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09acc494-a429-48bc-9fad-ff60e69000d3	unified-emulation-simulation-training	2304.01244	null	https://arxiv.org/abs/2304.01244v1	https://arxiv.org/pdf/2304.01244v1.pdf	Unified Emulation-Simulation Training Environment for Autonomous Cyber Agents	Autonomous cyber agents may be developed by applying reinforcement and deep reinforcement learning (RL/DRL), where agents are trained in a representative environment. The training environment must simulate with high-fidelity the network Cyber Operations (CyOp) that the agent aims to explore. Given the complexity of net-work CyOps, a good simulator is difficult to achieve. This work presents a systematic solution to automatically generate a high-fidelity simulator in the Cyber Gym for Intelligent Learning (CyGIL). Through representation learning and continuous learning, CyGIL provides a unified CyOp training environment where an emulated CyGIL-E automatically generates a simulated CyGIL-S. The simulator generation is integrated with the agent training process to further reduce the required agent training time. The agent trained in CyGIL-S is transferrable directly to CyGIL-E showing full transferability to the emulated "real" network. Experimental results are presented to demonstrate the CyGIL training performance. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real for leveraging RL agents in real-world cyber networks.	['Thomas Kunz', 'James Hailing Rao', 'Adrian Taylor', 'Jean-Pierre S. El Rami', 'Li Li']	2023-04-03	null	null	null	null	['offline-rl']	['playing-games']	[-2.73265868e-01 7.00822294e-01 1.72952175e-01 1.55207902e-01 -9.35752988e-02 -4.99123484e-01 6.97034419e-01 -1.57877102e-01 -4.26382750e-01 1.11261821e+00 -5.60904205e-01 -4.29658949e-01 -2.94015855e-01 -1.16191673e+00 -8.56384695e-01 -5.40041149e-01 -6.29573703e-01 9.45913196e-01 1.77784041e-01 -6.85358524e-01 -2.67357379e-01 8.26730609e-01 -1.67215562e+00 -1.44369498e-01 5.98737657e-01 5.81744254e-01 6.51287973e-01 1.17634773e+00 4.02479231e-01 1.31670666e+00 -1.04792714e+00 1.93669051e-01 4.84809399e-01 -5.48023760e-01 -7.95412242e-01 -4.80684306e-04 -5.43069541e-01 -4.26262677e-01 -3.37699890e-01 6.72887206e-01 3.75791401e-01 3.03144932e-01 1.69098988e-01 -1.96427345e+00 3.49397659e-01 9.29536164e-01 2.46220857e-01 -1.40318125e-01 6.10229671e-01 8.07886958e-01 4.24279898e-01 3.48848887e-02 9.69023585e-01 1.36449122e+00 4.63574201e-01 4.46922362e-01 -9.83803809e-01 -6.26541018e-01 9.43033770e-02 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d4dfb29a-59b4-4518-96d2-13f5d1ecdd0c	electromyography-signal-classification-using	2305.04006	null	https://arxiv.org/abs/2305.04006v1	https://arxiv.org/pdf/2305.04006v1.pdf	Electromyography Signal Classification Using Deep Learning	We have implemented a deep learning model with L2 regularization and trained it on Electromyography (EMG) data. The data comprises of EMG signals collected from control group, myopathy and ALS patients. Our proposed deep neural network consists of eight layers; five fully connected, two batch normalization and one dropout layers. The data is divided into training and testing sections by subsequently dividing the training data into sub-training and validation sections. Having implemented this model, an accuracy of 99 percent is achieved on the test data set. The model was able to distinguishes the normal cases (control group) from the others at a precision of 100 percent and classify the myopathy and ALS with high accuracy of 97.4 and 98.2 percents, respectively. Thus we believe that, this highly improved classification accuracies will be beneficial for their use in the clinical diagnosis of neuromuscular disorders.	['Abdulhamit Subasi', 'Selcuk Cankurt', 'Mekia Shigute Gaso']	2023-05-06	null	null	null	null	['electromyography-emg', 'l2-regularization']	['medical', 'methodology']	[ 3.00598592e-01 2.85540193e-01 -3.32311839e-01 -2.63567597e-01 -6.78174198e-01 2.96222746e-01 -5.49947731e-02 -5.84931135e-01 -8.76715243e-01 1.27888215e+00 -1.11968003e-01 4.54694629e-02 -2.67938673e-01 -3.47975165e-01 -5.59941947e-01 -8.76414001e-01 -4.01523083e-01 6.50853693e-01 3.34320106e-02 1.21675067e-01 1.99463472e-01 3.94415766e-01 -1.36413634e+00 5.02498984e-01 1.05946720e+00 9.03455615e-01 2.34018356e-01 5.54916024e-01 2.09397808e-01 7.05387235e-01 -7.74959862e-01 2.83752173e-01 3.61344635e-01 -2.75563061e-01 -7.53407836e-01 -5.35108633e-02 2.58378685e-01 -3.10252905e-01 -4.56387252e-01 8.48320842e-01 7.99931109e-01 1.66319847e-01 6.36029065e-01 -9.12507176e-01 -3.41643035e-01 5.22450209e-01 -6.25031054e-01 2.51318604e-01 -1.82793543e-01 3.42207700e-01 4.66477603e-01 -2.45846257e-01 8.75406682e-01 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4a659a4e-ab71-4236-839f-d45221bbab72	multi-relational-embedding-for-knowledge	null	null	https://ir.soken.ac.jp/?action=pages_view_main&active_action=repository_view_main_item_detail&item_id=6334&item_no=1&page_id=29&block_id=155	https://ir.soken.ac.jp/?action=pages_view_main&active_action=repository_view_main_item_detail&item_id=6334&item_no=1&page_id=29&block_id=155	Multi-Relational Embedding for Knowledge Graph Representation and Analysis	Multi-relational data, such as knowledge graphs, bibliographic data, and information networks are prevalent in real-world datasets. Managing, exploring, and utilizing these large and complex datasets effectively are challenging. In recent years, multi-relational embedding methods have emerged as a new effective approach to model multi-relational data by representing both the entities and the relations as embedding vectors in semantic space. On knowledge graphs, multi-relational embedding methods aim to model the interactions between these embedding vectors to predict the relational link between entities. These knowledge graph embedding methods solve the important inherent task of link prediction for knowledge graph completion, but also provide the embedding representations that have various potential applications. The goal of this thesis is first to study multi-relational embedding on knowledge graphs to propose a new embedding model that explains and improves previous methods, then to study the applications of multi-relational embedding in representation and analysis of knowledge graphs. For the first part of the thesis, we study the theoretical framework of knowledge graph embedding methods to explain and improve them. We review and analyze the popular class of semantic matching knowledge graph embedding methods, with a focus on the state-of-the-art trilinear-product-based models such as ComplEx. Based on our analysis, we identify two fundamental complementary aspects that a knowledge graph embedding model needs to address, that is, computational efficiency and model expressiveness. Previous trilinear-product-based models use specially designed interaction mechanisms to manually provide a trade-off between the two aspects. However, their interaction mechanisms are specially designed and fixed, potentially causing them to be suboptimal or difficult to extend. In this thesis, we propose the multi-partition embedding interaction (MEI) model with block term format to systematically address this problem. MEI divides each embedding into a multi-partition vector to efficiently restrict the interactions. Each local interaction is modeled with the Tucker tensor format and the full interaction is modeled with the block term tensor format, enabling MEI to control the trade-off between expressiveness and computational cost, learn the interaction mechanisms from data automatically. The model combines advanced tensor representation formats and modern deep learning techniques to achieve state-of-the-art performance on the link prediction task. The theoretical framework of the MEI model is then used as a general mechanism of knowledge graph embedding to analyze, explain, and generalize previous models. We also draw the connections to word embeddings and language modeling to provide some new insights and generalizations. For the second part of the thesis, we study how to apply multi-relational embedding in representation and analysis of knowledge graphs. Unlike word embedding, the semantic structures such as similarity and analogy structures in knowledge graph embedding space are not well-studied, and thus not usually utilized for data representation and analysis. To demonstrate the application of multi-relational embedding, we formalize a framework for data representation and analysis by semantic queries on the multi-relational embedding space. We build a knowledge graph from scholarly data and show how various tasks on the original datasets can be approximated by appropriate semantic queries, which are multi-linear algebraic operations on the multi-relational embedding spaces. We also theoretically study the entity analogy reasoning task in multi-relational embedding space, which can be formulated as an open-relational query by examples task, doing relational query on unseen relations. Using the above mathematical connections between knowledge graph embeddings and word embeddings, we analyze the semantic structures in the knowledge graph embedding space and propose potential solution to the above entity analogy reasoning task. The goal of this endeavor is to explore potential applications of recent advancements in multi-relational embedding to data representation and analysis, especially to improve its effectiveness on scholarly data.	['Hung Nghiep Tran']	2020-09-28	null	null	null	phd-dissertation-the-graduate-university-for	['knowledge-graph-embeddings', 'knowledge-graph-embeddings']	['graphs', 'methodology']	[-2.60561347e-01 3.25738341e-01 -5.48878133e-01 -9.28325951e-03 5.90382982e-03 -4.89967883e-01 5.00784457e-01 2.39097208e-01 1.01226367e-01 5.12054861e-02 2.23748952e-01 -4.54842269e-01 -8.94016445e-01 -1.23959935e+00 -5.29224873e-01 -3.61464351e-01 -3.36346209e-01 5.73655963e-01 1.00638799e-01 -4.18746084e-01 7.71175623e-02 5.09184539e-01 -1.53670025e+00 4.11078751e-01 6.92621350e-01 7.12184787e-01 -3.11997719e-02 4.91090238e-01 -5.97263455e-01 8.11092675e-01 -1.87968582e-01 -7.08431661e-01 8.25336128e-02 -7.24988058e-02 -1.07689214e+00 -1.92079455e-01 1.57732040e-01 1.18326351e-01 -7.69372165e-01 8.62730742e-01 2.88667500e-01 6.99634254e-02 6.72770679e-01 -1.77323401e+00 -1.16686678e+00 8.72852445e-01 -2.71401078e-01 1.77365690e-02 5.23303688e-01 -3.65795374e-01 1.49593794e+00 -9.43990648e-01 7.56878376e-01 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b1fe7d3e-abea-46b1-b347-7726aafc8669	easy-guided-decoding-in-providing-suggestions	2211.07093	null	https://arxiv.org/abs/2211.07093v2	https://arxiv.org/pdf/2211.07093v2.pdf	Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation	Machine translation technology has made great progress in recent years, but it cannot guarantee error free results. Human translators perform post editing on machine translations to correct errors in the scene of computer aided translation. In favor of expediting the post editing process, many works have investigated machine translation in interactive modes, in which machines can automatically refine the rest of translations constrained by human's edits. Translation Suggestion (TS), as an interactive mode to assist human translators, requires machines to generate alternatives for specific incorrect words or phrases selected by human translators. In this paper, we utilize the parameterized objective function of neural machine translation (NMT) and propose a novel constrained decoding algorithm, namely Prefix Suffix Guided Decoding (PSGD), to deal with the TS problem without additional training. Compared to the state of the art lexically constrained decoding method, PSGD improves translation quality by an average of $10.87$ BLEU and $8.62$ BLEU on the WeTS and the WMT 2022 Translation Suggestion datasets, respectively, and reduces decoding time overhead by an average of 63.4% tested on the WMT translation datasets. Furthermore, on both of the TS benchmark datasets, it is superior to other supervised learning systems trained with TS annotated data.	['Yuqi Zhang', 'Jiayi Wang', 'Yu Zhao', 'Xin Ge', 'Ke Wang']	2022-11-14	null	null	null	null	['nmt']	['computer-code']	[ 8.88319969e-01 -6.64400980e-02 -3.07653844e-01 -4.23435718e-01 -1.27600300e+00 -5.58377028e-01 3.44757706e-01 -2.23485798e-01 -5.80255389e-01 1.11097491e+00 -2.86533665e-02 -1.05172229e+00 3.47053379e-01 -4.79040504e-01 -8.77246141e-01 -2.98738152e-01 5.89902103e-01 1.00331557e+00 -2.33108595e-01 -4.97315168e-01 4.60579395e-01 -6.13278076e-02 -8.67789745e-01 4.37526256e-01 1.58358145e+00 6.02279305e-01 5.78972518e-01 3.21192473e-01 -3.90413165e-01 -2.27843583e-01 -4.75568324e-01 -8.98558915e-01 2.70546675e-01 -7.45250940e-01 -6.78904653e-01 -2.20925033e-01 2.22520381e-02 -1.98190615e-01 1.39497906e-01 1.13600314e+00 7.23205924e-01 -2.71009713e-01 4.90030885e-01 -8.04176867e-01 -9.64120388e-01 1.05762136e+00 -3.74862313e-01 1.30776465e-02 5.20672262e-01 -7.17763826e-02 8.42906177e-01 -1.54932237e+00 5.61675489e-01 1.17372811e+00 4.55516756e-01 7.55873680e-01 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52e76f88-c452-4470-aaef-750d42e953a1	tail-dependence-structure-and-extreme-risk	2303.11030	null	https://arxiv.org/abs/2303.11030v1	https://arxiv.org/pdf/2303.11030v1.pdf	Tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets	This paper combines the Copula-CoVaR approach with the ARMA-GARCH-skewed Student-t model to investigate the tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets, taking four main agricultural commodities, namely soybean, maize, wheat, and rice as examples. The empirical results indicate that the tail dependence structures for the four futures-spot pairs are quite different, and each of them exhibits a certain degree of asymmetry. In addition, the futures market for each agricultural commodity has significant and robust extreme downside and upside risk spillover effects on the spot market, and the downside risk spillover effects for both soybeans and maize are significantly stronger than their corresponding upside risk spillover effects, while there is no significant strength difference between the two risk spillover effects for wheat, and rice. This study provides a theoretical basis for strengthening global food cooperation and maintaining global food security, and has practical significance for investors to use agricultural commodities for risk management and portfolio optimization.	['Wei-Xing Zhou', 'Peng-Fei Dai', 'Yun-Shi Dai']	2023-03-20	null	null	null	null	['portfolio-optimization']	['time-series']	[-6.69479072e-01 -1.97964460e-01 -2.42255673e-01 -2.25047588e-01 -8.04301351e-02 -8.71098578e-01 1.71358451e-01 2.36207634e-01 2.35476986e-01 4.92822111e-01 3.03414226e-01 -1.16613185e+00 -4.01299894e-01 -1.16086125e+00 -3.06891590e-01 -1.27386105e+00 -5.02866983e-01 -7.45216310e-02 -9.67425555e-02 -4.58833396e-01 3.00214514e-02 3.10523510e-01 -9.44207311e-01 -3.79955292e-01 9.45851147e-01 1.03855038e+00 7.33750984e-02 1.78523734e-02 -3.03617328e-01 -1.39794007e-01 -7.44675621e-02 -6.31811559e-01 5.12017012e-01 -6.36366010e-02 1.47588953e-01 -5.22273004e-01 -8.79060507e-01 -5.19449115e-01 3.60089809e-01 1.36200988e+00 3.01485628e-01 -3.57238770e-01 9.16977763e-01 -1.35576999e+00 -1.03979504e+00 1.16180897e+00 -1.62196040e+00 1.79064332e-03 -1.96764588e-01 4.28850166e-02 8.11193228e-01 -6.56625152e-01 -2.37299725e-01 1.46289241e+00 6.69883728e-01 -2.94736236e-01 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863c7ece-43b1-485d-94ea-f3d824aa7c04	sceneformer-indoor-scene-generation-with	2012.09793	null	https://arxiv.org/abs/2012.09793v2	https://arxiv.org/pdf/2012.09793v2.pdf	SceneFormer: Indoor Scene Generation with Transformers	We address the task of indoor scene generation by generating a sequence of objects, along with their locations and orientations conditioned on a room layout. Large-scale indoor scene datasets allow us to extract patterns from user-designed indoor scenes, and generate new scenes based on these patterns. Existing methods rely on the 2D or 3D appearance of these scenes in addition to object positions, and make assumptions about the possible relations between objects. In contrast, we do not use any appearance information, and implicitly learn object relations using the self-attention mechanism of transformers. We show that our model design leads to faster scene generation with similar or improved levels of realism compared to previous methods. Our method is also flexible, as it can be conditioned not only on the room layout but also on text descriptions of the room, using only the cross-attention mechanism of transformers. Our user study shows that our generated scenes are preferred to the state-of-the-art FastSynth scenes 53.9% and 56.7% of the time for bedroom and living room scenes, respectively. At the same time, we generate a scene in 1.48 seconds on average, 20% faster than FastSynth.	['Matthias Nießner', 'Chandan Yeshwanth', 'Xinpeng Wang']	2020-12-17	null	null	null	null	['scene-generation']	['computer-vision']	[ 1.96711138e-01 2.37911865e-01 7.47902572e-01 -4.38510269e-01 -2.91688293e-01 -7.40974665e-01 7.15464830e-01 7.16673657e-02 -1.84438676e-01 5.56330442e-01 5.38022459e-01 -1.55740499e-01 2.01695442e-01 -9.69070971e-01 -9.17054713e-01 -2.87033409e-01 2.24486411e-01 4.34888065e-01 1.74908414e-01 -3.44664127e-01 4.83322628e-02 4.81314719e-01 -1.83477271e+00 4.69825953e-01 7.92345285e-01 7.02407420e-01 8.33656728e-01 1.11497808e+00 -1.31784514e-01 8.26070428e-01 -7.83074081e-01 2.51379069e-02 2.70038158e-01 -5.02566159e-01 -7.69518435e-01 4.94710088e-01 6.80038393e-01 -4.80097204e-01 -3.48592490e-01 5.19774258e-01 5.80237806e-01 4.04566199e-01 3.13042611e-01 -8.40378404e-01 -8.05318892e-01 3.68261725e-01 -1.39581174e-01 -3.19378287e-01 9.00411248e-01 2.94913739e-01 7.62286425e-01 -8.57020497e-01 5.87517142e-01 1.16052401e+00 2.77450383e-01 5.65271974e-01 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32241292-91d0-42e5-9e36-0f47f35147f4	cross-task-knowledge-transfer-for-visually	null	null	https://openreview.net/forum?id=ByGq7hRqKX	https://openreview.net/pdf?id=ByGq7hRqKX	Cross-Task Knowledge Transfer for Visually-Grounded Navigation	Recent efforts on training visual navigation agents conditioned on language using deep reinforcement learning have been successful in learning policies for two different tasks: learning to follow navigational instructions and embodied question answering. In this paper, we aim to learn a multitask model capable of jointly learning both tasks, and transferring knowledge of words and their grounding in visual objects across tasks. The proposed model uses a novel Dual-Attention unit to disentangle the knowledge of words in the textual representations and visual objects in the visual representations, and align them with each other. This disentangled task-invariant alignment of representations facilitates grounding and knowledge transfer across both tasks. We show that the proposed model outperforms a range of baselines on both tasks in simulated 3D environments. We also show that this disentanglement of representations makes our model modular, interpretable, and allows for zero-shot transfer to instructions containing new words by leveraging object detectors.	['Lisa Lee', 'Ruslan Salakhutdinov', 'Dhruv Batra', 'Devendra Singh Chaplot', 'Devi Parikh']	2019-05-01	null	null	null	iclr-2019-5	['embodied-question-answering']	['computer-vision']	[-9.92803648e-03 2.45683402e-01 1.09384447e-01 -2.21183285e-01 -5.57420850e-01 -8.22399199e-01 1.07647395e+00 4.16528322e-02 -7.47320592e-01 5.37107229e-01 5.58535635e-01 -4.69310820e-01 1.00735486e-01 -6.20482922e-01 -1.17171240e+00 -5.58189392e-01 -2.32914895e-01 6.19652867e-01 1.32130712e-01 -3.29657316e-01 3.74469012e-01 2.95836031e-01 -1.53122294e+00 5.64646900e-01 5.37532449e-01 4.49456930e-01 8.08108270e-01 8.52982819e-01 -1.64222568e-01 1.11095166e+00 -4.28925037e-01 6.13791719e-02 2.89590567e-01 -2.94725686e-01 -1.02213585e+00 -1.11528292e-01 7.35303283e-01 -7.87507176e-01 -5.85643768e-01 7.07371175e-01 1.39518395e-01 5.27108610e-01 9.60456133e-01 -1.21160138e+00 -1.50430548e+00 4.19921666e-01 -4.28137630e-01 2.62068361e-01 3.87595922e-01 4.63454455e-01 1.37450683e+00 -9.16521847e-01 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9.56695139e-01 -9.62878987e-02 -1.02709091e+00 -6.29133999e-01 -6.84463024e-01 -2.01927230e-01 3.92354190e-01 7.44224489e-01 -1.12976158e+00 1.86896231e-02 1.05368488e-01]	[4.55482816696167, 0.5253642201423645]
