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+"---\nabstract: 'In this work we report state-of-the-art theoretical calculations of the dipole polarizability of the argon atom. Frequency dependence of the polarizability is taken into account by means of the dispersion coefficients (Cauchy coefficients) which is sufficient for experimentally relevant wavelengths below the first resonant frequency. In the proposed theoretical framework, all known physical effects including the relativistic, quantum electrodynamics, finite nuclear mass, and finite nuclear size corrections are accounted for. We obtained $\\alpha_0=11.0775(19)$ for the static polarizability and $\\alpha_2=27.976(15)$ and $\\alpha_4=95.02(11)$ for the second and fourth dispersion coefficients, respectively. The result obtained for the static polarizability agrees (within the estimated uncertainty) with the most recent experimental data \\[C. Gaiser and B. Fellmuth, Phys. Rev. Lett. **120**, 123203 (2018)\\], but is less accurate. The dispersion coefficients determined in this work appear to be most accurate in the literature, improving by more than an order of magnitude upon previous estimates. By combining the experimentally determined value of the static polarizability with the dispersion coefficients from our calculations, the polarizability of argon can be calculated with accuracy of around $10\\,$ppm for wavelengths above roughly $450\\,$nm. This result is important from the point of view of quantum metrology, especially for a new"
+"---\nabstract: 'Gravitational-wave backgrounds are expected to arise from the superposition of gravitational wave signals from a large number of unresolved sources and also from the stochastic processes that occurred in the Early universe. So far, we have not detected any gravitational wave background, but with the improvements in the detectors\u2019 sensitivities, such detection is expected in the near future. The detection and inferences we draw from the search for a gravitational-wave background will depend on the source model, the type of search pipeline used, and the data generation in the gravitational-wave detectors. In this work, we focus on the effect of the data generation process, specifically the calibration of the detectors\u2019 digital output into strain data used by the search pipelines. Using the calibration model of the current LIGO detectors as an example, we show that for power-law source models and calibration uncertainties $\\lesssim 10 \\%$, the detection of isotropic gravitational wave background is not significantly affected. We also show that the source parameter estimation and upper limits calculations get biased. For calibration uncertainties of $\\lesssim 5 \\%$, the biases are not significant ($\\lesssim 2 \\%$), but for larger calibration uncertainties, they might become significant, especially when trying to"
+"---\nabstract: |\n    A class of graphs ${{\\mathscr C}}$ is [*[monadically stable]{}*]{} if for any unary expansion $\\widehat{{{\\mathscr C}}}$ of ${{\\mathscr C}}$, one cannot interpret, in first-order logic, arbitrarily long linear orders in graphs from $\\widehat{{{\\mathscr C}}}$. It is known that nowhere dense graph classes are monadically stable; these encompass most of the studied concepts of sparsity in graphs, including classes of graphs that exclude a fixed topological minor. On the other hand, monadic stability is a property expressed in purely model-theoretic terms and hence it is also suited for capturing structure in dense graphs.\n\n    For several years, it has been suspected that one can construct a structure theory for monadically stable graph classes that mirrors the theory of nowhere dense graph classes in the dense setting. In this work we provide a next step in this direction by giving a characterization of monadic stability through the [*[Flipper game]{}*]{}: a game on a graph played by [*[Flipper]{}*]{}, who in each round can complement the edge relation between any pair of vertex subsets, and [*[Connector]{}*]{}, who in each round is forced to localize the game to a ball of bounded radius. This is an analog of the [*[Splitter game]{}*]{}, which characterizes"
+"---\nabstract: 'A comprehensive wideband spectral analysis of the brightest black hole X-ray binary 4U $1543-47$ during its 2021 outburst is carried out for the first time using *NICER, NuSTAR,* and *AstroSat* observations by phenomenological and reflection modelling. The source attains a super-Eddington peak luminosity and remains in the soft state, with a small fraction ($< 3\\%$) of the inverse-Comptonized photons. The spectral modelling reveals a steep photon index ($\\Gamma \\sim 2-2.6$) and relatively high inner disk temperature (T$_{in}\\sim 0.9-1.27$ keV). The line-of-sight column density varies between ($0.45-0.54$)$\\times10^{22}$ cm$^{-2}$. Reflection modelling using the RELXILL model suggests that 4U $1543-47$ is a low-inclination system ($\\theta \\sim 32^\\circ - 40^\\circ$). The accretion disk is highly ionized (log $\\xi$ &gt; 3) and has super solar abundance (3.6$-$10 $A_{Fe,\\odot}$) over the entire period of study. We detected a prominent dynamic absorption feature between $\\sim 8-11$ keV in the spectra throughout the outburst. This detection is the first of its kind for X-ray binaries. We infer that the absorption of the primary X-ray photons by the highly ionized, fast-moving disk-winds can produce the observed absorption feature. The phenomenological spectral modelling also shows the presence of a neutral absorption feature $\\sim 7.1 - 7.4$ keV, and"
+"---\nabstract: 'The paper suggests a generalization of the Sign-Perturbed Sums (SPS) finite sample system identification method for the identification of closed-loop observable stochastic linear systems in state-space form. The solution builds on the theory of matrix-variate regression and instrumental variable methods to construct distributionfree confidence regions for the state-space matrices. Both direct and indirect identification are studied, and the exactness as well as the strong consistency of the construction are proved. Furthermore, a new, computationally efficient ellipsoidal outer/approximation algorithm for the confidence regions is proposed. The new construction results in a semidefinite optimization problem which has an order-of-magnitude smaller number of constraints, as if one applied the ellipsoidal outer/approximation after vectorization. The effectiveness of the approach is also demonstrated empirically via a series of numerical experiments.'\naddress:\n- 'Institute for Computer Science and Control (SZTAKI), E\u00f6tv\u00f6s Lor\u00e1nd Research Network (ELKH), Budapest, Hungary'\n- 'Department of Probability Theory and Statistics, Institute of Mathematics, E\u00f6tv\u00f6s Lor\u00e1nd University (ELTE), Budapest, Hungary'\nauthor:\n- Szabolcs Szentp\u00e9teri\n- Bal\u00e1zs Csan\u00e1d Cs\u00e1ji\nbibliography:\n- 'iv-mimo-clss-sps.bib'\ntitle: |\n     Non-Asymptotic State-Space Identification of Closed-Loop\\\n    Stochastic Linear Systems using Instrumental Variables\n---\n\nclosed-loop identification, distribution-free methods, non-asymptotic guarantees, instrumental variables\n\nIntroduction\n============\n\nEstimating a [*mathematical model*]{} from observations"
+"---\nabstract: 'Numerical approximations of partial differential equations (PDEs) are routinely employed to formulate the solution of physics, engineering and mathematical problems involving functions of several variables, such as the propagation of heat or sound, fluid flow, elasticity, electrostatics, electrodynamics, and more. While this has led to solving many complex phenomena, there are some limitations. Conventional approaches such as Finite Element Methods (FEMs) and Finite Differential Methods (FDMs) require considerable time and are computationally expensive. In contrast, data driven machine learning-based methods such as neural networks provide a faster, fairly accurate alternative, and have certain advantages such as discretization invariance and resolution invariance. This article aims to provide a comprehensive insight into how data-driven approaches can complement conventional techniques to solve engineering and physics problems, while also noting some of the major pitfalls of machine learning-based approaches. Furthermore, we highlight, a novel and fast machine learning-based approach ($\\sim$1000x) to learning the solution operator of a PDE operator learning. We will note how these new computational approaches can bring immense advantages in tackling many problems in fundamental and applied physics.'\naddress:\n- 'Department of Computer Science, Purdue University, West Lafayette, IN, USA '\n- 'Department of Materials Science and Engineering, MIT,"
+"---\nabstract: 'Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases. This might severely limit AI\u2019s clinical adoption. A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan. In this paper, we construct a Unified Tumor Transformer (CancerUniT) model to jointly detect tumor existence & location and diagnose tumor characteristics for eight major cancers in CT scans. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction. We decouple the object queries into organ queries, tumor detection queries and tumor diagnosis queries, and further establish hierarchical relationships among the three groups. This clinically-inspired architecture effectively assists inter- and intra-organ representation learning of tumors and facilitates the resolution of these complex, anatomically related multi-organ cancer image reading tasks. CancerUniT is trained end-to-end using a curated large-scale CT images of 10,042 patients including eight major types of cancers and occurring non-cancer tumors (all are pathology-confirmed with 3D tumor masks annotated by radiologists). On the test set of 631 patients, CancerUniT"
+"---\nabstract: 'Deep visual models have widespread applications in high-stake domains. Hence, their black-box nature is currently attracting a large interest of the research community. We present the first survey in Explainable AI that focuses on the methods and metrics for interpreting deep visual models. Covering the landmark contributions along the state-of-the-art, we not only provide a taxonomic organisation of the existing techniques, but also excavate a range of evaluation metrics and collate them as measures of different properties of model explanations. Along the insightful discussion on the current trends, we also discuss the challenges and future avenues for this research direction.'\nauthor:\n- 'Naveed Akhtar The University of Western Australia naveed.akhtar@uwa.edu.au'\n- First Author$^1$\n- Second Author$^2$\n- |\n    Third Author$^{2,3}$Fourth Author$^4$ $^1$First Affiliation\\\n    $^2$Second Affiliation\\\n    $^3$Third Affiliation\\\n    $^4$Fourth Affiliation {first, second}@example.com, third@other.example.com, fourth@example.com\nbibliography:\n- 'ijcai23.bib'\ntitle: 'A Survey of Explainable AI in Deep Visual Modeling: Methods and Metrics'\n---\n\nIntroduction\n============\n\nVisual computational models have widespread applications, ranging from casual use in handheld devices to high-stake tasks in forensics, surveillance, autonomous driving and medical diagnosis etc. Contemporary visual models rely heavily on deep learning, which is a black-box technology. Hence, the opacity of deep visual models is"
+"---\nabstract: 'Twitter (one example of microblogging) is widely being used by researchers to understand human behavior, specifically how people behave when a significant event occurs and how it changes user microblogging patterns. The changing microblogging behavior can reveal patterns that can help in detecting real-world events. However, the Twitter data that is available has limitations, such as, it is incomplete and noisy and the samples are irregular. In this paper we create a model, called *Twitter Behavior Agent-Based Model (TBAM)* to simulate Twitter pattern and behavior using Agent-Based Modeling (ABM). The generated data from ABM simulations can be used in place or to complement the real-world data toward improving the accuracy of event detection. We confirm the validity of our model by finding the cross-correlation between the real data collected from Twitter and the data generated using TBAM.'\nbibliography:\n- 'datafusion.bib'\n---\n\nIntroduction\n============\n\nThe widespread use of microblogging services, such as Twitter, which generate immense content has resulted in considerable research focusing on utilizing their counts and semantic content for many different practical applications. For example, researchers can use microblogging data to gain insight into events and how people behave when an event occurs. The change in microblogging"
+"---\nabstract: 'Model-based next state prediction and state value prediction are slow to converge. To address these challenges, we do the following: i) Instead of a neural network, we do model-based planning using a parallel memory retrieval system (which we term the *slow* mechanism); ii) Instead of learning state values, we guide the agent\u2019s actions using goal-directed exploration, by using a neural network to choose the next action given the current state and the goal state (which we term the *fast* mechanism). The goal-directed exploration is trained online using hippocampal replay of visited states and future imagined states every single time step, leading to fast and efficient training. Empirical studies show that our proposed method has a 92% solve rate across 100 episodes in a dynamically changing grid world, significantly outperforming state-of-the-art actor critic mechanisms such as PPO (54%), TRPO (50%) and A2C (24%). Ablation studies demonstrate that both mechanisms are crucial. We posit that the future of Reinforcement Learning (RL) will be to model goals and sub-goals for various tasks, and plan it out in a goal-directed memory-based approach.'\nauthor:\n- |\n    John Chong Min Tan\\\n    Department of Electrical and Computer Engineering\\\n    National University of Singapore\\\n    `johntancm@u.nus.edu.sg`\\\n    Mehul Motani\\"
+"---\nabstract: 'Anomaly detection in videos is a significant yet challenging problem. Previous approaches based on deep neural networks employ either reconstruction-based or prediction-based approaches. Nevertheless, existing reconstruction-based methods [**1)**]{} rely on old-fashioned convolutional autoencoders and are poor at modeling temporal dependency; [**2)**]{} are prone to overfit the training samples, leading to indistinguishable reconstruction errors of normal and abnormal frames during the inference phase. To address such issues, firstly, we get inspiration from transformer and propose [**S**]{}patio-[**T**]{}emporal [**A**]{}uto-[**T**]{}rans-[**E**]{}ncoder, dubbed as STATE, as a new autoencoder model for enhanced consecutive frame reconstruction. Our STATE is equipped with a specifically designed learnable convolutional attention module for efficient temporal learning and reasoning. Secondly, we put forward a novel reconstruction-based input perturbation technique during testing to further differentiate anomalous frames. With the same perturbation magnitude, the testing reconstruction error of the normal frames lowers more than that of the abnormal frames, which contributes to mitigating the overfitting problem of reconstruction. Owing to the high relevance of the frame abnormality and the objects in the frame, we conduct object-level reconstruction using both the raw frame and the corresponding optical flow patches. Finally, the anomaly score is designed based on the combination of the raw and"
+"---\nabstract: |\n    In real photonic quantum systems losses are an unavoidable factor limiting the scalability to many modes and particles, restraining their application in fields as quantum information and communication. For this reason, a considerable amount of engineering effort has been taken in order to improve the quality of particle sources and system components. At the same time, data analysis and collection methods based on post-selection have been used to mitigate the effect of particle losses. This has allowed for investigating experimentally multi-particle evolutions where the observer lacks knowledge about the system\u2019s intermediate propagation states. Nonetheless, the fundamental question how losses affect the behaviour of the surviving subset of a multi-particle system has not been investigated so far. For this reason, here we study the impact of particle losses in a quantum walk of two photons reconstructing the output probability distributions for one photon conditioned on the loss of the other in a known mode and temporal step of our evolution network. We present the underlying theoretical scheme that we have devised in order to model controlled particle losses, we describe an experimental platform capable of implementing our theory in a time multiplexing encoding. In the end we show"
+"---\nabstract: |\n    Vandermonde matrices are usually exponentially ill-conditioned and often result in unstable approximations. In this paper, we introduce and analyze the *multivariate Vandermonde with Arnoldi (V+A) method*, which is based on least-squares approximation together with a Stieltjes orthogonalization process, for approximating continuous, multivariate functions on $d$-dimensional irregular domains. The V+A method addresses the ill-conditioning of the Vandermonde approximation by creating a set of discrete orthogonal basis with respect to a discrete measure. The V+A method is simple and general. It relies only on the sample points from the domain and requires no prior knowledge of the domain. In this paper, we first analyze the sample complexity of the V+A approximation. In particular, we show that, for a large class of domains, the V+A method gives a well-conditioned and near-optimal $N$-dimensional least-squares approximation using $M=\\mathcal{O}(N^2)$ equispaced sample points or $M=\\mathcal{O}(N^2\\log N)$ random sample points, independently of $d$. We also give a comprehensive analysis of the error estimates and rate of convergence of the V+A approximation. Based on the multivariate V+A approximation, we propose a new variant of the weighted V+A least-squares algorithm that uses only $M=\\mathcal{O}(N\\log N)$ sample points to give a near-optimal approximation. Our numerical results confirm that"
+"---\nabstract: 'It has become a consensus that autonomous vehicles (AVs) will first be widely deployed on highways. However, the complexity of highway interchanges becomes the bottleneck for deploying AVs. An AV should be sufficiently tested under different highway interchanges, which is still challenging due to the lack of available datasets containing diverse highway interchanges. In this paper, we propose a model-driven method, [F<span style=\"font-variant:small-caps;\">lyover</span>]{}, to generate a dataset consisting of diverse interchanges with measurable diversity coverage. First, [F<span style=\"font-variant:small-caps;\">lyover</span>]{}proposes a labeled digraph to model the topology of an interchange. Second, [F<span style=\"font-variant:small-caps;\">lyover</span>]{}takes real-world interchanges as input to guarantee topology practicality and extracts different topology equivalence classes by classifying the corresponding topology models. Third, for each topology class, [F<span style=\"font-variant:small-caps;\">lyover</span>]{}identifies the corresponding geometrical features for the ramps and generates concrete interchanges using k-way combinatorial coverage and differential evolution. To illustrate the diversity and applicability of the generated interchange dataset, we test the built-in traffic flow control algorithm in SUMO and the fuel-optimization trajectory tracking algorithm deployed to Alibaba\u2019s autonomous trucks on the dataset. The results show that except for the geometrical difference, the interchanges are diverse in throughput and fuel consumption under the traffic flow control and trajectory tracking algorithms,"
+"---\nabstract: 'Numerous types of social biases have been identified in pre-trained language models (PLMs), and various intrinsic bias evaluation measures have been proposed for quantifying those social biases. Prior works have relied on human annotated examples to compare existing intrinsic bias evaluation measures. However, this approach is not easily adaptable to different languages nor amenable to large scale evaluations due to the costs and difficulties when recruiting human annotators. To overcome this limitation, we propose a method to compare intrinsic gender bias evaluation measures without relying on human-annotated examples. Specifically, we create multiple *bias-controlled* versions of PLMs using varying amounts of male vs. female gendered sentences, mined automatically from an unannotated corpus using gender-related word lists. Next, each bias-controlled PLM is evaluated using an intrinsic bias evaluation measure, and the rank correlation between the computed bias scores and the gender proportions used to fine-tune the PLMs is computed. Experiments on multiple corpora and PLMs repeatedly show that the correlations reported by our proposed method that does not require human annotated examples are comparable to those computed using human annotated examples in prior work.'\nauthor:\n- |\n    Masahiro Kaneko$^{1}$ Danushka Bollegala$^{2,3}$ Naoaki Okazaki$^{1}$\\\n    $^1$Tokyo Institute of Technology $^2$University of Liverpool"
+"---\nabstract: 'We analyze results of a recent experiment \\[D. Razmadze et al., *Phys. Rev. Lett.*, **125**, 116803 (2020)\\] on transport through a quantum dot between two full-shell nanowires and show that the observed effects are caused by the Kondo effect enhancement due to a nontrivial geometry (magnetic flux in a full-shell nanowire) rather than the presence of Majorana bound states. Moreover, we propose that such a setup presents a unique and convenient system to study the competition between superconductivity and the Kondo effect and has significant advantages in comparison to other known approaches, as the important parameter is controlled by the magnetic flux through the full-shell nanowire, which can be significantly varied with small changes of magnetic field, and does not require additional gates. This competition is of fundamental interest as it results in a quantum phase transition between an unscreened doublet and a many-body Kondo singlet ground states of the system.'\nauthor:\n- 'Aleksandr E. Svetogorov'\n- Daniel Loss\n- Jelena Klinovaja\nbibliography:\n- 'QD.bib'\ntitle: 'Enhancement of the Kondo effect in a quantum dot formed in a full-shell nanowire.'\n---\n\nIntroduction\n============\n\nSemiconducting nanowires with full superconducting shell were recently introduced as possible realizations of topological superconductors,"
+"---\nabstract: 'Bagging is an important technique for stabilizing machine learning models. In this paper, we derive a finite-sample guarantee on the stability of bagging for any model. Our result places no assumptions on the distribution of the data, on the properties of the base algorithm, or on the dimensionality of the covariates. Our guarantee applies to many variants of bagging and is optimal up to a constant. Empirical results validate our findings, showing that bagging successfully stabilizes even highly unstable base algorithms.'\nauthor:\n- 'Jake A. Soloff'\n- Rina Foygel Barber\n- Rebecca Willett\nbibliography:\n- 'reference.bib'\nnocite: '[@devroye1979distribution2]'\ntitle: 'Bagging Provides Assumption-free Stability'\n---\n\nIntroduction {#sec-intro}\n============\n\nAlgorithmic stability\u2014that is, how perturbing training data influences a learned model\u2014is fundamental to modern data analysis. In learning theory, certain forms of stability are necessary and sufficient for generalization [@bousquet2002stability; @poggio2004general; @shalev2010learnability]. In model selection, stability measures can reliably identify important features [@meinshausen2010stability; @shah2013variable; @ren2021derandomizing]. In scientific applications, stable methods promote reproducibility, a prerequisite for meaningful inference [@yu2013stability]. In distribution-free prediction, stability is a key assumption for the validity of jackknife prediction intervals [@barber2021predictive; @steinberger2023conditional].\n\nAnticipating various benefits of stability, @breiman1996bagging [@breiman1996heuristics] proposed bagging as an ensemble meta-algorithm to stabilize any"
+"---\nabstract: |\n    Picture-valued invariants are the main achievement of parity theory by V.O. Manturov. In the paper we give a general description of such invariants which can be assigned to a parity (in general, a trait) on diagram crossings. We distinguish two types of picture-valued invariants: derivations (Turaev bracker, index polynomial etc.) and functorial maps (Kauffman bracket, parity bracket, parity projection etc.). We consider some examples of binary functorial maps.\n\n    Besides known cases of functorial maps, we present two new examples. The order functorial map is closely connected with (pre)orderings of surface groups and leads to the notion of sibling knots, i.e. knots such that any diagram of one knot can be transformed to a diagram of the other by crossing switching. The other is the lifting map which is inverse to forgetting of under-overcrossings information which turns virtual knots into flat knots. We give some examples of liftable flat knots and flattable virtual ones.\n\n    An appendix of the paper contains description of some smoothing skein modules. In particular, we show that $\\Delta$-equivalence of tangles in a fixed surface is classified by the extended homotopy index polynomial.\nauthor:\n- Igor Nikonov\ntitle: Local transformations and functorial maps\n---\n\nKeywords:"
+"---\nabstract: 'Hazard detection and avoidance is a key technology for future robotic small body sample return and lander missions. Current state-of-the-practice methods rely on high-fidelity, *a priori* terrain maps, which require extensive human-in-the-loop verification and expensive reconnaissance campaigns to resolve mapping uncertainties. We propose a novel safety mapping paradigm that leverages deep semantic segmentation techniques to predict landing safety directly from a single monocular image, thus reducing reliance on high-fidelity, *a priori* data products. We demonstrate precise and accurate safety mapping performance on real *in-situ* imagery of prospective sample sites from the OSIRIS-REx mission.'\nauthor:\n- 'Travis Driver[^1], \u00a0Kento Tomita[^2], \u00a0Koki Ho[^3], \u00a0and Panagiotis Tsiotras[^4]'\nbibliography:\n- 'references.bib'\ntitle: |\n    Deep Monocular Hazard Detection for\\\n    Safe Small Body Landing\n---\n\nIntroduction\n============\n\nHazard detection and avoidance (HD&A) is a key technology for future robotic small body sample return and lander missions. Current approaches rely on high-fidelity digital elevation maps (DEMs) derived from digital terrain models (DTMs), local topography and albedo maps, generated on the ground\u00a0[@berry2022scitech]. However, DTM construction involves extensive human-in-the-loop verification, carefully designed image acquisition plans, and expensive reconnaissance campaigns to resolve mapping uncertainties\u00a0[@barnouin2020; @palmer2022practical]. We, instead, propose a novel safety mapping paradigm that leverages Bayesian"
+"---\nabstract: 'Estimating the Shannon entropy of a discrete distribution from which we have only observed a small sample is challenging. Estimating other information-theoretic metrics, such as the Kullback-Leibler divergence between two sparsely sampled discrete distributions, is even harder. Existing approaches to address these problems have shortcomings: they are biased, heuristic, work only for some distributions, and/or cannot be applied to all information-theoretic metrics. Here, we propose a fast, semi-analytical estimator for sparsely sampled distributions that is efficient, precise, and general. Its derivation is grounded in probabilistic considerations and uses a hierarchical Bayesian approach to extract as much information as possible from the few observations available. Our approach provides estimates of the Shannon entropy with precision at least comparable to the state of the art, and most often better. It can also be used to obtain accurate estimates of any other information-theoretic metric, including the notoriously challenging Kullback-Leibler divergence. Here, again, our approach performs consistently better than existing estimators.'\nauthor:\n- Angelo Piga\n- 'Lluc Font-Pomarol'\n- 'Marta Sales-Pardo'\n- Roger Guimer\u00e0\nbibliography:\n- 'sample.bib'\ntitle: 'Bayesian estimation of information-theoretic metrics for sparsely sampled distributions'\n---\n\nIntroduction\n============\n\nInformation theory is gaining momentum as a methodological framework to study complex"
+"---\nabstract: 'Force Sensing and Force Control are essential to many industrial applications. Typically, a 6-axis Force/Torque (F/T) sensor is mounted between the robot\u2019s wrist and the end-effector in order to measure the forces and torques exerted by the environment onto the robot (the external wrench). Although a typical 6-axis F/T sensor can provide highly accurate measurements, it is expensive and vulnerable to drift and external impacts. Existing methods aiming at estimating the external wrench using only the robot\u2019s internal signals are limited in scope: for example, wrench estimation accuracy was mostly validated in free-space motions and simple contacts as opposed to tasks like assembly that require high-precision force control. Here we present a Neural Network based method and argue that by devoting particular attention to the training data structure, it is possible to accurately estimate the external wrench in a wide range of scenarios based solely on internal signals. As an illustration, we demonstrate a pin insertion experiment with 100-micron clearance and a hand-guiding experiment, both performed without external F/T sensors or joint torque sensors. Our result opens the possibility of equipping the existing 2.7 million industrial robots with Force Sensing and Force Control capabilities without any additional hardware.'"
+"---\nabstract: 'In the framework of coupled 1D Gross-Pitaevskii equations, we explore the dynamics of a binary Bose-Einstein condensate where the intra-component interaction is repulsive, while the inter-component one is attractive. The existence regimes of stable self-trapped localized states in the form of symbiotic solitons have been analyzed. Imbalanced mixtures, where the number of atoms in one component exceeds the number of atoms in the other component, are considered in parabolic potential and box-like trap. When all the intra-species and inter-species interactions are repulsive, we numerically find a new type of symbiotic solitons resembling dark-bright solitons. A variational approach has been developed which allows us to find the stationary state of the system and frequency of small amplitude dynamics near the equilibrium. It is shown that the strength of inter-component coupling can be retrieved from the frequency of the localized state\u2019s vibrations.'\nauthor:\n- 'K. K. Ismailov$^{1,3}$, B. B. Baizakov$^1$, F. Kh. Abdullaev$^{1,2}$ and M. Salerno$^3$'\ntitle: 'Dynamics of imbalanced quasi-one-dimensional binary Bose-Einstein condensate in external potentials'\n---\n\nIntroduction\n============\n\nTwo-component Bose-Einstein condensates (BEC) may show a variety of interesting phenomena depending on the character and strength of intra-species and inter-species forces [@kevrekidis2016]. Among all possible settings the case of"
+"---\nabstract: 'Stellar candidates in the Ursa Minor (UMi) dwarf galaxy have been found using a new Bayesian algorithm applied to *Gaia* EDR3 data. Five of these targets are located in the extreme outskirts of UMi, from $\\sim5$ to 12 elliptical half-light radii (r$_h$), where r$_h$(UMi) $= 17.32 \\pm 0.11$ arcmin, and have been observed with the GRACES high resolution spectrograph at the Gemini-Northern telescope. Precise radial velocities ($\\sigma_{\\rm{RV}} < 2$ km s$^{-1}$) and metallicities ($\\sigma_{\\rm{\\FeH}} < 0.2$ dex) confirm their memberships of UMi. Detailed analysis of the brightest and outermost star (Target\u00a01, at $\\sim12$ r$_h$), yields precision chemical abundances for the $\\alpha$- (Mg, Ca, Ti), odd-Z (Na, K, Sc), Fe-peak (Fe, Ni, Cr), and neutron-capture (Ba) elements. With data from the literature and APOGEE DR17, we find the chemical patterns in UMi are consistent with an outside-in star formation history that includes yields from core collapse supernovae, asymptotic giant branch stars, and supernovae Ia. Evidence for a knee in the \\[$\\alpha$/Fe\\] ratios near $\\FeH\\sim-2.1$ indicates a low star formation efficiency similar to that in other dwarf galaxies. Detailed analysis of the surface number density profile shows evidence that UMi\u2019s outskirts have been populated by tidal effects, likely as"
+"---\nabstract: 'We introduce a general description of localised distortions in active nematics using the framework of active nematic multipoles. We give the Stokesian flows for arbitrary multipoles in terms of differentiation of a fundamental flow response and describe them explicitly up to quadrupole order. We also present the response in terms of the net active force and torque associated to the multipole. This allows the identification of the dipolar and quadrupolar distortions that generate self-propulsion and self-rotation respectively and serves as a guide for the design of arbitrary flow responses. Our results can be applied to both defect loops in three-dimensional active nematics and to systems with colloidal inclusions. They reveal the geometry-dependence of the self-dynamics of defect loops and provide insights into how colloids might be designed to achieve propulsive or rotational dynamics, and more generally for the extraction of work from active nematics. Finally, we extend our analysis also to two dimensions and to systems with chiral active stresses.'\nauthor:\n- 'Alexander J.H. Houston'\n- 'Gareth P. Alexander'\nbibliography:\n- 'ActiveNematicMultipoles.bib'\ntitle: 'Active Nematic Multipoles: Flow Responses and the Dynamics of Defects and Colloids'\n---\n\nIntroduction {#sec:intro}\n============\n\nActive liquid crystals model a wide range of materials,"
+"---\nabstract: 'Tuberculosis (TB) is still considered a leading cause of death and a substantial threat to global child health. Both TB infection and disease are curable using antibiotics. However, most children who die of TB are never diagnosed or treated. In clinical practice, experienced physicians assess TB by examining chest X-rays (CXR). Pediatric CXR has specific challenges compared to adult CXR, which makes TB diagnosis in children more difficult. Computer-aided diagnosis systems supported by Artificial Intelligence have shown performance comparable to experienced radiologist TB readings, which could ease mass TB screening and reduce clinical burden. We propose a multi-view deep learning-based solution which, by following a proposed template, aims to automatically regionalize and extract lung and mediastinal regions of interest from pediatric CXR images where key TB findings may be present. Experimental results have shown accurate region extraction, which can be used for further analysis to confirm TB finding presence and severity assessment. Code publicly available at: <https://github.com/dani-capellan/pTB_LungRegionExtractor>.'\nauthor:\n- 'Daniel Capell\u00e1n-Mart\u00edn'\n- 'Juan J. G\u00f3mez-Valverde'\n- 'Ramon Sanchez\u2011Jacob'\n- 'David Bermejo-Pel\u00e1ez'\n- 'Lara Garc\u00eda-Delgado'\n- 'Elisa L\u00f3pez-Varela'\n- 'Maria J. Ledesma-Carbayo'\nbibliography:\n- 'references.bib'\ntitle: 'Deep learning-based lung segmentation and automatic regional template in chest X-ray images for"
+"---\nabstract: 'This study aims at improving the performance of scoring student responses in science education automatically. BERT-based language models have shown significant superiority over traditional NLP models in various language-related tasks. However, science writing of students, including argumentation and explanation, is domain-specific. In addition, the language used by students is different from the language in journals and Wikipedia, which are training sources of BERT and its existing variants. All these suggest that a domain-specific model pre-trained using science education data may improve model performance. However, the ideal type of data to contextualize pre-trained language model and improve the performance in automatically scoring student written responses remains unclear. Therefore, we employ different data in this study to contextualize both BERT and SciBERT models and compare their performance on automatic scoring of assessment tasks for scientific argumentation. We use three datasets to pre-train the model: 1) journal articles in science education, 2) a large dataset of students\u2019 written responses (sample size over 50,000), and 3) a small dataset of students\u2019 written responses of scientific argumentation tasks. Our experimental results show that in-domain training corpora constructed from science questions and responses improve language model performance on a wide variety of downstream tasks."
+"---\nabstract: 'In this paper, we study the identifiability and the estimation of the parameters of a copula-based multivariate model when the margins are unknown and are arbitrary, meaning that they can be continuous, discrete, or mixtures of continuous and discrete. When at least one margin is not continuous, the range of values determining the copula is not the entire unit square and this situation could lead to identifiability issues that are discussed here. Next, we propose estimation methods when the margins are unknown and arbitrary, using pseudo log-likelihood adapted to the case of discontinuities. In view of applications to large data sets, we also propose a pairwise composite pseudo log-likelihood. These methodologies can also be easily modified to cover the case of parametric margins. One of the main theoretical result is an extension to arbitrary distributions of known convergence results of rank-based statistics when the margins are continuous. As a by-product, under smoothness assumptions, we obtain that the asymptotic distribution of the estimation errors of our estimators are Gaussian. Finally, numerical experiments are presented to assess the finite sample performance of the estimators, and the usefulness of the proposed methodologies is illustrated with a copula-based regression model for hydrological"
+"---\nabstract: 'Networked control systems (NCSs) are an example of task-oriented communication systems, where the purpose of communication is real-time control of processes over a network. In the context of NCSs, with the processes sending their state measurements to the remote controllers, the deterioration of control performance due to the network congestion can be partly mitigated by shaping the traffic injected into the network at the transport layer (TL). In this work, we conduct an extensive performance evaluation of selected TL protocols and show that existing approaches from communication and control theories fail to deliver sufficient control performance in realistic network scenarios. Moreover, we propose a new semantic-aware TL policy, which uses the process state information to filter the most relevant updates and the network state information to prevent delays due to network congestion. The proposed mechanism is shown to outperform all the considered TL protocols with respect to control performance.'\nauthor:\n- \nbibliography:\n- 'biblio.bib'\ntitle: |\n    [Towards Semantic-Aware Transport Layer Protocols: A Control Performance Perspective]{}\\\n    [^1]\n---\n\nIntroduction {#sec:intro}\n============\n\nIn the evolving post-Shannon 6G systems [@strinati20216g], the perspective on the network is shifting from reliable transmission of bits to delivering heterogeneous services with respect to application-specific goals."
+"---\nabstract: 'Expressive text-to-speech (TTS) aims to synthesize speech with varying speaking styles to better reflect human speech patterns. In this study, we attempt to use natural language as a style prompt to control the styles in the synthetic speech, *e.g.*, \u201cSigh tone in full of sad mood with some helpless feeling\". Considering that there is no existing TTS corpus that is suitable to benchmark this novel task, we first construct a speech corpus whose speech samples are annotated with not only content transcriptions but also style descriptions in natural language. Then we propose an expressive TTS model, named InstructTTS, which is novel in the sense of the following aspects: (1) We fully take advantage of self-supervised learning and cross-modal metric learning and propose a novel three-stage training procedure to obtain a robust sentence embedding model that can effectively capture semantic information from the style prompts and control the speaking style in the generated speech. (2) We propose to model acoustic features in discrete latent space and train a novel discrete diffusion probabilistic model to generate vector-quantized (VQ) acoustic tokens rather than the commonly-used mel spectrogram. (3) We jointly apply mutual information (MI) estimation and minimization during acoustic model training"
+"---\nabstract: 'We study the motion of charge carriers in curved Dirac materials, in the presence of a local Fermi velocity. An explicit parameterization of the latter emerging quantity for a nanoscroll cylindrical geometry is also provided, together with a discussion of related physical effects and observable properties.'\nauthor:\n- 'B.\u00a0Bagchi[^1]'\n- 'A.\u00a0Gallerati[^2]'\n- 'R.\u00a0Ghosh[^3]'\nbibliography:\n- 'bibliografia.bib'\ntitle: ' **Dirac equation in curved spacetime: the role of local Fermi velocity**'\n---\n\n[@r @X]{} **Keywords:** & Dirac equation, graphene, local Fermi velocity, nanoscrolls.\n\nIntroduction\n============\n\nDirac equation is one of the most relevant contributions in the history of quantum mechanics. Over the decades, its study has been conducted from different points of view [@thaller1992dirac; @bjorken1964relativistic; @Peskin:1995ev], with countless applications in many areas of physics. The reformulation of the Dirac formalism in curved backgrounds is an appealing field of research due to its remarkable applications in high-energy physics, quantum field theory, analogue gravity scenarios and condensed matter.\n\nA real, solid-state system where to observe the properties of Dirac spinorial quantum fields in a curved space is provided by graphene and other two-dimensional materials. The latter have attracted great interest because of their electronic, mechanical and optical characteristics [@novoselov2004electric;"
+"---\nabstract: 'The extensive damage caused by malware requires anti-malware systems to be constantly improved to prevent new threats. The current trend in malware detection is to employ machine learning models to aid in the classification process. We propose a new dataset with the objective of improving current anti-malware systems. The focus of this dataset is to improve host based intrusion detection systems by providing API call sequences for thousands of malware samples executed in Windows 10 virtual machines. A tutorial on how to create and expand this dataset is provided along with a benchmark demonstrating how to use this dataset to classify malware. The data contains long sequences of API calls for each sample, and in order to create models that can be deployed in resource constrained devices, three feature selection methods were tested. The principal innovation, however, lies in the multi-label classification system in which one sequence of APIs can be tagged with multiple labels describing its malicious behaviours.'\nauthor:\n- '\\'\nbibliography:\n- 'main.bib'\ntitle: 'Behavioural Reports of Multi-Stage Malware'\n---\n\n[Carpenter : Behavioural Reports of Multi-Stage Malware]{}\n\nIntroduction\n============\n\nThere are billions of malware attacks worldwide every year [@StatistaMalware2022]. Most malicious programs are created by cyber-criminals"
+"---\nabstract: 'In this paper we present a novel method, *Knowledge Persistence* ($\\mathcal{KP}$), for faster evaluation of Knowledge Graph (KG) completion approaches. Current ranking-based evaluation is quadratic in the size of the KG, leading to long evaluation times and consequently a high carbon footprint. $\\mathcal{KP}$ addresses this by representing the topology of the KG completion methods through the lens of topological data analysis, concretely using persistent homology. The characteristics of persistent homology allow $\\mathcal{KP}$ to evaluate the quality of the KG completion looking only at a fraction of the data. Experimental results on standard datasets show that the proposed metric is highly correlated with ranking metrics (Hits@N, MR, MRR). Performance evaluation shows that $\\mathcal{KP}$ is computationally efficient: In some cases, the evaluation time (validation+test) of a KG completion method has been reduced from 18 hours (using Hits@10) to 27 seconds (using $\\mathcal{KP}$), and on average (across methods & data) reduces the evaluation time (validation+test) by $\\approx$ **99.96**%.'\nauthor:\n- Anson Bastos\n- Kuldeep Singh\n- Abhishek Nadgeri\n- Johannes Hoffart\n- Toyotaro Suzumura\n- Manish Singh\nbibliography:\n- 'bibliography.bib'\ntitle: 'Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?'\n---\n\nIntroduction {#sec:introduction}\n============\n\nPublicly available"
+"---\nabstract: 'This paper makes a case for accelerating lattice-based post quantum cryptography (PQC) with memristor based crossbars, and shows that these inherently error-tolerant algorithms are a good fit for noisy analog MAC operations in crossbars. We compare different NIST round-3 lattice-based candidates for PQC, and identify that SABER is not only a front-runner when executing on traditional systems, but it is also amenable to acceleration with crossbars. SABER is a module-LWR based approach, which performs modular polynomial multiplications with rounding. We map the polynomial multiplications in SABER on crossbars and show that analog dot-products can yield a $1.7-32.5\\times$ performance and energy efficiency improvement, compared to recent hardware proposals. This initial design combines the innovations in multiple state-of-the-art works \u2013 the algorithm in SABER and the memristive acceleration principles proposed in ISAAC (for deep neural network acceleration). We then identify the bottlenecks in this initial design and introduce several additional techniques to improve its efficiency. These techniques are synergistic and especially benefit from SABER\u2019s power-of-two modulo operation. First, we show that some of the software techniques used in SABER, that are effective on CPU platforms, are unhelpful in crossbar-based accelerators. Relying on simpler algorithms further improves our efficiencies by $1.3-3.6\\times$."
+"---\nabstract: 'Simulation-based inference (SBI) techniques are now an essential tool for the parameter estimation of mechanistic and simulatable models with intractable likelihoods. Statistical approaches to SBI such as approximate Bayesian computation and Bayesian synthetic likelihood have been well studied in the well specified and misspecified settings. However, most implementations are inefficient in that many model simulations are wasted. Neural approaches such as sequential neural likelihood (SNL) have been developed that exploit all model simulations to build a surrogate of the likelihood function. However, SNL approaches have been shown to perform poorly under model misspecification. In this paper, we develop a new method for SNL that is robust to model misspecification and can identify areas where the model is deficient. We demonstrate the usefulness of the new approach on several illustrative examples.'\nauthor:\n- 'Ryan P. Kelly'\n- 'David J. Nott'\n- 'David T. Frazier'\n- 'David J. Warne'\n- Christopher Drovandi\nbibliography:\n- 'refs.bib'\ntitle: '**Misspecification-robust Sequential Neural Likelihood**'\n---\n\n[*Keywords: generative models, implicit models, likelihood-free inference, normalising flows, simulation-based inference*]{}\n\nIntroduction {#sec:intro}\n============\n\nStatistical inference for complex models can be challenging when the likelihood function is infeasible to evaluate many times. However, if the model is computationally inexpensive"
+"---\nabstract: 'Ranking algorithms in traditional search engines are powered by enormous training data sets that are meticulously engineered and curated by a centralized entity. Decentralized peer-to-peer (p2p) networks such as torrenting applications and Web3 protocols deliberately eschew centralized databases and computational architectures when designing services and features. As such, robust search-and-rank algorithms designed for such domains must be engineered specifically for decentralized networks, and must be lightweight enough to operate on consumer-grade personal devices such as a smartphone or laptop computer. We introduce G-Rank, an unsupervised ranking algorithm designed exclusively for decentralized networks. We demonstrate that accurate, relevant ranking results can be achieved in fully decentralized networks without any centralized data aggregation, feature engineering, or model training. Furthermore, we show that such results are obtainable with minimal data preprocessing and computational overhead, and can still return highly relevant results even when a user\u2019s device is disconnected from the network. G-Rank is highly modular in design, is not limited to categorical data, and can be implemented in a variety of domains with minimal modification. The results herein show that unsupervised ranking models designed for decentralized p2p networks are not only viable, but worthy of further research. *Author\u2019s note: the experiments"
+"---\nauthor:\n- Biswarup Mukhopadhyaya\n- ', Tousik Samui'\n- ', and Ritesh K. Singh'\ntitle: Dynamic Radius Jet Clustering Algorithm\n---\n\n[@counter&gt;0@toks=@toks=]{}\n\n[@counter&gt;0@toks=@toks=]{}\n\n[@counter&gt;0@toks=@toks=]{} [ !a! @toks= @toks= ]{}\n\n[abstract[ The study of standard QCD jets produced along with fat jets, which may appear as a result of the decay of a heavy particle, has become an essential part of collider studies. Current jet clustering algorithms, which use a fixed radius parameter for the formation of jets from the hadrons of an event, may be inadequate to capture the differing radius features. In this work, we develop an alternative jet clustering algorithm that allows the radius to vary dynamically based on local kinematics and distribution in the $\\eta$-$\\phi$ plane inside each evolving jet. We present the usefulness of this dynamic radius clustering algorithm through two Standard Model processes, and thereafter illustrate it for a scenario beyond the Standard Model at the 13\u00a0TeV LHC.]{}]{}\n\nIntroduction {#sec:intro}\n============\n\nThe physics extraction capacity of any high-energy collider depends crucially on the handling of coloured particles in various final states. These are produced as partons via either short-distance interactions of quantum chromodynamics (QCD) or electroweak processes[@Campbell:2006wx; @Ellis:1996mzs]. The partons, however, hadronize through"
+"---\nauthor:\n- 'J. Bouvier'\n- 'A. Sousa'\n- 'K. Pouilly'\n- 'J.M. Almenara'\n- 'J.-F. Donati'\n- 'S. Alencar'\n- 'A. Frasca'\n- 'K. Grankin'\n- 'A. Carmona'\n- 'G. Pantolmos'\n- 'B. Zaire'\n- 'X. Bonfils'\n- 'A. Bayo'\n- 'L.M. Rebull'\n- 'J. Alonso-Santiago'\n- 'J. F. Gameiro'\n- 'N. J. Cook'\n- 'E. Artigau'\n- 'the Spirou Legacy Survey (SLS) consortium'\nbibliography:\n- 'gmaur\\_rev0.bib'\ndate: 'Received 2 November 2022; accepted 5 January 2023'\nsubtitle: 'A semester-long optical and near-infrared spectrophotometric monitoring campaign[^1][^2]'\ntitle: 'Stable accretion and episodic outflows in the young transition disk system GM Aurigae. '\n---\n\n[We investigate the structure and dynamics of magnetospheric accretion and associated outflows on a scale smaller than 0.1 au around the young transitional disk system GM Aur.]{} [We devised a coordinated observing campaign to monitor the variability of the system on timescales ranging from days to months, including partly simultaneous high-resolution optical and near-infrared spectroscopy, multiwavelength photometry, and low-resolution near-infrared spectroscopy, over a total duration of six months, covering 30 rotational cycles. We analyzed the photometric and line profile variability to characterize the accretion and ejection processes.]{} [The optical and near-infrared light curves indicate that the luminosity of"
+"---\nabstract: 'The modern S-Matrix Bootstrap provides non-perturbative bounds on low-energy aspects of scattering amplitudes, leveraging the constraints of unitarity, analyticity and crossing. Typically, the solutions saturating such bounds also saturate the unitarity constraint as much as possible, meaning that they are almost exclusively elastic. This is expected to be unphysical in $d>2$ because of Aks\u2019 theorem. We explore this issue by adding inelasticity as an additional input, both using a primal approach in general dimensions which extends the usual ansatz, and establishing a dual formulation in the 2d case. We then measure the effects on the low-energy observables where we observe stronger bounds than in the standard setup.'\nauthor:\n- 'Ant\u00f3nio Antunes$^{a,b}$, Miguel S. Costa$^a$, Jos\u00e9 Pereira$^a$'\ntitle: 'Exploring Inelasticity in the S-Matrix Bootstrap '\n---\n\n[**Introduction.**]{} Scattering amplitudes are some of the most studied observables in quantum field theory. They encode the probability amplitudes of transitions between asymptotic states with a definite number of particles. The simplest non-trivial amplitude $_{\\textrm{in}}\\langle p_1,p_2|p_3,p_4\\rangle_{\\textrm{out}}$, corresponding to 2-2 scattering has been extensively studied for decades, notably through Feynman perturbation theory, which extracts the connected amplitude through the LSZ procedure which takes as input a four-point correlation function. Additionally, modern on-shell perturbative techniques"
+"---\nabstract: 'We study null and timelike constant radii geodesics in the environment of an over-spinning putative Kerr-type naked singularity. We are particularly interested in two topics: first, the differences of the shadows of the naked rotating singularity and the Kerr black hole; and second, the spinning down effect of the particles falling from the accretion disk. Our findings are as follows: around the naked singularity, the non-equatorial prograde orbits in the Kerr black hole remain intact up to a critical rotation parameter ($\\alpha=\\frac{4\\sqrt{2}}{3\\sqrt{3}}$) and cease to exist above this value. This has an important consequence in the shadow of the naked singularity if the shadow is registered by an observer on the rotation plane or close to it as the shadow cannot be distinguished from that of a Kerr black hole viewed from the same angle. We also show that the timelike retrograde orbits in the equatorial plane immediately (after about an 8% increase in mass) reduce the spin parameter of the naked singularity from larger values to $\\alpha=1$ at which an event horizon appears. This happens because the retrograde orbits have a larger capture cross-section than the prograde ones. So if a naked singularity happens to have an"
+"---\nauthor:\n- 'Jianyang\u00a0Qi,[!!]{}'\n- 'Noah Hood,'\n- 'Abigail Kopec,[!!]{}'\n- 'Yue Ma, Haiwen Xu, Min Zhong,'\n- 'Kaixuan\u00a0Ni[!!]{}'\nbibliography:\n- 'bibliography.bib'\ntitle: Low Energy Electronic Recoils and Single Electron Detection with a Liquid Xenon Proportional Scintillation Counter\n---\n\nIntroduction {#sec:intro}\n============\n\nDual-phase Liquid Xenon Time Projection Chambers (LXeTPCs) have traditionally been used in large scale rare event searches, and operate by detecting the prompt scintillation light (S1) and the proportional electroluminescence of ionization electrons (S2) from an energy deposition. However, these detectors have never achieved perfect charge collection efficiency in practice\u00a0[@XENON:2022ltv; @LZ:2022ufs]. Additionally, they display a background comprised of delayed single electrons that can last $\\mathcal{O}(1)$\u00a0s after a large S2 signal\u00a0[@XENONCollaborationSS:2021sgk; @LUX:2020vbj; @Kopec:2021ccm]. This background can impact low energy event searches which are only capable of producing S2s, such as those from Coherent Elastic Neutrino Nucleus Scattering (CE$\\nu$NS)\u00a0[@Ni:2021mwa]. Two main hypotheses for this background are that there are electrons trapped on impurities and then are released, or trapped at the liquid gas interface and are extracted later than most of the S2 electrons. This begs the question of whether or not it is possible to make a detector with a sensitivity to single"
+"---\nabstract: 'Communications between unmanned aerial vehicles (UAVs) play an important role in deploying aerial networks. Although some studies reveal that drone-based air-to-air (A2A) channels are relatively clear and thus can be modeled as free-space propagation, such an assumption may not be applicable to drones flying in low altitudes of built-up environments. In practice, low-altitude A2A channel modeling becomes more challenging in urban scenarios since buildings can obstruct the line-of-sight (LOS) path, and multipaths from buildings lead to additional losses. Therefore, we herein focus on modeling low-altitude A2A channels considering a generic urban deployment, where we introduce the evidence of the small-size first Fresnel zone at the millimeter-wave (mmWave) band to approximately derive the LOS probability. Then, the path loss under different propagation conditions is investigated to obtain an integrated path loss model. In addition, we incorporate the impact of imperfect beam alignment on the path loss, where the relation between path loss fluctuation and beam misalignment level is modeled as an exponential form. Finally, comparisons with the 3GPP model show the effectiveness of the proposed analytical model. Numerical simulations in different environments and heights provide practical deployment guidance for aerial networks.'\nauthor:\n- \ntitle: 'Path Loss Analysis for Low-Altitude"
+"---\nabstract: |\n    We propose a multigrid method to solve the linear system of equations arising from a hybrid discontinuous Galerkin (in particular, a single face hybridizable, a hybrid Raviart\u2013Thomas, or a hybrid Brezzi\u2013Douglas\u2013Marini) discretization of a Stokes problem. Our analysis is centered around the augmented Lagrangian approach and we prove uniform convergence in this setting. Numerical experiments underline our analytical findings.\\\n    <span style=\"font-variant:small-caps;\">Keywords.</span> Augmented Lagrangian approach, hybrid discontinuous Galerkin, multigrid method, Stokes equation.\naddress:\n- 'Department of Mathematics Sciences, Soochow University, Suzhou, 215006, China'\n- 'School of Mathematics, University of Minnesota, 206 Church St SE, Minneapolis, MN, 55455, USA'\n- 'Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Mathematikon, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany'\n- 'School of Engineering Science, Lappeenranta\u2013Lahti University of Technology, P.O. Box 20, 53851 Lappeenranta, Finland'\nauthor:\n- Peipei Lu\n- Wei Wang\n- Guido Kanschat\n- Andreas Rupp\nbibliography:\n- 'MultigridStokes.bib'\ntitle: Homogeneous multigrid method for HDG applied to the Stokes equation\n---\n\nIntroduction\n============\n\nHybrid discontinuous Galerkin (HDG) methods have been a very active field of research in the last years, and they have been applied to many different partial differential equations (PDEs). For Stokes problems, one advantage of HDG schemes is"
+"---\nabstract: |\n    Safety in the automotive domain is a well-known topic, which has been in constant development in the past years. The complexity of new systems that add more advanced components in each function has opened new trends that have to be covered from the safety perspective. In this case, not only the have to be covered but also scenarios, which cover all relevant information of the vehicle environment. Many of them are not yet still sufficient defined or considered. In this context, Safety of the Intended Functionality (SOTIF) appears to ensure the system when it might fail because of technological shortcomings or misuses by users.\n\n    An identification of the plausibly insufficiencies of ADAS/ADS functions has to be done to discover the potential triggering conditions that can lead to these unknown scenarios, which might effect a hazardous behaviour. The main goal of this publication is the definition of to identify these triggering conditions that have been applied to the collision avoidance function implemented in our self-developed mobile Hardware-in-Loop (HiL) platform.\nauthor:\n- 'V\u00edctor\u00a0J.\u00a0Exp\u00f3sito\u00a0Jim\u00e9nez'\n- Helmut\u00a0Martin\n- Christian\u00a0Schwarzl\n- Georg\u00a0Macher\n- Eugen\u00a0Brenner\nbibliography:\n- 'bibliography.bib'\ntitle: 'Triggering Conditions Analysis and for Validation of"
+"---\nabstract: |\n    In this paper, we use algebro-geometric methods in order to derive classification results for so-called $D$-bialgebra structures on the power series algebra $A[\\![z]\\!]$ for certain central simple non-associative algebras $A$. These structures are closely related to a version of the classical Yang-Baxter equation (CYBE) over $A$.\n\n    If $A$ is a Lie algebra, we obtain new proofs for pivotal steps in the known classification of non-degenerate topological Lie bialgebra structures on $A[\\![z]\\!]$ as well as of non-degenerate solutions of the usual CYBE.\n\n    If $A$ is associative, we achieve the classification of non-triangular topological balanced infinitesimal bialgebra structures on $A[\\![z]\\!]$ as well as of all non-degenerate solutions of an associative version of the CYBE.\naddress: |\n     ETH Z\u00fcrich\\\n    Department of Mathematics\\\n    R\u00e4mistrasse 101\\\n    8092 Zurich\\\n    Switzerland \nauthor:\n- Raschid Abedin\nbibliography:\n- 'Literatur.bib'\ntitle: 'Classification of $D$-bialgebra structures on power series algebras'\n---\n\nIntroduction\n============\n\nBackground and motivation {#background-and-motivation .unnumbered}\n-------------------------\n\nA Lie bialgebra $(L,\\delta)$ over a field $\\Bbbk$ consists of a Lie algebra $L$ over $\\Bbbk$ equipped with a skew-symmetric 1-cocycle $\\delta \\colon L \\to L \\otimes L$ such that the dual map $\\delta^*\\colon (L \\otimes L)^* \\to L^*$ restricted to $L^*\\otimes L^* \\subseteq (L\\otimes L)^*$ is a"
+"---\nabstract: 'It is widely believed that typical finite families of $d \\times d$ matrices admit finite products that attain the joint spectral radius. This conjecture is supported by computational experiments and it naturally leads to the following question: are these spectrum maximizing products typically unique, up to cyclic permutations and powers? We answer this question negatively. As discovered by Horowitz around fifty years ago, there are products of matrices that always have the same spectral radius despite not being cyclic permutations of one another. We show that the simplest Horowitz products can be spectrum maximizing in a robust way; more precisely, we exhibit a small but nonempty open subset of pairs of $2 \\times 2$ matrices $(A,B)$ for which the products $A^2 B A B^2$ and $B^2 A B A^2$ are both spectrum maximizing.'\naddress: 'Department of Mathematics, The Pennsylvania State University'\nauthor:\n- Jairo Bochi and Piotr Laskawiec\ndate: 'First version: January, 2023. Revision: August, 2023.'\ntitle: Spectrum Maximizing Products are not generically unique\n---\n\nIntroduction\n============\n\nThe *joint spectral radius* of a family of linear operators was introduced by Rota and Strang [@rota-strang] in 1960 and later became a topic of intense research. It measures the maximal"
+"---\nabstract: 'We provide a new perspective on shadow tomography by demonstrating its deep connections with the general theory of measurement frames. By showing that the formalism of measurement frames offers a natural framework for shadow tomography \u2014 in which \u201cclassical shadows\u201d correspond to unbiased estimators derived from a suitable dual frame associated with the given measurement \u2014 we highlight the intrinsic connection between standard state tomography and shadow tomography. Such perspective allows us to examine the interplay between measurements, reconstructed observables, and the estimators used to process measurement outcomes, while paving the way to assess the influence of the input state and the dimension of the underlying space on estimation errors. Our approach generalizes the method described in \\[H.-Y. Huang [*et al.*]{}, Nat. Phys. [**16**]{}, 1050 (2020)\\], whose results are recovered in the special case of covariant measurement frames. As an application, we demonstrate that a sought-after target of shadow tomography can be achieved for the entire class of tight rank-1 measurement frames \u2014 namely, that it is possible to accurately estimate a finite set of generic rank-1 bounded observables while avoiding the growth of the number of the required samples with the state dimension.'\nauthor:\n- 'L. Innocenti'"
+"---\nauthor:\n- 'Jonathan W. Bartlett'\n- Camila Olarte Parra\n- Emily Granger\n- 'Ruth H. Keogh'\n- 'Erik W. van Zwet'\n- 'Rhian M. Daniel'\ntitle: 'G-formula for causal inference via multiple imputation'\n---\n\n**Keywords:** G-formula, multiple imputation, synthetic imputation\n\nIntroduction\n============\n\nThe collection of methods referred to as G-methods, developed by James Robins and co-workers, can provide valid inference for the effects of time-varying exposures or treatments in the presence of time-varying confounders\u2014variables that affect treatment over time and the outcome of interest\u2014even when these are affected by previous values of treatment [@naimi2017introduction]. One such method is parametric G-formula (sometimes known as G-computation). Parametric G-formula involves postulating models for the time-varying confounders and outcomes. The expected outcome under specified longitudinal treatment regimes of interest can then be estimated and contrasted.\n\nThe evaluation of G-formula estimators often involves intractable integrals. To overcome this in practice, G-formula implementations make use of Monte-Carlo integration (simulation) [@daniel2011gformula; @mcgrath2020gformula]. The Monte-Carlo error in the resulting estimator can be reduced by increasing the number of simulations (simulated individuals). However, the number required to ensure this error is sufficiently small may be quite large - [@deStavola2015mediation] found in a data analysis that a Monte-Carlo sample"
+"---\nabstract: 'Image anomaly detection (IAD) is an urgent issue that needs to be addressed in modern industrial manufacturing (IM). Recently, many advanced algorithms have been released, but their performance varies greatly due to non-uniformed settings. That is, researchers find it difficult to analyze because they are designed for different or specific cases in IM. To eliminate this problem, we first propose a uniform IAD setting to systematically assess the effectiveness of these algorithms, mainly considering three aspects of supervision level (unsupervised, fully supervised), learning paradigm (few-shot, continual, noisy label), and efficiency (memory usage, inference speed). Then, we skillfully construct a comprehensive image anomaly detection benchmark (IM-IAD), which includes 19 algorithms on 7 major datasets with the same setting. Our extensive experiments (17,017 total) provide new insights into the redesign or selection of the IAD algorithm under uniform conditions. Importantly, the proposed IM-IAD presents feasible challenges and future directions for further work. We believe that this work can have a significant impact on the IAD field. To ensure reproducibility and accessibility, our source codes are uploaded to the website: <https://github.com/M-3LAB/open-iad>.'\nauthor:\n- 'Guoyang Xie$^{1}$,\u00a0 Jinbao Wang$^{1}$,\u00a0 Jiaqi Liu$^{1}$, Jiayi Lyu, Yong Liu, Chengjie Wang, Feng Zheng$^{*}$,\u00a0 and Yaochu Jin$^{*}$,\u00a0 [^1]"
+"---\nbibliography:\n- 'refs-emergencePHENO.bib'\n---\n\naddtoreset[equation]{}[section]{}\n\n\\#1 \\#1 2\n\n\\\n\nBased on Quantum Gravity arguments, it has been suggested that all kinetic terms of light particles below the UV cut-off could arise in the IR via quantum (loop) corrections. These loop corrections involve infinite towers of states becoming light (e.g. Kaluza-Klein or string towers). We study implications of this *Emergence Proposal* for fundamental scales in the Standard Model (SM). In this scheme all Yukawa couplings are of order one in the UV and small Yukawas for lighter generations appear via large anomalous dimensions induced by the towers of states. Thus, the observed hierarchies of quark and lepton masses are a reflection of the structure of towers of states that lie below the Quantum Gravity scale, $\\Lambda_{\\text{QG}}$. Small Dirac neutrino masses consistent with experimental observation appear due to the existence of a tower of SM singlet states of mass $m_{0}\\simeq Y_{\\nu_3}M_p\\simeq 7\\times 10^5$ GeV, opening up a new extra dimension, while the UV cut-off occurs at $\\Lambda_{\\text{QG}}\\lesssim 10^{14}$ GeV. Additional constraints relating the Electro-Weak (EW) and cosmological constant (c.c.) scales (denoted $M_{\\text{EW}}$ and $V_0$) appear if the Swampland condition $m_{\\nu_1}\\lesssim V_0^{1/4}$ is imposed (with $\\nu_1$ denoting the lightest neutrino), which itself"
+"---\nabstract: 'Recently, inversion methods have been exploring the incorporation of additional high-rate information from pretrained generators (such as weights or intermediate features) to improve the refinement of inversion and editing results from embedded latent codes. While such techniques have shown reasonable improvements in reconstruction, they often lead to a decrease in editing capability, especially when dealing with complex images that contain occlusions, detailed backgrounds, and artifacts. To address this problem, we propose a novel refinement mechanism called **Domain-Specific Hybrid Refinement** (DHR), which draws on the advantages and disadvantages of two mainstream refinement techniques. We find that the weight modulation can gain favorable editing results but is vulnerable to these complex image areas and feature modulation is efficient at reconstructing. Hence, we divide the image into two domains and process them with these two methods separately. We first propose a Domain-Specific Segmentation module to automatically segment images into in-domain and out-of-domain parts according to their invertibility and editability without additional data annotation, where our hybrid refinement process aims to maintain the editing capability for in-domain areas and improve fidelity for both of them. We achieve this through Hybrid Modulation Refinement, which respectively refines these two domains by weight modulation and"
+"---\nabstract: 'In the development of acoustic signal processing algorithms, their evaluation in various acoustic environments is of utmost importance. In order to advance evaluation in realistic and reproducible scenarios, several high-quality acoustic databases have been developed over the years. In this paper, we present another complementary database of acoustic recordings, referred to as the Multi-arraY Room Acoustic Database (MYRiAD). The MYRiAD database is unique in its diversity of microphone configurations suiting a wide range of enhancement and reproduction applications (such as assistive hearing, teleconferencing, or sound zoning), the acoustics of the two recording spaces, and the variety of contained signals including 1214 room impulse responses (RIRs), reproduced speech, music, and stationary noise, as well as recordings of live cocktail parties held in both rooms. The microphone configurations comprise a dummy head (DH) with in-ear omnidirectional microphones, two behind-the-ear (BTE) pieces equipped with 2 omnidirectional microphones each, 5 external omnidirectional microphones (XMs), and two concentric circular microphone arrays (CMAs) consisting of 12 omnidirectional microphones in total. The two recording spaces, namely the SONORA Audio Laboratory (SAL) and the Alamire Interactive Laboratory (AIL), have reverberation times of and , respectively. Audio signals were reproduced using 10 movable loudspeakers in the SAL"
+"---\nabstract: 'The spectral index images of the jet in the nearby radio galaxy M87 have previously been shown with Very Long Baseline Interferometric arrays at 2-43GHz. They exhibit flattening of the spectra at a location of inner (central) spine and toward outer ridges. This could imply optical depth effects, lower energy cutoff or stratification of the emitting particles energy distribution. In this paper we employ simulations of multifrequency VLBI observations of M87 radio jet with various model brightness distributions. CLEAN deconvolution errors produce significant features in the observed images. For intensity images they result in the appearance of the inner ridge line in the intrinsically edge brightened jet models. For spectral index images they flatten the spectra in a series of stripes along the jet. Another bias encountered in our simulations is steepening of the spectra in a low surface brightness jet regions. These types of the imaging artefacts do not depend on the model considered. We propose a methods for the compensation of the systematics using only the observed data.'\nauthor:\n- '\\'\nbibliography:\n- 'nee.bib'\ndate: 'Accepted \u2026Received \u2026; in original form \u2026'\n---\n\n\\[firstpage\\]\n\ngalaxies: active\u00a0\u2013 radio continuum: galaxies\u00a0\u2013 galaxies: jets\u00a0\u2013 methods: data"
+"---\nabstract: 'Most of today\u2019s communication systems are designed to target reliable message recovery after receiving the entire encoded message (codeword). However, in many practical scenarios, the transmission process may be interrupted before receiving the complete codeword. This paper proposes a novel rateless autoencoder (AE)-based code design suitable for decoding the transmitted message before the noisy codeword is fully received. Using particular dropout strategies applied during the training process, rateless AE codes allow to trade off between decoding delay and reliability, providing a graceful improvement of the latter with each additionally received codeword symbol. The proposed rateless AEs significantly outperform the conventional AE designs for scenarios where it is desirable to trade off reliability for lower decoding delay.'\nauthor:\n- \ntitle: |\n    Rateless Autoencoder Codes:\\\n    Trading off Decoding Delay and Reliability\\\n    [^1] \n---\n\nIntroduction {#intro}\n============\n\nThe design of short block-length error-correcting codes for unpredictable and time-varying wireless channels still remains a challenge [@Shirvanimoghaddam_2018]. Particularly challenging is the design of codes for channels experiencing prolonged deep fades or even complete channel failures that prevent the receiver to receive the complete (noisy) codeword. Such channels, referred to as *dying channels* in [@Zeng_2008] and [@Varshney_2012], arise in various communication systems, e.g., due"
+"---\nabstract: 'In this work, we create artistic closed loop curves that trace out images and 3D shapes, which we then hide in musical audio as a form of steganography. We use traveling salesperson art to create artistic plane loops to trace out image contours, and we use Hamiltonian cycles on triangle meshes to create artistic space loops that fill out 3D surfaces. Our embedding scheme is designed to faithfully preserve the geometry of these loops after lossy compression, while keeping their presence undetectable to the audio listener. To accomplish this, we hide each dimension of the curve in a different frequency, and we perturb a sliding window sum of the magnitude of that frequency to best match the target curve at that dimension, while hiding scale information in that frequency\u2019s phase. In the process, we exploit geometric properties of the curves to help to more effectively hide and recover them. Our scheme is simple and encoding happens efficiently with a nonnegative least squares framework, while decoding is trivial. We validate our technique quantitatively on large datasets of images and audio, and we show results of a crowd sourced listening test that validate that the hidden information is indeed unobtrusive.'"
+"---\nabstract: 'Semantic parsing is a means of taking natural language and putting it in a form that a computer can understand. There has been a multitude of approaches that take natural language utterances and form them into lambda calculus expressions - mathematical functions to describe logic. Here, we experiment with a sequence to sequence model to take natural language utterances, convert those to lambda calculus expressions, when can then be parsed, and place them in an XML format that can be used by a finite state machine. Experimental results show that we can have a high accuracy model such that we can bridge the gap between technical and nontechnical individuals in the robotics field.'\nauthor:\n- |\n    Jake Imyak\\\n    `imyak.1@osu.edu`\\\n    Parth Parekh\\\n    `parekh.86@osu.edu`\\\n    Cedric McGuire\\\n    `mcguire.389@osu.edu`\\\ndate: 'December 10th, 2021'\ntitle: Underwater Robotics Semantic Parser Assistant\n---\n\nCredits\n=======\n\nJake Imyak was responsible for the creation of the 1250 dataset terms and finding the RNN encoder/decoder model. This took 48 Hours. Cedric McGuire was responsible for the handling of the output logical form via the implementation of the Tokenizer and Parser. This took 44 Hours. Parth Parekh assembled the Python structure for behavior tree as well as created the"
+"---\nauthor:\n- 'Xiao Wang,'\n- 'Chi Tian[!!]{},'\n- and Fa Peng Huang\nbibliography:\n- 'ref.bib'\ntitle: 'Model-dependent analysis method for energy budget of the cosmological first-order phase transition'\n---\n\nIntroduction\n============\n\nThe cosmological first-order phase transition (FOPT) and its associated phase transition gravitational waves (GWs) open a new window to explore many fundamental problems in particle cosmology, such as electroweak baryogenesis\u00a0[@Trodden:1998ym; @Morrissey:2012db] and dark matter\u00a0[@Baker:2019ndr; @Chway:2019kft; @Huang:2017kzu; @Huang:2017rzf; @Elor:2021swj], etc. During an FOPT, GW signals can be generated by bubble collisions\u00a0[@Kosowsky:1991ua; @Kosowsky:1992vn; @Huber:2008hg], sound waves\u00a0[@Hindmarsh:2013xza; @Hindmarsh:2015qta; @Hindmarsh:2017gnf], and turbulence\u00a0[@Kosowsky:2001xp; @Caprini:2009yp; @RoperPol:2019wvy]. Future GW experiments, such as TianQin\u00a0[@TianQin:2015yph; @TianQin:2020hid], LISA\u00a0[@LISA:2017pwj], Taiji\u00a0[@Hu:2017mde], and Big Bang Observatory (BBO)\u00a0[@Corbin:2005ny], among others, may be able to detect phase transition GW signals and reveal various puzzles about our Universe. Recent studies\u00a0[@Hindmarsh:2013xza; @Hindmarsh:2015qta; @Hindmarsh:2017gnf] have shown that the sound wave is the dominant source of phase transition GWs in a thermal FOPT. To finally pin down the underlying physics of an FOPT, we need precise quantification of the GW spectra and their signal-to-noise ratio in future GW experiments. To that end, obtaining accurate estimations of phase transition parameters that determine the GW spectra become critical. The"
+"[**Integral equation method for microseismic wavefield modelling in anisotropic elastic media**]{}\\\n\n[**Ujjwal Shekhar$^{1,a}$, Morten Jakobsen$^{1,a}$, Einar Iversen$^{1,a}$, Inga Berre$^{1,b}$, Florin A. Radu$^{1,b}$\\\n(January 10, 2023)\\\n**$^1$ Center for Modeling of Coupled Subsurface Dynamics, University of Bergen, Bergen 5007, Norway\\\n$^a$ Department of Earth Science, University of Bergen\\\n$^b$ Department of Mathematics, University of Bergen\\\nEmail: Ujjwal.Shekhar@uib.no****]{}\n\nABSTRACT {#abstract .unnumbered}\n========\n\nIn this paper, we present a frequency-domain volume integral method to model the microseismic wavefield in heterogeneous anisotropic-elastic media. The elastic wave equation is written as an integral equation of the Lippmann-Schwinger type, and the seismic source is represented as a general moment tensor. The displacement field due to a moment tensor source can be computed using the spatial derivative of the elastodynamic Green\u2019s function. The existing matrix-based implementation of the integral equation is computationally inefficient to model the wavefield in a three-dimensional earth. An integral equation for the particle displacement is, hence, formulated in a matrix-free manner through the application of the Fourier transform. The biconjugate gradient stabilized method is used to iteratively obtain the solution of this equation. We apply the numerical scheme to three different models in order of increasing geological complexity and obtain the elastic displacement"
+"---\nabstract: 'The inclusion of long-range couplings in the Kitaev chain is shown to modify the universal scaling of topological states close to the critical point. By means of the scattering approach, we prove that the Majorana states *soften*, becoming increasingly delocalised at a universal rate which is only determined by the interaction range. This edge mechanism can be related to a change in the value of the bulk topological index at criticality, upon careful redefinition of the latter. The critical point turns out to be topologically akin to the trivial phase rather than interpolating between the two phases. Our treatment moreover showcases how various topological aspects of quantum models can be investigated analytically.'\nauthor:\n- Alessandro Tarantola\n- Nicol\u00f2 Defenu\nbibliography:\n- 'bib.bib'\ntitle: 'Softening of Majorana edge states by long-range couplings'\n---\n\nIntroduction {#sec:Intro}\n============\n\nEfficient quantum computing is probably the primary goal of modern physics research and the race for quantum advantage involves research groups all around the globe[@higgins2007fundamental; @carlo2009demonstration; @monroe2013trapped; @debnath2016small; @barends2014universal; @ofek2016extending; @arute2019quantum]. The first spark to this intense research activity came from the formulation of Shor\u2019s algorithm for prime factorization[@shor1994algorithms; @shor1997polynomial], which was followed by a large number of influential theoretical proposals[@cirac1995quantum; @beckman1996efficient; @lloyd1996universal;"
+"---\nabstract: 'Challenging the Nvidia monopoly, dedicated AI-accelerator chips have begun emerging for tackling the computational challenge that the inference and, especially, the training of modern deep neural networks (DNNs) poses to modern computers. The field has been ridden with studies assessing the performance of these contestants across various DNN model types. However, AI-experts are aware of the limitations of current DNNs and have been working towards the fourth AI wave which will, arguably, rely on more biologically inspired models, predominantly on spiking neural networks (SNNs). At the same time, GPUs have been heavily used for simulating such models in the field of computational neuroscience, yet AI-chips have not been tested on such workloads. The current paper aims at filling this important gap by evaluating multiple, cutting-edge AI-chips (Graphcore IPU, GroqChip, Nvidia GPU with Tensor Cores and Google TPU) on simulating a highly biologically detailed model of a brain region, the inferior olive (IO). This IO application stress-tests the different AI-platforms for highlighting architectural tradeoffs by varying its compute density, memory requirements and floating-point numerical accuracy. Our performance analysis reveals that the simulation problem maps extremely well onto the GPU and TPU architectures, which for networks of 125,000 cells leads"
+"---\nauthor:\n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \nbibliography:\n- 'epj\\_st\\_scordo.bib'\ntitle: New opportunities for kaonic atoms measurements from CdZnTe detectors\n---\n\nIntroduction {#sec_intro}\n============\n\nKaonic atoms are formed when a $\\mathrm{K^-}$ is moderated inside a target until it reaches a low enough kinetic energy to be stopped, replacing one of the outer electrons and forming an exotic atom in a highly excited state. The kaonic atom then undergoes atomic cascade to the ground state. These systems provide an ideal tool to study the low-energy regime of Quantum Chromodynamics (QCD) since, due to the much heavier $\\mathrm{K^-}$ mass with respect to the $\\mathrm{e^-}$ one, the lower levels are close enough to the nucleus to be influenced by the short-range strong interaction between the nucleus and the $\\mathrm{K^-}$ [@Napolitano:2022eik].\\\nKaonic atoms have been intensively studied in the 1970s and 1980s with a series of measurements, still representing today the main database for low-energy antikaon-nucleon studies [@Davies:1979; @Izycki:1980; @Bird:1983; @Wiegand:1971zz; @Baird:1983ub; @Friedman:1994hx]. More"
+"---\nabstract: 'Extreme events are unusual and rare large-amplitude fluctuations that occur can unexpectedly in nonlinear dynamical systems. Events above the extreme event threshold of the probability distribution of a nonlinear process characterize extreme events. Different mechanisms for the generation of extreme events and their prediction measures have been reported in the literature. [ Based on the properties of extreme events, such as rare in the frequency of occurrence and extreme in amplitude, various studies have shown that extreme events are both linear and nonlinear in nature.]{} Interestingly, in this work, we report on a special class of extreme events which are nonchaotic and nonperiodic. These nonchaotic extreme events appear in between the quasi-periodic and chaotic dynamics of the system. We report the existence of such extreme events with various statistical measures and characterization techniques.'\nauthor:\n- 'Premraj Durairaj$^1$, Sathiyadevi Kanagaraj$^1$, Suresh Kumarasamy$^1$, Karthikeyan Rajagopal$^{1,2}$'\ntitle: 'Emergence of extreme events in a quasi-periodic oscillator'\n---\n\nExtreme events are unanticipated, rare events that occur in many natural and engineering systems. Extreme events (EE) can exist in various forms, including floods, cyclones, droughts, pandemics, power outages, material ruptures, explosions, chemical contamination, and stock market crashes, among others [@albeverio06]. Such events have a"
+"[**Homoclinic orbit and the violation of the chaos bound around a black hole with anisotropic matter fields** ]{}\\\n\n[$\\mbox{Soyeon \\,\\, Jeong}^{\\dag}$]{}[^1], [$\\mbox{Bum-Hoon \\,\\, Lee}^{\\S\\dag}$]{}[^2], [$\\mbox{Hocheol \\,\\, Lee}^{\\dag}$]{}[^3], [$\\mbox{Wonwoo \\,\\, Lee}^{\\S}$]{}[^4]\\\n\n[\u00a7*Center for Quantum Spacetime, Sogang University, Seoul 04107, Korea*]{}\\\n[*Department of Physics, Sogang University, Seoul 04107, Korea*]{}\\\n\n[**Abstract**]{}\n\n[ We study the homoclinic orbit and the violation of chaos bound, which are obtained by particle motions around a black hole that coexist with anisotropic matter fields. The homoclinic one is associated with an unstable local maximum of the effective potential. By perturbing a particle located slightly away from the homoclinic one, we numerically compute Lyapunov exponents indicating the sensitivity of the initial value. Our results demonstrate that the violation of the chaos bound increases with higher angular momentum, and the anisotropic matter gives rise to violating the chaos bound further, even in the case of the nonextremal black hole. We utilize the Hamiltonian-Jacobi formalism to explicitly illustrate how the geodesic motion of a particle can be integrable in the procedure of obtaining our findings.]{}\n\nIntroduction \\[sec1\\]\n=====================\n\nBlack holes are not only theoretically predicted in the theory of gravitation, but also become real celestial objects to exist in the Universe"
+"---\nabstract: 'For a finite simple graph $G$, the bunkbed graph $G^\\pm$ is defined to be the product graph $G\\square K_2$. We will label the two copies of a vertex $v\\in V(G)$ as $v_-$ and $v_+$. The bunkbed conjecture, posed by Kasteleyn, states that for independent bond percolation on $G^\\pm$, percolation from $u_-$ to $v_-$ is at least as likely as percolation from $u_-$ to $v_+$, for any $u,v\\in V(G)$. Despite the plausibility of this conjecture, so far the problem in full generality remains open. Recently, Hutchcroft, Nizi\u0107-Nikolac, and Kent gave a proof of the conjecture in the $p\\uparrow 1$ limit. Here we present a new proof of the bunkbed conjecture in this limit, working in the more general setting of allowing different probabilities on different edges of $G^\\pm$.'\naddress: 'Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, United Kingdom'\nauthor:\n- Lawrence Hollom\nbibliography:\n- 'main.bib'\ntitle: 'A new proof of the bunkbed conjecture in the $p\\uparrow 1$ limit'\n---\n\nIntroduction {#sec:intro}\n============\n\nIn this introduction we state two forms of the bunkbed conjecture, discuss briefly the known results about special cases of the conjecture, and then proceed to state the"
+"---\nabstract: 'We introduce a new Bravais lattice determination algorithm. SELLA is a straight-forward algorithm and a program for determining Bravais lattice type based on Selling (Delone) reduction. It is a complete, closed solution, and it provides a clear metric of fit to each type.'\nauthor:\n- 'Herbert J.'\n- 'Nicholas K.'\nbibliography:\n- 'Reduced.bib'\ntitle: 'SELLA - A Program for Determining Bravais Lattice Types'\n---\n\n[**]{}\\\n\n[Bernstein]{}\n\n[Sauter]{}\n\nA method for determining likely Bravais lattice types based on Selling (Delone) reduction. It is a complete, closed solution.\n\n[**Note:**]{} Boris Delaunay in his later publications used the Russian version of his surname: Delone. We will follow that choice.\\\n\nIntroduction\n============\n\nWe introduce a new Bravais lattice determination algorithm. The Bravais lattice types were created by . and developed methods for the identification of the Bravais lattice type of a crystal using the measured unit cell dimensions; their methods were exact only if the cell parameters exactly corresponded to the actual type [@Patterson1957]. Delone discussed the issues of scalars that have nearly zero values, but the broader issues of other measurement errors were not discussed. review the literature on the efforts to create methods to utilize data that contain unavoidable measurement"
+"---\nabstract: 'Siamese networks are one of the most trending methods to achieve self-supervised visual representation learning (SSL). Since hand labeling is costly, SSL can play a crucial part by allowing deep learning to train on large unlabeled datasets. Meanwhile, Neural Architecture Search (NAS) is becoming increasingly important as a technique to discover novel deep learning architectures. However, early NAS methods based on reinforcement learning or evolutionary algorithms suffered from ludicrous computational and memory costs. In contrast, differentiable NAS, a gradient-based approach, has the advantage of being much more efficient and has thus retained most of the attention in the past few years. In this article, we present NASiam, a novel approach that uses for the first time differentiable NAS to improve the multilayer perceptron projector and predictor (encoder/predictor pair) architectures inside siamese-networks-based contrastive learning frameworks (e.g., SimCLR, SimSiam, and MoCo) while preserving the simplicity of previous baselines. We crafted a search space designed explicitly for multilayer perceptrons, inside which we explored several alternatives to the standard ReLU activation function. We show that these new architectures allow ResNet backbone convolutional models to learn strong representations efficiently. NASiam reaches competitive performance in both small-scale (i.e., CIFAR-10/CIFAR-100) and large-scale (i.e., ImageNet) image"
+"---\nabstract: 'Sinc-collocation methods are known to be efficient for Fredholm integral equations of the second kind, even if functions in the equations have endpoint singularity. However, existing methods have the disadvantage of inconsistent collocation points. This inconsistency complicates the implementation of such methods, particularly for large-scale problems. To overcome this drawback, this study proposes another Sinc-collocation methods with consistent collocation points. The results of a theoretical error analysis show that the proposed methods have the same convergence property as existing methods. Numerical experiments suggest the superiority of the proposed methods in terms of implementation and computational cost.'\nauthor:\n- 'Tomoaki Okayama[^1]'\ntitle: 'Sinc-collocation methods with consistent collocation points for Fredholm integral equations of the second kind'\n---\n\nIntroduction\n============\n\nThis paper is concerned with Fredholm integral equations of the second kind of the following form: $$u(t) - \\int_a^b k(t, s)u(s) {\\,\\mathrm{d}}s = g(t),\n\\quad a\\leq t\\leq b,\n\\label{eq:Fredholm}$$ where $k$ and $g$ are given continuous functions, and $u$ is the solution to be determined. Most numerical methods provided in the literature do not perform well when the functions $k$ and $g$ have derivative singularity at the endpoints. To overcome the difficulty, Rashidinia and Zarebnia\u00a0[@RZ1] proposed a Sinc-collocation method,"
+"---\nabstract: 'Pin fins are imperative in the cooling of turbine blades. The designs of pin fins, therefore, have seen significant research in the past. With the developments in metal additive manufacturing, novel design approaches toward complex geometries are now feasible. To that end, this article presents a Bayesian optimization approach for designing inline pins that can achieve low pressure loss. The pin-fin shape is defined using featurized (parametrized) piecewise cubic splines in 2D. The complexity of the shape is dependent on the number of splines used for the analysis. From a method development perspective, the study is performed using three splines. Owing to this piece-wise modeling, a unique pin fin design is defined using five features. After specifying the design, a computational fluid dynamics-based model is developed that computes the pressure drop during the flow. Bayesian optimization is carried out on a Gaussian processes-based surrogate to obtain an optimal combination of pin-fin features to minimize the pressure drop. The results show that the optimization tends to approach an aerodynamic design leading to low pressure drop corroborating with the existing knowledge. Furthermore, multiple iterations of optimizations are conducted with varying degree of input data. The results reveal that a convergence"
+"---\nabstract: 'Improving the deployment efficiency of transformer-based language models has been challenging given their high computation and memory cost. While INT8 quantization has recently been shown to be effective in reducing both the memory cost and latency while preserving model accuracy, it remains unclear whether we can leverage INT4 (which doubles peak hardware throughput) to achieve further latency improvement. In this study, we explore the feasibility of employing INT4 weight and activation (W4A4) quantization for language models. Our findings indicate that W4A4 quantization introduces no to negligible accuracy degradation for encoder-only and encoder-decoder models, but causes a significant accuracy drop for decoder-only models. To materialize the performance gain using W4A4, we develop a highly-optimized end-to-end W4A4 encoder inference pipeline supporting different quantization strategies. Our INT4 pipeline is $8.5\\times$ faster for latency-oriented scenarios and up to $3\\times$ for throughput-oriented scenarios compared to the inference of FP16, and improves the SOTA BERT INT8 performance from FasterTransformer by up to $1.7\\times$. We provide insights into the failure cases when applying W4A4 to decoder-only models, and further explore the compatibility of INT4 quantization with other compression methods, like pruning and layer reduction.'\nauthor:\n- DeepSpeed\ntitle: 'Random-LTD: Random and Layerwise Token Dropping Brings"
+"---\nabstract: 'We study generic conformally flat (analytic-)hypersurfaces in the Euclidean $4$-space $\\mathbb{R}^4$. Such a local-hypersurface is obtained as an evolution of surfaces issuing from a certain surface in $\\mathbb{R}^4$, and then, in consequence, the original surface is a (principal-)curvature surface of the hypersurface. The Poincar\u00e9 metric ${\\check g}_H$ of the upper half plane leads to a $6$-dimensional set of singular (analytic-)Riemannian $2$-metrics $g_0$ of $\\mathbb{R}^2$: on a simply connected open set in the regular domain of $g_0$, a curvature surface with the metric $g_0$ is determined. In this paper, we choose a suitable singular metric $g_0$ for ${\\check g}_H$ and clarify the structure of the curvature surfaces: the curvature surfaces extend analytically to what kind set of $\\mathbb{R}^2$ beyond the regular set of $g_0$; we explicitly catch the singularities and the points of infinity of $g_0$ for the surface. In this case, all principal curvature lines in the extended surface are expressed by a frame field of $\\mathbb{R}^4$ induced on the surface from hypersurfaces and they lie on some standard $2$-spheres $\\mathbb{S}^2$, respectively. We also provide a general method of constructing an approximation of such frame fields, and obtain the figures of those lines including the singular points of"
+"---\nabstract: 'Hierarchical Clustering is a popular unsupervised machine learning method with decades of history and numerous applications. We initiate the study of [*differentially private*]{} approximation algorithms for hierarchical clustering under the rigorous framework introduced by\u00a0@dasgupta2016cost. We show strong lower bounds for the problem: that any $\\epsilon$-DP algorithm must exhibit $O(|V|^2/ \\epsilon)$-additive error for an input dataset $V$. Then, we exhibit a polynomial-time approximation algorithm with $O(|V|^{2.5}/ \\epsilon)$-additive error, and an exponential-time algorithm that meets the lower bound. To overcome the lower bound, we focus on the stochastic block model, a popular model of graphs, and, with a separation assumption on the blocks, propose a private $1+o(1)$ approximation algorithm which also recovers the blocks exactly. Finally, we perform an empirical study of our algorithms and validate their performance.'\nauthor:\n- |\n    Jacob Imola[^1]\\\n    UC San Diego\\\n    <jimola@eng.ucsd.edu>\n- |\n    Alessandro Epasto\\\n    Google\\\n    <aepasto@google.com>\n- |\n    Mohammad Mahdian\\\n    Google\\\n    <mahdian@google.com>\n- |\n    Vincent Cohen-Addad\\\n    Google\\\n    <cohenaddad@google.com>\n- |\n    Vahab Mirrokni\\\n    Google\\\n    <mirrokni@google.com>\nbibliography:\n- 'citations.bib'\ntitle: 'Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees'\n---\n\nIntroduction {#sec:introduction}\n============\n\nHierarchical Clustering is a staple of unsupervised machine learning with more than 60 years of history\u00a0[@ward1963hierarchical]. Contrary to [*flat*]{} clustering methods (such"
+"---\nabstract: 'We investigate predictions of the trilinear Higgs self-coupling with radiative corrections in the context of the Inert Doublet Model. The triple Higgs vertex is computed at the one-loop level based on the on-shell renormalization scheme. We calculate its possible deviation from the predictions within the standard model, taking into account all relevant theoretical and experimental constraints, including dark matter searches and the latest bounds on the branching fraction of the Higgs boson decaying to invisible particles. By scanning the model\u2019s parameter space, we find that the deviation in the triple Higgs boson self-coupling from standard model expectations can be substantial, exceeding 100% in certain regions of the parameter space.'\nauthor:\n- |\n    Jaouad El Falaki$^{1\\,}$[^1]\\\n    [*$^1$ LPTHE, Physics Department, Faculty of Sciences, Ibnou Zohr University, P.O.B. 8106 Agadir, Morocco.*]{}\\\n    *I would like to dedicate this paper to my sister Fatima, who is bravely fighting against cancer*\nbibliography:\n- 'biblio.bib'\ntitle: |\n     **Revisiting one-loop corrections to the trilinear Higgs boson self-coupling in the Inert Doublet Model\\\n    **\n---\n\n=1\n\nIntroduction {#sec:introduction}\n============\n\nA great achievement in the history of high energy physics was made on July 4, 2012, with the discovery of the Higgs boson by ATLAS and CMS,"
+"---\nabstract: 'We investigate five English benchmark datasets (on the superGLUE leaderboard) and two Swedish datasets for bias, along multiple axes. The datasets are the following: , , , , , Swedish , and SWEDN. Bias can be harmful and it is known to be common in data, which models learn from. In order to mitigate bias in data, it is crucial to be able to estimate it objectively. We use bipol, a novel multi-axes bias metric with explainability, to estimate and explain how much bias exists in these datasets. Multilingual, multi-axes bias evaluation is not very common. Hence, we also contribute a new, large Swedish bias-labeled dataset (of 2 million samples), translated from the English version and train the model on it. In addition, we contribute new multi-axes lexica for bias detection in Swedish. We make the codes, model, and new dataset publicly available.'\nauthor:\n- |\n    \\\n    Tosin Adewumi\\*^+^, Isabella S\u00f6dergren^++^, Lama Alkhaled^+^, Sana Sabah Sabry^+^,\\\n    Foteini Liwicki^+^ and Marcus Liwicki^+^\\\n    ^+^Machine Learning Group, EISLAB, ^++^Digital Services and Systems\\\n    Lule\u00e5 University of Technology, Sweden\\\nbibliography:\n- 'ranlp2023.bib'\ntitle: 'Bipol: Multi-axes Evaluation of Bias with Explainability in Benchmark Datasets'\n---\n\n**Caution: This paper contains examples, from datasets, of what"
+"---\nabstract: 'We present a high time resolution, multi-frequency linear polarization analysis of Very Large Array (VLA) radio observations during some of the brightest radio flaring (${\\sim} 1{\\text{\\,Jy}\\xspace}$) activity of the 2015 outburst of V404 Cygni. The VLA simultaneously captured the radio evolution in two bands (each with two 1 GHz base-bands), recorded at 5/7and 21/26, allowing for a broadband polarimetric analysis. Given the source\u2019s high flux densities, we were able to measure polarization on timescales of ${\\sim}13\\,$minutes, constituting one of the highest temporal resolution radio polarimetric studies of a black hole X-ray binary (BHXB) outburst to date. Across all base-bands, we detect variable, weakly linearly polarized emission (${<} 1\\%$) with a single, bright peak in the time-resolved polarization fraction, consistent with an origin in an evolving, dynamic jet component. We applied two independent polarimetric methods to extract the intrinsic electric vector position angles and rotation measures from the 5 and 7$\\,$GHz base-band data and detected a variable intrinsic polarization angle, indicative of a rapidly evolving local environment or a complex magnetic field geometry. Comparisons to the simultaneous, spatially-resolved observations taken with the Very Long Baseline Array at 15.6, do not show a significant connection between the jet ejections and"
+"---\nabstract: 'In the power system, security assessment (SA) plays a pivotal role in determining the safe operation in a normal situation and some contingencies scenarios. Electrical variables as input variables of the model are mainly considered to indicate the power system operation as secure or insecure, according to the reliability criteria for contingency scenarios. In this approach, the features are in grid format data, where the relation between features and any knowledge of network topology is absent. Moreover, the traditional and common models, such as neural networks (NN), are not applicable if the input variables are in the graph format structure. Therefore, this paper examines the security analysis in the graph neural network (GNN) framework such that the GNN model incorporates the network connection and node\u2019s neighbors\u2019 influence for the assessment. Here the input features are separated graphs representing different network conditions in electrical and structural statuses. Topological characteristics defined by network centrality measures are added in the feature vector representing the structural properties of the network. The proposed model is simulated in the IEEE 118-Bus system for the voltage static security assessment (SSA). The performance indices validate the efficiency of the GNN-based model compared to the traditional NN"
+"---\nabstract: 'We extend the renormalizability study of the formulation of chiral effective field theory with a finite cutoff, applied to nucleon-nucleon scattering, by taking into account non-perturbative effects. We consider the nucleon-nucleon interaction up to next-to-leading order in the chiral expansion. The leading-order interaction is treated non-perturbatively. In contrast to the previously considered case when the leading-order interaction was assumed to be perturbative, new features related to the renormalization of the effective field theory are revealed. In particular, more severe constraints on the leading-order potential are formulated, which can enforce the renormalizability and the correct power counting for the next-to-leading order amplitude. To illustrate our theoretical findings, several partial waves in the nucleon-nucleon scattering, $^3P_0$, $^3S_1-{^3D_1}$ and $^1S_0$ are analyzed numerically. The cutoff dependence and the convergence of the chiral expansion for those channels are discussed.'\nauthor:\n- 'A.\u00a0M.\u00a0Gasparyan'\n- 'E.\u00a0Epelbaum'\nbibliography:\n- '5.bib'\ntitle: 'Renormalization of nuclear chiral effective field theory with non-perturbative leading order interactions'\n---\n\nIntroduction\n============\n\nOver the last decades, the effective field theory (EFT) approach has become a standard tool in studies of the nucleon-nucleon (NN), few-nucleon and many-nucleon systems due to the possibility to perform systematically improvable calculations in accordance"
+"---\nabstract: 'X-ray radiation, in particular radiation between 0.1 keV and 10 keV, is evident from both point-like sources, such as compact objects and T-Tauri young stellar objects, and extended emission from hot, cooling gas, such as in supernova remnants. The X-ray radiation is absorbed by nearby gas, providing a source of both heating and ionization. While protoplanetary chemistry models now often include X-ray emission from the central young stellar object, simulations of star-forming regions have yet to include X-ray emission coupled to the chemo-dynamical evolution of the gas. We present an extension of the [TreeRay]{} reverse raytrace algorithm implemented in the [Flash]{} magneto-hydrodynamic code which enables the inclusion of X-ray radiation from 0.1 keV $< E_{\\gamma} <$ 100 keV, dubbed [XrayTheSpot]{}. [XrayTheSpot]{} allows for the use of an arbitrary number of bins, minimum and maximum energies, and both temperature-independent and temperature-dependent user-defined cross sections, along with the ability to include both point and extended diffuse emission and is coupled to the thermochemical evolution. We demonstrate the method with several multi-bin benchmarks testing the radiation transfer solution and coupling to the thermochemistry. Finally, we show two example star formation science cases for this module: X-ray emission from protostellar accretion irradiating"
+"---\nabstract: 'This paper proposes EyeNet, a novel semantic segmentation network for point clouds that addresses the critical yet often overlooked parameter of coverage area size. Inspired by human peripheral vision, EyeNet overcomes the limitations of conventional networks by introducing a simple but efficient multi-scale input and a parallel processing network with connection blocks between parallel streams. The proposed approach effectively addresses the challenges of dense point clouds, as demonstrated by our ablation studies and state-of-the-art performance on Large-Scale Outdoor datasets.'\nauthor:\n- |\n    Sunghwan Yoo, Yeonjeong Jeong, Maryam Jameela, Gunho Sohn\\\n    Department of Earth and Space Science and Engineering\\\n    Lassonde School of Engineering York University, Canada\\\n    [(jacobyoo, yjjeong, maryumja, gsohn)@yorku.ca]{}\ntitle: 'Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scene'\n---\n\nIntroduction\n============\n\nRecently, there has been growing interest in developing digital twins of the three-dimensional world, driven by their various applications. With advancements in LiDAR devices and survey techniques, point cloud datasets have become more accurate, dense, and spatially extensive, both on the ground level[@paris2014; @iqmulus; @hackel2017semantic3d; @semantickitti; @toronto3d] and in the airborne level[@isprs2012; @dales; @sensaturban; @sum].\n\nHowever, the functional coverage area of an input batch is critical for effective feature learning in semantic segmentation"
+"---\nabstract: 'This paper presents a methodology for the simulation of non-Gaussian wind field as a stochastic wave using the 3rd-order Spectral Representation Method. Traditionally, the wind field is modeled as a stochastic vector process at discrete locations in space. But the simulation of vector process is well-known to be computationally challenging and numerically unstable when modeling wind at a large number of discrete points in space. Recently, stochastic waves have been used to model the field as a continuous process indexed both in time and space. We extend the classical Spectral Representation Method for simulation of Gaussian stochastic waves to a third-order representation modeling asymmetrically skewed non-Gaussian stochastic waves from a prescribed power spectrum and bispectrum. We present an efficient implementation using the fast Fourier transform, which reduces the computational time dramatically. We then apply the method for simulation of a non-Gaussian wind velocity field along a long-span bridge.'\naddress: 'Johns Hopkins University, Baltimore, United States'\nauthor:\n- Lohit Vandanapu\n- 'Michael D. Shields'\nbibliography:\n- 'elsarticle-template-1-num.bib'\ntitle: 'Simulation of non-Gaussian wind field as a $3^{rd}$-order stochastic wave'\n---\n\nwind field simulation ,stochastic wave ,non-Gaussian ,stochastic process ,simulation\n\nIntroduction {#sec:introduction}\n============\n\nDynamic wind loads can have unpredictable and devastating"
+"---\nabstract: 'Energy modelling can enable energy-aware software development and assist the developer in meeting an application\u2019s energy budget. Although many energy models for embedded processors exist, most do not account for processor-specific configurations, neither are they suitable for static energy consumption estimation. This paper introduces a set of comprehensive energy models for Arm\u2019s Cortex-M0 processor, ready to support energy-aware development of edge computing applications using either profiling- or static-analysis-based energy consumption estimation. We use a commercially representative physical platform together with a custom modified Instruction Set Simulator to obtain the physical data and system state markers used to generate the models. The models account for different processor configurations which all have a significant impact on the execution time and energy consumption of edge computing applications. Unlike existing works, which target a very limited set of applications, all developed models are generated and validated using a very wide range of benchmarks from a variety of emerging IoT application areas, including machine learning and have a prediction error of less than 5%.'\nauthor:\n- |\n    Kris Nikov, Kyriakos Georgiou, Zbigniew Chamski,\\\n    Kerstin Eder[^1] [ ]{}and Jose Nunez-Yanez[^2]\nbibliography:\n- 'shortedRef.bib'\ntitle: 'Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and"
+"---\nabstract: 'Long Term Evolution (LTE) signal is ubiquitously present in electromagnetic (EM) background environment, which make it an attractive signal source for the ambient backscatter communications (AmBC). In this paper, we propose a system, in which a backscatter device (BD) introduces artificial Doppler shift to the channel which is larger than the natural Doppler but still small enough such that it can be tracked by the channel estimator at the User Equipment (UE). Channel estimation is done using the downlink cell specific reference signals (CRS) that are present regardless the UE being attached to the network or not. FSK was selected due to its robust operation in a fading channel. We describe the whole AmBC system, use two receivers. Finally, numerical simulations and measurements are provided to validate the proposed FSK AmBC performance.'\nauthor:\n- 'Jingyi Liao, Xiyu Wang, Kalle Ruttik, Riku J\u00e4ntti, and Phan-Huy Dinh-Thuy [^1] [^2]'\nbibliography:\n- 'References.bib'\ntitle: Ambient FSK Backscatter Communications using LTE Cell Specific Reference Signals\n---\n\nAmbient Backscatter Communications, LTE Cell Specific Reference Signals, Channel Estimation\n\nIntroduction {#sec:Intro}\n============\n\n-12pt\n\nThe introduction of ambient backscatter communications (AmBC) [@liu2013ambient] in mobile networks [@PhDFara] has recently been proposed for the sustainable development of asset"
+"---\nauthor:\n- 'F.\u00a0de\u00a0Gasperin'\n- 'H.\u00a0W.\u00a0Edler'\n- 'W.\u00a0L.\u00a0Williams'\n- 'J.\u00a0R.\u00a0Callingham'\n- 'B.\u00a0Asabere'\n- 'M.\u00a0Br\u00fcggen'\n- 'G.\u00a0Brunetti'\n- 'T.\u00a0J.\u00a0Dijkema'\n- 'M.\u00a0J.\u00a0Hardcastle'\n- 'M.\u00a0Iacobelli'\n- 'A.\u00a0Offringa'\n- 'M.\u00a0J.\u00a0Norden'\n- 'H.\u00a0J.\u00a0A.\u00a0R\u00f6ttgering'\n- 'T.\u00a0Shimwell'\n- 'R.\u00a0J.\u00a0van\u00a0Weeren'\n- 'C.\u00a0Tasse'\n- 'D.\u00a0J.\u00a0Bomans'\n- 'A.\u00a0Bonafede'\n- 'A.\u00a0Botteon'\n- 'R.\u00a0Cassano'\n- 'K.\u00a0T.\u00a0Chy\u017cy'\n- 'V.\u00a0Cuciti'\n- 'K.\u00a0L.\u00a0Emig'\n- 'M.\u00a0Kadler'\n- 'G.\u00a0Miley'\n- 'B.\u00a0Mingo'\n- 'M.\u00a0S.\u00a0S.\u00a0L.\u00a0Oei'\n- 'I.\u00a0Prandoni'\n- 'D.\u00a0J.\u00a0Schwarz'\n- 'P.\u00a0Zarka'\nbibliography:\n- 'library.bib'\nsubtitle: 'II. First data release'\ntitle: The LOFAR LBA Sky Survey\n---\n\n[The Low Frequency Array (LOFAR) is the only existing radio interferometer able to observe at ultra-low frequencies ($<100$\u00a0MHz) with high resolution ($<15$) and high sensitivity ($<1$\u00a0). To exploit these capabilities, the LOFAR Surveys Key Science Project is using the LOFAR Low Band Antenna (LBA) to carry out a sensitive wide-area survey at $41-66$ MHz named the LOFAR LBA Sky Survey (LoLSS).]{} [LoLSS is covering the whole northern sky above declination $24\\deg$ with a"
+"---\nabstract: |\n    Diffusion-driven instability and bifurcation analysis are studied in a predator-prey model with herd behavior and quadratic mortality by incorporating multiple Allee effect into prey species. The existence and stability of the equilibria of the system are studied. The sufficient and necessary conditions for Turing instability occurring are obtained. And the stability and direction of Hopf and steady state bifurcations are explored by using the normal form method. Furthermore, some numerical simulations are presented to support our theoretical analysis. We found that too large diffusion rate of prey prevents Turing instability from emerging. The biomass conversion rate does affect the stability of the system and the occurrence of Turing instability. This indicates that the biomass conversion rate is essentially significant for the predator-prey system. Finally, we summarize our findings in the conclusion.\\\n    [**keywords**]{}: Turing instability; Hopf bifurcation; Steady state bifurcation; Multiple Allee effect; Herd behavior; Predator-prey;\nauthor:\n- |\n    Jianglong Xiao Yonghui Xia$\\footnote{Corresponding author. Yonghui Xia, yhxia@zjnu.cn; xiadoc@163.com.}$\\\n    [*$^a$ College of Mathematics Science, Zhejiang Normal University, 321004, Jinhua, China*]{}\\\n    [Email: jianglongxiao@zjnu.edu.cn; yhxia@zjnu.cn; xiadoc@163.com.]{}\ntitle: 'Turing instability in a diffusive predator-prey model with multiple Allee effect and herd behavior [^1]'\n---\n\nIntroduction\n============\n\nHistory\n-------\n\nModeling the interactions between"
+"---\nabstract: |\n    Verification of discrete time or continuous time dynamical systems over the reals is known to be undecidable. It is however known that undecidability does not hold for various classes of systems when considering *robust* systems: if robustness is defined as the fact that reachability relation is stable under infinitesimal perturbation, then their reachability relation is decidable. In other words, undecidability implies sensitivity under infinitesimal perturbation, a property usually not expected in systems considered \u201cin practice\u201d, and hence can be seen (somehow informally) as an artifact of the theory, that always assumes exactness. In a similar vein, it is known that, while undecidability holds for logical formulas over the reals, it does not hold when considering $\\delta$-undecidability: one must determine whether a property is true, or $\\delta$-far from being true.\n\n    We first extend the previous statements to a theory for general (discrete time, continuous-time, and even hybrid) dynamical systems, and we relate the two approaches. We also relate robustness to some geometric properties of reachability relation.\n\n    But mainly, when a system is robust, it then makes sense to quantify at which level of perturbation. We prove that assuming robustness to polynomial perturbations on precision leads to reachability verifiable"
+"---\nbibliography:\n- 'biblio.bib'\n---\n\n[**Long-range quenched bond disorder in the bi-dimensional Potts model**]{}\\\n[**Francesco\u00a0Chippari**]{}\\\nSorbonne Universit\u00e9 & CNRS, UMR 7589, LPTHE, F-75005, Paris, France\\\ne-mail: [fchippari@lpthe.jussieu.fr]{}\\\n[**Marco\u00a0Picco**]{}\\\nSorbonne Universit\u00e9 & CNRS, UMR 7589, LPTHE, F-75005, Paris, France\\\ne-mail: [picco@lpthe.jussieu.fr]{}\\\n[**Raoul\u00a0Santachiara**]{}\\\nParis-Saclay Universit\u00e9 & CNRS, UMR 8626, LPTMS, 91405, Saclay, France\\\ne-mail: [raoul.santachiara@gmail.com]{}\n\n(Dated: )\n\n.2in\n\n**ABSTRACT**\n\n> We study the bi-dimensional $q$-Potts model with long-range bond correlated disorder. Similarly to [@Chatelain], we implement a disorder bimodal distribution by coupling the Potts model to auxiliary spin-variables, which are correlated with a power-law decaying function. The universal behaviour of different observables, especially the thermal and the order-parameter critical exponents, are computed by Monte-Carlo techniques for $q=1,2,3$-Potts models for different values of the power-law decaying exponent $a$. On the basis of our conclusions, which are in agreement with previous theoretical and numerical results for $q=1$ and $q=2$, we can conjecture the phase diagram for $q\\in [1,4]$. In particular, we establish that the system is driven to a fixed point at finite or infinite long-range disorder depending on the values of $q$ and $a$. Finally, we discuss the role of the higher cumulants of the disorder distribution. This is done"
+"---\nabstract: 'Disclosure avoidance (DA) systems are used to safeguard the confidentiality of data while allowing it to be analyzed and disseminated for analytic purposes. These methods, e.g., cell suppression, swapping, and k-anonymity, are commonly applied and may have significant societal and economic implications. However, a formal analysis of their privacy and bias guarantees has been lacking. This paper presents a framework that addresses this gap: it proposes differentially private versions of these mechanisms and derives their privacy bounds. In addition, the paper compares their performance with traditional differential privacy mechanisms in terms of accuracy and fairness on US Census data release and classification tasks. The results show that, contrary to popular beliefs, traditional differential privacy techniques may be superior in terms of accuracy and fairness to differential private counterparts of widely used DA mechanisms.'\nauthor:\n- Keyu Zhu$^1$\n- Ferdinando Fioretto$^2$\n- 'Pascal Van Hentenryck$^{1}$'\n- |\n    Saswat Das$^3$ Christine Task$^4$ $^1$Georgia Institute of Technology\\\n    $^2$Syracuse University\\\n    $^3$National Institute of Science Education and Research\\\n    $^4$Knexus Research Corporation\\\n    keyu.zhu@gatech.edu, ffiorett@syr.edu, pvh@isye.gatech.edu, saswat.das@niser.ac.in, christine.task@knexusresearch.com\nbibliography:\n- 'ijcai23.bib'\ntitle: Privacy and Bias Analysis of Disclosure Avoidance Systems\n---\n\nIntroduction\n============\n\nDisclosure avoidance (DA) systems are methods used to protect confidentiality while still"
+"---\nabstract: 'A modular form on an even lattice $M$ of signature $(l,2)$ is called reflective if it vanishes only on quadratic divisors orthogonal to roots of $M$. In this paper we show that every reflective modular form on a lattice of type $2U\\oplus L$ induces a root system satisfying certain constrains. As applications, (1) we prove that there is no lattice of signature $(21,2)$ with a reflective modular form and that $2U\\oplus D_{20}$ is the unique lattice of signature $(22,2)$ and type $U\\oplus K$ which has a reflective Borcherds product; (2) we give an automorphic proof of Shvartsman and Vinberg\u2019s theorem, asserting that the algebra of modular forms for an arithmetic subgroup of $\\mathrm{O}(l,2)$ is never freely generated when $l\\geq 11$. We also prove several results on the finiteness of lattices with reflective modular forms.'\naddress: 'Center for Geometry and Physics, Institute for Basic Science (IBS), Pohang 37673, Korea'\nauthor:\n- Haowu Wang\nbibliography:\n- 'refs.bib'\ntitle: On the classification of reflective modular forms\n---\n\nIntroduction\n============\n\nThe need to study modular forms on orthogonal groups ${\\mathop{\\null\\mathrm {O}}\\nolimits}(l,2)$ was first pointed out by Weil [@Wei79] in his program for the study of $K3$ surfaces in the late 1950s. In"
+"---\nabstract: 'The bulk photovoltaic effect (BPVE) refers to the phenomenon of generating photocurrent or photovoltage in homogeneous noncentrosymmetric materials under illumination, and the intrinsic contribution to the BPVE is known as the shift current effect. We calculate the shift current conductivities of the ferroelectric SnTe monolayer using first-principles methods. We find that the monolayer SnTe has giant shift-current conductivity near the valley points. More remarkably, the linear optical absorption coefficient at this energy is very small, and therefore leads to an enormous Glass coefficient that is four orders of magnitude larger than that of BaTiO$_3$. The unusual shift-current effects are further investigated using a three-band model. We find that the giant shift current conductivities and Glass coefficient are induced by the nontrivial energy band geometries near the valley points, where the shift-vector diverges. This is a prominent example that the band geometry can play essential roles in the fundamental properties of solids.'\nauthor:\n- Gan Jin\n- Lixin He\ntitle: Peculiar band geometry induced giant shift current in ferroelectric SnTe monolayer\n---\n\nIntroduction {#introduction .unnumbered}\n============\n\nThe study of BPVE has a long history\u00a0[@Sturman_1992; @Baltz_1981; @Sipe_2000], and recently it has attracted great renewed interest because it potentially allows"
+"---\nabstract: 'We consider wave propagation problems over 2-dimensional domains with piecewise-linear boundaries, possibly including scatterers. Under the assumption that the initial conditions and forcing terms are radially symmetric and compactly supported, we propose an approximation of the propagating wave as the sum of some special space-time functions. Each term in this sum identifies a particular field component, modeling the result of a single reflection or diffraction effect. We describe an algorithm for identifying such components automatically, based on the domain geometry. To showcase our proposed method, we present several numerical examples, such as waves scattering off wedges and waves propagating through a room in presence of obstacles.'\nauthor:\n- 'Davide Pradovera[^1]'\n- Monica Nonino\n- Ilaria Perugia\nbibliography:\n- 'references.bib'\ntitle: 'Geometry-based approximation of waves in complex domains[^2]'\n---\n\n**Keywords:** wave propagation, surrogate modeling, scattering, geometrical theory of diffraction.\n\n**AMS subject classifications:** 35L05, 35Q60, 65M25, 78A45, 78M34.\n\nIntroduction {#sec:intro}\n============\n\nThe discretization of numerical models for the simulation of complex phenomena results in high-dimensional systems to be solved, usually at an extremely high cost in terms of computational time and storage memory. Among these models, wave propagation problems represent an extremely interesting topic: relevant applications can be found, e.g.,"
+"---\nabstract: |\n    Software Defined Networks have opened the door to statistical and AI-based techniques to improve efficiency of networking. Especially to ensure a certain *Quality of Service* (QoS) for specific applications by routing packets with awareness on content nature (VoIP, video, files, etc.) and its needs (latency, bandwidth, etc.) to use efficiently resources of a network.\n\n    Monitoring and predicting various Key Performance Indicators (KPIs) at any level may handle such problems while preserving network bandwidth.\n\n    The question addressed in this work is the design of efficient, low-cost adaptive algorithms for KPI estimation, monitoring and prediction. We focus on end-to-end latency prediction, for which we illustrate our approaches and results on data obtained from a public generator provided after the recent international challenge on GNN [@suarez2021graph].\n\n    In this paper, we improve [our]{} previously proposed low-cost estimators [@larrenie2022icccnt] by adding the adaptive dimension, and show that the performances are minimally modified while gaining the ability to track varying networks.\nauthor:\n- |\n    Pierre Larrenie\\\n    Thales SIX & LIGM\\\n    Universit\u00e9 Gustave Eiffel, CNRS\\\n    Marne-la-Vall\u00e9e, France\\\n    `pierre.larrenie@esiee.fr`\\\n    Jean-Fran\u00e7ois Bercher\\\n    LIGM\\\n    Universit\u00e9 Gustave Eiffel, CNRS\\\n    Marne-la-Vall\u00e9e, France\\\n    `jean-francois.bercher@esiee.fr`\\\n    Olivier Venard\\\n    ESYCOM\\\n    Universit\u00e9 Gustave Eiffel, CNRS\\\n    Marne-la-Vall\u00e9e, France\\\n    `olivier.venard@esiee.fr`\\\n    Iyad Lahsen-Cherif\\\n    Institut National des Postes"
+"---\nabstract: 'Given a dataset on actions and resulting long-term rewards, a direct estimation approach fits value functions that minimize prediction error on the training data. Temporal difference learning (TD) methods instead fit value functions by minimizing the degree of temporal inconsistency between estimates made at successive time-steps. Focusing on finite state Markov chains, we provide a crisp asymptotic theory of the statistical advantages of this approach. First, we show that an intuitive *inverse trajectory pooling coefficient* completely characterizes the percent reduction in mean-squared error of value estimates. Depending on problem structure, the reduction could be enormous or nonexistent. Next, we prove that there can be dramatic improvements in estimates of the difference in value-to-go for two states: TD\u2019s errors are bounded in terms of a novel measure \u2014 the problem\u2019s *trajectory crossing time* \u2014 which can be much smaller than the problem\u2019s time horizon.'\nauthor:\n- |\n    David Cheikhi Daniel Russo\\\n    Columbia University\nbibliography:\n- 'ref.bib'\ntitle: On the Statistical Benefits of Temporal Difference Learning\n---\n\nIntroduction\n============\n\nTemporal difference learning is a distinctive approach to estimation in long-term optimization problems. Its importance to reinforcement learning is hard to overstate. In their seminal book, @sutton2018reinforcement write: *If one had"
+"---\nabstract: 'During the last decade, forcing and response modes produced by resolvent analysis have demonstrated great potential to guide sensor and actuator placement and design in flow control applications. However, resolvent modes are frequency-dependent, which, although responsible for their success in identifying scale interactions in turbulence, complicates their use for control purposes. In this work, we seek orthogonal bases of forcing and response modes that are the most responsive and receptive, respectively, across all frequencies. We show that these frequency-independent bases of *representative* resolvent modes are given by the eigenvectors of the observability and controllability Gramians of the system considering full state inputs and outputs. We present several numerical examples where we leverage these bases by building orthogonal or interpolatory projectors onto the dominant forcing and response subspaces. Gramian-based forcing modes are used to identify dynamically relevant disturbances, to place point sensors to measure disturbances, and to design actuators for feedforward control in the subcritical linearized Ginzburg\u2013Landau equation. Gramian-based response modes are used to identify coherent structures and for point sensor placement aiming at state reconstruction in the turbulent flow in a minimal channel at $\\mathrm{Re}_{\\tau}=185$. The approach does not require data snapshots and relies only on knowledge of"
+"---\nabstract: 'Propyl cyanide (PrCN) (C$_3$H$_7$CN) with both linear and branched isomers is ubiquitous in interstellar space and is important for astrochemistry as it is one of the most complex molecules found to date in the interstellar medium. Furthermore, it is the only one observed species to share the branched atomic backbone of amino acids, some of the building blocks of life. Radical-radical chemical reactions are examined in detail using density functional theory, second order M$\\phi$ller Plesset perturbation theory, coupled cluster methods, and the energy resolved master equation formalism to compute the rate constants in the low pressure limit prevalent in the ISM. Quantum chemical studies are reported for the formation of propyl-cyanide (n-PrCN) and its branched isomer (iso-PrCN) from the gas phase association and surface reactions of radicals on a 34-water model ice cluster. We identify two and three paths for the formation of iso-PrCN, and n-PrCN respectively. The reaction mechanism involves the following radicals association: CH$_3$CHCH$_3$+CN, CH$_3$+CH$_3$CHCN for iso-PrCN formation and CH$_3$CH$_2$+CH$_2$CN, CH$_3$+CH$_2$CH$_2$CN, CN+CH$_3$CH$_2$CH$_2$ leading to n-PrCN formation. We employ the M062X/6-311$++$G(d,p) DFT functional and MP2/aug-cc-pVTZ for reactions on the ice model, and gas phase respectively to optimize the structures, compute minimum energy paths and zero-point vibrational energies"
+"---\nabstract: 'Self-attention weights and their transformed variants have been the main source of information for analyzing token-to-token interactions in Transformer-based models. But despite their ease of interpretation, these weights are not faithful to the models\u2019 decisions as they are only one part of an encoder, and other components in the encoder layer can have considerable impact on information mixing in the output representations. In this work, by expanding the scope of analysis to the whole encoder block, we propose , a novel context mixing score customized for Transformers that provides us with a deeper understanding of how information is mixed at each encoder layer. We demonstrate the superiority of our context mixing score over other analysis methods through a series of complementary evaluations with different viewpoints based on linguistically informed rationales, probing, and faithfulness analysis.[^1]'\nauthor:\n- |\n    Hosein Mohebbi$^{1}$ \u00a0 Willem Zuidema$^{2}$ \u00a0 Grzegorz Chrupa\u0142a$^{1}$ \u00a0 Afra Alishahi$^{1}$\\\n    $^1$ CSAI, Tilburg University \u00a0 $^2$ ILLC, University of Amsterdam\\\n    `{h.mohebbi, a.alishahi}@tilburguniversity.edu`\\\n    `w.h.zuidema@uva.nl`\\\n    `grzegorz@chrupala.me`\\\nbibliography:\n- 'custom.bib'\ntitle: Quantifying Context Mixing in Transformers\n---\n\n=1\n\nIntroduction\n============\n\nTransformers [@NIPS2017_3f5ee243], with their impressive empirical success, have become a prime choice of architecture to learn contextualized representations across a wide range of modalities, such as language"
+"---\nabstract: 'Here we analyze the Hawking radiation detected by an inertial observer in an arbitrary position in a Reissner-Nordstr\u00f6m spacetime, with special emphasis on the asymptotic behavior of the Hawking spectrum as an observer approaches the inner or outer horizon. Two different methods are used to analyze the Hawking flux: first, we calculate an effective temperature quantifying the rate of exponential redshift experienced by an observer from an emitter\u2019s vacuum modes, which reproduces the Hawking effect provided the redshift is sufficiently adiabatic. Second, we compute the full Bogoliubov graybody spectrum observed in the three regimes where the wave equation can be solved analytically (at infinity and at the outer and inner horizons). We find that for an observer at the event horizon, the effective Hawking temperature is finite and becomes negative when $(Q/M)^2>8/9$, while at the inner horizon, the effective temperature is always negative and infinite in every direction the observer looks, coinciding with an ultraviolet-divergent spectrum.'\nauthor:\n- Tyler McMaken\n- 'Andrew J. S. Hamilton'\nbibliography:\n- 'apsbib.bib'\ntitle: Hawking radiation inside a charged black hole\n---\n\n\\[sec:int\\]Introduction\n=======================\n\nSome of the most extraordinary effects in the study of quantum field theory in curved spacetime occur near the"
+"---\nabstract: 'In recent years, the attention mechanism has demonstrated superior performance in various tasks, leading to the emergence of GAT and Graph Transformer models that utilize this mechanism to extract relational information from graph-structured data. However, the high computational cost associated with the Transformer block, as seen in Vision Transformers, has motivated the development of alternative architectures such as MLP-Mixers, which have been shown to improve performance in image tasks while reducing the computational cost. Despite the effectiveness of Transformers in graph-based tasks, their computational efficiency remains a concern. The logic behind MLP-Mixers, which addresses this issue in image tasks, has the potential to be applied to graph-structured data as well. In this paper, we propose the Graph Mixer Network (GMN), also referred to as Graph Nasreddin Nets (GNasNets), a framework that incorporates the principles of MLP-Mixers for graph-structured data. Using a PNA model with multiple aggregators as the foundation, our proposed GMN has demonstrated improved performance compared to Graph Transformers. The source code is available publicly at <https://github.com/asarigun/GraphMixerNetworks>.'\nauthor:\n- |\n    Ahmet Sar\u0131g\u00fcn\\\n    \\\nbibliography:\n- 'reference.bib'\ntitle: Graph Mixer Networks\n---\n\nIntroduction\n============\n\nGraph Neural Networks (GNNs) are a powerful tool for working with graph-structured data, which"
+"---\nabstract: |\n    TextFormats is a software system for efficient and user-friendly creation of text format specifications, accessible from multiple programming languages (C/C++, Python, Nim) and the Unix command line. To work with a format, a specification written in the TextFormats Specification Language (TFSL) must be created. The specification defines datatypes for each part of the format.\n\n    The syntax for datatype definitions in TextFormats specifications is based on the text representation. Thus this system is well suited for the description of existing formats. However, when creating a new text format for representing existing data, the user may use different possible definitions, based on the type of value and the representation choices.\n\n    This study explores the possible definition syntax in the TextFormats Specification Language to be used for creating text representations of scalar values (e.g.\u00a0string, numeric value, boolean) and compound data structures (e.g.\u00a0array, mapping). The results of the analysis are presented systematically, together with examples for each each type of different values that can be represented, and usage advices.\nauthor:\n- Giorgio Gonnella\nbibliography:\n- 'references.bib'\ntitle: Designing text representations for existing data using the TextFormats Specification Language\n---\n\nTextFormats | Datatype definition | Parser | Text representation |"
+"---\nabstract: 'Ulam\u2019s method is a popular discretization scheme for stochastic operators that involves the construction of a transition probability matrix controlling a Markov chain on a set of cells covering some domain. We consider an application to satellite-tracked undrogued surface-ocean drifting buoy trajectories obtained from the NOAA Global Drifter Program dataset. Motivated by the motion of in the tropical Atlantic, we apply Transition Path Theory (TPT) to drifters originating off the west coast of Africa to the Gulf of Mexico. We find that the most common case of a regular covering by equal longitude\u2013latitude side cells can lead to a large instability in the computed transition times as a function of the number of cells used. We propose a different covering based on a clustering of the trajectory data which is stable against the number of cells in the covering. We also propose a generalization of the standard transition time statistic of TPT which can be used to construct a partition of the domain of interest into weakly dynamically connected regions.'\nauthor:\n- 'G.\u00a0Bonner'\n- 'F.J.\u00a0Beron-Vera'\n- 'M.J.\u00a0Olascoaga'\ntitle: Stability of temporal statistics in Transition Path Theory with sparse data\n---\n\n> Transition Path Theory (TPT)"
+"---\nabstract: 'Voice synthesis has seen significant improvements in the past decade resulting in highly intelligible voices. Further investigations have resulted in models that can produce variable speech, including conditional emotional expression. The problem lies, however, in a focus on phrase-level modifications and prosodic vocal features. Using the CREMA-D dataset we have trained a GAN conditioned on emotion to generate worth lengths for a given input text. These word lengths are relative to neutral speech and can be provided, through speech synthesis markup language (SSML) to a text-to-speech (TTS) system to generate more expressive speech. Additionally, a generative model is also trained using implicit maximum likelihood estimation (IMLE) and a comparative analysis with GANs is included. We were able to achieve better performances on objective measures for neutral speech, and better time alignment for happy speech when compared to an out-of-box model. However, further investigation of subjective evaluation is required.'\nauthor:\n- |\n    Navjot Kaur\\\n    `nka77@sfu.ca`\\\n    Paige Tuttosi\\\n    `ptuttosi@sfu.ca`\\\nbibliography:\n- 'references.bib'\ntitle: 'Time out of Mind: Generating Rate of Speech conditioned on emotion and speaker'\n---\n\nIntroduction\n============\n\nAs humans, we are particularly fascinated by the aspects of ourselves that are difficult to put words to, yet are inherent"
+"---\nabstract: |\n    Pulsar wind nebulae are fascinating systems, and archetypal sources for high-energy astrophysics in general. Due to their vicinity, brightness, to the fact that they shine at multi-wavelengths, and especially to their long-living emission at gamma-rays, modelling their properties is particularly important for the correct interpretation of the visible Galaxy. A complication in this respect is the variety of properties and morphologies they show at different ages. Here we discuss the differences among the evolutionary phases of pulsar wind nebulae, how they have been modeled in the past and what progresses have been recently made. We approach the discussion from a phenomenological, theoretical (especially numerical) and observational point of view, with particular attention to the most recent results and open questions about the physics of such intriguing sources.\\\n    Accepted for publication in PASA, 2023 January 30. Received 2022 December 14; in original form 2022 August 16.\nauthor:\n- 'Barbara Olmi$^{1,2}$[^1] and Niccol\u00f2 Bucciantini$^{2,3,4}$[^2]'\nbibliography:\n- 'biblio.bib'\ntitle: 'From young to old: the evolutionary path of Pulsar Wind Nebulae'\n---\n\nHigh Energy Astrophysics: Plasma Astrophysics \u2013 ISM: Supernova Remnants \u2013 ISM: Pulsar Wind Nebulae \u2013 ISM: Cometary Nebulae \u2013 Pulsars: General \u2013 Relativistic Processes \u2013 Methods: Numerical\n\nINTRODUCTION  {#sec:intro}"
+"---\nabstract: 'Many real-world domains require safe decision making in uncertain environments. In this work, we introduce a deep reinforcement learning framework for approaching this important problem. We consider a distribution over transition models, and apply a risk-averse perspective towards model uncertainty through the use of coherent distortion risk measures. We provide robustness guarantees for this framework by showing it is equivalent to a specific class of distributionally robust safe reinforcement learning problems. Unlike existing approaches to robustness in deep reinforcement learning, however, our formulation does not involve minimax optimization. This leads to an efficient, model-free implementation of our approach that only requires standard data collection from a single training environment. In experiments on continuous control tasks with safety constraints, we demonstrate that our framework produces robust performance and safety at deployment time across a range of perturbed test environments.'\nauthor:\n- |\n    James Queeney[^1]\\\n    Division of Systems Engineering\\\n    Boston University\\\n    `jqueeney@bu.edu`\\\n    Mouhacine Benosman\\\n    Mitsubishi Electric Research Laboratories\\\n    `benosman@merl.com`\\\nbibliography:\n- 'Queeney\\_NeurIPS23\\_CameraReady.bib'\ntitle: |\n    Risk-Averse Model Uncertainty for\\\n    Distributionally Robust Safe Reinforcement Learning\n---\n\nIntroduction\n============\n\nIn many real-world decision making applications, it is important to satisfy safety requirements while achieving a desired goal. In addition, real-world environments often involve"
+"---\nabstract: 'Precise robotic grasping of several novel objects is a huge challenge in manufacturing, automation, and logistics. Most of the current methods for model-free grasping are disadvantaged by the sparse data in grasping datasets and by errors in sensor data and contact models. This study combines data generation and sim-to-real transfer learning in a grasping framework that reduces the sim-to-real gap and enables precise and reliable model-free grasping. A large-scale robotic grasping dataset with dense grasp labels is generated using domain randomization methods and a novel data augmentation method for deep learning-based robotic grasping to solve data sparse problem. We present an end-to-end robotic grasping network with a grasp optimizer. The grasp policies are trained with sim-to-real transfer learning. The presented results suggest that our grasping framework reduces the uncertainties in grasping datasets, sensor data, and contact models. In physical robotic experiments, our grasping framework grasped single known objects and novel complex-shaped household objects with a success rate of 90.91%. In a complex scenario with multi-objects robotic grasping, the success rate was 85.71%. The proposed grasping framework outperformed two state-of-the-art methods in both known and unknown object robotic grasping.'\nauthor:\n- 'Lei Zhang$^{1,2}$, Kaixin Bai$^{1,2}$, Zhaopeng Chen$^{2,1}$\\*, Yunlei Shi$^{1,2}$,"
+"---\nabstract: 'AI enabled chat bots have recently been put to use to answer customer service queries, however it is a common feedback of users that bots lack a personal touch and are often unable to understand the real intent of the user\u2019s question. To this end, it is desirable to have human involvement in the customer servicing process. In this work, we present a system where a human support agent collaborates in real-time with an AI agent to satisfactorily answer customer queries. We describe the user interaction elements of the solution, along with the machine learning techniques involved in the AI agent.'\nauthor:\n- 'Debayan Banerjee Mathis Poser Christina Wiethof Varun Shankar Subramanian Richard Paucar Eva A. C. Bittner Chris Biemann'\nbibliography:\n- 'aaai23.bib'\ntitle: 'A System for Human-AI collaboration for Online Customer Support'\n---\n\n![image](images/ui1.png){width=\"90.00000%\"}\n\nIntroduction\n============\n\nIn the pursuit of operational efficiency, companies across the globe have been deploying automation technology aided by Artificial Intelligence (AI) for Online Customer Support (OCS) use cases [^1]. With the explosive growth of social media usage, incoming customer queries have grown exponentially and to handle this growth, the use of proper technology is critical. Some estimates say that by the year"
+"---\nabstract: 'Using computer simulations, we have studied the percolation and the electrical conductance of two-dimensional, random percolating networks of curved, zero-width metallic nanowires. We mimicked the curved nanowires using circular arcs. The percolation threshold decreased as the aspect ratio of the arcs increased. Comparison with published data on the percolation threshold of symmetric quadratic B\u00e9zier curves suggests that, when the percolation of slightly curved wires is simulated, the particular choice of curve to mimic the shape of real-world wires is of little importance. Considering the electrical properties, we took into account both the nanowire resistance per unit length and the junction (nanowire/nanowire contact) resistance. Using a mean-field approximation (MFA), we derived the total electrical conductance of the nanowire-based networks as a function of their geometrical and physical parameters. The MFA predictions have been confirmed by our Monte Carlo numerical simulations. For our random homogeneous and isotropic systems of conductive curved wires, the electric conductance decreased as the wire shape changed from a stick to a ring when the wire length remained fixed.'\nauthor:\n- 'Yuri\u00a0Yu.\u00a0Tarasevich'\n- 'Andrei\u00a0V.\u00a0Eserkepov'\n- 'Irina\u00a0V.\u00a0Vodolazskaya'\nbibliography:\n- 'arcs.bib'\ntitle: 'Percolation and electrical conduction in random systems of curved linear"
+"---\nabstract: 'The basic idea of this work is to achieve the observed relic density of a non-thermal dark matter(DM) and its connection with Cosmic Microwave Background (CMB) via additional relativistic degrees of freedom which are simultaneously generated during the period $T_{\\rm BBN}~{\\rm to}~T_{\\rm CMB}$ from a long-lived dark sector particle. To realize this phenomena we minimally extend the type-I seesaw scenario with a Dirac fermion singlet($\\chi$) and a complex scalar singlet ($\\varphi$) which transform non-trivially under an unbroken symmetry $\\mathcal{Z}_3$. $\\chi$ being the lightest particle in the dark sector acts as a stable dark matter candidate while the next to lightest state $\\varphi$ operates like a long lived dark scalar particle. The initial density of $\\varphi$ can be thermally produced through either self-interacting number changing processes ($3 \\varphi \\to 2 \\varphi$) within dark sector or the standard annihilation to SM particles ($2 \\varphi \\to 2~ {\\rm SM}$). The late time (after neutrino decoupling) non-thermal decay of $\\varphi$ can produce dark matter in association with active neutrinos. The presence of extra relativistic neutrino degrees of freedom at the time of CMB can have a significant impact on $\\Delta \\rm N_{eff}$. Thus the precise measurement of $\\Delta \\rm N_{ eff}$ by"
+"---\nabstract: |\n    In $p$-median location interdiction the aim is to find a subset of edges in a graph, such that the objective value of the $p$-median problem in the same graph without the selected edges is as large as possible.\n\n    We prove that this problem is $\\operatorname*{\\mathsf{NP}}$-hard even on acyclic graphs. Restricting the problem to trees with unit lengths on the edges, unit interdiction costs, and a single edge interdiction, we provide an algorithm which solves the problem in polynomial time. Furthermore, we investigate path graphs with unit and arbitrary lengths. For the former case, we present an algorithm, where multiple edges can get interdicted. Furthermore, for the latter case, we present a method to compute an optimal solution for one interdiction step which can also be extended to multiple interdicted edges.\nauthor:\n- 'L. Lei\u00df[^1]'\n- 'T. Heller'\n- 'L. Sch\u00e4fer'\n- 'M. Streicher'\n- 'S. Ruzika'\nbibliography:\n- 'mybibfile.bib'\ntitle: '$p$-median location interdiction on trees'\n---\n\n**Keywords:** Network Interdiction, Location Planning, Median Problems, Edge Interdiction, Network Location Planning\\\n\nIntroduction {#sec:intro}\n============\n\nLocation planning is a field of mathematical research which crosses our daily life more often, than we might think at first sight. The root of modern"
+"---\nabstract: 'The optimized certainty equivalent (OCE) is a family of risk measures that cover important examples such as entropic risk, conditional value-at-risk and mean-variance models. In this paper, we propose a new episodic risk-sensitive reinforcement learning formulation based on tabular Markov decision processes with recursive OCEs. We design an efficient learning algorithm for this problem based on value iteration and upper confidence bound. We derive an upper bound on the regret of the proposed algorithm, and also establish a minimax lower bound. Our bounds show that the regret rate achieved by our proposed algorithm has optimal dependence on the number of episodes and the number of actions.'\nbibliography:\n- 'main.bib'\n---\n\n**Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents**\n\n[Wenhao Xu]{}[^1], Xuefeng Gao[^2], Xuedong He[^3]\n\nIntroduction\n============\n\nReinforcement learning (RL) studies the problem of sequential decision making in an unknown environment by carefully balancing between exploration and exploitation [@sutton2018reinforcement]. In the classical setting, it describes how an agent takes actions to maximize *expected cumulative rewards* in an environment typically modeled by a Markov decision process (MDP, [@puterman2014markov]). However, optimizing the expected cumulative rewards alone is often not sufficient in many practical applications such as finance, healthcare"
+"---\nabstract: 'The characterization of finite-time thermodynamic processes is of crucial importance for extending equilibrium thermodynamics to nonequilibrium thermodynamics. The central issue is to quantify responses of thermodynamic variables and irreversible dissipation associated with non-quasistatic changes of thermodynamic forces applied to the system. In this study, we derive a simple formula that incorporates the non-quasistatic response coefficients with Onsager\u2019s kinetic coefficients, where the Onsager coefficients characterize the relaxation dynamics of fluctuation of extensive thermodynamic variables of semi-macroscopic systems. Moreover, the thermodynamic length and the dissipated availability that quantifies the efficiency of irreversible thermodynamic processes are formulated in terms of the derived non-quasistatic response coefficients. The present results are demonstrated by using an ideal gas model. The present results are, in principle, verifiable through experiments and are thus expected to provide a guiding principle for the nonequilibrium control of macroscopic thermodynamic systems.'\naddress: 'Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa 277-8561, Japan'\nauthor:\n- Yuki Izumida\ntitle: 'Non-quasistatic response coefficients and dissipated availability for macroscopic thermodynamic systems'\n---\n\nIntroduction\n============\n\nIn recent years, adiabatic control and even its shortcuts have received renewed and considerable interest both in theoretical and experimental points of"
+"---\nabstract: |\n    .\n\n    **Context:** DevOps responds the growing need of companies to streamline the software development process and, thus, has experienced widespread adoption in the past years. However, the successful adoption of DevOps requires companies to address significant cultural and organizational changes. Understanding the organizational structure and characteristics of teams adopting DevOps is key, and comprehending the existing theories and representations of team taxonomies is critical to guide companies in a more systematic and structured DevOps adoption process. As there was no unified theory to explain the different topologies of DevOps teams, in previous work, we built a theory to represent the organizational structure and characteristics of teams adopting DevOps, harmonizing the existing knowledge. **Objective:** In this paper, we expand the theory-building in the context of DevOps Team Taxonomies. Our main contributions are presenting and executing the Operationalization and Testing phases for a continuously evolving theory on DevOps team structures. **Method:** We operationalize the constructs and propositions that make up our theory to generate empirically testable hypotheses to confirm or disconfirm the theory. Specifically, we focus on the research operation side of the theory-research cycle: identifying propositions, deriving empirical indicators from constructs, establishing testable hypotheses, and testing them. **Results:**"
+"---\nabstract: |\n    We investigate unbiased high-dimensional mean estimators in differential privacy. We consider differentially private mechanisms whose expected output equals the mean of the input dataset, for every dataset drawn from a fixed bounded domain $K$ in ${\\mathbb{R}}^d$. A classical approach to private mean estimation is to compute the true mean and add unbiased, but possibly correlated, Gaussian noise to it. In the first part of this paper, we study the optimal error achievable by a Gaussian noise mechanism for a given domain $K$ when the error is measured in the $\\ell_p$ norm for some $p \\ge 2$. We give algorithms that compute the optimal covariance for the Gaussian noise for a given $K$ under suitable assumptions, and prove a number of nice geometric properties of the optimal error. These results generalize the theory of factorization mechanisms from domains $K$ that are symmetric and finite (or, equivalently, symmetric polytopes) to arbitrary bounded domains.\n\n    In the second part of the paper we show that Gaussian noise mechanisms achieve nearly optimal error among all private unbiased mean estimation mechanisms in a very strong sense. In particular, for *every input dataset*, an unbiased mean estimator satisfying concentrated differential privacy introduces approximately at"
+"---\nabstract: 'Differential equations are used to model and predict the behaviour of complex systems in a wide range of fields, and the ability to solve them is an important asset for understanding and predicting the behaviour of these systems. Complicated physics mostly involves difficult differential equations, which are hard to solve analytically. In recent years, physics-informed neural networks have been shown to perform very well in solving systems with various differential equations. The main ways to approximate differential equations are through penalty function and reparameterization. Most researchers use penalty functions rather than reparameterization due to the complexity of implementing reparameterization. In this study, we quantitatively compare physics-informed neural network models with and without reparameterization using the approximation error. The performance of reparameterization is demonstrated based on two benchmark mechanical engineering problems, a one-dimensional bar problem and a two-dimensional bending beam problem. Our results show that when dealing with complex differential equations, applying reparameterization results in a lower approximation error.'\nauthor:\n- |\n    Siddharth Nand\\\n    Department of Mathematics\\\n    The University of British Columbia\\\n    `sidnand@student.ubc.ca`\\\n    Yuecheng Cai\\\n    Department of Mechanical Engineering\\\n    The University of British Columbia\\\n    `ycai05@mail.ubc.ca`\\\ntitle: 'Physics-informed Neural Network: The Effect of Reparameterization in Solving Differential Equations'\n---\n\nIntroduction"
+"---\nabstract: 'We study the set of incentive compatible and efficient two-sided matching mechanisms. We classify all such mechanisms under an additional assumption \u2013 \u201cgender-neutrality\" \u2013 which guarantees that the two sides be treated symmetrically. All group strategy-proof, efficient, and gender-neutral mechanisms are recursive and the outcome is decided in a sequence of rounds. In each round two agents are selected, one from each side. These agents are either \u201cmatched-by-default\" or \u201cunmatched-by-default.\" In the former case either of the selected agents can unilaterally force the other to match with them while in the latter case, they may only match together if both agree. In either case, if this pair of agents is not matched together, each gets their top choices among the set of remaining agents. As an important step in the characterization, we first show that in one-sided matching all group strategy-proof and efficient mechanisms are sequential dictatorships. An immediate corollary is that there are no individually rational, group strategy-proof and efficient one-sided matching mechanisms.'\nauthor:\n- 'Sophie Bade[^1]'\n- 'Joseph Root[^2]'\nbibliography:\n- 'matching.bib'\ndate: January 2023\ntitle: 'Royal Processions: Incentives, Efficiency and Fairness in Two-sided Matching'\n---\n\nIntroduction\n============\n\ninitiated the study of stability in two-sided matching"
+"---\nabstract: 'Modern data aggregation often takes the form of a platform collecting data from a network of users. More than ever, these users are now requesting that the data they provide is protected with a guarantee of privacy. This has led to the study of optimal data acquisition frameworks, where the optimality criterion is typically the maximization of utility for the agent trying to acquire the data. This involves determining how to allocate payments to users for the purchase of their data at various privacy levels. The main goal of this paper is to characterize a *fair* amount to pay users for their data at a given privacy level. We propose an axiomatic definition of fairness, analogous to the celebrated Shapley value. Two concepts for fairness are introduced. The first treats the platform and users as members of a common coalition and provides a complete description of how to divide the utility among the platform and users. In the second concept, fairness is defined only among users, leading to a potential fairness-constrained mechanism design problem for the platform. We consider explicit examples involving private heterogeneous data and show how these notions of fairness can be applied. To the best"
+"---\nauthor:\n- 'G. Desprez[^1]'\n- 'V. Picouet'\n- 'T. Moutard'\n- 'S. Arnouts'\n- 'M. Sawicki [^2]'\n- 'J. Coupon'\n- 'S. Gwyn'\n- 'L. Chen'\n- 'J. Huang'\n- 'A. Golob'\n- 'H. Furusawa'\n- 'H. Ikeda'\n- 'S. Paltani'\n- 'C. Cheng'\n- 'W. Hartley'\n- 'B. C. Hsieh'\n- 'O. Ilbert'\n- 'O. B. Kauffmann'\n- 'H. J. McCracken'\n- 'M. Shuntov'\n- 'M. Tanaka'\n- 'S. Toft'\n- 'L. Tresse'\n- 'J. R. Weaver'\nbibliography:\n- 'references.bib'\ndate: 'Received date; accepted date'\nsubtitle: '$U+grizy(+YJHK_s)$ photometry and photometric redshifts for 18M galaxies in the $20~{\\rm deg}^2$ of the HSC-SSP Deep and ultraDeep fields'\ntitle: 'Combining the CLAUDS & HSC-SSP surveys'\n---\n\nIntroduction {#sec:intro}\n============\n\nDeep, wide-area multiband imaging surveys play a pivotal role in our studies of the Universe and of the formation and evolution of its content (e.g., CANDELS, @Grogin2011; COSMOS, @Scoville2007; CFHTLS, @Hudelot2012; DES, @Abbott2018). They are expected to continue to do so for the foreseeable future given the large investment of resources into projects such as LSST on the Rubin Observatory [@Ivezic2019], *Euclid* [@Laureijs2011], and Roman Space Telescope [@Akeson2019]. While spectroscopy \u2014 particularly when highly multiplexed \u2014 can yield detailed physical information on"
+"---\nabstract: |\n    In the last decades, due to the huge technological growth observed, it has become increasingly common that a collection of temporal data rapidly accumulates in vast amounts. This provides an opportunity for extracting valuable information through the estimation of increasingly precise models. But at the same time it imposes the challenge of continuously updating the models as new data become available.\n\n    Currently available methods for addressing this problem, the so-called online learning methods, use current parameter estimations and novel data to update the estimators. These approaches avoid using the full raw data and speeding up the computations.\n\n    In this work we consider three online learning algorithms for parameters estimation in the context of time series models. In particular, the methods implemented are: gradient descent, Newton-step and Kalman filter recursions. These algorithms are applied to the recently developed irregularly observed autoregressive (iAR) model. The estimation accuracy of the proposed methods is assessed by means of Monte Carlo experiments.\n\n    The results obtained show that the proposed online estimation methods allow for a precise estimation of the parameters that generate the data both for the regularly and irregularly observed time series. These online approaches are numerically efficient, allowing substantial computational"
+"---\nabstract: 'We consider a stochastic volatility model where the dynamics of the volatility are described by linear functions of the (time extended) signature of a primary underlying process, which is supposed to be some multidimensional continuous semimartingale. Under the additional assumption that this primary process is of polynomial type, we obtain closed form expressions for the VIX squared, exploiting the fact that the truncated signature of a polynomial process is again a polynomial process. Adding to such a primary process the Brownian motion driving the stock price, allows then to express both the log-price and the VIX squared as linear functions of the signature of the corresponding augmented process. This feature can then be efficiently used for pricing and calibration purposes. Indeed, as the signature samples can be easily precomputed, the calibration task can be split into an offline sampling and a standard optimization. For both the SPX and VIX options we obtain highly accurate calibration results, showing that this model class allows to solve the joint calibration problem without adding jumps or rough volatility.'\nauthor:\n- 'Christa Cuchiero[^1]'\n- 'Guido Gazzani[^2]'\n- 'Janka M\u00f6ller[^3]'\n- 'Sara Svaluto-Ferro[^4]'\ntitle: 'Joint calibration to SPX and VIX options with signature-based models'"
+"---\nabstract: 'A connected graph is called a block graph if each of its blocks is a complete graph. Let $\\mathbf{Bl}(\\textbf{k}, \\varphi)$ be the class of block graph on $\\textbf{k}$ vertices with given dissociation number $\\varphi$. In this article, we have obtained a block graph $\\mathbb{B}_{\\textbf{k},\\varphi}$ in $\\mathbf{Bl}(\\textbf{k}, \\varphi)$ that uniquely attains the maximum spectral radius $\\rho(G)$ among all graphs $G$ in $\\mathbf{Bl}(\\textbf{k}, \\varphi)$. Furthermore, we also provide bounds on $\\rho(\\mathbb{B}_{\\textbf{k},\\varphi})$.'\nauthor:\n- 'Joyentanuj Das[^1] and Sumit Mohanty[^2]'\ntitle: |\n    On the spectral radius of block graphs with a given\\\n    dissociation number \n---\n\n[*Keywords*:]{} complete graphs, block graphs, dissociation number, spectral radius, bounds.\n\n[**MSC**:]{} 05C50, 15A18\n\nIntroduction {#sec:intro}\n============\n\nLet $G=(V(G),E(G))$ be a finite, simple, connected graph with $V(G)$ as the set of vertices and $E(G)$ as the set of edges in $G$. We write $u\\sim v$ to indicate that the vertices $u,v \\in V(G)$ are adjacent in $G$. The degree of the vertex $v$, denoted by $d_G(v)$, equals the number of vertices in $V$ that are adjacent to $v$. A graph $H$ is said to be a subgraph of $G$ if $V(H) \\subset V(G)$ and $E(H) \\subset E(G)$. For any subset $S \\subset V (G)$, a subgraph $H$ of"
+"---\nabstract: 'We present a novel solver technique for the anisotropic heat flux equation, aimed at the high level of anisotropy seen in magnetic confinement fusion plasmas. Such problems pose two major challenges: (i) discretization accuracy and (ii) efficient implicit linear solvers. We simultaneously address each of these challenges by constructing a new finite element discretization with excellent accuracy properties, tailored to a novel solver approach based on algebraic multigrid (AMG) methods designed for advective operators. We pose the problem in a mixed formulation, introducing the heat flux as an auxiliary variable and discretizing the temperature and auxiliary fields in a discontinuous Galerkin space. The resulting block matrix system is then reordered and solved using an approach in which two advection operators are inverted using AMG solvers based on approximate ideal restriction (AIR), which is particularly efficient for upwind discontinuous Galerkin discretizations of advection. To ensure that the advection operators are non-singular, in this paper we restrict ourselves to considering open (acyclic) magnetic field lines. We demonstrate the proposed discretization\u2019s superior accuracy over other discretizations of anisotropic heat flux, achieving error $1000\\times$ smaller for anisotropy ratio of $10^9$, while also demonstrating fast convergence of the proposed iterative solver in highly"
+"---\nabstract: 'Originally proposed as a method for knowledge transfer from one model to another, some recent studies have suggested that knowledge distillation ([<span style=\"font-variant:small-caps;\">kd</span>]{}) is in fact a form of regularization. Perhaps the strongest argument of all for this new perspective comes from its apparent similarities with label smoothing ([<span style=\"font-variant:small-caps;\">ls</span>]{}). Here we re-examine this stated equivalence between the two methods by comparing the predictive confidences of the models they train. Experiments on four text classification tasks involving models of different sizes show that: (a)\u00a0In most settings, [<span style=\"font-variant:small-caps;\">kd</span>]{} and [<span style=\"font-variant:small-caps;\">ls</span>]{} drive model confidence in completely opposite directions, and (b) In [<span style=\"font-variant:small-caps;\">kd</span>]{}, the student inherits not only its knowledge but also its confidence from the teacher, reinforcing the classical knowledge transfer view.'\nauthor:\n- |\n    Md Arafat Sultan\\\n    IBM Research AI\\\n    arafat.sultan@ibm.com\nbibliography:\n- 'custom.bib'\ntitle: 'Knowledge Distillation $\\approx$ Label Smoothing: Fact or Fallacy?'\n---\n\n=1\n\nIntroduction {#section:introduction}\n============\n\nKnowledge distillation ([<span style=\"font-variant:small-caps;\">kd</span>]{}) was originally proposed as a mechanism for a small and lightweight *student* model to learn to perform a task, [*e.g.*]{}, classification, from a higher-capacity *teacher* model [@hinton2014distilling]. In recent years, however, this view of [<span style=\"font-variant:small-caps;\">kd</span>]{} as a knowledge transfer process has come"
+"---\nabstract: 'Some quality indicators have been proposed for benchmarking preference-based evolutionary multi-objective optimization algorithms using a reference point. Although a systematic review and analysis of the quality indicators are helpful for both benchmarking and practical decision-making, neither has been conducted. In this context, first, this paper reviews existing regions of interest and quality indicators for preference-based evolutionary multi-objective optimization using the reference point. We point out that each quality indicator was designed for a different region of interest. Then, this paper investigates the properties of the quality indicators. We demonstrate that an achievement scalarizing function value is not always consistent with the distance from a solution to the reference point in the objective space. We observe that the regions of interest can be significantly different depending on the position of the reference point and the shape of the Pareto front. We identify undesirable properties of some quality indicators. We also show that the ranking of preference-based evolutionary multi-objective optimization algorithms depends on the choice of quality indicators.'\nauthor:\n- 'Ryoji\u00a0Tanabe,\u00a0\u00a0and\u00a0Ke\u00a0Li,\u00a0 [^1] [^2]'\nbibliography:\n- 'reference.bib'\ntitle: 'Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis'\n---\n\nPreference-based evolutionary multi-objective"
+"---\naddress: |\n    $^{1}$CNR-IOM-Democritos, c/o SISSA (International School for Advanced Studies), Via Bonomea 265, I-34136, Trieste, Italy\\\n    $^{2}$Dipartimento di Fisica Teorica, Universit\u00e0 Trieste, Strada Costiera 11, I-34014 Trieste, Italy\\\n    $^{3}$Univ Lyon, Ens de Lyon, CNRS, Laboratoire de Physique, F-69342 Lyon, France\\\n    $^{4}$Dipartimento di Fisica \u201cE. R. Caianiello\u201d, Universit\u00e0 degli Studi di Salerno and CNR-SPIN, Via Giovanni Paolo II, I-84084 Fisciano (Sa), Italy\\\n    $^{5}$Dipartimento di Fisica e Astronomia \u201cGalileo Galilei\u201d, INFN and QTech, Universit\u00e0 di Padova, via Marzolo 8, I-35131 Padova, Italy\\\n    $^{6}$INO-CNR, Unit\u00e0 di Sesto Fiorentino, via Nello Carrara 1, I-50019 Sesto Fiorentino (Firenze), Italy\n---\n\nIntroduction\n============\n\nTrapped ultracold atomic gases offer a convenient and flexible platform to explore the fascinating aspects of many-body physics in one dimension [@cazalilla_review_bosons; @mistadikis_1d]. In particular, in the last years the one dimensional dipolar Bose gas has been largely investigated theoretically, see for instance Ref.\u00a0 [@baranov_condensed_2012]. This kind of study is nowadays a vibrant topic of research, triggered by recent observations of self-bound droplets in attractive bosonic mixtures [@tarruell2018a; @Semeghini2018; @derrico2019] and in dipolar atoms [@pfau2016; @luo2021]. In the real experiment, however, the system is not strictly one dimensional: the system made of identical atoms of mass $m$ is usually confined by a"
+"---\nabstract: |\n    This paper shows that to compute the Haar state on $\\mathcal{O}(SL_q(n))$, it suffices to compute the Haar states of a special type of monomials which we define as standard monomials. Then, we provide an algorithm to explicitly compute the Haar states of standard monomials on $\\mathcal{O}(SL_q(3))$ with reasonable computational cost. The numerical results on $\\mathcal{O}(SL_q(3))$ will be used in the future study of the $q$-deformed Weingarten function.\n\n    *Keywords* \u2014 Quantum groups; quantum special linear group; Haar state.\naddress: 'Math department, Texas A&M University, College Station, TX 77843, USA'\nauthor:\n- Ting Lu\nbibliography:\n- 'reference.bib'\ntitle: 'Computing the Haar state on ${\\mathcal{O}(SL_q(3))}$'\n---\n\nIntroduction\n============\n\nThe Haar measure on a compact topological group is a well-studied object. In particular, when the group is $U(n)$, the group of $n\\times n$ unitary matrices, there is an elegant formula for the integral of matrix coefficients with respect to the Haar measure. This formula is given by so-called Weingarten functions, introduced by Collins in 2003\u00a0[@collins2003moments]. The current paper will study a $q$-deformation of the Haar measure on the Drinfeld\u2013Jimbo\u00a0[@drinfeld1986quantum]\u00a0[@jimbo1985aq] quantum groups $\\mathcal{O}(SL_q(n))$ which is dual to $U_q(sl_n)$\u00a0[@korogodski1998algebras].\n\nIn the context of $\\mathcal{O}(SL_q(n))$, the most relevant algebraic structure"
+"---\nabstract: 'Dialogue summarization aims to condense a given dialogue into a simple and focused summary text. Typically, both the roles\u2019 viewpoints and conversational topics change in the dialogue stream. Thus how to effectively handle the shifting topics and select the most salient utterance becomes one of the major challenges of this task. In this paper, we propose a novel topic-aware Global-Local Centrality (GLC) model to help select the salient context from all sub-topics. The centralities are constructed at both the global and local levels. The global one aims to identify vital sub-topics in the dialogue and the local one aims to select the most important context in each sub-topic. Specifically, the GLC collects sub-topic based on the utterance representations. And each utterance is aligned with one sub-topic. Based on the sub-topics, the GLC calculates global- and local-level centralities. Finally, we combine the two to guide the model to capture both salient context and sub-topics when generating summaries. Experimental results show that our model outperforms strong baselines on three public dialogue summarization datasets: CSDS, MC, and SAMSUM. Further analysis demonstrates that our GLC can exactly identify vital contents from sub-topics.\u00a0[^1]'\nauthor:\n- |\n    Xinnian Liang^1^, Shuangzhi Wu^2^, Chenhao Cui^2^,"
+"---\nabstract: 'The Schr\u00f6dinger equation admits smooth and finite solutions that spontaneously evolve into a singularity, even for a free particle. This blowup is generally ascribed to the intrinsic dispersive character of the associated time evolution. We resort to the notion of quantum trajectories to reinterpret this singular behavior. We show that the blowup can be directly related to local phase variations, which generate an underlying velocity field responsible for driving the quantum flux toward the singular region.'\nauthor:\n- 'Angel S. Sanz'\n- 'Luis\u00a0L. S\u00e1nchez-Soto'\n- Andrea Aiello\ntitle: A quantum trajectory analysis of singular wave functions\n---\n\nIntroduction\n============\n\nThe Schr\u00f6dinger equation is, perhaps, the prototype of a dispersive equation; that is, if no boundary conditions are imposed, its wave solutions spread out in space as they evolve in time\u00a0[@Tao:2006aa]. A frequent way to quantify this dispersion is by the so-called dispersive estimates, a topic with a long history\u00a0[@Schlag:2007aa; @Mandel:2020aa; @Dietze:2021aa] and whose main goal is to establish tight bounds on the decay of the solutions.\n\nRecently, it has been pointed out that the Schr\u00f6dinger equation, even for a free particle, presents dispersive singularities\u00a0[@Peres:2002aa; @Bona:2010aa]: an initial square-integrable profile $\\psi(x,0)$ could result in a"
+"---\nabstract: 'We present in this reference paper an instrumental project dedicated to the monitoring of solar activity during solar cycle 25. It concerns the survey of fast evolving chromospheric events implied in Space Weather, such as flares, coronal mass ejections, filament instabilities and Moreton waves. Coronal waves are produced by large flares around the solar maximum and propagate with chromospheric counterparts; they are rare, faint, difficult to observe, and for that reason, challenging. They require systematic observations with automatic, fast and multi-channel optical instruments. MeteoSpace is a high cadence telescope assembly specially designed for that purpose. The large amount of data will be freely available to the solar community. We describe in details the optical design, the qualification tests and capabilities of the telescopes, and show how waves can be detected. MeteoSpace will be installed at Calern observatory (C\u00f4te d\u2019Azur, 1270 m) and will be in full operation in 2023.'\nauthor:\n- 'Jean-Marie Malherbe'\n- Thierry Corbard\n- Ga\u00eble Barbary\n- Fr\u00e9d\u00e9ric Morand\n- Claude Collin\n- Daniel Crussaire\n- Florence Guitton\nbibliography:\n- 'papier.bib'\ndate: 'Received: 15 December 2021 / Accepted: date'\ntitle: 'Monitoring fast solar chromospheric activity: the MeteoSpace project'\n---\n\nIntroduction {#sec:Intro}\n============\n\nSolar activity is"
+"---\nabstract: 'In addition to longitudinal spin angular momentum (SAM) along the axis of propagation of light, spatially structured electromagnetic fields such as evanescent waves and focused beams have recently been found to possess transverse SAM in the direction perpendicular to the axis of propagation. In particular, the SAM of SPPs with spatial structure has been extensively studied in the last decade after it became clear that evanescent fields with spatially structured energy flow generate three-dimensional spin texture. Here we present numerical calculations of the space-time surface plasmon polariton (ST-SPP) wave packet, a plasmonic bullet that propagates at an arbitrary group velocity while maintaining its spatial distribution. ST-SPP wave packets with complex spatial structure and energy flow density distribution determined by the group velocity are found to propagate with accompanying three-dimensional spin texture and finite topological charge density. Furthermore, the spatial distribution of the spin texture and topological charge density determined by the spatial structure of the SPP is controllable, and the deformation associated with propagation is negligible. ST-SPP wave packets, which can stably transport customizable three-dimensional spin textures and topological charge densities, can be excellent subjects of observation in studies of spinphotonics and optical topological materials.'\nauthor:\n- Naoki"
+"---\nabstract: 'Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE.'\nauthor:\n- Lan Tang\n- 'Xiaxi Li[^1]'\n- Jinyuan Zhang\n- Guiying Li\n- 'Peng Yang[^2] ()'\n- Ke Tang\nbibliography:\n- 'references.bib'\ntitle: 'Enabling surrogate-assisted evolutionary"
+"---\nabstract: 'There is a current interest in quantum thermodynamics in the context of open quantum systems. An important issue is the consistency of quantum thermodynamics, in particular the second law of thermodynamics, i.e., the flow of heat from a hot reservoir to a cold reservoir. Here recent emphasis has been on composite system and in particular the issue regarding the application of local or global master equations. In order to contribute to this discussion we discuss two cases, namely as an example a single qubit and as a simple composite system two coupled qubits driven by two heat reservoirs at different temperatures, respectively. Applying a global Lindblad master equation approach we present explicit expressions for the heat currents in agreement with the second law of thermodynamics. The analysis is carried out in the Born-Markov approximation. We also discuss issues regarding the possible presence of coherences in the steady state.'\nauthor:\n- 'Hans C. Fogedby'\ntitle: Heat currents in qubit systems\n---\n\nIntroduction\n============\n\nThere is a current interest in quantum thermodynamics [@Alicki79; @Barra15; @Kosloff13a; @Kosloff14; @Mari12; @Kosloff19; @Levy20; @Colla22; @Linden10]. Thermodynamics is a universal theory and must from a fundamental point of view emerge from a microscopic point of"
+"---\nabstract: 'In this paper, we investigate the mixed-state entanglement in a model of p-wave superconductivity phase transition using holographic methods. We calculate several entanglement measures, including holographic entanglement entropy (HEE), mutual information (MI), and entanglement wedge cross-section (EWCS). Our results show that these measures display critical behavior at the phase transition points, with the EWCS exhibiting opposite temperature behavior compared to the HEE. Additionally, we find that the critical exponents of all entanglement measures are twice those of the condensate. Moreover, we find that the EWCS is a more sensitive indicator of the critical behavior of phase transitions than the HEE. Furthermore, we uncover a universal inequality in the growth rates of EWCS and MI near critical points in thermal phase transitions, such as p-wave and s-wave superconductivity, suggesting that MI captures more information than EWCS when a phase transition first occurs.'\nauthor:\n- 'Zhe Yang $^{1}$'\n- 'Fang-Jing Cheng $^{2}$'\n- 'Chao Niu $^{1}$'\n- 'Cheng-Yong Zhang $^{1}$'\n- 'Peng Liu $^{1}$'\ntitle: ' The mixed-state entanglement in holographic p-wave superconductor model '\n---\n\n[^1]\n\nIntroduction {#sec:intro}\n============\n\nQuantum entanglement is the most crucial characteristic of the quantum system and lays the key foundation of quantum information theory."