0945ddd0-7d44-45fb-a18f-1b16ce7e7667	selective-experience-replay-compression-using	2302.11510	null	https://arxiv.org/abs/2302.11510v4	https://arxiv.org/pdf/2302.11510v4.pdf	Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging	Selective experience replay is a popular strategy for integrating lifelong learning with deep reinforcement learning. Selective experience replay aims to recount selected experiences from previous tasks to avoid catastrophic forgetting. Furthermore, selective experience replay based techniques are model agnostic and allow experiences to be shared across different models. However, storing experiences from all previous tasks make lifelong learning using selective experience replay computationally very expensive and impractical as the number of tasks increase. To that end, we propose a reward distribution-preserving coreset compression technique for compressing experience replay buffers stored for selective experience replay. We evaluated the coreset compression technique on the brain tumor segmentation (BRATS) dataset for the task of ventricle localization and on the whole-body MRI for localization of left knee cap, left kidney, right trochanter, left lung, and spleen. The coreset lifelong learning models trained on a sequence of 10 different brain MR imaging environments demonstrated excellent performance localizing the ventricle with a mean pixel error distance of 12.93 for the compression ratio of 10x. In comparison, the conventional lifelong learning model localized the ventricle with a mean pixel distance of 10.87. Similarly, the coreset lifelong learning models trained on whole-body MRI demonstrated no significant difference (p=0.28) between the 10x compressed coreset lifelong learning models and conventional lifelong learning models for all the landmarks. The mean pixel distance for the 10x compressed models across all the landmarks was 25.30, compared to 19.24 for the conventional lifelong learning models. Our results demonstrate that the potential of the coreset-based ERB compression method for compressing experiences without a significant drop in performance.	['Vishwa S. Parekh', 'Michael A. Jacobs', 'Vladimir Braverman', 'Samson Zhou', 'Guangyao Zheng']	2023-02-22	null	null	null	null	['tumor-segmentation', 'brain-tumor-segmentation']	['computer-vision', 'medical']	[-1.43500581e-01 -1.52205080e-02 -4.45030332e-01 -1.80735931e-01 -1.07541883e+00 -6.80662170e-02 2.29429096e-01 5.11952937e-01 -9.76886094e-01 8.80705893e-01 3.30074906e-01 8.00618008e-02 -1.74468845e-01 -3.42504233e-01 -7.34195948e-01 -9.08718765e-01 -7.62611032e-01 3.20671856e-01 1.32636994e-01 4.36609179e-01 1.64690480e-01 2.49008790e-01 -1.11225355e+00 4.37340438e-01 6.96473956e-01 7.78201282e-01 8.21886539e-01 8.19297612e-01 2.56135911e-01 1.07494736e+00 -4.45977271e-01 -3.85631174e-02 -1.39965238e-02 -4.33843493e-01 -8.31692219e-01 -2.63464302e-01 3.76484931e-01 -6.78849459e-01 -7.05539286e-01 7.84206331e-01 9.44114327e-01 2.58052796e-01 2.72251308e-01 -1.04674804e+00 -1.19444616e-01 7.31682003e-01 -3.52386206e-01 7.03086197e-01 -1.92801580e-02 2.04515070e-01 2.15388954e-01 -8.28227282e-01 8.92585099e-01 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3b53b1aa-1602-4ce5-bf31-0ed7d3bbe79d	weakly-supervised-text-instance-segmentation	2303.10848	null	https://arxiv.org/abs/2303.10848v2	https://arxiv.org/pdf/2303.10848v2.pdf	Weakly-Supervised Text Instance Segmentation	Text segmentation is a challenging vision task with many downstream applications. Current text segmentation methods require pixel-level annotations, which are expensive in the cost of human labor and limited in application scenarios. In this paper, we take the first attempt to perform weakly-supervised text instance segmentation by bridging text recognition and text segmentation. The insight is that text recognition methods provide precise attention position of each text instance, and the attention location can feed to both a text adaptive refinement head (TAR) and a text segmentation head. Specifically, the proposed TAR generates pseudo labels by performing two-stage iterative refinement operations on the attention location to fit the accurate boundaries of the corresponding text instance. Meanwhile, the text segmentation head takes the rough attention location to predict segmentation masks which are supervised by the aforementioned pseudo labels. In addition, we design a mask-augmented contrastive learning by treating our segmentation result as an augmented version of the input text image, thus improving the visual representation and further enhancing the performance of both recognition and segmentation. The experimental results demonstrate that the proposed method significantly outperforms weakly-supervised instance segmentation methods on ICDAR13-FST (18.95$\%$ improvement) and TextSeg (17.80$\%$ improvement) benchmarks.	['xiangyang xue', 'Bin Li', 'Haiyang Yu', 'Xinyan Zu']	2023-03-20	null	null	null	null	['weakly-supervised-instance-segmentation']	['computer-vision']	[ 7.75448143e-01 2.11075664e-01 -2.71864623e-01 -5.35651386e-01 -8.83205056e-01 -3.08387071e-01 4.18704391e-01 -5.93288708e-03 -5.29650748e-01 3.67178261e-01 -8.70694891e-02 -2.30865628e-01 3.72732043e-01 -4.53765631e-01 -7.51205564e-01 -7.95120776e-01 7.20850587e-01 7.11505532e-01 4.13817376e-01 3.23298007e-01 3.95908743e-01 7.68013224e-02 -1.19024420e+00 3.01049471e-01 1.24643803e+00 1.19208586e+00 4.07534838e-01 5.70870221e-01 -5.90564549e-01 5.95865488e-01 -5.27800620e-01 -3.62883300e-01 1.39704004e-01 -4.53603625e-01 -8.67585778e-01 7.64432907e-01 5.34184515e-01 -2.56377012e-01 -6.88520893e-02 1.10119128e+00 2.06274897e-01 1.38293192e-01 6.62629783e-01 -8.26829433e-01 -4.80221659e-01 7.82530367e-01 -1.25802433e+00 9.12582129e-02 -2.52477139e-01 1.43561363e-01 1.07296610e+00 -1.06739819e+00 2.76838869e-01 8.40083182e-01 4.61409181e-01 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2621839e-a33d-458b-88d9-98309a501697	neurosymbolic-ai-why-what-and-how	2305.00813	null	https://arxiv.org/abs/2305.00813v1	https://arxiv.org/pdf/2305.00813v1.pdf	Neurosymbolic AI - Why, What, and How	Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning. Human perception-inspired machine perception, in the context of AI, refers to large-scale pattern recognition from raw data using neural networks trained using self-supervised learning objectives such as next-word prediction or object recognition. On the other hand, machine cognition encompasses more complex computations, such as using knowledge of the environment to guide reasoning, analogy, and long-term planning. Humans can also control and explain their cognitive functions. This seems to require the retention of symbolic mappings from perception outputs to knowledge about their environment. For example, humans can follow and explain the guidelines and safety constraints driving their decision-making in safety-critical applications such as healthcare, criminal justice, and autonomous driving. This article introduces the rapidly emerging paradigm of Neurosymbolic AI combines neural networks and knowledge-guided symbolic approaches to create more capable and flexible AI systems. These systems have immense potential to advance both algorithm-level (e.g., abstraction, analogy, reasoning) and application-level (e.g., explainable and safety-constrained decision-making) capabilities of AI systems.	['Manas Gaur', 'Kaushik Roy', 'Amit Sheth']	2023-05-01	null	null	null	null	['object-recognition']	['computer-vision']	[ 6.27300799e-01 5.68442464e-01 -7.92162959e-03 -4.26336110e-01 2.66098291e-01 -4.87745851e-01 8.81728590e-01 5.39560974e-01 -1.83274835e-01 6.11354411e-01 8.87597650e-02 -4.95432407e-01 -3.47212106e-01 -1.08844233e+00 -7.51923025e-01 -8.93904045e-02 -1.14095584e-02 5.58458567e-01 1.36122197e-01 -6.65053010e-01 5.66043079e-01 8.99380088e-01 -2.01862884e+00 4.52480048e-01 8.73666346e-01 1.00827503e+00 2.78056383e-01 6.14491403e-01 -2.66451776e-01 1.09246480e+00 -1.57176092e-01 6.91866055e-02 -1.34978890e-01 -5.66062093e-01 -8.75412285e-01 -3.38291109e-01 -2.59010881e-01 1.70120951e-02 -1.47984266e-01 9.54322696e-01 -3.48738521e-01 3.49259794e-01 5.05574286e-01 -1.29547882e+00 -1.13808787e+00 4.01354283e-01 5.97629070e-01 -2.17414066e-01 6.11092210e-01 5.65887690e-01 4.60885465e-01 -5.68215907e-01 3.99971902e-01 1.49477446e+00 4.47975963e-01 6.54239893e-01 -1.35323608e+00 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06303e2c-c7dd-4ed4-927d-fa4761ee0582	efficient-high-resolution-template-matching	2306.15010	null	https://arxiv.org/abs/2306.15010v1	https://arxiv.org/pdf/2306.15010v1.pdf	Efficient High-Resolution Template Matching with Vector Quantized Nearest Neighbour Fields	Template matching is a fundamental problem in computer vision and has applications in various fields, such as object detection, image registration, and object tracking. The current state-of-the-art methods rely on nearest-neighbour (NN) matching in which the query feature space is converted to NN space by representing each query pixel with its NN in the template pixels. The NN-based methods have been shown to perform better in occlusions, changes in appearance, illumination variations, and non-rigid transformations. However, NN matching scales poorly with high-resolution data and high feature dimensions. In this work, we present an NN-based template-matching method which efficiently reduces the NN computations and introduces filtering in the NN fields to consider deformations. A vector quantization step first represents the template with $k$ features, then filtering compares the template and query distributions over the $k$ features. We show that state-of-the-art performance was achieved in low-resolution data, and our method outperforms previous methods at higher resolution showing the robustness and scalability of the approach.	['Ida-Maria Sintorn', 'Ankit Gupta']	2023-06-26	null	null	null	null	['template-matching', 'object-tracking', 'image-registration', 'quantization']	['computer-vision', 'computer-vision', 'computer-vision', 'methodology']	[ 6.15060270e-01 -5.37124634e-01 -1.65973008e-02 -5.67907870e-01 -9.14125085e-01 -4.56825823e-01 7.09499598e-01 -4.63601984e-02 -5.69038570e-01 1.42847419e-01 -3.46767530e-02 3.51961702e-01 -2.86396056e-01 -9.22259510e-01 -5.11178493e-01 -6.85133755e-01 1.88434988e-01 4.15571481e-01 9.53806281e-01 1.77503061e-02 4.48534161e-01 1.11549318e+00 -1.98320901e+00 1.86442435e-01 2.81649590e-01 1.32264566e+00 -3.18539701e-03 4.80152965e-01 -5.11883609e-02 -2.04685673e-01 -3.96228433e-01 -4.33978111e-01 6.33841693e-01 -6.47907630e-02 -5.94436347e-01 1.74144462e-01 1.23498023e+00 1.90234594e-02 -5.41294694e-01 1.20087683e+00 6.31795466e-01 2.79440045e-01 6.23547733e-01 -1.00644720e+00 -4.67621505e-01 -3.91995877e-01 -4.70122159e-01 3.32874209e-02 5.04471660e-01 -8.74269530e-02 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d22eddf7-56e7-475a-9324-fd8104491eb5	attention-based-multimodal-fusion-for-video	1701.03126	null	http://arxiv.org/abs/1701.03126v2	http://arxiv.org/pdf/1701.03126v2.pdf	Attention-Based Multimodal Fusion for Video Description	Currently successful methods for video description are based on encoder-decoder sentence generation using recur-rent neural networks (RNNs). Recent work has shown the advantage of integrating temporal and/or spatial attention mechanisms into these models, in which the decoder net-work predicts each word in the description by selectively giving more weight to encoded features from specific time frames (temporal attention) or to features from specific spatial regions (spatial attention). In this paper, we propose to expand the attention model to selectively attend not just to specific times or spatial regions, but to specific modalities of input such as image features, motion features, and audio features. Our new modality-dependent attention mechanism, which we call multimodal attention, provides a natural way to fuse multimodal information for video description. We evaluate our method on the Youtube2Text dataset, achieving results that are competitive with current state of the art. More importantly, we demonstrate that our model incorporating multimodal attention as well as temporal attention significantly outperforms the model that uses temporal attention alone.	['Kazuhiro Sumi', 'Teng-Yok Lee', 'Tim K. Marks', 'John R. Hershey', 'Takaaki Hori', 'Chiori Hori']	2017-01-11	attention-based-multimodal-fusion-for-video-1	http://openaccess.thecvf.com/content_iccv_2017/html/Hori_Attention-Based_Multimodal_Fusion_ICCV_2017_paper.html	http://openaccess.thecvf.com/content_ICCV_2017/papers/Hori_Attention-Based_Multimodal_Fusion_ICCV_2017_paper.pdf	iccv-2017-10	['video-description']	['computer-vision']	[ 1.71096608e-01 -1.49751469e-01 -3.58191103e-01 -3.07341307e-01 -8.59091520e-01 -3.05884749e-01 8.89266908e-01 -5.65329008e-02 -3.67793024e-01 6.06032908e-01 8.86303782e-01 1.25332609e-01 2.03039810e-01 -4.98042375e-01 -7.25577533e-01 -4.12904888e-01 -6.08183667e-02 -6.55685924e-03 1.14310361e-01 -2.00459331e-01 4.17116880e-01 2.55416095e-01 -1.55981898e+00 7.19250798e-01 2.73055434e-01 8.82421076e-01 4.75307703e-01 9.13146198e-01 -1.26131862e-01 1.15433276e+00 -3.63781869e-01 -3.06238681e-01 -3.18499386e-01 -6.33002460e-01 -8.68520498e-01 8.46484229e-02 2.97526747e-01 -5.43051124e-01 -7.48088598e-01 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a7a21f19-2375-46b6-b8aa-6aedf8997335	towards-social-generative-ai-for-education	2306.10063	null	https://arxiv.org/abs/2306.10063v1	https://arxiv.org/pdf/2306.10063v1.pdf	Towards social generative AI for education: theory, practices and ethics	This paper explores educational interactions involving humans and artificial intelligences not as sequences of prompts and responses, but as a social process of conversation and exploration. In this conception, learners continually converse with AI language models within a dynamic computational medium of internet tools and resources. Learning happens when this distributed system sets goals, builds meaning from data, consolidates understanding, reconciles differences, and transfers knowledge to new domains. Building social generative AI for education will require development of powerful AI systems that can converse with each other as well as humans, construct external representations such as knowledge maps, access and contribute to internet resources, and act as teachers, learners, guides and mentors. This raises fundamental problems of ethics. Such systems should be aware of their limitations, their responsibility to learners and the integrity of the internet, and their respect for human teachers and experts. We need to consider how to design and constrain social generative AI for education.	['Mike Sharples']	2023-06-14	null	null	null	null	['ethics']	['miscellaneous']	[ 2.73292903e-02 6.73137307e-01 -3.21767144e-02 -1.47267640e-01 1.72021985e-01 -7.56471038e-01 9.21139479e-01 3.90597105e-01 -1.15985408e-01 7.88704157e-01 5.54538250e-01 -4.12377805e-01 -2.17797235e-01 -1.16016746e+00 -4.57781643e-01 -2.35246152e-01 5.23476124e-01 6.02626443e-01 3.68408114e-01 -5.50419629e-01 3.63546848e-01 2.90045440e-01 -1.92360306e+00 -1.95555672e-01 1.60722530e+00 -1.16238542e-01 1.51481196e-01 6.24814928e-01 -5.84655821e-01 1.61156571e+00 -7.79176950e-01 -5.76246321e-01 -3.96223456e-01 -9.82672274e-01 -1.09897816e+00 -1.47484377e-01 1.57144800e-01 -3.24657738e-01 9.89943594e-02 1.12313104e+00 3.10230255e-01 2.91835815e-01 1.97151229e-01 -1.19091308e+00 -1.22836089e+00 9.07776773e-01 8.77680853e-02 1.43282795e-02 7.00492144e-01 1.93089515e-01 9.84528884e-02 -2.64261335e-01 8.71776044e-01 1.29693472e+00 3.60517859e-01 8.15277815e-01 -8.67067873e-01 -8.15748990e-01 -6.85228333e-02 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b717181d-9f75-48ae-84e0-bd9c5da46009	sieg-at-mediqa-2019-multi-task-neural	null	null	https://aclanthology.org/W19-5049	https://aclanthology.org/W19-5049.pdf	Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment	This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain. Recognizing textual inference relations and question similarity can address the issue of answering new consumer health questions by mapping them to Frequently Asked Questions on reputed websites like the NIH. We show that leveraging information from parallel tasks across domains along with medical knowledge integration allows our model to learn better biomedical feature representations. Our final models for the NLI and RQE tasks achieve the 4th and 2nd rank on the shared-task leaderboard respectively.	['James Route', 'Sai Abishek Bhaskar', 'Rashi Rungta', 'Teruko Mitamura', 'Eric Nyberg']	2019-08-01	null	null	null	ws-2019-8	['question-similarity']	['natural-language-processing']	[ 4.11544472e-01 4.81799275e-01 -4.81643200e-01 -6.73941135e-01 -1.59253812e+00 -4.27256495e-01 3.79664004e-01 8.01151514e-01 -6.64657593e-01 9.01505351e-01 6.98689759e-01 -5.57057500e-01 -6.15480840e-01 -3.30382079e-01 -9.76224244e-01 1.52585655e-01 3.40325385e-02 7.85172164e-01 5.54240367e-04 -2.86397427e-01 1.66093528e-01 -1.01582423e-01 -4.85560983e-01 1.20605576e+00 9.91175234e-01 1.16524136e+00 -1.61708340e-01 7.95585930e-01 -2.67583787e-01 1.58560050e+00 -3.94379318e-01 -7.58070230e-01 -3.86591822e-01 -2.68249214e-01 -1.79879522e+00 -4.87435132e-01 4.91214007e-01 -1.99718252e-01 -1.44312799e-01 7.77826607e-01 3.92320216e-01 -1.81036554e-02 8.04203987e-01 -1.14864540e+00 -1.11512446e+00 6.00324571e-01 -2.60165751e-01 6.55144930e-01 1.00450373e+00 -3.35096687e-01 1.65347874e+00 -8.94960344e-01 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c3805f26-ad2a-488b-8f07-533069987f88	depth-quality-aware-salient-object-detection	2008.04159	null	https://arxiv.org/abs/2008.04159v1	https://arxiv.org/pdf/2008.04159v1.pdf	Depth Quality Aware Salient Object Detection	The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D). The D quality usually varies from scene to scene, while the SOTA bi-stream approaches are depth quality unaware, which easily result in substantial difficulties in achieving complementary fusion status between RGB and D, leading to poor fusion results in facing of low-quality D. Thus, this paper attempts to integrate a novel depth quality aware subnet into the classic bi-stream structure, aiming to assess the depth quality before conducting the selective RGB-D fusion. Compared with the SOTA bi-stream methods, the major highlight of our method is its ability to lessen the importance of those low-quality, no-contribution, or even negative-contribution D regions during the RGB-D fusion, achieving a much improved complementary status between RGB and D.	['Chenglizhao Chen', 'Jipeng Wei', 'Hong Qin', 'Chong Peng']	2020-08-07	null	null	null	null	['rgb-d-salient-object-detection', 'salient-object-detection']	['computer-vision', 'computer-vision']	[ 2.01437950e-01 -2.16123983e-01 1.82954688e-02 -1.72547072e-01 -5.09966135e-01 -2.92958707e-01 6.19903326e-01 4.75338638e-01 -3.06082845e-01 2.96777606e-01 8.97230133e-02 -1.79111250e-02 -1.16417788e-01 -1.04401731e+00 -5.57498969e-02 -7.43645191e-01 2.85344303e-01 -1.45916387e-01 7.66487718e-01 -4.19476479e-01 2.16867328e-01 7.83637524e-01 -2.08845806e+00 5.84429801e-02 7.36390591e-01 1.44183302e+00 2.11484581e-01 4.92689788e-01 -4.04502779e-01 5.42756379e-01 -5.08657396e-01 -1.58157915e-01 5.99084973e-01 -5.51123679e-01 -2.41898254e-01 1.73205659e-01 2.82512069e-01 -4.85471427e-01 -2.93590486e-01 1.33193851e+00 6.20223701e-01 -5.66559359e-02 4.43709821e-01 -1.42501593e+00 -1.68212324e-01 5.41661680e-02 -9.26487327e-01 3.43956172e-01 6.42903566e-01 2.44200423e-01 7.57978439e-01 -8.66281331e-01 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134639a2-771a-4eec-9bf4-3e264555f289	human-scene-network-a-novel-baseline-with	2301.07923	null	https://arxiv.org/abs/2301.07923v1	https://arxiv.org/pdf/2301.07923v1.pdf	Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection	Video anomaly detection in surveillance systems with only video-level labels (i.e. weakly-supervised) is challenging. This is due to, (i) the complex integration of human and scene based anomalies comprising of subtle and sharp spatio-temporal cues in real-world scenarios, (ii) non-optimal optimization between normal and anomaly instances under weak supervision. In this paper, we propose a Human-Scene Network to learn discriminative representations by capturing both subtle and strong cues in a dissociative manner. In addition, a self-rectifying loss is also proposed that dynamically computes the pseudo temporal annotations from video-level labels for optimizing the Human-Scene Network effectively. The proposed Human-Scene Network optimized with self-rectifying loss is validated on three publicly available datasets i.e. UCF-Crime, ShanghaiTech and IITB-Corridor, outperforming recently reported state-of-the-art approaches on five out of the six scenarios considered.	['Francois Bremond', 'Gianpiero Francesca', 'Lorenzo Garattoni', 'Quan Kong', 'Rui Dai', 'Snehashis Majhi']	2023-01-19	null	null	null	null	['video-anomaly-detection']	['computer-vision']	[ 3.26861620e-01 -2.85747558e-01 3.57846953e-02 -5.10925174e-01 -5.48303366e-01 -5.01294315e-01 6.34649456e-01 1.82053044e-01 -6.06388032e-01 4.63293165e-01 9.71844494e-02 2.53538024e-02 -1.69962093e-01 -2.34096363e-01 -7.39216447e-01 -5.82060277e-01 -5.96632957e-01 2.08721414e-01 4.25754339e-01 -1.17409542e-01 2.48266049e-02 4.10816103e-01 -1.78873336e+00 4.03069735e-01 9.38085079e-01 1.37221098e+00 -3.30859870e-01 8.62467349e-01 4.01580483e-01 1.10066175e+00 -3.17434639e-01 -4.31509733e-01 7.04533577e-01 -2.79838443e-01 -4.39841509e-01 5.11727095e-01 1.23834825e+00 -6.12360358e-01 -6.99262321e-01 1.22510159e+00 8.40506330e-02 2.31533587e-01 6.63799226e-01 -1.56697178e+00 -3.32675606e-01 -2.68090844e-01 -7.06772268e-01 7.80260861e-01 4.05243814e-01 6.48132980e-01 7.85059214e-01 -7.19127715e-01 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5be8e567-f3cc-4f8e-84f3-e572b03fd26c	fine-grained-visual-textual-representation	1709.00340	null	http://arxiv.org/abs/1709.00340v4	http://arxiv.org/pdf/1709.00340v4.pdf	Fine-grained Visual-textual Representation Learning	Fine-grained visual categorization is to recognize hundreds of subcategories belonging to the same basic-level category, which is a highly challenging task due to the quite subtle and local visual distinctions among similar subcategories. Most existing methods generally learn part detectors to discover discriminative regions for better categorization performance. However, not all parts are beneficial and indispensable for visual categorization, and the setting of part detector number heavily relies on prior knowledge as well as experimental validation. As is known to all, when we describe the object of an image via textual descriptions, we mainly focus on the pivotal characteristics, and rarely pay attention to common characteristics as well as the background areas. This is an involuntary transfer from human visual attention to textual attention, which leads to the fact that textual attention tells us how many and which parts are discriminative and significant to categorization. So textual attention could help us to discover visual attention in image. Inspired by this, we propose a fine-grained visual-textual representation learning (VTRL) approach, and its main contributions are: (1) Fine-grained visual-textual pattern mining devotes to discovering discriminative visual-textual pairwise information for boosting categorization performance through jointly modeling vision and text with generative adversarial networks (GANs), which automatically and adaptively discovers discriminative parts. (2) Visual-textual representation learning jointly combines visual and textual information, which preserves the intra-modality and inter-modality information to generate complementary fine-grained representation, as well as further improves categorization performance.	['Yuxin Peng', 'Xiangteng He']	2017-08-31	null	null	null	null	['fine-grained-visual-categorization']	['computer-vision']	[ 9.17732567e-02 -4.55515593e-01 -3.33935231e-01 -3.15448940e-01 -4.07674283e-01 -8.37964237e-01 7.31653214e-01 5.65843098e-02 -1.30297601e-01 3.68788570e-01 4.61435258e-01 -1.06890149e-01 -9.88630578e-02 -7.07592845e-01 -6.58754945e-01 -7.33175039e-01 3.71930927e-01 2.47243926e-01 2.52572000e-01 -2.29025885e-01 2.03014061e-01 4.20138329e-01 -1.57247531e+00 6.83239043e-01 7.78954625e-01 1.34917247e+00 2.32778713e-01 2.40054950e-01 -4.71633196e-01 7.54343092e-01 -5.66999018e-01 -3.69717181e-01 9.58118141e-02 -4.53353435e-01 -6.69247031e-01 4.88818526e-01 4.55325902e-01 -8.48924145e-02 -3.62964332e-01 1.22084761e+00 1.15185440e-01 1.18538760e-01 1.08754480e+00 -1.35033774e+00 -1.09529340e+00 5.30473590e-01 -9.88634765e-01 2.44527891e-01 -8.73364434e-02 3.27742666e-01 9.84856009e-01 -8.22739005e-01 2.00023890e-01 1.46249831e+00 5.48904777e-01 3.85325462e-01 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463a5733-f47f-48bf-90b5-6d41c2d376ed	normalization-in-training-u-net-for-2d	1809.03783	null	http://arxiv.org/abs/1809.03783v3	http://arxiv.org/pdf/1809.03783v3.pdf	Normalization in Training U-Net for 2D Biomedical Semantic Segmentation	2D biomedical semantic segmentation is important for robotic vision in surgery. Segmentation methods based on Deep Convolutional Neural Network (DCNN) can out-perform conventional methods in terms of both accuracy and levels of automation. One common issue in training a DCNN for biomedical semantic segmentation is the internal covariate shift where the training of convolutional kernels is encumbered by the distribution change of input features, hence both the training speed and performance are decreased. Batch Normalization (BN) is the first proposed method for addressing internal covariate shift and is widely used. Instance Normalization (IN) and Layer Normalization (LN) have also been proposed. Group Normalization (GN) is proposed more recently and has not yet been applied to 2D biomedical semantic segmentation, however, no specific validations on GN were given. Most DCNNs for biomedical semantic segmentation adopt BN as the normalization method by default, without reviewing its performance. In this paper, four normalization methods - BN, IN, LN and GN are compared in details, specifically for 2D biomedical semantic segmentation. U-Net is adopted as the basic DCNN structure. Three datasets regarding the Right Ventricle (RV), aorta, and Left Ventricle (LV) are used for the validation. The results show that detailed subdivision of the feature map, i.e. GN with a large group number or IN, achieves higher accuracy. This accuracy improvement mainly comes from better model generalization. Codes are uploaded and maintained at Xiao-Yun Zhou's Github.	['Guang-Zhong Yang', 'Xiao-Yun Zhou']	2018-09-11	null	null	null	null	['2d-semantic-segmentation']	['computer-vision']	[ 1.89351514e-01 3.23109239e-01 -2.00145930e-01 -3.82223278e-01 8.33855867e-02 -2.02720433e-01 2.28587106e-01 2.02820525e-01 -6.63701534e-01 5.53162336e-01 -4.68688048e-02 -3.05191249e-01 -1.01509616e-01 -6.97936594e-01 -3.17885190e-01 -8.38093162e-01 1.61456779e-01 2.83095688e-01 2.86884397e-01 -6.09162077e-02 1.30094171e-01 8.00282657e-01 -1.10372674e+00 -1.01670913e-01 7.85713613e-01 9.67414200e-01 3.28672647e-01 4.04291481e-01 -3.98900032e-01 2.67634809e-01 -7.40301788e-01 2.24921312e-02 3.37418586e-01 -5.23130238e-01 -8.49190354e-01 -1.27806827e-01 -5.42888083e-02 -1.43926755e-01 -6.21275790e-02 1.14868593e+00 8.25823605e-01 9.66534317e-02 7.82536626e-01 -9.77857530e-01 -2.79632270e-01 7.05353320e-01 -4.46057260e-01 1.45544186e-01 -3.44356805e-01 -1.20328404e-01 2.10911661e-01 -4.16717947e-01 5.71220756e-01 1.06607795e+00 9.31214392e-01 7.59006143e-01 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-6.15495384e-01 3.86281461e-01 1.50232809e-02 -7.18109250e-01 -3.63793522e-01 3.50074396e-02]	[14.625676155090332, -2.5937044620513916]
5c8e8362-2b7b-43bf-aafd-a2b4f08299e0	community-detection-with-known-unknown-or	2301.04088	null	https://arxiv.org/abs/2301.04088v1	https://arxiv.org/pdf/2301.04088v1.pdf	Community Detection with Known, Unknown, or Partially Known Auxiliary Latent Variables	Empirical observations suggest that in practice, community membership does not completely explain the dependency between the edges of an observation graph. The residual dependence of the graph edges are modeled in this paper, to first order, by auxiliary node latent variables that affect the statistics of the graph edges but carry no information about the communities of interest. We then study community detection in graphs obeying the stochastic block model and censored block model with auxiliary latent variables. We analyze the conditions for exact recovery when these auxiliary latent variables are unknown, representing unknown nuisance parameters or model mismatch. We also analyze exact recovery when these secondary latent variables have been either fully or partially revealed. Finally, we propose a semidefinite programming algorithm for recovering the desired labels when the secondary labels are either known or unknown. We show that exact recovery is possible by semidefinite programming down to the respective maximum likelihood exact recovery threshold.	['Aria Nosratinia', 'Mohammad Esmaeili']	2023-01-08	null	null	null	null	['stochastic-block-model', 'community-detection']	['graphs', 'graphs']	[ 2.88555712e-01 5.52273035e-01 -5.11735678e-01 1.65278148e-02 -5.36029100e-01 -7.66389370e-01 2.84580350e-01 1.40789598e-02 6.49642125e-02 9.01143014e-01 1.23313859e-01 -1.83551684e-01 -4.88981962e-01 -6.14015818e-01 -7.41325498e-01 -1.05370212e+00 -4.03104484e-01 1.06316650e+00 -3.58549953e-01 3.78316194e-01 -1.47544131e-01 5.04408419e-01 -7.21748710e-01 -2.12331146e-01 7.48229444e-01 5.23318350e-02 -2.56825358e-01 8.95929337e-01 3.67961019e-01 6.23196006e-01 -2.42541388e-01 -1.28193751e-01 2.43983686e-01 -1.89580828e-01 -3.98366839e-01 8.39238703e-01 4.33035865e-02 -1.77387103e-01 -6.38671279e-01 1.38525379e+00 6.94753155e-02 -4.78540510e-01 9.81662333e-01 -1.81619251e+00 -7.14751244e-01 9.60308254e-01 -1.00744390e+00 -1.62725478e-01 2.50468433e-01 -2.73200512e-01 1.14445996e+00 -7.15103745e-01 7.11804330e-01 1.15141869e+00 6.04777217e-01 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1d37e617-379a-436d-8522-1c59ea984036	label-informed-graph-structure-learning-for	2108.04595	null	https://arxiv.org/abs/2108.04595v1	https://arxiv.org/pdf/2108.04595v1.pdf	Label-informed Graph Structure Learning for Node Classification	Graph Neural Networks (GNNs) have achieved great success among various domains. Nevertheless, most GNN methods are sensitive to the quality of graph structures. To tackle this problem, some studies exploit different graph structure learning strategies to refine the original graph structure. However, these methods only consider feature information while ignoring available label information. In this paper, we propose a novel label-informed graph structure learning framework which incorporates label information explicitly through a class transition matrix. We conduct extensive experiments on seven node classification benchmark datasets and the results show that our method outperforms or matches the state-of-the-art baselines.	['Liang Wang', 'Shu Wu', 'Fenyu Hu', 'Liping Wang']	2021-08-10	null	null	null	null	['graph-structure-learning']	['graphs']	[ 1.82905838e-01 1.91304654e-01 -8.11969161e-01 -3.56021047e-01 -1.06096320e-01 -4.39568311e-01 5.94138384e-01 3.85383785e-01 -3.27444315e-01 7.24829793e-01 -1.54684773e-02 -4.26016092e-01 -1.95518717e-01 -1.07653272e+00 -3.96389812e-01 -5.35346150e-01 -2.06813030e-02 3.64370584e-01 3.91902149e-01 -3.12700160e-02 1.25750735e-01 3.66954088e-01 -8.76344562e-01 -1.36735439e-01 6.67773485e-01 5.25176406e-01 -2.46141016e-01 2.63455600e-01 -2.86918402e-01 1.12506378e+00 -3.96389961e-01 -4.83749479e-01 1.88376412e-01 -2.96814024e-01 -8.24723780e-01 7.11898804e-02 1.70204520e-01 -4.32654507e-02 -8.14876378e-01 1.39667165e+00 2.41282761e-01 1.35297984e-01 7.29190111e-01 -1.46935368e+00 -7.30357587e-01 1.01231754e+00 -6.67222261e-01 1.34749815e-01 1.14486493e-01 -9.37222838e-02 1.42384124e+00 -4.89037484e-01 6.90821350e-01 1.13475609e+00 7.78772593e-01 3.91744435e-01 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62bbb4ba-6663-4067-8476-48d48f64ab4e	a-weakly-supervised-approach-to-emotion	2306.06979	null	https://arxiv.org/abs/2306.06979v1	https://arxiv.org/pdf/2306.06979v1.pdf	A Weakly Supervised Approach to Emotion-change Prediction and Improved Mood Inference	Whilst a majority of affective computing research focuses on inferring emotions, examining mood or understanding the \textit{mood-emotion interplay} has received significantly less attention. Building on prior work, we (a) deduce and incorporate emotion-change ($\Delta$) information for inferring mood, without resorting to annotated labels, and (b) attempt mood prediction for long duration video clips, in alignment with the characterisation of mood. We generate the emotion-change ($\Delta$) labels via metric learning from a pre-trained Siamese Network, and use these in addition to mood labels for mood classification. Experiments evaluating \textit{unimodal} (training only using mood labels) vs \textit{multimodal} (training using mood plus $\Delta$ labels) models show that mood prediction benefits from the incorporation of emotion-change information, emphasising the importance of modelling the mood-emotion interplay for effective mood inference.	['Roland Goecke', 'Ramanathan Subramanian', 'Akshay Asthana', 'Iman Abbasnejad', 'Ravikiran Parameshwara', 'Ibrahim Radwan', 'Soujanya Narayana']	2023-06-12	null	null	null	null	['metric-learning', 'metric-learning']	['computer-vision', 'methodology']	[ 5.39569795e-01 1.86744407e-02 9.49447230e-02 -9.27717626e-01 -6.97575271e-01 -6.71710491e-01 4.31579441e-01 3.36027056e-01 -2.95120776e-01 5.54646611e-01 3.59315723e-01 1.14911504e-01 -9.09868404e-02 -3.48851502e-01 -1.53306916e-01 -5.75294793e-01 -1.75172612e-01 1.91117793e-01 -8.23963761e-01 -2.65660018e-01 1.28064796e-01 -1.41386956e-01 -1.78069520e+00 3.49044085e-01 4.96072978e-01 1.53852081e+00 -5.33786893e-01 8.19872558e-01 -3.53375189e-02 1.16903019e+00 -5.31846941e-01 -8.05245280e-01 1.38453901e-01 -9.00415361e-01 -7.13766277e-01 1.68270692e-01 2.28891909e-01 1.63921982e-01 1.54495224e-01 8.91757488e-01 4.79035616e-01 5.69787323e-01 8.88028443e-01 -1.34655607e+00 -4.26678240e-01 2.02118397e-01 -2.39030883e-01 2.26824909e-01 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713feeeb-fa5f-4788-9c89-4e086c57c6fa	multimodal-contrastive-learning-for	2304.11080	null	https://arxiv.org/abs/2304.11080v1	https://arxiv.org/pdf/2304.11080v1.pdf	Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata	This work discusses the use of contrastive learning and deep learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals. While the ECG signals usually contain 12 leads (channels), many healthcare facilities and devices lack access to all these 12 leads. This raises the problem of how to use only fewer ECG leads to produce meaningful diagnoses with high performance. We introduce a simple experiment to test whether contrastive learning can be applied to this task. More specifically, we added the similarity between the embedding vectors when the 12 leads signal and the fewer leads ECG signal to the loss function to bring these representations closer together. Despite its simplicity, this has been shown to have improved the performance of diagnosing with all lead combinations, proving the potential of contrastive learning on this task.	['Hieu Pham', 'Phi Le Nguyen', 'Nhat H. Tran', 'Tue M. Cao']	2023-04-18	null	null	null	null	['electrocardiography-ecg']	['methodology']	[ 1.44166112e-01 9.98164713e-02 9.75528732e-02 -3.89131367e-01 -5.33153951e-01 -3.81124437e-01 5.50278313e-02 1.18095070e-01 -3.75004292e-01 7.34226525e-01 -3.47362049e-02 -5.96862078e-01 -4.33809280e-01 -5.00652790e-01 -3.53900433e-01 -6.36235893e-01 -6.46495044e-01 3.08727086e-01 -3.47948760e-01 -5.16388044e-02 -2.49665648e-01 5.91726840e-01 -8.96147668e-01 3.18464339e-01 5.44103503e-01 7.77952611e-01 -2.46621668e-01 8.04118216e-01 2.99489945e-01 5.63000202e-01 -9.88836169e-01 -3.22772712e-01 4.98469979e-01 -7.70143032e-01 -6.36391819e-01 -9.25646797e-02 2.32779354e-01 -5.43287694e-01 -3.05119246e-01 6.37336075e-01 1.02821767e+00 -1.42898396e-01 6.93070173e-01 -1.08222175e+00 -1.98050186e-01 5.77434540e-01 -4.12426174e-01 5.01012146e-01 3.48095894e-01 1.48510844e-01 8.57987523e-01 -4.94573444e-01 4.10179645e-01 8.35858285e-01 1.08882940e+00 3.73904735e-01 -1.20675135e+00 -5.46686172e-01 -3.68596971e-01 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990e03b6-46b6-4ca6-af58-b79a6acc2e55	streaming-parallel-transducer-beam-search	2203.15773	null	https://arxiv.org/abs/2203.15773v1	https://arxiv.org/pdf/2203.15773v1.pdf	Streaming parallel transducer beam search with fast-slow cascaded encoders	Streaming ASR with strict latency constraints is required in many speech recognition applications. In order to achieve the required latency, streaming ASR models sacrifice accuracy compared to non-streaming ASR models due to lack of future input context. Previous research has shown that streaming and non-streaming ASR for RNN Transducers can be unified by cascading causal and non-causal encoders. This work improves upon this cascaded encoders framework by leveraging two streaming non-causal encoders with variable input context sizes that can produce outputs at different audio intervals (e.g. fast and slow). We propose a novel parallel time-synchronous beam search algorithm for transducers that decodes from fast-slow encoders, where the slow encoder corrects the mistakes generated from the fast encoder. The proposed algorithm, achieves up to 20% WER reduction with a slight increase in token emission delays on the public Librispeech dataset and in-house datasets. We also explore techniques to reduce the computation by distributing processing between the fast and slow encoders. Lastly, we explore sharing the parameters in the fast encoder to reduce the memory footprint. This enables low latency processing on edge devices with low computation cost and a low memory footprint.	['Michael L Seltzer', 'Ozlem Kalinli', 'Vikas Chandra', 'Jiedan Zhu', 'Duc Le', 'Ke Li', 'Yangyang Shi', 'Jay Mahadeokar']	2022-03-29	null	null	null	null	['low-latency-processing']	['robots']	[ 6.49622083e-01 1.86421499e-01 4.48610820e-02 -4.12728041e-01 -1.20499146e+00 -5.73868871e-01 2.92408943e-01 6.17409572e-02 -5.10612130e-01 3.00939530e-01 5.19931972e-01 -6.54369533e-01 1.13392390e-01 -5.18880665e-01 -9.14264023e-01 -4.99811262e-01 1.32162318e-01 2.72100568e-01 7.05712259e-01 -2.44161282e-02 -1.91124796e-03 7.31798410e-02 -1.66890311e+00 7.97666609e-01 5.20768344e-01 8.20143998e-01 8.05157483e-01 1.47979808e+00 -5.15647270e-02 1.16387153e+00 -7.97740340e-01 -3.10642123e-01 1.36687636e-01 -5.56131840e-01 -5.32601595e-01 -4.67243254e-01 2.75829256e-01 -5.34532607e-01 -5.53180218e-01 7.66133249e-01 8.34654689e-01 5.68119064e-02 6.19495171e-04 -7.87810028e-01 -3.67544442e-01 1.33757603e+00 -3.80911618e-01 5.57941198e-01 3.79079461e-01 -4.89602610e-02 1.21020806e+00 -6.77411675e-01 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bb4de338-0466-4ba3-9172-e60d78d85510	agcn-adversarial-graph-convolutional-network	null	null	https://www.bmvc2021-virtualconference.com/assets/papers/1545.pdf	https://www.bmvc2021-virtualconference.com/assets/papers/1545.pdf	AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud Segmentation	3D point cloud segmentation provides a high-level semantic understanding of object structure that is valuable in applications such as medicine, robotics and self-driving. In this paper, we propose an Adversarial Graph Convolutional Network for 3D point cloud segmentation. Many current networks encounter problems such as low segmentation accuracy and high complexities due to their crude network architectures and local feature aggregation methods. To overcome these problems, we propose a) a graph convolutional network (GCN) in an adversarial learning scheme where a discriminator network provides a segmentation network with informative information to improve segmentation accuracy and b) a graph convolution, GeoEdgeConv, as a means of local feature aggregation to improve segmentation accuracy and space and time complexities. By using an embedding L2 loss as an adversarial loss, the proposed network is learned to reduce noisy labels by enforcing the consistency between neighbouring labels. Preserving geometric structures over convolution layers by using both point and relative position features, GeoEdgeConv helps learn fine details of complex structures, and thus improves segmentation accuracy in boundaries and reduces label noise inside a class without increased computational complexity. Experiments on ShapeNet Part demonstrate that our model outperforms the state-of-the-art (SOTA) with lower complexity and it has strong prospects in applications requiring low power but high segmentation performance.	['Daniel C. Alexander', 'Seunghoi Kim']	2021-11-25	null	null	null	british-machine-vision-conference-bmvc-2021	['3d-part-segmentation', 'point-cloud-segmentation']	['computer-vision', 'computer-vision']	[ 7.23878741e-02 4.35077310e-01 8.61399472e-02 -3.12942624e-01 -3.54485035e-01 -5.81694245e-01 2.78588623e-01 1.15093671e-01 -2.34467253e-01 2.87619174e-01 -5.43270767e-01 -2.46170774e-01 1.05272800e-01 -1.30768871e+00 -9.84995306e-01 -6.94712520e-01 -1.27562761e-01 5.67888260e-01 6.01341903e-01 -1.71691164e-01 9.50457528e-02 1.13380802e+00 -9.17126179e-01 -1.33870080e-01 1.19922793e+00 1.23063242e+00 -3.18147428e-02 2.50023305e-01 -4.81016666e-01 4.72310156e-01 -5.62943757e-01 -4.48154122e-01 7.20085740e-01 -2.49342933e-01 -9.06980574e-01 1.32219449e-01 4.00011927e-01 -8.70661065e-02 -3.76817197e-01 1.44106901e+00 4.20922935e-01 8.49178061e-03 5.91604888e-01 -1.32897151e+00 -7.07635581e-01 2.60779768e-01 -6.90624952e-01 -2.39969075e-01 -4.74076904e-02 3.92109931e-01 6.04819953e-01 -4.75223750e-01 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66c97663-9ef6-4d41-9119-3483a6a62bdf	differential-private-stack-generalization	1811.09491	null	https://arxiv.org/abs/1811.09491v3	https://arxiv.org/pdf/1811.09491v3.pdf	Differential Private Stack Generalization with an Application to Diabetes Prediction	To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms. In this paper, motivated by the success of improving predicting performance by ensemble learning, we propose to enhance privacy-preserving logistic regression by stacking. We show that this can be done either by sample-based or feature-based partitioning. However, we prove that when privacy-budgets are the same, feature-based partitioning requires fewer samples than sample-based one, and thus likely has better empirical performance. As transfer learning is difficult to be integrated with a differential privacy guarantee, we further combine the proposed method with hypothesis transfer learning to address the problem of learning across different organizations. Finally, we not only demonstrate the effectiveness of our method on two benchmark data sets, i.e., MNIST and NEWS20, but also apply it into a real application of cross-organizational diabetes prediction from RUIJIN data set, where privacy is of significant concern.	['Quanming Yao', 'WeiWei Tu', 'Wenyuan Dai', 'Qiang Yang', 'James T. Kwok', 'Yuqiang Chen', 'Xiawei Guo']	2018-11-23	null	null	null	null	['diabetes-prediction']	['medical']	[ 1.86639547e-01 5.05923294e-03 -1.04569279e-01 -5.48032284e-01 -7.33653426e-01 -4.16529715e-01 7.68834911e-03 3.88267934e-01 -4.85640496e-01 1.20339739e+00 2.91056708e-02 -3.70383114e-01 -2.14157477e-01 -9.05530930e-01 -9.70587134e-01 -9.20776725e-01 6.30524680e-02 1.17745697e-01 -2.59495258e-01 2.41645768e-01 1.47284985e-01 3.00262988e-01 -1.03919196e+00 3.77519339e-01 1.09779787e+00 9.05725598e-01 -5.08912265e-01 1.19773313e-01 3.13274384e-01 6.47348881e-01 -3.40883195e-01 -8.34640145e-01 7.81159818e-01 -3.32736999e-01 -6.09921515e-01 -8.19353238e-02 3.30297023e-01 -3.19404751e-01 -2.53776520e-01 1.07390261e+00 4.72645462e-01 -6.35773083e-03 5.80505669e-01 -1.48473608e+00 -4.53355253e-01 5.08792639e-01 -7.51450777e-01 -4.66556787e-01 -1.33200124e-01 -2.07974046e-01 8.97147179e-01 -6.47850215e-01 3.97584766e-01 8.99614990e-01 8.62497509e-01 4.00274932e-01 -1.56424129e+00 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3.51936758e-01 -2.57619202e-01 -3.24367225e-01 -1.99166596e-01]	[6.111204147338867, 6.628015518188477]