+"---\nabstract: 'We present LM-GAN, an HDR sky model that generates photorealistic environment maps with weathered skies. Our sky model retains the flexibility of traditional parametric models and enables the reproduction of photorealistic all-weather skies with visual diversity in cloud formations. This is achieved with flexible and intuitive user controls for parameters, including sun position, sky color, and atmospheric turbidity. Our method is trained directly from inputs fitted to real HDR skies, learning both to preserve the input\u2019s illumination and correlate it to the real reference\u2019s atmospheric components in an end-to-end manner. Our main contributions are a generative model trained on both sky appearance and scene rendering losses, as well as a novel sky-parameter fitting algorithm. We demonstrate that our fitting algorithm surpasses existing approaches in both accuracy and sky fidelity, and also provide quantitative and qualitative analyses, demonstrating LM-GAN\u2019s ability to match parametric input to photorealistic all-weather skies. The generated HDR environment maps are ready to use in 3D rendering engines and can be applied to a wide range of image-based lighting applications.'\nauthor:\n- Lucas Valen\u00e7a\n- Ian Maquignaz\n- Hadi Moazen\n- Rishikesh Madan\n- 'Yannick Hold-Geoffroy'\n- 'Jean-Fran\u00e7ois Lalonde'\ntitle: 'LM-GAN: A Photorealistic All-Weather Parametric Sky"
+"---\nabstract: 'The robustness of the Kalman filter to double talk and its rapid convergence make it a popular approach for addressing acoustic echo cancellation (AEC) challenges. However, the inability to model nonlinearity and the need to tune control parameters cast limitations on such adaptive filtering algorithms. In this paper, we integrate the frequency domain Kalman filter (FDKF) and deep neural networks (DNNs) into a hybrid method, called NeuralKalman, to leverage the advantages of deep learning and adaptive filtering algorithms. Specifically, we employ a DNN to estimate nonlinearly distorted far-end signals, a transition factor, and the nonlinear transition function in the state equation of the FDKF algorithm. Experimental results show that the proposed NeuralKalman improves the performance of FDKF significantly and outperforms strong baseline methods.'\naddress: |\n     $^1$The Ohio State University, Columbus, OH, USA\\\n    $^2$Tencent AI Lab, Bellevue, WA, USA\nbibliography:\n- 'mybib.bib'\ntitle: 'NeuralKalman: A Learnable Kalman Filter for Acoustic Echo Cancellation'\n---\n\n**Index Terms**: Acoustic echo cancellation, Kalman filter, deep learning, NeuralKalman\n\nIntroduction {#sec:intro}\n============\n\nAcoustic echo cancellation (AEC), as an active and challenging research problem in the domain of speech processing, has been studied for decades and is widely used in mobile communication and teleconferencing systems. The"
+"---\nabstract: 'Federated learning (FL), as an effective decentralized distributed learning approach, enables multiple institutions to jointly train a model without sharing their local data. However, the domain feature shift caused by different acquisition devices/clients substantially degrades the performance of the FL model. Furthermore, most existing FL approaches aim to improve accuracy without considering reliability (e.g., confidence or uncertainty). The predictions are thus unreliable when deployed in safety-critical applications. Therefore, aiming at improving the performance of FL in non-Domain feature issues while enabling the model more reliable. In this paper, we propose a novel reliable federated disentangling network, termed RFedDis, which utilizes feature disentangling to enable the ability to capture the global domain-invariant cross-client representation and preserve local client-specific feature learning. Meanwhile, to effectively integrate the decoupled features, an uncertainty-aware decision fusion is also introduced to guide the network for dynamically integrating the decoupled features at the evidence level, while producing a reliable prediction with an estimated uncertainty. To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling, which enhances the performance and reliability of FL in non-IID domain features. Extensive experimental results show"
+"---\nabstract: 'In this work, we present UV completions of the recently proposed number-changing Co-SIMP freeze-out mechanism. In contrast to the standard cannibalistic-type dark matter picture that occurs entirely in the dark sector, the $3\\to 2$ process setting the relic abundance in this case requires one Standard Model particle in the initial and final states. This prevents the dark sector from overheating and leads to rich experimental signatures. We generate the Co-SIMP interaction with a dark sector consisting of two scalars, with the mediator coupling to either nucleons or electrons. In either case, *the dark matter candidate is naturally light*: nucleophilic interactions favor the sub-GeV mass range and leptophilic interactions favor the sub-MeV mass range. Viable thermal models in these lighter mass regimes are particularly intriguing to study at this time, as new developments in low-threshold detector technologies will begin probing this region of parameter space. While particles in the sub-MeV regime can potentially impact light element formation and CMB decoupling, we show that a late-time phase transition opens up large fractions of parameter space. These thermal light dark matter models can instead be tested with dedicated experiments. We discuss the viable parameter space in each scenario in light of"
+"---\nabstract: 'Semantic segmentation is an important technique for environment perception in intelligent transportation systems. With the rapid development of convolutional neural networks (CNNs), road scene analysis can usually achieve satisfactory results in the source domain. However, guaranteeing good generalization to different target domain scenarios remains a significant challenge. Recently, semi-supervised learning and active learning have been proposed to alleviate this problem. Semi-supervised learning can improve model accuracy with massive unlabeled data, but some pseudo labels containing noise would be generated with limited or imbalanced training data. And there will be suboptimal models if human guidance is absent. Active learning can select more effective data to intervene, while the model accuracy can not be improved because the massive unlabeled data are not used. And the probability of querying sub-optimal samples will increase when the domain difference is too large, increasing annotation cost. This paper proposes an iterative loop method combining active and semi-supervised learning for domain adaptive semantic segmentation. The method first uses semi-supervised to learn massive unlabeled data to improve model accuracy and provide more accurate selection models for active learning. Secondly, combined with the predictive uncertainty sample selection strategy of active learning, manual intervention is used to correct"
+"---\nabstract: |\n    In this work, we present new data on the $^{182,183,184}$W($\\gamma,n$) cross sections, utilizing a quasi-monochromatic photon beam produced at the NewSUBARU synchrotron radiation facility. Further, we have extracted the nuclear level density and $\\gamma$-ray strength function of $^{186}$W from data on the $^{186}$W($\\alpha,\\alpha^\\prime\\gamma$)$^{186}$W reaction measured at the Oslo Cyclotron Laboratory. Combining previous measurements on the $^{186}$W($\\gamma,n$) cross section with our new $^{182,183,184}$W($\\gamma,n$) and ($\\alpha,\\alpha^\\prime\\gamma$)$^{186}$W data sets, we have deduced the $^{186}$W $\\gamma$-ray strength function in the range of $1 < E_\\gamma < 6$ MeV and $7 < E_\\gamma < 14$ MeV.\n\n    Our data are used to extract the level density and $\\gamma$-ray strength functions needed as input to the nuclear-reaction code , providing an indirect, experimental constraint for the $^{185}$W($n,\\gamma$)$^{186}$W cross section and reaction rate. Compared to the recommended Maxwellian-averaged cross section (MACS) in the KADoNiS-1.0 data base, our results are on average lower for the relevant energy range $k_B T \\in [5,100]$ keV, and we provide a smaller uncertainty for the MACS. The theoretical values of Bao *et al.* and the cross section experimentally constrained on photoneutron data of Sonnabend *et al.* are significantly higher than our result. The lower value by Mohr *et al.* is"
+"---\nabstract: 'This letter presents a continuous probabilistic modeling methodology for spatial point cloud data using finite Gaussian Mixture Models (GMMs) where the number of components are adapted based on the scene complexity. Few hierarchical and adaptive methods have been proposed to address the challenge of balancing model fidelity with size. Instead, state-of-the-art mapping approaches require tuning parameters for specific use cases, but do not generalize across diverse environments. To address this gap, we utilize a self-organizing principle from information-theoretic learning to automatically adapt the complexity of the GMM model based on the relevant information in the sensor data. The approach is evaluated against existing point cloud modeling techniques on real-world data with varying degrees of scene complexity.'\nauthor:\n- 'Kshitij Goel, Nathan Michael, and Wennie Tabib[^1][^2] [^3][^4]'\nbibliography:\n- 'refs.bib'\ntitle: 'Probabilistic Point Cloud Modeling via Self-Organizing Gaussian Mixture Models'\n---\n\nMapping, RGB-D Perception, Field Robots\n\nINTRODUCTION {#sec:intro}\n============\n\npoint cloud data are used in physical simulations\u00a0[@Ummenhofer2020Lagrangian], computer graphics\u00a0[@vedaldi_neural_2020], and robotic perception\u00a0[@tabib_autonomous_2021]. For robotic perception applications, in particular, three-dimensional (3D) perception algorithms do not operate directly on raw point cloud data; instead, they subsample, discretize, or create an intermediate representation\u00a0[@eckart_compact_2017]. Gaussian mixture models (GMMs) have"
+"---\nabstract: 'We introduce a symmetry class for higher dimensional partitions - *fully complementary higher dimensional partitions* (FCPs) - and prove a formula for their generating function. By studying symmetry classes of FCPs in dimension 2, we define variations of the classical symmetry classes for plane partitions. As a by-product we obtain conjectures for three new symmetry classes of plane partitions and prove that another new symmetry class, namely *quasi transpose complementary plane partitions* are equinumerous to symmetric plane partitions.'\naddress: 'University of Vienna, Austria'\nauthor:\n- 'Florian Schreier-Aigner'\nbibliography:\n- 'LiteraturListe.bib'\ntitle: Fully complementary higher dimensional partitions\n---\n\nIntroduction {#sec: intro}\n============\n\nA [*plane partition*]{} $\\pi$ is an array $(\\pi_{i,j})$ of non-negative integers with all but finitely many entries equal to $0$, which is weakly decreasing along rows and columns, i.e., $\\pi_{i,j} \\geq \\pi_{i+1,j}$ and $\\pi_{i,j} \\geq \\pi_{i,j+1}$; see Figure\u00a0\\[fig: PP\\] (left) for an example. MacMahon [@MacMahon97] introduced them at the end of the 19th century as two dimensional generalisations of ordinary partitions and proved in [@MacMahon16] two enumeration results: He showed that the generating function of plane partitions is given by $$\\sum_{\\pi} q^{|\\pi|} = \\prod_{i\\geq 1} \\frac{1}{(1-q^i)^i},$$ where the sum is over all plane partitions and $|\\pi|$"
+"---\nauthor:\n- '\u00c1ngel Rinc[\u00f3]{}n [^1]'\n- 'Grigoris Panotopoulos [^2]'\n- 'Il[\u00ed]{}dio Lopes [^3]'\nbibliography:\n- 'biblio\\_1.bib'\ndate: 'Received: date / Revised version: date'\ntitle: Anisotropic stars made of exotic matter within the complexity factor formalism\n---\n\n[leer.eps]{} gsave 72 31 moveto 72 342 lineto 601 342 lineto 601 31 lineto 72 31 lineto showpage grestore\n\nIntroduction\n============\n\nAny reasonable modern cosmological model must include Dark Energy (DE). Nevertheless, the nature and origin of Dark Energy remain a mystery despite its fundamental importance in modern theoretical cosmology [@SupernovaSearchTeam:1998fmf; @SupernovaCosmologyProject:1998vns; @Freedman:2003ys]. As it is well known, a cosmological model made of only matter and radiation cannot lead to accelerated solutions to the universe as predicted by Einstein\u2019s Theory of General Relativity (GR) [@Einstein:1916vd]. This kind of solution is obtained by including a constant $\\Lambda$ in Einstein\u2019s field equations [@Einstein:1917ce], i.e., by adding the contribution of the dark energy. Despite its simplicity, such accelerated cosmological model is in exceptional agreement with a vast amount of observational data. Such a cosmological model is known as the concordance cosmological model or the $\\Lambda$CDM model. Nevertheless, $\\Lambda$ suffers from the cosmological constant ongoing problem [@Weinberg:1988cp; @Zeldovich:1967gd]. Additionally, this $\\Lambda$\u2013problem is amplified by the current values"
+"---\nabstract: 'The dynamic Schr\u00f6dinger bridge problem provides an appealing setting for solving constrained time-series data generation tasks posed as optimal transport problems. It consists of learning non-linear diffusion processes using efficient iterative solvers. Recent works have demonstrated state-of-the-art results ([*e.g.*]{}, in modelling single-cell embryo RNA sequences or sampling from complex posteriors) but are limited to learning bridges with only initial and terminal constraints. Our work extends this paradigm by proposing the Iterative Smoothing Bridge (ISB). We integrate Bayesian filtering and optimal control into learning the diffusion process, enabling the generation of constrained stochastic processes governed by sparse observations at intermediate stages and terminal constraints. We assess the effectiveness of our method on synthetic and real-world data generation tasks and we show that the ISB generalises well to high-dimensional data, is computationally efficient, and provides accurate estimates of the marginals at intermediate and terminal times.'\nauthor:\n- |\n    Ella Tamir ella.tamir@aalto.fi\\\n    Department of Computer Science\\\n    Aalto University Martin Trapp martin.trapp@aalto.fi\\\n    Department of Computer Science\\\n    Aalto University Arno Solin arno.solin@aalto.fi\\\n    Department of Computer Science\\\n    Aalto University\ntitle: 'Transport with Support: Data-Conditional Diffusion Bridges'\n---\n\nIntroduction\n============\n\nGenerative diffusion models have gained increasing popularity and achieved impressive results in a variety of"
+"---\nabstract: 'We discuss a new type of delay differential equation that exhibits resonating transient oscillations. The power spectrum peak of the dynamical trajectory reaches its maximum height when the delay is suitably tuned. Furthermore, our analysis of the resonant conditions for this equation has revealed a new connection between the solutions of the transcendental trigonometric equation and the Lambert $W$ function. These results offer fresh insights into the nonlinear dynamics induced by delayed feedback.'\nauthor:\n- |\n    Kenta Ohira$^{1}$ and Toru Ohira$^{2}$\\\n    \u00a0$^{1}$Future Value Creation Research Center,\\\n    Graduate School of Informatics, Nagoya University, Japan\\\n    \u00a0$^{2}$Graduate School of Mathematics, Nagoya University, Japan\ntitle: 'Delay, resonance and the Lambert W function'\n---\n\n[**Keywords**]{}: Delay, Resonance, Transient Oscillation, Lambert W function, Transcendental equation\n\nIntroduction\n============\n\nThere has been interest in investigating the effect of delays in various fields such as biology, mathematics, economics, and engineering [@heiden1979; @bellman1963; @cabrera_1; @hayes1950; @insperger; @kcuhler; @longtinmilton1989a; @mackeyglass1977; @miltonetal2009b; @ohirayamane2000; @smith2010; @stepan1989; @stepaninsperger; @szydlowski2010]). Typically, delays introduce oscillations and complex behaviors to otherwise simple and well-behaved systems. Longer delays are known to induce an increase in the complexity of dynamics. The Mackey\u2013Glass equation[@mackeyglass1977], which exhibits various types of dynamics including chaos, serves as a representative example.\n\nMathematical"
+"---\nabstract: 'The interaction of rarefied gases with functionalized surfaces is of great importance in technical applications such as gas separation membranes and catalysis. To investigate the influence of functionalization and rarefaction on gas flow rate in a defined geometry, pressure-driven gas flow experiments with helium and carbon dioxide through plain and alkyl-functionalized microchannels are performed. The experiments cover Knudsen numbers from 0.01 to 200 and therefore the slip flow regime up to free molecular flow. To minimize the experimental uncertainty which is prevalent in micro flow experiments, a methodology is developed to make optimal use of the measurement data. The results are compared to an analytical model predicting rarefied gas flow in straight channels and to numerical simulations of the S-model and BGK equations. The experimental data shows no significant difference between plain and functionalized channels. This stands in contrast to previous measurements in smaller geometries and demonstrates that the surface-to-volume ratio seems to be too small for the functionalization to have an influence and highlights the importance of geometric scale for surface effects. These results also shed light on the molecular reflection characteristics described by the TMAC.'\nauthor:\n- |\n    Simon Kunze$^1$, Pierre Perrier$^2$, Rodion Groll$^{3,6}$, Benjamin Besser$^1$,\\"
+"---\nauthor:\n- Minzhao Liu\n- Changhun Oh\n- Junyu Liu\n- Liang Jiang\n- Yuri Alexeev\nbibliography:\n- 'boson.bib'\ntitle: Simulating lossy Gaussian boson sampling with matrix product operators\n---\n\n[**Gaussian boson sampling, a computational model that is widely believed to admit quantum supremacy, has already been experimentally demonstrated and is claimed to surpass the classical simulation capabilities of even the most powerful supercomputers today. However, whether the current approach limited by photon loss and noise in such experiments prescribes a scalable path to quantum advantage is an open question. To understand the effect of photon loss on the scalability of Gaussian boson sampling, we analytically derive the asymptotic operator entanglement entropy scaling, which relates to the simulation complexity. As a result, we observe that efficient tensor network simulations are likely possible under the $N_\\text{out}\\propto\\sqrt{N}$ scaling of the number of surviving photons orange$N_\\text{out}$ in the number of input photons $N$. We numerically verify this result using a tensor network algorithm with $U(1)$ symmetry, and overcome previous challenges due to the large local Hilbert space dimensions in Gaussian boson sampling with hardware acceleration. Additionally, we observe that increasing the photon number through larger squeezing does not increase the entanglement entropy"
+"---\nabstract: 'We explore the influences of the higher-order Gauss-Bonnet (GB) correction terms on the growth of perturbations at the early stage of a $(n+1)$-dimensional Friedmann-Robertson-Walker (FRW) universe. Considering a cosmological constant in the FRW background, we study the linear perturbations by adopting the spherically symmetric collapse (SC) formalism. In light of the modifications that appear in the field equations, we disclose the role of the GB coupling constant $\\alpha$, as well as the extra dimensions $n>3$ on the growth of perturbations. It, in essence, is done by defining a dimensionless parameter $\\tilde{\\beta}=(n-2)(n-3) \\alpha H_0^2$ in which $H_0$ is the Hubble constant. We find that the matter density contrast starts growing at the early stages of the universe and, as the universe expands, it grows faster compared to the standard cosmology. Besides, in the framework of GB gravity, the growth of matter perturbations in higher dimensions is faster than its standard counterpart $(n=3)$. Further, in the presence of $\\alpha$, the growth of perturbations increases as it increases. This is an expected result, since the higher order GB correction terms increase the strength of the gravity and thus support the growth of perturbations. For the existing cosmological model, we also investigate"
+"---\nabstract: 'We give a complete characterisation of the domain of attraction of fixed points of branching Brownian motion (BBM) with critical drift. Prior to this classification, we introduce a suitable metric space of locally finite point measures on which we prove 1) that the BBM with critical drift is a well-defined Markov process and 2) that it satisfies the Feller property. Several applications of this characterisation are given.'\naddress:\n- 'Beijing Normal University, School of Mathematical Sciences, China '\n- 'Universit\u00e9 Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 69622 Villeurbanne, France , Institut Universitaire de France (IUF) and Universit\u00e9 de Gen\u00e8ve (Unige)'\n- 'Tata Institute of Fundamental Research-CAM, Bangalore, India'\nauthor:\n- 'Xinxin Chen, Christophe Garban, Atul Shekhar'\nbibliography:\n- 'biblio.bib'\ntitle:  Domain of attraction of the fixed points of Branching Brownian motion \n---\n\n[**]{}\n\nIntroduction {#intro}\n============\n\nContext.\n--------\n\nIn this article we study the critical-drifted branching Brownian motion (BBM) seen as a Markov process, and answer some natural questions about it. A (binary)[^1] branching Brownian motion (BBM) can be described as follows: starting from a countable set of initial particles, each particle evolves independently of each other according to standard Brownian motions in ${\\mathbb{R}}$"
+"---\nauthor:\n- |\n    [Cameron Perot[^1]]{}\\\n    [Master\u2019s Thesis]{}\\\n    [submitted to]{}\\\n    [The Faculty of Mathematics, Computer Science, and Natural Sciences]{}\\\n    [of RWTH Aachen University]{}\\\n    [written at]{}\\\n    [J\u00fclich Supercomputing Centre]{}\\\n    [Forschungszentrum J\u00fclich]{}\\\n    [First Examiner: Prof. Dr. Kristel Michielsen[^2]^,^[^3]]{}\\\n    [Second Examiner: Prof. Dr. Holger Rauhut]{}\\\n    [Adviser: Dr. Dennis Willsch]{}\nbibliography:\n- 'references.bib'\ndate: 'July 4, 2022'\ntitle: |\n     [Quantum Boltzmann Machines]{}\\\n    [Applications in Quantitative Finance]{} \n---\n\nAbstract {#abstract .unnumbered}\n========\n\nIn this thesis we explore using the D-Wave Advantage 4.1 quantum annealer to sample from quantum Boltzmann distributions and train quantum Boltzmann machines (QBMs). We focus on the real-world problem of using QBMs as generative models to produce synthetic foreign exchange market data and analyze how the results stack up against classical models based on restricted Boltzmann machines (RBMs). Additionally, we study a small 12-qubit problem which we use to compare samples obtained from the Advantage 4.1 with theory, and in the process gain vital insights into how well the Advantage 4.1 can sample quantum Boltzmann random variables and be used to train QBMs. Through this, we are able to show that the Advantage 4.1 can sample classical Boltzmann random variables to some extent, but is limited in its ability to sample from"
+"---\nabstract: 'We study PAC learnability and PAC stabilizability of Hedonic Games (HGs), i.e., efficiently inferring preferences or core-stable partitions from samples. We first expand the known learnability/stabilizability landscape for some of the most prominent HGs classes, providing results for Friends and Enemies Games, Bottom Responsive, and Anonymous HGs. Then, having a broader view in mind, we attempt to shed light on the structural properties leading to learnability/stabilizability, or lack thereof, for specific HGs classes. Along this path, we focus on the fully expressive Hedonic Coalition Nets representation of HGs. We identify two sets of conditions that lead to efficient learnability, and which encompass all of the known positive learnability results. On the side of stability, we reveal that, while the freedom of choosing an ad hoc adversarial distribution is the most obvious hurdle to achieving PAC stability, it is not the only one. First, we show a distribution independent necessary condition for PAC stability. Then, we focus on ${\\ensuremath{\\mathcal{W}}}$-games, where players have individual preferences over other players and evaluate coalitions based on the least preferred member. We prove that these games are PAC stabilizable under the class of bounded distributions, which assign positive probability mass to all coalitions. Finally,"
+"Introduction {#sec:introduction}\n============\n\nIn the last decade, Natural Language Processing (NLP) has gained relevance in Legal Artificial Intelligence, transitioning from symbolic to subsymbolic techniques [@Villata2022ThirtyYO]. Such a shift is motivated partially by the nature of legal resources, which appear primarily in a textual format (legislation, legal proceedings, contracts, etc.). Following the advancements in NLP technologies, the legal NLP literature [@zhong-etal-2020-nlp; @nllp-2022-natural; @katz_natural_2023] is flourishing with many new resources, such as large legal corpora [@henderson_pile_2022], task-specific datasets [@shen2022multilexsum; @christen_resolving_2023; @brugger_multilegalsbd; @niklaus_automatic_2023], and pre-trained legal-oriented language models [@chalkidis-etal-2020-legal; @zlucia/custom-legalbert; @lawformer; @niklaus-giofre-2023-pretrain; @hua_legalrelectra_2022; @chalkidis-etal-2023-lexfiles]. @DBLP:journals/corr/abs-2308-05502 offer a comprehensive survey on the topic.\n\nSpecifically, the emergence of has led to significant performance boosts on popular benchmarks like GLUE [@GLUE] or SuperGLUE [@SUPERGLUE], emphasizing the need for more challenging benchmarks to measure progress. Legal benchmark suites have also been developed to systematically evaluate the performance of , showcasing the superiority of legal-oriented models over generic ones on downstream tasks such as legal document classification or question answering [@chalkidis-etal-2022-lexglue; @hwang2022a]. Even though these PLMs are shown to be effective for numerous downstream tasks, they are general-purpose models that are trained on broad-domain resources, such as Wikipedia or News, and therefore, can be insufficient to address tasks"
+"---\nabstract: |\n    This work is concerned to study the bouncing nature of the universe for an isotropic configuration of fluid $\\mathcal{T}_{\\alpha\\beta}$ and Friedmann-Lema\u00eetre-Robertson-Walker metric scheme. This work is carried out under the novel $f(\\mathcal{G},\\mathcal{T}_{\\alpha\n    \\beta} \\mathcal{T}^{\\alpha \\beta})$ gravitation by assuming a specific model i.e, $f(\\mathcal{G},\\mathcal{T}^2)=\\mathcal{G}+\\alpha\n    \\mathcal{G}^2+2\\lambda \\mathcal{T}^2$ with $\\alpha$ and $\\lambda$ are constants, serving as free parameters. [The terms $\\mathcal{G}$ and $\\mathcal{T}^2$ served as an Gauss-Bonnet invariant and square of the energy-momentum trace term as an inclusion in the gravitational action respectively, and is proportional to $\\mathcal{T}^2=\\mathcal{T}_{\\alpha \\beta}\n    \\mathcal{T}^{\\alpha \\beta}$.]{} A specific functional form of the Hubble parameter is taken to provide the evolution of cosmographic parameters. A well known equation of state parameter, $\\omega(t)=-\\frac{k \\log (t+\\epsilon\n    )}{t}-1$ is used to represent the dynamical behavior of energy density, matter pressure and energy conditions. A detailed graphical analysis is also provided to review the bounce. Furthermore, all free parameters are set in a way, to make the supposed Hubble parameter act as the bouncing solution and ensure the viability of energy conditions. Conclusively, all necessary conditions for a bouncing model are checked.\nauthor:\n- |\n    Z. Yousaf$^1$ [^1], M. Z. Bhatti$^1$ [^2], H. Aman$^1$ [^3], P.K. Sahoo$^2$ [^4]\\\n    $^1$Department of"
+"---\nabstract: 'In this work, we have presented a way to increase the contrast of an image. Our target is to find a transformation that will be image specific. We have used a fuzzy system as our transformation function. To tune the system according to an image, we have used Genetic Algorithm and Hill Climbing in multiple ways to evolve the fuzzy system and conducted several experiments. Different variants of the method are tested on several images and two variants that are superior to others in terms of fitness are selected. We have also conducted a survey to assess the visual improvement of the enhancements made by the two variants. The survey indicates that one of the methods can enhance the contrast of the images visually.'\nauthor:\n- Mohimenul Kabir\n- Jaiaid Mobin\n- Ahmad Hassanat\n- 'M. Sohel Rahman[^1]'\ntitle: Image Contrast Enhancement using Fuzzy Technique with Parameter Determination using Metaheuristics\n---\n\nIntroduction\n============\n\nImage enhancement is the procedure of improving an image\u2019s quality and information content. Image enhancement aims to increase visual differences among its features and make it more suitable for applications (e.g. increasing the brightness of dark images for viewing). Some common image enhancement techniques are"
+"---\nabstract: 'We present high S/N measurements of the [H$\\;$[I]{}]{}\u00a0[Ly$\\alpha$]{}\u00a0absorption line toward 16 Galactic targets which are at distances between approximately 190 and 2200 pc, all beyond the wall of the Local Bubble. We describe the models used to remove stellar emission and absorption features and the methods used to account for all known sources of error in order to compute high precision values of the [H$\\;$[I]{}]{}\u00a0column density with robust determinations of uncertainties. When combined with [H$_2$]{}\u00a0column densities from other sources, we find total H column densities ranging from 10$^{20.01}$ to 10$^{21.25}$ cm$^{-2}.$ Using deuterium column densities from [[*FUSE*]{}]{}\u00a0observations we determine the D/H ratio along the sight lines. We confirm and strengthen the conclusion that D/H is spatially variable over these [H$\\;$[I]{}]{}\u00a0column density and target distance regimes, which predominantly probe the ISM outside the Local Bubble. We discuss how these results affect models of Galactic chemical evolution. We also present an analysis of metal lines along the five sight lines for which we have high resolution spectra and, along with results reported in the literature, discuss the corresponding column densities in the context of a generalized depletion analysis. We find that D/H is only"
+"---\nauthor:\n- Xinyu\u00a0Zhou\n- Yang\u00a0Li\n- Jun Zhao\ntitle: Resource Allocation of Federated Learning Assisted Mobile Augmented Reality System in the Metaverse\n---\n\nIntroduction {#sec:introduction}\n============\n\nMetaverse has become a buzzword in recent years. It seeks to create a society integrated virtual/augmented reality and allows millions of people to communicate online with a virtual avatar. Augmented Reality (AR) technology, which has been expected to one of the most significant component of the Metaverse, is an enhanced version of the real physical world that is achieved through the use of digital visual elements, sound, or other sensory stimuli and delivered via technology. Mobile Augmented Reality (MAR) is to implement AR technology on mobile devices and allow users to experience services through AR devices (e.g. smart glasses, headsets, controllers, etc.).\n\n**Motivation**. According to Moore\u2019s law, the storage capacity and computing power of mobile devices will be further improved in the future, making it possible to implement machine learning models on mobile devices [@dionisio20133d]. However, limited by the small amount of personal data, it is difficult to train a high-performing MAR model on a single device. Federated learning (FL) [@mcmahan2017communication], presented in 2017, allows models from diverse participants to train"
+"---\nauthor:\n- 'Gustavo. P.\u00a0de Brito [^1]'\n- 'Astrid Eichhorn[^2]'\n- Christopher Pfeiffer\nbibliography:\n- 'refs.bib'\ntitle: 'Higher-order curvature operators in causal set quantum gravity '\n---\n\nIntroduction and motivation\n===========================\n\nIn this paper, we construct higher-order curvature operators for causal sets. Our motivation is twofold: First, geometric quantities such as curvature operators are important when one reconstructs a continuous spacetime from a discrete causal set \u2013 which is one of the key outstanding problems in causal set quantum gravity. Second, higher-order curvature operators are important when one uses causal sets to search for asymptotic safety in quantum gravity in Lorentzian signature \u2013 which is one of the key outstanding problems in asymptotically safe quantum gravity.\\\nBelow, we introduce these motivations in more detail.\\\nOur first motivation comes from the reconstruction of continuum geometry from a discrete causal set. Causal set quantum gravity is based on a discretization of Lorentzian spacetimes [@Bombelli:1987aa], see [@Surya:2019ndm] for a recent review. It substitutes Lorentzian continuum manifolds by networks of spacetime points, in which the links that connect the nodes of the network correspond to causal relations. Mathematically, such networks are partial orders. However, the set of partial orders which satisfy the causal-set"
+"---\nabstract: 'Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives. This paper addresses the question of whether such optimization actually provides the generator with gradients that make its distribution close to the target distribution. We derive , sufficient conditions for the discriminator to serve as the distance between the distributions by connecting the GAN formulation with the concept of sliced optimal transport. Furthermore, by leveraging these theoretical results, we propose a novel GAN training scheme, called slicing adversarial network (SAN). With only simple modifications, . Experiments on synthetic and image datasets support our theoretical results and the SAN\u2019s effectiveness as compared to usual GANs.'\nauthor:\n- |\n    Yuhta Takida${}^{1}$ Masaaki Imaizumi${}^{2}$ Takashi Shibuya${}^{1}$ Chieh-Hsin Lai${}^{1}$Toshimitsu Uesaka${}^{1}$ Naoki Murata${}^{1}$ Yuki Mitsufuji${}^{1,3}$\\\n    ${}^1$Sony AI, Tokyo, Japan\\\n    ${}^{2}$The University of Tokyo, Tokyo, Japan\\\n    ${}^{3}$Sony Group Corporation, Tokyo, Japan\nbibliography:\n- 'str\\_def\\_abrv.bib'\n- 'refs\\_dgm.bib'\n- 'refs\\_ml.bib'\ntitle: |\n    SAN: Inducing Metrizability of GAN with\\\n    Discriminative Normalized Linear Layer\n---\n\nIntroduction\n============\n\nA generative adversarial network (GAN)\u00a0[@goodfellow2014generative] is a popular approach for generative modeling. GANs have achieved remarkable performance in various domains such as image\u00a0[@brock2018large; @karras2019style; @karras2021alias], audio\u00a0[@kumar2019melgan; @donahue2019adversarial; @kong2020hifi], and"
+"---\nabstract: 'Solving optimization problems on quantum annealers usually requires each variable of the problem to be represented by a connected set of qubits called a logical qubit or a chain. Chain weights, in the form of ferromagnetic coupling between the chain qubits, are applied so that the physical qubits in a chain favor taking the same value in low energy samples. Assigning a good chain-strength value is crucial for the ability of quantum annealing to solve hard problems, but there are no general methods for computing such a value and, even if an optimal value is found, it may still not be suitable by being too large for accurate annealing results. In this paper, we propose an optimization-based approach for producing suitable logical qubits representations that results in smaller chain weights and show that the resulting optimization problem can be successfully solved using the augmented Lagrangian method. Experiments on the D-Wave Advantage system and the maximum clique problem on random graphs show that our approach outperforms both the default D-Wave method for chain-strength assignment as well as the quadratic penalty method.'\naddress: |\n     Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria;\\\n    Los Alamos National Laboratory,"
+"---\nabstract: 'We present 04-resolution imaging polarimetry at 8.7, 10.3, and 12.5 $\\mu$m, obtained with CanariCam at the Gran Telescopio Canarias (GTC), of the central 0.11 pc x 0.28 pc (42 x 108) region of W51 IRS2. The polarization, as high as $\\sim$14%, arises from silicate particles aligned by the interstellar magnetic field (B-field). We separate, or unfold, the polarization of each sightline into emission and absorption components, from which we infer the morphologies of the corresponding projected B-fields that thread the emitting and foreground-absorbing regions. We conclude that the projected B-field in the foreground material is part of the larger-scale ambient field. The morphology of the projected B-field in the mid-IR emitting region spanning the cometary region W51 IRS2W is similar to that in the absorbing region. Elsewhere, the two B-fields differ significantly with no clear relationship between them. The B-field across the W51 IRS2W cometary core appears to be an integral part of a champagne outflow of gas originating in the core and dominating the energetics there. The bipolar outflow, W51north jet, that appears to originate at or near SMA1/N1 coincides almost exactly with a clearly demarcated north-south swath of lower polarization. While speculative, comparison of mid-IR and"
+"---\nabstract: 'The relationship between structure and dynamics in glassy fluids remains an intriguing open question. Recent work has shown impressive advances in our ability to predict local dynamics using structural features, most notably due to the use of advanced machine learning techniques. Here we explore whether a simple linear regression algorithm combined with intelligently chosen structural order parameters can reach the accuracy of the current, most advanced machine learning approaches for predicting dynamic propensity. To do this we introduce a method to pinpoint the cage state of the initial configuration \u2013 i.e. the configuration consisting of the average particle positions when particle rearrangement is forbidden. We find that, in comparison to both the initial state and the inherent state, the structure of the cage state is highly predictive of the long-time dynamics of the system. Moreover, by combining the cage state information with the initial state, we are able to predict dynamic propensities with unprecedentedly high accuracy over a broad regime of time scales, including the caging regime.'\nauthor:\n- 'Rinske M. Alkemade'\n- Frank Smallenburg\n- Laura Filion\nbibliography:\n- 'myref.bib'\ntitle: Improving the prediction of glassy dynamics by pinpointing the local cage\n---\n\nIntroduction\n============\n\nUnderstanding the"
+"---\nabstract: 'Cluster states are versatile quantum resources and an essential building block for measurement-based quantum computing. The possibility to generate cluster states in specific systems may thus serve as an indicator regarding if and to what extent these systems can be harnessed for quantum technologies and quantum information processing in particular. Here, we apply this analysis to networks of degenerate optical parametric oscillators (DOPOs), also called coherent Ising machines (CIMs). CIMs are distinguished by their highly flexible coupling capabilities, which makes it possible to use them, e.g., to emulate large spin systems. As CIMs typically operate with coherent states (and superpositions thereof), it is natural to consider cluster states formed by superpositions of coherent states, i.e., coherent cluster states. As we show, such coherent cluster states can, under ideal conditions, be generated in DOPO networks with the help of beam splitters and classical pumps. Our subsequent numerical analysis provides the minimum requirements for the generation of coherent cluster states under realistic conditions. Moreover, we discuss how nonequilibrium pumps can improve the generation of coherent cluster states. In order to assess the quality of the cluster-state generation, we map the generated states to an effective spin space using modular variables,"
+"---\nabstract: 'Stabilization, disturbance rejection, and control of optical beams and optical spots are ubiquitous problems that are crucial for the development of optical systems for ground and space telescopes, free-space optical communication terminals, precise beam steering systems, and other types of optical systems. High-performance disturbance rejection and control of optical spots require the development of disturbance estimation and data-driven Kalman filter methods. Motivated by this, we propose a unified and experimentally verified data-driven framework for optical-spot disturbance modeling and tuning of covariance matrices of Kalman filters. Our approach is based on covariance estimation, nonlinear optimization, and subspace identification methods. Also, we use spectral factorization methods to emulate optical-spot disturbances with a desired power spectral density in an optical laboratory environment. We test the effectiveness of the proposed approaches on an experimental setup consisting of a piezo tip-tilt mirror, piezo linear actuator, and a CMOS camera.'\nauthor:\n- Aleksandar Haber\n- Michael Krainak\nbibliography:\n- 'sample.bib'\ntitle: 'Data-driven Estimation, Tracking, and System Identification of Deterministic and Stochastic Optical Spot Dynamics'\n---\n\nIntroduction\n============\n\nStabilization, disturbance rejection, and precise control of optical beams and optical spots are fundamental and ubiquitous problems that appear in a number of applications and optical systems."
+"---\nauthor:\n- |\n    Mehdi Sadeghi[^1] and Faramarz Rahmani[^2]\\\n    \\\n    \\\ntitle: '**The phase transition of Rastall AdS black hole with cloud of strings and quintessence**'\n---\n\n**Keywords:** Phase transition, Rastall theory of gravity, quintessence, cloud of strings.\n\nIntroduction {#intro}\n============\n\nBlack holes behave like thermodynamic systems [@Kubiznak:2014zwa]. This motivates us to study the thermodynamic behavior of the black holes. The Hawking-Page phase transition which is a first-order phase transition, describes the transition between phases which are static spherically symmetric vacuum solutions of the Einstein equations in AdS spacetime. According to the gauge/gravity duality [@Witten:1998qj]-[@Aharony], this must corresponds to a phase transition in gauge theory. Witten shows that this correspond to a confinement-deconfinement phase transition in the gauge theory side [@Witten:1998zw]. The thermodynamics of black hole is a combination of general relativity and quantum field theory which helps us to formulate quantum gravity. Bardeen, Carter and Hawking were the ones who introduced the four laws of black hole thermodynamics [@Bardeen:1973gs]. In this regard, the mass of a black hole and surface gravity on event horizon are interpreted as the enthalpy and the temperature of space-time respectively. This rather novel idea originates from a consideration of the Smarr relation [@Caldarelli:1999xj]-[@Smarr:1972kt]. The"
+"---\nabstract: 'Within the Friedmann-Lema\u00eetre-Robertson-Walker (FLRW) framework, the Hubble constant $H_0$ is an integration constant. Thus, mathematical consistency demands that $H_0$ is also observationally a constant. Building on earlier results, we demonstrate redshift evolution of flat $\\Lambda$CDM cosmological parameters $(H_0, \\Omega_{m})$ in Pantheon+ supernove (SN) in the redshift range $0 < z \\lesssim 2.26$. We compare the whole SN sample and the SN sample split into low and high redshift subsamples demarcated by redshift $z_{\\textrm{split}}$. We show that $z_{\\textrm{split}}=1$ has a marginal Bayesian preference through the Akaike Information Criterion for evolution in $H_0$ (also $\\Omega_m)$ compared to the whole sample. Such evolution is strictly forbidden in FLRW models. Through mock analysis, we estimate the evolution as a $ 1.4 \\sigma$ effect ($p=0.08$), and the presence of $\\Omega_m >1$ best fits, indicative of negative dark energy (DE) density, beyond $z_{\\textrm{split}} =1$ as $1.3 \\sigma$ ($p=0.1$) to $1.9 \\sigma$ effects $(p=0.026$) depending on the criteria. [Finally, using complementary profile distributions we confirm a robust $> 2 \\sigma$ shift in $H_0$ for SN with $z > 1$.]{}'\nauthor:\n- 'M. Malekjani'\n- 'R. Mc Conville'\n- 'E. \u00d3 Colg\u00e1in'\n- 'S. Pourojaghi'\n- 'M. M. Sheikh-Jabbari'\ntitle:  Negative Dark Energy Density from High"
+"---\nabstract: 'In this article, we study the inconsistency of a system of $\\max$-product fuzzy relational equations and of a system of $\\max$-Lukasiewicz fuzzy relational equations. For a system of $\\max-\\min$ fuzzy relational equations $A \\Box_{\\min}^{\\max} x = b$ and using the $L_\\infty$ norm, [@arxiv.2301.06141] showed that the Chebyshev distance $\\Delta = \\inf_{c \\in \\mathcal{C}} \\Vert b - c \\Vert$, where $\\mathcal{C}$ is the set of second members of consistent systems defined with the same matrix $A$, can be computed by an explicit analytical formula according to the components of the matrix $A$ and its second member $b$. In this article, we give analytical formulas analogous to that of [@arxiv.2301.06141] to compute the Chebyshev distance associated to the second member of a system of $\\max$-product fuzzy relational equations and that associated to the second member of a system of $\\max$-Lukasiewicz fuzzy relational equations.'\nauthor:\n- |\n    Isma\u00efl Baaj\\\n    Univ. Artois, CNRS, CRIL, F-62300 Lens, France\\\n    [baaj@cril.fr](baaj@cril.fr)\ntitle: 'Chebyshev distances associated to the second members of systems of max-product/Lukasiewicz fuzzy relational equations'\n---\n\nIntroduction\n============\n\nArtificial Intelligence (AI) applications based on systems of fuzzy relational equations emerged thanks to [@sanchez1976resolution; @sanchez1977]\u2019s seminal work on solving systems of $\\max-\\min$ fuzzy relational equations."
+"---\nabstract: 'The melting of a homopolymer double-stranded (ds) deoxyribonucleic acid (DNA) in the dilute limit is studied numerically in the presence of an attractive and impenetrable surface on a simple cubic lattice. The two strands of the DNA are modeled using two self-avoiding walks, capable of interacting at complementary sites, thereby mimicking the base pairing. The impenetrable surface is modeled by restricting the DNA configurations at the $z\\geq 0$ plane, with attractive interactions for monomers at $z=0$. Further, we consider two variants for $z=0$ occupations by ds segments, where one or two surface interactions are counted. This consideration has significant consequences, to the extent of changing the stability of the bound phase in the adsorbed state. Interestingly, adsorption changes from critical to first-order with a modified exponent on coinciding with the melting transition. For simulations, we use the pruned and enriched Rosenbluth algorithm.'\nauthor:\n- Debjyoti Majumdar\ntitle: Adsorption of melting deoxyribonucleic acid\n---\n\nIntroduction\n============\n\nThe denaturation of the double-stranded (ds) deoxyribonucleic acid (DNA) from a bound (ds) to an unbound single-stranded (ss) phase is an important step towards fundamental biological processes such as DNA replication, ribonucleic acid (RNA) transcription, packaging of DNA and repairing [@watson2003]. [*In vitro*]{},"
+"---\nauthor:\n- \n- \n- \n- \ntitle: Characterising Solutions of Anomalous Cancellation\n---\n\nIntroduction {#sec1}\n============\n\nA zeal for some interesting mathematical problems brought us to a very peculiar problem, a quest to find all odd digit integers $[a_1a_2\\ldots a_{2k+1}]$ (all $a_i$\u2019s are digits, $a_1\\neq 0$) such that the following property holds. $$[a_1a_2\\ldots a_k]\\cdot [a_{k+1}a_{k+2}\\ldots a_{2k+1}] = [a_1a_2\\ldots a_{k+1}]\\cdot [a_{k+2}a_{k+2}\\ldots a_{2k+1}]$$\n\nAn elementary example is the number 164, which has the property as shown $1\\times 64 = 16\\times 4$. Similarly $24\\times 996 = 249\\times 96$ implies that $24996$ also fits our problem requirement. Now the question is, can one find a way to generate all such numbers? A brute force algorithm always works, but it is never satisfying to leave things at that \u2013 a proof of being a mathematics aspirant. This prompted us to go through existing literature which, though scarce, hide a gold mine of information. Adding on to previous work, we arrived at several interesting results that demand a place in this paper. The paper has been structured in a format that the general reader can be presented with all the beautiful results, while the seasoned readers can move to the appendix to get a flavour of"
+"---\nabstract: 'Control and characterization of networks is a paramount step for the development of many quantum technologies. Even for moderate-sized networks, this amounts to explore an extremely vast parameters space in search for the couplings defining the network topology. Here we explore the use of a genetic algorithm to retrieve the topology of a network from the measured probability distribution obtained from the evolution of a continuous-time quantum walk on the network. Our result shows that the algorithm is capable of efficiently retrieving the required information even in the presence of noise.'\nauthor:\n- Claudia Benedetti\n- Ilaria Gianani\nbibliography:\n- 'main.bib'\ntitle:  Identifying network topologies via quantum walk distributions \n---\n\nNetworks are a fundamental model to understand the underlying properties of complex systems. They are invaluable tools to describe phenomena happening at different scales ranging from social interactions [@wasserman94; @Onnela07], to biological processes [@jeong00; @pastor01; @maslov02; @silva05; @plenio08], from the configurations of molecules [@Winterbach13; @dekeer21], to the structure of internet [@faloutsos99; @Caldarelli_2000; @Pastor04; @He2009] and physical systems alike [@zoller97; @kuzmich05; @mulken16; @krutitsky16; @nokkala16]. In the context of quantum technologies, networks constitute the prime structure of communication and computation protocols [@deutch89; @Christandl05; @bose07; @politi08; @aspuru12]. Understanding how quantum information"
+"---\nabstract: 'Besides the transient effect, the passage of a gravitational wave also causes a persistent displacement in the relative position of an interferometer\u2019s test masses through the *nonlinear memory effect*. This effect is generated by the gravitational backreaction of the waves themselves, and encodes additional information about the source. In this work, we explore the implications of using this information for the parameter estimation of massive binary black holes with LISA. Based on a Fisher analysis for nonprecessing black hole binaries, our results show that the memory can help to reduce the degeneracy between the luminosity distance and the inclination for binaries observed only for a short time ($\\sim$\u00a0few hours) before merger. To assess how many such short signals will be detected, we utilized state-of-the-art predictions for the population of massive black hole binaries and models for the gaps expected in the LISA data. We forecast from tens to few hundreds of binaries with observable memory, but only\u00a0$\\sim \\mathcal{O}(0.1)$ events in 4 years for which the memory helps to reduce the degeneracy between distance and inclination. Based on this, we conclude that the new information from the nonlinear memory, while promising for testing general relativity in the"
+"---\nabstract: 'The Milky Way halo is one of the few galactic haloes that provides a unique insight into galaxy formation by resolved stellar populations. Here, we present a catalogue of $\\sim$47 million halo stars selected independent of parallax and line-of-sight velocities, using a combination of Gaia DR3 proper motion and photometry by means of their reduced proper motion. We select high tangential velocity (halo) main sequence stars and fit distances to them using their simple colour-absolute-magnitude relation. This sample reaches out to $\\sim$21 kpc with a median distance of $6.6$ kpc thereby probing much further out than would be possible using reliable Gaia parallaxes. The typical uncertainty in their distances is $0.57_{-0.26}^{+0.56}$ kpc. Using the colour range $0.45<(G_0-G_\\text{RP,0})<0.715$ where the main sequence is narrower, gives an even better accuracy down to $0.39_{-0.12}^{+0.18}$ kpc in distance. The median velocity uncertainty for stars within this colour range is 15.5 km/s. The distribution of these sources in the sky, together with their tangential component velocities, are very well-suited to study retrograde substructures. We explore the selection of two complex retrograde streams: GD-1 and Jhelum. For these streams, we resolve the gaps, wiggles and density breaks reported in the literature more clearly. We"
+"---\nabstract: 'A famous theorem by R. Brauer shows how to modify a single eigenvalue of a matrix $A$ by a rank-one update without changing the remaining eigenvalues. A generalization of this theorem (due to R. Rado) is used to change a pair of eigenvalues $\\lambda, 1/\\lambda$ of a symplectic matrix $S$ in a structure-preserving way to desired target values $\\mu, 1/\\mu$. Universal bounds on the relative distance between $S$ and the newly constructed symplectic matrix $\\hat{S}$ with modified spectrum are given. The eigenvalues Segre characteristics of $\\hat{S}$ are related to those of $S$ and a statement on the eigenvalue condition numbers of $\\hat{S}$ is derived. The main results are extended to matrix pencils.'\ntitle: 'Structure-preserving eigenvalue modification of symplectic matrices and matrix pencils'\n---\n\nIntroduction {#sec:intro}\n============\n\nIn numerical linear algebra and matrix analysis one occasionally encounters the necessity of modifying special eigenvalues of a matrix without altering its remaining eigenvalues. Techniques for changing certain eigenvalues of a matrix have, for instance, been applied to solve nonnegative inverse eigenvalue problems [@Perfect1955; @Soto2006] or, in form of deflation methods, to remove dominant eigenvalues in eigenvalue computations [@saad_evals Sec.4.2]. Furthermore, the task of modifying eigenvalues of matrices is of interest in"
+"---\nabstract: |\n    Software Defined Networks have opened the door to statistical and AI-based techniques to improve efficiency of networking. Especially to ensure a certain *Quality of Service* (QoS) for specific applications by routing packets with awareness on content nature (VoIP, video, files, etc.) and its needs (latency, bandwidth, etc.) to use efficiently resources of a network.\n\n    Predicting various Key Performance Indicators (KPIs) at any level may handle such problems while preserving network bandwidth.\n\n    The question addressed in this work is the design of efficient and low-cost algorithms for KPI prediction, implementable at the local level. We focus on end-to-end latency prediction, for which we illustrate our approaches and results on a public dataset from the recent international challenge on GNN [@suarez2021graph]. We propose several low complexity, locally implementable approaches, achieving significantly lower wall time both for training and inference, with marginally worse prediction accuracy compared to state-of-the-art global GNN solutions.\nauthor:\n- |\n    Pierre Larrenie\\\n    Thales SIX & LIGM\\\n    Universit\u00e9 Gustave Eiffel, CNRS\\\n    Marne-la-Vall\u00e9e, France\\\n    `pierre.larrenie@esiee.fr`\\\n    Jean-Fran\u00e7ois Bercher\\\n    LIGM\\\n    Universit\u00e9 Gustave Eiffel, CNRS\\\n    Marne-la-Vall\u00e9e, France\\\n    `jean-francois.bercher@esiee.fr`\\\n    Olivier Venard\\\n    ESYCOM\\\n    Universit\u00e9 Gustave Eiffel, CNRS\\\n    Marne-la-Vall\u00e9e, France\\\n    `olivier.venard@esiee.fr`\\\n    Iyad Lahsen-Cherif\\\n    Institut National des Postes et T\u00e9l\u00e9communications (INPT)\\\n    Rabat, Morocco\\\n    `lahsencherif@inpt.ac.ma`\\\nbibliography:"
+"---\nabstract: 'We consider the problem Enum$\\cdot{\\textit{IP}}$ of enumerating prime implicants of Boolean functions represented by decision decomposable negation normal form (dec-DNNF) circuits. We study Enum$\\cdot{\\textit{IP}}$ from dec-DNNF within the framework of enumeration complexity and prove that it is in [OutputP]{}, the class of output polynomial enumeration problems, and more precisely in [IncP]{}, the class of polynomial incremental time enumeration problems. We then focus on two closely related, but seemingly harder, enumeration problems where further restrictions are put on the prime implicants to be generated. In the first problem, one is only interested in prime implicants representing subset-minimal abductive explanations, a notion much investigated in AI for more than three decades. In the second problem, the target is prime implicants representing sufficient reasons, a recent yet important notion in the emerging field of eXplainable AI, since they aim to explain predictions achieved by machine learning classifiers. We provide evidence showing that enumerating specific prime implicants corresponding to subset-minimal abductive explanations or to sufficient reasons is not in [OutputP]{}.'\nauthor:\n- |\n    Alexis de Colnet$^1$ Pierre Marquis$^{1,2}$ $^1$Univ. Artois, CNRS, Centre de Recherche en Informatique de Lens (CRIL), F-62300 Lens, France\\\n    $^2$Institut Universitaire de France {decolnet, marquis}@cril.fr\nbibliography:\n- 'enumIP.bib'\ntitle:"
+"---\nabstract: |\n    Thousands of papers have reported two-way cluster-robust (TWCR) standard errors. However, the recent econometrics literature points out the potential non-gaussianity of two-way cluster sample means, and thus invalidity of the inference based on the TWCR standard errors. Fortunately, simulation studies nonetheless show that the gaussianity is rather common than exceptional. This paper provides theoretical support for this encouraging observation. Specifically, we derive a novel central limit theorem for two-way clustered triangular arrays that justifies the use of the TWCR under very mild and interpretable conditions. We, therefore, hope that this paper will provide a theoretical justification for the legitimacy of most, if not all, of the thousands of those empirical papers that have used the TWCR standard errors. We provide a guide in practice as to when a researcher can employ the TWCR standard errors.\\\n    [**Keywords:**]{} asymptotic gaussianity, two-way clustering, triangular arrays, central limit theorem\\\nauthor:\n- 'Harold D. Chiang[^1]'\n- 'Yuya Sasaki[^2]'\nbibliography:\n- 'biblio.bib'\ntitle: 'On Using The Two-Way Cluster-Robust Standard Errors'\n---\n\nIntroduction\n============\n\nMulti-way clustering is ubiquitous in empirical studies. For example, market structures by construction induce two-way clustering, where common supply shocks cause cluster dependence within a firm across markets and common"
+"---\nabstract: 'We propose a generalization of the standard matched pairs design in which experimental units (often geographic regions or *geos*) may be combined into larger units/regions called \u201csupergeos\u201d in order to improve the average matching quality. Unlike optimal matched pairs design which can be found in polynomial time [@lu2011optimal], this generalized matching problem is NP-hard. We formulate it as a mixed-integer program (MIP) and show that experimental design obtained by solving this MIP can often provide a significant improvement over the standard design regardless of whether the treatment effects are homogeneous or heterogeneous. Furthermore, we present the conditions under which trimming techniques that often improve performance in the case of homogeneous effects [@chen2022robust], may lead to biased estimates and show that the proposed design does not introduce such bias. We use empirical studies based on real-world advertising data to illustrate these findings.'\nauthor:\n- |\n    Aiyou Chen\\\n    Google\\\n- |\n    Nick Doudchenko\\\n    Google\\\n- |\n    Shunhua Jiang\\\n    Columbia\\\n- |\n    Cliff Stein\\\n    Google\\\n- |\n    Bicheng Ying\\\n    Google\\\nbibliography:\n- 'ref.bib'\ntitle: 'Supergeo Design: Generalized Matching for Geographic Experiments'\n---\n\n=1\n\nIntroduction {#intro}\n============\n\nWith online advertising revenue in the US amounting to almost 200 billion dollars in 2021"
+"---\nauthor:\n- 'Thomas Gehrmann,'\n- 'Andreas von Manteuffel,'\n- 'and Tong-Zhi Yang'\nbibliography:\n- 'Renormalization.bib'\ntitle: 'Renormalization of twist-two operators in covariant gauge to three loops in QCD'\n---\n\nIntroduction {#sec:introduction}\n============\n\nThe operator product expansion (OPE)\u00a0[@Wilson:1969zs; @Frishman:1973pp] provides an elegant method to separate short-distance from long-distance contributions in quantum field theory. Its early application to deeply inelastic lepton-nucleon scattering processes\u00a0[@Gross:1974cs] in quantum chromodynamics (QCD) successfully predicted the violation of Bjorken scaling\u00a0[@Bjorken:1968dy; @Bjorken:1969ja], thereby enabling the development of the QCD-improved parton model\u00a0[@Altarelli:1977zs]. The anomalous dimensions of quark and gluon operators in the OPE are directly related to the Altarelli-Parisi splitting functions\u00a0[@Altarelli:1977zs; @Dokshitzer:1977sg; @Gribov:1972ri] of the QCD-improved parton model by an inverse Mellin transformation.\n\nThe splitting functions determine the scale evolution of the parton distributions, which are an essential ingredient to all quantitative predictions for high-energy hadron collider processes. The precise determination of parton distributions requires the iterated comparison of highly accurate experimental data for a multitude of processes with theoretical predictions at a comparable level of precision. These predictions require higher order perturbative corrections\u00a0[@Heinrich:2020ybq] to the underlying hard scattering processes as well as to the splitting functions.\n\nSplitting functions are currently known to"
+"---\nabstract: '\\[sec:abs\\] Amateurs working on mini-films and short-form videos usually spend lots of time and effort on the multi-round complicated process of setting and adjusting scenes, plots, and cameras to deliver satisfying video shots. We present Virtual Dynamic Storyboard ([VDS]{}) to allow users storyboarding shots in virtual environments, where the filming staff can easily test the settings of shots before the actual filming. [VDS]{}runs on a \u201cpropose-simulate-discriminate\u201d mode: Given a formatted story script and a camera script as input, it generates several character animation and camera movement proposals following predefined story and cinematic rules to allow an off-the-shelf simulation engine to render videos. To pick up the top-quality dynamic storyboard from the candidates, we equip it with a shot ranking discriminator based on shot quality criteria learned from professional manual-created data. [VDS]{}is comprehensively validated via extensive experiments and user studies, demonstrating its efficiency, effectiveness, and great potential in assisting amateur video production.'\nauthor:\n- Anyi Rao\n- Xuekun Jiang\n- Yuwei Guo\n- Linning Xu\n- Lei Yang\n- Libiao Jin\n- Dahua Lin\n- Bo Dai\nbibliography:\n- 'main.bib'\ntitle: 'Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production'\n---\n\n&lt;ccs2012&gt; &lt;concept&gt; &lt;concept\\_id&gt;10002951.10003227.10003251.10003256&lt;/concept\\_id&gt; &lt;concept\\_desc&gt;Information systems\u00a0Multimedia"
+"---\nabstract: |\n    We provide a game-theoretic analysis of the problem of front-running attacks. We use it to distinguish attacks from legitimate competition among honest users for having their transactions included earlier in the block. We also use it to introduce an intuitive notion of the severity of front-running attacks. We then study a simple commit-reveal protocol and discuss its properties. This protocol has costs because it requires two messages and imposes a delay. However, we show that it prevents the most severe front-running attacks while preserving legitimate competition between users, guaranteeing that the earliest transaction in a block belongs to the honest user who values it the most. When the protocol does not fully eliminate attacks, it nonetheless benefits honest users because it reduces competition among attackers (and overall expenditure by attackers).\\\n    **Keywords**: Front running, Game theory, MEV, Transactions reordering, commit-reveal\nauthor:\n- 'Andrea Canidio [^1] and Vincent Danos[^2]'\nbibliography:\n- 'bib.bib'\ntitle: ' Commitment Against Front-Running Attacks[^3]'\n---\n\nIntroduction\n============\n\nOn the Ethereum network, each validator decides how to order pending transactions to form the next block, hence determining the order in which these transactions are executed. As a consequence, users often compete with each other to have"
+"---\nabstract: 'When estimating causal effects, it is important to assess external validity, i.e., determine how useful a given study is to inform a practical question for a specific target population. One challenge is that the covariate distribution in the population underlying a study may be different from that in the target population. If some covariates are effect modifiers, the average treatment effect (ATE) may not generalize to the target population. To tackle this problem, we propose new methods to generalize or transport the ATE from a source population to a target population, in the case where the source and target populations have different sets of covariates. When the ATE in the target population is identified, we propose new doubly robust estimators and establish their rates of convergence and limiting distributions. Under regularity conditions, the doubly robust estimators provably achieve the efficiency bound and are locally asymptotic minimax optimal. A sensitivity analysis is provided when the identification assumptions fail. Simulation studies show the advantages of the proposed doubly robust estimator over simple plug-in estimators. Importantly, we also provide minimax lower bounds and higher-order estimators of the target functionals. The proposed methods are applied in transporting causal effects of dietary intake"
+"---\nabstract: 'In this work we extend the class of Consensus-Based Optimization (CBO) metaheuristic methods by considering memory effects and a random selection strategy. The proposed algorithm iteratively updates a population of particles according to a consensus dynamics inspired by social interactions among individuals. The consensus point is computed taking into account the past positions of all particles. While sharing features with the popular Particle Swarm Optimization (PSO) method, the exploratory behavior is fundamentally different and allows better control over the convergence of the particle system. We discuss some implementation aspects which lead to an increased efficiency while preserving the success rate in the optimization process. In particular, we show how employing a random selection strategy to discard particles during the computation improves the overall performance. Several benchmark problems and applications to image segmentation and Neural Networks training are used to validate and test the proposed method. A theoretical analysis allows to recover convergence guarantees under mild assumptions on the objective function. This is done by first approximating the particles evolution with a continuous-in-time dynamics, and then by taking the mean-field limit of such dynamics. Convergence to a global minimizer is finally proved at the mean-field level.'\nauthor:\n- 'Giacomo"
+"---\nabstract: 'The shot-down process is a strong Markov process which is annihilated, or shot down, when *jumping over* or to the complement of a given open subset of a\u00a0vector space. Due to specific features of the shot-down time, such processes suggest new type of boundary conditions for nonlocal differential equations. In this work we construct the shot-down process for the fractional Laplacian in Euclidean space. For smooth bounded sets $D$, we study its transition density and characterize Dirichlet form. We show that the corresponding Green function is comparable to that of the fractional Laplacian with Dirichlet conditions on $D$. However, for nonconvex $D$, the transition density of the shot-down stable process is incomparable with the Dirichlet heat kernel of the fractional Laplacian for $D$. Furthermore, Harnack inequality in general fails for harmonic functions of the shot-down process.'\naddress:\n- ' Krzysztof Bogdan Department of Pure and Applied Mathematics Wroc\u0142aw University of Science and Technology, Wroc\u0142aw, Poland '\n- ' Kajetan Jastrz\u0229bski Institute of Mathematics University of Wroc\u0142aw, Wroc\u0142aw, Poland '\n- ' Moritz Kassmann Fakult\u00e4t f\u00fcr Mathematik Universit\u00e4t Bielefeld, Germany '\n- ' Micha\u0142 Kijaczko Department of Pure and Applied Mathematics Wroc\u0142aw University of Science and Technology, Wroc\u0142aw,"
+"---\nabstract: 'This paper presents the development of a distributed application that facilitates the understanding and application of swarm intelligence in solving optimization problems. The platform comprises a search space of customizable random particles, allowing users to tailor the solution to their specific needs. By leveraging the power of Ray distributed computing, the application can support multiple users simultaneously, offering a flexible and scalable solution. The primary objective of this project is to provide a user-friendly platform that enhances the understanding and practical use of swarm intelligence in problem-solving.'\nauthor:\n- |\n    Karthik reddy Kanjula\\\n    School of Coumputing and Information\\\n    West Chester University of Pennsylvania\\\n    West Chester, PA 19383\\\n    `karthikreddykanjula99@gmail.com`\\\n    Sai Meghana Kolla\\\n    School of Mathematics and Computer Science\\\n    Pennsylvania state University\\\n    Harrisburg, PA 17057\\\n    `szk6163@psu.edu`\\\ntitle: Distributed Swarm Intelligence\n---\n\nIntroduction\n============\n\nThe Particle Swarm Optimization (PSO) algorithm is an approximation algorithm that finds the best solution from all the explored feasible solutions for any problem that can be formulated into a mathematical equation. In the field of algorithms and theoretical computer science, optimization problems are known by the name \u201capproximation\u201d algorithms. In this project, we built a web application that hosts a PSO algorithm with interactive features such"
+"---\nabstract: 'By flexibly manipulating the radio propagation environment, reconfigurable intelligent surface (RIS) is a promising technique for future wireless communications. However, the single-side coverage and double-fading attenuation faced by conventional RISs largely restrict their applications. To address this issue, we propose a novel concept of multi-functional RIS (MF-RIS), which provides reflection, transmission, and amplification simultaneously for the incident signal. With the aim of enhancing the performance of a non-orthogonal multiple-access (NOMA) downlink multiuser network, we deploy an MF-RIS to maximize the sum rate by jointly optimizing the active beamforming and MF-RIS coefficients. Then, an alternating optimization algorithm is proposed to solve the formulated non-convex problem by exploiting successive convex approximation and penalty-based method. Numerical results show that the proposed MF-RIS outperforms conventional RISs under different settings.'\nauthor:\n- 'Ailing\u00a0Zheng, Wanli\u00a0Ni, Wen\u00a0Wang, and Hui\u00a0Tian [^1] [^2]'\ntitle: 'Enhancing NOMA Networks via Reconfigurable Multi-Functional Surface'\n---\n\nMulti-functional reconfigurable intelligent surface, non-orthogonal multiple access, rate maximization.\n\nIntroduction {#Introduction}\n============\n\nCompared to orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) is capable of achieving high spectrum efficiency and massive connectivity [@LiuNOMA2017]. Prior investigations have shown that the differences between users\u2019 channel conditions can be exploited to enhance NOMA performance"
+"---\nabstract: 'In Bhattacharya et al. (Science Advances, 2020), a set of chemical reactions involved in the dynamics of actin waves in cells was studied. Both at the microscopic level, where the individual chemical reactions are directly modelled using Gillespie-type algorithms, and on a macroscopic level where a deterministic reaction-diffusion equation arises as the large-scale limit of the underlying chemical reactions. In this work, we derive, and subsequently study, the related mesoscopic stochastic reaction-diffusion system, or Chemical Langevin Equation, that arises from the same set of chemical reactions. We explain how the stochastic patterns that arise from this equation can be used to understand the experimentally observed dynamics from Bhattacharya et al. In particular, we argue that the mesoscopic stochastic model better captures the microscopic behaviour than the deterministic reaction-diffusion equation, while being more amenable for mathematical analysis and numerical simulations than the microscopic model.'\naddress:\n- |\n     Biometris - Wageningen University and Research\\\n    Wageningen; The Netherlands\\\n    Email: \n- |\n     Biometris - Wageningen University and Research\\\n    Wageningen; The Netherlands\\\n    Email: \nauthor:\n- 'C. H. S. Hamster'\n- 'P. van Heijster'\nbibliography:\n- 'ref.bib'\ntitle: Waves in a Stochastic Cell Motility Model\n---\n\n,\n\n,\n\nGillespie Algorithms, Cell Motility, Mesoscopic Patterns,"
+"---\nabstract: 'The standard model (SM) one-loop contributions to the most general $H^*Z^*Z^*$ coupling are obtained via the background field method in terms of Passarino-Veltman scalar functions, from which the contributions to the $H^*ZZ$ and $HZZ^*$ couplings are obtained in terms of two $CP$-conserving $h_{1,2}^V$ and one $CP$-violating $h_3^V$ form factors ($V=H, Z$). The current CMS constraints on the $HZZ$ coupling ratios are then used to obtain bounds on the real and absorptive parts of the anomalous $HZZ$ couplings. The former are up to two orders of magnitude tighter than previous ones, whereas the latter are the first one of this kind. The effects of the absorptive parts of the $HZZ$ anomalous couplings, which have been overlooked in the past, are analyzed via the partial decay width $\\Gamma_{H^\\ast\\rightarrow ZZ}$, and a significant deviation from the SM tree-level contribution is observed at low energies, though it becomes negligible at high energies. We also explore the possibility that polarized $Z$ gauge bosons are used for the study of non-SM $HZZ$ contributions via a new left-right asymmetry $\\mathcal{A}_{LR}$, which is sensitive to $CP$-violating complex form factors and can be as large as the unity at most, though in a more conservative scenario it"
+"---\nabstract: 'Gradient-based meta-learning methods have primarily been applied to classical machine learning tasks such as image classification. Recently, PDE-solving deep learning methods, such as neural operators, are starting to make an important impact on learning and predicting the response of a complex physical system directly from observational data. Since the data acquisition in this context is commonly challenging and costly, the call of utilization and transfer of existing knowledge to new and unseen physical systems is even more acute. Herein, we propose a novel meta-learning approach for neural operators, which can be seen as transferring the knowledge of solution operators between governing (unknown) PDEs with varying parameter fields. Our approach is a provably universal solution operator for multiple PDE solving tasks, with a key theoretical observation that underlying parameter fields can be captured in the first layer of neural operator models, in contrast to typical final-layer transfer in existing meta-learning methods. As applications, we demonstrate the efficacy of our proposed approach on PDE-based datasets and a real-world material modeling problem, illustrating that our method can handle complex and nonlinear physical response learning tasks while greatly improving the sampling efficiency in unseen tasks.'\naddress:\n- 'Department of Mathematics, Lehigh University,"
+"---\nabstract: |\n    Self organizing complex systems can be modeled using cellular automaton models. However, the parametrization of these models is crucial and significantly determines the resulting structural pattern. In this research, we introduce and successfully apply a sound statistical method to estimate these parameters. The method is based on constructing Gaussian likelihoods using characteristics of the structures such as the mean particle size. We show that our approach is robust with respect to the method parameters, domain size of patterns, or CA iterations.\\\n    <span style=\"font-variant:small-caps;\">Keywords.</span> Cellular automaton, discrete model, parameter identification, statistical approach.\naddress:\n- 'Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Mathematikon, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany'\n- 'Mathematical Institute for Machine Learning and Data Science, Catholic university of Eichst\u00e4tt-Ingolstadt, Goldknopfgasse 7, 85049 Ingolstadt, Germany'\n- 'School of Engineering Science, Lappeenranta\u2013Lahti University of Technology, P.O. Box 20, 53851 Lappeenranta, Finland'\nauthor:\n- Alexey Kazarnikov\n- Nadja Ray\n- Heikki Haario\n- Joona Lappalainen\n- Andreas Rupp\nbibliography:\n- 'CAMparameters.bib'\ntitle: Parameter estimation for cellular automata\n---\n\nIntroduction\n============\n\nCellular automaton (CA) models are widely used to describe self-organizing, complex systems such as tumor growth\u00a0[@moreira2002cellular], protein bioinformatics\u00a0[@xiao2011cellular], chemical reactions\u00a0[@menshutina2020cellular], formation and turnover of"
+"---\nauthor:\n- 'Zhongjie Huang$^{a,b}$,'\n- 'Bo Wang$^{a,b}$,'\n- 'Ellis Ye Yuan$^{a,b}$,'\n- 'Xinan Zhou$^{c}$'\nbibliography:\n- 'refs.bib'\ntitle: 'AdS super gluon scattering up to two loops: A position space approach'\n---\n\nIntroduction\n============\n\nThe AdS/CFT correspondence maps correlation functions of local operators in the CFT to on-shell scattering amplitudes in AdS. In the holographic limit, these observables are expanded in powers of $1/c$ with respect to the large central charge. At the leading order, the holographic correlators are just given by the generalized free field theory due to the large $N$ factorization and they can be computed simply by Wick contractions. However, to extract nontrivial dynamical information one needs to go to higher orders in $1/c$ . Computing these subleading contributions is in general intractable from the CFT side alone as the theory is strongly coupled. The weakly coupled dual description makes it possible, at least in principle, as holographic correlators can be computed as amplitudes at various loop orders by using the AdS generalization of the standard Feynman diagram expansion. However, it should be noted that such a recipe is rather impractical to use beyond the few simplest cases [@Freedman:1998tz; @DHoker:1999pj; @Arutyunov:2000py; @Arutyunov:2002fh; @Arutyunov:2003ae], due to the proliferation"
+"---\nabstract: 'We propose an optical clock based on narrow, spin-forbidden M1 and E2 transitions in laser-cooled neutral titanium. These transitions exhibit much smaller black body radiation shifts than those in alkaline earth atoms, small quadratic Zeeman shifts, and have wavelengths in the S, C, and L-bands of fiber-optic telecommunication standards, allowing for integration with robust laser technology. We calculate lifetimes; transition matrix elements; dynamic scalar, vector, and tensor polarizabilities; and black body radiation shifts of the clock transitions using a high-precision relativistic hybrid method that combines a configuration interaction and coupled cluster approaches. We also calculate the line strengths and branching ratios of the transitions used for laser cooling. To identify magic trapping wavelengths, we have completed the largest-to-date direct dynamical polarizability calculations. Finally, we identify new challenges that arise in precision measurements due to magnetic dipole-dipole interactions and describe an approach to overcome them. Direct access to a telecommunications-band atomic frequency standard will aid the deployment of optical clock networks and clock comparisons over long distances.'\nauthor:\n- Scott Eustice\n- Dmytro Filin\n- Jackson Schrott\n- Sergey Porsev\n- Charles Cheung\n- Diego Novoa\n- 'Dan M. Stamper-Kurn'\n- 'Marianna S. Safronova'\nbibliography:\n- 'ti\\_clock\\_refs.bib'\ntitle: 'Optical"
+"---\nabstract: '*Curriculum learning (CL)* - training using samples that are generated and presented in a meaningful order - was introduced in the machine learning context around a decade ago. While CL has been extensively used and analysed empirically, there has been very little mathematical justification for its advantages. We introduce a CL model for learning the class of $k$-parities on $d$ bits of a binary string with a neural network trained by stochastic gradient descent (SGD). We show that a wise choice of training examples, involving two or more product distributions, allows to reduce significantly the computational cost of learning this class of functions, compared to learning under the uniform distribution. We conduct experiments to support our analysis. Furthermore, we show that for another class of functions - namely the \u2018Hamming mixtures\u2019 - CL strategies involving a bounded number of product distributions are not beneficial, while we conjecture that CL with unbounded many curriculum steps can learn this class efficiently.'\nauthor:\n- 'Elisabetta Cornacchia[^1]'\n- 'Elchanan Mossel[^2]'\nbibliography:\n- 'references.bib'\ntitle: A Mathematical Model for Curriculum Learning\n---\n\nIntroduction\n============\n\nSeveral experimental studies have shown that humans and animals learn considerably better if the learning materials are presented in"
+"---\nabstract: 'In this paper we solve in the positive the question of whether any finite set of integers $A$, containing $0$, is the mapping degree set between two oriented closed connected manifolds of the same dimension. We extend this question to the rational setting, where an affirmative answer is also given.'\naddress:\n- 'CITIC, CITMAga, Departamento de Computaci\u00f3n, Universidade da Coru[\u00f1]{}a, 15071-A Coru[\u00f1]{}a, Spain.'\n- 'Instituto de Matem\u00e1tica Interdisciplinar and Departamento de \u00c1lgebra, Geometr\u00eda y Topolog\u00eda, Universidad Complutense de Madrid, Plaza de las Ciencias, 3, 28040-Madrid, Spain'\n- 'Departamento de \u00c1lgebra, Geometr\u00eda y Topolog\u00eda, Universidad de M\u00e1laga, Campus de Teatinos, s/n, 29071-M\u00e1laga, Spain'\nauthor:\n- 'C. Costoya'\n- Vicente Mu\u00f1oz\n- Antonio Viruel\nbibliography:\n- 'CMV-references.bib'\ntitle: Finite sets containing zero are mapping degree sets\n---\n\n[^1]\n\nIntroduction {#sec:intro}\n============\n\nIn this paper, we settle in the positive various questions which have been raised about $D(M,N)$, the set of mapping degrees between two oriented closed connected manifolds $M$ and $N$ of the same dimension: $$D(M,N)=\\{ d \\in {\\mathbb{Z}}\\, |\\, \\exists f:M\\to N, \\, \\deg(f)=d\\}.$$\n\nC. Neofytidis, S. Wang, and Z. Wang [@NWW Problem 1.1] discuss the problem of finding, for any set $A \\subset \\mathbb Z$ containing $0$, two"
+"---\nabstract: 'Magnetars are the most strongly magnetized neutron stars, and one of the most promising targets for X-ray polarimetric measurements. We present here the first Imaging X-ray Polarimetry Explorer ([[*IXPE*]{}]{}) observation of the magnetar [1RXS\u00a0J170849.0-400910]{}, jointly analysed with a new Swift observation and archival NICER data. The total (energy and phase integrated) emission in the 2\u20138\u00a0keV energy range is linerarly polarized, at a $\\sim 35$% level. The phase-averaged polarization signal shows a marked increase with energy, ranging from $\\sim$ 20% at 2\u20133\u00a0keV up to $\\sim$ 80% at 6\u20138\u00a0keV, while the polarization angle remain constant. This indicates that radiation is mostly polarized in a single direction. The spectrum is well reproduced by a combination of either two thermal (blackbody) components or a blackbody and a power law. Both the polarization degree and angle also show a variation with the spin phase, and the former is almost anti-correlated with the source counts in the 2\u20138\u00a0keV and 2\u20134\u00a0keV bands. We discuss the possible implications and interpretations, based on a joint analysis of the spectral, polarization and pulsation properties of the source. A scenario in which the surface temperature is not homogeneous, with a hotter cap covered"
+"---\nabstract: 'BERTScore is an effective and robust automatic metric for reference-based machine translation evaluation. In this paper, we incorporate multilingual knowledge graph into BERTScore and propose a metric named KG-BERTScore, which linearly combines the results of BERTScore and bilingual named entity matching for reference-free machine translation evaluation. From the experimental results on WMT19 QE as a metric without references shared tasks, our metric KG-BERTScore gets higher overall correlation with human judgements than the current state-of-the-art metrics for reference-free machine translation evaluation.[^1] Moreover, the pre-trained multilingual model used by KG-BERTScore and the parameter for linear combination are also studied in this paper.'\nauthor:\n- 'Zhanglin Wu, Min Zhang, Ming Zhu, Yinglu Li, Ting Zhu, Hao Yang\\*, Song Peng, Ying Qin'\ntitle: 'KG-BERTScore: Incorporating Knowledge Graph into BERTScore for Reference-Free Machine Translation Evaluation'\n---\n\nIntroduction {#sec1}\n============\n\nMachine translation (MT) evaluation is an important research topic in natural language processing, and its development plays a crucial role in the progress of machine translation. Although Human judgement is an ideal MT evaluation metric, automatic MT evaluation metrics are applied in most cases due to the former\u2019s long evaluation cycle and high labor consumption. With the continuous deepening of research, automatic MT evaluation"
+"---\nabstract: 'As human-robot collaboration increases in the workforce, it becomes essential for human-robot teams to coordinate efficiently and intuitively. Traditional approaches for human-robot scheduling either utilize exact methods that are intractable for large-scale problems and struggle to account for stochastic, time varying human task performance, or application-specific heuristics that require expert domain knowledge to develop. We propose a deep learning-based framework, called HybridNet, combining a heterogeneous graph-based encoder with a recurrent schedule propagator for scheduling stochastic human-robot teams under upper- and lower-bound temporal constraints. The HybridNet\u2019s encoder leverages Heterogeneous Graph Attention Networks to model the initial environment and team dynamics while accounting for the constraints. By formulating task scheduling as a sequential decision-making process, the HybridNet\u2019s recurrent neural schedule propagator leverages Long Short-Term Memory (LSTM) models to propagate forward consequences of actions to carry out fast schedule generation, removing the need to interact with the environment between every task-agent pair selection. The resulting scheduling policy network provides a computationally lightweight yet highly expressive model that is end-to-end trainable via Reinforcement Learning algorithms. We develop a virtual task scheduling environment for mixed human-robot teams in a multi-round setting, capable of modeling the stochastic learning behaviors of human workers. Experimental results"
+"---\nabstract: 'On a closed Riemannian surface $(M,\\bar g)$ with negative Euler characteristic, we study the problem of finding conformal metrics with prescribed volume $A>0$ and the property that their Gauss curvatures $f_\\lambda= f + \\lambda$ are given as the sum of a prescribed function $f \\in C^\\infty(M)$ and an additive constant $\\lambda$. Our main tool in this study is a new variant of the prescribed Gauss curvature flow, for which we establish local well-posedness and global compactness results. In contrast to previous work, our approach does not require any sign conditions on $f$. Moreover, we exhibit conditions under which the function $f_\\lambda$ is sign changing and the standard prescribed Gauss curvature flow is not applicable.'\nauthor:\n- 'Franziska Borer[^1]'\n- 'Peter Elbau[^2]'\n- 'Tobias Weth[^3]'\nbibliography:\n- 'articles.bib'\ntitle: A Variant Prescribed Curvature Flow on Closed Surfaces with Negative Euler Characteristic\n---\n\nAcknowledgment {#acknowledgment .unnumbered}\n==============\n\nThis work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project 408275461 (Smoothing and Non-Smoothing via Ricci Flow).\\\nWe would like to thank Esther Cabezas\u2013Rivas for helpful discussions.\n\nIntroduction\n============\n\nLet $(M,\\bar g)$ be a two-dimensional, smooth, closed, connected, oriented Riemann manifold endowed with a smooth background metric $\\bar g$. A"
+"---\nabstract: 'We have carried out the first spectro-polarimetric study of the bright NS-LMXB [GX\u00a09+9]{}using *IXPE* and *AstroSat* observations. We report a significant detection of polarization of $1.7\\pm 0.4\\%$ over the $2-8$\u00a0keV energy band, with a polarization angle of $63^{\\circ}\\pm 7^{\\circ}$. The polarization is found to be energy-dependent, with a $3\\sigma$ polarization degree consistent with null polarization in $2-4$\u00a0keV, and $3.2\\%$ in $4-8$\u00a0keV. Typical of the spectra seen in NS-LMXBs, we find that a combination of soft thermal emission from the accretion disc and Comptonized component from the optically thick corona produces a good fit to the spectra. We also attempt to infer the individual polarization of these components, and obtain a $3\\sigma$ upper limit of $\\sim 11\\%$ on the polarization degree of the thermal component, and constrain that of the Comptonized component to $\\sim 3\\%$. We comment on the possible corona geometry of the system based on our results.'\nauthor:\n- |\n    Rwitika Chatterjee$^{1}$[^1], Vivek K. Agrawal$^{1}$, Kiran M. Jayasurya$^{1}$, Tilak Katoch$^{2}$\\\n    $^{1}$Space Astronomy Group, ISITE Campus, U. R. Rao Satellite Center, ISRO, Bengaluru 560037, India\\\n    $^{2}$Department of Astronomy and Astrophysics, Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai 400005, India\nbibliography:\n-"
+"---\nabstract: 'The Arrhenius crossover temperature, $T_{A}$, corresponds to a thermodynamic state wherein the atomistic dynamics of a liquid becomes heterogeneous and cooperative; and the activation barrier of diffusion dynamics becomes temperature-dependent at temperatures below $T_{A}$. The theoretical estimation of this temperature is difficult for some types of materials, especially silicates and borates. In these materials, self-diffusion as a function of the temperature $T$ is reproduced by the Arrhenius law, where the activation barrier practically independent on the temperature $T$. The purpose of the present work was to establish the relationship between the Arrhenius crossover temperature $T_{A}$ and the physical properties of liquids directly related to their glass-forming ability. Using a machine learning model, the crossover temperature $T_{A}$ was calculated for silicates, borates, organic compounds and metal melts of various compositions. The empirical values of the glass transition temperature $T_{g}$, the melting temperature $T_{m}$, the ratio of these temperatures $T_{g}/T_{m}$ and the fragility index $m$ were applied as input parameters. It has been established that the temperatures $T_{g}$ and $T_{m}$ are significant parameters, whereas their ratio $T_{g}/T_{m}$ and the fragility index $m$ do not correlate much with the temperature $T_{A}$. An important result of the present work is the analytical"
+"---\nabstract: |\n    One way of introducing sparsity into deep networks is by attaching an external table of parameters that is sparsely looked up at different layers of the network. By storing the bulk of the parameters in the external table, one can increase the capacity of the model without necessarily increasing the inference time. Two crucial questions in this setting are then: what is the lookup function for accessing the table and how are the contents of the table consumed? Prominent methods for accessing the table include 1) using words/wordpieces token-ids as table indices, 2) LSH hashing the token vector in each layer into a table of buckets, and 3) learnable softmax style routing to a table entry. The ways to consume the contents include adding/concatenating to input representation, and using the contents as expert networks that specialize to different inputs. In this work, we conduct rigorous experimental evaluations of existing ideas and their combinations. We also introduce a new method, alternating updates, that enables access to an increased token dimension without increasing the computation time, and demonstrate its effectiveness in language modeling.\n\n    It has been well established that increasing scale in deep transformer networks leads to improved quality"
+"---\nabstract: 'Firms have access to abundant data on market participants. They use these data to target contracts to agents with specific characteristics, and describe these contracts in opaque terms. In response to such practices, recent proposed regulations aim to increase transparency, especially in digital markets. In order to understand when opacity arises in contracting and the potential effects of proposed regulations, we study a moral hazard model in which a risk-neutral principal faces a continuum of weakly risk-averse agents. The agents differ in an observable characteristic that affects the payoff of the principal. In a *described contract*, the principal sorts the agents into groups, and to each group communicates a distribution of output-contingent payments. Within each group, the realized distribution of payments must be consistent with the communicated contract. A described contract is *transparent* if the principal communicates the realized contract to the agent ex-ante, and otherwise it is *opaque*. We provide a geometric characterization of the principal\u2019s optimal described contract as well as conditions under which the optimal described mechanism is transparent and opaque.'\nauthor:\n- 'Andreas Haupt and Zo\u00eb Hitzig[^1]'\nbibliography:\n- 'refs.bib'\ntitle: Opaque Contracts\n---\n\nIntroduction\n============\n\nFirms have access to abundant data on consumers"
+"---\nabstract: 'In many modern statistical problems, the limited available data must be used both to develop the hypotheses to test, and to test these hypotheses\u2014that is, both for exploratory and confirmatory data analysis. Reusing the same dataset for both exploration and testing can lead to massive selection bias, leading to many false discoveries. Selective inference is a framework that allows for performing valid inference even when the same data is reused for exploration and testing. In this work, we are interested in the problem of selective inference for data clustering, where a clustering procedure is used to hypothesize a separation of the data points into a collection of subgroups, and we then wish to test whether these data-dependent clusters in fact represent meaningful differences within the data. Recent work by @gao2020selective provides a framework for doing selective inference for this setting, where a hierarchical clustering algorithm is used for producing the cluster assignments, which was then extended to k-means clustering by @chen2022selective. Both these works rely on assuming a known covariance structure for the data, but in practice, the noise level needs to be estimated\u2014and this is particularly challenging when the true cluster structure is unknown. In our work,"
+"---\nabstract: 'We study the tail of $p(U)$, the probability distribution of $U=\\vert\\psi(0,L)\\vert^2$, for $\\ln U\\gg 1$, $\\psi(x,z)$ being the solution to $\\partial_z\\psi -\\frac{i}{2m}\\nabla_{\\perp}^2 \\psi =g\\vert S\\vert^2\\, \\psi$, where $S(x,z)$ is a complex Gaussian random field, $z$ and $x$ respectively are the axial and transverse coordinates, with $0\\le z\\le L$, and both $m\\ne 0$ and $g>0$ are real parameters. We perform the first instanton analysis of the corresponding Martin-Siggia-Rose action, from which it is found that the realizations of $S$ concentrate onto long filamentary instantons, as $\\ln U\\to +\\infty$. The tail of $p(U)$ is deduced from the statistics of the instantons. The value of $g$ above which $\\langle U\\rangle$ diverges coincides with the one obtained by the completely different approach developed in Mounaix et al. 2006 [*Commun. Math. Phys.*]{} [**264**]{}\u00a0741. Numerical simulations clearly show a statistical bias of $S$ towards the instanton for the largest sampled values of $\\ln U$. The high maxima \u2014 or \u2018hot spots\u2019 \u2014 of $\\vert S(x,z)\\vert^2$ for the biased realizations of $S$ tend to cluster in the instanton region.'\nauthor:\n- Philippe Mounaix\ntitle: 'Schr\u00f6dinger Equation Driven by the Square of a Gaussian Field: Instanton Analysis in the Large Amplification Limit'\n---\n\nIntroduction {#intro}"
+"---\nabstract: '[There is a persistent lack of funding, especially for SMEs, that cyclically worsens. The factoring and invoice discounting market appears to address delays in paying commercial invoices: sellers bring still-to-be-paid invoices to financial organizations, intermediaries, typically banks that provide an advance payment. This article contains research on novel decentralized approaches to said lending services without intermediaries by using liquidity pools and its associated heuristics, creating an Automated Market Maker. In our approach, the contributed collateral and the invoice trades with risk is measured with a formula: The Kelly criterion is used to calculate the optimal premium to be contributed to a liquidity pool in the funding of the said invoices. The behavior of the algorithm is studied in several scenarios of streams of invoices with representative amounts, collaterals, payment delays, and nonpayments rates or mora. We completed the study with hack scenarios with bogus, nonpayable invoices. As a result, we have created a resilient solution that performs the best with partially collateralized invoices. The outcome is decentralized market developed with the Kelly criterion that is reasonably resilient to a wide variety of the invoicing cases that provides sound profit to liquidity providers, and several premium distribution policies were"
+"---\nabstract: 'A self-driven hybrid atomic spin oscillator is demonstrated in theory and experiment with a vapor Rb-Xe dual-spin system. The raw signal of Rb spin oscillation is amplified, phase-shifted and sent back to drive the Xe spins coherently. By fine tuning the driving field strength and phase, a self-sustaining spin oscillation signal with zero frequency shift is obtained. The effective coherence time is infinitely prolonged beyond the intrinsic coherence time of Xe spins, forming a hybrid atomic spin oscillator. Spectral analysis indicates that a frequency resolution of 13.1 nHz is achieved, enhancing the detection sensitivity for magnetic field. Allan deviation analysis shows that the spin oscillator can operate in continuous wave mode like a spin maser. The prototype spin oscillator can be easily implanted into other hybrid spin systems and enhance the detection sensitivity of alkali metal-noble gas comagnetometers.'\nauthor:\n- Erwei Li\n- Qianjin Ma\n- Guobin Liu\n- Peter Yun\n- Shougang Zhang\ntitle: 'Self-driven Hybrid Atomic Spin Oscillator'\n---\n\n[UTF8]{}[gbsn]{}\n\n[^1]\n\n[^2]\n\nAlkali metal-noble gas comagnetometer has been used for both fundamental and practical applications, such as the search for axion like particles or new physics beyond the standard model [@Bulatowicz2013PRL; @Domainwall2013PRL; @Limes2018PRL; @EDM2019PRL] and inertial"
+"---\nabstract: 'Nearest neighbor machine translation augments the Autoregressive Translation\u00a0(AT) with $k$-nearest-neighbor retrieval, by comparing the similarity between the token-level context representations of the target tokens in the query and the datastore. However, the token-level representation may introduce noise when translating ambiguous words, or fail to provide accurate retrieval results when the representation generated by the model contains indistinguishable context information, e.g., Non-Autoregressive Translation\u00a0(NAT) models. In this paper, we propose a novel $n$-gram nearest neighbor retrieval method that is model agnostic and applicable to both AT and NAT models. Specifically, we concatenate the adjacent $n$-gram hidden representations as the key, while the tuple of corresponding target tokens is the value. In inference, we propose tailored decoding algorithms for AT and NAT models respectively. We demonstrate that the proposed method consistently outperforms the token-level method on both AT and NAT models as well on general as on domain adaptation translation tasks. On domain adaptation, the proposed method brings $1.03$ and $2.76$ improvements regarding the average BLEU score on AT and NAT models respectively.'\nauthor:\n- |\n    Rui Lv$^{1}$[^1]\u00a0\u00a0, Junliang Guo$^2$\u00a0\u00a0, Rui Wang$^2$, Xu Tan$^2$, Qi Liu$^{1}$, Tao Qin$^{2}$\\\n    $^1$University of Science and Technology of China $^2$Microsoft Research"
+"---\nabstract: 'We report new examples of Sidon sets in abelian groups arising from generalized jacobians of curves, and discuss some of their properties with respect to size and structure.'\naddress:\n- 'Univ. Lille, CNRS, UMR 8524 - Laboratoire Paul Painlev\u00e9, F-59000 Lille, France'\n- 'CMLS, \u00c9cole polytechnique, F-91128 Palaiseau cedex, France'\n- 'D-MATH, ETH Z\u00fcrich, R\u00e4mistrasse 101, CH-8092 Z\u00fcrich, Switzerland'\nauthor:\n- Arthur Forey\n- Javier Fres\u00e1n\n- Emmanuel Kowalski\nbibliography:\n- 'sidon.bib'\ntitle: Sidon sets in algebraic geometry\n---\n\nIntroduction\n============\n\nLet $A$ be an abelian group. A subset $S$ of $A$ is called a *Sidon set* if $S$ does not contain non-trivial additive quadruples; that is, if any solution $(x_1,x_2,x_3,x_4)\\in S^4$ of the equation $$\\label{eq-sidon}\n  x_1+x_2=x_3+x_4$$ satisfies $x_1\\in\\{x_3,x_4\\}$ (see, e.g.,\u00a0[@eberhard-manners \u00a71]). In other words, up to transposition an element of\u00a0$A$ is in at most one way the sum of two elements of\u00a0$S$.\n\nWe will explain how to construct a range of new examples of Sidon sets using the theory of commutative algebraic groups. In fact, we sometimes most naturally obtain a slight variant: given an element\u00a0$a$ of\u00a0$A$, we say that a subset $S$ of\u00a0$A$ is a *symmetric Sidon set with center"
+"---\nabstract: 'While human evaluation remains best practice for accurately judging the faithfulness of automatically-generated summaries, few solutions exist to address the increased difficulty and workload when evaluating *long-form* summaries. Through a survey of [162]{}\u00a0papers on long-form summarization, we first shed light on current human evaluation practices surrounding long-form summaries. We find that 73% of these papers do not perform any human evaluation on model-generated summaries, while other works face new difficulties that manifest when dealing with long documents (e.g., low inter-annotator agreement). Motivated by our survey, we present [<span style=\"font-variant:small-caps;\">LongEval</span>]{}, a set of guidelines for human evaluation of faithfulness in long-form summaries that addresses the following challenges: (1) How can we achieve high inter-annotator agreement on faithfulness scores? (2) How can we minimize annotator workload while maintaining accurate faithfulness scores? and (3) Do humans benefit from automated alignment between summary and source snippets? We deploy [<span style=\"font-variant:small-caps;\">LongEval</span>]{}\u00a0in annotation studies on two long-form summarization datasets in different domains (SQuALITY and PubMed), and we find that switching to a finer granularity of judgment (e.g., clause-level) reduces inter-annotator variance in faithfulness scores (e.g., std-dev from 18.5 to 6.8). We also show that scores from a *partial* annotation of fine-grained units"
+"---\nauthor:\n- Rui Zhu\n- Di Tang\n- Siyuan Tang\n- Guanhong Tao\n- Shiqing Ma\n- Xiaofeng Wang\n- Haixu Tang\nbibliography:\n- 'reference.bib'\ntitle: 'Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering'\n---\n\nAbstract {#abstract .unnumbered}\n--------\n\nMost existing methods to detect backdoored machine learning (ML) models take one of the two approaches: trigger inversion (aka. reverse engineer) and weight analysis (aka. model diagnosis). In particular, the gradient-based trigger inversion is considered to be among the most effective backdoor detection techniques, as evidenced by the TrojAI competition\u00a0[@trojai], Trojan Detection Challenge\u00a0[@NIPS_competation] and backdoorBench\u00a0[@benchmark]. However, little has been done to understand why this technique works so well and, more importantly, whether it raises the bar to the backdoor attack. In this paper, we report the first attempt to answer this question by analyzing the change rate of the backdoored model around its trigger-carrying inputs. Our study shows that existing attacks tend to inject the backdoor characterized by a low change rate around trigger-carrying inputs, which are easy to capture by gradient-based trigger inversion. In the meantime, we found that the low change rate is not necessary for a backdoor attack to succeed: we design a new"
+"---\nabstract: 'Motivated by the markets operating on fast time scales, we present a framework for online coalitional games with time-varying coalitional values and propose real-time payoff distribution mechanisms. Specifically, we design two online distributed algorithms to track the Shapley value and the core, the two most widely studied payoff distribution criteria in coalitional game theory. We show that the payoff distribution trajectory resulting from our proposed algorithms converges to a neighborhood of the time-varying solutions. We adopt an operator-theoretic perspective to show the convergence of our algorithms. Numerical simulations of a real-time local electricity market and cooperative energy forecasting market illustrate the performance of our algorithms: [the difference between online payoffs and static payoffs (Shapley and the core) to the participants is little; online algorithms considerably improve the scalability of the mechanism with respect to the number of market participants.]{}'\nauthor:\n- 'Aitazaz Ali Raja and Sergio Grammatico [^1] [^2]'\nbibliography:\n- 'IEEEabrv.bib'\n- 'Bibliography.bib'\ntitle: 'Online coalitional games for real-time payoff distribution with applications to energy markets'\n---\n\nIntroduction {#sec: intro}\n============\n\nA technological transformation is currently underway converting key infrastructures, such as power grids, commerce, and trading platforms, into highly dynamic complex systems. In these domains, predictive"
+"---\nabstract: 'The Near-Infrared Spectrograph (NIRSpec) is one of the four focal plane instruments on the James Webb Space Telescope. In this paper, we summarize the in-orbit performance of NIRSpec, as derived from data collected during its commissioning campaign and the first few months of nominal science operations. More specifically, we discuss the performance of some critical hardware components such as the two NIRSpec Hawaii-2RG (H2RG) detectors, wheel mechanisms, and the micro-shutter array. We also summarize the accuracy of the two target acquisition procedures used to accurately place science targets into the slit apertures, discuss the current status of the spectrophotometric and wavelength calibration of NIRSpec spectra, and provide the \u2019as measured\u2019 sensitivity in all NIRSpec science modes. Finally, we point out a few important considerations for the preparation of NIRSpec science programs.'\nauthor:\n- 'T. B\u00f6ker'\n- 'T. L. Beck'\n- 'S. M. Birkmann'\n- 'G. Giardino'\n- 'C. Keyes'\n- 'N. Kumari'\n- 'J. Muzerolle'\n- 'T. Rawle'\n- 'P. Zeidler'\n- 'Y. Abul-Huda'\n- 'C. Alves de Oliveira'\n- 'S. Arribas'\n- 'K. Bechtold'\n- 'R. Bhatawdekar'\n- 'N. Bonaventura'\n- 'A. J. Bunker'\n- 'A. J. Cameron'\n- 'S. Carniani'\n- 'S. Charlot'\n- 'M. Curti'"
+"---\nabstract: 'Assuming the Riemann hypothesis, we prove the latest explicit version of the prime number theorem for short intervals. Using this result, and assuming the generalised Riemann hypothesis for Dirichlet $L$-functions is true, we then establish explicit formulae for $\\psi(x,\\chi)$, $\\theta(x,\\chi)$, and an explicit version of the prime number theorem for primes in arithmetic progressions that hold for general moduli $q\\geq 3$. Finally, we restrict our attention to $q\\leq 10\\,000$ and use an exact computation to refine these results.'\naddress: 'University of Bristol, School of Mathematics, Fry Building, Woodland Road, Bristol, BS8 1UG'\nauthor:\n- Ethan Simpson Lee\nbibliography:\n- 'refs.bib'\ntitle: The prime number theorem for primes in arithmetic progressions at large values\n---\n\n[^1]\n\nIntroduction\n============\n\nSuppose that $x\\geq 2$, $p$ are prime numbers, $\\chi$ is a Dirichlet character modulo $q\\geq 3$, $$    \\psi(x,\\chi) = \\sum_{n\\leq x} \\chi(n)\\Lambda(n),\n    \\quad\\text{and}\\quad\n    \\psi_1(x,\\chi) \n    = \\int_0^x \\psi(t,\\chi)\\,dt\n    = \\sum_{n\\leq x} \\chi(n) \\Lambda(n) (x-n).$$ The purpose of this paper is to prove the latest explicit and conditional version of the prime number theorem for primes in arithmetic progressions, which is a collection of asymptotic estimates for $$\\pi(x;q,a) \n    = \\sum_{\\substack{p \\leq x \\\\ p \\equiv a {\\,\\left(\\textnormal{mod }q\\right)\\,}}} 1,\n    \\quad\n    \\theta(x;q,a) \n    = \\sum_{\\substack{p"
+"---\naddress: |\n    $^{1}$ Robotics Department, University of Michigan, Ann Arbor, MI 48109, USA\\\n    $^{2}$ Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI 48109, USA;maanigj@umich.edu (M.G.)\\\n    $^{3}$ Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA 24061, USA; ematkins@vt.edu (E.M.A.)\\\n    $^{4}$ Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, USA; jwcutler@umich.edu (J.W.C.)\\\nbibliography:\n- 'refs.bib'\n---\n\nIntroduction\n============\n\nEarth\u2019s magnetic field provides a reference measurement anywhere on the planet to assist with navigation. Magnetometers, or 3D compasses, onboard many modern robotics platforms can measure the local magnetic field to assist with navigation. To do this, we often rely on simplifying assumptions (i.e., the magnetic field points northward and is constant in a target region) or on maps of Earth\u2019s magnetic field like the World Magnetic Model (WMM). However, when testing in indoor environments, these assumptions and worldwide maps are inaccurate due to the contribution of the magnetic field from metallic objects inside buildings. Because of this, measurements from magnetometers are often ignored for indoor navigation.\n\nThere are a number of existing works that leverage indoor magnetic field measurements to estimate position, or attitude [@Kuevor2021; @Kok2018; @Vallivaara2011; @Vallivaara2010; @Akai2015; @Akai2017; @Suksakulchai2000; @Li2012; @Haverinen2009; @Wu2019]. However, a common theme"
+"---\nabstract: |\n    A proper $k$-coloring of a graph $G$ is a *neighbor-locating $k$-coloring* if for each pair of vertices in the same color class, the sets of colors found in their neighborhoods are different. The neighbor-locating chromatic number $\\chi_{NL}(G)$ is the minimum $k$ for which $G$ admits a neighbor-locating $k$-coloring. A proper $k$-coloring of a graph $G$ is a *locating $k$-coloring* if for each pair of vertices $x$ and $y$ in the same color-class, there exists a color class $S_i$ such that $d(x,S_i)\\neq d(y,S_i)$. The locating chromatic number $\\chi_{L}(G)$ is the minimum $k$ for which $G$ admits a locating $k$-coloring. It follows that $\\chi(G)\\leq\\chi_L(G)\\leq\\chi_{NL}(G)$ for any graph $G$, where $\\chi(G)$ is the usual chromatic number of $G$.\n\n    We show that for any three integers $p,q,r$ with $2\\leq p\\leq q\\leq r$ (except when $2=p=q<r$), there exists a connected graph $G_{p,q,r}$ with $\\chi(G_{p,q,r})=p$, $\\chi_L(G_{p,q,r})=q$ and $\\chi_{NL}(G_{p,q,r})=r$. We also show that the locating chromatic number (resp., neighbor-locating chromatic number) of an induced subgraph of a graph $G$ can be arbitrarily larger than that of $G$.\n\n    Alcon *et al.* showed that the number $n$ of vertices of $G$ is bounded above by $k(2^{k-1}-1)$, where $\\chi_{NL}(G)=k$ and $G$ is connected (this bound is tight)."
+"---\nabstract: 'Navigating the diverse solution spaces of non-trivial software engineering tasks requires a combination of technical knowledge, problem-solving skills, and creativity. With multiple possible solutions available, each with its own set of trade-offs, it is essential for programmers to evaluate the various options and select the one that best suits the specific requirements and constraints of a project. Whether it is choosing from a range of libraries, weighing the pros and cons of different architecture and design solutions, or finding unique ways to fulfill user requirements, the ability to think creatively is crucial for making informed decisions that will result in efficient and effective software. However, the interfaces of current chatbot tools for programmers, such as OpenAI\u2019s ChatGPT or GitHub Copilot, are optimized for presenting a single solution, even for complex queries. While other solutions can be requested, they are not displayed by default and are not intuitive to access. In this paper, we present our work-in-progress prototype \u201c<span style=\"font-variant:small-caps;\">GPTCompare</span>\u201d, which allows programmers to visually compare multiple source code solutions generated by GPT-n models for the same programming-related query by highlighting their similarities and differences.'\nauthor:\n- \ntitle: |\n    Navigating Complexity in Software Engineering:\\\n    A Prototype for Comparing GPT-n"
+"---\nabstract: 'While the concepts of quantum many-body integrability and chaos are of fundamental importance for the understanding of quantum matter, their precise definition has so far remained an open question. In this work, we introduce an alternative indicator for quantum many-body integrability and chaos, which is based on the statistics of eigenstates by means of nearest-neighbor subsystem trace distances. We show that this provides us with a faithful classification through extensive numerical simulations for a large variety of paradigmatic model systems including random matrix theories, free fermions, Bethe-ansatz solvable systems, and models of many-body localization. While existing indicators, such as those obtained from level-spacing statistics, have already been utilized with great success, they also face limitations. This concerns for instance the quantum many-body kicked top, which is exactly solvable but classified as chaotic in certain regimes based on the level-spacing statistics, while our introduced indicator signals the expected quantum many-body integrability. We discuss the universal behaviors we observe for the nearest-neighbor trace distances and point out that our indicator might be useful also in other contexts such as for the many-body localization transition.'\nauthor:\n- Reyhaneh Khasseh\n- Jiaju Zhang\n- Markus Heyl\n- 'M.\u00a0A.\u00a0Rajabpour'\ntitle: 'Identifying"
+"---\nabstract: 'This paper describes the acceleration of high energies protons captured by electrostatic waves in the frame of jet arising in the area of instability of relativistic jet, where spiral structures are excited. The wave has a spatially heterogeneous structure of $\\exp(i k_\\parallel z +i m_\\phi \\phi)$. Protons can be captured in potential wells created by spiral waves, and thereby experience acceleration with a mechanism known as surfatron acceleration. Expressions of the maximum energy ($ E_p \\simeq 10^{19} eV $) and the energy spectrum from jet parameters are obtained.'\nauthor:\n- 'Ya. N. Istomin'\n- 'A. A. Gunya'\nbibliography:\n- 'References.bib'\ntitle: 'Surfatron acceleration of the protons to high-energy in the relativistic jets'\n---\n\nIntroduction {#section1}\n============\n\nAxisymmetric collimated quasi-stationary ejections, called relativistic jets, arise in the process of plasma accretion onto the central black hole from the surrounding disk. Such a flow of relativistic plasma with a nonthermal nature of radiation is typical mainly for a number of active galactic nuclei (AGNs) and microquasars [@2006MNRAS.370..399B]. The pioneering works, [@1977MNRAS.179..433B] and [@1982MNRAS.199..883B], describe the prerequisites for the emergence of axisymmetric jets. The quasi-cylindrical structure of the jet is formed by the ratio of the prevailing toroidal magnetic field over"
+"---\nabstract: 'Response generation is one of the critical components in task-oriented dialog systems. Existing studies have shown that large pre-trained language models can be adapted to this task. The typical paradigm of adapting such extremely large language models would be by fine-tuning on the downstream tasks which is not only time-consuming but also involves significant resources and access to fine-tuning data. Prompting [@schick2020exploiting] has been an alternative to fine-tuning in many NLP tasks. In our work, we explore the idea of using prompting for response generation in task-oriented dialog systems. Specifically, we propose an approach that performs *contextual dynamic prompting* where the prompts are learnt from dialog contexts. We aim to distill useful prompting signals from the dialog context. On experiments with MultiWOZ 2.2 dataset [@zang2020multiwoz], we show that contextual dynamic prompts improve response generation in terms of *combined score* [@mehri-etal-2019-structured] by 3 absolute points, and a massive 20 points when dialog states are incorporated. Furthermore, human annotation on these conversations found that agents which incorporate context were preferred over agents with vanilla prefix-tuning.'\nauthor:\n- |\n    Sandesh Swamy\\\n    AWS AI Labs\\\n    sanswamy@amazon.com Narges Tabari\\\n    AWS AI Labs\\\n    nargesam@amazon.com Chacha Chen[^1]\\\n    University of Chicago\\\n    chacha@uchicago.edu Rashmi Gangadharaiah\\\n    AWS AI"
+"---\nabstract: |\n    **Motivation:** As viruses that mainly infect bacteria, phages are key players across a wide range of ecosystems. Analyzing phage proteins is indispensable for understanding phages\u2019 functions and roles in microbiomes. High-throughput sequencing enables us to obtain phages in different microbiomes with low cost. However, compared to the fast accumulation of newly identified phages, phage protein classification remains difficult. In particular, a fundamental need is to annotate virion proteins, the structural proteins such as major tail, baseplate, etc. Although there are experimental methods for virion protein identification, they are too expensive or time-consuming, leaving a large number of proteins unclassified. Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein classification.\\\n    **Results:** In this work, we adapted the state-of-the-art image classification model, Vision Transformer, to conduct virion protein classification. By encoding protein sequences into unique images using chaos game representation, we can leverage Vision Transformer to learn both local and global features from sequence \u201cimages\u201d. Our method, PhaVIP, has two main functions: classifying PVP and non-PVP sequences and annotating the types of PVP, such as capsid and tail. We tested PhaVIP on several datasets with increasing difficulty and benchmarked it"
+"---\nabstract: 'The usage of Python idioms is popular among Python developers in a formative study of 101 Python idiom performance related questions on Stack Overflow, we find that developers often get confused about the performance impact of Python idioms and use anecdotal toy code or rely on personal project experience which is often contradictory in performance outcomes. There has been no large-scale, systematic empirical evidence to reconcile these performance debates. In the paper, we create a large synthetic dataset with 24,126 pairs of non-idiomatic and functionally-equivalent idiomatic code for the nine unique Python idioms identified in\u00a0[@zhang2022making], and reuse a large real-project dataset of 54,879 such code pairs provided in\u00a0[@zhang2022making]. We develop a reliable performance measurement method to compare the speedup or slowdown by idiomatic code against non-idiomatic counterpart, and analyze the performance discrepancies between the synthetic and real-project code, the relationships between code features and performance changes, and the root causes of performance changes at the bytecode level. We summarize our findings as some actionable suggestions for using Python idioms.'\nauthor:\n- \nbibliography:\n- 'debug.bib'\ntitle: 'Faster or Slower? Performance Mystery of Python Idioms Unveiled with Empirical Evidence '\n---\n\nIntroduction\n============\n\nPython supports many unique idioms"
+"---\nauthor:\n- 'Jeremy S. Sanders'\nbibliography:\n- 'refs.bib'\ntitle: Clusters of galaxies\n---\n\nIntroduction\n============\n\nMany galaxies in the universe are found to be gravitationally bound into objects known as groups and clusters of galaxies. The richest clusters of galaxies consist of thousands of individual galaxies, with total masses of $\\sim 10^{15} {\\hbox{$\\rm\\thinspace M_{\\odot}$}}$. Groups of galaxies are lower mass objects containing fewer galaxies ($\\lesssim 50$), although the boundary between clusters and groups is not exact. In the hierarchical theory of the formation of structure, clusters are expected to lie at the densest regions of the cosmic web, built up by the merger of smaller structures over time (e.g. [@Allen11]).\n\nIt was noted that a number of the X-ray sources in the sky discovered by the *Uhuru* X-ray observatory were associated with groups and clusters of galaxies [@Cavaliere71]. The discovery of an emission feature around 7 keV in an X-ray spectrum of the Perseus cluster observed using the *Ariel 5* X-ray telescope [@Mitchell76] provided key evidence that the X-ray emission from clusters of galaxies was thermal in nature, originating from Fe K-shell transitions within the hot plasma within the cluster. The presence of this line in Perseus and other"
+"---\nabstract: 'Let $l\\geq 3$ and $F$ be a modular form of weight $l/2-1$ on $\\mathrm{O}(l,2)$ which vanishes only on rational quadratic divisors. We prove that $F$ has only simple zeros and that $F$ is anti-invariant under every reflection fixing a quadratic divisor in the zeros of $F$. In particular, $F$ is a reflective modular form. As a corollary, the existence of $F$ leads to $l\\leq 20$ or $l=26$, in which case $F$ equals the Borcherds form on ${\\mathop{\\mathrm {II}}\\nolimits}_{26,2}$. This answers a question posed by Borcherds in 1995.'\naddress:\n- 'Center for Geometry and Physics, Institute for Basic Science (IBS), Pohang 37673, Korea'\n- 'Lehrstuhl A f\u00fcr Mathematik, RWTH Aachen, 52056 Aachen, Germany'\nauthor:\n- Haowu Wang\n- Brandon Williams\nbibliography:\n- 'refs.bib'\ntitle: 'On the non-existence of singular Borcherds products'\n---\n\nIntroduction\n============\n\nIn this paper we prove some nice properties of holomorphic automorphic products of singular weight on orthogonal groups ${\\mathop{\\null\\mathrm {O}}\\nolimits}(l,2)$, and resolve an open problem posed by Borcherds in 1995.\n\nLet $M$ be an even integral lattice of signature $(l,2)$ with $l\\geq 3$. Let $(-,-)$ denote the bilinear form on $M$ and let $M'$ be the dual lattice of $M$. Either of the two connected"
+"---\nabstract: 'Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt. While revolutionary, current state-of-the-art diffusion models may still fail in generating images that fully convey the semantics in the given text prompt. We analyze the publicly available Stable Diffusion model and assess the existence of *catastrophic neglect*, where the model fails to generate one or more of the subjects from the input prompt. Moreover, we find that in some cases the model also fails to correctly bind attributes (, colors) to their corresponding subjects. To help mitigate these failure cases, we introduce the concept of *Generative Semantic Nursing (GSN)*, where we seek to intervene in the generative process on the fly during inference time to improve the faithfulness of the generated images. Using an attention-based formulation of GSN, dubbed *Attend-and-Excite*, we guide the model to refine the cross-attention units to *attend* to all subject tokens in the text prompt and strengthen \u2014 or *excite* \u2014 their activations, encouraging the model to generate all subjects described in the text prompt. We compare our approach to alternative approaches and demonstrate that it conveys the desired concepts more faithfully across"
+"---\nabstract: 'Large pretrained language models are widely used in downstream NLP tasks via task-specific fine-tuning, but such procedures can be costly. Recently, Parameter-Efficient Fine-Tuning (PEFT) methods have achieved strong task performance while updating a much smaller number of parameters compared to full model fine-tuning (FFT). However, it is non-trivial to make informed design choices on the *PEFT configurations*, such as their architecture, the number of tunable parameters, and even the layers in which the PEFT modules are inserted. Consequently, it is highly likely that the current, manually designed configurations are suboptimal in terms of their performance-efficiency trade-off. Inspired by advances in neural architecture search, we propose [[<span style=\"font-variant:small-caps;\">autoPEFT</span>]{}]{}for automatic PEFT configuration selection: we first design an expressive configuration search space with multiple representative PEFT modules as building blocks. Using multi-objective Bayesian optimisation in a low-cost setup, we then discover a Pareto-optimal *set* of configurations with strong performance-cost trade-offs across different numbers of parameters that are also highly transferable across different tasks. Empirically, on GLUE and SuperGLUE tasks, we show that [[<span style=\"font-variant:small-caps;\">autoPEFT</span>]{}]{}-discovered configurations significantly outperform existing PEFT methods and are on par or better than FFT, without incurring substantial training efficiency costs.'\nauthor:\n- |\n    Han Zhou^1,\\*^ Xingchen Wan^2,\\*^"
+"---\nabstract: 'We calculate deviations in cosmological observables as a function of parameters in a class of connection\u2013based models of quantum gravity. In this theory non-trivial modifications to the background cosmology can occur due to a distortion of the wave function of the Universe at the transition from matter to dark energy domination (which acts as a \u201creflection\u201d in connection space). We are able to exclude some regions of parameter space and show with projected constraints that future experiments like DESI will be able to further constrain these models. An interesting feature of this theory is that there exists a region of parameter space that could naturally alleviate the $S_8$ tension.'\nauthor:\n- 'Michael W.\u00a0Toomey'\n- Savvas Koushiappas\n- Bruno Alexandre\n- Jo\u00e3o Magueijo\nbibliography:\n- 'bibo.bib'\ntitle: 'Quantum Gravity Signatures in the Late-Universe'\n---\n\nIntroduction\n============\n\nOne of the most striking features of $\\Lambda$CDM cosmology is the recent transition from decelerated to accelerated expansion. While the dynamics of this transition through the lens of the concordance model is well understood and has been studied *ad nauseam*, it has recently been pointed out [@Alexandre:2022ijm] that from the perspective of some theories of quantum gravity, such a transition can result"
+"---\nauthor:\n- 'Adam Davis,'\n- 'Tony Menzo,'\n- 'Ahmed Youssef,'\n- 'Jure Zupan,'\nbibliography:\n- 'CPV\\_ML\\_biblio.bib'\ntitle: 'Earth mover\u2019s distance as a measure of CP violation'\n---\n\n[ !a! @toks= @toks= ]{} [ !b! @toks= @toks= ]{}\n\n[@counter&gt;0@toks=@toks=]{} [@counter&gt;0@toks=@toks=]{} [@counter&gt;0@toks=@toks=]{} [@counter&gt;0@toks=@toks=]{}\n\n[abstract[We introduce a new unbinned two sample test statistic sensitive to CP violation utilizing the optimal transport plan associated with the Wasserstein (earth mover\u2019s) distance. The efficacy of the test statistic is shown via two examples of CP asymmetric distributions with varying sample sizes: the Dalitz distributions of $B^0 \\rightarrow K^+\\pi^-\\pi^0$ and of $D^0 \\rightarrow \\pi^+\\pi^-\\pi^0$ decays. The windowed version of the Wasserstein distance test statistic is shown to have comparable sensitivity to CP violation as the commonly used energy test statistic, but also retains information about the localized distributions of CP asymmetry over the Dalitz plot. For large statistic datasets we introduce two modified Wasserstein distance based test statistics \u2013 the binned and the sliced Wasserstein distance statistics, which show comparable sensitivity to CP violation, but improved computing time and memory scalings. Finally, general extensions and applications of the introduced statistics are discussed. ]{}]{}\n\nIntroduction\n============\n\nThe Wasserstein distance or earth mover\u2019s distance (EMD) is a measure"
+"---\nauthor:\n- 'Jenny Wagner,[!!]{}'\nbibliography:\n- 'charged\\_bh.bib'\ntitle: 'Observables for moving, stupendously charged and massive primordial black holes'\n---\n\nIntroduction {#sec:introduction}\n============\n\nPrimordial black holes (PBHs) have long been established as dark matter candidates, [@bib:Hawking1971; @bib:Zeldovic1966]. Their signatures are being searched for on mass scales ranging from sub-solar masses to primordial supermassive black holes (PSMBHs) with up to $10^{11} M_\\odot$, see, for instance, [@bib:Cappelluti2022; @bib:Carr2022] or [@bib:Villanueva2021] for recent overviews. As discussed in [@bib:Carr_stup], so-called Stupendously LArge Black holes (SLABs) beyond $10^{11} M_\\odot$ could also evolve and exist. Even though no clear evidence for the existence of PBHs has been found so far, they are good candidates for SLABs.\n\nConventional structure growth models, which also assume black hole formation only starts at the cosmic time when the first stars were created, were recently challenged from several James Webb Space Telescope (JWST) observations of fast galaxy evolution at high redshifts, see, for instance, [@bib:Boylan2023; @bib:Kocevski2023; @bib:Labbe2023; @bib:Matthee2023; @bib:Pacucci2023]. Thus, possible black hole formation scenarios may be extended or revised alongside explanations for these early, massive galaxies. As far as observational signatures are concerned, [@bib:Carr2021] and [@bib:Carr_stup] elaborately showed that SLABs up to $10^{16} M_\\odot$ would not leave significant anisotropic imprints"
+"---\nabstract: |\n    It is known that multiple partonic scatterings in high-energy proton-proton ($pp$) collisions must happen in parallel. However, a rigorous parallel scattering formalism, taking energy sharing properly into account, fails to reproduce factorization, which on the other hand is the basis of almost all $pp$ event generators. In addition, binary scaling in nuclear scatterings is badly violated. These problems are usually \u201csolved\u201d by simply not considering strictly parallel scatterings, which is not a solution. I will report on new ideas (leading to EPOS4), which allow recovering perfectly factorization, and also binary scaling in $AA$ collisions, in a rigorous unbiased parallel scattering formalism. In this new approach, dynamical saturation scales play a crucial role, and this seems to be the missing piece needed to reconcile parallel scattering with factorization. From a practical point of view, one can compute within the EPOS4 framework parton distribution functions (EPOS PDFs) and use them to compute inclusive $pp$ cross sections. So, for the first time, one may compute inclusive jet production (for heavy or light flavors) at very high transverse momentum ($p_{t}$) and at the same time in the same formalism study flow effects at low $p_{t}$ in high-multiplicity $pp$ events, making EPOS4"
+"---\nabstract: |\n    This paper presents a conditional convergence result of solutions to the Allen\u2013Cahn equation with arbitrary potentials to a De Giorgi type $ \\operatorname{BV}$-solution to multiphase mean curvature flow. Moreover we show that De Giorgi type $ \\operatorname{BV}$-solutions are De Giorgi type varifold solutions, and thus our solution is unique in a weak-strong sense.\\\n    **Keywords:** Gradient flows, Mean curvature flow, Allen\u2013Cahn equation\\\n    **Mathematical Subject Classification:** 35A15, 35K57, 35K93, 53E10, 74N20\nauthor:\n- 'Pascal Steinke[^1]'\nbibliography:\n- 'references.bib'\ntitle: 'Convergence of Allen\u2013Cahn equations to De Giorgi\u2019s multiphase mean curvature flow'\n---\n\nIntroduction\n============\n\nHistory and main results\n------------------------\n\nMultiphase mean curvature flow is an important geometric evolution equation which has been studied for a long time, bearing not only mathematical importance, but also for the applied sciences. Originally it was proposed to study the evolution of grain boundaries in annealed recrystallized metal, as described by Mullins in [@mullins_two_dimensional_motion_of_idealized_grain_boundaries], who cites Beck in [@beck_metal_interfaces] as already having observed such a behaviour in 1952.\n\nOver the years numerous different solution concepts for multiphase mean curvature flow have been proposed. Classically we have smooth solutions, where we require the evolution of the interfaces to be smooth, for example described by Huisken in"
+"---\nabstract: 'Combining ideas coming from Stone duality and Reynolds parametricity, we formulate in a clean and principled way a notion of profinite $\\lambda$-term which, we show, generalizes at every type the traditional notion of profinite word coming from automata theory. We start by defining the Stone space of profinite $\\lambda$-terms as a projective limit of finite sets of usual $\\lambda$-terms, considered modulo a notion of equivalence based on the finite standard model. One main contribution of the paper is to establish that, somewhat surprisingly, the resulting notion of profinite $\\lambda$-term coming from Stone duality lives in perfect harmony with the principles of Reynolds parametricity. In addition, we show that the notion of profinite $\\lambda$-term is compositional by constructing a cartesian closed category of profinite $\\lambda$-terms, and we establish that the embedding from $\\lambda$-terms modulo $\\beta\\eta$-conversion to profinite $\\lambda$-terms is faithful using Statman\u2019s finite completeness theorem. Finally, we prove that the traditional Church encoding of finite words into $\\lambda$-terms can be extended to profinite words, and leads to a homeomorphism between the space of profinite words and the space of profinite $\\lambda$-terms of the corresponding Church type.'\naddress:\n- Universit\u00e9 Paris Cit\u00e9\n- 'CNRS, Universit\u00e9 Paris Cit\u00e9, Inria'\n- 'Universit\u00e9"
+"---\nauthor:\n- \ntitle: 'Wright\u2019s Strict Finitistic Logic in the Classical Metatheory: The Propositional Case'\n---\n\nIntroduction\n============\n\nAims {#section: Aims}\n----\n\nThe present paper provides and explores the propositional part of \u2018strict finitistic logic\u2019 according to Wright, as obtained via classical counterparts of his strict finitistic models of arithmetic, under an additional assumption we call the \u2018atomic prevalence condition\u2019. \u2018Strict finitism\u2019 is the view of mathematics according to which an object, or a number in particular, is admitted iff it is constructible in practice, and a statement holds iff it is verifiable in practice. It is constructivist, in that it accepts a number and a statement on grounds of our cognitive capabilities; and more restrictive than intuitionism, since it uses the notion of \u2018in practice\u2019 in place of intuitionism\u2019s \u2018in principle\u2019. Strict finitism is finitistic, because as a consequence, it rejects the idea that there are infinitely many natural numbers. Strict finitistic logic is meant to be the abstract system of logical reasoning concerning actually constructible objects, based on actual verifiability.\n\nAmong the literary sources, Wright\u2019s in 1982 [@Wright1982], to us, is the most philosophically inspirational and appears to be in possession of most formal-logical contents. While [@Wright1982] is"
+"---\nabstract: 'Quorum sensing (QS) mimickers can be used as an effective tool to disrupt biofilms which consist of communicating bacteria and extracellular polymeric substances (EPS). In this paper, a stochastic biofilm disruption model based on the usage of QS mimickers is proposed. A chemical reaction network (CRN) involving four different states is employed to model the biological processes during the biofilm formation and its disruption via QS mimickers. In addition, a state-based stochastic simulation algorithm is proposed to simulate this CRN. The proposed model is validated by the *in vitro* experimental results of *Pseudomonas aeruginosa* biofilm and its disruption by rosmarinic acid as the QS mimicker. Our results show that there is an uncertainty in state transitions due to the effect of the randomness in the CRN. In addition to the QS activation threshold, the presented work demonstrates that there are underlying two more thresholds for the disruption of EPS and bacteria, which provides a realistic modeling for biofilm disruption with QS mimickers.'\nauthor:\n- 'Fatih Gulec,\u00a0 Andrew W. Eckford,\u00a0 [^1] [^2][^3]'\nbibliography:\n- 'ref\\_fg\\_biofilm\\_disruption.bib'\ntitle: A Stochastic Biofilm Disruption Model based on Quorum Sensing Mimickers\n---\n\nMolecular communication, biological communication, biofilm disruption, quorum sensing mimickers.\n\nIntroduction\n============\n\ncan"
+"---\nabstract: 'We study the classical scheduling problem on parallel machines where the precedence graph has the bounded depth $h$. Our goal is to minimize the maximum completion time. We focus on developing approximation algorithms that use only sublinear space or sublinear time. We develop the first one-pass streaming approximation schemes using sublinear space when all jobs\u2019 processing times differ no more than a constant factor $c$ and the number of machines $m$ is at most $\\tfrac {2n \\epsilon}{3 h c }$. This is so far the best approximation we can have in terms of $m$, since no polynomial time approximation better than $\\tfrac{4}{3}$ exists when $m = \\tfrac{n}{3}$ unless P=NP. The algorithms are then extended to the more general problem where the largest $\\alpha n$ jobs have no more than $c$ factor difference. We also develop the first sublinear time algorithms for both problems. For the more general problem, when $ m  \\le    \\tfrac {  \\alpha  n  \\epsilon}{20 c^2  \\cdot h  } $, our algorithm is a randomized $(1+\\epsilon)$-approximation scheme that runs in sublinear time. This work not only provides an algorithmic solution to the studied problem under big data environment, but also gives a methodological framework for designing"
+"---\nauthor:\n- 'Cheng Zhao (\u8d75\u6210)'\nbibliography:\n- 'FCFC.bib'\ndate: 'Received September 15, 1996; accepted March 16, 1997'\nsubtitle: 'A high-performance pair counting toolkit'\ntitle: Fast Correlation Function Calculator\n---\n\n[UTF8]{}[gbsn]{}\n\n[A novel high-performance exact pair counting toolkit called Fast Correlation Function Calculator ([`FCFC`]{}) is presented, which is publicly available at <https://github.com/cheng-zhao/FCFC>.]{} [As the rapid growth of modern cosmological datasets, the evaluation of correlation functions with observational and simulation catalogues has become a challenge. High-efficiency pair counting codes are thus in great demand.]{} [We introduce different data structures and algorithms that can be used for pair counting problems, and perform comprehensive benchmarks to identify the most efficient ones for real-world cosmological applications. We then describe the three levels of parallelisms used by [`FCFC`]{} \u2013 including SIMD, OpenMP, and MPI \u2013 and run extensive tests to investigate the scalabilities. Finally, we compare the efficiency of [`FCFC`]{} against alternative pair counting codes.]{} [The data structures and histogram update algorithms implemented in [`FCFC`]{} are shown to outperform alternative methods. [`FCFC`]{} does not benefit much from SIMD as the bottleneck of our histogram update algorithm is mostly cache latency. Nevertheless, the efficiency of [`FCFC`]{} scales well with the numbers of OpenMP threads and MPI"
+"---\nauthor:\n- 'Cat P. Le, Luke Dai, Michael Johnston, Yang Liu, Marilyn Walker, Reza Ghanadan'\nbibliography:\n- 'refs.bib'\n- 'athena.bib'\ntitle: 'Improving Open-Domain Dialogue Evaluation with a Causal Inference Model'\n---\n\nopen-domain dialogue, user ratings, dialogue evaluation, causal inference, user satisfaction\n\nIntroduction {#sec:intro}\n============\n\nEvaluation has always been a complex challenge for interactive dialogue systems. For task-oriented dialogues, frameworks such as Paradise\u00a0[@walker-etal-1997-paradise] model the relationship between user satisfaction, task completion, and cost factors such as dialogue length, word error rate, and dialogue behaviors. However, open-domain dialogue systems such as those built for the Alexa Prize SocialBot Grand Challenge\u00a0[@ram2018conversational; @gabriel2020further], where there is no clearly defined task, require new metrics and methods for evaluation that better reflect their affordances [@walker2021modeling; @kim2020speech; @ghazarian-etal-2019-better; @ghazarian2022wrong; @higashinaka2019improving; @sinha-etal-2020-learning].\n\nIn Alexa Prize SocialBot, user conversation ratings are elicited primarily for comparative evaluation of systems competing in the Grand Challenge. Numerous other use cases for dialogue ratings include selecting training data and running A/B tests to evaluate new capabilities, features, and models. Manually collected user ratings are limited in that only a fraction of users of \u201cAlexa Let\u2019s Chat\" leave ratings, and these ratings can be highly subjective. An alternative is the post-hoc"
+"---\nabstract: 'Dynamic treatment rules or policies are a sequence of decision functions over multiple stages that are tailored to individual features. One important class of treatment policies for practice, namely multi-stage stationary treatment policies, prescribe treatment assignment probabilities using the same decision function over stages, where the decision is based on the same set of features consisting of both baseline variables (e.g., demographics) and time-evolving variables (e.g., routinely collected disease biomarkers). Although there has been extensive literature to construct valid inference for the value function associated with the dynamic treatment policies, little work has been done for the policies themselves, especially in the presence of high dimensional feature variables. We aim to fill in the gap in this work. Specifically, we first estimate the multistage stationary treatment policy based on an augmented inverse probability weighted estimator for the value function to increase the asymptotic efficiency, and further apply a penalty to select important feature variables. We then construct one-step improvement of the policy parameter estimators. Theoretically, we show that the improved estimators are asymptotically normal, even if nuisance parameters are estimated at a slow convergence rate and the dimension of the feature variables increases with the sample size. Our"
+"---\nabstract: 'In compact and dense star-forming clouds a global star cluster wind could be suppressed. In this case the stellar feedback is unable to expel the leftover gas from the cluster. Young massive stars remain embedded into a dense residual gas and stir it moving in the gravitational well of the system. Here we present a self-consistent model for the molecular gas distribution in such young, enshrouded stellar clusters. It is assumed that the cloud collapse terminates and the star formation ceases when a balance between the turbulent pressure and gravity and between the turbulent energy dissipation and regeneration rates is established. These conditions result in an equation that determines the residual gas density distribution that, in turn, allows one to determine the other characteristics of the leftover gas and the star formation efficiency. It is shown that model predictions are in good agreement with several observationally determined properties of cloud D1 in nearby dwarf spheroidal galaxy NGC 5253 and its embedded cluster.'\nauthor:\n- Sergiy Silich\n- Jean Turner\n- Jonathan Mackey\n- 'Sergio Mart\u00ednez-Gonz\u00e1lez'\nbibliography:\n- 'TUR.bib'\ndate: 'Accepted . Received ; in original form'\ntitle: Molecular gas properties in young stellar clusters with a suppressed star"
+"---\nabstract: 'The spectrum of laser-plasma-generated X-rays is very important as it can characterize electron dynamics and also be useful for applications, and nowadays with the forthcoming high-repetition-rate laser-plasma experiments, there is a raising demand for online diagnosis for the X-ray spectrum. In this paper, scintillators and silicon PIN diodes are used to build a wideband online filter stack spectrometer. The genetic algorithm is used to optimize the arrangements of the X-ray sensors and filters by minimizing the condition number of the response matrix, thus the unfolding error can be significantly decreased according to the numerical experiments. The detector responses are quantitatively calibrated by irradiating the scintillator and PIN diode using different nuclides and comparing the measured $\\gamma$-ray peaks. Finally, a 15-channel spectrometer prototype has been implemented. The X-ray detector, front-end electronics, and back-end electronics are integrated into the prototype, and the prototype can determine the spectrum with 1 kHz repetition rates.'\naddress:\n- 'Science and Technology on Plasma Physics Laboratory, Laser Fusion Research Center, CAEP, Mianyang, 621900, Sichuan, China'\n- 'Department of Engineering Physics, Tsinghua University, Beijing, 100084, China'\n- 'Key Laboratory of Particle and Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, 100084, China'\n- 'School of Information"
+"---\nbibliography:\n- 'lb.bib'\n---\n\n[ **Finite temperature spin diffusion in the Hubbard model in the strong coupling limit** ]{}\n\nOleksandr Gamayun^1,2\\*^, Arthur Hutsalyuk^3^, Bal\u00e1zs Pozsgay^3^, Mikhail B. Zvonarev^4^\n\n[**1**]{} London Institute for Mathematical Sciences, Royal Institution, 21 Albemarle St, London W1S 4BS, UK,\n\n[**2**]{} Faculty of Physics, University of Warsaw, ul. Pasteura 5, 02-093 Warsaw, Poland,\n\n[**3**]{} MTA-ELTE \u201cMomentum\u201d Integrable Quantum Dynamics Research Group, Department of Theoretical Physics, E\u00f6tv\u00f6s Lor\u00e1nd University, P\u00e1zm\u00e1ny P\u00e9ter stny. 1A, 1117 Budapest, Hungary\\\n[**4**]{} Universit\u00e9 Paris-Saclay, CNRS, LPTMS, 91405, Orsay, France\\\n\\* og@lims.ac.uk\n\nAbstract {#abstract .unnumbered}\n========\n\n[**We investigate finite temperature spin transport in one spatial dimension by considering the spin-spin correlation function of the Hubbard model in the limiting case of infinitely strong repulsion. We find that in the absence of a magnetic field the transport is diffusive, and derive the spin diffusion constant. Our approach is based on asymptotic analysis of a Fredholm determinant representation. The obtained results are in agreement with Generalized Hydrodynamics approach.**]{}\n\nIntroduction\n============\n\nQuantum transport in the integrable systems attracts ever increasing attention of the physics community\u00a0[@transport-review]. Distinctive features of these systems \u2013 a completely elastic and factorized (two-body reducible) scattering, and a presence of an infinite"
+"---\nabstract: 'In online exploration systems where users with fixed preferences repeatedly arrive, it has recently been shown that $O(1)$, i.e., bounded regret, can be achieved when the system is modeled as a linear contextual bandit. This result may be of interest for recommender systems, where the popularity of their items is often short-lived, as the exploration itself may be completed quickly before potential long-run non-stationarities come into play. However, in practice, exact knowledge of the linear model is difficult to justify. Furthermore, potential existence of unobservable covariates, uneven user arrival rates, interpretation of the necessary rank condition, and users opting out of private data tracking all need to be addressed for practical recommender system applications. In this work, we conduct a theoretical study to address all these issues while still achieving bounded regret. Aside from proof techniques, the key differentiating assumption we make here is the presence of effective Synthetic Control Methods (SCM), which are shown to be a practical relaxation of the exact linear model knowledge assumption. We verify our theoretical bounded regret result using a minimal simulation experiment.'\nauthor:\n- \n- \nbibliography:\n- 'bib.bib'\ntitle: 'Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle [^1] '\n---"
+"---\nabstract: 'Optimizing the performance of thermal machines is an essential task of thermodynamics. the optimization of information engines that convert information about the state of a system into work. We a generalized finite-time Carnot cycle for a quantum information engine and optimize its power output in the regime of low dissipation. We derive a general formula for its efficiency at maximum power valid for arbitrary working media. We further investigate the optimal performance of a qubit information engine subjected to weak energy measurements.'\nauthor:\n- Paul Fadler\n- Alexander Friedenberger\n- Eric Lutz\ntitle: Efficiency at maximum power of a Carnot quantum information engine\n---\n\nHeat engines convert thermal energy into mechanical work by running cyclicly between two heat baths at different temperatures. They have been widely used to generate motion, from ancient steam engines to modern internal combustion motors [@cen01]. Information engines, on the other hand, extract energy from a single heat bath by processing information, for instance, via cyclic measurement and feedback operations [@cao09; @sag10; @abr11; @hor11; @bau12; @sag12; @esp12; @man13; @hor13; @um15; @par16; @yam16; @hor19]. They thus exploit information gained about the state of a system to produce useful work [@sei12; @sag12a]. Such machines may be"
+"---\nauthor:\n- 'Semyon Yurchenko$^{1}$, Mikhail Zhabitsky$^{2}$[^1][^2]'\ndate: |\n    $^1$Saint Petersburg State University, Laboratory of ultra-high energy physics, St.\u00a0Petersburg, Russia\\\n    $^2$Joint Institute for Nuclear Research, Dubna, Russia\\\ntitle: 'Genetic Algorithm for determination of the event collision time and particle identification by time-of-flight at NICA SPD'\n---\n\n**Keywords**: Genetic Algorithm; Time-Of-Flight; Particle identification\n\nIntroduction\n============\n\nThe Spin Physics Detector (SPD) is a future experiment that will be placed in one of the two interaction points of the NICA collider in the Joint Institute for Nuclear Research. By studying collisions of polarized proton and deuteron beams, the SPD collaboration will perform a comprehensive study of the unpolarized and polarized gluon content of nucleons and other spin related phenomena\u00a0[@CDRSPD]. With polarized proton-proton collision energies $\\sqrt{s}$ up to $27~\\text{GeV}$, SPD will cover a kinematic range between the low-energy measurements at ANKE-COSY\u00a0[@Dymov:2016jgy] and SATURNE and the high-energy measurements at RHIC\u00a0[@STAR:2021mfd] and LHC\u00a0[@Hadjidakis:2018ifr].\n\nThe SPD experimental setup is planned as a general-purpose $4\\pi$\u00a0detector with advanced tracking and particle identification capabilities. The particle identification will be performed by means of $dE/dx$, Time-Of-Flight (TOF), Electromagnetic calorimetry and Muon-filtering techniques. The experiment will use a system of Multigap Resistive Plate Chambers (MRPC)\u00a0[@ZeballosMRPC;"
+"---\nabstract: 'This article presents a fast direct solver, termed Algebraic Inverse Fast Multipole Method (from now on abbreviated as AIFMM), for linear systems arising out of $N$-body problems. AIFMM relies on the following three main ideas: (i) Certain sub-blocks in the matrix corresponding to $N$-body problems can be efficiently represented as low-rank matrices; (ii) The low-rank sub-blocks in the above matrix are leveraged to construct an extended sparse linear system; (iii) While solving the extended sparse linear system, certain fill-ins that arise in the elimination phase are represented as low-rank matrices and are \u201credirected\u201d though other variables maintaining zero fill-in sparsity. The main highlights of this article are the following: (i) Our method is completely algebraic (as opposed to the existing Inverse Fast Multipole Method\u00a0[@ambikasaran2014inverse; @doi:10.1137/15M1034477; @TAKAHASHI2017406], from now on abbreviated as IFMM). We rely on our new Nested Cross Approximation\u00a0[@gujjula2022nca] (from now on abbreviated as NNCA) to represent the matrix arising out of $N$-body problems. (ii) A significant contribution is that the algorithm presented in this article is more efficient than the existing IFMMs. In the existing IFMMs, the fill-ins are compressed and redirected as and when they are created. Whereas in this article, we update"
+"---\nabstract: 'We say that $\\Gamma$, the boundary of a bounded Lipschitz domain, is locally dilation invariant if, at each $x\\in \\Gamma$, $\\Gamma$ is either locally $C^1$ or locally coincides (in some coordinate system centred at $x$) with a Lipschitz graph $\\Gamma_x$ such that $\\Gamma_x=\\alpha_x\\Gamma_x$, for some $\\alpha_x\\in (0,1)$. In this paper we study, for such $\\Gamma$, the essential spectrum of $D_\\Gamma$, the double-layer (or Neumann-Poincar\u00e9) operator of potential theory, on $L^2(\\Gamma)$. We show, via localisation and Floquet-Bloch-type arguments, that this essential spectrum is the union of the spectra of related continuous families of operators $K_t$, for $t\\in [-\\pi,\\pi]$; moreover, each $K_t$ is compact if $\\Gamma$ is $C^1$ except at finitely many points. For the 2D case where, additionally, $\\Gamma$ is piecewise analytic, we construct convergent sequences of approximations to the essential spectrum of $D_\\Gamma$; each approximation is the union of the eigenvalues of finitely many finite matrices arising from Nystr\u00f6m-method approximations to the operators $K_t$. Through error estimates with explicit constants, we also construct functionals that determine whether any particular locally-dilation-invariant piecewise-analytic $\\Gamma$ satisfies the well-known spectral radius conjecture, that the essential spectral radius of $D_\\Gamma$ on $L^2(\\Gamma)$ is $<1/2$ for all Lipschitz $\\Gamma$. We illustrate this theory with"
+"---\nabstract: 'Structural pruning enables model acceleration by removing structurally-grouped parameters from neural networks. However, the parameter-grouping patterns vary widely across different models, making architecture-specific pruners, which rely on manually-designed grouping schemes, non-generalizable to new architectures. In this work, we study a highly-challenging yet barely-explored task, any structural pruning, to tackle general structural pruning of arbitrary architecture like CNNs, RNNs, GNNs and Transformers. The most prominent obstacle towards this goal lies in the structural coupling, which not only forces different layers to be pruned simultaneously, but also expects all removed parameters to be consistently unimportant, thereby avoiding structural issues and significant performance degradation after pruning. To address this problem, we propose a general and [fully automatic]{} method, *Dependency Graph* (DepGraph), to explicitly model the dependency between layers and comprehensively group coupled parameters for pruning. In this work, we extensively evaluate our method on several architectures and tasks, including ResNe(X)t, DenseNet, MobileNet and Vision transformer for images, GAT for graph, DGCNN for 3D point cloud, alongside LSTM for language, and demonstrate that, even with a simple norm-based criterion, the proposed method consistently yields gratifying performances.'\nauthor:\n- |\n    **Gongfan Fang$^1$ Xinyin Ma$^1$ Mingli Song$^2$ Michael Bi Mi$^3$ Xinchao Wang$^1$[^1]\\\n    [National University"
+"---\nabstract: 'Loss function learning is a new meta-learning paradigm that aims to automate the essential task of designing a loss function for a machine learning model. Existing techniques for loss function learning have shown promising results, often improving a model\u2019s training dynamics and final inference performance. However, a significant limitation of these techniques is that the loss functions are meta-learned in an offline fashion, where the meta-objective only considers the very first few steps of training, which is a significantly shorter time horizon than the one typically used for training deep neural networks. This causes significant bias towards loss functions that perform well at the very start of training but perform poorly at the end of training. To address this issue we propose a new loss function learning technique for adaptively updating the loss function online after each update to the base model parameters. The experimental results show that our proposed method consistently outperforms the cross-entropy loss and offline loss function learning techniques on a diverse range of neural network architectures and datasets.'\nauthor:\n- Christian Raymond\n- Qi Chen\n- |\n    Bing XueMengjie Zhang\\\n    Victoria University of Wellington, Wellington, New Zealand\\\n    {Christian.Raymond, Qi.Chen, Bing.Xue, Mengjie.Zhang}@ecs.vuw.ac.nz\nbibliography:\n- 'references.bib'"
+"---\nabstract: 'We studied integration contour deformations in the chiral random matrix theory of Stephanov\u00a0[@Stephanov:1996ki] with the goal of alleviating the finite-density sign problem. We considered simple ans\u00e4tze for the deformed integration contours, and optimized their parameters. We find that optimization of a single parameter manages to considerably improve on the severity of the sign problem. We show numerical evidence that the improvement achieved is exponential in the degrees of freedom of the system, i.e., the size of the random matrix. We also compare the optimization method with contour deformations coming from the holomorphic flow equations.'\nauthor:\n- Matteo Giordano\n- Attila P\u00e1sztor\n- D\u00e1vid Peszny\u00e1k\n- Zolt\u00e1n Tulip\u00e1nt\ntitle: Fighting the sign problem in a chiral random matrix model with contour deformations\n---\n\nIntroduction\n============\n\nEuclidean quantum field theories at non-zero particle density (or chemical potential) generally suffer from a complex action problem: the weights in the path integral representation are complex, and thus cannot be interpreted as a joint probability density function on the space of field configurations (up to a proportionality factor). This prevents the use of importance sampling methods for the direct simulation of these theories. In QCD, this complex action problem severely hampers first-principles"
+"---\nabstract: |\n    Motivated by the challenge of nonstationarity in sequential decision making, we study Online Convex Optimization (OCO) under the coupling of two problem structures: the domain is unbounded, and the comparator sequence $u_1,\\ldots,u_T$ is arbitrarily time-varying. As no algorithm can guarantee low regret simultaneously against all comparator sequences, handling this setting requires moving from minimax optimality to comparator adaptivity. That is, sensible regret bounds should depend on certain complexity measures of the comparator relative to one\u2019s prior knowledge.\n\n    This paper achieves a new type of these adaptive regret bounds via a sparse coding framework. The complexity of the comparator is measured by its energy and its sparsity on a user-specified dictionary, which offers considerable versatility. Equipped with a wavelet dictionary for example, our framework improves the state-of-the-art bound [@jacobsen2022parameter] by adapting to both ($i$) the magnitude of the comparator average ${\\left\\|{\\bar u}\\right\\|}={\\|{\\sum_{t=1}^Tu_t/T}\\|}$, rather than the maximum $\\max_t{\\left\\|{u_t}\\right\\|}$; and ($ii$) the comparator variability $\\sum_{t=1}^T{\\left\\|{u_t-\\bar u}\\right\\|}$, rather than the uncentered sum $\\sum_{t=1}^T{\\left\\|{u_t}\\right\\|}$. Furthermore, our analysis is simpler due to decoupling function approximation from regret minimization.\nauthor:\n- |\n    Zhiyu Zhang[^1]\\\n    Harvard University\\\n    `zhiyuz@seas.harvard.edu`\\\n- |\n    Ashok Cutkosky\\\n    Boston University\\\n    `ashok@cutkosky.com`\\\n- |\n    Ioannis Ch. Paschalidis\\\n    Boston University\\\n    `yannisp@bu.edu`\\\nbibliography:\n-"
+"---\nabstract: |\n    I study mechanism design with blockchain-based tokens, that is, tokens that can be used within a mechanism but can also be saved and traded outside of the mechanism. I do so by considering a repeated, private-value auction, in which the auctioneer accepts payments in a blockchain-based token he creates and initially owns. I show that the present-discounted value of the expected revenues is the same as in a standard auction with dollars, but these revenues accrue earlier and are less variable. The optimal monetary policy involves the burning of tokens used in the auction, a common feature of many blockchain-based auctions. I then introduce non-contractible effort and the possibility of misappropriating revenues. I compare the auction with tokens to an auction with dollars in which the auctioneer can also issue financial securities. An auction with tokens is preferred when there are sufficiently severe contracting frictions, while the opposite is true when contracting frictions are low.\\\n    **JEL classification**: D44, E42, L86\\\n    **Keywords**: Mechanism design, Auctions, Blockchain, Cryptocurrencies, Tokens, Private Money\nauthor:\n- 'Andrea Canidio [^1]'\nbibliography:\n- 'bibliography.bib'\ntitle: ' Auctions with Tokens: Monetary Policy as a Mechanism Design Choice.[^2] '\n---\n\nFirst version: September 30, 2021. This"
+"---\nauthor:\n- \n- \n- \n- \nbibliography:\n- 'sn-bibliography.bib'\ntitle: A Human Word Association based model for topic detection in social networks\n---\n\nWith the widespread use of social networks, detecting the topics discussed in these networks has become a significant challenge. The current works are mainly based on frequent pattern mining or semantic relations, and the language structure is not considered. The meaning of language structural methods is to discover the relationship between words and how humans understand them. Therefore, this paper uses the Concept of the Imitation of the Mental Ability of Word Association to propose a topic detection framework in social networks. This framework is based on the Human Word Association method. A special extraction algorithm has also been designed for this purpose. The performance of this method is evaluated on the FA-CUP dataset. It is a benchmark dataset in the field of topic detection. The results show that the proposed method is a good improvement compared to other methods, based on the Topic-recall and the keyword F1 measure. Also, most of the previous works in the field of topic detection are limited to the English language, and the Persian language, especially microblogs written in this language,"
+"---\nabstract: 'Existing algorithms for ensuring fairness in AI use a single-shot training strategy, where an AI model is trained on an annotated training dataset with sensitive attributes and then fielded for utilization. This training strategy is effective in problems with stationary distributions, where both training and testing data are drawn from the same distribution. However, it is vulnerable with respect to distributional shifts in the input space that may occur after the initial training phase. As a result, the time-dependent nature of data can introduce biases into the model predictions. Model retraining from scratch using a new annotated dataset is a naive solution that is expensive and time-consuming. We develop an algorithm to adapt a fair model to remain fair under domain shift using solely new unannotated data points. We recast this learning setting as an unsupervised domain adaptation problem. Our algorithm is based on updating the model such that the internal representation of data remains unbiased despite distributional shifts in the input space. We provide extensive empirical validation on three widely employed fairness datasets to demonstrate the effectiveness of our algorithm.'\nauthor:\n- 'Serban Stan and Mohammad Rostami University of Southern California {rostamim,sstan}@usc.edu'\n- First Author$^1$\n- Second"
+"---\nabstract: 'We consider optimal sensor placement for a family of linear Bayesian inverse problems characterized by a deterministic hyper-parameter. The hyper-parameter describes distinct configurations in which measurements can be taken of the observed physical system. To optimally reduce the uncertainty in the system\u2019s model with a single set of sensors, the initial sensor placement needs to account for the non-linear state changes of all admissible configurations. We address this requirement through an observability coefficient which links the posteriors\u2019 uncertainties directly to the choice of sensors. We propose a greedy sensor selection algorithm to iteratively improve the observability coefficient for all configurations through orthogonal matching pursuit. The algorithm allows explicitly correlated noise models even for large sets of candidate sensors, and remains computationally efficient for high-dimensional forward models through model order reduction. We demonstrate our approach on a large-scale geophysical model of the Perth Basin, and provide numerical studies regarding optimality and scalability with regard to classic optimal experimental design utility functions.'\naddress:\n- 'Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E 24th St, Austin, TX 78712, USA'\n- 'School of Computational Science and Engineering, Georgia Institute of Technology, 756 W Peachtree St NW,"
+"---\nabstract: 'Quantum batteries are quantum systems that store energy which can then be used for quantum tasks. One relevant question about such systems concerns the differences and eventual advantages over their classical counterparts, whether in the efficiency of the energy transference, input power, total stored energy or other relevant physical quantities. Here, we show how a purely quantum effect related to the vacuum of the electromagnetic field can enhance the charging of a quantum battery. In particular, we demonstrate how an anti-Jaynes Cummings interaction derived from an off-resonant Raman configuration can be used to increase the stored energy of an effective two-level atom when compared to its classically driven counterpart, eventually achieving full charging of the battery with zero entropic cost.'\nauthor:\n- 'Tiago F. F. Santos'\n- Yohan Vianna de Almeida\n- 'Marcelo F. Santos'\ntitle: Vacuum enhanced charging of a quantum battery\n---\n\nThe quest for advanced quantum technologies or the irreversible role of measurements in quantum dynamics are examples of subjects that have stimulated the study of thermodynamics in the microscopic world. An important recent topic of investigation involves the role played by quantum resources in the storage and use of energy by quantized systems\u00a0[@karen2013;"
+"---\nauthor:\n- |\n    Chenqi\u00a0Kong, Kexin\u00a0Zheng, Yibing\u00a0Liu, Shiqi\u00a0Wang,\u00a0,\\\n    Anderson\u00a0Rocha,\u00a0, and Haoliang\u00a0Li,\u00a0\nbibliography:\n- 'main.bib'\ntitle: 'M$^{3}$FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System'\n---\n\n[Shell : Bare Demo of IEEEtran.cls for Computer Society Journals]{}\n\nface recognition (AFR) systems have been prevalently deployed on mobile devices and play a vital role around the globe. It is reported that the market of AFR will reach USD 3.35B by 2024\u00a0[@AFR_market]. Despite AFR\u2019s extraordinary success, face presentation attacks (FPA), also known as face spoofing, have recently posed high-security risks over impersonation, financial fraud, and privacy leakage. 2D FPA, comprising photo print and video replay attacks, is the most detrimental and notorious attack type due to its low costs\u00a0[@patel2016secure]. Malicious attackers can easily launch it by accessing the target person\u2019s face images/videos on social media and presenting them to the target AFR systems. The abuse of 2D FPA will certainly lead to trust destruction and tangible concerns in the long run. Therefore, safeguarding AFR systems from 2D FPA and suppressing the pressing security concerns is of utmost importance.\n\n![Illustration of M$^3$FAS system. The mobile device employs the front camera to capture the input"
+"---\nabstract: 'Carrier relaxation measurements in moir\u00e9 materials offer a unique probe of the microscopic interactions, in particular the ones that are not easily measured by transport. Umklapp scattering between phonons is a ubiquitous momentum-nonconserving process that governs the thermal conductivity of semiconductors and insulators. In contrast, Umklapp scattering between electrons and phonons has not been demonstrated experimentally. Here, we study the cooling of hot electrons in moir\u00e9 graphene using time- and frequency-resolved photovoltage measurements as a direct probe of its complex energy pathways including electron-phonon coupling. We report on a dramatic speedup in hot carrier cooling of twisted bilayer graphene near the magic angle: the cooling time is a few picoseconds from room temperature down to 5 K, whereas in pristine graphene coupling to acoustic phonons takes nanoseconds. Our analysis indicates that this ultrafast cooling is a combined effect of the formation of a superlattice with low-energy moir\u00e9 phonons, spatially compressed electronic Wannier orbitals, and a reduced superlattice Brillouin zone, enabling Umklapp scattering that overcomes electron-phonon momentum mismatch. These results demonstrate a way to engineer electron-phonon coupling in twistronic systems, an approach that could contribute to the fundamental understanding of their transport properties and enable applications in thermal management"
+"---\nabstract: 'There is a long-standing question of whether it is possible to extend the formalism of equilibrium thermodynamics to the case of non-equilibrium systems in steady states. We have made such an extension for an ideal gas in a heat flow \\[Ho\u0142yst *et al.*, J. Chem. Phys. 157, 194108 (2022)\\]. Here we investigate whether such a description exists for the system with interactions: the Van der Waals gas in a heat flow. We introduce the parameters of state, each associated with a single way of changing energy. The first law of non-equilibrium thermodynamics follows from these parameters. The internal energy $U$ for the non-equilibrium states has the same form as in equilibrium thermodynamics. For the Van der Waals gas, $U(S^*, V, N, a^*,b^* )$ is a function of only 5 parameters of state (irrespective of the number of parameters characterizing the boundary conditions): the entropy $S^*$, volume $V$, number of particles $N$, and the rescaled Van der Waals parameters $a^*$, $b^*$. The state parameters, $a^*$, $b^*$, together with $S^*$, determine the net heat exchange with the environment.'\nauthor:\n- Robert Ho\u0142yst\n- Karol Makuch\n- Konrad Gi\u017cy\u0144ski\n- Anna Macio\u0142ek\n- 'Pawe\u0142 J. \u017buk'\ntitle: Steady thermodynamic fundamental relation"
+"---\nabstract: 'We generate anti-self-polar polytopes via a numerical implementation of the gradient flow induced by the diameter functional on the space of all finite subsets of the sphere, and prove related results on the critical points of the diameter functional as well as results about the combinatorics of such polytopes. We also discuss potential connections to Borsuk\u2019s conjecture.'\naddress:\n- 'Bar Ilan University.'\n- 'The Ohio State University.'\n- 'University of Utah.'\nauthor:\n- Mikhail Katz\n- Facundo M\u00e9moli\n- Qingsong Wang\ntitle: 'Extremal spherical polytopes and Borsuk\u2019s conjecture'\n---\n\nIntroduction\n============\n\nLet $(X, d_X)$ be a metric space. The *Kuratowski embedding* $x\n\\mapsto d_X(x, \\cdot)$ is an embedding of $X$ into $L^{\\infty}(X)$, the space of all bounded real-valued functions on $X$ with the uniform norm. When $X$ is the unit sphere with its geodesic distance, the homotopy types of the $r$-neighborhoods $B_r(X, L^{\\infty}(X))$ in the Kuratowski embedding of $X$ were studied by Katz in\u00a0[@katz1991neighborhoods]. The values at which the homotopy type changes are closely related to the critical configurations of the diameter functional ${\\mathrm{diam}}$ of $X$ which maps a finite subset $A$ of $X$ to ${\\mathrm{diam}}(A):=\\max_{a,a'\\in A}d_X(a,a')$. When $X$ is the unit circle, such critical values turn"
+"---\nabstract: 'Image denoising is a typical ill-posed problem due to complex degradation. Leading methods based on normalizing flows have tried to solve this problem with an invertible transformation instead of a deterministic mapping. However, the implicit bijective mapping is not explored well. Inspired by a latent observation that noise tends to appear in the high-frequency part of the image, we propose a fully invertible denoising method that injects the idea of disentangled learning into a general invertible neural network to split noise from the high-frequency part. More specifically, we decompose the noisy image into clean low-frequency and hybrid high-frequency parts with an invertible transformation and then disentangle case-specific noise and high-frequency components in the latent space. In this way, denoising is made tractable by inversely merging noiseless low and high-frequency parts. Furthermore, we construct a flexible hierarchical disentangling framework, which aims to decompose most of the low-frequency image information while disentangling noise from the high-frequency part in a coarse-to-fine manner. Extensive experiments on real image denoising, JPEG compressed artifact removal, and medical low-dose CT image restoration have demonstrated that the proposed method achieves competing performance on both quantitative metrics and visual quality, with significantly less computational cost.'\nauthor:\n-"
+"---\nabstract: 'We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology. In contrast to formal mathematics, where large databases of formal proofs are available (e.g., the Lean Mathematical Library), current datasets of natural-language mathematics, used to benchmark language models, either cover only elementary mathematics or are very small. We address this by publicly releasing two new datasets: GHOSTS and miniGHOSTS. These are the first natural-language datasets curated by working researchers in mathematics that (1) aim to cover graduate-level mathematics, (2) provide a holistic overview of the mathematical capabilities of language models, and (3) distinguish multiple dimensions of mathematical reasoning. These datasets also test whether ChatGPT and GPT-4 can be helpful assistants to professional mathematicians by emulating use cases that arise in the daily professional activities of mathematicians. We benchmark the models on a range of fine-grained performance metrics. For advanced mathematics, this is the most detailed evaluation effort to date. We find that ChatGPT can be used most successfully as a mathematical assistant for querying facts, acting as a mathematical search engine and knowledge base interface."