ee13b7ce-29e8-416b-b413-b46c0bce608d	modelling-radiological-language-with	1609.08409	null	http://arxiv.org/abs/1609.08409v1	http://arxiv.org/pdf/1609.08409v1.pdf	Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks	Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and negation detection. We investigate whether learning several types of word embeddings improves BiLSTM's performance on those tasks. Using a large dataset of chest x-ray reports, we compare the proposed model to a baseline dictionary-based NER system and a negation detection system that leverages the hand-crafted rules of the NegEx algorithm and the grammatical relations obtained from the Stanford Dependency Parser. Compared to these more traditional rule-based systems, we argue that BiLSTM offers a strong alternative for both our tasks.	['Samuel Withey', 'Giovanni Montana', 'Robert Bakewell', 'Savelie Cornegruta']	2016-09-27	modelling-radiological-language-with-1	https://aclanthology.org/W16-6103	https://aclanthology.org/W16-6103.pdf	ws-2016-11	['negation-detection', 'medical-named-entity-recognition']	['natural-language-processing', 'natural-language-processing']	[ 2.55496919e-01 6.43742919e-01 -2.36525118e-01 -3.87390196e-01 -1.05618227e+00 -1.43225893e-01 4.10734028e-01 7.93844819e-01 -1.28181994e+00 6.03431702e-01 6.01660669e-01 -8.45379829e-01 -9.38668028e-02 -7.83281922e-01 -4.13144171e-01 -4.15944785e-01 -2.33075723e-01 4.37203139e-01 2.72929788e-01 -3.44224125e-01 1.49288967e-01 4.88304943e-01 -9.32414174e-01 5.07235944e-01 5.31991065e-01 7.84836531e-01 2.55897164e-01 9.55584645e-01 -2.45009005e-01 1.34618080e+00 -4.85609800e-01 -4.55982715e-01 -3.58785450e-01 -2.10086033e-01 -1.03964210e+00 -4.68435973e-01 8.75035897e-02 -1.74784198e-01 -4.91167754e-01 9.30617571e-01 7.74442375e-01 1.50628472e-02 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7db4622f-586a-4309-b0e5-1ca2fc95b6ed	towards-open-vocabulary-scene-graph	2208.08165	null	https://arxiv.org/abs/2208.08165v3	https://arxiv.org/pdf/2208.08165v3.pdf	Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning	Scene graph generation (SGG) is a fundamental task aimed at detecting visual relations between objects in an image. The prevailing SGG methods require all object classes to be given in the training set. Such a closed setting limits the practical application of SGG. In this paper, we introduce open-vocabulary scene graph generation, a novel, realistic and challenging setting in which a model is trained on a set of base object classes but is required to infer relations for unseen target object classes. To this end, we propose a two-step method that firstly pre-trains on large amounts of coarse-grained region-caption data and then leverages two prompt-based techniques to finetune the pre-trained model without updating its parameters. Moreover, our method can support inference over completely unseen object classes, which existing methods are incapable of handling. On extensive experiments on three benchmark datasets, Visual Genome, GQA, and Open-Image, our method significantly outperforms recent, strong SGG methods on the setting of Ov-SGG, as well as on the conventional closed SGG.	['Yuan-Fang Li', 'Jingkuan Song', 'Lianli Gao', 'Tao He']	2022-08-17	null	null	null	null	['scene-graph-generation']	['computer-vision']	[ 6.25045180e-01 4.17840123e-01 6.46227598e-02 -3.21286261e-01 -6.00086749e-01 -6.68126464e-01 8.63199830e-01 1.75229281e-01 -8.41106400e-02 4.72914785e-01 -8.32997710e-02 -4.42610472e-01 2.51817942e-01 -9.12368774e-01 -1.15615904e+00 -5.93845248e-01 2.53204882e-01 6.12989485e-01 5.16704679e-01 -1.18629798e-01 9.01879650e-03 2.44380176e-01 -1.52798617e+00 1.85601503e-01 7.05668628e-01 1.06144917e+00 3.53859693e-01 5.85494876e-01 1.88988209e-01 8.20666969e-01 -2.77723640e-01 -5.70528626e-01 2.64676869e-01 -3.11314285e-01 -7.73498654e-01 5.52390695e-01 8.02006304e-01 -2.18997762e-01 -3.46434087e-01 1.11556327e+00 2.13592693e-01 3.49758685e-01 8.47430706e-01 -1.34100318e+00 -7.80150890e-01 4.64985400e-01 -5.08764565e-01 1.15135036e-01 2.16223612e-01 3.65801811e-01 1.22072124e+00 -9.65997934e-01 7.31639564e-01 1.33201182e+00 1.67310849e-01 5.75716555e-01 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-3.24681759e-01 7.75975943e-01 2.78978348e-01 -8.77265871e-01 -2.87605226e-01 4.11177069e-01]	[10.33988094329834, 1.6138097047805786]
8619df05-b023-4c63-be88-c42a87dba003	the-best-of-both-worlds-combining-human-and	2305.12737	null	https://arxiv.org/abs/2305.12737v1	https://arxiv.org/pdf/2305.12737v1.pdf	The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning	Multilingual semantic parsing aims to leverage the knowledge from the high-resource languages to improve low-resource semantic parsing, yet commonly suffers from the data imbalance problem. Prior works propose to utilize the translations by either humans or machines to alleviate such issues. However, human translations are expensive, while machine translations are cheap but prone to error and bias. In this work, we propose an active learning approach that exploits the strengths of both human and machine translations by iteratively adding small batches of human translations into the machine-translated training set. Besides, we propose novel aggregated acquisition criteria that help our active learning method select utterances to be manually translated. Our experiments demonstrate that an ideal utterance selection can significantly reduce the error and bias in the translated data, resulting in higher parser accuracies than the parsers merely trained on the machine-translated data.	['Gholamreza Haffari', 'Raj V. Tumuluri', 'Philip R. Cohen', 'Lizhen Qu', 'Zhuang Li']	2023-05-22	null	null	null	null	['semantic-parsing']	['natural-language-processing']	[ 3.22498202e-01 4.44687963e-01 -7.82229722e-01 -8.38859618e-01 -1.63336527e+00 -7.19699621e-01 1.40784115e-01 1.47349805e-01 -5.57955325e-01 8.68671417e-01 3.22553337e-01 -3.79418999e-01 3.77721459e-01 -8.22407126e-01 -8.51799726e-01 -3.69400114e-01 6.23063266e-01 7.02076495e-01 -3.48429382e-02 -2.71157086e-01 -5.48413023e-02 -2.09627166e-01 -1.05616105e+00 3.55692416e-01 1.67701411e+00 5.35394192e-01 5.02588987e-01 -1.21261477e-02 -5.60003996e-01 8.11269701e-01 -4.34259623e-01 -8.55429888e-01 3.74486983e-01 -6.62231147e-01 -7.41198480e-01 1.06602140e-01 5.63472211e-02 -2.59210259e-01 3.55973691e-01 1.25208032e+00 4.33437616e-01 -5.83662540e-02 1.84364282e-02 -9.12236273e-01 -6.71614587e-01 1.21490884e+00 -3.40213776e-01 -2.11082548e-01 4.87539768e-01 -1.64305011e-03 1.26405764e+00 -1.20086336e+00 5.89930892e-01 1.15693617e+00 5.13499677e-01 7.02037990e-01 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-1.12525916e+00 9.16700214e-02 5.13667166e-01 -1.03232354e-01 -5.38858235e-01 -3.20638835e-01 9.55772251e-02]	[11.379995346069336, 10.184192657470703]
fa50bdaf-ca10-43b7-bbf8-c2fddb20ed6b	videomix-rethinking-data-augmentation-for	2012.03457	null	https://arxiv.org/abs/2012.03457v1	https://arxiv.org/pdf/2012.03457v1.pdf	VideoMix: Rethinking Data Augmentation for Video Classification	State-of-the-art video action classifiers often suffer from overfitting. They tend to be biased towards specific objects and scene cues, rather than the foreground action content, leading to sub-optimal generalization performances. Recent data augmentation strategies have been reported to address the overfitting problems in static image classifiers. Despite the effectiveness on the static image classifiers, data augmentation has rarely been studied for videos. For the first time in the field, we systematically analyze the efficacy of various data augmentation strategies on the video classification task. We then propose a powerful augmentation strategy VideoMix. VideoMix creates a new training video by inserting a video cuboid into another video. The ground truth labels are mixed proportionally to the number of voxels from each video. We show that VideoMix lets a model learn beyond the object and scene biases and extract more robust cues for action recognition. VideoMix consistently outperforms other augmentation baselines on Kinetics and the challenging Something-Something-V2 benchmarks. It also improves the weakly-supervised action localization performance on THUMOS'14. VideoMix pretrained models exhibit improved accuracies on the video detection task (AVA).	['Jinhyung Kim', 'Dongyoon Han', 'Byeongho Heo', 'Seong Joon Oh', 'Sangdoo Yun']	2020-12-07	null	null	null	null	['weakly-supervised-action-localization']	['computer-vision']	[ 6.61266208e-01 -1.39869079e-01 -6.14311814e-01 -1.10254191e-01 -8.37647736e-01 -3.05174857e-01 8.23649824e-01 -1.51169419e-01 -5.95867455e-01 5.58596611e-01 3.04814756e-01 1.74292848e-01 5.41565776e-01 -2.46874273e-01 -1.09472716e+00 -1.08297133e+00 -1.70387682e-02 2.98743099e-01 5.59665918e-01 1.95751056e-01 8.25408697e-02 1.95618004e-01 -1.56081414e+00 8.84417832e-01 3.62022728e-01 1.15694046e+00 1.06876288e-02 6.30294204e-01 6.52172253e-04 1.34336698e+00 -5.24039745e-01 -1.17861189e-01 3.29564333e-01 -5.19563973e-01 -7.53227830e-01 4.77678835e-01 1.15984392e+00 -4.68395203e-01 -7.52045691e-01 9.02780414e-01 2.28366151e-01 2.06639454e-01 5.80072582e-01 -1.72188270e+00 -3.63946587e-01 4.91596431e-01 -7.82218874e-01 7.68934190e-01 1.81158409e-01 3.12573999e-01 7.39015520e-01 -1.00786078e+00 6.71027780e-01 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-1.03419185e+00 -2.71092504e-01 -1.12664914e+00 -2.49862492e-01 5.93312383e-01 3.26563746e-01 -1.07514882e+00 -2.71680057e-01 3.51862729e-01]	[8.5353422164917, 0.7424302697181702]
681064e9-fc80-4b58-b0f2-e04242a0cf0f	ultra-low-power-and-real-time-ecg-1	1905.02954	null	https://arxiv.org/abs/1905.02954v4	https://arxiv.org/pdf/1905.02954v4.pdf	Ultra Low-Power and Real-time ECG Classification Based on STDP and R-STDP Neural Networks for Wearable Devices	This paper presents a novel ECG classification algorithm for real-time cardiac monitoring on ultra low-power wearable devices. The proposed solution is based on spiking neural networks which are the third generation of neural networks. In specific, we employ spike-timing dependent plasticity (STDP), and reward-modulated STDP (R-STDP), in which the model weights are trained according to the timings of spike signals, and reward or punishment signals. Experiments show that the proposed solution is suitable for real-time operation, achieves comparable accuracy with respect to previous methods, and more importantly, its energy consumption is significantly smaller than previous neural network based solutions.	['Alireza Amirshahi', 'Matin Hashemi']	2019-05-08	ultra-low-power-and-real-time-ecg	null	null	arxiv190502954-2019-5	['ecg-classification']	['medical']	[ 5.25797725e-01 -5.32828391e-01 -1.47042394e-01 -1.72479391e-01 1.51947945e-01 -2.43630037e-01 -8.18028580e-04 4.02597845e-01 -6.55983210e-01 1.15504038e+00 -3.88230801e-01 9.26870108e-02 -1.75972953e-01 -6.79005682e-01 -4.90241021e-01 -8.94121766e-01 -9.73725915e-02 -5.11183143e-02 5.47127783e-01 -1.26016378e-01 5.16752183e-01 3.10689092e-01 -1.37695932e+00 8.30777641e-03 7.60170400e-01 1.24787450e+00 4.47646063e-03 3.22351158e-01 4.16690767e-01 3.85591447e-01 -5.08131683e-01 2.54167676e-01 -1.93646640e-01 -5.72992682e-01 -8.53610933e-02 -7.28925228e-01 -6.63294375e-01 2.25007787e-01 -4.03224021e-01 8.29711556e-01 9.35198367e-01 -2.01675788e-01 4.94561642e-01 -1.15362906e+00 -4.05571312e-01 5.34835100e-01 -3.95825744e-01 7.75737286e-01 -9.45129544e-02 9.31790769e-02 1.82691157e-01 -4.48646426e-01 2.96043515e-01 2.92156279e-01 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f030bfe6-ffc9-4dd8-80e5-112db70fb5a4	vision-language-pre-training-for-multimodal	2204.07955	null	https://arxiv.org/abs/2204.07955v2	https://arxiv.org/pdf/2204.07955v2.pdf	Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis	As an important task in sentiment analysis, Multimodal Aspect-Based Sentiment Analysis (MABSA) has attracted increasing attention in recent years. However, previous approaches either (i) use separately pre-trained visual and textual models, which ignore the crossmodal alignment or (ii) use vision-language models pre-trained with general pre-training tasks, which are inadequate to identify finegrained aspects, opinions, and their alignments across modalities. To tackle these limitations, we propose a task-specific Vision-Language Pre-training framework for MABSA (VLPMABSA), which is a unified multimodal encoder-decoder architecture for all the pretraining and downstream tasks. We further design three types of task-specific pre-training tasks from the language, vision, and multimodal modalities, respectively. Experimental results show that our approach generally outperforms the state-of-the-art approaches on three MABSA subtasks. Further analysis demonstrates the effectiveness of each pretraining task. The source code is publicly released at https://github.com/NUSTM/VLP-MABSA.	['Jianfei Yu', 'Rui Xia', 'Yan Ling']	2022-04-17	null	https://aclanthology.org/2022.acl-long.152	https://aclanthology.org/2022.acl-long.152.pdf	acl-2022-5	['aspect-based-sentiment-analysis']	['natural-language-processing']	[-2.71539409e-02 -1.64002404e-01 -9.57794115e-02 -5.80876529e-01 -1.01704657e+00 -4.79412228e-01 8.26825202e-01 -3.73756252e-02 -5.88062882e-01 2.01695323e-01 2.98505992e-01 -4.11140740e-01 5.02435744e-01 -3.66701990e-01 -6.83002174e-01 -5.35972416e-01 6.50123298e-01 3.78429443e-01 -1.14180177e-01 -2.81577080e-01 1.50379181e-01 -1.34677291e-01 -1.15504253e+00 8.54466200e-01 6.93833292e-01 1.16659558e+00 2.52381742e-01 6.22560561e-01 -4.76959050e-01 8.32240582e-01 -1.92665204e-01 -8.71556759e-01 -2.77660429e-01 -5.39746463e-01 -8.70287895e-01 2.77813435e-01 3.39494944e-01 -2.66042173e-01 -2.25596637e-01 1.11019254e+00 3.82496864e-01 -1.37801498e-01 5.80991924e-01 -1.13431132e+00 -1.15001261e+00 3.26819927e-01 -1.03322160e+00 -1.87560543e-01 2.03059793e-01 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3ec08b76-5520-4453-ba1d-010ec27383d7	inter-view-depth-consistency-testing-in-depth	2301.11752	null	https://arxiv.org/abs/2301.11752v1	https://arxiv.org/pdf/2301.11752v1.pdf	Inter-View Depth Consistency Testing in Depth Difference Subspace	Multiview depth imagery will play a critical role in free-viewpoint television. This technology requires high quality virtual view synthesis to enable viewers to move freely in a dynamic real world scene. Depth imagery at different viewpoints is used to synthesize an arbitrary number of novel views. Usually, depth images at multiple viewpoints are estimated individually by stereo-matching algorithms, and hence, show lack of interview consistency. This inconsistency affects the quality of view synthesis negatively. This paper proposes a method for depth consistency testing in depth difference subspace to enhance the depth representation of a scene across multiple viewpoints. Furthermore, we propose a view synthesis algorithm that uses the obtained consistency information to improve the visual quality of virtual views at arbitrary viewpoints. Our method helps us to find a linear subspace for our depth difference measurements in which we can test the inter-view consistency efficiently. With this, our approach is able to enhance the depth information for real world scenes. In combination with our consistency-adaptive view synthesis, we improve the visual experience of the free-viewpoint user. The experiments show that our approach enhances the objective quality of virtual views by up to 1.4 dB. The advantage for the subjective quality is also demonstrated.	['Markus Flierl', 'Pravin Kumar Rana']	2023-01-27	null	null	null	null	['stereo-matching-1']	['computer-vision']	[ 5.09056300e-02 -1.98233113e-01 2.08076835e-01 -3.45531017e-01 -5.48141301e-01 -6.41519904e-01 3.43380004e-01 -4.37442243e-01 5.78600261e-03 4.98640120e-01 8.94979015e-02 7.02541843e-02 5.49789853e-02 -8.97448003e-01 -4.19633329e-01 -6.80733383e-01 2.82456636e-01 2.70188421e-01 4.97990668e-01 -2.78622657e-01 3.48267317e-01 5.79523623e-01 -1.89721394e+00 4.37097669e-01 8.29146087e-01 8.25420916e-01 7.29098916e-01 7.01153159e-01 1.96497262e-01 4.54849929e-01 -3.66201073e-01 -5.95790781e-02 5.73972821e-01 -3.84928256e-01 -3.65595102e-01 5.42676866e-01 7.34791934e-01 -9.69074368e-01 -1.62835553e-01 1.30254340e+00 4.69026834e-01 5.96786924e-02 1.04251385e-01 -9.70845759e-01 1.36341676e-01 -9.37226340e-02 -6.41946018e-01 -3.45496424e-02 8.97603214e-01 3.23062181e-03 7.28571653e-01 -9.24623370e-01 1.03052473e+00 1.08106267e+00 -1.44441584e-02 3.86743009e-01 -1.09521222e+00 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2.44210154e-01 4.87085998e-01 -5.37131786e-01 -6.54337585e-01 -2.84796320e-02]	[9.219407081604004, -2.5282692909240723]
19bced65-165f-4083-9e04-1b680274cd39	avatarbooth-high-quality-and-customizable-3d	2306.09864	null	https://arxiv.org/abs/2306.09864v1	https://arxiv.org/pdf/2306.09864v1.pdf	AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation	We introduce AvatarBooth, a novel method for generating high-quality 3D avatars using text prompts or specific images. Unlike previous approaches that can only synthesize avatars based on simple text descriptions, our method enables the creation of personalized avatars from casually captured face or body images, while still supporting text-based model generation and editing. Our key contribution is the precise avatar generation control by using dual fine-tuned diffusion models separately for the human face and body. This enables us to capture intricate details of facial appearance, clothing, and accessories, resulting in highly realistic avatar generations. Furthermore, we introduce pose-consistent constraint to the optimization process to enhance the multi-view consistency of synthesized head images from the diffusion model and thus eliminate interference from uncontrolled human poses. In addition, we present a multi-resolution rendering strategy that facilitates coarse-to-fine supervision of 3D avatar generation, thereby enhancing the performance of the proposed system. The resulting avatar model can be further edited using additional text descriptions and driven by motion sequences. Experiments show that AvatarBooth outperforms previous text-to-3D methods in terms of rendering and geometric quality from either text prompts or specific images. Please check our project website at https://zeng-yifei.github.io/avatarbooth_page/.	['Xun Cao', 'Hao Zhu', 'Yao Yao', 'Xinya Ji', 'Yuanxun Lu', 'Yifei Zeng']	2023-06-16	null	null	null	null	['text-to-3d']	['computer-vision']	[-9.15330555e-03 3.25554490e-01 4.21449006e-01 -3.33553761e-01 -5.05530596e-01 -4.73995209e-01 6.18053317e-01 -5.09243429e-01 1.17708929e-01 4.70048457e-01 1.92574039e-01 3.64848822e-01 2.04335049e-01 -6.06492281e-01 -5.52561283e-01 -5.33833325e-01 4.91917193e-01 6.42771065e-01 7.08360672e-02 -3.54125440e-01 -2.43013054e-01 6.92571938e-01 -1.38912678e+00 -1.03227280e-01 8.00698996e-01 9.33398664e-01 3.31055552e-01 7.48331666e-01 1.96925610e-01 4.76143986e-01 -5.59338570e-01 -6.34851813e-01 3.83613110e-01 -5.09599149e-01 -2.98067927e-01 4.51601416e-01 6.96475327e-01 -6.97794139e-01 -1.85273632e-01 6.71486974e-01 8.86002719e-01 2.91301072e-01 5.06453812e-01 -1.19897008e+00 -7.39870071e-01 4.90337722e-02 -8.35035026e-01 -5.70760429e-01 7.89473593e-01 2.83857346e-01 6.43735528e-01 -9.59622324e-01 9.31486309e-01 1.37455404e+00 3.27255785e-01 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f35dbc0b-42c0-473d-95f7-6bc87b599ba7	a-study-of-acoustic-features-in-arabic	2110.12304	null	https://arxiv.org/abs/2110.12304v1	https://arxiv.org/pdf/2110.12304v1.pdf	A Study of Acoustic Features in Arabic Speaker Identification under Noisy Environmental Conditions	One of the major parts of the voice recognition field is the choice of acoustic features which have to be robust against the variability of the speech signal, mismatched conditions, and noisy environments. Thus, different speech feature extraction techniques have been developed. In this paper, we investigate the robustness of several front-end techniques in Arabic speaker identification. We evaluate five different features in babble, factory and subway conditions at the various signal to noise ratios (SNR). The obtained results showed that two of the auditory feature i.e. gammatone frequency cepstral coefficient (GFCC) and power normalization cepstral coefficients (PNCC), unlike their combination performs substantially better than a conventional speaker features i.e. Mel-frequency cepstral coefficients (MFCC).	['Abderrahmane Amrouche', 'Kawthar Yasmine Zergat', 'Zhor Benhafid']	2021-10-23	null	null	null	null	['speaker-identification']	['speech']	[-2.46912494e-01 -6.67287827e-01 5.12918890e-01 -1.13005809e-01 -5.08268774e-01 -5.69816887e-01 7.26991177e-01 1.38720751e-01 -5.00765324e-01 6.62229121e-01 4.08049196e-01 -1.84969738e-01 -3.39446276e-01 -2.46653184e-01 8.77235457e-02 -8.72217417e-01 -2.59140104e-01 -2.55205780e-01 3.53146493e-01 -4.88848895e-01 4.93334204e-01 7.38405406e-01 -2.00061798e+00 -6.11351691e-02 4.06675845e-01 8.93103182e-01 2.88184345e-01 1.09020972e+00 -6.22121170e-02 6.17647655e-02 -1.03325808e+00 -1.08237825e-01 -2.32460782e-01 -3.09726268e-01 -4.92894381e-01 6.05133129e-03 -3.32927495e-01 -5.26369289e-02 5.07931784e-02 1.05969357e+00 8.17681611e-01 4.20996308e-01 8.29295635e-01 -8.55021775e-01 -9.83792767e-02 4.84032840e-01 1.11406326e-01 6.82157755e-01 7.29232609e-01 -8.05761293e-02 3.30670416e-01 -1.12406790e+00 3.18086743e-02 1.31674659e+00 5.06837785e-01 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cb1f583c-4de3-41e2-a016-661ff1a714e4	call-larisa-ivanovna-code-switching-fools	2109.14350	null	https://arxiv.org/abs/2109.14350v2	https://arxiv.org/pdf/2109.14350v2.pdf	Call Larisa Ivanovna: Code-Switching Fools Multilingual NLU Models	Practical needs of developing task-oriented dialogue assistants require the ability to understand many languages. Novel benchmarks for multilingual natural language understanding (NLU) include monolingual sentences in several languages, annotated with intents and slots. In such setup models for cross-lingual transfer show remarkable performance in joint intent recognition and slot filling. However, existing benchmarks lack of code-switched utterances, which are difficult to gather and label due to complexity in the grammatical structure. The evaluation of NLU models seems biased and limited, since code-switching is being left out of scope. Our work adopts recognized methods to generate plausible and naturally-sounding code-switched utterances and uses them to create a synthetic code-switched test set. Based on experiments, we report that the state-of-the-art NLU models are unable to handle code-switching. At worst, the performance, evaluated by semantic accuracy, drops as low as 15\% from 80\% across languages. Further we show, that pre-training on synthetic code-mixed data helps to maintain performance on the proposed test set at a comparable level with monolingual data. Finally, we analyze different language pairs and show that the closer the languages are, the better the NLU model handles their alternation. This is in line with the common understanding of how multilingual models conduct transferring between languages	['Ekaterina Artemova', 'Alexey Birshert']	2021-09-29	null	null	null	null	['intent-recognition']	['natural-language-processing']	[ 7.59096444e-02 5.77829480e-01 -2.49440670e-01 -3.90106916e-01 -1.10273743e+00 -7.84258425e-01 6.48439169e-01 5.46788052e-02 -1.84934884e-01 1.07815540e+00 2.40998492e-01 -8.32997978e-01 2.32640579e-01 -2.73511022e-01 -8.07933450e-01 -9.44700390e-02 -2.13824473e-02 1.00751972e+00 1.00029288e-02 -6.53476238e-01 -7.56860748e-02 -3.93436432e-01 -1.52066302e+00 6.34334207e-01 1.35198498e+00 3.10572922e-01 5.62104523e-01 6.86903417e-01 -6.49461746e-01 1.06356120e+00 -6.22708082e-01 -4.88049686e-01 1.20342001e-01 -6.69382632e-01 -1.31491387e+00 2.15334333e-02 4.49088722e-01 -5.18652238e-02 2.19918132e-01 9.52001035e-01 5.87369800e-02 -3.29161882e-01 6.88700080e-01 -1.08050811e+00 -5.88236094e-01 1.08016860e+00 1.81561112e-02 -2.97704637e-01 1.08832276e+00 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dc44ffb3-768f-4d55-9911-61a5eb97e715	braid-weaving-symbolic-and-statistical	2011.13354	null	https://arxiv.org/abs/2011.13354v4	https://arxiv.org/pdf/2011.13354v4.pdf	Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations	Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching (unification) of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of knowledge (the "knowledge acquisition" problem). To address these issues, we devise a novel logical reasoner called Braid, that supports probabilistic rules, and uses the notion of custom unification functions and dynamic rule generation to overcome the brittle matching and knowledge-gap problem prevalent in traditional reasoners. In this paper, we describe the reasoning algorithms used in Braid, and their implementation in a distributed task-based framework that builds proof/explanation graphs for an input query. We use a simple QA example from a children's story to motivate Braid's design and explain how the various components work together to produce a coherent logical explanation. Finally, we evaluate Braid on the ROC Story Cloze test and achieve close to state-of-the-art results while providing frame-based explanations.	['David Ferrucci', 'Tom Breloff', 'Aditya Kalyanpur']	2020-11-26	null	null	null	null	['cloze-test']	['natural-language-processing']	[-7.07746819e-02 8.27995956e-01 -1.97079957e-01 -7.00228810e-01 -8.51849377e-01 -4.27913964e-01 6.08772457e-01 3.60656917e-01 1.63427740e-01 8.95081401e-01 2.42196217e-01 -6.14709556e-01 -8.28447938e-01 -9.39063609e-01 -7.88519979e-01 -1.28022926e-02 1.48358047e-01 1.01991320e+00 5.99399030e-01 -2.93274313e-01 1.45166725e-01 1.04767159e-01 -1.96004832e+00 5.82259178e-01 1.31530929e+00 7.26644516e-01 -2.94295758e-01 4.83426750e-01 -4.26352561e-01 1.30549014e+00 -4.30023462e-01 -9.16191399e-01 -3.34249467e-01 -2.94948459e-01 -1.21213889e+00 -6.97416604e-01 4.32376087e-01 -3.44957471e-01 4.64756135e-03 7.84318566e-01 9.77525190e-02 3.41659598e-02 6.99939787e-01 -1.65204918e+00 -6.59224451e-01 1.48482192e+00 1.28215805e-01 -1.96741194e-01 9.68790412e-01 -2.20628634e-01 1.08403873e+00 -4.01745677e-01 7.24452436e-01 1.55432594e+00 8.13115597e-01 6.17427647e-01 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141f64d8-2d29-46ab-b09b-65131b0cbde6	progressive-upsampling-audio-synthesis-via	null	null	https://openreview.net/forum?id=Skg9jnVFvH	https://openreview.net/pdf?id=Skg9jnVFvH	Progressive Upsampling Audio Synthesis via Effective Adversarial Training	This paper proposes a novel generative model called PUGAN, which progressively synthesizes high-quality audio in a raw waveform. PUGAN leverages on the recently proposed idea of progressive generation of higher-resolution images by stacking multiple encode-decoder architectures. To effectively apply it to raw audio generation, we propose two novel modules: (1) a neural upsampling layer and (2) a sinc convolutional layer. Compared to the existing state-of-the-art model called WaveGAN, which uses a single decoder architecture, our model generates audio signals and converts them in a higher resolution in a progressive manner, while using a significantly smaller number of parameters, e.g., 20x smaller for 44.1kHz output, than an existing technique called WaveGAN. Our experiments show that the audio signals can be generated in real-time with the comparable quality to that of WaveGAN with respect to the inception scores and the human evaluation.	['Jaegul Choo', 'Gerard Jounghyun Kim', 'Minwook Chang', 'Youngwoo Cho']	2019-09-25	null	null	null	null	['audio-generation']	['audio']	[ 4.24003035e-01 3.43113780e-01 2.27410316e-01 4.58053462e-02 -1.32607377e+00 -3.96540165e-01 5.30768812e-01 -4.70520228e-01 3.98488641e-02 7.43440807e-01 4.12917376e-01 -1.50836393e-01 4.17249203e-01 -1.00416720e+00 -9.42228496e-01 -4.13858086e-01 -5.42481095e-02 -1.88011825e-02 2.61496067e-01 -1.73100233e-01 -1.71345487e-01 -6.98936284e-02 -1.66492844e+00 5.86814761e-01 6.02076173e-01 1.13224828e+00 -1.93096120e-02 1.18877077e+00 3.31424147e-01 9.25180256e-01 -9.55740750e-01 -4.37620014e-01 2.77614594e-01 -9.79860485e-01 -4.86933380e-01 3.39348167e-02 4.60349888e-01 -8.07340622e-01 -4.36126798e-01 8.51873338e-01 9.22238827e-01 -2.15372130e-01 3.10360789e-01 -8.93405139e-01 -8.45808804e-01 1.05813968e+00 -3.47392797e-01 -6.60422444e-02 6.27566457e-01 3.20714474e-01 1.04921520e+00 -7.21254826e-01 4.29434150e-01 1.25488460e+00 7.59705484e-01 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645654a6-3acf-4736-ae09-83929ff6ed4a	global-universal-approximation-of-functional	2306.03303	null	https://arxiv.org/abs/2306.03303v1	https://arxiv.org/pdf/2306.03303v1.pdf	Global universal approximation of functional input maps on weighted spaces	We introduce so-called functional input neural networks defined on a possibly infinite dimensional weighted space with values also in a possibly infinite dimensional output space. To this end, we use an additive family as hidden layer maps and a non-linear activation function applied to each hidden layer. Relying on Stone-Weierstrass theorems on weighted spaces, we can prove a global universal approximation result for generalizations of continuous functions going beyond the usual approximation on compact sets. This then applies in particular to approximation of (non-anticipative) path space functionals via functional input neural networks. As a further application of the weighted Stone-Weierstrass theorem we prove a global universal approximation result for linear functions of the signature. We also introduce the viewpoint of Gaussian process regression in this setting and show that the reproducing kernel Hilbert space of the signature kernels are Cameron-Martin spaces of certain Gaussian processes. This paves the way towards uncertainty quantification for signature kernel regression.	['Josef Teichmann', 'Philipp Schmocker', 'Christa Cuchiero']	2023-06-05	null	null	null	null	['gaussian-processes']	['methodology']	[-2.86659598e-02 4.46512043e-01 2.25925729e-01 -3.54390264e-01 -5.28841615e-01 -2.39648938e-01 5.15125930e-01 -6.26637554e-03 -6.28573179e-01 5.95581710e-01 1.27218112e-01 -3.38635802e-01 -2.74805099e-01 -1.05793357e+00 -7.56322742e-01 -1.13509822e+00 -5.06453812e-01 3.08484375e-01 7.44493902e-02 -2.18491703e-01 -1.04906641e-01 6.35673106e-01 -1.16930914e+00 -3.13033871e-02 6.05629563e-01 1.12659633e+00 -5.03477931e-01 1.08855104e+00 -7.95897618e-02 5.68578839e-01 -2.25195229e-01 -2.74137914e-01 3.16246420e-01 -3.80077958e-01 -6.74580634e-01 -1.60664752e-01 -1.05981603e-01 2.91859433e-02 -3.13949555e-01 1.18802202e+00 1.81050599e-01 7.43207037e-01 1.18741548e+00 -1.19322586e+00 -1.12493706e+00 7.68083930e-01 -6.14148080e-02 -1.31225601e-01 -1.46531612e-01 -1.21088915e-01 8.43940020e-01 -1.11805117e+00 2.70983756e-01 1.17839527e+00 1.07085037e+00 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f5858e2c-5cf7-4ae4-9907-76dbfc2542eb	sensala-a-dynamic-semantics-system-for	null	null	https://aclanthology.org/C18-2027	https://aclanthology.org/C18-2027.pdf	Sensala: a Dynamic Semantics System for Natural Language Processing	Here we describe Sensala , an open source framework for the semantic interpretation of natural language that provides the logical meaning of a given text. The framework{'}s theory is based on a lambda calculus with exception handling and uses contexts, continuations, events and dependent types to handle a wide range of complex linguistic phenomena, such as donkey anaphora, verb phrase anaphora, propositional anaphora, presuppositions and implicatures.	['Ekaterina Lebedeva', 'Daniyar Itegulov', 'Bruno Woltzenlogel Paleo']	2018-08-01	sensala-a-dynamic-semantics-system-for-1	https://aclanthology.org/C18-2027	https://aclanthology.org/C18-2027.pdf	coling-2018-8	['implicatures']	['natural-language-processing']	[-2.23995462e-01 5.79404354e-01 -3.79666418e-01 -4.18924868e-01 8.87642205e-02 -7.91636586e-01 9.94216919e-01 6.94507718e-01 -4.98682559e-01 1.17149091e+00 6.07104540e-01 -4.98328775e-01 -4.68767136e-01 -1.04043412e+00 -2.36175969e-01 -1.18919782e-01 3.73985269e-03 3.60778540e-01 1.03635120e+00 -9.07630503e-01 4.57938731e-01 3.66395205e-01 -1.48994434e+00 5.99516988e-01 -1.06926657e-01 6.39173269e-01 -1.29028887e-03 2.08813310e-01 -7.70310760e-01 1.40575397e+00 -5.61521769e-01 -9.59580839e-01 -4.08395648e-01 1.01118624e-01 -1.52846849e+00 -4.92536038e-01 -2.08490863e-01 -2.10450608e-02 7.49520212e-02 1.25780320e+00 1.19638056e-01 2.79523015e-01 1.94604427e-01 -1.46125329e+00 -3.92970651e-01 8.95795822e-01 7.24510774e-02 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7300f3b4-709f-4c0d-b71d-660cf37073de	weakly-supervised-instance-segmentation-using-3	null	null	http://papers.nips.cc/paper/8885-weakly-supervised-instance-segmentation-using-the-bounding-box-tightness-prior	http://papers.nips.cc/paper/8885-weakly-supervised-instance-segmentation-using-the-bounding-box-tightness-prior.pdf	Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior	This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations. The major difficulty lies in the uncertain figure-ground separation within each bounding box since there is no supervisory signal about it. We address the difficulty by formulating the problem as a multiple instance learning (MIL) task, and generate positive and negative bags based on the sweeping lines of each bounding box. The proposed deep model integrates MIL into a fully supervised instance segmentation network, and can be derived by the objective consisting of two terms, i.e., the unary term and the pairwise term. The former estimates the foreground and background areas of each bounding box while the latter maintains the unity of the estimated object masks. The experimental results show that our method performs favorably against existing weakly supervised methods and even surpasses some fully supervised methods for instance segmentation on the PASCAL VOC dataset.	['Yung-Yu Chuang', 'Yen-Yu Lin', 'Chung-Chi Tsai', 'Kuang-Jui Hsu', 'Cheng-Chun Hsu']	2019-12-01	null	null	null	neurips-2019-12	['box-supervised-instance-segmentation', 'weakly-supervised-instance-segmentation']	['computer-vision', 'computer-vision']	[ 3.90002012e-01 5.85337400e-01 -4.96760994e-01 -6.93005979e-01 -1.08613837e+00 -6.30066514e-01 3.68151337e-01 1.60797387e-01 -3.65245491e-01 8.87269318e-01 -4.89989638e-01 -1.20835632e-01 9.60119218e-02 -6.43135428e-01 -1.08900845e+00 -1.00582576e+00 1.46193296e-01 8.01895976e-01 5.59454620e-01 1.52544513e-01 1.80133179e-01 2.37191647e-01 -1.40225089e+00 4.79132175e-01 9.84085739e-01 1.32307160e+00 -1.62155345e-01 5.76365888e-01 -3.72292548e-01 8.44631255e-01 -7.89512634e-01 -2.43286431e-01 3.70405018e-01 -1.26811340e-01 -8.56512964e-01 4.98623610e-01 8.00379753e-01 -1.93703607e-01 2.15978429e-01 1.01829660e+00 7.88205490e-02 4.23645899e-02 7.19404757e-01 -1.44931066e+00 -5.32804653e-02 4.04625535e-01 -9.14071381e-01 2.89471447e-02 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2bb2b33c-7952-46a2-a0f1-c38490992f1e	unified-io-a-unified-model-for-vision	2206.08916	null	https://arxiv.org/abs/2206.08916v2	https://arxiv.org/pdf/2206.08916v2.pdf	Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks	We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical computer vision tasks, including pose estimation, object detection, depth estimation and image generation, vision-and-language tasks such as region captioning and referring expression, to natural language processing tasks such as question answering and paraphrasing. Developing a single unified model for such a large variety of tasks poses unique challenges due to the heterogeneous inputs and outputs pertaining to each task, including RGB images, per-pixel maps, binary masks, bounding boxes, and language. We achieve this unification by homogenizing every supported input and output into a sequence of discrete vocabulary tokens. This common representation across all tasks allows us to train a single transformer-based architecture, jointly on over 90 diverse datasets in the vision and language fields. Unified-IO is the first model capable of performing all 7 tasks on the GRIT benchmark and produces strong results across 16 diverse benchmarks like NYUv2-Depth, ImageNet, VQA2.0, OK-VQA, Swig, VizWizGround, BoolQ, and SciTail, with no task-specific fine-tuning. Code and demos for Unified-IO are available at: https://unified-io.allenai.org.	['Aniruddha Kembhavi', 'Roozbeh Mottaghi', 'Rowan Zellers', 'Christopher Clark', 'Jiasen Lu']	2022-06-17	null	null	null	null	['object-categorization']	['computer-vision']	[ 1.36945605e-01 -6.83213770e-02 8.60921759e-03 -5.28361261e-01 -8.64298880e-01 -7.29292870e-01 7.50552535e-01 -1.41307130e-01 -4.16291624e-01 4.65502739e-01 1.20585971e-01 -4.02340591e-01 4.23935056e-01 -7.47754037e-01 -8.88742626e-01 -3.67484510e-01 3.55267555e-01 4.61404949e-01 4.40242141e-01 -2.93598473e-01 8.49645734e-02 4.88479108e-01 -1.47010100e+00 4.46766555e-01 5.08964300e-01 1.50280440e+00 3.05562705e-01 8.22611630e-01 -2.46614426e-01 1.12960184e+00 -4.55646694e-01 -5.39284945e-01 2.65926480e-01 -8.23389813e-02 -9.12438631e-01 -2.37913500e-03 9.64508057e-01 -6.85432911e-01 -5.75763822e-01 8.12267959e-01 6.76355541e-01 8.00857469e-02 6.09258771e-01 -1.44158602e+00 -1.04451168e+00 3.44364941e-01 -5.43555260e-01 1.06251001e-01 5.35669327e-01 7.00056016e-01 9.86545861e-01 -9.83778656e-01 6.79079354e-01 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c57ab201-9e5b-4836-b50e-2f66ca25e937	disentangling-human-dynamics-for-pedestrian	1911.01138	null	https://arxiv.org/abs/1911.01138v2	https://arxiv.org/pdf/1911.01138v2.pdf	Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision	We tackle the problem of Human Locomotion Forecasting, a task for jointly predicting the spatial positions of several keypoints on the human body in the near future under an egocentric setting. In contrast to the previous work that aims to solve either the task of pose prediction or trajectory forecasting in isolation, we propose a framework to unify the two problems and address the practically useful task of pedestrian locomotion prediction in the wild. Among the major challenges in solving this task is the scarcity of annotated egocentric video datasets with dense annotations for pose, depth, or egomotion. To surmount this difficulty, we use state-of-the-art models to generate (noisy) annotations and propose robust forecasting models that can learn from this noisy supervision. We present a method to disentangle the overall pedestrian motion into easier to learn subparts by utilizing a pose completion and a decomposition module. The completion module fills in the missing key-point annotations and the decomposition module breaks the cleaned locomotion down to global (trajectory) and local (pose keypoint movements). Further, with Quasi RNN as our backbone, we propose a novel hierarchical trajectory forecasting network that utilizes low-level vision domain specific signals like egomotion and depth to predict the global trajectory. Our method leads to state-of-the-art results for the prediction of human locomotion in the egocentric view. Project pade: https://karttikeya.github.io/publication/plf/	['Adrien Gaidon', 'Kuan-Hui Lee', 'Juan Carlos Niebles', 'Ehsan Adeli', 'Karttikeya Mangalam']	2019-11-04	null	null	null	null	['human-dynamics']	['computer-vision']	[-2.16481790e-01 -4.68198098e-02 -1.75522361e-02 -3.23676705e-01 -6.67675078e-01 -3.14988643e-01 5.88456810e-01 -3.48247081e-01 -3.59778315e-01 6.59959912e-01 6.51352882e-01 2.40926728e-01 2.85052985e-01 -7.69910812e-01 -8.94127011e-01 -8.08488667e-01 -1.79056913e-01 5.47923326e-01 3.48869324e-01 -3.32822919e-01 -9.01212618e-02 2.86867708e-01 -1.85997343e+00 4.62021708e-01 6.46333754e-01 7.42040455e-01 2.45387524e-01 1.00225329e+00 4.48951989e-01 8.53376567e-01 1.45935547e-02 -5.54109216e-01 2.50679165e-01 -1.96218744e-01 -8.34472001e-01 1.91946313e-01 6.72147691e-01 -6.25428379e-01 -7.53220797e-01 6.37373924e-01 5.80188036e-01 3.10824603e-01 5.66009343e-01 -1.44551432e+00 -8.24819654e-02 1.92424625e-01 -4.32269871e-01 -3.83823970e-03 5.22346616e-01 3.50034416e-01 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ed7b3717-473e-47c7-9e7e-6a53edf036eb	intersection-warning-system-for-occlusion	2303.07227	null	https://arxiv.org/abs/2303.07227v1	https://arxiv.org/pdf/2303.07227v1.pdf	Intersection Warning System for Occlusion Risks using Relational Local Dynamic Maps	This work addresses the task of risk evaluation in traffic scenarios with limited observability due to restricted sensorial coverage. Here, we concentrate on intersection scenarios that are difficult to access visually. To identify the area of sight, we employ ray casting on a local dynamic map providing geometrical information and road infrastructure. Based on the area with reduced visibility, we first model scene entities that pose a potential risk without being visually perceivable yet. Then, we predict a worst-case trajectory in the survival analysis for collision risk estimation. Resulting risk indicators are utilized to evaluate the driver's current behavior, to warn the driver in critical situations, to give suggestions on how to act safely or to plan safe trajectories. We validate our approach by applying the resulting intersection warning system on real world scenarios. The proposed system's behavior reveals to mimic the general behavior of a correctly acting human driver.	