+"---\nabstract: 'Ensemble methods can deliver surprising performance gains but also bring significantly higher computational costs, e.g., can be up to 2048X in large-scale ensemble tasks. However, we found that the majority of computations in ensemble methods are redundant. For instance, over 77% of samples in CIFAR-100 dataset can be correctly classified with only a single ResNet-18 model, which indicates that only around 23% of the samples need an ensemble of extra models. To this end, we propose an inference efficient ensemble learning method, to simultaneously optimize for effectiveness and efficiency in ensemble learning. More specifically, we regard ensemble of models as a sequential inference process and learn the optimal halting event for inference on a specific sample. At each timestep of the inference process, a common selector judges if the current ensemble has reached ensemble effectiveness and halt further inference, otherwise filters this challenging sample for the subsequent models to conduct more powerful ensemble. Both the base models and common selector are jointly optimized to dynamically adjust ensemble inference for different samples with various hardness, through the novel optimization goals including sequential ensemble boosting and computation saving. The experiments with different backbones on real-world datasets illustrate our method can"
+"---\nabstract: 'Physics-informed neural networks (PINNs) have been widely used to solve partial differential equations in a forward and inverse manner using deep neural networks. However, training these networks can be challenging for multiscale problems. While statistical methods can be employed to scale the regression loss on data, it is generally challenging to scale the loss terms for equations. This paper proposes a method for scaling the mean squared loss terms in the objective function used to train PINNs. Instead of using automatic differentiation to calculate the temporal derivative, we use backward Euler discretization. This provides us with a scaling term for the equations. In this work, we consider the two and three-dimensional Navier-Stokes equations and determine the kinematic viscosity using the spatio-temporal data on the velocity and pressure fields. We first consider numerical datasets to test our method. We test the sensitivity of our method to the time step size, the number of timesteps, noise in the data, and spatial resolution. Finally, we use the velocity field obtained using Particle Image Velocimetry (PIV) experiments to generate a reference pressure field. We then test our framework using the velocity and reference pressure field.'\nauthor:\n- Sukirt Thakur\n- Maziar Raissi"
+"---\nabstract: 'The flipped classroom is a new pedagogical strategy that has been gaining increasing importance recently. Spoken discussion dialog commonly occurs in flipped classroom, which embeds rich information indicating processes and progression of students\u2019 learning. This study focuses on learning analytics from spoken discussion dialog in the flipped classroom, which aims to collect and analyze the discussion dialogs in flipped classroom in order to get to know group learning processes and outcomes. We have recently transformed a course using the flipped classroom strategy, where students watched video-recorded lectures at home prior to group-based problem-solving discussions in class. The in-class group discussions were recorded throughout the semester and then transcribed manually. After features are extracted from the dialogs by multiple tools and customized processing techniques, we performed statistical analyses to explore the indicators that are related to the group learning outcomes from face-to-face discussion dialogs in the flipped classroom. Then, machine learning algorithms are applied to the indicators in order to predict the group learning outcome as High, Mid or Low. The best prediction accuracy reaches 78.9%, which demonstrates the feasibility of achieving automatic learning outcome prediction from group discussion dialog in flipped classroom.'\nauthor:\n- 'Hang\u00a0Su, Borislav\u00a0Dzodzo,"
+"---\nabstract: 'Previous work suggests that performance of cross-lingual information retrieval correlates highly with the quality of Machine Translation. However, there may be a threshold beyond which improving query translation quality yields little or no benefit to further improve the retrieval performance. This threshold may depend upon multiple factors including the source and target languages, the existing MT system quality and the search pipeline. In order to identify the benefit of improving an MT system for a given search pipeline, we investigate the sensitivity of retrieval quality to the presence of different levels of MT quality using experimental datasets collected from actual traffic. We systematically improve the performance of our MT systems quality on language pairs as measured by MT evaluation metrics including Bleu and Chrf to determine their impact on search precision metrics and extract signals that help to guide the improvement strategies. Using this information we develop techniques to compare query translations for multiple language pairs and identify the most promising language pairs to invest and improve.'\nauthor:\n- |\n    Bryan Hang Zhang\\\n    Amazon.com\\\n    `bryzhang@amazon.com`\\\n    Amita Misra\\\n    Amazon.com\\\n    `misrami@amazon.com`\\\nbibliography:\n- 'anthology.bib'\n- 'others.bib'\n- 'acl2021.bib'\n- 'reference.bib'\ntitle: 'Machine Translation Impact in E-commerce Multilingual Search '\n---"
+"---\nabstract: 'The minimum completion (fill-in) problem is defined as follows: Given a graph family\u00a0$\\mathcal{F}$ (more generally, a property\u00a0$\\Pi$) and a graph\u00a0$G$, the completion problem asks for the minimum number of non-edges needed to be added to $G$ so that the resulting graph belongs to the graph family\u00a0$\\mathcal{F}$ (or has property\u00a0$\\Pi$). This problem is NP-complete for many subclasses of perfect graphs and polynomial solutions are available only for minimal completion sets. We study the minimum completion problem of a $P_4$-sparse graph\u00a0$G$ with an added edge. For any optimal solution of the problem, we prove that there is an optimal solution whose form is of one of a small number of possibilities. This along with the solution of the problem when the added edge connects two non-adjacent vertices of a spider or connects two vertices in different connected components of the graph enables us to present a polynomial-time algorithm for the problem.'\nauthor:\n- Anna Mpanti\n- 'Stavros D. Nikolopoulos'\n- Leonidas Palios\ntitle: 'Adding an Edge in a $P_4$-sparse Graph [^1] '\n---\n\nIntroduction\n============\n\nOne instance of the general (${\\cal C},+k$)-MinEdgeAddition problem [@NP05] is the ($P_4$-sparse,[$+$]{}$1$)-MinEdgeAddition Problem. In this problem, we add $1$"
+"---\nabstract: 'We look at a stochastic time-varying optimization problem and we formulate online algorithms to find and track its optimizers in expectation. The algorithms are derived from the intuition that standard prediction and correction steps can be seen as a nonlinear dynamical system and a measurement equation, respectively, yielding the notion of nonlinear filter design. The optimization algorithms are then based on an extended Kalman filter in the unconstrained case, and on a bilinear matrix inequality condition in the constrained case. Some special cases and variations are discussed, notably the case of parametric filters, yielding certificates based on LPV analysis and, if one wishes, matrix sum-of-squares relaxations. Supporting numerical results are presented from real data sets in ride-hailing scenarios. The results are encouraging, especially when predictions are accurate, a case which is often encountered in practice when historical data is abundant.'\nauthor:\n- 'Andrea Simonetto [^1]Paolo Massioni [^2]'\nbibliography:\n- 'PaperCollection00.bib'\ntitle: 'Nonlinear Optimization Filters for Stochastic Time-Varying Convex Optimization'\n---\n\nIntroduction {#sec1}\n============\n\nWe look at time-varying optimization problems of the form $$~\\label{eq:tv}\n\\min_{\\x \\in {\\mathbb R}^n} f(\\x; \\y(t)) + g(\\x),\\qquad t\\geq 0,$$ where $f: {\\mathbb R}^n \\times {\\mathbb R}^d \\to {\\mathbb R}$ is a smooth strongly convex"
+"---\nabstract: 'Dantzig-Wolfe (DW) decomposition is a well-known technique in mixed-integer programming (MIP) for decomposing and convexifying constraints to obtain potentially strong dual bounds. We investigate cutting planes that can be derived using the DW decomposition algorithm and show that these cuts can provide the same dual bounds as DW decomposition. More precisely, we generate one cut for each DW block, and when combined with the constraints in the original formulation, these cuts imply the objective function cut one can simply write using the DW bound. This approach typically leads to a formulation with lower dual degeneracy that consequently has a better computational performance when solved by standard MIP solvers in the original space. We also discuss how to strengthen these cuts to improve the computational performance further. We test our approach on the Multiple Knapsack Assignment Problem and the Temporal Knapsack Problem, and show that the proposed cuts are helpful in accelerating the solution time without the need to implement branch and price.'\nauthor:\n- |\n    Rui Chen$^1$, Oktay G\u00fcnl\u00fck$^2$, Andrea Lodi$^1$\\\n    $^1$ Cornell Tech, Cornell University ({rui.chen,andrea.lodi}@cornell.edu)\\\n    $^2$ School of ORIE, Cornell University (oktay.gunluk@cornell.edu)\nbibliography:\n- 'ref.bib'\ntitle: 'Recovering Dantzig-Wolfe Bounds by Cutting Planes'\n---\n\nIntroduction\n============\n\nIn"
+"---\nabstract: 'The presence of Galactic cirrus is an obstacle for studying both faint objects in our Galaxy and low surface brightness extragalactic structures. With the aim of studying individual cirrus filaments in SDSS Stripe\u00a082 data, we develop techniques based on machine learning and neural networks that allow one to isolate filaments from foreground and background sources in the entirety of Stripe\u00a082 with a precision similar to that of the human expert. Our photometric study of individual filaments indicates that only those brighter than 26 mag arcsec$^{-2}$ in the SDSS $r$ band are likely to be identified in SDSS Stripe\u00a082 data by their distinctive colours in the optical bands. We also show a significant impact of data processing (e.g. flat-fielding, masking of bright stars, and sky subtraction) on colour estimation. . Our work provides a useful framework for an analysis of all types of low surface brightness features (cirri, tidal tails, stellar streams, etc.) in existing and future deep optical surveys. For practical purposes, we provide the catalogue of dust filaments.'\nauthor:\n- |\n    Anton\u00a0A.\u00a0Smirnov,$^{1,2}$[^1] Sergey\u00a0S.\u00a0Savchenko,$^{1,2,5}$ Denis\u00a0M.\u00a0Poliakov$^{1,2}$ Alexander A. Marchuk,$^{1,2}$ Aleksandr\u00a0V.\u00a0Mosenkov,$^{3,1}$ Vladimir\u00a0B.\u00a0Il\u2019in,$^{1,2,4}$ George\u00a0A.\u00a0Gontcharov,$^{1}$ Javier\u00a0Rom[\u00e1]{}n,$^{6,7,8}$"
+"---\nabstract: 'Supernova (SN) plays an important role in galaxy formation and evolution. In high-resolution galaxy simulations using massively parallel computing, short integration timesteps for SNe are serious bottlenecks. This is an urgent issue that needs to be resolved for future higher-resolution galaxy simulations. One possible solution would be to use the Hamiltonian splitting method, in which regions requiring short timesteps are integrated separately from the entire system. To apply this method to the particles affected by SNe in a smoothed-particle hydrodynamics simulation, we need to detect the shape of the shell on and within which such SN-affected particles reside during the subsequent global step in advance. In this paper, we develop a deep learning model, 3D-MIM, to predict a shell expansion after a SN explosion. Trained on turbulent cloud simulations with particle mass $m_{\\rm gas}=1$ M$_\\odot$, the model accurately reproduces the anisotropic shell shape, where densities decrease by over 10 per cent by the explosion. We also demonstrate that the model properly predicts the shell radius in the uniform medium beyond the training dataset of inhomogeneous turbulent clouds. We conclude that our model enables the forecast of the shell and its interior where SN-affected particles will be present.'\nauthor:"
+"---\ntitle: |\n    **Tachyons and Misaligned Supersymmetry in Closed String Vacua**\n\n    \\\n---\n\n<span style=\"font-variant:small-caps;\">Abstract</span>\\\n\nIn a remarkable paper, Dienes discovered that the absence of physical tachyons in closed string theory is intimately related to oscillations in the net number of bosonic minus fermionic degrees of freedom, a pattern predicted by an underlying misaligned supersymmetry. The average of these oscillations was linked to an exponential growth controlled by an effective central charge $C_\\text{eff}$ smaller than the expected inverse Hagedorn temperature. Dienes also conjectured that $C_\\text{eff}$ should vanish when tachyons are absent.\n\nIn this paper, we revisit this problem and show that boson-fermion oscillations are realised even when tachyons are present in the physical spectrum. In fact, we prove that the average growth rate $C_\\text{eff}$ is set by the mass of the \u201clightest\u201d state, be it massless or tachyonic, and coincides with the effective inverse Hagedorn temperature of the associated thermal theory. We also provide a general proof that the necessary and sufficient condition for classical stability is the vanishing of the sector averaged sum which implies $C_\\text{eff} =0$, in agreement with Dienes\u2019 conjecture.\n\n-   [carlo.angelantonj@unito.it]{}\\\n    [iflorakis@uoi.gr]{}\\\n    [giorgio.leone@unito.it]{}\n\nIntroduction\n============\n\nSuperstring vacua are typically unstable when space-time supersymmetry is absent. The"
+"---\naddress: '$^{1}$ Max Planck Institute for the Physics of Complex Systems, Dresden; crt@pks.mpg.de\\'\n---\n\nIntroduction\n============\n\nOne of the central areas of study in quantum chaos is that of the spectral statistics of quantum chaotic systems and how they relate to classical chaos and random matrix theory (RMT) [@StoeBook; @HaakeBook]. The spectral form factor (SFF) is one of the most widely used spectral statistics, due to the stark contrast in behaviour between the chaotic and integrable regimes. However, the SFF is not a self averaging quantity [@prange1997spectral], meaning that the typical value at may be far from the average value. Because of this, its numerical computation remains challenging, and its practical evaluation requires some sort of smoothing procedure, either by computing disorder averages (only possible when considering systems with disorder) or local time averages. Nevertheless, the SFF has been used as the fundamental indicator of quantum chaos in many of the central rigorous results. A heuristic proof of the quantum chaos (Bohigas-Giannoni-Schmit) conjecture [@BGS; @CVG], that was initiated by Berry [@berry1985semiclassical] developed by Sieber and Richter [@Sieber2001] and later completed by the group of Haake\u00a0[@Mueller2004a; @Mueller2004b; @Mueller2005] clearly relates random matrix spectral correlations to correlations among classical unstable"
+"---\nabstract: 'According to recent new definitions, a multi-party behavior is *genuinely multipartite nonlocal* (GMNL) if it cannot be modeled by measurements on an underlying network of bipartite-only nonlocal resources, possibly supplemented with local (classical) resources shared by all parties. The new definitions differ on whether to allow entangled measurements upon, and/or superquantum behaviors among, the underlying bipartite resources. Here, we categorize the full hierarchy of these new candidate definitions of GMNL in three-party quantum networks, highlighting the intimate link to device-independent witnesses of network effects. A key finding is the existence of a behavior in the simplest nontrivial multi-partite measurement scenario (3 parties, 2 measurement settings, and 2 outcomes) that cannot be simulated in a bipartite network prohibiting entangled measurements and superquantum resources \u2013 thus witnessing the most general form of GMNL \u2013 but can be simulated with bipartite-only quantum states *with* an entangled measurement, indicating an approach to device independent certification of entangled measurements with fewer settings than in previous protocols. Surprisingly, we also find that this (3,2,2) behavior, as well as the others previously studied as device-independent witnesses of entangled measurements, can all be simulated at a higher echelon of the GMNL hierarchy that allows superquantum bipartite"
+"---\nabstract: 'We study a one-dimensional sluggish random walk with space-dependent transition probabilities between nearest-neighbour lattice sites. Motivated by trap models of slow dynamics, we consider a model in which the trap depth increases logarithmically with distance from the origin. This leads to a random walk which has symmetric transition probabilities that decrease with distance $|k|$ from the origin as $1/|k|$ for large $|k|$. We show that the typical position after time $t$ scales as $t^{1/3}$ with a nontrivial scaling function for the position distribution which has a trough (a cusp singularity) at the origin. even though the transition probabilities are symmetric. We also compute the survival probability of the walker in the presence of a sink at the origin and show that it decays as $t^{-1/3}$ at late times. Furthermore we compute the distribution of the maximum position, $M(t)$, to the right of the origin up to time $t$, and show that it has a nontrivial scaling function. Finally we provide a generalisation of this model where the transition probabilities decay as $1/|k|^\\alpha$ with $\\alpha >0$.'\naddress:\n- '$^1$ Department of Physics, Indian Institute of Science Education and Research, Dr. Homi Bhabha Road, Pune 411008, India'\n- '$^2$ Department"
+"---\nabstract: |\n    **Background** Alzheimer\u2019s disease and related dementia (ADRD) are characterized by multiple and progressive anatomo-clinical changes including accumulation of abnormal proteins in the brain, brain atrophy and severe cognitive impairment. Understanding the sequence and timing of these changes is of primary importance to gain insight into the disease natural history and ultimately allow earlier diagnosis. Yet, modeling changes over disease course from cohort data is challenging as the usual timescales (time since inclusion, chronological age) are inappropriate and time-to-clinical diagnosis is available on small subsamples of participants with short follow-up durations prior to diagnosis. One solution to circumvent this challenge is to define the disease time as a latent variable.\n\n    **Methods** We developed a multivariate mixed model approach that realigns individual trajectories into the latent disease time to describe disease progression. In contrast with the existing literature, our methodology exploits the clinical diagnosis information as a partially observed and approximate reference to guide the estimation of the latent disease time. The model estimation was carried out in the Bayesian Framework using Stan. We applied the methodology to the MEMENTO study, a French multicentric clinic-based cohort of 2186 participants with 5-year intensive follow-up. Repeated measures of 12 ADRD markers"
+"---\nabstract: 'We investigate policy transfer using image-to-semantics translation to mitigate learning difficulties in vision-based robotics control agents. This problem assumes two environments: a simulator environment with semantics, that is, low-dimensional and essential information, as the state space, and a real-world environment with images as the state space. By learning mapping from images to semantics, we can transfer a policy, pre-trained in the simulator, to the real world, thereby eliminating real-world on-policy agent interactions to learn, which are costly and risky. In addition, using image-to-semantics mapping is advantageous in terms of the computational efficiency to train the policy and the interpretability of the obtained policy over other types of sim-to-real transfer strategies. To tackle the main difficulty in learning image-to-semantics mapping, namely the human annotation cost for producing a training dataset, we propose two techniques: pair augmentation with the transition function in the simulator environment and active learning. We observed a reduction in the annotation cost without a decline in the performance of the transfer, and the proposed approach outperformed the existing approach without annotation.'\nauthor:\n- \nbibliography:\n- 'rl.bib'\ntitle: 'Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning [^1] '\n---\n\ndeep reinforcement learning, policy transfer, sim-to-real\n\nIntroduction"
+"---\nabstract: 'We present a comprehensive study of the temperature and magnetic-field dependent photoluminescence (PL) of individual NV centers in diamond, spanning the temperature-range from cryogenic to ambient conditions. We directly observe the emergence of the NV\u2019s room-temperature effective excited state structure and provide a clear explanation for a previously poorly understood broad quenching of NV PL at intermediate temperatures around $50~$K. We develop a model that quantitatively explains all of our findings, including the strong impact that strain has on the temperature-dependence of the NV\u2019s PL. These results complete our understanding of orbital averaging in the NV excited state and have significant implications for the fundamental understanding of the NV center and its applications in quantum sensing.'\nauthor:\n- Jodok Happacher\n- Juanita Bocquel\n- 'Hossein T. Dinani'\n- 'M\u00e4rta A. Tschudin'\n- Patrick Reiser\n- 'David A. Broadway'\n- 'Jeronimo R. Maze'\n- Patrick Maletinsky\nbibliography:\n- 'Bibliography\\_NV\\_Temperature\\_Dependence.bib'\ntitle: Temperature Dependent Photophysics of Single NV Centers in Diamond\n---\n\nColor centers in solid state hosts are crucial for a variety of quantum technologies, including spin-based quantum sensors[@Degen2017a], highly stable fluorescent labels[@Alkahtani2018a], and single-photon light sources for advanced microscopy[@Nelz2020a]. Among the many potential systems, the nitrogen vacancy (NV) lattice"
+"---\nabstract: 'Facial expression recognition (FER) plays an important role in a variety of real-world applications such as human-computer interaction. POSTER achieves the state-of-the-art (SOTA) performance in FER by effectively combining facial landmark and image features through two-stream pyramid cross-fusion design. However, the architecture of POSTER is undoubtedly complex. It causes expensive computational costs. In order to relieve the computational pressure of POSTER, in this paper, we propose POSTER++. It improves POSTER in three directions: cross-fusion, two-stream, and multi-scale feature extraction. In cross-fusion, we use window-based cross-attention mechanism replacing vanilla cross-attention mechanism. We remove the image-to-landmark branch in the two-stream design. For multi-scale feature extraction, POSTER++ combines images with landmark\u2019s multi-scale features to replace POSTER\u2019s pyramid design. Extensive experiments on several standard datasets show that our POSTER++ achieves the SOTA FER performance with the minimum computational cost. For example, POSTER++ reached 92.21% on RAF-DB, 67.49% on AffectNet (7 cls) and 63.77% on AffectNet (8 cls), respectively, using only 8.4G floating point operations (FLOPs) and 43.7M parameters (Param). This demonstrates the effectiveness of our improvements.'\nauthor:\n- |\n    Jiawei Mao$^\\dag$ Rui Xu$^\\dag$ Xuesong Yin[[^1]]{} Yuanqi Chang Binling Nie Aibin Huang$^*$\\\n    School of Media and Design, Hangzhou Dianzi University, Hangzhou, China\\\n    [{jiaweima0,211330017,yinxs,yuanqichang,binlingnie,huangaibin}@hdu.edu.cn"
+"---\nabstract: |\n    User interaction data in recommender systems is a form of dyadic relation that reflects the preferences of users with items. Learning the representations of these two discrete sets of objects, users and items, is critical for recommendation. Recent multimodal recommendation models leveraging multimodal features ([*e.g.*,]{}images and text descriptions) have been demonstrated to be effective in improving recommendation accuracy. However, state-of-the-art models enhance the dyadic relations between users and items by considering either user-user or item-item relations, leaving the high-order relations of the other side ([*i.e.*,]{}users or items) unexplored. Furthermore, we experimentally reveal that the current multimodality fusion methods in the state-of-the-art models may degrade their recommendation performance. That is, without tainting the model architectures, these models can achieve even better recommendation accuracy with uni-modal information. On top of the finding, we propose a model that enhances the dyadic relations by learning ual epresenttions of both users and items via constructing homogeneous raphs for multimdal recommedation. We name our model as [`DRAGON`]{}. Specifically, [`DRAGON`]{}constructs the user-user graph based on the commonly interacted items and the item-item graph from item multimodal features. It then utilizes graph learning on both the user-item heterogeneous graph and the homogeneous graphs (user-user and"
+"---\nabstract: 'Culture shapes people\u2019s behavior, both online and offline. Surprisingly, there is sparse research on how cultural context affects network formation and content consumption on social media. We analyzed the friendship networks and dyadic relations between content producers and consumers across 73 countries through a cultural lens in a closed-network setting. Closed networks allow for intimate bonds and self-expression, providing a natural setting to study cultural differences in behavior. We studied three theoretical frameworks of culture - individualism, relational mobility, and tightness. We found that friendship networks formed across different cultures differ in egocentricity, meaning the connectedness between a user\u2019s friends. Individualism, mobility, and looseness also significantly negatively impact how tie strength affects content consumption. Our findings show how culture affects social media behavior, and we outline how researchers can incorporate this in their work. Our work has implications for content recommendations and can improve content engagement.'\nauthor:\n- Agrima Seth\n- Jiyin Cao\n- Xiaolin Shi\n- Ron Dotsch\n- Yozen Liu\n- 'Maarten W. Bos'\nbibliography:\n- 'sample-base.bib'\ntitle: 'Cultural Differences in Friendship Network Behaviors: A Snapchat Case Study'\n---\n\n&lt;ccs2012&gt; &lt;concept&gt; &lt;concept\\_id&gt;10003120.10003130.10003131.10003292&lt;/concept\\_id&gt; &lt;concept\\_desc&gt;Human-centered computing\u00a0Social networks&lt;/concept\\_desc&gt; &lt;concept\\_significance&gt;500&lt;/concept\\_significance&gt; &lt;/concept&gt; &lt;concept&gt; &lt;concept\\_id&gt;10003120.10003130.10003131.10011761&lt;/concept\\_id&gt; &lt;concept\\_desc&gt;Human-centered computing\u00a0Social media&lt;/concept\\_desc&gt; &lt;concept\\_significance&gt;300&lt;/concept\\_significance&gt; &lt;/concept&gt;"
+"---\nabstract: 'Graph-structured data can be found in numerous domains, yet the scarcity of labeled instances hinders its effective utilization of deep learning in many scenarios. Traditional unsupervised domain adaptation (UDA) strategies for graphs primarily hinge on adversarial learning and pseudo-labeling. These approaches fail to effectively leverage graph discriminative features, leading to class mismatching and unreliable label quality. To navigate these obstacles, we develop the Denoising and Nuclear-Norm Wasserstein Adaptation Network (DNAN). DNAN employs the Nuclear-norm Wasserstein discrepancy (NWD), which can simultaneously achieve domain alignment and class distinguishment. DANA also integrates a denoising mechanism via a variational graph autoencoder that mitigates data noise. This denoising mechanism helps capture essential features of both source and target domains, improving the robustness of the domain adaptation process. Our comprehensive experiments demonstrate that DNAN outperforms state-of-the-art methods on standard UDA benchmarks for graph classification.'\nauthor:\n- |\n    Mengxi Wu mengxiwu@usc.edu\\\n    USC Computer Science Department\\\n    Mohammad Rostami rostamim@usu.edu\\\n    USC Computer Science Department\nbibliography:\n- 'main.bib'\ntitle: 'Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data'\n---\n\nIntroduction\n============\n\nWhile deep learning has made substantial progress in handling graph-structured data, it shares a substantial drawback with other methods in the same"
+"---\nabstract: 'This note modifies the reference encoding of Turing machines in the $\\l$-calculus by Dal Lago and Accattoli [@DBLP:journals/corr/abs-1711-10078], which is tuned for time efficiency, as to accommodate logarithmic space. There are two main changes: Turing machines now have *two* tapes, an input tape and a work tape, and the input tape is encoded differently, because the reference encoding comes with a linear space overhead for managing tapes, which is excessive for studying logarithmic space.'\nauthor:\n- Beniamino Accattoli\n- Ugo Dal Lago\n- Gabriele Vanoni\nbibliography:\n- 'main.bib'\ntitle: |\n    A Log-Sensitive Encoding of\\\n    Turing Machines in the $\\l$-Calculus\n---\n\nIntroduction\n============\n\nThis note presents a new encoding of Turing machines into the $\\l$-calculus and and proves its correctness. It is based over Dal Lago and Accattoli\u2019s reference encoding of single tape Turing machines [@DBLP:journals/corr/abs-1711-10078]. The new encoding is tuned for studying logarithmic space complexity even though such a study is not carried out here but in a companion paper. The aim of this note is to provide the formal definition of the encoding and the tedious calculations to prove its correctness.\n\nThe key points of the new encoding with respect to the reference one are:\n\n-   *Log-sensitivity*:"
+"---\nabstract: 'The instability of a cryogenic $^4$He jet exiting through a small nozzle into vacuum leads to the formation of $^4$He drops which are considered as ideal matrices for spectroscopic studies of embedded atoms and molecules. Here, we present a He-DFT description of droplet formation resulting from jet breaking and contraction of superfluid $^4$He filaments. Whereas the fragmentation of long jets closely follows the predictions of linear theory for inviscid fluids, leading to droplet trains interspersed with smaller satellite droplets, the contraction of filaments with an aspect ratio larger than a threshold value leads to the nucleation of vortex rings which hinder their breakup into droplets.'\nauthor:\n- Francesco Ancilotto\n- Manuel Barranco\n- Mart\u00ed Pi\ntitle: |\n    Nanoscopic jets and filaments of superfluid $^4$He at zero temperature:\\\n    a DFT study\n---\n\nIntroduction\n============\n\nLiquid $^4$He droplets at low temperature offer a unique environment for molecular spectroscopy[@Leh98; @Cho06; @Cal11] and the study of superfluidity on the atomic scale,[@Sin89; @Kri90; @Gre98] including the study of quantum vortices.[@Gom14; @Lan18; @Ges19; @Oco20] Usually, $^4$He droplets are produced by expansion of cooled $^4$He gas or by instability of a cryogenic $^4$He jet exiting a source chamber into vacuum throughout a nozzle, whose temperature"
+"---\nabstract: 'Cross-modality data translation has attracted great interest in image computing. Deep generative models (*e.g.*, GANs) show performance improvement in tackling those problems. Nevertheless, as a fundamental challenge in image translation, the problem of Zero-shot-Learning Cross-Modality Data Translation with fidelity remains unanswered. This paper proposes a new unsupervised zero-shot-learning method named Mutual Information guided Diffusion cross-modality data translation Model (MIDiffusion), which learns to translate the unseen source data to the target domain. The MIDiffusion leverages a score-matching-based generative model, which learns the prior knowledge in the target domain. We propose a differentiable local-wise-MI-Layer ($LMI$) for conditioning the iterative denoising sampling. The $LMI$ captures the identical cross-modality features in the statistical domain for the diffusion guidance; thus, our method does not require retraining when the source domain is changed, as it does not rely on any direct mapping between the source and target domains. This advantage is critical for applying cross-modality data translation methods in practice, as a reasonable amount of source domain dataset is not always available for supervised training. We empirically show the advanced performance of MIDiffusion in comparison with an influential group of generative models, including adversarial-based and other score-matching-based models.'\nauthor:\n- |\n    Zihao Wang$^1$[^1], Yingyu"
+"---\nabstract: 'A new variant of Newton\u2019s method for empirical risk minimization is studied, where at each iteration of the optimization algorithm, the gradient and Hessian of the objective function are replaced by robust estimators taken from existing literature on robust mean estimation for multivariate data. After proving a general theorem about the convergence of successive iterates to a small ball around the population-level minimizer, consequences of the theory in generalized linear models are studied when data are generated from Huber\u2019s epsilon-contamination model and/or heavy-tailed distributions. An algorithm for obtaining robust Newton directions based on the conjugate gradient method is also proposed, which may be more appropriate for high-dimensional settings, and conjectures about the convergence of the resulting algorithm are offered. Compared to robust gradient descent, the proposed algorithm enjoys the faster rates of convergence for successive iterates often achieved by second-order algorithms for convex problems, i.e., quadratic convergence in a neighborhood of the optimum, with a stepsize that may be chosen adaptively via backtracking linesearch.'\nauthor:\n- Eirini Ioannou\n- Muni Sreenivas Pydi\n- 'Po-Ling Loh'\nbibliography:\n- 'refs.bib'\ntitle: 'Robust empirical risk minimization via Newton\u2019s method'\n---\n\nIntroduction\n============\n\nStatistical estimation via classical procedures often depends on strong"
+"---\nabstract: 'Percolation theory shows that removing a small fraction of critical nodes can lead to the disintegration of a large network into many disconnected tiny subnetworks. The *network dismantling* task focuses on how to efficiently select the least such critical nodes. Most existing approaches focus on measuring nodes\u2019 importance from either functional or topological viewpoint. Different from theirs, we argue that nodes\u2019 importance can be measured from both of the two complementary aspects: The functional importance can be based on the nodes\u2019 competence in relaying network information; While the topological importance can be measured from nodes\u2019 regional structural patterns. In this paper, we propose an unsupervised learning framework for network dismantling, called DCRS, which encodes and fuses both node iffusion ompetence and ole ignificance. Specifically, we propose a graph diffusion neural network which emulates information diffusion for competence encoding; We divide nodes with similar egonet structural patterns into a few roles, and construct a role graph on which to encode node role significance. The DCRS converts and fuses the two encodings to output a final ranking score for selecting critical nodes. Experiments on both real-world networks and synthetic networks demonstrate that our scheme significantly outperforms the state-of-the-art competitors for"
+"---\nauthor:\n- 'Devojyoti Kansabanik,$^{1}$ Surajit Mondal$^{2}$, Divya Oberoi $^1$, Puja Majee $^1$'\nbibliography:\n- 'example.bib'\ntitle: 'Space Weather Research using Spectropolarimetric Radio Imaging Combined With Aditya-L1 and PUNCH Missions'\n---\n\nIntroduction\n============\n\nThe space weather around the Earth is determined by the Sun. The most important phenomenon determining the space weather is coronal mass ejection (CME). CMEs are large-scale eruptions of magnetized plasma from the Sun into the heliosphere. It is well-established that the magnetic fields play important roles in their propagation and determining their geo-effectiveness. While propagating CMEs interact with other heliospheric components like solar wind, co-rotating interaction regions, and stream interaction regions and change their propagation direction and magnetic field topology [@Manchester2017]. These deformations complicate the prediction of CME arrival times or the $B_\\mathrm{z}$ component of the magnetic field at 1 AU. Hence, tracking and measuring the magnetic fields of a CME as it propagates from the corona into the heliosphere, is essential for improving space-weather forecasting.\n\nThere are several state-of-the-art CME models [@Isavnin_2016; @M\u00f6stl2018] developed over the last few years to incorporate these deformations of CMEs into account. These models have multiple independent parameters. One needs to constrain these model parameters of a CME during its"
+"---\nabstract: 'One of the alternative theories of gravitation with a possible UV completion of general relativity is Horava-Lifshitz gravity. Regarding a particular class of pure $F(R)$ gravity in three dimensions, we obtain an analytical rotating Lifshitz-like black hole solution. We first investigate some geometrical properties of the obtained solution that reduces to a charged rotating BTZ black hole in a special limit. Then, we study the optical features of such a black hole like the photon orbit and the energy emission rate and discuss how electric charge, angular momentum, and exponents affect them. In order to have an acceptable optical behavior, we should apply some constraints on the exponents. We continue our investigation with the study of the thermodynamic behavior of the solutions in the extended phase space and examine the validity of the first law of thermodynamics besides local thermal stability by using the heat capacity. Evaluating the existence of van der Waals-like phase transition, we obtain critical quantities and show how they change under the variation of black hole parameters. Finally, we construct a holographic heat engine of such a black hole and obtain its efficiency in a cycle. By comparing the obtained efficiency with the Carnot"
+"---\nabstract: 'Despite the great success of state-of-the-art deep neural networks, several studies have reported models to be over-confident in predictions, indicating miscalibration. Label Smoothing has been proposed as a solution to the over-confidence problem and works by softening hard targets during training, typically by distributing part of the probability mass from a \u2018one-hot\u2019 label uniformly to all other labels. However, neither model nor human confidence in a label are likely to be uniformly distributed in this manner, with some labels more likely to be confused than others. In this paper we integrate notions of model confidence and human confidence with label smoothing, respectively *Model Confidence LS* and *Human Confidence LS*, to achieve better model calibration and generalization. To enhance model generalization, we show how our model and human confidence scores can be successfully applied to curriculum learning, a training strategy inspired by learning of \u2018easier to harder\u2019 tasks. A higher model or human confidence score indicates a more recognisable and therefore easier sample, and can therefore be used as a scoring function to rank samples in curriculum learning. We evaluate our proposed methods with four state-of-the-art architectures for image and text classification task, using datasets with multi-rater label annotations"
+"---\nabstract: 'Ultrarelativistic gamma-ray burst (GRB) jets are strong gravitational wave (GW) sources with memory-type signals. The plateau (or shallow decay) phases driven by the energy injection might appear in the early X-ray afterglows of GRBs. In this paper, we investigate the GW signal as well as X-ray afterglow emission in the framework of GRB jets with energy injection, and both short- and long-duration GRBs are considered. We find that, regardless of the case, because of the antibeaming and time delay effects, a rising slope emerging in the waveform of GW signal due to the energy injection lags far behind the energy ejection, and the typical frequency of the characteristic amplitudes falls within a low-frequency region of $\\sim10^{-4}-10^{-6} \\,{\\rm Hz}$; and we consider that the GW memory triggered by GRB jets with energy injection are previously unaware and the nearby GRBs with strong energy injection might disturb the measurement of the stochastic GW background. Such GW memory detection would provide a direct test for models of energy injection in the scenario of GRB jets.'\nauthor:\n- 'Bao-Quan Huang'\n- Tong Liu\n- Li Xue\n- 'Yan-Qing Qi'\ntitle: 'Low-frequency gravitational wave memory from gamma-ray burst afterglows with energy injection'\n---"
+"[to $~{}$ revised version ]{}\n\n2.0cm\n\n[**Primordial black holes from Volkov\u2013Akulov\u2013Starobinsky\\\n.1in supergravity**]{}\n\n.3in\n\nYermek Aldabergenov\u00a0${}^{a,b,}$[^1] and Sergei V. Ketov\u00a0${}^{c,d,e,}$[^2] .1in\n\n${}^a$\u00a0[*Department of Physics, Faculty of Science, Chulalongkorn University,\\\nPhayathai Road, Pathumwan, Bangkok 10330, Thailand*]{}\\\n${}^b$\u00a0[*Department of Theoretical and Nuclear Physics, Al-Farabi Kazakh National University,\\\n71 Al-Farabi Ave., Almaty 050040, Kazakhstan*]{}\\\n${}^c$\u00a0[*Department of Physics, Tokyo Metropolitan University\\\n1-1 Minami-ohsawa, Hachioji-shi, Tokyo 192-0397, Japan*]{}\\\n${}^d$\u00a0[*Research School of High-Energy Physics, Tomsk Polytechnic University\\\n2a Lenin Avenue, Tomsk 634028, Russian Federation*]{}\\\n${}^e$\u00a0[*Kavli Institute for the Physics and Mathematics of the Universe (WPI)\\\nThe University of Tokyo Institutes for Advanced Study, Kashiwa 277-8583, Japan*]{}\\\n.1in\n\n.3in\n\n[**Abstract**]{} .2in\n\nWe study the formation of primordial black holes (PBH) in the Starobinsky supergravity coupled to the nilpotent superfield describing Volkov\u2013Akulov goldstino. By using the no-scale K\u00e4hler potential and a polynomial superpotential, we find that under certain conditions our model can describe effectively single-field inflation with the ultra-slow-roll phase that appears near a critical (near-inflection) point of the scalar potential. This can lead to the formation of PBH as part of (or whole) dark matter, while keeping the inflationary spectral tilt and the tensor-to-scalar ratio in good agreement with the"
+"---\nabstract: |\n    We propose A-Crab (Actor-Critic Regularized by Average Bellman error), a new algorithm for offline reinforcement learning (RL) in complex environments with insufficient data coverage. Our algorithm combines the marginalized importance sampling framework with the actor-critic paradigm, where the critic returns evaluations of the actor (policy) that are pessimistic relative to the offline data and have a small average (importance-weighted) Bellman error. Compared to existing methods, our algorithm simultaneously offers a number of advantages:\n\n    1.  It is practical and achieves the optimal statistical rate of $1/\\sqrt{N}$\u2014where $N$ is the size of offline dataset\u2014in converging to the best policy covered in the offline dataset, even when combined with general function approximators.\n\n    2.  It relies on a weaker *average* notion of policy coverage (compared to the $\\ell_\\infty$ single-policy concentrability) that exploits the structure of policy visitations.\n\n    3.  It outperforms the data-collection behavior policy over a wide-range of hyperparameter and is the first algorithm to do so *without* solving a minimax optimization problem.\n\nbibliography:\n- 'references.bib'\ntitle: 'Importance Weighted Actor-Critic for Optimal Conservative Offline Reinforcement Learning'\n---\n\nIntroduction {#sec:intro}\n============\n\nOffline reinforcement learning (RL) algorithms aim at learning a good policy based only on historical interaction data. This paradigm allows for"
+"---\nabstract: |\n    We initiate the study of strategic behavior in screening processes with *multiple* classifiers. We focus on two contrasting settings: a \u201cconjunctive\u201d setting in which an individual must satisfy all classifiers simultaneously, and a sequential setting in which an individual to succeed must satisfy classifiers one at a time. In other words, we introduce the combination of *strategic classification* with screening processes.\n\n    We show that sequential screening pipelines exhibit new and surprising behavior where individuals can exploit the sequential ordering of the tests to \u201czig-zag\u201d between classifiers without having to simultaneously satisfy all of them. We demonstrate an individual can obtain a positive outcome using a limited manipulation budget even when far from the intersection of the positive regions of every classifier. Finally, we consider a learner whose goal is to design a sequential screening process that is robust to such manipulations, and provide a construction for the learner that optimizes a natural objective.\nauthor:\n- 'Lee Cohen[^1]'\n- 'Saeed Sharifi-Malvajerdi[^2]'\n- 'Kevin Stangl[^3]'\n- 'Ali Vakilian[^4]'\n- 'Juba Ziani [^5]'\nbibliography:\n- 'strategic-pipeline.bib'\ntitle: Sequential Strategic Screening\n---\n\nIntroduction {#sec:intro}\n============\n\nScreening processes\u00a0[@arunachaleswaran2022pipeline; @blum2022multi; @cohen2019efficient] involve evaluating and selecting individuals for a specific, pre-defined purpose, such"
+"---\nabstract: 'Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an $n$-qubit gapped local Hamiltonian after learning from only $\\mathcal{O}(\\log(n))$ data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $\\mathcal{O}(n^c)$ data for a large constant $c$. Furthermore, the training and prediction time of the proposed ML model scale as $\\mathcal{O}(n \\log n)$ in the number of qubits $n$. Numerical experiments on physical systems with up to $45$ qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.'\nauthor:\n- Laura Lewis\n- 'Hsin-Yuan Huang'\n- 'Viet T. Tran'\n- Sebastian Lehner\n- Richard Kueng\n- John Preskill\ntitle: |\n    Improved machine learning algorithm for\\\n    predicting ground state properties\n---\n\nIntroduction\n============\n\nFinding the ground state of a quantum many-body system is a fundamental problem with far-reaching consequences for physics, materials science, and chemistry. Many powerful methods [@HohenbergKohn; @NobelKohn; @CEPERLEY555; @SandvikSSE; @becca_sorella_2017; @DMRG1; @DMRG2]"
+"---\nabstract: 'In many applications of online decision making, the environment is non-stationary and it is therefore crucial to use bandit algorithms that handle changes. Most existing approaches are designed to protect against non-smooth changes, constrained only by total variation or Lipschitzness over time, where they guarantee $\\tilde \\Theta(T^{2/3})$ regret. However, in practice environments are often changing [**smoothly**]{}, so such algorithms may incur higher-than-necessary regret in these settings and do not leverage information on the rate of change. We study a non-stationary two-armed bandits problem where we assume that an arm\u2019s mean reward is a $\\beta$-H\u00f6lder function over (normalized) time, meaning it is $(\\beta-1)$-times Lipschitz-continuously differentiable. We show the first separation between the smooth and non-smooth regimes by presenting a policy with $\\tilde O(T^{3/5})$ regret for $\\beta=2$. We complement this result by an ${\\Omega}(T^{(\\beta+1)/(2\\beta+1)})$ lower bound for any integer $\\beta\\ge 1$, which matches our upper bound for $\\beta=2$.'\nauthor:\n- 'Su Jia, Qian Xie, Nathan Kallus, Peter I. Frazier'\nbibliography:\n- 'bandits.bib'\ntitle: '**Smooth Non-Stationary Bandits**'\n---\n\nIntroduction\n============\n\nAs a fundamental variant of the MAB problem, non-stationary bandits provide a middleground between the stochastic bandits [@lai1985asymptotically] and adversarial bandits [@auer2002nonstochastic]. In the standard non-stationary model [@besbes2014stochastic], the mean reward"
+"---\nabstract: 'We use 28 quasar fields with high-resolution (HIRES and UVES) spectroscopy from the MUSE Analysis of Gas Around Galaxies survey to study the connection between Ly$\\alpha$ emitters (LAEs) and metal-enriched ionized gas traced by \u00a0in absorption at redshift $z\\approx3-4$. In a sample of 220 \u00a0absorbers, we identify 143 LAEs connected to \u00a0gas within a line-of-sight separation $\\pm500\\rm\\,km\\,s^{-1}$, equal to a detection rate of $36\\pm5$ per cent once we account for multiple LAEs connected to the same \u00a0absorber. The luminosity function of LAEs associated with \u00a0absorbers shows a $\\approx 2.4$ higher normalization factor compared to the field. \u00a0with higher equivalent width and velocity width are associated with brighter LAEs or multiple galaxies, while weaker systems are less often identified near LAEs. The covering fraction in groups is up to $\\approx 3$ times larger than for isolated galaxies. Compared to the correlation between optically-thick \u00a0absorbers and LAEs, \u00a0systems are twice less likely to be found near LAEs especially at lower equivalent width. Similar results are found using \u00a0as tracer of ionized gas. We propose three components to model the gas environment of LAEs: i) the circumgalactic medium of galaxies, accounting for the strongest correlations between absorption and emission; ii) overdense"
+"---\nabstract: 'We generalize a Maximum Principle for optimal control problems involving sweeping systems previously derived in [@nosso_2022] to cover the case where the moving set may be nonsmooth. Noteworthy, we consider problems with constrained end point. A remarkable feature of our work is that we rely upon an ingenious smooth approximating family of standard differential equations in the vein of that used in [@nosso_2019].'\nauthor:\n- 'M. d. R. de Pinho, M. Margarida A. Ferreira [^1] and Georgi Smirnov [^2]'\ntitle: A Maximum Principle for Optimal Control Problems involving Sweeping Processes with a Nonsmooth Set\n---\n\n**Keywords:** Sweeping Process Optimal Control, Maximum Principle, Approximations\n\nIntroduction\n============\n\nIn recent years, there has been a surge of interest in optimal control problems involving the controlled sweeping process of the form $$\\label{SP}\n\\dot x(t) \\in f(t,x(t),u(t))- N_{C(t)}(x(t)), ~u(t)\\in U, ~~x(0) \\in C_0.$$ In this respect, we refer to, for example, [@ArCo17], [@BrKr], [@MoCa17], [@CoPa16], [@CoHeHoMo], [@KuMa00], [@zeidan2020], [@nosso_2019] (see also accompanying correction [@correction_2019]), [@CCBN_2021], [@Palladino2022] and [@nosso_2022]. Sweeping processes first appeared in the seminal paper [@Mo74] by J.J. Moreau as a mathematical framework for problems in plasticity and friction theory. They have proved of interest to tackle problems in mechanics, engineering, economics"
+"---\nabstract: 'High-dimensional biphoton states are promising resources for quantum applications, ranging from high-dimensional quantum communications to quantum imaging. A pivotal task is fully characterising these states, which is generally time-consuming and not scalable when projective measurement approaches are adopted. However, new advances in coincidence imaging technologies allow for overcoming these limitations by parallelising multiple measurements. Here, we introduce biphoton digital holography, in analogy to off-axis digital holography, where coincidence imaging of the superposition of an unknown state with a reference one is used to perform quantum state tomography. We apply this approach to single photons emitted by spontaneous parametric down-conversion in a nonlinear crystal when the pump photons possess various quantum states. The proposed reconstruction technique allows for a more efficient (3 order-of-magnitude faster) and reliable (an average fidelity of 87%) characterisation of states in arbitrary spatial modes bases, compared with previously performed experiments. Multi-photon digital holography may pave the route toward efficient and accurate computational ghost imaging and high-dimensional quantum information processing.'\naddress:\n- 'Dipartimento di Fisica, Sapienza Universit\u00e0 di Roma, Piazzale Aldo Moro 5, I-00185 Roma, Italy'\n- 'Nexus for Quantum Technologies, University of Ottawa, Ottawa, K1N 6N5, ON, Canada'\n- 'Nexus for Quantum Technologies, University of"
+"---\nabstract: 'We report the discovery with [*TESS*]{}\u00a0of a third set of eclipses from V994 Herculis (TIC 424508303), previously only known as a doubly-eclipsing system. The key implication of this discovery and our analyses is that V994 Her is the second fully-characterized (2+2) + 2 sextuple system, in which all three binaries eclipse. In this work, we use a combination of ground-based observations and [*TESS*]{}\u00a0data to analyze the eclipses of binaries A and B in order to update the parameters of the inner quadruple\u2019s orbit (with a derived period of 1062 $\\pm$ 2d). The eclipses of binary C that were detected in the [*TESS*]{}\u00a0data were also found in older ground-based observations, as well as in more recently obtained observations. The eclipse timing variations of all three pairs were studied in order to detect the mutual perturbations of their constituent stars, as well as those of the inner pairs in the (2+2) core. At the longest periods they arise from apsidal motion, which may help constraining parameters of the component stars\u2019 internal structure. We also discuss the relative proximity of the periods of binaries A and B to a 3:2 mean motion resonance. This work represents a step"
+"---\nabstract: 'Team diversity can be seen as a double-edged sword. It brings additional cognitive resources to teams at the risk of increased conflict. Few studies have investigated how different types of diversity impact software teams. This study views diversity through the lens of the *categorization-elaboration model (CEM)*. We investigated how diversity in gender, age, role, and cultural background impacts team effectiveness and conflict, and how these associations are moderated by psychological safety. Our sample consisted of 1,118 participants from 161 teams and was analyzed with Covariance-Based Structural Equation Modeling (CB-SEM). We found a positive effect of age diversity on team effectiveness and gender diversity on relational conflict. Psychological safety contributed directly to effective teamwork and less conflict but did not moderate the diversity-effectiveness link. While our results are consistent with the CEM theory for age and gender diversity, other types of diversity did not yield similar results. We discuss several reasons for this, including curvilinear effects, moderators such as task interdependence, or the presence of a diversity mindset. With this paper, we argue that a dichotomous nature of diversity is oversimplified. Indeed, it is a complex relationship where context plays a pivotal role. A more nuanced understanding of diversity"
+"---\nabstract: 'ALMA observations of the disk around HD 163296 have resolved a crescent-shape substructure at around 55 au, inside and off-center from a gap in the dust that extends from 38 au to 62 au. In this work we propose that both the crescent and the dust rings are caused by a compact pair (period ratio $\\simeq 4:3$) of sub-Saturn-mass planets inside the gap, with the crescent corresponding to dust trapped at the $L_5$ Lagrange point of the outer planet. This interpretation also reproduces well the gap in the gas recently measured from the CO observations, which is shallower than what is expected in a model where the gap is carved by a single planet. Building on previous works arguing for outer planets at $\\approx 86$ and $\\approx 137$ au, we provide with a global model of the disk that best reproduces the data and show that all four planets may fall into a long resonant chain, with the outer three planets in a 1:2:4 Laplace resonance. We show that this configuration is not only an expected outcome from disk-planet interaction in this system, but it can also help constraining the radial and angular position of the planet candidates"
+"---\nabstract: 'In this work we study the neutron star phenomenology of $R^p$ attractor theories in the Einstein frame. The Einstein frame $R^p$ attractor theories have the attractor property that they originate from a large class of Jordan frame scalar theories with arbitrary non-minimal coupling. These theories in the Einstein frame provide a viable class of inflationary models, and in this work we investigate their implications on static neutron stars. We numerically solve the Tolman-Oppenheimer-Volkoff equations in the Einstein frame, for three distinct equations of state, and we provide the mass-radius diagrams for several cases of interest of the $R^p$ attractor theories. We confront the results with several timely constraints on the radii of specific mass neutron stars, and as we show, only a few cases corresponding to specific equations of state pass the stringent tests on neutron stars phenomenology.'\nauthor:\n- |\n    Vasilis K. Oikonomou$^{1,2}$\\\n    $^{1}$ Department of Physics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece\\\n    $^{2}$ Institut f\u00fcr Theoretische Physik, Goethe Universit\u00e4t Frankfurt, Max-von-Laue-Str.1, 60438 Frankfurt am Main, Germany\ntitle: $R^p$ Attractors Static Neutron Star Phenomenology\n---\n\n\\[firstpage\\]\n\nstars: neutron; Physical Data and Processes, cosmology: theory\n\nIntroduction {#introduction .unnumbered}\n============\n\nThe direct gravitational wave observation GW170817 [@TheLIGOScientific:2017qsa; @Abbott:2020khf]"
+"---\nabstract: 'The surprising ability of Large Language Models (LLMs) to perform well on complex reasoning with only few-shot chain-of-thought prompts is believed to emerge only in very large-scale models (100+ billion parameters). We show that such abilities can, in fact, be distilled down from GPT-3.5 ($\\ge$ 175B) to T5 variants ($\\le$ 11B). We propose *model specialization*, to specialize the model\u2019s ability towards a target task. The hypothesis is that large models (commonly viewed as larger than 100B) have strong modeling power, but are spread on a large spectrum of tasks. Small models (commonly viewed as smaller than 10B) have limited model capacity, but if we concentrate their capacity on a specific target task, the model can achieve a decent improved performance. We use multi-step math reasoning as our testbed because it is a very typical emergent ability. We show two important aspects of model abilities: (1). there exists a very complex balance/ tradeoff between language models\u2019 multi-dimensional abilities; (2). by paying the price of decreased generic ability, we can clearly lift up the scaling curve of models smaller than 10B towards a specialized multi-step math reasoning ability. We further give comprehensive discussions about important design choices for better generalization,"
+"---\nabstract: 'Atom controlled sub-nanometer MoS$_2$ pores have been recently fabricated. Oxidative environments are of particular interest for MoS$_2$ applications in electronics, sensing and energy storage. In this work we carried out first-principles calculations of oxygen adsorption in plain and sub-nanometer MoS$_2$ nanopores. The chemical stability of the layers and pores towards oxygen was verified using density-functional theory. Dissociation and diffusion barriers have been calculated in order to understand surface and pore oxidation and its electronic properties at the atomic scale.'\nauthor:\n- Murilo Kendjy Onita\n- Flavio Bento de Oliveira\n- Andr\u00e9ia Luisa da Rosa\ntitle: 'Interaction of oxygen with pristine and defective $\\rm MoS_2$ monolayers'\n---\n\nIntroduction\n============\n\nOwing to their fascinating properties, two dimensional transition metal dichalcogenides (TMDs) have been explored for a variety of applications, including electronics and optoelectronics, photonics, catalysis and energy storage[@Feng2015; @Feng2016; @Nature2016; @Karmodak2021; @Bhim2021]. In particular, molybdenum disulfide (MoS$_2$), the most promising TMDCs, is efficiently exfoliated in monolayer or multilayers[@Santosh2015; @NatComm2017]. Recently the fabrication of MoS2 sub-nanometer pores offer several opportunities being promising candidates for several technological applications such as membranes for DNA translocation,[@Feng2015; @Sen2021; @Graf2019], water filtration and desalination[@Cao2020; @Kou2016; @Nature2015; @Wang2021], energy harvesting[@Graf2019a] and hydrogen evolution reaction[@Wu2019; @Li2019; @Frenkeldefects2022].\n\nControl"
+"---\nauthor:\n- 'H[\u00e9]{}ctor R. Olivares S.'\n- 'Monika A. Mo[\u015b]{}cibrodzka'\n- Oliver Porth\nbibliography:\n- 'references.bib'\ndate:  \ntitle: General relativistic hydrodynamic simulations of perturbed transonic accretion\n---\n\n[Comparison of horizon-scale observations of Sgr\u00a0A\\* and M87\\* with numerical simulations has provided considerable insight in their interpretation. Most of these simulations are variations of the same physical scenario consisting of a rotation supported torus seeded with a poloidal magnetic fields. However, this approach has several well known limitations such as secular decreasing trends in mass accretion rate that render long term variability studies difficult, a lack of connection with the large-scale accretion flow which is replaced by an artificial medium emulating vacuum, and important differences with respect to the predictions of models of accretion onto Sgr\u00a0A\\* fed by stellar winds.]{} [We aim to study the flow patterns that arise at horizon scales in more general accretion scenarios, that have a clearer connection with the large scale flow and are at the same time controlled by a reduced set of parameters.]{} [As a first step in this direction, we perform three dimensional general relativistic hydrodynamic simulations of rotating transonic flows with velocity perturbations injected from a spherical boundary located far"
+"---\nabstract: 'We employ the Feedback In Realistic Environments (FIRE-2) physics model to study how the properties of giant molecular clouds (GMCs) evolve during galaxy mergers. We conduct a pixel-by-pixel analysis of molecular gas properties in both the simulated control galaxies and galaxy major mergers. The simulated GMC-pixels in the control galaxies follow a similar trend in a diagram of velocity dispersion ($\\sigma_v$) versus gas surface density ($\\Sigma_{\\mathrm{mol}}$) to the one observed in local spiral galaxies in the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) survey. For GMC-pixels in simulated mergers, we see a significant increase of factor of 5 \u2013 10 in both $\\Sigma_{\\mathrm{mol}}$ and $\\sigma_v$, which puts these pixels above the trend of PHANGS galaxies in the $\\sigma_v$ vs $\\Sigma_{\\mathrm{mol}}$ diagram. This deviation may indicate that GMCs in the simulated mergers are much less gravitationally bound compared with simulated control galaxies with virial parameter ($\\alpha_{\\mathrm{vir}}$) reaching 10 \u2013 100. Furthermore, we find that the increase in $\\alpha_{\\mathrm{vir}}$ happens at the same time as the increase in global star formation rate (SFR), which suggests stellar feedback is responsible for dispersing the gas. We also find that the gas depletion time is significantly lower for high [$\\alpha_{\\mathrm{vir}}$]{}GMCs during"
+"---\nauthor:\n- \n- \n- \n- \n- \nbibliography:\n- 'sn-bibliography.bib'\ntitle: 'Physics-agnostic and Physics-infused machine learning for thin films flows: modeling, and predictions from small data'\n---\n\nIntroduction {#sec1}\n============\n\nThe study of multiphase flows is often limited by the computational effort involved in solving the Navier-Stokes equations [@glasser1997fully]. One such example, the flow of thin films of liquid on inclined planes, has fascinated researchers not only because of the wide range of industrial applications but also because of the interesting dynamics of the liquid-air interface [@Kalliadasis2012]. The Navier-Stokes (NS) equations accurately describe the fluid motion and also the evolution of the surface but suffer from high computational cost [@Pettas2019a; @Pettas2019b]. To this end, significant effort has led to several approximate interface evolution equations that are much simpler to solve but are nevertheless valid under specific assumptions and limitations. Beyond their limits of validity, it is often found that they yield nonphysical solutions, or even blow up [@Kalliadasis2012], posing significant restrictions to their applicability.\n\nIn order to drastically enable Computational Fluid Dynamics and break new barriers in flow control, uncertainty quantification and shape optimization, it is crucial to develop novel, robust and efficient data-driven/data-assisted models that combine physical and mathematical"
+"---\nabstract: 'Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. By conducting a human evaluation on ten LLMs across different pretraining methods, prompts, and model scales, we make two important observations. First, we find instruction tuning, and not model size, is the key to the LLM\u2019s zero-shot summarization capability. Second, existing studies have been limited by low-quality references, leading to underestimates of human performance and lower few-shot and finetuning performance. To better evaluate LLMs, we perform human evaluation over high-quality summaries we collect from freelance writers. Despite major stylistic differences such as the amount of paraphrasing, we find that LMM summaries are judged to be on par with human written summaries.'\nauthor:\n- |\n    Tianyi Zhang$\\phantom{}^{1}$[[^1]]{}, Faisal Ladhak$\\phantom{}^{2*}$, Esin Durmus$\\phantom{}^{1}$, Percy Liang$\\phantom{}^{1}$,\\\n    **Kathleen McKeown$\\phantom{}^{2}$, Tatsunori B. Hashimoto$\\phantom{}^{1}$**\\\n    $\\phantom{}^{1}$Stanford Univeristy $\\phantom{}^{2}$Columbia Univeristy\\\nbibliography:\n- 'tacl2021.bib'\ntitle: Benchmarking Large Language Models for News Summarization\n---\n\nIntroduction\n============\n\nLarge language models (LLMs) have shown promising results in zero-/few-shot tasks across a wide range of domains\u00a0[@palm; @anthropic; @gpt3; @opt] and raised significant interest for their potential for automatic summarization\u00a0[@gpt3-era; @Liu2022RevisitingTG]. However, the design decisions contributing to its success on summarization remain"
+"---\nauthor:\n- 'Evan Grohs [^1] and George M. Fuller'\nbibliography:\n- 'references.bib'\ntitle: Big Bang Nucleosynthesis\n---\n\nIntroduction\n============\n\nThe success of Big Bang Nucleosynthesis (BBN) theory in predicting the primordial abundances of helium and deuterium and the baryon (ordinary matter) content of the universe represents one of the greatest triumphs of modern physics \\[see for reviews of the various physical phenomena present in BBN\\]. It is all the more remarkable that this success is born of very simplistic assumptions about the universe and its evolution. These are: (1) General Relativity (GR) is a correct description of spacetime dynamics and that the distribution of mass-energy on any 3-dimensional spacelike hypersurface at a given value of the time $t$ (age of the universe) is homogeneous and isotropic; and (2) that the standard model of particle physics and, more specifically, simple nuclear physics obtains at very early times in the history of the universe.\n\nIn fact, the Friedmann-LeMa\u00eetre-Robertson-Walker metric, the solution to the field equations in the symmetry implied by homogeneity and isotropy \\[see [@1973grav.book.....M]\\], was worked out in [@1917SPAW.......142E] shortly after Einstein\u2019s original work on GR. [@1922ZPhy...10..377F] and [@1924ZPhy...21..326F] showed that the solutions to the GR field equations led to"
+"---\nabstract: 'Methods to detect malignant lesions from screening mammograms are usually trained with fully annotated datasets, where images are labelled with the localisation and classification of cancerous lesions. However, real-world screening mammogram datasets commonly have a subset that is fully annotated and another subset that is weakly annotated with just the global classification (i.e., without lesion localisation). Given the large size of such datasets, researchers usually face a dilemma with the weakly annotated subset: to not use it or to fully annotate it. The first option will reduce detection accuracy because it does not use the whole dataset, and the second option is too expensive given that the annotation needs to be done by expert radiologists. In this paper, we propose a middle-ground solution for the dilemma, which is to formulate the training as a weakly- and semi-supervised learning problem that we refer to as malignant breast lesion detection with incomplete annotations. To address this problem, our new method comprises two stages, namely: 1) pre-training a multi-view mammogram classifier with weak supervision from the whole dataset, and 2) extending the trained classifier to become a multi-view detector that is trained with semi-supervised student-teacher learning, where the training set contains"
+"---\nabstract: 'Observations with the Hubble Space Telescope unexpectedly revealed that the dwarf galaxy ESO006-001 is a near neighbor to the Local Group at a distance of $2.70\\pm0.11$\u00a0Mpc. The stellar population in the galaxy is well resolved into individual stars to a limit of $M_I\\sim-0.5$\u00a0mag. The dominant population is older than 12 Gyr yet displays a significant range in metallicity of $-2 < \\rm{[Fe/H]} < -1$, as evidenced by a Red Giant Branch with substantial width. Superimposed on the dominant population are stars on the Main Sequence with ages less than 100\u00a0Myr and Helium burning Blue Loop stars with ages of several hundred Myr. ESO006-001 is an example of a transition dwarf; a galaxy dominated by old stars but one that has experienced limited recent star formation in a swath near the center. No [H[i]{}]{}\u00a0gas is detected at the location of the optical galaxy in spite of the evidence for young stars. Intriguingly, an [H[i]{}]{}\u00a0cloud with a similar redshift is detected 9 kpc away in projection. Otherwise, ESO006-001 is a galaxy in isolation with its nearest known neighbor IC3104, itself a dwarf, at a distance of $\\sim 500$\u00a0kpc.'\nauthor:\n- 'Lidia N. Makarova'\n-"
+"---\nabstract: 'Artificial learners often behave differently from human learners in the context of neural agent-based simulations of language emergence and change. A common explanation is the lack of appropriate cognitive biases in these learners. However, it has also been proposed that more naturalistic settings of language learning and use could lead to more human-like results. We investigate this latter account focusing on the word-order/case-marking trade-off, a widely attested language universal that has proven particularly hard to simulate. We propose a new Neural-agent Language Learning and Communication framework where pairs of speaking and listening agents first learn a miniature language via supervised learning, and then optimize it for communication via reinforcement learning. Following closely the setup of earlier human experiments, we succeed in replicating the trade-off with the new framework without hard-coding specific biases in the agents. We see this as an essential step towards the investigation of language universals with neural learners.'\nauthor:\n- |\n    Yuchen Lian$^\\diamond$ $^\\dagger$ Arianna Bisazza$^\\ddagger$ Tessa Verhoef$^\\dagger$\\\n    \u00a0\\\n    $^\\diamond$Faculty of Electronic and Information Engineering, Xi\u2019an Jiaotong University\\\n    $^\\dagger$Leiden Institute of Advanced Computer Science, Leiden University\\\n    `{y.lian, t.verhoef}@liacs.leidenuniv.nl`\\\n    $^\\ddagger$Center for Language and Cognition, University of Groningen\\\n    `a.bisazza@rug.nl`\nbibliography:\n- 'tacl2021.bib'\n- 'anthology.bib'\ntitle: |\n    Communication"
+"---\nauthor:\n- Simone Veronese\n- 'W. J. G. de Blok'\n- 'F. Walter'\nbibliography:\n- 'reference.bib'\ndate: 'Received &lt;date&gt; / Accepted &lt;date&gt;'\ntitle: Extended neutral hydrogen filamentary network in NGC 2403\n---\n\nIntroduction {#sec:intro}\n============\n\nGalaxies form stars through the collapsing of giant molecular (mainly molecular hydrogen, H[ii]{}) clouds on timescales of $\\sim10^7$ yr [@meidt15; @schinnerer19; @walter20] and over the cosmic time [@madau14] galaxies progressively deplete their molecular gas content. In a perfect steady state, the H[ii]{} reservoir is replenished by the cooling of the atomic hydrogen (H[i]{}) in the interstellar medium [@clark12; @walch15], which will cause a reduction in the content of the atomic gas. However, both simulations and observations reveal that the H[i]{} content in galaxies is almost constant from $z\\sim1$ [@dave17; @chen21]. Consequently, in order to maintain star formation over cosmic time, galaxies must accrete H[i]{}.\\\nPrevious studies of H[i]{} interaction features and dwarf galaxies have shown that they do not provide enough cold gas to replenish the material reservoir for star formation [@sancisi08; @putman12; @blok20]. Other extraplanar gas observed closer to the galaxy disks is usually related to galactic fountains [@putman12; @li21; @marasco22]: star formation and supernova explosions eject interstellar medium from the disk to"
+"---\nabstract: 'Colloidal gels consist of percolating networks of interconnected arms. Their mechanical properties depend on the individual arms, and on their collective behaviour. We use numerical simulations to pull on a single arm, built from a model colloidal gel-former with short-ranged attractive interactions. Under elongation, the arm breaks by a necking instability. We analyse this behaviour at three different length scales: a rheological continuum model of the whole arm; a microscopic analysis of the particle structure and dynamics; and the local stress tensor. Combining these different measurements gives a coherent picture of the necking and failure: the neck is characterised by plastic flow that occurs for stresses close to the arm\u2019s yield stress. The arm has an amorphous local structure and has large residual stresses from its initialisation. We find that neck formation is associated with increased plastic flow, a reduction in the stability of the local structure, and a reduction in the residual stresses; this indicates that [the]{} system loses its solid character and starts to behave more like a viscous fluid. We discuss the implications of these results for the modelling of gel dynamics.'\nauthor:\n- Kristian Thijssen\n- 'Tanniemola B. Liverpool'\n- 'C. Patrick Royall'\n-"
+"---\nabstract: '[ Simultaneous optimization of multiple objective functions results in a set of trade-off, or Pareto, solutions. Choosing a, in some sense, best solution in this set is in general a challenging task: In the case of three or more objectives the Pareto front is usually difficult to view, if not impossible, and even in the case of just two objectives constructing the whole Pareto front so as to visually inspect it might be very costly. Therefore, optimization over the Pareto (or efficient) set has been an active area of research. Although there is a wealth of literature involving finite dimensional optimization problems in this area, there is a lack of problem formulation and numerical methods for optimal control problems, except for the convex case. In this paper, we formulate the problem of optimizing over the Pareto front of nonconvex constrained and time-delayed optimal control problems as a bi-level optimization problem. Motivated by existing solution differentiability results, we propose an algorithm incorporating (i) the Chebyshev scalarization, (ii) a concept of the essential interval of weights, and (iii) the simple but effective bisection method, for optimal control problems with two objectives. We illustrate the working of the algorithm on two"
+"---\nabstract: 'Millimetre-wave (mmWave) radars can generate 3D point clouds to represent objects in the scene. However, the accuracy and density of the generated point cloud can be lower than a laser sensor. Although researchers have used mmWave radars for various applications, there are few quantitative evaluations on the quality of the point cloud generated by the radar and there is a lack of a standard on how this quality can be assessed. This work aims to fill the gap in the literature. A radar simulator is built to evaluate the most common data processing chains of 3D point cloud construction and to examine the capability of the mmWave radar as a 3D imaging sensor under various factors. It will be shown that the radar detection can be noisy and have an imbalance distribution. To address the problem, a novel super-resolution point cloud construction (SRPC) algorithm is proposed to improve the spatial resolution of the point cloud and is shown to be able to produce a more natural point cloud and reduce outliers.'\nauthor:\n- \nbibliography:\n- 'ref.bib'\ntitle: 'Millimetre-wave Radar for Low-Cost 3D Imaging: A Performance Study'\n---\n\nmmWave radar, 3D imaging, point cloud\n\nIntroduction\n============\n\nMillimetre-wave (mmWave) radars"
+"---\nabstract: 'Accurate acne detection plays a crucial role in acquiring precise diagnosis and conducting proper therapy. However, the ambiguous boundaries and arbitrary dimensions of acne lesions severely limit the performance of existing methods. In this paper, we address these challenges via a novel Decoupled Sequential Detection Head (DSDH), which can be easily adopted by mainstream two-stage detectors. DSDH brings two simple but effective improvements to acne detection. Firstly, the offset and scaling tasks are explicitly introduced, and their incompatibility is settled by our task-decouple mechanism, which improves the capability of predicting the location and size of acne lesions. Second, we propose the task-sequence mechanism, and execute offset and scaling sequentially to gain a more comprehensive insight into the dimensions of acne lesions. In addition, we build a high-quality acne detection dataset named ACNE-DET to verify the effectiveness of DSDH. Experiments on ACNE-DET and the public benchmark ACNE04 show that our method outperforms the state-of-the-art methods by significant margins. Our code and dataset are publicly available at (temporarily anonymous).'\nauthor:\n- Xin Wei$^1$\n- Lei Zhang$^1$\n- Jianwei Zhang$^1$\n- Junyou Wang$^1$\n- Wenjie Liu$^1$\n- |\n    Jiaqi Li$^2$ Xian Jiang$^2$ $^1$College of Computer Science, Sichuan University, Chengdu 610065, China\\"
+"---\nabstract: 'Dimensionality reduction is a crucial technique in data analysis, as it allows for the efficient visualization and understanding of high-dimensional datasets. The circular coordinate is one of the topological data analysis techniques associated with dimensionality reduction but can be sensitive to variations in density. To address this issue, we propose new circular coordinates to extract robust and density-independent features. Our new methods generate a new coordinate system that depends on a shape of an underlying manifold preserving topological structures. We demonstrate the effectiveness of our methods through extensive experiments on synthetic and real-world datasets.'\naddress:\n- 'Department of Mathematical Sciences and Research Institute of Mathematics, Seoul National University'\n- 'Department of Mathematical Sciences and Research Institute of Mathematics, Seoul National University'\nauthor:\n- Taejin Paik\n- Jaemin Park\nbibliography:\n- 'main.bib'\ntitle: 'Circular Coordinates for Density-Robust Analysis'\n---\n\nIntroduction\n============\n\nDimensionality reduction allows us to understand high-dimensional data and gives us intuitive information about a dataset. One of the key challenges in this area is preserving the intrinsic topological structure. Different dimensionality reduction strategies try to handle this problem in different ways.\n\nPrincipal component analysis (PCA) [@pearson1901liii; @hotelling1933analysis] is one of the most basic techniques for linear dimensionality"
+"---\nabstract: 'Magnetic resonance imaging (MRI) is a common technique to scan brains for strokes, tumors, and other abnormalities that cause forms of dementia. However, correctly diagnosing forms of dementia from MRIs is difficult, as nearly 1 in 3 patients with Alzheimer\u2019s were misdiagnosed in 2019, an issue neural networks can rectify. The performance of these neural networks have been shown to be improved by applying quantum algorithms. This proposed novel neural network architecture uses a fully-connected (FC) layer, which reduces the number of features to obtain an expectation value by implementing a variational quantum circuit (VQC). This study found that the proposed hybrid quantum-classical convolutional neural network (QCCNN) provided 97.5% and 95.1% testing and validation accuracies, respectively, which was considerably higher than the classical neural network (CNN) testing and validation accuracies of 91.5% and 89.2%. Additionally, using a testing set of 100 normal and 100 dementia MRI images, the QCCNN detected normal and demented images correctly 95% and 98% of the time, compared to the CNN accuracies of 89% and 91%. With hospitals like Massachusetts General Hospital beginning to adopt machine learning applications for biomedical image detection, this proposed architecture would approve accuracies and potentially save more lives. Furthermore,"
+"---\nabstract: 'We propose a highly efficient and fast method of translational cooling for high-angular-momentum atoms. Optical pumping and stimulated transitions, combined with magnetic forces, can be used to compress phase-space density, and the efficiency of each compression step increases with the angular momentum. Entropy is removed by spontaneously emitted photons, and particle number is conserved. This method may be an attractive alternative to evaporative cooling of atoms and possibly molecules in order to produce quantum degenerate gases.'\naddress:\n- '$^1$ Department of Physics, The University of Texas at Austin, Austin, Texas, 78712, USA'\n- '$^2$Johannes Gutenberg-Universit[\u00e4]{}t Mainz, Helmholtz-Institut Mainz, GSI Helmholtzzentrum f[\u00fc]{}r Schwerionenforschung, 55128 Mainz, Germany'\n- '$^3$ Department of Physics, University of California, Berkeley, California 94720, USA'\n- '$^4$Rochester Scientific, LLC, El Cerrito, California 94530, USA'\nauthor:\n- 'Logan E. Hillberry$^1$, Dmitry Budker$^{2,3}$, Simon M. Rochester$^4$, Mark G. Raizen$^1$'\nbibliography:\n- 'bibliography.bib'\ntitle: 'Efficient cooling of high-angular-momentum atoms'\n---\n\nMay 2023\n\n[*Keywords*]{}: atomic physics, cold atoms, phase-space compression\n\nIntroduction\n============\n\nLaser cooling, first proposed almost half a century ago, remains the standard approach for producing ultracold atoms [@metcalf1999laser; @schreck2021laser]. This method relies on momentum transfer from light to atoms as photons are repeatedly scattered, enabling the production and"
+"---\nabstract: 'Virtual Mental Health Assistants (VMHAs) have become a prevalent method for receiving mental health counseling in the digital healthcare space. An assistive counseling conversation commences with natural open-ended topics to familiarize the client with the environment and later converges into more fine-grained domain-specific topics. Unlike other conversational systems, which are categorized as open-domain or task-oriented systems, VMHAs possess a hybrid conversational flow. These counseling bots need to comprehend various aspects of the conversation, such as dialogue-acts, intents, etc., to engage the client in an effective and appropriate conversation. Although the surge in digital health research highlights applications of many general-purpose response generation systems, they are barely suitable in the mental health domain \u2013 the prime reason is the lack of understanding in the mental health counseling conversation. Moreover, in general, dialogue-act guided response generators are either limited to a template-based paradigm or lack appropriate semantics in dialogue generation. To this end, we propose [`READER`]{}\u00a0\u2013 a **RE**sponse-[**A**]{}ct guided reinforced **D**ialogue gen**ER**ation model for the mental health counseling conversations. [`READER`]{}\u00a0is built on transformer to jointly predict a potential dialogue-act $d_{t+1}$ for the next utterance (*aka* response-act) and to generate an appropriate response ($u_{t+1}$). Through the transformer-reinforcement-learning (TRL) with"
+"---\nabstract: 'Preferential attachment (PA) network models have a wide range of applications in various scientific disciplines. Efficient generation of large-scale PA networks helps uncover their structural properties and facilitate the development of associated analytical methodologies. Existing software packages only provide limited functions for this purpose with restricted configurations and efficiency. We present a generic, user-friendly implementation of weighted, directed PA network generation with package . The core algorithm is based on an efficient binary tree approach. The package further allows adding multiple edges at a time, heterogeneous reciprocal edges, and user-specified preference functions. The engine under the hood is implemented in . Usages of the package are illustrated with detailed explanation. A benchmark study shows that is efficient for generating general PA networks not available in other packages. In restricted settings that can be handled by existing packages, provides comparable efficiency.'\naddress:\n- 'Department of Statistics, , '\n- 'Shanghai Center for Mathematical Sciences, , '\n- 'Department of Biostatistics, , '\nauthor:\n- \n- \n- \n- \nbibliography:\n- 'generatePA.bib'\ntitle: Generating General Preferential Attachment Networks with Package \n---\n\n.\n\nIntroduction {#sec:intro}\n============\n\nPreferential attachment (PA) networks are important network models in scientific research. The standard PA model\u00a0[@Barabasi1999emergence]"
+"---\nabstract: 'The design of feedback channels in frequency division duplex (FDD) systems is a major challenge because of the limited available feedback bits. We consider non-orthogonal multiple access (NOMA) systems that incorporate reconfigurable intelligent surfaces (RISs). In limited feedback RIS-aided NOMA systems, the RIS-aided channel and the direct channel gains should be quantized and fed back to the transmitter. This paper investigates the rate loss of the overall RIS-aided NOMA systems suffering from quantization errors. We first consider random vector quantization for the overall RIS-aided channel and identical uniform quantizers for the direct channel gains. We then obtain an upper bound for the rate loss, due to the quantization error, as a function of the number of feedback bits and the size of RIS. Our numerical results indicate the sum rate performance of the limited feedback system approaches that of the system with full CSI as the number of feedback bits increases.'\nauthor:\n- '[^1]'\nbibliography:\n- 'IEEEabrv.bib'\n- 'references.bib'\ntitle: 'Reconfigurable Intelligent Surface-Aided NOMA with Limited Feedback'\n---\n\nIntroduction {#sec:introduction}\n============\n\nReconfigurable intelligent surfaces (RISs) are presumed as an attractive solution to enhance the spectral, power efficiency, and coverage of wireless communication systems\u00a0[@8741198]. These surfaces consist of"
+"---\nabstract: 'The Krylov subspace methods, being one category of the most important classical numerical methods for linear algebra problems, their quantum generalisation can be much more powerful. However, quantum Krylov subspace algorithms are prone to errors due to inevitable statistical fluctuations in quantum measurements. To address this problem, we develop a general theoretical framework to analyse the statistical error and measurement cost. Based on the framework, we propose a quantum algorithm to construct the Hamiltonian-power Krylov subspace that can minimise the measurement cost. In our algorithm, the product of power and Gaussian functions of the Hamiltonian is expressed as an integral of the real-time evolution, such that it can be evaluated on a quantum computer. We compare our algorithm with other established quantum Krylov subspace algorithms in solving two prominent examples. It is shown that the measurement number in our algorithm is typically $10^4$ to $10^{12}$ times smaller than other algorithms. Such an improvement can be attributed to the reduced cost of composing projectors onto the ground state. These results show that our algorithm is exceptionally robust to statistical fluctuations and promising for practical applications.'\nauthor:\n- Zongkang Zhang\n- Anbang Wang\n- Xiaosi Xu\n- Ying Li\nbibliography:"
+"---\nabstract: 'Throughout 2021, GitGuardian\u2019s monitoring of public GitHub repositories revealed a two-fold increase in the number of secrets (database credentials, API keys, and other credentials) exposed compared to 2020, accumulating more than six million secrets. To our knowledge, the challenges developers face to avoid checked-in secrets are not yet characterized. *The goal of our paper is to aid researchers and tool developers in understanding and prioritizing opportunities for future research and tool automation for mitigating checked-in secrets through an empirical investigation of challenges and solutions related to checked-in secrets*. We extract 779 questions related to checked-in secrets on Stack Exchange and apply qualitative analysis to determine the challenges and the solutions posed by others for each of the challenges. We identify 27 challenges and 13 solutions. The four most common challenges, in ranked order, are: (i) store/version of secrets during deployment; (ii) store/version of secrets in source code; (iii) ignore/hide of secrets in source code; and (iv) sanitize VCS history. The three most common solutions, in ranked order, are: (i) move secrets out of source code/version control and use template config file; (ii) secret management in deployment; and (iii) use local environment variables. Our findings indicate that the same"
+"---\nabstract: 'Optimal decision-making compels us to anticipate the future at different horizons. However, in many domains connecting together predictions from multiple time horizons and abstractions levels across their organization becomes all the more important, else decision-makers would be planning using separate and possibly conflicting views of the future. This notably applies to smart grid operation. To optimally manage energy flows in such systems, accurate and *coherent* predictions must be made across varying aggregation levels and horizons. Such hierarchical structures are said to be coherent when values at different scales are equal when brought to the same level, else would need to be reconciled. With this work, we propose a novel multi-dimensional hierarchical forecasting method built upon structurally-informed machine-learning regressors and established hierarchical reconciliation taxonomy. A generic formulation of multi-dimensional hierarchies, reconciling spatial and temporal hierarchies under a common frame is initially defined. Next, a coherency-informed hierarchical learner is developed built upon a custom loss function leveraging optimal reconciliation methods. Coherency of the produced hierarchical forecasts is then secured using similar reconciliation technics. The outcome is a unified and coherent forecast across all examined dimensions, granting decision-makers a common view of the future serving aligned decision-making. The method is evaluated"
+"---\nauthor:\n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \n- \ntitle: '<span style=\"font-variant:small-caps;\">Grasian</span>: Towards the first demonstration of gravitational quantum states of atoms with a cryogenic hydrogen beam'\n---\n\nIntroduction {#sec1}\n============\n\nQuantum bouncers were first predicted in 1928 [@Bre:1928pr]. Nearly 75 years later, this phenomenon was demonstrated through the observation of neutron ($n$) gravitational quantum states (<span style=\"font-variant:small-caps;\">gqs</span>) [@Lus:1978jl; @Nes:2000nima; @Nes:2002Nat; @Nes:2003prd; @Nes:2003prdbis; @Nes:2005epjc; @Wes:2007epjc]. Confined by the gravitational- and the mirror potential, the $n$ are settled in gravitationally bound quantum states.\n\nStudies of $n$ <span style=\"font-variant:small-caps;\">gqs</span> have a broad impact on fundamental and applied physics. They serve as a unique method to study the interaction of a particle in a quantum state with a gravitational field. For example, paired with more recent measurements of $n$ whispering gallery states (<span style=\"font-variant:small-caps;\">wgs</span>) - quantum states trapped by the centrifugal- and the mirror potential [@Nes:2010NatPhys], they result in the first direct demonstration of the validity of the weak equivalence principle for a particle in a pure quantum state.\n\nThe observation of <span style=\"font-variant:small-caps;\">gqs</span> initiated active analysis of the pecularities of this phenomenon [@Rob:2004pr; @Ber:2005jmc; @Mat:2006pra; @ber:2006plb; @Rom:2007prl; @Del:2009prl; @Gar:2012pr; @Bel:2014pr] and their application to"
+"---\nabstract: 'Measuring empathy in conversation can be challenging, as empathy is a complex and multifaceted psychological construct that involves both cognitive and emotional components. Human evaluations can be subjective, leading to inconsistent results. Therefore, there is a need for an automatic method for measuring empathy that reduces the need for human evaluations. In this paper, we proposed a novel approach EMP-EVAL, a simple yet effective automatic empathy evaluation method. The proposed technique takes the influence of Emotion, Cognitive and Emotional empathy. To the best knowledge, our work is the first to systematically measure empathy without the human-annotated provided scores. Experimental results demonstrate that our metrics can correlate with human preference, achieving comparable results with human judgments.'\nauthor:\n- |\n    Bushra Amjad , Muhammad Zeeshan, Mirza Omer Beg\\\n    Department of Artificial Intelligence and Data Science\\\n    National University of Computer and Emerging Sciences\\\n    Islamabad, Pakistan\\\n    `{bushra.amjad,i191711,omer.beg}@nu.edu.pk`\nbibliography:\n- 'custom.bib'\ntitle: 'EMP-EVAL: A Framework for Measuring Empathy in Open Domain Dialogues'\n---\n\n=1\n\nIntroduction\n============\n\nEmpathy is a vital component of human communication, and it is increasingly being recognized as an important aspect of conversational agents and chatbots. Empathy refers to the ability to understand and share the feelings of others, which"
+"---\nabstract: 'Assortment optimization has received active explorations in the past few decades due to its practical importance. Despite the extensive literature dealing with optimization algorithms and latent score estimation, uncertainty quantification for the optimal assortment still needs to be explored and is of great practical significance. Instead of estimating and recovering the complete optimal offer set, decision-makers may only be interested in testing whether a given property holds true for the optimal assortment, such as whether they should include several products of interest in the optimal set, or how many categories of products the optimal set should include. This paper proposes a novel inferential framework for testing such properties. We consider the widely adopted multinomial logit (MNL) model, where we assume that each customer will purchase an item within the offered products with a probability proportional to the underlying preference score associated with the product. We reduce inferring a general optimal assortment property to quantifying the uncertainty associated with the sign change point detection of the marginal revenue gaps. We show the asymptotic normality of the marginal revenue gap estimator, and construct a maximum statistic via the gap estimators to detect the sign change point. By approximating the distribution"
+"---\nabstract: 'As global digitization continues to grow, technology becomes more affordable and easier to use, and social media platforms thrive, becoming the new means of spreading information and news. Communities are built around sharing and discussing current events. Within these communities, users are enabled to share their opinions about each event. Using Sentiment Analysis to understand the polarity of each message belonging to an event, as well as the entire event, can help to better understand the general and individual feelings of significant trends and the dynamics on online social networks. In this context, we propose a new ensemble architecture, EDSA-Ensemble (Event Detection Sentiment Analysis Ensemble), that uses Event Detection and Sentiment Analysis to improve the detection of the polarity for current events from Social Media. For Event Detection, we use techniques based on Information Diffusion taking into account both the time span and the topics. To detect the polarity of each event, we preprocess the text and employ several Machine and Deep Learning models to create an ensemble model. The preprocessing step includes several word representation models, i.e., raw frequency, $TFIDF$, Word2Vec, and Transformers. The proposed EDSA-Ensemble architecture improves the event sentiment classification over the individual Machine and"
+"---\nabstract: 'The effect of viscosity contrast between a jet and its surroundings is experimentally investigated using density-matched fluids. A gravity-driven flow is established with a jet of saltwater emerging into an ambient medium composed of high-viscosity propylene glycol. Jet Reynolds numbers, $Re$, ranging from 1600 to 3400 were studied for an ambient-to-jet viscosity ratio, $M$, between 1 and 50. Visualization suggests that at low values of the viscosity ratio, the jet breakdown mode is axisymmetric, while helical modes develop at high values of viscosity ratio. The transition between these two modes is attempted to be delineated using a variety of diagnostic tools. Hot film anemometry measurements indicate that the onset of the helical mode is accompanied by the appearance of a discrete peak in the frequency spectrum of velocity fluctuations, which exhibits little spatial variation for the first several diameters in the downstream direction. Laser-Induced Fluorescence (LIF) is used to identify the jet boundary against the background. An analysis of high-speed images acquired using the LIF technique enables identification of the spatial growth rate of waves on the jet boundary, as well as the frequency of oscillation of the weakly diffusive interface. Temporal fluctuations of fluorescence intensity are found"
+"---\nabstract: 'Autonomous robots are required to reason about the behaviour of dynamic agents in their environment. The creation of models to describe these relationships is typically accomplished through the application of causal discovery techniques. However, as it stands observational causal discovery techniques struggle to adequately cope with conditions such as causal sparsity and non-stationarity typically seen during online usage in autonomous agent domains. Meanwhile, interventional techniques are not always feasible due to domain restrictions. In order to better explore the issues facing observational techniques and promote further discussion of these topics we carry out a benchmark across 10 contemporary observational temporal causal discovery methods in the domain of autonomous driving. By evaluating these methods upon causal scenes drawn from real world datasets in addition to those generated synthetically we highlight where improvements need to be made in order to facilitate the application of causal discovery techniques to the aforementioned use-cases. Finally, we discuss potential directions for future work that could help better tackle the difficulties currently experienced by state of the art techniques.'\nbibliography:\n- 'references.bib'\ntitle: 'Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour'\n---\n\nCausal Discovery, Time Series Data Analysis, Autonomous Driving\n\nIntroduction\n============"
+"---\nabstract: 'Eukaryotes swim with coordinated flagellar (ciliary) beating and steer by fine-tuning the coordination. The model organism for studying flagellate motility, *C. reinhardtii* (CR), employs synchronous, breast-stroke-like flagellar beating to swim, and it modulates the beating amplitudes differentially to steer. This strategy hinges on both inherent flagellar asymmetries (e.g. different response to chemical messengers) and such asymmetries being effectively coordinated in the synchronous beating. In CR, the synchrony of beating is known to be supported by a mechanical connection between flagella, however, how flagellar asymmetries persist in the synchrony remains elusive. For example, it has been speculated for decades that one flagellum leads the beating, as its dynamic properties (i.e. frequency, waveform, etc.) appear to be copied by the other one. In this study, we combine experiments, computations, and modeling efforts to elucidate the roles played by each flagellum in synchronous beating. With a non-invasive technique to selectively load each flagellum, we show that the coordinated beating essentially responds to only load exerted on the *cis* flagellum; and that such asymmetry in response derives from a unilateral coupling between the two flagella. Our results highlight a distinct role for each flagellum in coordination and have implication for biflagellates\u2019 tactic"
+"---\nabstract: 'Summation-by-parts (SBP) operators allow us to systematically develop energy-stable and high-order accurate numerical methods for time-dependent differential equations. Until recently, the main idea behind existing SBP operators was that polynomials can accurately approximate the solution, and SBP operators should thus be exact for them. However, polynomials do not provide the best approximation for some problems, with other approximation spaces being more appropriate. We recently addressed this issue and developed a theory for *one-dimensional* SBP operators based on general function spaces, coined function-space SBP (FSBP) operators. In this paper, we extend the theory of FSBP operators to *multiple dimensions*. We focus on their existence, connection to quadratures, construction, and mimetic properties. A more exhaustive numerical demonstration of multi-dimensional FSBP (MFSBP) operators and their application will be provided in future works. Similar to the one-dimensional case, we demonstrate that most of the established results for polynomial-based multi-dimensional SBP (MSBP) operators carry over to the more general class of MFSBP operators. Our findings imply that the concept of SBP operators can be applied to a significantly larger class of methods than is currently done. This can increase the accuracy of the numerical solutions and/or provide stability to the methods.'\nauthor:\n-"
+"---\nabstract: 'Ultralong-range Rydberg molecules (ULRM) are highly imbalanced bound systems formed via the low-energy scattering of a Rydberg electron with a ground-state atom. We investigate for $^{23}$Na the $d$-state and the energetically close-by trilobite state, exhibiting avoided crossings that lead to the breakdown of the adiabatic Born-Oppenheimer (BO) approximation. We develop a coupled-channel approach to explore the non-adiabatic interaction effects between these electronic states. The resulting spectrum exhibits stark differences in comparison to the BO spectra, such as the existence of above-threshold resonant states without any adiabatic counterparts, and a significant rearrangement of the spectral structure as well as the localization of the eigenstates. Our study motivates the use of $^{23}$Na ULRM, as a probe to explore vibronic interaction effects on exaggerated time and length scales.'\nauthor:\n- Rohan Srikumar\n- Frederic Hummel\n- Peter Schmelcher\nbibliography:\n- 'references.bib'\ntitle: 'Non-adiabatic interaction effects in the spectra of ultralong-range Rydberg molecules'\n---\n\nIntroduction {#Sec1}\n============\n\nRydberg atoms are an important player in modern quantum physics due to their unique and extreme properties. Their size and dipole moment scale as $n^2$, and lifetimes and polarizability scale as $n^3$ and $n^7$, respectively, where $n$ is the principal quantum number [@gallagher_1994; @sibalic_2018]. They"
+"---\nabstract: 'We present a new algorithm to train a robust malware detector. Malware is a prolific problem and malware detectors are a front-line defense. Modern detectors rely on machine learning algorithms. Now, the adversarial objective is to devise alterations to the malware code to decrease the chance of being detected whilst *preserving the functionality and realism of the malware*. Adversarial learning is effective in improving robustness but generating functional and realistic adversarial malware samples is non-trivial. Because: i)\u00a0in contrast to tasks capable of using gradient-based feedback, adversarial learning in a domain without a *differentiable mapping function* from the *problem space* (malware code inputs) to the *feature space* is hard; and ii)\u00a0it is difficult to ensure the adversarial malware is realistic and functional. This presents a challenge for developing scalable adversarial machine learning algorithms for large datasets at a production or commercial scale to realize robust malware detectors. We propose an alternative; perform adversarial learning in the *feature space* in contrast to the problem space. We *prove* the projection of perturbed, yet valid malware, in the problem space into feature space will always be a subset of adversarials generated in the feature space. Hence, by generating a robust"
+"---\nabstract: 'This paper presents a new approach to Model Predictive Control for environments where essential, discrete variables are partially observed. Under this assumption, the belief state is a probability distribution over a finite number of states. We optimize a *control-tree* where each branch assumes a given state-hypothesis. The control-tree optimization uses the probabilistic belief state information. This leads to policies more optimized with respect to likely states than unlikely ones, while still guaranteeing robust constraint satisfaction at all times. We apply the method to both linear and non-linear MPC with constraints. The optimization of the *control-tree* is decomposed into optimization subproblems that are solved in parallel leading to good scalability for high number of state-hypotheses. We demonstrate the real-time feasibility of the algorithm on two examples and show the benefits compared to a classical MPC scheme optimizing w.r.t. one single hypothesis.'\nauthor:\n- 'Camille Phiquepal$^{1}$ and Marc Toussaint$^{2}$ [^1][^2]'\nbibliography:\n- 'references.bib'\ntitle: '**Control-Tree Optimization: an approach to MPC under discrete Partial Observability** '\n---\n\nINTRODUCTION\n============\n\nIn the field of receding horizon motion planning, uncertainty about the robot environment is often neglected or integrated in the environment representation through heuristics (e.g. by adding safety distances, or potential fields"
+"---\nabstract: 'Artificial intelligence (AI) in healthcare has the potential to improve patient outcomes, but clinician acceptance remains a critical barrier. We developed a novel decision support interface that provides interpretable treatment recommendations for sepsis, a life-threatening condition in which decisional uncertainty is common, treatment practices vary widely, and poor outcomes can occur even with optimal decisions. This system formed the basis of a mixed-methods study in which 24 intensive care clinicians made AI-assisted decisions on real patient cases. We found that explanations generally increased confidence in the AI, but concordance with specific recommendations varied beyond the binary acceptance or rejection described in prior work. Although clinicians sometimes ignored or trusted the AI, they also often prioritized aspects of the recommendations to follow, reject, or delay in a process we term \u201cnegotiation.\u201d These results reveal novel barriers to adoption of treatment-focused AI tools and suggest ways to better support differing clinician perspectives.'\nauthor:\n- Venkatesh Sivaraman\n- 'Leigh A. Bukowski'\n- Joel Levin\n- 'Jeremy M. Kahn'\n- Adam Perer\nbibliography:\n- 'references.bib'\ntitle: 'Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care'\n---\n\n&lt;ccs2012&gt; &lt;concept&gt; &lt;concept\\_id&gt;10003120.10003121.10003129&lt;/concept\\_id&gt; &lt;concept\\_desc&gt;Human-centered computing\u00a0Interactive systems and tools&lt;/concept\\_desc&gt; &lt;concept\\_significance&gt;500&lt;/concept\\_significance&gt; &lt;/concept&gt;"
+"---\nabstract: 'Quadrature formulas (QFs) based on radial basis functions (RBFs) have become an essential tool for multivariate numerical integration of scattered data. Although numerous works have been published on RBF-QFs, their stability theory can still be considered as underdeveloped. Here, we strive to pave the way towards a more mature stability theory for global and function-independent RBF-QFs. In particular, we prove stability of these for compactly supported RBFs under certain conditions on the shape parameter and the data points. As an alternative to changing the shape parameter, we demonstrate how the least-squares approach can be used to construct stable RBF-QFs by allowing the number of data points used for numerical integration to be larger than the number of centers used to generate the RBF approximation space. Moreover, it is shown that asymptotic stability of many global RBF-QFs is independent of polynomial terms, which are often included in RBF approximations. While our findings provide some novel conditions for stability of global RBF-QFs, the present work also demonstrates that there are still many gaps to fill in future investigations.'\nauthor:\n- 'Jan Glaubitz[^1]'\n- 'Jonah Reeger[^2]'\nbibliography:\n- 'literature.bib'\ntitle: 'Towards stability results for global radial basis function based quadrature formulas[^3]'"
+"---\nabstract: |\n    The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self-supervised vision transformers, a recent breakthrough in computer vision and deep learning. Our proposed algorithm uses a combination of machine learning, computer vision and hedonic pricing models trained on real estate data to estimate the value of a given property. We collected and pre-processed a data set of real estate properties in the city of Boulder, Colorado and used it to train, validate and test our algorithm. Our data set consisted of qualitative images (including house interiors, exteriors, and street views) as well as quantitative features such as the number of bedrooms, bathrooms, square footage, lot square footage, property age, crime rates, and proximity to amenities. We evaluated the performance of our model using metrics such as Root Mean Squared Error (RMSE). Our findings indicate that these techniques are able to accurately predict the value of properties, with a low RMSE. The proposed algorithm outperforms traditional appraisal methods that do not leverage property images and has the potential to be used in real-world applications.\\\n    *keywords*: housing price"
+"---\nabstract: 'We develop a novel computational framework to approximate solution operators of evolution partial differential equations (PDEs). By employing a general nonlinear reduced-order model, such as a deep neural network, to approximate the solution of a given PDE, we realize that the evolution of the model parameters is a control problem in the parameter space. Based on this observation, we propose to approximate the solution operator of the PDE by learning the control vector field in the parameter space. From any initial value, this control field can steer the parameter to generate a trajectory such that the corresponding reduced-order model solves the PDE. This allows for substantially reduced computational cost to solve the evolution PDE with arbitrary initial conditions. We also develop comprehensive error analysis for the proposed method when solving a large class of semilinear parabolic PDEs. Numerical experiments on different high-dimensional evolution PDEs with various initial conditions demonstrate the promising results of the proposed method.'\nauthor:\n- 'Nathan Gaby[^1]'\n- 'Xiaojing Ye[^2]'\n- 'Haomin Zhou[^3]'\nbibliography:\n- 'library.bib'\ntitle: 'Neural Control of Parametric Solutions for High-Dimensional Evolution PDEs[^4]'\n---\n\nIntroduction {#sec:intro}\n============\n\nPartial differential equations (PDEs) are ubiquitous in modeling and are vital in numerous applications from"
+"---\nabstract: 'Generative models have the ability to synthesize data points drawn from the data distribution, however, not all generated samples are high quality. In this paper, we propose using a combination of coresets selection methods and \u201centropic regularization\u201d to select the highest fidelity samples. We leverage an Energy-Based Model which resembles a variational auto-encoder with an inference and generator model for which the latent prior is complexified by an energy-based model. In a semi-supervised learning scenario, we show that augmenting the labeled data-set, by adding our selected subset of samples, leads to better accuracy improvement rather than using all the synthetic samples.'\nauthor:\n- |\n    Omead Pooladzandi$^*$,\u00a0\u00a0Pasha Khosravi$^*$,\u00a0\u00a0 Erik Nijkamp$^*$,\u00a0\u00a0 Baharan Mirzasoleiman\\\n    University of California, Los Angeles\\\n    ` {opooladz, pashak, enijkamp, baharan}@ucla.edu`\nbibliography:\n- 'main.bib'\ntitle: |\n    Generating High Fidelity Synthetic Data\\\n    via Coreset selection and Entropic Regularization\n---\n\nIntroduction\n============\n\nIn machine learning, augmenting data-sets with synthetic data has become a common practice which potentially provides significant improvements in downstream tasks such as classification. For example, in the case of images, recent methods like MixMatch, FixMatch and Mean Teacher [@berthelot2019mixmatch] [@raffel2020fixmatch] [@tarvainen2017mean] have proposed data augmentation techniques which rely on simple pre-defined transformations such as cropping, resizing,"
+"---\nabstract: 'In federated learning (FL), the global model at the server requires an efficient mechanism for weight aggregation and a systematic strategy for collaboration selection to manage and optimize communication payload. We introduce a practical and cost-efficient method for regularized weight aggregation and propose a laborsaving technique to select collaborators per round. We illustrate the performance of our method, regularized similarity weight aggregation (RegSimAgg), on the Federated Tumor Segmentation (FeTS) 2022 challenge\u2019s federated training (weight aggregation) problem. Our scalable approach is principled, frugal, and suitable for heterogeneous non-IID collaborators. Using FeTS2021 evaluation criterion, our proposed algorithm RegSimAgg stands at 3rd position in the final rankings of FeTS2022 challenge in the weight aggregation task. Our solution is open sourced at: <https://github.com/dskhanirfan/FeTS2022>'\nauthor:\n- Muhammad Irfan Khan\n- Mohammad Ayyaz Azeem\n- Esa Alhoniemi\n- Elina Kontio\n- 'Suleiman A. Khan'\n- Mojtaba Jafaritadi\nbibliography:\n- 'main.bib'\ntitle: Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation\n---\n\nIntroduction\n============\n\nFederated learning (FL) is on the horizon to replace the current paradigm of data sharing, allowing for privacy-preserving cross-institutional research including a wide range of biomedical disciplines. In simple terms, FL is a machine learning paradigm in a distributed or"
+"---\nabstract: 'Few-shot learning allows pre-trained language models to adapt to downstream tasks while using a limited number of training examples. However, practical applications are limited when all model parameters must be optimized. In this work we apply a new technique for parameter efficient few shot learning while adopting a strict definition of parameter efficiency. Our training method combines 1) intermediate training by reformulating natural language tasks as entailment tasks [@wang_entailment_2021] and 2) differentiable optimization of template and label tokens [@zhang_differentiable_2021]. We quantify the tradeoff between parameter efficiency and performance in the few shot regime and propose a simple model agnostic approach that can be extended to any task By achieving competitive performance while only optimizing 3% of a model\u2019s parameters and allowing for batched inference, we allow for more efficient practical deployment of models.'\nauthor:\n- Anonymous\n- |\n    Ethan Kim\\\n    Harvard University\\\n    Jerry Yang\\\n    Harvard University\\\nbibliography:\n- 'anthology.bib'\n- 'custom.bib'\ntitle: Differentiable Entailment for Parameter Efficient Few Shot Learning\n---\n\n=1\n\nIntroduction\n============\n\nLarge pre-trained language models have demonstrated adaptability to solve natural language processing (NLP) tasks. Typically, such language models are adapted to a downstream task through fine-tuning [@howard_universal_2018]. Although fine-tuning improves performance on downstream tasks,"
+"---\nauthor:\n- 'Dimitra Tsigkari, George Iosifidis, and Thrasyvoulos Spyropoulos [^1] [^2] [^3] [^4] [^5]'\nbibliography:\n- 'IEEEabrv.bib'\n- 'tsigkari\\_quid-pro-quo.bib'\ntitle: |\n    Quid pro Quo in Streaming Services:\\\n    Algorithms for Cooperative Recommendations\n---\n\nIntroduction\n============\n\nBackground and Motivation\n-------------------------\n\nRecommender systems\u00a0(RSs) permeate today\u2019s on-demand streaming services such as Netflix, Disney+, etc.; and are affecting substantially the content requests issued by their subscribers. In Netflix, for example, it is estimated that $80\\%$ of the requests stem from the recommendations that are offered to its users\u00a0[@gomez2016netflix]. Indeed, by proposing contents that are relevant to their users\u2019 interests, Content Providers\u00a0(CPs) can increase the viewing activity in their platforms, reduce the user churn, and eventually boost their revenues\u00a0[@gomez2016netflix]. Therefore, it is not surprising that CPs comprehend the business value of these systems and invest research and financial resources to improve their accuracy.\n\nAt the same time, recommendations can be leveraged by content caching networks to steer user requests towards nearby-cached contents. These caching networks are either today\u2019s traditional Content Delivery Networks\u00a0(CDNs) or edge cache providers in future wireless architectures\u00a0(we will use, hereafter, the term CDN to imply any such caching network provider). The recently-coined terms of cache/network-friendly recommendations"
+"---\nabstract: 'We consider the varifold associated to the Allen\u2013Cahn phase transition problem in $\\mathbb R^{n+1}$(or $n+1$-dimensional Riemannian manifolds with bounded curvature) with integral $L^{q_0}$ bounds on the Allen\u2013Cahn mean curvature (first variation of the Allen\u2013Cahn energy) in this paper. It is shown here that there is an equidistribution of energy between the Dirichlet and Potential energy in the phase field limit and that the associated varifold to the total energy converges to an integer rectifiable varifold with mean curvature in $L^{q_0}, q_0 > n$. The latter is a diffused version of Allard\u2019s convergence theorem for integer rectifiable varifolds.'\naddress:\n- |\n    School of Mathematical Sciences\\\n    Queen Mary University of London\\\n    Mile End Road\\\n    London E1 4NS\n- |\n    School of Mathematical Sciences\\\n    Queen Mary University of London\\\n    Mile End Road\\\n    London E1 4NS\nauthor:\n- Huy The Nguyen\n- Shengwen Wang\nbibliography:\n- 'AllardAllenCahn.bib'\ntitle: 'Quantization of the Energy for the inhomogeneous Allen\u2013Cahn mean curvature'\n---\n\nIntroduction\n============\n\nLet $\\Omega\\subset\\mathbb(M^{n+1},g)$ be an open subset in a Riemannian manifold with bounded curvature. Consider $u\\in W^{2,p}(\\Omega)$ satisfying the following equation $$\\begin{aligned}\n\\label{PFVe}\n    {\\varepsilon}\\Delta u_{\\varepsilon}-\\frac{W'(u_{\\varepsilon})}{{\\varepsilon}}=f_{\\varepsilon},\n    \\end{aligned}$$ where $W(t)=\\frac{(1-t^2)^2}{2}$ is a double-well potential. The equation can be viewed as a prescribed first variation"
+"---\nabstract: 'This paper investigates big Ramsey degrees of unrestricted relational structures in (possibly) infinite languages. While significant progress has been made in studying big Ramsey degrees, many classes of structures with finite small Ramsey degrees still lack an understanding of their big Ramsey degrees. We show that if there are only finitely many relations of every arity greater than one, then unrestricted relational structures have finite big Ramsey degrees, and give some evidence that this is tight. This is the first time that finiteness of big Ramsey degrees has been established for an infinite-language random structure. Our results represent an important step towards a better understanding of big Ramsey degrees for structures with relations of arity greater than two.'\naddress:\n- 'Computer Science Institute of Charles University (I\u00daUK), Charles University, Malostransk\u00e9 n\u00e1m\u011bst\u00ed\u00a025, Praha\u00a01, Czech Republic'\n- 'Department of Applied Mathematics (KAM), Charles University, Malostransk\u00e9 n\u00e1m\u011bst\u00ed\u00a025, Praha\u00a01, Czech Republic'\n- 'Laboratoire Paul Painlev\u00e9, Universit\u00e9 de Lille, 59 655 Villeneuve d\u2019Ascq C\u00e9dex, France'\n- 'Department of Applied Mathematics (KAM), Charles University, Malostransk\u00e9 n\u00e1m\u011bst\u00ed\u00a025, Praha\u00a01, Czech Republic'\n- 'Department of Mathematics, University of Toronto, Toronto, Canada, M5S 2E4'\n- 'Department of Applied Mathematics (KAM), Charles University,"
+"---\nauthor:\n- Tien Mai\n- Avinandan Bose\n- Arunesh Sinha\n- 'Thanh H. Nguyen'\nbibliography:\n- 'refs.bib'\ntitle: Tackling Stackelberg Network Interdiction against a Boundedly Rational Adversary\n---\n\nIntroduction\n============\n\nNetwork interdiction is a well-studied topic in Artificial Intelligence. There are many practical problems\u00a0[@smith2020survey], such as in cyber systems and illicit supply networks, that can be modelled as a network interdiction problem. In literature, many variations in models of network interdiction exist, and consequentially, a variety of techniques have been used for solving different types of these problems. Our work focuses on a particular type in which there is a set of critical nodes ${{\\mathcal{L}}}$ to protect within a larger network of ${{\\mathcal{S}}}$ nodes. We employ a popular network interdiction model\u00a0[@fulkerson1977maximizing; @israeli2002shortest], where the interdictor (defender) uses a randomized allocation of limited defense resources for the critical nodes in ${{\\mathcal{L}}}$. The adversary traverses the graphs starting from an origin $s_o$ and reaching a destination $s_d$. There is an interaction with the defender only if the adversary crosses any node in ${{\\mathcal{L}}}$. The interaction is modelled using a leader-follower (Stackelberg) game setting where the defender first allocates resources in a randomized fashion and then the adversary chooses its"
+"---\nabstract: |\n    Pen testing is the problem of selecting high-capacity resources when the only way to measure the capacity of a resource expends its capacity. We have a set of $n$ pens with unknown amounts of ink and our goal is to select a feasible subset of pens maximizing the total ink in them. We are allowed to gather more information by writing with them, but this uses up ink that was previously in the pens. Algorithms are evaluated against the standard benchmark, i.e, the optimal pen testing algorithm, and the omniscient benchmark, i.e, the optimal selection if the quantity of ink in the pens are known.\n\n    We identify optimal and near optimal pen testing algorithms by drawing analogues to auction theoretic frameworks of deferred-acceptance auctions and virtual values. Our framework allows the conversion of any near optimal deferred-acceptance mechanism into a near optimal pen testing algorithm. Moreover, these algorithms guarantee an additional overhead of at most $(1+o(1)) \\ln n$ in the approximation factor of the omniscient benchmark. We use this framework to give pen testing algorithms for various combinatorial constraints like matroid, knapsack and general downward-closed constraints and also for online environments.\nauthor:\n- Aadityan Ganesh\n- Jason"
+"---\nabstract: 'The recent emergence of 6G raises the challenge of increasing the transmission data rate even further in order to break the barrier set by the Shannon limit. Traditional communication methods fall short of the 6G goals, paving the way for Semantic Communication (SemCom) systems. These systems find applications in wide range of fields such as economics, metaverse, autonomous transportation systems, healthcare, smart factories, etc. In SemCom systems, only the relevant information from the data, known as semantic data, is extracted to eliminate unwanted overheads in the raw data and then transmitted after encoding. In this paper, we first use the shared knowledge base to extract the keywords from the dataset. Then, we design an auto-encoder and auto-decoder that only transmit these keywords and, respectively, recover the data using the received keywords and the shared knowledge. We show analytically that the overall semantic distortion function has an upper bound, which is shown in the literature to converge. We numerically compute the accuracy of the reconstructed sentences at the receiver. Using simulations, we show that the proposed methods outperform a state-of-the-art method in terms of the average number of words per sentence.'\nauthor:\n- '[^1]'\nbibliography:\n- 'references.bib'\ntitle: 'Knowledge-Aware"
+"---\nabstract: 'Contrary to common assumptions, a transcritical domain exists during the early times of liquid hydrocarbon fuel injection at supercritical pressure. A sharp two-phase interface is sustained before substantial heating of the liquid. Thus, two-phase dynamics has been shown to drive the early three-dimensional deformation and atomisation. A recent study of a transcritical liquid jet shows distinct deformation features caused by interface thermodynamics, low surface tension, and intraphase diffusive mixing. In the present work, the vortex identification method $\\lambda_\\rho$, which considers the fluid compressibility, is used to study the vortex dynamics in a cool liquid *n*-decane transcritical jet surrounded by a hotter oxygen gaseous stream at supercritical pressures. The relationship between vortical structures and the liquid surface evolution is detailed, along with the vorticity generation mechanisms, including variable-density effects. The roles of hairpin and roller vortices in the early deformation of lobes, the layering and tearing of liquid sheets, and the formation of fuel-rich gaseous blobs are analysed. At these high pressures, enhanced intraphase mixing and ambient gas dissolution affect the local liquid structures (i.e., lobes). Thus, liquid breakup differs from classical sub-critical atomisation. Near the interface, liquid density and viscosity drop by up to 10% and 70%, respectively,"
+"---\nabstract: |\n    Superconducting diode effect, in analogy to the nonreciprocal resistive charge transport in semiconducting diode, is a nonreciprocity of dissipationless supercurrent. Such an exotic phenomenon originates from intertwining between symmetry-constrained supercurrent transport and intrinsic quantum functionalities of helical/chiral superconductors. In this article, research progress of superconducting diode effect including fundamental concepts, material aspects, device prospects, and theoretical/experimental development is reviewed. First, fundamental mechanisms to cause superconducting diode effect including simultaneous space-inversion and time-reversal symmetry breaking, magnetochiral anisotropy, interplay between spin-orbit interaction energy and the characteristic energy scale of supercurrent carriers, and finite-momentum Cooper pairing are discussed. Second, the progress of superconducting diode effect from theoretical predictions to experimental observations are reviewed. Third, interplay between various system parameters leading to superconducting diode effect with optimal performance is presented. Then, it is explicitly highlighted that nonreciprocity of supercurrent can be characterized either by current-voltage relation obtained from resistive direct-current measurements in the metal-superconductor fluctuation region ($T\\approx T_c$) or by current-phase relation and nonreciprocity of superfluid inductance obtained from alternating-current measurements in the superconducting phase ($T<T_c$). Finally, insight into future directions in this active research field is provided with a perspective analysis on intertwining between band-topology and helical superconductivity, which could"
+"---\nauthor:\n- 'Ji Yao[^1], Huanyuan Shan[^2], Pengjie Zhang[^3], Xiangkun Liu, Catherine Heymans, Benjamin Joachimi, Marika Asgari, Maciej Bilicki, Hendrik Hildebrandt, Konrad Ku\u0133ken, Tilman Tr\u00f6ster, Jan Luca van den Busch, Angus Wright,'\n- Ziang Yan\nbibliography:\n- 'references.bib'\ndate: 'Received January 30, 2023; accepted ?'\ntitle: 'KiDS-1000: Cross-correlation with Planck cosmic microwave background lensing and intrinsic alignment removal with self-calibration'\n---\n\n[Galaxy shear and cosmic microwave background (CMB) lensing convergence cross-correlations contain additional information on cosmology with respect to auto-correlations. While remaining immune to certain systemic effects, these cross-correlations are nonetheless affected by the galaxy\u2019s intrinsic alignments (IA). These effects may, in fact, be responsible for the reported low lensing amplitude of the galaxy shear $\\times$ CMB convergence cross-correlations, compared to the standard Planck $\\Lambda$CDM (cosmological constant and cold dark matter) cosmology predictions.]{} [In this work, we investigate how IA affects the Kilo-Degree Survey (KiDS) galaxy lensing shear and Planck CMB lensing convergence cross-correlation and we compare it to previous treatments, both with and without IA taken into consideration.]{} [We compared the marginalization over IA parameters and the IA self-calibration (SC) method (with additional observables defined only from the source galaxies) to demonstrate that SC can efficiently break the degeneracy"
+"---\nauthor:\n- 'Reetika Joshi , Manuel Luna , Brigitte Schmieder , Fernando Moreno-Insertis ,'\n- Ramesh Chandra\nbibliography:\n- 'references-4.bib'\ntitle: 'Interaction of solar jets with filaments: Triggering of large-amplitude filament oscillations'\n---\n\n[Large-amplitude oscillations (LAOs) are often detected in filaments. Using multiwavelength observations, their origin can be traced back to the interaction with eruptions and jets. ]{} [We present two different case studies as observational evidence in support of 2.5D MHD numerical experiments that show that the LAOs in the filament channels can be initiated by solar jets. ]{} [ We use longitudinal magnetic field observations using the Helioseismic Magnetic Imager to study the evolution of the filament channels. The LAOs in the filaments are analysed using two techniques. The first one is time-distance diagnostics with EUV and H$\\alpha$ datasets. In the second method, the oscillations in different parts of the filaments are examined using Fourier analysis of the brightness variations of all pixels in H$\\alpha$ observations. ]{} [In the two studied events, we can identify a quadrupolar configuration with an X-point at the top of the parasitic region suggestive of a classical null-point. The X-point evolves into a flat structure suggestive of a breakout current sheet. A"
+"---\nabstract: |\n    We study the (variable-length) codes of the form\u00a0$X \\cup {\\left\\{a^n\\right\\}}$ where\u00a0$X \\subseteq a^*\\omega a^*$ and\u00a0${\\left|X\\right|} = n$. We extend various notions and results from *factorizations of cyclic groups* theory to this type of codes.\n\n    In particular, when\u00a0$n$ is the product of at most three primes or has the form\u00a0$p^k q$ (with\u00a0$p$ and\u00a0$q$ prime), we prove that they are composed of prefix and suffix codes. We provide counterexamples for other\u00a0$n$. It implies that the long-standing *triangle conjecture* is true for this type of\u00a0$n$. We also prove a conjecture about the size of a potential counterexample to the conjecture.\nauthor:\n- 'Christophe Cordero[^1]'\nbibliography:\n- 'biblio.bib'\ndate: |\n     [$^*$Dipartimento di Informatica ed Applicazioni, Universit\u00e0 di Salerno,\\\n    via Giovanni Paolo II 132, Fisciano, 84084, ITALY.]{}\\\n    Nov 20, 2022\ntitle: Factorizations of Cyclic Groups and Bayonet Codes\n---\n\nIntroduction {#introduction .unnumbered}\n============\n\nSch\u00fctzenberger founded and developed the theory of (variable-length) *codes* in the\u00a01960s in order to study encoding problems raised by Shannon\u2019s information theory. Since then, the theory has undergone its own development and links with monoids, automata, combinatorics on words and factorizations of cyclic groups have emerged. We refer the"
+"---\nabstract: 'The first order variation of the matter energy-momentum tensor $T_{\\mu \\nu}$ with respect to the metric tensor $g^{\\alpha \\beta}$ plays an important role in modified gravity theories with geometry-matter coupling, and in particular in the $f(R,T)$ modified gravity theory. We obtain the expression of the variation $\\delta T_{\\mu \\nu}/\\delta g^{\\alpha \\beta}$ for the baryonic matter described by an equation given in a parametric form, with the basic thermodynamic variables represented by the particle number density, and by the specific entropy, respectively. The first variation of the matter energy-momentum tensor turns out to be independent on the matter Lagrangian, and can be expressed in terms of the pressure, the energy-momentum tensor itself, and the matter fluid four-velocity. We apply the obtained results for the case of the $f(R,T)$ gravity theory, where $R$ is the Ricci scalar, and $T$ is the trace of the matter energy-momentum tensor, which thus becomes a unique theory, also independent on the choice of the matter Lagrangian. A simple cosmological model, in which the Hilbert-Einstein Lagrangian is generalized through the addition of a term proportional to $T^n$ is considered in detail, and it is shown that it gives a very good description of the observational"
+"---\nabstract: 'In this paper, we study compactifications of the moduli of smooth del Pezzo surfaces using K-stability and the line arrangement. We construct K-moduli of log del Pezzo pairs with sum of lines as boundary divisors, and prove that for $d=2,3,4$, these K-moduli of pairs are isomorphic to the K-moduli spaces of del Pezzo surfaces. For $d=1$, we prove that they are different by exhibiting some walls.'\naddress: '851 S Morgan St, 60607, Chicago, Illinois, USA'\nauthor:\n- Junyan Zhao\nbibliography:\n- 'citation.bib'\ntitle: 'Compactifications of moduli of del Pezzo surfaces via line arrangement and K-stability'\n---\n\nIntroduction\n============\n\nThe moduli spaces of del Pezzo surfaces have been extensively studied thanks to the development of different approaches to construct moduli spaces.\n\nA weighted marked del Pezzo surface is a pair $(S,B)$ with $S$ a del Pezzo surface and $B=\\sum b_iB_i$ a boundary divisor, where $b_i\\in[0,1]$ and $B_i$\u2019s are all the finitely many lines on $S$. The KSBA moduli spaces parameterizes all such pairs such that $(S,B)$ is semi log canonical (slc) and $K_S+B$ is ample. For example, when $b_i=1$ for each $i$, the moduli of such quartic del Pezzo pairs is isomorphic to the moduli ${\\overline{M}}_{0,5}$ of rational curves"
+"---\nabstract: 'Conventional ASR systems use frame-level phoneme posterior to conduct force-alignment\u00a0(FA) and provide timestamps, while end-to-end ASR systems especially AED based ones are short of such ability. This paper proposes to perform timestamp prediction\u00a0(TP) while recognizing by utilizing continuous integrate-and-fire\u00a0(CIF) mechanism in non-autoregressive ASR model - Paraformer. Foucing on the fire place bias issue of CIF, we conduct post-processing strategies including fire-delay and silence insertion. Besides, we propose to use scaled-CIF to smooth the weights of CIF output, which is proved beneficial for both ASR and TP task. Accumulated averaging shift\u00a0(AAS) and diarization error rate\u00a0(DER) are adopted to measure the quality of timestamps and we compare these metrics of proposed system and conventional hybrid force-alignment system. The experiment results over manually-marked timestamps testset show that the proposed optimization methods significantly improve the accuracy of CIF timestamps, reducing 66.7% and 82.1% of AAS and DER respectively. Comparing to Kaldi force-alignment trained with the same data, optimized CIF timestamps achieved 12.3% relative AAS reduction.'\nauthor:\n- Xian Shi\n- Yanni Chen\n- Shiliang Zhang\n- Zhijie Yan\ntitle: 'Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model'\n---\n\nIntroduction\n============\n\nTimestamp prediction is one of"
+"---\nabstract: 'We propose a novel way of modulating exceptional topology by implementing Floquet engineering in non-hermitian (NH) systems. We introduce Floquet exceptional topological insulator which results from shining light on a conventional three-dimensional NH topological insulator. Light-matter interaction facilitates the quantum phases of matter to exhibit a novel phenomenon, where, the point gaps in the bulk host surface states. These distinct surface states either fill the point gap in the complex eigenspectrum or exhibit exceptional points in the presence of a magnetic field. We also highlight the existence of a quantum anomaly generated by photo-induced modulation. The existence of the Floquet biorthogonal Chern number and spectral winding number show that the momentum slices exhibit NH skin effect, even though the system as a whole does not. We also employ wave-dynamics evolution to illustrate the NH surface skin effect.'\nauthor:\n- Gaurab Kumar Dash\n- Subhajyoti Bid\n- Manisha Thakurathi\nbibliography:\n- 'sample.bib'\ntitle: Floquet Exceptional Topological Insulator\n---\n\n*Introduction.* The non-hermitian (NH) topological phases have been the center of attraction for theoretical as well as experimental studies [@RevModPhys.93.015005; @Sbid]. Many experimentally realizable models of NH systems have been proposed in condensed matter physics hosting exceptional points (EPs), lines, rings,"
+"---\nabstract: 'This paper describes an online off-policy data-driven reinforcement learning based-algorithm to regulate and control the relative position of a deputy satellite in an autonomous satellite docking problem. The optimal control policy is learned under the framework of output regulation problem and adaptive dynamic programming (ADP) by considering the continuous-time linearized model of the satellite. The linearized model of relative motion is used to describe the motion between satellites, and the satellite docking problem is formulated as a linear optimal output regulation problem, in which the feedback-forward optimal controller is used to track a class of references and rejecting a class of disturbances while maintaining the overall system\u2019s closed-loop stability. The optimal control problem is presented using a data-driven reinforcement learning based method to regulate the relative position and velocity of the deputy to safely dock with the chief. Using the adaptive optimal output regulation framework, the learned optimal feedback-feedforward gains guarantee optimal transient and steady state performances without any prior knowledge of the dynamics of the studied system. [The states/input information of the underlying dynamical system are instead used to compute the approximated optimal feedback-feedforward control gain matrices.]{} Reference tracking and disturbance rejection are achieved in an optimal"
+"---\nabstract: |\n    As variable renewable energy sources comprise a growing share of total electricity generation, energy storage technologies are becoming increasingly critical for balancing energy generation and demand.\n\n    In this study, we modeled an existing thermal energy storage unit with estimated capital costs that are sufficiently low to enable large-scale deployment in the electric power system. Our analysis emphasizes the value of using such units to cost-effectively improve renewable energy dispatchability. This study modeled an existing real-world grid rather than simulating hypothetical future electric power systems. The storage unit coupled with a photovoltaic (PV) system was modeled with different storage capacities, whereas each storage unit size had various discharge capacities.\n\n    The modeling was performed under a baseline case with no emission constraints and under hypothetical scenarios in which CO$_2$ emissions were reduced. The results show that power availability increases with increasing storage size and vastly increases in the hypothetical CO$_2$ reduction scenarios, as the storage unit is utilized differently. When CO$_2$ emissions are reduced, the power system must be less dependent on fossil fuel technologies that currently serve the grid, and thus rely more on the power that is served from the PV + storage unit.\n\n    The proposed approach"
+"---\nabstract: 'We study a very restrictive graph exploration problem. In our model, an agent without persistent memory is placed on a vertex of a graph and only sees the adjacent vertices. The goal is to visit every vertex of the graph, return to the start vertex, and terminate. The agent does not know through which edge it entered a vertex. The agent may color the current vertex and can see the colors of the neighboring vertices in an arbitrary order. The agent may not recolor a vertex. We investigate the number of colors necessary and sufficient to explore all graphs. We prove that $n-1$ colors are necessary and sufficient for exploration in general, $3$ colors are necessary and sufficient if only trees are to be explored, and $\\min\\{2k-3,n-1\\}$ colors are necessary and $\\min\\{2k-1,n-1\\}$ colors are sufficient on graphs of size $n$ and circumference $k$, where the circumference is the length of a longest cycle. This only holds if an algorithm has to explore all graphs and not merely certain graph classes. We give an example for a graph class where each graph can be explored with $4$ colors, although the graphs have maximal circumference. Moreover, we prove that recoloring"
+"---\nabstract: 'Port scanning is the process of attempting to connect to various network ports on a computing endpoint to determine which ports are open and which services are running on them. It is a common method used by hackers to identify vulnerabilities in a network or system. By determining which ports are open, an attacker can identify which services and applications are running on a device and potentially exploit any known vulnerabilities in those services. Consequently, it is important to detect port scanning because it is often the first step in a cyber attack. By identifying port scanning attempts, cybersecurity professionals can take proactive measures to protect the systems and networks before an attacker has a chance to exploit any vulnerabilities. Against this background, researchers have worked for over a decade to develop robust methods to detect port scanning. While there have been various surveys, none have focused solely on machine learning based detection schemes specific to port scans. Accordingly, we provide a systematic review of 15 papers published between February 2021 and January 2023. We extract critical information such as training dataset, algorithm used, technique, and model accuracy. We also collect unresolved challenges and ideas for future work."
+"---\nabstract: 'Proximity induced superconductivity with a clean interface has attracted much attention in recent years. We discuss how the commonly-employed electron tunneling approximation can be hybridized with first-principles calculation to achieve a quantitative characterization starting from the microscopic atomic structure. By using the graphene-Zn heterostructure as an example, we compare this approximated treatment to the full *ab inito* anisotropic Eliashberg formalism. Based on the calculation results, we discuss how superconductivity is affected by the interfacial environment.'\nauthor:\n- Yunhao Li\n- Zimeng Zeng\n- Jizheng Wu\n- Chen Si\n- Zheng Liu\nbibliography:\n- 'references.bib'\ntitle: 'Quantifying proximity-induced superconductivity from first-principles calculations'\n---\n\nIntroduction\n============\n\nSince the establishment of the BCS theory[@10; @11], the occurrence of superconductivity (SC) in non-superconducting materials (N) placed in proximity to a superconductor (S) has been studied by various semiempirical approximations[@5; @6; @12]. Historically, the dirty interface was better modelled, for which the detailed interfacial structure is less important and the motion of the superconducting electrons can be described by a simplified diffusion equation[@6; @13]. In contrast, for the clean NS heterostructure, an atomic characterization of the interfacial coupling is more complicated.\n\nIn the past two decades, the first-principles calculation within the framework of"
+"---\nabstract: 'This work presents an approach for incrementally updating deep neural network (DNN) models in a non-stationary environment. DNN models are sensitive to changes in input data distribution, which limits their application to problem settings with stationary input datasets. In a non-stationary environment, updating a DNN model requires parameter re-training or model fine-tuning. We propose an unsupervised source-free method to update DNN classification models. The contributions of this work are two-fold. First, we use trainable Gaussian prototypes to generate representative samples for future iterations; second, using unsupervised domain adaptation, we incrementally adapt the existing model using unlabelled data. Unlike existing methods, our approach can update a DNN model incrementally for non-stationary source and target tasks without storing past training data. We evaluated our work on incremental sentiment prediction and incremental disease prediction applications and compared our approach to state-of-the-art continual learning, domain adaptation, and ensemble learning methods. Our results show that our approach achieved improved performance compared to existing incremental learning methods. We observe minimal forgetting of past knowledge over many iterations, which can help us develop unsupervised self-learning systems.'\nbibliography:\n- 'ref.bib'\n---\n\nIntroduction\n============\n\nDeep neural networks (DNN) have shown exceptional performance in several practical classification problems"
+"---\nabstract: 'Shaping light deep inside complex media, such as biological tissue, is critical to many research fields. Although the coherent control of scattered light via wavefront shaping has made significant advances in addressing this challenge, controlling light over extended or multiple targets without physical access inside a medium remains elusive. Here we present a phase conjugation method for spatially incoherent light, which enables the non-invasive light control based on incoherent emission from multiple target positions. Our method characterizes the scattering responses of hidden sources by retrieving mutually incoherent scattered fields from speckle patterns. By time-reversing scattered fluorescence with digital phase conjugation, we experimentally demonstrate focusing of light on individual and multiple targets. We also demonstrate maximum energy delivery to an extended target through a scattering medium by exploiting transmission eigenchannels. This paves the way to control light propagation in complex media using incoherent contrasts mechanisms.'\nauthor:\n- YoonSeok Baek\n- 'Hilton B. de Aguiar'\n- Sylvain Gigan\ntitle:\n-  Phase conjugation with spatially incoherent light in complex media\n- \n---\n\nMain\n====\n\nDelivering optical energy and transmitting information through complex media remains an important challenge in many fields of studies, including optical manipulation [@vcivzmar2010], deep-tissue imaging [@ntziachristos2010; @kubby2019] and"
+"---\nauthor:\n- |\n    Jinxing Zhou, Xuyang Shen, Jianyuan Wang, Jiayi Zhang, Weixuan Sun, Jing Zhang,\\\n    Stan Birchfield, Dan Guo, Lingpeng Kong, Meng Wang,\u00a0, and Yiran Zhong\nbibliography:\n- 'IEEEabrv.bib'\n- 'sample-base.bib'\ntitle: 'Audio-Visual Segmentation with Semantics'\n---\n\n[Shell : Bare Advanced Demo of IEEEtran.cls for IEEE Computer Society Journals]{}\n\nIntroduction {#intro}\n============\n\nlargely rely on visual and auditory cues to understand their environmental surroundings. For example, a dog barking can be distinguished from a bird calling based on both their sound and appearance. Such audio-visual information is integrated with the brain in a synthesis process\u00a0[@wei2022learning], crucial for comprehensively perceiving the world. Inspired by this cognitive ability of humans, we explore audio-visual learning with deep models via the integration of multi-modal signals.\n\n![image](./figures/task_illustration_AVS_SSL_v3.pdf){width=\"\\textwidth\"}\n\nOver the years, researchers have studied various problems within audio-visual artificial perception. For instance, some researchers investigate the audio-visual correspondence (AVC) problem\u00a0[@arandjelovic2017look; @arandjelovic2018objects; @aytar2016soundnet], which aims to determine whether an audio signal and a visual image describe the same scene. AVC is based on the phenomenon that these two signals usually occur simultaneously, such as a barking dog, a singing person, and a humming car. Others study the audio-visual event localization (AVEL)\u00a0[@lin2019dual; @lin2020audiovisual;"
+"---\nabstract: 'In this paper, we present a powerful method (Atomistic Green\u2019s Function, AGF) for calculating the effective Hamiltonian of acoustic and elastic wave-scatterers. The ability to calculate the effective Hamiltonian allows for the study of scattering problems in infinite systems without the introduction of any artificial truncating boundaries such as perfectly matched layers or Dirichlet to Neumann (DtN) maps. Furthermore, the AGF formalism also allows for the efficient calculation of the Green\u2019s function of the scatterer as well as all relevant scattering metrics including reflection and transmission ratios. The formalism presented here is especially suited to scattering problems involving waveguides, phononic crystals, metamaterials, and metasurfaces. We show the application of the method to three scattering problems: scattering from a slab (1D), scattering from a finite phononic crystal (1D), and scattering from defects in a waveguide (2D).'\nauthor:\n- 'Hossein Khodavirdi$^a$, Zhun-Yong Ong$^b$, Ankit Srivastava$^a$'\ntitle: 'The Atomistic Green\u2019s Function method for acoustic and elastic wave-scattering problems'\n---\n\n[^1]\n\nIntroduction {#sec:intro}\n============\n\nIn this paper, we consider the broad problem of wave scattering from a finite scatterer into an infinite environment from the perspective of the Atomic Green\u2019s Function (AGF) method. The AGF method is appealing as it results in"
+"---\nabstract: 'Microfluidic droplet screens serve as an innovative platform for high-throughput biotechnology, enabling significant advancements in discovery, product optimization, and analysis. This review sheds light on the emerging trend of interaction assays in microfluidic droplets, underscoring the unique suitability of droplets for these applications. Encompassing a diverse range of biological entities such as antibodies, enzymes, DNA, RNA, various microbial and mammalian cell types, drugs, and other molecules, these assays demonstrate their versatility and scope. Recent methodological breakthroughs have escalated these screens to novel scales of bioanalysis and biotechnological product design. Moreover, we highlight pioneering advancements that extend droplet-based screens into new domains: cargo delivery within human bodies, application of synthetic gene circuits in natural environments, 3D-printing, and the development of droplet structures responsive to environmental signals. The potential of this field is profound and only set to increase.'\nauthor:\n- Carolus Vitalis\n- Tobias Wenzel\nbibliography:\n- 'cas-refs.bib'\ntitle: Leveraging Interactions in Microfluidic Droplets for Enhanced Biotechnology Screens\n---\n\nbiotechnology ,microfluidics ,droplets ,droplet screen ,interactions ,single cell ,molecular interactions 87.80.-y ,87.80.Rb ,87.14.-g ,47.80.-v 76T30 ,78A70 ,68U10\n\nThe Power of Interaction Screens in Droplets {#sec:introduction}\n============================================\n\nIn the rapidly advancing field of biotechnology, interaction screens in droplets are emerging as"
+"---\nabstract: 'Motivated by the corrected form of the entropy\u2013area law, and with the help of von Neumann entropy of quantum matter, we construct an emergent spacetime by the virtue of the geometric language of statistical information manifolds. We discuss the link between Wald and Jacobson approaches of thermodynamic/gravity correspondence and Fisher *pseudo*\u2013Riemannian metric of information manifold. We derive in detail Einstein\u2019s field equations in statistical information geometric forms. This results in finding a quantum origin of a positive cosmological constant that is founded on Fisher metric. This cosmological constant resembles those found in Lovelock\u2019s theories in a de Sitter background as a result of using the complex extension of spacetime and the Gaussian exponential families of probability distributions, and we find a time varying dynamical gravitational constant as a function of Fisher metric together with the corresponding Ryu\u2013Takayanagi formula of such system. Consequently, we obtain a dynamical equation for the entropy in information manifold using Liouville\u2013von Neumann equation from the Hamiltonian of the system. This Hamiltonian is suggested to be non\u2013Hermitian, which corroborates the approaches that relate non\u2013unitary conformal field theories to information manifolds. This provides some insights on resolving \u201cthe problem of time\u201d.'\nauthor:\n- 'Hassan Alshal [^1]'"
+"---\nauthor:\n- \n- \n- \nbibliography:\n- 'sn-bibliography.bib'\ntitle: Theoretical aspects in penalty hyperparameters optimization\n---\n\nAcknowledgment {#acknowledgment .unnumbered}\n==============\n\nThe authors would like to thank Prof. A. M. Candela and Prof. N. Del Buono from Universit\u00e0 degli Studi di Bari Aldo Moro for the deep discussions on the preliminary version of this manuscript. This work was supported by INDAM-GNCS.\n\nIntroduction\n============\n\nTraining a Machine Learning (ML) algorithm is quite important to produce data-driven models, which can be successfully applied in real life applications. These processes often require to specify several variables by the users, namely hyperparameters, which must be set before the learning procedure starts. Hyperparameters govern the whole learning process and play a crucial role in guaranteeing good model performances. They are often manually specified, and the lack of an automatic tuning procedure makes the field of Hyperparameter Optimization (HPO) an ever-evolving topic. The literature offers various solutions for hyperparameters tuning, from Gradient-based to Black-Box or Bayesian\u2019s approaches, beside some naive but daily used methods such as Grid and Random search. A brief overview on existing methods can be found in\u00a0[@nostro]. Hyperparameters can be of different types (discrete, continuous, categorical), and in most cases, the number of"
+"---\nabstract: 'Photoionization of the rubidium (Rb) atoms cooled in a magneto-optical trap, characterized by the coexistence of the ground 5$S_{1/2}$ and the excited 5$P_{3/2}$ states, is investigated experimentally and theoretically with the 400 nm femtosecond laser pulses at intensities of $I=3\\times10^9$ W/cm$^2$ $-4.5\\times10^{12}$ W/cm$^2$. Recoil-ion momentum distribution (RIMD) of Rb$^+$ exhibits rich ring-like structures and their energies correspond to one-photon ionization of the 5$P_{3/2}$ state, two-photon and three-photon ionizations of the 5$S_{1/2}$ state, respectively. With the increasing of $I$, we find that experimental signals near zero-momentum (NZM) in RIMDs resulted from the 5$P_{3/2}$ state enhance dramatically and its peaked Rb$^+$ momenta dwindle obviously while that from the 5$S_{1/2}$ state is maintained. Meanwhile, the ion-yield ratio of the 5$S_{1/2}$ over the 5$P_{3/2}$ states varies from $I$ to $I^{1.5}$ as $I$ increases. These features indicate a transition from perturbative ionization to strong-perturbative ionization for the 5$P_{3/2}$ state. Numerical simulations by solving the time-dependent Schr\u00f6dinger equation (TDSE) can qualitatively explain the measurements of RIMD, photoion angular distributions, as well as ion-yield ratio. However, some discrepancies still exist, especially for the NZM dip, which could stem from the electron-electron correlation that is neglected in the present TDSE simulations since we have adopted the"
+"---\nabstract: 'Within the framework of Einstein\u2019s General Relativity we study strange quark stars assuming an interacting equation-of-state. Taking into account the presence of anisotropies in a sphere made of ultra dense matter, we employ the formalism based on the complexity factor. We integrate the structure equations numerically imposing the appropriate conditions both at the center and at the surface of the stars, thus obtaining interior solutions describing hydrostatic equilibrium. Making use of well-established criteria, we demonstrate that the solutions obtained here are well behaved and realistic. A comparison with another, more conventional approach, is made as well. Our numerical results are summarized in a number of figures. .'\naddress: |\n     ${}^a$ Departamento de F[\u00ed]{}sica Aplicada, Universidad de Alicante, Campus de San Vicente del Raspeig, E-03690 Alicante, Spain;\\\n    ${}^b$ Departamento de Ciencias F\u00edsicas, Universidad de la Frontera, Casilla 54-D, 4811186 Temuco, Chile.\\\n    ${}^c$ Centro de Astrof[\u00ed]{}sica e Gravita[\u00e7]{}[\u00e3]{}o-CENTRA, Instituto Superior T[\u00e9]{}cnico-IST, Universidade de Lisboa-UL, Av. Rovisco Pais, 1049-001 Lisboa, Portugal. \nauthor:\n- '\u00c1ngel Rinc\u00f3n [${}^{a}$ [^1] ]{} Grigoris Panotopoulos [${}^{b}$ [^2] ]{} Il[\u00ed]{}dio Lopes [${}^{c}$ [^3] ]{}'\ntitle: Anisotropic Quark Stars with an Interacting Quark Equation of State within the Complexity Factor Formalism\n---\n\nIntroduction {#intro}\n============\n\nUnlike many other"
+"---\nabstract: 'This paper proposes a new approach to estimating the distribution of a response variable conditioned on observing some factors. The proposed approach possesses desirable properties of flexibility, interpretability, tractability and extendability. The conditional quantile function is modeled by a mixture (weighted sum) of basis quantile functions, with the weights depending on factors. The calibration problem is formulated as a convex optimization problem. It can be viewed as conducting quantile regressions for all confidence levels simultaneously while avoiding quantile crossing by definition. The calibration problem is equivalent to minimizing the continuous ranked probability score (CRPS). Based on the canonical polyadic (CP) decomposition of tensors, we propose a dimensionality reduction method that reduces the rank of the parameter tensor and propose an alternating algorithm for estimation. Additionally, based on Risk Quadrangle framework, we generalize the approach to conditional distributions defined by Conditional Value-at-Risk (CVaR), expectile and other functions of uncertainty measures. Although this paper focuses on using splines as the weight functions, it can be extended to neural networks. Numerical experiments demonstrate the effectiveness of our approach.'\nauthor:\n- 'Cheng Peng[^1] and Stan Uryasev[^2]'\nbibliography:\n- 'FM\\_reference.bib'\ntitle: Factor Model of Mixtures \n---\n\nIntroduction\n============\n\nThis paper proposes a new"
+"---\nabstract: 'Deep neural networks (DNNs) are known to be vulnerable to adversarial geometric transformation. This paper aims to verify the robustness of large-scale DNNs against the combination of multiple geometric transformations with a provable guarantee. Given a set of transformations (e.g., rotation, scaling, etc.), we develop GeoRobust, a black-box robustness analyser built upon a novel global optimisation strategy, for locating the [*worst-case*]{} combination of transformations that affect and even alter a network\u2019s output. GeoRobust can provide [*provable guarantees*]{} on finding the [*worst-case*]{} combination based on recent advances in Lipschitzian theory. Due to its black-box nature, GeoRobust can be deployed on large-scale DNNs regardless of their architectures, activation functions, and the number of neurons. In practice, GeoRobust can locate the worst-case geometric transformation with high precision for the ResNet50 model on ImageNet in a few seconds on average. We examined 18 ImageNet classifiers, including the ResNet family and vision transformers, and found a positive correlation between the geometric robustness of the networks and the parameter numbers. We also observe that increasing the depth of DNN is more beneficial than increasing its width in terms of improving its geometric robustness. Our tool **GeoRobust** is available at <https://github.com/TrustAI/GeoRobust>.'\nauthor:\n- |\n    Written"
+"---\nabstract: 'Blazar emission is dominated by nonthermal radiation processes that are highly variable across the entire electromagnetic spectrum. Turbulence, which can be a major source of nonthermal particle acceleration, can widely exist in the blazar emission region. The Turbulent Extreme Multi-Zone (TEMZ) model has been used to describe turbulent radiation signatures. Recent particle-in-cell (PIC) simulations have also revealed the stochastic nature of the turbulent emission region and particle acceleration therein. However, radiation signatures have not been systematically studied via first-principle-integrated simulations. In this paper, we perform combined PIC and polarized radiative transfer simulations to study synchrotron emission from magnetic turbulence in the blazar emission region. We find that the multi-wavelength flux and polarization are generally characterized by stochastic patterns. Specifically, the variability time scale and average polarization degree (PD) are governed by the correlation length of the turbulence. Interestingly, magnetic turbulence can result in polarization angle (PA) swings with arbitrary amplitudes and duration, in either direction, that are not associated with changes in flux or PD. Surprisingly, these swings, which are stochastic in nature, can appear either bumpy or smooth, although large amplitude swings ($>180^{\\circ}$) are very rare, as expected. Our radiation and polarization signatures from first-principle-integrated simulations are"
+"---\nabstract: 'The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this setting is backpropagation through the solution of an optimization problem, which typically lacks a closed form. One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver. While flexible and general, unrolling can encounter accuracy and efficiency issues in practice. These issues can be avoided by analytical differentiation of the optimization, but current frameworks impose rigid requirements on the optimization problem\u2019s form. This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation. Additionally, it proposes a unifying view of unrolling and analytical differentiation through optimization mappings. Experiments over various model-based learning tasks demonstrate the advantages of the approach both computationally and in terms of enhanced expressiveness.'\nauthor:\n- James Kotary\n- |\n    My H Dinh Ferdinando Fioretto\\\n    University of Virginia {jk4pn, fqw2tz, fioretto}@virginia.edu\nbibliography:\n- 'bib.bib'\ntitle:  Backpropagation of Unrolled Solvers with Folded Optimization \n---\n\nIntroduction {#sec:introduction}\n============\n\nThe integration of optimization problems as components in neural networks has shown to"
+"---\nabstract: 'We present an efficient robust value iteration for `s`-rectangular robust Markov Decision Processes (MDPs) with a time complexity comparable to standard (non-robust) MDPs which is significantly faster than any existing method. We do so by deriving the optimal robust Bellman operator in concrete forms using our $L_p$ water filling lemma. We unveil the exact form of the optimal policies, which turn out to be novel threshold policies with the probability of playing an action proportional to its advantage.'\nauthor:\n- Navdeep Kumar\n- Kfir Levy\n- Kaixin Wang\n- Shie Mannor\nbibliography:\n- 'main.bib'\ntitle: 'An Efficient Solution to s-Rectangular Robust Markov Decision Processes'\n---\n\nIntroduction\n============\n\nIn Markov Decision Processes (MDPs), an agent interacts with the environment and learns to optimally behave in it \u00a0[@Sutton1998]. However, the MDP solution may be very sensitive to little changes in the model parameters \u00a0[@BiasVarianceShie]. Hence we should be cautious applying the solution of the MDP, when the model is changing or when there is uncertainty in the model parameters. Robust MDPs provide a way to address this issue, where an agent can learn to optimally behave even when the model parameters are uncertain \u00a0[@HKuhn2013; @tamar14; @Iyenger2005]. Another motivation to study"
+"---\nabstract: 'Population-wise matching of the cortical fold is necessary to identify biomarkers of neurological or psychiatric disorders. The difficulty comes from the massive inter-individual variations in the morphology and spatial organization of the folds. This task is challenging at both methodological and conceptual levels. In the widely used registration-based techniques, these variations are considered as noise and the matching of folds is only implicit. Alternative approaches are based on the extraction and explicit identification of the cortical folds. In particular, representing cortical folding patterns as graphs of sulcal basins \u2013 termed *sulcal graphs* \u2013 enables to formalize the task as a graph-matching problem. In this paper, we propose to address the problem of sulcal graph matching directly at the population level using multi-graph matching techniques. First, we motivate the relevance of multi-graph matching framework in this context. We then introduce a procedure to generate populations of artificial sulcal graphs, which allows us benchmarking several state of the art multi-graph matching methods. Our results on both artificial and real data demonstrate the effectiveness of multi-graph matching techniques to obtain a population-wise consistent labeling of cortical folds at the sulcal basins level.'\nauthor:\n- |\n    R.Yadav$^{\\star\\ddag\\dagger}$, F.X. Dup\u00e9$^{\\dagger}$, S. Takerkart$^{\\star}$, G."
+"---\nauthor:\n- 'Junji Hisano,'\n- 'Teppei Kitahara,'\n- 'Naohiro Osamura,'\n- and Atsuyuki Yamada\nbibliography:\n- 'ref.bib'\ntitle: 'Novel loop-diagrammatic approach to QCD parameter and application to the left-right model'\n---\n\nIntroduction\n============\n\nThe QCD $\\theta$ term is $P$- and $T$-odd, and then $CP$-odd under the $CPT$ invariance. Because it is identical to the total derivative, it never locally affects physics at the classical level (as long as the momentum conservation holds), while its effect occurs only via nonperturbative processes [@Belavin:1975fg; @tHooft:1976rip; @Callan:1976je; @Jackiw:1976pf; @Witten:1979ey]. It is known that this interaction induces the neutron electric dipole moment (EDM) [@Baluni:1978rf; @Crewther:1979pi]. Measurement of the neutron EDM by the nEDM collaboration has set the severe upper bound: $|d_n|_{\\rm exp} < 1.8 \\times  10^{-26} ~e\\,\\text{cm}$ (90% CL)\u00a0[@Abel:2020pzs]. Using the latest lattice result $d_n = - 0.00148(34)\\, \\bar{\\theta}\\, e\\,\\text{fm}$ [@Liang:2023jfj][^1] and assuming that $\\bar{\\theta}$ is the only source of $CP$ violation, one obtains the upper bound on the angle, $$\\begin{aligned}\n  |\\bar{\\theta} | \\lesssim 1.2 \\times 10^{-10} \\quad (90\\%~\\text{CL})\\,,\n  \\label{eq:constraint theta}\\end{aligned}$$ where $\\bar\\theta$ is the physical $CP$-violating angle in the QCD Lagrangian which will be defined explicitly in the next section.\n\nAlthough the experimental bound requires that $\\bar{\\theta}$ must be around zero, such"
+"---\nabstract: 'A *sparsification* of a given graph $G$ is a sparser graph (typically a subgraph) which aims to approximate or preserve some property of $G$. Examples of sparsifications include but are not limited to spanning trees, Steiner trees, spanners, emulators, and distance preservers. Each vertex has the same priority in all of these problems. However, real-world graphs typically assign different \u201cpriorities\u201d or \u201clevels\u201d to different vertices, in which higher-priority vertices require higher-quality connectivity between them. Multi-priority variants of the Steiner tree problem have been studied in prior literature but this generalization is much less studied for other sparsification problems. In this paper, we define a generalized multi-priority problem and present a rounding-up approach that can be used for a variety of graph sparsifications. Our analysis provides a systematic way to compute approximate solutions to multi-priority variants of a wide range of graph sparsification problems given access to a single-priority subroutine.'\nauthor:\n- Reyan Ahmed\n- Keaton Hamm\n- Stephen Kobourov\n- Mohammad Javad Latifi Jebelli\n- Faryad Darabi Sahneh\n- Richard Spence\nbibliography:\n- 'references.bib'\ntitle: 'Multi-Priority Graph Sparsification[^1] '\n---\n\nIntroduction\n============\n\nA *sparsification* of a graph $G$ is a graph ${H}$ which preserves some property of $G$."
+"---\nabstract: 'Accurately clustering high-dimensional measurements is vital for adequately analyzing scientific data. Deep learning machinery has remarkably improved clustering capabilities in recent years due to its ability to extract meaningful representations. In this work, we are given unlabeled samples from multiple source domains, and we aim to learn a shared classifier that assigns the examples to various clusters. Evaluation is done by using the classifier for predicting cluster assignments in a previously unseen domain. This setting generalizes the problem of unsupervised domain generalization to the case in which no supervised learning samples are given (completely unsupervised). Towards this goal, we present an end-to-end model and evaluate its capabilities on several multi-domain image datasets. Specifically, we demonstrate that our model is more accurate than schemes that require fine-tuning using samples from the target domain or some level of supervision.'\nbibliography:\n- 'spice.bib'\n---\n\n[ ]{}\n\nIntroduction {#sec:introduction}\n============\n\nClustering is a fundamental machine-learning task that categorizes unlabeled vectors into homogeneous groups. Clustering high dimensional measurements is a difficult task, and classical methods such as $K$-means [@journals/tit/Lloyd82], Spectral Clustering [@NIPS2001_801272ee], or density-based methods [@conf/kdd/EsterKSX96] often fail to group semantically related examples. Several recent works have developed deep learning-based frameworks to overcome"
+"---\nabstract: 'There is a growing need to characterize the engineering material properties of the shallow subsurface in three-dimensions for advanced engineering analyses. However, imaging the near-surface in three-dimensions at spatial resolutions required for such purposes remains in its infancy and requires further study before it can be adopted into practice. To enable and accelerate research in this area, we present a large subsurface imaging dataset acquired using a dense network of three-component (3C) nodal stations acquired in 2019 at the Garner Valley Downhole Array (GVDA) site. Acquisition of this dataset involved the deployment of 196, 3C nodal stations positioned on a 14 by 14 grid with a 5-m spacing. The array was used to acquire active-source data generated by a vibroseis truck and an instrumented sledgehammer, and passive-wavefield data containing ambient noise. The active-source acquisition included 66 vibroseis and 209 instrumented sledgehammer source locations. Multiple source impacts were recorded at each source location to enable stacking of the recorded signals. The active-source recordings are provided in terms of both raw, uncorrected units of counts and corrected engineering units of meters per second. For each source impact, the force output from the vibroseis or instrumented sledgehammer was recorded and is"
+"---\nabstract: 'As few-shot object detectors are often trained with abundant base samples and fine-tuned on few-shot novel examples, the learned models are usually biased to base classes and sensitive to the variance of novel examples. To address this issue, we propose a meta-learning framework with two novel feature aggregation schemes. More precisely, we first present a Class-Agnostic Aggregation (CAA) method, where the query and support features can be aggregated regardless of their categories. The interactions between different classes encourage class-agnostic representations and reduce confusion between base and novel classes. Based on the CAA, we then propose a Variational Feature Aggregation (VFA) method, which encodes support examples into class-level support features for robust feature aggregation. We use a variational autoencoder to estimate class distributions and sample variational features from distributions that are more robust to the variance of support examples. Besides, we decouple classification and regression tasks so that VFA is performed on the classification branch without affecting object localization. Extensive experiments on PASCAL VOC and COCO demonstrate that our method significantly outperforms a strong baseline (up to 16%) and previous state-of-the-art methods (4% in average). Code will be available at: <https://github.com/csuhan/VFA>'\nauthor:\n- Author Name\n- 'First Author Name,^1,2^"
+"---\nauthor:\n- \n- \nbibliography:\n- 'sor21.bib'\ntitle: 'Interval Transportation Problem: Feasibility, Optimality and the Worst Optimal Value'\n---\n\nIntroduction {#sec:intro}\n============\n\nTransportation problems\u00a0[@Hitchcock:1941:TP] often arise in various practical applications, and as such they belong to the commonly studied and used models in operations research. The goal of a transportation problem is to find a\u00a0transportation plan for shipping a given commodity from a\u00a0set of supply centers to the customers, while minimizing the total transportation costs and respecting the available supply levels and the customer demand.\n\nSince uncertainty is an important factor present in many real-world problems, we address the issue that the values of supply and demand and even the unit transportation costs may not always be known exactly in advance. In this paper, we formulate the transportation problem as a model of interval linear programming\u00a0(see e.g. [@Rohn:2006:Intervallinearprogramminga] for general introduction to interval linear programming), where we assume that only lower and upper bounds on the exact quantities are given and the values can be perturbed independently within these bounds. Such uncertain model is known in the literature as the *interval transportation problem*\u00a0[@Chanas:1993:ITP].\n\nOne of the essential tasks in interval programming is computing the optimal"
+"---\nabstract: 'To mitigate gender bias in contextualized language models, different intrinsic mitigation strategies have been proposed, alongside many bias metrics. Considering that the end use of these language models is for downstream tasks like text classification, it is important to understand how these intrinsic bias mitigation strategies actually translate to fairness in downstream tasks and the extent of this. In this work, we design a probe to investigate the effects that some of the major intrinsic gender bias mitigation strategies have on downstream text classification tasks. We discover that instead of resolving gender bias, intrinsic mitigation techniques and metrics are able to hide it in such a way that significant gender information is retained in the embeddings. Furthermore, we show that each mitigation technique is able to hide the bias from some of the intrinsic bias measures but not all, and each intrinsic bias measure can be fooled by some mitigation techniques, but not all. We confirm experimentally, that none of the intrinsic mitigation techniques used without any other fairness intervention is able to consistently impact extrinsic bias. We recommend that intrinsic bias mitigation techniques should be combined with other fairness interventions for downstream tasks.'\nauthor:\n- 'Ewoenam Kwaku"
+"---\nabstract: 'We consider Lorentz-symmetry properties of the ghost-free massive gravity theory proposed by de Rham, Gabadadze, and Tolley. In particular, we present potentially observable effects in gravitational-wave propagation and in Newton\u2019s law, including Lorentz-violating signals.'\naddress:\n- |\n    $^1$Departamento de F\u00edsica, Faculdade de Ci\u00eancias e Tecnologia,\\\n    Universidade do Algarve, 8005-139 Faro, Portugal\n- |\n    $^2$Centro de Astrof\u00edsica e Gravita\u00e7\u00e3o, Instituto Superior T\u00e9cnico,\\\n    Universidade de Lisboa, Avenida Rovisco Pais, 1049-001 Lisbon, Portugal\nauthor:\n- 'R.\u00a0Potting$^{1,2}$'\ntitle: Massive Gravity and Lorentz Symmetry\n---\n\nThe de Rham\u2013Tolley\u2013Gabadadze action\n===================================\n\nOne of the motivations for considering massive gravity is the possibility that modifications of General Relativity over large distances may yield a solution to the cosmological constant problem. A ghost-free theory of non-interacting gravitons was constructed in 1939 by Fierz and Pauli.[@FierzPauli] However, attempts to generalize it to the nonlinear level failed during decades, with the work of Boulware and Deser showing that generically such theories will suffer from ghost instabilities.[@BoulwareDeser] Recently, however, de Rham, Gabadadze, and Tolley (dRGT) showed that there exists a non-linear extension of Fierz\u2013Pauli massive gravity that does not suffer from ghosts.[@deRhamGabadadzeTolley] It can be shown that their model can be maximally extended to the action:[@HassanRosen] $$S ="
+"---\nabstract: |\n    The detection of small and medium-sized objects in three dimensions has always been a frontier exploration problem. This technology has a very wide application in sports analysis, games, virtual reality, human animation and other fields. The traditional three-dimensional small target detection technology has the disadvantages of high cost, low precision and inconvenience, so it is difficult to apply in practice. With the development of machine learning and deep learning, the technology of computer vision algorithms is becoming more mature. Creating an immersive media experience is considered to be a very important research work in sports.\n\n    The main work is to explore and solve the problem of football detection under the 36 cameras, aiming at the research and implementation of the live broadcast system of football matches. Using multi-cameras detects a target ball that is about 2 \\* 10$^-5$ of the original image and determines its position in three dimension. This problem is relatively advanced in the AI live broadcast system. It\u2019s full of challenges due to the occlusion, motion, uneven illumination of the target object.\n\n    This paper designed and implemented football detection system under multiple cameras for the detection and capture of targets in real-time matches. The"
+"---\nabstract: 'A novel smooth immersed boundary method (IBM) based on a direct-forcing formulation is proposed to simulate incompressible dense particle-laden flows. This IBM relies on a regularization of the transfer function between the Eulerian grid points (to discretise the fluid governing equations) and Lagrangian markers (to represent the particle surface) to fulfill the no-slip condition at the surfaces of the particles, allowing both symmetrical and non-symmetrical interpolation and spreading supports to be used. This enables that local source term contributions to the Eulerian grid, accounting for the boundary condition enforced at a Lagrangian marker on the surface of a particle, can be present on the inside of the particle only when this is beneficial, for instance when the Lagrangian marker is near another particle surface or near a domain boundary. However, when the Lagrangian marker is not near another particle surface or a domain boundary, the interpolation and spreading operators are locally symmetrical, meaning a \u201cclassic\u201d IBM scheme is adopted. This approach, named hybrid IBM (HyBM), is validated with a number of test-cases from the literature. These results show that the HyBM achieves more accurate results compared to a classical IBM framework, especially at coarser mesh resolutions, when there"
+"---\nabstract: 'Vision Transformers (ViTs) are becoming a very popular paradigm for vision tasks as they achieve state-of-the-art performance on image classification. However, although early works implied that this network structure had increased robustness against adversarial attacks, some works argue ViTs are still vulnerable. This paper presents our first attempt toward detecting adversarial attacks during inference time using the network\u2019s input and outputs as well as latent features. We design four quantifications (or derivatives) of input, output, and latent vectors of ViT-based models that provide a signature of the inference, which could be beneficial for the attack detection, and empirically study their behavior over clean samples and adversarial samples. The results demonstrate that the quantifications from input (images) and output (posterior probabilities) are promising for distinguishing clean and adversarial samples, while latent vectors offer less discriminative power, though they give some insights on how adversarial perturbations work.'\nauthor:\n- 'Hugo Lemarchant,^1^ Liangzi Li,^1^ Yiming Qian,^1^ Yuta Nakashima,^1^ Hajime Nagahara'\nbibliography:\n- 'aaai23.bib'\ntitle: Inference Time Evidences of Adversarial Attacks for Forensic on Transformers\n---\n\nIntroduction\n============\n\nAll deep learning models used nowadays are vulnerable to adversarial attacks [@goodfellow2013intriguing; @NaseerIntriguingViT2021], especially in the domain of image classification. This is inherent to"
+"---\nauthor:\n- 'Yichen\u00a0Yang, Jingtao\u00a0Li, Nishil\u00a0Talati, Subhankar\u00a0Pal, Siying\u00a0Feng, Chaitali\u00a0Chakrabarti, Trevor\u00a0Mudge, Ronald\u00a0Dreslinski'\nbibliography:\n- 'refs.bib'\ntitle: 'Accelerating Graph Analytics on a Reconfigurable Architecture with a Data-Indirect Prefetcher '\n---\n\nIntroduction\n============\n\nWith the end of Dennard scaling and Moore\u2019s law, traditional architectures like CPU and GPU do not meet the performance and power requirements of modern data centers and high-performance computing. Domain-specific accelerators have been proposed to achieve high efficiency, however, their programmability is limited. Prior works proposed low-power manycore reconfigurable architectures (MRA) to bridge the programmability-efficiency gap. A recent example is Transmuter (TM)\u00a0[@transmuter], which has demonstrated superior performance on many kernels and large end-to-end applications\u00a0[@cosparse; @outerspace].\n\nGraph analytics is an important domain with many real-world applications\u00a0[@graph1; @graph2]. They typically involve irregular memory accesses\u00a0[@prodigy], which makes their performance limited on commercial hardware platforms. Even on a state-of-the-art (SoTA) MRA such as TM, running five graph analytics workloads (Sec.\u00a0\\[method\\]) shows an average L1 miss rate of 34%. Clearly, this indicates room for performance optimizations. Several prior works such as\u00a0[@gapbs; @prodigy] propose software (SW) and hardware (HW) optimization techniques to accelerate graph analytics on the CPU. Among these, data"
+"---\nabstract: 'Demographic parity is the most widely recognized measure of group fairness in machine learning, which ensures equal treatment of different demographic groups. Numerous works aim to achieve demographic parity by pursuing the commonly used metric $\\Delta DP$\u00a0[^1]. Unfortunately, in this paper, we reveal that the fairness metric $\\Delta DP$ can not precisely measure the violation of demographic parity, because it inherently has the following drawbacks: *i)* zero-value $\\Delta DP$ does not guarantee zero violation of demographic parity, *ii)* $\\Delta DP$ values can vary with different classification thresholds. To this end, we propose two new fairness metrics, rea etween robability density function urves () and rea etween umulative density function urves (), to precisely measure the violation of demographic parity at the distribution level. The new fairness metrics directly measure the difference between the distributions of the prediction probability for different demographic groups. Thus our proposed new metrics enjoy: *i)* zero-value / guarantees zero violation of demographic parity; *ii)* / guarantees demographic parity while the classification thresholds are adjusted. We further re-evaluate the existing fair models with our proposed fairness metrics and observe different fairness behaviors of those models under the new metrics. The code is available at"
+"---\nauthor:\n- 'M.\u00a0Perger'\n- 'M.\u00a0Perger G.\u00a0Anglada-Escud\u00e9'\n- 'D.\u00a0Baroch'\n- 'M.\u00a0Lafarga'\n- 'I.\u00a0Ribas'\n- 'J.C.\u00a0Morales'\n- 'E.\u00a0Herrero'\n- 'P.J.\u00a0Amado'\n- 'J.R.Barnes'\n- 'J.A.\u00a0Caballero'\n- 'S.V.\u00a0Jeffers'\n- 'A.\u00a0Quirrenbach'\n- 'A.\u00a0Reiners'\nbibliography:\n- 'bibtex.bib'\ndate:\n- \n- 'Received 29.09.2022 / Accepted 01.03.2023'\ntitle:\n- Radial velocities with Machine Learning\n- |\n    A Machine Learning approach for\\\n    correcting radial velocities using physical observables\n---\n\n[Precision radial velocity (RV) measurements continue to be a key tool to detect and characterise extrasolar planets. While instrumental precision keeps improving, stellar activity remains a barrier to obtain reliable measurements below 1-2ms$^{-1}$ accuracy.]{} [Using simulations and real data, we investigate the capabilities of a Deep Neural Network approach to produce activity free Doppler measurements of stars.]{} [As case studies we use observations of two known stars ($\\epsilon$Eridani and AUMicroscopii, both with clear signals of activity induced Doppler variability. Synthetic observations using the [starsim]{} code are generated for the observables (inputs) and the resulting Doppler signal (labels), and then they are used to train a Deep Neural Network algorithm to predict Doppler corrections. We identify a relatively simple architecture consisting of convolutional layers followed by fully"
+"---\nabstract: 'We determine the Casimir energies and forces in a variety of potentially experimentally viable setups, consisting of parallel plates made of perfect electromagnetic conductors (PEMCs), which generalize perfect electric conductors (PECs) and perfect magnetic conductors (PMCs), and Weyl semimetals (WSMs). Where comparison is possible, our results agree with the Casimir forces calculated elsewhere in the literature, albeit with different methods. We find a multitude of known but also new cases where repulsive Casimir forces are in principle possible, but restricting the setup to PECs combined with the aforementioned WSM geometry, results in purely attractive Casimir forces.'\nauthor:\n- David Dudal\n- Thomas Oosthuyse\nbibliography:\n- 'bibliography.bib'\ntitle: 'Interplay between chiral media and perfect electromagnetic conductor plates: repulsive vs.\u00a0attractive Casimir force transitions'\n---\n\nMotivation\n==========\n\nIt is well known by now that according to the no-go theorem [@Kenneth:2006vr], the Casimir force is always attractive between dielectrics which are related by reflection. However, when this reflection symmetry is absent there are no clear rules regarding if the Casimir force is attractive or repulsive, and it has to be considered on a case by case basis. Therefore it is interesting to look at the well known, and geometrically symmetric, parallel"
+"---\nauthor:\n- som\n- Anton Kun\u010dinas\n- Odd Magne Ogreid\n- Per Osland\n- 'M. Nesbitt Rebelo'\nbibliography:\n- 'ref.bib'\ntitle: 'Revisiting two dark matter candidates in $S_3$-symmetric three-Higgs-doublet models'\n---\n\nIntroduction\n============\n\nIn spite of the success of the Standard Model (SM) of Particle Physics and existing cosmological observations we have a limited knowledge of the Universe\u00a0[@ParticleDataGroup:2022pth]. The standard cosmological model implies that a significant part of the matter of the Universe is made up of a hypothetical Dark Matter (DM). Apart from the experimental bounds and compelling evidence coming from different cosmological scales and phenomena we still do not know what DM could be. In scenarios beyond the SM it is common to invoke models with an extended scalar sector. Such extensions are appealing due to their simplicity from the mathematical point of view and the ability to deal with several shortcomings of the SM. Therefore, models with a non-minimal Higgs sector are well motivated, despite the fact that the properties of the observed Higgs boson are in good agreement with the SM predictions\u00a0[@10years]. In the Inert Doublet Model (IDM)\u00a0[@IDM] the scalar sector of the SM is enlarged by an additional copy of the"
+"---\nabstract: 'Many numerical algorithms have been established to reconstruct pressure fields from measured kinematic data with noise by Particle Image Velocimetry (PIV), such as the Pressure Poisson solver and the Omni-Directional Integration (ODI) method. This study adopts Gaussian Process Regression (GPR), a probabilistic framework with an intrinsic de-noising mechanism to tackle drawbacks of traditional Pressure Poisson solver and compares the performance with ODI. To evaluate the accuracy of the algorithm, GPR and ODI are tested in detail in a canonical setup of forced homogeneous isotropic turbulence from the Johns Hopkins Turbulence Database. According to the result, GPR has the same level of accuracy as ODI with optimized hyper-parameters for the isotropic turbulence flow. However, GPR has the tendency to flatten impulsive signals. Therefore, without further modifications, it is not suitable to detect flow structures with impulsive true signals. The error propagation of the proposed framework is also analyzed and discussed in both physical and spectral spaces.'\nauthor:\n- 'Zejian You[^1] and Qi Wang[^2]'\n- 'Xiaofeng Liu[^3]'\nbibliography:\n- 'main.bib'\ntitle: Pressure Reconstruction from the Measured Pressure Gradient Using Gaussian Process Regression\n---\n\nNomenclature {#nomenclature .unnumbered}\n============\n\n[@l @[=]{} l@]{} $p(\\boldsymbol{x})$ & pressure field\\\n$\\widetilde{p}(\\boldsymbol{x})$ & true pressure field\\\n$\\boldsymbol{x}$"
+"---\nabstract: 'In the vector-field guided path-following problem, a sufficiently smooth vector field is designed such that its integral curves converge to and move along a one-dimensional geometric desired path. The existence of singular points where the vector field vanishes creates a topological obstruction to global convergence to the desired path and some associated topological analysis has been conducted in [@yao2021topo]. In this paper, we strengthen the result in [@yao2021topo Theorem 2] by showing that the domain of attraction of the desired path, which is a compact asymptotically stable one-dimensional embedded submanifold of an $n$-dimensional ambient manifold ${{\\mathcal{M}}}$, is homeomorphic to ${\\mathbb{R}^{n-1}} \\times \\mathbb{S}^1$, and not just homotopy equivalent to $\\mathbb{S}^1$ as shown in [@yao2021topo Theorem 2]. This result is extended for a $k$-dimensional compact manifold for $k \\ge 2$.'\nauthor:\n- 'Weijia Yao, , Bohuan Lin, Brian D. O. Anderson, , and Ming Cao, [^1]'\nbibliography:\n- 'ref.bib'\ntitle: |\n    The Domain of Attraction of the Desired Path\\\n    in Vector-field Guided Path Following\n---\n\nPath following, domain of attraction, nonlinear systems\n\nIntroduction\n============\n\nIn mobile robotics and some control applications, it is fundamental for trajectories of a dynamical system to converge to and accurately follow a predefined one-dimensional desired"
+"---\nabstract: 'The entropy production of an open system coupled to a reservoir initialized in a canonical state can be expressed as a sum of two microscopic information-theoretic contributions: the system-bath mutual information and the relative entropy measuring the displacement of the environment from equilibrium. We investigate whether this result can be generalized to situations where the reservoir is initialized in a microcanonical or in a certain pure state (e.g., an eigenstate of a nonintegrable system), such that the reduced dynamics and thermodynamics of the system are the same as for the thermal bath. We show that while in such a case the entropy production can still be expressed as a sum of the mutual information between the system and the bath and a properly redefined displacement term, the relative weight of those contributions depends on the initial state of the reservoir. In other words, different statistical ensembles for the environment predicting the same reduced dynamics for the system give rise to the same total entropy production but to different information-theoretic contributions to the entropy production.'\nauthor:\n- Krzysztof Ptaszy\u0144ski\n- Massimiliano Esposito\ntitle: 'Ensemble dependence of information-theoretic contributions to the entropy production'\n---\n\nOne of the main goals of"
+"---\nauthor:\n- 'Yuanpeng He [^1]'\nbibliography:\n- 'cite.bib'\ntitle: 'Time Series Forecasting via Semi-Asymmetric Convolutional Architecture with Global Atrous Sliding Window'\n---\n\n[Shell : Bare Demo of IEEEtran.cls for Computer Society Journals]{}\n\nseries is a sequence taken at successive equally spaced points in time which is also known as dynamic series. Precise prediction of time series has close connections to human society, for instance, it may help people format schedules and company make adjustments on investment strategy. Moreover, foreseeing future behaviour based on analysis of known historical data is of great importance in lots of fields such as epidemic [@DBLP:journals/tim/SharmaKMR21], medical treatment [@DBLP:journals/tnn/TanYMYYWY21], finance [@DBLP:journals/pr/ChengYXL22; @DBLP:journals/tfs/LiuXLC20] and industrial Internet [@DBLP:journals/tii/UchitelevaPLHS22]. Time series forecasting therefore attracts attention from researchers around the world. Nevertheless, how to fully utilize observation to generate accurate and reasonable predictions is still an unsolved problem.\n\nTo realize accurate prediction of future, researchers develop various kinds of solutions. RNN model has been favored by researchers since it was proposed. Because of its recurrent architecture design, RNN models can effectively model long-term dependencies [@DBLP:journals/tits/HeLZT22], therefore achieve an effective understanding of temporal data [@DBLP:journals/eswa/LiuGYC20; @DBLP:conf/nips/DennisAMSSSJ19] as well. However, RNN may encounter memory overflow due to continuous storage of previous"
+"---\nabstract: |\n    Eight pairs of rare-earth normally - deformed (ND) nuclei around the isobaric nuclei with A = 164 and have identical values of F-spin, $\\pm $ $ F_{0} $ and $ N_{p} $ $ N_{n} $ ($ N_{p} $ and $ N_{n} $ are the number of valence protons and valence neutrons respectively ) have been studied. These pairs of identical bands (IB\u2019s) cover 16 mass units and are classified as (i) 3 pairs of nuclei separated by (2p,2n) :($ ^{162}Yb-^{166}Hf $), ($ ^{162}Er-^{166}Yb $), ($ ^{162}Dy-^{166}Er $) (ii) 2 pairs of nuclei separated by (4p,4n): ($ ^{160}Dy-^{168}Yb $), ($ ^{160}Er-^{168}Hf $) (iii) 2 pairs of nuclei separated by (6p,6n): ($ ^{158}Er-^{170}W $) ($ ^{158}Dy-^{170}Hf $) and (iv) one pair of nuclei separated by (8p,8n): ($ ^{156}Dy-^{172}W $).\n\n    We suggested a theoretical collective rotational formula containing three parameters (CRF3) as an extended version of Bohr-Mottelson model to calculate the ground state positive parity excitation energies. Also, the sd-version of the interacting boson model (IBM) has been used to describe the nuclear shapes by using the intrinsic coherent-state. The optimized models parameters for each nucleus are adjusted by using a simulation search program to minimize the root mean square"
+"---\nabstract: 'The main objective of this paper is to study the flow characteristics of a rotating, isothermally heated circular cylinder with a vertical arc-shaped control plate placed downstream. Stream function-Vorticity ($\\psi-\\omega$) formulation of two dimensional (2-D) Navier-Stokes (N-S) equations is considered as the governing equation and the simulations are performed for different distances of the control plate ($0.5$, $1$, $2$, $3$), rotational rates ($0.5$, $1$, $2.07$, $3.25$) at Prandtl number $0.7$ and Reynolds number $150$. The governing equations are discretized using the Higher Order Compact (HOC) scheme and the system of algebraic equations, arising from HOC discretization, is solved using the Bi-Conjugate Gradient Stabilized approach. Present computed results show that the vortex shedding plane is shifted upward from the centerline of the flow domain by the cylinder\u2019s rotational motion. The structure of the wake varies based on the plate\u2019s position. The size of vortices is greatly reduced when the control plate is set at $d/R_0=3$ and the rotational rate is very high. At greater rotational rates, the impact of varied positions of the arc-shaped control plate is very significant. The rotation of the cylinder and the location of the plate can be used to lower or enhance the values"
+"---\nabstract: 'In this perspective, the various measures of electron correlation used in wavefunction theory, density functional theory and quantum information theory are briefly reviewed. We then focus on a more traditional metric based on dominant weights in the full configuration solution and discuss its behaviour with respect to the choice of the $N$-electron and the one-electron basis. The impact of symmetry is discussed and we emphasize that the distinction between determinants, configuration state functions and configurations as reference functions is useful because the latter incorporate spin-coupling into the reference and should thus reduce the complexity of the wavefunction expansion. The corresponding notions of single determinant, single spin-coupling and single configuration wavefunctions are discussed and the effect of orbital rotations on the multireference character is reviewed by analysing a simple model system. In molecular systems, the extent of correlation effects should be limited by finite system size and in most cases the appropriate choices of one-electron and $N$-electron bases should be able to incorporate these into a low-complexity reference function, often a single configurational one.'\nauthor:\n- R\u00f3bert Izs\u00e1k\n- 'Aleksei V. Ivanov'\n- 'Nick S. Blunt'\n- Nicole Holzmann\n- Frank Neese\ntitle: 'Measuring Electron Correlation. The Impact of"
+"---\nabstract: 'The role of magnetic fields in galaxy evolution is still an unsolved question in astrophysics. We have previously shown that magnetic fields play a crucial role in major mergers between disc galaxies; in hydrodynamic simulations of such mergers, the Auriga model produces compact remnants with a distinctive bar and ring morphology. In contrast, in magnetohydrodynamic (MHD) simulations, remnants form radially-extended discs with prominent spiral arm structure. In this paper, we analyse a series of cosmological \u201czoom-in\u201d simulations of major mergers and identify exactly *how* magnetic fields are able to alter the outcome of the merger. We find that magnetic fields modify the transport of angular momentum, systematically hastening the merger progress. The impact of this altered transport depends on the orientation of the field, with a predominantly non-azimuthal (azimuthal) orientation increasing the central baryonic concentration (providing support against collapse). Both effects act to suppress an otherwise existent bar-instability, which in turn leads to a fundamentally different morphology and manifestation of feedback. We note, in particular, that stellar feedback is substantially less influential in MHD simulations, which allows for the later accretion of higher angular momentum gas and the subsequent rapid radial growth of the remnant disc. A corollary"
+"---\nabstract: 'Quasitoposes encompass a wide range of structures, including various categories of graphs. They have proven to be a natural setting for reasoning about the metatheory of algebraic graph rewriting. In this paper we propose and motivate the notion of *fuzzy presheaves*, which generalises fuzzy sets and fuzzy graphs. We prove that fuzzy presheaves are rm-adhesive quasitoposes, proving our recent conjecture for fuzzy graphs. Furthermore, we show that simple fuzzy graphs categories are quasitoposes.'\nauthor:\n- Alo\u00efs Rosset ^^\n- Roy Overbeek\n- J\u00f6rg Endrullis\nbibliography:\n- 'main.bib'\ntitle: Fuzzy Presheaves are Quasitoposes\n---\n\nThe algebraic graph transformation tradition uses category theory to define graph rewriting formalisms. The categorical approach enables rewriting a large variety of structures, and allows meta-properties to be studied in uniform ways, such as concurrency, parallelism, termination, and confluence. Different formalisms have been developed and studied since 1973, such as DPO\u00a0[@Ehrig_Pfender_Schneider_1973_DPO], SPO\u00a0[@Lowe_1993_SPO], DPU\u00a0[@Bauderon_1995_DPU], SqPO\u00a0[@Corradini_Heindel_Hermann_Konig_2006_SqPO], AGREE\u00a0[@Corradini_Duval_Echahed_Prost_Ribeiro_2015_AGREE], PBPO\u00a0[@Corradini_Duval_Echahed_Prost_Ribeiro_2019_PBPO], and [PBPO$^{+}$]{}\u00a0[@Overbeek_Endrullis_Rosset_2020_PBPO+].\n\nThe study of meta-properties on a categorical level has given new motivation to study existing concepts, such as quasitoposes\u00a0[@Wyler_1991], and has given rise to new ones, such as different notions of adhesivity\u00a0[@Lack_Sobocinski_Walukiewicz_2004_Adhesive_categories; @Lack_Sobocinski_2005_Adhesive_quasiadhesive_categories]. Quasitoposes are categories with rich"
+"---\nabstract: 'Quantum deviations or coherent noise are a typical type of noise when implementing gate operations in quantum computers, and their impact on the performance of quantum error correction (QEC) is still elusive. Here, we consider the topological surface code, with both stochastic noise and coherent noise on the multi-qubit entanglement gates during stabilizer measurements in both initial state preparation and error detection. We map a multi-round error detection protocol to a three-dimensional statistical mechanical model consisting of $\\mathbb{Z}_2$ gauge interactions and related the error threshold to its phase transition point. Specifically, two error thresholds are identified distinguishing different error correction performances. Below a finite error threshold, in stark contrast to the case with only stochastic errors, unidentifiable measurement errors can cause the failure of QEC in the large code distance limit. This problem can only be fixed at the perfect initial state preparation point. For a finite or small code with distance $d$, we find that if the preparation error rate is below a crossover scale $\\propto 1/\\log d$, the logical errors can still be suppressed. We conclude that this type of unavoidable coherent noise has a significant impact on QEC performance, and becomes increasingly detrimental as the"
+"---\nabstract: 'Fast matching of regular expressions with *bounded repetition*, aka *counting*, such as ${\\texttt{(ab)\\{50,100\\}}}$, i.e., matching linear in the length of the text and independent of the repetition bounds, has been an open problem for at least two decades. We show that, for a wide class of regular expressions with counting, which we call *synchronizing*, fast matching is possible. We empirically show that the class covers nearly all counting used in usual applications of regex matching. This complexity result is based on an improvement and analysis of a recent matching algorithm that compiles regexes to deterministic counting-set automata (automata with registers that hold sets of numbers).'\nauthor:\n- Luk\u00e1\u0161 Hol\u00edk\n- Juraj S\u00ed\u010d\n- Lenka Turo\u0148ov\u00e1\n- Tom\u00e1\u0161 Vojnar\nbibliography:\n- 'literature\\_usenix.bib'\n- 'bibliography.bib'\ntitle: 'Fast Matching of Regular Patterns with Synchronizing Counting (Technical Report)[^1]'\n---\n\nIntroduction\n============\n\nFast matching of regular expressions with *bounded repetition*, aka *counting*, has been an open problem for at least two decades (cf., e.g., [@Sperberg-McQueen-ExtendedFAWeb]). The time complexity of the standard matching algorithms run on a regex such as ${\\texttt{.*a.\\{100\\}}}$ is, at best, dominated by the *length of the text multiplied by the repetition bounds*. This makes matching prone to unacceptable slowdowns since"
+"---\nabstract: 'Various congestion control protocols have been designed to achieve high performance in different networking environments. Modern online learning solutions that delegate the congestion control actions to a machine cannot properly converge in the stringent time scales of data centers. We leverage multi-agent reinforcement learning to design a system for automatic and dynamic tuning of congestion control parameters at end-hosts in a data center. The system includes agents at the end-hosts to monitor and report the network and traffic states, and agents to run the reinforcement learning algorithm given the states. Based on the state of the environment, the system generates congestion control parameters that optimize network performance metrics such as throughput and latency. As a case study, we examine BBR, an example of a prominent recently-developed congestion control protocol. Our experiments demonstrate that the proposed system has the potential to mitigate the problems of static parameters ([*i.e.*]{}, reduces convergence time by $2.7$x and round-trip time by $40\\%$).'\nauthor:\n- \nbibliography:\n- 'ref.bib'\ntitle: A Deep Reinforcement Learning Framework for Optimizing Congestion Control in Data Centers\n---\n\nIntroduction\n============\n\nCongestion control is a crucial component in computer networking, which determines how limited shared resources (link bandwidth, buffers, etc) are"
+"---\nabstract: 'The Josephson vortex formed in a superconductor-insulator-superconductor (SIS) junction can affect the quantum mechanics of quasiparticles by creating an effective adiabatic potential determined by the inhomogeneous distribution of the phase difference of the order parameter along the junction. Starting from the quasiclassical version of the Bogoliubov-de Gennes (BdG) theory, we found the quasiparticle spectrum and the local density of states (DOS) both for the isolated Josephson vortex and the vortex chain. The spatially resolved DOS reveals a peculiar two-peak structure for each Josephson vortex, which can be detected experimentally using the scanning tunneling microscopy/spectroscopy techniques.'\nauthor:\n- Vadim\u00a0Plastovets\n- 'A.\u00a0S.\u00a0Mel\u2019nikov'\ntitle: Electronic structure of a Josephson vortex in a SIS junction\n---\n\nIntroduction\n============\n\nThe observation of a rich variety of vortex phases in superconductors and superfluids is known to be one of the convincing manifestations of the quantum coherence in these systems. According to a textbook picture (see, e.g. Ref. [@Eshrig]) each vortex has a $2\\pi$ circulation of the order parameter phase and carries the magnetic flux quantum $\\Phi_0=\\pi\\hbar c/e$ in the bulk systems. Such vortices affect the local gap function and, thus, perturb the quasiparticle spectrum provoking the formation of the subgap quasiparticle"
+"---\nabstract: 'Recent claims of achieving exponential quantum advantage have attracted attention to Gaussian boson sampling (GBS), a potential application of which is dense subgraph finding. We investigate the effects of sources of error including loss and spectral impurity on GBS applied to dense subgraph finding algorithms. We find that the effectiveness of these algorithms is remarkably robust to errors, to such an extent that there exist efficient classical algorithms that can simulate the underlying GBS. These results imply that the speedup of GBS-based algorithms for the dense subgraph problem over classical approaches is at most polynomial, though this could be achieved on a quantum device with dramatically less stringent requirements on loss and photon purity than general GBS.'\nauthor:\n- 'Naomi R. Solomons'\n- 'Oliver F. Thomas'\n- 'Dara P. S. McCutcheon'\nbibliography:\n- 'references.bib'\ntitle: |\n    **Gaussian-boson-sampling-enhanced dense subgraph finding shows\\\n    limited advantage over efficient classical algorithms**\n---\n\nIntroduction\n============\n\nGaussian boson sampling (GBS) is a non-universal model of quantum computation which samples from the photon number distribution of Gaussian squeezed states passed through a passive linear interferometer\u00a0[@hamilton2017gaussian]. Unlike universal quantum computers, GBS can be realised by currently available quantum devices at a scale which is not"
+"---\nabstract: 'We find an explicit form of entropy solutions to a Riemann problem for a degenerate nonlinear parabolic equation with piecewise constant velocity and diffusion coefficients. It is demonstrated that this solution corresponds to the minimum point of some strictly convex function of a finite number of variables.'\nauthor:\n- |\n    Evgeny\u00a0Yu.\u00a0Panov\\\n    Yaroslav-the-Wise Novgorod State University, Veliky Novgorod, Russian Federation,\\\n    Research and Development Center, Veliky Novgorod, Russian Federation.\ntitle: On the structure of entropy solutions to the Riemann problem for a degenerate nonlinear parabolic equation\n---\n\nIntroduction\n============\n\nIn a half-plane $\\Pi=\\{ (t,x) \\ | \\ t>0, x\\in{{\\mathbb R}}\\}$, we consider a nonlinear parabolic equation $$\\label{1}\nu_t+v(u)u_x-t(a^2(u)u_x)_x=0,$$ where $v(u),a(u)\\in L^\\infty({{\\mathbb R}})$, $a(u)\\ge 0$. Since the diffusion coefficient $a(u)$ may take zero value, equation (\\[1\\]) is degenerate. In the case when $a(u)\\equiv 0$ it reduces to a first order conservation law $$\\label{con}\nu_t+\\varphi(u)_x=0,$$ where $\\varphi'(u)=v(u)$. Similarly, a general equation (\\[1\\]) can be written in the conservative form $$u_t+\\varphi(u)_x-tA(u)_{xx}=0$$ with $A'(u)=a^2(u)$, which allows to define weak solutions of this equation. Unfortunately, weak solutions to a Cauchy problem for equation (\\[1\\]) are not unique in general, and some additional entropy conditions are required. We consider the Cauchy problem with initial"
+"---\nabstract: 'One of the few firm predictions of string theory is the existence of a massless scalar field coupled to gravity, the dilaton. In its presence, the value of the fundamental constants of the universe, such as the fine-structure constant, will vary with the time-dependent vacuum expectation value of this field, in direct violation of the Einstein equivalence principle. The *runaway dilaton* proposed by Damour, Piazza, and Veneziano provides a physically motivated cosmological scenario which reconciles the existence of a massless dilaton with observations, while still providing nonstandard and testable predictions. Furthermore, the field can provide a natural candidate for dynamical dark energy. While this model has been previously constrained from local laboratory experiments and low-redshift observations, we provide here the first full self-consistent constraints, also including high redshift data, in particular from the cosmic microwave background. We consider various possible scenarios in which the field could act as quintessence. Despite the wider parameter space, we make use of recent observational progress to significantly improve constraints on the model, showing that order unity couplings (which would be natural in string theory) are ruled out.'\nauthor:\n- L\u00e9o Vacher\n- Nils Sch\u00f6neberg\n- 'J. D. F. Dias'\n- 'C. J."
+"---\nabstract: 'This paper is devoted to the study of the properties of entropy as a function of the Hurst index, which corresponds to the fractional Gaussian noise. Since the entropy of the Gaussian vector depends on the determinant of the covariance matrix, and the behavior of this determinant as a function of the Hurst index is rather difficult to study analytically at high dimensions, we also consider simple alternative entropy functionals, whose behavior, on the one hand, mimics the behavior of entropy and, on the other hand, is not difficult to study. Asymptotic behavior of the normalized entropy (so called entropy rate) is also studied for the entropy and for the alternative functionals.'\naddress:\n- '$^1$ Division of Mathematics and Physics, M\u00e4lardalen University, 721 23 V\u00e4ster[\u00e5]{}s, Sweden'\n- '$^2$ Department of Probability Theory, Statistics and Actuarial Mathematics, Taras Shevchenko National University of Kyiv, 64/13, Volodymyrska Street, 01601 Kyiv, Ukraine'\n- '$^3$ Sydney Mathematical Research Institute, The University of Sydney, Sydney NSW 2006, Australia'\nauthor:\n- Anatoliy Malyarenko$^1$\n- 'Yuliya Mishura[$^{1,2}$]{}'\n- 'Kostiantyn Ralchenko$^{2,3}$'\n- Sergiy Shklyar$^2$\nbibliography:\n- 'biblio.bib'\ntitle: Entropy and alternative entropy functionals of\u00a0fractional Gaussian noise as the functions of\u00a0Hurst index\n---\n\n[^1]\n\nIntroduction\n============"
+"---\nabstract: 'Learning models that are robust to distribution shifts is a key concern in the context of their real-life applicability. Invariant Risk Minimization (IRM) is a popular framework that aims to learn robust models from multiple environments. The success of IRM requires an important assumption: the underlying causal mechanisms/features remain invariant across environments. When not satisfied, we show that IRM can over-constrain the predictor and to remedy this, we propose a relaxation via *partial invariance*. In this work, we theoretically highlight the sub-optimality of IRM and then demonstrate how learning from a partition of training domains can help improve invariant models. Several experiments, conducted both in linear settings as well as with deep neural networks on tasks over both language and image data, allow us to verify our conclusions.'\nauthor:\n- '[Moulik Choraria^1,^[^1], Ibtihal Ferwana^1^, Ankur Mani^2^, Lav R. Varshney^1^]{}'\n- Author Name\n- 'First Author Name,^1,2^ Second Author Name, ^2^ Third Author Name ^1^'\nbibliography:\n- 'full.bib'\n- 'conf\\_full.bib'\n- 'pirm.bib'\ntitle:\n- Learning Optimal Features via Partial Invariance\n- 'My Publication Title \u2014 Single Author'\n- 'My Publication Title \u2014 Multiple Authors'\n---\n\nIntroduction\n============\n\nStandard machine learning models trained using classical Empirical Risk Minimization (ERM) can"
+"---\nabstract: 'Most session-based recommender systems (SBRSs) focus on extracting information from the observed items in the current session of a user to predict a next item, ignoring the causes outside the session (called outer-session causes, OSCs) that influence the user\u2019s selection of items. However, these causes widely exist in the real world, and few studies have investigated their role in SBRSs. In this work, we analyze the causalities and correlations of the OSCs in SBRSs from the perspective of causal inference. We find that the OSCs are essentially the confounders in SBRSs, which leads to spurious correlations in the data used to train SBRS models. To address this problem, we propose a novel SBRS framework named COCO-SBRS (**CO**unterfactual **CO**llaborative **S**ession-**B**ased **R**ecommender **S**ystems) to learn the causality between OSCs and user-item interactions in SBRSs. COCO-SBRS first adopts a self-supervised approach to pre-train a recommendation model by designing pseudo-labels of causes for each user\u2019s selection of the item in data to guide the training process. Next, COCO-SBRS adopts counterfactual inference to recommend items based on the outputs of the pre-trained recommendation model considering the causalities to alleviate the data sparsity problem. As a result, COCO-SBRS can learn the causalities in data,"
+"---\nabstract: 'In this paper, we derive some explicit expansion formulas associated to Brenke polynomials using operational rules based on their corresponding generating functions. The obtained coefficients are expressed either in terms of finite double sums or finite sums or sometimes in closed hypergeometric terms. The derived results are applied to Generalized Gould-Hopper polynomials and Generalized Hermite polynomials introduced by Szeg\u00f6 and Chihara. Some well-known duplication and convolution formulas are deduced as particular cases.'\naddress:\n- 'Mathematics Department, College of Science, King Khalid University, Abha, Kingdom of Saudi Arabia/D\u00e9partement de Math\u00e9matiques, \u00c9cole Sup\u00e9rieure des Sciences et de la Technologie, Sousse University, Tunisia.'\n- 'D\u00e9partement de Math\u00e9matiques, Institut Pr\u00e9paratoire aux \u00c9tudes d\u2019Ing\u00e9nieur, Sfax University, Tunisia.'\n- 'D\u00e9partement de Math\u00e9matiques, \u00c9cole Sup\u00e9rieure des Sciences et de la Technologie, Sousse University, Tunisia.'\nauthor:\n- Hamza Chaggara\n- Abdelhamid Gahami\n- Neila Ben Romdhane\ntitle: Some Expansion Formulas for Brenke Polynomial Sets\n---\n\n\\[page:firstblob\\]\n\nIntroduction\n============\n\nLet $\\mathcal{P}$ be the vector space of polynomials with coefficients in $\\mathbb{C}$. A polynomial sequence in $\\mathcal{P}$ is called *polynomial set* (PS for short) if $\\deg P_n=n$, for all $n$.\\\nThe connection and linearization problems are defined as follows.\\\nGiven two PSs $\\{P_n\\}_{n\\geq0}$ and $\\{Q_n\\}_{n\\geq0}$, the so-called *connection"
+"---\nabstract: 'We consider the problem of *collaborative tree exploration* posed by Fraigniaud, Gasieniec, Kowalski, and Pelc\u00a0[@fraigniaud2006collective] where a team of $k$ agents is tasked to collectively go through all the edges of an unknown tree as fast as possible. Denoting by $n$ the total number of nodes and by $D$ the tree depth, the $\\mathcal{O}(n/\\log(k)+D)$ algorithm of [@fraigniaud2006collective] achieves the best-known competitive ratio with respect to the cost of offline exploration which is $\\Theta(\\max{\\{2n/k,2D\\}})$. Brass, Cabrera-Mora, Gasparri, and Xiao [@brass2011multirobot] consider an alternative performance criterion, namely the additive overhead with respect to $2n/k$, and obtain a $2n/k+\\mathcal{O}((D+k)^k)$ runtime guarantee. In this paper, we introduce \u2018Breadth-First Depth-Next\u2019 (BFDN), a novel and simple algorithm that performs collaborative tree exploration in time $2n/k+\\mathcal{O}(D^2\\log(k))$, thus outperforming [@brass2011multirobot] for all values of $(n,D)$ and being order-optimal for all trees with depth $D=o_k(\\sqrt{n})$. Moreover, a recent result from [@disser2017general] implies that no exploration algorithm can achieve a $2n/k+\\mathcal{O}(D^{2-\\epsilon})$ runtime guarantee. The dependency in $D^2$ of our bound is in this sense optimal. The proof of our result crucially relies on the analysis of an associated two-player game. We extend the guarantees of BFDN to: scenarios with limited memory and communication, adversarial setups where robots"
+"---\nabstract: 'Developing systems that can synthesize natural and life-like motions for simulated characters has long been a focus for computer animation. But in order for these systems to be useful for downstream applications, they need not only produce high-quality motions, but must also provide an accessible and versatile interface through which users can direct a character\u2019s behaviors. Natural language provides a simple-to-use and expressive medium for specifying a user\u2019s intent. Recent breakthroughs in natural language processing (NLP) have demonstrated effective use of language-based interfaces for applications such as image generation and program synthesis. In this work, we present PADL, which leverages recent innovations in NLP in order to take steps towards developing language-directed controllers for physics-based character animation. PADL allows users to issue natural language commands for specifying both high-level tasks and low-level skills that a character should perform. We present an adversarial imitation learning approach for training policies to map high-level language commands to low-level controls that enable a character to perform the desired task and skill specified by a user\u2019s commands. Furthermore, we propose a multi-task aggregation method that leverages a language-based multiple-choice question-answering approach to determine high-level task objectives from language commands. We show that our"
+"---\nabstract: 'The scattering of Dirac fermions in the background fields of topological solitons of the $(2+1)$-dimensional $\\mathbb{CP}^{N-1}$ model is studied using analytical and numerical methods. It is shown that the exact solutions for fermionic wave functions can be expressed in terms of the confluent Heun functions. The question of the existence of bound states for the fermion-soliton system is then investigated. General formulae describing fermion scattering are obtained, and a symmetry property for the partial phase shifts is derived. The amplitudes and cross-sections of the fermion-soliton scattering are obtained in an analytical form within the framework of the Born approximation, and the symmetry properties and asymptotic forms of the Born amplitudes are investigated. The dependences of the first few partial phase shifts on the fermion momentum are obtained by numerical methods, and some of their properties are investigated and discussed.'\nauthor:\n- 'A.\u00a0Yu.\u00a0Loginov'\nbibliography:\n- 'article.bib'\ntitle: 'Fermion scattering on topological solitons in the $\\mathbb{CP}^{N-1}$ model'\n---\n\n\\[sec:I\\] Introduction\n======================\n\nA number of $(2 + 1)$-dimensional field models admit the existence of planar topological solitons [@Manton; @E_Weinberg; @Zakrzewski], which play an important role in field theory, high-energy physics, condensed matter physics, cosmology, and hydrodynamics. The vortices of"
+"---\nabstract: 'Mutation testing is an established software quality assurance technique for the assessment of test suites. While it is well-suited to estimate the general fault-revealing capability of a test suite, it is not practical and informative when the software under test must be validated against specific requirements. This is often the case for embedded software, where the software is typically validated against rigorously-specified safety properties. In such a scenario (i) a mutant is relevant only if it can impact the satisfaction of the tested properties, and (ii) a mutant is meaningfully-killed with respect to a property only if it causes the violation of that property. To address these limitations of mutation testing, we introduce *property-based mutation testing*, a method for assessing the capability of a test suite to exercise the software with respect to a given property. We evaluate our property-based mutation testing framework on Simulink models of safety-critical Cyber-Physical Systems (CPS) from the automotive and avionic domains and demonstrate how property-based mutation testing is more informative than regular mutation testing. These results open new perspectives in both mutation testing and test case generation of CPS.'\nauthor:\n- \n- \n- \n- \nbibliography:\n- 'refs.bib'\ntitle: 'Property-Based Mutation Testing'\n---"
+"---\nabstract: 'With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challenging as it requires considering both the and the , Recent graph-based approaches have made impressive progress in tackling the challenges of this field. In this survey, we conduct a comprehensive and up-to-date review of Graph-based TSAD (G-TSAD). First, we explore the significant potential of graph representation learning for time-series data. Then, we review state-of-the-art graph anomaly detection techniques in the context of time series and discuss their strengths and drawbacks. Finally, we discuss the technical challenges and potential future directions for possible improvements in this research field.'\nauthor:\n- |\n    Thi Kieu Khanh Ho, Ali Karami, Narges Armanfard Department of Electrical and Computer Engineering, McGill University\\\n    Mila - Quebec AI Institute, Montreal, QC, Canada {thi.k.ho, ali.karami, narges.armanfard}@mcgill.ca\n- First Author$^1$\n- Second Author$^2$\n- |\n    Third Author$^{2,3}$Fourth Author$^4$ $^1$First Affiliation\\\n    $^2$Second Affiliation\\\n    $^3$Third Affiliation\\\n    $^4$Fourth Affiliation {first, second}@example.com, third@other.example.com, fourth@example.com\nbibliography:\n- 'ijcai23.bib'\ntitle: 'Graph-based"
+"---\nabstract: 'Forthcoming fixed-target coherent elastic neutrino-nucleus scattering experiments aim at measurements with $\\cal{O}(\\text{tonne})$-scale detectors and substantially reduced systematic and statistical uncertainties. With such high quality data, the extraction of point-neutron distributions mean-square radii requires a better understanding of possible theoretical uncertainties. We quantify the impact of single-nucleon electromagnetic mean-square radii on the weak-charge form factor and compare results from weak-charge form factor parametrizations and weak-charge form factor decompositions in terms of elastic vector proton and neutron form factors, including nucleon form factors $Q$-dependent terms up to order $Q^2$. We assess as well the differences arising from results derived using weak-charge form factor decompositions in terms of elastic vector proton and neutron form factors and a model-independent approach based solely on the assumption of spherically symmetric nuclear ground state. We demonstrate the impact of the main effects by assuming pseudo-data from a one-tonne LAr detector and find that, among the effects and under the assumptions considered in this paper, weak-charge form factor parametrizations and weak-charge form factor decompositions in terms of elastic vector proton and neutron form factors enable the extraction of the $^{40}\\text{Ar}$ point-neutron distribution mean-square radius with a $\\sim 15\\%$ accuracy. With a substantial reduction of the beam-related"
+"---\nabstract: |\n    While many organizations have shifted to working remotely during the COVID-19 pandemic, how the remote workforce and the remote teams are influenced by and would respond to this and future shocks remain largely unknown. Software developers have relied on remote collaborations long before the pandemic, working in virtual teams (GitHub repositories). The dynamics of these repositories through the pandemic provide a unique opportunity to understand how remote teams react under shock. This work presents a systematic analysis.\n\n    We measure the overall effect of the early pandemic on public GitHub repositories by comparing their sizes and productivity with the counterfactual outcomes forecasted as if there were no pandemic. We find that the productivity level and the number of active members of these teams vary significantly during different periods of the pandemic. We then conduct a finer-grained investigation and study the heterogeneous effects of the shock on individual teams. We find that the resilience of a team is highly correlated to certain properties of the team before the pandemic. Through a bootstrapped regression analysis, we reveal which types of teams are robust or fragile to the shock.\nauthor:\n- 'Xuan Lu,^1^ Wei Ai,^2^ Yixin Wang,^3^ Qiaozhu Mei^1^\\'\n- 'Author"
+"---\nabstract: 'In this research, we address the challenge faced by existing deep learning-based human mesh reconstruction methods in balancing accuracy and computational efficiency. These methods typically prioritize accuracy, resulting in large network sizes and excessive computational complexity, which may hinder their practical application in real-world scenarios, such as virtual reality systems. To address this issue, we introduce a modular multi-stage lightweight graph-based transformer network for human pose and shape estimation from 2D human pose, a pose-based human mesh reconstruction approach that prioritizes computational efficiency without sacrificing reconstruction accuracy. Our method consists of a 2D-to-3D lifter module that utilizes graph transformers to analyze structured and implicit joint correlations in 2D human poses, and a mesh regression module that combines the extracted pose features with a mesh template to produce the final human mesh parameters.'\nauthor:\n- \nbibliography:\n- 'main.bib'\ntitle: 'A Modular Multi-stage Lightweight Graph Transformer Network for Human Pose and Shape Estimation from 2D Human Pose'\n---\n\n**Introduction**\n================\n\nThe field of computer vision has witnessed significant advancements with the advent of deep learning models in human pose estimation from monocular images. Specifically, 2D pose estimation involves determining the locations of human joint coordinates within a single image. In"
+"---\nabstract: 'We present the results of a survey of \u00a0 emission in 14 infrared luminous dusty star forming galaxies (DSFGs) at $2 < z < 4$ with the NSF\u2019s Karl G. Jansky Very Large Array. All sources are detected in $\\rm ^{12}$CO(1$-$0), with an $\\sim 1\\arcsec$ angular resolution. Seven sources show extended and complex structure. We measure $\\rm ^{12}$CO luminosities of $(\\mu)L^\\prime_{\\rm CO(1-0)} = 0.4-2.9\\times10^{11}$Kkms$^{-1}$pc$^2$, and molecular gas masses of $(\\mu)M_{\\rm H_2} = 1.3-8.6\\times10^{11}$M$_\\odot$, where $(\\mu)$ is the magnification factor. The derived molecular gas depletion times of $t_{\\rm dep} = 40-460$Myr, cover the expected range of both normal star forming galaxies and starbursts. Comparing to the higher$-J$ $\\rm ^{12}$CO transitions previously observed for the same sources, we find CO temperature brightness ratios of $r_{32/10} = 0.4-1.4$, $r_{43/10} = 0.4-1.7$, and $r_{54/10} = 0.3-1.3$. We find a wide range of CO spectral line energy distributions (SLEDs), in agreement with other high-$z$ DSFGs, with the exception of three sources that are most comparable to the Cloverleaf and APM08279$+$5255. Based on radiative transfer modelling of the $\\rm ^{12}$CO SLEDs we determine densities of $\\rm n_{H_2} = 0.3-8.5\\times10^3$cm$^{-3}$ and temperatures of $\\rm T_K = 100 - 200\\,K$. Lastly, four sources are detected in the"
+"---\nabstract: 'Classification is one of the main applications of supervised learning. Recent advancement in developing quantum computers has opened a new possibility for machine learning on such machines. Due to the noisy performance of near-term quantum computers, error mitigation techniques are essential for extracting meaningful data from noisy raw experimental measurements. Here, we propose two ensemble-learning error mitigation methods, namely bootstrap aggregating and adaptive boosting, which can significantly enhance the performance of variational quantum classifiers for both classical and quantum datasets. The idea is to combine several weak classifiers, each implemented on a shallow noisy quantum circuit, to make a strong one with high accuracy. While both of our protocols substantially outperform error-mitigated primitive classifiers, the adaptive boosting shows better performance than the bootstrap aggregating. The protocols have been exemplified for classical handwriting digits as well as quantum phase discrimination of a symmetry-protected topological Hamiltonian, in which we observe a significant improvement in accuracy. Our ensemble-learning methods provide a systematic way of utilising shallow circuits to solve complex classification problems.'\nauthor:\n- Qingyu Li\n- Yuhan Huang\n- Xiaokai Hou\n- Ying Li\n- Xiaoting Wang\n- Abolfazl Bayat\nbibliography:\n- 'reference.bib'\ntitle: 'Ensemble-learning error mitigation for variational quantum"
+"---\nauthor:\n- Dong Yin\n- Sridhar Thiagarajan\n- Nevena Lazic\n- Nived Rajaraman\n- Botao Hao\n- Csaba Szepesvari\nbibliography:\n- 'reference.bib'\ntitle: Sample Efficient Deep Reinforcement Learning via Local Planning\n---\n\nIntroduction {#sec:intro}\n============\n\nSimulators are ubiquitous in modern reinforcement learning (RL). They correspond to either to the environment itself (as in chess, go, and video games\u00a0[@bellemare2013arcade]) or to a simplified model of the true environment (such as robotic arm manipulation [@qassem2010modeling; @akkaya2019solving], car driving [@bojarski2016end; @aradi2020survey], or plasma shape control in fusion [@degrave2022magnetic]). Simulators have been widely used in RL research. Many standard benchmarks in RL involve simulators, for example, Atari games\u00a0[@bellemare2013arcade], Mujoco simulation engine\u00a0[@todorov2012mujoco], OpenAI Gym\u00a0[@brockman2016openai], DeepMind control suite\u00a0[@tassa2018deepmind], and DeepMind Lab\u00a0[@beattie2016deepmind]. Somewhat surprisingly, the majority of RL algorithms use the agent-environment interaction protocols that mimic learning in the real world during training, and do not explicitly take advantage of favorable simulator properties. In particular, the standard interaction protocol, called *online access*, assumes that the agent can only follow the dynamics of the environment during learning. In this work, we consider the *local access* protocol [@yin2021efficient], where the agent is allowed to revisit any previously observed state in addition to"
+"---\nabstract: 'Deep reinforcement learning has recently been applied to a variety of robotics applications, but learning locomotion for robots with unconventional configurations is still limited. Prior work has shown that, despite the simple modeling of articulated swimmer robots, such systems struggle to find effective gaits using reinforcement learning due to the heterogeneity of the search space. In this work, we leverage insight from geometric models of these robots in order to focus on promising regions of the space and guide the learning process. We demonstrate that our augmented learning technique is able to produce gaits for different learning goals for swimmer robots in both low and high Reynolds number fluids.'\nauthor:\n- 'Jiaheng Hu$^{1}$ and Tony Dear$^{1}$[^1]'\nbibliography:\n- 'IEEEexample.bib'\ntitle: '**Guided Deep Reinforcement Learning for Articulated Swimming Robots** '\n---\n\nINTRODUCTION\n============\n\nArticulated swimming robots have attracted much interest from researchers due to their effective locomotive capabilities as well as the richness of their geometric structure. The basis of their locomotion arises from the interaction between actuation of their joints and the surrounding fluid environment. Such interactions depend highly on the nature of the fluid, but previous work has shown that in the cases of extremely low or"
+"---\nabstract: 'Closed shell molecular structures are under normal conditions time-reversal invariant. Experimental evidences point, however, towards that this invariance may be locally violated when the structure is in contact with a particle reservoir. The mechanisms behind such local symmetry breaking are not clear by any means. By considering a minimal model for a closed shell structure, here we propose that the symmetry breaking may result from a combination of internal and/or external interactions. It is shown that a magnetic moment of a localized electron level can be generated and maintained under the influence of such combination. The theoretical results should cast new light on the mechanisms that may form magnetic properties in molecular compounds.'\nauthor:\n- 'M. Shiranzaei'\n- 'S. Kalh\u00f6fer'\n- 'J. Fransson'\nbibliography:\n- 'ref.bib'\ntitle: Emergent magnetism as a cooperative effect of interactions and reservoir\n---\n\nMolecules, as well as single atoms, which are in closed shell configurations when isolated can acquire a magnetic state when, e.g., immersed in solution [@ChemSci.1.631; @Science.331.445], attached on a surface [@Nature.468.1476; @NatPhysics.8.497; @JMaterChemC.3.11986; @Science.352.318; @RSCAdv.9.34421; @PhysRevMaterials.5.114801; @ACSNano.16.13049], or being in embedded in clusters comprising several components [@JACS.125.8694; @NatComms.6.10139; @ChemSci.7.6132; @JPhysChemLett.7.4988; @JPhysChemC.121.12159; @ApplPhysExpress.13.113001; @ACSNano14.16624; @JPhysChemC.125.9875]. Such properties can be exploited in,"
+"---\nabstract: 'We initiate the study of convex geometry over ordered hyperfields. We define convex sets and halfspaces over ordered hyperfields, presenting structure theorems over hyperfields arising as quotients of fields. We prove hyperfield analogues of Helly, Radon and Carath\u00e9odory theorems. We also show that arbitrary convex sets can be separated via hemispaces. Comparing with classical convexity, we begin classifying hyperfields for which halfspace separation holds. In the process, we demonstrate many properties of ordered hyperfields, including a classification of stringent ordered hyperfields.'\nauthor:\n- 'James Maxwell[^1]'\n- 'Ben Smith[^2]'\nbibliography:\n- 'References.bib'\ntitle: Convex geometry over ordered hyperfields\n---\n\nIntroduction\n============\n\nHyperfields are structures that generalise fields by allowing the addition operation to be multivalued, i.e. the sum $a\\boxplus b$ may be a set rather than a singleton. Multivalued operations were first considered by Marty\u00a0[@MAR] in the context of hypergroups, but it was Krasner who first introduced the notion of a hyperfield\u00a0[@KRA:57; @KRA:83]. They have since been studied within the context of a number of mathematical problems in which multivalued addition is required, including number theory\u00a0[@CC], real fields\u00a0[@Mars] and more recently matroid theory\u00a0[@BB]. They rose to prominence within tropical geometry via the articles of"
+"---\nabstract: 'Active galactic nuclei (AGN) can launch and sustain powerful winds featuring mildly relativistic velocity and wide opening angle. Such winds, known as ultra-fast outflows (UFOs), can develop a bubble structure characterized by a forward shock expanding in the host galaxy and a wind termination shock separating the fast cool wind from the hot shocked wind. In this work we explore whether diffusive shock acceleration can take place efficiently at the wind termination shock of UFOs. We calculate the spectrum of accelerated particles and find that protons can be energized up to the EeV range promoting UFOs to promising candidates for accelerating ultra-high energy cosmic rays (UHECRs). We also compute the associated gamma-ray and neutrino fluxes and compare them with available data in the literature. We observe that high-energy (HE) neutrinos are efficiently produced up to hundreds of PeV while the associated gamma rays could be efficiently absorbed beyond a few tens of GeV by the optical-ultraviolet AGN photon field. By assuming a typical source density of non-jetted AGN we expect that UFOs could play a dominant role as diffuse sources of UHECRs and HE neutrinos. We finally apply our model to the recently observed NGC1068 and we find"
+"---\nauthor:\n- |\n    Alexander\u00a0E.\u00a0Ulanov,[^1^]{} Thibault Wildi,[^1^]{} Nikolay\u00a0G.\u00a0Pavlov,[^2^]{}\\\n    John\u00a0D.\u00a0Jost,[^2^]{} Maxim Karpov,[^2^]{} Tobias Herr[^1,3,\\*^]{}\nbibliography:\n- 'bibliography.bib'\ndate: |\n     $^1$Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany\\\n    $^2$Enlightra Sarl, Rue de Lausanne 64, 1020 Renens, Switzerland\\\n    $^3$Physics Department, Universit\u00e4t Hamburg UHH, Luruper Chaussee 149, 22607 Hamburg, Germany\\\n    $^*$tobias.herr@desy.de \ntitle:\n- 'Synthetic-reflection self-injection-locked microcombs'\n- |\n    Synthetic-self-injection locked microcombs -\\\n    Supplemental Information\n---\n\n**Laser-driven microresonators have enabled chip-integrated light sources with unique properties, including the self-organized formation of ultrashort soliton pulses and frequency combs (microcombs). While poised to impact major photonic applications, such as spectroscopy, sensing and optical data processing, microcombs still necessitate complex scientific equipment to achieve and maintain suitable single-pulse operation. Here, to address this challenge, we demonstrate microresonators with programmable synthetic reflection providing an injection-feedback to the driving laser. When designed appropriately, synthetic reflection enables [deterministic]{} access to self-injection-locked microcombs operating exclusively in the single-soliton regime. These results provide a route to easily-operable microcombs for portable sensors, autonomous navigation, or extreme-bandwidth data processing. [The novel concept of synthetic reflection may also be generalized to other integrated photonic systems.]{}**\n\nLaser-driven microresonators provide access to nonlinear optical phenomena, already with low-power continuous-wave excitation [@vahala:2003]. Leveraging"
+"---\nabstract: 'Convolutions are one of the most important operations in signal processing. They often involve large arrays and require significant computing time. Moreover, in practice, the signal data to be processed by convolution may be corrupted by noise. In this paper, we introduce a new method for computing the convolutions in the quantized tensor train (QTT) format and removing noise from data using the QTT decomposition. We demonstrate the performance of our method using a common mathematical model for synthetic aperture radar (SAR) processing that involves a sinc kernel and present the entire cost of decomposing the original data array, computing the convolutions, and then reformatting the data back into full arrays.'\nauthor:\n- 'Alina Chertock[^1],\u00a0 Chris Leonard[^2],\u00a0 Semyon Tsynkov[^3],\u00a0 Sergey Utyuzhnikov[^4]'\nbibliography:\n- 'ref.bib'\ntitle: |\n    Denoising Convolution Algorithms and Applications\\\n    to SAR Signal Processing\n---\n\nIntroduction\n============\n\nConvolution operations are used in different practical applications. They often involve large arrays of data and require optimization with respect to memory and computational cost. While input data are usually available only in a discrete form, the standard realization based on a vector-matrix representation is not often efficient since it leads to using sparse matrices. On the other hand, a"
+"---\nabstract: 'Real-world classifiers can benefit from the option of *abstaining* from predicting on samples where they have low confidence. Such abstention is particularly useful on samples which are close to the learned decision boundary, or which are outliers with respect to the training sample. These settings have been the subject of extensive but disjoint study in the *selective classification* (*SC*) and *out-of-distribution* (*OOD*) detection literature. Recent work on *selective classification with OOD detection* (*SCOD*) has argued for the unified study of these problems; however, the formal underpinnings of this problem are still nascent, and existing techniques are heuristic in nature. In this paper, we propose new plugin estimators for SCOD that are theoretically grounded, effective, and generalise existing approaches from the SC and OOD detection literature. In the course of our analysis, we formally explicate how na\u00efve use of existing SC and OOD detection baselines may be inadequate for SCOD. We empirically demonstrate that our approaches yields competitive SC and OOD detection performance compared to baselines from both literatures.'\nauthor:\n- |\n    Harikrishna Narasimhan\\\n    Google Research, Mountain View\\\n- |\n    Aditya Krishna Menon\\\n    Google Research, New York\\\n- |\n    Wittawat Jitkrittum\\\n    Google Research, New York\\\n- |\n    Sanjiv Kumar\\"
+"---\nabstract: 'In the wake of dwindling Moore\u2019s law, integrated electro-optic (E-O) computing circuits have shown revolutionary potential to provide progressively faster and more efficient hardware for computing. The E-O circuits for computing from the literature can operate with minimal latency at high bit-rates. However, they face shortcomings due to their operand handling complexity, non-amortizable high area and static power overheads, and general unsuitability for large-scale integration on reticle-limited chips. To alleviate these shortcomings, in this paper, we present a microring resonator (MRR) based polymorphic E-O logic gate (MRR-PEOLG) that can be dynamically programmed to implement different logic functions at different times. Our MRR-PEOLG can provide compactness and polymorphism to E-O circuits, to consequently improve their operand handling and amortization of area and static power overheads. We model our MRR-PEOLG using photonics foundry-validated tools to perform frequency and time-domain analysis of its polymorphic logic functions. Our evaluation shows that the use of our MRR-PEOLG in two E-O circuits from prior works can reduce their area-energy-delay product by up to 82.6$\\times$. A tutorial on the modeling and simulation of our MRR-PEOLG, along with related codes and files, is available on https://github.com/uky-UCAT/MRR-PEOLG.'\nauthor:\n- \n- \n- \n- \nbibliography:\n- 'ref.bib'\ntitle: |"
+"---\nauthor:\n- 'Saba Asif Baig,'\n- Sanjit Shashi\nbibliography:\n- 'refs.bib'\ntitle: Transport across interfaces in symmetric orbifolds\n---\n\nIntroduction\n============\n\n2-dimensional conformal field theories with boundaries have a long history in the literature [@Cardy:1984bb; @Cardy:2004hm], with applications to both condensed matter theory [@Affleck:1995ge; @Affleck2000] and worldsheet string theory [@Sagnotti:1987tw; @Polchinski:1995mt; @Gaberdiel:2002my; @Recknagel:2013uja]. However, given a generic (particularly, irrational) theory, the underlying classification principles of such boundaries consistent with conformal structure are unknown.\n\nAs a first pass to studying conformal boundaries, we can examine physical data \u201cencoded\" by the boundary. One example is the ground-state degeneracy $g$ (called the $g$-function) of the boundary state [@Affleck:1991tk]. This is a $c$-number that counts the \u201cboundary degrees of freedom.\" It is also associated with a thermodynamic *boundary entropy* $S_{\\text{b}} = \\log g$ [@Cardy:2004hm].\n\nInstead of a CFT with a boundary, we may consider two CFTs glued along a defect surface (in 2d, a line) preserving (reduced) conformal symmetry. This defect is an interface between the constituent systems [@Wong:1994np], so the full theory is called an *interface CFT (ICFT)*. Like with boundaries, a general classification of conformal interfaces is unknown. However, an ICFT can be mapped to a boundary (B)CFT by folding along"
+"---\nabstract: '*Randomized Controlled Trials (RCT)s* are relied upon to assess new treatments, but suffer from limited power to guide personalized treatment decisions. On the other hand, observational (i.e., non-experimental) studies have large and diverse populations, but are prone to various biases (e.g. residual confounding). To safely leverage the strengths of observational studies, we focus on the problem of *falsification*, whereby RCTs are used to validate causal effect estimates learned from observational data. In particular, we show that, given data from both an RCT and an observational study, assumptions on internal and external validity have an observable, testable implication in the form of a set of *Conditional Moment Restrictions (CMRs)*. Further, we show that expressing these CMRs with respect to the causal effect, or \u201ccausal contrast\u201d, as opposed to individual counterfactual means, provides a more reliable falsification test. In addition to giving guarantees on the asymptotic properties of our test, we demonstrate superior power and type I error of our approach on semi-synthetic and real world datasets. Our approach is interpretable, allowing a practitioner to visualize which subgroups in the population lead to falsification of an observational study.'\nbibliography:\n- 'ref.bib'\n---\n\nINTRODUCTION\n============\n\nObservational studies, prevalent in healthcare, economics,"
+"---\nabstract: 'Increasing observations of white dwarf atmospheric pollution and disrupting planetesimals is driving increased studies into the fate of exo-asteroids around post-main-sequence stars. Planetesimal populations in the Solar System which are most likely to survive the violent post-main-sequence evolution, such as the Kuiper Belt, display a large binary fraction with a propensity for near equal-mass components and provide a previously unexplored population of planetesimals which are likely to exist around white dwarfs. Here we simulate the dynamical evolution of equal-mass binary asteroid systems around white dwarfs using the N-body integrator `REBOUND` for $1$\u00a0Gyr. We confirm that giant planets are efficient at dissociating and ejecting binary asteroid systems on eccentric orbits, while Earth-mass planets are better at keeping planetesimals in their planetary systems. We find binary systems can be dissociated and ejected from their systems across Myr timescales, producing interstellar objects. We do not expect a population of free-floating binary asteroid systems as all ejected planetesimals are gravitationally unbound from each other. Further, we discuss the influence of asteroid binarity on the white dwarf pollution process and find there is little to no impact on how close a body can get to a star. However, the orbital evolution of"
+"---\nabstract: 'We revisit the concept of a minimal basis through the lens of the theory of modules over a commutative ring $R$. We first review the conditions for the existence of a basis for submodules of $R^n$ where $R$ is a B\u00e9zout domain. Then, we define the concept of invertible basis of a submodule of $R^n$ and, when $R$ is an elementary divisor domain, we link it to the Main Theorem of \\[G. D. Forney Jr., SIAM J. Control 13, 493\u2013520, 1975\\]. Over an elementary divisor domain, the submodules admitting an invertible basis are precisely the free pure submodules of $R^n$. As an application, we let $\\Omega \\subseteq \\C$ be either a connected compact set or a connected open set, and we specialize to $R=\\A$, the ring of functions that are analytic on $\\Omega$. We show that, for any matrix $A(z) \\in \\A^{m \\times n}$, $\\ker A(z) \\cap \\A^n$ is a free $\\A$-module and admits an invertible basis, or equivalently a basis that is full rank upon evaluation at any $\\lambda \\in \\Omega$. Finally, given $\\lambda \\in \\Omega$, we use invertible bases to define and study maximal sets of root vectors at $\\lambda$ for $A(z)$. This in particular allows"
+"---\nabstract: 'We introduce a method for solving the \u201cinverse\u201d phase equilibria problem: How should the interactions among a collection of molecular species be designed in order to achieve a target phase diagram? Using techniques from convex optimization theory, we show how to solve this problem for phase diagrams containing a large number of components and many coexisting phases with prescribed compositions. We apply our approach to commonly used mean-field models of multicomponent fluids and then use molecular simulations to verify that the designed interactions result in the target phase diagrams. Our approach enables the rational design of \u201cprogrammable\u201d fluids, such as biopolymer and colloidal mixtures, with complex phase behavior.'\nauthor:\n- Fan Chen\n- 'William M.\u00a0Jacobs'\ntitle: Programmable phase behavior in fluids with designable interactions\n---\n\nIntroduction\n============\n\nThe observation that proteins and nucleic acids can demix to form \u201cbiomolecular condensates\u201d within living cells\u00a0[@hyman2014liquid; @shin2017liquid; @alberti2019considerations] has sparked intense interest in understanding the phase behavior of complex, multicomponent fluids\u00a0[@jacobs2017phase; @mao2019phase; @jacobs2021self; @shrinivas2021phase; @carugno2022instabilities; @graf2022thermodynamic; @zwicker2022evolved]. Although multicomponent phase equilibria is a foundational topic of chemical physics\u00a0[@gibbs1878equilibrium], and many important theoretical contributions[@griffiths1970critical; @sear2003instabilities; @jacobs2013predicting] in this area predate the current popularity of biomolecular condensate research, intracellular"
+"---\nabstract: 'Nitrogen vacancy (NV) centers in diamond have emerged in recent years as leading quantum sensors in various modalities. Most applications benefit from shallow NVs, enabling higher sensitivity and resolution. However, near surface NVs ($<$ 20 nm depth) suffer from reduced stability and coherence properties due to additional noise. We demonstrate a novel surface termination technique based on nitrogen plasma under non-damaging conditions, achieving significant improvement in NV optical stability and quantum coherence.'\nauthor:\n- 'R. Malkinson'\n- 'M. K. Kuntumalla'\n- 'A. Hoffman'\n- 'N. Bar-Gill'\nbibliography:\n- 'ref.bib'\ntitle: Enhanced quantum properties of shallow diamond atomic defects through nitrogen surface termination\n---\n\nIntroduction\n============\n\nRecent years have seen significant interest in optically active atomic defects in the solid-state in the context of various quantum science and technology applications, notably in quantum sensing [@degen2017review; @taylor2008magnetometer; @acosta2009diamonds; @doherty2013nitrogen]. One of the leading realizations of such a platform is the nitrogen vacancy (NV) center in diamond, which exhibits favorable spin properties even at room temperature [@doherty2013nitrogen; @bargill2013coherence; @childress2006coherent].\n\nFor many sensing applications, it is desirable to optimize samples with shallow defects, such that their sensitivity to signals of interest outside of their host material will be maximized. However, it has"
+"---\nabstract: 'Gaussian elimination with partial pivoting (GEPP) [is a widely used]{} method to solve dense linear systems. Each GEPP step uses a row transposition pivot movement if needed to ensure the leading pivot entry is maximal in magnitude for the leading column of the remaining untriangularized subsystem. We will use theoretical and numerical approaches to study how often this pivot movement is needed. We provide full distributional descriptions for the number of pivot movements needed using GEPP using particular Haar random ensembles, as well as compare these models to other common transformations from randomized numerical linear algebra. Additionally, we introduce new random ensembles with fixed pivot movement counts and fixed sparsity, $\\alpha$. Experiments estimating the empirical spectral density (ESD) of these random ensembles leads to a new conjecture on a universality class of random matrices with fixed sparsity whose scaled ESD converges to a measure on the complex unit disk that depends on $\\alpha$ and is an interpolation of the uniform measure on the unit disk and the Dirac measure at the origin.'\naddress: 'Department of Mathematics, University of Arizona'\nauthor:\n- \u00a0\nbibliography:\n- 'references.bib'\ntitle: Distribution of the number of pivots needed using Gaussian elimination with partial pivoting"
+"---\nauthor:\n- Elisa\u00a0El\u00a0Sergany\n- Matthieu\u00a0Wyart\n- 'Tom\u00a0W.J.\u00a0de\u00a0Geus'\nbibliography:\n- 'library.bib'\ntitle: Armouring of a frictional interface by mechanical noise\n---\n\nIntroduction\n============\n\nWe study systems in which disorder and elasticity compete, leading to intermittent, avalanche-type response under loading. Examples include an elastic line being pulled over a disordered pinning potential, or frictional interfaces \u00a0[@Fisher1998; @Narayan1993; @Kardar1998]. When subject to an external load $f$, such systems are pinned by disorder when the load is below a critical value $f_c$. At $f > f_c$, the system moves forward at a finite rate. At $f = f_c$ the system displays a crackling-type response described by avalanches whose sizes and durations are distributed according to powerlaws.\n\nA key aspect of such systems is the distribution of soft spots [@Muller2015]. If we define $x$ as the force increase needed to trigger an instability locally, then increasing the remotely applied force by $\\Delta f$ will trigger $n_a \\propto \\int_0^{\\Delta f} P(x) dx$ avalanches, with $P(x)$ the probability density of $x$. The relevant behaviour of $P(x)$ therefore is that at small $x$. Let us assume that $P(x) \\sim x^\\theta$ at small $x$, such that $n_a \\propto (\\Delta f)^{\\theta + 1}$."
+"---\nabstract: 'Due to its incommensurate nature, moir\u00e9 superlattices host not only acoustic phonons but also another type of soft collective modes called phasons. Here, we investigate the impact of electron-phason scattering on the transport properties of moir\u00e9 systems. We show that the resistivity can scale linearly with temperature down to temperatures much lower than the Bloch-Gr\u00fcneisen scale defined by electron kinematics on the Fermi surface. This result stems from the friction between layers, which transfers phason spectral weight to a broad diffusive low-energy peak in the mechanical response of the system. As a result, phason scattering becomes a very efficient channel for entropy production at low temperatures. We also consider the contributions of phasons to thermodynamic properties at low temperatures and find a \u201cmetallic-like\u201d linear-in-$T$ behavior for the specific heat, despite the fact that this behavior is due to mechanical and not electronic degrees of freedom. We discuss the implications of this finding to reports of linear-in-$T$ resistivity in the phase diagram of twisted bilayer graphene.'\nauthor:\n- 'H\u00e9ctor Ochoa$^{1,2,3}$ and Rafael M. Fernandes$^4$'\nbibliography:\n- 'references.bib'\ntitle: 'Extended linear-in-$T$ resistivity due to electron-phason scattering in moir\u00e9 superlattices'\n---\n\nIntroduction\n============\n\nElucidating the nature of the metallic state of"
+"---\nabstract: 'Light carries energy and momentum. It can therefore alter the motion of objects from atomic to astronomical scales. Being widely available, readily controllable and broadly biocompatible, light is also an ideal tool to propel microscopic particles, drive them out of thermodynamic equilibrium and make them active. Thus, light-driven particles have become a recent focus of research in the field of soft active matter. In this perspective, we discuss recent advances in the control of soft active matter with light, which has mainly been achieved using light intensity. We also highlight some first attempts to utilize light\u2019s additional degrees of freedom, such as its wavelength, polarization, and momentum. We then argue that fully exploiting light with all of its properties will play a critical role to increase the level of control over the actuation of active matter as well as the flow of light itself through it. This enabling step will advance the design of soft active matter systems, their functionalities and their transfer towards technological applications.'\nauthor:\n- Marcel Rey\n- Giovanni Volpe\n- Giorgio Volpe\nbibliography:\n- 'achemso-demo.bib'\ntitle: 'Light, Matter, Action: Shining light on active matter'\n---\n\n![image](toc4.png){width=\"8.25cm\"}\n\nIntroduction\n============\n\nIn the last half century, the"
+"---\nabstract: 'The high dimensionality and complex dynamics of turbulent flows remain an obstacle to the discovery and implementation of control strategies. Deep reinforcement learning (RL) is a promising avenue for overcoming these obstacles, but requires a training phase in which the RL agent iteratively interacts with the flow environment to learn a control policy, which can be prohibitively expensive when the environment involves slow experiments or large-scale simulations. We overcome this challenge using a framework we call \u201cDManD-RL\" (data-driven manifold dynamics-RL), which generates a data-driven low-dimensional model of our system that we use for RL training. With this approach, we seek to minimize drag in a direct numerical simulation (DNS) of a turbulent minimal flow unit of plane Couette flow at ${\\mbox{\\textit{Re}}}=400$ using two slot jets on one wall. We obtain, from DNS data with $\\mathcal{O}(10^5)$ degrees of freedom, a $25$-dimensional DManD model of the dynamics by combining an autoencoder and neural ordinary differential equation. Using this model as the environment, we train an RL control agent, yielding a $440$-fold speedup over training on the DNS, with equivalent control performance. The agent learns a policy that laminarizes 84% of unseen DNS test trajectories within $900$ time units, significantly outperforming"
+"---\nabstract: 'Semantic segmentation of various tissue and nuclei types in histology images is fundamental to many downstream tasks in the area of computational pathology (CPath). In recent years, Deep Learning (DL) methods have been shown to perform well on segmentation tasks but DL methods generally require a large amount of pixel-wise annotated data. Pixel-wise annotation sometimes requires expert\u2019s knowledge and time which is laborious and costly to obtain. In this paper, we present a consistency based semi-supervised learning (SSL) approach that can help mitigate this challenge by exploiting a large amount of unlabelled data for model training thus alleviating the need for a large annotated dataset. However, SSL models might also be susceptible to changing context and features perturbations exhibiting poor generalisation due to the limited training data. We propose an SSL method that learns robust features from both labelled and unlabelled images by enforcing consistency against varying contexts and feature perturbations. The proposed method incorporates context-aware consistency by contrasting pairs of overlapping images in a pixel-wise manner from changing contexts resulting in robust and context invariant features. We show that cross-consistency training makes the encoder features invariant to different perturbations and improves the prediction confidence. Finally, entropy minimisation"
+"---\nabstract: 'We consider the dynamics of a charged inertial active Ornstein-Uhlenbeck particle in a viscoelastic suspension under the action of an uniform magnetic field. With the help of both numerical simulation and analytical framework, we exactly investigate the viscoelastic response of the particle to the magnetic field by means of particle trajectories, mean displacement, and mean square displacement (MSD) calculations. The simulated particle trajectories and MSD calculations reveal that the steady state response of a confined harmonic particle to the magnetic field show interesting features due to the complex interplay of underlying physical processes such as elastic dissipation and active fluctuations in the medium. When the activity time scale of the dynamics is larger than the elastic dissipation timescale (or vice-versa), the steady state MSD shows an enhancement (or suppression) with increase in the field strength. In both the cases, for very strong magnetic field the MSD interestingly approaches the value at the equilibrium limit where the generalized fluctuation dissipation relation is satisfied and the the particle behaves like an inertial passive Brownian particle. Thus, for a finite magnetic field, the system exhibits a re-entrant type transition from active to passive and then to active behaviour with persistence of"
+"---\nabstract: 'Mounting compact and lightweight base stations on unmanned aerial vehicles (UAVs) is a cost-effective and flexible solution to provide seamless coverage on the existing terrestrial networks. While the coverage probability in UAV-assisted cellular networks has been widely investigated, it provides only the first-order statistic of signal-to-interference-plus-noise ratio (SINR). In this paper, to analyze high-order statistics of SINR and characterize the disparity among individual links, we provide a meta distribution (MD)-based analytical framework for UAV-assisted cellular networks, in which the probabilistic line-of-sight channel and realistic antenna pattern are taken into account for air-to-ground transmissions. To accurately characterize the interference from UAVs, we relax the widely applied uniform off-boresight angle (OBA) assumption and derive the exact distribution of OBA. Using stochastic geometry, for both steerable and vertical antenna scenarios, we obtain mathematical expressions for the moments of condition success probability, the SINR MD, and the mean local delay. Moreover, we study the asymptotic behavior of the moments as network density approaches infinity. Numerical results validate the tightness of the theoretical results and show that the uniform OBA assumption underestimates the network performance, especially in the regime of moderate altitude of UAV. We also show that when UAVs are equipped with"
+"---\nabstract: 'The rapid increase in demand for wireless controlled Smart Lighting has created a need to automate the mapping between the identifiers for individual light sources and their physical locations. To control Smart Lights, their IDs and physical locations relative to each other must be determined. Nowadays, skilled technicians perform this process manually, which requires a lot of effort, is time-consuming, and incurs high costs, particularly with non-stationary lights. Visible Light Communication has been presented as a possible solution to this problem. This paper presents an approach based on Visible Light Communication that leverages Machine Learning to automate the mapping process between the identifiers and the relative physical location of Smart Lights. We show that our approach provides a better location-mapping performance compared to existing methods.'\nauthor:\n- Jagdeep Singh\n- Dan Watkinson\n- Tim Farnham\n- Daniele Puccinelli\nbibliography:\n- 'references.bib'\ntitle: Detecting and Controlling Smart Lights with \n---\n\n&lt;ccs2012&gt; &lt;concept&gt; &lt;concept\\_id&gt;10010147.10010371.10010382.10010383&lt;/concept\\_id&gt; &lt;concept\\_desc&gt;Computing methodologies\u00a0Image processing&lt;/concept\\_desc&gt; &lt;concept\\_significance&gt;500&lt;/concept\\_significance&gt; &lt;/concept&gt; &lt;concept&gt; &lt;concept\\_id&gt;10003033.10003099.10003101&lt;/concept\\_id&gt; &lt;concept\\_desc&gt;Networks\u00a0Location based services&lt;/concept\\_desc&gt; &lt;concept\\_significance&gt;500&lt;/concept\\_significance&gt; &lt;/concept&gt; &lt;/ccs2012&gt;\n\nIntroduction {#sec:intro}\n============\n\nAs light-emitting diodes (LEDs) become increasingly ubiquitous in commercial environments due to their energy efficiency, there has been a growing demand for wirelessly controlled Smart Lights. The Smart"
+"---\nabstract: 'Room layout estimation is a long-existing robotic vision task that benefits both environment sensing and motion planning. However, layout estimation using point clouds (PCs) still suffers from data scarcity due to annotation difficulty. As such, we address the semi-supervised setting of this task based upon the idea of model exponential moving averaging. But adapting this scheme to the state-of-the-art (SOTA) solution for PC-based layout estimation is not straightforward. To this end, we define a quad set matching strategy and several consistency losses based upon metrics tailored for layout quads. Besides, we propose a new online pseudo-label harvesting algorithm that decomposes the distribution of a hybrid distance measure between quads and PC into two components. This technique does not need manual threshold selection and intuitively encourages quads to align with reliable layout points. Surprisingly, this framework also works for the fully-supervised setting, achieving a new SOTA on the ScanNet benchmark. Last but not least, we also push the semi-supervised setting to the realistic omni-supervised setting, demonstrating significantly promoted performance on a newly annotated ARKitScenes testing set. Our codes, data and models are made publicly available[^1].'\nauthor:\n- |\n    Huan-ang Gao$^{1,2}$, Beiwen Tian$^{1,2}$, Pengfei Li$^{1,2}$, Xiaoxue Chen$^{1,2}$\\\n    Hao Zhao$^{3,4}$, Guyue"
+"---\nabstract: 'In the <span style=\"font-variant:small-caps;\">Fully Leafed Induced Subtrees</span>, one is given a graph $G$ and two integers $a$ and $b$ and the question is to find an induced subtree of $G$ with $a$ vertices and at least $b$ leaves. This problem is known to be -complete even when the input graph is $4$-regular. Polynomial algorithms are known when the input graph is restricted to be a tree or series-parallel. In this paper we generalize these results by providing an algorithm parameterized by treewidth. We also provide a polynomial algorithm when the input graph is restricted to be a chordal graph.'\nauthor:\n- |\n    Julien Baste$^*$\\\n    \\\n    $^*$[Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France]{}\\\n    [julien.baste@univ-lille.fr]{}\ntitle: |\n    The <span style=\"font-variant:small-caps;\">Leafed Induced Subtree</span>\\\n    in chordal and bounded treewidth graphs\n---\n\n**Keyword:** Fully Leafed Induced Subtrees, algorithms, chordal graphs, treewidth\n\nIntroduction\n============\n\nAn important focus in the graph community is to find substructure in a graph. Since the 80\u2019s, a particular attention is given to subgraphs that are trees\u00a0[@ErSaSo1986; @KlWe1991; @PaTcXu1984; @WaArUn2014]. While finding a spanning tree in a graph is almost trivial, adding constraints on the required spanning tree quickely increase the complexity of the problem."
+"---\nabstract: 'Entangled states play a fundamental role in Quantum Mechanics and are at the core of many contemporary applications, such as quantum communication and quantum computing. Therefore, determining whether a state is entangled or not is an important task. Here, we propose a method to detect the entanglement of unknown two-qubit quantum states. Our method is based on the violation of the Clauser-Horne-Shimony-Holt inequality. This maximizes the value of the inequality even when contains an unknown quantum state. The method iteratively generates local measurement settings that lead to increasing values of the inequality.'\nauthor:\n- 'J. Cort\u00e9s-Vega'\n- 'J. F. Barra'\n- 'L. Pereira'\n- 'A. Delgado'\ntitle: 'Detecting entanglement of unknown states by violating the Clauser-Horne-Shimony-Holt inequality'\n---\n\nIntroduction\n============\n\nQuantum mechanics predicts the existence of quantum states of composite systems that cannot be written as products of states of their individual components [@von_Neumann]. These are the so called entangled states. Today, these states play a central role in quantum information theory [@Alber; @Horodecki1] and in many applications, such as, for instance, quantum cryptography [@Ekert], quantum teleportation [@Bennett; @Boschi1998], frequency standards improvement [@Wineland; @Huelga; @Giovannetti], one-way quantum computing [@Raussendorf], clock synchronization [@Jozsa], and entanglement assisted orientation in space"
+"---\nabstract: 'Recently, deep metric learning techniques received attentions, as the learned distance representations are useful to capture the similarity relationship among samples and further improve the performance of various of supervised or unsupervised learning tasks. We propose a novel supervised metric learning method that can learn the distance metrics in both geometric and probabilistic space for image recognition. In contrast to the previous metric learning methods which usually focus on learning the distance metrics in Euclidean space, our proposed method is able to learn better distance representation in a hybrid approach. To achieve this, we proposed a ***Generalized Hybrid Metric Loss*** (GHM-Loss) to learn the general hybrid proximity features from the image data by controlling the trade-off between geometric proximity and probabilistic proximity. To evaluate the effectiveness of our method, we first provide theoretical derivations and proofs of the proposed loss function, then we perform extensive experiments on two public datasets to show the advantage of our method compared to other state-of-the-art metric learning methods.'\nauthor:\n- \n- \nbibliography:\n- 'ref.bib'\ntitle: Learning Generalized Hybrid Proximity Representation for Image Recognition\n---\n\nDeep metric learning, proximity, probability distribution, representation learning, image classification\n\nIntroduction\n============\n\nMetric learning takes input data to"
+"---\nabstract: 'NectarCAM is a Cherenkov camera which is going to equip the (MST) of the northern site of the Cherenkov Telescope Array Observatory (CTAO). NectarCAM is equipped with 265 modules, each consisting of 7 photo-multiplier tubes (PMTs), a Front-End Board and a local camera trigger system used for data acquisition. This paper addresses the timing [[performance]{}]{} of NectarCAM which are crucial to reduce the noise in shower images and as well as [to]{} discriminate between gamma-ray photons and cosmic-ray background [and finally to allow coincidence identification with neighbouring telescopes for stereoscopic operations]{}. have been performed in a dark room using various light sources to illuminate the first NectarCAM unit.'\nauthor:\n- 'F.\u00a0Bradascio'\n- 'H.\u00a0Rueda'\n- 'J.A.\u00a0Barrio'\n- 'J.\u00a0Biteau'\n- 'F.\u00a0Brun'\n- 'C.\u00a0Champion'\n- 'J-F.\u00a0Glicenstein'\n- 'D.\u00a0Hoffmann'\n- 'P.\u00a0Jean'\n- 'J.P.\u00a0Lenain'\n- 'F.\u00a0Louis'\n- 'A.\u00a0P\u00e9rez'\n- 'M.\u00a0Punch'\n- 'P.\u00a0Sizun'\n- 'K.-H.\u00a0Sulanke'\n- 'L.\u00a0A.\u00a0Tejedor'\n- 'B.\u00a0Vallage'\nbibliography:\n- 'mybibfile.bib'\ntitle: The NectarCAM Timing System\n---\n\nNectarCAM ,gamma ray ,Cherenkov , ,timing resolution ,PMT transit time ,trigger\n\n![image](figure1.pdf){width=\"90.00000%\"}\n\n[\\[fig:trigger\\]]{}\n\nIntroduction\n============\n\nThe imaging atmospheric Cherenkov technique is [[one of the major methods]{}]{} for observations"
+"---\nabstract: 'This paper proposes a new cognitive model, acting as the main component of an AGI agent. The model is introduced in its mature state, and as an extension of previous models, DENN, and especially AKREM, by including operational models (frames/classes) and will. In addition, it is mainly based on the duality principle in every known intelligent aspect, such as exhibiting both top-down and bottom-up model learning, generalization verse specialization , and more. Furthermore, a holistic approach is advocated for AGI designing and cognition under constraints or efficiency is proposed, in the form of reusability and simplicity. Finally, reaching this mature state is described via a cognitive evolution from infancy to adulthood, utilizing a consolidation principle. The final product of this cognitive model is a dynamic operational memory of models and instances.'\nbibliography:\n- '../Shimon\\_paper02.bib'\n- '../Shimon5.bib'\ntitle: 'Purposeful and Operation-based Cognitive System for AGI'\n---\n\nIntroduction\n============\n\nOur consistent goal is to construct a basic realistic model for *AGI* (Artificial General Intelligence). It is a gradual process with many versions along the way. Hence, this paper presents *MOM* (Model Of Models), the next version of *AKREM* (Associative Knowledge Representation) [@10.1007/978-3-031-19907-3_6]. *AKREM* is a mature-state knowledge representation model, based"
+"---\nabstract: |\n    We consider large uniform labeled random graphs in different classes with few induced $P_4$ ($P_4$ is the graph consisting of a single line of $4$ vertices) which generalize the case of cographs. Our main result is the convergence to a Brownian limit object in the space of graphons. As a by-product we obtain new asymptotic enumerative results for all these graph classes. We also obtain typical density results for a wide variety of induced subgraphs. These asymptotics hold at a smaller scale than what is observable through the graphon convergence.\n\n    Our proofs rely on tree encoding of graphs. We then use mainly combinatorial arguments, including the symbolic method and singularity analysis.\nauthor:\n- Th\u00e9o Lenoir\nbibliography:\n- 'biblio.bib'\ntitle: 'Graph classes with few $P_4$\u2019s: Universality and Brownian graphon limits'\n---\n\nIntroduction\n============\n\nMotivation\n----------\n\nRandom graphs are one of the most studied objects in probability theory and in combinatorics. A natural question is to investigate the scaling limits of a uniformly chosen graph in a given family (an important example for this paper are the cographs).\n\nCographs have been studied since the seventies by various authors, especially for their algorithmic properties: recognizing cographs can be solved in"
+"---\nauthor:\n- 'Rahul Shah,[!!]{}'\n- 'Arko Bhaumik,'\n- Purba Mukherjee\n- and Supratik Pal\nbibliography:\n- 'biblio.bib'\ntitle: 'A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher Forecast, MCMC and Machine Learning'\n---\n\n\\[sec:introduction\\]Introduction\n================================\n\nWhile the standard $\\Lambda$CDM (cosmological constant $+$ cold dark matter) model of cosmology has provided an excellent fit to a wide range of cosmological datasets over the last two decades, state-of-the-art observational facilities in the era of precision cosmology have recently started to shed light on its inadequacies. The latter have manifested in the form of discrepancies or *tensions* between the values of one or more parameter(s) of the baseline $\\Lambda$CDM model, inferred from different datasets. Among the key tensions observed so far, perhaps the most serious one is the so-called Hubble tension, which has emerged between the value of the Hubble constant ($H_0$) inferred from cosmic microwave background (CMB) data at a redshift of $z\\sim1080$ and direct model-independent measurement of $H_0$ from low-redshift $(z\\sim0.01-1)$ Cepheid-calibrated type Ia supernovae (SNIa) data. First identified after the *Planck* 2013 data release [@novosyadlyj; @hazra], it has steadily grown into a serious problem in the standard cosmological paradigm that can no longer be"
+"---\nabstract: 'We present ${\\text{V0LVER}}$, an AMM protocol which solves an incentivization trilemma between users, passive liquidity providers, and block producers. ${\\text{V0LVER}}$ enables users and passive liquidity providers to interact without paying MEV or incurring uncontrolled loss-versus-rebalancing to the block producer. ${\\text{V0LVER}}$ is an AMM protocol built on an encrypted transaction mempool, where transactions are decrypted after being allocated liquidity by the AMM. ${\\text{V0LVER}}$ ensures this liquidity, given some external market price, is provided at that price in expectancy. This is done by incentivizing the block producer to move the pool price to the external market price. With this, users transact in expectancy at the external market price in exchange for a fee, with AMMs providing liquidity in expectancy at the external market price. Under block producer and liquidity provider competition, all of the fees in ${\\text{V0LVER}}$ approach zero. Without block producer arbitrage, ${\\text{V0LVER}}$ guarantees fall back to those of an AMM, albeit free from loss-versus-rebalancing and user-level MEV.'\nauthor:\n- Conor McMenamin\n- Vanesa Daza\nbibliography:\n- 'references.bib'\ntitle: 'An AMM minimizing user-level extractable value and loss-versus-rebalancing'\n---\n\n[^1]\n\nIntroduction\n============\n\nAMMs have emerged as a dominant medium for decentralized token exchange. This is due to several important properties"
+"---\nabstract: 'Obviously, the object is the key factor of the 3D single object tracking (SOT) task. However, previous Siamese-based trackers overlook the negative effects brought by randomly dropped object points during backbone sampling, which hinder trackers to predict accurate bounding boxes (BBoxes). Exploring an approach that seeks to maximize the preservation of object points and their object-aware features is of particular significance. Motivated by this, we propose an Object Preserving Siamese Network (**OPSNet**), which can significantly maintain object integrity and boost tracking performance. Firstly, the **object highlighting module** enhances the object-aware features and extracts discriminative features from template and search area. Then, the **object-preserved sampling** selects object candidates to obtain object-preserved search area seeds and drop the background points that contribute less to tracking. Finally, the **object localization network** precisely locates 3D BBoxes based on the object-preserved search area seeds. Extensive experiments demonstrate our method outperforms the state-of-the-art performance ($\\sim$9.4% and $\\sim$2.5% success gain on KITTI and Waymo Open Dataset respectively).'\nauthor:\n- |\n    Kaijie Zhao, Haitao Zhao, Zhongze Wang, Jingchao Peng, Zhengwei Hu\\\n    East China University of Science and Technology\\\n    No.130, Meilong Rd, Shanghai\\\nbibliography:\n- 'egbib.bib'\ntitle: Object Preserving Siamese Network for Single Object Tracking on Point"
+"---\nabstract: 'The human cerebral cortex is highly convoluted into convex gyri and concave sulci. It has been demonstrated that gyri and sulci are significantly different in their anatomy, connectivity, and function: besides exhibiting opposite shape patterns, long-distance axonal fibers connected to gyri are much denser than those connected to sulci, and neural signals on gyri are more complex in low-frequency while sulci are more complex in high-frequency. Although accumulating evidence shows significant differences between gyri and sulci, their primary roles in brain function have not been elucidated yet. To solve this fundamental problem, we design a novel Twin-Transformer framework to unveil the unique functional roles of gyri and sulci as well as their relationship in the whole brain function. Our Twin-Transformer framework adopts two structure-identical (twin) Transformers to disentangle spatial-temporal patterns of gyri and sulci: one focuses on the information of gyri and the other is on sulci. The Gyro-Sulcal interactions, along with the tremendous but widely existing variability across subjects, are characterized in the loss design. We validated our Twin-Transformer on the HCP task-fMRI dataset, for the first time, to elucidate the different roles of gyri and sulci in brain function. Our results suggest that gyri and sulci"
+"---\nabstract: 'Identifying high-quality webpages is crucial for real-world search engines, which can fulfil users\u2019 information need with less cognitive burden. Early studies of *webpage quality assessment* usually design hand-crafted features that may only work on particular categories of webpages (e.g., shopping websites, medical websites). They can hardly be applied to real-world search engines that serve trillions of webpages with various types and purposes. In this paper, we propose a novel layout-aware webpage quality assessment model currently deployed in our search engine. Intuitively, layout is a universal and critical dimension for the quality assessment of different categories of webpages. Based on this, we directly employ the meta-data that describes a webpage, i.e., Document Object Model (DOM) tree, as the input of our model. The DOM tree data unifies the representation of webpages with different categories and purposes and indicates the layout of webpages. To assess webpage quality from complex DOM tree data, we propose a graph neural network (GNN) based method that extracts rich layout-aware information that implies webpage quality in an end-to-end manner. Moreover, we improve the GNN method with an attentive readout function, external web categories and a category-aware sampling method. We conduct rigorous offline and online experiments"
+"---\nabstract: 'The obstructed atomic insulators are insulators with both atomic limits and boundary states. In this work, we study the obstructed atomic insulators under correlation. We use the symmetry indicators by constructing many-body wavefunctions in momentum space to prove the obstruction properties in different models including the SSH chain, anisotropic square lattice model, the quadrupole insulator model and etc. We demonstrate that the obstruction properties with boundary modes persist at large $U$ where the charge freedom is well-gapped, namely, this insulator phase can smoothly connect to its Mott phase without Mott transition.'\nauthor:\n- Kun Jiang\n- Hongming Weng\n- Jiangping Hu\nbibliography:\n- 'reference.bib'\ntitle: The destiny of obstructed atomic insulator under correlation\n---\n\n*Introduction* How to find and classify phases of matter is one central theme in condensed matter physics. The discovery of topological insulators (TIs) and other topological phases profoundly changed our view on matter classification [@qi_RevModPhys.83.1057; @kane_RevModPhys.82.3045]. Re-tracking the development of topological physics, the combination with density functional theory greatly boosts the development of predicting and identifying topological properties [@fangchen; @xiangang_wan; @Topological_quantum_chemistry; @bernevig; @zhanghaijun; @Hongming_PhysRevX.5.011029]. A variety of topological materials have been observed or confirmed by modern experimental techniques in the past two decades after"
+"---\nbibliography:\n- 'lit.bib'\n---\n\n=cmss10 =cmss10 at 7pt\n\n[ **Anomalous triple gauge couplings\\\nin electroweak dilepton tails at the LHC\\\nand interference resurrection** ]{}\n\n[Haeyun Hwang$^{\\, a}$, Ui Min$^{\\, b}$, Junghyeon Park$^{\\, b}$, Minho Son$^{\\, b}$ and Jae Hyeok Yoo$^{\\, a}$]{}\n\n$^{a}$ [*Department of Physics, Korea University, Seoul, Republic of Korea* ]{} $^{b}$ [*Department of Physics, Korea Advanced Institute of Science and Technology,\\\n291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea* ]{}\n\n**Abstract**\n\nIntroduction {#sec:intro}\n============\n\nAlthough the LHC has been performing great including the discovery of the Higgs boson\u00a0[@ATLAS:2012yve; @CMS:2012qbp], it continuously shows no evidence for the new physics, or beyond the Standard Model (BSM), only confirming the Standard Model (SM) to a better precision. It indicates that either new particles, if they exist, are very weakly coupled to the SM or they may be hidden in the energy scale beyond the LHC reach, especially, if a new physics has a sizeable coupling to the SM. Given the strong indication for the mass gap between the electroweak and new physics scales, the effective field theory approach makes sense to parametrize the possible new physics effects encoded in the higher-dimensional operators. Deviating from the SM with the Higgs"
+"---\nabstract: 'With the rise of deep reinforcement learning (RL) methods, many complex robotic manipulation tasks are being solved. However, harnessing the full power of deep learning requires large datasets. Online-RL does not suit itself readily into this paradigm due to costly and time-taking agent environment interaction. Therefore recently, many offline-RL algorithms have been proposed to learn robotic tasks. But mainly, all such methods focus on a single task or multi-task learning, which requires retraining every time we need to learn a new task. Continuously learning tasks without forgetting previous knowledge combined with the power of offline deep-RL would allow us to scale the number of tasks by keep adding them one-after-another. In this paper, we investigate the effectiveness of regularisation-based methods like synaptic intelligence for sequentially learning image-based robotic manipulation tasks in an offline-RL setup. We evaluate the performance of this combined framework against common challenges of sequential learning: catastrophic forgetting and forward knowledge transfer. We performed experiments with different task combinations to analyze the effect of task ordering. We also investigated the effect of the number of object configurations and density of robot trajectories. We found that learning tasks sequentially helps in the propagation of knowledge from previous"
+"---\nabstract: 'Connected automated driving has the potential to significantly improve urban traffic efficiency, e.g., by alleviating issues due to occlusion. Cooperative behavior planning can be employed to jointly optimize the motion of multiple vehicles. Most existing approaches to automatic intersection management, however, only consider fully automated traffic. In practice, mixed traffic, i.e., the simultaneous road usage by automated and human-driven vehicles, will be prevalent. The present work proposes to leverage reinforcement learning and a graph-based scene representation for cooperative multi-agent planning. We build upon our previous works that showed the applicability of such machine learning methods to fully automated traffic. The scene representation is extended for mixed traffic and considers uncertainty in the human drivers\u2019 intentions. In the simulation-based evaluation, we model measurement uncertainties through noise processes that are tuned using real-world data. The paper evaluates the proposed method against an enhanced first in - first out scheme, our baseline for mixed traffic management. With increasing share of automated vehicles, the learned planner significantly increases the vehicle throughput and reduces the delay due to interaction. Non-automated vehicles benefit virtually alike.'\nauthor:\n- '\\'\nbibliography:\n- 'references.bib'\ntitle: |\n    Automatic Intersection Management in\\\n    Mixed Traffic Using Reinforcement Learning\\\n    and Graph"
+"---\nabstract: 'We introduce DPACCs, a generalized commitment scheme based on smart contract wallets and non-interactive zero knowledge proofs. DPACCs allow any smart contract wallet holder to collateralize a claim, request, or commitment in general, in a private and anonymous manner. DPACCs can prove arbitrarily much or little about the wallet generating the commitment, and/or the transaction which is being committed. This can be used to convince a prospective block builder or relayer that the wallet generating the DPACC has enough funds to pay required fees, that the wallet is committed to performing certain actions, and importantly, that the wallet loses some amount of collateral if this commitment is broken. DPACCs delegate typically expensive zero knowledge operations off-chain, only requiring an additional one or two mapping checks when compared to transactions being sent from basic externally owned accounts. We demonstrate that DPACCs can be applied to effectively eliminate MEV in DeFi where it currently occurs, shifting MEV instead to censorship. Although still a concern, censorship can be made prohibitively expensive, making DPACCs a viable solution to most sources of MEV.'\nauthor:\n- Conor McMenamin\n- Vanesa Daza\nbibliography:\n- 'references.bib'\ntitle: 'Dynamic, Private, Anonymous, Collateralizable Commitments vs. MEV'\n---\n\n[^1]"
+"---\nabstract: 'The widespread use of (generalized) Kohn-Sham density functional theory (KS-DFT) lies in the fact that hierarchical sets of approximations of the exchange-correlation (XC) energy functional can be designed, offering versatile choices to satisfy different levels of accuracy needs. The XC functionals standing on the fifth (top) rung of the Jacob\u2019s ladder incorporate the information of unoccupied Kohn-Sham orbitals, and by doing so can describe seamlessly non-local electron correlations that the lower-rung functionals fail to capture. The doubly hybrid approximations (DHAs) and random phase approximation (RPA) based methods are two representative classes of fifth-rung functionals that have been under active development over the past two decades. In this review, we recapitulate the basic concepts of DHAs and RPA, derive their underlying theoretical formulation from the perspective of adiabatic-connection fluctuation-dissipation theory, and describe the implementation algorithms based on the resolution-of-identity technique within an atomic-orbital basis-set framework. Illustrating examples of practical applications of DHAs and RPA are presented, highlighting the usefulness of these functionals in resolving challenging problems in computational materials science. The most recent advances in the realms of these two types of functionals are briefly discussed.'\naddress:\n- '$^1$ Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai, Key"
+"---\nabstract: 'In this paper, we address the problem of estimating the in-hand 6D pose of an object in contact with multiple vision-based tactile sensors. We reason on the possible spatial configurations of the sensors along the object surface. Specifically, we filter contact hypotheses using geometric reasoning and a Convolutional Neural Network (CNN), trained on simulated object-agnostic images, to promote those that better comply with the actual tactile images from the sensors. We use the selected sensors configurations to optimize over the space of 6D poses using a Gradient Descent-based approach. We finally rank the obtained poses by penalizing those that are in collision with the sensors. We carry out experiments in simulation using the DIGIT vision-based sensor with several objects, from the standard YCB model set. The results demonstrate that our approach estimates object poses that are compatible with actual object-sensor contacts in $87.5\\%$ of cases while reaching an average positional error in the order of $2$ centimeters. Our analysis also includes qualitative results of experiments with a real DIGIT sensor.'\nauthor:\n- 'Gabriele M. Caddeo$^{1, 2}$, Nicola A. Piga$^{1}$, Fabrizio Bottarel$^{1}$ and Lorenzo Natale$^{1}$[^1][^2]'\nbibliography:\n- 'biblio.bib'\ntitle: |\n    **Collision-aware In-hand 6D Object Pose Estimation\\\n    using Multiple Vision-based"
+"---\nabstract: 'Automated bidding, an emerging intelligent decision making paradigm powered by machine learning, has become popular in online advertising. Advertisers in automated bidding evaluate the cumulative utilities and have private financial constraints over multiple ad auctions in a long-term period. Based on these distinct features, we consider a new ad auction model for automated bidding: the values of advertisers are public while the financial constraints, such as budget and return on investment (ROI) rate, are private types. We derive the truthfulness conditions with respect to private constraints for this multi-dimensional setting, and demonstrate any feasible allocation rule could be equivalently reduced to a series of non-decreasing functions on budget. However, the resulted allocation mapped from these non-decreasing functions generally follows an irregular shape, making it difficult to obtain a closed-form expression for the auction objective. To overcome this design difficulty, we propose a family of truthful automated bidding auction with personalized rank scores, similar to the Generalized Second-Price (GSP) auction. The intuition behind our design is to leverage personalized rank scores as the criteria to allocate items, and compute a critical ROI to transform the constraints on budget to the same dimension as ROI. The experimental results demonstrate that"
+"---\nabstract: 'Policy gradient methods have become a standard for training reinforcement learning agents in a scalable and efficient manner. However, they do not account for transition uncertainty, whereas learning robust policies can be computationally expensive. In this paper, we introduce robust policy gradient (RPG), a policy-based method that efficiently solves rectangular robust Markov decision processes (MDPs). We provide a closed-form expression for the worst occupation measure. Incidentally, we find that the worst kernel is a rank-one perturbation of the nominal. Combining the worst occupation measure with a robust Q-value estimation yields an explicit form of the robust gradient. Our resulting RPG can be estimated from data with the same time complexity as its non-robust equivalent. Hence, it relieves the computational burden of convex optimization problems required for training robust policies by current policy gradient approaches.'\nauthor:\n- |\n    Navdeep Kumar[^1]\\\n    Technion\\\n    Esther Derman\\\n    MILA, Universit\u00e9 de Montr\u00e9al\\\n    Matthieu Geist\\\n    Goodle Deepmind\\\n    Kfir Levy\\\n    Technion\\\n    Shie Mannor\\\n    Technion, NVIDIA Research\\\nbibliography:\n- 'main.bib'\ntitle: Policy Gradient for Rectangular Robust Markov Decision Processes\n---\n\nIntroduction\n============\n\n=-1 Markov decision processes (MDPs) provide an analytical framework to solve sequential decision-making problems and seek the best performance in a fixed environment. Since the"
+"---\nabstract: 'Evaluating the performance of machine learning models under distribution shift is challenging, especially when we only have unlabeled data from the shifted (target) domain, along with labeled data from the original (source) domain. Recent work suggests that the notion of disagreement, the degree to which two models trained with different randomness differ on the same input, is a key to tackle this problem. Experimentally, disagreement and prediction error have been shown to be strongly connected, which has been used to estimate model performance. Experiments have led to the discovery of the *disagreement-on-the-line* phenomenon, whereby the classification error under the target domain is often a linear function of the classification error under the source domain; and whenever this property holds, disagreement under the source and target domain follow the same linear relation. In this work, we develop a theoretical foundation for analyzing disagreement in high-dimensional random features regression; and study under what conditions the disagreement-on-the-line phenomenon occurs in our setting. Experiments on CIFAR-10-C, Tiny ImageNet-C, and Camelyon17 are consistent with our theory and support the universality of the theoretical findings.'\nauthor:\n- 'Donghwan Lee[^1] [^2] Behrad Moniri[^3] Xinmeng Huang Edgar Dobriban[^4] Hamed Hassani'\nbibliography:\n- 'ArXiv/Arxiv\\_Refs.bib'\ntitle: 'Demystifying Disagreement-on-the-Line"
+"---\nabstract: 'In this work, we established a novel theory for the dynamics of oscillating bubbles such as cavitation bubbles, underwater explosion bubbles, and air bubbles. For the first time, we proposed bubble dynamics equations that can simultaneously take into consideration the effects of boundaries, bubble interaction, ambient flow field, gravity, bubble migration, fluid compressibility, viscosity, and surface tension while maintaining a unified and elegant mathematical form. The present theory unifies different classical bubble equations such as the Rayleigh-Plesset equation, the Gilmore equation, and the Keller-Miksis equation. Furthermore, we validated the theory with experimental data of bubbles with a variety in scales, sources, boundaries, and ambient conditions and showed the advantages of our theory over the classical theoretical models, followed by a discussion on the applicability of the present theory based on a comparison to simulation results with different numerical methods. Finally, as a demonstration of the potential of our theory, we modeled the complex multi-cycle bubble interaction with wide ranges of energy and phase differences and gained new physical insights into inter-bubble energy transfer and coupling of bubble-induced pressure waves.'\nauthor:\n- 'A-Man Zhang (\u5f20\u963f\u6f2b)\\*'\n- 'Shi-Min Li (\u674e\u4e16\u6c11)'\n- 'Pu Cui (\u5d14\u749e)'\n- 'Shuai Li (\u674e\u5e05)'\n- 'Yun-Long"
+"---\nabstract: 'Archive collections are nowadays mostly available through search engines interfaces, which allow a user to retrieve documents by issuing queries. The study of these collections may be, however, impaired by some aspects of search engines, such as the overwhelming number of documents returned or the lack of contextual knowledge provided. New methods that could work independently or in combination with search engines are then required to access these collections. In this position paper, we propose to extend *TimeLine Summarization* (TLS) methods on archive collections to assist in their studies. We provide an overview of existing TLS methods and we describe a conceptual framework for an *Archive TimeLine Summarization* (ATLS) system, which aims to generate informative, readable and interpretable timelines.'\nauthor:\n- |\n    Nicolas Gutehrl\u00e9\\\n    Laboratoire CRIT,\\\n    University of Bourgogne\\\n    Franche-Comt\u00e9, France\\\n    `nicolas.gutehrle`\\\n    `@univ-fcomte.fr` Antoine Doucet\\\n    Laboratoire L3i,\\\n    University of La Rochelle,\\\n    France\\\n    `antoine.doucet`\\\n    `@univ-lr.fr`\\\n    Adam Jatowt\\\n    Dept. of Computer Science &\\\n    Digital Science Center,\\\n    University of Innsbruck, Austria\\\n    `adam.jatowt`\\\n    `@uibk.ac.at`\\\nbibliography:\n- 'biblio.bib'\ntitle: 'Archive TimeLine Summarization (ATLS): Conceptual Framework for Timeline Generation over Historical Document Collections'\n---\n\n=1\n\nIntroduction {#sec:introduction}\n============\n\nExploring archives\n------------------\n\nIn the recent years, archives and libraries across the world have frequently conducted"
+"---\nauthor:\n- Abhinit Kumar Ambastha\n- Leong Tze Yun\nbibliography:\n- 'main.bib'\ntitle: 'TIDo: Source-free Task Incremental Learning in Non-stationary Environments'\n---\n\nINTRODUCTION\n============\n\nTask incremental learning problem applies to non-stationary problem settings where an agent needs to update an existing task model but does not have access to large amounts of labeled data. An example of such a setting is an autonomous agent learning an incremental object detection model. Object detection and computer vision models are helpful in various domains, such as robotics, healthcare, e-commerce, and security. A fixed label set and a stationary input data distribution limits a classification model\u2019s generalization ability in non-stationary or open-set problem settings. We can overcome these bottlenecks using unsupervised task incremental learning and update the model without access to a large replay memory.\n\nIn a task incremental learning problem, the agent aims to learn an optimal hypothesis for both source and target domains [@kundu2020class; @masana2020class; @mi2020generalized; @shermin2020adversarial; @saito2018maximum]. The source and target are assumed to have undergone a dataset shift [@blitzer2006domain]. Hence, their shared class instances have a covariate discrepancy. We assume access to a few labeled target private class instances. The current works address task incremental settings at a single"
+"---\nabstract: 'We describe an approach to optically trapping small, chemically stable molecules at cryogenic temperatures by buffer-gas loading a deep optical dipole trap. The trap depth will be produced by a tightly focused, 1064-nm cavity capable of reaching intensities of hundreds of . Molecules will be directly buffer-gas loaded into the trap using a helium buffer gas at . The very far-off-resonant, quasielectrostatic trapping mechanism is insensitive to a molecule\u2019s internal state, energy level structure, and its electric and magnetic dipole moment. Here, we theoretically investigate the trapping and loading dynamics, as well as the heating and loss rates, and conclude that $10^4$\u2013$10^6$ molecules are likely to be trapped. Our trap would open new possibilities in molecular spectroscopy, studies of cold chemical reactions, and precision measurement, amongst other fields of physics.'\nauthor:\n- Ashwin Singh\n- Lothar Maisenbacher\n- Ziguang Lin\n- |\n    \\\n    Jeremy J. Axelrod\n- 'Cristian D. Panda'\n- Holger M\u00fcller\nbibliography:\n- 'bibliography.bib'\ntitle: 'Dynamics of a buffer-gas-loaded, deep optical trap for molecules'\n---\n\nIntroduction\n============\n\nBackground\n----------\n\nCooling and trapping of atoms and ions has enabled unparalleled quantum control of both internal and external degrees of freedom [@2001_Metcalf_Book_Laser_cooling_and_Trapping]. It has led to advances in"
+"---\nabstract: |\n    High-cardinality categorical features are pervasive in actuarial data (e.g.\u00a0occupation in commercial property insurance). Standard categorical encoding methods like one-hot encoding are inadequate in these settings.\n\n    In this work, we present a novel *Generalised Linear Mixed Model Neural Network* (\u201cGLMMNet\u201d) approach to the modelling of high-cardinality categorical features. The GLMMNet integrates a generalised linear mixed model in a deep learning framework, offering the predictive power of neural networks and the transparency of random effects estimates, the latter of which cannot be obtained from the entity embedding models. Further, its flexibility to deal with any distribution in the exponential dispersion (ED) family makes it widely applicable to many actuarial contexts and beyond.\n\n    We illustrate and compare the GLMMNet against existing approaches in a range of simulation experiments as well as in a real-life insurance case study. Notably, we find that the GLMMNet often outperforms or at least performs comparably with an entity embedded neural network, while providing the additional benefit of transparency, which is particularly valuable in practical applications.\n\n    Importantly, while our model was motivated by actuarial applications, it can have wider applicability. The GLMMNet would suit any applications that involve high-cardinality categorical variables and where the response"
+"---\nabstract: 'This paper introduces the [*[3D Peskin problem]{}*]{}: a two-dimensional elastic membrane immersed in a three-dimensional steady Stokes flow. We obtain the equations that model this free boundary problem and show that they admit a boundary integral reduction, providing an evolution equation for the elastic interface. We consider general nonlinear elastic laws, i.e., the fully nonlinear Peskin problem, and prove that the problem is well-posed in low-regularity H\u00f6lder spaces. Moreover, we prove that the elastic membrane becomes smooth instantly in time.'\naddress:\n- '$^{\\ast}$Departamento de An\u00e1lisis Matem\u00e1tico, Universidad de Sevilla, C/Tarfia s/n, Campus Reina Mercedes, 41012, Sevilla, Spain. <egarciajuarez@ub.edu>'\n- '$^\\dagger$Department of Mathematics, University of Pennsylvania, David Rittenhouse Lab., 209 South 33rd St., Philadelphia, PA 19104, USA. $^{\\ddagger}$<kuopo@sas.upenn.edu> $^{\\mathsection}$<y1mori@math.upenn.edu> $^{\\mathparagraph}$<strain@math.upenn.edu>'\nauthor:\n- 'Eduardo Garc\u00eda-Ju\u00e1rez$^{\\ast}$'\n- 'Po-Chun Kuo$^{\\dagger,\\ddagger}$'\n- 'Yoichiro Mori$^{\\dagger,\\mathsection}$'\n- 'Robert M. Strain$^{\\dagger,\\mathparagraph}$'\nbibliography:\n- 'references.bib'\ntitle: 'Well-Posedness of the 3D Peskin Problem'\n---\n\n[^1]\n\n[^2]\n\n[^3]\n\n[^4]\n\nIntroduction\n============\n\nThe immersed boundary method, introduced by Peskin [@Peskin77; @Peskin02] to study the blood flow around heart valves, has been widely applied to numerically study fluid-structure interaction (FSI) problems. These FSI problems, in which a fluid interacts with elastic structures, appear naturally in many engineering and biophysics applications [@Pozrikidis03;"
+"---\nabstract: 'Studying mental models has recently received more attention, aiming to understand the cognitive aspects of human-computer interaction. However, there is not enough research on the elicitation of mental models in complex dynamic systems. We present *Thought Bubbles* as an approach for eliciting mental models and an avenue for understanding players\u2019 mental model development in interactive virtual environments. We demonstrate the use of Thought Bubbles in two experimental studies involving 250 participants playing a supply chain game. In our analyses, we rely on Situation Awareness (SA) levels, including perception, comprehension, and projection, and show how experimental manipulations such as disruptions and information sharing shape players\u2019 mental models and drive their decisions depending on their behavioral profile. Our results provide evidence for the use of thought bubbles in uncovering cognitive aspects of behavior by indicating how disruption location and availability of information affect people\u2019s mental model development and influence their decisions.'\nauthor:\n- Omid Mohaddesi\n- Noah Chicoine\n- Min Gong\n- Ozlem Ergun\n- Jacqueline Griffin\n- David Kaeli\n- Stacy Marsella\n- Casper Harteveld\nbibliography:\n- 'references.bib'\ntitle: 'Thought Bubbles: A Proxy into Players\u2019 Mental Model Development'\n---\n\n&lt;ccs2012&gt; &lt;concept&gt; &lt;concept\\_id&gt;10003120.10003121.10011748&lt;/concept\\_id&gt; &lt;concept\\_desc&gt;Human-centered computing\u00a0Empirical studies in HCI&lt;/concept\\_desc&gt; &lt;concept\\_significance&gt;500&lt;/concept\\_significance&gt;"
+"---\nabstract: 'In this paper, we have realized the left-right symmetric model with modular symmetry. We have used $\\Gamma$(3) modular group which is isomorphic to non-abelian discrete symmetry group $A_4$. The advantage of using modular symmetry is the non-requirement for the use of extra particles called \u2019flavons\u2019. In this model, the Yukawa couplings are expressed in terms of modular forms $(Y_1,Y_2,Y_3)$. In this work, we have studied minimal Left-Right Symmetric Model for both type-I and type-II dominances. Here, we have calculated the values for the Yukawa couplings and then plotted it against the sum of the neutrino masses. The results obtained are well within the experimental limits for the desired values of sum of neutrino masses. We have also briefly analyzed the effects of the implications of modular symmetry on neutrinoless double beta decay with the new physics contributions within Left-Right Symmetric Model.'\nauthor:\n- Ankita Kakoti\n- Bichitra Bijay Boruah\n- Mrinal Kumar Das\nbibliography:\n- 'cite.bib'\ntitle: 'Minimal Left-Right Symmetric Model with $A_4$ modular symmetry'\n---\n\n**Introduction**\n================\n\nDespite the huge and continued success of the Standard Model (SM) of particle physics, it leaves some of the puzzles unanswered like the existence of neutrino masses, baryon asymmetry of"
+"---\nabstract: 'We present a new paradigm for fine-tuning large-scale vision-language pre-trained models on downstream task, dubbed Prompt Regularization (ProReg). Different from traditional fine-tuning which easily overfits to the downstream task data, ProReg uses the prediction by prompting the pretrained model to regularize the fine-tuning. The motivation is: by prompting the large model \u201ca photo of a [`[CLASS]`]{}\u201d, the fill-in answer is only dependent on the pretraining encyclopedic knowledge while independent of the task data distribution, which is usually biased. Specifically, given a training sample prediction during fine-tuning, we first calculate its Kullback-Leibler loss of the prompt prediction and Cross-Entropy loss of the ground-truth label, and then combine them with a proposed sample-wise adaptive trade-off weight, which automatically adjusts the transfer between the pretrained and downstream domains. On various out-of-distribution benchmarks, we show the consistently strong performance of ProReg compared with conventional fine-tuning, zero-shot prompt, prompt tuning, and other state-of-the-art methods.'\nauthor:\n- '**Beier Zhu**^1^, **Yulei Niu**^2^[^1], **Saeil Lee**^3^, **Minhoe Hur**^4^, **Hanwang Zhang**^1^'\nbibliography:\n- 'aaai23.bib'\ntitle: 'Debiased Fine-Tuning for Vision-Language Models by Prompt Regularization'\n---\n\nIntroduction {#sec:1}\n============\n\n![image](images/figure1.pdf){width=\"100.00000%\"}\n\nPlease think about it: when you want to train a vision model for a task, what is the first thing"
+"---\nabstract: 'We study two natural problems about rational behaviors in multiplayer non-zero-sum sequential infinite duration games played on graphs: checking problems, that consist in deciding whether a strategy profile, defined by a Mealy machine, is rational; and rational verification, that consists in deciding whether all the rational answers to a given strategy satisfy some specification. We give the complexities of those problems for two major concepts of rationality: Nash equilibria and subgame-perfect equilibria, and for five major classes of payoff functions: parity, quantitative reachability, energy, discounted-sum, and mean-payoff.'\nauthor:\n- L\u00e9onard Brice\n- 'Jean-Fran\u00e7ois Raskin'\n- Marie van den Bogaard\nbibliography:\n- 'bibli.bib'\ntitle: 'Rational Verification and Checking for Nash and Subgame-perfect Equilibria in Graph Games'\n---\n\nIntroduction\n============\n\nFormal methods are essential to guarantee the correctness of safety critical computer systems. Techniques like model-checking\u00a0[@DBLP:reference/mc/2018] or automated theorem proving\u00a0[@DBLP:journals/pacmpl/EbnerURAM17] are now routinely used to develop systematically hardware pieces as well as embedded control systems. Nevertheless, there are contexts in which formal methods have not yet been applied successfully large-scale. That is the case for instance of multi-agent systems, which still represent a challenge for formal verification techniques, because they are usually composed of [*heterogeneous*]{} components, ranging from"
+"---\nabstract: 'The causal set and Wolfram model approaches to discrete quantum gravity both permit the formulation of a manifestly covariant notion of entanglement entropy for quantum fields. In the causal set case, this is given by a construction (due to Sorkin and Johnston) of a 2-point correlation function for a Gaussian scalar field from causal set Feynman propagators and Pauli-Jordan functions, from which an eigendecomposition, and hence an entanglement entropy, can be computed. In the Wolfram model case, it is given instead in terms of the Fubini-Study metric on branchial graphs, whose tensor product structure is inherited functorially from that of finite-dimensional Hilbert spaces. In both cases, the entanglement entropies in question are most naturally defined over an extended spacetime region (hence the manifest covariance), in contrast to the generically non-covariant definitions over single spacelike hypersurfaces common to most continuum quantum field theories. In this article, we show how an axiomatic field theory for a free, massless scalar field (obeying the appropriate bosonic commutation relations) may be rigorously constructed over multiway causal graphs: a combinatorial structure sufficiently general as to encompass both causal sets and Wolfram model evolutions as special cases. We proceed to show numerically that the entanglement"
+"---\nabstract: 'Due to the emergence of graph neural networks (GNNs) and their widespread implementation in real-world scenarios, the fairness and privacy of GNNs have attracted considerable interest since they are two essential social concerns in the era of building trustworthy GNNs. Existing studies have respectively explored the fairness and privacy of GNNs and exhibited that both fairness and privacy are at the cost of GNN performance. However, the interaction between them is yet to be explored and understood. In this paper, we investigate the interaction between the fairness of a GNN and its privacy for the first time. We empirically identify that edge privacy risks increase when the individual fairness of nodes is improved. Next, we present the intuition behind such a trade-off and employ the influence function and Pearson correlation to measure it theoretically. To take the performance, fairness, and privacy of GNNs into account simultaneously, we propose implementing fairness-aware re-weighting and privacy-aware graph structure perturbation modules in a retraining mechanism. Experimental results demonstrate that our method is effective in implementing GNN fairness with limited performance cost and restricted privacy risks.'\n---\n\nIntroduction\n============\n\nIn recent years, in addition to competent performance, there has been an increasing desire"
+"---\nabstract: 'The Bayesian evidence is a key tool in model selection, allowing a comparison of models with different numbers of parameters. Its use in analysis of cosmological models has been limited by difficulties in calculating it, with current numerical algorithms requiring supercomputers. In this paper we give exact formulae for the Bayesian evidence in the case of Gaussian likelihoods with arbitrary correlations and top-hat priors, and approximate formulae for the case of likelihood distributions with leading non-Gaussianities (skewness and kurtosis). We apply these formulae to cosmological models with and without isocurvature components, and compare with results we previously obtained using numerical thermodynamic integration. We find that the results are of lower precision than the thermodynamic integration, while still being good enough to be useful.'\nauthor:\n- |\n    Juan Garc[\u00ed]{}a-Bellido\\\n    Departamento de F\u00edsica Te\u00f3rica \u00a0C-XI, Universidad Aut\u00f3noma de Madrid,\\\n    Cantoblanco, 28049 Madrid, Spain\ndate: 'April 14th, 2005'\ntitle: An analytical approach to Bayesian evidence computation\n---\n\nepsf\n\nIntroduction\n============\n\nModel selection refers to the statistical problem of deciding which model description of observational data is the best [@jeff; @mackay]. It differs from parameter estimation, where the choice of a single model (i.e.\u00a0choice of parameters to be varied) has already"
+"---\nabstract: 'We investigate the rate-distortion-leakage region of the Chief Executive Officer (CEO) problem with a passive eavesdropper and privacy constraints, considering a general distortion measure. While an inner bound directly follows from the previous work, [an]{} outer bound is newly developed in this paper. To derive this bound, we introduce a new lemma tailored for analyzing privacy constraints. As a specific instance, we demonstrate that the tight bound for discrete and Gaussian sources is obtained when the eavesdropper can only observe the messages under logarithmic loss distortion. We further investigate the rate-distortion-leakage region for a scenario where the eavesdropper possesses the messages and side information under the same distortion, and provide an outer bound for this particular case. The derived [outer]{} bound [differs]{} from the inner bound by only a minor quantity [that]{} appears in the constraints associated with the privacy-leakage rates, and it becomes tight when the distortion is large.'\nauthor:\n- 'Vamoua\u00a0Yachongka\u00a0\u00a0\u00a0Hideki\u00a0Yagi\u00a0\u00a0\u00a0\u00a0Hideki\u00a0Ochiai [^1] [^2]'\ntitle: |\n    Outer Bounds on the CEO Problem with\\\n    Privacy Constraints\n---\n\nEavesdropper, logarithmic loss, privacy leakage, rate region, the CEO problem.\n\nIntroduction\n============\n\nThe Chief Executive Officer (CEO) problem, introduced by Berger et al. in [@berger1996], can"
+"---\nabstract: 'Click-through rate (CTR) prediction is crucial in recommendation and online advertising systems. Existing methods usually model user behaviors, while ignoring the informative context which influences the user to make a click decision, e.g., click pages and pre-ranking candidates that inform inferences about user interests, leading to suboptimal performance. In this paper, we propose a Decision-Making Context Interaction Network (DCIN), which deploys a carefully designed Context Interaction Unit (CIU) to learn decision-making contexts and thus benefits CTR prediction. In addition, the relationship between different decision-making context sources is explored by the proposed Adaptive Interest Aggregation Unit (AIAU) to improve CTR prediction further. In the experiments on public and industrial datasets, DCIN significantly outperforms the state-of-the-art methods. Notably, the model has obtained the improvement of CTR+2.9%/CPM+2.1%/GMV+1.5% for online A/B testing and served the main traffic of Meituan Waimai advertising system.'\nauthor:\n- |\n    Xiang Li^1^, Shuwei Chen^1^, Jian Dong^1^, Jin Zhang^1^, Yongkang Wang^1^,\\\n    Xingxing Wang^1^, Dong Wang^1^\n- Author Name\n- 'First Author Name,^1,2^ Second Author Name, ^2^ Third Author Name ^1^'\nbibliography:\n- 'aaai23.bib'\ntitle:\n- |\n    Decision-Making Context Interaction Network\\\n    for Click-Through Rate Prediction\n- 'My Publication Title \u2014 Single Author'\n- 'My Publication Title \u2014 Multiple Authors'"
+"---\nabstract: 'Non-Hermitian (NH) Hamiltonians have been shown to exhibit unique signatures, including the NH skin effect and an exponential spectral sensitivity with respect to boundary conditions. Here, we investigate as to what extent these remarkable phenomena, recently predicted and observed in a broad range of settings, may also occur in closed correlated fermionic systems that are governed by a Hermitian many-body Hamiltonian. There, an effectively NH quasiparticle description naturally arises in the Green\u2019s function formalism due to inter-particle scattering that represents an inherent source of dissipation. As a concrete platform we construct an extended interacting Su-Schrieffer-Heeger (SSH) model subject to varying boundary conditions, which we analyze using exact diagonalization and non-equilibrium Green\u2019s function methods. That way, we clearly identify the presence of the aforementioned NH phenomena in the quasi-particle properties of this Hermitian model system.'\nauthor:\n- Tommaso Micallo\n- Carl Lehmann\n- Jan Carl Budich\ntitle: 'Correlation-Induced Sensitivity and Non-Hermitian Skin Effect of Quasiparticles'\n---\n\nIntroduction\n============\n\nLeveraging the concept of effective non-Hermitian (NH) Hamiltonians, a number of intriguing phenomena unique to dissipative systems have been recently experimentally discovered and explained with theory in a wide range of physical settings [@Rudner2009; @Zeuner2015; @Lee2016; @Gong2018; @Zhou2018; @Kunst2018; @Imhof2018; @Yao2018NHBands;"
+"---\nabstract: 'The aim of sequential pattern mining (SPM) is to discover potentially useful information from a given sequence. Although various SPM methods have been investigated, most of these focus on mining all of the patterns. However, users sometimes want to mine patterns with the same specific prefix pattern, called co-occurrence pattern. Since sequential rule mining can make better use of the results of SPM, and obtain better recommendation performance, this paper addresses the issue of maximal co-occurrence nonoverlapping sequential rule (MCoR) mining and proposes the MCoR-Miner algorithm. To improve the efficiency of support calculation, MCoR-Miner employs depth-first search and backtracking strategies equipped with an indexing mechanism to avoid the use of sequential searching. To obviate useless support calculations for some sequences, MCoR-Miner adopts a filtering strategy to prune the sequences without the prefix pattern. To reduce the number of candidate patterns, MCoR-Miner applies the frequent item and binomial enumeration tree strategies. To avoid searching for the maximal rules through brute force, MCoR-Miner uses a screening strategy. To validate the performance of MCoR-Miner, eleven competitive algorithms were conducted on eight sequences. Our experimental results showed that MCoR-Miner outperformed other competitive algorithms, and yielded better recommendation performance than frequent co-occurrence pattern"
+"---\nauthor:\n- \n- \n- \n- \n- \n- \nbibliography:\n- 'reference.bib'\ntitle: '**Force moment partitioning and scaling analysis of vortices shed by a 2D pitching wing in quiescent fluid**'\n---\n\nIntroduction {#sec.intro}\n============\n\nThe unsteady flow and vortex dynamics associated with flapping wings/foils have been extensively studied for understanding the complex lift/thrust generation mechanism of animal flight and swimming [@ellington1996leading; @lentink2009rotational; @anderson1998oscillating; @triantafyllou2000hydrodynamics], as well as developing bio-inspired flapping-wing micro air vehicles [MAVs, @ho2003unsteady; @shyy2010recent; @jafferis2019untethered], oscillating-foil autonomous underwater vehicles [AUVs, @barrett1996propulsive; @zhu2019tuna; @zhong2021tunable] and energy-harvesting devices [@xiao2014review; @young2014review]. Flapping wings/foils were commonly studied in the presence of an ambient flow to match real flying/swimming settings. Numerous studies have focused on identifying scaling laws that characterize the formation and development of the shed vortices, along with the corresponding aerodynamic loads [@buchholz2008wake; @buchholz2011scaling; @baik2012unsteady; @onoue2016vortex]. In the absence of a freestream flow, the problem is associated with the hovering flight of insects [@wang2005dissecting; @bergou2007passive; @kang2014analytical] and the starting motion of fish [@heathcote2004flexible; @epps2007impulse; @devoria2012vortex; @devoria2013force; @shinde2013jet; @david2018kinematic]. In these studies, the pitching panel was usually hinged at the leading-edge to mimic the corresponding biological appendage, and more emphasis was placed on the vorticity-induced lift and thrust. On the other hand, the"
+"---\nabstract: 'In environmental health research there is often interest in the effect of an exposure on a health outcome assessed on the same day and several subsequent days or lags. Distributed lag nonlinear models (DLNM) are a well-established statistical framework for estimating an exposure-lag-response function. We propose methods to allow for prior information to be incorporated into DLNMs. First, we impose a monotonicity constraint in the exposure-response at lagged time periods which matches with knowledge on how biological mechanisms respond to increased levels of exposures. Second, we introduce variable selection into the DLNM to identify lagged periods of susceptibility with respect to the outcome of interest. The variable selection approach allows for direct application of informative priors on which lags have nonzero association with the outcome. We propose a tree-of-trees model that uses two layers of trees: one for splitting the exposure time frame and one for fitting exposure-response functions over different time periods. We introduce a zero-inflated alternative to the tree splitting prior in Bayesian additive regression trees to allow for lag selection and the addition of informative priors. We develop a computational approach for efficient posterior sampling and perform a comprehensive simulation study to compare our method"
+"---\nabstract: 'Table-based reasoning has shown remarkable progress in a wide range of table-based tasks. It is a challenging task, which requires reasoning over both free-form natural language (NL) questions and (semi-)structured tabular data. However, previous table-based reasoning solutions usually suffer from significant performance degradation on \u201chuge\u201d evidence (tables). In addition, most existing methods struggle to reason over complex questions since the essential information is scattered in different places. To alleviate the above challenges, we exploit large language models (LLMs) as decomposers for effective table-based reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning, and (ii) decompose a complex question into simpler sub-questions for text reasoning. First, we use a powerful LLM to decompose the evidence involved in the current question into the sub-evidence that retains the relevant information and excludes the remaining irrelevant information from the \u201chuge\u201d evidence. Second, we propose a novel \u201cparsing-execution-filling\u201d strategy to decompose a complex question into simper step-by-step sub-questions by generating intermediate SQL queries as a bridge to produce numerical and logical sub-questions with a powerful LLM. Finally, we leverage the decomposed sub-evidence and sub-questions to get the final"
+"---\nabstract: 'In mobile computation offloading (MCO), mobile devices (MDs) can choose to either execute tasks locally or to have them executed on a remote edge server (ES). This paper addresses the problem of assigning both the wireless communication bandwidth needed, along with the ES capacity that is used for the task execution, so that task completion time constraints are satisfied. The objective is to obtain these allocations so that the average power consumption of the mobile devices is minimized, subject to a cost budget constraint. The paper includes contributions for both soft and hard task completion deadline constraints. The problems are first formulated as mixed integer nonlinear programs (MINLPs). Approximate solutions are then obtained by decomposing the problems into a collection of convex subproblems that can be efficiently solved. Results are presented that demonstrate the quality of the proposed solutions, which can achieve near optimum performance over a wide range of system parameters.'\nauthor:\n- '[^1]'\nbibliography:\n- 'IEEEabrv.bib'\n- 'mybib.bib'\ntitle: Wireless and Service Allocation for Mobile Computation Offloading with Task Deadlines\n---\n\nEdge computing, mobile computation offloading, soft and hard task completion deadlines, cost budget constraints, power efficiency.\n\nIntroduction {#sec:introduction}\n============\n\nMobile computation offloading (MCO) can be"
+"---\nabstract: 'For a vertex operator algebra with Heisenberg symmetries, we construct spaces of coinvariants at principally polarized abelian varieties with respect to the action of an infinite-dimensional Lie algebra. We show how these spaces globalize to twisted $\\mathcal{D}$-modules on moduli of principally polarized abelian varieties, and we determine the Atiyah algebra of a line bundle acting on them. We prove analogous results on the universal abelian variety.'\naddress: 'Nicola Tarasca Department of Mathematics & Applied Mathematics Virginia Commonwealth University, Richmond, VA 23284'\nauthor:\n- Nicola Tarasca\nbibliography:\n- 'Biblio.bib'\ntitle: |\n    Coinvariants of vertex algebras\\\n    on moduli of abelian varieties\n---\n\nIntroduction {#introduction .unnumbered}\n============\n\nSpaces of coinvariants have classically been constructed by assigning representations of affine Lie algebras, and more generally, vertex operator algebras, to algebraic curves [@tuy; @bzf]. These spaces globalize to quasi-coherent sheaves carrying a twisted $\\mathcal{D}$-module structure on moduli of curves. Said structure is induced by an equivariant action of the algebra on vertex operator algebra modules and on moduli of curves with marked points and formal coordinates. The construction generalizes to produce twisted $\\mathcal{D}$-modules on moduli spaces parametrizing pointed curves with extra features: line bundles, principal bundles, and $\\mathcal{D}$-bundles [@frenkel2004twisted; @bzf; @ben2010mathcal].\n\nRemoving curves"
+"---\nabstract: 'The reliability of the results of self monitoring of the vitals in the wild using medical devices or wearables or camera based smart phone solutions is subject to variabilities such as position of placement, hardware of the device and environmental factors. In this first of its kind study, we demonstrate that this variability in self monitoring of Blood Pressure (BP), Blood oxygen saturation level (SpO2) and Heart rate (HR) is statistically significant ($p<0.05$) on 203 healthy subjects by quantifying positional and hardware variability. We also establish the existance of this variability in camera based solutions for self-monitoring of vitals in smart phones and thus prove that the use of camera based smart phone solutions is similar to the use of medical devices or wearables for self-monitoring in the wild.'\nauthor:\n- 'Nikhil S. Narayan\\'\n- 'Shashanka B. R. [^1]\\'\n- 'Rohit Damodaran$^*$\\'\n- 'Dr. Chandrashekhar Jayaram$^*$\\'\n- 'Dr. M. A. Kareem\\'\n- 'Dr. Mamta P.$^*$\\'\n- 'Dr. Saravanan K. R.$^*$\\'\n- 'Dr. Monu Krishnan$^*$\\'\n- |\n    Dr. Raja Indana\\\n    \\\n    MFine\\\nbibliography:\n- 'egbib.bib'\ntitle: 'Factors that affect Camera based Self-Monitoring of Vitals in the Wild'\n---\n\nIntroduction {#sec:intro}\n============\n\nMonitoring of vitals is important for identifying and"
+"---\nabstract: 'The social brain hypothesis states that the relative size of the neocortex is larger for species with higher social complexity as a result of evolution. Various lines of empirical evidence have supported the social brain hypothesis, including evidence from the structure of social networks. Social complexity may itself positively impact cooperation among individuals, which occurs across different animal taxa and is a key behavior for successful group living. Theoretical research has shown that particular structures of social networks foster cooperation more easily than others. Therefore, we hypothesized that species with a relatively large neocortex tend to form social networks that better enable cooperation. In the present study, we combine data on brain and body mass, data on social networks, and theory on the evolution of cooperation on networks to test this hypothesis in primates. We have found a positive effect of brain size on cooperation in social networks even after controlling for the effect of other structural properties of networks that are known to promote cooperation.'\nauthor:\n- 'Neil G. MacLaren$^1$'\n- 'Lingqi Meng$^{1,\\dagger}$'\n- Melissa Collier$^2$\n- 'Naoki Masuda$^{1,3}$'\nbibliography:\n- 'refs.bib'\ntitle: Cooperation and the social brain hypothesis in primate social networks\n---\n\nIntroduction {#sec:intro}\n============"
+"---\nabstract: 'In this paper we consider mean-field optimal control problems with selective action of the control, where the constraint is a continuity equation involving a non-local term and diffusion. First order optimality conditions are formally derived in a general framework, accounting for boundary conditions. Hence, the optimality system is used to construct a reduced gradient method, where we introduce a novel algorithm for the numerical realization of the forward and the backward equations, based on exponential integrators. We illustrate extensive numerical experiments on different control problems for collective motion in the context of opinion formation and pedestrian dynamics.'\nauthor:\n- Giacomo Albi\n- Marco Caliari\n- Elisa Calzola\n- Fabio Cassini\nbibliography:\n- 'biblio\\_mfexpint.bib'\ntitle: 'Exponential integrators for mean-field selective optimal control problems'\n---\n\nIntroduction\n============\n\nThe study of collective motion of interacting agents systems is of paramount importance to understand the formation of coherent global behaviors at various scales, with applications to the study of biological, social, and economic phenomena. In recent years, there has been a surge of literature on the collective behavior of multi-agent systems, covering a wide range of topics such as cell aggregation and motility, coordinated animal motion [@cucker2007emergent; @d2006self], opinion formation [@MR2887663; @motsch2014heterophilious;"
+"---\nabstract: 'We introduce a strategy to compute EPRL spin foam amplitudes with many internal faces numerically. We work with `sl2cfoam-next`, the state-of-the-art framework to numerically evaluate spin foam transition amplitudes. We find that uniform sampling Monte Carlo is exceptionally effective in approximating the sum over internal quantum numbers of a spin foam amplitude, considerably reducing the computational resources necessary. We apply it to compute large volume divergences of the theory and find surprising numerical evidence that the EPRL vertex renormalization amplitude is instead finite.'\nauthor:\n- |\n    \\\n    \\\ntitle: |\n    Summing bulk quantum numbers with Monte Carlo\\\n    in spin foam theories\n---\n\nIntroduction {#sec:intro}\n============\n\nSpin foam theory is the Lorentz covariant version of loop quantum gravity (LQG) and provides a tentative background independent path integral for gravity. It gives dynamics to LQG kinematical states defining transition amplitudes between spin network states [@Rovelli:2014ssa; @Perez:2012wv]. The most promising spin foam theory is the EPRL-FK model [@Engle:2007wy; @Freidel:2007py]. Various generalizations include the extension to general triangulations [@Kaminski:2009fm], the inclusion of a cosmological constant [@Han:2011aa; @Han:2021tzw], and boundary with different signature [@Conrady:2010kc]. These theories promisingly connect with discrete general relativity in the double limit of finer discretization and large areas [@Barrett:2009mw;"
+"---\nabstract: 'Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning. However, the incremental learning issue of CL has rarely been studied, which brings the limitation in applying it to real-world applications. Contrastive learning identifies the samples with the negative ones from the noise distribution that changes in the incremental scenarios. Therefore, only fitting the change of data without noise distribution causes bias, and directly retraining results in low efficiency. To bridge this research gap, we propose a self-supervised *Incremental Contrastive Learning* (ICL) framework consisting of (i) a novel Incremental InfoNCE (NCE-II) loss function by estimating the change of noise distribution for old data to guarantee no bias with respect to the retraining, (ii) a meta-optimization with deep reinforced Learning Rate Learning (LRL) mechanism which can adaptively learn the learning rate according to the status of the training processes and achieve fast convergence which is critical for incremental learning. Theoretically, the proposed ICL is equivalent to retraining, which is based on solid mathematical derivation. In practice, extensive experiments in different domains demonstrate that, without retraining a new model, ICL achieves up to $16.7\\times$ training"
+"---\nauthor:\n- som\n- 'Amanda L. Steinhebel'\n- Regina Caputo\n- Henrike Fleischhack\n- Nicolas Striebig\n- Manoj Jadhav\n- Yusuke Suda\n- Ricardo Luz\n- Daniel Violette\n- Carolyn Kierans\n- Hiroyasu Tajima\n- Yasushi Fukazawa\n- Richard Leys\n- Ivan Peric\n- Jessica Metcalfe\n- Michela Negro\n- 'Jeremy S. Perkins'\nbibliography:\n- 'astropix.bib'\ntitle: 'AstroPix: CMOS pixels in space'\n---\n\nMotivation and Previous Work {#sec:intro}\n============================\n\nThe AstroPix project aims to develop and test pixelated silicon sensors for use in space-based gamma-ray instruments. This novel space-based technology is based upon work done with similar detectors at the Large Hadron Collider [@schoning2020mupix], and could contribute to a host of future instruments such as a next-generation wide-field gamma-ray explorer whose time domain capabilities is prioritized in the Astro2020 Decadal Survey [@decadal]. This work will overview the design, testing, and development of AstroPix version 2. This version is a step toward a flight prototype which will be realized with version 3 (Section\u00a0\\[sec:future\\]). The long-term goal is to continue this development and testing in order to determine AstroPix performance and its suitability for use in space (Fig.\u00a0\\[fig:projectOverview\\]).\n\n![AstroPix project overview - collider-based particle physics silicon detectors have been"
+"---\nauthor:\n- 'Andrea Solfanelli,[!!]{}'\n- Stefano Ruffo\n- Sauro Succi\n- Nicol\u00f2 Defenu\ntitle: 'Logarithmic, Fractal and Volume-Law Entanglement in a Kitaev chain with long-range hopping and pairing'\n---\n\nIntroduction\n============\n\nIn recent years, the quantum community\u2019s interest in long-range physics has steadily increased due to the emergence of promising platforms for quantum technological applications: long-range interacting quantum systems. These systems are characterized by coupling energies between pairs of microscopic constituents $V_{i,j}$ that decay as a power law of their distance $r=|i-j|$, with $\\alpha>0$ [@Campa2014; @DefenuArXiv2021]. This increased interest is largely due to the systems\u2019 stability against external perturbations, which allows for the mitigation of the detrimental effects of dynamically generated excitations [@DefenuArXiv2021; @xuPhysics2022]. An example of the rigidity of long-range interacting platforms against external drivings and of its utility for quantum technological applications is the possibility for such systems to host clean discrete Floquet time crystal phases [@RussomannoPRB2017; @SuracePRB2019; @PizziNatComm2021; @GiachettiArXiv2022]. Another example is the recently introduced advantage in the finite time performance of quantum heat-engines with a working substance hosting long-range couplings [@Solfanelli2022ArXiv]. Moreover, this technological and theoretical interest is also supported from the experimental side by the possibility to implement long-range interacting systems in typical quantum"
+"---\nabstract: 'From natural language processing to genome sequencing, large-scale machine learning models are bringing advances to a broad range of fields. Many of these models are too large to be trained on a single machine, and instead must be distributed across multiple devices. This has motivated the research of new compute and network systems capable of handling such tasks. In particular, recent work has focused on developing management schemes which decide *how* to allocate distributed resources such that some overall objective, such as minimising the job completion time (JCT), is optimised. However, such studies omit explicit consideration of *how much* a job should be distributed, usually assuming that maximum distribution is desirable. In this work, we show that maximum parallelisation is sub-optimal in relation to user-critical metrics such as throughput and blocking rate. To address this, we propose PAC-ML (artitioning for synchronous omputing with achine earning). PAC-ML leverages a graph neural network and reinforcement learning to learn how much to partition computation graphs such that the number of jobs which meet arbitrary user-defined JCT requirements is maximised. In experiments with five real deep learning computation graphs on a recently proposed optical architecture across four user-defined JCT requirement distributions, we"
+"---\nabstract: 'In this work we report the independent discovery and analysis of nine new compact triply eclipsing triple star systems found with the [*TESS*]{} mission: TICs 47151245, 81525800, 99013269, 229785001, 276162169, 280883908, 294803663, 332521671, and 356324779. Each of these nine systems exhibits distinct third-body eclipses where the third (\u2018tertiary\u2019) star occults the inner eclipsing binary (EB), or vice versa. We utilize a photodynamical analysis of the [*TESS*]{} photometry, archival photometric data, [*TESS*]{} eclipse timing variations of the EBs, available archival spectral energy distribution curves (SED), and, in some cases, newly acquired radial velocity observations, to solve for the parameters of all three stars, as well as most of the orbital elements. From these analyses we find that the outer orbits of all nine systems are viewed nearly edge on (i.e., within $\\lesssim 4^\\circ$), and 6 of the systems are coplanar to within $5^\\circ$; the others have mutual inclination angles of $20^\\circ$, $41^\\circ$, and possibly $179^\\circ$ (i.e., a retrograde outer orbit). The outer orbital periods range from 47.8 days to 604 days, with eccentricities spanning 0.004 to 0.61. The masses of all 18 EB stars are in the range of 0.9\u20132.6M$_\\odot$ and are mostly situated near the main sequence. By"
+"---\nabstract: 'Giant anomalous Hall effect (AHE) and magneto-optical activity can emerge in magnets with topologically non-trivial degeneracies. However, identifying the specific band structure features like Weyl points, nodal lines or planes which generate the anomalous response is a challenging issue. Since the low-energy interband transitions can govern the static AHE, we addressed this question in the prototypical magnetic Weyl semimetal Co$_3$Sn$_2$S$_2$ also hosting nodal lines by broadband polarized reflectivity and magneto-optical Kerr effect spectroscopy with a focus on the far-infrared range. In the linear dichroism spectrum we observe a strong resonance at 40meV, which also shows up in the optical Hall conductivity spectrum and primarily determines the static AHE, thus, confirms its intrinsic origin. Our material-specific theory reproduces the experimental data remarkably well and shows that strongly tilted nodal line segments around the Fermi energy generate the resonance. While the Weyl points only give vanishing contributions, these segments of the nodal lines gapped by the spin-orbit coupling dominate the low-energy optical response.'\nauthor:\n- 'F.\u00a0Schilberth'\n- 'M.-C. Jiang'\n- 'S. Minami'\n- 'M.\u00a0A.\u00a0Kassem'\n- 'F.\u00a0Mayr'\n- 'J.\u00a0Deisenhofer'\n- 'T. Koretsune'\n- 'Y.\u00a0Tabata'\n- 'T.\u00a0Waki'\n- 'H.\u00a0Nakamura'\n- 'G.-Y. Guo'\n- 'R."