['Julian Eggert', 'Benedict Flade', 'Tim Puphal', 'Yuda Li', 'Florian Damerow']	2023-03-13	null	null	null	null	['survival-analysis']	['miscellaneous']	[ 3.55988741e-01 5.24219334e-01 1.68899357e-01 -2.20307127e-01 -7.07018733e-01 -3.48596215e-01 6.09188318e-01 7.58807361e-01 -6.99591756e-01 6.07047975e-01 9.60437134e-02 -9.01025951e-01 -4.21500683e-01 -1.09037161e+00 -4.68717337e-01 -5.37097454e-01 -2.44810045e-01 2.61171043e-01 6.01971865e-01 -3.43597025e-01 2.02251673e-01 1.13989282e+00 -1.93157411e+00 -1.54777318e-01 7.21871495e-01 7.80157804e-01 1.89562023e-01 6.53191626e-01 3.24786246e-01 4.13133770e-01 -5.34231067e-01 -8.57414529e-02 2.30148494e-01 2.51024097e-01 -3.08830202e-01 -1.83704138e-01 1.17191173e-01 -4.88049775e-01 -8.24404880e-02 8.01447272e-01 2.32297570e-01 3.67694616e-01 6.97703183e-01 -1.23399496e+00 8.95690382e-01 -4.08349670e-02 7.30907843e-02 4.40371484e-01 7.28758276e-01 4.24450755e-01 2.61985987e-01 -3.59112263e-01 5.62542915e-01 1.05532026e+00 2.11523518e-01 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83bc998d-ae91-44ee-8db8-26f3fadfb093	exploring-phonetic-context-in-lip-movement	2305.19556	null	https://arxiv.org/abs/2305.19556v1	https://arxiv.org/pdf/2305.19556v1.pdf	Exploring Phonetic Context in Lip Movement for Authentic Talking Face Generation	Talking face generation is the task of synthesizing a natural face synchronous to driving audio. Although much progress has been made in terms of visual quality, lip synchronization, and facial motion of the talking face, current works still struggle to overcome issues of crude and asynchronous lip movement, which can result in puppetry-like animation. We identify that the prior works commonly correlate lip movement with audio at the phone level. However, due to co-articulation, where an isolated phone is influenced by the preceding or following phones, the articulation of a phone varies upon the phonetic context. Therefore, modeling lip motion with the phonetic context can generate more spatio-temporally aligned and stable lip movement. In this respect, we investigate the phonetic context in lip motion for authentic talking face generation. We propose a Context-Aware Lip-Sync framework (CALS), which leverages phonetic context to generate more spatio-temporally aligned and stable lip movement. The CALS comprises an Audio-to-Lip module and a Lip-to-Face module. The former explicitly maps each phone to a contextualized lip motion unit, which guides the latter in synthesizing a target identity with context-aware lip motion. In addition, we introduce a discriminative sync critic that enforces accurate lip displacements within the phonetic context through audio-visual sync loss and visual discriminative sync loss. From extensive experiments on LRW, LRS2, and HDTF datasets, we demonstrate that the proposed CALS effectively enhances spatio-temporal alignment, greatly improving upon the state-of-the-art on visual quality, lip-sync quality, and realness. Finally, we show the authenticity of the generated video through a lip readability test and achieve 97.7% of relative word prediction accuracy to real videos.	['Yong Man Ro', 'Jeongsoo Choi', 'Minsu Kim', 'Se Jin Park']	2023-05-31	null	null	null	null	['talking-face-generation', 'face-generation']	['computer-vision', 'computer-vision']	[ 3.09064478e-01 -5.42525761e-02 -3.83273989e-01 -8.92407522e-02 -1.06141996e+00 -4.77523804e-01 5.81492424e-01 -6.71702683e-01 2.04417944e-01 4.84322131e-01 5.87745309e-01 -3.67065280e-04 5.03266633e-01 -3.35927218e-01 -7.47233927e-01 -7.98512876e-01 2.91257739e-01 -1.94522992e-01 1.55892968e-02 8.58051237e-03 3.16934697e-02 4.27000046e-01 -1.82967043e+00 3.95586997e-01 7.79448092e-01 9.92038190e-01 1.43479571e-01 9.24225509e-01 6.88977689e-02 5.46230555e-01 -4.18207943e-01 -4.49892968e-01 2.04711318e-01 -8.02470744e-01 -3.72920483e-01 9.58301947e-02 7.04687655e-01 -3.19052517e-01 -2.07859993e-01 8.86658072e-01 8.50216746e-01 -1.10228091e-01 5.88736594e-01 -1.67360771e+00 -3.93009275e-01 2.39574924e-01 -6.06086075e-01 -2.72719562e-01 6.70146883e-01 6.94734991e-01 9.54562068e-01 -1.10853910e+00 6.77635431e-01 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b4c0ee68-6c8d-4e4a-8bcf-6bcf7f3d3708	osu-multimodal-machine-translation-system	1710.02718	null	http://arxiv.org/abs/1710.02718v2	http://arxiv.org/pdf/1710.02718v2.pdf	OSU Multimodal Machine Translation System Report	This paper describes Oregon State University's submissions to the shared WMT'17 task "multimodal translation task I". In this task, all the sentence pairs are image captions in different languages. The key difference between this task and conventional machine translation is that we have corresponding images as additional information for each sentence pair. In this paper, we introduce a simple but effective system which takes an image shared between different languages, feeding it into the both encoding and decoding side. We report our system's performance for English-French and English-German with Flickr30K (in-domain) and MSCOCO (out-of-domain) datasets. Our system achieves the best performance in TER for English-German for MSCOCO dataset.	['Mingbo Ma', 'Dapeng Li', 'Liang Huang', 'Kai Zhao']	2017-10-07	osu-multimodal-machine-translation-system-1	https://aclanthology.org/W17-4751	https://aclanthology.org/W17-4751.pdf	ws-2017-9	['multimodal-machine-translation']	['natural-language-processing']	[ 5.05130649e-01 -1.22741777e-02 6.72370866e-02 -5.45696080e-01 -1.47726023e+00 -7.41398096e-01 7.45787263e-01 -5.58585644e-01 -8.63361716e-01 1.06373501e+00 1.70947149e-01 -4.90158886e-01 7.58675992e-01 -2.25858241e-01 -9.69207644e-01 -2.84961075e-01 4.62486118e-01 3.95536065e-01 1.76140919e-01 -3.45809728e-01 -1.41073987e-01 9.30924341e-02 -7.66112328e-01 1.10878170e+00 5.36397099e-01 5.99656999e-01 5.55282772e-01 1.18908525e+00 4.13313396e-02 5.27440071e-01 -3.90250176e-01 -1.02763736e+00 3.48476499e-01 -6.82823837e-01 -1.28248429e+00 -1.24846129e-02 8.56513441e-01 -3.85663301e-01 -6.26481533e-01 1.07683170e+00 8.55847180e-01 -1.74454734e-01 5.43004632e-01 -1.49117959e+00 -1.08897138e+00 5.17875314e-01 -6.87259078e-01 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1.41638190e-01 8.68200839e-01 7.97290027e-01 5.30027270e-01 2.58479655e-01 2.81326890e-01 -5.75252950e-01 -2.54709572e-01 -9.10371602e-01 -1.03165075e-01 3.03743482e-01 1.40039787e-01 9.13262516e-02 3.79315168e-02 4.80869025e-01]	[11.449946403503418, 1.51760995388031]
96140b8c-cd56-43b5-8380-c27f93ba7934	exploring-regions-of-interest-visualizing	2305.20058	null	https://arxiv.org/abs/2305.20058v1	https://arxiv.org/pdf/2305.20058v1.pdf	Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning	Computer aided detection and diagnosis systems based on deep learning have shown promising performance in breast cancer detection. However, there are cases where the obtained results lack justification. In this study, our objective is to highlight the regions of interest used by a convolutional neural network (CNN) for classifying histological images as benign or malignant. We compare these regions with the regions identified by pathologists. To achieve this, we employed the VGG19 architecture and tested three visualization methods: Gradient, LRP Z, and LRP Epsilon. Additionally, we experimented with three pixel selection methods: Bins, K-means, and MeanShift. Based on the results obtained, the Gradient visualization method and the MeanShift selection method yielded satisfactory outcomes for visualizing the images.	['Mohammed Amine Chikh', 'Khadidja Abi Ayad', 'Said Mahmoudi', 'Mohammed Brahimi', 'Imane Nedjar']	2023-05-31	null	null	null	null	['breast-cancer-detection', 'breast-cancer-detection']	['knowledge-base', 'medical']	[-3.97998728e-02 1.68210968e-01 -3.36579569e-02 -2.87319243e-01 -2.38299370e-01 -1.67872459e-01 7.06927359e-01 6.70874178e-01 -6.28068924e-01 5.60042620e-01 -1.75373152e-01 -8.87901902e-01 -1.80573702e-01 -7.80425847e-01 -2.52799928e-01 -9.61199820e-01 -3.85625035e-01 -5.77780306e-02 2.24708915e-01 -2.26407200e-02 5.31675637e-01 1.20135570e+00 -1.33808720e+00 6.27233028e-01 5.39063931e-01 9.51221287e-01 -8.15949589e-02 6.25946164e-01 -4.07026887e-01 5.69672525e-01 -5.95691383e-01 -2.72243116e-02 1.40555250e-02 -2.44002998e-01 -6.82074726e-01 -2.79623449e-01 2.97518134e-01 -6.09296635e-02 -1.12009645e-02 9.64376271e-01 5.72733998e-01 -1.08779080e-01 7.65553236e-01 -9.96700585e-01 -4.42820996e-01 3.31745803e-01 -7.14480579e-01 5.59387684e-01 7.06434483e-03 1.53300270e-01 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1b17ba6d-0349-4f58-91ee-54680c60419e	shadow-neural-radiance-fields-for-multi-view	2104.09877	null	https://arxiv.org/abs/2104.09877v1	https://arxiv.org/pdf/2104.09877v1.pdf	Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry	We present a new generic method for shadow-aware multi-view satellite photogrammetry of Earth Observation scenes. Our proposed method, the Shadow Neural Radiance Field (S-NeRF) follows recent advances in implicit volumetric representation learning. For each scene, we train S-NeRF using very high spatial resolution optical images taken from known viewing angles. The learning requires no labels or shape priors: it is self-supervised by an image reconstruction loss. To accommodate for changing light source conditions both from a directional light source (the Sun) and a diffuse light source (the sky), we extend the NeRF approach in two ways. First, direct illumination from the Sun is modeled via a local light source visibility field. Second, indirect illumination from a diffuse light source is learned as a non-local color field as a function of the position of the Sun. Quantitatively, the combination of these factors reduces the altitude and color errors in shaded areas, compared to NeRF. The S-NeRF methodology not only performs novel view synthesis and full 3D shape estimation, it also enables shadow detection, albedo synthesis, and transient object filtering, without any explicit shape supervision.	['Dario Izzo', 'Dawa Derksen']	2021-04-20	null	null	null	null	['shadow-detection']	['computer-vision']	[ 4.89877820e-01 -1.61570489e-01 2.41973132e-01 -4.74373817e-01 -2.71430224e-01 -6.95139706e-01 6.41080797e-01 -4.01244074e-01 -4.97424975e-02 8.59012067e-01 1.50860921e-01 -9.94112715e-02 1.56679109e-01 -9.51901913e-01 -9.27278578e-01 -1.08435595e+00 3.35113764e-01 4.18350995e-01 3.07118595e-01 -4.19735849e-01 -8.82255435e-02 9.22275841e-01 -1.58503890e+00 -2.01441437e-01 9.65763927e-01 7.76617110e-01 5.53231299e-01 7.51593292e-01 7.34681562e-02 7.38273740e-01 -4.12070245e-01 1.66077584e-01 5.79604447e-01 -1.75560027e-01 -2.01953426e-01 4.86683458e-01 9.52964067e-01 -5.89694619e-01 -2.87034035e-01 7.55466342e-01 3.84619474e-01 3.97619903e-01 5.36324263e-01 -8.86514544e-01 -4.85041678e-01 -3.88924003e-01 -5.03165185e-01 -1.51245251e-01 1.49845004e-01 1.40016496e-01 7.15396285e-01 -9.53273118e-01 6.61522567e-01 9.49422300e-01 5.43479979e-01 1.84802070e-01 -1.39919853e+00 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-1.07446322e-02 9.66295600e-01 -7.99196243e-01 -8.85762155e-01 1.06017798e-01]	[9.864337921142578, -3.005108594894409]
86c1c1fd-d53a-498e-9ab1-7503cc576055	copula-entropy-based-variable-selection-for	2209.01561	null	https://arxiv.org/abs/2209.01561v1	https://arxiv.org/pdf/2209.01561v1.pdf	Copula Entropy based Variable Selection for Survival Analysis	Variable selection is an important problem in statistics and machine learning. Copula Entropy (CE) is a mathematical concept for measuring statistical independence and has been applied to variable selection recently. In this paper we propose to apply the CE-based method for variable selection to survival analysis. The idea is to measure the correlation between variables and time-to-event with CE and then select variables according to their CE value. Experiments on simulated data and two real cancer data were conducted to compare the proposed method with two related methods: random survival forest and Lasso-Cox. Experimental results showed that the proposed method can select the 'right' variables out that are more interpretable and lead to better prediction performance.	['Jian Ma']	2022-09-04	null	null	null	null	['variable-selection', 'survival-analysis']	['methodology', 'miscellaneous']	[ 1.11887179e-01 -2.17470735e-01 -5.14333606e-01 -4.93766665e-01 -6.05574965e-01 6.90126419e-02 1.05766758e-01 4.46984261e-01 -5.45876741e-01 1.49631500e+00 2.10267529e-01 -3.00521523e-01 -4.31224763e-01 -8.68068993e-01 -1.18717467e-02 -9.75013733e-01 -5.82757056e-01 5.55383623e-01 -1.90911219e-01 5.57684004e-02 1.23930566e-01 3.19935083e-01 -1.19237757e+00 -4.30238962e-01 1.05536437e+00 6.97367191e-01 -8.79637823e-02 4.98629153e-01 1.72886178e-02 4.00605738e-01 -5.05684912e-01 -2.38727406e-01 -8.32150206e-02 -8.38283479e-01 -4.19394761e-01 -4.47877169e-01 -5.88251948e-01 4.01245594e-01 2.90468276e-01 8.69276106e-01 5.90410411e-01 2.04979345e-01 1.12507653e+00 -1.35507011e+00 -1.34086818e-01 7.52205431e-01 -7.76796043e-01 2.31252447e-01 1.64488226e-01 -5.10464907e-01 9.15569425e-01 -5.35924792e-01 6.47368848e-01 1.30588365e+00 6.77783847e-01 4.91034478e-01 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ca65a0a3-b157-4fc8-bf86-59db5b64dadc	deep-ultrasound-denoising-using-diffusion	2306.07440	null	https://arxiv.org/abs/2306.07440v1	https://arxiv.org/pdf/2306.07440v1.pdf	Deep Ultrasound Denoising Using Diffusion Probabilistic Models	Ultrasound images are widespread in medical diagnosis for musculoskeletal, cardiac, and obstetrical imaging due to the efficiency and non-invasiveness of the acquisition methodology. However, the acquired images are degraded by acoustic (e.g. reverberation and clutter) and electronic sources of noise. To improve the Peak Signal to Noise Ratio (PSNR) of the images, previous denoising methods often remove the speckles, which could be informative for radiologists and also for quantitative ultrasound. Herein, a method based on the recent Denoising Diffusion Probabilistic Models (DDPM) is proposed. It iteratively enhances the image quality by eliminating the noise while preserving the speckle texture. It is worth noting that the proposed method is trained in a completely unsupervised manner, and no annotated data is required. The experimental blind test results show that our method outperforms the previous nonlocal means denoising methods in terms of PSNR and Generalized Contrast to Noise Ratio (GCNR) while preserving speckles.	['Hassan Rivaz', 'Adrian Basarab', 'Sobhan Goudarzi', 'Hojat Asgariandehkordi']	2023-06-12	null	null	null	null	['medical-diagnosis']	['medical']	[ 4.14571464e-01 -2.35426098e-01 5.56281090e-01 -8.22203085e-02 -5.36330700e-01 -1.76136538e-01 9.82104391e-02 3.61275412e-02 -4.65314716e-01 5.90305269e-01 2.36314446e-01 5.58924079e-02 -3.91139507e-01 -5.09077430e-01 -1.39154315e-01 -1.44074738e+00 1.80522073e-02 -2.82763422e-01 4.39790159e-01 1.83248356e-01 1.77517548e-01 3.98532450e-01 -1.40304303e+00 -1.04035892e-01 1.15062332e+00 9.76107001e-01 5.03222942e-01 5.41833818e-01 1.21786475e-01 6.51239932e-01 -5.59223115e-01 -1.86857775e-01 5.49631864e-02 -6.02217495e-01 -2.52419174e-01 3.38172585e-01 -1.33093938e-01 -4.94167984e-01 -4.56333369e-01 1.44002223e+00 8.53057563e-01 2.76445419e-01 5.04321635e-01 -3.47981393e-01 -6.24099314e-01 1.55882537e-01 -8.56320739e-01 4.36718792e-01 -1.47504779e-02 6.36375993e-02 1.24879614e-01 -8.71670008e-01 4.58430111e-01 9.78025496e-01 5.79158247e-01 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1a0fbdf9-276b-4db3-8426-097473c03835	debiasing-stance-detection-models-with	2212.10392	null	https://arxiv.org/abs/2212.10392v1	https://arxiv.org/pdf/2212.10392v1.pdf	Debiasing Stance Detection Models with Counterfactual Reasoning and Adversarial Bias Learning	Stance detection models may tend to rely on dataset bias in the text part as a shortcut and thus fail to sufficiently learn the interaction between the targets and texts. Recent debiasing methods usually treated features learned by small models or big models at earlier steps as bias features and proposed to exclude the branch learning those bias features during inference. However, most of these methods fail to disentangle the ``good'' stance features and ``bad'' bias features in the text part. In this paper, we investigate how to mitigate dataset bias in stance detection. Motivated by causal effects, we leverage a novel counterfactual inference framework, which enables us to capture the dataset bias in the text part as the direct causal effect of the text on stances and reduce the dataset bias in the text part by subtracting the direct text effect from the total causal effect. We novelly model bias features as features that correlate with the stance labels but fail on intermediate stance reasoning subtasks and propose an adversarial bias learning module to model the bias more accurately. To verify whether our model could better model the interaction between texts and targets, we test our model on recently proposed test sets to evaluate the understanding of the task from various aspects. Experiments demonstrate that our proposed method (1) could better model the bias features, and (2) outperforms existing debiasing baselines on both the original dataset and most of the newly constructed test sets.	['Bing Qin', 'Yanyan Zhao', 'Jianhua Yuan']	2022-12-20	null	null	null	null	['counterfactual-inference', 'stance-detection']	['miscellaneous', 'natural-language-processing']	[ 9.36877802e-02 9.62778181e-02 -8.83953512e-01 -6.23594463e-01 -5.37324250e-01 -7.16373563e-01 1.05960822e+00 5.21794967e-02 -4.00571376e-01 9.13635254e-01 7.30306089e-01 -3.30231428e-01 1.36502072e-01 -9.71309304e-01 -9.57773685e-01 -6.47047281e-01 3.91310900e-01 4.59169924e-01 1.38714775e-01 -4.61617082e-01 5.86624324e-01 -8.93514305e-02 -1.11360550e+00 5.48761666e-01 1.15070689e+00 7.42188871e-01 -5.13716578e-01 1.87537372e-01 1.68035850e-01 1.32643306e+00 -8.71360302e-01 -7.17759609e-01 2.13347286e-01 -6.04711890e-01 -7.04745770e-01 -4.47705746e-01 6.79516733e-01 -6.80735171e-01 -4.68322873e-01 1.05319500e+00 5.88161170e-01 -2.22958043e-01 9.59717035e-01 -1.41928244e+00 -7.45071769e-01 1.35510600e+00 -8.01898360e-01 3.90056849e-01 1.43238157e-01 3.71001869e-01 1.42762458e+00 -6.40343487e-01 5.59235692e-01 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368989ce-3b85-4bec-a0ae-7a37908990f7	inferring-community-characteristics-in	2105.13762	null	https://arxiv.org/abs/2105.13762v2	https://arxiv.org/pdf/2105.13762v2.pdf	The Feature-First Block Model	Labelled networks are an important class of data, naturally appearing in numerous applications in science and engineering. A typical inference goal is to determine how the vertex labels (or features) affect the network's structure. In this work, we introduce a new generative model, the feature-first block model (FFBM), that facilitates the use of rich queries on labelled networks. We develop a Bayesian framework and devise a two-level Markov chain Monte Carlo approach to efficiently sample from the relevant posterior distribution of the FFBM parameters. This allows us to infer if and how the observed vertex-features affect macro-structure. We apply the proposed methods to a variety of network data to extract the most important features along which the vertices are partitioned. The main advantages of the proposed approach are that the whole feature-space is used automatically and that features can be rank-ordered implicitly according to impact.	['Lawrence Tray', 'Ioannis Kontoyiannis']	2021-05-28	null	null	null	null	['stochastic-block-model']	['graphs']	[ 4.18739945e-01 1.30351782e-01 -2.34807849e-01 -4.79874432e-01 -2.18822911e-01 -5.09310007e-01 1.13057768e+00 2.51837313e-01 -2.16974676e-01 8.97975445e-01 9.50318575e-02 -1.53659135e-01 -7.72813857e-01 -1.26695669e+00 -6.05285525e-01 -7.72629023e-01 -2.10872516e-01 8.92509758e-01 4.91403073e-01 1.51960582e-01 5.05925655e-01 8.57238770e-01 -1.79170513e+00 -3.28582413e-02 6.54036701e-01 6.49917424e-01 7.62909353e-02 5.92593729e-01 -3.59775066e-01 6.21421754e-01 -2.97280759e-01 -3.42434764e-01 -1.24916419e-01 -2.71030694e-01 -9.05692995e-01 7.76730776e-02 -1.82241514e-01 3.73327881e-02 2.95198616e-02 1.10478187e+00 5.69811203e-02 1.91935301e-01 1.41859567e+00 -1.22548735e+00 2.94110805e-01 7.35881805e-01 -4.51061904e-01 2.57405698e-01 2.85259008e-01 -1.61132336e-01 1.26959026e+00 -8.14742446e-01 8.99129748e-01 1.56811011e+00 2.85127878e-01 -1.78405970e-01 -1.51604736e+00 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1.19177535e-01 1.99614596e-02 -1.11027431e+00 -3.32570195e-01 -1.29134431e-01]	[7.0947442054748535, 4.71970272064209]