+"---\nabstract: 'Deep neural networks (DNNs) have achieved remarkable success in numerous domains, and their application to PDE-related problems has been rapidly advancing. This paper provides an estimate for the generalization error of learning Lipschitz operators over Banach spaces using DNNs with applications to various PDE solution operators. The goal is to specify DNN width, depth, and the number of training samples needed to guarantee a certain testing error. Under mild assumptions on data distributions or operator structures, our analysis shows that deep operator learning can have a relaxed dependence on the discretization resolution of PDEs and, hence, lessen the curse of dimensionality in many PDE-related problems including elliptic equations, parabolic equations, and Burgers equations. Our results are also applied to give insights about discretization-invariance in operator learning.'\nauthor:\n- |\n    Ke Chen\\\n    Department of Mathematics\\\n    University of Maryland College Park\\\n    College Park, MD, USA\\\n    `kechen@umd.edu`\\\n    Chunmei Wang\\\n    Department of Mathematics\\\n    University of Florida\\\n    Gainesville\\\n    `chunmei.wang@ufl.edu`\\\n    Haizhao Yang\\\n    Department of Mathematics\\\n    University of Maryland College Park\\\n    College Park, MD, USA\\\n    `hzyang@umd.edu`\\\nbibliography:\n- 'ref.bib'\ntitle: Deep Operator Learning Lessens the Curse of Dimensionality for PDEs \n---\n\nIntroduction {#sec:introduction}\n============\n\nNonlinear operator learning aims to learn a mapping from a parametric"
+"---\nabstract: 'Motion prediction for automated vehicles in complex environments is a difficult task that is to be mastered when automated vehicles are to be used in arbitrary situations. Many factors influence the future motion of traffic participants starting with traffic rules and reaching from the interaction between each other to personal habits of human drivers. Therefore, we present a novel approach for a graph-based prediction based on a heterogeneous holistic graph representation that combines temporal information, properties and relations between traffic participants as well as relations with static elements such as the road network. The information is encoded through different types of nodes and edges that both are enriched with arbitrary features. We evaluated the approach on the INTERACTION and the Argoverse dataset and conducted an informative ablation study to demonstrate the benefit of different types of information for the motion prediction quality.'\nauthor:\n- 'Daniel Grimm$^{1}$, Philip Sch\u00f6rner$^{1}$, Moritz Dre\u00dfler$^{2}$ and J.-Marius Z\u00f6llner$^{1,2}$[^1][^2]'\nbibliography:\n- 'library.bib'\ntitle: '**Holistic Graph-based Motion Prediction** '\n---\n\nat (current page.south) ;\n\nIntroduction\n============\n\nMachine learning has improved in recent years and excels in domains where it is hard to find an explicit mathematical description of the solution. In autonomous driving machine learning"
+"---\nabstract: 'Sparseness and robustness are two important properties for many machine learning scenarios. In the present study, regarding the *maximum correntropy criterion* (MCC) based robust regression algorithm, we investigate to integrate the MCC method with the *automatic relevance determination* (ARD) technique in a Bayesian framework, so that MCC-based robust regression could be implemented with \u2018*adaptive sparseness*\u2019. To be specific, we use an inherent noise assumption from the MCC to derive an explicit likelihood function, and realize the maximum a posteriori (MAP) estimation with the ARD prior by variational Bayesian inference. Compared to the existing robust and sparse $L_1$-regularized MCC regression, the proposed MCC-ARD regression can eradicate the troublesome tuning for the regularization hyper-parameter which controls the regularization strength. Further, MCC-ARD achieves superior prediction performance and feature selection capability than $L_1$-regularized MCC, as demonstrated by a noisy and high-dimensional simulation study.'\nauthor:\n- \nbibliography:\n- 'bibli.bib'\ntitle: |\n    Adaptive sparseness for correntropy-based\\\n    robust regression via automatic\\\n    relevance determination\\\n    [^1] [^2] [^3] [^4]\n---\n\nadaptive sparseness, robustness, maximum correntropy criterion, automatic relevance determination, variational Bayes\n\nIntroduction {#sec:introduction}\n============\n\nRegression aims at a prediction model for continuous variables from the input of covariate variables or some derived features, which is also closely"
+"---\nabstract: 'Multi-structural (MS) games are combinatorial games that capture the number of quantifiers of first-order sentences. On the face of their definition, MS games differ from Ehrenfeucht-Fra\u00efss\u00e9 (EF) games in two ways: first, MS games are played on two sets of structures, while EF games are played on a pair of structures; second, in MS games, Duplicator can make any number of copies of structures. In the first part of this paper, we perform a finer analysis of MS games and develop a closer comparison of MS games with EF games. In particular, we point out that the use of sets of structures is of the essence and that when MS games are played on pairs of structures, they capture Boolean combinations of first-order sentences with a fixed number of quantifiers. After this, we focus on another important difference between MS games and EF games, namely, the necessity for Spoiler to play on top of a previous move in order to win some MS games. Via an analysis of the types realized during MS games, we delineate the expressive power of the variant of MS games in which Spoiler never plays on top of a previous move. In the"
+"---\nabstract: 'We report on the discovery of two low-luminosity, broad-line active galactic nuclei (AGN) at $z>5$ identified using JWST NIRSpec spectroscopy from the Cosmic Evolution Early Release Science (CEERS) Survey. We detect broad H$\\alpha$ emission in the spectra of both sources, with FWHM of $2038\\pm286$ and $1807\\pm207$ km s$^{-1}$, resulting in virial black hole (BH) masses that are 1-2 dex below that of existing samples of luminous quasars at $z>5$. The first source, CEERS 1670 at $z=5.242$, is 2\u20133 dex fainter than known quasars at similar redshifts and was previously identified as a candidate low-luminosity AGN based on its morphology and rest-frame optical spectral energy distribution (SED). We measure a BH mass of $M_{\\rm BH}=1.3\\pm0.4\\times 10^{7}~{M_\\odot}$, confirming that this AGN is powered by the least-massive BH known in the universe at the end of cosmic reionization. The second source, CEERS 3210 at $z=5.624$, is inferred to be a heavily obscured, broad-line AGN caught in a transition phase between a dust-obscured starburst and an unobscured quasar. We estimate its BH mass to be in the range of $M_{\\rm BH}\\simeq 0.9-4.7 \\times 10^{7}~M_{\\odot}$, depending on the level of dust obscuration assumed. We perform SED fitting to derive host stellar masses, $M_\\star$,"
+"---\nabstract: |\n    The Keller-Segel-Navier-Stokes system governs chemotaxis in liquid environments. This system is to be solved for the organism and chemoattractant densities and for the fluid velocity and pressure. It is known that if the total initial cell density mass is below $2\\pi$ there exist globally defined generalised solutions, but what is less understood is whether there are blow-up solutions beyond such a threshold and its optimality.\n\n    Motivated by this issue, a numerical blow-up scenario is investigated. Approximate solutions computed via a stabilised finite element method founded on a shock capturing technique are such that they satisfy *a priori* bounds as well as lower and $L^1(\\Omega)$ bounds for the cell and chemoattractant densities. In particular, this latter properties are essential in detecting numerical blow-up configurations, since the non-satisfaction of these two requirements might trigger numerical oscillations leading to non-realistic finite-time collapses into persistent Dirac-type measures.\n\n    Our findings show that the existence threshold value $2\\pi$ encountered for the cell density mass may not be optimal and hence it is conjectured that the critical threshold value $4\\pi$ may be inherited from the fluid-free Keller-Segel equations. Additionally it is observed that the formation of singular points can be neglected if the fluid"
+"---\nabstract: 'Programmable quantum devices are now able to probe wave functions at unprecedented levels. This is based on the ability to project the many-body state of atom and qubit arrays onto a measurement basis which produces snapshots of the system wave function. Extracting and processing information from such observations remains, however, an open quest. One often resorts to analyzing low-order correlation functions - that is, discarding most of the available information content. Here, we introduce wave function networks - a mathematical framework to describe wave function snapshots based on network theory. For many-body systems, these networks can become scale free - a mathematical structure that has found tremendous success and applications in a broad set of fields, ranging from biology to epidemics to internet science. We demonstrate the potential of applying these techniques to quantum science by introducing protocols to extract the Kolmogorov complexity corresponding to the output of a quantum simulator, and implementing tools for fully scalable cross-platform certification based on similarity tests between networks. We demonstrate the emergence of scale-free networks analyzing experimental data obtained with a Rydberg quantum simulator manipulating up to 100 atoms. Our approach illustrates how, upon crossing a phase transition, the simulator complexity"
+"---\nabstract: 'Multi-task learning (MTL) aims at solving multiple related tasks simultaneously and has experienced rapid growth in recent years. However, MTL models often suffer from performance degeneration with negative transfer due to learning several tasks simultaneously. Some related work attributed the source of the problem is the conflicting gradients. In this case, it is needed to select useful gradient updates for all tasks carefully. To this end, we propose a novel optimization approach for MTL, named GDOD, which manipulates gradients of each task using an orthogonal basis decomposed from the span of all task gradients. GDOD decomposes gradients into task-shared and task-conflict components explicitly and adopts a general update rule for avoiding interference across all task gradients. This allows guiding the update directions depending on the task-shared components. Moreover, we prove the convergence of GDOD theoretically under both convex and non-convex assumptions. Experiment results on several multi-task datasets not only demonstrate the significant improvement of GDOD performed to existing MTL models but also prove that our algorithm outperforms state-of-the-art optimization methods in terms of *AUC* and *Logloss* metrics.'\nauthor:\n- Xin Dong\n- Ruize Wu\n- Chao Xiong\n- Hai Li\n- Lei Cheng\n- Yong He\n- Shiyou"
+"---\nabstract: 'With the sustained attention on carbon neutrality, the personal carbon trading (PCT) scheme has been embraced as an auspicious paradigm for scaling down carbon emissions. To facilitate the simultaneous clearance of energy and carbon allowance inside the energy community while hedging against uncertainty, a joint trading framework is proposed in this article. The energy trading is implemented in a peer-to-peer (P2P) manner without the intervention of a central operator, and the uncertainty trading is materialized through procuring reserve of conventional generators and flexibility of users. Under the PCT scheme, carbon allowance is transacted via a sharing mechanism. Possible excessive carbon emissions due to uncertainty balance are tackled by obliging renewable agents to procure sufficient carbon allowances, following the consumption responsibility principle. A two-stage iterative method consisting of tightening McCormick envelope and alternating direction method of multipliers (ADMM) is devised to transform the model into a mixed-integer second-order cone program (MISOCP) and to allow for a fully decentralized market-clearing procedure. Numerical results have validated the effectiveness of the proposed market model.'\nauthor:\n- Yuanxi Wu\n- Zhi Wu\n- Wei Gu\n- Zheng Xu\n- Zheng Shu\n- Qirun Sun\ntitle: Decentralized Energy Market Integrating Carbon Allowance Trade and"
+"---\nabstract: 'Confirmation of accelerated expansion of the universe probed the concept of dark energy theory, and since then, numerous models have been introduced to explain its origin and nature. The present work is based on reconstructing dark energy by parametrization of the deceleration parameter in the FLRW universe filled with radiation, dark matter and dark energy. We have chosen some well-motivated parametrized models 1-3 in an attempt to investigate the energy density in terms of deceleration parameters by estimating the cosmological parameters with the help of different observational datasets. Also, we have introduced a new model 4 for the parametrization of the deceleration parameter. Then we analyzed the cosmography parameters using the best-fit values of the parameters. Using the information criteria, we have examined the viability of the models.'\nauthor:\n- Himanshu Chaudhary\n- Amine Bouali\n- Ujjal Debnath\n- Tanusree Roy\n- 'G.Mustafa'\nbibliography:\n- 'mybib.bib'\ntitle: Constraints on the Parameterized Deceleration Parameter in FLRW Universe\n---\n\nIntroduction {#Introduction}\n============\n\nThe concept of cosmic acceleration was probably one of the most promising discoveries in the modern cosmology paradigm. Recently, two independent research works involving distant supernovae suggested that in the present epoch, the universe is undergoing an accelerated"
+"---\nauthor:\n- \n- \n- \n- \nbibliography:\n- 'Manuscript.bib'\ntitle: 'SSR-TA: Sequence to Sequence based expert recurrent recommendation for ticket automation'\n---\n\nIntroduction {#sec1}\n============\n\nIn an IT software system, maintaining the normal operation is the essential requirement to provide high-quality services. In practice, the relationship among components of the IT software system is complicated. Once an exception occurs in a component, the exception may spread rapidly to other components and generate many concurrent warnings, causing the system unable to provide services. For example, IBM Cloud suffered two outages within five days in 2021\u00a0[@IBM]. The services were hit in the UK, the US, Sydney, Tokyo, and more for several hours due to incorrect routing settings by the external network provider. However, manually assigning an expert to address the exception is time-consuming and expensive. In order to catch exceptions and solve problems in time, the ticket automation is widely used in IT software systems. Fig.\u00a0\\[fig.ticketsystem\\] illustrates the workflow of the ticket automation. In detail, when an exception is detected by the engineer/monitor system, a ticket containing the exception description and the corresponding system information will be submitted to the ticket system. Further, the ticket system assigns an expert to"
+"---\nabstract: 'Tsunami-risk and flood-risk mitigation planning has particular importance for communities like those of the Pacific Northwest, where coastlines are extremely dynamic and a seismically-active subduction zone looms large. The challenge does not stop here for risk managers: mitigation options have multiplied since communities have realized the viability and benefits of nature-based solutions. To identify suitable mitigation options for their community, risk managers need the ability to rapidly evaluate several different options through fast and accessible tsunami models, but may lack high-performance computing infrastructure. The goal of this work is to leverage the newly developed Google\u2019s Tensor Processing Unit (TPU), a high-performance hardware accessible via the Google Cloud framework, to enable the rapid evaluation of different tsunami-risk mitigation strategies available to all communities. We establish a starting point through a numerical solver of the nonlinear shallow-water equations that uses a fifth-order Weighted Essentially Non-Oscillatory method with the Lax-Friedrichs flux splitting, and a Total Variation Diminishing third-order Runge-Kutta method for time discretization. We verify numerical solutions through several analytical solutions and benchmarks, reproduce several findings about one particular tsunami-risk mitigation strategy, and model tsunami runup at Crescent City, California whose topography comes from a high-resolution Digital Elevation Model. The direct"
+"---\nabstract: 'Solar wind charge exchange produces emissions in the soft X-ray energy range which can enable the study of near-Earth space regions such as the magnetopause, the magnetosheath and the polar cusps by remote sensing techniques. The Solar wind\u2013Magnetosphere\u2013Ionosphere Link Explorer (SMILE) and Lunar Environment heliospheric X-ray Imager (LEXI) missions aim to obtain soft X-ray images of near-Earth space thanks to their Soft X-ray Imager (SXI) instruments. While earlier modeling works have already simulated soft X-ray images as might be obtained by SMILE SXI during its mission, the numerical models used so far are all based on the magnetohydrodynamics description of the space plasma. To investigate the possible signatures of ion-kinetic-scale processes in soft X-ray images, we use for the first time a global hybrid-Vlasov simulation of the geospace from the Vlasiator model. The simulation is driven by fast and tenuous solar wind conditions and purely southward interplanetary magnetic field. We first produce global X-ray images of the dayside near-Earth space by placing a virtual imaging satellite at two different locations, providing meridional and equatorial views. We then analyze regional features present in the images and show that they correspond to signatures in soft X-ray emissions of mirror-mode wave"
+"---\nabstract: 'Global-scale quantum communication networks will require efficient long-distance distribution of quantum signals. Optical fibre communication channels have range constraints due to exponential losses in the absence of quantum memories and repeaters. Satellites enable intercontinental quantum communication by exploiting more benign inverse square free-space attenuation and long sight lines. However, the design and engineering of satellite quantum key distribution (QKD) systems is difficult and characteristic differences to terrestrial QKD networks and operations pose additional challenges. The typical approach to modelling satellite QKD (SatQKD) has been to estimate performances with a fully optimised protocol parameter space and with few payload and platform resource limitations. Here, we analyse how practical constraints affect the performance of SatQKD for the Bennett-Brassard 1984 (BB84) weak coherent pulse decoy state protocol with finite key size effects. We consider engineering limitations and trade-offs in mission design including limited in-orbit tunability, quantum random number generation rates and storage, and source intensity uncertainty. We quantify practical SatQKD performance limits to determine the long-term key generation capacity and provide important performance benchmarks to support the design of upcoming missions.'\nauthor:\n- 'Jasminder S. Sidhu'\n- Thomas Brougham\n- Duncan McArthur\n- 'Roberto G. Pousa'\n- 'Daniel K. L. Oi'"
+"---\nabstract: 'We investigate properties of neural networks that use both ReLU and $x^2$ as activation functions and build upon previous results to show that both analytic functions and functions in Sobolev spaces can be approximated by such networks of constant depth to arbitrary accuracy, demonstrating optimal order approximation rates across all nonlinear approximators, including standard ReLU networks. We then show how to leverage low local dimensionality in some contexts to overcome the curse of dimensionality, obtaining approximation rates that are optimal for unknown lower-dimensional subspaces.'\naddress: 'Department of Mathematics, Imperial College London & Mathematical Institute, University of Oxford, United Kingdom'\nauthor:\n- 'Vincent P.H. Goverse$^{*}$, Jad Hamdan$^{\\dagger}$, and Jared Tanner$^{\\dagger}$'\nbibliography:\n- 'main.bib'\nnocite: '[@*]'\ntitle: 'Optimal Approximation Complexity of High-Dimensional Functions with Neural Networks'\n---\n\n$$\\begin{aligned}\n   &\\small {}^* \\textit{Imperial College London}\\\\\n   &\\small {}^\\dagger \\textit{University of Oxford}\\end{aligned}$$\n\nIntroduction {#submission}\n============\n\nThe number of parameters needed to approximate smooth high-dimensional functions, $W^{n,\\infty}([0,1]^d)$, within a prescribed $\\epsilon$ accuracy in the $\\ell_{\\infty}$ norm was lower bounded by [@devore] to have a dependence on $\\epsilon$ that is proportional to $\\epsilon^{-d/n}$. [@yarotsky] has subsequently shown that a simple feedforward neural network with ${\\mathrm{ReLU}}(x):=\\max\\{0,x\\}$ nonlinear activation is nearly optimal in terms of the number of"
+"---\nabstract: 'Electric Vertical Takeoff and Landing (eVTOL) vehicles will open new opportunities in aviation. This paper describes the design and wind tunnel analysis of an eVTOL uncrewed aircraft system (UAS) prototype with a traditional aircraft wing, tail, and puller motor along with four vertical thrust pusher motors. Vehicle design and construction are summarized. Dynamic thrust from propulsion modules is experimentally determined at different airspeeds over a large sweep of propeller angles of attack. Wind tunnel tests with the vehicle prototype cover a suite of hover, transition and cruise flight conditions. Net aerodynamic forces and moments are distinctly computed and compared for plane, quadrotor and hybrid flight modes. Coefficient-based models are developed. Polynomial curve fits accurately capture observed data over all test configurations. To our knowledge, the presented wind tunnel experimental analysis for a multi-mode eVTOL platform is novel. Increased drag and reduced dynamic thrust likely due to flow interactions will be important to address in future designs.'\nauthor:\n- 'Akshay Mathur[^1]'\n- 'Ella Atkins [^2]'\nbibliography:\n- 'references.bib'\ntitle: Wind Tunnel Testing and Aerodynamic Characterization of a QuadPlane Uncrewed Aircraft System\n---\n\nNomenclature {#nomenclature .unnumbered}\n============\n\n[@l @[=]{} l@]{} $C_{L},C_{D},C_{M}$ & coefficients of lift, drag and pitching moment for"
+"---\nabstract: 'The computational study of equilibria involving constraints on players\u2019 strategies has been largely neglected. However, in real-world applications, players are usually subject to constraints ruling out the feasibility of some of their strategies, such as, *e.g.*, safety requirements and budget caps. Computational studies on constrained versions of the Nash equilibrium have lead to some results under very stringent assumptions, while finding constrained versions of the *correlated equilibrium* (CE) is still unexplored. In this paper, we introduce and computationally characterize *constrained Phi-equilibria*\u2014a more general notion than constrained CEs\u2014in *normal-form games*. We show that computing such equilibria is in general computationally intractable, and also that the set of the equilibria may *not* be convex, providing a sharp divide with unconstrained CEs. Nevertheless, we provide a polynomial-time algorithm for computing a constrained (approximate) Phi-equilibrium maximizing a given linear function, when either the number of constraints or that of players\u2019 actions is fixed. Moreover, in the special case in which a player\u2019s constraints do *not* depend on other players\u2019 strategies, we show that an exact, function-maximizing equilibrium can be computed in polynomial time, while *one* (approximate) equilibrium can be found with an efficient decentralized no-regret learning algorithm.'\nauthor:\n- |\n    Martino Bernasconi\\"
+"---\nabstract: 'Although industrial anomaly detection (AD) technology has made significant progress in recent years, generating realistic anomalies and learning priors of normal remain challenging tasks. In this study, we propose an end-to-end industrial anomaly detection method called FractalAD. Training samples are obtained by synthesizing fractal images and patches from normal samples. This fractal anomaly generation method is designed to sample the full morphology of anomalies. Moreover, we designed a backbone knowledge distillation structure to extract prior knowledge contained in normal samples. The differences between a teacher and a student model are converted into anomaly attention using a cosine similarity attention module. The proposed method enables an end-to-end semantic segmentation network to be used for anomaly detection without adding any trainable parameters to the backbone and segmentation head, and has obvious advantages over other methods in training and inference speed.. The results of ablation studies confirmed the effectiveness of fractal anomaly generation and backbone knowledge distillation. The results of performance experiments showed that FractalAD achieved competitive results on the MVTec AD dataset and MVTec 3D-AD dataset compared with other state-of-the-art anomaly detection methods.'\nauthor:\n- |\n    Xuan Xia$^1$, Weijie Lv$^2$, Xing He$^1$, Nan Li$^1$, Chuanqi Liu$^3$, Ning Ding$^1$\\\n    1.Shenzhen Institute"
+"---\nabstract: |\n    People increasingly use videos on the Web as a source for learning, be it for daily tasks or in formal as well as informal educational settings. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning from computer science databases for the period from 2016 to 2021 using the PRISMA guidelines. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories:\n\n    audio features,\n\n    visual features,\n\n    textual features,\n\n    instructor behavior,\n\n    learners\u2019 activities,\n\n    interactive features (quizzes, etc.),\n\n    production style, and\n\n    instructional design.\n\n    Also, we identify four representative research directions:\n\n    proposals of tools to support video-based learning,\n\n    studies with controlled experiments,\n\n    data analysis studies, and\n\n    proposals of design guidelines for learning videos.\n\n    We find that the most explored characteristics are textual features followed by visual features, learner\u2019s activities, and"
+"---\nabstract: 'Addressing the discrepancy between the late and early time measurements of the Hubble parameter, $H_0$, and the so-called $S_8$ parameter has been a challenge in precision cosmology. Several models are present to address these tensions, but very few of them can do so simultaneously. In the past, we have suggested Banks-Zaks/Unparticles as an emergent Dark Energy model and claimed that it can ameliorate the Hubble tension. In this work, we test this claim and perform a likelihood analysis of the model and its parameters are given current data and compare it to $\\Lambda$CDM. The model offers a possible resolution of Hubble tension and softens the Large Scale Structure (LSS) tension without employing a scalar field or modifying the gravitational sector. Our analysis shows a higher value of $H_0 \\sim 70 - 73$ km/sec/Mpc and a slightly lower value of $S_8$ for various combinations of data sets. Consideration of Planck CMB data combined with the Pantheon sample and SH0ES priors lowers the $H_0$ and $S_8$ tension to $0.96  \\sigma$ and $0.94 \\sigma$ respectively with best-fit $\\Delta \\chi^2 \\approx -10$ restoring cosmological concordance. Significant improvement in the likelihood persists for other combinations of data sets as well. Evidence for the"
+"---\nauthor:\n- Samantha Pilgrim\nbibliography:\n- 'mybibliography2.bib'\ntitle: On Sharp Bounds for the Dynamic Asymptotic Dimension\n---\n\n#### Abstract:\n\nWe prove the dynamic asymptotic dimension of a free isometric action on a space of finite doubling dimension is either infinite or equal to the asymptotic dimension of the acting group; and give a full description of the dynamic asymptotic dimension of translation actions on compact Lie groups in terms of the amenability and asymptotic dimension of the acting group.\n\nIntroduction\n============\n\nThe dynamic asymptotic dimension (DAD) of a group action $\\Gamma{\\curvearrowright}X$ was first introduced by Guentner, Willett, and Yu [@dasdimGWY], and was shown to relate to conditions used by Bartels, L\u00fcck, and Reich in work on the Farrell Jones conjecture on manifold topology [@bartels] [@bartels_et_al], as well as to the nuclear dimension of the crossed product [@winter2009nuclear] and calculations of its $K$-theory [@Guentner2016DynamicAD]. The DAD is also related to other dynamical dimension theories [@kerr2017dimension] known to take into account both the asymptotic dimension of $\\Gamma$ and the topological dimension of $X$. However, it has been suspected since its introduction that the dynamic asymptotic dimension may actually coincide with the asymptotic dimension of $\\Gamma$ whenever it is finite (it is"
+"---\nabstract: 'Pellet injection is used for fuelling and controlling discharges in tokamaks, and it is foreseen in ITER. During pellet injection, a movement of the ablated material towards the low-field side (or outward major radius direction) occurs because of the inhomogeneity of the magnetic field. Due to the complexity of the theoretical models, computer codes developed to simulate the cross-field drift are computationally expensive. Here, we present a one-dimensional semi-analytical model for the radial displacement of ablated material after pellet injection, taking into account both the Alfv\u00e9n and ohmic currents which short-circuit the charge separation creating the drift. The model is suitable for rapid calculation of the radial drift displacement, and can be useful for e.g.\u00a0modelling of disruption mitigation via pellet injection.'\nauthor:\n- 'O.\u00a0Vallhagen, I.\u00a0Pusztai, P.\u00a0Helander, S.\u00a0L.\u00a0Newton,'\n- 'T.\u00a0F\u00fcl\u00f6p'\nbibliography:\n- 'bibliography.bib'\ntitle: Drift of ablated material after pellet injection in a tokamak\n---\n\nIntroduction\n============\n\nPellet injection is an effective tool for modifying the density profile in fusion devices, and can be used for both fuelling and plasma control [@Pegourie2007]. It has also been employed successfully to mitigate transient events in tokamaks, e.g.\u00a0edge localized modes [@Lang_2015] and disruptions [@Reux2021]."
+"---\nabstract: 'Sample path properties of random processes an interesting and extensively studied topic especially in the case of Gaussian processes. In this article we study continuity properties of hypercontractive fields, providing natural extensions for some known Gaussian results beyond Gaussianity. Our results to both random processes and random fields alike.'\naddress:\n- 'Patrik Nummi, Department of Mathematics and Statistics, University of Helsinki, Finland'\n- 'Lauri Viitasaari, Department of Mathematics, Uppsala University, Sweden'\nauthor:\n- Patrik Nummi\n- Lauri Viitasaari\nbibliography:\n- 'main-revised-no-red.bib'\ntitle: Necessary and sufficient conditions for continuity of hypercontractive processes and fields\n---\n\n[^1]\n\n[**Mathematics Subject Classifications (2020)**]{}: 60G17, 60G60, 60G15\n\n[**Keywords:**]{} Hypercontractive fields, sample path continuity, H\u00f6lder continuity, Kolmogorov-Chentsov criterion,\n\nIntroduction\n============\n\nSample path regularity of random processes and fields is an important and extensively studied topic in the literature, and can be used as a tool, for example, to study convergence of certain statistical estimators or to study convergence of numerical schemes for stochastic partial differential equations. Indeed, various application areas arise from the fact that sharp modulus of continuity estimates can be translated to supremum tail bounds, providing sharp tail behaviour of the supremum.\n\nThe problem is particularly well-studied in the case of Gaussian"
+"---\nabstract: 'Modern-day autonomous vehicles are increasingly becoming complex multidisciplinary systems composed of mechanical, electrical, electronic, computing and information sub-systems. Furthermore, the individual constituent technologies employed for developing autonomous vehicles have started maturing up to a point, where it seems beneficial to start looking at the synergistic integration of these components into sub-systems, systems, and potentially, system-of-systems. Hence, this work applies the principles of mechatronics approach of system design, verification and validation for the development of autonomous vehicles. Particularly, we discuss leveraging multidisciplinary co-design practices along with virtual, hybrid and physical prototyping and testing within a concurrent engineering framework to develop and validate a scaled autonomous vehicle using the AutoDRIVE Ecosystem. We also describe a case-study of autonomous parking application using a modular probabilistic framework to illustrate the benefits of the proposed approach.'\nauthor:\n- 'Chinmay Samak$^{\\ast}$, Tanmay Samak$^{\\ast}$, Venkat Krovi[^1][^2]'\nbibliography:\n- 'IEEEabrv.bib'\n- 'References.bib'\ntitle: 'Towards Mechatronics Approach of System Design, Verification and Validation for Autonomous Vehicles'\n---\n\nAutonomous vehicles, mechatronics approach, multidisciplinary design, simulation and virtual prototyping, rapid prototyping, verification and validation.\n\nIntroduction {#Section: Introduction}\n============\n\nvehicles have evolved significantly over the course of time [@Beiker2016]. The gradual transition from purely mechanical automobiles to those with greater"
+"=1\n\nThe study of the classical soliton solutions in field theory can be traced back to the pioneering paper by Skyrme [@skyrme; @Skyrme:1962vh] (for a review, see [@Brown:2010api; @Manton:2022; @Manton:2004tk; @Shnir:2018yzp]). The Skyrme model was introduced in 1961 as a simple version of the nonlinear sigma model in $3+1$ dimensions, which can be used as an effective theory of atomic nuclei. It has been shown by Witten [@Witten:1983tw; @Witten:1983tx] that the Skyrme model can be derived from the $1/N_c$ expansion of the QCD low-energy effective Lagrangian. The Skyrmions are topological solitons, in this framework the topological charge corresponding to the physical baryon number.\n\nThe simplest (and original) version of the Skyrme model can be constructed for the ${\\rm SU}(2)$ valued chiral field. Then the model contains only three free parameters which set the length and energy scales and the mass of the pion field, respectively. An appropriate fitting of these parameters together with the assumption that the slowly rotating Skyrmion can be considered as rigid body, allows to evaluate various quantities like, e.g., mean square radii, $g$-factors of nucleons, and their magnetic moments [@Adkins:1983ya]. It turns out that the agreement with the corresponding experimental data is surprisingly better than what"
+"---\nabstract: 'We have implemented a gated-based quantum version of a restricted Boltzmann machine for approximating the ground state of a Pauli-decomposed qubit Hamiltonian. During the implementation and evaluation, we have noticed a variety of unexpected topics. It starts from limitations due to the structure of the algorithm itself and continues with constraints induced by specific quantum software development kits, which did not (yet) support necessary features for an efficient implementation. In this paper we systematically summarize our findings and categorize them according to their relevance for the implementation of similar quantum algorithms. We also discuss the feasibility of executing such implementations on current NISQ devices.'\nauthor:\n- \n- \n- \nbibliography:\n- 'main.bib'\ntitle: |\n    Clever Design, Unexpected Obstacles: Insights on Implementing a Quantum Boltzmann Machine\\\n    [^1] \n---\n\nquantum software engineering, NISQ devices, quantum machine learning, limitations and constraints\n\nIntroduction\n============\n\nThe advent of quantum computers accessible to the public has given an immense boost to the development of new quantum algorithms in the last decade. Many algorithms promise advantages over previously known classical ones, e.g., with regard to a speedup\u00a0[@Harrow2009] or enhanced solution quality\u00a0[@Havlicek2019]. However, to raise these advantages the conceptual algorithms first have to be applied"
+"---\nabstract: |\n    Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph contrastive learning (GCL). Existing hardness-aware CL methods typically treat negative instances that are most similar to the anchor instance as hard negatives, which helps improve the CL performance, especially on image data. However, this approach often fails to identify the hard negatives but leads to many false negatives on graph data. This is mainly due to that the learned graph representations are not sufficiently discriminative due to over-smooth representations and/or non-i.i.d. issues in graph data. To tackle this problem, this paper proposes a novel approach that builds a discriminative model on *collective affinity* information (i.e., two sets of pairwise affinities between the negative instances and the anchor instance) to mine hard negatives in GCL. In particular, the proposed approach evaluates how confident/uncertain the discriminative model is about the affinity of each negative instance to an anchor instance to determine its hardness weight relative to the anchor instance. This uncertainty information is then incorporated into existing GCL loss functions via a weighting term to enhance their performance. The enhanced GCL is theoretically grounded that the resulting GCL loss is equivalent"
+"---\nabstract: |\n    Individual-based epidemiological models support the study of fine-grained preventive measures, such as tailored vaccine allocation policies, *in silico*. As individual-based models are computationally intensive, it is pivotal to identify optimal strategies within a reasonable computational budget. Moreover, due to the high societal impact associated with the implementation of preventive strategies, uncertainty regarding decisions should be communicated to policy makers, which is naturally embedded in a Bayesian approach.\n\n    We present a novel technique for evaluating vaccine allocation strategies using a multi-armed bandit framework in combination with a Bayesian anytime $m$-top exploration algorithm. $m$-top exploration allows the algorithm to learn $m$ policies for which it expects the highest utility, enabling experts to inspect this small set of alternative strategies, along with their quantified uncertainty. The anytime component provides policy advisors with flexibility regarding the computation time and the desired confidence, which is important as it is difficult to make this trade-off beforehand.\n\n    We consider the Belgian COVID-19 epidemic using the individual-based model STRIDE, where we learn a set of vaccination policies that minimize the number of infections and hospitalisations. Through experiments we show that our method can efficiently identify the $m$-top policies, which is validated in a scenario where"
+"---\nabstract: 'Many instances of similar or almost-identical industrial machines or tools are often deployed at once, or in quick succession. For instance, a particular model of air compressor may be installed at hundreds of customers. Because these tools perform distinct but highly similar tasks, it is interesting to be able to quickly produce a high-quality controller for machine $N+1$ given the controllers already produced for machines $1..N$. This is even more important when the controllers are learned through Reinforcement Learning, as training takes time, energy and other resources. In this paper, we apply Policy Intersection, a Policy Shaping method, to help a Reinforcement Learning agent learn to solve a new variant of a compressors control problem faster, by transferring knowledge from several previously learned controllers. We show that our approach outperforms loading an old controller, and significantly improves performance in the long run.'\nauthor:\n- |\n    H\u00e9l\u00e8ne Plisnier helene.plisnier@vub.be\\\n    Vrije Universiteit Brussel Denis Steckelmacher denis.steckelmacher@vub.be\\\n    Vrije Universiteit Brussel Jeroen Willems jeroen.willems@flandersmake.be\\\n    FlandersMake Bruno Depraetere bruno.depraetere@flandersmake.be\\\n    FlandersMake Ann Now\u00e9 ann.nowe@vub.be\\\n    Vrije Universiteit Brussel\nbibliography:\n- 'main.bib'\ntitle: Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem\n---\n\nIntroduction\n============\n\nA Reinforcement Learning algorithm takes an input,"
+"---\nabstract: 'Networked systems that occur in various domains, such as the power grid, the brain, and opinion networks, are known to obey conservation laws. For instance, electric networks obey Kirchoff\u2019s laws, and social networks display opinion consensus. Such conservation laws are often modeled as balance equations that relate appropriate injected flows and potentials at the nodes of the networks. A recent line of work considers the problem of estimating the unknown structure of such networked systems from observations of node potentials (and only the knowledge of the statistics of injected flows). Given the dynamic nature of the systems under consideration, an equally important task is estimating the change in the structure of the network from data \u2013 the so called *differential network analysis* problem. That is, given two sets of node potential observations, the goal is to estimate the structural differences between the underlying networks. We formulate this novel differential network analysis problem for systems obeying conservation laws and devise a convex estimator to learn the edge changes directly from node potentials. We derive conditions under which the estimate is unique in the high-dimensional regime and devise an efficient ADMM-based approach to perform the estimation. Finally, we demonstrate the"
+"---\nauthor:\n- 'R.\u00a0Cesaroni'\n- '\u00a0F.\u00a0Faustini'\n- '\u00a0D.\u00a0Galli'\n- '\u00a0A.\u00a0Lorenzani'\n- '\u00a0S.\u00a0Molinari'\n- '\u00a0L.\u00a0Testi'\ndate: 'Received date / Accepted date'\ntitle: ' A Herschel study of the high-mass protostar '\n---\n\nIntroduction {#sint}\n============\n\nThe study of massive stars (conventionally those in excess of $\\sim$8\u00a0) is hampered by various problems, the most important of which probably being their large distances and their formation in clusters. As a consequence, precise estimates of crucial physical quantities such as the stellar luminosity in addition to accretion and outflow rates had been difficult to obtain until recently. With the substantial improvement of sensitivity and angular resolution provided by the new instruments that have recently become available online, these limitations can be overcome, at least in part. In this study we exploit the potential of the ESA Herschel Space Observatory (Pilbratt et al. [@pilb]) to image the continuum and line emission from a massive (proto)star in the far-IR at a few 10\u00a0resolution in the wake of previous studies by other authors (e.g. Green et al.\u00a0[@green13]; Manoj et al.\u00a0[@manoj13; @manoj16]; Karska et al.\u00a0[@karska13; @karska14; @karska18]; Nisini et al.\u00a0[@nisi]; Mottram"
+"---\nabstract: 'A general method is introduced for constructing two-dimensional (2D) surface meshes embedded in three-dimensional (3D) space time, *and* 3D hypersurface meshes embedded in four-dimensional (4D) space time. In particular, we begin by dividing the space-time domain into time slabs. Each time slab is equipped with an initial plane (hyperplane), in conjunction with an unstructured simplicial surface (hypersurface) mesh that covers the initial plane. We then obtain the vertices of the terminating plane (hyperplane) of the time slab from the vertices of the initial plane using a space-time trajectory-tracking approach. Next, these vertices are used to create an unstructured simplicial mesh on the terminating plane (hyperplane). Thereafter, the initial and terminating boundary vertices are stitched together to form simplicial meshes on the intermediate surfaces or *sides* of the time slab. After describing this new mesh-generation method in rigorous detail, we provide the results of multiple numerical experiments which demonstrate its validity and flexibility.'\naddress:\n- 'Department of Mechanical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802'\n- 'Department of Mechanical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802'\n- 'Laboratories for Computational Physics and Fluid Dynamics, Naval Research Laboratory, Washington, DC 20375'\nauthor:\n- 'Jude T. Anderson'"
+"---\nabstract: 'In this work we present a hybrid physics-based and data-driven learning approach to construct surrogate models for concurrent multiscale simulations of complex material behavior. We start from robust but inflexible physics-based constitutive models and increase their expressivity by allowing a subset of their material parameters to change in time according to an evolution operator learned from data. This leads to a flexible hybrid model combining a data-driven encoder and a physics-based decoder. Apart from introducing physics-motivated bias to the resulting surrogate, the internal variables of the decoder act as a memory mechanism that allows path dependency to arise naturally. We demonstrate the capabilities of the approach by combining an FNN encoder with several plasticity decoders and training the model to reproduce the macroscopic behavior of fiber-reinforced composites. The hybrid models are able to provide reasonable predictions of unloading/reloading behavior while being trained exclusively on monotonic data. Furthermore, in contrast to traditional surrogates mapping strains to stresses, the specific architecture of the hybrid model allows for lossless dimensionality reduction and straightforward enforcement of frame invariance by using strain invariants as the feature space of the encoder.'\nauthor:\n- 'I.B.C.M. Rocha'\n- 'P. Kerfriden'\n- 'F.P. van der Meer'\nbibliography:"
+"---\nabstract: 'We show that, unlike vacuum General Relativity, Einstein-scalar theories allow *balanced* static, neutral, asymptotically flat, double-black hole solutions, for scalar field models minimally coupled to gravity, with appropriate self-interactions. These are scalar hairy versions of the double-Schwarzschild (or Bach-Weyl) solution, *but regular* on and outside the two (topologically spherical) horizons. The balancing repulsive force is provided by the scalar field. An explicit illustration is presented, using a Weyl-type construction adapted to numerical solutions, requiring no partial linearisation, or integrability structure, of the Einstein-scalar equations. Fixing the couplings of the model, the balanced configurations form a one-parameter family of solutions, labelled by the proper distance between the black holes.'\n---\n\n[ **Two Schwarzschild-like black holes\\\nbalanced by their scalar hair** ]{}\\\n[[**Carlos A. R. Herdeiro and Eugen Radu** ]{}\\\n$^{\\ddagger }$[Departamento de Matem\u00e1tica da Universidade de Aveiro and ]{}\\\n[Centre for Research and Development in Mathematics and Applications (CIDMA),]{}\\\n[Campus de Santiago, 3810-183 Aveiro, Portugal]{} ]{}\n\n Introduction\n=============\n\nA remarkable solution of \u201cvacuum\" General Relativity (GR) is the Bach-Weyl (BW) or double-Schwarzschild metric, describing two static, neutral black holes (BHs) placed at some non-zero distance, in a four dimensional, asymptotically flat spacetime\u00a0[@BW].[^1] The gravitational attraction between the BHs"
+"---\nabstract: |\n    Our study is based on an epidemiological compartmental model, the SIRS model. In the SIRS model, each individual is in one of the states susceptible (S), infected(I) or recovered (R), depending on its state of health. In compartment R, an individual is assumed to stay immune within a finite time interval only and then transfers back to the S compartment. We extend the model and allow for a feedback control of the infection rate by mitigation measures which are related to the number of infections. A finite response time of the feedback mechanism is supposed that changes the low-dimensional SIRS model into an infinite-dimensional set of integro-differential (delay-differential) equations. It turns out that the retarded feedback renders the originally stable endemic equilibrium of SIRS (stable focus) into an unstable focus if the delay exceeds a certain critical value. Nonlinear solutions show persistent regular oscillations of the number of infected and susceptible individuals. In the last part we include noise effects from the environment and allow for a fluctuating infection rate. This results in multiplicative noise terms and our model turns into a set of stochastic nonlinear integro-differential equations. Numerical solutions reveal an irregular behavior of repeated disease"
+"---\nabstract: 'Recently discovered kagome superconductors $A\\textrm{V}_{3}\\textrm{Sb}_{5}$ ($A =$ K, Rb, Cs) exhibit exotic bulk and surface physical properties such as charge-density wave (CDW) and chirality, whereas their origins remain unresolved. By using micro-focused angle-resolved photoemission spectroscopy, we discovered that $A\\textrm{V}_{3}\\textrm{Sb}_{5}$ commonly exhibits two distinct polar surfaces depending on the termination; electron- and hole-doped ones for the $A$- and Sb-termination, respectively. We observed that the kagome-derived band shows a clear splitting in the $A$-terminated surface while it is absent in the Sb-terminated counterpart, indicative of the polarity-dependent CDW at the surface. Close comparison of the band-dependent splitting reveals that the three-dimensional CDW structure of the K-terminated surface is different from that of the Rb- or Cs-terminated surface, suggesting the diversity of the CDW ground state. These results provide important insight into the origin of CDW in kagome superconductors $A\\textrm{V}_{3}\\textrm{Sb}_{5}$.'\nauthor:\n- Takemi Kato\n- Yongkai Li\n- Min Liu\n- Kosuke Nakayama\n- Zhiwei Wang\n- Seigo Souma\n- Miho Kitamura\n- Koji Horiba\n- Hiroshi Kumigashira\n- Takashi Takahashi\n- Yugui Yao\n- Takafumi Sato\ntitle: 'Surface-termination-dependent electronic states in kagome superconductors $A\\textrm{V}_{3}\\textrm{Sb}_{5}$ ($A =$ K, Rb, Cs) studied by micro-ARPES'\n---\n\n[^1]\n\n[^2]\n\n[^3]\n\n[^4]\n\n[^5]\n\n[^6]\n\nIntroduction"
+"---\nabstract: 'Various approaches have been used in the literature for eliminating nonresonant levels in atomic systems and deriving effective Hamiltonians. Important among these are elimination techniques at the level of probability amplitudes, operator techniques to project the dynamics on to the subspace of resonant levels, Green\u2019s function techniques, the James\u2019 effective Hamiltonian approach, etc. None of the previous approaches is suitable for deriving effective Hamiltonians in intracavity situations. However, the James\u2019 approach does work in the case of only two-photon transitions in a cavity. A generalization of the James\u2019 approach works in the case of three-photon transitions in a cavity, but only under Raman-like resonant conditions. Another important approach for adiabatic elimination is based on an adaptation of the Markov approximation well-known in the theory of system-bath interactions. However, this approach has not been shown to work in intracavity situations. In this paper, we present a method of adiabatic elimination for atoms inside cavities in the presence of multiphoton transitions. We work in the Heisenberg picture, and our approach has the advantage that it allows one to derive effective Hamiltonians even when Raman-like resonance conditions do not hold.'\n---\n\n\\\nProsenjit Maity\\\n\n*Department of Physics, Ramakrishna Mission Residential College,\\"
+"---\nabstract: 'The valley Zeeman physics of excitons in monolayer transition metal dichalcogenides provides valuable insight into the spin and orbital degrees of freedom inherent to these materials. Being atomically-thin materials, these degrees of freedom can be influenced by the presence of adjacent layers, due to proximity interactions that arise from wave function overlap across the 2D interface. Here, we report 60 T magnetoreflection spectroscopy of the A- and B- excitons in monolayer WS$_2$, systematically encapsulated in monolayer graphene. While the observed variations of the valley Zeeman effect for the A- exciton are qualitatively in accord with expectations from the bandgap reduction and modification of the exciton binding energy due to the graphene-induced dielectric screening, the valley Zeeman effect for the B- exciton behaves markedly different. We investigate prototypical WS$_2$/graphene stacks employing first-principles calculations and find that the lower conduction band of WS$_2$ at the $K/K''$ valleys (the $CB^-$ band) is strongly influenced by the graphene layer on the orbital level. This leads to variations in the valley Zeeman physics of the B- exciton, consistent with the experimental observations. Our detailed microscopic analysis reveals that the conduction band at the $Q$ point of WS$_2$ mediates the coupling between $CB^-$ and"
+"---\nabstract: 'We study reinforcement learning with linear function approximation and adversarially changing cost functions, a setup that has mostly been considered under simplifying assumptions such as full information feedback or exploratory conditions. We present a computationally efficient policy optimization algorithm for the challenging general setting of unknown dynamics and bandit feedback, featuring a combination of mirror-descent and least squares policy evaluation in an auxiliary MDP used to compute exploration bonuses. Our algorithm obtains an $\\widetilde O(K^{6/7})$ regret bound, improving significantly over previous state-of-the-art of $\\widetilde O (K^{14/15})$ in this setting. In addition, we present a version of the same algorithm under the assumption a simulator of the environment is available to the learner (but otherwise no exploratory assumptions are made), and prove it obtains state-of-the-art regret of $\\widetilde O (K^{2/3})$.'\nauthor:\n- 'Uri Sherman[^1]'\n- 'Tomer Koren[^2]'\n- 'Yishay Mansour[^3]'\nbibliography:\n- 'main.bib'\ntitle: |\n    Improved Regret for Efficient Online Reinforcement Learning\\\n    with Linear Function Approximation\n---\n\nIntroduction\n============\n\nReinforcement Learning (RL; [@sutton2018reinforcement; @mmt2022rlbook]) studies online decision making problems in which an agent learns through experience within a dynamic environment, with the goal to minimize a loss function associated with the agent-environment interaction. Modern applications of RL such as"
+"---\nabstract: 'Nanomechanical spectroscopy (NMS) is a recently developed approach to determine optical absorption spectra of nanoscale materials via mechanical measurements. It is based on measuring changes in the resonance frequency of a membrane resonator vs. the photon energy of incoming light. This method is a direct measurement of absorption, which has practical advantages compared to common optical spectroscopy approaches. In the case of two-dimensional (2D) materials, NMS overcomes limitations inherent to conventional optical methods, such as the complications associated with measurements at high magnetic fields and low temperatures. In this work, we develop a protocol for NMS of 2D materials that yields two orders of magnitude improved sensitivity compared to previous approaches, while being simpler to use. To this end, we use electrical sample actuation, which simplifies the experiment and provides a reliable calibration for greater accuracy. Additionally, the use of low-stress silicon nitride membranes as our substrate reduces the noise-equivalent power to $NEP$ = $\\SI{890}{fW/\\sqrt{Hz}}$, comparable to commercial semiconductor photodetectors. We use our approach to spectroscopically characterize a two-dimensional transition metal dichalcogenide (WS$_2$), a layered magnetic semiconductor (CrPS$_4$), and a plasmonic supercrystal consisting of gold nanoparticles.'\nauthor:\n- '**Jan N. Kirchhof**'\n- '**Yuefeng Yu**'\n- '**Denis Yagodkin**'\n-"
+"---\nbibliography:\n- 'library.bib'\n---\n\n= =-2-2in =\n\n\\\n\\\n\n\\\n\\\n\n  ------------------ -------------------------------------------------------\n  Author:            [Sven Elflein]{}\n  Supervisor:        [Prof. Dr. Stephan G\u00fcnnemann]{}\n  Advisor:           [M. Sc. Daniel Z\u00fcgner, M. Sc. Bertrand Charpentier]{}\n  Submission Date:   [15. August 2021]{}\n  ------------------ -------------------------------------------------------\n\n[Munich]{}, [15. August 2021]{} [Sven Elflein]{}\n\nToday, deep learning is increasingly applied in security-critical situations such as autonomous driving and medical diagnosis. Despite its success, the behavior and robustness of deep networks are not fully understood yet, posing a significant risk.\n\nIn particular, researchers recently found that neural networks are overly confident in their predictions, even on data they have never seen before. To tackle this issue, one can differentiate two approaches in the literature. One accounts for uncertainty in the predictions, while the second estimates the underlying density of the training data to decide whether a given input is close to the training data, and thus the network is able to perform as expected.\n\nFor the latter approach, can be used as density estimators. are freely specified by a single function mapping from the data domain to a single value. This allows the usage of existing, powerful classification architectures.\n\nIn this thesis, we investigate the capabilities of at the task of"
+"---\nabstract: 'Fair allocation of indivisible goods has attracted extensive attention over the last two decades, yielding numerous elegant algorithmic results and producing challenging open questions. The problem becomes much harder in the presence of *strategic* agents. Ideally, one would want to design *truthful* mechanisms that produce allocations with fairness guarantees. However, in the standard setting without monetary transfers, it is generally impossible to have truthful mechanisms that provide non-trivial fairness guarantees. Recently, @AmanatidisBFLLR21 suggested the study of mechanisms that produce fair allocations in their equilibria. Specifically, when the agents have additive valuation functions, the simple Round-Robin algorithm always has pure Nash equilibria and the corresponding allocations are *envy-free up to one good* ([$\\textrm{\\MakeUppercase{ef}1}$]{}) with respect to the agents\u2019 *true valuation functions*. Following this agenda, we show that this outstanding property of the Round-Robin mechanism extends much beyond the above default assumption of additivity. In particular, we prove that for agents with *cancelable* valuation functions (a natural class that contains, e.g., additive and budget-additive functions), this simple mechanism always has equilibria and even its approximate equilibria correspond to approximately [$\\textrm{\\MakeUppercase{ef}1}$]{}allocations with respect to the agents\u2019 true valuation functions. Further, we show that the approximate [$\\textrm{\\MakeUppercase{ef}1}$]{}fairness of approximate equilibria surprisingly holds"
+"---\nabstract: 'Bacteriophages are central to microbial ecosystems for balancing bacterial populations and promoting evolution by applying strong selection pressure. Here we review some of the known aspects that modulate phage-bacteria interaction in a way that naturally promotes their coexistence. We focus on the modulations that arise from structural, physical, or physiological constraints. We argue they should play roles in many phage-bacteria systems providing sustainable diversity.'\nauthor:\n- Namiko Mitarai\n- Anastasios Marantos\n- Kim Sneppen\ntitle: 'Sustainable Diversity of Phage-Bacteria Systems'\n---\n\nvirulent phage ,kill the winner ,colony ,spatial refuge ,dormancy 0000 ,1111 0000 ,1111\n\nIntroduction {#sec:intro}\n============\n\nBacteriophages play a critical role in balancing and sometimes reshaping microbial ecosystems, supporting the diversity of bacterial strains [@thingstad2000elements; @Thingstad2014; @haerter2014phage; @marantos2022kill] and in facilitating the transmission of genes between different bacterial strains [@dixit2015recombinant] and species [@koonin2013virocentric]. Genetic engineering and gene-editing in modern biotechnology are by-products of the fitness gain of bacteria when they protect themselves from phages by restriction-modification enzymes [@eriksen2022emergence; @tesson2023synergy] or CRISPR systems [@jinek2012programmable; @ishino2018history; @rostol2019ph; @tesson2023synergy]. Future explorations in phage resistance mechanisms will likely unravel other aspects [@scholl2005escherichia; @song2020primary; @chaudhry2020mucoidy; @laughlin2022architecture; @millman2022expanded] of the aeon-long war between these dominating life forms [@whitman1998prokaryotes; @breitbart2005here] on our planet. Here"
+"---\nabstract: 'We prove that every connected $P_5$-free graph has cop number at most two, solving a conjecture of Sivaraman. In order to do so, we first prove that every connected $P_5$-free graph $G$ with independence number at least three contains a three-vertex induced path with vertices $a \\hbox{-} b \\hbox{-} c$ in order, such that every neighbour of $c$ is also adjacent to one of $a,b$.'\nauthor:\n- |\n    Maria Chudnovsky[^1]\\\n    Princeton University, Princeton, NJ 08544\\\n    \\\n    Sergey Norin[^2]\\\n    Department of Mathematics and Statistics, McGill University, Montr\u00e9al, QC, Canada\\\n    \\\n    Paul Seymour[^3]\\\n    Princeton University, Princeton, NJ 08544\\\n    \\\n    J\u00e9r\u00e9mie Turcotte[^4]\\\n    Department of Mathematics and Statistics, McGill University, Montr\u00e9al, QC, Canada\nbibliography:\n- 'refs.bib'\ndate: 'December 19, 2022; revised January 25, 2023'\ntitle: 'Cops and robbers on $P_5$-free graphs'\n---\n\nIntroduction\n============\n\nWe denote the $t$-vertex path by $P_t$. There are a number of well-known open questions about $P_5$-free graphs (a graph $G$ is [*$H$-free*]{} if no induced subgraph of $G$ is isomorphic to $H$, and $|G|$ denotes the number of vertices of $G$). For instance:\n\n-   the Erd\u0151s-Hajnal [@erdos_ramsey-type_1989] conjecture implies that for some $c>0$, every $P_5$-free graph $G$ has a clique or stable set of size at least"
+"---\nbibliography:\n- 'arxiv.bib'\n---\n\nIntroduction\n============\n\nProbabilistic graphical models (PGMs) propose a rigorous and elegant way to represent the full joint probability density function of high-dimensional data and to express assumptions about its hidden structure. Learning and inference algorithms let us analyze data under those assumptions and recover the hidden structure that best explains our observations. However, performing exact inference in complex PGMs is often intractable. To mitigate this problem, several techniques have been proposed for approximate inference, among which a popular one is variational inference (VI) [@wainwright2008graphical; @bishop2006pattern]. In this paper, we consider directed acyclic PGMs\u2014also named Bayesian networks (BNs)\u2014with binary variables and noisy-OR conditional distribution [@pearl1988probabilistic]. The resulting noisy-OR BNs have been used for medical diagnosis [@jaakkola1999variational], data compression [@vsingliar2006noisy], text mining [@liu2016representing], and more recently overparametrized learning [@buhai2020empirical] and topic modeling on large sparse datasets [@ji2020variational]. Noisy-OR BNs have an intractable posterior: most of the aforementioned applications rely on VI for approximate inference. Some existing VI methods [@buhai2020empirical] use a recognition network and amortize the approximate inference via a single forward pass, which cannot induce \u201cexplaining-away\u201d (see Section \\[sec:noisy-or\\]). In contrast, @jaakkola1999variational [@vsingliar2006noisy; @ji2020variational] assume a mean-field (MF) posterior, which is vulnerable to bad local optima."
+"---\nabstract: 'We consider the problem of satisfiability of sets of constraints in a given set of finite uniform hypergraphs. While the problem under consideration is similar in nature to the problem of satisfiability of constraints in graphs, the classical complexity reduction to finite-domain CSPs that was used in the proof of the complexity dichotomy for such problems cannot be used as a black box in our case. We therefore introduce an algorithmic technique inspired by classical notions from the theory of finite-domain CSPs, and prove its correctness based on symmetries that depend on a linear order that is external to the structures under consideration. Our second main result is a P/NP-complete complexity dichotomy for such problems over many sets of uniform hypergraphs. The proof is based on the translation of the problem into the framework of constraint satisfaction problems (CSPs) over infinite uniform hypergraphs. Our result confirms in particular the Bodirsky-Pinsker conjecture for CSPs of first-order reducts of some homogeneous hypergraphs. This forms a vast generalization of previous work by Bodirsky-Pinsker (STOC\u201911) and Bodirsky-Martin-Pinsker-Pongr\u00e1cz (ICALP\u201916) on graph satisfiability.'\naddress:\n- 'Hamburg University of Technology, Research Group on Theoretical Computer Science, Germany'\n- 'Institut f\u00fcr Diskrete Mathematik und Geometrie, FG"
+"---\nabstract: |\n    The Minkowski symmetral of an $\\alpha$-concave function is defined, and some of its fundamental properties are deduced. It is shown that almost all sequences of random Minkowski symmetrizations of a quasiconcave function converge in the $L^p$ metric ($p\\geq 1$) to a spherical decreasing mean width rearrangement. A sharp extended Urysohn\u2019s type inequality for quasiconcave functions is then derived.\n\n    Using inner linearizations from convex optimization, an analogue of polytopes inscribed in a convex body is studied for log concave functions: Given a log concave function, we consider log-affine minorants defined in terms of the inner linearizations of the corresponding convex base function. As an application of the functional Minkowski symmetrizations, it is shown that any functional defined on the class of log concave functions which satisfies certain conditions is maximized by the reflectional hypo-symmetrization. As a corollary, it is shown that among all log concave functions of a given mean width, the reflectional hypo-symmetrization is hardest to approximate by \u201clog-affine\" minorants with a fixed number of break points. This result generalizes the classical fact which states that among all convex bodies of a given mean width, a Euclidean ball is hardest to approximate by inscribed polytopes with a"
+"---\nabstract: 'Generative graph self-supervised learning (SSL) aims to learn node representations by reconstructing the input graph data. However, most existing methods focus on unsupervised learning tasks only and very few work has shown its superiority over the state-of-the-art graph contrastive learning (GCL) models, especially on the classification task. While a very recent model has been proposed to bridge the gap, its performance on unsupervised learning tasks is still unknown. In this paper, to comprehensively enhance the performance of generative graph SSL against other GCL models on both unsupervised and supervised learning tasks, we propose the [<span style=\"font-variant:small-caps;\">SeeGera</span>]{}\u00a0model, which is based on the family of self-supervised variational graph auto-encoder (VGAE). Specifically, [<span style=\"font-variant:small-caps;\">SeeGera</span>]{}\u00a0adopts the semi-implicit variational inference framework, a hierarchical variational framework, and mainly focuses on feature reconstruction and structure/feature masking. On the one hand, [<span style=\"font-variant:small-caps;\">SeeGera</span>]{}\u00a0co-embeds both nodes and features in the encoder and reconstructs both links and features in the decoder. Since feature embeddings contain rich semantic information on features, they can be combined with node embeddings to provide fine-grained knowledge for feature reconstruction. On the other hand, [<span style=\"font-variant:small-caps;\">SeeGera</span>]{}\u00a0adds an additional layer for structure/feature masking to the hierarchical variational framework, which boosts the"
+"---\nabstract: |\n    We address the problem of testing conditional mean and conditional variance for non-stationary data. We build e-values and p-values for four types of non-parametric composite hypotheses with specified mean and variance as well as other conditions on the shape of the data-generating distribution. These shape conditions include symmetry, unimodality, and their combination. Using the obtained e-values and p-values, we construct tests via e-processes, also known as testing by betting, as well as some tests based on combining p-values for comparison. Although we mainly focus on one-sided tests, the two-sided test for the mean is also studied. Simulation and empirical studies are conducted under a few settings, and they illustrate features of the methods based on e-processes.\\\n    **Keywords**: P-values, e-values, e-processes, symmetry, unimodality\nauthor:\n- 'Yixuan Fan[^1]'\n- 'Zhanyi Jiao[^2]'\n- 'Ruodu Wang [Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada. E-mail: <wang@uwaterloo.ca>.]{}'\ntitle: 'Testing mean and variance by e-processes'\n---\n\nIntroduction\n============\n\nTesting mean and variance in various settings is a classic problem in statistics. In parametric inference concerning testing the mean, well-known tests like Student\u2019s t-test and z-test, as well as tests related to variance such as the Chi-Squared test and the"
+"---\nabstract: 'The power graph of a finite group $G$ is a simple undirected graph with vertex set $G$ and two vertices are adjacent if one is a power of the other. The enhanced power graph of a finite group $G$ is a simple undirected graph whose vertex set is the group $G$ and two vertices $a$ and $b$ are adjacent if there exists $c \\in G$ such that both $a$ and $b$ are powers of $c$. In this paper, we study the difference graph $\\mathcal{D}(G)$ of a finite group $G$ which is the difference of the enhanced power graph and the power graph of $G$ with all isolated vertices removed. We characterize all the finite nilpotent groups $G$ such that the genus (or cross-cap) of the difference graph $\\mathcal{D}(G)$ is at most $2$.'\naddress: '$\\text{}^1$Department of Mathematics, Birla Institute of Technology and Science Pilani, Pilani 333031, India'\nauthor:\n- 'Parveen, Jitender Kumar$^{*}$'\ntitle: Nilpotent groups whose Difference graphs have positive genus\n---\n\nHistorical Background and Main result\n=====================================\n\nThere are number of graphs attached to groups, in particular: Cayley graphs, commuting graphs, power graphs, enhanced power graph etc. These graphs have been studied extensively by various researchers because of"
+"---\nabstract: 'We give necessary and sufficient condition that an element of an arbitrary $C^{*}$-algebra is an isolated vertex of the orthograph related to the mutual strong Birkhoff-James orthogonality. Also, we prove that for all $C^{*}$-algebras except $\\mathbb{C},\\mathbb{C}\\oplus\\mathbb{C}$ and $M_2(\\mathbb{C})$ all non isolated points make a single connected component of the orthograph which diameter is less than or equal to $4$, i.e.\u00a0any two non isolated points can be connected by a path with at most $4$ edges. Some related results are given.'\naddress:\n- |\n    University of Belgrade\\\n    Faculty of Mathematics\\\n    Studentski trg 16-18\\\n    11000 Beograd\\\n    Serbia\n- |\n    University of Belgrade\\\n    Faculty of Mathematics\\\n    Studentski trg 16-18\\\n    11000 Beograd\\\n    Serbia\nauthor:\n- 'Dragoljub J. Ke\u010dki'' c'\n- 'Srdjan Stefanovi'' c'\nbibliography:\n- 'Orthograph2023Feb.bib'\ntitle: 'Isolated vertices and diameter of the $BJ$-orthograph in $C^*$-algebras'\n---\n\n[^1]\n\nIntroduction\n============\n\nBirkhoff-James orthogonality in Banach spaces has been studied for many years. It is defined as follows.\n\nLet $X$ be a Banach space, and let $x$, $y\\in X$. We say that $x$ is Birkhoff-James orthogonal to $y$, and denote $x\\perp_{BJ} y$, or simply $x\\perp y$, if for any $\\lambda\\in \\mathbb C$ there holds $$\\label{BJord}\n\\|x+\\lambda y\\|\\ge\\|x\\|.$$\n\nIt is easy to see that"
+"---\nabstract: |\n    In this paper, we analyze hashing from a worst-case perspective. To this end, we study a new property of hash families that is strongly related to $d$-perfect hashing, namely $c$-ideality. On the one hand, this notion generalizes the definition of perfect hashing, which has been studied extensively; on the other hand, it provides a direct link to the notion of $c$-approximativity . We focus on the usually neglected case where the average load $\\alpha$ is at least $1$ and prove upper and lower parametrized bounds on the minimal size of $c$-ideal hash families.\n\n    As an aside, we show how $c$-ideality helps to analyze the *advice complexity* of hashing. The concept of advice, introduced a decade ago, lets us measure the information content of an online problem. We prove hashing\u2019s advice complexity to be linear in the hash table size.\nauthor:\n- Fabian Frei\n- David Wehner\nbibliography:\n- 'bib.bib'\ntitle: 'Bounds for $c$-Ideal Hashing'\n---\n\nIntroduction\n============\n\nSay you wake up one morning in a hotel with the desire to stroll around and view some sights of the city. Considering your peculiar proclivity towards paranoia, you want to know for every car you see whether you have"
+"---\nabstract: 'Let $\\Sigma$ be a closed minimal surface immersed in a Riemannian 3-manifold carrying an orthonormal Killing frame. This class of ambient spaces includes Lie groups with a bi-invariant metric. In this paper, we prove that the sum of the Morse index and the nullity of $\\Sigma$ is bounded from below by a constant times its genus.'\naddress:\n- 'Institute of Mathematics, Federal University of Alagoas, CEP 57072-970, Macei\u00f3, Alagoas, Brazil'\n- 'Universidade Estadual de Feira de Santana, Feira de Santana-BA, Brazil'\nauthor:\n- Marcos Petr\u00facio Cavalcante\n- 'Darlan F. de Oliveira'\n- 'Robson dos S. Silva'\nbibliography:\n- 'bibliography.bib'\ntitle: 'Index bounds for closed minimal surfaces in 3-manifolds with the Killing property'\n---\n\nIntroduction\n============\n\nMinimal hypersurfaces are critical points for the area functional, while constant mean curvature (CMC) hypersurfaces are critical points for the area functional for volume preserving variations. If such a hypersurface minimizes area up to the second order, then we say that it is *stable*.\n\nMore generally, we may define the *index* of a minimal or a CMC hypersurface as the dimension of the maximal subspace in the respective space of variations, where the second variation of the area functional is negative definite. It"
+"---\nabstract: 'Identifying the effects of causes and causes of effects is vital in virtually every scientific field. Often, however, the needed probabilities may not be fully identifiable from the data sources available. This paper shows how partial identifiability is still possible for several probabilities of causation. We term this $\\epsilon\\text{-identifiability}$ and demonstrate its usefulness in cases where the behavior of certain subpopulations can be restricted to within some narrow bounds. In particular, we show how unidentifiable causal effects and counterfactual probabilities can be narrowly bounded when such allowances are made. Often those allowances are easily measured and reasonably assumed. Finally, $\\epsilon\\text{-identifiability}$ is applied to the unit selection problem.'\nauthor:\n- Ang Li\n- |\n    Scott MuellerJudea Pearl Cognitive Systems Laboratory, Department of Computer Science,\\\n    University of California, Los Angeles,\\\n    Los Angeles, California, USA.\\\n    {angli, scott, judea}@cs.ucla.edu\nbibliography:\n- 'ijcai23.bib'\ntitle: '$\\epsilon$-Identifiability of Causal Quantities'\n---\n\nIntroduction\n============\n\nBoth Effects of Causes (EoC) and Causes of Effects (CoE) play an important role in several fields, such as health science, social science, and business. For example, the causal effects identified by the adjustment [@pearl1993aspects] formula helps decision-maker avoid randomized controlled trial using purely observational data. For another example, probabilities of causation"
+"---\nabstract: |\n    Pairs of similar compounds that only differ by a small structural modification but exhibit a large difference in their binding affinity for a given target are known as activity cliffs\u00a0(ACs). It has been hypothesised that QSAR models struggle to predict ACs and that ACs thus form a major source of prediction error. However, a study to explore the AC-prediction power of modern QSAR methods and its relationship to general QSAR-prediction performance is lacking. We systematically construct nine distinct QSAR models by combining three molecular representation methods (extended-connectivity fingerprints, physicochemical-descriptor vectors and graph isomorphism networks) with three regression techniques (random forests, k-nearest neighbours and multilayer perceptrons); we then use each resulting model to classify pairs of similar compounds as ACs or non-ACs and to predict the activities of individual molecules in three case studies: dopamine receptor D2, factor Xa, and SARS-CoV-2 main protease.\n\n    We observe low AC-sensitivity amongst the tested models when the activities of both compounds are unknown, but a substantial increase in AC-sensitivity when the actual activity of one of the compounds is given. Graph isomorphism features are found to be competitive with or superior to classical molecular representations for AC-classification and can thus be"
+"---\nabstract: 'Recently, deformed quantum systems gather lots of attention in the literature. Dunkl formalism differs from others by containing the difference-differential and reflection operator. It is one of the most interesting deformations since it let us discuss the solutions according to the even and odd solutions. In this work, we studied the ideal Bose gas and the blackbody radiation via the Dunkl formalism. To this end, we made a liaison between the coordinate and momentum operators with the creation and annihilation operators which allowed us to obtain the expressions of the partition function, the condensation temperature, and the ground state population of the Bose gas. We found that Dunkl-condensation temperature increases with increasing $\\theta$ value. In the blackbody radiation phenomena, we found how the Dunkl formalism modifies total radiated energy. Then, we examined the thermal quantities of the system. We found that the Dunkl deformation causes an increase in entropy and specific heat functions as well as in the total radiation energy. However, we observed a decrease in the Dunk-corrected Helmholtz free energy in this scenario. Finally, we found that the equation of state is invariant even in the considered formalism.'\nauthor:\n- |\n    F. Merabtine[^1]\\\n    Laboratory for Theoretical"
+"---\nabstract: |\n    ChatGPT has the ability to generate grammatically flawless and seemingly-human replies to different types of questions from various domains. The number of its users and of its applications is growing at an unprecedented rate. Unfortunately, use and abuse come hand in hand. In this paper, we study whether a machine learning model can be effectively trained to accurately distinguish between original human and seemingly human (that is, ChatGPT-generated) text, especially when this text is short. Furthermore, we employ an explainable artificial intelligence framework to gain insight into the reasoning behind the model trained to differentiate between ChatGPT-generated and human-generated text. The goal is to analyze model\u2019s decisions and determine if any specific patterns or characteristics can be identified. Our study focuses on short online reviews, conducting two experiments comparing human-generated and ChatGPT-generated text. The first experiment involves ChatGPT text generated from custom queries, while the second experiment involves text generated by rephrasing original human-generated reviews. We fine-tune a Transformer-based model and use it to make predictions, which are then explained using SHAP. We compare our model with a perplexity score-based approach and find that disambiguation between human and ChatGPT-generated reviews is more challenging for the ML model"
+"---\nabstract: 'A free boundary value problem related to the genesis of multispecies granular biofilms is presented. The granular biofilm is modelled as a spherical free boundary domain with radial symmetry. The proposed model is conceived in the framework of continuum mechanics and consists of: nonlinear hyperbolic PDEs which model the advective transport and growth of attached species that constitute the granular biofilm matrix; semilinear elliptic PDEs which govern the diffusive transport and conversion of nutrients; and semilinear elliptic PDEs describing the invasion phenomena and conversion of planktonic cells suspended in the surrounding environment. The evolution of the free boundary is governed by an ODE accounting for microbial growth, attachment, and detachment. By using the method of characteristics, the system of equations constituting the granular biofilm model is converted into an equivalent integral system. Existence and uniqueness of solutions are discussed and proved for the attachment regime using the fixed point theorem.'\nauthor:\n- 'F.\u00a0Russo[^1^]{}'\n- 'M.R.\u00a0Mattei[^1^]{}'\n- 'A.\u00a0Tenore[^1^]{}'\n- 'B.\u00a0D\u2019Acunto[^1^]{}'\n- 'V.\u00a0Luongo[^1^]{}'\n- 'L.\u00a0Frunzo[^1^]{}'\nbibliography:\n- 'Bibliography.bib'\ntitle: Analysis of a spherical free boundary problem modelling granular biofilms\n---\n\n[[^1^]{}Department of Mathematics and Applications \u201cRenato Caccioppoli\u201d, University of Naples Federico II, Via Cintia"
+"---\nauthor:\n- 'Mandar Saraf,[!!]{}'\n- 'Pandi Raj Chinnappan,'\n- 'Aditya Deodhar,'\n- 'Mamta Jangra,'\n- 'J. Krishnamoorthi,'\n- 'Gobinda Majumder,'\n- 'Veera Padmavathy,'\n- 'K.C. Ravindran,'\n- 'Raj Shah,'\n- 'Ravindra Shinde,'\n- 'and B. Satyanarayana'\ntitle: 'Design, fabrication and large scale qualification of cosmic muon veto scintillator detectors'\n---\n\nIntroduction {#sec:intro}\n============\n\nThe India-based Neutrino Observatory (INO) collaboration has proposed a 50kT magnetised Iron Calorimeter (ICAL) detector, to study the mass hierarchy and mixing parameters of atmospheric neutrinos\u00a0[@ino]. The ICAL will consist of 150 layers of Resistive Plate Chambers (RPCs) as active detector elements, sandwiched between 56mm thick iron plates. The ICAL will be placed inside an underground laboratory in a cavern under a mountain near Pottipuram in South India. The tall mountain will provide a rock cover of more than 1.3km, corresponding to an overburden of $\\sim$3500mwe on all the sides of the ICAL, and thus reduce the atmospheric muon background by an order of $10^6$\u00a0[@muonflux]. This reduction in the muon flux is essential for the detection of neutrinos because neutrinos will be detected indirectly through the muons they form after interaction with the target mass, and only a handful of neutrino interactions may happen"
+"---\nabstract: 'We study a family of conservative interacting particle systems with degenerate rates called noncooperative kinetically constrained lattice gases. We prove for all models in this family the diffusive scaling of the relaxation time, the positivity of the diffusion coefficient, and the positivity of the self-diffusion coefficient.'\naddress: 'MAP5, Universit\u00e9 Paris Cit\u00e9'\nauthor:\n- Assaf Shapira\nbibliography:\n- 'noncooperative.bib'\ntitle: Noncooperative models of kinetically constrained lattice gases\n---\n\n\\#1[\\#1]{} \u00b6 \\#1[\\#1]{} \\#1[\\#1]{}\n\nIntroduction\n============\n\nKinetically constrained lattice gases are interacting particle systems introduced by physicists in order to better understand glassy materials (see, e.g., [@KobAndersen; @RitortSollich]). The basic underlying hypothesis behind these models is that glassy behavior is a dynamic effect, and the role of interactions is irrelevant. Under this hypothesis, we can explain why glasses are rigid using the *cage effect*\u2014even though their microscopic structure is amorphous, glasses at low temperatures have a very high density, and molecules are unable to move since they are blocked by neighboring molecules.\n\nIn order to model this effect, we consider the lattice $\\Z^{d}$, describing a coarse graining of the glass. Each site, corresponding to some region in the glass, could be either *occupied* or *empty*, representing dense or dilute zones. We"
+"---\nabstract: 'This work presents an optimal sampling-based method to solve the real-time motion planning problem in static and dynamic environments, exploiting the Rapid-exploring Random Trees (RRT) algorithm and the Model Predictive Path Integral (MPPI) algorithm. The RRT algorithm provides a nominal mean value of the random control distribution in the MPPI algorithm, resulting in satisfactory control performance in static and dynamic environments without a need for fine parameter tuning. We also discuss the importance of choosing the right mean of the MPPI algorithm, which balances exploration and optimality gap, given a fixed sample size. In particular, a sufficiently large mean is required to explore the state space enough, and a sufficiently small mean is required to guarantee that the samples reconstruct the optimal controls. The proposed methodology automates the procedure of choosing the right mean by incorporating the RRT algorithm. The simulations demonstrate that the proposed algorithm can solve the motion planning problem in real-time for static or dynamic environments.'\nauthor:\n- 'Chuyuan Tao$^{\\dagger}$, Hunmin Kim$^{\\ddagger}$, and Naira Hovakimyan$^{\\dagger}$ [^1] [^2][^3]'\nbibliography:\n- 'citation.bib'\ntitle: RRT Guided Model Predictive Path Integral Method\n---\n\nIntroduction\n============\n\nMotion planning problems have been widely discussed in recent years in the field of"
+"---\nabstract: |\n    Over the past few years, more and more Internet advertisers have started using automated bidding for optimizing their advertising campaigns. Such advertisers have an optimization goal (e.g. to maximize conversions), and some constraints (e.g. a budget or an upper bound on average cost per conversion), and the automated bidding system optimizes their auction bids on their behalf. Often, these advertisers participate on multiple advertising channels and try to optimize across these channels. A central question that remains unexplored is how automated bidding affects optimal auction design in the [*multi-channel setting*]{}.\n\n    In this paper, we study the problem of setting auction reserve prices in the multi-channel setting. In particular, we shed light on the revenue implications of whether each channel optimizes its reserve price [*locally*]{}, or whether the channels optimize them [*globally*]{} to maximize total revenue. Motivated by practice, we consider two models: one in which the channels have full freedom to set reserve prices, and another in which the channels have to respect floor prices set by the publisher. We show that in the first model, welfare and revenue loss from [*local*]{} optimization is bounded by a function of the advertisers\u2019 inputs, but is independent of the"
+"---\nabstract: 'In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a *conversation* with a user and *ground* them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities). To this end, we develop a dataset where user questions are annotated with <span style=\"font-variant:small-caps;\">Sparql</span> parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers.[^1]'\nauthor:\n- |\n    Laura Perez-Beltrachini$^1$, Parag Jain$^1$, Emilio Monti$^2$, Mirella Lapata$^1$\\\n    $^1$ School of Informatics, University of Edinburgh\\\n    $^2$ Amazon Alexa\\\n    `{lperez,parag.jain,mlap}@inf.ed.ac.uk, monti@amazon.co.uk`\nbibliography:\n- 'seqspar2.bib'\n- 'anthology.bib'\ntitle: |\n    Semantic Parsing for Conversational\\\n    Question Answering over Knowledge Graphs\n---\n\n=1\n\nIntroduction\n============\n\nConversational information seeking is the process of acquiring information through conversations [@Zamani:ea:2022]. Recent years have seen an increasing number of applications aiming to build conversational interfaces based on information"
+"---\nauthor:\n- 'Guanglin\u00a0Zhou, Shaoan\u00a0Xie, Guang-Yuan\u00a0Hao, Shiming\u00a0Chen, Biwei\u00a0Huang, Xiwei\u00a0Xu, Chen\u00a0Wang, Liming\u00a0Zhu, Lina\u00a0Yao, , Kun\u00a0Zhang'\nbibliography:\n- 'ref.bib'\ntitle: 'Emerging Synergies in Causality and Deep Generative Models: A Survey'\n---\n\n[Shell : A Sample Article Using IEEEtran.cls for IEEE Journals]{}\n\nIntroduction\n============\n\nand accurately modeling data-generating processes (DGPs) are pivotal objectives within the domains of artificial intelligence (AI) and machine learning (ML) [@domingos2012few; @pearl2009causal; @Goodfellow2014GenerativeAN; @Kingma2014AutoEncodingVB]. The proficient modeling of these processes is not merely foundational but also has far-reaching implications across diverse applications. It plays a crucial role in data analytics [@shalev2014understanding; @kaur2022modeling], synthesizing novel and high-fidelity data samples [@kobyzev2020normalizing; @Dhariwal2021DiffusionMB; @wang2022controllable], as well as facilitating informed and reliable decision-making processes [@pearl2009causal; @arora2021survey]. As a result, various methodologies have emerged to tackle the multifaceted challenges inherent to this pivotal area of research [@Goodfellow-et-al-2016; @Kingma2014AutoEncodingVB; @pearl2009causal].\n\n![ Comparison of data-generating processes within deep generative models (DGMs) and causality. DGMs primarily draw latent variables from a simple distribution such as Gaussian [@Goodfellow2014GenerativeAN; @Kingma2014AutoEncodingVB], while causality is rooted in variables defined by causal relationships [@von2021self]. This divergence in foundation grants causality superior extrapolation and interpretability, and DGMs an edge in managing high-dimensional spaces,"
+"---\nabstract: 'The nuclear equation of state (EOS) is at the center of numerous theoretical and experimental efforts in nuclear physics. With advances in microscopic theories for nuclear interactions, the availability of experiments probing nuclear matter under conditions not reached before, endeavors to develop sophisticated and reliable transport simulations to interpret these experiments, and the advent of multi-messenger astronomy, the next decade will bring new opportunities for determining the nuclear matter EOS, elucidating its dependence on density, temperature, and isospin asymmetry. Among controlled terrestrial experiments, collisions of heavy nuclei at intermediate beam energies (from a few tens of MeV/nucleon to about 25 GeV/nucleon in the fixed-target frame) probe the widest ranges of baryon density and temperature, enabling studies of nuclear matter from a few tenths to about 5 times the nuclear saturation density and for temperatures from a few to well above a hundred MeV, respectively. Collisions of neutron-rich isotopes further bring the opportunity to probe effects due to the isospin asymmetry. However, capitalizing on the enormous scientific effort aimed at uncovering the dense nuclear matter EOS, both at RHIC and at FRIB as well as at other international facilities, depends on the continued development of state-of-the-art hadronic transport simulations."
+"---\nabstract: 'The recently published paper by Gupta and Agrawal [@gupta2018design] exploited the sum-difference co-array (SDCA) to enhance the virtual aperture of sparse arrays. We argue that the key SDCA property established in [@gupta2018design] requires a critical necessary and sufficient condition that is valid for a very rare case only.'\nauthor:\n- and\u00a0\nbibliography:\n- 'rootSBL.bib'\ntitle: 'Disproving Sum-Difference Co-Array Property '\n---\n\nDirection-of-arrival (DOA) estimation, noncircular signal, sparse array, sum-difference co-array.\n\nIntroduction\n============\n\nDirection-of-arrival (DOA) estimation is one of the most important topics in array signal processing, and has attracted wide attention in radar, sonar and wireless communications. The classical subspace-type algorithms can resolve up to $(N-1)$ sources for an $N$-element uniform linear array (ULA). To identify more sources than the number of sensors, many sparse array geometries have been proposed in the last decade.\n\nRecently, [@gupta2018design] claimed that the property of noncircular sources can be utilized to significantly enhance the virtual aperture of sparse arrays. Considering noncircular sources, its main idea is to combine the difference co-array and sum co-array of the sparse array configuration into a sum-difference co-array (SDCA), so that the degree-of-freedom (DoF) can be substantially increased. Then, [@gupta2018design] designed the sparse array based on such"
+"---\nabstract: 'Dissipative dynamics of nuclear fission is a well confirmed phenomenon described either by a Kramers-modified statistical model or by a dynamical model employing the Langevin equation. Though dynamical models as well as statistical models incorporating fission delay are found to explain the measured fission observables in many studies, it nonetheless shows conflicting results for shell closed nuclei in the mass region 200. Analysis of recent data for neutron shell closed nuclei in excitation energy range 40$-$80 MeV failed to arrive at a satisfactory description of the data and attributed the mismatch to shell effects and/or entrance channel effects, without reaching a definite conclusion. In the present work we show that a well established stochastic dynamical code simultaneously reproduces the available data of pre-scission neutron multiplicities, fission and evaporation residue excitation functions for neutron shell closed nuclei $^{210}$Po and $^{212}$Rn and their isotopes $^{206}$Po and $^{214,216}$Rn without the need for including any extra shell or entrance channel effects. The calculations are performed by using a phenomenological universal friction form factor with no ad-hoc adjustment of model parameters. However, we note significant deviation, beyond experimental errors, in some cases of Fr isotopes.'\nauthor:\n- Divya Arora\n- 'P. Sugathan'\n-"
+"---\nabstract: 'Size-invariant shape transformation gives rise to the so-called quantum shape effect in strongly confined systems. While quantum size and shape effects are often thought to be difficult to distinguish because of their coexistence, it is actually possible to separate them and focus solely on the shape effect. In fact, quantum shape effect is a quite different phenomenon from quantum size effects, as it can have the opposite influence on the physical properties of nanoscale systems. Here we explore the origin of the quantum shape effect by theoretically investigating the simplest system that can produce the same physics: quantum particles in a box separated by a moving partition. The partition moves quasistatically from one end of the box to the other, allowing the system to remain in equilibrium with a reservoir throughout the process. The partition and the boundaries are impenetrable by particles, forming two effectively interconnected regions. Position of the partition becomes the shape variable. We investigate quantum shape effect on the thermodynamic properties of confined particles. In addition, we applied a new analytical model based on dimensional transitions to accurately predict thermodynamic properties under the quantum shape effect. A fundamental understanding of quantum shape effects could pave"
+"---\nabstract: 'Zhang (2019) presented a general estimation approach based on the Gaussian distribution for general parametric models where the likelihood of the data is difficult to obtain or unknown, but the mean and variance-covariance matrix are known. Castilla and Zografos (2021) extended the method to density power divergence-based estimators, which are more robust than the likelihood-based Gaussian estimator against data contamination. In this paper we introduce the restricted minimum density power divergence Gaussian estimator (MDPDGE) and study its main asymptotic properties. Also, we examine it robustness through its influence function analysis. Restricted estimators are required in many practical situations, in special in testing composite null hypothesis, and provide here constrained estimators to inherent restrictions of the underlying distribution. Further, we derive robust Rao-type test statistics based on the MDPDGE for testing simple null hypothesis and we deduce explicit expressions for some main important distributions. Finally, we empirically evaluate the efficiency and robustness of the method through a simulation study.'\nauthor:\n- |\n    A. Felipe, M. Jaenada$,$ P. Miranda and L. Pardo\\\n    [Department of Statistics and O.R., Complutense University of Madrid, Spain]{}\ndate:\n- \n- \ntitle: '**Restricted distance-type Gaussian estimators based on density power divergence and their applications in hypothesis"
+"---\nabstract: 'Inspired by natural evolutionary processes, Evolutionary Computation (EC) has established itself as a cornerstone of Artificial Intelligence. EC possesses distinctive attributes, including adaptability and the capability to explore expansive problem spaces, making it invaluable in domains that require intricate black-box optimization. Recently, with the surge in data-intensive applications and large-scale complex systems, the demand for scalable EC solutions has grown significantly. However, many existing EC libraries, which were originally designed for modest scales, fall short in catering to the heightened demands of modern problems. While the advent of some pioneering GPU-accelerated EC libraries is a step forward, they too grapple with limitations, particularly in terms of flexibility and architectural robustness. To address these limitations, we introduce : a computing framework tailored for automated, distributed, and heterogeneous execution of EC algorithms. At the core of lies a functional programming model that simplifies the development of parallelized EC algorithms, seamlessly integrated with a high-performance computation model designed specifically for distributed GPU-accelerated execution. Building upon foundation, we have crafted an extensive library comprising a wide spectrum of 45 EC algorithms for both single- and multi-objective optimization. Furthermore, the library offers comprehensive support for a diverse set of benchmark problems, ranging from"
+"---\nabstract: 'In this note we determine the graviton-graviton OPE and the null states in any $w_{1+\\infty}$ symmetric theory on the celestial sphere. Our analysis shows that there exists a discrete *infinite* family of such theories. The MHV-sector and the quantum self dual gravity are two members of this infinite family. Although the Bulk Lagrangian description of this family of theories is not currently known to us, the graviton scattering amplitudes in these theories are heavily constrained due to the existence of null states. Presumably they are exactly solvable in the same way as the minimal models of $2$-D CFT.'\nauthor:\n- Shamik Banerjee\n- Harshal Kulkarni\n- Partha Paul\ntitle: 'An infinite family of $w_{1+\\infty}$ invariant theories on the celestial sphere'\n---\n\nIntroduction\n============\n\nCelestial holography [@Strominger:2017zoo] is an attempt to formulate a dual theory of quantum gravity in asymptotically flat space time. When the bulk is $(3+1)$ dimensional, the dual theory is conjectured to be two dimensional and it lives on the celestial sphere. The correlation functions of this theory are given by the Mellin transform of the bulk $S$-matrix elements [@Pasterski:2016qvg; @Pasterski:2017kqt; @Banerjee:2018gce] and an important question is how to compute them in the dual formulation.\n\nNow"
+"---\nabstract: 'We propose a deep learning algorithm for solving high-dimensional parabolic integro-differential equations (PIDEs) and high-dimensional forward-backward stochastic differential equations with jumps (FBSDEJs), where the jump-diffusion process are derived by a Brownian motion and an independent compensated Poisson random measure. In this novel algorithm, a pair of deep neural networks for the approximations of the gradient and the integral kernel is introduced in a crucial way based on deep FBSDE method. To derive the error estimates for this deep learning algorithm, the convergence of Markovian iteration, the error bound of Euler time discretization, and the simulation error of deep learning algorithm are investigated. Two numerical examples are provided to show the efficiency of this proposed algorithm.'\nauthor:\n- 'Wansheng Wang[^1]'\n- 'Jie Wang[^2]'\n- 'Jinping Li[^3]'\n- 'Feifei Gao[^4]'\n- 'Yi Fu[^5]'\ntitle: 'Deep learning numerical methods for high-dimensional fully nonlinear PIDEs and coupled FBSDEs with jumps[^6]'\n---\n\nparabolic integro-differential equations, forward-backward stochastic differential equations with jumps, deep learning, error estimates\n\n60H35, 65C20, 65M15, 65C30, 60H10, 65M75\n\nIntroduction\n============\n\nThe purpose of this paper is to derive error estimates for the proposed deep learning algorithm for solving high-dimensional parabolic integro-partial differential equations (PIDEs) which can be represented by high-dimensional"
+"---\nabstract: 'In most qubit realizations, prototype devices are available and are already utilized in both industry and academic research. Despite being severely constrained, hardware- and algorithm-aware quantum circuit mapping techniques have been developed for enabling successful algorithm executions during the NISQ era, targeting mostly technologies with high qubit counts. Not so much attention has been paid to the implementation of compilation methods for quantum processors based on spin-qubits due to the scarce availability of current experimental devices and their small sizes. However, based on their high scalability potential and their rapid progress it is timely to start exploring quantum circuit mapping solutions for these spin-qubit devices. In this work, we discuss the unique mapping challenges of a scalable spin-qubit crossbar architecture with shared control [@li2018crossbar] and introduce *SpinQ*, the first native compilation framework for scalable spin-qubit architectures that maps quantum algorithms on this crossbar architecture. At the core of *SpinQ* is the *Integrated Strategy* that addresses the unique operational constraints of the crossbar while considering compilation (execution time) scalability, having a $O(n)$ computational complexity. To evaluate the performance of *SpinQ* on this novel architecture, we compiled a broad set of well-defined quantum circuits and performed an in-depth analysis based"
+"---\nabstract: 'We adopt the *integral* definition of the fractional Laplace operator and study an optimal control problem on *Lipschitz domains* that involves a fractional elliptic partial differential equation (PDE) as state equation and a control variable that enters the state equation as a coefficient; pointwise constraints on the control variable are considered as well. We establish the existence of optimal solutions and analyze first and, necessary and sufficient, second order optimality conditions. Regularity estimates for optimal variables are also analyzed. We [[develop]{}]{} two finite element discretization [[strategies]{}]{}: a semidiscrete scheme [[in which]{}]{} the control variable is not discretized, and a fully discrete scheme [[in which]{}]{} the control variable is discretized with piecewise constant functions. For both [[schemes]{}]{}, we analyze [[the]{}]{} convergence properties of discretizations and derive error estimates.'\nauthor:\n- 'Francisco Bersetche[^1]'\n- 'Francisco Fuica[^2]'\n- 'Enrique Ot\u00e1rola[^3]'\n- 'Daniel Quero[^4]'\nbibliography:\n- 'bilinear\\_ref\\_sin\\_url.bib'\ndate: 'Draft version of .'\ntitle: '[[Bilinear optimal control for the fractional Laplacian: [[analysis and discretization]{}]{}]{}]{}[^5]'\n---\n\noptimal control, fractional diffusion, integral fractional Laplacian, first and second order optimality conditions, regularity estimates, finite elements, convergence, error estimates.\n\n35R11, 49J20, 49K20, 49M25, 65K10, 65N15, 65N30.\n\nIntroduction {#sec:intro}\n============\n\nIn this paper, we are interested in the"
+"---\nabstract: 'Variants of the BERT architecture specialised for producing full-sentence representations often achieve better performance on downstream tasks than sentence embeddings extracted from vanilla BERT. However, there is still little understanding of what properties of inputs determine the properties of such representations. In this study, we construct several sets of sentences with pre-defined lexical and syntactic structures and show that SOTA sentence transformers have a strong nominal-participant-set bias: cosine similarities between pairs of sentences are more strongly determined by the overlap in the set of their noun participants than by having the same predicates, lengthy nominal modifiers, or adjuncts. At the same time, the precise syntactic-thematic functions of the participants are largely irrelevant.'\nauthor:\n- |\n    Dmitry Nikolaev Sebastian Pad[\u00f3]{}\\\n    IMS, University of Stuttgart\\\n    `dnikolaev@fastmail.com` `pado@ims.uni-stuttgart.de`\nbibliography:\n- 'anthology.bib'\n- 'custom.bib'\ntitle: Representation biases in sentence transformers\n---\n\n=1\n\nIntroduction\n============\n\nTransformer-based encoder-only models derived from the BERT architecture and pre-trained using similar objective and training regimens [@devlin-etal-2019-bert; @liu2019roberta] have become the standard tool for downstream tasks at the level of individual tokens and token sequences [@tenney-etal-2019-bert; @wang2021ner]. Whole-sentence representations can also be easily extracted from the outputs of these models by either using the embedding of the special"
+"---\nabstract: 'Sensing is a universal task in science and engineering. Downstream tasks from sensing include inferring full state estimates of a system (system identification), control decisions, and forecasting. These tasks are exceptionally challenging to achieve with limited sensors, noisy measurements, and corrupt or missing data. Existing techniques typically use current (static) sensor measurements to perform such tasks and require principled sensor placement or an abundance of randomly placed sensors. In contrast, we propose a [*SHallow REcurrent Decoder*]{} (SHRED) neural network structure which incorporates (i) a recurrent neural network (LSTM) to learn a latent representation of the temporal dynamics of the sensors, and (ii) a shallow decoder that learns a mapping between this latent representation and the high-dimensional state space. By explicitly accounting for the time-history, or trajectory, of the sensor measurements, SHRED enables accurate reconstructions with far fewer sensors, outperforms existing techniques when more measurements are available, and is agnostic towards sensor placement. In addition, a compressed representation of the high-dimensional state is directly obtained from sensor measurements, which provides an [*on-the-fly*]{} compression for modeling physical and engineering systems. Forecasting is also achieved from the sensor time-series data alone, producing an efficient paradigm for predicting temporal evolution with an"
+"---\nabstract: |\n    We explore the decoupling of massive ghost mode in the $4D$ (four-dimensional) theory of the conformal factor of the metric. The model was introduced by Antoniadis and Mottola in [@antmot] and can be regarded as a close analog of the fourth-derivative quantum gravity. The analysis of the derived one-loop nonlocal form factors includes their asymptotic behavior in the UV and IR limits. In the UV (high energy) domain, our results reproduce the Minimal Subtraction scheme-based beta functions of [@antmot]. In the IR (i.e., at low energies), the diagrams with massive ghost internal lines collapse into tadpole-type graphs without nonlocal contributions and become irrelevant. On the other hand, those structures that contribute to the running of parameters of the action and survive in the IR, are well-correlated with the divergent part (or the leading in UV contributions to the form factors), coming from the effective low-energy theory of the conformal factor. This effective theory describes only the light propagating mode. Finally, we discuss whether these results may shed light on the possible running of the cosmological constant at low energies.\n\n    *Keywords:* \u00a0Higher derivatives, quantum gravity, massive ghosts, cosmological constant, decoupling, conformal anomaly\n---\n\n[**Effective approach to the Antoniadis-Mottola"
+"---\nabstract: 'In dense stellar clusters like galactic nuclei and globular clusters stellar densities are so high that stars might physically collide with each other. In galactic nuclei the energy and power output can be close, and even exceed, to those from supernovae events. We address the event rate and the electromagnetic characteristics of collisions of main sequence stars (MS) and red giants (RG). We also investigate the case in which the cores form a binary and emit gravitational waves. In the case of RGs this is particularly interesting because the cores are degenerate. We find that MS event rate can be as high as tens per year, and that of RGs one order of magnitude larger. The collisions are powerful enough to mimic supernovae- or tidal disruptions events. We find Zwicky Transient Facility observational data which seem to exhibit the features we describe. The cores embedded in the gaseous debris experience a friction force which has an impact on the chirping mass of the gravitational wave. As a consequence, the two\u00a0small cores in principle mimic two supermassive black holes merging. However, their evolution in frequency along with the precedent electromagnetic burst and the ulterior afterglow are efficient tools"
+"---\nabstract: 'We argue in favour of developing a comprehensive dynamical theory for rationalizing, predicting, [designing]{}, and machine learning nonequilibrium phenomena that occur in soft matter. To give guidance for navigating the theoretical and practical challenges that lie ahead, we discuss and exemplify the limitations of dynamical density functional theory. Instead of the implied adiabatic sequence of equilibrium states that this approach provides as a makeshift for the true time evolution, we posit that the pending theoretical tasks lie in developing a systematic understanding of the dynamical functional relationships that govern the genuine nonequilibrium physics. While static density functional theory gives a comprehensive account of the equilibrium properties of many-body systems, we argue that power functional theory is the only present contender to shed similar insights into nonequilibrium dynamics, including the recognition and implementation of exact sum rules that result from the Noether theorem. As\u00a0a\u00a0demonstration of the power functional point of view, we consider an idealized steady sedimentation flow of the three-dimensional Lennard-Jones fluid and machine-learn the kinematic map from the mean motion to the internal force field. [The trained model is capable of both predicting and designing the steady state dynamics universally for various target density modulations."
+"---\nabstract: 'This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modeling structure designed for the typical daily count data related to the pandemic. The resulting specification permits a flexible yet parsimonious model with a low computational cost. The model is extended to allow for unreported cases using a mixed-frequency setting. Results suggest that these cases\u2019 effects on the parameter estimates might be sizeable. Full sample results show that the flexible framework accurately captures the successive waves of the pandemic. A real-time exercise indicates that the proposed structure delivers timely and precise information on the pandemic\u2019s current stance. This superior performance, in turn, transforms into accurate predictions of the confirmed and death cases.'\nauthor:\n- 'Cem \u00c7akmakl[i]{} [^1]'\n- 'Yasin \u015eim\u015fek [^2]'\nbibliography:\n- 'ref.bib'\ntitle: 'Bridging the Covid-19 Data and the Epidemiological Model using Time-Varying Parameter SIRD Model\\'\n---\n\n**Keywords**: *Covid-19, SIRD, Observation driven models, Score models, Count data, Time-varying parameters* **JEL Classification**: C13, C32, C51, I19\n\nIntroduction\n============\n\nThe outbreak of the new coronavirus, the Covid-19 pandemic,"
+"---\nabstract: 'We propose a novel algorithm, **Salient Conditional Diffusion** (**Sancdifi**), a state-of-the-art defense against backdoor attacks. [**Sancdifi** ]{}uses a denoising diffusion probabilistic model (DDPM) to degrade an image with noise and then recover said image. Critically, we compute saliency-based masks to condition our diffusion, allowing for stronger diffusion on the most salient pixels. As a result, [**Sancdifi** ]{}is highly effective at diffusing out triggers in data poisoned by backdoor attacks. At the same time, it reliably recovers salient features when applied to clean data. This performance is achieved without requiring access to the model parameters of the Trojan network, meaning [**Sancdifi** ]{}operates as a black-box defense.'\nauthor:\n- |\n    Brandon B. May$^\\dagger$ N. Joseph Tatro$^\\dagger$[^1] Dylan Walker Piyush Kumar Nathan Shnidman\\\n    Vision & Image Understanding\\\n    Systems & Technology Research\\\n    Woburn, MA 01801\\\nbibliography:\n- 'main.bib'\ntitle: Salient Conditional Diffusion for Defending Against Backdoor Attacks\n---\n\nIntroduction\n============\n\nWith the increasing societal adoption of deep learning models, adversarial robustness, the ability of deep neural networks (DNNs) to withstand adversarial attacks, has quickly become a topic of interest in the machine learning community [@madry2017towards]. As the field develops, attention is being given to sophisticated attacks that align more closely to practical"
+"---\nabstract: 'Localization is a key challenge in many robotics applications. In this work we explore LIDAR-based global localization in both urban and natural environments and develop a method suitable for online application. Our approach leverages efficient deep learning architecture capable of learning compact point cloud descriptors directly from 3D data. The method uses an efficient feature space representation of a set of segmented point clouds to match between the current scene and the prior map. We show that down-sampling in the inner layers of the network can significantly reduce computation time without sacrificing performance. We present substantial evaluation of LIDAR-based global localization methods on nine scenarios from six datasets varying between urban, park, forest, and industrial environments. Part of which includes post-processed data from 30 sequences of the Oxford RobotCar dataset, which we make publicly available. Our experiments demonstrate a factor of three reduction of computation, 70% lower memory consumption with marginal loss in localization frequency. The proposed method allows the full pipeline to run on robots with limited computation payload such as drones, quadrupeds, and UGVs as it does not require a GPU at run time.'\nauthor:\n- 'Georgi Tinchev, Adrian Penate-Sanchez and Maurice Fallon [^1] [^2]'\nbibliography:"
+"---\nabstract: 'mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals. This technology offers privacy protection and is resilient to weather and lighting conditions. However, its generalization performance is yet unknown and limits its practical deployment. To address this problem, in this paper, a non-synthetic dataset is collected and analyzed to reveal the presence of spatial and temporal domain shifts in mmWave gait biometric data, which significantly impacts identification accuracy. To mitigate this issue, a novel self-aligned domain adaptation method called GaitSADA is proposed. GaitSADA improves system generalization performance by using a two-stage semi-supervised model training approach. The first stage employs semi-supervised contrastive learning to learn a compact gait representation from both source and target domain data, aligning source-target domain distributions implicitly. The second stage uses semi-supervised consistency training with centroid alignment to further close source-target domain gap by pseudo-labelling the target-domain samples, clustering together the samples belonging to the same class but from different domains, and pushing the class centroid close to the weight vector of each class. Experiments show that GaitSADA outperforms representative domain adaptation methods with an improvement ranging from 15.41% to 26.32% on average accuracy"
+"---\nabstract: 'Personalized decentralized learning is a promising paradigm for distributed learning, enabling each node to train a local model on its own data and collaborate with other nodes to improve without sharing any data. However, this approach poses significant privacy risks, as nodes may inadvertently disclose sensitive information about their data or preferences through their collaboration choices. In this paper, we propose Private Personalized Decentralized Learning (**[PPDL]{}**), a novel approach that combines secure aggregation and correlated adversarial multi-armed bandit optimization to protect node privacy while facilitating efficient node selection. By leveraging dependencies between different arms, represented by potential collaborators, we demonstrate that [PPDL]{} can effectively identify suitable collaborators solely based on aggregated models. Additionally, we show that [PPDL]{} surpasses previous non-private methods in model performance on standard benchmarks under label and covariate shift scenarios.'\nauthor:\n- 'Edvin Listo Zec[^1]'\n- Johan \u00d6stman\n- Olof Mogren\n- Daniel Gillblad\nbibliography:\n- 'references.bib'\ntitle: Efficient Node Selection in Private Personalized Decentralized Learning\n---\n\nIntroduction {#intro}\n============\n\nCollaborative machine learning is a recent paradigm where multiple actors train a joint model without revealing their local datasets\u00a0[@mcmahan2017fl]. Instead, only the locally trained model parameters are shared among the actors. In applications pertaining"
+"---\nabstract: 'Multilingual pretrained language models (mPLMs) have shown their effectiveness in multilingual word alignment induction. However, these methods usually start from mBERT or XLM-R. In this paper, we investigate whether multilingual sentence Transformer [LaBSE ]{}is a strong multilingual word aligner. This idea is non-trivial as [LaBSE ]{}is trained to learn language-agnostic sentence-level embeddings, while the alignment extraction task requires the more fine-grained word-level embeddings to be language-agnostic. We demonstrate that the vanilla [LaBSE ]{}outperforms other mPLMs currently used in the alignment task, and then propose to finetune [LaBSE ]{}on parallel corpus for further improvement. Experiment results on seven language pairs show that our best aligner outperforms previous state-of-the-art models of all varieties. In addition, our aligner supports different language pairs in a single model, and even achieves new state-of-the-art on zero-shot language pairs that does not appear in the finetuning process.'\nauthor:\n- |\n    Weikang Wang$^{1}$Guanhua Chen$^{2}$[^1]\u00a0\u00a0 Hanqing Wang$^{1}$ Yue Han$^{1}$ Yun Chen$^{1}$[^2]\u00a0\u00a0\\\n    $^1$Shanghai University of Finance and Economics\\\n    $^2$Southern University of Science and Technology\\\n    wwk@163.sufe.edu.cn ghchen08@gmail.com\\\n    {whq,hanyue}@163.sufe.edu.cn yunchen@sufe.edu.cn\\\nbibliography:\n- 'custom.bib'\ntitle: Multilingual Sentence Transformer as A Multilingual Word Aligner\n---\n\n=1\n\nIntroduction\n============\n\nWord alignment aims to find the correspondence between words in parallel texts [@brown1993mathematics]."
+"---\nabstract: |\n    **ABSTRACT**\\\n    In rare-earth compounds with valence fluctuation, the proximity of the 4f level to the Fermi energy leads to instabilities of the charge configuration and the magnetic moment. Here, we provide direct experimental evidence for an induced magnetic polarization of the Eu$^{3+}$ atomic shell with J=0, due to intra-atomic exchange and spin-orbital coupling interactions with Eu$^{2+}$ atomic shell. By applying external pressure, a transition from antiferromagnetic to a fluctuating behavior in a EuNiGe$_3$ single crystals is probed. Magnetic polarization is observed for both valence states of Eu$^{2+}$ and Eu$^{3+}$ across the entire pressure range. The anomalous magnetism is discussed in terms of a homogeneous intermediate valence state where frustrated Dzyaloshinskii-Moriya couplings are enhanced by the onset of spin-orbital interaction and engender a chiral spin-liquid-like precursor.\nauthor:\n- '[K.\u00a0Chen$^{1,*}$, C.\u00a0Luo$^{2}$, Y.\u00a0Zhao$^3$ F.\u00a0Baudelet$^{4}$, A.\u00a0Maurya$^{5}$, A.\u00a0Thamizhavel$^5$, U.K.\u00a0R\u00f6\u00dfler$^6$, D.\u00a0Makarov$^7$, and F.\u00a0Radu$^{2,*}$]{}'\ntitle: 'Evidence of The Anomalous Fluctuating Magnetic State by Pressure Driven 4f Valence Change in EuNiGe$_3$'\n---\n\nNational Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230026, Anhui, China\n\nHelmholtz-Zentrum Berlin f\u00fcr Materialien und Energie, Albert-Einstein-Strasse 15, 12489, Berlin, Germany\n\nCenter for High Pressure Science and Technology Advanced"
+"---\nauthor:\n- som\n- 'Frank M. Rieger'\nbibliography:\n- 'references.bib'\ntitle: 'Theory of Gamma-Ray Loud AGNs'\n---\n\nIntroduction\n============\n\nRadio-loud or [*jetted AGNs*]{} [@Padovani2017], i.e. AGNs with strong relativistic jets, are the most persistent, powerful sources in the Universe. Their detection at gamma-ray energies reveals that a significant amount of their power is deposited a highest energies, and demonstrates them to be exceptional cosmic particle accelerators [@Biteau2020]. All types of jetted AGNs have been seen at gamma-ray energies, from more misaligned radio galaxies to blazar-type sources where the jets are seen face-on [@Urry1995; @Costamante2020; @Rulten2022].\\\nGamma-ray astrophysics is thus expected to play a fundamental role in resolving key issues in AGN physics, such as: [*(i) How are relativistic jets being formed?*]{} Are they preferentially ergospheric or disk-driven? If the former, what is their plasma source; if the latter, what is their accretion-disk connection? [*(ii) What makes jets radiate?*]{} What is the dominant gamma-ray radiation process and particle acceleration mechanism? Where is it located? What is the plasma composition? [*(iii) How are small and large scales connected?*]{} How is the energy transported from the black hole (BH) to the outer lobes scales? How are jets confined? What kind of instabilities"
+"---\nabstract: 'Identifying causal relations among multi-variate time series is one of the most important elements towards understanding the complex mechanisms underlying the dynamic system. It provides critical tools for forecasting, simulations and interventions in science and business analytics. In this paper, we proposed a graph neural network approach with score-based method aiming at learning a sparse DAG that captures the causal dependencies in a discretized time temporal graph. We demonstrate methods with graph neural network significantly outperformed other state-of-the-art methods with dynamic bayesian networking inference. In addition, from the experiments, the structural causal model can be more accurate than a linear SCM discovered by the methods such as Notears.'\nauthor:\n- '[Yang Sun](mailto:<sunyang.46135@bytedance.com>?Subject=Your UAI 2023 paper)'\n- Yifan Xie\nbibliography:\n- 'sample-base.bib'\ntitle: 'GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network'\n---\n\nIntroduction\n============\n\nMulti-variate time series data has been carefully studied for many years. Sensors have been recording large amount of collected time series data including traffics, electricity consumption and so on. Interestingly, these time series variables are subtly internally interlinked. These links with causal dependencies carry critical information for decision makers.\n\nCausality has been formalized as a theory for causal and counterfactual inference [@pearl_2009] from"
+"---\nabstract: |\n    In this paper, we present *YunMa*, an exoplanet cloud simulation and retrieval package, which enables the study of cloud microphysics and radiative properties in exoplanetary atmospheres. *YunMa* simulates the vertical distribution and sizes of cloud particles and their corresponding scattering signature in transit spectra. We validated *YunMa* against results from the literature.\n\n    When coupled to the *TauREx 3* platform, an open Bayesian framework for spectral retrievals, *YunMa* enables the retrieval of the cloud properties and parameters from transit spectra of exoplanets. The sedimentation efficiency ($f_{\\mathrm{sed}}$), which controls the cloud microphysics, is set as a free parameter in retrievals. We assess the retrieval performances of *YunMa* through 28 instances of a K2-18 b-like atmosphere with different fractions of H$_2$/He and N$_2$, and assuming water clouds. Our results show a substantial improvement in retrieval performances when using *YunMa* instead of a simple opaque cloud model and highlight the need to include cloud radiative transfer and microphysics to interpret the next-generation data for exoplanet atmospheres. This work also inspires instrumental development for future flagships by demonstrating retrieval performances with different data quality.\nauthor:\n- Sushuang Ma\n- Yuichi Ito\n- 'Ahmed Faris Al-Refaie'\n- Quentin Changeat\n- Billy Edwards\n-"
+"---\nabstract: 'We study the problem of online learning and online regret minimization when samples are drawn from a general unknown *non-stationary* process. We introduce the concept of a *dynamic changing process* with cost $K$, where the *conditional* marginals of the process can vary arbitrarily, but that the number of different conditional marginals is bounded by $K$ over $T$ rounds. For such processes we prove a tight (upto $\\sqrt{\\log T}$ factor) bound $O(\\sqrt{KT\\cdot{\\mathsf{VC}(\\mathcal{H})}\\log T})$ for the *expected worst case* regret of any finite VC-dimensional class $\\mathcal{H}$ under absolute loss (i.e., the expected miss-classification loss). We then improve this bound for general mixable losses, by establishing a tight (up to $\\log^3 T$ factor) regret bound $O(K\\cdot{\\mathsf{VC}(\\mathcal{H})}\\log^3 T)$. We extend these results to general *smooth adversary* processes with *unknown* reference measure by showing a sub-linear regret bound for $1$-dimensional threshold functions under a general bounded convex loss. Our results can be viewed as a first step towards regret analysis with non-stationary samples in the *distribution blind* (universal) regime. This also brings a new viewpoint that shifts the study of complexity of the hypothesis classes to the study of the complexity of processes generating data.'\nbibliography:\n- 'bibliography.bib'\ntitle: Online Learning in Dynamically"
+"---\nabstract: 'Transformer-based deep neural networks have achieved great success in various sequence applications due to their powerful ability to model long-range dependency. The key module of Transformer is self-attention (SA) which extracts features from the entire sequence regardless of the distance between positions. Although SA helps Transformer performs particularly well on long-range tasks, SA requires quadratic computation and memory complexity with the input sequence length. Recently, attention map reuse, which groups multiple SA layers to share one attention map, has been proposed and achieved significant speedup for speech recognition models. In this paper, we provide a comprehensive study on attention map reuse focusing on its ability to accelerate inference. We compare the method with other SA compression techniques and conduct a breakdown analysis of its advantages for a long sequence. We demonstrate the effectiveness of attention map reuse by measuring the latency on both CPU and GPU platforms.'\nauthor:\n- Kyuhong Shim\n- Jungwook Choi\n- Wonyong Sung\nbibliography:\n- 'reference.bib'\ntitle: Exploring Attention Map Reuse for Efficient Transformer Neural Networks\n---\n\nIntroduction {#sec:introduction}\n============\n\nThe ability to learn long-range dependency is essential for various sequence processing tasks such as language modeling, machine translation, text summarizing, question answering, and"
+"---\nabstract: 'In this paper we study exponential utility indifference pricing of pure endowment policies in a stochastic-factor model for an insurance company, which can also invest in a financial market. Specifically, we propose a modeling framework where the hazard rate is described by an observable general diffusion process and the risky asset price evolves as a jump diffusion affected by a continuous-time finite-state Markov chain representing regimes of the economy. Using the classical stochastic control approach based on the Hamilton-Jacobi-Bellman equation, we describe the optimal investment strategies with and without the insurance derivative and characterize the indifference price in terms of a classical solution to a linear PDE. We also provide its probabilistic representation via an extension of the Feynman-Kac formula show that it satisfies a final value problem. Furthermore, we also discuss the indifference price for a portfolio of insurance policies and for a term life insurance. Finally, some numerical experiments are performed to address sensitivity analyses.'\naddress:\n- 'Alessandra Cretarola(), Department of Mathematics and Computer Science, University of Perugia, Via Luigi Vanvitelli, 1, I-06123 Perugia, Italy.'\n- 'Benedetta Salterini, Department of Mathematics and Computer Science, University of Firenze, Viale Morgagni, 67/A, I-50134 Firenze, Italy.'\nauthor:\n- Alessandra"
+"---\nabstract: 'Benefiting from transformer-based pre-trained language models, neural ranking models have made significant progress. More recently, the advent of multilingual pre-trained language models provides great support for designing neural cross-lingual retrieval models. However, due to unbalanced pre-training data in different languages, multilingual language models have already shown a performance gap between high and low-resource languages in many downstream tasks. And cross-lingual retrieval models built on such pre-trained models can inherit language bias, leading to suboptimal result for low-resource languages. Moreover, unlike the English-to-English retrieval task, where large-scale training collections for document ranking such as MS MARCO are available, the lack of cross-lingual retrieval data for low-resource language makes it more challenging for training cross-lingual retrieval models. In this work, we propose : timal ransport distllation for low-resource ross-lingual information retriev. To transfer a model from high to low resource languages, forms the cross-lingual token alignment task as an optimal transport problem to learn from a well-trained monolingual retrieval model. By separating the cross-lingual knowledge from knowledge of query document matching, only needs **bitext data** for distillation training, which is more feasible for low-resource languages. Experimental results show that, with minimal training data, significantly outperforms strong baselines on low-resource languages,"
+"---\nauthor:\n- 'C.\u00a0L\u00f3pez-Sanjuan'\n- 'H.\u00a0V\u00e1zquez Rami\u00f3'\n- 'K.\u00a0Xiao'\n- 'H.\u00a0Yuan'\n- 'J.\u00a0M.\u00a0Carrasco'\n- 'J.\u00a0Varela'\n- 'D.\u00a0Crist\u00f3bal-Hornillos'\n- 'P.\u00a0-E.\u00a0Tremblay'\n- 'A.\u00a0Ederoclite'\n- 'A.\u00a0Mar\u00edn-Franch'\n- 'A.\u00a0J.\u00a0Cenarro'\n- 'P.\u00a0R.\u00a0T.\u00a0Coelho'\n- 'S.\u00a0Daflon'\n- 'A.\u00a0del Pino'\n- 'H.\u00a0Dom\u00ednguez S\u00e1nchez'\n- 'J.\u00a0A.\u00a0Fern\u00e1ndez-Ontiveros'\n- 'A.\u00a0Hern\u00e1n-Caballero'\n- 'F.\u00a0M.\u00a0Jim\u00e9nez-Esteban'\n- 'J.\u00a0Alcaniz'\n- 'R.\u00a0E.\u00a0Angulo'\n- 'R.\u00a0A.\u00a0Dupke'\n- 'C.\u00a0Hern\u00e1ndez-Monteagudo'\n- 'M.\u00a0Moles'\n- 'L.\u00a0Sodr\u00e9 Jr.'\nbibliography:\n- 'biblio.bib'\ndate: 'Received XX XX 2023 / Accepted XX XX XX'\ntitle: 'J-PLUS: Towards an homogeneous photometric calibration using [*Gaia*]{} BP/RP low-resolution spectra'\n---\n\nIntroduction {#sec:intro}\n============\n\nA fundamental step in the data processing of any imaging survey is its photometric calibration, that translates the observed counts in the reduced images to a physical flux scale referred to the top of the atmosphere. Accurate colors are needed to derive atmospheric parameters for Milky Way stars, photometric redshifts for galaxies and quasars, and surface composition for minor bodies in the Solar System; while reliable absolute fluxes directly affect the estimation of the luminosity and the mass of galaxies and stars. Within this"
+"---\nabstract: 'Image guidance is an effective strategy for depth super-resolution. Generally, most existing methods employ hand-crafted operators to decompose the high-frequency (HF) and low-frequency (LF) ingredients from low-resolution depth maps and guide the HF ingredients by directly concatenating them with image features. However, the hand-designed operators usually cause inferior HF maps (e.g., distorted or structurally missing) due to the diverse appearance of complex depth maps. Moreover, the direct concatenation often results in weak guidance because not all image features have a positive effect on the HF maps. In this paper, we develop a recurrent structure attention guided (RSAG) framework, consisting of two important parts. First, we introduce a deep contrastive network with multi-scale filters for adaptive frequency-domain separation, which adopts contrastive networks from large filters to small ones to calculate the pixel contrasts for adaptive high-quality HF predictions. Second, instead of the coarse concatenation guidance, we propose a recurrent structure attention block, which iteratively utilizes the latest depth estimation and the image features to jointly select clear patterns and boundaries, aiming at providing refined guidance for accurate depth recovery. In addition, we fuse the features of HF maps to enhance the edge structures in the decomposed LF maps. Extensive"
+"---\nabstract: 'The probability of simultaneous tunnelling of two particles is modified when the system is in a non-separable state, either entangled or symmetrised. We compare both effects in the rectangular potential barrier by evaluating the transmission rates in superposition states. For large momenta, their simultaneous presence greatly changes the form of the transmission rates. The joint effects are much larger than the superposition ones. Moreover, there are significant differences between bosons and fermions. We present an unified view of the combined effects as a quantum interference phenomenon. The analysis also illustrates a novel aspect of exclusion in entangled systems, the existence of superposition states one of whose terms is forbidden by Pauli\u2019s principle.'\nauthor:\n- |\n    Pedro Sancho\\\n    Centro de L\u00e1seres Pulsados CLPU\\\n    Parque Cient\u00edfico, 37085 Villamayor, Salamanca, Spain\ntitle: Comparing entanglement and identity effects in tunnelling\n---\n\nIntroduction.\n=============\n\nThe relation between the two types of non-separability in quantum mechanics, entanglement and identity, is a subject of increasing interest. On the one hand, the problem of the definition of entanglement measures for identical particles has not been completely settled yet [@tri]. On the other hand, several schemes have been proposed to convert the non-separability of systems of identical"
+"---\nabstract: 'Visual arts play an important role in cultural life and provide access to social heritage and self-enrichment, but most visual arts are inaccessible to blind people. Researchers have explored different ways to enhance blind people\u2019s access to visual arts (e.g., audio descriptions, tactile graphics). However, how blind people adopt these methods remains unknown. We conducted semi-structured interviews with 15 blind visual arts patrons to understand how they engage with visual artwork and the factors that influence their adoption of visual arts access methods. We further examined interview insights in a follow-up survey (N=220). We present: 1) current practices and challenges of accessing visual artwork in-person and online (e.g., Zoom tour), 2) motivation and cognition of perceiving visual arts (e.g., imagination), and 3) implications for designing visual arts access methods. Overall, our findings provide a roadmap for technology-based support for blind people\u2019s visual arts experiences.'\nauthor:\n- Franklin Mingzhe Li\n- Lotus Zhang\n- Maryam Bandukda\n- Abigale Stangl\n- Kristen Shinohara\n- Leah Findlater\n- Patrick Carrington\nbibliography:\n- 'main.bib'\ntitle: Understanding Visual Arts Experiences of Blind People\n---\n\n&lt;ccs2012&gt; &lt;concept&gt; &lt;concept\\_id&gt;10003120.10011738.10011773&lt;/concept\\_id&gt; &lt;concept\\_desc&gt;Human-centered computing\u00a0Empirical studies in accessibility&lt;/concept\\_desc&gt; &lt;concept\\_significance&gt;500&lt;/concept\\_significance&gt; &lt;/concept&gt; &lt;/ccs2012&gt;\n\nIntroduction\n============\n\nArt allows the expression of"
+"---\nabstract: 'Open radio access network (ORAN) provides an open architecture to implement radio access network (RAN) of the fifth generation (5G) and beyond mobile communications. As a key technology for the evolution to the sixth generation (6G) systems, cell-free massive multiple-input multiple-output (CF-mMIMO) can effectively improve the spectrum efficiency, peak rate and reliability of wireless communication systems. Starting from scalable implementation of CF-mMIMO, we study a cell-free RAN (CF-RAN) under the ORAN architecture. Through theoretical analysis and numerical simulation, we investigate the uplink and downlink spectral efficiencies of CF-mMIMO with the new architecture. We then discuss the implementation issues of CF-RAN under ORAN architecture, including time-frequency synchronization and over-the-air reciprocity calibration, low layer splitting, deployment of ORAN radio units (O-RU), artificial intelligent based user associations. Finally, we present some representative experimental results for the uplink distributed reception and downlink coherent joint transmission of CF-RAN with commercial off-the-shelf O-RUs.'\nauthor:\n- 'Yang Cao, Ziyang Zhang, Xinjiang Xia, Pengzhe Xin, Dongjie Liu, Kang Zheng, Mengting Lou, Jing Jin, Qixing Wang, Dongming Wang, Yongming Huang, Xiaohu You, Jiangzhou Wang [^1]'\nbibliography:\n- 'oran\\_cfran.bib'\ntitle: 'From ORAN to Cell-Free RAN: Architecture, Performance Analysis, Testbeds and Trials'\n---\n\n\\[1\\][ \\[0pt\\]\\[0pt\\][^$#1$^]{}]{}\n\ncell-free massive MIMO, radio"
+"---\nabstract: 'We show that, for the purpose of quantum communication via a quantum field, it is essential to view the field not only as a medium for transmission but also as a source of entanglement that can aid in the communication task. To this end, we consider the quantum communication scenario where Alice is initially entangled with an ancilla and intends to communicate with Bob through a quantum field, so as to make Bob entangled with the ancilla. We find that if Alice and Bob communicate by directly coupling to the quantum field, then they can generate negativity between Bob and the ancilla only at orders that are higher than second perturbative order. We then present a protocol based on quantum teleportation in which Alice and Bob consume entanglement that they obtained from the field via interaction or harvesting. We show that this protocol can transfer negativity already to second perturbative order.'\nauthor:\n- Koji Yamaguchi\n- Achim Kempf\nbibliography:\n- 'references.bib'\ntitle: Entanglement is better teleported than transmitted\n---\n\nIntroduction\n============\n\nConventional communication technologies focus on the transfer of classical information. For the development of quantum communication technologies it is important to model not only the transfer of"
+"---\nabstract: 'The emergence of the fifth-generation (5G) New Radio (NR) brings additional possibilities to vehicle-to-everything (V2X) network with improved quality of services. In order to obtain accurate channel state information (CSI) in high-mobility V2X networks, pilot signals and frequent handover between vehicles and infrastructures are required to establish and maintain the communication link, which increases the overheads and reduces the communication throughput. To address this issue, integrated sensing and communications (ISAC) was employed at the base station (BS) in the vehicle-to-infrastructure (V2I) network to reduce a certain amount of overheads, thus improve the spectral efficiency. Nevertheless, the exact amount of overheads reduction remains unclear, particularly for practical NR based V2X networks. In this paper, we study a link-level NR based V2I system employing ISAC signaling to facilitate the communication beam management, where the Extended Kalman filtering (EKF) algorithm is performed to realize the functions of tracking and predicting the motion of the vehicle. We provide detailed analysis on the overheads reduction with the aid of ISAC, and show that up to 43.24% overheads can be reduced under assigned NR frame structure. In addition, numerical results are provided to validate the improved performance on the beam tracking and communication throughput.'"
+"---\nabstract: 'We study a model of internal waves under periodic forcing in an effectively 2-dimensional aquarium. When the underlying classical dynamics has sufficiently irrational rotation number, we prove that the solution to the internal waves equation remains bounded in energy space in time. This result is in contrast with the works by Colin de Verdi\u00e8re\u2013Saint-Raymond and Dyatlov\u2013Wang\u2013Zworski, which gave a description of singular profiles when the underlying dynamics has periodic attractors. Our result is proved by showing the spectral measure near the forcing frequency is very small.'\naddress: 'Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139'\nauthor:\n- Zhenhao Li\nbibliography:\n- 'internal\\_waves.bib'\ntitle: 2D internal waves in an ergodic setting\n---\n\nIntroduction\n============\n\nBelow the wind-mixed surface layer of the ocean and an interfacial layer of seasonal pycnocline where the density increases rapidly, the density field of the ocean stabilizes and increases gradually with depth. To first order in this region, the density is considered to be stable-stratified, i.e. the density depends only on the vertical direction and increases with depth. Internal waves describe linear perturbations of such stable-stratified fluids, and are therefore a central topic in oceanography. These perturbations occur naturally and can arise mechanically"
+"---\nabstract: 'BabelBrain is an open-source standalone graphic-user-interface application designed for studies of neuromodulation using transcranial focused ultrasound. It calculates the transmitted acoustic field in the brain tissue, taking into account the distortion effects caused by the skull barrier. The simulation is prepared using scans from magnetic resonance imaging (MRI) and, if available, computed tomography and zero-echo time MRI scans. It also calculates the thermal effects based on a given ultrasound regime, such as the total duration of exposure, the duty cycle, and acoustic intensity. The tool is designed to work in tandem with neuronavigation and visualization software, such as 3DSlicer. It uses image processing to prepare domains for ultrasound simulation and uses the BabelViscoFDTD library for transcranial modeling calculations. BabelBrain supports multiple GPU backends, including Metal, OpenCL, and CUDA, and works on all major operating systems including Linux, macOS, and Windows. This tool is particularly optimized for Apple ARM64 systems, which are common in brain imaging research. The paper presents the modeling pipeline used in BabelBrain and a numerical study where different methods of acoustic properties mapping were tested to select the best method that can reproduce the transcranial pressure transmission efficiency reported in the literature.'\nauthor:\n- |"
+"---\nabstract: 'This work extends the Multiscale Sparse Representation (MSR) framework developed for static Point Cloud Geometry Compression (PCGC) to support the dynamic PCGC through the use of multiscale inter conditional coding. To this end, the reconstruction of the preceding Point Cloud Geometry (PCG) frame is progressively downscaled to generate multiscale temporal priors which are then scale-wise transferred and integrated with lower-scale spatial priors from the same frame to form the contextual information to improve occupancy probability approximation when processing the current PCG frame from one scale to another. Following the Common Test Conditions (CTC) defined in the standardization committee, the proposed method presents State-Of-The-Art (SOTA) compression performance, yielding 78% lossy BD-Rate gain to the latest standard-compliant V-PCC and 45% lossless bitrate reduction to the latest G-PCC. Even for recently-emerged learning-based solutions, our method still shows significant performance gains.'\nauthor:\n- 'Jianqiang Wang, Dandan Ding, Hao Chen, and Zhan Ma [^1]'\nbibliography:\n- 'refs.bib'\ntitle: Dynamic Point Cloud Geometry Compression Using Multiscale Inter Conditional Coding\n---\n\nDynamic point cloud geometry, Multiscale temporal prior, Inter conditional coding.\n\nIntroduction\n============\n\nDynamic point clouds are of great importance for applications like holographic communication, autonomous machinery, etc., for which the efficient compression of dynamic"
+"---\nabstract: 'The phase transition patterns displayed by a model of two coupled complex scalar fields are studied at finite temperature and chemical potential. Possible phenomena like symmetry persistence and inverse symmetry breaking at high temperatures are analyzed. The effect of finite density is also considered and studied in combination with the thermal effects. The nonperturbative optimized perturbation theory method is considered and the results contrasted with perturbation theory. Applications of the results obtained are considered in the context of an effective model for condensation of kaons at high densities, which is of importance in the understanding of the color-flavor locked phase of quantum chromodynamics.'\nauthor:\n- 'Manuella C. Silva'\n- 'Rudnei O. Ramos'\n- 'Ricardo L. S. Farias'\ntitle: Phase transition patterns for coupled complex scalar fields at finite temperature and density\n---\n\nIntroduction {#intro}\n============\n\nIn statistical physics, we can describe a dynamical system in the ensemble as characterized by a Hamiltonian, with charges (or quantum numbers) and corresponding chemical potentials. A chemical potential, which enters as a Lagrange multiplier in the (quantum) grand-canonical partition function, is assigned to each conserved charge of the system. This is the starting point of any setup aiming to study how a"
+"---\nabstract: 'The lack of data for information extraction (IE) from semi-structured business documents is a real problem for the IE community. Publications relying on large-scale datasets use only proprietary, unpublished data due to the sensitive nature of such documents. Publicly available datasets are mostly small and domain-specific. The absence of a large-scale public dataset or benchmark hinders the reproducibility and cross-evaluation of published methods. The DocILE 2023 competition, hosted as a lab at the CLEF 2023 conference and as an ICDAR 2023 competition, will run the first major benchmark for the tasks of *Key Information Localization and Extraction* (KILE) and *Line Item Recognition* (LIR) from business documents. With thousands of annotated real documents from open sources, a hundred thousand of generated synthetic documents, and nearly a million unlabeled documents, the DocILE lab comes with the largest publicly available dataset for KILE and LIR. We are looking forward to contributions from the Computer Vision, Natural Language Processing, Information Retrieval, and other communities. The data, baselines, code and up-to-date information about the lab and competition are available at <https://docile.rossum.ai/>.'\nauthor:\n- \u0160t\u011bp\u00e1n \u0160imsa\n- Milan \u0160ulc\n- Maty\u00e1\u0161 Skalick\u00fd\n- |\n    \\\n    Yash Patel\n- Ahmed Hamdi\nbibliography:\n- 'bibliography.bib'\ntitle:"
+"---\nabstract: 'Understanding the nature of local-itinerant transition of strongly correlated electrons is one of the central problems in condensed matter physics. Heavy fermion systems describe the $f$-electron delocalization through Kondo interactions with conduction electrons. Tremendous efforts have been devoted to the so-called Kondo-destruction scenario, which predicts a dramatic local-to-itinerant quantum phase transition of $f$-electrons at zero temperature. On the other hand, two-fluid behaviors have been observed in many materials, suggesting coexistence of local and itinerant $f$-electrons over a broad temperature range but lacking a microscopic theoretical description. To elucidate this fundamental issue, here we propose an exactly solvable Kondo-Heisenberg model in which the spins are defined in the momentum space and the $\\textbf{k}$-space Kondo interaction corresponds to a highly nonlocal spin scattering in the coordinate space. Its solution reveals a continuous evolution of the Fermi surfaces with Kondo interaction and two-fluid behaviors similar to those observed in real materials. The electron density violates the usual Luttinger\u2019s theorem, but follows a generalized one allowing for partially enlarged Fermi surfaces due to partial Kondo screening in the momentum space. Our results highlight the consequence of nonlocal Kondo interaction relevant for strong quantum fluctuation regions, and provide important insight into the microscopic"
+"---\nabstract: 'Black hole evaporation is generally considered inevitable for low-mass black holes, yet there is no confirmation of this remarkable hypothesis. Here, we propose a phenomenological model that appeals to the possible survival of light quasi-extremal primordial black holes as a significant dark matter component and show that the related cosmological and astrophysical constraints disappear for reasonable degrees of quasi-extremality. The results obtained are general, conservative and should be taken as a proof of principle for future, model-specific analyses.'\nauthor:\n- 'Jose A. de Freitas Pacheco'\n- Elias Kiritsis\n- Matteo Lucca\n- Joseph Silk\nbibliography:\n- 'bibliography.bib'\ntitle: 'Quasi-extremal primordial black holes are a viable dark matter candidate'\n---\n\n[ 0= ]{} [ 0= ]{} [ 0= ]{}\n\nIntroduction\n============\n\nSince first postulated in the late \u201860s [@Zeldovich1967Hypothesis; @Hawking1971Gravitationally; @carr:pbhsformation; @chapline:pbhformation], primordial black holes (PBHs) are regarded as a viable dark matter (DM) candidate, albeit over a highly constrained mass range (see e.g., [@Sasaki2018Primordial; @Carr2020Constraints; @Carr2020Primordial; @Villanueva-Domingo2021Brief]). The most appealing example is given by a range roughly between $10^{17}$ and $10^{22}$\u00a0g, bounded from below by constraints coming from the impact of the energy injection following Hawking evaporation of the PBHs [@Hawking1974Black] on observables such as cosmic microwave"
+"---\nabstract: 'Cerebral microbleeds detection is an important and challenging task. With the gaining popularity of the MRI, the ability to detect cerebral microbleeds also raises. Unfortunately, for radiologists, it is a time-consuming and laborious procedure. For this reason, various solutions to automate this process have been proposed for several years, but none of them is currently used in medical practice. In this context, the need to systematize the existing knowledge and best practices has been recognized as a factor facilitating the imminent synthesis of a real CMBs detection system practically applicable in medicine. To the best of our knowledge, all available publications regarding automatic cerebral microbleeds detection have been gathered, described, and assessed in this paper in order to distinguish the current research state and provide a starting point for future studies.'\nauthor:\n- |\n    Maria Ferlin\\\n    Gda\u0144sk University of Technology\\\n    Gda\u0144sk, Poland\\\n    `maria.ferlin@pg.edu.pl`\\\n    Zuzanna Klawikowska\\\n    Gda\u0144sk University of Technology\\\n    Gda\u0144sk, Poland\\\n    `zuzanna.klawikowska@pg.edu.pl`\\\n    Micha\u0142 Grochowski\\\n    Gda\u0144sk University of Technology\\\n    Gda\u0144sk, Poland\\\n    `michal.grochowski@pg.edu.pl`\\\n    Ma\u0142gorzata Grzywi\u0144ska\\\n    Medical University of Gda\u0144sk\\\n    Gda\u0144sk, Poland\\\n    `malgorzata.grzywinska@gumed.edu.pl`\\\n    Edyta Szurowska\\\n    Medical University of Gda\u0144sk\\\n    Gda\u0144sk, Poland\\\n    `eszurowska@gumed.edu.pl`\\\ntitle: Review of methods for automatic cerebral microbleeds detection\n---\n\n=1\n\nIntroduction {#sec:introduction}\n============\n\nCerebral microbleeds (CMBs) are defined"
+"---\nabstract: 'We classify periodic Y-systems of rank 2 satisfying the symplectic property. We find that there are six such Y-systems. In all cases, the periodicity follows from the existence of two reddening sequences associated with the time evolution of the Y-systems in positive and negative directions, which gives rise to quantum dilogarithm identities associated with Donaldson-Thomas invariants. We also consider $q$-series called the Nahm sums associated with these Y-systems. We see that they are included in Zagier\u2019s list of rank 2 Nahm sums that are likely to be modular functions. It was recently shown by Wang that they are indeed modular functions.'\nauthor:\n- Yuma Mizuno\ntitle: 'Periodic Y-systems and Nahm sums: the rank 2 case'\n---\n\nIntroduction\n============\n\nBackground\n----------\n\nThe Y-system is a system of algebraic relations satisfied by coefficients of a cluster algebra, which has the following form: $$\\label{eq:y-system}\n  Y_i (u) Y_i (u - r_i) = \n  \\prod_{j \\in I} \\prod_{p=1}^{r_i-1} Y_j(u-p)^{{[n_{ij;p}]_{+}}} \\bigl(1 + Y_j(u - p) \\bigr)^{- n_{ij;p} }$$ where $I$ is a finite index set, $Y_i (u)$ for $i \\in I$, $u \\in \\mathbb{Z}$ are commuting variables, $r_i \\in \\mathbb{Z}_{\\geq 1}$, and $n_{ij;p} \\in \\mathbb{Z}$. We also use the notation ${[n]_{+}} \\coloneqq \\max(0, n)$. Such"
+"---\nabstract: 'We study a recommender system for quantum data using the linear contextual bandit framework. In each round, a learner receives an observable (the context) and has to recommend from a finite set of unknown quantum states (the actions) which one to measure. The learner has the goal of maximizing the reward in each round, that is the outcome of the measurement on the unknown state. Using this model we formulate the low energy quantum state recommendation problem where the context is a Hamiltonian and the goal is to recommend the state with the lowest energy. For this task, we study two families of contexts: the Ising model and a generalized cluster model. We observe that if we interpret the actions as different phases of the models then the recommendation is done by classifying the correct phase of the given Hamiltonian and the strategy can be interpreted as an online quantum phase classifier.'\nauthor:\n- 'Shrigyan Brahmachari$^1$, Josep Lumbreras$^1$, Marco Tomamichel$^{1,2}$'\nbibliography:\n- 'biblio.bib'\ntitle: |\n    Quantum contextual bandits and\\\n    recommender systems for quantum data\n---\n\nIntroduction\n============\n\nRecommender systems are a class of online reinforcement learning algorithms that interact sequentially with an environment suggesting relevant items to a"
+"---\nabstract: 'Open source intelligence is a powerful tool for cybersecurity analysts to gather information both for analysis of discovered vulnerabilities and for detecting novel cybersecurity threats and exploits. However the scale of information that is relevant for information security on the internet is always increasing, and is intractable for analysts to parse comprehensively. Therefore methods of condensing the available open source intelligence, and automatically developing connections between disparate sources of information, is incredibly valuable. In this research, we present a system which constructs a Neo4j graph database formed by shared connections between open source intelligence text including blogs, cybersecurity bulletins, news sites, antivirus scans, social media posts (e.g., Reddit and Twitter), and threat reports. These connections are comprised of possible indicators of compromise (e.g., IP addresses, domains, hashes, email addresses, phone numbers), information on known exploits and techniques (e.g., CVEs and MITRE ATT&CK Technique ID\u2019s), and potential sources of information on cybersecurity exploits such as twitter usernames. The construction of the database of potential IoCs is detailed, including the addition of machine learning and metadata which can be used for filtering of the data for a specific domain (for example a specific natural language) when needed. Examples of utilizing"
+"---\nabstract: |\n    Many important problems in Bioinformatics (e.g., assembly or multi-assembly) admit multiple solutions, while the final objective is to report only one. A common approach to deal with this uncertainty is finding *safe* partial solutions (e.g., contigs) which are common to all solutions. Previous research on safety has focused on polynomially-time solvable problems, whereas many successful and natural models are NP-hard to solve, leaving a lack of \u201csafety tools\" for such problems. We propose the first method for computing all safe solutions for an NP-hard problem, *minimum flow decomposition*. We obtain our results by developing a \u201csafety test\u201d for paths based on a general Integer Linear Programming (ILP) formulation. Moreover, we provide implementations with practical optimizations aimed to reduce the total ILP time, the most efficient of these being based on a recursive group-testing procedure.\\\n    **Results:** Experimental results on the transcriptome datasets of Shao and Kingsford (TCBB, 2017) show that all safe paths for minimum flow decompositions correctly recover up to 90% of the full RNA transcripts, which is at least 25% more than previously known safe paths, such as (C\u00e1ceres et al. TCBB, 2021), (Zheng et al., RECOMB\u00a02021), (Khan et al., RECOMB\u00a02022, ESA\u00a02022). Moreover,"
+"---\nabstract: 'We constrain the photon mass from well-localized fast radio bursts (FRBs) using Bayes inference method. The probability distributions of dispersion measures (DM) of host galaxy and intergalactic medium are properly taken into account. The photon mass is tightly constrained from 17 well-localized FRBs in the redshift range $0<z<0.66$. Assuming that there is no redshift evolution of host DM, the $1\\sigma$ and $2\\sigma$ upper limits of photon mass are constrained to be $m_\\gamma<4.8\\times 10^{-51}$ kg and $m_\\gamma<7.1\\times 10^{-51}$ kg, respectively. Monte Carlo simulations show that, even enlarging the FRB sample to 200 and extending the redshift range to $0<z<3$ couldn\u2019t significantly improve the constraining ability on photon mass. This is because of the large uncertainty on the DM of intergalactic medium.'\nauthor:\n- |\n    Hai-Nan Lin$^{1,2}$, Li Tang$^{3}$[^1] and Rui Zou$^{1,2}$\\\n    $^{1}$Department of Physics, Chongqing University, Chongqing 401331, China\\\n    $^{2}$Chongqing Key Laboratory for Strongly Coupled Physics, Chongqing University, Chongqing 401331, China\\\n    $^{3}$Department of Math and Physics, Mianyang Normal University, Mianyang 621000, China\\\nbibliography:\n- 'reference.bib'\ndate: 'Accepted 2022; Received 2022; in original form 2022'\ntitle: 'Revised constraints on the photon mass from well-localized fast radio bursts'\n---\n\n\\[firstpage\\]\n\nfast radio bursts \u2013 intergalactic medium \u2013 radio continuum: transients\n\nIntroduction"
+"---\nabstract: 'By interacting, synchronizing, and cooperating with its physical counterpart in real time, digital twin is promised to promote an intelligent, predictive, and optimized modern city. Via interconnecting massive physical entities and their virtual twins with inter-twin and intra-twin communications, the Internet of digital twins (IoDT) enables free data exchange, dynamic mission cooperation, and efficient information aggregation for composite insights across vast physical/virtual entities. However, as IoDT incorporates various cutting-edge technologies to spawn the new ecology, severe known/unknown security flaws and privacy invasions of IoDT hinders its wide deployment. Besides, the intrinsic characteristics of IoDT such as *decentralized structure*, *information-centric routing* and *semantic communications* entail critical challenges for security service provisioning in IoDT. To this end, this paper presents an in-depth review of the IoDT with respect to system architecture, enabling technologies, and security/privacy issues. Specifically, we first explore a novel distributed IoDT architecture with cyber-physical interactions and discuss its key characteristics and communication modes. Afterward, we investigate the taxonomy of security and privacy threats in IoDT, discuss the key research challenges, and review the state-of-the-art defense approaches. Finally, we point out the new trends and open research directions related to IoDT.'\nauthor:\n- '\\'\nbibliography:\n- 'ref.bib'\ntitle:"
+"---\nabstract: 'Average distance between two points in a unit-volume body $K \\subset \\mathbb{R}^n$ tends to infinity as $n \\to \\infty$. However, for two small subsets of volume $\\varepsilon > 0$ the situation is different. For unit-volume cubes and euclidean balls the largest distance is of order $\\sqrt{-\\ln \\varepsilon}$, for simplexes and hyperoctahedrons \u2013 of order $-\\ln \\varepsilon$, for $\\ell_p$ balls with $p \\in [1;2]$ \u2013 of order $(-\\ln \\varepsilon)^{\\frac{1}{p}}$. These estimates are not dependent on the dimensionality $n$. The goal of the paper is to study this phenomenon. Isoperimetric inequalities will play a key role in our approach.'\nbibliography:\n- 'survey.bib'\nnocite: '[@*]'\n---\n\nDimension-free estimates on distances between subsets of volume $\\varepsilon$ inside a unit-volume body\n\nAbdulamin Ismailov[^1] Alexei Kanel-Belov Fyodor Ivlev\n\nIntroduction.\n=============\n\nIn high dimensions we observe a variety of different phenomena. For example, Vladimir Igorevich Arnold liked to ask his students the following question: &lt;&lt;What percent of the overall mass is occupied by the pulp of the $100$-dimensional watermelon of diameter $1$ meter, if the crust is of width $1$ centimeter?&gt;&gt; The answer is approximately $1 - e^{-1}$. This question in a simple way demonstrates the concentration of measure phenomenon: how most of the mass"
+"---\nabstract: 'The magnetic response is a state-defining property of superconductors. The magnetic flux penetrates type-II bulk superconductors by forming quantum vortices when the enclosed magnetic flux is equal to the magnetic flux quantum. The flux quantum is the universal quantity that depends only on the ratio of fundamental constants: the electron charge and the Planck constant. This work investigates the vortex state in the hole-overdoped by using scanning superconducting quantum interference device (SQUID) magnetometry. We observed quantum vortices that carry only a fraction of the flux quantum, which vary continuously with temperature. This finding establishes the phenomenon that superconductors support quantum vortices with non-universally quantized magnetic flux. Furthermore, the demonstrations of the mobility of the fractional vortices and the manipulability of their positions open up a route for future fluxonics applications.'\nauthor:\n- |\n    Yusuke Iguchi$^{1,2}$, Ruby Shi$^{1,2,3}$, Kunihiro Kihou$^{4}$, Chul-Ho Lee$^{4}$,\\\n    Vadim Grinenko$^{5}$, Egor Babaev$^{6}$, and Kathryn A. Moler$^{1,2,3,7}$\ntitle: 'Observation of superconducting vortices carrying a temperature-dependent fraction of the flux quantum'\n---\n\nIntroduction\n============\n\nA classical electrically conducting fluid allows the creation of arbitrary vortices. By contrast, the fundamental property of an ordinary superconductor is the quantization of vorticity. This implies the quantization of the magnetic flux"
+"---\nabstract: |\n    Coverability in Petri nets finds applications in verification of safety properties of reactive systems. We study coverability in the equivalent model: Vector Addition Systems with States (VASS).\n\n    A $k$-VASS can be seen as $k$ counters and a finite automaton whose transitions are labelled with $k$ integers. Counter values are updated by adding the respective transition labels. A configuration in this system consists of a state and $k$ counter values. Importantly, the counters are never allowed to take negative values. The coverability problem asks whether one can traverse the $k$-VASS from the initial configuration to a configuration with at least the counter values of the target.\n\n    In a well-established line of work on $k$-VASS, coverability in 2-VASS is already -hard when the integer updates are encoded in binary. This lower bound limits the practicality of applications, so it is natural to focus on restrictions. In this paper we initiate the study of 2-VASS with one unary counter. Here, one counter receives binary encoded updates and the other receives unary encoded updates. Our main result is that coverability in 2-VASS with one unary counter is in . This improves upon the inherited state-of-the-art upper bound. Our main technical contribution"
+"---\nabstract: 'In this study, Higgs production processes, Higgsstrahlung and vector boson (W and Z) fusion processes, were investigated for four different future lepton colliders (CEPC, ILC, CLIC, and FCC-ee). The cross sections for each production process and corresponding backgrounds were calculated considering the ISR and beamstrahlung effects. Various cuts and the b-tagging method were used to reduce the background. Finally, the number of events for each collider was determined, and significance calculations were performed. In our calculations, high event numbers were obtained for all four colliders for the Higgsstrahlung, W, and Z fusion process. This shows that electron-positron colliders will play an important role in future Higgs physics research.'\nauthor:\n- |\n    **Deniz YILMAZ$^{1}$[^1] , Mehmet SAHIN$^{2}$, Dogukan Hazar YAVUZ$^{1}$**\\\n    $^{1}$Physics Engineering Department, Ankara University, Ankara,Turkey\\\n    $^{2}$Department of Computer Engineering, Usak University, Usak,Turkey\ntitle: 'Higgs production at next generation $e^{+}e^{-}$ colliders'\n---\n\nIntroduction {#section1}\n============\n\nThe discovery of the Higgs boson at the Large Hadron Collider (LHC) [@citation1; @citation2] confirmed the electroweak symmetry breaking mechanism of the Standard Model (SM) [@citation3; @citation4; @citation5; @citation6]. However, there is still some unknown about the observed Higgs boson: is it the fundamental scalar of the SM, or a more complex object, or"
+"---\nabstract: 'Modeling user interfaces (UIs) from visual information allows systems to make inferences about the functionality and semantics needed to support use cases in accessibility, app automation, and testing. Current datasets for training machine learning models are limited in size due to the costly and time-consuming process of manually collecting and annotating UIs. We crawled the web to construct WebUI, a large dataset of 400,000 rendered web pages associated with automatically extracted metadata. We analyze the composition of WebUI and show that while automatically extracted data is noisy, most examples meet basic criteria for visual UI modeling. We applied several strategies for incorporating semantics found in web pages to increase the performance of visual UI understanding models in the mobile domain, where less labeled data is available: *(i)* element detection, *(ii)* screen classification and *(iii)* .'\nauthor:\n- Jason Wu\n- Siyan Wang\n- Siman Shen\n- 'Yi-Hao Peng'\n- Jeffrey Nichols\n- 'Jeffrey P. Bigham'\nbibliography:\n- 'main.bib'\ntitle: 'WebUI: A Dataset for Enhancing Visual UI Understanding with Web Semantics'\n---\n\nIntroduction\n============\n\nComputational modeling of user interfaces (UIs) allows us to understand design decisions [@deka2017rico; @kumar2013webzeitgeist], improve their accessibility [@zhang2021screen], and automate their usage [@li2020mapping; @li2017sugilite; @burns2022interactive]."
+"---\nauthor:\n- 'Ciro Pinto and Dominic J. Walton'\ntitle: |\n    Ultra-luminous X-ray sources:\\\n    extreme accretion and feedback\n---\n\nIntroduction {#sec:intro}\n============\n\nUltra-luminous X-ray sources (ULXs) are non-nuclear astronomical objects mainly found in external galaxies. Under the assumption of isotropic emission, their X-ray luminosities surpass the Eddington limit for a standard 10 stellar-remnant black hole (BH), or $10^{39}$ergs$^{-1}$, and in rare cases reaching a few times $10^{41}$ergs$^{-1}$. This makes them the most extreme amongst X-ray binaries (XRBs) and excellent targets to study super-Eddington accretion or to search for unusually massive black holes. In both cases, it is clear that ULXs provide an important workbench to study the origin of the early supermassive black holes that have been discovered at high redshifts when the Universe was young [@Fan2003; @Banados2018].\n\nX-ray surveys {#sec:surveys}\n-------------\n\nULXs were discovered about 40 years ago with the *Einstein* observatory [@Long1981; @Fabbiano1989]. Dedicated studies using the ROSAT all-sky survey have underlined the strong connection between ULXs and star formation based on the fact that ULXs are preferentially seen in late-type galaxies, especially in star-forming regions such as spiral arms [@Liu2005]. Typically, ULX candidates are identified by searching the sky area[^1] subtended by known galaxies for X-ray sources"
+"---\nabstract: 'The division between two vectors belonging to the same vector space is obtained by elementary procedures of vector algebra and is defined by a matrix. This representation is obtained for two and three dimensional vector spaces. A new vector multiplication is defined and an the inverse vector. Through this multiplication we can obtain the division previously defined.The meaning of vector division and multiplication are analyzed.'\ntitle: Division and new multiplication between vectors\n---\n\n[2]{}\n\nIntroduction\n============\n\nThe division between vectors in the framework of vector algebra is a subject not widely treated. Few studies have been carried out about this subject and those that have been carried out conclude that it is impossible to define this operation. In $1966$, $\\text{Kenneth O. May}$ wrote an article,[@may1966impossibility], in which he states that it is impossible to divide two vectors in a three-dimensional space. This impossibility is essentially due to the ways that have been used to define division between vectors, which have not achieved this goal because they have tried to obtain it by one of the existing products between vectors, either by the dot product or by the vector product, these products not being proper multiplications like those defined"
+"---\nabstract: 'Inter neuron communication happens through the exchange of neurotransmitters at the synapse by a process known as exocytosis. This makes exocytosis a fundamental process of information exchange in the body. The exocytosis process has a distinct geometry as it involves a vesicle that attaches to the cell membrane and then releases the neurotransmitters through a pore. Significant recent research, both experimental and numerical, attempt to understand the time dynamics of exocytosis. In this manuscript, we share an analytical model that predicts the key output parameters of exocytosis based on the geometry of the vesicle and pore. Our analytical predictions are well supported by detailed numerical simulations. This model could help extract geometrical parameters from experimental data and hence could be of broad interest.'\nauthor:\n- Sundeep Kapila\n- 'Pradeep R. Nair'\nbibliography:\n- 'predictivemodel.bib'\ntitle: Geometry aware predictive models for exocytosis\n---\n\n\\[sec:intro\\]Introduction\n=========================\n\nIt is well known that information exchange between neurons is mediated through transfer of neurotransmitters[@intro1]. Accordingly, the process of exocytosis in which vesicles release neurotransmitters to the pre-synaptic cleft is of fundamental importance. Extensive research, both experimental and numerical [@analytical1] [@analytical2], has been reported to analyze the time dynamics of exocytosis and hence decipher"
+"---\nabstract: |\n    In this work, we aim to confirm the impact of the Fourier series on the quantum machine learning model. We will propose models, tests, and demonstrations to achieve this objective. We designed a quantum machine learning leveraged on the Hamiltonian encoding. With a subtle change, we performed the trigonometric interpolation, binary and multiclass classifier, and a quantum signal processing application. We also proposed a block diagram of determining approximately the Fourier coefficient based on quantum machine learning. We performed and tested all the proposed models using the Pennylane framework.\n\n    **KeyWords:** Quantum Computing, Quantum Machine Learning, Fourier Series, QML, QSP, Interpolation, Regression model, quantum classifiers\nauthor:\n- 'Parfait Atchade-Adelomou'\n- Kent Larson\nbibliography:\n- 'main.bib'\ndate: Feb 2023\ntitle: Fourier series weight in quantum machine learning\n---\n\nIntroduction {#sec:intro}\n============\n\nThe Fourier series is a mathematical tool that allows us to model any arbitrary periodic signal with a combination of sines and cosines. Its main advantage is that very little signal information is lost during the transformation from one domain to another. Indeed, this series does not exist for all signals (Dirichlet conditions[@chaudhuri2022navier]); however, in various fields and sectors, the Fourier series is the tool used to transform"
+"---\nabstract: 'Electronic Health Record (EHR) has become an essential tool in the healthcare ecosystem, providing authorized clinicians with patients\u2019 health-related information for better treatment. While most developed countries are taking advantage of EHRs to improve their healthcare system, it remains challenging in developing countries to support clinical decision-making and public health [using a computerized patient healthcare information system]{}. This paper proposes a novel EHR architecture suitable for developing countries\u2014an architecture that fosters inclusion and provides solutions tailored to all social classes and socioeconomic statuses. Our architecture foresees an internet-free (offline) solution to allow medical transactions between healthcare organizations, and the storage of EHRs in geographically underserved and rural areas. Moreover, we discuss how artificial intelligence can leverage anonymous health-related information to enable better public health policy and surveillance.'\nauthor:\n- |\n    Jean Marie Tshimula,$^{1,2,4}$ D\u2019Jeff K. Nkashama,$^{1,2}$ Kalonji Kalala,$^{1,3}$ Maximilien V. Dialufuma,$^{1,7}$\\\n    [**Mbuyi Mukendi Didier,$^{1,6,8}$ Hugues Kanda,$^{1}$ Jean Tshibangu Muabila,$^{1,5}$ Christian N. Mayemba$^{1,9}$**]{}\\\n    $^{1}$Groupe de Recherche de Prospection et Valorisation des Donn\u00e9es (Greprovad) $^{2}$Universit\u00e9 de Sherbrooke\\\n    $^{3}$EECS, University of Ottawa $^{4}$Universit\u00e9 T\u00c9LUQ $^{5}$LISV-UVSQ, Universit\u00e9 Paris-Saclay $^{6}$University of\\\n    Kinshasa $^{7}$Montreal Behavioural Medicine Centre, CIUSSS-NIM/UQAM/Concordia $^{8}$Biomedical Research\\\n    Unit, Hospital Monkole, Kinshasa $^{9}$Department of Anatomical Pathology, University of Kinshasa\\\n    [{2101, kabj2801}contact@greprovad.org]{}\\\nbibliography:"
+"---\nabstract: 'Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted significant attention due to their ability to generate high-quality synthetic images. In this work, we show that diffusion models memorize individual images from their training data and emit them at generation time. With a generate-and-filter pipeline, we extract over a thousand training examples from state-of-the-art models, ranging from photographs of individual people to trademarked company logos. We also train hundreds of diffusion models in various settings to analyze how different modeling and data decisions affect privacy. Overall, our results show that diffusion models are much less private than prior generative models such as GANs, and that mitigating these vulnerabilities may require new advances in privacy-preserving training.'\nauthor:\n- |\n    Nicholas Carlini$^{*1}$ Jamie Hayes$^{*2}$ Milad Nasr$^{*1}$\\\n    Matthew Jagielski$^{+1}$ Vikash Sehwag$^{+4}$ Florian Tram\u00e8r$^{+3}$\\\n    Borja Balle$^{\\dag2}$ Daphne Ippolito$^{\\dag1}$ Eric Wallace$^{\\dag5}$\\\n    *$^1$Google $^2$DeepMind $^3$ETHZ $^4$Princeton $^5$UC Berkeley*\\\n    *$^*$Equal contribution$^+$Equal contribution$^\\dag$Equal contribution*\\\n    \\\nbibliography:\n- 'main.bib'\ntitle: Extracting Training Data from Diffusion Models\n---\n\nIntroduction\n============\n\nDenoising diffusion models are an emerging class of generative neural networks that produce images from a training distribution via an iterative denoising process\u00a0[@sohl2015deep; @song2019generative; @ho2020denoising]. Compared to prior approaches such as GANs\u00a0[@goodfellow2020generative]"
+"---\nabstract: 'In this work the confined domains for a point-like particle propagating within the boundary of an ideally reflecting paraboloid mirror are derived. Thereby it is proven that all consecutive flight parabola foci points lie on the surface of a common sphere of radius $R$. The main results are illustrated in various limiting cases and are compared to its two-dimensional counterpart.'\naddress: 'Gymnasium Holzkirchen, Germany'\nauthor:\n- Daniel Jaud\ntitle: 'Gravitational billiards - bouncing inside a paraboloid cavity'\n---\n\n[ ***Keywords\u2014*** **Keywords:** billiards, gravity, foci, paraboloid mirror, confined trajectories ]{}\n\n[ ***Keywords\u2014*** **MSC-Classification:** 14H81, 37C50, 37N05 ]{}\n\nIntroduction {#sec:Introduction}\n============\n\nOver the last decades, the dynamics of a point-like particle confined to some domain under the influence of a constant gravitational force, shortly called gravitational billiards, has been studied from various perspectives. Starting from [@Lehihet1986] who first performed a numerical study of the simplest imaginable system, the wedge, showing that the system can be integrable for certain angles of the wedge. Further study of the system have e.g. been performed in [@Anderson; @Korsch1999]. Extensions to other two dimensional boundaries (circular, elliptic, oval) or potentials have been performed in e.g. [@Costa2015; @Korsch_1991], who showed that for the quadratic and Coulomb"
+"---\nabstract: 'We study age of information in multi-hop multi-cast cache-enabled networks where the inter-update times on the links are not necessarily exponentially distributed. We focus on the set of non-arithmetic distributions for inter-update times, which includes continuous probability distributions as a subset. We first characterize instantaneous age of information at each node for arbitrary networks. We then explicate the recursive equations for instantaneous age of information in multi-hop networks and derive closed form expressions for expected age of information at an end-user. We show that expected age in multi-hop networks exhibits an additive structure. Further, we show that the expected age at each user is directly proportional to the variance of inter-update times at all links between a user and the source. We expect the analysis in this work to help alleviate the over-dependence on Poisson processes for future work in age of information.'\nauthor:\n- |\n    Priyanka Kaswan Sennur Ulukus\\\n    Department of Electrical and Computer Engineering\\\n    University of Maryland, College Park, MD 20742\\\n    *pkaswan@umd.edu* *ulukus@umd.edu*\nbibliography:\n- 'ref\\_priyanka.bib'\ntitle: 'Age of Information With Non-Poisson Updates in Cache-Updating Networks'\n---\n\nIntroduction {#sect:introduction}\n============\n\nEmerging time-sensitive applications with progressively dynamic data coupled with storage capacity becoming increasingly cheap has prompted"
+"---\nabstract: 'Low-mass X-ray binaries have long been theorised as potential sources of continuous gravitational-wave radiation, yet there is no observational evidence from recent LIGO/Virgo observing runs. Even for the theoretically \u2018loudest\u2019 source, Sco\u00a0X-1, the upper limit on gravitational-wave strain has been pushed ever lower. Such searches require precise measurements of the source properties for sufficient sensitivity and computational feasibility. Collating over 20 years of high-quality spectroscopic observations of the system, we present a precise and comprehensive ephemeris for Sco\u00a0X-1 through radial velocity measurements, performing a full homogeneous reanalysis of all relevant datasets and correcting previous analyses. Our Bayesian approach accounts for observational systematics and maximises not only precision, but also the fidelity of uncertainty estimates \u2014 crucial for informing principled continuous-wave searches. Our extensive dataset and analysis also enables us to construct the highest signal-to-noise, highest resolution phase-averaged spectrum of a low-mass X-ray binary to date. Doppler tomography reveals intriguing transient structures present in the accretion disk and flow driven by modulation of the accretion rate, necessitating further characterisation of the system at high temporal and spectral resolution. Our ephemeris corrects and supersedes previous ephemerides, and provides a factor three reduction in the number of templates in"
+"---\nabstract: 'Woronowicz proved the existence of the Haar state for compact quantum groups under a separability assumption later removed by Van Daele in a new existence proof. A minor adaptation of Van Daele\u2019s proof yields an idempotent state in any non-empty weak-\\* compact convolution-closed convex subset of the state space. Such subsets, and their associated idempotent states, are studied in the case of quantum permutation groups.'\nauthor:\n- 'J.P. McCarthy'\ntitle: Analysis for idempotent states on quantum permutation groups\n---\n\n=16.5pt\n\nIntroduction {#introduction .unnumbered}\n============\n\nIt is sometimes quipped that *quantum groups are neither quantum nor groups*. Whatever about compact quantum groups not being quantum, compact quantum groups are, of course, not in general classical groups. On the other hand, compact Hausdorff groups *are* compact quantum groups. Furthermore, the classical theorems of the existence of the Haar measure, Peter\u2013Weyl, Tannaka\u2013Krein duality, etc., can all be viewed as special cases of the quantum analogues proved by Woronowicz [@wo2; @wo3], and thus naturally the theory of compact quantum groups has many commonalities with the theory of compact groups.\n\nNot all classical theorems generalise so nicely:\n\n\\[kawito\\] Let $G$ be a compact separable group. Then a probability distribution on $G$ is idempotent"
+"---\nabstract: 'Recent work has formulated the task for computational construction grammar as producing a constructicon given a corpus of usage. Previous work has evaluated these unsupervised grammars using both internal metrics (for example, Minimum Description Length) and external metrics (for example, performance on a dialectology task). This paper instead takes a linguistic approach to evaluation, first learning a constructicon and then analyzing its contents from a linguistic perspective. This analysis shows that a learned constructicon can be divided into nine major types of constructions, of which *Verbal* and *Nominal* are the most common. The paper also shows that both the token and type frequency of constructions can be used to model variation across registers and dialects.'\nauthor:\n- |\n    Jonathan Dunn\\\n    Department of Linguistics and\\\n    New Zealand Institute for Language, Brain and Behaviour\\\n    University of Canterbury\\\n    Christchurch, New Zealand\\\n    `jonathan.dunn@canterbury.ac.nz`\nbibliography:\n- 'References.bib'\ntitle: |\n    Exploring the Constructicon:\\\n    Linguistic Analysis of a Computational CxG\n---\n\n=1\n\nIntroduction\n============\n\nConstruction Grammar (CxG) is a usage-based approach to language which views grammatical structure as a set of form-meaning mappings called a *constructicon* [@l08]. From this usage-based perspective, a *construction* could belong in the grammar either (i) because it is sufficiently entrenched"
+"---\nabstract: 'We numerically investigate tidally induced surface refreshing on Apophis during its close approach with Earth within a perigee distance of 5.96 Earth radii on April 13, 2029. We implement a tidal resurfacing model with two stages: dynamics modeling of the entire body to determine time-varying accelerations and surface slope profiles felt by each surface patch during the 6-h-long closest encounter, and DEM modeling to track motions of surface grains in localized patches. The surface slope profiles and measured grain motions are combined to statistically extrapolate the \u2018expected\u2019 percentage of resurfaced area. Using the tidal resurfacing model, we present surface maps showing the total expected resurfacing on Apophis given 3 representative encounter orientations. Our simulation results indicate that tidal resurfacing, limited to certain localized regions, will likely occur half an hour before perigee and on the scale of 1 per cent of Apophis\u2019s entire surface area. Our models indicate that the most likely locations to detect tidal resurfacing are: initially high-sloped regions ($> 30^{\\circ}$) regardless of the encounter orientation of Apophis, and mid-sloped regions ($15^{\\circ}$\u2013$30^{\\circ}$) that experience a significant positive slope variation ($> 0.5^{\\circ}$), which is mainly controlled by the encounter orientation. Expected data from ground-based observations of the"
+"---\nauthor:\n- 'J\u00e9r\u00e9my Fensch, Fr\u00e9d\u00e9ric Bournaud, No\u00e9 Brucy, Yohan Dubois, Patrick Hennebelle and Joakim Rosdahl'\nbibliography:\n- 'library.bib'\ndate: 'Submitted Nov. the 17th 2023, Accepted Jan the 30th 2023'\ntitle: 'Universal gravity-driven isothermal turbulence cascade in disk galaxies'\n---\n\nIntroduction {#sec:intro}\n============\n\nGas in galaxies is supersonically turbulent [see review by @Elmegreen2004; @Hennebelle2012]. The turbulent energy decays on a timescale much shorter than the time for which molecular clouds are turbulent. Thus, there must be a mechanism to continuously provide turbulent energy to molecular clouds.\n\nProcesses suggested so far have been shear from galactic rotation [@Fleck1981], mass transport [@Krumholz2016; @Krumholz2018] stellar feedback [see e.g. @MacLow2004; @Ostriker2011; @Faucher-Giguere2013; @Padoan2016; @Hayward2017], gravitational instabilities [see e.g. @Bournaud2010] and gas accretion [@Klessen2010; @Goldbaum2015; @Goldbaum2016; @Ginzburg2022; @Forbes2022]. However, distinguishing their respective roles is not an easy task.\n\nFor instance, @Krumholz2016 developed an analytical prediction for the dependence of the gas velocity dispersion on star formation rate in galaxies with gravity- and feedback-dominated turbulence injection. They found that observational data is best fitted by gravity-dominated injection for high-redshift galaxies, whereas the low-redshift case is less clear, predictions of the two models being very close to each other [see also @Varidel2020; @Girard2021; @Yu2021]. Using numerical simulations"
+"---\nabstract: 'In this work we establish an approach to model miscibility gaps of alloys using statistical physics, lattice dynamics from first-principles calculations. We carefully calculate the entropy to include all processes introducing disorder to the system, i.e., combining the electronic, phononic, and configuration entropies. Furthermore we present our algorithm for generating Special Quasirandom Structures (SQS). We model the miscibility gap in tungsten - chromium and tungsten - molybdenum systems, obtaining the agreement with the experimental data. Furthermore, we propose an enhancement for the tungsten-chromium W~70~Cr~30~ alloy with tantalum and hafnium, leading to the modified stabilization temperatures $T_S$, where the solid solution is miscible.'\nauthor:\n- Andrzej Piotr K\u0105dzielawa\n- Dominik Legut\nbibliography:\n- 'bibliography.bib'\nnocite: '[@*]'\ntitle: 'On the miscibility gap in tungsten-based alloys'\n---\n\nIntroduction {#sec:introduction}\n============\n\nWith the rapid search of carbon-neutral power producing facilities, the need for working fusion reactors is higher than ever before. Successful Ignition Events [@AbuShawareb2022; @Zylstra2022], followed by net-positive cycle [@Zylstra2022b], increased the priority of research on the crucial elements of a fusion reactor, e.g., the so-called first walls. One family of compounds, namely tungsten-based alloys represent prospective candidates to replace pure tungsten, as the alloy\u2019s features (e.g., selfpassivation) make them resistant"
+"---\nabstract: 'Programmable unitary photonic devices are emerging as promising tools to implement unitary transformation for quantum information processing, machine learning, and optical communication. These devices typically use a rectangular mesh of Mach-Zehnder interferometers (MZIs), which has a clear mathematical structure and can be configured deterministically. However, this mesh architecture is sensitive to fabrication errors, and the correction techniques are still under investigation. In contrast, the multi-plane light conversion (MPLC) architecture is more robust against fabrication errors, but a deterministic method for configuring the converter has not yet been developed due to its complex mathematical structure. In this work, we propose a fast iterative configuration method for MPLC, following the mathematical review of the matrix distance and proposal of a new norm. We show through numerical simulations that adding a few redundant layers significantly improves the convergence of the MPLC architecture, making it a practical and attractive option. We also consider the effects of finite resolution and crosstalk in phase shifters in our simulations. In addition, we propose a phase-insensitive distance suited for applications using only intensity detections. Our method demonstrates orders of magnitude better accuracy and a 20-fold speed-up compared to previous approaches.'\nauthor:\n- Yoshitaka Taguchi\n- Yunzhuo"
+"---\nabstract: 'Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread. We argue that while misinformation and disinformation detection have been studied, there has been a lack of investment in the important open challenge of detecting harmful agendas in news articles; identifying harmful agendas is critical to flag news campaigns with the greatest potential for real world harm. Moreover, due to real concerns around censorship, harmful agenda detectors must be interpretable to be effective. In this work, we propose this new task and release a dataset, <span style=\"font-variant:small-caps;\">NewsAgendas</span>, of annotated news articles for agenda identification. We show how interpretable systems can be effective on this task and demonstrate that they can perform comparably to black-box models.'\nauthor:\n- |\n    Melanie Subbiah$^*$\\\n    Columbia University\\\n    `m.subbiah@columbia.edu`\\\n    Amrita Bhattacharjee$^*$\\\n    Arizona State University\\\n    `abhatt43@asu.edu`\\\n    Yilun Hua\\\n    Columbia University\\\n    `yh3228@columbia.edu`\\\n    Tharindu Kumarage\\\n    Arizona State University\\\n    `kskumara@asu.edu`\\\n    Huan Liu\\\n    Arizona State University\\\n    `huanliu@asu.edu`\\\n    Kathleen McKeown\\\n    Columbia University\\\n    `kathy@cs.columbia.edu`\nbibliography:\n- 'anthology.bib'\n- 'custom.bib'\ntitle: 'Towards Detecting Harmful Agendas in News Articles\\'\n---\n\n=1\n\nIntroduction\n============\n\nIn recent years, the spread of misinformation and disinformation has become a particularly persistent and harmful issue online [@bastick2021would; @mueller2020internet; @vosoughi2018spread; @zhang2020overview]."