60a24841-f8b1-4a7d-bfe2-fa40f7a55c54	a-survey-on-offline-model-based-reinforcement	2305.03360	null	https://arxiv.org/abs/2305.03360v1	https://arxiv.org/pdf/2305.03360v1.pdf	A Survey on Offline Model-Based Reinforcement Learning	Model-based approaches are becoming increasingly popular in the field of offline reinforcement learning, with high potential in real-world applications due to the model's capability of thoroughly utilizing the large historical datasets available with supervised learning techniques. This paper presents a literature review of recent work in offline model-based reinforcement learning, a field that utilizes model-based approaches in offline reinforcement learning. The survey provides a brief overview of the concepts and recent developments in both offline reinforcement learning and model-based reinforcement learning, and discuss the intersection of the two fields. We then presents key relevant papers in the field of offline model-based reinforcement learning and discuss their methods, particularly their approaches in solving the issue of distributional shift, the main problem faced by all current offline model-based reinforcement learning methods. We further discuss key challenges faced by the field, and suggest possible directions for future work.	['Haoyang He']	2023-05-05	null	null	null	null	['model-based-reinforcement-learning']	['reasoning']	[-1.61451489e-01 -2.64359176e-01 -9.06488419e-01 -3.36798429e-01 -9.89557624e-01 -6.15176976e-01 3.88202935e-01 3.29055667e-01 -8.50668311e-01 1.04558361e+00 -1.10039108e-01 -3.95012379e-01 -4.22604173e-01 -8.69803131e-01 -6.61779523e-01 -6.79140985e-01 -5.79373837e-01 7.11788118e-01 4.48045880e-02 -6.33947372e-01 4.29368913e-01 3.62012416e-01 -1.52681303e+00 -5.08757941e-02 7.74810076e-01 6.82847857e-01 1.46068767e-01 8.24753165e-01 -2.45852321e-01 9.10713375e-01 -8.72896194e-01 -2.50121444e-01 1.63010865e-01 -6.42770171e-01 -7.31546640e-01 -6.91180751e-02 1.24572970e-01 -5.41642010e-01 -6.78749323e-01 8.42805386e-01 6.97976589e-01 5.30634046e-01 4.85663623e-01 -1.50462425e+00 -2.81059623e-01 7.58252203e-01 -4.45358306e-01 6.33598149e-01 4.25807923e-01 1.98277116e-01 9.14213836e-01 -4.68733281e-01 3.30157459e-01 1.05981898e+00 8.36468160e-01 6.63641453e-01 -1.15209281e+00 -7.36995101e-01 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9930afad-656a-4641-86ac-3dad72faecef	negation-typology-and-general-representation	null	null	https://aclanthology.org/2021.naacl-srw.3	https://aclanthology.org/2021.naacl-srw.3.pdf	Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish.	Negation is a linguistic universal that poses difficulties for cognitive and computational processing. Despite many advances in text analytics, negation resolution remains an acute and continuously researched question in Natural Language Processing. Reliable negation parsing affects results in biomedical text mining, sentiment analysis, machine translation, and many other fields. The availability of multilingual pre-trained general representation models makes it possible to experiment with negation detection in languages that lack annotated data. In this work we test the performance of two state-of-the-art contextual representation models, Multilingual BERT and XLM-RoBERTa. We resolve negation scope by conducting zero-shot transfer between English, Spanish, French, and Russian. Our best result amounts to a token-level F1-score of 86.86{\%} between Spanish and Russian. We correlate these results with a linguistic negation typology and lexical capacity of the models.	['Fabio Rinaldi', 'Anastassia Shaitarova']	2021-06-01	null	null	null	naacl-2021-4	['negation-detection', 'negation-scope-resolution']	['natural-language-processing', 'natural-language-processing']	[ 2.20240787e-01 2.14888617e-01 -3.53854954e-01 -5.16655803e-01 -7.68665016e-01 -6.11796677e-01 5.39950550e-01 8.85420144e-01 -1.00021625e+00 1.19812489e+00 4.74412471e-01 -3.73916715e-01 1.78748131e-01 -8.06822538e-01 -5.61632156e-01 -6.09785728e-02 2.53901243e-01 4.58083600e-01 1.33104652e-01 -7.83404171e-01 5.77396080e-02 1.88856095e-01 -1.19461381e+00 8.13162506e-01 9.46997285e-01 4.27713931e-01 -5.88939674e-02 3.27002734e-01 -4.12602186e-01 9.19181705e-01 -6.00732327e-01 -7.67907977e-01 -2.79101193e-01 -5.96407712e-01 -8.01022470e-01 -6.60144091e-01 1.34708539e-01 2.98107028e-01 2.24648938e-01 1.37180305e+00 4.75343049e-01 -1.13324709e-01 4.57611382e-01 -4.43412364e-01 -8.59970450e-01 8.33807647e-01 -3.42111677e-01 5.52053392e-01 8.37189257e-01 -1.14507966e-01 9.44181085e-01 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3d160552-3cf8-4d69-9d72-a2e8d9b71a2f	multi-graph-tensor-networks	2010.13209	null	https://arxiv.org/abs/2010.13209v4	https://arxiv.org/pdf/2010.13209v4.pdf	Multi-Graph Tensor Networks	The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms. To this end, recent efforts have generalized existing algorithms to irregular domains through graphs, with the aim to gain additional insights from data through the underlying graph topology. At the same time, tensor-based methods have demonstrated promising results in bypassing the bottlenecks imposed by the Curse of Dimensionality. In this paper, we introduce a novel Multi-Graph Tensor Network (MGTN) framework, which exploits both the ability of graphs to handle irregular data sources and the compression properties of tensor networks in a deep learning setting. The potential of the proposed framework is demonstrated through an MGTN based deep Q agent for Foreign Exchange (FOREX) algorithmic trading. By virtue of the MGTN, a FOREX currency graph is leveraged to impose an economically meaningful structure on this demanding task, resulting in a highly superior performance against three competing models and at a drastically lower complexity.	['Danilo P. Mandic', 'Kriton Konstantinidis', 'Yao Lei Xu']	2020-10-25	null	null	null	null	['algorithmic-trading']	['time-series']	[-2.97975808e-01 9.25287083e-02 -2.65344948e-01 -1.85602922e-02 -3.87858957e-01 -6.40362144e-01 7.08708167e-01 2.66392112e-01 1.31866723e-01 4.90203232e-01 4.25992996e-01 -4.07091588e-01 -6.47177637e-01 -9.80592251e-01 -6.51094437e-01 -5.33960938e-01 -5.09464800e-01 5.84438741e-01 -3.77294391e-01 -2.60348827e-01 1.84652105e-01 5.87233603e-01 -1.08052814e+00 -6.44557774e-02 7.58357406e-01 1.33846104e+00 -1.38637275e-01 1.47130713e-01 -1.65538669e-01 8.86778295e-01 -1.69304833e-01 -8.60648632e-01 6.72831297e-01 -7.37469643e-02 -6.49037182e-01 3.50612670e-01 3.66966486e-01 -3.89577538e-01 -6.63519263e-01 9.83008146e-01 1.45143270e-01 1.97209474e-02 2.62733817e-01 -1.26990235e+00 -8.55298281e-01 7.90128708e-01 -5.32161295e-01 2.36527666e-01 -8.58967602e-02 2.43990511e-01 1.73115826e+00 -5.97193718e-01 5.27414858e-01 1.20783544e+00 6.19897842e-01 -2.60473229e-03 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6.69710159e-01 5.85761786e-01 -7.02679813e-01 -2.91125774e-01 -3.38619679e-01]	[6.867447376251221, 5.995962619781494]
4b434595-8b41-4fed-9763-2fe6c7b1b612	a-new-sentence-ordering-method-using-bert	2108.11994	null	https://arxiv.org/abs/2108.11994v1	https://arxiv.org/pdf/2108.11994v1.pdf	A New Sentence Ordering Method Using BERT Pretrained Model	Building systems with capability of natural language understanding (NLU) has been one of the oldest areas of AI. An essential component of NLU is to detect logical succession of events contained in a text. The task of sentence ordering is proposed to learn succession of events with applications in AI tasks. The performance of previous works employing statistical methods is poor, while the neural networks-based approaches are in serious need of large corpora for model learning. In this paper, we propose a method for sentence ordering which does not need a training phase and consequently a large corpus for learning. To this end, we generate sentence embedding using BERT pre-trained model and measure sentence similarity using cosine similarity score. We suggest this score as an indicator of sequential events' level of coherence. We finally sort the sentences through brute-force search to maximize overall similarities of the sequenced sentences. Our proposed method outperformed other baselines on ROCStories, a corpus of 5-sentence human-made stories. The method is specifically more efficient than neural network-based methods when no huge corpus is available. Among other advantages of this method are its interpretability and needlessness to linguistic knowledge.	['Heshaam Faili', 'Seyedeh Zahra Razavi', 'Melika Golestani']	2021-08-26	null	null	null	null	['sentence-ordering']	['natural-language-processing']	[ 4.96733725e-01 8.05554986e-02 -1.39280111e-01 -5.84980249e-01 -4.14829999e-01 -2.83893794e-01 8.86551678e-01 6.99274719e-01 -6.43091440e-01 8.26756120e-01 6.60629034e-01 -2.07628667e-01 -1.65900141e-01 -1.05883622e+00 -5.89609027e-01 -3.08870465e-01 -2.41957739e-01 5.50625384e-01 3.04745674e-01 -5.15591204e-01 8.54295790e-01 1.42110169e-01 -1.44941449e+00 7.76948333e-01 8.70739520e-01 7.92921901e-01 5.98125935e-01 8.72778893e-01 -3.28517497e-01 1.48049045e+00 -6.03998303e-01 -1.77785501e-01 -9.36586857e-02 -8.08304071e-01 -1.10012627e+00 -1.70217752e-01 1.28207684e-01 2.65047280e-03 -2.12497219e-01 7.32693553e-01 3.11296523e-01 3.17952603e-01 8.99787962e-01 -7.37046123e-01 -6.03444099e-01 1.04383397e+00 -2.09881723e-01 7.12350845e-01 6.24804914e-01 -4.06100303e-01 1.35029566e+00 -8.56072545e-01 5.60622394e-01 1.27045548e+00 5.41554809e-01 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b4e561db-aff4-41da-8605-17229c015c70	frame-semantic-enhanced-sentence-modeling-for	null	null	https://aclanthology.org/2021.emnlp-main.331	https://aclanthology.org/2021.emnlp-main.331.pdf	Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization	Sentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task. In particular, intra-sentence level semantics leverage Frames and Frame Elements to model internal semantic structure within a sentence, while inter-sentence level semantics leverage Frame-to-Frame relations to model relationships among sentences. Extensive experiments on two benchmark corpus CNN/DM and NYT demonstrate that our model outperforms six state-of-the-art methods significantly.	['Hongye Tan', 'XiaoLi Li', 'Ru Li', 'Shaoru Guo', 'Yong Guan']	null	null	null	null	emnlp-2021-11	['extractive-document-summarization']	['natural-language-processing']	[ 6.49699688e-01 4.85668004e-01 -5.55448532e-01 -6.32624626e-01 -9.00972307e-01 -2.15155602e-01 5.85634708e-01 7.33150005e-01 -1.75645739e-01 9.34402168e-01 1.55572820e+00 2.05734119e-01 2.43779272e-01 -6.27298474e-01 -6.25510991e-01 -1.40420943e-01 4.04152781e-01 -2.61492934e-03 2.82122046e-01 -4.33157086e-01 6.18430018e-01 -2.47837394e-01 -1.34112036e+00 9.95701909e-01 8.32310617e-01 6.99326456e-01 4.60770220e-01 8.73437047e-01 -5.82281291e-01 1.38522816e+00 -1.01565588e+00 -1.22880220e-01 -5.05200684e-01 -6.64422572e-01 -1.16055405e+00 3.08229536e-01 5.62123835e-01 -2.77678013e-01 -3.21369708e-01 9.36734617e-01 5.51982284e-01 3.46109748e-01 3.25525701e-01 -4.11042273e-01 -6.13121688e-01 1.28653574e+00 -4.65665370e-01 5.69168687e-01 7.79211760e-01 -2.03176200e-01 1.48613954e+00 -8.15829098e-01 9.57258165e-01 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4.65877622e-01 -8.74156773e-01 -4.74471748e-01 -1.35910797e+00 -2.49464348e-01 4.32630002e-01 4.16845798e-01 8.87539908e-02 -8.99055004e-02 2.25826904e-01]	[12.53211784362793, 9.491437911987305]
29aa008c-cdb4-4ae7-aff8-88555a03d628	are-neural-nets-modular-inspecting-functional-1	2010.02066	null	https://arxiv.org/abs/2010.02066v3	https://arxiv.org/pdf/2010.02066v3.pdf	Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks	Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc. Understanding if and how NNs are modular could provide insights into how to improve them. Current inspection methods, however, fail to link modules to their functionality. In this paper, we present a novel method based on learning binary weight masks to identify individual weights and subnets responsible for specific functions. Using this powerful tool, we contribute an extensive study of emerging modularity in NNs that covers several standard architectures and datasets. We demonstrate how common NNs fail to reuse submodules and offer new insights into the related issue of systematic generalization on language tasks.	['Jürgen Schmidhuber', 'Sjoerd van Steenkiste', 'Róbert Csordás']	2020-10-05	are-neural-nets-modular-inspecting-functional	https://openreview.net/forum?id=7uVcpu-gMD	https://openreview.net/pdf?id=7uVcpu-gMD	iclr-2021-1	['systematic-generalization']	['reasoning']	[ 3.94642293e-01 3.85113120e-01 5.76537438e-02 -2.76887059e-01 3.06796938e-01 -8.28995764e-01 3.17588955e-01 -1.41507387e-01 3.07850074e-02 7.43342161e-01 -6.25463724e-02 -5.83965480e-01 -6.70823753e-01 -8.74327004e-01 -7.51062751e-01 -6.20991826e-01 -4.78496999e-01 -1.88527599e-01 4.32850540e-01 -3.74315590e-01 1.69025525e-01 5.65373003e-01 -1.85725582e+00 3.72827977e-01 1.01331079e+00 5.88498890e-01 4.92228180e-01 5.01048326e-01 -1.20881461e-01 5.55636585e-01 -7.30327249e-01 -7.69748151e-01 1.62897244e-01 -2.43854240e-01 -8.10673714e-01 -3.25237840e-01 3.99951518e-01 -1.02783674e-02 -9.28756520e-02 1.15054119e+00 1.04733042e-01 -1.65506139e-01 8.39455485e-01 -1.25685668e+00 -7.96356440e-01 1.12960863e+00 -6.02987111e-02 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76ebf7dc-0e3c-4411-80ce-0acf08ebd890	uvim-a-unified-modeling-approach-for-vision	2205.10337	null	https://arxiv.org/abs/2205.10337v3	https://arxiv.org/pdf/2205.10337v3.pdf	UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes	We introduce UViM, a unified approach capable of modeling a wide range of computer vision tasks. In contrast to previous models, UViM has the same functional form for all tasks; it requires no task-specific modifications which require extensive human expertise. The approach involves two components: (I) a base model (feed-forward) which is trained to directly predict raw vision outputs, guided by a learned discrete code and (II) a language model (autoregressive) that is trained to generate the guiding code. These components complement each other: the language model is well-suited to modeling structured interdependent data, while the base model is efficient at dealing with high-dimensional outputs. We demonstrate the effectiveness of UViM on three diverse and challenging vision tasks: panoptic segmentation, depth prediction and image colorization, where we achieve competitive and near state-of-the-art results. Our experimental results suggest that UViM is a promising candidate for a unified modeling approach in computer vision.	['Neil Houlsby', 'Jeremiah Harmsen', 'Xiaohua Zhai', 'Lucas Beyer', 'André Susano Pinto', 'Alexander Kolesnikov']	2022-05-20	null	null	null	null	['colorization']	['computer-vision']	[ 4.31051821e-01 2.21924428e-02 -3.00829839e-02 -5.68749130e-01 -6.44051790e-01 -4.18035448e-01 9.30365324e-01 -1.14679575e-01 -3.42091173e-01 6.17365949e-02 -3.03650737e-01 -4.68024671e-01 1.91725135e-01 -6.01449728e-01 -6.68725729e-01 -7.18158484e-01 2.02368498e-01 4.65443760e-01 3.85721803e-01 5.90659082e-02 4.58804041e-01 4.12177235e-01 -1.75405347e+00 4.69301760e-01 1.05018926e+00 1.04843640e+00 6.92553639e-01 1.08329463e+00 -2.87633121e-01 1.19640315e+00 -9.30192620e-02 -2.35703662e-01 1.73777372e-01 -4.46798801e-01 -7.27498293e-01 5.39608359e-01 4.96870577e-01 -1.48647517e-01 -1.03443570e-01 1.06803882e+00 3.73416319e-02 -8.43251199e-02 9.53940988e-01 -9.19281483e-01 -8.83516729e-01 5.59918769e-02 -6.80064142e-01 -1.92574024e-01 1.15378127e-01 2.24588901e-01 9.34457242e-01 -1.01500285e+00 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5.88812053e-01 -9.65005875e-01 -4.64442581e-01 -6.80231154e-01 -4.30743068e-01 3.71414721e-01 5.32517493e-01 -4.05081809e-01 1.22052627e-02 6.77278221e-01]	[10.239792823791504, 1.6016793251037598]
fc4584e6-8afe-4fb2-a411-bad020fb2e8b	deconfounded-video-moment-retrieval-with	2106.01534	null	https://arxiv.org/abs/2106.01534v1	https://arxiv.org/pdf/2106.01534v1.pdf	Deconfounded Video Moment Retrieval with Causal Intervention	We tackle the task of video moment retrieval (VMR), which aims to localize a specific moment in a video according to a textual query. Existing methods primarily model the matching relationship between query and moment by complex cross-modal interactions. Despite their effectiveness, current models mostly exploit dataset biases while ignoring the video content, thus leading to poor generalizability. We argue that the issue is caused by the hidden confounder in VMR, {i.e., temporal location of moments}, that spuriously correlates the model input and prediction. How to design robust matching models against the temporal location biases is crucial but, as far as we know, has not been studied yet for VMR. To fill the research gap, we propose a causality-inspired VMR framework that builds structural causal model to capture the true effect of query and video content on the prediction. Specifically, we develop a Deconfounded Cross-modal Matching (DCM) method to remove the confounding effects of moment location. It first disentangles moment representation to infer the core feature of visual content, and then applies causal intervention on the disentangled multimodal input based on backdoor adjustment, which forces the model to fairly incorporate each possible location of the target into consideration. Extensive experiments clearly show that our approach can achieve significant improvement over the state-of-the-art methods in terms of both accuracy and generalization (Codes: \color{blue}{\url{https://github.com/Xun-Yang/Causal_Video_Moment_Retrieval}}	['Tat-Seng Chua', 'Meng Wang', 'Wei Ji', 'Fuli Feng', 'Xun Yang']	2021-06-03	null	null	null	null	['moment-retrieval']	['computer-vision']	[ 1.01070255e-01 -3.75048101e-01 -7.93859124e-01 -7.48310462e-02 -7.57069468e-01 -6.97415590e-01 9.57335770e-01 -2.58207858e-01 -1.09407464e-02 2.53479242e-01 5.65730274e-01 -2.34102085e-01 -3.92341465e-01 -5.11484623e-01 -9.17408168e-01 -5.24020612e-01 8.31722748e-03 -1.47816017e-01 -1.19882695e-01 -7.38851354e-02 3.55293185e-01 1.46603197e-01 -1.54185748e+00 5.67836225e-01 5.60058296e-01 8.25537205e-01 1.28676221e-01 4.02968347e-01 2.68636137e-01 9.09133017e-01 -2.77346134e-01 -5.05463421e-01 2.59194225e-01 -3.76521707e-01 -3.97663623e-01 -1.46669358e-01 5.07218897e-01 -4.63462919e-01 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bc2d33cc-4dd5-4047-ab92-77e85d286777	exploring-model-dynamics-for-accumulative	2306.03726	null	https://arxiv.org/abs/2306.03726v1	https://arxiv.org/pdf/2306.03726v1.pdf	Exploring Model Dynamics for Accumulative Poisoning Discovery	Adversarial poisoning attacks pose huge threats to various machine learning applications. Especially, the recent accumulative poisoning attacks show that it is possible to achieve irreparable harm on models via a sequence of imperceptible attacks followed by a trigger batch. Due to the limited data-level discrepancy in real-time data streaming, current defensive methods are indiscriminate in handling the poison and clean samples. In this paper, we dive into the perspective of model dynamics and propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information. By implicitly transferring the changes in the data manipulation to that in the model outputs, Memorization Discrepancy can discover the imperceptible poison samples based on their distinct dynamics from the clean samples. We thoroughly explore its properties and propose Discrepancy-aware Sample Correction (DSC) to defend against accumulative poisoning attacks. Extensive experiments comprehensively characterized Memorization Discrepancy and verified its effectiveness. The code is publicly available at: https://github.com/tmlr-group/Memorization-Discrepancy.	['Bo Han', 'Liang Wang', 'Tongliang Liu', 'Shuo Yuan', 'Li He', 'Chao Du', 'Jiangchao Yao', 'Xiawei Guo', 'Jianing Zhu']	2023-06-06	null	null	null	null	['memorization']	['natural-language-processing']	[ 1.74524322e-01 -4.86845821e-01 2.32539866e-02 5.68661429e-02 -9.98259664e-01 -1.08468115e+00 6.69086516e-01 5.46940386e-01 -4.39940661e-01 4.71308053e-01 -9.14798975e-02 -3.50149333e-01 3.49048115e-02 -6.34779096e-01 -8.20401371e-01 -1.05395865e+00 -2.50100166e-01 1.34623617e-01 1.69319615e-01 -2.49098346e-01 4.88070071e-01 5.00872076e-01 -9.94177043e-01 4.28486586e-01 7.14433670e-01 7.18130112e-01 -3.71595681e-01 7.83566773e-01 2.07744896e-01 8.31332207e-01 -1.00542474e+00 -3.80846679e-01 6.45406246e-01 -5.24936974e-01 -5.50097167e-01 -2.64414668e-01 2.60245323e-01 -6.03277028e-01 -6.97245896e-01 1.41773510e+00 4.60118055e-01 -3.47081423e-01 2.98516095e-01 -1.64636850e+00 -5.91109574e-01 7.68384576e-01 -6.69079542e-01 6.01867080e-01 2.30533049e-01 6.85535371e-01 4.93806809e-01 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416d876d-8b88-4a97-81a1-279c57e61d0e	radioses-mmwave-based-audioradio-speech	2204.07092	null	https://arxiv.org/abs/2204.07092v1	https://arxiv.org/pdf/2204.07092v1.pdf	RadioSES: mmWave-Based Audioradio Speech Enhancement and Separation System	Speech enhancement and separation have been a long-standing problem, especially with the recent advances using a single microphone. Although microphones perform well in constrained settings, their performance for speech separation decreases in noisy conditions. In this work, we propose RadioSES, an audioradio speech enhancement and separation system that overcomes inherent problems in audio-only systems. By fusing a complementary radio modality, RadioSES can estimate the number of speakers, solve source association problem, separate and enhance noisy mixture speeches, and improve both intelligibility and perceptual quality. We perform millimeter-wave sensing to detect and localize speakers, and introduce an audioradio deep learning framework to fuse the separate radio features with the mixed audio features. Extensive experiments using commercial off-the-shelf devices show that RadioSES outperforms a variety of state-of-the-art baselines, with consistent performance gains in different environmental settings. Compared with the audiovisual methods, RadioSES provides similar improvements (e.g., ~3 dB gains in SiSDR), along with the benefits of lower computational complexity and being less privacy concerning.	['K. J. Ray Liu', 'Min Wu', 'Beibei Wang', 'Chenshu Wu', 'Muhammed Zahid Ozturk']	2022-04-14	null	null	null	null	['speech-separation']	['speech']	[ 1.07618511e-01 -2.31453910e-01 1.66260481e-01 -2.81513512e-01 -1.45241141e+00 -4.61449146e-01 2.89060891e-01 -2.82230526e-02 -3.23672712e-01 5.49291253e-01 7.24272490e-01 -1.70326993e-01 -1.70650147e-02 -4.31437522e-01 -3.62366825e-01 -9.92866457e-01 -8.59390572e-02 -1.98611245e-01 9.62913856e-02 -2.31355533e-01 -3.25238258e-01 3.08696777e-01 -1.50686383e+00 3.47758038e-03 7.99109459e-01 1.38757026e+00 3.46962869e-01 9.38475728e-01 5.03156818e-02 6.75689399e-01 -1.04745388e+00 7.85285085e-02 2.70811886e-01 5.01007512e-02 1.80215761e-01 -1.83527783e-01 5.34131944e-01 -4.33519781e-01 -1.07710528e+00 1.07543933e+00 1.44373536e+00 1.40416980e-01 2.87053555e-01 -1.07011127e+00 -5.09886742e-01 5.50932169e-01 -6.46023214e-01 3.55130136e-01 4.70569223e-01 -3.27643692e-01 7.31983840e-01 -8.86520743e-01 -3.37751925e-01 1.18849349e+00 7.48466671e-01 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-5.29541731e-01 -8.26391697e-01 -8.99664819e-01 -4.07448709e-01 9.34918165e-01 -4.98112768e-01 -6.60475016e-01 -3.50850731e-01]	[14.900307655334473, 5.928405284881592]