+"---\nabstract: 'Face image translation has made notable progress in recent years. However, when training on limited data, the performance of existing approaches significantly declines. Although some studies have attempted to tackle this problem, they either failed to achieve the few-shot setting (less than 10) or can only get suboptimal results. In this paper, we propose **G**AN **P**rior **D**istillation (GPD) to enable effective few-shot face image translation. GPD contains two models: a teacher network with GAN Prior and a student network that fulfills end-to-end translation. Specifically, we adapt the teacher network trained on large-scale data in the source domain to the target domain with only a few samples, where it can learn the target domain\u2019s knowledge. Then, we can achieve few-shot augmentation by generating source domain and target domain images simultaneously with the same latent codes. We propose an anchor-based knowledge distillation module that can fully use the difference between the training and the augmented data to distill the knowledge of the teacher network into the student network. The trained student network achieves excellent generalization performance with the absorption of additional knowledge. Qualitative and quantitative experiments demonstrate that our method achieves superior results than state-of-the-art approaches in a few-shot setting.'"
+"---\nauthor:\n- Alessandro Sonnenfeld\n- 'Shun-Sheng Li'\n- Giulia Despali\n- Raphael Gavazzi\n- 'Anowar J. Shajib'\n- 'Edward N. Taylor'\nbibliography:\n- 'references.bib'\ntitle: Strong lensing selection effects\n---\n\n[ Strong lenses are a biased subset of the general population of galaxies. ]{} [ The goal of this work is to quantify how lens galaxies and lensed sources differ from their parent distribution, namely the strong lensing bias. ]{} [ We first studied how the strong lensing cross-section varies as a function of lens and source properties. Then, we simulated strong lensing surveys with data similar to that expected for *Euclid* and measured the strong lensing bias in different scenarios. We focused particularly on two quantities: the stellar population synthesis mismatch parameter, $\\asps$, defined as the ratio between the true stellar mass of a galaxy and the stellar mass obtained from photometry, and the central dark matter mass at fixed stellar mass and size. ]{} [ Strong lens galaxies are biased towards higher stellar masses, smaller half-mass radii, and higher dark matter masses. The amplitude of the bias depends on the intrinsic scatter in the mass-related parameters of the galaxy population and on the completeness in Einstein radius"
+"---\nabstract: 'We analyze the boson mixing in curved spacetime and compute the expectation value of the energy-momentum tensor of bosons on the flavor vacuum in spatially flat Friedmann-Lema\u00eetre-Robertson-Walker metrics. We show that the energy-momentum tensor of the flavor vacuum behaves as the effective energy-momentum tensor of a perfect fluid. Assuming a fixed de Sitter background, we show that the equation of state can assume values in the interval $[-1, 1]$, and, in the flat space-time limit has a value $-1$, which is the one of the dark energy. The results here presented show that vacuum of mixed bosons like neutrino super-partners, can represent a possible component of the dark energy of the Universe.'\nauthor:\n- Antonio Capolupo\n- Aniello Quaranta\ntitle: 'Boson mixing and flavor vacuum in the expanding Universe: a possible candidate for the dark energy'\n---\n\nIntroduction\n============\n\nOne of the most important discoveries of modern cosmology is represented by the observation of the accelerated expansion of the universe [@CMBR; @Spergel; @LSS; @Szalay; @DE1; @Peebles; @SNeIa]. In the context of general relativity, classical sources of matter generate only positive pressures, while an accelerating expansion of the universe requires negative pressures. Therefore, the understanding of cosmic acceleration turns"
+"---\nabstract: 'In this work we study the robustness to adversarial attacks, of early-stopping strategies on gradient-descent (GD) methods for linear regression. More precisely, we show that early-stopped GD is optimally robust (up to an absolute constant) against Euclidean-norm adversarial attacks. However, we show that this strategy can be arbitrarily sub-optimal in the case of general Mahalanobis attacks. This observation is compatible with recent findings in the case of classification\u00a0[@Vardi2022GradientMP] that show that GD provably converges to non-robust models. To alleviate this issue, we propose to apply instead a GD scheme on a transformation of the data adapted to the attack. This data transformation amounts to apply feature-depending learning rates and we show that this modified GD is able to handle any Mahalanobis attack, as well as more general attacks under some conditions. Unfortunately, choosing such adapted transformations can be hard for general attacks. To the rescue, we design a simple and tractable estimator whose adversarial risk is optimal up to within a multiplicative constant of 1.1124 in the population regime, and works for any norm.'\nbibliography:\n- 'litterature.bib'\n---\n\nIntroduction\n============\n\nMachine learning models are highly sensitive to small perturbations known as *adversarial examples*\u00a0[@szegedy2013intriguing], which are often"
+"---\nauthor:\n- 'J.\u00a0Cernicharo, J.\u00a0R.\u00a0Pardo, C.\u00a0Cabezas, M.\u00a0Ag\u00fandez, B.\u00a0Tercero, N.\u00a0Marcelino, R.\u00a0Fuentetaja, M.\u00a0Gu\u00e9lin, and P.\u00a0de\u00a0Vicente'\ndate: 'Submitted 29/12/2022; Accepted 30/1/2023'\ntitle: 'Discovery of the C$_7$N$^-$ anion in TMC-1 and IRC+10216[^1]'\n---\n\nIntroduction\n============\n\nThe presence of carbon-chain negative ions in space was predicted on the grounds that electron radiative attachment is efficient for open-shell molecules with large electron affinities [@Dalgarno1973; @Sarre1980; @Herbst1981]. The predicted abundance ratio between an anion and its corresponding neutral species increases with the size of the molecule, which is a consequence of the increasing density of vibrational states as the number of atoms in the molecule increases.\n\nThe first anion detected in space, C$_6$H$^-$, was observed towards TMC-1 [@McCarthy2006]. Lines from this species were already reported as unidentified features in the line survey of IRC+10216 performed with the Nobeyama 45m telescope by @Kawaguchi1995. @Aoki2000 suggested that the carrier of these lines was C$_6$H$^-$ from *ab initio* calculations, which was finally confirmed by the laboratory observations of @McCarthy2006. The presence of this anion in space attracted significant attention and motivated searches for other hydrocarbon anions in interstellar and circumstellar clouds. C$_4$H$^-$ was first discovered in the circumstellar cloud"
+"=1\n\nIntroduction\n============\n\nA Lie group $G$ is called symplectic if it has a left-invariant closed 2-form whose rank is equal to $\\dim(G)$. In this case, $\\mathfrak{g} = \\mathrm{Lie}(G)$ would be a quasi-Frobenius Lie algebra. Specifically, there exists $\\omega \\in Z^2(\\mathfrak{g})$ such that $\\omega$ is non-degenerate, see Section \\[qFrobenius\\] for more details. Symplectic Lie groups include, for example, abelian groups of even dimension and the group of affine transformations of ${\\mathbb K}^n$, for $n\\geq 1$, where ${\\mathbb K}={\\mathbb R}$ or ${\\mathbb C}$, see [@BC; @MR2].\n\nMedina and Revoy classified symplectic nilpotent groups by their Lie algebras in [@MR2]. Symplectic double extensions were introduced in their work, and they showed that each nilpotent quasi-Frobenius Lie algebra can be obtained by a sequence of symplectic double extensions from the trivial Lie algebra. As a group, $(G, \\omega)$ is a double extension of the group $(H, \\Omega)$, meaning\u00a0$(H, \\Omega)$ is a Marsden\u2013Weinstein reduced manifold of $(G, \\omega)$. Therefore, symplectic nilmanifolds can be obtained by symplectic double extensions.\n\nThe notion of symplectic double extension is the symplectic analog to the notion of double extension studied earlier in [@FS; @MR1] when the bilinear form is non-degenerate, invariant and symmetric (abbreviated NIS in [@BKLS]). In"
+"---\nabstract: 'We introduce , a novel model for interpretable semantic image segmentation, which constructs its predictions using similar patches from the training set. To achieve accuracy comparable to baseline methods, we adapt the mechanism of prototypical parts and introduce a diversity loss function that increases the variety of prototypes within each class. We show that discovers semantic concepts, in contrast to standard segmentation models. Experiments conducted on Pascal VOC and Cityscapes datasets confirm the precision and transparency of the presented method.'\nauthor:\n- Miko\u0142aj Sacha$^1$\n- 'Dawid Rymarczyk$^{1,2}$'\n- \u0141ukasz Struski$^1$\n- Jacek Tabor$^1$\n- |\n    Bartosz Zieli\u0144ski$^{1,3}$\\\n    $^1$ Faculty of Mathematics and Computer Science, Jagiellonian University, Krak\u00f3w, Poland\\\n    $^2$ Ardigen, Krak\u00f3w, Poland $^3$ IDEAS NCBR, Warsaw, Poland\\\n    [{mikolaj.sacha;dawid.rymarczyk}@doctoral.uj.edu.pl]{}\\\n    [{lukasz.struski;jacek.tabor;bartosz.zielinski}@uj.edu.pl]{}\nbibliography:\n- 'egbib.bib'\ntitle: ': Interpretable Semantic Segmentation with Prototypical Parts'\n---\n\nIntroduction\n============\n\nSemantic segmentation is an essential component in many visual understanding systems. However, while deep learning-based models have achieved promising performance on challenging benchmarks\u00a0[@minaee2021image], their decisions remain unclear due to lack of explanation\u00a0[@rudin2019stop]. This issue may appear particularly problematic in critical applications, such as medical imaging or autonomous driving.\n\n![In contrast to existing methods, provides an interpretation of resulted segmentation. For this purpose, it"
+"---\nabstract: 'Phasor measurement units (PMUs) provide accurate and high-fidelity measurements in order to monitor the state of the power grid and support various control and planning tasks. However, PMUs have a high installation cost prohibiting their massive deployment. Minimizing the number of installed PMUs needs to be achieved while also maintaining full observability of the network. At the same time, data integrity attacks on PMU measurements can cause mislead power system control and operation routines. In this paper, a bi-level stochastic non-cooperative game-based placement model is proposed for PMU allocation in the presence of cyber-attack risks. In the first level, the protection of individual PMU placed in a network is addressed, while considering the interaction between the grid operator and the attacker with respective resource constraints. In the second level, the attacker observes the placement of the PMUs and compromises them, with the aim of maximizing the state estimation error and reducing the observability of the network. The proposed technique is deployed in the IEEE-9 bus test system. The results demonstrate a 9% reduction in the cost incurred by the power grid operator for deploying PMUs while considering cyber-risks.'\nauthor:\n- |\n    \\\n    \\\nbibliography:\n- 'ref1.bib'\ntitle: 'A"
+"---\nabstract: 'When using ecological momentary assessment data (EMA), missing data is pervasive as participant attrition is a common issue. Thus, any EMA study must have a missing data plan. In this paper, we discuss missingness in time series analysis and the appropriate way to handle missing data when the data is modeled as a discrete time continuous measure state-space model. We found that Missing Completely At Random, Missing At Random, and Time-dependent Missing At Random data have less bias and variability than Autoregressive Time-dependent Missing At Random and Missing Not At Random. The Kalman filter excelled at handling missing data. Contrary to the literature, we found that, with either default package settings or a lag-1 imputation model, multiple imputation struggled to recover the parameters.'\nauthor:\n- \nbibliography:\n- 'Bib1.bib'\ntitle: 'Missing Data in Discrete Time State-Space Modeling of Ecological Momentary Assessment Data: A Monte-Carlo Study of Imputation Methods'\n---\n\nmissing data; time series; ecological momentary assessment; state-space model\n\nIntroduction\n============\n\nMissing data is ubiquitous in psychological data, no matter cross-sectional or time series. This is problematic as many statistical methods are not directly applicable to data sets with missing data, and those that are face problems with statistical power,"
+"---\nabstract: 'Persistent nonequilibrium effects such as the memory of the initial state, the ballistic diffusion, and the break of the equipartition theorem and the ergodicity in Brownian motions are investigated by analytically solving the generalized Langevin equation of nonrelativistic Brownian particles with colored noise. These effects can also be observed in the Brownian motion of relativistic particles by numerically solving the generalized Langevin equation for specially chosen memory kernels. Our analyses give rise to think about the possible anomalous motion of heavy quarks in relativistic heavy-ion collisions.'\nauthor:\n- Weiguo Chen\n- Carsten Greiner\n- 'Zhe Xu [^1]'\ntitle: Persistent nonequilibrium effects in generalized Langevin dynamics of nonrelativistic and relativistic particles\n---\n\nIntroduction\n============\n\nThe Brownian motion is a famous stochastic process exhibiting the relationship between the fluctuation and the dissipation in statistical physics. Mathematically, it is described by the Langevin equation. The common knowledge from text books by solving the Langevin equation is that the Brownian motion is a random motion of the Brownian particle in a fluid or gas. In the long-time limit and with the ensemble average, the Brownian particle will reach the thermal equilibrium with the matter where it is suspended and diffuse linearly with"
+"---\nauthor:\n- Hao Xu\nbibliography:\n- 'draft-bibliography.bib'\ntitle: '[Decoherence and thermalization of Unruh-DeWitt detector in arbitrary dimensions]{}'\n---\n\nIntroduction\n============\n\nThe relationship between general relativity and quantum physics has been one of the most significant open topics in modern physics. Despite the belief that there should exist a more fundamental theory of quantum gravity which degenerates into general relativity and quantum physics in certain situations, the pursuit of quantum gravity has not been very successful. It appears that there are considerable differences between the fundamental laws regulating macroscopic and microscopic processes. For example, Einstein\u2019s equation, the fundamental equation of general relativity, is nonlinear, whereas Schr\u00f6dinger\u2019s equation, the fundamental equation of quantum mechanics, is linear. Because of the linearity of Schr\u00f6dinger\u2019s equation, the linear superposition of the two solutions is still a solution of the equation, leading to the principle of superposition and a probabilistic interpretation of our universe in the macroscopic world. We cannot help but wonder: if the universe is governed by fundamental quantum mechanics principles at the microscopic level, why does the macroworld appear to be classical?\n\nOne of the reasons for this problem is that we used the incorrect assumption of idealized closed system. Although the"
+"---\nabstract: |\n    Diversity and inclusion are necessary prerequisites for shaping technological innovation that benefits society as a whole. A common indicator of diversity consideration is the representation of different social groups among software engineering (SE) researchers, developers, and students. However, this does not necessarily entail that diversity is considered in the SE research itself.\n\n    In our study, we examine how diversity is embedded in SE research, particularly research that involves participant studies. To this end, we have selected 79 research papers containing 105 participant studies spanning three years of ICSE technical tracks. Using a content analytical approach, we identified how SE researchers report the various diversity categories of their study participants and investigated: 1) the extent to which participants are described, 2) what diversity categories are commonly reported, and 3) the function diversity serves in the SE studies.\n\n    We identified 12 different diversity categories reported in SE participant studies. Our results demonstrate that even though most SE studies report on the diversity of participants, SE research often emphasizes professional diversity data, such as occupation and work experience, over social diversity data, such as gender or location of the participants. Furthermore, our results show that participant diversity is seldom analyzed"
+"---\nabstract: 'It is known that materials with broken time-reversal symmetry can have Hall responses. Here we show that in addition to the conventional currents, either linear or nonlinear in the electric field, another Hall current can occur in the time-reversal breaking materials within the second-order response to in-plane electric and vertical magnetic fields. Such a Hall response is generated by the oscillation of the electromagnetic field and has a quantum origin arising from a novel dipole associated with the Berry curvature and band velocity. We demonstrate that the massive Dirac model of LaAlO3/LaNiO3/LaAlO3 quantum well can be used to detect this Hall effect. Our work widens the theory of the Hall effect in the time-reversal breaking materials by proposing a new kind of nonlinear electromagnetic response.'\nauthor:\n- Anwei Zhang\n- 'Jun-Won Rhim'\ntitle: 'Nonlinear electromagnetic response for Hall effect in time-reversal breaking materials'\n---\n\n**Introduction** Since the discovery of the Hall effect\u00a0[@hall1879new], which is the phenomenon of the voltage drop perpendicular to the applied magnetic field and charge current, the transport properties under the magnetic field have been one of the essential solid-state research areas. As the magnetic field becomes strong enough, the system enters the quantum"
+"---\nabstract: 'A weak-doublet with hypercharge $-3/2$ is one of only a handful of fermions which has a renormalisable interaction with Standard Model fields. This should make it worthy of attention, but it has thus far received little consideration in the literature. In this paper, we perform a thorough investigation of the phenomenology which results from the introduction of this field, $F$. After expressing the model in terms of its effective field theory at dimension-6, we compute a range of electroweak and leptonic observables, the most stringent of which probe up to $M_F \\sim 300$ TeV. The simplicity of this scenario makes it very predictive and allows us to correlate the different processes. We then study how this new fermion can connect the SM to various simple but distinct dark sectors. Some of the most minimal cases of $F$-mediated dark matter (DM) involve frozen-in keV-scale scalar DM, which may produce x-ray lines, and frozen-out TeV-scale fermionic DM.'\nauthor:\n- Rupert Coy\nbibliography:\n- 'frogsbib.bib'\ntitle: 'A forgotten fermion: the hypercharge $-3/2$ doublet, its phenomenology and connections to dark matter'\n---\n\n0.7cm\n\nIntroduction {#sec:intro}\n============\n\nMotivation for physics beyond the Standard Model (SM) typically falls into two categories. One is purely"
+"---\nabstract: 'Massive pre-trained language models have garnered attention and controversy due to their ability to generate human-like responses: attention due to their frequent indistinguishability from human-generated phraseology and narratives, and controversy due to the fact that their convincingly presented arguments and facts are frequently simply false. Just how human-like are these responses when it comes to dialogues about physics, in particular about the standard content of introductory physics courses? This study explores that question by having ChatGTP, the pre-eminent language model in 2023, work through representative assessment content of an actual calculus-based physics course and grading the responses in the same way human responses would be graded. As it turns out, ChatGPT would narrowly pass this course while exhibiting many of the preconceptions and errors of a beginning learner.'\nauthor:\n- Gerd Kortemeyer\nbibliography:\n- 'ChatGPT.bib'\ntitle: 'Could an Artificial-Intelligence agent pass an introductory physics course?'\n---\n\nIntroduction\n============\n\n\u201cEducators may have concerns about ChatGPT, a large language model trained by OpenAI, for a number of reasons. First and foremost, there is the concern that a tool like ChatGPT could potentially be used to cheat on exams or assignments. ChatGPT can generate human-like text, which means that a student"
+"---\naddress: |\n    $^{1}$ Dipartimento di Fisica, Universit\u00e0 degli Studi di Genova, Via Dodecaneso 33, 16146 Genova, Italy; traversoziani@fisica.unige.it (N.T.Z.); sassetti@fisica.unige.it (M.S.)\\\n    $^{2}$ Institute for Theoretical Physics and Astrophysics, University of W\u00fcrzburg, D-97074 W\u00fcrzburg, Germany; alessio.calzona@physik.uni-wuerzburg.de (A.C.); trauzettel@physik.uni-wuerzburg.de (B.T.)\\\n    $^{3}$ CNR-SPIN, Via Dodecaneso 33, 16146 Genova, Italy\\\n    $^{4}$ Centro de F\u00edsica de Materiales (CFM-MPC), Centro Mixto CSIC-UPV/EHU, ; fs.bergeret@csic.es\\\n    $^{5}$ Donostia International Physics Center (DIPC), E-20018 Donostia\u2013San Sebasti\u00e1n, Spain\\\n    $^{6}$ W\u00fcrzburg-Dresden Cluster of Excellence ct.qmat, Germany\n---\n\nIntroduction {#sec:introduction}\n============\n\nTopological phases of quantum systems have been at the forefront of research in condensed matter over the last two decades [@hasan; @qi]. One of these phases takes place in quantum spin Hall (QSH) insulators, which are two-dimensional topological insulators (2DTIs) hosting topologically protected and counter-propagating helical edge states on their . The interplay of superconductivity and the QSH effect has been widely studied in view of applications in spintronics and in (topological) quantum computation [@hart; @pribiag]. To this end, topological Josephson junctions (JJs) appear as fundamental building blocks [@deacon; @dolcini]. In a topological JJ, two superconducting electrodes are connected through the helical edge state channels of the QSH insulator. If the junction is pierced by a magnetic flux, it realizes"
+"---\naddress: |\n    $^{1}$ Departamento de F\u00edsica, Escuela Superior de F\u00edsica y Matem\u00e1ticas, Instituto Polit\u00e9cnico Nacional, U. P. Adolfo L\u00f3pez Mateos, Zacatenco, C.P. 07738, CDMX, M\u00e9xico.; ediazf1001@alumno.ipn.mx, gadpau@hotmail.com\\\n    $^{2}$ Investigadores por M\u00e9xico-CONACYT, SCIESMEX-LANCE, Instituto de Geof\u00edsica, Unidad Michoac\u00e1n, Universidad Nacional Aut\u00f3noma de M\u00e9xico, C.P. 58190 Morelia, Michoac\u00e1n, M\u00e9xico.; jjgonzalez@igeofisica.unam.mx\nbibliography:\n- 'Two\\_fluid\\_jets\\_mdpi\\_2023.bib'\n---\n\nIntroduction\n============\n\nSolar jet-type phenomena are ubiquitous in the solar atmosphere. Its importance has generated numerous investigations on its origin and evolution [see, e.g., @2016SSRv..201....1R; @2021RSPSA.47700217S and references therein]. Although there are many open questions about its nature, enormous progress has been made in understanding its dynamics, particularly in 2D, 2.5D, and 3D MHD numerical simulations. These models have swept a broad spectrum of complexity from ideal MHD to resistive MHD and two-fluid MHD [see, e.g., @10.1093/pasj/58.2.423; @Pariat_2009; @Archontis_et_al_2010; @Jiang_2012; @Leake_2013; @Soler_2013; @10.1093/mnras/sty2306].\n\nJet-type phenomena have been proposed as promising candidates to generate large amounts of heat at the upper atmospheric layers of the Sun [@Beckers_1968]. Their frequency of occurrence is high compared to other solar phenomena, which gives the impression that jets are ongoing events that could represent a continuous source of energy [@De_Pontieu_2009]. These collimated plasma jets are called spicules and are cataloged according to"
+"---\nabstract: 'In combinatorial causal bandits (CCB), the learning agent chooses a subset of variables in each round to intervene and collects feedback from the observed variables to minimize expected regret or sample complexity. Previous works study this problem in both general causal models and binary generalized linear models (BGLMs). However, all of them require prior knowledge of causal graph structure. This paper studies the CCB problem without the graph structure on binary general causal models and BGLMs. We first provide an exponential lower bound of cumulative regrets for the CCB problem on general causal models. To overcome the exponentially large space of parameters, we then consider the CCB problem on BGLMs. We design a regret minimization algorithm for BGLMs even without the graph skeleton and show that it still achieves $O(\\sqrt{T}\\ln T)$ expected regret. This asymptotic regret is the same as the state-of-art algorithms relying on the graph structure. Moreover, we sacrifice the regret to $O(T^{\\frac{2}{3}}\\ln T)$ to remove the weight gap covered by the asymptotic notation. At last, we give some discussions and algorithms for pure exploration of the CCB problem without the graph structure.'\nauthor:\n- 'Shi Feng[^1]'\n- Nuoya Xiong\n- 'Wei Chen [^2]'\nbibliography:\n-"
+"---\nabstract: 'Non-Hermitian systems have been widely explored in platforms ranging from photonics to electric circuits. A defining feature of non-Hermitian systems is exceptional points (EPs), where both eigenvalues and eigenvectors coalesce. Tropical geometry is an emerging field of mathematics at the interface between algebraic geometry and polyhedral geometry, with diverse applications to science. Here, we introduce and develop a unified tropical geometric framework to characterize different facets of non-Hermitian systems. We illustrate the versatility of our approach using several examples, and demonstrate that it can be used to select from a spectrum of higher-order EPs in gain and loss models, predict the skin effect in the non-Hermitian Su-Schrieffer-Heeger model, and extract universal properties in the presence of disorder in the Hatano-Nelson model. Our work puts forth a new framework for studying non-Hermitian physics and unveils a novel connection of tropical geometry to this field.'\nauthor:\n- Ayan Banerjee\n- Rimika Jaiswal\n- Madhusudan Manjunath\n- Awadhesh Narayan\nbibliography:\n- 'references.bib'\ntitle: A Tropical Geometric Approach To Exceptional Points\n---\n\nIntroduction\n============\n\nSeveral branches of mathematics show an *unreasonable effectiveness* in formulating and understanding a myriad of physical phenomena\u00a0[@wigner1990unreasonable]. Striking recent examples include the role of topology in condensed"
+"---\nabstract: 'We prove that, if $\\Gamma$ is a finite connected cubic vertex-transitive graph, then either there exists a semiregular automorphism of $\\Gamma$ of order at least $6$, or the number of vertices of $\\Gamma$ is bounded above by an absolute constant.'\naddress:\n- 'Dipartimento di Matematica\u201cFelice Casorati\", University of Pavia, Via Ferrata 5, 27100 Pavia, Italy'\n- 'Dipartimento di Matematica e Applicazioni, University of Milano-Bicocca, Via Cozzi 55, 20125 Milano, Italy'\n- 'Dipartimento di Matematica e Applicazioni, University of Milano-Bicocca, Via Cozzi 55, 20125 Milano, Italy'\nauthor:\n- Marco Barbieri\n- Valentina Grazian\n- Pablo Spiga\nbibliography:\n- 'semiregularBib.bib'\ntitle: 'On the order of semiregular automorphisms of cubic vertex-transitive graphs'\n---\n\n\\[section\\] \\[thm\\][Lemma]{} \\[thm\\][Corollary]{} \\[thm\\][Hypothesis]{} \\[thm\\][Definition]{} \\[thm\\][Remark]{}\n\nIntroduction\n============\n\nA fascinating old-standing question in the theory of group actions on graphs is the so-called *Polycirculant Conjecture*: non-identity $2$-closed transitive permutation groups contain non-identity semiregular elements. This formulation of the conjecture was introduced by Klin [@Klin1998]. However, the question was previously posed independently by Maru\u0161i\u010d [@Marusic1981 Problem\u00a02.4] and Jordan [@Jordan1988] in terms of graphs: vertex-transitive graphs having more than one vertex admit non-identity semiregular automorphisms.\n\nIn this paper, we focus our attention on cubic graphs. In [@MarusicScappellatoSemiregular], Marusi\u010d and"
+"---\nabstract: 'We study estimation problems in safety-critical applications with streaming data. Since estimation problems can be posed as optimization problems in the probability space, we devise a stochastic projected Wasserstein gradient flow that keeps track of the belief of the estimated quantity and can consume samples from online data. We show the convergence properties of our algorithm. Our analysis combines recent advances in the Wasserstein space and its differential structure with more classical stochastic gradient descent. We apply our methodology for predictive maintenance of safety-critical processes: Our approach is shown to lead to superior performance when compared to classical least squares, enabling, among others, improved robustness for decision-making.'\naddress:\n- 'Automatic Control Laboratory, ETH Z\u00fcrich, Physikstrasse 3, 8092 Z\u00fcrich, Switzerland.'\n- |\n    University of British Columbia Department of Mechanical Engineering, 2054-6250 Applied Science Lane,\\\n    Vancouver, BC V6T 1Z4, Canada.\nauthor:\n- Nicolas Lanzetti\n- 'Efe C. Balta'\n- 'Dominic Liao-McPherson'\n- Florian D\u00f6rfler\nbibliography:\n- 'ifacconf.bib'\ntitle: Stochastic Wasserstein Gradient Flows using Streaming Data with an Application in Predictive Maintenance\n---\n\nWasserstein gradient flows, streaming data, predictive maintenance\n\nIntroduction {#sec:introduction}\n============\n\nProviding performance guarantees for parameter estimation algorithms operating with streaming data is a key challenge when developing methods"
+"---\nabstract: 'Lepton portal dark matter (DM) models are a class of models where the DM candidates solely couple to charged leptons through a mediator carrying a lepton number. These models are very interesting since they avoid constraints from direct detection experiments even for coupling of order ${\\cal O}(1)$, they have small annihilation cross sections, and can be probed efficiently at lepton colliders. In this work, we consider a minimal lepton portal DM model which consists of extending the SM with two $SU(2)_L$ singlets: a charged scalar singlet and an electrically neutral right-handed fermion. We systematically study the production mechanisms of DM at multi-TeV muon colliders. After considering all the possible theoretical and experimental constraints and studying the phenomenology of lepton flavour violation and DM in the muon-philic scenario, we analyse the production rates of 54 channels (26 channels for prompt DM production and 28 channels for charged scalar production) at multi-TeV muon colliders. Finally, we discuss the possible collider signatures of some channels and the corresponding backgrounds. We find that at least 9 channels for DM production can be very efficient in testing DM with masses up to about $1$ TeV.'\nauthor:\n- Adil Jueid\n- and Salah Nasri"
+"---\nabstract: 'We combine the unbiased estimators in Rhee and Glynn (Operations Research: 63(5), 1026-1043, 2015) and the Heston model with stochastic interest rates. Specifically, we first develop a semi-exact log-Euler scheme for the Heston model with stochastic interest rates. Then, under mild assumptions, we show that the convergence rate in the $L^2$ norm is $O(h)$, where $h$ is the step size. The result applies to a large class of models, such as the Heston-Hull-While model, the Heston-CIR model and the Heston-Black-Karasinski model. Numerical experiments support our theoretical convergence rate.'\nauthor:\n- 'Chao Zheng [^1]'\n- 'Jiangtao Pan [^2]'\ntitle: Unbiased estimators for the Heston model with stochastic interest rates\n---\n\n**Keywords:** Heston model, stochastic interest rates, unbiased estimators, convergence rate, Heston-Hull-While model, Heston-CIR model\n\n**AMS subject classifications (2000):** 60H35, 65C30, 91G60\n\nIntroduction\n============\n\nThe classical Heston model (Heston [@He]) is one of the fundamental models in finance, and it has been widely applied in various financial markets, such as the equity, fixed income and foreign exchange markets, due to its tractability in modelling the term structure of implied volatility. However, in this model, the interest rate is constant, and this assumption is often not appropriate for long-maturity options, because"
+"---\nabstract: 'A recent line of work in NLP focuses on the (dis)ability of models to generalise compositionally for artificial languages. However, when considering natural language tasks, the data involved is not strictly, or *locally*, compositional. Quantifying the compositionality of data is a challenging task, which has been investigated primarily for short utterances. We use recursive neural models (Tree-LSTMs) with bottlenecks that limit the transfer of information between nodes. We illustrate that comparing data\u2019s representations in models with and without the bottleneck can be used to produce a compositionality metric. The procedure is applied to the evaluation of arithmetic expressions using synthetic data, and sentiment classification using natural language data. We demonstrate that compression through a bottleneck impacts non-compositional examples disproportionately and then use the bottleneck compositionality metric (BCM) to distinguish compositional from non-compositional samples, yielding a compositionality ranking over a dataset.'\nauthor:\n- |\n    Verna Dankers$^1$ [and]{.nodecor} Ivan Titov$^{1,2}$\\\n    $^1$ILCC, University of Edinburgh\\\n    $^2$ILLC, University of Amsterdam\\\n    `vernadankers@gmail.com`, `ititov@inf.ed.ac.uk`\nbibliography:\n- 'references.bib'\ntitle: 'Recursive Neural Networks with Bottlenecks Diagnose (Non-)Compositionality'\n---\n\n=1\n\nIntroduction\n============\n\n*Compositional generalisation* in contemporary NLP research investigates models\u2019 ability to compose the meanings of expressions from their parts and is often investigated with artificial languages"
+"---\nabstract: 'In the usual perception, surface superconductivity is associated with the surface nucleation of a superconducting condensate above the upper critical field in type-II superconductors or with a rearrangement of phonon properties and the electron-phonon coupling near surfaces/interfaces. Recently, it has been found that there is another example when the surface superconducting temperature is increased up to $20$-$25\\%$ as compared to the bulk one due to constructive interference of superconducting pair states. In the present work, we demonstrate that in fact, such an interference-induced enhancement can be much more pronounced, up to nearly $70\\%$. Furthermore, here it is shown that such an interference enhancement persists over a wide range of microscopic parameters.'\nauthor:\n- Yunfei Bai\n- Yajiang Chen\n- 'M. D. Croitoru'\n- 'A. A. Shanenko'\n- Xiaobing Luo\n- Yunbo Zhang\n---\n\nIntroduction {#int}\n============\n\nThere are two well-known examples of the surface superconductivity. The first one concerns the surface nucleation of a pare condensate in type-II superconductors below the third critical field $H_{c3}$, when the applied external magnetic field $H$ is in the interval from $H_{c2}$ to $H_{c3}$, see the pioneering works\u00a0[@SJames1963; @Gennes1964; @SJames1965; @Gennes1966; @SJames1969]. The second variant is related to an enhancement (and"
+"---\nauthor:\n- 'M. Girardi, S. Zarattini, W. Boschin, M. Nonino, I. Bartalucci , A. Mercurio, N. Nocerino, P. Rosati'\nbibliography:\n- 'biblio.bib'\ndate: 'Received / Accepted '\ntitle: 'A newly identified galaxy group thanks to tidal streams of intragroup light [^1]'\n---\n\nIntroduction {#intro}\n============\n\nGalaxy clusters evolve and increase in mass through a hierarchical merging process from poor groups to rich clusters. As shown by numerical simulations, accretion of smaller systems at the galaxy or group scale is the main channel of cluster growth (e.g., @benavides2020 and references therein). In this scenario, some of the intracluster diffuse light could originate from the group environment.\n\nDiffuse light is the light from stars that float freely in the gravitational potential of galaxy systems and that are not bound to a galaxy. For simplicity, we use the term ICL when referring to both intragroup and intracluster light. Diffuse light in galaxy systems was first discovered by @zwicky1951 in the Coma cluster and studied in the following years. We refer to @contini2021 and @montes2022 as two very recent reviews on this topic. Most of the ICL in clusters is concentrated around the brightest cluster galaxy (BCG) where it is considered to be"
+"---\nabstract: 'Recently, there has been a remarkable increase in the interest towards skeleton-based action recognition within the research community, owing to its various advantageous features, including computational efficiency, representative features, and illumination invariance. Despite this, researchers continue to explore and investigate the most optimal way to represent human actions through skeleton representation and the extracted features. As a result, the growth and availability of human action recognition datasets have risen substantially. In addition, deep learning-based algorithms have gained widespread popularity due to the remarkable advancements in various computer vision tasks. Most state-of-the-art contributions in skeleton-based action recognition incorporate a Graph Neural Network (GCN) architecture for representing the human body and extracting features. Our research demonstrates that Convolutional Neural Networks (CNNs) can attain comparable results to GCN, provided that the proper training techniques, augmentations, and optimizers are applied. Our approach has been rigorously validated, and we have achieved a score of $95\\%$ on the NTU-60 dataset.'\nauthor:\n- \nbibliography:\n- 'main.bib'\ntitle: 'Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN) '\n---\n\n**Introduction**\n================\n\nRecently, the research community has been witnessing a surging interest in skeleton-based action recognition, owing to its many advantageous attributes such as computational efficiency, informative"
+"---\nabstract: 'In the plane wave spacetime, we find that there will be a precession angle deviation between two free-falling gyroscopes when gravitational waves passed through. This kind of angle deviation is closely related to the well-known standard velocity memory effect. Initial conditions such as the separation velocity or displacement between the two gyroscopes will affect this angle deviation. The evolutions of the angle deviation are calculated for different cases. We find that in some extreme circumstance, the angle deviation\u2019s order of magnitude produced by a rotating compact binary source could be $10^{-14}$ rads. Therefore, this memory effect caused by the gravitational wave is likely to be detected in the future.'\nauthor:\n- Ke Wang\n- 'Chao-Jun Feng'\nbibliography:\n- 'GW-memory.bib'\ntitle: 'Spin vector deviation and the gravitational wave memory effect between two free-falling gyroscopes in the plane wave spacetime'\n---\n\n[^1]\n\nIntroduction\n============\n\nAfter decades of exploration, the gravitational waves from a Binary Black Hole merger was successfully detected in 2016 [@lasky_detecting_2016][@abbott_observation_2016], which marked the arrival of the era of multi-messenger astronomy.\n\nGravitational wave memory arises from the non-oscillating components of gravitational waves. The research on memory effect can be traced back to 1974 [@Zeldovich:1974gvh]. Zel[\u2019]{}dovich and Polnarev claimed"
+"---\nabstract: 'Link prediction on graphs is a fundamental problem, addressing the task of predicting potential connections between entities in a network, enabling the identification of missing or future relationships. Its applications span various domains, including social networks, recommendation systems, and biological networks, where predicting links can enhance our understanding of network structure and facilitate more effective information dissemination or resource allocation. Subgraph representation learning approaches (SGRLs), by transforming link prediction to graph classification on the subgraphs around the links, have achieved state-of-the-art performance in link prediction. However, SGRLs are computationally expensive and not scalable to large-scale graphs due to expensive subgraph-level operations. To unlock the scalability of SGRLs, we propose a new class of SGRLs, that we call *Scalable Simplified SGRL* ([*S3GRL*]{}). Aimed at faster training and inference, [*S3GRL*]{}simplifies the message passing and aggregation operations in each link\u2019s subgraph. [*S3GRL*]{}, as a scalability framework, accommodates various subgraph sampling strategies and diffusion operators to emulate computationally expensive SGRLs. We propose multiple instances of [*S3GRL*]{}and empirically study them on small to large-scale graphs. Our extensive experiments demonstrate that the proposed [*S3GRL*]{}models scale up SGRLs without significant performance compromise (even with considerable gains in some cases), while offering substantially lower computational footprints"
+"---\nabstract: 'The mechanical behavior of porous materials depends strongly on porosity and pore geometry, but also on morphological parameters characterizing the spatial arrangement of pores. Here we use bond-based peridynamics to study effects of disorder on the deformation and failure behavior of brittle porous solids both in the quasi-static limit and in case of dynamic loading scenarios. We show that structural disorder, which has a strong influence on stiffness, strength and toughness in the quasi-static limit, becomes less relevant under dynamic loading conditions.'\naddress:\n- '$^1$ Department of Materials Science, WW8-Materials Simulation, Friedrich-Alexander Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), Dr.-Mack-str. 77, 90762 F\u00fcrth, Germany'\n- '$^2$ Central Institute for Scientific Computing (ZISC), Friedrich-Alexander Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), Martensstrasse 5a, 91058 Erlangen, Germany'\nauthor:\n- 'Jonas Ritter$^1$, Shucheta Shegufta$^{1,2}$ and Michael Zaiser$^1$'\nbibliography:\n- 'periref.bib'\ntitle: Effects of disorder on deformation and failure of brittle porous materials\n---\n\n[*Keywords*]{}: Failure | Porous material | Disorder | Peridynamics\n\nIntroduction {#sec_Intro}\n============\n\nThere exist an abundance of porous materials ranging from natural or engineered cellular materials [@gibson1989modelling] over geomaterials such as sandstone [@gal1998physical] or snow [@gerling2017measuring; @sayers2021porosity] to building materials like concrete [@yaman2002active]. Inevitably the mechanical behavior and transport properties of such materials depend strongly on"
+"---\nabstract: 'In this paper, nonlinear time-history dynamic analyses of selected earthquake ground motions are conducted on designated wood-frame podium buildings and the resulting inter-story drifts are analyzed. We aim to construct a reliable region where performance-based seismic design criteria are met, such that a two-step analysis procedure can be used with high confidence. We develop a kriging metamodel with tailored adaptive sampling methods to achieve this goal in a computationally efficient manner. The input variables we consider are the normalized stiffness ratio and the normalized mass ratio of the podium building. We took a six-story wood frame built upon a one-story concrete podium as a case study for our methodology, where our results indicate that the two-step analysis procedure may be used with high confidence if its normalized stiffness ratio is at least 38 and its normalized mass ratio is between 0.5 and 1.5.'\nauthor:\n- Yuying Huang\n- Zhiyong Chen\n- 'Samuel W. K. Wong'\nbibliography:\n- 'bib.bib'\ntitle: A Kriging Metamodel with Adaptive Sampling for Seismic Evaluation of Podium Buildings\n---\n\nINTRODUCTION\\[Intro\\]\n=====================\n\nThe wood-frame podium building is a common type of mid-rise timber structure made up of a wood-frame structure (often three to six stories) built"
+"---\nabstract: 'Deep neural networks are known to be susceptible to adversarial attacks. In this work, we focus on improving adversarial robustness in the challenging zero-shot image classification setting. To address this issue, we propose LAAT, a novel Language-driven, Anchor-based Adversarial Training strategy. LAAT utilizes a text encoder to generate fixed *anchors* (normalized feature embeddings) for each category and then uses these anchors for adversarial training. By leveraging the semantic consistency of the text encoders, LAAT can enhance the adversarial robustness of the image model on novel categories without additional examples. We identify the large cosine similarity problem of recent text encoders and design several effective techniques to address it. The experimental results demonstrate that LAAT significantly improves zero-shot adversarial performance, outperforming previous state-of-the-art adversarially robust one-shot methods. Moreover, our method produces substantial zero-shot adversarial robustness when models are trained on large datasets such as ImageNet-1K and applied to several downstream datasets.'\nauthor:\n- |\n    Xiao Li$^{1}$ \u00a0\u00a0Wei Zhang$^{1}$ \u00a0\u00a0Yining Liu$^{2}$ \u00a0\u00a0Zhanhao Hu$^{1}$ \u00a0\u00a0Bo Zhang$^{1}$ \u00a0\u00a0Xiaolin Hu$^{1, 3}$[^1]\\\n    $^{1}$Department of Computer Science and Technology, BNRist, Institute for Artificial Intelligence,\\\n    IDG/McGovern Institute for Brain Research, Tsinghua Laboratory of Brain and Intelligence,\\\n    Tsinghua University, Beijing, China\\\n    $^{2}$Harbin Institute of Technolog, Weihai, China\\"
+"---\nabstract: 'Nonlinear plasmonic effects in perspective 2D materials containing low-dimensional quantum emitters can be a basis of a novel technological platform for the fabrication of fast all-plasmonic triggers, transistors, and sensors. This article considers the conditions for achieving a strong plasmon-exciton coupling in the system with quantum nanowire (NW) placed in proximity to the nanostructured graphene sheets. In the condition of strong coupling, nonlinear interaction between two surface plasmon-polariton (SPP) modes propagating along the graphene waveguide integrated with a stub nanoresonator loaded with a core-shell semiconductor NWs is investigated. Using the 2D full-wave electromagnetic simulation, we studied the different transmittance regimes of the stub with NW for both the strong pump SPP and weak signal SPP tuned to interband and intraband transition in NW, respectively. We found such a regime of NW-SPP interaction that corresponds to the destructive interference with the signal SPP transmittance through the stub less than $7 \\%$ in the case for pump SPP to be turned off. In contrast, the turning on the pump SPP leads to a transition to constructive interference in the stub and enhancement of signal SPP transmittance to $93 \\%$. In our model, the effect of plasmonic switching occurs with a"
+"---\nabstract: '[The observation of magnetic ordering in atomically thin CrI$_3$ and Cr$_2$Ge$_2$Te$_6$ monolayers has aroused intense interest in condensed matter physics and material science. Studies of van de Waals two-dimensional (2D) magnetic materials are of both fundamental importance and application interest. In particular, exciton-enhanced magneto-optical properties revealed in CrI$_3$ and CrBr$_3$ monolayers have expanded the understanding of exciton physics in 2D materials. Unlike CrI$_3$ and CrBr$_3$ with out-of-plane magnetization, CrSBr has an in-plane magnetic moment, therefore, providing a good opportunity to study the magnetic linear dichroism and high-order magneto-optical effects. Here, based on the many-body perturbation method within density-functional theory, we have studied quasiparticle electronic structure, exciton, and optical properties in CrSBr monolayer. Strongly bounded exciton has been identified with the first bright exciton located at 1.35 eV, in good agreement with an experiment of photoluminescence (Nat. Mater. **20**, 1657 (2021)). Strong contrast in the optical absorption is found between the electric fields lying along the in-plane two orthogonal directions. In accordance with a typical and realistic experimental setup, we show that the rotation angle of linear polarized light, either reflected or transmitted, could be comparable with those revealed in black phosphorene. Such large linear dichroism arises mainly from"
+"---\nabstract: 'In fluid dynamics, predicting and characterizing bifurcations, from the onset of unsteadiness to the transition to turbulence, is of critical importance for both academic and industrial applications. Different tools from dynamical systems theory can be used for this purpose. In this review, we present a concise theoretical and numerical framework focusing on practical aspects of the computation and stability analyses of steady and time-periodic solutions, with emphasis on high-dimensional systems such as those arising from the spatial discretization of the Navier-Stokes equations. Using a matrix-free approach based on Krylov methods, we extend the capabilities of the open-source high-performance spectral element-based time-stepper `Nek5000`. The numerical methods discussed are implemented in `nekStab`, an open-source and user-friendly add-on toolbox dedicated to the study of stability properties of flows in complex three-dimensional geometries. The performance and accuracy of the methods are illustrated and examined using standard benchmarks from the fluid mechanics literature. Thanks to its flexibility and domain-agnostic nature, the methodology presented in this work can be applied to develop similar toolboxes for other solvers, most importantly outside the field of fluid mechanics.'\nauthor:\n- 'Ricardo S.\u00a0Frantz'\n- 'Jean-Christophe Loiseau'\n- 'Jean-Christophe Robinet'\nbibliography:\n- 'biblio.bib'\ntitle: 'Krylov methods for large-scale"
+"---\nabstract: 'This note aims to clarify the deep relationship between birational modifications of a variety and semiorthogonal decompositions of its derived category of coherent sheaves. The result is a conjecture on the existence and properties of canonical semiorthogonal decompositions, which is a noncommutative analog of the minimal model program. We identify a mechanism for constructing semiorthogonal decompositions using Bridgeland stability conditions, and we propose that through this mechanism the quantum differential equation of the variety controls the conjectured semiorthogonal decompositions. We establish several implications of the conjectures: one direction of Dubrovin\u2019s conjecture on the existence of full exceptional collections; the $D$-equivalence conjecture; the existence of new categorical birational invariants for varieties of positive genus; and the existence of minimal noncommutative resolutions of singular varieties. Finally, we verify the conjectures for smooth projective curves by establishing a previously conjectured description of the stability manifold of $\\bP^1$.'\nauthor:\n- 'Daniel Halpern-Leistner'\nbibliography:\n- 'refs.bib'\ntitle: 'The noncommutative minimal model program.'\n---\n\nThe derived category of coherent sheaves $\\operatorname{D^b}(X)$ of a smooth projective variety $X$ often reveals hidden structure in the geometry of $X$. We recall two examples of this phenomenon:\n\nIn their seminal preprint [@BondalOrlov], Bondal and Orlov first glimpsed a"
+"---\nabstract: 'Within a robotic context, we merge the techniques of *passivity-based control* (PBC) and *reinforcement learning* (RL) with the goal of eliminating some of their reciprocal weaknesses, as well as inducing novel promising features in the resulting framework. We frame our contribution in a scenario where PBC is implemented by means of *virtual energy tanks*, a control technique developed to achieve closed-loop passivity for any arbitrary control input. Albeit the latter result is heavily used, we discuss why its practical application at its current stage remains rather limited, which makes contact with the highly debated claim that passivity-based techniques are associated with a loss of performance. The use of RL allows us to learn a control policy that can be passivized using the energy tank architecture, combining the versatility of learning approaches and the system theoretic properties which can be inferred due to the energy tanks. Simulations show the validity of the approach, as well as novel interesting research directions in energy-aware robotics.'\nauthor:\n- 'Riccardo Zanella^1^, Gianluca Palli^1^, Stefano Stramigioli^2^, Federico Califano^2^ [^1] [^2] [^3]'\nbibliography:\n- 'main.bib'\ntitle: '**Learning passive policies with virtual energy tanks in robotics** '\n---\n\n[Zanella : Learning passive control policies]{}\n\nIntroduction\n============"
+"---\nabstract: 'A wave near an isolated turning point is typically assumed to have an Airy function profile with respect to the separation distance. This description is incomplete, however, and is insufficient to describe the behavior of more realistic wavefields that are not simple plane waves. Asymptotic matching to a prescribed incoming wavefield generically introduces a phasefront curvature term that changes the characteristic wave behavior from the Airy function to that of the hyperbolic umbilic function. This function, which is one of the seven classic \u2018elementary\u2019 functions from catastrophe theory along with the Airy function, can be understood intuitively as the solution for a linearly focused Gaussian beam propagating in a linearly varying density profile, as we show. The morphology of the caustic lines that govern the intensity maxima of the diffraction pattern as one alters the density lengthscale of the plasma, the focal length of the incident beam, and also the injection angle of the incident beam are presented in detail. This morphology includes a Goos\u2013H\u00e4nchen shift and focal shift at oblique incidence that do not appear in a reduced ray-based description of the caustic. The enhancement of the intensity swelling factor for a focused wave compared to the"
+"---\nabstract: 'The uplink from a very small aperture terminal (VSAT) towards multiple satellites is considered, in this paper. VSATs can be equipped with multiple antennas, allowing parallel transmission to multiple satellites. A low-complexity precoder based on imperfect positional information of the satellites is presented. The probability distribution of the position uncertainty and the statistics of the channel elements are related by the characteristic function of the position uncertainty. This knowledge is included in the precoder design to maximize the mean signal-to-leakage-and-noise ratio (SLNR) at the satellites. Furthermore, the performance w.r.t. the inter-satellite distance is numerically evaluated. It is shown that the proposed approach achieves the capacity for perfect position knowledge and sufficiently large inter-satellite distances. In case of imperfect position knowledge, the performance degradation of the robust precoder is relatively small.'\nauthor:\n- \nbibliography:\n- './lib/IEEEabrv.bib'\n- './lib/references\\_short.bib'\ntitle: 'Robust Precoding via Characteristic Functions for VSAT to Multi-Satellite Uplink Transmission'\n---\n\nLEO satellites, 3D networks, massive MIMO, beamforming, beamspace MIMO\n\nIntroduction\n============\n\nIn future mobile networks, the terrestrial infrastructure will be extended by , consisting of , and communication satellites, forming a global heterogeneous 3D network. An integral component to these spatial networks are constellations of small satellites in"
+"---\nauthor:\n- 'Alexander Moskvin[^1], Evgenii Vasinovich, Anton Shadrin'\ntitle: 'Simple Realistic Model of Spin Reorientation in 4f-3d Compounds'\n---\n\n**Abstract:** Spin reorientation is an important phenomenon of rare-earth perovskites, orthoferrites and orthochromites. In this study, we consider a simple but realistic microscopic theory of the spontaneous spin-reorientation transitions induced by the 4f-3d interaction, more specifically, the interaction of the main Kramers doublet or non-Kramers quasi-doublet of the 4f ion with an effective magnetic field induced by the 3d sublattice. The obtained results indicate that the cause of both the temperature and the character of the spin-reorientation transition is a competition between the second and fourth order spin anisotropy of the 3d sublattice, the crystal field for 4f ions, and the 4f-3d interaction.\n\n**Keywords:** 4f-3d interaction; (quasi)doublets; spin reorientation\n\nIntroduction\n============\n\nRare-earth orthorhombic perovskites, orthoferrites *R*FeO$_3$ and orthochromites *R*CrO$_3$ (where *R* is a rare-earth ion and yttrium), exhibit many important features such as weak ferro- and antiferromagnetism, magnetization reversal, anomalous circular magnetooptics, and the phenomenon of the spontaneous spin reorientation. The spin reorientation (SR) is one of their unique properties that have attracted a lot of attention back in the 70s of the last century[@BZKL; @KP], though their exact microscopic"
+"---\nabstract: |\n    We present a fast, differentially private algorithm for high-dimensional *covariance-aware* mean estimation with nearly optimal sample complexity. Only exponential-time estimators were previously known to achieve this guarantee. Given $n$ samples from a (sub-)Gaussian distribution with unknown mean $\\mu$ and covariance $\\Sigma$, our $({\\varepsilon},\\delta)$-differentially private estimator produces $\\tilde{\\mu}$ such that $\\|\\mu - \\tilde{\\mu}\\|_{\\Sigma} \\leq \\alpha$ as long as $n \\gtrsim \\tfrac d {\\alpha^2} + \\tfrac{d \\sqrt{\\log 1/\\delta}}{\\alpha {\\varepsilon}}+\\frac{d\\log 1/\\delta}{{\\varepsilon}}$. The Mahalanobis error metric $\\|\\mu - \\hat{\\mu}\\|_{\\Sigma}$ measures the distance between $\\hat \\mu$ and $\\mu$ relative to $\\Sigma$; it characterizes the error of the sample mean. Our algorithm runs in time $\\tilde{O}(nd^{\\omega - 1} + nd/{\\varepsilon})$, where $\\omega < 2.38$ is the matrix multiplication exponent.\n\n    We adapt an exponential-time approach of @brown2021covariance, giving efficient variants of stable mean and covariance estimation subroutines that also improve the sample complexity to the nearly optimal bound above.\n\n    Our stable covariance estimator can be turned to private covariance estimation for unrestricted subgaussian distributions. With $n\\gtrsim d^{3/2}$ samples, our estimate is accurate in spectral norm. This is the first such algorithm using $n= o(d^2)$ samples, answering an open question posed by\u00a0[@alabi2022privately]. With $n\\gtrsim d^2$ samples, our estimate is accurate in Frobenius norm. This"
+"---\nabstract: 'We consider the problem of mixed linear regression (MLR), where each observed sample belongs to one of $K$ unknown linear models. In practical applications, the proportions of the $K$ components are often imbalanced. Unfortunately, most MLR methods do not perform well in such settings. Motivated by this practical challenge, in this work we propose [[`Mix-IRLS`]{}]{}, a novel, simple and fast algorithm for MLR with excellent performance on both balanced and imbalanced mixtures. In contrast to popular approaches that recover the $K$ models simultaneously, [[`Mix-IRLS`]{}]{}does it sequentially using tools from robust regression. Empirically, [[`Mix-IRLS`]{}]{}succeeds in a broad range of settings where other methods fail. These include imbalanced mixtures, small sample sizes, presence of outliers, and an unknown number of models $K$. In addition, [[`Mix-IRLS`]{}]{}outperforms competing methods on several real-world datasets, in some cases by a large margin. We complement our empirical results by deriving a recovery guarantee for [[`Mix-IRLS`]{}]{}, which highlights its advantage on imbalanced mixtures.'\nauthor:\n- 'Pini Zilber [^1]'\n- Boaz Nadler\nbibliography:\n- 'MLR.bib'\ntitle: Imbalanced Mixed Linear Regression\n---\n\n[keywords: mixture regression model, mixture of linear models, robust regression, iteratively reweighted least squares]{}\n\nIntroduction\n============\n\nIn this paper we consider a simple generalization of the"
+"---\nabstract: 'Pitch is a foundational aspect of our perception of audio signals. Pitch contours are commonly used to analyze speech and music signals and as input features for many audio tasks, including music transcription, singing voice synthesis, and prosody editing. In this paper, we describe a set of techniques for improving the accuracy of widely-used neural pitch and periodicity estimators to achieve state-of-the-art performance on both speech and music. We also introduce a novel entropy-based method for extracting periodicity and per-frame voiced-unvoiced classifications from statistical inference-based pitch estimators (e.g., neural networks), and show how to train a neural pitch estimator to simultaneously handle both speech and music data (i.e., *cross-domain* estimation) without performance degradation. While neural pitch trackers have historically been significantly slower than signal processing based pitch trackers, our estimator implementations approach the speed of state-of-the-art DSP-based pitch estimators on a standard CPU, but with significantly more accurate pitch and periodicity estimation. Our experiments show that an accurate, cross-domain pitch and periodicity estimator written in PyTorch with a hopsize of ten milliseconds can run 11.2x faster than real-time on a Intel i9-9820X 10-core 3.30 GHz CPU or 408x faster than real-time on a NVIDIA GeForce RTX 3090 GPU,"
+"---\nabstract: 'This paper considers a LQR optimal control design problem for distributed control systems with multi-agents. To control large-scale distributed systems such as smart-grid and multi-agent robotic systems over wireless communication networks, it is desired to design a feedback controller by considering various constraints on communication such as limited power, limited energy, or limited communication bandwidth, etc. In this paper, we focus on the reduction of communication energy in an LQR optimal control design problem on wireless communication networks. By considering the characteristic of wireless communication, i.e., Radio Frequency (RF) signal can spread in all directions in a broadcast way, we formulate a low-rank LQR optimal control model to reduce the communication energy in the distributed feedback control system. To solve the problem, we propose an Alternating Direction Method of Multipliers (ADMM) based algorithm. Through various numerical experiments, we demonstrate that a feedback controller designed using low-rank structure can outperform the previous work on sparse LQR optimal control design, which focuses on reducing the number of communication links in a network, in terms of energy consumption, system stability margin against noise and error in communication.'\nauthor:\n- 'Myung\u00a0Cho, Abdallah\u00a0Abdallah, and Mohammad\u00a0Rasouli[^1]'\ntitle: 'Low-rank LQR Optimal Control"
+"---\nabstract: 'Magnetic phenomena are in chemistry and condensed matter physics considered to be associated with low temperatures. That a magnetic state, or order, is stable below a critical temperature as well as becoming stronger the lower the temperature is a nearly unquestioned paradigm. It is, therefore, surprising that recent experimental observation made on supramolecular aggregates suggest that, for instance, the magnetic coercivity may increase with increasing temperature, as well as the chiral induced spin selectivity effect may be enhanced. Here, a mechanism for vibrationally stabilized magnetism is proposed and a theoretical model is introduced with which the qualitative aspects of the recent experimental findings can be explained. It is argued that anharmonic vibrations, which become increasingly occupied with increasing temperature, enables nuclear vibrations to both stabilize and sustain magnetic states. The theoretical proposal, hence, pertains to structures without inversion and/or reflection symmetries, for instance, chiral molecules and crystals.'\nauthor:\n- 'J. Fransson'\nbibliography:\n- 'CPISPref.bib'\ntitle: Vibrationally Induced Magnetism in Supramolecular Aggregates\n---\n\nMagnetism and magnetic phenomena concern central questions in physics and chemistry and remain to be major fields of research. The phenomenology of magnetism pertains to seemingly separated questions of, for instance, room temperature ferromagnetism [@NatPhysics.5.840; @NatNanotech.13.289;"
+"---\nauthor:\n- \nbibliography:\n- 'refs.bib'\n---\n\nAbstract {#abstract .unnumbered}\n========\n\nProbabilistic graphical models (PGMs) provide a compact and flexible framework to model very complex real-life phenomena. They combine the probability theory which deals with uncertainty and logical structure represented by a graph which allows to cope with the\u00a0computational complexity and also interprete and communicate the obtained knowledge. In the thesis we consider two different types of PGMs: Bayesian networks (BNs) which are static, and continuous time Bayesian networks which, as the name suggests, have temporal component. We are interested in recovering their true structure, which is the first step in learning any PGM. This is a challenging task, which is interesting in\u00a0itself from the causal point of view, for the purposes of interpretation of the model and the\u00a0decision making process. All approaches for structure learning in the thesis are united by the same idea of maximum likelihood estimation with LASSO penalty. The\u00a0problem of\u00a0structure learning is reduced to the\u00a0problem of finding non-zero coefficients in\u00a0the\u00a0LASSO estimator for a generalized linear model. In case of CTBNs we consider the problem both for complete and incomplete data. We support the theoretical results with experiments.\n\n**Keywords"
+"---\nabstract: 'Brain Computer Interface (BCI) technologies have the potential to improve the lives of millions of people around the world, whether through assistive technologies or clinical diagnostic tools. Despite advancements in the field, however, at present consumer and clinical viability remains low. A key reason for this is that many of the existing BCI deployments require substantial data collection per end-user, which can be cumbersome, tedious, and error-prone to collect. We address this challenge via a deep learning model, which, when trained across sufficient data from multiple subjects, offers reasonable performance out-of-the-box, and can be customized to novel subjects via a transfer learning process. We demonstrate the fundamental viability of our approach by repurposing an older but well-curated electroencephalography (EEG) dataset and benchmarking against several common approaches/techniques. We then partition this dataset into a transfer learning benchmark and demonstrate that our approach significantly reduces data collection burden per-subject. This suggests that our model and methodology may yield improvements to BCI technologies and enhance their consumer/clinical viability.'\nauthor:\n- \nbibliography:\n- 'references.bib'\ntitle: 'Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface'\n---\n\nIntroduction {#sec:intro}\n============\n\nBCI captures, measures and records brain function via brainwaves and relates those measurements"
+"---\nabstract: 'This paper presents a new convergent Plug-and-Play (PnP) algorithm. PnP methods are efficient iterative algorithms for solving image inverse problems formulated as the minimization of the sum of a data-fidelity term and a regularization term. PnP methods perform regularization by plugging a pre-trained denoiser in a proximal algorithm, such as Proximal Gradient Descent (PGD). To ensure convergence of\u00a0PnP schemes, many works study specific parametrizations of deep denoisers. However, existing results require either unverifiable or suboptimal hypotheses on the denoiser, or assume restrictive conditions on the parameters of the inverse problem. Observing that these limitations can be due to the proximal algorithm in use, we study a relaxed version of the PGD algorithm for minimizing the sum of a convex function and a weakly convex one. When plugged with a relaxed proximal denoiser, we show that the proposed PnP-$\\alpha$PGD algorithm converges for a wider range of regularization parameters, thus allowing more accurate image restoration.'\nauthor:\n- 'Samuel Hurault, Antonin Chambolle, Arthur Leclaire, Nicolas Papadakis'\nbibliography:\n- 'mybibliography.bib'\ntitle: 'A relaxed proximal gradient descent algorithm for convergent Plug-and-Play with proximal denoiser'\n---\n\nIntroduction\n============\n\nWe focus on the convergence of plug-and-play methods associated to the class of inverse problems:"
+"---\nabstract: 'We present new closed-form expressions for certain improper integrals of Mathematical Physics such as Ising, Box, and Associated integrals. The techniques we employ here include (a) the Method of Brackets and its modifications and suitable extensions and (b) the evaluation of the resulting Mellin-Barnes representations via the recently discovered Conic Hull method. Analytic continuations of these series solutions are then produced using the automated method of Olsson. Thus, combining all the recent advances allows for closed-form solutions for the hitherto unknown $B_3(s)$ and related integrals in terms of multivariable hypergeometric functions. Along the way, we also discuss certain complications while using the Original Method of Brackets for these evaluations and how to rectify them. The interesting cases of $C_{5,k}$ is also studied. It is not yet fully resolved for the reasons we discuss in this paper.'\nauthor:\n- 'B. Ananthanarayan [^1] Tanay Pathak[^2] Kartik Sharma[^3]'\nbibliography:\n- 'reference.bib'\ndate: |\n    Centre for High Energy Physics, Indian Institute of Science,\\\n    Bangalore-560012, Karnataka, India \ntitle: Closed Form Expressions for Certain Improper Integrals of Mathematical Physics \n---\n\nIntroduction\n============\n\nIn studies of theoretical physics and mathematics, various integrals appear whose symbolic evaluation is sought after. Gradshyteyn and Ryzik [@gradshteyn2014table] compiled a"
+"---\nabstract: 'Optimization and control of complex unsteady flows remains an important challenge due to the large cost of performing a function evaluation, i.e. a full computational fluid dynamics (CFD) simulation. Reducing the number of required function evaluations would help to decrease the computational cost of the overall optimization procedure. In this article, we consider the stochastic derivative-free surrogate-model based Dynamic COordinate search using Response Surfaces (DYCORS) algorithm [@regis_combining_2013] and propose several enhancements: First, the gradient information is added to the surrogate model to improve its accuracy and enhance the convergence rate of the algorithm. Second, the internal parameters of the radial basis function employed to generate the surrogate model are optimized by minimizing the leave-one-out error in the case of the original algorithm and by using the gradient information in the case of the gradient-enhanced version. We apply the resulting optimization algorithm to the minimization of the total pressure loss through a linear cascade of blades, and we compare the results obtained with the stochastic algorithms at different Reynolds numbers with a gradient-based optimization algorithm. The results show that stochastic optimization outperforms gradient-based optimization even at very low $Re$ numbers, and that the proposed gradient-enhanced version improves the convergence"
+"---\nabstract: 'Existing bounds on the generalization error of deep networks assume some form of smooth or bounded dependence on the input variable, falling short of investigating the mechanisms controlling such factors in practice. In this work, we present an extensive experimental study of the empirical Lipschitz constant of deep networks undergoing double descent, and highlight non-monotonic trends strongly correlating with the test error. Building a connection between parameter-space and input-space gradients for SGD around a critical point, we isolate two important factors \u2013 namely loss landscape curvature and distance of parameters from initialization \u2013 respectively controlling optimization dynamics around a critical point and bounding model function complexity, even beyond the training data. Our study presents novel insights on implicit regularization via overparameterization, and effective model complexity for networks trained in practice.\u00a0[^1][^2]'\nauthor:\n- |\n    Matteo Gamba\\\n    KTH\\\n    Sweden\\\n    `mgamba@kth.se`\\\n    Hossein Azizpour\\\n    KTH\\\n    Sweden\\\n    `azizpour@kth.se`\\\n    Mrten Bj\u00f6rkman\\\n    KTH\\\n    Sweden\\\n    `celle@kth.se`\\\nbibliography:\n- 'references.bib'\ntitle: On the Lipschitz Constant of Deep Networks and Double Descent\n---\n\nIntroduction {#sec:introduction}\n============\n\nA longstanding question towards understanding the remarkable generalization ability of deep networks is characterizing the hypothesis class of models *trained in practice*, thus isolating properties of the networks\u2019 model function that"
+"---\nabstract: |\n    In this article, we consider weak del Pezzo surfaces defined over a finite field, and their associated, singular, anticanonical models.\n\n    We first define arithmetic types for such surfaces, by considering the Frobenius actions on their Picard groups; this extends the classification of Swinnerton-Dyer and Manin for ordinary del Pezzo surfaces. We also show that some invariants of the surfaces only depend on the above type.\n\n    Then we study an inverse Galois problem for singular del Pezzo surfaces having degree $3\\leq d\\leq 6$: we describe which types can occur over a given finite field (of odd characteristic when $3\\leq d\\leq 4$).\naddress:\n- 'LAMIA, Universit\u00e9 des Antilles'\n- 'Institut de Math\u00e9matiques de Toulouse, UMR 5219'\nauthor:\n- R\u00e9gis Blache\n- Emmanuel Hallouin\nbibliography:\n- 'SingularDelPezzo.bib'\ntitle: Classification of singular del Pezzo surfaces over finite fields\n---\n\nIntroduction {#introduction .unnumbered}\n============\n\nIn this article, we study certain del Pezzo surfaces defined over a finite field. Recall that a smooth projective surface $X$ is a *weak del Pezzo surface* when its anticanonical divisor $-K_X$ is big and nef; its *degree* is the self-intersection number $d:=K_X^{\\cdot2}$. If moreover $-K_X$ is ample, we call $X$ an *ordinary* del Pezzo surface. When a"
+"---\nabstract: 'Federated learning (FL) has recently emerged as a promising paradigm that trains machine learning (ML) models on clients\u2019 devices in a distributed manner without the need of transmitting clients\u2019 data to the FL server. In many applications of ML (e.g., image classification), the labels of training data need to be generated manually by human agents (e.g., recognizing and annotating objects in an image), which are usually costly and error-prone. In this paper, we study FL with crowdsourced data labeling where the local data of each participating client of FL are labeled manually by the client. We consider the strategic behavior of clients who may not make desired effort in their local data labeling and local model computation (quantified by the mini-batch size used in the stochastic gradient computation), and may misreport their local models to the FL server. We first characterize the performance bounds on the training loss as a function of clients\u2019 data labeling effort, local computation effort, and reported local models, which reveal the impacts of these factors on the training loss. With these insights, we devise Labeling and Computation Effort and local Model Elicitation (LCEME) mechanisms which incentivize strategic clients to make truthful efforts as"
+"---\nabstract: 'Neural network controllers (NNCs) have shown great promise in autonomous and cyber-physical systems. Despite the various verification approaches for neural networks, the safety analysis of NNCs remains an open problem. Existing verification approaches for neural network control systems (NNCSs) either can only work on a limited type of activation functions, or result in non-trivial over-approximation errors with time evolving. This paper proposes a verification framework for NNCS based on Lipschitzian optimisation, called DeepNNC. We first prove the Lipschitz continuity of closed-loop NNCSs by unrolling and eliminating the loops. We then reveal the working principles of applying Lipschitzian optimisation on NNCS verification and illustrate it by verifying an adaptive cruise control model. Compared to state-of-the-art verification approaches, DeepNNC shows superior performance in terms of efficiency and accuracy over a wide range of NNCs. We also provide a case study to demonstrate the capability of DeepNNC to handle a real-world, practical, and complex system. Our tool **DeepNNC** is available at <https://github.com/TrustAI/DeepNNC>.'\nauthor:\n- 'Chi Zhang^1^, Wenjie Ruan^1^$^{\\dagger}$, Peipei Xu ^2^'\nbibliography:\n- 'aaai23.bib'\ntitle: Reachability Analysis of Neural Network Control Systems\n---\n\nIntroduction\n============\n\nNeural network controllers have gained increasing interest in the autonomous industry and cyber-physical systems because of"
+"---\nabstract: 'We perform physical and numerical experiments to study the stick-slip response of a stack of slabs in contact through dry frictional interfaces driven in quasistatic shear. The ratio between the drive\u2019s stiffness and the slab\u2019s shear stiffness controls the presence or absence of slip synchronization. A sufficiently high stiffness ratio leads to synchronization, comprising periodic slip events in which all interfaces slip simultaneously. A lower stiffness ratio leads to asynchronous slips and, experimentally, to the stick-slip amplitude being broadly distributed as the number of layers in the stack increases. We interpret this broadening in light of the combined effect of surface disorder, complex loading paths of the asynchronous slips, and creep. Consequently, the ageing rate can be readily extracted from the stick-slip cycle. The extracted aging rate is found to be of the same order of magnitude as existing experimental results on a similar material. Finally, we discuss the emergence of slow slips and an increase in creep-rate variations when more slabs are added to the stack.'\nauthor:\n- Samuel Poincloux\n- 'Pedro M.\u00a0Reis'\n- 'Tom W.J.\u00a0de Geus'\nbibliography:\n- 'library.bib'\ntitle: 'Stick-slip synchronization in stack of elastically coupled frictional interfaces'\n---\n\nIntroduction\n============\n\nMultiple frictional"
+"---\nabstract: 'This paper addresses the problem of visual feature representation learning with an aim to improve the performance of end-to-end reinforcement learning (RL) models. Specifically, a novel architecture is proposed that uses a heterogeneous loss function, called CRC loss, to learn improved visual features which can then be used for policy learning in RL. The CRC-loss function is a combination of three individual loss functions, namely, contrastive, reconstruction and consistency loss. The feature representation is learned in parallel to the policy learning while sharing the weight updates through a Siamese Twin encoder model. This encoder model is augmented with a decoder network and a feature projection network to facilitate computation of the above loss components. Through empirical analysis involving latent feature visualization, an attempt is made to provide an insight into the role played by this loss function in learning new action-dependent features and how they are linked to the complexity of the problems being solved. The proposed architecture, called CRC-RL, is shown to outperform the existing state-of-the-art methods on the challenging Deep mind control suite environments by a significant margin thereby creating a new benchmark in this field.'\nauthor:\n- |\n    Darshita Jain$^1$, Anima Majumder$^1$, Samrat Dutta$1$, Swagat"
+"---\nabstract: 'Slip is a very common phenomena present in wheeled mobile robotic systems. It has undesirable consequences such as wasting energy and impeding system stability. To tackle the challenge of mobile robot trajectory tracking under slippery conditions, we propose a hierarchical framework that learns and adapts gains of the tracking controllers simultaneously online. Concretely, a reinforcement learning (RL) module is used to auto-tune parameters in a lateral predictive controller and a longitudinal speed PID controller. Experiments show the necessity of simultaneous gain tuning, and have demonstrated that our online framework outperforms the best baseline controller using fixed gains. By utilizing online gain adaptation, our framework achieves robust tracking performance by rejecting slip and reducing tracking errors when the mobile robot travels through various terrains.'\naddress: 'Department of Mechanical Engineering, University of California, Berkeley, CA 94720 USA (e-mail: {hgao9, ruizhouzr, tomizuka, zhuoxu}@berkeley.edu)'\nauthor:\n- Huidong Gao\n- Rui Zhou\n- Masayoshi Tomizuka\n- Zhuo Xu\nbibliography:\n- 'references.bib'\ntitle: Online Learning Based Mobile Robot Controller Adaptation for Slip Reduction\n---\n\nTrajectory Tracking, Slip Rejection, Reinforcement Learning, Hierarchical Control\n\nIntroduction\n============\n\nBackground and Motivation\n-------------------------\n\nMobile robots are used in various industrial applications such as manufacturing, process and aerospace. They often"
+"---\nauthor:\n- 'M. Antonelli, U. Dal Lago, D. Davoli, I. Oitavem, P. Pistone'\nbibliography:\n- 'bib.bib'\ntitle: '**An Arithmetic Theory for the Poly-Time Random Functions**'\n---\n\nWe introduce a new bounded theory ${\\textsf{\\textbf{RS}}^1_2}$, and show that the functions which are $\\Sigma^b_1$-representable in it are precisely random functions which can be computed in polynomial time. Concretely, we pass through the class of oracle functions over strings ${\\mathcal{POR}}$, which is introduced in Section\u00a0\\[sec:introRS\\], together with ${\\textsf{\\textbf{RS}}^1_2}$. Then, we show that functions computed by poly-time PTMs are *arithmetically* characterized by a class of probabilistic bounded formulas: in Section\u00a0\\[sec:RStoPOR\\], we prove that the class of poly-time oracle functions is equivalent to that of functions which are $\\Sigma^b_1$-representable in ${\\textsf{\\textbf{RS}}^1_2}$, and in Section\u00a0\\[sec:PORandPTM\\] we establish the converse.\n\nOverview\n========\n\nUsual characterizations of poly-time (deterministic) functions in bounded arithmetic are obtained by two \u201cmacro\u201d results\u00a0[@Buss86; @Ferreira90]. Some Cobham-style algebra for poly-time functions is introduced and shown equivalent to (1) that of functions computed by TMs running in polynomial time, and (2) that of functions which are $\\Sigma^b_1$-representable in the proper bounded theory. The global structure of our proof follows a similar path, with an algebra of oracle recursive function, called ${\\mathcal{POR}}$,"
+"---\nabstract: 'Growing robots based on the eversion principle are known for their ability to extend rapidly, from within, along their longitudinal axis, and, in doing so, reach deep into hitherto inaccessible, remote spaces. Despite many advantages, eversion robots also present significant challenges, one of which is maintaining sensory payload at the tip without restricting the eversion process. A variety of tip mechanisms have been proposed by the robotics community, among them rounded caps of relatively complex construction that are not always compatible with functional hardware, such as sensors or navigation pouches, integrated with the main eversion structure. Moreover, many tip designs incorporate rigid materials, reducing the robot\u2019s flexibility and consequent ability to navigate through narrow openings. Here, we address these shortcomings and propose a design to overcome them: a soft, entirely fabric based, cylindrical cap that can easily be slipped onto the tip of eversion robots. Having created a series of caps of different sizes and materials, an experimental study was conducted to evaluate our new design in terms of four key aspects: eversion robot made from multiple layers of everting material, solid objects protruding from the eversion robot, squeezability, and navigability. In all scenarios, we can show that"
+"---\nabstract: 'The majority of computer vision algorithms fail to find higher-order (abstract) patterns in an image so are not robust against adversarial attacks, unlike human lateralized vision. Deep learning considers each input pixel in a homogeneous manner such that different parts of a \u201clocality-sensitive hashing table\u201d are often not connected, meaning higher-order patterns are not discovered. Hence these systems are not robust against noisy, irrelevant, and redundant data, resulting in the wrong prediction being made with high confidence. Conversely, vertebrate brains afford heterogeneous knowledge representation through lateralization, enabling modular learning at different levels of abstraction. This work aims to verify the effectiveness, scalability, and robustness of a lateralized approach to real-world problems that contain noisy, irrelevant, and redundant data. The experimental results of multi-class ($200$ classes) image classification show that the novel system effectively learns knowledge representation at multiple levels of abstraction making it more robust than other state-of-the-art techniques. Crucially, the novel lateralized system outperformed all the state-of-the-art deep learning-based systems for the classification of normal and adversarial images by $19.05\\% - 41.02\\%$ and $1.36\\% - 49.22\\%$, respectively. Findings demonstrate the value of heterogeneous and lateralized learning for computer vision applications.'\nauthor:\n- 'Abubakar\u00a0Siddique,\u00a0 Will\u00a0N.\u00a0Browne,"
+"---\nauthor:\n- 'Patricio\u00a0E.\u00a0Cubillos'\n- Luca\u00a0Fossati\n- Tommi\u00a0Koskinen\n- Chenliang\u00a0Huang\n- 'A.\u00a0G.\u00a0Sreejith'\n- Kevin\u00a0France\n- 'P. Wilson Cauley'\n- 'Carole\u00a0A.\u00a0Haswell'\nbibliography:\n- 'nuv\\_HD189733b.bib'\ntitle: 'The [[[*Hubble*]{}]{}]{}/STIS Near-ultraviolet Transmission Spectrum of HD\u00a0189733b'\n---\n\n[The benchmark hot Jupiter [[HD\u00a0189733b]{}]{} has been a key target to lay out the foundations of comparative planetology for giant exoplanets. As such, [[HD\u00a0189733b]{}]{} has been extensively studied across the electromagnetic spectrum. Here, we report the observation and analysis of three transit light curves of [[HD\u00a0189733b]{}]{} obtained with [[[*Hubble*]{}]{}]{}/STIS in the near ultraviolet, the last remaining unexplored spectral window to be probed with present-day instrumentation for this planet. The NUV is a unique window for atmospheric mass-loss studies owing to the strong resonance lines and large photospheric flux. Overall, from a low-resolution analysis ($R=50$) we found that the planet\u2019s near-ultraviolet spectrum is well characterized by a relatively flat baseline, consistent with the optical-infrared transmission, plus two regions at $\\sim$2350 and $\\sim$2600 [\u00c5]{} that exhibit a broad and significant excess absorption above the continuum. From an analysis at a higher resolution ($R=4700$), we found that the transit depths at the core of the magnesium resonance"
+"---\nabstract: 'Suppose $Q(x)$ is a real $n\\times n$ regular symmetric positive semidefinite matrix polynomial. Then it can be factored as $$Q(x) = G(x)^TG(x),$$ where $G(x)$ is a real $n\\times n$ matrix polynomial with degree half that of $Q(x)$ if and only if $\\det(Q(x))$ is the square of a nonzero real polynomial. We provide a constructive proof of this fact, rooted in finding a skew-symmetric solution to a modified algebraic Riccati equation $$XSX - XR + R^TX + P = 0,$$ where $P,R,S$ are real $n\\times n$ matrices with $P$ and $S$ real symmetric. In addition, we provide a detailed algorithm for computing the factorization.'\naddress: 'Department of Mathematics, Drexel University, Philadelphia, PA 19104, USA'\nauthor:\n- Sarah Gift\n- 'Hugo J. Woerdeman'\nbibliography:\n- 'FPaperV4.bib'\ntitle: Real Factorization of Positive Semidefinite Matrix Polynomials \n---\n\nPositive semidefinite matrix polynomial ,Algebraic Riccati equation ,Matrix factorization 47A68 ,46C20 ,15B48 ,93B05\n\nIntroduction\n============\n\nThe Fej\u00e9r-Riesz factorization was first shown for matrix polynomials by Rosenblatt [@494] and Helson [@307]. Its version on the real line is the following: given a matrix polynomial $Q(x) = \\sum_{i=0}^{2m}Q_ix^i$ with $Q_i$ Hermitian and $Q(x)$ positive semidefinite for all $x\\in{\\mathbb{R}}$, we can factorize it as $$Q(x) = G(x)^*G(x) ,$$"
+"---\nabstract: 'What is the impact of human-computer interaction research on industry? [While it is impossible to track all research impact pathways, the growing literature on translational research impact measurement offers patent citations as one measure of how industry recognizes and draws on research in its inventions. In this paper, we perform a large-scale measurement study primarily of patent citations to premier HCI research venues, tracing how HCI research are cited in United States patents over the last 30 years. We observe that 20.1% of papers from these venues, including 60\u201380% of papers at UIST and 13% of papers in a broader dataset of SIGCHI-sponsored venues overall, are cited by patents\u2014far greater than premier venues in science overall (9.7%) and NLP (11%).]{} However, the time lag between a patent and its paper citations is long (10.5 years) and getting longer, suggesting that HCI research and practice may not be efficiently connected.'\nauthor:\n- Hancheng Cao\n- Yujie Lu\n- Yuting Deng\n- 'Daniel A. McFarland'\n- 'Michael S. Bernstein'\nbibliography:\n- 'reference.bib'\n---\n\n&lt;ccs2012&gt; &lt;concept&gt; &lt;concept\\_id&gt;10003120.10003121.10011748&lt;/concept\\_id&gt; &lt;concept\\_desc&gt;Human-centered computing\u00a0Empirical studies in HCI&lt;/concept\\_desc&gt; &lt;concept\\_significance&gt;500&lt;/concept\\_significance&gt; &lt;/concept&gt; &lt;/ccs2012&gt;\n\nIntroduction\n============\n\nWhat is the impact of human-computer interaction research beyond academia? Does HCI research"
+"---\nauthor:\n- \n- '[^1]'\ntitle: 'Massive MIMO in 5G: How Beamforming, Codebooks, and Feedback Enable Larger Arrays'\n---\n\nAbstract {#abstract .unnumbered}\n========\n\nMassive multiple-input multiple-output (MIMO) is an important technology in fifth generation (5G) cellular networks and beyond. To help design the beamforming at the base station, 5G has introduced new support in the form of flexible feedback and configurable antenna array geometries. In this article, we present an overview of MIMO throughout the mobile standards, highlight the new beam-based feedback system in 5G NR, and describe how this feedback system enables massive MIMO through beam management. Finally, we conclude with challenges related to massive MIMO in 5G.\n\nIntroduction {#introduction .unnumbered}\n============\n\nMultiple-input-multiple-output (MIMO) is a general class of technologies that incorporates a number of transmission and reception techniques using multiple antennas. Spatial multiplexing (SM), among the most well known MIMO methods, involves the transmission of multiple data streams, called layers in 3GPP specifications. In a single user (SU-)MIMO setting, spatial multiplexing involves directing multiple streams to one user, resulting in an increase in the spectral efficiency proportional to the number of streams. Similarly, the concept can be applied in a multiple user (MU-)MIMO setting, where the layers may"
+"---\nabstract: 'A generalized spline on an edge labeled graph $(G,\\alpha)$ is defined as a vertex labeling, such that the difference of labels on adjacent vertices lies in the ideal generated by the edge label. We study generalized splines over greatest common divisor domains and present a determinantal basis condition for generalized spline modules on arbitrary graphs. The main result of the paper answers a conjecture that appeared in several papers.'\naddress:\n- TED University\n- Hacettepe University\nauthor:\n- Seher F\u0130\u015fekc\u0130\n- 'Samet Sar[i]{}o\u011flan'\nbibliography:\n- 'fs.bib'\ntitle: Basis condition for generalized spline modules\n---\n\nIntroduction {#intro}\n============\n\nSplines are known as piecewise polynomial functions defined on faces of polyhedral complexes with a smoothness condition. They control the curvature of a surface in engineering and industry. They also appear in numerical analysis, optimization theory, computer-aided design and modeling, and solutions of differential equations.\n\nThe notion of a generalized spline is introduced by Gilbert, Viel, and Tymoczko [@GPT]. As a generalization of classical splines, generalized splines are defined on edge labeled graphs over arbitrary rings instead of polyhedral complexes over polynomial rings. The set of all generalized splines on an edge labeled graph over a base ring $R$ has a"
+"---\nabstract: 'Spreadsheets are a vital tool for end-user data management. Using large language models for formula authoring assistance in these environments can be difficult, as these models are expensive to train and challenging to deploy due to their size (up to billions of parameters). We present [`FLAME`]{}, a transformer-based model trained exclusively on Excel formulas that leverages domain insights to achieve competitive performance while being substantially smaller (60M parameters) and training on two orders of magnitude less data. We curate a training dataset using sketch deduplication, introduce an Excel-specific formula tokenizer, and use domain-specific versions of masked span prediction and noisy auto-encoding as pre-training objectives. We evaluate [`FLAME`]{} on formula repair, formula completion, and similarity-based formula retrieval. [`FLAME`]{} can outperform much larger models, such as the Davinci (175B) and Cushman (12B) variants of Codex and CodeT5 (220M), in 10 of 14 evaluation settings for the repair and completion tasks. For formula retrieval, [`FLAME`]{} outperforms CodeT5, CodeBERT, and GraphCodeBERT.'\nauthor:\n- |\n    Harshit Joshi^1^[^1], Abishai Ebenezer, Jos\u00e9 Cambronero Sanchez^2^[^2], Sumit Gulwani^2^,\\\n    Aditya Kanade^3^, Vu Le^2^, Ivan Radi\u010dek, Gust Verbruggen^2^\nbibliography:\n- 'ref.bib'\ntitle: 'FLAME: A Small Language Model for Spreadsheet Formulas'\n---\n\n![Model performance (top-5 candidate cutoff) for last-mile repair"
+"---\nabstract: 'We investigate the use of amplitude amplification on the gate-based model of quantum computing as a means for solving combinatorial optimization problems. This study focuses primarily on QUBO (quadratic unconstrained binary optimization) problems, which are well-suited for qubit superposition states. Specifically, we demonstrate circuit designs which encode QUBOs as \u2018cost oracle\u2019 operations $U_{\\textrm{C}}$, which when combined with the standard Grover diffusion operator $U_{\\textrm{s}}$ lead to high probabilities of measurement for states corresponding to the optimal and near optimal solutions. In order to achieve these probabilities, a single scalar parameter $p_{\\textrm{s}}$ is required, which we show can be found through a variational quantum-classical hybrid approach.'\nauthor:\n- 'Daniel Koch$^{1}$$^{*}$, Massimiliano Cutugno$^{1}$, Saahil Patel$^{1}$, Laura Wessing$^{1}$, Paul M. Alsing$^{1}$'\ntitle: Variational Amplitude Amplification for Solving QUBO Problems\n---\n\nIntroduction\n============\n\nAmplitude amplification is a quantum algorithm strategy that is capable of circumventing one of quantum computing\u2019s most difficult challenges: probabilistic measurements. Originally proposed by Grover in 1996 [@grover], and later shown to be optimal [@boyer; @bennett], the combination of his oracle $U_{\\textrm{G}}$ and \u2018diffusion\u2019 $U_{\\textrm{s}}$ operators is able to drive a quantum system to a superposition state where one (or multiple) basis state(s) has nearly 100% probability of being measured."
+"---\nabstract: 'The popularity and relative openness of Android means it is a popular target for malware. Over the years, various studies have found that machine learning models can effectively discriminate malware from benign applications. However, as the operating system evolves, so does malware, bringing into question the findings of these previous studies, many of which used small, outdated, and often imbalanced datasets. In this paper, we reimplement 16 representative past works and evaluate them on a balanced, relevant and up-to-date dataset comprising 124,000 Android applications. We also carry out new experiments designed to fill holes in existing knowledge, and use our findings to identify the most effective features and models to use for Android malware detection within a contemporary environment. Our results suggest that accuracies of up to 96.8% can be achieved using static features alone, with a further 1% achievable using more expensive dynamic analysis approaches. We find the best models to be random forests built from API call usage and TCP network traffic features.'\nauthor:\n- 'Ali Muzaffar, Hani Ragab Hassen,\u00a0 Hind Zantout, Michael A Lones,\u00a0'\nbibliography:\n- 'bib.bib'\n- 'library.bib'\ntitle: A Comprehensive Investigation of Feature and Model Importance in Android Malware Detection\n---\n\nAndroid,"
+"---\nabstract: 'We present a cut finite element method for the heat equation on two overlapping meshes: a stationary background mesh and an overlapping mesh that evolves inside/\u201con top\u201d of it. Here the overlapping mesh is prescribed a simple discontinuous evolution, meaning that its location, size, and shape as functions of time are *discontinuous* and *piecewise constant*. For the discrete function space, we use continuous Galerkin in space and discontinuous Galerkin in time, with the addition of a discontinuity on the boundary between the two meshes. The finite element formulation is based on Nitsche\u2019s method. The simple discontinuous mesh evolution results in a space-time discretization with a slabwise product structure between space and time which allows for existing analysis methodologies to be applied with only minor modifications. We follow the analysis methodology presented by Eriksson and Johnson in\u00a0[@Eriksson1991; @Eriksson1995]. The greatest modification is the introduction of a Ritzlike \u201cshift operator\u201d that is used to obtain the discrete strong stability needed for the error analysis. The shift operator generalizes the original analysis to some methods for which the discrete subspace at one time does not lie in the space of the stiffness form at the subsequent time. The error analysis"
+"---\nabstract: 'Graph Neural Networks (GNNs) are powerful in learning rich network representations that aid the performance of downstream tasks. However, recent studies showed that GNNs are vulnerable to adversarial attacks involving node injection and network perturbation. Among these, node injection attacks are more practical as they don\u2019t require manipulation in the existing network and can be performed more realistically. In this paper, we propose a novel problem statement \u2013 a [*class-specific poison attack*]{} on graphs in which the attacker aims to misclassify specific nodes in the target class into a different class using node injection. Additionally, nodes are injected in such a way that they camouflage as benign nodes. We propose [`NICKI`]{}, a novel attacking strategy that utilises an optimization-based approach to sabotage the performance of GNN-based node classifiers. [`NICKI`]{}\u00a0works in two phases \u2013 it first learns the node representation and then generates the features and edges of the injected nodes. Extensive experiments and ablation studies on four benchmark networks show that [`NICKI`]{}\u00a0is consistently better than four baseline attacking strategies for misclassifying nodes in the target class. We also show that the injected nodes are properly camouflaged as benign, thus making the poisoned graph indistinguishable from its"
+"---\nabstract: |\n    **Purpose**: Generalization is one of the main challenges of computational pathology. Slide preparation heterogeneity and the diversity of scanners lead to poor model performance when used on data from medical centers not seen during training. In order to achieve stain invariance in breast invasive carcinoma patch classification, we implement a stain translation strategy using cycleGANs for unsupervised image-to-image translation. Those models often suffer from lack of proper metrics to monitor and stop the training at a particular point. In this work, we also introduce a novel method to solve this issue.\n\n    **Approach**: We compare three cycleGAN-based approaches to a baseline classification model obtained without any stain invariance strategy. Two of the proposed approaches use cycleGAN\u2019s translations at inference or training in order to build stain-specific classification models. The last method uses them for stain data augmentation during training. This constrains the classification model to learn stain-invariant features. Regarding CycleGANs\u2019 training monitoring, we leverage Fr\u00e9chet Inception Distance (FID) between generated and real samples and use it as a stopping criterion. We compare CycleGANs\u2019 models stopped using this criterion and models stopped at a fixed number of epochs.\n\n    **Results**: Baseline metrics are set by training and testing the baseline"
+"---\nabstract: 'We propose a Small Area Estimation model based on Generalized Additive Models for Location, Scale and Shape (SAE-GAMLSS), for the estimation of household economic indicators. SAE-GAMLSS release the exponential family distributional assumption and allow each distributional parameter to depend on covariates. A bootstrap approach to estimate MSE is proposed. The SAE-GAMLSS estimator shows a largely better performance than the well-known EBLUP, under various simulated scenarios. Based on SAE-GAMLSS per-capita consumption of Italian and foreign households in Italian regions, in urban and rural areas, is estimated. Results show that the well-known Italian North-South divide does not hold for foreigners.'\nauthor:\n- 'Lorenzo Mori[[^1] ]{}'\n- Maria Rosaria Ferrante\ntitle: 'Small Area Estimation of Household Economic Indicators under Unit-Level Generalized Additive Models for Location, Scale and Shape'\n---\n\n[***Keywords:*** GAMLSS, kurtosis, per-capita expenditure, skewness, urban-rural disparity]{}\n\nIntroduction {#sec1}\n============\n\nSample surveys are designed to provide reliable estimates for the whole reference population and, occasionally, for large sub-populations. For specific sub-populations, the sample size could be too small to produce design-based estimates with an acceptable level of variability. In this case, small area estimation (SAE) models are used to estimate sub-population parameters, where sub-population can be defined using geographical areas and/or"
+"---\nabstract: 'Company financial risk is ubiquitous and early risk assessment for listed companies can avoid considerable losses. Traditional methods mainly focus on the financial statements of companies and lack the complex relationships among them. However, the financial statements are often biased and lagged, making it difficult to identify risks accurately and timely. To address the challenges, we redefine the problem as **company financial risk assessment on tribe-style graph** by taking each listed company and its shareholders as a tribe and leveraging financial news to build inter-tribe connections. Such tribe-style graphs present different patterns to distinguish risky companies from normal ones. However, most nodes in the tribe-style graph lack attributes, making it difficult to directly adopt existing graph learning methods (e.g., Graph Neural Networks(GNNs)). In this paper, we propose a novel **H**ierarchical **G**raph **N**eural **N**etwork (TH-GNN) for **T**ribe-style graphs via two levels, with the first level to encode the structure pattern of the tribes with contrastive learning, and the second level to diffuse information based on the inter-tribe relations, achieving effective and efficient risk assessment. Extensive experiments on the real-world company dataset show that our method achieves significant improvements on financial risk assessment over previous competing methods. Also, the extensive"
+"---\nabstract: 'We introduce the [*Blackwell discount factor*]{} for Markov Decision Processes (MDPs). Classical objectives for MDPs include discounted, average, and Blackwell optimality. Many existing approaches to computing average-optimal policies solve for discounted optimal policies with a discount factor close to $1$, but they only work under strong or hard-to-verify assumptions such as ergodicity or weakly communicating MDPs. In this paper, we show that when the discount factor is larger than the [*Blackwell discount factor*]{} ${\\gamma_{\\sf bw}}$, all discounted optimal policies become Blackwell- and average-optimal, and we derive a general upper bound on ${\\gamma_{\\sf bw}}$. The upper bound on ${\\gamma_{\\sf bw}}$ provides the first reduction from average and Blackwell optimality to discounted optimality, [*without any assumptions*]{}, and new polynomial-time algorithms for average- and Blackwell-optimal policies. Our work brings new ideas from the study of polynomials and algebraic numbers to the analysis of MDPs. Our results also apply to robust MDPs, enabling the first algorithms to compute robust Blackwell-optimal policies.'\nauthor:\n- |\n    Julien Grand-Cl[\u00e9]{}ment\\\n    Information System and Operations Management Department, HEC Paris\\\n    `grand-clement@hec.fr`\n- |\n    Marek Petrik\\\n    Department of Computer Science, University of New Hampshire\\\n    `mpetrik@cs.unh.edu`\nbibliography:\n- 'ref.bib'\ntitle: Reducing Blackwell and Average Optimality to Discounted MDPs via the"
+"---\nabstract: |\n    Recent works have shown that in contrast to classical linear elastic fracture mechanics, endowing crack fronts in a brittle solid with Steigmann-Ogden surface elasticity yields a model that predicts bounded strains at the crack tips for plane-strain problems. However, a logarithmic singularity is still present in general for anti-plane shear (mode-III fracture) even when Steigmann-Ogden surface elasticity is incorporated.\n\n    Motivated by obtaining a model of brittle fracture capable of predicting bounded strains for all modes of loading, we formulate an exact general theory of a bulk solid containing a boundary surface with strain-gradient surface elasticity. For planar reference surfaces parameterized by flat coordinates, the form of surface elasticity reduces to that introduced by Hilgers and Pipkin, and when the surface energy is independent of the surface gradient of the stretching, the theory reduces to that of Steigmann and Ogden. We give a full discussion of material symmetry using Murdoch and Cohen\u2019s extension of Noll\u2019s theory. We present a model quadratic surface energy that incorporates resistance to geodesic distortion, satisfies strong ellipticity, and requires the same material constants found in the Steigmann-Ogden theory.\n\n    Finally, we derive and apply the linearized theory to mode-III fracture in an infinite plate."
+"---\nabstract: 'Ensuring safe, real-time motion planning in arbitrary environments requires a robotic manipulator to avoid collisions, obey joint limits, and account for uncertainties in the mass and inertia of objects and the robot itself. This paper proposes Autonomous Robust Manipulation via Optimization with Uncertainty-aware Reachability (ARMOUR), a provably-safe, receding-horizon trajectory planner and tracking controller framework for robotic manipulators to address these challenges. ARMOUR first constructs a robust controller that tracks desired trajectories with bounded error despite uncertain dynamics. ARMOUR then uses a novel recursive Newton-Euler method to compute all inputs required to track any trajectory within a continuum of desired trajectories. Finally, ARMOUR over-approximates the swept volume of the manipulator; this enables one to formulate an optimization problem that can be solved in real-time to synthesize provably-safe motions. This paper compares ARMOUR to state of the art methods on a set of challenging manipulation examples in simulation and demonstrates its ability to ensure safety on real hardware in the presence of model uncertainty without sacrificing performance. Project page: <https://roahmlab.github.io/armour/>.'\nauthor:\n- 'Jonathan Michaux$^{1}$, Patrick Holmes$^{1}$, Bohao Zhang$^{1}$, Che Chen$^{1}$, Baiyue Wang$^{1}$, Shrey Sahgal$^{1}$, Tiancheng Zhang$^{1}$, Sidhartha Dey$^{4}$, Shreyas Kousik$^{2}$, and Ram Vasudevan$^{1,3}$ [^1] [^2] [^3] [^4] [^5]'\nbibliography:\n-"
+"---\nabstract: |\n    The canonical algorithm for differentially private mean estimation is to first clip the samples to a bounded range and then add noise to their empirical mean. Clipping controls the sensitivity and, hence, the variance of the noise that we add for privacy. But clipping also introduces statistical bias. We prove that this tradeoff is inherent \u2013 no algorithm can simultaneously have low bias, low variance, and low privacy loss for arbitrary distributions.\n\n    On the positive side, we show that unbiased mean estimation is possible under approximate differential privacy if we assume that the distribution is symmetric. Furthermore, we show that, even if we assume that the data is sampled from a Gaussian, unbiased mean estimation is impossible under pure or concentrated differential privacy.\nauthor:\n- 'Gautam Kamath[^1]'\n- 'Argyris Mouzakis[^2]'\n- 'Matthew Regehr[^3]'\n- 'Vikrant Singhal[^4]'\n- 'Thomas Steinke[^5]'\n- 'Jonathan Ullman[^6]'\nbibliography:\n- 'biblio.bib'\nnocite: '[@Steinke2022]'\ntitle: ' A Bias-Variance-Privacy Trilemma for Statistical Estimation[^7] '\n---\n\nIntroduction\n============\n\nWhile the goal of statistical inference and machine learning is to learn about a population, rather than the sample, most statistical and learning algorithms reveal lot of information that is specific to their sample, raising concerns about the"
+"---\nabstract: 'In last decades, legal case search has received more and more attention. Legal practitioners need to work or enhance their efficiency by means of class case search. In the process of searching, legal practitioners often need the search results under several different causes of cases as reference. However, existing work tends to focus on the relevance of the judgments themselves, without considering the connection between the causes of action. Several well-established diversity search techniques already exist in open-field search efforts. However, these techniques do not take into account the specificity of legal search scenarios, e.g., the subtopic may not be independent of each other, but somehow connected. Therefore, we construct a diversity legal retrieval model. This model takes into account both diversity and relevance, and is well adapted to this scenario. At the same time, considering the lack of dataset with diversity labels, we constructed a diversity legal retrieval dataset and obtained labels by manual labeling. experiments confirmed that our model is effective.'\nauthor:\n- Ruizhe Zhang\n- Qingyao Ai\n- Yueyue Wu\n- Yixiao Ma\n- Yiqun Liu\nbibliography:\n- 'sample-base.bib'\ntitle: Diverse legal case search\n---\n\n&lt;ccs2012&gt; &lt;concept&gt; &lt;concept\\_id&gt;10002951.10003317.10003338.10003340&lt;/concept\\_id&gt; &lt;concept\\_desc&gt;Information systems\u00a0Probabilistic retrieval models&lt;/concept\\_desc&gt; &lt;concept\\_significance&gt;300&lt;/concept\\_significance&gt; &lt;/concept&gt;"
+"---\naddress:\n- ', , '\n- ', , '\n- ', , , '\n- ', , , '\nauthor:\n- \n- \n- \n- \n- \n- \nbibliography:\n- 'lit.bib'\ntitle: Towards Learned Emulation of Interannual Water Isotopologue Variations in General Circulation Models\n---\n\nIntroduction {#sec:intro}\n============\n\nReliable analysis of current climate change, as well as robust prediction of future Earth system behavior, have become a crucial foundation for all endeavors to protect humanity\u2019s prosperity, mitigate ecological disasters, or formulate plans for adaptation [@langsdorfClimateChange20222022]. This analysis hinges on an accurate understanding and modeling of complex mechanisms in the climate system, which in turn relies on knowledge of the system\u2019s past behavior. To analyze past climatic conditions outside the comparatively short period of instrumental measurements, we depend on environmental processes recording and preserving information on the climate system in natural \u201cclimate archives\u201d. One way to recover past climate information from such archives is to measure the relative abundance of isotopes, particularly of the isotopes of the constituents of water molecules [@mookEnvironmentalIsotopesHydrological2000]. Due to differences in mass, molecules with varying isotopic compositions, so-called isotopologues, differ in their behavior in chemical reactions and phase transitions. For the special case of water, molecules containing"
+"---\nabstract: 'Handling trust is one of the core requirements for facilitating effective interaction between the human and the AI agent. Thus, any decision-making framework designed to work with humans must possess the ability to estimate and leverage human trust. In this paper, we propose a mental model based theory of trust that not only can be used to infer trust, thus providing an alternative to psychological or behavioral trust inference methods, but also can be used as a foundation for any trust-aware decision-making frameworks. First, we introduce what trust means according to our theory and then use the theory to define trust evolution, human reliance and decision making, and a formalization of the appropriate level of trust in the agent. Using human subject studies, we compare our theory against one of the most common trust scales (Muir scale) to evaluate 1) whether the observations from the human studies match our proposed theory and 2) what aspects of trust are more aligned with our proposed theory.'\nauthor:\n- \n- \n- \nbibliography:\n- 'arxiv.bib'\ntitle: A Mental Model Based Theory of Trust\n---\n\nIntroduction\n============\n\nAs AI-powered systems are becoming evermore pervasive in our day-to-day lives, the need to develop effective"
+"---\nabstract: |\n    We introduce a new family of Schur functions $s_{\\lambda/\\mu;a,b}(x/y)$ that depend on two sets of variables and two sequences of parameters. These *free fermionic Schur functions* generalize and unify double, supersymmetric, and dual Schur functions from literature. These functions have a hidden symmetry that is manifested in the supersymmetric Cauchy identity $$\\sum_{\\lambda}s_{\\lambda;a,b}(x/y)\\widehat{s}_{\\lambda;a,b}(z/w) = \\prod_{i,j}\\frac{1+y_iz_j}{1-x_iz_j}\\frac{1+x_iw_j}{1-y_iw_j},$$ where $\\widehat{s}_{\\lambda;a,b}(z/w) = s_{\\lambda';b',a'}(w/z)$ are the dual functions.\n\n    Our approach is based on the integrable six vertex model with free fermionic Boltzmann weights. We show that these weights satisfy the *refined Yang-Baxter equations*, which allows us to prove the generalizations of well-known properties of the Schur functions. We emphasize that some of our results and proofs are novel even in special cases.\naddress: |\n    Department of Mathematics\\\n    University of North Carolina at Chapel Hill\\\n    Chapel Hill, North Carolina, 27599, United States of America\nauthor:\n- Slava Naprienko\nbibliography:\n- 'ffschur.bib'\ntitle: Free Fermionic Schur Functions\n---\n\nIntroduction\n============\n\nThe *Schur functions* $s_\\lambda(x)$ are the symmetric functions that occur in various parts of mathematics. In representation theory, they are the characters of the polynomial irreducible representations of the general linear group; they are also the representatives of the characters of the irreducible representations of"
+"---\nabstract: 'In a 1992 paper of ours the role of opacity-driven thermal instabilities in shaping the course of stellar evolution was amply illustrated. This included the classical issue of \u201c[*why stars become red giants\"*]{} as well as the subsequent formation of extended \u201cCepheids\" [*blue loops*]{} during the helium burning phases. Our explanation of these evolutionary phenomena has been occasionally dismissed with just a few words in refereed or not refereed publications. In a most recent case, the fact that, through the years, I did not reply to these criticisms is interpreted as evidence that they were well founded. In this paper it is made clear that this is not at all the case, the leading role of such instabilities is instead reaffirmed and the criticisms are shown to be insubstantial.'\nauthor:\n- |\n    Alvio Renzini$^{1}$[^1]\\\n    $^{1}$INAF - Osservatorio Astronomico di Padova, Vicolo dell\u2019Osservatorio 5, I-35122 Padova, Italy\n- 'A.Renzini'\ndate: 'Accepted 2023 January 10. Received 2022 December 16'\ntitle: |\n     Why stars inflate to and deflate from red giant dimensions, II:\\\n    replies to critics\n---\n\n\\[firstpage\\]\n\nstars: evolution \u2013 stars: interiors \u2013 Hertzsprung-Russell and colour-magnitude diagrams\n\nIntroduction {#sec:intro}\n============\n\nThe founding fathers of stellar evolution, Subrahmanyan Chandrasekhar, Martin Schwarzschild,"
+"---\nabstract: 'Deep learning (DL) is revolutionizing the scientific computing community. To reduce the data gap caused by usually expensive simulations or experimentation, active learning has been identified as a promising solution for the scientific computing community. However, the deep active learning (DAL) literature is currently dominated by image classification problems and pool-based methods, which are not directly transferrable to scientific computing problems, dominated by regression problems with no pre-defined \u2019pool\u2019 of unlabeled data. Here for the first time, we investigate the robustness of DAL methods for scientific computing problems using ten state-of-the-art DAL methods and eight benchmark problems. We show that, to our surprise, the majority of the DAL methods are not robust even compared to random sampling when the ideal pool size is unknown. We further analyze the effectiveness and robustness of DAL methods and suggest that diversity is necessary for a robust DAL for scientific computing problems.'\nauthor:\n- |\n    Simiao Ren, Yang Deng, Willie J. Padilla and Leslie Collins\\\n    Department of Electrical and Computer Engineering\\\n    Duke University\\\n    Durham, NC 27705, USA\\\n    Jordan M. Malof\\\n    Department of Computer Science\\\n    University of Montana\\\n    Missoula, MT 59812, USA\\\nbibliography:\n- 'references.bib'\ntitle: Deep Active Learning for Scientific Computing in"
+"---\nauthor:\n- 'Beatriz Hern\u00e1ndez-Molinero,'\n- 'Carmelita Carbone,'\n- 'Raul Jimenez,'\n- 'Carlos Pe\u00f1a Garay,'\ntitle: 'Cosmic background neutrinos deflected by gravity: DEMNUni simulation analysis'\n---\n\nIntroduction\n============\n\nOne second after the Big Bang, neutrinos decoupled from matter and streamed freely from gravity potential wells with a frozen Fermi-Dirac distribution. After this event, the neutrino content decoheres in mass eigenstates because neutrino energy and density redshift. The estimated temperature of the Fermi-Dirac distribution is $1.95 K$\u00a0[@lesgourges; @RouletBook] nowadays. Because this distribution is frozen, all neutrinos, independently of their masses, have the same momentum distribution at present. However, the distribution of velocities is not the same because momentum is related to velocity by neutrino mass.\n\nNeutrinos, as no-charged fermions, can be either Dirac or Majorana. The current experimental route to discover their nature is neutrinoless double-beta decay\u00a0[@Goeppert-Mayer; @furry]. However, cosmological observations already tell us that the sum of neutrino masses is small ($< 0.1$eV)\u00a0[@Planck:2018vyg; @shapefit] and their hierarchy is normal\u00a0[@hierarchy; @hierarchy2]; this would imply that double-beta decay experiments have to be in the several tons range, which is challenging. It is therefore crucial to look at cosmological signals. Experiments sensitive to the cosmic neutrino background ($C_\\nu B$)"
+"---\nabstract: 'Archetypal analysis is a matrix factorization method with convexity constraints. Due to local minima, a good initialization is essential, but frequently used initialization methods yield either sub-optimal starting points or are prone to get stuck in poor local minima. In this paper, we propose archetypal analysis++ (AA++), a probabilistic initialization strategy for archetypal analysis that sequentially samples points based on their influence on the objective, similar to $k$-means++. In fact, we argue that $k$-means++ already approximates the proposed initialization method. Furthermore, we suggest to adapt an efficient Monte Carlo approximation of $k$-means++ to AA++. In an extensive empirical evaluation of 13 real-world data sets of varying sizes and dimensionalities and considering two pre-processing strategies, we show that AA++ nearly always outperforms all baselines, including the most frequently used ones.'\nauthor:\n- Sebastian Mair\n- Jens Sj\u00f6lund\nbibliography:\n- 'references.bib'\ntitle: 'Archetypal Analysis++: Rethinking the Initialization Strategy'\n---\n\nIntroduction {#intro}\n============\n\nArchetypal analysis (AA) [@cutler1994] is a matrix factorization method with convexity constraints. The idea is to represent every data point as a convex combination of points, called archetypes, located on the boundary of the data set. Thus, archetypes can be seen as well-separated observations that summarize the most"
+"---\nabstract: |\n    Consider a mechanism that cannot observe how many players there are directly, but instead must rely on their self-reports to know how many are participating. Suppose the players can create new identities to report to the auctioneer at some cost $c$. The usual mechanism design paradigm is equivalent to implicitly assuming that $c$ is infinity for all players, while the usual Sybil attacks literature is that it is zero or finite for one player (the attacker) and infinity for everyone else (the \u2019honest\u2019 players). The false-name proof literature largely assumes the cost to be 0. We consider a model with variable costs that unifies these disparate streams.\n\n    A paradigmatic normal form game can be extended into a Sybil game by having the action space by the product of the feasible set of identities to create action where each player chooses how many players to present as in the game and their actions in the original normal form game. A mechanism is (dominant) false-name proof if it is (dominant) incentive-compatible for all the players to self-report as at most one identity. We study mechanisms proposed in the literature motivated by settings where anonymity and self-identification are the norms,"
+"---\nabstract: 'By classical results of Rochlin, Thom, Wallace and Lickorish, it is well-known that any two 3-manifolds (with diffeomorphic boundaries) are related one to the other by surgery operations. Yet, by restricting the type of the surgeries, one can define several families of non-trivial equivalence relations on the sets of (diffeomorphism classes of) 3-manifolds. In this expository paper, which is based on lectures given at the school \u201c*Winter Braids XI*\u201d (Dijon, December 2021), we explain how certain filtrations of mapping class groups of surfaces enter into the definitions and the mutual comparison of these surgery equivalence relations. We also survey the ways in which concrete invariants of 3-manifolds (such as finite-type invariants) can be used to characterize such relations.'\naddress: 'Institut de Math\u00e9matiques de Bourgogne, UMR 5584, CNRS, Universit\u00e9 de Bourgogne, 21000 Dijon, France'\nauthor:\n- Gw\u00e9na\u00ebl Massuyeau\ndate: 'July 21, 2023'\ntitle: 'Surgery equivalence relations for $3$-manifolds'\n---\n\nIntroduction {#introduction .unnumbered}\n============\n\nIt is a classical result of Rochlin and Thom, dating back to the early\u00a050\u2019s, that any closed oriented $3$-manifold $M$ is the boundary of a compact oriented $4$-manifold\u00a0$W$. By elementary differential topology arguments (considering a handle decomposition of $W$), it follows that $M$ is"
+"---\nabstract: 'Human parsing is a key topic in image processing with many applications, such as surveillance analysis, human-robot interaction, person search, and clothing category classification, among many others. Recently, due to the success of deep learning in computer vision, there are a number of works aimed at developing human parsing algorithms using deep learning models. As methods have been proposed, a comprehensive survey of this topic is of great importance. In this survey, we provide an analysis of state-of-the-art human parsing methods, covering a broad spectrum of pioneering works for semantic human parsing. We introduce five insightful categories: (1) structure-driven architectures exploit the relationship of different human parts and the inherent hierarchical structure of a human body, (2) graph-based networks capture the global information to achieve an efficient and complete human body analysis, (3) context-aware networks explore useful contexts across all pixel to characterize a pixel of the corresponding class, (4) LSTM-based methods can combine short-distance and long-distance spatial dependencies to better exploit abundant local and global contexts, and (5) combined auxiliary information approaches use related tasks or supervision to improve network performance. We also discuss the advantages/disadvantages of the methods in each category and the relationships between methods"
+"---\nabstract: 'While 5G networks are being rolled out, the definition of the potential 5G-Advanced features and the identification of disruptive technologies for 6G systems are being addressed by the scientific and academic communities to tackle the challenges that 2030 communication systems will face, such as terabit-capacity and always-on networks. In this framework, it is globally recognised that Non-Terrestrial Networks (NTN) will play a fundamental role in support to a fully connected world, in which physical, human, and digital domains will converge. In this framework, one of the main challenges that NTN have to address is the provision of the high throughput requested by the new ecosystem. In this paper, we focus on Cell-Free Multiple Input Multiple Output (CF-MIMO) algorithms for NTN. In particular: i) we discuss the architecture design supporting centralised and federated CF-MIMO in NTN, with the latter implementing distributed MIMO algorithms from multiple satellites in the same formation (swarm); ii) propose a novel location-based CF-MIMO algorithm, which does not require Channel State Information (CSI) at the transmitter; and iii) design novel normalisation approaches for federated CF-MIMO in NTN, to cope with the constraints on non-colocated radiating elements. The numerical results substantiate the good performance of the proposed"
+"---\nabstract: 'State-of-the-art summarization systems can generate highly fluent summaries. These summaries, however, may contain factual inconsistencies and/or information not present in the source. Hence, an important component of assessing the quality of summaries is to determine whether there is information consistency between the source and the summary. Existing approaches are typically based on lexical matching or representation-based methods. In this work, we introduce an alternative scheme based on standard information-theoretic measures in which the information present in the source and summary is directly compared. We propose a Multiple-choice Question Answering and Generation framework, MQAG, which approximates the information consistency by computing the expected statistical distance between summary and source answer distributions over automatically generated multiple-choice questions. This approach exploits multiple-choice answer probabilities, as predicted answer distributions can be compared. We conduct experiments on four summary evaluation datasets: QAG-CNNDM/XSum, XSum-Hallucination, Podcast Assessment, and SummEval. Experiments show that MQAG, using models trained on SQuAD or RACE, outperforms existing evaluation methods on the majority of tasks.[^1]'\nauthor:\n- |\n    Potsawee Manakul, Adian Liusie, Mark J. F. Gales\\\n    ALTA Institute, Department of Engineering, University of Cambridge\\\n    `pm574@cam.ac.uk, al826@cam.ac.uk, mjfg@eng.cam.ac.uk`\nbibliography:\n- 'anthology.bib'\n- 'custom.bib'\ntitle: |\n    <span style=\"font-variant:small-caps;\">MQAG</span>: Multiple-choice Question Answering and Generation"
+"---\nabstract: 'Motivated by applications such as voluntary carbon markets and educational testing, we consider a market for goods with varying but hidden levels of quality in the presence of a third-party certifier. The certifier can provide informative signals about the quality of products, and can charge for this service. Sellers choose both the quality of the product they produce and a certification. Prices are then determined in a competitive market. Under a single-crossing condition, we show that the levels of certification chosen by producers are uniquely determined at equilibrium. We then show how to reduce a revenue-maximizing certifier\u2019s problem to a monopolistic pricing problem with non-linear valuations, and design an FPTAS for computing the optimal slate of certificates and their prices. In general, both the welfare-optimal and revenue-optimal slate of certificates can be arbitrarily large.'\nauthor:\n- Andreas Haupt\n- Nicole Immorlica\n- 'Brendan Lucier[^1]'\nbibliography:\n- 'refs.bib'\ntitle: Certification Design for a Competitive Market\n---\n\nIntroduction\n============\n\nMany markets sell goods that have different features but appear identical to consumers. Examples include the market for carbon credits, the market for contract workers such as electricians or plumbers, or even some markets for physical goods like milk and eggs."
+"---\nabstract: 'In a standard quantum field theory the norm $\\langle \\Omega\\vert \\Omega\\rangle$ of the vacuum state is taken to be finite. In this paper we provide a procedure, based on constructing an equivalent wave mechanics, for determining whether or not $\\langle \\Omega\\vert \\Omega\\rangle$ actually is finite. We provide an example based on a second-order plus fourth-order scalar field theory, a prototype for quantum gravity, in which it is not. In this example the Minkowski path integral with a real measure diverges though the Euclidean path integral does not. Thus in this example contributions from the Wick rotation contour cannot be ignored. Since $\\langle \\Omega\\vert \\Omega\\rangle$ is not finite, use of the standard Feynman rules is not valid. And while these rules not only lead to states with negative norm, they in fact lead to states with infinite negative norm. However, if the fields in that theory are continued into the complex plane, we show that then there is a domain in the complex plane known as a Stokes wedge in which one can define an appropriate time-independent, positive and finite inner product, viz. the $\\langle L\\vert R\\rangle$ overlap of left-eigenstates and right-eigenstates of the Hamiltonian; with the vacuum state"
+"---\nauthor:\n- 'Stefan Reissl [[![image](orcid.pdf){width=\"10pt\"}](https://orcid.org/0000-0001-5222-9139)]{}'\n- Philipp Nguyen\n- 'Lucas M. Jordan'\n- 'Ralf S. Klessen [[![image](orcid.pdf){width=\"10pt\"}](https://orcid.org/0000-0002-0560-3172)]{}'\nbibliography:\n- './bibtex.bib'\ntitle: 'The rotational disruption of porous dust aggregates from ab-initio kinematic calculations'\n---\n\n[The sizes of dust grains in the interstellar medium follows a distribution where most of the dust mass is in smaller grains. However, the re-distribution from larger grains towards smaller sizes especially by means of rotational disruption is poorly understood.]{} [We aim to study the dynamics of porous grain aggregates under accelerated ration. Especially, we determine the deformation of the grains and the maximal angular velocity up to the rotational disruption event by caused by centrifugal forces.]{} [We pre-calculate porous grain aggregate my means of ballistic aggregation analogous to the interstellar dust as input for subsequent numerical simulations. In detail, we perform three-dimensional N-body simulations mimicking the radiative torque spin-up process up to the point where the grain aggregates become rotationally disrupted.]{} [Our simulations results are in agreement with theoretical models predicting a characteristic angular velocity $\\omega_{\\mathrm{disr}}$ of the order of ${ 10^8 - 10^9\\ \\mathrm{rad\\ s^{-1}} }$, where grains become rotationally disrupted. In contrast to the theoretical predictions, we show that for large porous grain"
+"---\nabstract: 'Epitaxial heterostructures composed of complex correlated metal oxides grown along specific crystallographic orientations offer a route to investigating emergent phenomena such as topological states and spin liquids through geometrical lattice engineering. A$_2$Ru$_2$O$_7$ pyrochlore ruthenates in particular exhibit a metal-insulator transition with varying A cation whose mechanism is not fully understood. We report on the epitaxial growth, structural and electrical properties of metallic pyrochlore bismuth ruthenate heterostructures grown along both the \\[001\\] and \\[111\\] directions. Ordered pyrochlore thin films were obtained with highly oriented texture along the \\[001\\] and \\[111\\] crystallographic directions. Density functional theory calculations of the electronic band structure and density of states indicated that Bi$_2$Ru$_2$O$_7$ is semimetallic and that hybridization of the Ru 4$d$ and Bi 6$p$ orbitals via the anion network at the Fermi energy was responsible for the metallicity. Electrical conductivity measurements confirmed that the compound is weakly metallic in agreement with the reported conductivity for the stoichiometric bulk compound. The carrier concentration and mobility of the electrons compared favorably with previous reports on bulk material and indicate strong electron-electron interactions. The measured and computed optical conductivities were found to share coincident spectral features and confirm the electronic correlation. Comparison of the electrical and"
+"---\nabstract: 'Recent work has empirically shown that deep neural networks latch on to the Fourier statistics of training data and show increased sensitivity to Fourier-basis directions in the input. Understanding and modifying this Fourier-sensitivity of computer vision models may help improve their robustness. Hence, in this paper we study the frequency sensitivity characteristics of deep neural networks using a principled approach. We first propose a ***basis trick***, proving that unitary transformations of the of a function can be used to compute its gradient in the basis induced by the transformation. Using this result, we propose a general measure of any differentiable model\u2019s $\\textit{\\textbf{Fourier-sensitivity}}$ using the unitary Fourier-transform of its . When applied to deep neural networks, we find that computer vision models are consistently sensitive to particular frequencies dependent on the dataset, training method and architecture. Based on this measure, we further propose a $\\textit{\\textbf{Fourier-regularization}}$ framework to modify the Fourier-sensitivities and frequency bias of models. Using our proposed regularizer-family, we demonstrate that deep neural networks obtain improved classification accuracy on robustness evaluations.'\nauthor:\n- |\n    Kiran Krishnamachari kirank@u.nus.edu\\\n    Institute for Infocomm Research, A\\*STAR, Singapore\\\n    School of Computing, National University of Singapore, Singapore See-Kiong Ng seekiong@nus.edu.sg\\\n    Institute of Data Science,"
+"---\nabstract: 'Calculating the trace of the product of $m$ $n$-qubit density matrices (multivariate trace) is a crucial subroutine in quantum error mitigation and information measures estimation. We propose an unified multivariate trace estimation (UMT) which conceptually unifies the previous qubit-optimal and depth-optimal approaches with tunable quantum circuit depth and the number of qubits. The constructed circuits have $\\lceil(m-1)/s\\rceil$ or $n\\lceil(m-1)/s\\rceil$ depth corresponding to $(s+m)n$ or $s+mn$ qubits for $s\\in\\{1,\\cdots,\\lfloor m/2\\rfloor\\}$, respectively. Such flexible circuit structures enable people to choose suitable circuits according different hardware devices. We apply UMT to virtual distillation for achieving exponential error suppression and design a family of concrete circuits to calculate the trace of the product of $8$ and $9$ $n$-qubit density matrices. Numerical example shows that the additional circuits still mitigate the noise expectation value under the global depolarizing channel.'\nauthor:\n- 'Jin-Min Liang'\n- 'Qiao-Qiao Lv'\n- 'Zhi-Xi Wang'\n- 'Shao-Ming Fei'\ntitle: Unified multivariate trace estimation and quantum error mitigation\n---\n\nIntroduction\n============\n\nFault-tolerant quantum (FTQ) computers may provide novel computational advantages over classical computers for many tasks [@Shor1994; @Harrow2009Quantum; @Biamonte2017Quantum; @Liang2019Quantum]. As the perfect FTQ computers are not available yet, a preferable substitute is the the noisy intermediate-scale quantum (NISQ) devices"
+"---\nabstract: 'We theoretically investigate the recovery of global spectrum (monopole) from visibilities (cross-correlation only) measured by the interferometer array and the feasibility of extracting 21 cm signal of cosmic dawn. In our approach, the global spectrum is obtained by solving the monopole and higher-order components simultaneously from the visibilities measured with up to thousands of baselines. Using this algorithm, the monopole of both foreground and the 21 cm signal can be correctly recovered in a broad range of conditions. We find that a 3D baseline distribution can have much better performance than a 2D (planar) baseline distribution, particularly when there is a lack of shorter baselines. We simulate for ground-based 2D and 3D array configurations, and a cross-shaped space array located at the Sun-Earth L2 point that can form 3D baselines through orbital precession. In all simulations we obtain good recovered global spectrum, and successfully extract the 21 cm signal from it, with reasonable number of antennas and observation time.'\nauthor:\n- Xin Zhang\n- Bin Yue\n- Yuan Shi\n- Fengquan Wu\n- Xuelei Chen\nbibliography:\n- 'refe.bib'\ntitle: On Measuring the 21 cm Global Spectrum of the Cosmic Dawn with an Interferometer Array \n---\n\nIntroduction\n============\n\nThe"
+"---\nabstract: 'We use the two-flavor linear sigma model with quarks to study the phase structure of isospin asymmetric matter at zero temperature. The meson degrees of freedom provide the mean field chiral- and isospin-condensates on top of which we compute the effective potential accounting for constituent quark fluctuations at one-loop order. Using the renormalizability of the model, we absorb the ultraviolet divergences into suitable counter-terms that are added respecting the original structure of the theory. These counter-terms are determined from the stability conditions which require the effective potential to have minima in the condensates directions at the classical values, as well as the transition from the non-condensed to the condensed phase to be smooth as a function of the isospin chemical potential. We use the model to study the evolution of the condensates as well as the pressure, energy and isospin densities and the sound velocity as functions of the isospin chemical potential. The approach does a good average description up to isospin chemical potentials values not too large as compared to the vacuum pion mass.'\nauthor:\n- 'Alejandro Ayala$^{1,2,3}$, Aritra Bandyopadhyay$^{3,4,5}$, Ricardo L. S. Farias$^3$, L. A. Hern\u00e1ndez$^{6,2}$, Jos\u00e9 Luis Hern\u00e1ndez$^{1,7,8,9}$'\nbibliography:\n- 'bibliography.bib'\ntitle: 'QCD equation of"
+"---\nauthor:\n- |\n    Yuqiang Li[^1] Shengchen Li[^2] George Fazekas[^3]\\\n    `{yuqiang.li19@student., shengchen.li@}xjtlu.edu.cn`\\\n    `g.fazekas@qmul.ac.uk`\nbibliography:\n- 'MIR.bib'\ntitle: An Comparative Analysis of Different Pitch and Metrical Grid Encoding Methods in the Task of Sequential Music Generation\n---\n\nAbstract {#abstract .unnumbered}\n========\n\nPitch and meter are two fundamental music features for symbolic music generation tasks, where researchers usually choose different encoding methods depending on specific goals. However, the advantages and drawbacks of different encoding methods have not been frequently discussed. This paper presents a integrated analysis of the influence of two low-level feature, pitch and meter, on the performance of a token-based sequential music generation model. First, the commonly used MIDI number encoding and a less used pitch class-octave encoding are compared. Second, an dense intra-bar metric grid is imposed to the encoded sequence as auxiliary features. Different complexity and resolution settings of the metric grid are compared. For complexity, the single token approach and the multiple token approach are compared; for grid resolution, 0 (ablation), 1 (bar-level), 4 (downbeat-level) 12, (8th-triplet-level) up to 64 (64th-note-grid-level) are compared; for durational resolution, 4, 8, 12 and 16 subdivisions per beat are compared. All different encodings are tested on separately trained Transformer-XL model for"
+"---\nabstract: 'Retrieval-Augmented Language Modeling (RALM) methods, which condition a language model (LM) on relevant documents from a grounding corpus during generation, were shown to significantly improve language modeling performance. In addition, they can mitigate the problem of factually inaccurate text generation and provide natural source attribution mechanism. Existing RALM approaches focus on modifying the LM architecture in order to facilitate the incorporation of external information, significantly complicating deployment. This paper considers a simple alternative, which we dub *In-Context RALM*: leaving the LM architecture unchanged and prepending grounding documents to the input, *without any further training of the LM*. We show that In-Context RALM that builds on off-the-shelf general purpose retrievers provides surprisingly large LM gains across model sizes and diverse corpora. We also demonstrate that the document retrieval and ranking mechanism can be specialized to the RALM setting to further boost performance. We conclude that In-Context RALM has considerable potential to increase the prevalence of LM grounding, particularly in settings where a pretrained LM must be used without modification or even via API access.[^1]'\nauthor:\n- |\n    Ori Ram[^2]\u00a0\u00a0\u00a0\u00a0\u00a0Yoav Levine$^*$\u00a0\u00a0\u00a0\u00a0Itay Dalmedigos\u00a0\u00a0\u00a0\u00a0Dor Muhlgay\\\n    **Amnon Shashua\u00a0\u00a0\u00a0\u00a0Kevin Leyton-Brown\u00a0\u00a0\u00a0\u00a0Yoav Shoham**\\\n    AI21 Labs\\\n    `{orir,yoavl,itayd,dorm,amnons,kevinlb,yoavs}@ai21.com`\nbibliography:\n- 'anthology.bib'\n- 'custom.bib'"
+"---\nabstract: 'Transfomer-based approaches advance the recent development of multi-camera 3D detection both in academia and industry. In a vanilla transformer architecture, queries are randomly initialised and optimised for the whole dataset, without considering the differences among input frames. In this work, we propose to leverage the predictions from an image backbone, which is often highly optimised for 2D tasks, as priors to the transformer part of a 3D detection network. The method works by (1). augmenting image feature maps with 2D priors, (2). sampling query locations via ray-casting along 2D box centroids, as well as (3). initialising query features with object-level image features. Experimental results shows that 2D priors not only help the model converge faster, but also largely improve the baseline approach by up to $12\\%$ in terms of average precision.'\nauthor:\n- 'Di Feng$^{*}$, Francesco Ferroni$^{*}$'\nbibliography:\n- 'bibliography.bib'\ntitle: |\n    Priors are Powerful: Improving a Transformer for\\\n    Multi-camera 3D Detection with 2D Priors [^1] \n---\n\nIntroduction {#sec:introduction}\n============\n\nTowards 360-degree 3D perception, self-driving vehicles are usually equipped with multiple monocular cameras, and reliable and accurate multi-camera 3D detection has become an important research challenge and industrial effort. The traditional approach takes advantage of the convolution neural"
+"---\nabstract: 'Image augmentations are quintessential for effective visual representation learning across self-supervised learning techniques. While augmentation strategies for natural imaging have been studied extensively, medical images are vastly different from their natural counterparts. Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent. To address this challenge, in this study, we systematically assess the effect of various augmentations on the quality and robustness of the learned representations. We train and evaluate Siamese Networks for abnormality detection on chest X-Rays across three large datasets ([MIMIC-CXR]{}, [CheXpert]{} and [VinDr-CXR]{}). We investigate the efficacy of the learned representations through experiments involving linear probing, fine-tuning, zero-shot transfer, and data efficiency. Finally, we identify a set of augmentations that yield robust representations that generalize well to both out-of-distribution data and diseases, while outperforming supervised baselines using just zero-shot transfer and linear probes by up to 20%. Our code is available at <https://github.com/StanfordMIMI/siaug>.'\nbibliography:\n- 'paper\\_bibliography.bib'\ntitle: 'Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays '\n---\n\nData Augmentations, Self-Supervised Learning, Medical Imaging.\n\nIntroduction {#sec:intro}\n============\n\nDeep learning algorithms enable high-accuracy medical image analysis, yet are constrained by limitations of labelled data."
+"---\nabstract: |\n    A long-standing challenge for search and conversational assistants is query intention detection in ambiguous queries. Asking clarifying questions in conversational search has been widely studied and considered an effective solution to resolve query ambiguity. Existing work have explored various approaches for clarifying question ranking and generation. However, due to the lack of real conversational search data, they have to use artificial datasets for training, which limits their generalizability to real-world search scenarios. As a result, the industry has shown reluctance to implement them in reality, further suspending the availability of real conversational search interaction data. The above dilemma can be formulated as a cold start problem of clarifying question generation and conversational search in general. Furthermore, even if we do have large-scale conversational logs, it is not realistic to gather training data that can comprehensively cover all possible queries and topics in open-domain search scenarios. The risk of fitting bias when training a clarifying question retrieval/generation model on incomprehensive dataset is thus another important challenge.\n\n    In this work, we innovatively explore generating clarifying questions in a zero-shot setting to overcome the cold start problem and we propose a constrained clarifying question generation system which uses both question"
+"---\nabstract: 'Sonification as a complement of visualization is been under research for decades as a new ways of data deployment. ICAD conferences, gather together specialists from different disciplines to discuss about sonification. Different tools as sonoUno, starSound and Web Sandbox are attempt to reach a tool to open astronomical data sets and sonify it in conjunction to visualization. In this contribution, the sonoUno web version is presented, this version allows user to explore data sets without any installation. The data can be uploaded or a pre-loaded file can be opened, the sonification and the visual characteristics of the plot can be customized on the same window. The plot, sound and marks can be saved. The web interface were tested with the main used screen readers in order to confirm their good performance.'\nauthor:\n- Gonzalo De La Vega\n- Leonardo Martin Exequiel Dominguez\n- Johanna Casado\n- Beatriz Garc\u00eda\ntitle: 'SonoUno web: an innovative user centred web interface[^1]'\n---\n\nIntroduction\n============\n\nThe need to explore data sets beyond the visual field has led the community to study new ways to represent it, this is the case of sonification. In this sense, since 1992 the ICAD conferences[@icadWeb] has existed bringing"
+"---\nabstract: '[ Alfven wave is the single most important physical phenomenon of magneto-hydrodynamic turbulence and has far-reaching impact to almost all studies related to astrophysical magnetic field. Yet the restoration of the Alfven wave fluctuations from a given magnetic field, aka the local Alfven wave problem, is never properly addressed in literature albeit its importance. Previous works model the Alfven wave fluctuation as the perturbation along a straight-line, constant magnetic field. However, [Lazarian & Pogosyan (2012)]{} suggested that the decomposition of Alfven wave along a straight line, aka. the global frame decomposition, has a factor of discrepancy to the true local Alfven wave fluctuation. Here we provide a geometric interpretation on how the local Alfven wave is related to the global frame through the use of vector frame formulation. We prove both analytically and numerically that the local frame Alfven wave is an orthogonal transformation of that of the global frame and related by the local Alfvenic Mach number. In other words, when we observe Alfven wave in the global frame of reference, some of the Alfven wave will be mistaken as compressible waves. The importance of frame choices have a far-reaching impact to the analytical studies of MHD"
+"---\nabstract: |\n    We study the complement problem in projective spaces ${{\\mathbb P}}^n$ over any algebraically closed field: If $H, H' \\subseteq {{\\mathbb P}}^n$ are irreducible hypersurfaces of degree $d$ such that the complements ${{\\mathbb P}}^n \\setminus H$, ${{\\mathbb P}}^n \\setminus H'$ are isomorphic, are the hypersurfaces $H$, $H'$ isomorphic?\n\n    For $n = 2$, the answer is positive if $d\\leq 7$ and there are counterexamples when $d = 8$. In contrast we provide counterexamples for all $n, d \\geq 3$ with $(n, d) \\neq (3, 3)$. Moreover, we show that the complement problem has an affirmative answer for $d = 2$ and give partial results in case $(n, d) = (3, 3)$. In the course of the exposition, we prove that rational normal projective surfaces admitting a desingularisation by trees of smooth rational curves are piecewise isomorphic if and only if they coincide in the Grothendieck ring, answering affirmatively a question posed by Larsen and Lunts for such surfaces.\naddress:\n- 'J\u00e9r\u00e9my Blanc, Universit\u00e4t Basel, Departement Mathematik und Informatik, Spiegelgasse $1$, CH-$4051$ Basel, Switzerland.'\n- 'Pierre-Marie\u00a0Poloni, Universit\u00e4t Basel, Departement Mathematik und Informatik, Spiegelgasse $1$, CH-$4051$ Basel, Switzerland.'\n- 'Immanuel Van Santen, Universit\u00e4t Basel, Departement Mathematik und Informatik, Spiegelgasse $1$,"
+"---\nabstract: 'We present a novel [[framework for]{}]{} spin-adapted coupled cluster theory. [[The]{}]{} approach [exploits]{} the entanglement of an open-shell molecule with electrons in a non-interacting bath. form a closed-shell electron correlation can be [[included using]{}]{} the standard spin-adapted closed-shell coupled cluster formalism. A projection operator, which enforces conditions on the electrons in the bath, is used to obtain the desired state of the This *entanglement coupled cluster theory* is [[outlined]{}]{}, and proof-of-concept calculations for doublet states are reported. The approach is [[further]{}]{} extendable to open-shell systems with other values of the total spin.'\nauthor:\n- Sarai Dery Folkestad\n- Bendik St\u00f8a Sannes\n- Henrik Koch\nbibliography:\n- 'main.bib'\ntitle: 'Entanglement Coupled Cluster Theory: Exact Spin-Adaptation'\n---\n\nIntroduction\n============\n\nThe complication with spin-unrestricted methods is the appearance of spin contamination. For UHF, spin contamination appears in two different situations.[@krylov2000] For open-shell systems, it arises from differences in the mean field experienced by the $\\alpha$\u2013 and $\\beta$\u2013electrons. For closed-shell systems, it appears when static correlation becomes important, for instance, when bonds are stretched .[@andrews1991; @krylov2000; @helgaker2014] While spin contamination can be significant at low levels of theory, it is reduced as the description of the electronic structure improves. [@stanton1994; @krylov2000] In"
+"---\nabstract: 'Multiple-$Q$ states manifest themselves in a variety of noncollinear and noncoplanar magnetic structures depending on the magnetic interactions and lattice structures. In particular, cubic-lattice systems can host a plethora of multiple-$Q$ states, such as magnetic skyrmion and hedgehog lattices. We here classify momentum-dependent anisotropic exchange interactions in the cubic-lattice systems based on the magnetic representation analysis. We construct an effective spin model for centrosymmetric cubic space groups, $Pm\\bar{3}m$ and $Pm\\bar{3}$, and noncentrosymmetric ones, $P\\bar{4}3m$, $P432$, and $P23$: The former include the symmetric anisotropic exchange interaction, while the latter additionally include the Dzyaloshinskii-Moriya interaction. We demonstrate that the anisotropic exchange interaction becomes the origin of the multiple-$Q$ states by applying the anisotropic spin model to the case under $Pm\\bar{3}$. We show several multiple-$Q$ instabilities in the ground state by performing simulated annealing. Our results will be a reference for not only exploring unknown multiple-$Q$ states but also understanding the origin of the multiple-$Q$ states observed in both noncentrosymmetric and centrosymmetric magnets like EuPtSi and SrFeO$_3$.'\nauthor:\n- Ryota Yambe\n- Satoru Hayami\nbibliography:\n- 'main.bib'\ntitle: ' Anisotropic spin model and multiple-$Q$ states in cubic systems '\n---\n\nIntroduction\n============\n\nFrustration arising from competing interactions gives rise to intriguing"
+"---\nabstract: 'Industrial Control Systems (ICS) are often built from geographically distributed components and often use programmable logic controllers for localized processes. Since verification of such systems is challenging because of both time sensitivity of the system specifications and the inherent asynchrony in distributed components, developing runtime assurance that verifies not just the correctness of different components, but also generates aggregated statistics of the systems is of interest. In this paper, we first present a general technique for runtime monitoring of distributed applications whose behavior can be modeled as input/output [*streams*]{} with an internal computation module in the partially synchronous semantics, where an imperfect clock synchronization algorithm is assumed. Second, we propose a generalized stream-based decentralized runtime verification technique. We also rigorously evaluate our algorithm on extensive synthetic experiments and several ICS and aircraft SBS message datasets.'\nauthor:\n- \n- \nbibliography:\n- 'bibliography.bib'\ntitle: 'Stream-based Decentralized Runtime Verification '\n---\n\n\\[DOWHILE\\][Do]{}[doWhile]{}\\[1\\][\u00a0\\#1]{}\n\nIntroduction {#sec:intro}\n============\n\nIndustrial Control Systems (ICS) are information systems to control industrial processes such as manufacturing, product handling, distribution, etc. It includes supervisory control and data acquisition systems used to control geographically dispersed assets and distributed control systems using a programmable logic controller for each of the"
+"---\nabstract: 'The search for the chiral magnetic effect (CME) in relativistic heavy-ion collisions helps us understand the $\\mathcal{CP}$ symmetry breaking in strong interactions and the topological nature of the QCD vacuum. Since the background and signal of the CME have different correlations with respect to the spectator and participant planes, a two-plane method has been proposed to extract the fraction of the CME signal inside the CME observable of $\\Delta\\gamma$ from the experimental measurements relative to the two planes. Using a multiphase transport model with different strengths of the CME, we reexamine the two-plane method in isobar collisions at $\\sqrt{s_{_{\\rm NN}}} = 200$ GeV. The ratios of the CME signal and background relative to the two different planes are found to be different, which is inconsistent with the assumptions made in the current experimental measurements. The difference arises from the decorrelation of the chiral magnetic effect relative to spectator and participant planes, which originates from final state interactions. Our finding suggests that the current experimental measurements may overestimate the fraction of the CME signal in the CME observable in relativistic heavy-ion collisions.'\nauthor:\n- 'Bang-Xiang Chen'\n- 'Xin-Li Zhao'\n- 'Guo-Liang Ma'\nbibliography:\n- 'fcme.bib'\ntitle: 'On the difference"
+"---\nabstract: 'It is explained that any scalar field in $f(R,T)$ gravity model could present a new subclass of the k-essence model for inflation. While the case of the quintessence has been studied, there is an empty gap required to be filled by investigating more types of field models. Here, we are aiming to partially fill the gap and concentrate on the tachyon field. In this study, we explore the phenomenon of tachyon inflation in the context of the alternative gravity theory $f(R,T) = R + \\eta T$. By applying slow-roll approximations, we examine the model in detail for three different potentials: power-law, generalized T-mode, and inverse hyperbolic cosh. Using observational data and Python coding, we determine a range of values for the model\u2019s free parameters that allow it to fit the data perfectly. In essence, this study provides a comprehensive analysis of tachyon inflation in the $R + \\eta T$ gravity theory, offering new insights into this alternative framework and its potential to explain tachyon inflation.'\nauthor:\n- Abolhassan Mohammadi\n- Fardin Kheirandish\nbibliography:\n- 'REFtest.bib'\ntitle: 'Exploring new subclass of k-inflation: tachyon inflation in $R+\\eta T$ gravity model'\n---\n\nIntroduction {#Sec_intro}\n============\n\nEinstein\u2019s general theory of relativity, proposed"
+"---\nabstract: 'There has been much interest over many years in studying charged systems after the artificial imposition of periodic boundary conditions, and correcting for the resulting divergence of the electrostatic energy density. A correction for cubic cells was derived by Makov and Payne in 1995, and its leading error term is of the form $L^{-5}$ for a cube of side $L$. Most modern Density Functional Theory codes use a different treatment of the \u2018Z$\\alpha$\u2019 energy term to that used by Makov and Payne, resulting in an error term of the form $L^{-3}$ if their correction is used unmodified. This paper shows how the Makov and Payne result can be made consistent with modern practice.'\nauthor:\n- 'M.J.\u00a0Rutter'\ntitle: Pseudopotential contributions to the quadrupole moment in charged periodic systems\n---\n\nIntroduction\n============\n\nMany Density Functional Theory codes assume three dimensional periodicity which enables the use of simple techniques such as plane waves for the basis set, and the 3D Ewald sum for electrostatic interactions. Abinit[@Abinit], <span style=\"font-variant:small-caps;\">Castep</span>[@CASTEP], Quantum Espresso[@QE], <span style=\"font-variant:small-caps;\">Vasp</span>[@VASP] and others use this approach.\n\nIf the system to be modelled is not periodic in all three dimensions, then it can be repeatedly tiled to generate periodicity, with"
+"---\nabstract: |\n    In this paper we consider the filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. This is a challenging problem which requires the use of advanced numerical schemes based upon time-discretization of the diffusion process and then the application of particle filters. Perhaps the state-of-the-art method for moderate dimensional problems is the multilevel particle filter of [@mlpf]. This is a method that combines multilevel Monte Carlo and particle filters. The approach in that article is based intrinsically upon an Euler discretization method. We develop a new particle filter based upon the antithetic truncated Milstein scheme of [@ml_anti]. We show that for a class of diffusion problems, for $\\epsilon>0$ given, that the cost to produce a mean square error (MSE) in estimation of the filter, of $\\mathcal{O}(\\epsilon^2)$ is $\\mathcal{O}(\\epsilon^{-2}\\log(\\epsilon)^2)$. In the case of multidimensional diffusions with non-constant diffusion coefficient, the method of [@mlpf] has a cost of $\\mathcal{O}(\\epsilon^{-2.5})$ to achieve the same MSE. We support our theory with numerical results in several examples.\\\n    **Key words**: Multilevel Monte Carlo, Particle Filters, Diffusion Processes, Filtering.\n---\n\n[**Antithetic Multilevel Particle Filters**]{}\n\nBY AJAY JASRA$^{1}$, MOHAMED MAAMA$^{1}$ & HERNANDO OMBAO$^{2}$\n\n[$^{1}$Applied Mathematics and Computational Science Program, $^{2}$Statistics"
+"---\nabstract: 'Understanding the pathways taken by a quantum particle during a transport process is an enormous challenge. There are broadly two different aspects of the problem that affect the route taken. First is obviously the couplings between the various sites, which translates into the intrinsic \u201cstrength\u201d of a state-to-state channel. Apart from the inter-state couplings, the solvents affecting the energies of the state, and their relative coupling strengths and time-scales form the second factor. This impact of dissipative media is significantly more difficult to analyze. Building on recently derived relations between coherences and population derivatives, we present an analysis of the transport that allows us to account for both the effects in a rigorous manner. We demonstrate the richness hidden behind the transport even for a relatively simple system, a 4-site coarse-grained model of the Fenna-Matthews-Olson complex. The effect of the local dissipative media is highly non-trivial. We show that while the impact on the total site population may be small, there are dramatic changes to the pathway taken by the transport process. The ability to untangle the dynamics at a greater granularity opens up possibilities in terms of design of novel systems with an eye towards quantum control.'"
+"---\nabstract: 'This paper introduces state polynomials, i.e., polynomials in noncommuting variables and formal states of their products. A state analog of Artin\u2019s solution to Hilbert\u2019s 17th problem is proved showing that state polynomials, positive over all matrices and matricial states, are sums of squares with denominators. Somewhat surprisingly, it is also established that a Krivine-Stengle Positivstellensatz fails to hold in the state polynomial setting. Further, archimedean Positivstellens\u00e4tze in the spirit of Putinar and Helton-McCullough are presented leading to a hierarchy of semidefinite relaxations converging monotonically to the optimum of a state polynomial subject to state constraints. This hierarchy can be seen as a state analog of the Lasserre hierarchy for optimization of polynomials, and the Navascu\u00e9s-Pironio-Ac\u00edn scheme for optimization of noncommutative polynomials. The motivation behind this theory arises from the study of correlations in quantum networks. Determining the maximal quantum violation of a polynomial Bell inequality for an arbitrary network is reformulated as a state polynomial optimization problem. Several examples of quadratic Bell inequalities in the bipartite and the bilocal tripartite scenario are analyzed. To reduce the size of the constructed SDPs, sparsity, sign symmetry and conditional expectation of the observables\u2019 group structure are exploited. To obtain the above-mentioned"
+"---\nabstract: 'Learning-based control approaches have shown great promise in performing complex tasks directly from high-dimensional perception data for real robotic systems. Nonetheless, the learned controllers can behave unexpectedly if the trajectories of the system divert from the training data distribution, which can compromise safety. In this work, we propose a control filter that wraps any reference policy and effectively encourages the system to stay in-distribution with respect to offline-collected safe demonstrations. Our methodology is inspired by Control Barrier Functions (CBFs), which are model-based tools from the nonlinear control literature that can be used to construct minimally invasive safe policy filters. While existing methods based on CBFs require a known low-dimensional state representation, our proposed approach is directly applicable to systems that rely solely on high-dimensional visual observations by learning in a latent state-space. We demonstrate that our method is effective for two different visuomotor control tasks in simulation environments, including both top-down and egocentric view settings.'\nauthor:\n- |\n    \\\n    University of California, Berkeley\\\n    Toyota Research Institute, Los Altos, CA\\\n    Toyota Research Institute, Los Altos, CA\\\n    University of California, Berkeley\\\n    Toyota Research Institute, Los Altos, CA\nbibliography:\n- 'references.bib'\ntitle: 'In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution"
+"---\nabstract: 'Interpretability of reinforcement learning policies is essential for many real-world tasks but learning such interpretable policies is a hard problem. Particularly rule-based policies such as decision trees and rules lists are difficult to optimize due to their non-differentiability. While existing techniques can learn verifiable decision tree policies there is no guarantee that the learners generate a decision that performs optimally. In this work, we study the optimization of size-limited decision trees for Markov Decision Processes (MPDs) and propose OMDTs: Optimal MDP Decision Trees. Given a user-defined size limit and MDP formulation OMDT directly maximizes the expected discounted return for the decision tree using Mixed-Integer Linear Programming. By training optimal decision tree policies for different MDPs we empirically study the optimality gap for existing imitation learning techniques and find that they perform sub-optimally. We show that this is due to an inherent shortcoming of imitation learning, namely that complex policies cannot be represented using size-limited trees. In such cases, it is better to directly optimize the tree for expected return. While there is generally a trade-off between the performance and interpretability of machine learning models, we find that OMDTs limited to a depth of 3 often perform close to"
+"---\nabstract: |\n    This study shows a method of enhancing the thermoelectric properties of GeTe-based materials by Ti and Bi co-doping on cation sites along with self-doping with Ge via simultaneous optimization of electronic (via crystal field engineering, and precise Fermi level optimization) and thermal (via point-defect scattering) transport properties. The pristine GeTe possesses high carrier concentration ($n$) due to intrinsic Ge vacancies, low Seebeck coefficient ($\\alpha$), and high thermal conductivity ($\\kappa$). The Ge vacancy optimization and crystal field engineering results in an enhanced $\\alpha$ via excess Ge and Ti doping, which is further improved by band structure engineering through Bi doping. As a result of improved $\\alpha$ and optimized Fermi level (carrier concentration), an enhanced power factor ($\\alpha^2\\sigma$) is obtained for Ti\u2013Bi co-doped Ge$_{1.01}$Te. These experimental results are also evidenced by theoretical calculations of band structure, and thermoelectric parameters using density functional theory and Boltztrap calculations, respectively. A significant reduction in the phonon thermal conductivity ($\\kappa_{ph}$) from $\\sim$ 3.5 W\u00b7m$^{-1}$\u00b7K$^{-1}$ to $\\sim$ 1.06 W\u00b7m$^{-1}$\u00b7K$^{-1}$ at 300K for Ti\u2013Bi co-doping in GeTe, attributed to point-defect scattering due to mass and strain field fluctuation, in line with the Debye-Callaway model. The phonon dispersion calculations show a decreasing group velocity in Ti\u2013Bi"
+"---\nbibliography:\n- 'refs.bib'\n---\n\n[**RGB Arabic Alphabets Sign Language Dataset**]{}\\\n0.5cm\n\n**Authors**\\\n\n-   Muhammad Al-Barham ^a,\\*^\n\n-   Adham Alsharkawi ^b^\n\n-   Musa Al-Yaman ^b^\n\n-   Mohammad Al-Fetyani ^c^\n\n-   Ashraf Elnagar ^d^\n\n-   Ahmad Abu Sa\u2019aleek ^e^\n\n-   Mohammad Al-Odat ^f^\n\n0.5cm **Affiliations**\\\n\n-   ^a^ MLALP Research Group, University of Sharjah, United Arab Emirates\n\n-   ^b^ Mechatronics Engineering Department, The University of Jordan\n\n-   ^c^ AppsWave for Information Technology, Jordan\n\n-   ^d^ Department of Computer Science, University of Sharjah, United Arab Emirates\n\n-   ^e^ Al-Wefaq Control Systems, Doha, Qatar\n\n-   ^f^ Student Guidance Department, The University of Jordan, Jordan\n\n0.5cm\n\n**Corresponding author\u2019s email address and Twitter handle**\n\n-   muhammadal-barham@ieee.org\n\n-   twitter: @MuhammadBarham\\_\n\n0.5cm **Keywords**\\\n0.5cm\n\n-   Sign-Language\n\n-   Dataset\n\n-   Deaf\n\n-   Arabic\n\n-   Alphabet\n\n0.5cm **Abstract**\\\n0.5cm\n\nThis paper introduces the RGB Arabic Alphabet Sign Language (AASL) dataset. AASL comprises 7,857 raw and fully labelled RGB images of the Arabic sign language alphabets, which to our best knowledge is the first publicly available RGB dataset. The dataset is aimed to help those interested in developing real-life Arabic sign language classification models. AASL was collected from more than 200 participants and with different settings such as lighting, background, image"
+"---\nabstract: |\n    Measurements in heavy-flavour azimuthal angular correlation provide insight into the production, propagation, and hadronization of heavy-flavour jets in ultra-relativistic hadronic and heavy-ion collisions. These measurements across different particle species help to isolate the possible modification in particle production and fragmentation due to different mass and quark contents. Jet correlation studies give direct access to the initial parton dynamics produced in these collisions.\\\n    This article studies the azimuthal angular correlations of heavy-flavour hadrons (charm and beauty mesons and charm baryons) in pp collisions at $\\sqrt{s} =$ 7 TeV using PYTHIA8. We study the production of heavy-flavour jets with different parton-level processes, including multi-parton interactions and different color reconnection prescriptions. The heavy-flavour hadrons correlations are calculated in the different triggers and associated [$p_{\\rm{T}}$]{}intervals to characterize the impact of hard and soft scattering. The yields and the widths associated with the near-side (NS) and away-side (AS) correlation peaks are calculated and studied as a function of associated [$p_{\\rm{T}}$]{}for different trigger [$p_{\\rm{T}}$]{}ranges.\nauthor:\n- 'Ravindra Singh,[^1]'\n- Swapnesh Khade\n- Ankhi Roy\ntitle: '[Jet fragmentation via azimuthal angular correlations of heavy-flavours in pp collisions at $\\sqrt{s} = $ 7 TeV]{}'\n---\n\nIntroduction {#intro}\n============\n\nThe Relativistic Heavy-Ion Collider (RHIC) at BNL,"
+"---\nabstract: 'We investigate electrons tunneling in AB bilayer graphene through a triple electrostatic barrier in the presence of a perpendicular magnetic field. By way of the transfer matrix method and using the continuity conditions at the different interfaces, transmission probability is determined. Additional resonances appear for two-band tunneling and at normal incidence, and their number is proportional to the value of $U_4$ in cases where $U_2<U_4$ is present. However, when $U_2>U_4$, anti-Klein tunneling increases with $U_2$. The transmission probability exhibits an interesting oscillatory behavior when $U_3>U_2=U_4$ and $U_3 <U_2=U_4$. For fixed energy $E=0.39\\gamma_1$, increasing barrier widths increases the number of oscillations and decreases Klein tunneling. An interlayer bias creates a gap for: $U_2<U_3<U_4$ and $U_3>U_2=U_4$. In the four-band tunneling case, transmission decreases in $T^+_+$, \u00a0$T^-_+$ and \u00a0$T^-_-$ channels in comparison with the single barrier case. It does, however, increase for $T^+_-$ when compared to the single barrier case. Transmission is suppressed in the gap region when an interlayer bias is introduced. Our results are relevant for electron confinements in AB bilayer graphene and for the development of graphene-based transistors.'\nauthor:\n- Mouhamadou Hassane Saley\n- Ahmed Jellal\ntitle: Magnetic field effect on tunneling through triple barrier in AB bilayer"
+"---\nabstract: 'We investigate spatial correlations of strain fluctuations in sheared colloidal glasses and simulations of sheared amorphous solids. The correlations reveal a quadrupolar symmetry reminiscent of the strain field due to an Eshelby\u2019s inclusion. However, they display an algebraic decay $1/r^{\\alpha}$, where the exponent $\\alpha$ is close to $1$ in the steady state, unlike the Eshelby field, for which $\\alpha=3$ . The exponent takes values between $3$ to $1$ in the transient stages of deformation. We explain these observations using a simple model based on interacting Eshelby inclusions. As the system is sheared beyond the linear response to plastic flow, the density correlations of inclusions are enhanced and it emerges as key to understanding the elastoplastic response of the system to applied shear.'\nauthor:\n- 'Sagar Malik$^{1}$, Meenakshi L.$^{2}$, Atharva Pandit$^{1}$, Antina Ghosh$^{1}$, Peter Schall$^{3}$, Bhaskar Sengupta$^{2}$, Vijayakumar Chikkadi$^1$'\ntitle: Microscopic strain correlations in sheared amorphous solids\n---\n\nAmorphous solids are an important class of materials that appear in various forms ranging from metallic glasses to polymeric glasses and soft materials made of emulsions, foams and granular matter [@Barrat18; @Biroli11]. Even though their mechanical properties differ significantly, they display similar elastic and plastic properties. Therefore, the elastoplastic deformation of"
+"---\nabstract: '*We study singularity formation of K$\\ddot{\\text{a}}$hler-Ricci flow on a K$\\ddot{\\text{a}}$hler manifold that admits a horizontally homothetic conformal submersion into another K$\\ddot{\\text{a}}$hler manifold. We will derive necessary and sufficient conditions for the preservation of horizontally homothetic conformal submersion along the flow and establish the formation of type I singularity together with a standard splitting of the Cheeger-Gromov limit. This generalizes the setup of Calabi symmetry that was discussed in [@1] and [@2], thus gives new proofs for the results listed there.*'\nauthor:\n- NGUYEN THE HOAN\nbibliography:\n- 'references.bib'\nnocite: '[@*]'\ntitle: 'K$\\ddot{\\text{A}}$HLER-RICCI FLOW AND CONFORMAL SUBMERSION'\n---\n\nIntroduction\n============\n\nIn [@2], Song and Weinkove showed that for a K$\\ddot{\\text{a}}$hler-Ricci flow on a Hirzebruch surface (a $\\mathbb{P}^1$ bundle over a projective space) which satisfies certain rotational symmetry conditions (Calabi symmetry), then either it will collapse the $\\mathbb{P}^1$ fibers, shrink the base to a point or contract the exceptional divisor. In all of these cases, the manifold considered as a metric spaces induced by K$\\ddot{\\text{a}}$hler metrics along the flow is known to perform a Gromov-Hausdorff convergence.\n\nThe singularity behaviors of the flow in these cases are also of particular interests. Since it was proved in [@8] that the flow could"
+"---\nabstract: 'When an exposure of interest is confounded by unmeasured factors, an instrumental variable (IV) can be used to identify and estimate certain causal contrasts. Identification of the marginal average treatment effect (ATE) from IVs relies on strong untestable structural assumptions. When one is unwilling to assert such structure, IVs can nonetheless be used to construct bounds on the ATE. Famously, [@balke1997bounds] proved tight bounds on the ATE for a binary outcome, in a randomized trial with noncompliance and no covariate information. We demonstrate how these bounds remain useful in observational settings with baseline confounders of the IV, as well as randomized trials with measured baseline covariates. The resulting bounds on the ATE are non-smooth functionals, and thus standard nonparametric efficiency theory is not immediately applicable. To remedy this, we propose (1) under a novel margin condition, influence function-based estimators of the bounds that can attain parametric convergence rates when the nuisance functions are modeled flexibly, and (2) estimators of smooth approximations of these bounds. We propose extensions to continuous outcomes, explore finite sample properties in simulations, and illustrate the proposed estimators in an observational study targeting the effect of higher education on wages.'\nauthor:\n- |\n    Alexander W."
+"---\nabstract: 'Machine learning (ML) compilers are an active area of research because they offer the potential to automatically speedup tensor programs. Kernel fusion is often cited as an important optimization performed by ML compilers. However, there exists a knowledge gap about how XLA, the most common ML compiler, applies this nuanced optimization, what kind of speedup it can afford, and what low-level effects it has on hardware. Our paper aims to bridge this knowledge gap by studying key compiler passes of XLA\u2019s source code. Our evaluation on a reinforcement learning environment Cartpole shows how different fusion decisions in XLA are made in practice. Furthermore, we implement several XLA kernel fusion strategies that can achieve up to 10.56x speedup compared to our baseline implementation.'\nauthor:\n- |\n    [Daniel Snider Ruofan Liang]{}\\\n    University of Toronto [^1]\\\n    {firstname.lastname}@mail.utoronto.ca\nbibliography:\n- 'main.bib'\ntitle: 'Operator Fusion in XLA: Analysis and Evaluation'\n---\n\nIntroduction\n============\n\nMachine learning (ML) becomes more and more important in various computer tasks including computer vision, natural language processing, robotic control, etc. The computation efficiency of ML also arises as a popular topic for computer system and architecture research. Today\u2019s machine learning (ML) applications rely on specialized hardware accelerators like GPUs"
+"---\nabstract: 'Gas leakage is a serious hazard in a variety of sectors, including industrial, domestic, and gas-powered vehicles. The installation of gas leak detection systems has emerged as a critical preventative measure to solve this problem. Traditional gas sensors, such as electrochemical, infrared point, and Metal Oxide Semiconductor sensors, have been widely used for gas leak detection. However, these sensors have limitations in terms of their adaptation to various gases, as well as their high cost and difficulties in scaling. In this paper, a novel non-contact gas detection technique based on a 40 kHz ultrasonic signal is described. The proposed approach employs the reflections of the emitted ultrasonic wave to detect the gas leaks and also able to identify the exact gas in real-time. To confirm the method\u2019s effectiveness, trials were carried out using Hydrogen, Helium, Argon, and Butane gas. The system identified gas flow breaches in 0.01 seconds, whereas the gas identification procedure took 0.8 seconds. An interesting extension of the proposed approach is real-time visualisation of gas flow employing an array of transducers.'\nauthor:\n- \ntitle: 'Ultrasound based Gas Detection: Analyzing Acoustic Impedance for High-Performance and Low-Cost Solutions'\n---\n\nGas leakage, Detection, Identification, Ultrasound, Real-time.\n\nIntroduction\n============"
+"---\nabstract: 'The classical homotopy optimization approach has the potential to deal with highly nonlinear landscape, such as the energy landscape of QAOA problems. Following this motivation, we introduce Hamiltonian-Oriented Homotopy QAOA (HOHo-QAOA), that is a heuristic method for combinatorial optimization using QAOA, based on classical homotopy optimization. The method consists of a homotopy map that produces an optimization problem for each value of interpolating parameter. Therefore, HOHo-QAOA decomposes the optimization of QAOA into several loops, each using a mixture of the mixer and the objective Hamiltonian for cost function evaluation. Furthermore, we conclude that the HOHo-QAOA improves the search for low energy states in the nonlinear energy landscape and outperforms other variants of QAOA.'\nauthor:\n- 'Akash Kundu$^{1,2}$[^1], Ludmila Botelho$^{1,2 *}$[^2], Adam Glos$^{1,3}$'\nbibliography:\n- 'reference.bib'\ntitle: 'Hamiltonian-Oriented Homotopy QAOA'\n---\n\nIntroduction\n============\n\nSpeedup of practical applications is yet to be realized for quantum devices as they are small and noise prone. The limitations of available hardware initiated the Noisy Intermediate Scale Quantum (NISQ) era [@preskill2018quantum]. The NISQ algorithms [@bharti2022noisy] can operate on limited amount of resources., in particular by distributing tasks between quantum and classical devices. Many of those algorithms are represented by a broad class of variational"
+"---\nabstract: 'We prove that topological disks with positive curvature and strictly convex boundary of large length are close to round spherical caps of constant boundary curvature in the Gromov-Hausdorff sense. This proves stability for a theorem of F.\u00a0Hang and X.\u00a0Wang in [@HW], and can be viewed as an affirmative answer to a convex stability version of the Min-Oo Conjecture in dimension two. As an intermediate step, we obtain a compactness result for a Liouville-type PDE problem.'\naddress: 'The University of Pennsylvania, Department of Mathematics, David Rittenhouse Lab., 209 South 33rd Street, Philadelphia, PA 19104, USA.'\nauthor:\n- Hunter Stufflebeam\nbibliography:\n- 'Stab\\_Conv\\_Disks\\_v5\\_arxiv.bib'\ntitle: Stability of Convex Disks\n---\n\nIntroduction\n============\n\nInequalities in geometric analysis, such as the isoperimetric and systolic, Faber-Krahn and Penrose, relate given geometric objects to understood model cases, taking as input data bounds on curvatures, volumes, eigenvalues, etc. Via such relationships, much work has been done to understand the structure of spaces with natural geometric conditions phrased in terms of such quantities.\n\nGiven an inequality for which one has some understanding of extremizers (the geometric objects which realize equality), one might ask if an object *nearly* realizing equality must somehow share characteristics with the"
+"---\nabstract: |\n    A polynomial homotopy is a family of polynomial systems, typically in one parameter $t$. Our problem is to compute power series expansions of the coordinates of the solutions in the parameter $t$, accurately, using multiple double arithmetic. One application of this problem is the location of the nearest singular solution in a polynomial homotopy, via the theorem of Fabry. Power series serve as input to construct Pad\u00e9 approximations.\n\n    Exploiting the massive parallelism of Graphics Processing Units capable of performing several trillions floating-point operations per second, the objective is to compensate for the cost overhead caused by arithmetic with power series in multiple double precision. The application of Newton\u2019s method for this problem requires the evaluation and differentiation of polynomials, followed by solving a blocked lower triangular linear system. Experimental results are obtained on NVIDIA GPUs, in particular the RTX 2080, P100 and V100.\n\n    Code generated by the CAMPARY software is used to obtain results in double double, quad double, and octo double precision. The programs in this study are self contained, available in a public github repository under the GPL-v3.0 License.\nauthor:\n- 'Jan Verschelde[^1]'\ndate: 29 January 2023\ntitle: |\n    GPU Accelerated Newton for Taylor Series"
+"---\nabstract: |\n    Auto-bidding has recently become a popular feature in ad auctions. This feature enables advertisers to simply provide high-level constraints and goals to an automated agent, which optimizes their auction bids on their behalf. These auto-bidding intermediaries interact in a decentralized manner in the underlying auctions, leading to new interesting practical and theoretical questions on auction design, for example, in understanding the bidding equilibrium properties between auto-bidder intermediaries for different auctions. In this paper, we examine the effect of different auctions on the incentives of advertisers to report their constraints to the auto-bidder intermediaries. More precisely, we study whether canonical auctions such as first price auction (FPA) and second price auction (SPA) are [*auto-bidding incentive compatible (AIC)*]{}: whether an advertiser can gain by misreporting their constraints to the autobidder.\n\n    We consider value-maximizing advertisers in two important settings: when they have a budget constraint and when they have a target cost-per-acquisition constraint. The main result of our work is that for both settings, FPA and SPA are not AIC. This contrasts with FPA being AIC when auto-bidders are constrained to bid using a (sub-optimal) uniform bidding policy. We further extend our main result and show that any (possibly randomized)"
+"---\nabstract: 'Free-running Recurrent Neural Networks (RNNs), especially probabilistic models, generate an ongoing information flux that can be quantified with the mutual information $I\\left[\\vec{x}(t),\\vec{x}(t\\!+\\!1)\\right]$ between subsequent system states $\\vec{x}$. Although, former studies have shown that $I$ depends on the statistics of the network\u2019s connection weights, it is unclear (1) how to maximize $I$ systematically and (2) how to quantify the flux in large systems where computing the mutual information becomes intractable. Here, we address these questions using Boltzmann machines as model systems. We find that in networks with moderately strong connections, the mutual information $I$ is approximately a monotonic transformation of the root-mean-square averaged Pearson correlations between neuron-pairs, a quantity that can be efficiently computed even in large systems. Furthermore, evolutionary maximization of $I\\left[\\vec{x}(t),\\vec{x}(t\\!+\\!1)\\right]$ reveals a general design principle for the weight matrices enabling the systematic construction of systems with a high spontaneous information flux. Finally, we simultaneously maximize information flux and the mean period length of cyclic attractors in the state space of these dynamical networks. Our results are potentially useful for the construction of RNNs that serve as short-time memories or pattern generators.'\nauthor:\n- Claus Metzner\n- 'Marius E. Yamakou'\n- Dennis Voelkl\n- Achim Schilling\n-"
+"---\nabstract: 'Let $K \\subseteq {\\mathbb{R}}^d$ be a convex body and let ${\\mathbf{w}}\\in {\\operatorname{int}}(K)$ be an interior point of\u00a0$K$. The *coefficient of asymmetry* ${\\operatorname{ca}}(K,{\\mathbf{w}}) := \\min\\{ \\lambda \\geq 1 : {\\mathbf{w}}- K \\subseteq \\lambda (K - {\\mathbf{w}}) \\}$ has been studied extensively in the realm of Hensley\u2019s conjecture on the maximal volume of a $d$-dimensional lattice polytope that contains a fixed positive number of interior lattice points. We study the coefficient of asymmetry for *lattice zonotopes*, i.e., Minkowski sums of line segments with integer endpoints. Our main result gives the existence of an interior lattice point whose coefficient of asymmetry is bounded above by an explicit constant in $\\Theta(d \\log\\log d)$, for any lattice zonotope that has an interior lattice point. Our work is both inspired by and feeds on Wills\u2019 lonely runner conjecture from Diophantine approximation: we make intensive use of a discrete version of this conjecture, and reciprocally, we reformulate the lonely runner conjecture in terms of the coefficient of asymmetry of a zonotope.'\naddress:\n- |\n    Department of Mathematics\\\n    San Francisco State University\\\n    San Francisco, CA 94132\\\n    U.S.A.\n- |\n    Institut f\u00fcr Mathematik\\\n    Universit\u00e4t Rostock\\\n    Campus Ulmenstra\u00dfe 69\\\n    D-18051 Rostock\\\n    Germany\nauthor:\n- Matthias Beck\n-"
+"---\nabstract: 'Household robots operate in the same space for years. Such robots incrementally build dynamic maps that can be used for tasks requiring remote object localization. However, benchmarks in robot learning often test generalization through inference on tasks in unobserved environments. In an observed environment, locating an object is reduced to choosing from among all object proposals in the environment, which may number in the 100,000s. Armed with this intuition, using only a generic vision-language scoring model with minor modifications for 3d encoding and operating in an embodied environment, we demonstrate an absolute performance gain of 9.84% on remote object grounding above state of the art models for REVERIE and of 5.04% on FAO. When allowed to pre-explore an environment, we also exceed the previous state of the art pre-exploration method on REVERIE. Additionally, we demonstrate our model on a real-world TurtleBot platform, highlighting the simplicity and usefulness of the approach. Our analysis outlines a \u201cbag of tricks\u201d essential for accomplishing this task, from utilizing 3d coordinates and context, to generalizing vision-language models to large 3d search spaces.'\nauthor:\n- |\n    Gunnar A. Sigurdsson \u00a0\u00a0\u00a0\u00a0 Jesse Thomason \u00a0\u00a0\u00a0 Gaurav S. Sukhatme \u00a0\u00a0\u00a0 Robinson Piramuthu\\\n    Amazon Alexa AI\nbibliography:\n- 'main.bib'\ntitle: 'RREx-BoT:"
+"---\nabstract: 'To date, epsilon near zero (ENZ) responses, characterized by an infinite phase velocity, are primarily achieved by applying a monochromatic light source to a tailored metamaterial. Here, we derive the equations for inducing a dynamically generated broadband ENZ response in a large class of many-body systems via tracking and feedback control. We further find that this response leads to a current-energy relationship identical to that of an ideal inductor. Using a Fermi-Hubbard model, we numerically confirm these results which have the potential to advance optical computation on the nanoscale.'\nauthor:\n- Jacob Masur\n- 'Denys I. Bondar'\n- Gerard McCaul\nbibliography:\n- 'refs.bib'\ntitle: 'Dynamical Generation of Epsilon-Near-Zero Behaviour via Tracking and Feedback Control'\n---\n\n#### Introduction.\n\nOne of the principal research goals of the twenty first century optics has been the development and application of epsilon-near-zero (ENZ) materials. These form a subclass of near zero index (NZI) materials, whose common feature is the extraordinary optical properties they possess [@WuENZPhotonics; @ziolkowski2004propagation; @ENZPhotonics; @nziPhotonics; @riseNZItech; @nziPhotonicMaterials]. Such materials exhibit a near zero permittivity or permeability at a given frequency, leading to a decoupling of the electric and magnetic fields [@ENZPhotonics; @nziPhotonics; @engheta2006metamaterials; @ziolkowski2004propagation; @engheta2013pursuing]. Such behaviour has exceptional potential"
+"---\nabstract: 'Hit song prediction, one of the emerging fields in music information retrieval (MIR), remains a considerable challenge. Being able to understand what makes a given song a hit is clearly beneficial to the whole music industry. Previous approaches to hit song prediction have focused on using audio features of a record. This study aims to improve the prediction result of the top 10 hits among Billboard Hot 100 songs using more alternative metadata, including song audio features provided by Spotify, song lyrics, and novel metadata-based features (title topic, popularity continuity and genre class). Five machine learning approaches are applied, including: k-nearest neighbours, Na\u00efve Bayes, Random Forest, Logistic Regression and Multilayer Perceptron. Our results show that Random Forest (RF) and Logistic Regression (LR) with all features (including novel features, song audio features and lyrics features) outperforms other models, achieving 89.1% and 87.2% accuracy, and 0.91 and 0.93 AUC, respectively. Our findings also demonstrate the utility of our novel music metadata features, which contributed most to the models\u2019 discriminative performance.'\nauthor:\n- Mengyisong Zhao\n- Morgan Harvey\n- David Cameron\n- Frank Hopfgartner\n- 'Valerie J. Gillet'\ntitle: An Analysis of Classification Approaches for Hit Song Prediction using Engineered Metadata"
+"---\nabstract: 'We propose a Model-Based Reinforcement Learning (MBRL) algorithm named VF-MC-PILCO, specifically designed for application to mechanical systems where velocities cannot be directly measured. This circumstance, if not adequately considered, can compromise the success of MBRL approaches. To cope with this problem, we define a velocity-free state formulation which consists of the collection of past positions and inputs. Then, VF-MC-PILCO uses Gaussian Process Regression to model the dynamics of the velocity-free state and optimizes the control policy through a particle-based policy gradient approach. We compare VF-MC-PILCO with our previous MBRL algorithm, MC-PILCO4PMS, which handles the lack of direct velocity measurements by modeling the presence of velocity estimators. Results on both simulated (cart-pole and UR5 robot) and real mechanical systems (Furuta pendulum and a ball-and-plate rig) show that the two algorithms achieve similar results. Conveniently, VF-MC-PILCO does not require the design and implementation of state estimators, which can be a challenging and time-consuming activity to be performed by an expert user.'\nauthor:\n- 'Fabio Amadio$^1$, Alberto Dalla Libera$^1$, Daniel Nikovski$^2$, Ruggero Carli$^1$, Diego Romeres$^2$ [^1] [^2]'\nbibliography:\n- 'references.bib'\ntitle: '**Learning Control from Raw Position Measurements**'\n---\n\nIntroduction\n============\n\nModel-Based Reinforcement Learning (MBRL) [@polydoros2017survey] proved to be a promising strategy"
+"---\nabstract: 'In this paper we investigate the ability of modern machine learning algorithms in inferring basic offline activities,\u00a0e.g., shopping and dining, from location data. Using anonymized data of thousands of users of a prominent location-based social network, we empirically demonstrate that not only state-of-the-art machine learning excels at the task at hand\u00a0(F1 score$>$0.9) but also tabular models are among the best performers. The findings we report here not only fill an existing gap in the literature, but also highlight the potential risks of such capabilities given the ubiquity of location data and the high accessibility of tabular machine learning models.'\nauthor:\n- \n- \nbibliography:\n- 'bibs.bib'\ntitle: 'Where You Are Is What You Do: On Inferring Offline Activities From Location Data'\n---\n\nlocation data, activity inference, privacy\n\nIntroduction {#sec1}\n============\n\nWith the proliferation of social media, smartphones, Internet-of-things (IOT) devices and low Earth orbit (LEO) satellites, we live in a world where location data (Data with reference to a physical location) is ubiquitous. Recent estimates [@huang2020big] suggest that location data make up over 80% of the data created on a daily basis. While location data can be and have been used for widely agreed on, positive applications"
+"---\nabstract: 'We analyze the effect of circuit parameter variation on the performance of Josephson traveling-wave parametric amplifiers (JTWPAs). Specifically, the JTWPA concept we investigate is using flux-biased nonhysteretic rf-SQUIDs in a transmission line configuration, which harnesses the three-wave mixing (3WM) regime. Dispersion engineering enables phase-matching to achieve power gain of $\\sim$20\u00a0dB, while suppressing the generation of unwanted mixing processes. Two dispersion engineering concepts using a 3WM-JTWPA circuit model, i.e., resonant phase-matching (RPM) and periodic capacitance modulation (PCM), are discussed, with results potentially also applicable to four-wave-mixing (4WM) JTWPAs. We propose suitable circuit parameter sets and evaluate amplifier performance with and without circuit parameter variance using transient circuit simulations. This approach inherently takes into account microwave reflections, unwanted mixing products, imperfect phase-matching, pump depletion, etc. In the case of RPM the resonance frequency spread is critical, while PCM is much less sensitive to parameter spread. We discuss degrees of freedom to make the JTWPA circuits more tolerant to parameter spread. Finally, our analysis shows that the flux-bias point where rf-SQUIDs exhibit Kerr-free nonlinearity is close to the sweet spot regarding critical current spread.'\nauthor:\n- 'C. Kissling, V. Gaydamachenko, F. Kaap, M. Khabipov, R. Dolata, A. B. Zorin, L."
+"---\nabstract: 'We apply the Tenerife Inversion Code (TIC) to the plage spectropolarimetric observations obtained by the Chromospheric LAyer SpectroPolarimeter (CLASP2). These unprecedented data consist of full Stokes profiles in the spectral region around the h and k lines for a single slit position, with around two thirds of the 200\u00a0arcsec slit crossing a plage region and the rest crossing an enhanced network. A former analysis of these data had allowed us to infer the longitudinal component of the magnetic field by applying the weak field approximation (WFA) to the circular polarization profiles, and to assign the inferred magnetic fields to different layers of the solar atmosphere based on the results of previous theoretical radiative transfer investigations. In this work, we apply the recently developed TIC to the same data. We obtain the stratified model atmosphere that fits the intensity and circular polarization profiles at each position along the spectrograph slit and we compare our results for the longitudinal component of the magnetic field with the previously obtained WFA results, highlighting the generally good agreement in spite of the fact that the WFA is known to produce an underestimation when applied to the outer lobes of the h and"
+"---\nabstract: 'In the present paper, the electronic, structural, thermodynamic, and elastic properties of cubic $PbGeO_3$ perovskite oxide are presented and computed using the WIEN2k code. The structural properties have been evaluated and they are in good agreement with the theoretical and experimental data. A phonon dispersion is made and it reveals that the cubic $PbGeO_3$ perovskite is dynamically stable. In addition, the electronic properties of $PbGeO_3$ shows an opening gap energy, meaning a semiconductor behavior with an indirect band gap equal to $1.67\\;eV$. Moreover, the obtained elastic constants of cubic $PbGeO_3$ satisfy Born\u2019s mechanical stability criteria, and this indicates that our compound behaves as a stable ductile material. The temperature-pressure effects on thermodynamic parameters are investigated using the Gibbs2 package. Finally, based on the obtained results about the cubic $PbGeO_3$ perovskite properties, we assume that this compound will have potential applications.'\nauthor:\n- |\n    M. Agouri$^{1}$, A. Waqdim$^{1}$, A. Abbassi$^{1,}$[^1], M. Ouali$^{1}$, S. Taj$^{1}$, B. Manaut$^{1,}$[^2], M. Driouich$^1$\\\n    [*[$^1$ Laboratory of Research in Physics and Engineering Sciences,]{}*]{}\\\n    [*[Sultan Moulay Slimane University, Polydisciplinary Faculty, Beni Mellal, 23000, Morocco.]{}*]{}\\\ndate:\n- \n- \ntitle: '\\'\n---\n\nKeywords: DFT, Elastic, Thermodynamic, Perovskite, WIEN2K, mBJ.\n\nIntroduction\n============\n\nSince the discovery of $CaTiO_3$ [@1], the"
+"---\nabstract: 'This note addresses the problem of evaluating the impact of an attack on discrete-time nonlinear stochastic control systems. The problem is formulated as an optimal control problem with a joint chance constraint that forces the adversary to avoid detection throughout a given time period. Due to the joint constraint, the optimal control policy depends not only on the current state, but also on the entire history, leading to an explosion of the search space and making the problem generally intractable. However, we discover that the current state and whether an alarm has been triggered, or not, is sufficient for specifying the optimal decision at each time step. This information, which we refer to as the alarm flag, can be added to the state space to create an equivalent optimal control problem that can be solved with existing numerical approaches using a Markov policy. Additionally, we note that the formulation results in a policy that does not avoid detection once an alarm has been triggered. We extend the formulation to handle multi-alarm avoidance policies for more reasonable attack impact evaluations, and show that the idea of augmenting the state space with an alarm flag is valid in this extended"
+"---\nabstract: 'WebAssembly (abbreviated WASM) has emerged as a promising language of the Web and also been used for a wide spectrum of software applications such as mobile applications and desktop applications. These applications, named as WASM applications, commonly run in WASM runtimes. Bugs in WASM runtimes are frequently reported by developers and cause the crash of WASM applications. However, these bugs have not been well studied. To fill in the knowledge gap, we present a systematic study to characterize and detect bugs in WASM runtimes. We first harvest a dataset of 311 real-world bugs from hundreds of related posts on GitHub. Based on the collected high-quality bug reports, we distill 31 bug categories of WASM runtimes and summarize their common fix strategies. Furthermore, we develop a pattern-based bug detection framework to automatically detect bugs in WASM runtimes. We apply the detection framework to five popular WASM runtimes and successfully uncover 53 bugs that have never been reported previously, among which 14 have been confirmed and 6 have been fixed by runtime developers.'\nauthor:\n- Yixuan Zhang\n- Shangtong Cao\n- Haoyu Wang\n- Zhenpeng Chen\n- Xiapu Luo\n- Dongliang Mu\n- Yun Ma\n- Gang Huang\n- Xuanzhe"
+"---\nabstract: 'Enumerating the directed acyclic graphs (DAGs) of a Markov equivalence class (MEC) is an important primitive in causal analysis. The central resource from the perspective of computational complexity is the delay, that is, the time an algorithm that lists all members of the class requires between two consecutive outputs. Commonly used algorithms for this task utilize the rules proposed by Meek (1995) or the transformational characterization by Chickering (1995), both resulting in superlinear delay. In this paper, we present the first linear-time delay algorithm. On the theoretical side, we show that our algorithm can be generalized to enumerate DAGs represented by models that incorporate background knowledge, such as MPDAGs; on the practical side, we provide an efficient implementation and evaluate it in a series of experiments. Complementary to the linear-time delay algorithm, we also provide intriguing insights into Markov equivalence itself: All members of an MEC can be enumerated such that two successive DAGs have structural Hamming distance at most three.'\nauthor:\n- Author Name\n- 'First Author Name,^1^ Second Author Name, ^2^ Third Author Name ^1^'\n- 'Marcel Wien\u00f6bst,^1^ Malte Luttermann,^2^ Max Bannach,^1^ Maciej Li\u015bkiewicz^1^'\nbibliography:\n- 'main.bib'\ntitle:\n- 'Efficient Enumeration of Markov Equivalent DAGs[^1]'\n-"
+"Introduction\n============\n\nFederated Learning (FL) is an emerging privacy-preserving distributed machine learning paradigm. The model is transmitted to the clients by the server, and when the clients have completed local training, the parameter updates are sent back to the server for integration. Clients are not required to provide local raw data during this procedure, maintaining their privacy. However, the non-IID nature of clients\u2019 local distribution hinders the performance of FL algorithms\u00a0[@mcmahan2016communication; @li2018federated; @karimireddy2020scaffold; @li2021model], and the distribution shifts among clients become a main challenge in FL. =-1\n\n#### Distribution shifts in FL.\n\nAs identified in the seminal surveys\u00a0[@kairouz2021advances; @moreno2012unifying; @lu2018learning], there are three types of distribution shifts across clients that bottleneck the deployment of FL (see Figure\u00a0\\[fig:Illustration of the scenario we constructed\\]):\n\n-   *Concept shift*: For tasks of using feature $\\xx$ to predict label $y$, the conditional distributions of labels $\\cP(y|\\xx)$ may differ across clients, even if the marginal distributions of labels $\\cP(y)$ and features $\\cP(\\xx)$ are shared. =-1\n\n-   *Label distribution shift*: The marginal distributions of labels $\\cP(y)$ may vary across clients, even if the conditional distribution of features $\\cP(\\xx | y)$ is the same.\n\n-   *Feature distribution shift*: The marginal distribution of features $\\cP(\\xx)$ may"
+"---\nabstract: 'In turbulence modeling, we are concerned with finding closure models that represent the effect of the subgrid scales on the resolved scales. Recent approaches gravitate towards machine learning techniques to construct such models. However, the stability of machine-learned closure models and their abidance by physical structure (e.g. symmetries, conservation laws) are still open problems. To tackle both issues, we take the \u2018discretize first, filter next\u2019 approach. In this approach we apply a spatial averaging filter to existing fine-grid discretizations. The main novelty is that we introduce an additional set of equations which dynamically model the energy of the subgrid scales. Having an estimate of the energy of the subgrid scales, we can use the concept of energy conservation to derive stability. The subgrid energy containing variables are determined via a data-driven technique. The closure model is used to model the interaction between the filtered quantities and the subgrid energy. Therefore the total energy should be conserved. Abiding by this conservation law yields guaranteed stability of the system. In this work, we propose a novel skew-symmetric convolutional neural network architecture that satisfies this law. The result is that stability is guaranteed, independent of the weights and biases of the"
+"---\nabstract: |\n    Self-supervised sequential recommendation significantly improves recommendation performance by maximizing mutual information with well-designed data augmentations. However, the mutual information estimation is based on the calculation of Kullback\u2013Leibler divergence with several limitations, including the exponential need of the sample size and training instability. Also, existing data augmentations are mostly stochastic and can potentially break sequential correlations with random modifications. These two issues motivate us to investigate an alternative robust mutual information measurement capable of modeling uncertainty and alleviating KL divergence\u2019s limitations.\n\n    To this end, we propose a novel self-supervised learning framework based on the **M**utual Wasser**Stein** discrepancy minimization\u00a0() for the sequential recommendation. We propose the Wasserstein Discrepancy Measurement to measure the mutual information between augmented sequences. Wasserstein Discrepancy Measurement builds upon the 2-Wasserstein distance, which is more robust, more efficient in small batch sizes, and able to model the uncertainty of stochastic augmentation processes. We also propose a novel contrastive learning loss based on Wasserstein Discrepancy Measurement. Extensive experiments on four benchmark datasets demonstrate the effectiveness of over baselines. More quantitative analyses show the robustness against perturbations and training efficiency in batch size. Finally, improvements analysis indicates better representations of popular users/items with significant uncertainty. The source"
+"---\nabstract: 'In recent years, cloud and edge architectures have gained tremendous focus for offloading computationally heavy applications. From machine learning and Internet of Thing (IOT) to industrial procedures and robotics, cloud computing have been used extensively for data processing and storage purposes, thanks to its \u201cinfinite\u201d resources. On the other hand, cloud computing is characterized by long time delays due to the long distance between the cloud servers and the machine requesting the resources. In contrast, edge computing provides almost real-time services since edge servers are located significantly closer to the source of data. This capability sets edge computing as an ideal option for real-time applications, like high level control, for resource-constrained platforms. In order to utilize the edge resources, several technologies, with basic ones as containers and orchestrators like Kubernetes, have been developed to provide an environment with many features, based on each application\u2019s requirements. In this context, this works presents the implementation and evaluation of a novel edge architecture based on Kubernetes orchestration for controlling the trajectory of a resource-constrained Unmanned Aerial Vehicle (UAV) by enabling Model Predictive Control (MPC).'\nauthor:\n- 'Achilleas Santi Seisa, Sumeet Gajanan Satpute and George Nikolakopoulos[^1] [^2] [^3]'\nbibliography:\n- './IEEEtranBST/IEEEabrv.bib'\n-"
+"---\nabstract: |\n    Creating a taxonomy of interests is expensive and human-effort intensive: not only do we need to identify nodes and interconnect them, in order to use the taxonomy, we must also connect the nodes to relevant entities such as users, pins, and queries. Connecting to entities is challenging because of ambiguities inherent to language but also because individual interests are dynamic and evolve.\n\n    Here, we offer an alternative approach that begins with bottom-up discovery of [$\\mu$-topics]{}called pincepts. The discovery process itself connects these [$\\mu$-topics]{}dynamically with relevant queries, pins, and users at high precision, automatically adapting to shifting interests. Pincepts cover all areas of user interest and automatically adjust to the specificity of user interests and are thus suitable for the creation of various kinds of taxonomies. Human experts associate taxonomy nodes with [$\\mu$-topics]{}(on average, 3 [$\\mu$-topics]{}per node), and the [$\\mu$-topics]{}offer a high-level data layer that allows quick definition, immediate inspection, and easy modification. Even more powerfully, [$\\mu$-topics]{}allow easy exploration of nearby semantic space, enabling curators to spot and fill gaps. Curators\u2019 domain knowledge is heavily leveraged and we thus don\u2019t need untrained mechanical Turks, allowing further cost reduction. These [$\\mu$-topics]{}thus offer a satisfactory \u201csymbolic\u201d stratum over which to"
+"---\nabstract: 'Anomaly detection methods are part of the systems where rare events may endanger an operation\u2019s profitability, safety, and environmental aspects. Although many state-of-the-art anomaly detection methods were developed to date, their deployment is limited to the operation conditions present during the model training. Online anomaly detection brings the capability to adapt to data drifts and change points that may not be represented during model development resulting in prolonged service life. This paper proposes an online anomaly detection algorithm for existing real-time infrastructures where low-latency detection is required and novel patterns in data occur unpredictably. The online inverse cumulative distribution-based approach is introduced to eliminate common problems of offline anomaly detectors, meanwhile providing dynamic process limits to normal operation. The benefit of the proposed method is the ease of use, fast computation, and deployability as shown in two case studies of real microgrid operation data.'\nauthor:\n- \nbibliography:\n- 'main.bib'\ntitle: 'Real-Time Outlier Detection with Dynamic Process Limits\\'\n---\n\nanomaly detection, interpretable machine learning, online machine learning, real-time systems, streaming analytics\n\nIntroduction {#Introduction}\n============\n\nThe era of Industry 4.0 is ruled by data. Effective data-based decision-making is driven by the quantity of collected data. Internet of Things (IoT) devices"
+"=1\n\nIntroduction\n============\n\nThe goal of this paper is to present explicit formulas for certain algebraic Poisson brackets on\u00a0${{\\mathbb P}}^5$.\n\nRecall that two Poisson brackets $\\{\\cdot,\\cdot\\}_1$, $\\{\\cdot,\\cdot\\}_2$ are called [*compatible*]{} if any linear combination $\\{\\cdot,\\cdot\\}_1+{\\lambda}\\cdot \\{\\cdot,\\cdot\\}_2$ is still a Poisson bracket (i.e., satisfies the Jacobi identity). Pairs of compatible Poisson brackets play an important role in the theory of integrable systems.\n\nWith every normal elliptic curve $C$ in ${{\\mathbb P}}^n$ one can associate naturally a Poisson bracket on ${{\\mathbb P}}^n$, called a [*Feigin\u2013Odesskii bracket of type $q_{n+1,1}$*]{}. The corresponding quadratic Poisson brackets on ${{\\mathbb A}}^{n+1}$ arise as quasi-classical limits of Feigin\u2013Odesskii elliptic algebras. On the other hand, they can be constructed using the geometry of vector bundles on $C$ (see\u00a0[@FO95; @P98]).\n\nIt was discovered by Odesskii\u2013Wolf\u00a0[@OW] that for every $n$ there exists a family of $9$ linearly independent mutually compatible Poisson brackets on ${{\\mathbb P}}^n$, such that their generic linear combinations are Feigin\u2013Odesskii brackets of type $q_{n+1,1}$. In\u00a0[@HP], this construction was explained and extended in terms of anticanonical line bundles on del Pezzo surfaces. It was observed in\u00a0[@HP Example\u00a04.6] that in this framework one also obtains $10$ linearly independent mutually compatible Poisson brackets on"
+"---\nabstract: 'In financial markets, the market order sign exhibits strong persistence, widely known as the long-range correlation (LRC) of order flow; specifically, the sign autocorrelation function (ACF) displays long memory with power-law exponent $\\gamma$, such that $C(\\tau) \\propto \\tau^{-\\gamma}$ for large time-lag $\\tau$. One of the most promising microscopic hypotheses is the order-splitting behaviour at the level of individual traders. Indeed, Lillo, Mike, and Farmer (LMF) introduced in 2005 a simple microscopic model of order-splitting behaviour, which predicts that the macroscopic sign correlation is quantitatively associated with the microscopic distribution of metaorders. While this hypothesis has been a central issue of debate in econophysics, its direct quantitative validation has been missing because it requires large microscopic datasets with high resolution to observe the order-splitting behaviour of all individual traders. Here we present the first quantitative validation of this LMF prediction by analysing a large microscopic dataset in the Tokyo Stock Exchange market for more than nine years. On classifying all traders as either order-splitting traders or random traders as a statistical clustering, we directly measured the metaorder-length distributions $P(L)\\propto L^{-\\alpha-1}$ as the microscopic parameter of the LMF model and examined the theoretical prediction on the macroscopic order correlation $\\gamma"
+"---\nabstract: 'We develop the first end-to-end sample complexity of model-free policy gradient (PG) methods in discrete-time infinite-horizon Kalman filtering. Specifically, we introduce the receding-horizon policy gradient (RHPG-KF) framework and demonstrate $\\tilde{\\mathcal{O}}(\\epsilon^{-2})$ sample complexity for RHPG-KF in learning a stabilizing filter that is $\\epsilon$-close to the optimal Kalman filter. Notably, the proposed RHPG-KF framework does not require the system to be open-loop stable nor assume any prior knowledge of a stabilizing filter. Our results shed light on applying model-free PG methods to control a linear dynamical system where the state measurements could be corrupted by statistical noises and other (possibly adversarial) disturbances.'\nauthor:\n- 'Xiangyuan Zhang Bin Hu Tamer Ba\u015far[^1]'\nbibliography:\n- 'main.bib'\ntitle: '**Learning the Kalman Filter with Fine-Grained Sample Complexity**'\n---\n\nIntroduction\n============\n\nIn recent years, policy-based reinforcement learning (RL) methods [@sutton2000policy; @kakade2002natural; @schulman2015trust; @schulman2017proximal] have gained increasing attention in continuous control applications [@schulman2015high; @lillicrap2015continuous; @recht2019tour]. While traditional model-based techniques synthesize controller designs in a case-by-case manner [@anderson1979optimal; @anderson1990optimal], model-free policy gradient (PG) methods promise a universal framework that learns controller designs in an end-to-end fashion. The universality of model-free PG methods makes them desired candidates in complex control applications that involve nonlinear system dynamics and imperfect state"
+"---\nabstract: 'As AI technologies are rolled out into healthcare, academia, human resources, law, and a multitude of other domains, they become de-facto arbiters of truth. But truth is highly contested, with many different definitions and approaches. This article discusses the struggle for truth in AI systems and the general responses to date. It then investigates the production of truth in InstructGPT, a large language model, highlighting how data harvesting, model architectures, and social feedback mechanisms weave together disparate understandings of veracity. It conceptualizes this performance as an *operationalization of truth*, where distinct, often conflicting claims are smoothly synthesized and confidently presented into truth-statements. We argue that these same logics and inconsistencies play out in Instruct\u2019s successor, ChatGPT, reiterating truth as a non-trivial problem. We suggest that enriching sociality and thickening \u201creality\u201d are two promising vectors for enhancing the truth-evaluating capacities of future language models. We conclude, however, by stepping back to consider AI truth-telling as a social practice: what kind of \u201ctruth\u201d do we as listeners desire?'\nauthor:\n- Luke Munn\n- Liam Magee\n- Vanicka Arora\ndate: January 2023\ntitle: 'Truth Machines: Synthesizing Veracity in AI Language Models'\n---\n\n[ ***Keywords\u2014*** truthfulness, veracity, AI, large language model, GPT-3,"
+"---\nabstract: 'Fast radio bursts (FRBs) are being detected with increasing regularity. However, their spontaneous and often once-off nature makes high-precision burst position and frequency-time structure measurements difficult without specialised real-time detection techniques and instrumentation. The Australian Square Kilometre Array Pathfinder (ASKAP) has been enabled by the Commensal Real-time ASKAP Fast Transients Collaboration (CRAFT) to detect FRBs in real-time and save raw antenna voltages containing FRB detections. We present the CRAFT Effortless Localisation and Enhanced Burst Inspection pipeline (CELEBI), an automated [[[offline]{}]{}]{} software pipeline that extends CRAFT\u2019s existing software to process ASKAP voltages in order to produce sub-arcsecond precision localisations and polarimetric data at time resolutions as fine as 3ns of FRB events. We use Nextflow to link together Bash and Python code that performs software correlation, interferometric imaging, and beamforming, making use of common astronomical software packages.'\nauthor:\n- 'D.\u00a0R.\u00a0Scott'\n- 'H.\u00a0Cho'\n- 'C.\u00a0K.\u00a0Day'\n- 'A.\u00a0T.\u00a0Deller'\n- 'M.\u00a0Glowacki'\n- 'K.\u00a0Gourdji'\n- 'K.\u00a0W.\u00a0Bannister'\n- 'A.\u00a0Bera'\n- 'S.\u00a0Bhandari'\n- 'C.\u00a0W.\u00a0James'\n- 'R.\u00a0M.\u00a0Shannon'\nbibliography:\n- 'references.bib'\ntitle: 'CELEBI: The CRAFT Effortless Localisation and Enhanced Burst Inspection Pipeline'\n---\n\nFast Radio Bursts ,Radio Interferometry ,Astronomy Software"
+"---\nabstract: 'Gromov\u2013Wasserstein (GW) transport is inherently invariant under isometric transformations of the data. Having this property in mind, we propose to estimate dynamical systems by transfer operators derived from GW transport plans, when merely the initial and final states are known. We focus on entropy regularized GW transport, which allows to utilize the fast Sinkhorn algorithm and a spectral clustering procedure to extract coherent structures. Moreover, the GW framework provides a natural quantitative assessment on the shape-coherence of the extracted structures. We discuss fused and unbalanced variants of GW transport for labelled and noisy data, respectively. Our models are verified by three numerical examples of dynamical systems with governing rotational forces.'\nauthor:\n- 'Florian Beier,'\ntitle: 'Gromov\u2013Wasserstein Transfer Operators[^1] '\n---\n\nIntroduction\n============\n\nOptimal transport (OT) aims to find an optimal mass transport between two input (marginal) measures according to an underlying cost function. To improve the speed of the numerical computation, Cuturi [@cuturi] introduced a regularized OT version which can be solved by the fast and parallelizable Sinkhorn algorithm. Further effort has been made to generalize the OT for different settings as, e.g., unbalanced optimal transport [@LMS18], which relaxes the hard matching of the marginal measures. Another line"
+"---\nabstract: |\n    We introduce a new algorithm promoting sparsity called [*Support Exploration Algorithm (SEA)*]{} and analyze it in the context of support recovery/model selection problems.\n\n    The algorithm can be interpreted as an instance of the [*straight-through estimator (STE)*]{} applied to the resolution of a sparse linear inverse problem. SEA uses a non-sparse exploratory vector and makes it evolve in the input space to select the sparse support. We put to evidence an oracle update rule for the exploratory vector and consider the STE update.\n\n    The theoretical analysis establishes general sufficient conditions of support recovery. The general conditions are specialized to the case where the matrix $A$ performing the linear measurements satisfies the [*Restricted Isometry Property (RIP)*]{}.\n\n    Experiments show that SEA can efficiently improve the results of any algorithm. Because of its exploratory nature, SEA also performs remarkably well when the columns of $A$ are strongly coherent.\nauthor:\n- Mimoun Mohamed\n- Fran\u00e7ois Malgouyres\n- Valentin Emiya\n- Caroline Chaux\nbibliography:\n- 'abbr.bib'\n- 'bibliography.bib'\ntitle: Support Exploration Algorithm for Sparse Support Recovery\n---\n\n0.3in\n\nIntroduction\n============\n\nSparse representations and sparsity-inducing algorithms are widely used in statistics and machine learning\u00a0[@Hastie_T_2015_Statistical_SLwS], as well as in signal processing\u00a0[@Elad2010]. For instance,"
+"---\nabstract: |\n    We consider the gradient flow of the Ambrosio-Tortorelli functional at fixed $\\epsilon >0$, proving existence and uniqueness of a solution in dimension 2. The strategy of the proof essentially follows the one in the first part of [@feng2004analysis], but as suggested in a footnote by the authors of [@feng2004analysis] it employs a different and simpler technique, which is used for a different equation in [@barrett2006convergence] and in the end it allows to prove better estimates than the ones obtained in the original article. In particular we prove that if $U \\subset \\mathbb{R}^2$ is a bounded Lipshitz domain, the initial data $(u_0, z_0) \\in [H^1 (U)]^2$ and $0 \\leq z_0 \\leq 1$, then for every $T>0$ there exists a unique gradient flow $(u(t), z(t))$ of the Ambrosio-Tortorelli functional such that\n\n    $$(u,z) \\in [L^2 (0,T; H^2 (U)) \\cap L^\\infty (0,T; H^1 (U)) \\cap H^1 (0,T; L^2 (U)) ]^2.$$\n\n    The basic difference from [@feng2004analysis], as already said, is a better regularity result and a simpler proof: while in [@feng2004analysis] they used a localization argument based on an idea by Struwe (see [@struwe1996geometric]), here crucial estimates on the fourth powers of the $L^4 _t (L^4 _x )$ norms of the gradients"
+"---\nabstract: |\n    Nowadays traffic on the Internet has been widely encrypted to protect its confidentiality and privacy. However, traffic encryption is always abused by attackers to conceal their malicious behaviors. Since the encrypted malicious traffic has similar features to benign flows, it can easily evade traditional detection methods. Particularly, the existing encrypted malicious traffic detection methods are supervised and they rely on the prior knowledge of known attacks (e.g., labeled datasets). Detecting unknown encrypted malicious traffic in real time, which does not require prior domain knowledge, is still an open problem.\n\n    In this paper, we propose [HyperVision]{}, a realtime unsupervised machine learning (ML) based malicious traffic detection system. Particularly, [HyperVision]{}is able to detect unknown patterns of encrypted malicious traffic by utilizing a compact in-memory graph built upon the traffic patterns. The graph captures flow interaction patterns represented by the graph structural features, instead of the features of specific known attacks. We develop an unsupervised graph learning method to detect abnormal interaction patterns by analyzing the connectivity, sparsity, and statistical features of the graph, which allows [HyperVision]{}to detect various encrypted attack traffic without requiring any labeled datasets of known attacks. Moreover, we establish an information theory model to demonstrate that"
+"---\nabstract: 'Since random initial models in Federated Learning (FL) can easily result in unregulated Stochastic Gradient Descent (SGD) processes, existing FL methods greatly suffer from both slow convergence and poor accuracy, especially for non-IID scenarios. To address this problem, we propose a novel FL method named CyclicFL, which can quickly derive effective initial models to guide the SGD processes, thus improving the overall FL training performance. Based on the concept of Continual Learning (CL), we prove that CyclicFL approximates existing centralized pre-training methods in terms of classification and prediction performance. Meanwhile, we formally analyze the significance of data consistency between the pre-training and training stages of CyclicFL, showing the limited Lipschitzness of loss for the pre-trained models by CyclicFL. Unlike traditional centralized pre-training methods that require public proxy data, CyclicFL pre-trains initial models on selected clients cyclically without exposing their local data. Therefore, they can be easily integrated into any security-critical FL methods. Comprehensive experimental results show that CyclicFL can not only improve the classification accuracy by up to $16.21\\%$, but also significantly accelerate the overall FL training processes.'\nauthor:\n- Pengyu Zhang\n- Yingbo Zhou\n- Ming Hu\n- Xin Fu\n- Xian Wei\n- Mingsong Chen\nbibliography:"
+"---\nabstract: 'Optical nanofiber cavity research has mainly focused on the fundamental mode. Here, a Fabry-P\u00e9rot fiber cavity with an optical nanofiber supporting the higher-order modes, TE$_{01}$, TM$_{01}$, HE$_{21}^o$, and HE$_{21}^e$, is demonstrated. Using cavity spectroscopy, with mode imaging and analysis, we observe cavity resonances that exhibit complex, inhomogeneous states of polarization with topological features containing Stokes singularities such as C-points, Poincar\u00e9 vortices, and L-lines. *In situ* tuning of the intracavity birefringence enables the desired profile and polarization of the cavity mode to be obtained. These findings open new research possibilities for cold atom manipulation and multimode cavity quantum electrodynamics using the evanescent fields of higher-order mode optical nanofibers.'\naddress: 'Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa 904-0495, Japan'\nauthor:\n- 'Maki Maeda, Jameesh Keloth, and S\u00edle [Nic Chormaic]{}'\ntitle: 'Manipulation of polarization topology using a Fabry-P\u00e9rot fiber cavity with a higher-order mode optical nanofiber'\n---\n\nIntroduction {#sec:introduction}\n============\n\nNovel phenomena that can be revealed in non-paraxial light, such as transverse spin and spin-orbit coupling, have led to increasing interest in the tightly confined light observed in nano-optical devices [@Bliokh2015]. Optical nanofibers (ONFs), where the waist is subwavelength in size, are useful in this context because they"
+"---\nabstract: 'Graph alignment, which aims at identifying corresponding entities across multiple networks, has been widely applied in various domains. As the graphs to be aligned are usually constructed from different sources, the inconsistency issues of structures and features between two graphs are ubiquitous in real-world applications. Most existing methods follow the \u201cembed-then-cross-compare\u201d paradigm, which computes node embeddings in each graph and then processes node correspondences based on cross-graph embedding comparison. However, we find these methods are unstable and sub-optimal when structure or feature inconsistency appears. To this end, we propose SLOTAlign, an unsupervised graph alignment framework that jointly performs Structure Learning and Optimal Transport Alignment. We convert graph alignment to an optimal transport problem between two intra-graph matrices without the requirement of cross-graph comparison. We further incorporate multi-view structure learning to enhance graph representation power and reduce the effect of structure and feature inconsistency inherited across graphs. Moreover, an alternating scheme based algorithm has been developed to address the joint optimization problem in SLOTAlign, and the provable convergence result is also established. Finally, we conduct extensive experiments on six unsupervised graph alignment datasets and the DBP15K knowledge graph (KG) alignment benchmark dataset. The proposed SLOTAlign shows superior performance and"
+"---\nabstract: 'Black hole (BH) evaporation is caused by creation of entangled particle-antiparticle pairs near the event horizon, with one carrying positive energy to infinity and the other carrying negative energy into the BH. Since under the event horizon, particles always move toward the BH center, they can only be absorbed but not emitted at the center. This breaks absorption-emission symmetry and, as a result, annihilation of the particle at the BH center is described by a non-Hermitian Hamiltonian. We show that due to entanglement between photons moving inside and outside the event horizon, non-unitary absorption of the negative energy photons near the BH center, alters the outgoing radiation. As a result, radiation of the evaporating BH is not thermal, it carries information about BH interior and entropy is preserved during evaporation.'\nauthor:\n- 'Anatoly A. Svidzinsky'\ntitle: Nonthermal radiation of evaporating black holes\n---\n\nIntroduction\n============\n\nAccording to principles of quantum mechanics, state of an isolated system remains pure during evolution. This is the case for both types of quantum mechanical evolution - a unitary evolution governed by the Schr\u00f6dinger equation and a non-unitary state vector collapse brought about by a measurement. If the system remains in a pure"
+"---\nabstract: 'The remarkable ability of language models (LMs) has also brought challenges at the interface of AI and security. A critical challenge pertains to how much information these models retain and leak about the training data. This is particularly urgent as the typical development of LMs relies on huge, often highly sensitive data, such as emails and chat logs. To contrast this shortcoming, this paper introduces Context-Aware Differentially Private Language Model (CADP-LM) , a privacy-preserving LM framework that relies on two key insights: First, it utilizes the notion of *context* to define and audit the potentially sensitive information. Second, it adopts the notion of Differential Privacy to protect sensitive information and characterize the privacy leakage. A unique characteristic of CADP-LM is its ability to target the protection of sensitive sentences and contexts only, providing a highly accurate private model. Experiments on a variety of datasets and settings demonstrate these strengths of CADP-LM.'\nauthor:\n- |\n    My H.\u00a0Dinh\\\n    Syracuse University\\\n    `mydinh@syr.edu`\\\n    Ferdinando Fioretto\\\n    Syracuse University\\\n    `ffiorett@syr.edu`\\\nbibliography:\n- 'references.bib'\ntitle: 'Context-Aware Differential Privacy for Language Modeling'\n---\n\nIntroduction\n============\n\nLanguage models (LMs) are essential components of state-of-the-art natural language processing. Their recent development has focused on training increasingly large"
+"---\nabstract: 'Robotic grasping aims to detect graspable points and their corresponding gripper configurations in a particular scene, and is fundamental for robot manipulation. Existing research works have demonstrated the potential of using a transformer model for robotic grasping, which can efficiently learn both global and local features. However, such methods are still limited in grasp detection on a 2D plane. In this paper, we extend a transformer model for 6-Degree-of-Freedom (6-DoF) robotic grasping, which makes it more flexible and suitable for tasks that concern safety. The key designs of our method are a serialization module that turns a 3D voxelized space into a sequence of feature tokens that a transformer model can consume and skip-connections that merge multiscale features effectively. In particular, our method takes a Truncated Signed Distance Function (TSDF) as input. After serializing the TSDF, a transformer model is utilized to encode the sequence, which can obtain a set of aggregated hidden feature vectors through multi-head attention. We then decode the hidden features to obtain per-voxel feature vectors through deconvolution and skip-connections. Voxel feature vectors are then used to regress parameters for executing grasping actions. On a recently proposed pile and packed grasping dataset, we showcase that"
+"---\nabstract: 'With the advent of technologies such as Edge computing, the horizons of remote computational applications have broadened multidimensionally. Autonomous Unmanned Aerial Vehicle (UAV) mission is a vital application to utilize remote computation to catalyze its performance. However, offloading computational complexity to a remote system increases the latency in the system. Though technologies such as 5G networking minimize communication latency, the effects of latency on the control of UAVs are inevitable and may destabilize the system. Hence, it is essential to consider the delays in the system and compensate for them in the control design. Therefore, we propose a novel Edge-based predictive control architecture enabled by 5G networking, PACED-5G (Predictive Autonomous Control using Edge for Drones over 5G). In the proposed control architecture, we have designed a state estimator for estimating the current states based on the available knowledge of the time-varying delays, devised a Model Predictive controller (MPC) for the UAV to track the reference trajectory while avoiding obstacles, and provided an interface to offload the high-level tasks over Edge systems. The proposed architecture is validated in two experimental test cases using a quadrotor UAV.'\nauthor:\n- |\n    Viswa Narayanan Sankaranarayanan$^{1}$, Gerasimos Damigos$^{2}$, Achilleas Santi Seisa$^{1}$, Sumeet Gajanan"
+"---\nabstract: |\n    Can we infer sources of errors from outputs of the complex data analytics software? Bidirectional programming promises that we can reverse flow of software, and translate corrections of output into corrections of either input or data analysis.\n\n    This allows us to achieve holy grail of automated approaches to debugging, risk reporting and large scale distributed error tracking.\n\n    Since processing of risk reports and data analysis pipelines can be frequently expressed using a sequence relational algebra operations, we propose a replacement of this traditional approach with a data summarization algebra that helps to determine an impact of errors. It works by defining data analysis of a necessarily complete summarization of a dataset, possibly in multiple ways along multiple dimensions. We also present a description to better communicate how the complete summarizations of the input data may facilitates easier debugging and more efficient development of analysis pipelines. This approach can also be described as an generalization of axiomatic theories of accounting into data analytics, thus dubbed data accounting.\n\n    We also propose formal properties that allow for transparent assertions about impact of individual records on the aggregated data and ease debugging by allowing to find minimal changes that change behaviour"
+"---\nabstract: 'The famous Hadwiger-Nelson problem asks for the minimum number of colors needed to color the points of the Euclidean plane so that no two points unit distance apart are assigned the same color. In this note we consider a variant of the problem in Minkowski metric planes, where the unit circle is a regular polygon of even and at most $22$ vertices. We present a simple lattice\u2013sublattice coloring scheme that uses $6$ colors, proving that the chromatic number of the Minkowski planes above are at most 6. This result is new for regular polygons having more than 8 vertices.'\nauthor:\n- '*Panna Geh\u00e9r [^1]*'\ntitle: |\n    Note on the chromatic number of Minkowski planes:\\\n    the regular polygon case\n---\n\nIntroduction\n============\n\nIn 1950, Nelson raised the following question: What is the minimum number of colors that are needed to color the Euclidean plane so that no two points of the same color determine unit distance? We refer to such a coloring with $k$ color classes as a proper $k$-coloring. Thus Nelson\u2019s question asks for the smallest $k$ value such that the plane can be properly $k$-colored. This value is known as the chromatic number of the Euclidean plane,"
+"---\nabstract: 'We present a general lower bound for the fundamental tone for the $p$-Laplacian on Riemannian manifolds carrying a special kind of function. We then apply our result to the cases of negatively curved simply connected manifolds, a class of warped product manifolds and for a class of Riemannian submersions.'\naddress:\n- ' Universidade Federal do Piau\u00ed, Picos, PI, Brazil'\n- 'Universidade Federal de Alagoas, Macei\u00f3, AL, Brazil'\nauthor:\n- 'Francisco G. de S. Carvalho'\n- 'Marcos P. de A. Cavalcante'\nbibliography:\n- 'biblio.bib'\ndate:\n- \n- \ntitle: 'On the fundamental tone of the $p$-Laplacian on Riemannian manifolds and applications'\n---\n\nIntroduction {#intro}\n============\n\nLet $\\Omega$ be a bounded domain in a $n$-dimensional Riemannian manifold $M$, $n\\geq 2$. The first eigenvalue of the Dirichlet problem for the Laplace-Beltrami operator on $\\Omega$ can be characterized variationally as $$\\lambda_ 1(\\Omega) = \\inf \\bigg\\{\\frac{\\int_\\Omega \\|\\nabla u\\|^2 \\, dM}{\\int_\\Omega u^2\\, dM}:\nu\\in W^{1,2}_0(\\Omega), u\\neq 0 \\bigg \\},$$ where $W^{1,2}_0(\\Omega)$ denotes the Sobolev space with trace in $\\partial \\Omega$. If $M$ is not compact, *the first eigenvalue* of $M$ is defined as the limit $$\\lambda_1(M)=\\lim_{k\\to \\infty}\\lambda_1(\\Omega_k),$$ if it exists, where $\\Omega_1\\subset \\Omega_2\\subset\\cdots$ is an exhaustion of $M$. It is easy to see that this"
+"---\nabstract: 'Specification inference techniques aim at (automatically) inferring a set of assertions that capture the exhibited software behaviour by generating and filtering assertions through dynamic test executions and mutation testing. Although powerful, such techniques are computationally expensive due to a large number of assertions, test cases and mutated versions that need to be executed. To overcome this issue, we demonstrate that a small subset, i.e., 12.95% of the mutants used by mutation testing tools is sufficient for assertion inference, this subset is significantly different, i.e., 71.59% different from the subsuming mutant set that is frequently cited by mutation testing literature, and can be statically approximated through a learning based method. In particular, we propose [[*AIMS*]{}]{}, an approach that selects [[*Assertion Inferring Mutants*]{}]{}, i.e., a set of mutants that are well-suited for assertion inference, with 0.58 MCC, 0.79 Precision, and 0.49 Recall. We evaluate [[*AIMS*]{}]{}on 46 programs and demonstrate that it has comparable inference capabilities with full mutation analysis (misses 12.49% of assertions) while significantly limiting execution cost (runs 46.29 times faster). A comparison with randomly selected sets of mutants, shows the superiority of [[*AIMS*]{}]{}by inferring 36% more assertions while requiring approximately equal amount of execution time. We also show"
+"---\nabstract: 'Tidal disruption events (TDEs) are transient, multi-wavelength events in which a star is ripped apart by a supermassive black hole. Observations show that in a small fraction of TDEs, a short-lived, synchrotron emitting jet is produced. We observed the newly discovered TDE AT2022cmc with a slew of radio facilities over the first 100 days after its discovery. The light curve from the AMI\u2013LA radio interferometer shows day-timescale variability which we attribute to a high brightness temperature emitting region as opposed to scintillation. We measure a brightness temperature of 2$\\times$10^15^K, which is unphysical for synchrotron radiation. We suggest that the measured high brightness temperature is a result of relativistic beaming caused by a jet being launched at velocities close to the speed of light along our line of sight. We infer from day-timescale variability that the jet associated with AT2022cmc has a relativistic Doppler factor of at least 16, which corresponds to a bulk Lorentz factor of at least 8 if we are observing the jet directly on axis. Such an inference is the first conclusive evidence that the radio emission observed from some TDEs is from relativistic jets because it does not rely on an outflow model. We"
+"---\nabstract: 'In this paper we extensively explore the suitability of YOLO architectures to monitor the process flow across a Fischertechnik industry 4.0 application. Specifically, different YOLO architectures in terms of size and complexity design along with different prior-shapes assignment strategies are adopted. To simulate the real world factory environment, we prepared a rich dataset augmented with different distortions that highly enhance and in some cases degrade our image qualities. The degradation is performed to account for environmental variations and enhancements opt to compensate the color correlations that we face while preparing our dataset. The analysis of our conducted experiments shows the effectiveness of the presented approach evaluated using different measures along with the training and validation strategies that we tailored to tackle the unavoidable color correlations that the problem at hand inherits by nature.'\nauthor:\n- 'Slavomira Schneidereit, Ashkan Mansouri Yarahmadi'\n- Toni Schneidereit\n- 'Michael Breu[\u00df]{}'\n- Marc Gebauer\nbibliography:\n- 'ref.bib'\ntitle: 'YOLO-based Object Detection in Industry 4.0 Fischertechnik Model Environment'\n---\n\nIntroduction {#sec:Introduction}\n============\n\nWithin the context of computer vision, the task of simultaneously performing object localisation and recognition in an given image is perhaps one of the most interesting and important tasks with a broad"
+"---\nabstract: 'The StyleGAN family succeed in high-fidelity image generation and allow for flexible and plausible editing of generated images by manipulating the semantic-rich latent style space. However, projecting a real image into its latent space encounters an inherent trade-off between inversion quality and editability. Existing encoder-based or optimization-based StyleGAN inversion methods attempt to mitigate the trade-off but see limited performance. To fundamentally resolve this problem, we propose a novel two-phase framework by designating two separate networks to tackle editing and reconstruction respectively, instead of balancing the two. Specifically, in Phase I, a $\\mathcal{W}$-space-oriented StyleGAN inversion network is trained and used to perform image inversion and editing, which assures the editability but sacrifices reconstruction quality. In Phase II, a carefully designed rectifying network is utilized to rectify the inversion errors and perform ideal reconstruction. Experimental results show that our approach yields near-perfect reconstructions without sacrificing the editability, thus allowing accurate manipulation of real images. Further, we evaluate the performance of our rectifying network, and see great generalizability towards unseen manipulation types and out-of-domain images.'\nauthor:\n- 'Bingchuan Li, Tianxiang Ma, Peng Zhang, Miao Hua, Wei Liu, Qian He, Zili Yi[^1]'\n- Author Name\nbibliography:\n- 'aaai23.bib'\ntitle:\n- 'ReGANIE: Rectifying"
+"---\nabstract: 'The analysis of data stored in multiple sites has become more popular, raising new concerns about the security of data storage and communication. Federated learning, which does not require centralizing data, is a common approach to preventing heavy data transportation, securing valued data, and protecting personal information protection. Therefore, determining how to aggregate the information obtained from the analysis of data in separate local sites has become an important statistical issue. The commonly used averaging methods may not be suitable due to data nonhomogeneity and incomparable results among individual sites, and applying them may result in the loss of information obtained from the individual analyses. Using a sequential method in federated learning with distributed computing can facilitate the integration and accelerate the analysis process. We develop a data-driven method for efficiently and effectively aggregating valued information by analyzing local data without encountering potential issues such as information security and heavy transportation due to data communication. In addition, the proposed method can preserve the properties of classical sequential adaptive design, such as data-driven sample size and estimation precision when applied to generalized linear models. We use numerical studies of simulated data and an application to COVID-19 data collected from"
+"---\nabstract: 'Trustworthy machine learning aims at combating distributional uncertainties in training data distributions compared to population distributions. Typical treatment frameworks include the Bayesian approach, (min-max) distributionally robust optimization (DRO), and regularization. However, two issues have to be raised: 1) All these methods are biased estimators of the true optimal cost; 2) the prior distribution in the Bayesian method, the radius of the distributional ball in the DRO method, and the regularizer in the regularization method are difficult to specify. This paper studies a new framework that unifies the three approaches and that addresses the two challenges mentioned above. The asymptotic properties (e.g., consistency and asymptotic normalities), non-asymptotic properties (e.g., unbiasedness and generalization error bound), and a Monte\u2013Carlo-based solution method of the proposed model are studied. The new model reveals the trade-off between the robustness to the unseen data and the specificity to the training data.'\nauthor:\n- |\n    Shixiong Wang\\\n    Institute of Data Science\\\n    National University of Singapore, Singapore\\\n    `s.wang@u.nus.edu`\\\n- |\n    Haowei Wang\\\n    Department of Industrial Systems Engineering and Management\\\n    National University of Singapore, Singapore\\\n    `haowei_wang@u.nus.edu`\\\n- |\n    Jean Honorio\\\n    Department of Computer Science\\\n    Purdue University, USA\\\n    `jhonorio@purdue.edu`\nbibliography:\n- 'References.bib'\ntitle: '**Learning Against Distributional Uncertainty: On the"
+"---\nabstract: 'The graph removal lemma is a fundamental result in extremal graph theory which says that for every fixed graph $H$ and $\\varepsilon > 0$, if an $n$-vertex graph $G$ contains $\\varepsilon n^2$ edge-disjoint copies of $H$ then $G$ contains $\\delta n^{v(H)}$ copies of $H$ for some $\\delta = \\delta(\\varepsilon,H) > 0$. The current proofs of the removal lemma give only very weak bounds on $\\delta(\\varepsilon,H)$, and it is also known that $\\delta(\\varepsilon,H)$ is not polynomial in $\\varepsilon$ unless $H$ is bipartite. Recently, Fox and Wigderson initiated the study of minimum degree conditions guaranteeing that $\\delta(\\varepsilon,H)$ depends polynomially or linearly on $\\varepsilon$. In this paper we answer several questions of Fox and Wigderson on this topic.'\nauthor:\n- 'Lior Gishboliner[^1]'\n- Zhihan Jin\n- Benny Sudakov\nbibliography:\n- 'library.bib'\ntitle:  The Minimum Degree Removal Lemma Thresholds \n---\n\nIntroduction\n============\n\nThe graph removal lemma, first proved by Ruzsa and Szemer\u00e9di [@ruzsatriple], is a fundamental result in extremal graph theory. It also have important applications to additive combinatorics and property testing. The lemma states that for every fixed graph $H$ and $\\varepsilon > 0$, if an $n$-vertex graph $G$ contains $\\varepsilon n^2$ edge-disjoint copies of $H$ then $G$ it contains $\\delta"
+"---\nabstract: 'In this work we consider the KAM renormalizability problem for small pseudodifferential perturbations of the semiclassical isochronous transport operator with Diophantine frequencies on the torus. Assuming that the symbol of the perturbation is real analytic and globally bounded, we prove convergence of the quantum Lindstedt series and describe completely the set of semiclassical measures and quantum limits of the renormalized system. Each of these measures is given by symplectic deformation of the Haar measure on an invariant torus for the unperturbed classical system.'\naddress: 'Laboratoire de Math\u00e9matiques Jean Leray, Nantes Universit\u00e9. UMR CNRS 6629, 2 rue de la Houssini\u00e8re, 44322 Nantes Cedex 03, France.'\nauthor:\n- V\u00edctor Arnaiz\nbibliography:\n- 'Referencias.bib'\ntitle: Convergence of quantum Lindstedt series and semiclassical renormalization\n---\n\nIntroduction and main results\n=============================\n\nThe present work is concerned with the *renormalization problem* for quantum Hamiltonian systems. Let $\\mathbb{T}^d := {\\mathbb{R}}^d/2\\pi \\mathbb{Z}^d$ be the flat torus, we consider the linear Hamiltonian $\\mathcal{L}_\\omega : T^*\\mathbb{T}^d \\to {\\mathbb{R}}$ defined by $$\\label{classical_unperturbed_hamiltonian}\n\\mathcal{L}_\\omega (x,\\xi) = \\omega \\cdot \\xi, \\quad (x,\\xi) \\in \\mathbb{T}^d \\times {\\mathbb{R}}^d \\simeq T^*\\mathbb{T}^d,$$ where the vector of frequencies $\\omega$ satisfies the Diophantine condition . The renormalization problem in the classical framework [@Elia89; @Gall82; @Gen96] wonders if,"
+"---\nabstract: 'Weakly supervised image segmentation approaches in the literature usually achieve high segmentation performance using tight bounding box supervision and decrease the performance greatly when supervised by loose bounding boxes. However, compared with loose bounding box, it is much more difficult to acquire tight bounding box due to its strict requirements on the precise locations of the four sides of the box. To resolve this issue, this study investigates whether it is possible to maintain good segmentation performance when loose bounding boxes are used as supervision. For this purpose, this work extends our previous parallel transformation based multiple instance learning (MIL) for tight bounding box supervision by integrating an MIL strategy based on polar transformation to assist image segmentation. The proposed polar transformation based MIL formulation works for both tight and loose bounding boxes, in which a positive bag is defined as pixels in a polar line of a bounding box with one endpoint located inside the object enclosed by the box and the other endpoint located at one of the four sides of the box. Moreover, a weighted smooth maximum approximation is introduced to incorporate the observation that pixels closer to the origin of the polar transformation are"
+"---\nabstract: 'Millions of smart contracts have been deployed onto the Ethereum platform, posing potential attack subjects. Therefore, analyzing contract binaries is vital since their sources are unavailable, involving identification comprising function entry identification and detecting its boundaries. Such boundaries are critical to many smart contract applications, e.g. reverse engineering and profiling. Unfortunately, it is challenging to identify functions from these stripped contract binaries due to the lack of internal function call statements and the compiler-inducing instruction reshuffling. Recently, several existing works excessively relied on a set of handcrafted heuristic rules which impose several faults. To address this issue, we propose a novel neural network-based framework for EVM bytecode Function Entries and Boundaries Identification (neural-FEBI) that does not rely on a fixed set of handcrafted rules. Instead, it used a two-level bi-Long Short-Term Memory network and a Conditional Random Field network to locate the function entries. The suggested framework also devises a control flow traversal algorithm to determine the code segments reachable from the function entry as its boundary. Several experiments on 38,996 publicly available smart contracts collected as binary demonstrate that neural-FEBI confirms the lowest and highest F1-scores for the function entries identification task across different datasets of 88.3"
+"---\nabstract: 'The surging adoption of electric vehicles (EV) calls for accurate and efficient approaches to coordinate with the power grid operation. By being responsive to distribution grid limits and time-varying electricity prices, EV charging stations can minimize their charging costs while aiding grid operation simultaneously. In this study, we investigate the economic benefit of vehicle-to-grid (V2G) using real-time price data from New York State and a real-world charging network dataset. We incorporate nonlinear battery models and price uncertainty into the V2G management design to provide a realistic estimation of cost savings from different V2G options. The proposed control method is computationally tractable when scaling up to real-world applications. We show that our proposed algorithm leads to an average of 35% charging cost savings compared to uncontrolled charging when considering unidirectional charging, and bi-directional V2G enables additional 18$\\%$ cost savings compared to unidirectional smart charging. Our result also shows the importance of using more accurate nonlinear battery models in V2G controllers and evaluating the cost of price uncertainties over V2G.'\nauthor:\n- 'Joshua Jaworski, Ningkun Zheng,\u00a0, Matthias Preindl,\u00a0, Bolun\u00a0Xu,\u00a0 [^1]'\nbibliography:\n- 'IEEEabrv.bib'\n- 'literature\\_v1.bib'\ntitle: |\n    Vehicle-to-Grid Fleet Service Provision\\\n    considering Nonlinear Battery Behaviors\n---\n\nEnergy"
+"---\nabstract: 'The thermodynamic evolution of Coronal Mass Ejections (CMEs) in the inner corona ($\\leq$ 1.5 R$_{sun} $) is not yet completely understood. In this work, we study the evolution of thermodynamic properties of a CME core observed in the inner corona on July 20, 2017, by combining the MLSO/K-Cor white-light and the MLSO/CoMP Fe XIII 10747 [\u00c5]{} line spectroscopic data. We also estimate the emission measure weighted temperature (T$_{EM}$) of the CME core by applying the Differential Emission Measure (DEM) inversion technique on the SDO/AIA six EUV channels data and compare it with the effective temperature (T$_{eff}$) obtained using Fe XIII line width measurements. We find that the T$_{eff}$ and T$_{EM}$ of the CME core show similar variation and remain almost constant as the CME propagates from $\\sim$1.05 to 1.35 R$_{sun}$. The temperature of the CME core is of the order of million-degree kelvin, indicating that it is not associated with a prominence. Further, we estimate the electron density of this CME core using K-Cor polarized brightness (pB) data and found it decreasing by a factor of $\\sim$3.6 as the core evolves. An interesting finding is that the temperature of the CME core remains almost constant despite expected adiabatic"
+"---\nauthor:\n- Simone Magaletti\n- Ludovic Mayer\n- Xuan Phuc Le\n- Thierry Debuisschert\ntitle: Magnetic sensitivity enhancement via polarimetric excitation and detection of an ensemble of NV centers\n---\n\nIntroduction {#introduction .unnumbered}\n============\n\nThe negatively charged nitrogen-vacancy center (NV) is a spin-1 color center in diamond composed by a nitrogen atom and a carbon vacancy in two adjacent positions of the lattice (\\[fig:fig2\\]a). It presents remarkable spin-dependent optical properties and a long spin-coherence time (ms) at room-temperature that allow for optical polarization, optical read-out and coherent manipulation of its electron spin [@Doherty2013]. These features make it an appealing tool for quantum technologies and in particular for magnetic field sensing [@Rondin2014], where magnetic field sensitivity as low as a few pT$\\cdot$Hz$^{-1/2}$ have been demonstrated [@Zhang2021; @Wolf2015; @Wang2022]. The NV center magnetic sensitivity \u00a0scales as [@Barry2020]: $$\\eta\\propto\\frac{\\Delta\\nu}{C\\sqrt{S_0}}\n\\label{eq:SensitivityScaling}$$ where () is the linewidth of the spin resonance [@Bauch2018], (S~0~) is the detected photoluminescence (PL) and (C) is the contrast of the measurement, namely the relative photoluminescence variation between the system on resonance and out of resonance. Since the sensitivity scales with the square root of S~0~, magnetic field measurements that do not require a nanometer-scale resolution are usually performed with"
+"---\nabstract: 'Graph neural networks (GNNs) are the de facto standard deep learning architectures for machine learning on graphs. This has led to a large body of work analyzing the capabilities and limitations of these models, particularly pertaining to their representation and extrapolation capacity. We offer a novel theoretical perspective on the representation and extrapolation capacity of GNNs, by answering the question: how do GNNs behave as the number of graph nodes become very large? Under mild assumptions, we show that when we draw graphs of increasing size from the Erd\u0151s-R[\u00e9]{}nyi model, the probability that such graphs are mapped to a particular output by a class of GNN classifiers tends to either *zero* or to *one*. This class includes the popular graph convolutional network architecture. The result establishes \u2018zero-one laws\u2019 for these GNNs, and analogously to other convergence laws, entails theoretical limitations on their capacity. We empirically verify our results, observing that the theoretical asymptotic limits are evident already on relatively small graphs.'\nauthor:\n- |\n    Sam Adam-Day[^1]\\\n    Department of Mathematics\\\n    University of Oxford\\\n    Oxford, UK\\\n    `sam.adam-day@cs.ox.ac.uk`\\\n    Theodor-Mihai Iliant\\\n    Department of Computer Science\\\n    University of Oxford\\\n    Oxford, UK\\\n    `theodor-mihai.iliant@lmh.ox.ac.uk`\\\n    smail [\u0130]{}lkan Ceylan\\\n    Department of Computer Science\\\n    University of Oxford\\\n    Oxford,"
+"---\nabstract: 'In this paper, we propose a bilateral peer-to-peer (P2P) energy trading scheme under single-contract and multi-contract market setups, both as an assignment game, a special class of coalitional games. [The proposed market formulation allows for efficient computation of a market equilibrium while keeping the desired economic properties offered by the coalitional games. Furthermore, our market model allows buyers to have heterogeneous preferences (product differentiation) over the energy sellers, which can be economic, social, or environmental. To address the problem of scalability in coalitional games, we design a novel distributed negotiation mechanism that utilizes the geometric structure of the equilibrium solution to improve the convergence speed. Our algorithm enables market participants (prosumers) to reach a consensus on a set of \u201cstable\" and \u201cfair\" bilateral contracts which encourages prosumer participation.]{} The negotiation process is executed with virtually minimal information requirements on a time-varying communication network that in turn preserves privacy. We use operator-theoretic tools to rigorously prove its convergence. [Numerical simulations illustrate the benefits of our negotiation protocol and show that the average execution time of a negotiation step is much faster than the benchmark.]{}'\nbibliography:\n- 'bibliography.bib'\n---\n\nIntroduction\n============\n\nof power systems is rapidly materializing under the smart"
+"---\nabstract: 'We present a source of states for Quantum Key Distribution (QKD) based on a modular design exploiting the iPOGNAC, a stable, low-error, and calibration-free polarization modulation scheme, for both intensity and polarization encoding. This source is immune to the security vulnerabilities of other state sources such as side channels and some quantum hacking attacks. Furthermore, our intensity modulation scheme allows full tunability of the intensity ratio between the decoy and signal states, and mitigates patterning effects. The source was implemented and tested at the near-infrared optical band around 800 nm, of particular interest for satellite-based QKD. Remarkably, the modularity of the source simplifies its development, testing, and qualification, especially for space missions. For these reasons, our work paves the way for the development of the second generation of QKD satellites that can guarantee excellent performances at higher security levels.'\nauthor:\n- Federico\u00a0Berra\n- Costantino\u00a0Agnesi\n- Andrea\u00a0Stanco\n- Marco\u00a0Avesani\n- Sebastiano\u00a0Cocchi\n- Paolo\u00a0Villoresi\n- Giuseppe\u00a0Vallone\nbibliography:\n- 'modularQKD.bib'\ntitle: 'Modular source for near-infrared quantum communication'\n---\n\n[^1]\n\n[^2]\n\nIntroduction\n============\n\nQuantum Key Distribution (QKD)\u00a0[@Gisin2002; @Pirandola2019rev] is essential to ensure the safe exchange of sensitive data between distant parties. Establishing its security"
+"---\nabstract: 'In this paper we study $g$-fractional diffusion on bounded domains in $\\mathbb{R}^d$ with absorbing boundary conditions. A new general and explicit representation of the solution is obtained. We study the first-passage time distribution, showing the dependence on the particular choice of the function $g$. Then, we specialize the analysis to the interesting case of a rectangular domain. Finally, we briefly discuss the connection of this general theory with the physical application to the so-called fractional Dodson diffusion model, recently discussed in the literature.'\naddress:\n- '$^1$ Istituto dei Sistemi Complessi, Consiglio Nazionale delle Ricerche, P.le A. Moro 2, 00185 Roma, Italy'\n- '$^2$ Dipartimento di Fisica, Sapienza Universit\u00e0 di Roma, P.le A. Moro 2, 00185 Roma, Italy'\n- '$^3$ Section of Mathematics, International Telematic University Uninettuno, Corso Vittorio Emanuele II, 39, 00186 Roma, Italy'\nauthor:\n- 'L. Angelani$^{1,2}$ and R. Garra$^{3}$'\ntitle: 'G-fractional diffusion on bounded domains in $\\mathbb{R}^d$'\n---\n\nIntroduction\n============\n\nTime-fractional diffusion processes are widely studied in the literature for their relation to continuous-time random walks (see, e.g., [@ksbook]) and for pervasive applications in various fields, from applied physics to pure mathematics and probability (see, for example, the recent monograph [@lenzi] and references therein). More recently,"
+"---\nabstract: 'As the number of open and shared scientific datasets on the Internet increases under the open science movement, efficiently retrieving these datasets is a crucial task in information retrieval (IR) research. In recent years, the development of large models, particularly the pre-training and fine-tuning paradigm, which involves pre-training on large models and fine-tuning on downstream tasks, has provided new solutions for IR match tasks. In this study, we use the original BERT token in the embedding layer, improve the Sentence-BERT model structure in the model layer by introducing the SimCSE and K-Nearest Neighbors method, and use the cosent loss function in the optimization phase to optimize the target output. Our experimental results show that our model outperforms other competing models on both public and self-built datasets through comparative experiments and ablation implementations. This study explores and validates the feasibility and efficiency of pre-training techniques for semantic retrieval of Chinese scientific datasets.'\nauthor:\n- |\n    Xintao Chu\\\n    College of Computer Science and Engineering\\\n    North Minzu University\\\n    Yinchuan\\\n    `xtchu@stu.nun.edu.cn`\\\n    Jianping Liu\\\n    College of Computer Science and Engineering\\\n    North Minzu University\\\n    Yinchuan\\\n    `liujianping01@nmu.edu.cn`\\\n    Jian Wang\\\n    Agricultural Information Institute\\\n    Chinese Academy of Agricultural Sciences\\\n    Beijing\\\n    `wangjian01@caas.cn`\\\n    Xiaofeng Wang\\\n    College of Computer Science"
+"---\nabstract: 'We briefly review the origins and development of Borsuk\u2019s Theory of Shapes and the Multivalued Shape of Sanjurjo. We use a construction over metric compacta using hyperspaces to define a finite type version.'\nauthor:\n- Diego Mond\u00e9jar\nbibliography:\n- 'bibFinmul.bib'\ntitle: '**[On a finite type Multivalued Shape]{}**'\n---\n\n*Dedicated with respect to Professor J.M.R. Sanjurjo for his 70th birthday*\n\nIntroduction {#sec:intro}\n============\n\nShape Theory is a suitable extension of homotopy theory for topological spaces with bad local properties, where this theory does not give relevant information. The paradigmatic example is the Warsaw circle $\\mathcal{W}$ of Figure \\[warsawcircle\\]. It can be defined as the graph of function $f(x)=\\sin\\left(\\frac{1}{x}\\right)$ in the interval $(0,\\frac{2}{\\pi}]$ adding its closure (that is, the segment joining $(0,-1)$ and $(0,1)$) and closing the space by any simple (not intersecting itself or the rest of the space) arc joining the points $(0,-1)$ and $(\\frac{\\pi}{2},1)$.\n\nplot (, [sin((1/)r)]{}); (0,1)\u2013(0,-1.5)\u2013(,-1.5)\u2013(,[sin((1/)r)]{});\n\nIt is readily seen that the fundamental group of $\\mathcal{W}$ is trivial, since there are not continuous maps from $S^1$ to $\\mathcal{W}$ appart from the class of nullhomotopic maps. Moreover, so are all its homology and homotopy groups. But it is also easy to see that $\\mathcal{W}$ is not"
+"---\nabstract: 'A derangement is a permutation with no fixed point, and a nonderangement is a permutation with at least one fixed point. There is a one-term recurrence for the number of derangements of\u00a0$n$\u00a0elements, and we describe a bijective proof of this recurrence which can be found using a recursive map. We then show the combinatorial interpretation of this bijection and how it compares with other known bijections, and show how this gives an involution on $\\mathfrak{S}_n$. Nonderangements satisfy a similar recurrence. We convert the bijective proof of the one-term identity for derangements into a bijective proof of the one-term identity for nonderangements.'\nauthor:\n- Melanie Ferreri\nbibliography:\n- 'references.bib'\ntitle: Naturally emerging maps for derangements and nonderangements\n---\n\nIntroduction\n============\n\nA *derangement* is a permutation $\\s \\in \\S_n$ such that for all $i \\in [n]$, $$\\s(i) \\neq i,$$ i.e. a permutation which does not fix any element. We denote by $D_n$ the set of derangements on $n$ elements, and let $d_n = |D_n|$. Let $E_n$ be the set of permutations of $n$ elements with exactly one fixed point, and let $e_n = |E_n|$. Two well-known recurrence relations for counting derangements are $$\\label{eqn:twoterm}\n    d_n = (n-1)d_{n-1} + (n-1)d_{n-2}$$"
+"---\nabstract: 'A likelihood function on a smooth very affine variety gives rise to a twisted de Rham complex. We show how its top cohomology vector space degenerates to the coordinate ring of the critical points defined by the likelihood equations. We obtain a basis for cohomology from a basis of this coordinate ring. We investigate the dual picture, where twisted cycles correspond to critical points. We show how to expand a twisted cocycle in terms of a basis, and apply our methods to Feynman integrals from physics.'\nauthor:\n- 'Saiei-Jaeyeong Matsubara-Heo and Simon Telen'\nbibliography:\n- 'references.bib'\ntitle: Twisted Cohomology and Likelihood Ideals\n---\n\nIntroduction\n============\n\nVery affine varieties are closed subvarieties of an algebraic torus. They have applications in algebraic statistics [@huh2014likelihood] and particle physics [@mizera2018scattering]. We study smooth such varieties given by hypersurface complements in the algebraic torus. Fix $\\ell$ Laurent polynomials $f_1, \\ldots, f_\\ell \\in \\mathbb{C}[x_1^{\\pm 1}, \\ldots, x_n^{\\pm 1}]$ in $n$ variables. Localizing at the product $f_1 \\cdots f_\\ell$ gives the very affine variety $$\\label{eq:defX}\n X \\, = \\, \\{ x \\in (\\mathbb{C}^*)^n \\, : \\, f_i(x) \\neq 0 , \\text{ for all } i\\} \\, = \\, (\\mathbb{C}^*)^n \\setminus V(f_1 \\cdots f_\\ell).$$ This is"
+"---\nabstract: 'While neural text-to-speech (TTS) has achieved human-like natural synthetic speech, multilingual TTS systems are limited to resource-rich languages due to the need for paired text and studio-quality audio data. This paper proposes a method for zero-shot multilingual TTS using text-only data for the target language. The use of text-only data allows the development of TTS systems for low-resource languages for which only textual resources are available, making TTS accessible to thousands of languages. Inspired by the strong cross-lingual transferability of multilingual language models, our framework first performs masked language model pretraining with multilingual text-only data. Then we train this model with a paired data in a supervised manner, while freezing a language-aware embedding layer. This allows inference even for languages not included in the paired data but present in the text-only data. Evaluation results demonstrate highly intelligible zero-shot TTS with a character error rate of less than 12% for an unseen language.'\nauthor:\n- Anonymous authors\n- Takaaki Saeki$^1$\n- Soumi Maiti$^2$\n- Xinjian Li$^2$\n- Shinji Watanabe$^2$\n- |\n    \\\n    Shinnosuke Takamichi$^1$ Hiroshi Saruwatari$^1$ $^1$The University of Tokyo, Japan\\\n    $^2$Carnegie Mellon University, USA takaaki\\_saeki@ipc.i.u-tokyo.ac.jp, {smaiti, swatanab}@andrew.cmu.edu\nbibliography:\n- 'ijcai23.bib'\ntitle: 'Learning to Speak from Text: Zero-Shot Multilingual"
+"---\nabstract: 'In previous theoretical studies of phonon-mediated superconductors, the electron-phonon coupling is treated by solving the Migdal-Eliashberg equations under the bare vertex approximation, whereas the effect of Coulomb repulsion is incorporated by introducing one single pseudopotential parameter. These two approximations become unreliable in low carrier-density superconductors in which the vertex corrections are not small and the Coulomb interaction is poorly screened. Here, we shall go beyond these two approximations and employ the Dyson-Schwinger equation approach to handle the interplay of electron-phonon interaction and Coulomb interaction in a self-consistent way. We first derive the exact Dyson-Schwinger integral equation of the full electron propagator. Such an equation contains several unknown single-particle propagators and fermion-boson vertex functions, and thus seems to be intractable. To solve this difficulty, we further derive a number of identities satisfied by all the relevant propagators and vertex functions and then use these identities to show that the exact Dyson-Schwinger equation of electron propagator is actually self-closed. This self-closed equation takes into account not only all the vertex corrections, but also the mutual influence between electron-phonon interaction and Coulomb interaction. Solving it by using proper numerical methods leads to the superconducting temperature $T_{c}$ and other quantities. As an"
+"---\nabstract: 'Large-scale pre-trained language models (PLMs) have shown great potential in natural language processing tasks. Leveraging the capabilities of PLMs to enhance automatic speech recognition (ASR) systems has also emerged as a promising research direction. However, previous works may be limited by the inflexible structures of PLMs and the insufficient utilization of PLMs. To alleviate these problems, we propose the hierarchical knowledge distillation (HKD) on the continuous integrate-and-fire (CIF) based ASR models. To transfer knowledge from PLMs to the ASR models, HKD employs cross-modal knowledge distillation with contrastive loss at the acoustic level and knowledge distillation with regression loss at the linguistic level. Compared with the original CIF-based model, our method achieves 15% and 9% relative error rate reduction on the AISHELL-1 and LibriSpeech datasets, respectively.'\naddress: |\n     $^1$Institute of Automation, Chinese Academy of Sciences\\\n    $^2$School of Artificial Intelligence, University of Chinese Academy of Sciences\\\n    $^3$School of Future Technology, University of Chinese Academy of Sciences \nbibliography:\n- 'mybib.bib'\ntitle: 'Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation'\n---\n\n**Index Terms**: continuous integrate-and-fire, knowledge distillation, contrastive learning, pre-trained language models\n\nIntroduction\n============\n\nEnd-to-end (E2E) models have recently made remarkable progress on automatic speech recognition (ASR)"
+"---\nabstract: 'Let ${\\mathcal{M}}$ be an invariant subvariety in the moduli space of translation surfaces. We contribute to the study of the dynamical properties of the horocycle flow on ${\\mathcal{M}}$. In the context of dynamics on the moduli space of translation surfaces, we introduce the notion of a \u2018weak classification of horocycle invariant measures\u2019 and we study its consequences. Among them, we prove genericity of orbits and related uniform equidistribution results, asymptotic equidistribution of sequences of pushed measures, and counting of saddle connection holonomies. As an example, we show that invariant varieties of rank one, Rel-dimension one and related spaces obtained by adding marked points satisfy the \u2018weak classification of horocycle invariant measures\u2019. Our results extend prior results obtained by Eskin-Masur-Schmoll, Eskin-Marklof-Morris, and Bainbridge-Smillie-Weiss.'\naddress:\n- 'University of Utah [chaika@math.utah.edu ]{}'\n- 'Dept. of Mathematics, Tel Aviv University, Tel Aviv, Israel [barakw@post.tau.ac.il]{}'\n- 'Dept. of Mathematics, Tel Aviv University, Tel Aviv, Israel [florentygouf@mail.tau.ac.il]{}'\nauthor:\n- Jon Chaika\n- Barak Weiss\n- Florent Ygouf\nbibliography:\n- 'Rel\\_weak\\_classification.bib'\ntitle: 'Horocycle dynamics in rank one invariant subvarieties I: weak measure classification and equidistribution'\n---\n\nIntroduction\n============\n\nAny stratum of translation surfaces ${\\mathcal{H}}$ is endowed with an action of the group $G {{\\, \\stackrel{\\mathrm{def}}{=}\\,"
+"In this section we provide convergence rate and sample complexity results for Algorithm \\[alg:PG\\_MAG\\]. We extend the MLMC analysis of @dorfman2022 to the actor-critic setting, where we combine it with the two-timescale finite-time analysis of @wu2020finite to obtain non-asymptotic convergence guarantees for MAC (cf. Algorithm \\[alg:PG\\_MAG\\]). Salient features of our approach: **(1)** it avoids uniform ergodicity assumptions required in previous finite-time analyses [@zou2019finite; @wu2020finite; @chen2022finite]; **(2)** it explicitly characterizes convergence rate dependence on the mixing times encountered during training; **(3)** it (i) clarifies the trade-offs between mixing times and MLMC rollout length $T_{\\max}$, and (ii) extends the standard analysis to handle additional sources of bias in the MLMC estimator, both of which were missing from the analysis of @dorfman2022; **(4)** it leverages modified Adagrad stepsizes to avoid the slower convergence rates of previous two-timescale analyses [@wu2020finite] (cf. Theorem \\[thm:convergence\\_rate\\]). The rest of this section is structured as follows. We first outline standard assumptions (cf. Sec. \\[aaaumptions\\]) from the literature and provide some preliminary results. Second, we analyze the policy gradient norm (cf. Sec. \\[actor\\_convergence\\]) associated with Algorithm \\[alg:PG\\_MAG\\], which provides a preliminary convergence rate and characterizes its dependence on the error arising from the critic estimation procedure, the MLMC bias"
+"---\nabstract: 'In this paper, we develop the mathematical framework to describe the physical phenomenon behind the equilibrium configuration joining two antiferromagnetic domains. We firstly define the total energy of the system and deduce the governing equations by minimizing it with respect to the field variables. Then, we solve the resulting system of nonlinear PDEs together with proper initial and boundary conditions by varying the orientation of the 90$^{\\circ}$ domain wall (DW) configuration along the sample. Finally, the angular dependence of elastic and magnetoelastic energies as well as of incompatibility-driven volume effects is computed.'\nauthor:\n- Pierandrea Vergallo\n- Bennet Karetta\n- Giancarlo Consolo\n- 'Olena Gomonay [^1]'\nbibliography:\n- 'Ref.bib'\ndate:  \ntitle: 'Domain-wall orientation in antiferromagnets controlled by magnetoelastic effects'\n---\n\nIntroduction {#sec::introduction}\n============\n\nAntiferromagnets are considered as prospective materials for spintronic applications because of their fast internal magnetic dynamics and robustness with respect to the external magnetic field. Those of antiferromagnets that are suitable for practical applications due to high ordering temperature and high, teraherz, frequencies are also known for pronounced magnetoelastic coupling. Magnetoelastic effects are usually neglected while considering antiferromagnetic dynamics and switching. However, they can pin the domain walls [@Gomonay:PhysRevLett.91.237205], modify magnon spectra [@Bossini:PhysRevLett.127.077202; @Gomonay_2021; @Bauer:PhysRevB.104.014403],"
+"---\nabstract: 'Prior work has provided strong evidence that, within organizational settings, teams that bring a diversity of information and perspectives to a task are more effective than teams that do not. If this form of *informational diversity* confers performance advantages, why do we often see largely homogeneous teams in practice? One canonical argument is that the benefits of informational diversity are in tension with *affinity bias*. To better understand we analyze a sequential model of team formation in which individuals care about their team\u2019s performance (captured in terms of accurately predicting some future outcome based on a set of features) but experience a cost as a result of interacting with teammates who use different approaches to the prediction task. Our results formalize a fundamental limitation of utility-based motivations to drive informational diversity in organizations and hint at interventions that may improve informational diversity and performance simultaneously.'\nauthor:\n- |\n    \\\n    Carnegie Mellon University\\\n    [hheidari@cmu.edu](hheidari@cmu.edu)\\\n- |\n    \\\n    Cornell University\\\n    [sbarocas@cornell.edu](sbarocas@cornell.edu)\\\n- |\n    \\\n    Cornell University\\\n    [kleinberg@cornell.edu](kleinberg@cornell.edu)\\\n- |\n    \\\n    Cornell University\\\n    [karen.levy@cornell.edu](karen.levy@cornell.edu)\\\nbibliography:\n- 'biblio.bib'\ntitle: |\n    Informational Diversity and Affinity Bias\\\n    in Team Growth Dynamics\n---\n\nIntroduction\n============\n\nA long line of work in the social sciences has"
+"---\nauthor:\n- Maryam Khalid\nbibliography:\n- 'Related.bib'\ntitle: ' **Traffic Prediction in Cellular Networks using Graph Neural Networks** '\n---\n\nIntroduction\n============\n\nCellular networks are ubiquitous entities that provide major means of communications all over the world. A cellular network is composed of three main components, service provider, cellular users and base stations that connect the cellular users to the service provider. The density of base stations and cellular users is extremely high and is much more significant than the backhaul setup. Thus, the base station and cellular users constitute major part of cellular network whose study and evaluation is extremely critical as the number of users and their requirements are changing rapidly and new challenges are emerging.\\\nOne major challenge in cellular networks is dynamic change in number of users and their usage of telecommunication service which results in overloading at certain base stations. One class of solution to deal with this overloading issue is deployment of drones [@uav] that can act as temporary base stations and offload the traffic from the overloaded base station. There are two main challenges in development of this solution. Firstly, the drone is expected to be present around the base station where"
+"---\nabstract: |\n    In this paper, we introduce a new, spectral notion of approximation between directed graphs, which we call *singular value (SV) approximation*. SV-approximation is stronger than previous notions of spectral approximation considered in the literature, including spectral approximation of Laplacians for undirected graphs\u00a0[@ST04], standard approximation for directed graphs\u00a0[@CKPPRSV17], and unit-circle (UC) approximation for directed graphs\u00a0[@AKMPSV20]. Further, SV approximation enjoys several useful properties not possessed by previous notions of approximation, e.g., it is preserved under products of random-walk matrices and bounded matrices.\n\n    We provide a nearly linear-time algorithm for SV-sparsifying (and hence UC-sparsifying) Eulerian directed graphs, as well as $\\ell$-step random walks on such graphs, for any $\\ell\\leq {\\operatorname{poly}}(n)$. Combined with the Eulerian scaling algorithms of [@CKKPPRS18], given an arbitrary (not necessarily Eulerian) directed graph and a set $S$ of vertices, we can approximate the stationary probability mass of the $(S,S^c)$ cut in an $\\ell$-step random walk to within a multiplicative error of $1/{\\operatorname{polylog}}(n)$ and an additive error of $1/{\\operatorname{poly}}(n)$ in nearly linear time. As a starting point for these results, we provide a simple black-box reduction from SV-sparsifying Eulerian directed graphs to SV-sparsifying undirected graphs; such a directed-to-undirected reduction was not known for previous notions"
+"---\nabstract: 'Minerals, metals, and plastics are indispensable for a functioning modern society. Yet, their supply is limited causing a need for optimizing ore extraction and recuperation from recyclable materials. Typically, those processes must be meticulously adapted to the precise properties of the processed materials. Advancing our understanding of these materials is thus vital and can be achieved by crushing them into particles of micrometer size followed by their characterization. Current imaging approaches perform this analysis based on segmentation and characterization of particles imaged with computed tomography (CT), and rely on rudimentary postprocessing techniques to separate touching particles. However, their inability to reliably perform this separation as well as the need to retrain methods for each new image, these approaches leave untapped potential to be leveraged. Here, we propose ParticleSeg3D, an instance segmentation method able to extract individual particles from large CT images of particle samples containing different materials. Our approach is based on the powerful nnU-Net framework, introduces a particle size normalization, uses a border-core representation to enable instance segmentation, and is trained with a large dataset containing particles of numerous different sizes, shapes, and compositions of various materials. We demonstrate that ParticleSeg3D can be applied out-of-the-box to a"
+"---\nabstract: 'Shear and bulk viscosity of liquid water and Argon are evaluated from first principles in the Density Functional Theory (DFT) framework, by performing Molecular Dynamics simulations in the NVE ensemble and using the Kubo-Greenwood equilibrium approach. Standard DFT functional is corrected in such a way to allow for a reasonable description of van der Waals (vdW) effects. For liquid Argon the thermal conductivity has been also calculated. Concerning liquid water, to our knowledge this is the first estimate of the bulk viscosity and of the shear-viscosity/bulk-viscosity ratio from first principles. By analyzing our results we can conclude that our first-principles simulations, performed at a nominal average temperature of 366 K to guarantee that the systems is liquid-like, actually describe the basic dynamical properties of liquid water at about 330 K. In comparison with liquid water, the normal, monatomic liquid Ar is characterized by a much smaller bulk-viscosity/shear-viscosity ratio (close to unity) and this feature is well reproduced by our first-principles approach which predicts a value of the ratio in better agreement with experimental reference data than that obtained using the empirical Lennard-Jones potential. The computed thermal conductivity of liquid Argon is also in good agreement with the experimental"
+"---\nabstract: 'The process of designing costmaps for off-road driving tasks is often a challenging and engineering-intensive task. Recent work in costmap design for off-road driving focuses on training deep neural networks to predict costmaps from sensory observations using corpora of expert driving data. However, such approaches are generally subject to overconfident mis-predictions and are rarely evaluated in-the-loop on physical hardware. We present an inverse reinforcement learning-based method of efficiently training deep cost functions that are uncertainty-aware. We do so by leveraging recent advances in highly parallel model-predictive control and robotic risk estimation. In addition to demonstrating improvement at reproducing expert trajectories, we also evaluate the efficacy of these methods in challenging off-road navigation scenarios. We observe that our method significantly outperforms a geometric baseline, resulting in 44% improvement in expert path reconstruction and 57% fewer interventions in practice. We also observe that varying the risk tolerance of the vehicle results in qualitatively different navigation behaviors, especially with respect to higher-risk scenarios such as slopes and tall grass. [ Appendix](https://drive.google.com/file/d/18LitjcoVRY_s6K-3jK0hRccd7PYAcpsT/view?usp=sharing)'\nauthor:\n- |\n    Samuel Triest$^{1}$, Mateo Guaman Castro$^{1}$, Parv Maheshwari$^{2}$,\\\n    Matthew Sivaprakasam$^{1}$, Wenshan Wang$^{1}$, and Sebastian Scherer$^{1}$ [^1][^2][^3]\nbibliography:\n- 'refs.bib'\ntitle: 'Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for"
+"---\nabstract: 'Vision-language alignment learning for video-text retrieval arouses a lot of attention in recent years. Most of the existing methods either transfer the knowledge of image-text pretraining model to video-text retrieval task without fully exploring the multi-modal information of videos, or simply fuse multi-modal features in a brute force manner without explicit guidance. In this paper, we integrate multi-modal information in an explicit manner by tagging, and use the tags as the anchors for better video-text alignment. Various pretrained experts are utilized for extracting the information of multiple modalities, including object, person, motion, audio, etc. To take full advantage of these information, we propose the TABLE (TAgging Before aLignmEnt) network, which consists of a visual encoder, a tag encoder, a text encoder, and a tag-guiding cross-modal encoder for jointly encoding multi-frame visual features and multi-modal tags information. Furthermore, to strengthen the interaction between video and text, we build a joint cross-modal encoder with the triplet input of \\[vision, tag, text\\] and perform two additional supervised tasks, Video Text Matching (VTM) and Masked Language Modeling (MLM). Extensive experimental results demonstrate that the TABLE model is capable of achieving State-Of-The-Art (SOTA) performance on various video-text retrieval benchmarks, including MSR-VTT, MSVD, LSMDC"
+"---\nbibliography:\n- 'References.bib'\n---\n\n**Article title**\\\nLow-cost fluorescence microscope with microfluidic device fabrication for optofluidic applications\n\n**Authors**\\\nNagaraj Nagalingam$^{a,*}$, Aswin Raghunathan$^{a,*}$, Vikram Korede$^{a}$, Edwin F. J. Overmars$^{a}$, Shih-Te Hung$^{b}$, Remco Hartkamp$^{a}$, Johan T. Padding$^{a}$, Carlas S. Smith$^{b}$ and Huseyin Burak Eral$^{a}$\n\n$^*$denotes equal contribution\n\n**Affiliations**\\\n$^{a}$Process & Energy Department, Delft University of Technology, Leeghwaterstraat 39, 2628 CB Delft, The Netherlands.\\\n$^{b}$Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands\n\n**Corresponding author\u2019s email address and Twitter handle**\\\nn.nagalingam@tudelft.nl (Nagaraj Nagalingam)\n\n**Abstract**\\\nOptofluidic devices have revolutionized the manipulation and transportation of fluid at smaller length scales ranging from micrometers to millimeters. We describe a dedicated optical setup for studying laser-induced cavitation inside a microchannel. In a typical experiment, we use a tightly focused laser beam to locally evaporate the solution laced with a dye resulting in the formation of a microbubble. The evolving bubble interface is tracked using high-speed microscopy and digital image analysis. Furthermore, we extend this system to analyze fluid flow through fluorescence$-$Particle Image Velocimetry (PIV) technique with minimal adaptations. In addition, we demonstrate the protocols for the in-house fabrication of a microchannel tailored to function as a sample holder in"
+"---\nabstract: 'We present global 3D radiation magnetohydrodynamical simulations of accretion onto a 6.62 solar mass black hole with quasi-steady state accretion rates reaching 0.016 to 0.9 times the critical accretion rate, which is defined as the accretion rate to power the Eddington luminosity assuming a 10% radiative efficiency, in different runs. The simulations show no sign of thermal instability over hundreds of thermal timescales at 10\u00a0$r_{\\rm g}$. The energy dissipation happens close to the mid-plane in the near-critical runs and near the disk surface in the low accretion rate run. The total radiative luminosity inside $\\sim$20\u00a0$r_{\\rm g}$ is about 1% to 30% the Eddington limit, with a radiative efficiency of about 6% and 3%, respectively, in the sub- and near-critical accretion regimes. In both cases, self-consistent turbulence generated by the magnetorotational instability (MRI) leads to angular momentum transfer, and the disk is supported by magnetic pressure. Outflows from the central low-density funnel with a terminal velocity of $\\sim$0.1$c$ are seen only in the near-critical runs. We conclude that these magnetic pressure dominated disks are thermally stable and thicker than the $\\alpha$ disk, and the effective temperature profiles are much flatter than that in the $\\alpha$ disks. The"
+"---\nabstract: 'Local search is an effective method for solving large-scale combinatorial optimization problems, and it has made remarkable progress in recent years through several subtle mechanisms. In this paper, we found two ways to improve the local search algorithms in solving Pseudo-Boolean Optimization(PBO): Firstly, some of those mechanisms such as unit propagation are merely used in solving MaxSAT before, which can be generalized to solve PBO as well; Secondly, the existing local search algorithms utilize the heuristic on variables, so-called score, to mainly guide the search. We attempt to gain more insights into the clause, as it plays the role of a middleman who builds a bridge between variables and the given formula. Hence, we first extended the combination of unit propagation-based decimation algorithm to PBO problem, giving a further generalized definition of unit clause for PBO problem, and apply it to the existing solver LS-PBO for constructing an initial assignment; then, we introduced a new heuristic on clauses, dubbed care, to set a higher priority for the clauses that are less satisfied in current iterations. Experiments on benchmarks from the most recent PB Competition, as well as three real-world application benchmarks including minimum-width confidence band, wireless sensor network"
+"---\nabstract: 'We report spectral and polarimeter observations of two weak, low frequency (${\\approx}$85-60MHz) solar coronal type II radio bursts that occurred on 2020 May 29 within a time interval ${\\approx}$2min. The bursts had fine structures, and were due to harmonic plasma emission. Our analysis indicates that the magnetohydrodynamic (MHD) shocks responsible for the 1st and 2nd type II bursts were generated by the leading edge (LE) of an extreme-ultraviolet (EUV) flux rope/coronal mass ejection (CME) and interaction of its flank with a neighbouring coronal structure, respectively. The CME deflected from the radial direction by ${\\approx}25^{\\arcdeg}$ during propagation in the near-Sun corona. The estimated power spectral density (PSD) and magnetic field strength ($B$) near the location of the 1st burst at heliocentric distance $r{\\approx}1.35R_{\\odot}$ are $\\rm {\\approx}2{\\times}10^{-3}\\,W^{2}m$ and ${\\approx}$1.8G, respectively. The corresponding values for the 2nd burst at the same $r$ are $\\rm {\\approx}10^{-3}\\,W^{2}m$ and ${\\approx}$0.9G. The significant spatial scales of the coronal turbulence at the location of the two type II bursts are ${\\approx}$62-1Mm. Our conclusions from the present work are that the turbulence and magnetic field strength in the coronal region near the CME LE are higher compared to the corresponding values close to its flank. The derived"
+"---\nabstract: 'Microquasar SS 433 located at the geometric center of radio nebula W50 is a suitable source for investigating the physical process of how galactic jets affect the surrounding interstellar medium (ISM). Previous studies have searched for evidence of the interaction between the SS 433 jet and ISM, such as neutral hydrogen gas and molecular clouds; however, it is still unclear which ISM interacts with the jet. We looked for new molecular clouds that possibly interact at the terminal of the SS 433 eastern jet using the Nobeyama 45-m telescope and the Atacama Submillimeter Telescope Experiment (ASTE). We identified two molecular clouds, comprising many small clumps, in the velocity range of 30.1\u201336.5 km s$^{-1}$ for the first time. These clouds have complex velocity structures, and one of them has a density gradient toward SS 433. Although it is difficult to conclude the relation between the molecular clouds and the SS 433/W50 system, there is a possibility that the eastern structure of W50 constructed by the SS 433 jet swept up tiny molecular clumps drifting in the surroundings and formed the molecular clouds that we identified in this study.'\nauthor:\n- 'Haruka <span style=\"font-variant:small-caps;\">Sakemi</span>$^{1,*}$, Mami <span style=\"font-variant:small-caps;\">Machida</span>$^{2}$, Hiroaki <span style=\"font-variant:small-caps;\">Yamamoto</span>$^{3}$,"
+"---\nabstract: 'The paper presents a\u00a0novel methodology to build surrogate models of complicated functions by an active learning-based sequential decomposition of the input random space and construction of localized polynomial chaos expansions, referred to as domain adaptive localized polynomial chaos expansion (). The approach utilizes sequential decomposition of the input random space into smaller sub-domains approximated by low-order polynomial expansions. This allows approximation of functions with strong nonlinearties, discontinuities, and/or singularities. Decomposition of the input random space and local approximations alleviates the Gibbs phenomenon for these types of problems and confines error to a\u00a0very small vicinity near the non-linearity. The global behavior of the surrogate model is therefore significantly better than existing methods as shown in numerical examples. The whole process is driven by an active learning routine that uses the recently proposed $\\Theta$ criterion to assess local variance contributions [@NovVorSadShi:CMAME:21]. The proposed approach balances both *exploitation* of the surrogate model and *exploration* of the input random space and thus leads to efficient and accurate approximation of the original mathematical model. The numerical results show the superiority of the in comparison to (i) a\u00a0single global polynomial chaos expansion and (ii) the recently proposed stochastic spectral embedding (SSE)"
+"---\nabstract: 'Quantum thermodynamics allows for the interconversion of quantum coherence and mechanical work. Quantum coherence is thus a potential physical resource for quantum machines. However, formulating a general nonequilibrium thermodynamics of quantum coherence has turned out to be challenging. In particular, precise conditions under which coherence is beneficial to or, on the contrary, detrimental for work extraction from a system have remained elusive. We here develop a generic dynamic-Bayesian-network approach to the far-from-equilibrium thermodynamics of coherence. We concretely derive generalized fluctuation relations and a maximum-work theorem that fully account for quantum coherence at all times, for both closed and open dynamics. We obtain criteria for successful coherence-to-work conversion, and identify a nonequilibrium regime where maximum work extraction is increased by quantum coherence for fast processes beyond linear response.'\nauthor:\n- 'Franklin L. S. Rodrigues'\n- Eric Lutz\ntitle: Nonequilibrium thermodynamics of quantum coherence beyond linear response\n---\n\nCoherence is a central feature of quantum theory. It is intimately associated with linear superpositions of states and related interference phenomena [@str17]. In the past decades, it has been recognized as an essential physical resource for quantum technologies that can outperform their classical counterparts [@nie00], from quantum communication [@gis07] and quantum computation"
+"---\nabstract: 'While acute stress has been shown to have both positive and negative effects on performance, not much is known about the impacts of stress on students\u2019 grades during examinations. To answer this question, we examined whether a correlation could be found between physiological stress signals and exam performance. We conducted this study using multiple physiological signals of ten undergraduate students over three different exams. The study focused on three signals, i.e., skin temperature, heart rate, and electrodermal activity. We extracted statistics as features and fed them into a variety of binary classifiers to predict relatively higher or lower grades. Experimental results showed up to 0.81 ROC-AUC with $k$-nearest neighbor algorithm among various machine learning algorithms.'\nauthor:\n- |\n    Willie Kang$^{*1, 2}$, Sean Kim$^{*1, 3}$, Eliot Yoo$^{*1, 4}$, and Samuel Kim$^{1}$\\\n    \\\n    $^1$ IF Research Lab, La Palma, CA, USA\\\n    $^2$ El Toro High School, Lake Forest, CA, USA\\\n    $^3$ Oxford Academy, Cypress, CA, USA\\\n    $^4$ Cypress High School, Cypress, CA, USA\\\n    [{wildmanwillie25, seankim.hahjean, philliot1304}@gmail.com]{}\\\n    [sam@ifresearchlab.com]{} [^1]\ntitle: |\n    **Predicting Students\u2019 Exam Scores\\\n    Using Physiological Signals**\n---\n\nINTRODUCTION\n============\n\nCollege students are prone to stress due to the highly transitional and demanding nature of their lives, which may be"
+"---\nabstract: 'Sparse model identification enables nonlinear dynamical system discovery from data. However, the control of false discoveries for sparse model identification is challenging, especially in the low-data and high-noise limit. In this paper, we perform a theoretical study on ensemble sparse model discovery, which shows empirical success in terms of accuracy and robustness to noise. In particular, we analyse the bootstrapping-based sequential thresholding least-squares estimator. We show that this bootstrapping-based ensembling technique can perform a provably correct variable selection procedure with an exponential convergence rate of the error rate. In addition, we show that the ensemble sparse model discovery method can perform computationally efficient uncertainty estimation, compared to expensive Bayesian uncertainty quantification methods via MCMC. We demonstrate the convergence properties and connection to uncertainty quantification in various numerical studies on synthetic sparse linear regression and sparse model discovery. The experiments on sparse linear regression support that the bootstrapping-based sequential thresholding least-squares method has better performance for sparse variable selection compared to LASSO, thresholding least-squares, and bootstrapping-based LASSO. In the sparse model discovery experiment, we show that the bootstrapping-based sequential thresholding least-squares method can provide valid uncertainty quantification, converging to a delta measure centered around the true value with increased"
+"---\nabstract: |\n    We propose another interpretation of well-known derivatives computations from regular expressions, due to Brzozowski, Antimirov or Lombardy and Sakarovitch, in order to abstract the underlying data structures (*e.g.* sets or linear combinations) using the notion of monad. As an example of this generalization advantage, we first introduce a new derivation technique based on the graded module monad and then show an application of this technique to generalize the parsing of expression with capture groups and back references.\n\n    We also extend operators defining expressions to any $n$-ary functions over value sets, such as classical operations (like negation or intersection for Boolean weights) or more exotic ones (like algebraic mean for rational weights).\n\n    Moreover, we present how to compute a (non-necessarily finite) automaton from such an extended expression, using the Colcombet and Petrisan categorical definition of automata. These category theory concepts allow us to perform this construction in a unified way, whatever the underlying monad.\n\n    Finally, to illustrate our work, we present a Haskell implementation of these notions using advanced techniques of functional programming, and we provide a web interface to manipulate concrete examples.\nauthor:\n- Samira Attou\n- Ludovic Mignot\n- Cl\u00e9ment Miklarz\n- Florent Nicart\nbibliography:\n-"
+"---\nabstract: 'Infrared small object segmentation (ISOS) aims to segment small objects only covered with several pixels from clutter background in infrared images. It\u2019s of great challenge due to: 1) small objects lack of sufficient intensity, shape and texture information; 2) small objects are easily lost in the process where detection models, say deep neural networks, obtain high-level semantic features and image-level receptive fields through successive downsampling. This paper proposes a reliable segmentation model for ISOS, dubbed UCFNet (U-shape network with central difference convolution and fast Fourier convolution), which can handle well the two issues. It builds upon central difference convolution (CDC) and fast Fourier convolution (FFC). On one hand, CDC can effectively guide the network to learn the contrast information between small objects and the background, as the contrast information is very essential in human visual system dealing with the ISOS task. On the other hand, FFC can gain image-level receptive fields and extract global information on high-resolution features maps while preventing small objects from being overwhelmed. Experiments on several public datasets demonstrate that our method significantly outperforms the state-of-the-art ISOS models, and can provide useful guidelines for designing better ISOS deep models. Code are available at https://github.com/wcyjerry/BasicISOS.'\nauthor:"
+"---\nabstract: 'Motivated by quantum simulation, we consider lattice Hamiltonians for Yang-Mills gauge theories with finite gauge group, for example a finite subgroup of a compact Lie group. We show that the electric Hamiltonian admits an interpretation as a certain natural, non-unique Laplacian operator on the finite Abelian or non-Abelian group, and derive some consequences from this fact. Independently of the chosen Hamiltonian, we provide a full explicit description of the physical, gauge-invariant Hilbert space for pure gauge theories and derive a simple formula to compute its dimension. We illustrate the use of the gauge-invariant basis to diagonalize a dihedral gauge theory on a small periodic lattice.'\nauthor:\n- 'A.\u00a0Mariani$^1$, S.\u00a0Pradhan$^2$, E.\u00a0Ercolessi$^2$'\nbibliography:\n- 'biblio.bib'\ndate: |\n     $^1$\\\n    $^2$\\\ntitle: |\n    Hamiltonians and gauge-invariant Hilbert\\\n    space for lattice Yang-Mills-like theories\\\n    with finite gauge group\n---\n\nIntroduction\n============\n\nQuantum simulation is a field of growing interest both experimentally and theoretically [@Feynman; @buluta2009simulators; @cirac2012goals; @georgescu2014simulation]. It holds the promise to overcome technical difficulties, such as the *sign problem* [@Troyer_Wiese; @condmattsignproblem; @banuls2020lgtreview], which affect numerical Monte Carlo simulations in several interesting regimes, with applications to particle physics and condensed matter systems [@AartsReview; @condmattsignproblem]. In recent years, advances in experimental techniques"
+"---\nabstract: 'Nonlinear optical effects including stimulated Brillouin scattering (SBS) and four-wave mixing (FWM) play an important role in microwave photonics, optical frequency combs, and quantum photonics. Harnessing SBS and FWM in a low-loss and versatile integrated platform would open the path to building large-scale Brillouin/Kerr-based photonic integrated circuits. In this letter, we investigate the Brillouin and Kerr properties of a low-index (n=1.513\u00a0@\u00a01550\u00a0nm) silicon oxynitride (SiON) platform. We observed, for the first time, backward SBS in SiON waveguides with a Brillouin gain coefficient of 0.3\u00a0m$^{-1}$W$^{-1}$, which can potentially be increased to 0.95\u00a0m$^{-1}$W$^{-1}$ by just tailoring the waveguide cross-section. We also performed FWM experiments in SiON rings and obtained the nonlinear parameter $\\gamma$, of 0.02\u00a0m$^{-1}$W$^{-1}$. Our results point to a low-loss and low-index photonic integrated platform that is both Brillouin and Kerr active.'\nauthor:\n- Kaixuan\u00a0Ye\n- Yvan\u00a0Klaver\n- Oscar\u00a0A\u00a0Jimenez\u00a0Gordillo\n- Roel\u00a0Botter\n- Okky\u00a0Daulay\n- Francesco Morichetti\n- Andrea Melloni\n- David\u00a0Marpaung\nbibliography:\n- 'library.bib'\ntitle: 'Brillouin and Kerr nonlinearities of a low-index silicon oxynitride platform'\n---\n\nIntroduction {#introduction .unnumbered}\n============\n\nStimulated Brillouin scattering (SBS), which is an interaction between optical and acoustic waves, is currently revolutionizing"
+"---\nabstract: 'Real-time social media data can provide useful information on evolving hazards. Alongside traditional methods of disaster detection, the integration of social media data can considerably enhance disaster management. In this paper, we investigate the problem of detecting geolocation-content communities on Twitter and propose a novel distributed system that provides in near real-time information on hazard-related events and their evolution. We show that content-based community analysis leads to better and faster dissemination of reports on hazards. Our distributed disaster reporting system analyzes the social relationship among worldwide geolocated tweets, and applies topic modeling to group tweets by topics. Considering for each tweet the following information: user, timestamp, geolocation, retweets, and replies, we create a publisher-subscriber distribution model for topics. We use content similarity and the proximity of nodes to create a new model for geolocation-content based communities. Users can subscribe to different topics in specific geographical areas or worldwide and receive real-time reports regarding these topics. As misinformation can lead to increase damage if propagated in hazards related tweets, we propose a new deep learning model to detect fake news. The misinformed tweets are then removed from display. We also show empirically the scalability capabilities of the proposed system.'"
+"---\nabstract: 'This work investigates online learning techniques for a cognitive radar network utilizing feedback from a central coordinator. The network attempts to optimize radar tracking accuracy by learning the optimal channel selection for spectrum sharing and radar performance. We define optimal selection for such a network in relation to the radar observation quality obtainable in a given channel. Since the presence of primary users appears as interference, the approach also improves spectrum sharing performance. In other words, maximizing radar performance also minimizes interference to primary users. Each node is able to learn the quality of several available channels through repeated sensing. Importantly, each part of the network acts as an online learner, observing the environment to inform future actions. We show that in interference-limited spectrum, where the signal-to-interference-plus-noise ratio , a cognitive radar network is able to use information from the central coordinator in order to reduce the amount of time necessary to learn the optimal channel selection. We also show that even limited use of a central coordinator can eliminate collisions, which occur when two nodes select the same channel. We provide several reward functions which capture different aspects of the dynamic radar scenario and describe the online"
+"---\nabstract: 'In-ice radio detectors are a promising tool for the discovery of EeV neutrinos. For astrophysics, the implications of such a discovery will rely on the reconstruction of the neutrino arrival direction. This paper describes a first complete neutrino arrival direction reconstruction for detectors employing deep antennas such as RNO-G or planning to employ them like IceCube-Gen2. We will didactically introduce the challenges of neutrino direction reconstruction using radio emission in ice, elaborate on the detail of the algorithm used, and describe the obtainable performance based on a simulation study and discuss its implication for astrophysics.'\nauthor:\n- Ilse Plaisier\n- Sjoerd Bouma\n- Anna Nelles\nbibliography:\n- 'BIB.bib'\ndate: 'Received: date / Accepted: date'\ntitle: 'Reconstructing the arrival direction of neutrinos in deep in-ice radio detectors'\n---\n\nIntroduction {#sec:intro}\n============\n\nNeutrinos with energies up to have been detected numerously and IceCube has discovered a component of astrophysical neutrinos above the atmospheric neutrino background [@IceCube:2014stg]. These extraterrestrial neutrinos are expected to be created in extreme cosmic sources that accelerate charged particles, cosmic rays, to high energies, which produce secondary particles in their interactions with ambient matter and photon fields: neutrinos e.g.\u00a0[@Margolis:1977wt; @Berezinsky:1969erk; @ParticleDataGroup:2022pth]. Several point sources are revealing"
+"---\nbibliography:\n- 'ecjsample.bib'\n---\n\nIntroduction\n============\n\nContinual learning (CL), also named lifelong learning, is a machine learning paradigm that progressively accumulates knowledge over a continuous data stream to support future learning while maintaining previously digested information (@CGL_survey2019 [@CGL_survey2021]). Neural networks can overshadow human-level performance on single tasks under ideal scenarios (@AlphaGo [@He_2015_ICCV]). However, being exposed to a continuous data stream, they dramatically deteriorate their performance on past tasks or steps over time, known as catastrophic forgetting (CF) (@MCCLOSKEY1989109). Retraining a model from scratch is a naive method to overcome CF. Nevertheless, previous data might be inaccessible due to privacy concerns or limited memory capacity (@CGL_survey2021); Also, revisiting a large batch of old data is expensive. To circumvent CF, a model should strike a balance between the plasticity to accommodate novel knowledge and the stability to prevent existing knowledge from being perturbed by incoming inputs, which is referred to as the stability\u2013plasticity dilemma (@grossbergstudies [@SP-delimma]). Most CL studies focus on data represented in the Euclidean space where the data are arranged regularly (e.g., computer vision), whereas CL upon graph-structured data is still in its infancy.\n\nGraphs are widely applied to represent relational data (e.g., citation network, social network and traffic"
+"---\nabstract: 'Being around for decades, the problem of Authorship Attribution is still very much in focus currently. Some of the more recent instruments used are the pre-trained language models, the most prevalent being BERT. Here we used such a model to detect the authorship of texts written in the Romanian language. The dataset used is highly unbalanced, i.e., significant differences in the number of texts per author, the sources from which the texts were collected, the time period in which the authors lived and wrote these texts, the medium intended to be read (i.e., paper or online), and the type of writing (i.e., stories, short stories, fairy tales, novels, literary articles, and sketches). The results are better than expected, sometimes exceeding 87% macro-accuracy.'\nauthor:\n- 'Sanda-Maria Avram'\nbibliography:\n- 'main.bib'\ndate: January 2023\ntitle: 'BERT-based Authorship Attribution on the Romanian Dataset called ROST'\n---\n\nIntroduction\n============\n\nThe problem of automated Authorship Attribution (AA) is not new, however it is still relevant nowadays. It is defined as the task of determining authorship of an unknown text based on the textual characteristics of the text itself\u00a0[@oliveira2013comparing].\n\nMost approaches that treat the AA problem are in the area of artificial intelligence"
+"---\nabstract: 'Motion planning for autonomous vehicles sharing the road with human drivers remains challenging. The difficulty arises from three challenging aspects: human drivers are 1) multi-modal, 2) interacting with the autonomous vehicle, and 3) actively making decisions based on the current state of the traffic scene. We propose a motion planning framework based on Branch Model Predictive Control to deal with these challenges. The multi-modality is addressed by considering multiple future outcomes associated with different decisions taken by the human driver. The interactive nature of humans is considered by modeling them as reactive agents impacted by the actions of the autonomous vehicle. Finally, we consider a model developed in human neuroscience studies as a possible way of encoding the decision making process of human drivers. We present simulation results in various scenarios, showing the advantages of the proposed method and its ability to plan assertive maneuvers that convey intent to humans.'\nauthor:\n- \n- \n- \nbibliography:\n- 'references.bib'\ntitle: 'Interaction and Decision Making-aware Motion Planning using Branch Model Predictive Control[^1] '\n---\n\nIntroduction\n============\n\nAutonomous vehicles (AVs) must drive in the presence of other traffic participants, such as human-driven vehicles (HVs) and pedestrians. To this date, sharing the road"
+"---\nauthor:\n- Roberto Serafinelli\n- Valentina Braito\n- 'James N. Reeves'\n- Paola Severgnini\n- |\n    \\\n    Alessandra De Rosa\n- Roberto Della Ceca\n- Tracey Jane Turner\nbibliography:\n- 'biblio.bib'\ndate: 'Received XXX; accepted YYY'\ntitle: 'The [*NuSTAR*]{} view of the changing look AGN ESO 323-G77'\n---\n\n[The presence of an obscuring torus at parsec-scale distances from the central black hole is the main ingredient for the Unified Model of Active Galactic Nuclei (AGN), as obscured sources are thought to be seen through this structure. However, the Unified Model fails to describe a class of sources that undergo dramatic spectral changes, transitioning from obscured to unobscured and vice-versa through time. The variability in such sources, so-called Changing Look AGN (CLAGN), is thought to be produced by a clumpy medium at much smaller distances than the conventional obscuring torus. ESO 323-G77 is a CLAGN that was observed in various states through the years with [*Chandra*]{}, [*Suzaku*]{}, [*Swift*]{}-XRT and [*XMM-Newton*]{}, from unobscured ($N_{\\rm H}<3\\times10^{22}$ cm$^{-2}$) to Compton-thin ($N_{\\rm H}\\sim1-6\\times10^{23}$ cm$^{-2}$) and even Compton-thick ($N_{\\rm H}>1\\times10^{24}$ cm$^{-2}$), with timescales as short as one month. We present the analysis of the first [*NuSTAR*]{} monitoring of ESO 323-G77, consisting of 5 observations taken"
+"---\nauthor:\n- 'Xin\u00a0Li, Mingqiang\u00a0Wei, and Songcan\u00a0Chen'\nbibliography:\n- 'egbib.bib'\ntitle: 'PointSmile: Point Self-supervised Learning via Curriculum Mutual Information'\n---\n\nIntroduction {#sec:intro}\n============\n\nThere is an increasing demand to capture the real world by 3D sensing techniques for applications such as Metaverse and digital twins [@zhuzhetvcg]. The captured scenes are often represented in a simple and flexible form, i.e., point cloud [@Wei2022AGConv]. Recent years have witnessed considerable efforts of using deep learning to understand point clouds [@gulipeng]. The first step for point cloud understanding is to extract discriminative geometric features [@xianzhili], which is referred to as geometric representation learning (GRL). Ideally, when fed with sufficient annotated data, GRL will become powerful that can combine various neural networks, e.g., PointNet [@DBLP:conf/cvpr/QiSMG17], PointNet++ [@DBLP:conf/nips/QiYSG17], and DGCNN [@DBLP:journals/tog/WangSLSBS19], to facilitate the downstream tasks such as classification and segmentation. However, real-world scenarios often lack labeled 3D scans, and human annotations of those scans are very laborious due to their irregular structures [@honghuachen]. Although training on synthetic scans is promising to alleviate the shortage of labeled real-world data, such trained GRL will inevitably suffer from domain shifts. Self-supervised learning, as an unsupervised learning paradigm, can relieve the shortcomings of supervised models, and"
+"---\nabstract: |\n    We study the interior of black holes in the presence of charged scalar hair of small amplitude $\\epsilon$ on the event horizon and show their terminal boundary is a crushing Kasner-like singularity. These spacetimes are spherically symmetric, spatially homogeneous and they differ significantly from the hairy black holes with uncharged matter previously studied in *\\[M. Van de Moortel, Violent nonlinear collapse inside charged hairy black holes, arxiv.2109.10932\\]* in that the electric field is dynamical.\n\n    We prove that the backreaction of charged matter on the electric field induces the following phenomena, that ultimately impact upon the formation of the spacelike singularity:\n\n    -   :\u00a0oscillatory growth of the scalar hair, nonlinearly induced by the collapse.\n\n    -   :\u00a0the final Kasner exponents\u2019 dependency in ${\\epsilon}$ is via an expression of the form\\\n        $|\\sin\\left(\\omega_0 \\cdot {\\epsilon}^{-2}+ O(\\log ({\\epsilon}^{-1}))\\right)|$.\n\n    -   :\u00a0a transition from an unstable Kasner metric to a different stable Kasner metric.\n\n    The Kasner inversion occurring in our spacetime is reminiscent of the celebrated BKL scenario in cosmology.\n\n    We additionally propose a construction indicating the relevance of the above phenomena \u2013 including Kasner inversions \u2013 to spacelike singularities inside more general black holes, beyond the hairy case.\n\n    Our spacetime finally"
+"---\nabstract: '[A major challenge in generating single photons with a single emitter is to excite the emitter while avoiding laser leakage into the collection path. Ideally, any scheme to suppress this leakage should not result in a loss in efficiency of the single-photon source. Here, we investigate a scheme in which a single emitter, a semiconductor quantum dot, is embedded in a microcavity. The scheme exploits the splitting of the cavity mode into two orthogonally-polarised modes: one mode is used for excitation, the other for collection. By linking experiment to theory, we show that the best population inversion is achieved with a laser pulse detuned from the quantum emitter. The Rabi oscillations have an unusual dependence on pulse power. Our theory describes them quantitatively allowing us to determine the absolute photon creation probability. For the optimal laser detuning, the population innversion is 98%. The Rabi oscillations depend on the sign of the laser-pulse detuning. We show that this arises from the non-trivial effect of phonons on the exciton dynamics. The exciton-phonon interaction is included in the theory and gives excellent agreement with all the experimental results.]{}'\nauthor:\n- Alisa Javadi\n- Natasha Tomm\n- 'Nadia O. Antoniadis'\n- 'Alistair"
+"---\nabstract: 'A recent advancement in the domain of biomedical Entity Linking is the development of powerful two-stage algorithms \u2013 an initial *candidate retrieval* stage that generates a shortlist of entities for each mention, followed by a *candidate ranking* stage. However, the effectiveness of both stages are inextricably dependent on computationally expensive components. Specifically, in candidate retrieval via dense representation retrieval it is important to have *hard* negative samples, which require repeated forward passes and nearest neighbour searches across the entire entity label set throughout training. In this work, we show that pairing a proxy-based metric learning loss with an adversarial regularizer provides an efficient alternative to hard negative sampling in the candidate retrieval stage. In particular, we show competitive performance on the recall@1 metric, thereby providing the option to leave out the expensive candidate ranking step. Finally, we demonstrate how the model can be used in a zero-shot setting to discover out of knowledge base biomedical entities.'\nauthor:\n- |\n    Maciej Wiatrak^1^[^1], Eirini Arvaniti^1^, Angus Brayne^1^, Jonas Vetterle^1,\\ 2^, Aaron Sim^1^\\\n    ^1^BenevolentAI ^2^Moonfire Ventures\\\n    London, United Kingdom\\\n    `{maciej.wiatrak, eirini.arvaniti, angus.brayne, aaron.sim}@benevolent.ai`\\\n    `jonas@moonfire.com`\nbibliography:\n- 'anthology.bib'\n- 'custom.bib'\ntitle: 'Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrieval'\n---\n\n=1\n\nIntroduction"
+"---\nabstract: 'Biedl et al. introduced the minimum ply cover problem in CG 2021 following the seminal work of Erlebach and van Leeuwen in SODA 2008. They showed that determining the minimum ply cover number for a given set of points by a given set of axis-parallel unit squares is NP-hard, and gave a polynomial time $2$-approximation algorithm for instances in which the minimum ply cover number is bounded by a constant. Durocher et al. recently presented a polynomial time $(8 + \\epsilon)$-approximation algorithm for the general case when the minimum ply cover number is $\\omega(1)$, for every fixed $\\epsilon > 0$. They divide the problem into subproblems by using a standard grid decomposition technique. They have designed an involved dynamic programming scheme to solve the subproblem where each subproblem is defined by a unit side length square gridcell. Then they merge the solutions of the subproblems to obtain the final ply cover. We use a horizontal slab decomposition technique to divide the problem into subproblems. Our algorithm uses a simple greedy heuristic to obtain a $(27+\\epsilon)$-approximation algorithm for the general problem, for a small constant $\\epsilon>0$. Our algorithm runs considerably faster than the algorithm of Durocher et al. We"
+"---\nabstract: 'Intravascular ultrasound (IVUS) is the preferred modality for capturing real-time and high resolution cross-sectional images of the coronary arteries, and evaluating the stenosis. Accurate and real-time segmentation of IVUS images involves the delineation of lumen and external elastic membrane borders. In this paper, we propose a two-stream framework for efficient segmentation of 60 MHz high resolution IVUS images. It combines shallow and deep networks, namely, CSDN. The shallow network with thick channels focuses to extract low-level details. The deep network with thin channels takes charge of learning high-level semantics. Treating the above information separately enables learning a model to achieve high accuracy and high efficiency for accurate real-time segmentation. To further improve the segmentation performance, mutual guided fusion module is used to enhance and fuse both different types of feature representation. The experimental results show that our CSDN accomplishes a good trade-off between analysis speed and segmentation accuracy.'\naddress: |\n    $^{1\\ }$Institute of Artificial Intelligence, Insight Lifetech, Shenzhen, China\\\n    $^{2\\ }$School of Biomedical Engineering, Southern Medical University, Guangzhou, China\nbibliography:\n- 'refs.bib'\ntitle: 'CSDN: Combining Shallow and Deep Networks for Accurate Real-time Segmentation of High-definition Intravascular Ultrasound Images'\n---\n\nShallow network, Deep network, Real-time segmentation, Intravascular ultrasound images,"
+"---\nabstract: 'The slow viscous flow through a doubly-periodic array of cylinders does not have an analytical solution. However, as a reduced model for the flow within fibrous porous media, this solution is important for many real-world systems. We asymptotically determine the flow around a doubly-periodic array of infinite slender cylinders, by placing doubly-periodic two-dimensional singularity solutions within the cylinder and expanding the no-slip condition on the cylinder\u2019s surface in powers of the cylinder radius. The asymptotic solution provides a closed-form estimate for the flow and forces as a function of the radius and the dimensions of the cell. The force is compared to results from lattice-Boltzmann simulations of low-Reynolds-number flows in the same geometry, and the accuracy of the no-slip condition on the surface of the cylinder, predicted by the asymptotic theory, is checked. Finally, the behaviour of the flow, flux, force and effective permeability of the cell is investigated as a function of the geometric parameters. The structure of the asymptotic permeability is consistent with other models for the flow parallel to an array of rods. These models could be used to help understand the flows within porous systems composed of fibres and systems involving periodic arrays such"
+"---\naddress: 'Observatoire Astronomique de l\u2019Universit\u00e9 de Gen\u00e8ve, d\u00e9partement d\u2019astronomie Chemin Pegasi 51, CH-1290 Versoix, Switzerland; william.pluriel@unige.ch\\'\n---\n\nIntroduction {#intro}\n============\n\nGiant planets are diverse and complex objects\u00a0[@Guillot2006; @Showman2008; @Guerlet2014; @Miguel2018]. Thanks to the hundreds of hot giant exoplanets discovered so far, the\u00a0study of their atmospheres is at the forefront of exoplanet research. Spectroscopic observations are now being used to probe these worlds in the search of the molecular features, physical properties and dynamics of their atmospheres. Thanks to recent space (JWST) or ground-based (Espresso, NIRPS, CRIRES) instruments, a\u00a0sort of revolution is occurring in the field since we will observe more planets in one year than has ever been observed by Hubble and Spitzer! Moreover, we are at the edge of measuring accurate abundances and accurate radial velocity and are working with temporal resolutions high enough to see the impact of the 3D structures of these exoplanets\u2019 atmospheres. Such studies are crucial in the pursuit of understanding the diverse nature of the chemical compositions, atmospheric processes and internal structures of exoplanets, as\u00a0well as the conditions required for planetary\u00a0formation.\n\nIn recent years, there has been a surge in transit spectroscopy observations using both space-borne and ground-based"
+"---\nabstract: 'A general upper bound for topological entropy of switched nonlinear systems is constructed, using an asymptotic average of upper limits of the matrix measures of Jacobian matrices of strongly persistent individual modes, weighted by their active rates. A general lower bound is constructed as well, using a similar weighted average of lower limits of the traces of these Jacobian matrices. In a case of interconnected structure, the general upper bound is readily applied to derive upper bounds for entropy that depend only on \u201cnetwork-level\u201d information. In a case of block-diagonal structure, less conservative upper and lower bounds for entropy are constructed. In each case, upper bounds for entropy that require less information about the switching signal are also derived. The upper bounds for entropy and their relations are illustrated by numerical examples of a switched Lotka\u2013Volterra ecosystem model.'\nauthor:\n- 'Guosong\u00a0Yang, Daniel\u00a0Liberzon, and Jo\u00e3o\u00a0P.\u00a0Hespanha [^1] [^2] [^3] [^4]'\nbibliography:\n- 'reference-abbr.bib'\n- 'reference-temp.bib'\ntitle: |\n    **Topological entropy of switched nonlinear and\\\n    interconnected systems**\n---\n\nIntroduction {#sec:intro}\n============\n\nTopological entropy is a fundamental concept in dynamical systems theory. Roughly speaking, it describes the rate at which uncertainty about the state of a system grows as"
+"---\nabstract: 'We study photoinduced dynamics triggered by an inhomogeneity due to competition between charge density waves (CDWs) and superconductivity. As a simple example, we consider the superconducting (SC) interface between two CDW domains with opposite signs. The real-time dynamics are calculated within the time-dependent Hartree\u2013Fock\u2013Bogoliubov framework, where the order parameter dynamics and the nonequilibrium quasiparticle distribution functions are studied. We also calculate the various dynamical response functions within a generalized random phase approximation. Through comparisons between the real time dynamics and the analysis of the response functions, it is found that the photo-driven SC interface can emit collective modes of the SC order parameter. This is analogous to the spin wave emission from the magnetic domain wall in an antiferromagnet, particularly in the case of a low driving frequency, where the order parameters can be mapped onto the pseudospin picture. In the high-frequency case, we find a domain wall melting caused by changes in the quasiparticle distribution, which induces superconductivity in the whole system.'\nauthor:\n- Yukihiro Matsubayashi\n- Yusuke Masaki\n- Hiroaki Matsueda\nbibliography:\n- 'reference.bib'\ntitle: |\n    Photoinduced pseudospin-wave emission from\\\n    charge-density-wave domain wall with superconductivity\n---\n\nINTRODUCTION\n============\n\nRecent advances in terahertz laser technologies have enabled"
+"---\nabstract: 'In this work, we elaborate on a measure-theoretic approach to negative probabilities. We study a natural notion of contextuality measure and characterize its main properties. Then, we apply this measure to relevant examples of quantum physics. In particular, we study the role played by contextuality in quantum computing circuits.'\nauthor:\n- 'Elisa Monchietti$^{1}$, C\u00e9sar Massri$^{2}$, Acacio de Barros$^{3}$ and Federico Holik$^{4}$'\ntitle: 'Measure-theoretic approach to negative probabilities'\n---\n\n\\[section\\] \\[theo\\][Definition]{} \\[theo\\][Lemma]{} \\[theo\\][Proposition]{} \\[theo\\][Corollary]{} \\[theo\\][Example]{} \\[theo\\][Remark]{} \\[theo\\][Remark]{} \\[theo\\][Corollary]{} \\[theo\\][Example]{} \\[theo\\][Principle]{}\n\n\\[theo\\][Axiom]{}\n\n1 - Universidad Nacional de Rosario.\\\n2 - Instituto de Investigaciones Matem\u00e1ticas \u201cLuis A. Santalo\u201d.\\\n3 - School of Liberal Studies, San Francisco State University, 1900 Holloway Ave., San Francisco, California, USA.\\\n4 - Instituto de F\u00edsica La Plata (CONICET-UNLP), Calle 113 entre 64 y 64 S/N, 1900, La Plata, Buenos Aires, Argentina.\n\nIntroduction\n============\n\nAs reported by Peter Shor [^1], R. P. Feynmann was very interested in *negative probabilities*. He thought that, perhaps, they could be used to give a natural explanation to the violation of Bell inequalities by quantum systems. In a subsequent paper, Feynmann gave arguments for considering negative probabilities as an interesting option for handling different problems of modern physics [@feynman_negative_1987]. They also called"
+"---\nabstract: 'Solar flares are efficient particle accelerators with a large fraction of released magnetic energy ($10-50$%) converted into energetic particles such as hard X-ray producing electrons. This energy transfer process is not well constrained, with competing theories regarding the acceleration mechanism(s), including MHD turbulence. We perform a detailed parameter study examining how various properties of the acceleration region, including its spatial extent and the spatial distribution of turbulence, affect the observed electron properties, such as those routinely determined from X-ray imaging and spectroscopy. Here, a time-independent Fokker-Planck equation is used to describe the acceleration and transport of flare electrons through a coronal plasma of finite temperature. Motivated by recent non-thermal line broadening observations that suggested extended regions of turbulence in coronal loops, an extended turbulent acceleration region is incorporated into the model. We produce outputs for the density weighted electron flux, a quantity directly related to observed X-rays, modelled in energy and space from the corona to chromosphere. We find that by combining several spectral and imaging diagnostics (such as spectral index differences or ratios, energy or spatial-dependent flux ratios, and electron depths into the chromosphere) the acceleration properties, including the timescale and velocity dependence, can be constrained alongside"
+"---\nabstract: 'The shear measurement from DECaLS (Dark Energy Camera Legacy Survey) provides an excellent opportunity for galaxy-galaxy lensing study with DESI (Dark Energy Spectroscopic Instrument) galaxies, given the large ($\\sim 9000$ deg$^2$) sky overlap. We explore this potential by combining the DESI 1% survey and DECaLS DR8. With $\\sim 106$ deg$^2$ sky overlap, we achieve significant detection of galaxy-galaxy lensing for BGS and LRG as lenses. [Scaled to the full BGS sample, we expect the statistical errors to improve from $18(12)\\%$ to a promising level of $2(1.3)\\%$ at $\\theta>8^{''}(<8^{''})$. This brings stronger requirements for future systematics control.]{} To fully realize such potential, we need to control the residual multiplicative shear bias $|m|<0.01$ and the bias in the mean redshift $|\\Delta z|<0.015$. We also expect significant detection of galaxy-galaxy lensing with DESI LRG/ELG full samples as lenses, and cosmic magnification of ELG through cross-correlation with low-redshift DECaLS shear. [If such systematical error control can be achieved,]{} we find the advantages of DECaLS, comparing with KiDS (Kilo Degree Survey) and HSC (Hyper-Suprime Cam), are at low redshift, large-scale, and in measuring the shear-ratio (to $\\sigma_R\\sim 0.04$) and cosmic magnification.'\nauthor:\n- |\n    Ji Yao$^{1,2,3}$[^1], Huanyuan Shan$^{1,4}$[^2], Pengjie Zhang$^{2,3,5}$[^3], Eric Jullo$^{6}$, Jean-Paul"
+"---\nabstract: 'During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay [@brockmann_hidden_2013; @may_spatial_1984; @rice_variation_2021; @viboud_synchrony_2006; @Davis2021; @Balcan21484]. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility [@watts_multiscale_2005; @alessandretti_scales_2020; @susswein_ignoring_2021]. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing non-pharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales [@oliu_barton_green_2021]. Here we use weekly anonymized and aggregated human mobility data and spatially highly resolved data on COVID-19 cases, deaths and hospitalizations at the municipality level in Mexico to investigate how behavioural changes in response to the pandemic have altered the spatial scales of transmission and interventions during its first wave (March - June 2020). We find that the epidemic dynamics in Mexico were initially driven by SARS-CoV-2 exports from Mexico State and Mexico City, where early outbreaks occurred. The mobility network shifted after the implementation of interventions in late March 2020, and the mobility network communities became more disjointed while epidemics in these communities became increasingly synchronised. Our results provide actionable and dynamic insights into"
+"---\nauthor:\n- Federico Lelli\n- 'Zhi-Yu Zhang'\n- 'Thomas G. Bisbas'\n- Lingrui Lin\n- Padelis Papadopoulos\n- 'James M. Schombert'\n- Enrico Di Teodoro\n- Antonino Marasco\n- 'Stacy S. McGaugh'\nbibliography:\n- 'highz.bib'\ndate: 'Received 30/09/2022; accepted 30/01/2023'\ntitle: |\n    Cold gas disks in main-sequence galaxies at cosmic noon:\\\n    Low turbulence, flat rotation curves, and disk-halo degeneracy\n---\n\nIntroduction {#sec:intro}\n============\n\nDuring the past decades, there has been outstanding progress in studying the internal dynamics of high-$z$ galaxies. Near-infrared (NIR) spectroscopy with integral field units (IFUs) allowed for the kinematics of warm ($T\\simeq10^4$ K) ionized gas to be traced using the H$\\alpha$ emission line at $z\\simeq1.0-2.5$ [e.g., @Forster2009; @Wisnioski2015; @Stott2016] and the $\\lambda$5007 \u00c5\u00a0line up to $z\\simeq3.5$ [e.g., @Gnerucci2011; @Turner2017a]. Radio and submillimeter observations with the Jansky Very Large Array (JVLA) and the NOrthern Extended Millimeter Array (NOEMA) allowed for the kinematics of cold neutral gas ($T\\simeq10-100$ K) to be traced using CO transitions at $z\\simeq1-4$ [e.g., @Hodge2012; @Ubler2018]. Moreover, the Atacama Large Millimeter Array (ALMA) made it possible to study gas dynamics using \u00a0lines at $z\\simeq1-3$ [@Lelli2018; @Dye2022; @Gururajan2022], the \u00a0line at $z\\simeq4-7$ [@DeBreuck2014; @Jones2017; @Smit2018], and high-$J$ CO lines [@Tadaki2017; @Talia2018].\n\nThe first IFU"
+"---\nabstract: 'In evolved and dusty circumstellar discs, two planets with masses comparable to Jupiter and Saturn that migrate outwards while maintaining an orbital resonance can produce distinctive features in the dust distribution. Dust accumulates at the outer edge of the common gas gap, which behaves as a dust trap, where the local dust concentration is significantly enhanced by the planets\u2019 outward motion. Concurrently, an expanding cavity forms in the dust distribution inside the planets\u2019 orbits, because dust does not filter through the common gaseous gap and grain depletion in the region continues via inward drifting. There is no cavity in the gas distribution because gas can filter through the gap, although ongoing gas accretion on the planets can reduce the gas density in the inner disc. Such behaviour was demonstrated by means of simulations neglecting the effects of dust diffusion due to turbulence and of dust backreaction on the gas. Both effects may alter the formation of the dust peak at the gap outer edge and of the inner dust cavity, by letting grains filter through the dust trap. We performed high resolution hydrodynamical simulations of the coupled evolution of gas and dust species, the latter treated as pressureless"
+"---\nabstract: 'An integro-differential equation for the probability density of the generalized stochastic Ornstein-Uhlenbeck process with jump diffusion is considered for a special case of the Laplacian distribution of jumps. It is shown that for a certain ratio between the intensity of jumps and the speed of reversion, the fundamental solution can be found explicitly, as a finite sum. Alternatively, the fundamental solution can be represented as converging power series. The properties of this solution are investigated. The fundamental solution makes it possible to obtain explicit formulas for the density at each instant of time, which is important, for example, for testing numerical methods.'\naddress: ' Mathematics and Mechanics Department, Lomonosov Moscow State University, Leninskie Gory, Moscow, 119991, Russian Federation'\nauthor:\n- 'Olga S. Rozanova\\*, Nikolai A. Krutov'\ntitle: ' The fundamental solution of the master equation for a jump-diffusion Ornstein-Uhlenbeck process '\n---\n\nIntroduction\n============\n\nModels using stochastic dynamics have natural applications in various areas of physics, biology, and financial mathematics. In recent decades, it has become clear that many phenomena cannot be explained by adding only standard Wiener processes to deterministic models, it is necessary to consider models that take into account differently distributed jumps, that is, use"
+"---\nabstract: 'The bright pulsar PSR B0329+54 was previously known for many years to have two emission modes. Sensitive observations of individual pulses reveal that the central component of pulse profile, which is called core component, is found to be very weakened occasionally for some periods and then recovered. This is the newly identified core-weak mode. Based on our long observations of PSR B0329+54 by the Jiamusi 66-m telescope at 2250 MHz, we report here that the profile components of individual pulses, including these for the core and the leading and trailing peaks, are relatedly varying over some periods even before and after the core-weak mode, forming a regular pattern in the phase-vs-time plot for a train of period-folded pulses. The pattern has a similar structure for the core-weak mode with a time scale of 3 to 14 periods. It starts with an intensity brightening at the trailing phase of the core component, and then the core intensity declines to a very low level, as if the core component is drifting out from the normal radiation window within one or two periods. Then the intensity for the trailing components is enhanced, and then the leading component appears at an advanced"
+"---\nauthor:\n- |\n    Yangguang Li, Bin Huang, Zeren Chen, Yufeng Cui, Feng Liang, Mingzhu Shen,\\\n    Fenggang Liu, Enze Xie, Lu Sheng, Wanli Ouyang, Jing Shao\ntitle: 'Fast-BEV: A Fast and Strong Bird\u2019s-Eye View Perception Baseline'\n---\n\nIntroduction\n============\n\nA fast and accurate 3D perception system is essential for autonomous driving. Classic methods\u00a0[@zhou2018voxelnet; @lang2019pointpillars; @shi2019pointrcnn] rely on the accurate 3D information provided by Lidar point clouds. However, Lidar sensors usually cost thousands of dollars\u00a0[@neuvition_2022], hindering their applications on economical vehicles. Pure camera-based Bird\u2019s-Eye View (BEV) methods\u00a0[@wang2022detr3d; @xie2022m; @huang2021bevdet; @liu2022petr; @li2022bevformer; @li2022bevdepth; @liu2022bevfusion] have recently shown great potential for their impressive 3D perception capability and economical cost. They basically follow the paradigm: multi-camera 2D image features are transformed to the 3D BEV feature in ego-car coordinates, then specific heads are applied on the unified BEV representation to perform specific 3D tasks, , 3D detection, segmentation, . The unified BEV representation can handle a single task separately or multi-tasks simultaneously, which is highly efficient and flexible.\n\nTo perform 3D perception from 2D image features, state-of-the-art BEV methods on nuScenes\u00a0[@caesar2020nuscenes] either uses query based transformation\u00a0[@wang2022detr3d; @li2022bevformer] or implicit/explicit depth based transformation\u00a0[@philion2020lift; @huang2021bevdet; @li2022bevdepth]. However, they are difficult"
+"---\nabstract: 'We investigate the ability of a free pro-$\\CC$ group of infinite rank to abstractly solve abstract embedding problems, and conclude that for some varieties $\\CC$, the profinite completion of any order, of a free pro-$\\CC$ group of infinite rank, is a free pro-$\\CC$ group as well, of the corresponding rank.'\nauthor:\n- 'Tamar Bar-On'\nbibliography:\n- 'references.bib'\n- '../../Results\\_for\\_PHD/references.bib'\ntitle: 'Profinite Completion of Free Pro-$\\mathcal{C}$ Groups'\n---\n\nIntroduction {#introduction .unnumbered}\n============\n\nDuring the paper, $\\mm$ will be an infinite cardinal, and $\\CC$ be a variety of finite groups, i.e, a collection of finite groups which is closed under taking subgroups, quotients and finite products. The free pro-$\\CC$ group of rank $\\mm$ is defined as follows:\n\nConsider a discrete topological space of cardinality $\\mm$, $X$. Denote by $X\\cup \\{\\ast\\}$ its one-point compactification. One easily checks that this is a pointed profinite space- i.e, a topological space which is compact, Hausdorff and totally disconnected, with $\\ast$ being the distinguished point. The free pro-$\\CC$ group over $X\\cup \\{\\ast\\}$, also known as the free pro-$\\CC$ group over the set $X$ converging to 1, is a pro-$\\CC$ group $F_{\\CC}(\\mm)$, considered as a pointed profinite space with the identity element serving as the distinguished"
+"---\nabstract: |\n    Reduced graphene oxide (rGO) is a bulk-processable quasi-amorphous 2D material with broad spectral coverage and fast electronic response. rGO sheets are suspended in a polymer matrix and sequentially photoreduced while measuring the evolving optical spectra and ultrafast electron relaxation dynamics. Photoreduced rGO yields optical absorption spectra that fit with the same Fano lineshape parameters as monolayer graphene. With increasing photoreduction time, rGO transient absorption kinetics accelerate monotonically, reaching an optimal point that matches the hot electron cooling in graphene. All stages of rGO ultrafast kinetics are simulated with a hot-electron cooling model mediated by disorder-assisted supercollisions. While the rGO room temperature 0.31 ps$^{-1}$ electronic cooling rate matches monolayer graphene, subsequent photoreduction can rapidly increase the rate by 10-12$\\times$. Such accelerated supercollision rates imply a reduced mean-free scattering length caused by photoionized point-defects on the rGO sp$^2$ sub-lattice. For visible range excitations of rGO, photoreduction shows three increasing spectral peaks that match graphene quantum dot (GQD) transitions, while a broad peak from oxygenated defect edge states shrinks. These three confined GQD states donate their hot carriers to the graphene sub-lattice with a 0.17 ps rise-time that accelerates with photoreduction. Collectively, many desirable photophysical properties of 2D graphene are"
+"---\nabstract: 'The Ornstein-Uhlenbeck process is interpreted as Brownian motion in a harmonic potential. This Gaussian Markov process has a bounded variance and admits a stationary probability distribution, in contrast to the standard Brownian motion. It also tends to a drift towards its mean function, and such a process is called mean-reverting. Two examples of the generalized Ornstein-Uhlenbeck process are considered. In the first one, we study the Ornstein-Uhlenbeck process on a comb model, as an example of the harmonically bounded random motion in the topologically constrained geometry. The main dynamical characteristics (as the first and the second moments) and the probability density function are studied in the framework of both the Langevin stochastic equation and the Fokker-Planck equation. The second example is devoted to the study of the effects of stochastic resetting on the Ornstein-Uhlenbeck process, including stochastic resetting in the comb geometry. Here, the non-equilibrium stationary state is the main question in task, where the two divergent forces, namely the resetting and the drift towards the mean, lead to compelling results both in the case of the Ornstein-Uhlenbeck process with resetting and its generalization on the two dimensional comb structure.'\naddress:\n- '^1^*Research Center for Computer Science and"
+"---\nabstract: 'Topological defects and smooth excitations determine the properties of systems showing collective order. We introduce a generic non-singular field theory that comprehensively describes defects and excitations in systems with $O(n)$ broken rotational symmetry. Within this formalism, we explore fast events, such as defect nucleation/annihilation and dynamical phase transitions where the interplay between topological defects and non-linear excitations is particularly important. To highlight its versatility, we apply this formalism in the context of Bose-Einstein condensates, active nematics, and crystal lattices.'\nauthor:\n- Vidar Skogvoll\n- Jonas R\u00f8nning\n- Marco Salvalaglio\n- Luiza Angheluta\nbibliography:\n- 'Bibliography.bib'\ntitle: 'A unified field theory of topological defects and non-linear local excitations'\n---\n\nIntroduction {#Sec:Intro}\n============\n\nTopological defects are hallmarks of systems exhibiting collective order. They are widely encountered from condensed matter, including biological systems, to elementary particles, and the very early Universe\u00a0[@merminTopologicalTheoryDefects1979; @TWBKibble_1976; @michel1980symmetry; @vilenkin1994cosmic; @chaikin_lubensky_1995; @nelson2002defects; @ozawa2019topological; @ardavseva2022topological]. The small-scale dynamics of interacting topological defects are crucial for the emergence of large-scale non-equilibrium phenomena, such as quantum turbulence in superfluids\u00a0[@madeira2020quantum], spontaneous flows in active matter\u00a0[@alert2022active], or dislocation plasticity in crystals\u00a0[@papanikolaou2017avalanches]. In fact, classical discrete modeling approaches such as point vortex models\u00a0[@eyink2006onsager] and discrete dislocation dynamics\u00a0[@zhouDiscreteDislocationDynamics2010]"
+"---\nabstract: 'Monte Carlo (MC) methods are the most widely used methods to estimate the performance of a policy. Given an interested policy, MC methods give estimates by repeatedly running this policy to collect samples and taking the average of the outcomes. Samples collected during this process are called online samples. To get an accurate estimate, MC methods consume massive online samples. When online samples are expensive, e.g., online recommendations and inventory management, we want to reduce the number of online samples while achieving the same estimate accuracy. To this end, we use off-policy MC methods that evaluate the interested policy by running a different policy called behavior policy. We design a tailored behavior policy such that the variance of the off-policy MC estimator is provably smaller than the ordinary MC estimator. Importantly, this tailored behavior policy can be efficiently learned from existing offline data, i,e., previously logged data, which are much cheaper than online samples. With reduced variance, our off-policy MC method requires fewer online samples to evaluate the performance of a policy compared with the ordinary MC method. Moreover, our off-policy MC estimator is always unbiased.'\nauthor:\n- |\n    Shuze Liu shuzeliu@virginia.edu\\\n    Department of Computer Science\\\n    University of"
+"---\nabstract: |\n    Deep neural networks (DNNs) have demonstrated superior performance over classical machine learning to support many features in safety-critical systems. Although DNNs are now widely used in such systems (e.g., automobiles), there is limited progress towards the development of methods that support functional safety analysis of DNN-based systems. In particular, there is a lack of automated approaches for the identification of root causes of errors, to drive both risk analysis and DNN retraining. The identification of such root causes is indeed important in safety-analysis as they are the basis on which to identify and evaluate the risks associated to safety violations.\n\n    In this paper, we propose [SAFE]{}, a black-box approach to automatically detect the root causes of DNN errors. [SAFE]{}relies on a transfer learning model pre-trained on ImageNet to extract the features from error-inducing images. It then applies a density-based clustering algorithm to detect arbitrary shaped clusters of images representing plausible causes of error. Last, clusters are used to effectively retrain and improve the DNN. The black-box nature of [SAFE]{}is due to the fact that it does not require any internal information about the DNN or its modification. This property facilitates its adoption among practitioners as it is"
+"---\nabstract: |\n    There has been an increasing recognition of the value of data and of data-based decision making. As a consequence, the development of data science as a field of study has intensified in recent years. However, there is no systematic and comprehensive treatment and understanding of data science. This article describes a systematic and end-to-end framing of the field based on an inclusive definition. It identifies the core components making up the data science ecosystem, presents its lifecycle modeling the development process, and argues its interdisciplinarity.\n\n    (I will write a better abstract in due course.)\nauthor:\n- |\n    M. Tamer \u00d6zsu\\\n    University of Waterloo, Canada\\\n    [`tamer.ozsu@uwaterloo.ca`](mailto:tamer.ozsu@uwaterloo.ca)\nbibliography:\n- 'ds-references.bib'\ntitle: 'Foundations and Scoping of Data Science[^1]'\n---\n\nIntroduction {#sec:intro}\n============\n\nThere is a data-driven revolution underway in science and society, disrupting every form of enterprise. We are collecting and storing data more rapidly than ever before. The value of data as a central asset in an organization is now well-established and generally accepted. Economist called it \u201cthe world\u2019s most valuable resource\u201d\u00a0[@Economist2017]. World Economic Forum\u2019s briefing paper *A New Paradigm for Business of Data* states \u201cAt the heart of digital economy and society is the explosion of insight,"
+"---\nabstract: 'We present an efficient implementation of analytical non-adiabatic derivative coupling elements for the coupled cluster singles and doubles model. The derivative coupling elements are evaluated in a biorthonormal formulation in which the nuclear derivative acts on the right electronic state, where this state is biorthonormal with respect to the set of left states. This stands in contrast to earlier implementations based on normalized states and a gradient formula for the derivative coupling. As an illustration of the implementation, we determine a minimum energy conical intersection between the $n\\pi^\\ast$ and $\\pi\\pi^\\ast$ states in the nucleobase thymine.'\nauthor:\n- 'Eirik F.\u00a0Kj[\u00f8]{}nstad'\n- Henrik Koch\nbibliography:\n- 'apssamp.bib'\ntitle: 'Communication: Non-adiabatic derivative coupling elements for the coupled cluster singles and doubles model'\n---\n\nIntroduction\n============\n\nThe nuclear dynamics that follows photoexcitation typically involves non-adiabatic population transfer between several electronic states. For example, in the nucleobase thymine, photoexcitation to the bright $\\pi \\pi^\\ast$ state is followed by rapid ($60$ fs) non-adiabatic population transfer to the dark $n \\pi^\\ast$ state.[@wolf2017probing] As is well known, the approximate description of the electronic structure can have a dramatic qualitative impact on the simulated nuclear dynamics, often complicating the task of correctly identifying the actual physics"
+"---\nabstract: 'Quantization is the process of mapping an input signal from an infinite continuous set to a countable set with a finite number of elements. It is a non-linear irreversible process, which makes the traditional methods of system identification no longer applicable. In this work, we propose a method for parsimonious linear time invariant system identification when only quantized observations, discerned from noisy data, are available. More formally, given a priori information on the system, represented by a compact set containing the poles of the system, and quantized realizations, our algorithm aims at identifying the least order system that is compatible with the available information. The proposed approach takes also into account that the available data can be subject to fragmentation. Our proposed algorithm relies on an ADMM approach to solve a $\\ell_{p},(0<p<1),$ quasi-norm objective problem. Numerical results highlight the performance of the proposed approach when compared to the $\\ell_{1}$ minimization in terms of the sparsity of the induced solution.'\nauthor:\n- |\n    Omar M.Sleem and Constantino M. Lagoa\\\n    Dep. of Electrical Engineering, Pennsylvania State University, State College, PA 16801, USA.\\\n    Email: {\u00a0oms46@psu.edu, cml18@psu.edu }\nbibliography:\n- 'Ref.bib'\ntitle: Parsimonious System Identification from Fragmented Quantized Measurements\n---\n\nSystem"
+"---\nabstract: 'Knowledge of syntax includes knowledge of rare, idiosyncratic constructions. LLMs must overcome frequency biases in order to master such constructions. In this study, I prompt GPT-3 to give acceptability judgments on the English-language Article + Adjective + Numeral + Noun construction (e.g., \u201ca lovely five days\u201d). I validate the prompt using the CoLA corpus of acceptability judgments and then zero in on the AANN construction. I compare GPT-3\u2019s judgments to crowdsourced human judgments on a subset of sentences. GPT-3\u2019s judgments are broadly similar to human judgments and generally align with proposed constraints in the literature but, in some cases, GPT-3\u2019s judgments and human judgments diverge from the literature and from each other.'\nauthor:\n- |\n    Kyle Mahowald\\\n    `mahowald@utexas.edu`\\\n    Department of Linguistics\\\n    The University of Texas at Austin\nbibliography:\n- 'anthology.bib'\n- 'custom.bib'\ntitle: |\n    A Discerning Several Thousand Judgments:\\\n    GPT-3 Rates the Article + Adjective + Numeral + Noun Construction\n---\n\nIntroduction\n============\n\nConsider the English Article + Adjective + Numeral + Noun (AANN) construction: \u201c **a beautiful 228 pages** \\[iWeb\\]\u201d or \u201cThe president has had **a terrible five weeks** \\[COCA\\]\u201d. Usually cardinal numbers precede the adjective (\u201cfive terrible weeks\u201d), but here the adjective precedes the numeral. More"
+"---\nabstract: 'We report the successful operation of a functional pixel detector with gaseous helium cooling. Using an accurate mock-up beforehand, the cooling was validated. We use a miniature turbo compressor to propel the helium at under ambient pressure conditions with gas temperatures above . Our earlier results based on computational fluid dynamics simulations and a much simpler mock-up are confirmed. With this, we paved the path to cool pixel detectors in experimental particle physics at heat densities up to using helium. This enables cooling of detectors with very low mass requirements, minimising the effects of multiple Coulomb scattering effectively. The concept presented here is not limited to pixel detector applications and can be used to cool any surface with comparable heat-densities, only limited by shaping the helium gas flow.'\naddress:\n- 'Physikalisches Institut, Universit\u00e4t Heidelberg, Im Neuenheimer Feld\u00a0226, 69120\u00a0Heidelberg, Germany'\n- 'Laboratory for Particle Physics, Paul Scherrer Institut, Forschungsstrasse\u00a0111, 5232\u00a0Villigen, Switzerland'\n- 'Institute of Thermal and Fluid Engineering, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Klosterzelgstrasse\u00a02, 5210\u00a0Windisch, Switzerland'\nauthor:\n- Thomas Theodor Rudzki\n- Frank Meier Aeschbacher\n- Marin Deflorin\n- Niculin Flucher\nbibliography:\n- 'mu3eHeliumVertex.bib'\ntitle: |\n    Successful cooling of"
+"---\nabstract: '**\\[Context and motivation\\]** The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for that behaviour. **\\[Question / problem\\]** We see major uncertainty in how to specify training data and runtime monitoring for critical ML models and by this specifying the final functionality of the system. In this interview-based study we investigate the underlying challenges for these difficulties. **\\[Principal ideas/results\\]** Based on ten interviews with practitioners who develop ML models for critical applications in the automotive and telecommunication sector, we identified 17 underlying challenges in 6 challenge groups that relate to the challenge of specifying training data and runtime monitoring. **\\[Contribution\\]** The article provides a list of the identified underlying challenges related to the difficulties practitioners experience when specifying training data and runtime monitoring for ML models. Furthermore, interconnection between the challenges were found and based on these connections recommendation proposed to overcome the root causes for the challenges.'\nauthor:\n- 'Hans-Martin Heyn'\n- Eric Knauss\n- |\n    \\\n    Iswarya Malleswaran\n-"
+"---\nabstract: 'OAuth 2.0 is a popular authorization framework that allows third-party clients such as websites and mobile apps to request limited access to a user\u2019s account on another application. The specification classifies clients into different types based on their ability to keep client credentials confidential. It also describes different grant types for obtaining access to the protected resources, with the authorization code and implicit grants being the most commonly used. Each client type and associated grant type have their unique security and usability considerations. In this paper, we propose a new approach for OAuth ecosystem that combines different client and grant types into a unified singular protocol flow for OAuth (USPFO), which can be used by both confidential and public clients. This approach aims to reduce the vulnerabilities associated with implementing and configuring different client types and grant types. Additionally, it provides built-in protections against known OAuth 2.0 vulnerabilities such as client impersonation, token (or code) thefts and replay attacks through integrity, authenticity, and audience binding. The proposed USPFO is largely compatible with existing Internet Engineering Task Force (IETF) Proposed Standard Request for Comments (RFCs), OAuth 2.0 extensions and active internet drafts.'\nauthor:\n- Jaimandeep Singh\n- Naveen Kumar"
+"---\nabstract: |\n    In this series of papers, we propose a theory of enumerative invariants counting self-dual objects in self-dual categories. Ordinary enumerative invariants in abelian categories can be seen as invariants for the structure group ${\\mathrm{GL}}(n)$, and our theory is an extension of this to structure groups ${\\mathrm{O}}(n)$ and ${\\mathrm{Sp}}(2n)$. Examples of our invariants include invariants counting principal orthogonal or symplectic bundles, and invariants counting self-dual quiver representations.\n\n    In the present paper, we take the motivic approach, and define our invariants as elements in a ring of motives. One can also extract numerical invariants from these invariants. We prove wall-crossing formulae relating our invariants for different stability conditions. We also provide an explicit algorithm computing invariants for quiver representations, and we present some numerical results.\nauthor:\n- Chenjing Bu\nbibliography:\n- 'main.bib'\ntitle: |\n    Enumerative invariants in self-dual categories.\\\n    I. Motivic invariants\n---\n\n[ ]{}\n\nIntroduction\n============\n\nA theory of *enumerative invariants* is a certain way to assign numbers, or other types of data, to certain types of moduli stacks. One major branch of the study of such invariants is the study of moduli stacks of objects in an *abelian category*, such as the category of coherent sheaves on"
+"---\nabstract: 'Computational material modeling using advanced numerical techniques speeds up the design process and reduces the costs of developing new engineering products. In the field of multiscale modeling, huge computation efforts are expected for modeling heterogeneous materials while trying to reach high accuracy levels. In this work, a machine learning approach, namely the convolutional neural network (CNN), is developed as a solution providing a high level of accuracy, while being computationally efficient. The input for the CNN model consists of two-/three-dimensional images of artificial periodic and biphasic microstructures in the form of nonoverlapping and overlapping, mono- and polydisperse circular/spherical disk systems, which are generated by a random sequential inhibition process. These correspond to Statistical Volume Elements (SVE). Considering linear magnetostatics at the microscale, the output is the apparent permeability of the SVE. Training and testing data for the apparent properties is produced with finite element method-based two-scale asymptotic homogenization. The model efficiency is revealed by employing some representative examples in two- and three-dimensional settings. In this regard, the performance of the CNN model is assessed with the applied computational homogenization method relating to the accuracy and computational efficiency. The results with the CNN model show high accuracy in predicting"
+"---\nabstract: 'Spiking Neural Networks (SNNs) use discrete spike sequences to transmit information, which significantly mimics the information transmission of the brain. Although this binarized form of representation dramatically enhances the energy efficiency and robustness of SNNs, it also leaves a large gap between the performance of SNNs and Artificial Neural Networks based on real values. There are many different spike patterns in the brain, and the dynamic synergy of these spike patterns greatly enriches the representation capability. Inspired by spike patterns in biological neurons, this paper introduces the dynamic Burst pattern and designs the Leaky Integrate and Fire or Burst (LIFB) neuron that can make a trade-off between short-time performance and dynamic temporal performance from the perspective of network information capacity. LIFB neuron exhibits three modes, resting, Regular spike, and Burst spike. The burst density of the neuron can be adaptively adjusted, which significantly enriches the characterization capability. We also propose a decoupling method that can losslessly decouple LIFB neurons into equivalent LIF neurons, which demonstrates that LIFB neurons can be efficiently implemented on neuromorphic hardware. We conducted experiments on the static datasets CIFAR10, CIFAR100, and ImageNet, which showed that we greatly improved the performance of the SNNs while"
+"---\nabstract: 'We analyze a non-linear elliptic boundary value problem, that involves $(p, q)$ Laplace operator, for the existence of its positive solution in an arbitrary smooth bounded domain. The non-linearity here is driven by a continuous function in $(0,\\infty)$ which is singular, monotonically increasing and eventually positive. We prove the existence of a positive solution of this problem using a fixed point theorem due to Amann[@amann1976fixed]. In addition, for a specific nonlinearity we derive that the obtained solution is maximal in nature. The main results obtained here are first of its kind for a $(p, q)$ Laplace operator in an arbitrary bounded domain.'\naddress: 'School of Mathematics, Indian Institute of Science Education and Research Thiruvananthapuram, Maruthamala, Thiruvananthapuram, 695551'\nauthor:\n- 'R. Dhanya, R. Harish, Sarbani Pramanik'\nbibliography:\n- 'References.bib'\ntitle: 'On a class of infinite semipositone problems for (p,q) Laplace operator'\n---\n\nIntroduction\n============\n\nWe consider the following boundary value problem $$\\begin{aligned}\n\\label{problem}\n    -{\\Delta}_p u - {\\Delta}_q u &= {\\lambda}\\frac{f(u)}{u^{\\beta}} \\ \\text{in} \\ {\\Omega}\\nonumber \\\\\n    u &>0 \\ \\text{in} \\ {\\Omega}\\tag{$P_\\lambda$} \\\\\n    u &=0 \\ \\text{on} \\ {\\partial}{\\Omega}\\nonumber\\end{aligned}$$ where ${\\Omega}$ is a bounded domain in $\\mathbb{R}^N$ with a smooth boundary, $0<{\\beta}<1$, $1<q<p<\\infty$ and ${\\lambda}>0$ is a parameter. The term"
+"---\nabstract: 'Using molecular dynamics and thermodynamic integration, we report on the solvation process in water and in cyclohexane of seven polypeptides (GLY, ALA, ILE, ASN, LYS, ARG, GLU). The polypeptides are selected to cover the full hydrophobic scale while varying their chain length from tri- to undeca-homopeptides provide indications on possible non-additivity effects as well as the role of the peptide backbone in the overall stability of the polypeptides. The use of different solvents and different polypeptides allows us to investigate the relation between *solvent quality* \u2013 the capacity of a given solvent to fold a given biopolymer often described on a scale ranging from \u201cgood\u201d to \u201cpoor\u201d, and *solvent polarity* \u2013 related to the specific interactions of any solvent with respect to a reference solvent. Undeca-glycine is found to be the only polypeptides to have a proper stable collapse in water (polar solvent), with the other hydrophobic polypetides displaying in water repeated folding and unfolding events and with polar polypeptides presenting a even more complex behavior. By contrast, all polypeptides but none are found to keep an extended conformation in cyclohexane, irrespective of their polarity. All considered polypeptides are also found to have a favorable solvation free energy"
+"---\nabstract: 'We study the non-relativistic limit of quantum fields for an inertial and a non-inertial observer. We show that non-relativistic particle states appear as a superposition of relativistic and non-relativistic particles in different frames. Hence, the non-relativistic limit is frame-dependent. We detail this result when the non-inertial observer has uniform constant acceleration. Only for low accelerations, the accelerated observer agrees with the inertial frame about the non-relativistic nature of particles locally. In such a quasi-inertial regime, both observers agree about the number of particles describing quantum field states. The same does not occur when the acceleration is arbitrarily large (e.g., the Unruh effect). We furthermore prove that wave functions of particles in the inertial and the quasi-inertial frame are identical up to the coordinate transformation relating the two frames.'\nauthor:\n- Riccardo Falcone\n- Claudio Conti\nbibliography:\n- 'bibliography.bib'\ntitle: 'Frame-dependence of the non-relativistic limit of quantum fields'\n---\n\nIntroduction\n============\n\nSince the theoretical proposal of the Unruh effect [@PhysRevD.7.2850; @Davies:1974th; @PhysRevD.14.870] as the equivalent of the Hawking effect [@Hawking:1975vcx] in accelerated frames, there has been a wide interest in detectors able to reveal such effect. In their pioneering works, Unruh and DeWitt [@PhysRevD.14.870; @PhysRevD.29.1047; @hawking1980general] considered a particle"
+"---\nabstract: '*Orcinus orca* (killer whales) exhibit complex calls. They last about a second. In a call, an orca typically uses multiple frequencies simultaneously, varies the frequencies, and varies their volumes. Behavior data is hard to obtain because orcas live under water and travel quickly. Sound data is relatively easy to capture. As a science goal, we would like to know whether orca vocalizations constitute a semantic language. We do this by studying whether machine learning can predict behavior from vocalizations. Such prediction would also help scientific research and safety applications because one would like to predict behavior while only having to capture sound. A significant challenge in this process is lack of labeled data. We work with recent recordings of McMurdo Sound orcas\u00a0[@Wellard20:Cold] where each recording is labeled with the behaviors observed during the recording. This yields a dataset where sound *segments*\u2014continuous vocalizations that can be thought of as call *sequences* or more general structures\u2014within the recordings are labeled with superfluous behaviors. Despite that, with a careful combination of recent machine learning techniques, we achieve 96.4% classification accuracy. This suggests that orcas do use a semantic language. It is also promising for research and applications.'\nauthor:\n- 'Sophia"
+"---\nabstract: 'The supersymmetrized DFSZ axion model is especially compelling in that it contains 1. the SUSY solution to the gauge hierarchy problem, 2. the Peccei-Quinn (PQ) solution to the strong CP problem and 3. the Kim-Nilles solution to the SUSY $\\mu$ problem. In a string setting, where a discrete $R$-symmetry (${\\bf Z}_{24}^R$ for example) may emerge from the compactification process, a high-quality accidental axion (accion) can emerge from the accidental, approximate remnant global $U(1)_{PQ}$ symmetry where the decay constant $f_a$ is linked to the SUSY breaking scale, and is within the cosmological sweet zone. In this setup, one also expects the presence of stringy remnant moduli fields $\\phi_i$. Here, we consider the situation of a single light modulus $\\phi$ coupled to the PQMSSM in the early universe, with mixed axion plus higgsino-like WIMP dark matter. We evaluate dark matter and dark radiation production via nine coupled Boltzmann equations and assess the severity of the cosmological moduli problem (CMP) along with dark matter and dark radiation production rates. We find that typically the light modulus mass should be $m_{\\phi}\\agt 10^4$ TeV to avoid the moduli-induced dark matter overproduction problem. If one is able to (anthropically) tune the modulus field amplitude,"
+"---\nabstract: 'The continuous computational power growth in the last decades has made solving several optimization problems significant to humankind a tractable task; however, tackling some of them remains a challenge due to the overwhelming amount of candidate solutions to be evaluated, even by using sophisticated algorithms. In such a context, a set of nature-inspired stochastic methods, called meta-heuristic optimization, can provide robust approximate solutions to different kinds of problems with a small computational burden, such as derivative-free real function optimization. Nevertheless, these methods may converge to inadequate solutions if the function landscape is too harsh, e.g., enclosing too many local optima. Previous works addressed this issue by employing a hypercomplex representation of the search space, like quaternions, where the landscape becomes smoother and supposedly easier to optimize. Under this approach, meta-heuristic computations happen in the hypercomplex space, whereas variables are mapped back to the real domain before function evaluation. Despite this latter operation being performed by the Euclidean norm, we have found that after the optimization procedure has finished, it is usually possible to obtain even better solutions by employing the Minkowski $p$-norm instead and fine-tuning $p$ through an auxiliary sub-problem with neglecting additional cost and no hyperparameters. Such"
+"---\nauthor:\n- som\n- Tamas Almos Vami\n- Morris Swartz\nbibliography:\n- 'main.bib'\ntitle: 'Simulated performance and calibration of CMS Phase-2 Upgrade Inner Tracker sensors'\n---\n\nIntroduction\n============\n\nThe Large Hadron Collider (LHC) will be upgraded to its High Luminosity phase (HL-LHC) in the shutdown period from 2026. The HL-LHC will reach the instantaneous luminosity of $7.5\\,\\times\\,10^{34} \\,\\,\\,\\text{s}^{-1}\\text{cm}^{-2}$ which is four times the Run-2 value of $7.5\\,\\times\\,10^{34} \\,\\,\\,\\text{s}^{-1}\\text{cm}^{-2}$. The number of simultaneous proton-proton interactions per 25 ns bunch crossing (pileup) is expected to be between 140 and 200, which is a similar fourfold increase with respect to the current value of 55. The current detectors would not be able to operate in such conditions, which motivates the need to upgrade them. This is referred to as the Phase-2 Upgrade of CMS.\n\nThe upgraded CMS tracker detector [@CERN-LHCC-2017-009] will provide increased acceptance for tracking up to $|\\eta| < 4$ and significantly reduced mass through the use of carbon fiber mechanics, and $\\text{CO}_2$ cooling. The layout of the upgraded tracker is shown in Fig.\u00a0\\[fig:Phase2Tracker\\]. It consists of two parts, the Outer Tracker (OT) and the Inner Tracker (IT). The OT has six barrel layers and five pairs of forward disks,"
+"---\nabstract: 'Magnetic topological materials are a class of compounds with the underlying interplay of nontrivial band topology and magnetic spin configuration. Extensive interests have been aroused due to their application potential involved with an array of exotic quantum states. With angle-resolved photoemission spectroscopy and first-principles calculations, here we study the electronic properties of two magnetic Weyl semimetal candidates PrAlSi and SmAlSi. Though the two compounds harbor distinct magnetic ground states (ferromagnetic and antiferromagnetic for PrAlSi and SmAlSi, respectively) and 4$f$ shell fillings, we find that they share quite analogous low-energy band structure. By the measurements across the magnetic transitions, we further reveal that there is no evident evolution of the band structure in both compounds and the experimental spectra can be well reproduced by the nonmagnetic calculations, together suggesting a negligible effect of the magnetism on their electronic structures and a possibly weak coupling between the localized 4$f$ electrons and the itinerant conduction electrons. Our results offer essential insights into the interactions between magnetism, electron correlations, and topological orders in the $R$Al$X$ ($R$ = light rare earth and $X$ = Si or Ge) family.'\nauthor:\n- Rui Lou\n- Alexander Fedorov\n- Lingxiao Zhao\n- Alexander Yaresko\n- 'Bernd"
+"---\nabstract: 'There are various research strategies used for non-Hermitian systems, which typically involve introducing non-Hermitian terms to pre-existing Hermitian Hamiltonians. It can be challenging to directly design non-Hermitian many-body models that exhibit unique features not found in Hermitian systems. In this Letter, we propose a new method to construct non-Hermitian many-body systems by generalizing the parent Hamiltonian method into non-Hermitian regimes. This allows us to build a local Hamiltonian using given matrix product states as its left and right ground states. We demonstrate this method by constructing a non-Hermitian spin-$1$ model from the asymmetric Affleck-Kennedy-Lieb-Tasaki (AKLT) state, which preserves both chiral order and symmetry-protected topological order. Our approach opens up a new paradigm for systematically constructing and studying non-Hermitian many-body systems, providing guiding principles to explore new properties and phenomena in non-Hermitian physics.'\nauthor:\n- Ruohan Shen\n- Yuchen Guo\n- Shuo Yang\nbibliography:\n- 'ref.bib'\ntitle: 'Construction of Non-Hermitian Parent Hamiltonian from Matrix Product States'\n---\n\n[^1]\n\n[^2]\n\n*Introduction.* Non-Hermitian physics has attracted much attention both theoretically\u00a0[@Konotop2016; @Leykam2017; @Gong2018; @Kawabata2019; @Ashida2020; @Bergholtz2021; @Yamamoto2022; @Ding2022; @Guo2022; @Chen2022] and experimentally\u00a0[@Guo2009; @Schindler2011; @Zeuner2015; @El2018; @Hokmabadi2019; @Zhang2022A; @Gu2022] for describing open systems\u00a0[@Vega2017], such as photonics\u00a0[@Takata2018] and acoustics\u00a0[@Zhang2021A;"
+"---\nabstract: |\n    In this paper, we investigate the equilibria and their stability in an asymmetric duopoly model of Kopel by using several tools based on symbolic computations. We explore the possible positions of the equilibria in Kopel\u2019s model. We discuss the possibility of the existence of multiple positive equilibria and establish a necessary and sufficient condition for a given number of equilibria to exist. Furthermore, if the two duopolists adopt the best response reactions or homogeneous adaptive expectations, we establish rigorous conditions for the existence of distinct numbers of positive equilibria for the first time.\n\n    *Keywords: duopoly; Kopel\u2019s model; equilibrium; local stability; symbolic computation*\nauthor:\n- Xiaoliang Li\n- 'Kongyan Chen[^1]'\ntitle: Equilibria and their stability in an asymmetric duopoly model of Kopel\n---\n\n[UTF8]{}[gbsn]{}\n\nIntroduction\n============\n\nDuopoly is an intermediate market between monopoly and perfect competition [@guirao_extensions_2010]. The study of duopolistic competitions lies at the core of the field of industrial organization. Since about three decades ago, economists have been making efforts to model duopolistic competitions by discrete dynamical systems of form with unimodal reaction functions. Among them, Kopel [@kopel_simple_1996] proposed a famous duopoly model by assuming a linear demand function and nonlinear cost functions, which can be"
+"---\nauthor:\n- 'Noah Glennon,'\n- 'Anthony E. Mirasola,'\n- 'Nathan Musoke,'\n- 'Mark C. Neyrinck,'\n- 'Chanda Prescod-Weinstein'\nbibliography:\n- 'bib.bib'\ntitle: Scalar dark matter vortex stabilization with black holes\n---\n\nIntroduction\n============\n\nScalar dark matter (SDM, closely related to, or also known as superfluid, wave, and fuzzy dark matter) models have seen substantial recent attention as a promising alternative to the longstanding WIMP cold dark matter (WIMP CDM) model. Simulations of SDM show that these alternative models behave similarly to WIMP CDM in large-scale structure formation (where WIMP CDM has been successful), but ultralight scalars radically differ on galactic scales. WIMP CDM encounters well-known issues on these scales; there is an apparent deficit of observed satellite galaxies compared to simple estimates from $N$-body simulations of WIMPs, and there is a disagreement between simulated and observed density profiles in galactic cores\u00a0[@Weinberg2015; @Moore1994; @Papastergis2015]. Some work has suggested that baryonic effects could possibly account for these discrepancies\u00a0[@Kim2018; @Governato2010], but this motivation for SDM or warm dark matter remains. There also are other motivations for SDM, beyond addressing observational problems such as the missing-satellite problem. They are well-motivated in string theory\u00a0[@Svrcek2006; @Arvanitaki2010axiverse; @Marsh2016; @Hui2017].\n\nA crucial piece of"
+"---\nabstract: 'The incomplete fusion has been proved as the formation and emission of the $\\alpha$ particle by the increase in the rotational energy of the very mass-asymmetric dinuclear system. The results of the dinuclear system model have confirmed that the incomplete fusion in heavy-ion collisions occurs at a large orbital angular momentum ($L > 30 \\hbar$) due to the strong increase of the intrinsic fusion barrier.'\nauthor:\n- 'A.K. Nasirov$^{1,2}$'\n- 'B.M. Kayumov$^{2,3}$'\n- 'O.K. Ganiev$^{2,4,5}$'\n- 'G.A. Yuldasheva$^{2}$'\nbibliography:\n- 'References\\_PRL.bib'\ntitle: 'A new dynamical mechanism of incomplete fusion in heavy-ion collision'\n---\n\nThe incomplete fusion (ICF) of nuclei at the heavy-ion collision is observed in reactions of light projectiles with the intermediate-mass target nucleus. This phenomenon was first observed more than 60 years ago [@Knox1960]. From the analysis of experimental data it was found that the peripheral collisions are a favorable condition for the incomplete fusion. This phenomenon is studied by observation of the $\\alpha$ particle flying in the forward angles or other light clusters or by identification of the evaporation residue accompanied with the emitted fast light clusters.\n\nA mean value $<L> = 40 \\hbar$ of the angular momentum distribution of the entrance channel corresponding to"
+"---\nabstract: 'In this paper, we study the fundamental theorem of calculus and its consequences from an algebraic point of view. For functions with singularities, this leads to a generalized notion of evaluation. We investigate properties of such integro-differential rings and discuss many examples. We also construct corresponding integro-differential operators and provide normal forms via rewrite rules. They are then used to derive several identities and properties in a purely algebraic way, generalizing well-known results from analysis. In identities like shuffle relations for nested integrals and the Taylor formula, additional terms are obtained that take singularities into account. Another focus lies on treating basics of linear ODEs in this framework of integro-differential operators. These operators can have matrix coefficients, which allow to treat systems of arbitrary size in a unified way. In the appendix, using tensor reduction systems, we give the technical details of normal forms and prove them for operators including other functionals besides evaluation.'\nauthor:\n- 'Clemens G.\u00a0Raab^a,^[^1]\u00a0\u00a0and Georg Regensburger^a,b^'\nbibliography:\n- 'ref.bib'\ntitle: The fundamental theorem of calculus in differential rings\n---\n\n^a^Institute for Algebra, Johannes Kepler University Linz, Austria\\\n^b^Institute of Mathematics, University of Kassel, Germany\\\n<clemensr@algebra.uni-linz.ac.at>\\\n<regensburger@mathematik.uni-kassel.de>\n\n#### Keywords\n\nIntegro-differential rings, integro-differential operators,"
+"---\nabstract: 'Given a representation of a finite group $G$ over some commutative base ring $\\mathbf{k}$, the *cofixed space* is the largest quotient of the representation on which the group acts trivially. If $G$ acts by $\\mathbf{k}$-algebra automorphisms, then the cofixed space is a module over the ring of $G$-invariants. When the order of $G$ is not invertible in the base ring, little is known about this module structure. We study the cofixed space in the case that $G$ is the symmetric group on $n$ letters acting on a polynomial ring by permuting its variables. When $\\mathbf{k}$ has characteristic 0, the cofixed space is isomorphic to an ideal of the ring of symmetric polynomials in $n$ variables. Localizing $\\mathbf{k}$ at a prime integer $p$ while letting $n$ vary reveals striking behavior in these ideals. As $n$ grows, the ideals stay stable in a sense, then jump in complexity each time $n$ reaches a multiple of $p$.'\nauthor:\n- Alexandra Pevzner\nbibliography:\n- 'biblio.bib'\ntitle: Symmetric Group Fixed Quotients of Polynomial Rings\n---\n\nIntroduction\n============\n\nFix a commutative ring ${\\mathbf{k}}$ with unit. Given a representation $U$ of a finite group $G$ over ${\\mathbf{k}}$, there are two natural ${\\mathbf{k}}G$-modules one can associate"
+"---\nabstract: 'The global trend toward renewable power generation has drawn great attention to hydrogen Fuel Cells (FCs), which have a wide variety of applications, from utility power stations to laptops. The Multi-stack Fuel Cell System (MFCS), which is an assembly of FC stacks, can be a remedy for obstacles in high-power applications. However, the output voltage of FC stacks varies dramatically under variable load conditions; hence, in order for MFCS to be efficiently operated and guarantee an appropriate load-current sharing among the FC stacks, advanced converter controllers for power conditioning need to be designed. An accurate circuit model is essential for controller design, which accounts for the fact that the parameters of some converter components may change due to aging and repetitive stress in long-term operations. Existing control frameworks and parametric and non-parametric system identification techniques do not consider the aforementioned challenges. Thus, this paper investigates the potential of a data-driven method that, without system identification, directly implements control on paralleled converters using raw data. Based on pre-collected input/output trajectories, a non-parametric representation of the overall circuit is produced for implementing predictive control. While approaching equal current sharing within the MFCS, the proposed method considers the minimization of load-following"
+"---\nabstract: 'On a general open set of the euclidean space, we study the relation between the embedding of the homogeneous Sobolev space $\\mathcal{D}^{1,p}_0$ into $L^q$ and the summability properties of the distance function. We prove that in the superconformal case (i.e. when $p$ is larger than the dimension) these two facts are equivalent, while in the subconformal and conformal cases (i.e. when $p$ is less than or equal to the dimension) we construct counterexamples to this equivalence. In turn, our analysis permits to study the asymptotic behaviour of the positive solution of the Lane-Emden equation for the $p-$Laplacian with sub-homogeneous right-hand side, as the exponent $p$ diverges to $\\infty$. The case of first eigenfunctions of the $p-$Laplacian is included, as well. As particular cases of our analysis, we retrieve some well-known convergence results, under optimal assumptions on the open sets. We also give some new geometric estimates for generalized principal frequencies.'\naddress:\n- 'Dipartimento di Matematica e Informatica Universit\u00e0 degli Studi di Ferrara Via Machiavelli 35, 44121 Ferrara, Italy'\n- 'Dipartimento di Scienze Agrarie, Alimentari e Agro-ambientali Universit\u00e0 di Pisa Via del Borghetto 80, 56124 Pisa, Italy'\n- 'Dipartimento di Scienze Matematiche, Fisiche e Informatiche Universit\u00e0 di Parma Parco"
+"---\nabstract: 'The early kinetic decoupling (eKD) effect is an inevitable ingredient in calculating the relic density of dark matter (DM) for various well-motivated scenarios. It appears naturally in forbidden dark matter annihilation, the main focus of this work, which contains fermionic DM and a light singlet scalar that connects the DM and standard model (SM) leptons. The strong suppression of the scattering between DM and SM particles happens quite early in the DM depletion history, where the DM temperature drops away from the thermal equilibrium, $T_\\chi < T_{\\rm SM}$, leading to the decreased kinetic energy of DM. The forbidden annihilation thus becomes inefficient since small kinetic energy cannot help exceed the annihilation threshold, naturally leading to a larger abundance. To show the eKD discrepancy, we numerically solve the coupled Boltzmann equations that govern the evolution of DM number density and temperature. It is found that eKD significantly affects the DM abundance, resulting in almost an order of magnitude higher than that by the traditional calculation. We also discuss the constraints from experimental searches on the model parameters, where the viable parameter space shrinks when considering the eKD effect.'\nauthor:\n- Yu Liu\n- Xuewen Liu\n- Bin Zhu\nbibliography:"
+"---\nabstract: |\n    Rank-metric codes were studied by E. Gabidulin in 1985 after a brief introduction by Delsarte in 1978 as an equivalent of Reed-Solomon codes, but based on linearized polynomials. They have found applications in many areas, including linear network coding and space-time coding. They are also used in cryptography to reduce the size of the keys compared to Hamming metric codes at the same level of security. However, some families of rank-metric codes suffer from structural attacks due to the strong algebraic structure from which they are defined. It therefore becomes interesting to find new code families in order to address these questions in the landscape of rank-metric codes.\n\n    In this paper, we provide a generalization of Subspace Subcodes in Rank metric introduced by Gabidulin and Loidreau. We also characterize this family by giving an algorithm which allows to have its generator and parity-check matrices based on the associated extended codes. We have also studied the specific case of Gabidulin codes whose underlying decoding algorithms are known. Bounds for the cardinalities of these codes, both in the general case and in the case of Gabidulin codes, are also provided.\nauthor:\n- Ousmane Ndiaye\n- Peter Arnaud Kidoudou\n-"
+"---\nabstract: 'The Linux kernel makes considerable use of Berkeley Packet Filter (BPF) to allow user-written BPF applications to execute in the kernel space. BPF employs a verifier to statically check the security of user-supplied BPF code. Recent attacks show that BPF programs can evade security checks and gain unauthorized access to kernel memory, indicating that the verification process is not flawless. In this paper, we present , a system that isolates potentially malicious BPF programs using Intel Memory Protection Keys (MPK). Enforcing BPF program isolation with MPK is not straightforward; \u00a0is carefully designed to alleviate technical obstacles, such as limited hardware keys and supporting a wide variety of kernel BPF helper functions. We have implemented in a prototype kernel module, and our evaluation shows that delivers low-cost isolation of BPF programs under various real-world usage scenarios, such as the isolation of a packet-forwarding BPF program for the `memcached` database with an average throughput loss of 6%.'\nauthor:\n- \nbibliography:\n- 'main.bib'\ntitle: '**: Towards Safe BPF Kernel Extension**'\n---\n\nIntroduction {#sec:intro}\n============\n\nIt is common to extend kernel functionality by allowing user applications to download code into the kernel space. In 1993, the well-known was introduced for this purpose"
+"---\nabstract: 'Regression tests are often used to ensure that software behaves as intended, as software evolution takes place. However, tests in general and regression tests in particular are inherently partial as behavioural descriptions, and thus regression tests may fail in clearly capturing how software behaviour is altered by code changes. In this paper, we propose an assertion-based approach to capture software evolution, through the notion of *commit-relevant specification*. A commit-relevant specification summarises the program properties that have changed as a consequence of a *commit* (understood as a specific software modification), via two sets of assertions, the *delta-added assertions*, properties that did not hold in the pre-commit version but hold on the post-commit, and the *delta-removed assertions*, those that were valid in the pre-commit, but no longer hold after the code change. We also present [<span style=\"font-variant:small-caps;\">DeltaSpec</span>]{}, an approach that combines test generation and dynamic specification inference to automatically compute commit-relevant specifications from given commits. We evaluate [<span style=\"font-variant:small-caps;\">DeltaSpec</span>]{} on two datasets that include a total of 57 commits (63 classes and 797 methods). We show that commit-relevant assertions can precisely describe the semantic deltas of code changes, providing a useful mechanism for validating the behavioural evolution of software. We"
+"---\nabstract: 'Cosmological observations precisely measure primordial variations in the density of the Universe at megaparsec and larger scales, but much smaller scales remain poorly constrained. However, sufficiently large initial perturbations at small scales can lead to an abundance of ultradense dark matter minihalos that form during the radiation epoch and survive into the late-time Universe. Because of their early formation, these objects can be compact enough to produce detectable microlensing signatures. We investigate whether the EROS, OGLE, and HSC surveys can probe these halos by fully accounting for finite source size and extended lens effects. We find that current data may already constrain the amplitudes of primordial curvature perturbations in a new region of parameter space, but this conclusion is strongly sensitive to yet undetermined details about the internal structures of these ultradense halos. Under optimistic assumptions, current and future HSC data would constrain a power spectrum that features an enhancement at scales $k \\sim  10^7/{\\rm Mpc}$, and an amplitude as low as $\\mathcal{P}_\\zeta\\simeq 10^{-4}$ may be accessible. This is a particularly interesting regime because it connects to primordial black hole formation in a portion of the LIGO/Virgo/Kagra mass range and the production of scalar-induced gravitational waves in the"
+"---\nabstract: 'Numerical simulations of neutron star mergers represent an essential step toward interpreting the full complexity of multimessenger observations and constraining the properties of supranuclear matter. Currently, simulations are limited by an array of factors, including computational performance and input physics uncertainties, such as the neutron star equation of state. In this work, we expand the range of nuclear phenomenology efficiently available to simulations by introducing a new analytic parametrization of cold, beta-equilibrated matter that is based on the relativistic enthalpy. We show that the new *enthalpy parametrization* can capture a range of nuclear behavior, including strong phase transitions. We implement the enthalpy parametrization in the [`SpECTRE`]{} code, simulate isolated neutron stars, and compare performance to the commonly used spectral and polytropic parametrizations. We find comparable computational performance for nuclear models that are well represented by either parametrization, such as simple hadronic EoSs. We show that the enthalpy parametrization further allows us to simulate more complicated hadronic models or models with phase transitions that are inaccessible to current parametrizations.'\nauthor:\n- Isaac Legred\n- Yoonsoo Kim\n- Nils Deppe\n- Katerina Chatziioannou\n- Francois Foucart\n- Fran\u00e7ois H\u00e9bert\n- 'Lawrence E.\u00a0Kidder'\nbibliography:\n- 'references.bib'\ntitle: ' Simulating neutron"
+"---\nabstract: 'In this paper, we propose several new stochastic second-order algorithms for policy optimization that only require gradient and Hessian-vector product in each iteration, making them computationally efficient and comparable to policy gradient methods. Specifically, we propose a dimension-reduced second-order method (DR-SOPO) which repeatedly solves a projected two-dimensional trust region subproblem. We show that DR-SOPO obtains an $\\mathcal{O}(\\epsilon^{-3.5})$ complexity for reaching approximate first-order stationary condition and certain subspace second-order stationary condition. In addition, we present an enhanced algorithm (DVR-SOPO) which further improves the complexity to $\\mathcal{O}(\\epsilon^{-3})$ based on the variance reduction technique. Preliminary experiments show that our proposed algorithms perform favorably compared with stochastic and variance-reduced policy gradient methods.'\n---\n\nIntroduction\n============\n\nThe policy gradient (PG) method, pioneered by @williams1992simple, is a widely used approach for finding the optimal policy in reinforcement learning (RL). The main idea behind PG is to directly maximize the total reward by using the (stochastic) gradient of the cumulative rewards. PG is particularly useful for high dimensional continuous state and action spaces due to its ease in implementation. In recent years, the PG method has gained much attention due to its significant empirical successes in a variety of challenging RL applications\u00a0[@lillicrap2015continuous; @silver2014deterministic].\n\nDespite"
+"---\nabstract: 'A multiscale mathematical model describing the genesis and ecology of algal-bacterial photogranules and the metals biosorption on their solid matrix within a sequencing batch reactor (SBR) is presented. The granular biofilm is modelled as a spherical free boundary domain with radial symmetry and a vanishing initial value. The free boundary evolution is governed by an ordinary differential equation (ODE) accounting for microbial growth, attachment and detachment phenomena. The model is based on systems of partial differential equations (PDEs) derived from mass conservation principles. Specifically, two systems of nonlinear hyperbolic PDEs model the growth of attached species and the dynamics of free adsorption sites; and two systems of quasi-linear parabolic PDEs govern the diffusive transport and conversion of nutrients and metals. The model is completed with systems of impulsive ordinary differential equations (IDEs) describing the evolution of dissolved substrates, metals, and planktonic and detached biomasses within the granular-based SBR. All main phenomena involved in the process are considered in the mathematical model. Moreover, the dual effect of metal presence on the formation process of photogranules is accounted: metal stimulates the production of EPS by sessile species and negatively affects the metabolic activities of microbial species. To describe the effects"
+"---\nabstract: |\n    The Black-Litterman model extends the framework of the Markowitz Modern Portfolio Theory to incorporate investor views. We consider a case where multiple view estimates, including uncertainties, are given for the same underlying subset of assets at a point in time. This motivates our consideration of data fusion techniques for combining information from multiple sources. In particular, we consider consistency-based methods that yield fused view and uncertainty pairs; such methods are not common to the quantitative finance literature. We show a relevant, modern case of incorporating machine learning model-derived view and uncertainty estimates, and the impact on portfolio allocation, with an example subsuming Arbitrage Pricing Theory. Hence we show the value of the Black-Litterman model in combination with information fusion and artificial intelligence-grounded prediction methods.\\\n    [**Keywords:**]{} Black-Litterman portfolio allocation, information fusion, machine learning, financial time-series analysis.\nauthor:\n- 'Trent Spears[^1] [^2], Stefan Zohren$^*$, Stephen Roberts$^*$'\nbibliography:\n- 'bibs.bib'\ntitle: 'View fusion vis-\u00e0-vis a Bayesian interpretation of Black-Litterman for portfolio allocation.'\n---\n\nIntroduction\n============\n\nThe Black-Litterman model extends the Markowitz portfolio optimisation framework to incorporate investor beliefs about asset returns [@Black91]. Such beliefs are termed \u2018views\u2019. In its original formulation, the Black-Litterman framework is such that a given view"
+"---\nbibliography:\n- 'refs.bib'\n---\n\nThis thesis is distributed under license \u201c**Creative Commons Atributtion - Non Commercial - Non Derivatives**\u201d.\\\n![image](figures/creativecommons.png){width=\"4.2cm\"}\n\n*A mis padres,\\\nCrist\u00f3bal y Margarita.*\n\nAcknowledgements {#acknowledgements .unnumbered}\n================\n\nI owe a great deal to the people who have helped and advised me over the last three years, without whom I could not have written this thesis.\n\nFirst and foremost, I am tremendously grateful to my supervisors, who gave me such an absorbing topic to work on and offered their guidance, Eduardo J.S. Villase\u00f1or, J. Fernando Barbero G. and Juan Margalef Bentabol. I want to thank your incredible patience, continuous encouragement and your priceless time answering all of my questions. It has been a privilege to learn from you and work under your supervision.\n\nI thank Tom\u00e1s Ort\u00edn for making me fall in love with general relativity in my master studies and his patience with my endless and possibly pointless questions about spacetime and black holes. I also inevitably need to thank Mercedes Mart\u00edn Benito for encouraging me in the first place to pursue a PhD in this particular field of research.\n\nSpecial thanks also to Laura Hiscott for correcting my English, and to Pablo H. Ufarte for"
+"---\nabstract: 'We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs. The method is built of two main types of blocks: (i) We introduce unsupervised kernel machine layers propagating the node features in a one-hop neighborhood, using implicit node feature mappings. (ii) We specify a semi-supervised classification kernel machine through the lens of the Fenchel-Young inequality. We derive an effective initialization scheme and efficient end-to-end training algorithm in the dual variables for the full architecture. The main idea underlying GCKM is that, because of the unsupervised core, the final model can achieve higher performance in semi-supervised node classification when few labels are available for training. Experimental results demonstrate the effectiveness of the proposed framework.'\nauthor:\n- 'Sonny Achten, Francesco Tonin, Panagiotis Patrinos, Johan A.K. Suykens'\nbibliography:\n- 'achten.bib'\ntitle: |\n    Unsupervised Neighborhood Propagation Kernel Layers\\\n    for Semi-supervised Node Classification\n---\n\nIntroduction\n============\n\nSemi-supervised node classification has been an important research area for several years. In many real-life applications, one has structured data for which the entire graph can be observed (e.g., a social network where users are represented as nodes and the relationships between users as edges). However, the node labels can only be"
+"---\nabstract: |\n    We introduce partitioned matching games as a suitable model for international kidney exchange programmes, where in each round the total number of available kidney transplants needs to be distributed amongst the participating countries in a \u201cfair\u201d way. A partitioned matching game $(N,v)$ is defined on a graph $G=(V,E)$ with an edge weighting $w$ and a partition $V=V_1 \\cup \\dots \\cup V_n$. The player set is $N = \\{ 1, \\dots, n\\}$, and player $p \\in N$ owns the vertices in $V_p$. The value $v(S)$ of a coalition\u00a0$S \\subseteq N$ is the maximum weight of a matching in the subgraph of $G$ induced by the vertices owned by the players in\u00a0$S$. If $|V_p|=1$ for all $p\\in N$, then we obtain the classical matching game. Let $c=\\max\\{|V_p| \\; |\\; 1\\leq p\\leq n\\}$ be the width of $(N,v)$. We prove that checking core non-emptiness is polynomial-time solvable if $c\\leq 2$ but co-[[NP]{}]{}-hard if $c\\leq 3$. We do this via pinpointing a relationship with the known class of $b$-matching games and completing the complexity classification on testing core non-emptiness for $b$-matching games. With respect to our application, we prove a number of complexity results on choosing, out of possibly"
+"---\nabstract: 'Digital communication has made the public discourse considerably more complex, and new actors and strategies have emerged as a result of this seismic shift. Aside from the often-studied interactions among individuals during opinion formation, which have been facilitated on a large scale by social media platforms, the changing role of traditional media and the emerging role of \u201cinfluencers\u201d are not well understood, and the implications of their engagement strategies arising from the incentive structure of the attention economy even less so. Here we propose a novel opinion dynamics model that accounts for these different roles, namely that media and influencers change their own positions on slower time scales than individuals, while influencers dynamically gain and lose followers. Numerical simulations show the importance of their relative influence in creating qualitatively different opinion formation dynamics: with influencers, fragmented but short-lived clusters emerge, which are then counteracted by more stable media positions. Mean-field approximations by partial differential equations reproduce this dynamic. Based on the mean-field model, we study how strategies of influencers to gain more followers can influence the overall opinion distribution. We show that moving towards extreme positions can be a beneficial strategy for influencers to gain followers. Finally, we"
+"---\nauthor:\n- Giorgio Nicoletti\n- Matteo Bruzzone\n- Samir Suweis\n- Marco Dal Maschio\n- Daniel Maria Busiello\ntitle: '[Adaptation maximizes information and minimizes dissipation across biological scales]{}'\n---\n\nSensing and adaptation mechanisms in biological systems span a wide range of temporal and spatial scales, from cellular to multi-cellular level, forming a basis for decision-making and the optimization of limited resources [@bialek_review; @signaling_review; @gnesotto2018broken; @nemenman2012information; @nakajima2015biologically; @whiteley2017progress; @perkins2009strategies; @koshland1982amplification]. Prominent examples include the modulation of flagellar motion operated by bacteria according to changes in the local nutrient concentration [@tu_chemotaxis; @tu2008nonequilibrium; @mattingly2021escherichia], the regulation of immune responses through feedback mechanisms [@cheong_tnf; @wajant2003tumor], and the maintenance of high sensitivity in varying environments for olfactory or visual sensing in mammalian neurons [@tu_tradeoff; @menini1999calcium; @kohn2007visual; @lesica2007adaptation; @benucci2013adaptation].\n\nIn the last decade, advances in experimental techniques fostered the quest for the core biochemical mechanisms governing information processing. Simultaneous recordings of hundreds of biological signals made it possible to infer distinctive features directly from data [@maxent1; @maxent2; @kurtz2015sparse; @tunstrom2013collective]. However, many approaches fall short of describing the connection between the underlying chemical processes and the observed behaviors [@nicoletti2021mutual; @nicoletti2022mutual; @de2010advantages; @nicoletti2022information]. As a step in this direction, a multitude of works focused on the architecture"
+"---\nabstract: 'This paper links sizes of model classes to the minimum lengths of their defining formulas, that is, to their description complexities. Limiting to models with a fixed domain of size n, we study description complexities with respect to the extension of propositional logic with the ability to count assignments. This logic, called GMLU, can alternatively be conceived as graded modal logic over Kripke models with the universal accessibility relation. While GMLU is expressively complete for defining multisets of assignments, we also investigate its fragments GMLU(d) that can count only up to the integer threshold d. We focus in particular on description complexities of equivalence classes of GMLU(d). We show that, in restriction to a poset of type realizations, the order of the equivalence classes based on size is identical to the order based on description complexities. This also demonstrates a monotone connection between Boltzmann entropies of model classes and description complexities. Furthermore, we characterize how the relation between domain size n and counting threshold d determines whether or not there exists a dominating class, which essentially means a model class with limit probability one. To obtain our results, we prove new estimates on r-associated Stirling numbers. As another"
+"---\nabstract: |\n    We introduce `MOSAIC`, a Python program for machine learning models. Our framework is developed with in mind accelerating machine learning studies through making implementing and testing arbitrary network architectures and data sets simpler, faster and less error-prone. `MOSAIC` features a full execution pipeline, from declaring the models, data and related hyperparameters within a simple configuration file, to the generation of ready-to-interpret figures and performance metrics. It also includes an advanced run management, stores the results within a database, and incorporates several run monitoring options. Through all these functionalities, the framework should provide a useful tool for researchers, engineers, and general practitioners of machine learning.\\\n    `MOSAIC` is available from the dedicated [PyPI](https://pypi.org/project/ml-mosaic/) repository.\nauthor:\n- |\n    Matt\u00e9o Papin matpapin0@gmail.com\\\n    \u00c9cole 42, 75017 Paris, France Yann Beaujeault-Taudi\u00e8re yann.beaujeault-taudiere@ijclab.in2p3.fr\\\n    Universit\u00e9 Paris-Saclay, CNRS/IN2P3, IJCLab, 91405 Orsay, France\\\n    Laboratoire Leprince-Ringuet (LLR), \u00c9cole polytechnique, CNRS/IN2P3, 91120 Palaiseau, France Fr\u00e9d\u00e9ric Magniette frederic.magniette@llr.in2p3.fr\\\n    Laboratoire Leprince-Ringuet (LLR), \u00c9cole polytechnique, CNRS/IN2P3, 91120 Palaiseau, France\nbibliography:\n- 'biblio.bib'\ntitle: '`MOSAIC`, a comparison framework for machine learning models'\n---\n\nOpen-source software, network optimization, classification, regression, Python, neural networks\n\nIntroduction\n============\n\nConceiving optimal machine learning models, especially with artificial neural networks, is known to be a complex art. Although substantial"
+"---\nabstract: 'Current multilingual semantic parsing (MSP) datasets are almost all collected by translating the utterances in the existing datasets from the resource-rich language to the target language. However, manual translation is costly. To reduce the translation effort, this paper proposes the first active learning procedure for MSP (AL-MSP). AL-MSP selects only a subset from the existing datasets to be translated. We also propose a novel selection method that prioritizes the examples diversifying the logical form structures with more lexical choices, and a novel hyperparameter tuning method that needs no extra annotation cost. Our experiments show that AL-MSP significantly reduces translation costs with ideal selection methods. Our selection method with proper hyperparameters yields better parsing performance than the other baselines on two multilingual datasets.'\nauthor:\n- |\n    Zhuang Li, Gholamreza Haffari\\\n    Openstream.AI\\\nbibliography:\n- 'anthology.bib'\n- 'custom.bib'\ntitle: Active Learning for Multilingual Semantic Parser\n---\n\n=1\n\nIntroduction {#sec:intro}\n============\n\nMultilingual semantic parsing converts multilingual natural language utterances into logical forms (LFs) using a single model. However, there is a severe data imbalance among the MSP datasets. Currently, most semantic parsing datasets are in English, while only a limited number of non-English datasets exist. To tackle the data imbalance issue, almost"
+"---\nabstract: 'Hybrid metal-dielectric structures, which combine the advantages of both metal and dielectric materials, support high-confined but low-loss magnetic and electric resonances under deliberate arrangements. However, their potential for enhancing magnetic emission has not been explored. Here, we study the simultaneous magnetic and electric Purcell enhancement supported by a hybrid structure consisting of a dielectric nanoring and a silver nanorod. Such a structure enables low Ohmic loss and highly-confined field under the mode hybridization of magnetic resonances on nanoring and electric resonances on nanorod in the optical communication band. So, the 60-fold magnetic Purcell enhancement and 45-fold electric Purcell enhancement can be achieved simultaneously with $>95\\%$ of the radiation transmitted to far field. The position of emitter has a several-ten-nanometer tolerance for sufficiently large Purcell enhancement, which brings convenience to experimental fabrications. Moreover, an array formed by this hybrid nanostructure can further enhance the magnetic Purcell factors. The findings provide a possibility to selectively excite the magnetic and electric emission in integrated photon circuits. It may also facilitate brighter magnetic emission sources and light-emitting metasurfaces in a simpler arrangement.'\nauthor:\n- Lingxiao Shan$^1$\n- 'Qi Liu$^{1,2}$'\n- Yun Ma$^1$\n- Yali Jia$^1$\n- Hai Lin$^1$\n- 'Guowei Lu$^{1,2,3,4}$'\n-"
+"---\nabstract: 'Detecting anomalies in multivariate time series (MTS) data plays an important role in many domains. The abnormal values could indicate events, medical abnormalities, cyber-attacks, or faulty devices which if left undetected could lead to significant loss of resources, capital, or human lives. In this paper, we propose a novel and innovative approach to anomaly detection called *Bayesian State-Space Anomaly Detection (`BSSAD`)*. The `BSSAD` consists of two modules: the neural network module and the Bayesian state-space module. The design of our approach combines the strength of Bayesian state-space algorithms in predicting the next state and the effectiveness of recurrent neural networks and autoencoders at understanding the relationship between the data to achieve high accuracy in detecting anomalies. The modular design of our approach allows flexibility in implementation with the option of changing the parameters of the Bayesian state-space models or swapping neural network algorithms to achieve different levels of performance. In particular, we focus on using Bayesian state-space models of particle filters and ensemble Kalman filters. We conducted extensive experiments on five different datasets. The experimental results show the superior performance of our model over baselines, achieving an F1-score greater than 0.95. In addition, we also propose using a"
+"---\nabstract: 'The stratified inclined duct (SID) sustains an exchange flow in a long, gently sloping duct as a model for continuously-forced density-stratified flows such as those found in estuaries. Experiments have shown that the emergence of interfacial waves and their transition to turbulence as the tilt angle is increased appears linked to a threshold in the exchange flow rate given by inviscid two-layer hydraulics. We uncover these hydraulic mechanisms with (i) recent direct numerical simulations (DNS) providing full flow data in the key flow regimes (Zhu & Atoufi *et al.*, [arXiv:2301.09773]{}, 2023), (ii) averaging these DNS into two layers, (iii) an inviscid two-layer shallow water and instability theory to diagnose interfacial wave behaviour and provide physical insight. The laminar flow is subcritical and stable throughout the duct and hydraulically controlled at the ends of the duct. As the tilt is increased, the flow becomes everywhere supercritical and unstable to long waves. An internal undular jump featuring stationary waves first appears near the centre of the duct, then leads to larger-amplitude travelling waves, and to stronger jumps, wave breaking and intermittent turbulence at the largest tilt angle. Long waves described by the (nonlinear) shallow water equation are locally interpreted as"
+"---\nabstract: 'We report our analysis results for the globular cluster (GC) NGC\u00a06341 (M92), as a millisecond pulsar (MSP) J1717$+$4308A has recently been reported found in this GC. The data used are from the Large Area Telescope onboard the [*Fermi Gamma-ray Space Telescope (Fermi)*]{}. We detect $\\gamma$-ray pulsations of the MSP at a $4.4\\sigma$ confidence level (the corresponding weighted H-test value is $\\sim$28.4). This MSP, the fourth $\\gamma$-ray pulsar found in a GC, does not have significant off-pulse emission and has $\\gamma$-ray luminosity and efficiency $1.3\\times10^{34}$ergs$^{-1}$ and 1.7% respectively. In order to have a clear view on the properties of the known GC $\\gamma$-ray MSPs, we re-analyze the [[*Fermi*]{}]{}\u00a0LAT data for the other three ones. These four MSPs share the properties of either having high $\\dot{E}$ ($\\sim 10^{36}$ergs$^{-1}$) or being in the GCs that contain only limited numbers of known MSPs. In addition, we find that PSRs\u00a0J1823$-$3021A and B1821$-$24, in NGC\u00a06624 and NGC\u00a06626 respectively, have detectable off-pulse $\\gamma$-ray emission and PSR J1835$-$3259B in NGC\u00a06652 does not. Using the obtained off-pulse spectra or spectral upper limits, we constrain the numbers of other MSPs in the four GCs. The results are consistent with the numbers of"
+"---\nabstract: |\n    Using the Shioda-Tate theorem and an adaptation of Silverman\u2019s specialization theorem, we reduce the specialization of Mordell-Weil ranks for abelian varieties over fields finitely generated over infinite finitely generated fields $k$ to the the specialization theorem for N\u00e9ron-Severi ranks recently proved by Ambrosi in positive characteristic. More precisely, we prove that after a blow-up of the base surface $S$, for all vertical curves $S_x$ of a fibration $S \\to U \\subseteq {{\\mathbf{P}}}^1_k$ with $x$ from the complement of a sparse subset of $|U|$, the Mordell-Weil rank of an abelian scheme over $S$ stays the same when restricted to $S_x$.\n\n    *Keywords:* specialization of Mordell-Weil ranks; abelian schemes over higher-dimensional bases; specialization of N\u00e9ron-Severi groups; rational points.\n\n    *2020 Mathematics* subject classification: 11G10 (primary); 11G05; 11G35\naddress: |\n    Lehrstuhl Mathematik\u00a0II (Computeralgebra)\\\n    Universit\u00e4t Bayreuth\\\n    Universit\u00e4tsstra\u00dfe 30\\\n    95440 Bayreuth, Germany\nauthor:\n- Timo Keller\ntitle: |\n    Specialization of Mordell-Weil ranks\\\n    of abelian schemes over surfaces to curves\n---\n\n[^1]\n\nIntroduction\n============\n\nMotivation and overview\n-----------------------\n\nSilverman\u2019s specialization theorem\u00a0[@Silverman1983] states that for a family ${\\mathscr{A}}$ of abelian varieties fibered over a curve $C$ over a number field $K$, the specialization homomorphism from the generic fiber ${\\mathscr{A}}(K(C))$ to a special fiber ${\\mathscr{A}}(\\kappa(x))$"
+"---\nabstract: 'Applying pressure around megabar is indispensable in the synthesis of high-temperature superconducting hydrides, such as H$_3$S and LaH$_{10}$. Stabilizing the high-pressure phase of hydride around ambient condition is a severe challenge. Based on the density-functional theory calculations, we give the first example that the structure of hydride CaBH$_5$ predicted above 280 GPa, can maintain its dynamical stability with pressure down to 1 GPa, by modulating the charge transfer from metal atoms to hydrogen atoms via the replacement of Ca with alkali metal atoms e.g. Cs, in which the \\[BH$_5$\\]$^{2-}$ anion shrinks along $c$ axis and expands in the $ab$ plane, experiencing an anisotropic virtual high pressure. This mechanism, namely charge transfer modulated virtual high pressure effect, plays a vital role in enhancing the structural stability and leading to the reemergence of ambient-pressure-forbidden \\[BH$_5$\\]$^{2-}$ anion around 1 GPa in CsBH$_5$. Moreover, we find that CsBH$_5$ is a strongly coupled superconductor, with transition temperature as high as 98 K, well above the liquid-nitrogen temperature. Our findings provide a novel mechanism to reduce the critical pressure required by hydrogen-rich compound without changing its crystal structure, and also shed light on searching ambient-pressure high-temperature superconductivity in metal borohydrides.'\nauthor:\n- Miao Gao"
+"---\nabstract: 'This paper presents a complementary approach to establish stability of finite receding horizon control with a terminal cost. First a new augmented stage cost is defined by rotating the terminal cost. Then a one-step optimisation problem is defined based on this augmented stage cost. It is shown that a slightly modified Model Predictive Control (MPC) algorithm is stable if the value function of the augmented one-step cost (OSVF) is a control Lyapunov function. The proposed stability condition is completely complementary to the existing terminal cost based MPC stability conditions in the sense that they are mutually excluded with each other. By using this approach, we are able to establish stability for MPC algorithms with zero terminal cost or even negative terminal cost as special cases. Combining this new approach with the existing MPC stability theory, we are able to significantly relax the stability requirement on MPC and extend the design space where stability are guaranteed. The proposed approach will help to further reduce the gap between stability theory and practical applications of MPC and other optimisation based control methods.'\nauthor:\n- |\n    Wen-Hua Chen\\\n    Department of Aeronautical and Automotive Engineering\\\n    Loughborough University\\\n    Loughborough LE11 3TU, U.K.\\\n    `w.chen@lboro.ac.uk`\\\n    Yunda"
+"---\nabstract: 'In this paper, we shall first introduce the Pascal determinantal array of order $k$ as a generalization of the standard Pascal array. We also present an algorithm to produce determinantal arrays of any order. Then we investigate geometric properties of these new determinantal arrays. As a by product, we give a proof of the correctness of our proposed algorithm. Then we give a geometric interpretation of the determinant of any $k$ by $k$ subarray of the Pascal array. Finally, we will give a generalization of Rahimpour\u2019s determinantal identity, using the above geometric interpretation.'\naddress:\n- ' Department of Computer Science, Faculty of Mathematical and Computer Sciences, Allameh Tabataba\u2019i University, Tehran, Iran.\\'\n- 'Ministry of Information and Communication Technology (ICT), Zanjan, Iran.'\nauthor:\n- 'H. Teimoori and H. Khodakarami'\ntitle: 'Pascal Determinantal Arrays and A Generalization of Rahimpour\u2019s Determinantal Identity'\n---\n\nIntroduction\n============\n\nIn [@m1], the first author and M. Bayat proved a conjecture by Rahimpour about determinants inside the Pascal array. After more investigations around the previous project, the second author of this paper made a generalization of the Rahimpour\u2019s determinantal identity. Here, we first introduce a new infinite class of squared arrays which are called the Pascal"
+"---\nauthor:\n- 'Anushree Datta$^{1,2,3}$'\n- 'M.J. Calder\u00f3n$^1$'\n- 'A. Camjayi$^{4,5}$'\n- 'E. Bascones$^1$'\ntitle: Heavy quasiparticles and cascades without symmetry breaking in twisted bilayer graphene\n---\n\nAbstract {#abstract .unnumbered}\n========\n\n[**Among the variety of correlated states exhibited by twisted bilayer graphene cascades in the spectroscopic properties and in the compressibility , twist angle and temperature to other effects. we show that the spectral weight associated the formation of local moments and heavy quasiparticles can explain the cascade without invoking symmetry breaking orders. The phenomena reproduced here include the cascade flow of spectral weight, the oscillations of remote band energies, and the asymmetric jumps of the inverse compressibility. We also predict a strong momentum differentiation in the incoherent spectral weight associated the fragile topology of .** ]{}\n\nIntroduction {#introduction .unnumbered}\n============\n\nA large variety of correlated states including insulating, superconducting and topological phases\u00a0[@CaoNat2018_1; @CaoNat2018_2; @LuNature2019; @SharpeScience2019; @SaitoNatPhys2019; @ChoiNatPhys2019; @XieNat2019; @JiangNature2019; @NuckollsNature2020; @WongNature2020; @ChoiNature2021; @WuNatMaterials2021; @StepanovPRL2021; @XieNature2021] have been detected in . Among these phenomena, scanning tunneling microscopy (STM) experiments find a strong doping dependent broadening of the density of states (DOS) with a spectral weight reorganization up to several tens of meV\u00a0[@KerelskyNat2019; @ChoiNatPhys2019; @JiangNature2019; @XieNat2019]. This reorganization happens"
+"---\nabstract: 'This work is concerned with fractional Gaussian fields, i.e. Gaussian fields whose covariance operator is given by the inverse fractional Laplacian $(-\\Delta)^{-s}$ (where, in particular, we include the case $s >1$). We define a lattice discretization of these fields and show that their scaling limits \u2013 with respect to the optimal Besov space topology (up to an endpoint case) \u2013 are the original continuous fields. As a byproduct, in dimension $d<2s$, we prove the convergence in distribution of the maximum of the fields. A key tool in the proof is a sharp error estimate for the natural finite difference scheme for $(-\\Delta)^s$ under minimal regularity assumptions, which is also of independent interest.'\naddress:\n- 'Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg, Department of Data Science, Chair for Dynamics, Control and Numerics (Alexander von Humboldt Professorship), Cauerstr. 11, 91058 Erlangen, Germany.'\n- 'Weizmann Institute of Science, Department of Mathematics, Rehovot 7610001, Israel.'\nauthor:\n- Nicola De Nitti\n- Florian Schweiger\nbibliography:\n- 'FGF-ref.bib'\ntitle: Scaling limits for fractional polyharmonic Gaussian fields\n---\n\nIntroduction {#sec:intro}\n============\n\n\\\n\\\n\nFractional Gaussian Fields {#s:introFGF}\n--------------------------\n\nFractional Gaussian fields form a natural one-parameter family of Gaussian interface models. For a fixed parameter $s\\ge0$, the $s$-fractional Gaussian field is"
+"---\nauthor:\n- 'Carlos Mora$^\\dagger$'\n- 'Jonathan Tammer Eweis-Labolle[^1]'\n- Tyler Johnson\n- Likith Gadde\n- 'Ramin Bostanabad[^2]'\nbibliography:\n- '01\\_Ref.bib'\ntitle: 'Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Sets'\n---\n\nIn many applications in engineering and sciences analysts have simultaneous access to multiple data sources. In such cases, the overall cost of acquiring information can be reduced via data fusion or multi-fidelity (MF) modeling where one leverages inexpensive low-fidelity (LF) sources to reduce the reliance on expensive high-fidelity (HF) data. In this paper, we employ neural networks (NNs) for data fusion in scenarios where data is very scarce and obtained from an arbitrary number of sources with varying levels of fidelity and cost. We introduce a unique NN architecture that converts MF modeling into a nonlinear manifold learning problem. Our NN architecture inversely learns non-trivial (e.g., non-additive and non-hierarchical) biases of the LF sources in an interpretable and visualizable manifold where each data source is encoded via a low-dimensional distribution. This probabilistic manifold quantifies model form uncertainties such that LF sources with small bias are encoded close to the HF source. Additionally, we endow the output of our NN with a parametric distribution"
+"---\nabstract: 'In this work we explore in detail the presence of scalar resonances in the $WW$ fusion process in the context of the LHC experiments working in the theoretical framework provided by Higgs effective field theories (HEFTs). While the phenomenology of vector resonances is reasonably understood in the framework of Weinberg sum-rules and unitarization studies, scalar resonances are a lot less constrained and, more importantly do depend on HEFT low-energy effective couplings different from the ones of vector resoances that are difficult to constrain experimentally. More specifically, unitarization techniques combined with the requirement of causality allows us to set nontrivial bounds on Higgs self-interactions. This is due to the need for considering coupled channels in the scalar case along the unitarization process. As a byproduct, we can gain some relevant information on the Higgs sector from $WW\\to WW$ elastic processes without needing to consider two-Higgs production.'\nauthor:\n- I\u00f1igo Asi\u00e1in\n- Dom\u00e8nec Espriu\n- Federico Mescia\nbibliography:\n- 'bibliography.bib'\ntitle: ' Introducing tools to test Higgs interactions via $WW$ scattering. II. The coupled channel formalism and scalar resonances '\n---\n\nIntroduction {#sec:intro}\n============\n\nIn a companion paper Ref. [@Asiain:2021lch], we highlighted the importance of the $W_LW_L$ scattering in investigating"
+"---\nabstract: |\n    In the practical business of asset management by investment trusts and the like, the general practice is to manage over the medium to long term owing to the burden of operations and increase in transaction costs with the increase in turnover ratio. However, when machine learning is used to construct a management model, the number of learning data decreases with the increase in the long-term time scale; this causes a decline in the learning precision. Accordingly, in this study, data augmentation was applied by the combined use of not only the time scales of the target tasks but also the learning data of shorter term time scales, demonstrating that degradation of the generalization performance can be inhibited even if the target tasks of machine learning have long-term time scales. Moreover, as an illustration of how this data augmentation can be applied, we conducted portfolio management in which machine learning of a multifactor model was done by an autoencoder and mispricing was used from the estimated theoretical values. The effectiveness could be confirmed in not only the stock market but also the FX market, and a general-purpose management model could be constructed in various financial markets.\n\n    Highlights {#highlights"
+"---\nabstract: 'Coherent optical manipulation of electronic bandstructures via Floquet Engineering is a promising means to control quantum systems on an ultrafast timescale. However, the ultrafast switching on/off of the driving field comes with questions regarding the limits of validity of the Floquet formalism, which is defined for an infinite periodic drive, and to what extent the transient changes can be driven adibatically. Experimentally addressing these questions has been difficult, in large part due to the absence of an established technique to measure coherent dynamics through the duration of the pulse. Here, using multidimensional coherent spectroscopy we explicitly excite, control, and probe a coherent superposition of excitons in the $K$ and $K^\\prime$ valleys in monolayer WS$_2$. With a circularly polarized, red-detuned, pump pulse, the degeneracy of the $K$ and $K^\\prime$ excitons can be lifted and the phase of the coherence rotated. We demonstrate phase rotations during the 100 fs driving pulse that exceed $\\pi$, and show that this can be described by a combination of the AC-Stark shift of excitons in one valley and Bloch-Siegert shift of excitons in the opposite valley. Despite showing a smooth evolution of the phase that directly follows the intensity envelope of the pump pulse,"
+"---\nabstract: 'In $\\R^d$, it is well-known that cumulants provide an alternative to moments that can achieve the same goals with numerous benefits such as lower variance estimators. In this paper we extend cumulants to reproducing kernel Hilbert spaces (RKHS) using tools from tensor algebras and show that they are computationally tractable by a kernel trick. These kernelized cumulants provide a new set of all-purpose statistics; the classical maximum mean discrepancy and Hilbert-Schmidt independence criterion arise as the degree one objects in our general construction. We argue both theoretically and empirically (on synthetic, environmental, and traffic data analysis) that going beyond degree one has several advantages and can be achieved with the same computational complexity and minimal overhead in our experiments.'\nauthor:\n- |\n    Patric Bonnier$^{1*}$ Harald Oberhauser $^{1}$ Zolt[\u00e1]{}n Szab[\u00f3]{}$^{2}$\\\n    $^{1}$Mathematical Institute, University of Oxford $^{2}$Department of Statistics, London School of Economics\\\n    `bonnier,oberhauser@maths.ox.ac.uk`\\\n    `z.szabo@lse.ac.uk`\nbibliography:\n- 'BIB/refs\\_smoothed.bib'\ntitle: 'Kernelized Cumulants: Beyond Kernel Mean Embeddings'\n---\n\n#### Keywords:\n\nkernel, cumulant, mean embedding, Hilbert-Schmidt independence criterion, kernel Lancaster interaction, kernel Streitberg interaction, maximum mean discrepancy, maximum variance discrepancy\n\nIntroduction {#sec:intro}\n============\n\nThe moments of a random variable are arguably the most popular all-purpose statistic. However, cumulants are often more favorable statistics"
+"---\nabstract: 'Stein thinning is a promising algorithm proposed by [@riabiz2020optimal] for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize the kernelized Stein discrepancy (KSD), which only requires the gradient of the log-target distribution, and is thus well-suited for Bayesian inference. The main advantages of Stein thinning are the automatic remove of the burn-in period, the correction of the bias introduced by recent MCMC algorithms, and the asymptotic properties of convergence towards the target distribution. Nevertheless, Stein thinning suffers from several empirical pathologies, which may result in poor approximations, as observed in the literature. In this article, we conduct a theoretical analysis of these pathologies, to clearly identify the mechanisms at stake, and suggest improved strategies. Then, we introduce the regularized Stein thinning algorithm to alleviate the identified pathologies. Finally, theoretical guarantees and extensive experiments show the high efficiency of the proposed algorithm. An implementation of regularized Stein thinning as the `kernax` library in python and JAX is available at <https://gitlab.com/drti/kernax>.'\nauthor:\n- |\n    **Cl\u00e9ment B\u00e9nard**$^{1}$ **Brian Staber**$^{1}$ **S\u00e9bastien Da Veiga**$^{2}$\\\n    $^1$ Safran Tech, Digital Sciences & Technologies, 78114 Magny-Les-Hameaux, France\\\n    $^2$ ENSAI, CREST, F-35000 Rennes, France\\\n    `{clement.benard, brian.staber}@{safrangroup.com}`\\\n    `sebastien.da-veiga@ensai.fr`\nbibliography:\n- 'bibfile.bib'"
+"---\nabstract: 'A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we learn an optimal model in training data, it could have better generalization performance in testing tasks. However, learn such model is not possible in standard machine learning frameworks as the distribution of the test data is unknown. To tackle this challenge, we propose a novel robust meta learning method, which is more robust to the image-based testing tasks which is unknown and has distribution shifts with traning tasks. Our robust meta learning method can provide robust optimal models even when data from each distribution are scarce. In experiments, we demonstrate that our algorithm not only has better generalization performance, but also robust to different unknown testing tasks.'\nauthor:\n- 'Penghao Jiang, Ke Xin, Zifeng Wang, Chunxi Li[^1]'\ntitle: '**Robust Meta Learning for Image based tasks** '\n---\n\nIntroduction\n============\n\nDeep learning achieves good performance in many realworld tasks such as visual recognition [@20; @11], but relies on large training data, which indicates it is unable to generalize to small data regimes. To overcome this limitation of deep learning, recently, researchers have explored metalearning [@15; @29] approaches, whose goal is to"
+"---\nauthor:\n- 'S. Zambito,[!!]{}'\n- 'M. Milanesio,'\n- 'T. Moretti,'\n- 'L. Paolozzi,'\n- 'M. Munker,'\n- 'R. Cardella,'\n- 'T. Kugathasan,'\n- 'F. Martinelli,'\n- 'A. Picardi,'\n- 'M. Elviretti,'\n- 'H. R\u00fccker,'\n- 'A. Trusch,'\n- 'F. Cadoux,'\n- 'R. Cardarelli[!!]{},'\n- 'S. D\u00e9bieux,'\n- 'Y. Favre,'\n- 'C. A. Fenoglio,'\n- 'D. Ferrere,'\n- 'S. Gonzalez-Sevilla,'\n- 'L. Iodice,'\n- 'R. Kotitsa,'\n- 'C. Magliocca,'\n- 'M. Nessi,'\n- 'A. Pizarro-Medina,'\n- 'J. Sabater Iglesias,'\n- 'J. Saidi,'\n- 'M. Vicente Barreto Pinto'\n- 'and G. Iacobucci'\nbibliography:\n- 'bibliography.bib'\ntitle: '20 ps Time Resolution with a Fully-Efficient Monolithic Silicon Pixel Detector without Internal Gain Layer'\n---\n\nIntroduction {#sec:intro}\n============\n\nMonolithic Active Pixel Sensors (MAPS)\u00a0[@peric], which contain the sensor in the same CMOS substrate utilised for the electronics, are particularly appealing since they offer all the advantages of industrial standard CMOS processing, avoiding the production complexity and high cost of the bump-bonded hybrid pixel sensors that are commonly used in particle-physics experiments. Today MAPS represent a mature technology that matches the performance of hybrid silicon pixel sensors. Indeed MAPS are already used in a large LHC experiment\u00a0[@alice].\n\nThe large pile-up of events expected during"
+"---\nabstract: 'While Chain-of-Thought (CoT) prompting boosts Language Models\u2019 (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka. *faithfulness*). We propose **Faithful CoT**, a reasoning framework involving two stages: **Translation** (Natural Language query $\\rightarrow$ symbolic reasoning chain) and **Problem Solving** (reasoning chain $\\rightarrow$ answer), using an LM and a deterministic solver respectively. This guarantees that the reasoning chain provides a faithful explanation of the final answer. Aside from interpretability, Faithful CoT also improves empirical performance: it outperforms standard CoT on 9 of 10 benchmarks from 4 diverse domains, with a relative accuracy gain of 6.3% on Math Word Problems (MWP), 3.4% on Planning, 5.5% on Multi-hop Question Answering (QA), and 21.4% on Relational Inference. Furthermore, with GPT-4 and Codex, it sets the new state-of-the-art few-shot performance on 7 datasets (with 95.0+ accuracy on 6 of them), showing a strong synergy between faithfulness and accuracy.[^1]'\nauthor:\n- |\n    Qing Lyu [^2] Shreya Havaldar Adam Stein Li Zhang\\\n    **[Delip Rao]{} **[Eric Wong]{} **[Marianna Apidianaki]{} **[Chris Callison-Burch]{}\\\n    University of Pennsylvania\\\n    `{lyuqing, shreyah, steinad, zharry, exwong,`\\\n    `marapi, ccb}@seas.upenn.edu, deliprao@gmail.com`********\nbibliography:\n- 'custom.bib'\ntitle: 'Faithful Chain-of-Thought Reasoning'\n---\n\n=1"
+"---\nabstract: 'Two-sample testing tests whether the distributions generating two samples are identical. We pose the two-sample testing problem in a new scenario where the sample measurements (or sample features) are inexpensive to access, but their group memberships (or labels) are costly. We devise the first *active sequential two-sample testing framework* that not only sequentially but also *actively queries* sample labels to address the problem. Our test statistic is a likelihood ratio where one likelihood is found by maximization over all class priors, and the other is given by a classification model. The classification model is adaptively updated and then used to guide an active query scheme called bimodal query to label sample features in the regions with high dependency between the feature variables and the label variables. The theoretical contributions in the paper include proof that our framework produces an *anytime-valid* $p$-value; and, under reachable conditions and a mild assumption, the framework asymptotically generates a minimum normalized log-likelihood ratio statistic that a passive query scheme can only achieve when the feature variable and the label variable have the highest dependence. Lastly, we provide a *query-switching (QS)* algorithm to decide when to switch from passive query to active query and"
+"---\nabstract: 'A highly accurate and efficient method to compute the expected values of the count, sum, and squared norm of the sum of the centre vectors of a random maximal sized collection of non-overlapping unit diameter disks touching a fixed unit-diameter disk is presented. This extends earlier work on R\u00e9nyi\u2019s parking problem \\[Magyar Tud. Akad. Mat. Kutat\u00f3 Int. K\u00f6zl. 3 (1-2), 1958, pp. 109-127\\]. Underlying the method is a splitting of the the problem conditional on the value of the first disk. This splitting is proven and then used to derive integral equations for the expectations. These equations take a lower block triangular form. They are solved using substitution and approximation of the integrals to very high accuracy using a polynomial approximation within the blocks.'\naddress:\n- 'Mathematical Sciences Institute, The Australian National University, Australian Capital Territory\u00a02601, <span style=\"font-variant:small-caps;\">Australia</span>.'\n- 'Mathematical Sciences Institute, The Australian National University, Australian Capital Territory\u00a02601, <span style=\"font-variant:small-caps;\">Australia</span>.'\n- 'School of Engineering, The Australian National University, Australian Capital Territory\u00a02601, <span style=\"font-variant:small-caps;\">Australia</span>.'\nauthor:\n- 'M.\u00a0Hegland'\n- 'C.J.\u00a0Burden'\n- 'Z.\u00a0Stachurski'\nbibliography:\n- 'eg.bib'\n- 'eg.bib'\ntitle: Computing expected moments of the R\u00e9nyi parking problem on the circle\n---\n\nIntroduction\n============\n\nConsider"
+"---\nabstract: 'We explore the magnetohydrodynamics of Dirac fermions in neutral graphene in the Corbino geometry. Based on the fully consistent hydrodynamic description derived from a microscopic framework and taking into account all peculiarities of graphene-specific hydrodynamics, we report the results of a comprehensive study of the interplay of viscosity, disorder-induced scattering, recombination, energy relaxation, and interface-induced dissipation. In the clean limit, magnetoresistance of a Corbino sample is determined by viscosity. Hence the Corbino geometry could be used to measure the viscosity coefficient in neutral graphene.'\nauthor:\n- Vanessa Gall\n- 'Boris N. Narozhny'\n- 'Igor V. Gornyi'\nbibliography:\n- 'hydro-refs.bib'\n- 'refs-books.bib'\ntitle: Corbino magnetoresistance in neutral graphene\n---\n\nTransport measurements remain one of the most common experimental tools in condensed matter physics. Having dramatically evolved past the original task of establishing bulk material characteristics such as electrical and thermal conductivities, modern experiments often involve samples that are tailor-made to target particular properties or behavior.\n\nIn recent years considerable efforts have been devoted to uncovering the collective or hydrodynamic flows of charge carriers in ultraclean materials as predicted theoretically [@pg; @me0; @luc; @rev]. Several dedicated experiments focused on answering two major questions: is the observed electronic flow really hydrodynamic"
+"---\nabstract: '\\[S U M M A R Y\\] Self-supervised auto-encoders have emerged as a successful framework for representation learning in computer vision and natural language processing in recent years, However, their application to graph data has been met with limited performance due to the non-Euclidean and complex structure of graphs in comparison to images or text, as well as the limitations of conventional auto-encoder architectures. In this paper, we investigate factors impacting the performance of auto-encoders on graph data and propose a novel auto-encoder model for graph representation learning. Our model incorporates a hierarchical adaptive masking mechanism to incrementally increase the difficulty of training in order to mimic the process of human cognitive learning, and a trainable corruption scheme to enhance the robustness of learned representations. Through extensive experimentation on ten benchmark datasets, we demonstrate the superiority of our proposed method over state-of-the-art graph representation learning models.'\naddress: 'College of Computer Science and Technology, Jilin University, Changchun, 130000, China'\nauthor:\n- Chengyu Sun\n- Liang Hu\n- Hongtu Li\n- Shuai Li\n- Tuohang Li\n- Ling Chi\nbibliography:\n- 'myrefs.bib'\ntitle: ' HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruption'\n---\n\ngraph representation learning ,generative"
+"---\nabstract: 'We establish sharp global regularity of a class of multilinear oscillatory integral operators that are associated to nonlinear dispersive equations with both Banach and quasi-Banach target spaces. As a consequence we also prove the (local in time) continuous dependence on the initial data for solutions of a large class of coupled systems of dispersive partial differential equations.'\naddress:\n- 'A.\u00a0Bergfeldt, Department of Mathematics, Uppsala University, SE-751 06 Uppsala, Sweden'\n- 'S.\u00a0Rodr\u00edguez-L\u00f3pez, Department of Mathematics, Stockholm University, SE-106 91 Stockholm, Sweden'\n- 'D.\u00a0Rule, Department of Mathematics, Link\u00f6ping University, SE-581 83 Link\u00f6ping, Sweden'\n- 'W.\u00a0Staubach, Department of Mathematics, Uppsala University, SE-751 06 Uppsala, Sweden'\nauthor:\n- Aksel Bergfeldt\n- 'Salvador Rodr\u00edguez-L\u00f3pez'\n- David Rule\n- Wolfgang Staubach\ntitle: Multilinear oscillatory integrals and estimates for coupled systems of dispersive PDEs\n---\n\n[^1]\n\nIntroduction {#sec:intro}\n============\n\nIn this paper, we consider the regularity of multilinear oscillatory integral operators (multilinear OIOs for short) that are associated to nonlinear dispersive equations in the realm of Banach and quasi-Banach function spaces. Examples include non-linear water-wave and capillary wave equations, nonlinear wave and Klein\u2013Gordon equations, the nonlinear Schr\u00f6dinger equations, the Korteweg\u2013deVries-type equations, and higher order nonlinear dispersive equations. To achieve this, we"
+"---\nabstract: 'Variational quantum algorithms represent a promising approach to quantum machine learning where classical neural networks are replaced by parametrized quantum circuits. However, both approaches suffer from a clear limitation, that is a lack of interpretability. Here, we present a variational method to quantize projective simulation (PS), a reinforcement learning model aimed at interpretable artificial intelligence. Decision making in PS is modeled as a random walk on a graph describing the agent\u2019s memory. To implement the quantized model, we consider quantum walks of single photons in a lattice of tunable Mach-Zehnder interferometers trained via variational algorithms. Using an example from transfer learning, we show that the quantized PS model can exploit quantum interference to acquire capabilities beyond those of its classical counterpart. Finally, we discuss the role of quantum interference for training and tracing the decision making process, paving the way for realizations of interpretable quantum learning agents.'\nauthor:\n- Fulvio Flamini\n- Marius Krumm\n- 'Lukas J. Fiderer'\n- Thomas M\u00fcller\n- 'Hans J. Briegel'\nbibliography:\n- 'qPSrefList.bib'\ntitle: 'Towards interpretable quantum machine learning via single-photon quantum walks'\n---\n\n[^1]\n\n[^2]\n\nIntroduction\n============\n\nClassical machine learning methods are revolutionizing science and technology, with applications ranging from drug discovery"
+"---\nabstract: 'We consider the problem of entanglement detection in the presence of faulty, potentially malicious detectors. A common\u2014and, as of yet, the only\u2014approach to this problem is to perform a Bell test in order to identify nonlocality of the measured entangled state. However, there are two significant drawbacks in this approach: the requirement to exceed a critical, and often high, detection efficiency, and much lower noise tolerance. In this paper, we propose an alternative approach to this problem, which is resilient to the detection loophole and is based on the standard tool of entanglement witness. We discuss how the two main techniques to detection losses, namely the discard and assignment strategies, apply to entanglement witnessing. We demonstrate using the example of a two-qubit Bell state that the critical detection efficiency can be significantly reduced compared to the Bell test approach.'\naddress:\n- '$^1$ International Centre for Theory of Quantum Technologies (ICTQT), University of Gdansk, 80-309 Gda\u0144sk, Poland'\n- '$^2$ Heinrich Heine University D\u00fcsseldorf, Universit\u00e4tsstra\u00dfe 1, 40225 D\u00fcsseldorf, Germany'\n- '$^3$ Institute for Theoretical Physics, University of Cologne, Germany'\n- '$^4$ Department of Computer Science, Technical University of Darmstadt, Darmstadt, 64289 Germany'\nauthor:\n- 'Giuseppe Viola$^1$, Nikolai Miklin$^{1,2}$, Mariami Gachechiladze$^{3,4}$,"
+"---\nabstract: 'The nature of dark matter (DM) remains one of the unsolved mysteries of modern physics. An intriguing possibility is to assume that DM consists of ultralight bosonic particles in the Bose-Einstein condensate (BEC) state. We study stationary DM structures by using the system of the Gross-Pitaevskii and Poisson equations, including the effective temperature effect with parameters chosen to describe the Milky Way galaxy. We have investigated DM structure with BEC core and isothermal envelope. We compare the spherically symmetric and vortex core states, which allows us to analyze the impact of the core vorticity on the halo density, velocity distribution, and, therefore, its gravitational field. Gravitational field calculation is done in the gravitoelectromagnetism approach to include the impact of the core rotation, which induces a gravimagnetic field. As result, the halo with a vortex core is characterized by smaller orbital velocity in the galactic disk region in comparison with the non-rotating halo. It is found that the core vorticity produces gravimagnetic perturbation of celestial body dynamics, which can modify the circular trajectories.'\nauthor:\n- 'K. Korshynska'\n- 'Y.M. Bidasyuk'\n- 'E.V. Gorbar'\n- Junji Jia\n- 'A.I. Yakimenko'\nbibliography:\n- 'Bibl.bib'\ntitle: Dynamical galactic effects induced by stable"
+"---\nabstract: 'Silicon is indispensable in semiconductor industry. Understanding its high-temperature thermodynamic properties is essential both for theory and applications. However, first-principle description of high-temperature thermodynamic properties of silicon (thermal expansion coefficient and specific heat) is still incomplete. Strong deviation of its specific heat at high temperatures from the Dulong-Petit law suggests substantial contribution of anharmonicity effects. We demonstrate, that anharmonicity is mostly due to two transverse phonon modes, propagating in (111) and (100) directions, and can be quantitatively described with formation of the certain type of nanostructured planar defects of the crystal structure. Calculation of these defects\u2019 formation energy enabled us to determine their input into the specific heat and thermal expansion coefficient. This contribution turns out to be significantly greater than the one calculated in quasi-harmonic approximation.'\nauthor:\n- 'M. V. Kondrin'\n- 'Y.B. Lebed'\n- 'V.V. Brazhkin'\ntitle: 'Planar defects as a way to account for explicit anharmonicity in high temperature thermodynamic properties of silicon.'\n---\n\nIntroduction\n============\n\nSilicon is a widely used semiconductor with immense impact in industry. Understanding its high-temperature thermodynamic properties is necessary both for theory and applications. Also, due to its archetypal diamond structure, silicon is among the touchstones for testing new theories."
+"---\nabstract: 'Data sharing partnerships are increasingly an imperative for research institutions and, at the same time, a challenge for established models of data governance and ethical research oversight. We analyse four cases of data partnership involving academic institutions and examine the role afforded to the research partner in negotiating the relationship between risk, value, trust and ethics. Within this terrain, far from being a restraint on financialisation, the instrumentation of ethics forms part of the wider mobilisation of infrastructure for the realisation of profit in the big data economy. Under what we term \u2018combinatorial data governance\u2019 academic structures for the management of research ethics are instrumentalised as organisational functions that serve to mitigate reputational damage and societal distrust. In the alternative model of \u2018experimental data governance\u2019 researchers propose frameworks and instruments for the rethinking of data ethics and the risks associated with it \u2014 a model that is promising but limited in its practical application.'\nauthor:\n- Tsvetelina Hristova\n- Liam Magee\n- Emma Kearney\ndate: January 2023\ntitle: 'Academic Institutions in Multilateral Data Governance: Emerging Arrangements for Negotiating Risk, Value and Ethics in the Big Data Economy'\n---\n\n[ ***Keywords\u2014*** Big Data, Data Governance, Data Partnerships, Data Ecologies,"
+"---\nabstract: 'Human agency and autonomy have always been fundamental concepts in HCI. New developments, including ubiquitous AI and the growing integration of technologies into our lives, make these issues ever pressing, as technologies increase their ability to influence our behaviours and values. However, in HCI understandings of autonomy and agency remain ambiguous. Both concepts are used to describe a wide range of phenomena pertaining to sense-of-control, material independence, and identity. It is unclear to what degree these understandings are compatible, and how they support the development of research programs and practical interventions. We address this by reviewing 30 years of HCI research on autonomy and agency to identify current understandings, open issues, and future directions. From this analysis, we identify ethical issues, and outline key themes to guide future work. We also articulate avenues for advancing clarity and specificity around these concepts, and for coordinating integrative work across different HCI communities.'\nauthor:\n- Daniel Bennett\n- Oussama Metatla\n- Anne Roudaut\n- 'Elisa D. Mekler'\nbibliography:\n- 'references.bib'\n- 'additional\\_refs.bib'\n- 'corpus.bib'\ntitle: 'How does HCI Understand Human Agency and Autonomy?'\n---\n\n&lt;ccs2012&gt; &lt;concept&gt; &lt;concept\\_id&gt;10003120.10003121.10003126&lt;/concept\\_id&gt; &lt;concept\\_desc&gt;Human-centered computing\u00a0HCI theory, concepts and models&lt;/concept\\_desc&gt; &lt;concept\\_significance&gt;500&lt;/concept\\_significance&gt; &lt;/concept&gt; &lt;/ccs2012&gt;\n\nIntroduction\n============\n\nAutonomy and"
+"---\nabstract: 'Distributed stochastic optimization has drawn great attention recently due to its effectiveness in solving large-scale machine learning problems. Though numerous algorithms have been proposed and successfully applied to general practical problems, their theoretical guarantees mainly rely on certain boundedness conditions on the stochastic gradients, varying from uniform boundedness to the relaxed growth condition. In addition, how to characterize the data heterogeneity among the agents and its impacts on the algorithmic performance remains challenging. In light of such motivations, we revisit the classical Federated Averaging (FedAvg) algorithm [@mcmahan2017communication] as well as the more recent SCAFFOLD method [@karimireddy2020scaffold] for solving the distributed stochastic optimization problem and establish the convergence results under only a mild variance condition on the stochastic gradients for smooth nonconvex objective functions. Almost sure convergence to a stationary point is also established under the condition. Moreover, we discuss a more informative measurement for data heterogeneity as well as its implications.'\nauthor:\n- 'Kun Huang, Xiao Li, , and Shi Pu, [^1] [^2]'\nbibliography:\n- 'references\\_all.bib'\ntitle: Distributed Stochastic Optimization under a General Variance Condition\n---\n\nDistributed Optimization, Stochastic Optimization, Nonconvex Optimization\n\nIntroduction {#sec:introduction}\n============\n\nconsider solving the following optimization problem by a group of agents $[n]:={\\{1,2,\\cdots, n\\}}$:"
+"---\nabstract: 'Quantum systems driven by a time-periodic field are a platform of condensed matter physics where effective (quasi)stationary states, termed \u201cFloquet states\u201d, can emerge with external-field-dressed quasiparticles during driving. They appear, for example, as a prethermal intermediate state in isolated driven quantum systems or as a nonequilibrium steady state in driven open quantum systems coupled to environment. Floquet states may have various intriguing physical properties, some of which can be drastically different from those of the original undriven systems in equilibrium. In this article, we review fundamental aspects of Floquet states, and discuss recent topics and applications of Floquet states in condensed matter physics.'\nauthor:\n- Naoto Tsuji\nbibliography:\n- 'ref.bib'\ntitle:  Floquet States \n---\n\nKey objectives\n==============\n\n-   Introduce Floquet states in periodically driven quantum systems.\n\n-   Describe the basic aspects of the Floquet theory for isolated and open quantum systems.\n\n-   Discuss several examples of Floquet states, including ac Wannier-Stark effects and Floquet topological phases.\n\n-   Review experimental observations of Floquet states in solids and cold-atom systems.\n\nIntroduction\n============\n\nDriving quantum systems far from equilibrium provides various possibilities to control quantum states and realize new phases of matter that are otherwise inaccessible within thermal equilibrium. The interest"
+"---\nauthor:\n- 'James A. C. Young'\nbibliography:\n- 'references.bib'\ndate: Summer 2021\nnocite: '[@*]'\ntitle: On the Cauchy Integral and Jump Decomposition\n---\n\nIntroduction {#introduction .unnumbered}\n============\n\nPreface {#preface .unnumbered}\n-------\n\nThe purpose of these notes is to introduce the reader to the Cauchy type integral and the Sokhotski formulae (also know as the Jump decomposition, or simply the Jump problem). The article is split into two main chapters. The first is based on the contents of the first few sections of the book \u201cBoundary Value Problems\" by F.D. Gakhov [@gakhov2014boundary]. The style of presentation is geared towards readers who have a background at the level of a first course in complex variables, and are comfortable with basic arguments in analysis. One of the goals of the text is to be expository, and for that reason nearly all proofs will contain thoroughly worked out arguments and explanations. After establishing the basic ideas in the first chapter, we will skip past much rigorous training to (informally) discuss some open questions and advancements in the field lying on the fringe of our understanding in the second chapter. We will see how many disciplines work together to tell a richer story, some"
+"---\nabstract: 'Hypergraph clustering is a basic algorithmic primitive for analyzing complex datasets and systems characterized by multiway interactions, such as group email conversations, groups of co-purchased retail products, and co-authorship data. This paper presents a practical $O(\\log n)$-approximation algorithm for a broad class of hypergraph *ratio cut* clustering objectives. This includes objectives involving generalized hypergraph cut functions, which allow a user to penalize cut hyperedges differently depending on the number of nodes in each cluster. Our method is a generalization of the cut-matching framework for graph ratio cuts, and relies only on solving maximum s-t flow problems in a special reduced graph. It is significantly faster than existing hypergraph ratio cut algorithms, while also solving a more general problem. In numerical experiments on various types of hypergraphs, we show that it quickly finds ratio cut solutions within a small factor of optimality.'\nauthor:\n- Nate Veldt\nbibliography:\n- 'arxiv-full-updated.bib'\ntitle: 'Cut-matching Games for Generalized Hypergraph Ratio Cuts'\n---\n\nIntroduction\n============\n\nGraphs are a popular way to model social networks and other datasets characterized by pairwise interactions, but there has been a growing realization that many complex systems of interactions are characterized by *multiway* relationships that cannot be directly encoded"
+"---\nauthor:\n- 'Sara Venturini[^1] , Andrea Cristofari[^2], Francesco Rinaldi[^3], Francesco Tudisco[^4]'\nbibliography:\n- 'main.bib'\ntitle: |\n    Laplacian-based Semi-Supervised Learning in\\\n    Multilayer Hypergraphs by Coordinate Descent\n---\n\n**Abstract.** Graph Semi-Supervised learning is an important data analysis tool, where given a graph and a set of labeled nodes, the aim is to infer the labels to the remaining unlabeled nodes. In this paper, we start by considering an optimization-based formulation of the problem for an undirected graph, and then we extend this formulation to multilayer hypergraphs. We solve the problem using different coordinate descent approaches and compare the results with the ones obtained by the classic gradient descent method. Experiments on synthetic and real-world datasets show the potential of using coordinate descent methods with suitable selection rules.\\\n**Key words.** semi-supervised learning, coordinate methods, multilayer hypergraphs.\\\n\nIntroduction\n============\n\nConsider a finite, weighted and undirected graph $G = (V,E,w)$, with node set $V$, edge set $E \\subseteq V\\times V$ and edge-weight function $w$ such that $w(e)=w(uv)>0$ if $e = (u,v)\\in E$ and $0$ otherwise. Suppose each node $u\\in V$ can be assigned to one of $m$ classes, or labels, $C_1,\\dots,C_m$. In graph-based Semi-Supervised Learning (SSL), given a graph $G$ and an observation set"
+"---\nabstract: 'The parabolic Airy line ensemble $\\mathfrak A$ is a central limit object in the KPZ universality class and related areas. On any compact set $K = \\{1, \\dots, k\\} \\times [a, a + t]$, the law of the recentered ensemble $\\mathfrak A - \\mathfrak A(a)$ has a density $X_K$ with respect to the law of $k$ independent Brownian motions. We show that $$X_K(f) = \\exp \\left(-\\textsf{S}(f) + o(\\textsf{S}(f))\\right)$$ where $\\textsf{S}$ is an explicit, tractable, non-negative function of $f$. We use this formula to show that $X_K$ is bounded above by a $K$-dependent constant, give a sharp estimate on the size of the set where $X_K < \\epsilon$ as $\\epsilon \\to 0$, and prove a large deviation principle for $\\mathfrak A$. We also give density estimates that take into account the relative positions of the Airy lines, and prove sharp two-point tail bounds that are stronger than those for Brownian motion. These estimates are a key input in the classification of geodesic networks in the directed landscape [@dauvergne2023geodesic]. The paper is essentially self-contained, requiring only tail bounds on the Airy point process and the Brownian Gibbs property as inputs.'\nauthor:\n- Duncan Dauvergne\nbibliography:\n- 'bibliography.bib'\ntitle: Wiener densities"
+"---\nabstract: 'A bootstrap approach to the effective action in quantum field theory is discussed which entails the invariance under quantum fluctuations of the effective action describing physical reality (via the S-matrix).'\n---\n\n[0.9cm On selfconsistency in quantum field theory\\\n]{}\n\n[K. Scharnhorst ]{}\\\n[ Vrije Universiteit Amsterdam, Faculty of Sciences, Department of Physics and Astronomy, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands]{}\\\n\n\\[introduction\\]Introduction\n============================\n\nThese are times of contemplation and reorientation in quantum field theory. With the experimental detection of the Higgs boson in 2012 finally the finishing stone of the Standard Model of elementary particle physics [@1992donoghue] surfaced. On the theoretical side, the Standard Model is based on the concept of renormalized local quantum field theory. The confidence in this concept originally and primarily relies on the extraordinary success of the centerpiece of the Standard Model, quantum electrodynamics (QED), which exhibits an impressive agreement between theory and experiment (Cf., e.g., [@2006mohr], for more comprehensive reviews see [@1990kinoshita].). The successful application of renormalized local quantum field theory to the other components of the Standard Model, the electroweak theory and to quantum chromodynamics (QCD), have further advanced this confidence. On the other hand, many practicing quantum field theorists are"
+"---\nabstract: 'Many practical applications of robotics require systems that can operate safely despite uncertainty. In the context of motion planning, two types of uncertainty are particularly important when planning safe robot trajectories. The first is environmental uncertainty \u2014 uncertainty in the locations of nearby obstacles, stemming from sensor noise or (in the case of obstacles\u2019 future locations) prediction error. The second class of uncertainty is uncertainty in the robots own state, typically caused by tracking or estimation error. To achieve high levels of safety, it is necessary for robots to consider both of these sources of uncertainty. In this paper, we propose a risk-bounded trajectory optimization algorithm, known as Sequential Convex Optimization with Risk Optimization (SCORA), to solve chance-constrained motion planning problems despite both environmental uncertainty and tracking error. Through experiments in simulation, we demonstrate that SCORA significantly outperforms state-of-the-art risk-aware motion planners both in planning time and in the safety of the resulting trajectories.'\nauthor:\n- \nbibliography:\n- 'references.bib'\ntitle: 'Chance-Constrained Trajectory Optimization for High-DOF Robots in Uncertain Environments'\n---\n\nIntroduction\n============\n\nFor most robots, developed in the cradle of a well-controlled, carefully structured simulation or laboratory environment, primary challenge posed by deployment in the outside world is"
+"---\nabstract: 'In this paper we give a physically transparent picture of singular-continuous spectrum in disordered systems which possess a non-ergodic extended phase. We present a simple model of identically and independently distributed level spacing in the spectrum of local density of states and show how a fat tail appears in this distribution at the broad distribution of eigenfunction amplitudes. For the model with a power-law local spacing distribution we derive the correlation function $K(\\omega)$ of the local density of states and show that depending on the relation between the eigenfunction fractal dimension $D_{2}$ and the spectral fractal dimension $D_{s}$ encoded in the power-law spacing distribution, a singular continuous spectrum of a random Cantor set or that of an isolated mini-band may appear. In the limit of an infinite number of degrees of freedom the function $K(\\omega)$ in the non-ergodic extended phase is singular at $\\omega=0$ with the branch-cut singularity for the case of a random Cantor set and with the $\\delta$-function singularity for the case of an isolated mini-band. For an absolutely continuous spectrum $K(\\omega)$ tends to a finite limit as $\\omega\\rightarrow 0$. For an arbitrary local spacing distribution function we formulated a criterion of fractality of local spectrum"
+"---\nabstract: 'Model Predictive Control (MPC) is attracting tremendous attention in the autonomous driving task as a powerful control technique. The success of an MPC controller strongly depends on an accurate internal dynamics model. However, the static parameters, usually learned by system identification, often fail to adapt to both internal and external perturbations in real-world scenarios. In this paper, we firstly (1) reformulate the problem as a Partially Observed Markov Decision Process (POMDP) that absorbs the uncertainties into observations and maintains Markov property into hidden states; and (2) learn a recurrent policy continually adapting the parameters of the dynamics model via Recurrent Reinforcement Learning (RRL) for optimal and adaptive control; and (3) finally evaluate the proposed algorithm (referred as $\\textit{MPC-RRL}$) in CARLA simulator and leading to robust behaviours under a wide range of perturbations.'\nauthor:\n- 'Yuan Zhang$^{1}$, Chuxuan Li$^{2}$, Joschka Boedecker$^{1}$ and Guyue Zhou$^{2}$ [^1] [^2] [^3]'\nbibliography:\n- 'ref.bib'\ntitle: ' **Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Faster Adaption in Autonomous Driving** '\n---\n\nReinforcement Learning, Model Learning for Control, Robust/Adaptive Control\n\nINTRODUCTION {#sec:introduction}\n============\n\nModel Predictive Control (MPC) has become the primary control method for enormous fields, e.g. autonomous driving [@reiteremcore] and robotics [@song2020learning]."
+"---\nabstract: 'We investigate the tunneling phenomenon of particles through the horizon of a magnetized Ernst-like black hole. We employ the modified Lagrangian equation with the extended uncertainty principle for this black hole. We determine a tunneling rate and the related Hawking temperature for this black hole by using the WKB approach in the field equation. In addition, we examine the graph behavior of the Hawking temperature in relation to the black hole event horizon. We explore the stability analysis of this black hole by taking into account the impact of quantum gravity on Hawking temperatures. The temperature for a magnetized Ernst-like black hole rises as the correction parameter is decreased. Moreover, we analyze the thermodynamics quantities such as Hawking temperature, heat capacity and Bekenstein entropy by using the different approach. We obtain the corrected entropy to study the impact of logarithmic corrections on the different thermodynamic quantities. It is shown that these correction terms makes the system stable under thermal fluctuations.'\nauthor:\n- Riasat Ali\n- Zunaira Akhtar\n- Kazuharu Bamba\n- 'M. Umar Khan'\ntitle: 'Tunneling and thermodynamics evolution of the magnetized Ernst-like black hole'\n---\n\nIntroduction\n============\n\nThe black holes (BHs) is a real physical object, due"
+"---\nauthor:\n- 'Ying Yu$^{1}$, Xun-Wang Yan$^{2}$, Fengjie Ma$^{3}$, Miao Gao$^{1,4}$[^1], and Zhong-Yi Lu$^{5}$'\ntitle: 'Cubic C$_{20}$: An intrinsic superconducting carbon allotrope'\n---\n\nThe studies of carbon-based superconductor can be traced back to the 1960s. Superconductivity was first discovered in graphite intercalation compounds (GICs) below 1 K, such as KC$_8$ [@Hannay-PRL14]. Under ambient pressure, the highest $T_c$ for GICs is 11.5 K in CaC$_6$ [@Emery-PRL95]. Graphene, the two-dimensional form of graphite, has gapless Dirac bands. Superconductivity in doped graphene has been extensively investigated [@Uchoa-PRL98; @Profeta-NP8; @Chapman-SR6]. Recently, correlated insulating states were observed in twisted bilayer graphene, and superconductivity emerges at 1.7 K after electrostatic doping [@Cao-Nature556-1; @Cao-Nature556-2]. For twisted trilayer graphene, rich phase diagram and better tunability of electric field were realized, compared with the bilayer case [@Zhu-PRL125; @Park-N590], providing a fascinating playground to explore the interplay between correlated states and superconductivity. Insulator to superconductor transition was reported in boron-doped diamond [@Ekimov-Nature428], in which the $T_c$ exhibits a positive dependence on the proportion of boron that incorporated into diamond [@Takano-DRM16]. It was found that the onset $T_c$ of 27% boron-doped Q-carbon is 55 K [@Bhaumik-AN11]. Fullerene shows superconductivity at 18 K, after the potassium intercalation with stoichiometry of K$_3$C$_{60}$ [@Hebard-Nature350]."
+"---\nabstract: 'Systems with 1-bit quantization and oversampling are promising for the Internet of Things (IoT) devices in order to reduce the power consumption of the analog-to-digital-converters. The novel time-instance zero-crossing (TI ZX) modulation is a promising approach for this kind of channels but existing studies rely on optimization problems with high computational complexity and delay. In this work, we propose a practical waveform design based on the established TI ZX modulation for a multiuser multi-input multi-output (MIMO) downlink scenario with 1-bit quantization and temporal oversampling at the receivers. In this sense, the proposed temporal transmit signals are constructed by concatenating segments of coefficients which convey the information into the time-instances of zero-crossings according to the TI ZX mapping rules. The proposed waveform design is compared with other methods from the literature. The methods are compared in terms of bit error rate and normalized power spectral density. Numerical results show that the proposed technique is suitable for multiuser MIMO system with 1-bit quantization while tolerating some small amount of out-of-band radiation.'\naddress: |\n    Centre for Telecommunications Studies,\\\n    Pontifical Catholic University of Rio de Janeiro,\\\n    Rio de Janeiro, Brazil 22453-900\\\n    Email: diana;lukas.landau;delamare@cetuc.puc-rio.br\nbibliography:\n- 'ref.bib'\ntitle:  \n---\n\nZero-crossing precoding, oversampling, Moore"
+"---\nabstract: 'We construct a $q$-analogue of truncated version of symmetric multiple zeta values which satisfies the double shuffle relation. Using it, we define a $q$-analogue of symmetric multiple zeta values and see that it satisfies many of the same relations as symmetric multiple zeta values, which are the inverse relation and a part of the double shuffle relation and the Ohno-type relation.'\naddress: 'Department of Mathematics, Institute of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan'\nauthor:\n- Yoshihiro Takeyama\ntitle: 'A $q$-analogue of symmetric multiple zeta value'\n---\n\n[^1]\n\nIntroduction\n============\n\nThe *multiple zeta value* (MZV) is the real value defined by $$\\begin{aligned}\n  \\zeta({\\mathbf{k}})=\\sum_{0<m_{1}<\\cdots <m_{r}}\n  \\frac{1}{m_{1}^{k_{1}} \\cdots m_{r}^{k_{r}}}\n  \\label{eq:intro-MZV}\\end{aligned}$$ for a tuple of positive integers ${\\mathbf{k}}=(k_{1}, \\ldots , k_{r})$ with $k_{r}\\ge 2$. We set $\\zeta(\\varnothing)=1$ and regard it as a MZV. We denote by $\\mathcal{Z}$ the $\\mathbb{Q}$-linear subspace of $\\mathbb{R}$ spanned by the MZVs. It is known that $\\mathcal{Z}$ forms a $\\mathbb{Q}$-algebra with respect to the usual multiplication on $\\mathbb{R}$.\n\nKaneko and Zagier introduced two kinds of variants of MZVs called finite multiple zeta values and symmetric multiple zeta values (see, e.g., [@Kaneko]). The symmetric multiple zeta value (SMZV) is defined as an element of"
+"---\nabstract: 'Joint machine learning models that allow synthesizing and classifying data often offer uneven performance between those tasks or are unstable to train. In this work, we depart from a set of empirical observations that indicate the usefulness of internal representations built by contemporary deep diffusion-based generative models not only for generating but also predicting. We then propose to extend the vanilla diffusion model with a classifier that allows for stable joint end-to-end training with shared parameterization between those objectives. The resulting joint diffusion model outperforms recent state-of-the-art hybrid methods in terms of both classification and generation quality on all evaluated benchmarks. On top of our joint training approach, we present how we can directly benefit from shared generative and discriminative representations by introducing a method for visual counterfactual explanations.'\nauthor:\n- Kamil Deja\n- Tomasz Trzcinski\n- 'Jakub M. Tomczak'\nbibliography:\n- 'bibliography.bib'\ntitle: |\n    Learning Data Representations\\\n    with Joint Diffusion Models\n---\n\nIntroduction {#sec:Introduction}\n============\n\nTraining a single machine learning model that can jointly synthesize new data as well as to make predictions about input samples remains a long-standing goal of machine learning\u00a0[@jebara2012machine; @lasserre2006principled]. Shared representations created with a combination of those two objectives promise benefits"
+"---\nabstract: |\n    We address the following open problem, implicit in the 1990 article *Automorphisms of one-sided subshifts of finite type* of Boyle, Franks and Kitchens (BFK):\n\n    > Does there exists an element $\\psi$ in the group of automorphisms of the one-sided shift Aut($\\{0,1,\\ldots,n-1\\}^{\\mathbb{N}}, {\\sigma_{n}}$) so that all points of $\\{0,1,\\ldots,n-1\\}^{{\\mathbb{N}}}$ have orbits of length $n$ under $\\psi$ and $\\psi$ is not conjugate to a permutation?\n\n    Here, by a *permutation* we mean an automorphism of one-sided shift dynamical system induced by a permutation of the symbol set $\\{0,1,\\ldots,n-1\\}$.\n\n    We resolve this question by showing that any $\\psi$ with properties as above must be conjugate to a permutation.\n\n    Our techniques naturally extend those of BFK using the strongly synchronizing automata technology developed here and in several articles of the authors and collaborators (although, this article has been written to be largely self-contained).\nauthor:\n- Collin Bleak and Feyishayo Olukoya\nbibliography:\n- 'references.bib'\ntitle: 'Conjugacy for certain automorphisms of the one-sided shift via transducers'\n---\n\nIntroduction\n============\n\nLet $n$ be a positive integer and set ${X_{n}}{:=}\\{0,1,\\ldots,n-1\\}$. We will use ${X_{n}}$ to represent our standard alphabet of size $n$ and we will denote by ${\\sigma_{n}}$ the usual shift map on ${X_n^{\\mathbb{N}}}$. The group"
+"---\nabstract: 'Adversarial nets have proved to be powerful in various domains including generative modeling (GANs), transfer learning, and fairness. However, successfully training adversarial nets using first-order methods remains a major challenge. Typically, careful choices of the learning rates are needed to maintain the delicate balance between the competing networks. In this paper, we design a novel learning rate scheduler that dynamically adapts the learning rate of the adversary to maintain the right balance. The scheduler is driven by the fact that *the loss of an ideal adversarial net is a constant known a priori*. The scheduler is thus designed to keep the loss of the optimized adversarial net close to that of an ideal network. We run large-scale experiments to study the effectiveness of the scheduler on two popular applications: GANs for image generation and adversarial nets for domain adaptation. Our experiments indicate that adversarial nets trained with the scheduler are less likely to diverge and require significantly less tuning. For example, on CelebA, a GAN with the scheduler requires only one-tenth of the tuning budget needed without a scheduler. Moreover, the scheduler leads to statistically significant improvements in model quality, reaching up to $27\\%$ in Frechet Inception Distance"
+"---\nabstract: 'Vision Transformer (ViT) has recently gained significant attention in solving computer vision (CV) problems due to its capability of extracting informative features and modeling long-range dependencies through the self-attention mechanism. Whereas recent works have explored the trustworthiness of ViT, including its robustness and explainability, the issue of fairness has not yet been adequately addressed. We establish that the existing fairness-aware algorithms designed for CNNs do not perform well on ViT, which highlights the need for developing our novel framework via ebiased elf-ttention (DSA). DSA is a fairness-through-blindness approach that enforces ViT to eliminate spurious features correlated with the sensitive label for bias mitigation and simultaneously retain real features for target prediction. Notably, DSA leverages adversarial examples to locate and mask the spurious features in the input image patches with an additional attention weights alignment regularizer in the training objective to encourage learning real features for target prediction. Importantly, our DSA framework leads to improved fairness guarantees over prior works on multiple prediction tasks without compromising target prediction performance.'\nauthor:\n- |\n    Yao Qiang Chengyin Li Prashant Khanduri Dongxiao Zhu\\\n    Department of Computer Science, Wayne State University\\\n    `{yao,cyli,khanduri.prashant,dzhu}@wayne.edu`\nbibliography:\n- 'egbib.bib'\ntitle: 'Fairness-aware Vision Transformer via Debiased Self-Attention'\n---"
+"---\nabstract: 'In this study, we address a control-constrained optimal control problem pertaining to the transformation of quantum states. Our objective is to navigate a quantum system from an initial state to a desired target state while adhering to the principles of the Liouville-von Neumann equation. To achieve this, we introduce a cost functional that balances the dual goals of fidelity maximization and energy consumption minimization. We derive optimality conditions in the form of the Pontryagin Maximum Principle (PMP) for the matrix-valued dynamics associated with this problem. Subsequently, we present a time-discretized computational scheme designed to solve the optimal control problem. This computational scheme is rooted in an indirect method grounded in the PMP, showcasing its versatility and efficacy. To illustrate the practicality and applicability of our methodology, we employ it to address the case of a spin $\\frac{1}{2}$ particle subjected to interaction with a magnetic field. Our findings shed light on the potential of this approach to tackle complex quantum control scenarios and contribute to the broader field of quantum state transformations.'\nauthor:\n- 'Nahid Binandeh Dehaghani and A. Pedro Aguiar [^1]'\ntitle: 'Quantum State Transfer Optimization: Balancing Fidelity and Energy Consumption using Pontryagin Maximum Principle'\n---\n\n[Shell :"
+"---\nabstract: 'Many experimental time series measurements share unobserved causal drivers. Examples include genes targeted by transcription factors, ocean flows influenced by large-scale atmospheric currents, and motor circuits steered by descending neurons. Reliably inferring this unseen driving force is necessary to understand the intermittent nature of top-down control schemes in diverse biological and engineered systems. Here, we introduce a new unsupervised learning algorithm that uses recurrences in time series measurements to gradually reconstruct an unobserved driving signal. Drawing on the mathematical theory of skew-product dynamical systems, we identify recurrence events shared across response time series, which implicitly define a recurrence graph with glass-like structure. As the amount or quality of observed data improves, this recurrence graph undergoes a percolation transition manifesting as weak ergodicity breaking for random walks on the induced landscape\u2014revealing the shared driver\u2019s dynamics, even in the presence of strongly corrupted or noisy measurements. Across several thousand random dynamical systems, we empirically quantify the dependence of reconstruction accuracy on the rate of information transfer from a chaotic driver to the response systems, and we find that effective reconstruction proceeds through gradual approximation of the driver\u2019s dominant orbit topology. Through extensive benchmarks against classical and neural-network-based signal processing techniques,"
+"---\nauthor:\n- som\n- Christian Bespin\n- Ivan Caicedo\n- Jochen Dingfelder\n- Tomasz Hemperek\n- Toko Hirono\n- Fabian H\u00fcgging\n- Hans Kr\u00fcger\n- Konstantinos Moustakas\n- Heinz Pernegger\n- Petra Riedler\n- Lars Schall\n- Walter Snoeys\n- Norbert Wermes\nbibliography:\n- 'bibliography.bib'\ntitle: 'Charge collection and efficiency measurements of the TJ-Monopix2 DMAPS in 180nm CMOS technology'\n---\n\nIntroduction\n============\n\nIn recent years, advances in CMOS technologies have fueled the development of a new generation of monolithic active pixel sensors (MAPS) with fast readout and high radiation tolerance by depleting the charge sensitive volume\u00a0[@peric_hvcmos]. These depleted MAPS (DMAPS) devices are therefore an interesting candidate for high-energy particle physics experiments with high radiation environments and high particle rate. Depletion is achieved by either using high-voltage add-ons in the CMOS technology and/or high resistivity substrates. The increasing availability of these features in commercial CMOS processes could combine the features of the detector concept with possibly faster and cheaper production than common hybrid pixel detectors for the mentioned purposes. The idea behind and measurements results from one of multiple DMAPS prototypes, TJ-Monopix2\u00a0[@moustakas_cmos_design; @moustakas_thesis], will be presented in the following.\n\nDesign of TJ-Monopix2\n=====================\n\nTJ-Monopix2 is the latest DMAPS"
+"---\nabstract: 'By performing global fits to inverse beta decay (IBD) yield measurements from existing neutrino experiments based at highly $^{235}$U enriched reactor cores and conventional low-enriched cores, we explore current direct bounds on neutrino production by the sub-dominant fission isotope $^{241}$Pu. For this nuclide, we determine an IBD yield of $\\sigma_{241}$ = 8.16 $\\pm$ 3.47 cm$^2$/fission, a value (135 $\\pm$ 58)% that of current beta conversion models. This constraint is shown to derive from the non-linear relationship between burn-in of $^{241}$Pu and $^{239}$Pu in conventional reactor fuel. By considering new hypothetical neutrino measurements at high-enriched, low-enriched, mixed-oxide, and fast reactor facilities, we investigate the feasible limits of future knowledge of IBD yields for [$^{235}$U]{}, [$^{238}$U]{}, [$^{239}$Pu]{}, [$^{241}$Pu]{}, and [$^{240}$Pu]{}. We find that first direct measurement of the [$^{240}$Pu]{}\u00a0IBD yield can be performed at plutonium-burning fast reactors, while a suite of correlated measurements at multiple reactor types can achieve a precision in direct [$^{238}$U]{}, [$^{239}$Pu]{}, and [$^{241}$Pu]{}\u00a0yield knowledge that meets or exceeds that of current theoretical predictions.'\nauthor:\n- Yoshi Fujikake\n- Bryce Littlejohn\n- 'Ohana B. Rodrigues'\n- Pranava Teja Surukuchi\nbibliography:\n- 'main.bib'\ntitle: 'Exploring Current Constraints on Antineutrino Production by [$^{241}$Pu]{}\u00a0and Paths Towards the"
+"---\nabstract: 'Synthesizing high-fidelity complex images from text is challenging. Based on large pretraining, the autoregressive and diffusion models can synthesize photo-realistic images. Although these large models have shown notable progress, there remain three flaws. 1) These models require tremendous training data and parameters to achieve good performance. 2) The multi-step generation design slows the image synthesis process heavily. 3) The synthesized visual features are difficult to control and require delicately designed prompts. To enable high-quality, efficient, fast, and controllable text-to-image synthesis, we propose Generative Adversarial CLIPs, namely GALIP. GALIP leverages the powerful pretrained CLIP model both in the discriminator and generator. Specifically, we propose a CLIP-based discriminator. The complex scene understanding ability of CLIP enables the discriminator to accurately assess the image quality. Furthermore, we propose a CLIP-empowered generator that induces the visual concepts from CLIP through bridge features and prompts. The CLIP-integrated generator and discriminator boost training efficiency, and as a result, our model only requires about $3\\%$ training data and $6\\%$ learnable parameters, achieving comparable results to large pretrained autoregressive and diffusion models. Moreover, our model achieves $\\sim$120$\\times$faster synthesis speed and inherits the smooth latent space from GAN. The extensive experimental results demonstrate the excellent performance of"
+"---\nabstract: 'The Quantum Fourier Transformation (QFT) is a well-known subroutine for algorithms on qubit-based universal quantum computers. In this work, the known QFT circuit is used to derive an efficient circuit for the multidimensional QFT. The complexity of the algorithm is $\\mathcal{O}(  \\log^2(M)/d )$ for an array with $M=(2^n)^d$ elements $(n \\in \\mathbb{N})$ equally separated along $d$ dimensions. Relevant properties for application are discussed. An example on current hardware is depicted by a 6 qubit 2D-QFT with an IBM quantum computer.'\nauthor:\n- Philipp Pfeffer\nbibliography:\n- 'references.bib'\ntitle: Multidimensional Quantum Fourier Transformation\n---\n\nThe Quantum Fourier Transformation [@QFT] (QFT) is a key subroutine in quantum information processing, most prominently used within the quantum phase estimation [@Kit_QPE] and the factoring algorithm of Shor [@Shor_F]. Comparing to its classical counterpart, the fast fourier transformation [@FFT] (FFT) solves the same problem with effort $\\mathcal{O}(N\\log(N))$ while the QFT needs $\\mathcal{O}(\\log^2(N))$ operations [@NielsenChua] for a vector with $N=2^n$ elements ($n \\in \\mathbb{N}$). Though this speed-up does not lead to the broad replacement of the FFT, the aspect of quantum parallelism is important for the construction of further quantum algorithms. Single operations acting on a large quantum state, such that the whole state is"
+"---\nabstract: 'We present results of numerical simulations of the tensor-valued elliptic-parabolic PDE model for biological network formation. The numerical method is based on a non-linear finite difference scheme on a uniform Cartesian grid in a 2D domain. The focus is on the impact of different discretization methods and choices of regularization parameters on the symmetry of the numerical solution. In particular, we show that using the symmetric alternating-direction implicit (ADI) method for time discretization helps preserve the symmetry of the solution, compared to the (non symmetric) ADI method. Moreover, we study the effect of regularization by isotropic background permeability $r>0$, showing that increased condition number of the elliptic problem due to decreasing value of $r$ leads to loss of symmetry. [We show that in this case, neither the use of the symmetric ADI method preserves the symmetry of the solution.]{} Finally, we perform numerical error analysis of our method [making use of]{} Wasserstein distance.'\nauthor:\n- Clarissa Astuto\n- Daniele Boffi\n- Jan Haskovec\n- Peter Markowich\n- Giovanni Russo\ntitle: 'Asymmetry and condition number of an elliptic-parabolic system for biological network formation'\n---\n\nIntroduction\n============\n\nPrinciples of formation, adaptation and functioning of biological transportation networks have been a"
+"---\nabstract: 'Using natural language, Conversational Bot offers unprecedented ways to many challenges in areas such as information searching, item recommendation, and question answering. Existing bots are usually developed through retrieval-based or generative-based approaches, yet both of them have their own advantages and disadvantages. To assemble this two approaches, we propose a **h**ybrid r**e**t**r**ieval-generati**o**n **net**work (*HeroNet*) with the three-fold ideas: 1). To produce high-quality sentence representations, *HeroNet* performs multi-task learning on two subtasks: Similar Queries Discovery and Query-Response Matching. Specifically, the retrieval performance is improved while the model size is reduced by training two lightweight, task-specific adapter modules that share only one underlying T5-Encoder model. 2). By introducing adversarial training, *HeroNet* is able to solve both retrieval&generation tasks simultaneously while maximizing performance of each other. 3). The retrieval results are used as prior knowledge to improve the generation performance while the generative result are scored by the discriminator and their scores are integrated into the generator\u2019s cross-entropy loss function. The experimental results on a open dataset demonstrate the effectiveness of the *HeroNet* and our code is available at <https://github.com/TempHero/HeroNet.git>'\nauthor:\n- Bolin Zhang\n- Yunzhe Xu\n- 'Zhiying Tu [^1] and Dianhui Chu'\nbibliography:\n- 'ref.bib'\ntitle: 'HeroNet: A Hybrid"
+"---\nabstract: 'The question under which conditions oscillators with slightly different frequencies synchronize appears in various settings. We show that synchronization can be achieved even for *harmonic* oscillators that are bilinearly coupled via a purely dissipative interaction. By appropriately tuned gain/loss stable dynamics may be achieved where for the cases studied in this work all oscillators are synchronized. These findings are interpreted using the complex eigenvalues and eigenvectors of the non-Hermitian matrix describing the dynamics of the system.'\nauthor:\n- 'Juan N.\u00a0Moreno'\n- 'Christopher W.\u00a0W\u00e4chtler'\n- Alexander Eisfeld\ntitle: Synchronized states in dissipatively coupled harmonic oscillator networks\n---\n\nIntroduction\n============\n\nSynchronization is a fascinating phenomenon, which can be interpreted as a display of cooperative behavior appearing in many complex systems [@pikovsky2003synchronization; @StrogatzBook2018]. Since the first observation by Huygens in the late 1600s [@bennett2002huygens], it has been studied in diverse communities, where it plays an important role in our understanding for example in electric networks in engineering, circadian rhythms in biology, pattern formation in statistical mechanics, and chemical reactions in chemistry [@Strogatz1993; @Rosenblum2003; @arenas2008synchronization]. By now, it is seen as a universal phenomenon that is important both in fundamental studies and in technical applications, ranging from laser networks [@thornburg1997chaos],"
+"---\nabstract: |\n    In our paper, we introduce [*special-generic-like*]{} maps or [*SGL*]{} maps as smooth maps, present their fundamental algebraic topological and differential topological theory and give non-trivial examples.\n\n    The new class generalize the class of so-called [*special generic*]{} maps. Special generic maps are smooth maps which are locally projections or the product maps of Morse functions and the identity maps on disks. Morse functions with exactly two singular points on spheres or Morse functions in Reeb\u2019s theorem are simplest examples. Special generic maps and the manifolds of their domains have been studied well. Their structures are simple and this help us to study explicitly. As important properties, they have been shown to restrict the topologies and the differentiable structures of the manifolds strongly by Saeki and Sakuma, followed by Nishioka, Wrazidlo and the author. To cover wider classes of manifolds as the domains, the author previously introduced a class generalizing the class of special generic maps and smaller than our class: [*simply generalized special generic maps*]{}.\naddress: |\n    Institute of Mathematics for Industry, Kyushu University, 744 Motooka, Nishi-ku Fukuoka 819-0395, Japan\\\n    TEL (Office): +81-92-802-4402\\\n    FAX (Office): +81-92-802-4405\\\nauthor:\n- Naoki Kitazawa\ntitle: Smooth maps like special generic maps\n---"
+"---\nabstract: 'Stream processing engines (SPEs) are widely used for large scale streaming analytics over unbounded time-ordered data streams. Modern day streaming analytics applications exhibit diverse compute characteristics and demand strict latency and throughput requirements. Over the years, there has been significant attention in building hardware-efficient stream processing engines (SPEs) that support several query optimization, parallelization, and execution strategies to meet the performance requirements of large scale streaming analytics applications. However, in this work, we observe that these strategies often fail to generalize well on many real-world streaming analytics applications due to several inherent design limitations of current SPEs. We further argue that these limitations stem from the shortcomings of the fundamental design choices and the query representation model followed in modern SPEs. To address these challenges, we first propose *TiLT*, a novel intermediate representation (IR) that offers a highly expressive temporal query language amenable to effective query optimization and parallelization strategies. We subsequently build a compiler backend for TiLT that applies such optimizations on streaming queries and generates hardware-efficient code to achieve high performance on multi-core stream query executions. We demonstrate that TiLT achieves up to $326\\times$ ($20.49\\times$ on average) higher throughput compared to state-of-the-art SPEs (e.g., Trill) across"
+"---\nabstract: 'The basic idea of lifelike computing systems is the transfer of concepts in living systems to technical use that goes even beyond existing concepts of self-adaptation and self-organisation (SASO). As a result, these systems become even more autonomous and changeable - up to a runtime transfer of the actual target function. Maintaining controllability requires a complete and dynamic (self-)quantification of the system behaviour with regard to aspects of SASO but also, in particular, lifelike properties. In this article, we discuss possible approaches for such metrics and establish a first metric for transferability. We analyse the behaviour of the metric using example applications and show that it is suitable for describing the system\u2019s behaviour at runtime.'\nauthor:\n- 'Martin Goller$^{1}$'\n- |\n    Sven Tomforde$^1$\\\n    \\\n    $^1$Intelligent Systems, Christian-Albrechts-Universit\u00e4t zu Kiel, Germany\\\n    goller.cau@gmail.com / st@informatik.uni-kiel.de\nbibliography:\n- 'references.bib'\ntitle: A Quantification Approach for Transferability in Lifelike Computing Systems\n---\n\nI. Introduction\n===============\n\nThe last two decades have seen a trend towards more autonomous behaviour in technical systems \\[[@zhang2017current]\\]. On the one hand, this leads to increased controllability by the human administrator, as self-adaptation of behaviour and self-organisation of the system structure occurs through the technical systems themselves (here summarised as"
+"---\nabstract: 'Many deep learning tasks require annotations that are too time consuming for human operators, resulting in small dataset sizes. This is especially true for dense regression problems such as crowd counting which requires the location of every person in the image to be annotated. Techniques such as data augmentation and synthetic data generation based on simulations can help in such cases. In this paper, we introduce PromptMix, a method for artificially boosting the size of existing datasets, that can be used to improve the performance of lightweight networks. First, synthetic images are generated in an end-to-end data-driven manner, where text prompts are extracted from existing datasets via an image captioning deep network, and subsequently introduced to text-to-image diffusion models. The generated images are then annotated using one or more high-performing deep networks, and mixed with the real dataset for training the lightweight network. By extensive experiments on five datasets and two tasks, we show that PromptMix can significantly increase the performance of lightweight networks by up to 26%.'\nauthor:\n- 'Arian\u00a0Bakhtiarnia, Qi\u00a0Zhang, and\u00a0Alexandros\u00a0Iosifidis [^1] [^2]'\nbibliography:\n- 'references.bib'\ntitle: 'PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks'\n---\n\nEfficient deep learning, lightweight"
+"---\nabstract: 'We introduce a causal framework for designing optimal policies that satisfy fairness constraints. We take a pragmatic approach asking what we can do with an action space available to us and only with access to historical data. We propose two different fairness constraints: a moderation breaking constraint which aims at blocking moderation paths from the action and sensitive attribute to the outcome, and by that at reducing disparity in outcome levels as much as the provided action space permits; and an equal benefit constraint which aims at distributing gain from the new and maximized policy equally across sensitive attribute levels, and thus at keeping pre-existing preferential treatment in place or avoiding the introduction of new disparity. We introduce practical methods for implementing the constraints and illustrate their uses on experiments with semi-synthetic models.'\nauthor:\n- |\n    Limor Gultchin\\\n    University of Oxford, The Alan Turing Institute, DeepMind Siyuan Guo\\\n    University of Cambridge, Max Planck Institute for Intelligent Systems Alan Malek\\\n    DeepMind Silvia Chiappa\\\n    DeepMind Ricardo Silva\\\n    University College London\nbibliography:\n- 'bib.bib'\ntitle: 'Pragmatic Fairness: Developing Policies with Outcome Disparity Control'\n---\n\nIntroduction {#sec:intro}\n============\n\nThe fairness of decisions made by machine learning models involving underprivileged groups has seen"
+"---\nabstract: 'HTTP Adaptive Streaming (HAS) is nowadays a popular solution for multimedia delivery. The novelty of HAS lies in the possibility of continuously adapting the streaming session to current network conditions, facilitated by Adaptive Bitrate (ABR) algorithms. Various popular streaming and Video on Demand services such as Netflix, Amazon Prime Video, and Twitch use this method. Given this broad consumer base, ABR algorithms continuously improve to increase user satisfaction. The insights for these improvements are, among others, gathered within the research area of Quality of Experience (QoE). Within this field, various researchers have dedicated their works to identifying potential impairments and testing their impact on viewers\u2019 QoE. Two frequently discussed visual impairments influencing QoE are stalling events and quality switches. So far, it is commonly assumed that those stalling events have the worst impact on QoE. This paper challenged this belief and reviewed this assumption by comparing stalling events with multiple quality and high amplitude quality switches. Two subjective studies were conducted. During the first subjective study, participants received a monetary incentive, while the second subjective study was carried out with volunteers. The statistical analysis demonstrated that stalling events do not result in the worst degradation of QoE. These"
+"---\nabstract: 'We establish existence and uniqueness of the solution to the Dyson equation for the density-density response function in time-dependent density functional theory (TDDFT) in the random phase approximation (RPA). We show that the poles of the RPA density-density response function are forward-shifted with respect to those of the non-interacting response function, thereby explaining mathematically the well known empirical fact that the non-interacting poles (given by the spectral gaps of the time-independent Kohn-Sham equations) underestimate the true transition frequencies. Moreover we show that the RPA poles are solutions to an eigenvalue problem, justifying the approach commonly used in the physics community to compute these poles.'\nauthor:\n- 'Thiago Carvalho Corso[^1]'\n- 'Mi-Song Dupuy[^2]'\n- 'Gero Friesecke[^3]'\nbibliography:\n- 'linear-response.bib'\ntitle: 'The density-density response function in time-dependent density functional theory: mathematical foundations and pole shifting'\n---\n\n\\[section\\] \\[theo\\][Proposition]{} \\[theo\\][Remark]{} \\[theo\\][Lemma]{} \\[theo\\][Corollary]{} \\[theo\\][Definition]{}\n\nIntroduction\n============\n\n[While ground state properties of molecules are very successfully captured by time-independent Kohn-Sham density functional theory (KS-DFT), excitation energies provide a much greater challenge. In particular, the excitation energies of the time-independent Kohn-Sham equations do not accurately capture the true excitation energies, and [*have no theoretically supported meaning*]{}.]{}\n\nInstead, time-dependent density functional theory (TDDFT) in the"
+"---\nabstract: |\n    The Delone (Selling) scalars, which are used in unit cell reduction and in lattice type determination, are studied in [$\\bf{C^{3}}$]{}, the space of three complex variables. The three complex coordinate planes are composed of the six Delone scalars.\n\n    [**Note:**]{} In his later publications, Boris Delaunay used the Russian version of his surname, Delone.\\\nauthor:\n- 'Herbert J.'\nbibliography:\n- 'Reduced.bib'\ntitle: 'Delone lattice studies in [$\\bf{C^{3}}$]{}, the space of three complex variables'\n---\n\n[**]{}\\\n\n[Bernstein]{}\n\nThe space [$\\bf{C^{3}}$]{} is explained in more detail than in the original description. Boundary transformations of the fundamental unit are described in detail. A graphical presentation of the basic coordinates is described and illustrated.\n\nIntroduction\n============\n\nThe scalars used by\u00a0 in his formulation of Selling reduction \u00a0[@Selling1874] are (in the conventional order) $b \\cdot c$, $a \\cdot c$, $a \\cdot b$, $a \\cdot d$, $b \\cdot d$, $c \\cdot d$, where $d = -a-b-c$. (As a mnemonic device, observe that the first three terms use $\\alpha$, $\\beta$, and $\\gamma$, in that order, and the following terms use $a$, $b$, $c$, in that order.)\n\n\u00a0 chose to represent the Selling scalars in the space [$\\bf{S^{6}}$]{}, [{[$s_1$]{}, [$s_2$]{}, [$s_3$]{}, [$s_4$]{}, [$s_5$]{}, [$s_6$]{}}]{} (defined in the"
+"---\nabstract: 'We study the positive solutions of the logistic elliptic equation with a nonlinear Neumann boundary condition that models coastal fishery harvesting ([@GUU19]). An essential role is played by the smallest eigenvalue of the Dirichlet eigenvalue problem, with respect to which a noncritical case is studied in [@Um2022]. In this paper, we extend our analysis to the critical case and further study the noncritical case for a more precise description of the positive solution set. Our approach relies on the energy method, sub- and supersolutions, and implicit function analysis.'\naddress: 'Department of Mathematics, Faculty of Education, Ibaraki University, Mito 310-8512, Japan'\nauthor:\n- Kenichiro Umezu\ntitle: Logistic elliptic equation with a nonlinear boundary condition arising from coastal fishery harvesting II\n---\n\n[^1]\n\nIntroduction\n============\n\nThis paper is devoted to the study of the positive solutions for the following logistic elliptic equation with a nonlinear boundary condition arising from coastal fishery harvesting ([@GUU19]): $$\\begin{aligned}\n \\label{p}\n\\begin{cases}\n-\\Delta u = u-u^{p} & \\mbox{ in } \\Omega, \\\\\nu\\geq0 & \\mbox{ in } \\Omega, \\\\ \n\\frac{\\partial u}{\\partial \\nu}  = -\\lambda u^q & \\mbox{ on } \\partial\\Omega. \n\\end{cases} $$ Here, $\\Omega\\subset \\mathbb{R}^N$, $N\\geq1$, is a bounded domain with smooth boundary $\\partial\\Omega$, $\\Delta ="
+"---\nabstract: 'In this paper, we investigate the uplink performance of cell-free (CF) extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, which is a promising technique for future wireless communications. More specifically, we consider the practical scenario with multiple base stations (BSs) and multiple user equipments (UEs). To this end, we derive exact achievable spectral efficiency (SE) expressions for any combining scheme. It is worth noting that we derive the closed-form SE expressions for the CF XL-MIMO with maximum ratio (MR) combining. Numerical results show that the SE performance of the CF XL-MIMO can be hugely improved compared with the small-cell XL-MIMO. It is interesting that a smaller antenna spacing leads to a higher correlation level among patch antennas. Finally, we prove that increasing the number of UE antennas may decrease the SE performance with MR combining.'\nauthor:\n- |\n    Hao Lei, Zhe Wang, Huahua Xiao, Jiayi Zhang,\u00a0, Bo Ai,\u00a0\\\n    [^1]\nbibliography:\n- 'IEEEabrv.bib'\n- 'Ref.bib'\ntitle: 'Uplink Performance of Cell-Free Extremely Large-Scale MIMO Systems'\n---\n\nIntroduction\n============\n\nThe extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technique for the future communication system where the system is equipped with a massive number of antennas in a compact space [@bjornson2019massive], [@9113273]. Compared with"
+"---\nabstract: |\n    Over the years, Software Quality Engineering has increased interest, demonstrated by significant research papers published in this area. Determining when a software artifact is qualitatively valid is tricky, given the impossibility of providing an objective definition valid for any perspective, context, or stakeholder. Many quality model solutions have been proposed that reference specific quality attributes in this context. However, these approaches do not consider the context in which the artifacts will operate and the stakeholder\u2019s perspective who evaluate its validity. Furthermore, these solutions suffer from the limitations of being artifact-specific and not extensible.\n\n    In this paper, we provide a generic and extensible mechanism that makes it possible to aggregate and prioritize quality attributes. The user, taking into account his perspective and the context in which the software artifact will operate, is guided in defining all the criteria for his quality model. The management of these criteria is then facilitated through Multi-Criteria Decision Making (MCDM). In addition, we present the PRETTEF model, a concrete instance of the proposed approach for assessing and selecting MVC frameworks.\nauthor:\n- \n- \n- \n- \nbibliography:\n- 'bib.bib'\ntitle: 'A customizable approach to assess software quality through Multi-Criteria Decision Making'\n---\n\nQuality Model,"
+"---\nauthor:\n- som\n- Kristof De Bruyn\n- Robert Fleischer\n- Eleftheria Malami\n- Philine van Vliet\ntitle: 'Studies of New Physics in $B^0_q-\\bar B^0_q$ Mixing and Implications for Leptonic Decays'\n---\n\nIntroduction\n============\n\nThe phenomenon of $B^0_q$-$\\bar{B}^0_q$ mixing (where $q=d,s$) arises only from loop processes in the Standard Model (SM) and is sensitive to possible New Physics (NP) contributions, which could enter the loop topologies or even at the tree level, for instance in $Z'$ models. Associated to the mixing phenomenon are the mixing parameters and the CP-violating phases for which we have impressive experimental data. In this presentation, we follow Ref.\u00a0[@DeBruyn:2022zhw] and explore the space allowed for NP by current measurements and the state-of-the-art parameters. In addition, we point out interesting connections to the studies of leptonic rare $B$ decays.\n\nIn order to determine the parameter space of possible NP effects to $B_q^0$\u2013$\\bar{B}_q^0$ mixing, we have to compare the SM predictions of the mixing parameters with the corresponding experimental values. For these SM predictions, a careful analysis of the Unitarity Triangle (UT) apex is required. We pay special attention to the different determinations of the Cabibbo-Kobayashi-Maskawa (CKM) parameters and the tensions that arise between the extractions"
+"---\nabstract: 'Hematite is a canted antiferromagnetic insulator, promising for applications in spintronics. Here, we present [*ab initio*]{} calculations of the tensorial exchange interactions of hematite and use them to understand its magnetic properties by parameterizing a semiclassical Heisenberg spin model. Using atomistic spin dynamics simulations, we calculate the equilibrium properties and phase transitions of hematite, most notably the Morin transition. The computed isotropic and Dzyaloshinskii\u2013Moriya interactions result in a N\u00e9el temperature and weak ferromagnetic canting angle that are in good agreement with experimental measurements. Our simulations show how dipole-dipole interactions act in a delicate balance with first and higher-order on-site anisotropies to determine the material\u2019s magnetic phase. Comparison with spin-Hall magnetoresistance measurements on a hematite single-crystal reveals deviations of the critical behavior at low temperatures. Based on a mean-field model, we argue that these differences result from the quantum nature of the fluctuations that drive the phase transitions.'\nauthor:\n- Tobias Dannegger\n- Andr\u00e1s De\u00e1k\n- Levente R\u00f3zsa\n- 'E. Galindez-Ruales'\n- Shubhankar Das\n- Eunchong Baek\n- Mathias Kl\u00e4ui\n- L\u00e1szl\u00f3 Szunyogh\n- Ulrich Nowak\ntitle: 'Magnetic properties of hematite revealed by an [*ab initio*]{} parameterized spin model'\n---\n\nIntroduction\n============\n\nAs a prototypical weak ferromagnet, the insulating"
+"---\nabstract: 'In the context of adiabatic quantum computation (AQC), it has been argued that first-order quantum phase transitions (QPTs) due to localisation phenomena cause AQC to fail by exponentially decreasing the minimal spectral gap of the Hamiltonian along the annealing path. The vanishing of the spectral gap is often linked to the localisation of the ground state in a local minimum, requiring the system to tunnel into the global minimum at a later stage of the annealing. Recent methods have been proposed to avoid this phenomena by carefully designing the involved Hamiltonians. However, it remains a challenge to formulate a comprehensive theory on the effect of the various parameters and the conditions under which QPTs make the AQC algorithm fail. Equipped with concepts from graph theory, in this work we link graph quantities associated to the Hamiltonians along the anneal path with the occurrence of QPTs. These links allow us to derive bounds on the location of the minimal spectral gap along the anneal path, augmenting the toolbox for the design of strategies to improve the runtime of AQC algorithms.'\nauthor:\n- Matthias Werner\n- 'Artur Garc\u00eda-S\u00e1ez'\n- 'Marta P. Estarellas'\nbibliography:\n- 'references.bib'\ntitle: 'Bounding first-order quantum phase"
+"---\nabstract: 'Collectives of actively-moving particles can spontaneously separate into dilute and dense phases\u2014a fascinating phenomenon known as motility-induced phase separation (MIPS). MIPS is well-studied for randomly-moving particles with no directional bias. However, many forms of active matter exhibit collective chemotaxis, directed motion along a chemical\u00a0gradient that the constituent particles can generate themselves. Here, using theory and simulations, we demonstrate that collective chemotaxis strongly competes with MIPS\u2014in some cases, arresting or completely suppressing phase separation, or in other cases, generating fundamentally new dynamic instabilities. We establish quantitative principles describing this competition, thereby helping to reveal and clarify the rich physics underlying active matter systems that perform chemotaxis, ranging from cells to robots.'\nauthor:\n- Hongbo Zhao\n- Andrej Ko\u0161mrlj\n- 'Sujit S. Datta'\ntitle: 'Chemotactic motility-induced phase separation'\n---\n\nThe thermodynamics of active matter\u2014collections of active agents that consume energy\u2014has been studied extensively due to its fundamental richness as well as its importance to biological and engineering applications\u00a0[@Marchetti2013; @Gompper2020]. One prominent class of active matter is that composed of self-propelled agents, ranging from enzymes\u00a0[@Mohajerani2018; @Agudo-Canalejo2018; @Jee2018], motile microorganisms\u00a0[@Murray2007; @Liu2019], and mammalian cells\u00a0[@Alert2019; @Scarpa2016] to synthetic microswimmers and robots\u00a0[@Palacci2013; @Theurkauff2012; @Palagi2018]. These forms of active"
+"---\nabstract: 'The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre-trained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer trainable parameters. We also demonstrate the model\u2019s emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions.'\nbibliography:\n- 'main.bib'\n---\n\nIntroduction\n============\n\n\\[sec:intro\\] Vision-language pre-training (VLP) research has witnessed a rapid advancement in the past few years, where pre-trained models with increasingly larger scale have been developed to continuously push the state-of-the-art on various downstream tasks\u00a0[@clip; @ALBEF; @blip; @ofa; @flamingo; @beit3]. However, most state-of-the-art vision-language models incur a high computation cost during pre-training, due to end-to-end training using large-scale models and datasets."
+"---\nauthor:\n- 'V'' it\u011bzslav Kala'\ntitle: ' Universal quadratic forms and indecomposables in number fields: A survey '\n---\n\nThe goal of this survey article is to give an overview of the arithmetic theory of universal quadratic forms. I will primarily focus on the results over number fields obtained since 2015. For other surveys focusing on different facets of the area, see\u00a0[@Ea; @Han; @Kim].\n\nWhile I try to explain the broad ideas behind the proofs and the tools that are used, many details are nevertheless missing and there are numerous simplifications. The interested reader is therefore always encouraged to look into the original papers or to contact me. Overall the notes are more aimed at a\u00a0junior audience rather than the experts in the field (who might at least prefer to start reading only in Sections\u00a0\\[sec:4\\] or\u00a0\\[sec:5\\]).\n\nThe paper is primarily based on the notes from my lectures at the *XXIII International Workshop for Young Mathematicians* in Krakow, Poland (and from some of my other talks). Parts of the text are also taken from the introduction to my habilitation thesis\u00a0[@Ka3].\n\nIntroduction {#sec:1}\n============\n\nThe study of representations of integers by quadratic forms has a\u00a0long"
+"---\nabstract: 'The reconfigurable intelligent surface (RIS) is useful to effectively improve the coverage and data rate of end-to-end communications. In contrast to the well-studied coverage-extension use case, in this paper, multiple RIS panels are introduced, aiming to enhance the data rate of multi-input multi-output (MIMO) channels in presence of insufficient scattering. Specifically, via the operator-valued free probability theory, the asymptotic mutual information of the large-dimensional RIS-assisted MIMO channel is obtained under the Rician fading with Weichselberger\u2019s correlation structure, in presence of both the direct and the reflected links. Although the mutual information of Rician MIMO channels scales linearly as the number of antennas and the signal-to-noise ratio (SNR) in decibels, numerical results show that it requires sufficiently large SNR, proportional to the Rician factor, in order to obtain the theoretically guaranteed linear improvement. This paper shows that the proposed multi-RIS deployment is especially effective to improve the mutual information of MIMO channels under the large Rician factor conditions. When the reflected links have similar arriving and departing angles across the RIS panels, a small number of RIS panels are sufficient to harness the spatial degree of freedom of the multi-RIS assisted MIMO channels.'\nauthor:\n- 'Zhong\u00a0Zheng, \u00a0 Siqiang\u00a0Wang,"
+"---\nabstract: |\n    In this paper, we analyze a speed restarting scheme for the dynamical system given by $$\\ddot{x}(t) + \\dfrac{\\alpha}{t}\\dot{x}(t) + \\nabla \\phi(x(t)) + \\beta \\nabla^2 \\phi(x(t))\\dot{x}(t)=0,$$ where $\\alpha$ and $\\beta$ are positive parameters, and $\\phi:{{\\mathbb R}}^n \\to {{\\mathbb R}}$ is a smooth convex function. If $\\phi$ has quadratic growth, we establish a linear convergence rate for the function values along the restarted trajectories. As a byproduct, we improve the results obtained by Su, Boyd and Cand\u00e8s [@JMLR:v17:15-084], obtained in the strongly convex case for $\\alpha=3$ and $\\beta=0$. Preliminary numerical experiments suggest that both adding a positive Hessian driven damping parameter $\\beta$, and implementing the restart scheme help improve the performance of the dynamics and corresponding iterative algorithms as means to approximate minimizers of $\\phi$.\\\n    **Keywords:** Convex optimization $\\cdot$ Hessian driven damping $\\cdot$ First order methods $\\cdot$ Restarting $\\cdot$ Differential equations.\\\n    **MSC2020:** 37N40 $\\cdot$ 90C25 $\\cdot$ 65K10 (primary). 34A12 (secondary).\nauthor:\n- 'Juan Jos\u00e9 Maul\u00e9n[^1], Juan Peypouquet[^2]'\nbibliography:\n- 'referencias.bib'\ntitle: A speed restart scheme for a dynamics with Hessian driven damping\n---\n\nIntroduction\n============\n\nIn convex optimization, first-order methods are iterative algorithms that use function values and (generalized) derivatives to build minimizing sequences. Perhaps the oldest and simplest"
+"---\nabstract: 'Two different approaches exist to handle missing values for prediction: either imputation, prior to fitting any predictive algorithms, or dedicated methods able to natively incorporate missing values. While imputation is widely (and easily) use, it is unfortunately biased when low-capacity predictors (such as linear models) are applied afterward. However, in practice, naive imputation exhibits good predictive performance. In this paper, we study the impact of imputation in a high-dimensional linear model with MCAR missing data. We prove that zero imputation performs an implicit regularization closely related to the ridge method, often used in high-dimensional problems. Leveraging on this connection, we establish that the imputation bias is controlled by a ridge bias, which vanishes in high dimension. As a predictor, we argue in favor of the averaged SGD strategy, applied to zero-imputed data. We establish an upper bound on its generalization error, highlighting that imputation is benign in the $d \\gg \\sqrt{n}$ regime. Experiments illustrate our findings.'\nauthor:\n- |\n    Alexis Ayme, Claire Boyer, Aymeric Dieuleveut\\\n    & Erwan Scornet\nbibliography:\n- 'references.bib'\ntitle: ' Naive imputation implicitly regularizes high-dimensional linear models. '\n---\n\nIntroduction\n============\n\nMissing data has become an inherent problem in modern data science. Indeed, most real-world"
+"---\nabstract: 'In this paper, we study dimension reduction techniques for large-scale controlled stochastic differential equations (SDEs). The drift of the considered SDEs contains a polynomial term satisfying a one-sided growth condition. Such nonlinearities in high dimensional settings occur, e.g., when stochastic reaction diffusion equations are discretized in space. We provide a brief discussion around existence, uniqueness and stability of solutions. (Almost) stability then is the basis for new concepts of Gramians that we introduce and study in this work. With the help of these Gramians, dominant subspace are identified leading to a balancing related highly accurate reduced order SDE. We provide an algebraic error criterion and an error analysis of the propose model reduction schemes. The paper is concluded by applying our method to spatially discretized reaction diffusion equations.'\nauthor:\n- 'Martin Redmann[^1]'\ntitle: Model reduction for stochastic systems with nonlinear drift\n---\n\n**Keywords:** model order reduction$\\cdot$ nonlinear stochastic systems $\\cdot$ Gramians $\\cdot$ L\u00e9vy processes\n\n**MSC classification:** 60G51 $\\cdot$ 60H10 $\\cdot$ 65C30 $\\cdot$ 93C10 $\\cdot$ 93E03 $\\cdot$ 93E15\n\nIntroduction\n============\n\nModel order reduction (MOR) aims to find low-order approximations for high-/infinite-dimensional systems of differential equations reducing the complexity of the original problem. Many MOR schemes are based on projections"
+"---\nabstract: 'The great success of Deep Neural Networks (DNNs) has inspired the algorithmic development of DNN-based Fixed-Point (DNN-FP) for computer vision tasks. DNN-FP methods, trained by Back-Propagation Through Time or computing the inaccurate inversion of the Jacobian, suffer from inferior representation ability. Motivated by the representation power of the Transformer, we propose a framework to unroll the FP and approximate each unrolled process via Transformer blocks, called FPformer. To reduce the high consumption of memory and computation, we come up with FPRformer by sharing parameters between the successive blocks. We further design a module to adapt Anderson acceleration to FPRformer to enlarge the unrolled iterations and improve the performance, called FPAformer. In order to fully exploit the capability of the Transformer, we apply the proposed model to image restoration, using self-supervised pre-training and supervised fine-tuning. 161 tasks from 4 categories of image restoration problems are used in the pre-training phase. Hereafter, the pre-trained FPformer, FPRformer, and FPAformer are further fine-tuned for the comparison scenarios. Using self-supervised pre-training and supervised fine-tuning, the proposed FPformer, FPRformer, and FPAformer achieve competitive performance with state-of-the-art image restoration methods and better training efficiency. FPAformer employs only 29.82% parameters used in SwinIR models, and provides"
+"---\nabstract: 'This work describes experiments on thermal dynamics of pure excited by hydrodynamic cavitation, which has been reported to facilitate the spin conversion of para- and ortho-isomers at water interfaces. Previous measurements by NMR and capillary methods of excited samples demonstrated changes of proton density by 12-15%, the surface tension up to 15.7%, which can be attributed to a non-equilibrium para-/ortho- ratio. Beside these changes, we also expect a variation of heat capacity. Experiments use a differential calorimetric approach with two devices: one with an active thermostat for diathermic measurements, another is fully passive for long-term measurements. Samples after excitation are degassed at -0.09MPa and thermally equalized in a water bath. Conducted attempts demonstrated changes in the heat capacity of experimental samples by 4.17%\u20135.72% measured in the transient dynamics within 60 min after excitation, which decreases to 2.08% in the steady-state dynamics 90-120 min after excitation. Additionally, we observed occurrence of thermal fluctuations at the level of 10$^{-3}$ $^\\circ C$ relative temperature on 20-40 min mesoscale dynamics and a long-term increase of such fluctuations in experimental samples. Obtained results are reproducible in both devices and are supported by previously published outcomes on four-photon scattering spectra in the range from"
+"---\nabstract: |\n    Measurements in heavy flavor azimuthal angular correlation provide insight into the production, propagation, and hadronization of heavy flavor jets in ultra-relativistic hadronic and heavy-ion collisions. These measurements across different colliding systems, like p\u2013A and A\u2013A, help us isolate the possible modification in particle production due to cold nuclear matter (CNM) effects and the formation of Quark-Gluon Plasma (QGP), respectively. Jet correlation studies give direct access to the initial parton dynamics produced in these collisions.\n\n    This article studies the azimuthal angular correlations of electrons from heavy flavor hadron decays in pp, p\u2013Pb, and Pb\u2013Pb collisions at [$\\sqrt{s_{\\rm{NN}}}$]{}= 5.02 TeV using PYTHIA8+Angantyr. We study the production of heavy flavor jets with different parton level processes, including multi-parton interactions, different color reconnection prescriptions, and initial and final state radiation processes. In addition, we add the hadron-level processes, i.e., Bose-Einstein and rescattering effects, to quantify the effect due to these processes. The heavy flavor electron correlations are calculated in the different trigger and associated [$p_{\\rm{T}}$]{}intervals to characterize the impact of hard and soft scattering in the various colliding systems. The yields and the sigmas associated with the near-side (NS) and away-side (AS) correlation peaks are calculated and studied as a function"