efc6f216-0345-45a2-acda-0c551be93fcd	inclg-inpainting-for-non-cleft-lip-generation	2305.10589	null	https://arxiv.org/abs/2305.10589v1	https://arxiv.org/pdf/2305.10589v1.pdf	INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network	We present a software that predicts non-cleft facial images for patients with cleft lip, thereby facilitating the understanding, awareness and discussion of cleft lip surgeries. To protect patients privacy, we design a software framework using image inpainting, which does not require cleft lip images for training, thereby mitigating the risk of model leakage. We implement a novel multi-task architecture that predicts both the non-cleft facial image and facial landmarks, resulting in better performance as evaluated by surgeons. The software is implemented with PyTorch and is usable with consumer-level color images with a fast prediction speed, enabling effective deployment.	['Hubert P. H. Shum', 'Edmond S. L. Ho', 'Amir Atapour-Abarghouei', 'Shuang Chen']	2023-05-17	null	null	null	null	['image-inpainting']	['computer-vision']	[ 5.82314953e-02 4.89593267e-01 -3.91729474e-01 -5.01438022e-01 -1.14327669e+00 -2.98823029e-01 -1.86310783e-02 -1.74243003e-01 -4.02911782e-01 2.07877606e-01 4.39041942e-01 -4.36897159e-01 3.96092504e-01 -4.89016324e-01 -6.17958188e-01 -6.63046718e-01 1.76093057e-01 7.83060715e-02 -2.92205989e-01 4.07956243e-01 1.66552573e-01 4.64101464e-01 -1.71611452e+00 9.33711588e-01 4.45391893e-01 1.21293569e+00 -8.38404372e-02 6.69896662e-01 -6.68118000e-02 4.54745919e-01 -4.92995143e-01 -8.39158297e-01 4.76246953e-01 1.45008385e-01 -5.76016009e-01 -2.66500115e-01 1.04031467e+00 -9.80082214e-01 -8.50890279e-02 7.83870637e-01 7.88349569e-01 -5.46684861e-01 6.25637099e-02 -1.24858630e+00 -2.88287193e-01 -1.71428189e-01 -6.11005366e-01 -6.03431940e-01 2.46936336e-01 3.39053690e-01 7.37527192e-01 -9.08183873e-01 8.59177351e-01 9.91433084e-01 9.20390725e-01 1.06527460e+00 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a849bbfb-7dcf-4c6f-832c-9459f98eb450	sadtalker-learning-realistic-3d-motion	2211.12194	null	https://arxiv.org/abs/2211.12194v2	https://arxiv.org/pdf/2211.12194v2.pdf	SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation	Generating talking head videos through a face image and a piece of speech audio still contains many challenges. ie, unnatural head movement, distorted expression, and identity modification. We argue that these issues are mainly because of learning from the coupled 2D motion fields. On the other hand, explicitly using 3D information also suffers problems of stiff expression and incoherent video. We present SadTalker, which generates 3D motion coefficients (head pose, expression) of the 3DMM from audio and implicitly modulates a novel 3D-aware face render for talking head generation. To learn the realistic motion coefficients, we explicitly model the connections between audio and different types of motion coefficients individually. Precisely, we present ExpNet to learn the accurate facial expression from audio by distilling both coefficients and 3D-rendered faces. As for the head pose, we design PoseVAE via a conditional VAE to synthesize head motion in different styles. Finally, the generated 3D motion coefficients are mapped to the unsupervised 3D keypoints space of the proposed face render, and synthesize the final video. We conducted extensive experiments to demonstrate the superiority of our method in terms of motion and video quality.	['Fei Wang', 'Ying Shan', 'Yu Guo', 'Xi Shen', 'Yong Zhang', 'Xuan Wang', 'Xiaodong Cun', 'Wenxuan Zhang']	2022-11-22	null	http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_SadTalker_Learning_Realistic_3D_Motion_Coefficients_for_Stylized_Audio-Driven_Single_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_SadTalker_Learning_Realistic_3D_Motion_Coefficients_for_Stylized_Audio-Driven_Single_CVPR_2023_paper.pdf	cvpr-2023-1	['talking-head-generation']	['computer-vision']	[-1.02811567e-01 1.33460656e-01 2.19037294e-01 -7.18481481e-01 -9.12666142e-01 -3.73776704e-01 4.73322123e-01 -1.13504291e+00 8.17270856e-03 4.03844506e-01 6.80735469e-01 4.24528807e-01 2.97341436e-01 -2.56788224e-01 -7.95063674e-01 -8.80690038e-01 8.48663598e-02 1.33429110e-01 -4.13480759e-01 -1.90373421e-01 -9.09232050e-02 5.32474339e-01 -1.78149557e+00 3.69477987e-01 4.15570527e-01 1.14551878e+00 -4.35096398e-02 9.48211432e-01 -1.25975147e-01 9.42913949e-01 -8.92091990e-01 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3d4cdba6-c8f8-494f-9d30-121836f8490e	desra-detect-and-delete-the-artifacts-of-gan	2307.02457	null	https://arxiv.org/abs/2307.02457v1	https://arxiv.org/pdf/2307.02457v1.pdf	DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models	Image super-resolution (SR) with generative adversarial networks (GAN) has achieved great success in restoring realistic details. However, it is notorious that GAN-based SR models will inevitably produce unpleasant and undesirable artifacts, especially in practical scenarios. Previous works typically suppress artifacts with an extra loss penalty in the training phase. They only work for in-distribution artifact types generated during training. When applied in real-world scenarios, we observe that those improved methods still generate obviously annoying artifacts during inference. In this paper, we analyze the cause and characteristics of the GAN artifacts produced in unseen test data without ground-truths. We then develop a novel method, namely, DeSRA, to Detect and then Delete those SR Artifacts in practice. Specifically, we propose to measure a relative local variance distance from MSE-SR results and GAN-SR results, and locate the problematic areas based on the above distance and semantic-aware thresholds. After detecting the artifact regions, we develop a finetune procedure to improve GAN-based SR models with a few samples, so that they can deal with similar types of artifacts in more unseen real data. Equipped with our DeSRA, we can successfully eliminate artifacts from inference and improve the ability of SR models to be applied in real-world scenarios. The code will be available at https://github.com/TencentARC/DeSRA.	['Chao Dong', 'Jiantao Zhou', 'Ying Shan', 'Gen Li', 'Xiangyu Chen', 'Xintao Wang', 'Liangbin Xie']	2023-07-05	null	null	null	null	['image-super-resolution', 'super-resolution']	['computer-vision', 'computer-vision']	[ 5.93011081e-01 6.95251068e-03 3.07857901e-01 -2.56567970e-02 -9.11223829e-01 -4.66220886e-01 1.80787489e-01 -6.60169721e-01 1.58655539e-01 9.68038678e-01 1.53414950e-01 1.56080804e-03 2.31973045e-02 -8.37780476e-01 -6.62963808e-01 -8.44736159e-01 4.05972958e-01 -9.31648761e-02 4.98135649e-02 -1.52114734e-01 9.41714272e-02 4.22217876e-01 -1.42522311e+00 4.06774104e-01 1.36892498e+00 7.13543534e-01 2.03373909e-01 4.78268594e-01 4.54427581e-03 9.24667478e-01 -1.13423860e+00 -3.87455285e-01 3.93456995e-01 -8.85297954e-01 -3.33260655e-01 4.89010625e-02 3.78423542e-01 -4.50687647e-01 -2.40036815e-01 1.36306107e+00 7.92614520e-01 7.34690055e-02 3.32528561e-01 -1.05006540e+00 -7.66869307e-01 4.39372718e-01 -8.19074750e-01 1.91625044e-01 2.47933015e-01 4.00684953e-01 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0a7cf5c1-9600-4ade-83bf-3eec52948d96	open-information-extraction-via-chunks	2305.03299	null	https://arxiv.org/abs/2305.03299v1	https://arxiv.org/pdf/2305.03299v1.pdf	Open Information Extraction via Chunks	Open Information Extraction (OIE) aims to extract relational tuples from open-domain sentences. Existing OIE systems split a sentence into tokens and recognize token spans as tuple relations and arguments. We instead propose Sentence as Chunk sequence (SaC) and recognize chunk spans as tuple relations and arguments. We argue that SaC has better quantitative and qualitative properties for OIE than sentence as token sequence, and evaluate four choices of chunks (i.e., CoNLL chunks, simple phrases, NP chunks, and spans from SpanOIE) against gold OIE tuples. Accordingly, we propose a simple BERT-based model for sentence chunking, and propose Chunk-OIE for tuple extraction on top of SaC. Chunk-OIE achieves state-of-the-art results on multiple OIE datasets, showing that SaC benefits OIE task.	['XiaoLi Li', 'Jung-jae Kim', 'Aixin Sun', 'Kuicai Dong']	2023-05-05	null	null	null	null	['open-information-extraction', 'chunking']	['natural-language-processing', 'natural-language-processing']	[-2.79808581e-01 1.02888751e+00 -7.31170952e-01 -3.57578695e-01 -7.82603681e-01 -6.20286822e-01 3.23795915e-01 6.46302521e-01 -1.96918651e-01 1.02896369e+00 6.30945802e-01 -3.44357342e-01 -1.17525697e-01 -1.19718611e+00 -9.71710205e-01 4.61264074e-01 -3.65155458e-01 9.60389912e-01 5.34547508e-01 -3.65383297e-01 8.06967244e-02 1.98745906e-01 -1.46607625e+00 1.15930116e+00 1.07719100e+00 1.18454671e+00 -6.06069863e-01 6.47076011e-01 -9.17642474e-01 1.45859432e+00 -9.53272998e-01 -8.86796474e-01 1.95295960e-01 -4.54073027e-02 -1.62846494e+00 -3.29998583e-01 8.09929445e-02 -9.23180208e-02 -7.63288513e-02 5.62467217e-01 1.52902693e-01 -1.92458808e-01 6.11662209e-01 -1.37042093e+00 -6.96608245e-01 1.67750287e+00 -7.75523186e-02 1.12358116e-01 9.12551582e-01 -2.68769532e-01 1.50716329e+00 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5.49835503e-01 3.42781961e-01 4.88033593e-01 -5.88589683e-02 -4.81477469e-01 -1.08670998e+00 -7.80937612e-01 4.01902646e-01 5.99928871e-02 -3.48430395e-01 1.56347573e-01 -4.01829362e-01]	[9.59016227722168, 8.618504524230957]
88f44097-7046-4b9b-9611-9ffa00fa1aa4	dermatological-diagnosis-explainability	2302.12084	null	https://arxiv.org/abs/2302.12084v1	https://arxiv.org/pdf/2302.12084v1.pdf	Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks	In recent years, large strides have been taken in developing machine learning methods for dermatological applications, supported in part by the success of deep learning (DL). To date, diagnosing diseases from images is one of the most explored applications of DL within dermatology. Convolutional neural networks (ConvNets) are the most common (DL) method in medical imaging due to their training efficiency and accuracy, although they are often described as black boxes because of their limited explainability. One popular way to obtain insight into a ConvNet's decision mechanism is gradient class activation maps (Grad-CAM). A quantitative evaluation of the Grad-CAM explainability has been recently made possible by the release of DermXDB, a skin disease diagnosis explainability dataset which enables explainability benchmarking of ConvNet architectures. In this paper, we perform a literature review to identify the most common ConvNet architectures used for this task, and compare their Grad-CAM explanations with the explanation maps provided by DermXDB. We identified 11 architectures: DenseNet121, EfficientNet-B0, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, NASNetMobile, ResNet50, ResNet50V2, VGG16, and Xception. We pre-trained all architectures on an clinical skin disease dataset, and fine-tuned them on a DermXDB subset. Validation results on the DermXDB holdout subset show an explainability F1 score of between 0.35-0.46, with Xception displaying the highest explainability performance. NASNetMobile reports the highest characteristic-level explainability sensitivity, despite it's mediocre diagnosis performance. These results highlight the importance of choosing the right architecture for the desired application and target market, underline need for additional explainability datasets, and further confirm the need for explainability benchmarking that relies on quantitative analyses.	['Alfiia Galimzianova', 'Ole Winther', 'Raluca Jalaboi']	2023-02-23	null	null	null	null	['medical-diagnosis']	['medical']	[-1.06613757e-02 4.44085062e-01 -4.38828439e-01 -4.04667675e-01 -2.10378096e-01 -4.16054696e-01 5.50460279e-01 1.26828685e-01 -1.41900823e-01 6.34962380e-01 1.37424260e-01 -5.22597909e-01 -6.80446863e-01 -5.72412431e-01 -3.67607206e-01 -3.89846057e-01 2.44263858e-02 4.52538788e-01 -9.55914184e-02 -1.85945868e-01 -8.04135948e-02 5.65581203e-01 -1.19275260e+00 8.25574756e-01 1.05447459e+00 9.45899367e-01 1.89767376e-01 9.79160547e-01 -3.14967573e-01 7.88416862e-01 -4.70530599e-01 -6.31479084e-01 -1.19096316e-01 -4.54039752e-01 -1.02635741e+00 -3.87340821e-02 2.56478906e-01 -2.10211232e-01 -1.43271372e-01 5.23551464e-01 4.44163799e-01 -5.17981648e-01 6.71802104e-01 -1.20123327e+00 -7.93982863e-01 4.15332049e-01 -2.27532312e-01 3.13406676e-01 3.84287462e-02 3.48829627e-01 8.35940480e-01 -6.17158473e-01 8.62868011e-01 9.37498212e-01 9.36138332e-01 1.07946324e+00 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6aac6388-e50c-422b-9259-65dfb916a26d	boosting-text-classification-performance-on	null	null	https://aclanthology.org/W18-5114	https://aclanthology.org/W18-5114.pdf	Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs	Text classification models have been heavily utilized for a slew of interesting natural language processing problems. Like any other machine learning model, these classifiers are very dependent on the size and quality of the training dataset. Insufficient and imbalanced datasets will lead to poor performance. An interesting solution to poor datasets is to take advantage of the world knowledge in the form of knowledge graphs to improve our training data. In this paper, we use ConceptNet and Wikidata to improve sexist tweet classification by two methods (1) text augmentation and (2) text generation. In our text generation approach, we generate new tweets by replacing words using data acquired from ConceptNet relations in order to increase the size of our training set, this method is very helpful with frustratingly small datasets, preserves the label and increases diversity. In our text augmentation approach, the number of tweets remains the same but their words are augmented (concatenation) with words extracted from their ConceptNet relations and their description extracted from Wikidata. In our text augmentation approach, the number of tweets in each class remains the same but the range of each tweet increases. Our experiments show that our approach improves sexist tweet classification significantly in our entire machine learning models. Our approach can be readily applied to any other small dataset size like hate speech or abusive language and text classification problem using any machine learning model.	['Stan Matwin', 'Borna Jafarpour', 'Sima Sharifirad']	2018-10-01	null	null	null	ws-2018-10	['text-augmentation']	['natural-language-processing']	[ 3.42682064e-01 4.81116027e-01 -3.69707853e-01 -3.22109044e-01 2.03129813e-01 -3.99922848e-01 8.57263088e-01 6.10459983e-01 -5.09098530e-01 1.11040306e+00 3.10625166e-01 -2.13282228e-01 9.19044111e-03 -1.33517504e+00 -2.10035771e-01 -4.43032265e-01 2.91108161e-01 8.13573420e-01 2.92102963e-01 -8.37736249e-01 4.29393768e-01 8.70946422e-02 -1.54919946e+00 2.15431854e-01 9.72129464e-01 6.22399986e-01 -4.47299063e-01 4.23583388e-01 -8.96680236e-01 9.47716415e-01 -8.52465630e-01 -7.36436963e-01 1.53126314e-01 -3.05987805e-01 -1.12413442e+00 5.45343570e-02 1.42296970e-01 2.60522775e-02 -1.63040727e-01 9.45660710e-01 4.51640368e-01 1.94523543e-01 6.47854567e-01 -1.61888099e+00 -4.24509615e-01 1.14671922e+00 -8.81256461e-01 1.09381028e-01 1.41655266e-01 -3.90290618e-01 7.93787479e-01 -6.96313620e-01 6.52201772e-01 1.44448495e+00 6.36965573e-01 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7bde9b3c-1a29-4b3b-a367-c0021a0680d9	rethinking-of-the-image-salient-object	2008.05397	null	https://arxiv.org/abs/2008.05397v1	https://arxiv.org/pdf/2008.05397v1.pdf	Rethinking of the Image Salient Object Detection: Object-level Semantic Saliency Re-ranking First, Pixel-wise Saliency Refinement Latter	The real human attention is an interactive activity between our visual system and our brain, using both low-level visual stimulus and high-level semantic information. Previous image salient object detection (SOD) works conduct their saliency predictions in a multi-task manner, i.e., performing pixel-wise saliency regression and segmentation-like saliency refinement at the same time, which degenerates their feature backbones in revealing semantic information. However, given an image, we tend to pay more attention to those regions which are semantically salient even in the case that these regions are perceptually not the most salient ones at first glance. In this paper, we divide the SOD problem into two sequential tasks: 1) we propose a lightweight, weakly supervised deep network to coarsely locate those semantically salient regions first; 2) then, as a post-processing procedure, we selectively fuse multiple off-the-shelf deep models on these semantically salient regions as the pixel-wise saliency refinement. In sharp contrast to the state-of-the-art (SOTA) methods that focus on learning pixel-wise saliency in "single image" using perceptual clues mainly, our method has investigated the "object-level semantic ranks between multiple images", of which the methodology is more consistent with the real human attention mechanism. Our method is simple yet effective, which is the first attempt to consider the salient object detection mainly as an object-level semantic re-ranking problem.	['Zhen-Yu Wu', 'Hong Qin', 'Shuai Li', 'Chenglizhao Chen', 'Aimin Hao']	2020-08-10	null	null	null	null	['salient-object-detection']	['computer-vision']	[ 3.69417340e-01 1.92763761e-01 -1.54165879e-01 -1.84048876e-01 -5.87898970e-01 -2.43130215e-02 4.88525391e-01 5.17871559e-01 -4.82476324e-01 4.23654139e-01 3.79728913e-01 6.02702498e-02 5.67573421e-02 -6.23736978e-01 -8.44114065e-01 -6.24488890e-01 4.17474061e-01 8.80212858e-02 1.05445123e+00 -4.99106884e-01 7.08605945e-01 1.72808200e-01 -1.95050097e+00 3.31207931e-01 9.14501846e-01 9.55548644e-01 7.84328818e-01 2.75222838e-01 -3.82411480e-02 7.21828938e-01 -2.16589123e-01 -2.09231049e-01 -1.00778081e-01 -3.97965640e-01 -1.14410150e+00 1.09529644e-01 3.87545973e-01 -3.02555803e-02 1.56573623e-01 1.57685018e+00 2.59908378e-01 3.42434160e-02 2.77186841e-01 -1.23047411e+00 -8.78433347e-01 6.16596282e-01 -1.00011981e+00 5.76837122e-01 2.34581277e-01 1.28214240e-01 1.22066498e+00 -1.08413947e+00 3.86878282e-01 1.28379524e+00 3.07035178e-01 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6ec69aec-3cec-4a3c-b078-5f3087ebc678	synchronous-dual-network-with-cross-type	null	null	https://aclanthology.org/2021.emnlp-main.219	https://aclanthology.org/2021.emnlp-main.219.pdf	Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction	Joint entity and relation extraction is challenging due to the complex interaction of interaction between named entity recognition and relation extraction. Although most existing works tend to jointly train these two tasks through a shared network, they fail to fully utilize the interdependence between entity types and relation types. In this paper, we design a novel synchronous dual network (SDN) with cross-type attention via separately and interactively considering the entity types and relation types. On the one hand, SDN adopts two isomorphic bi-directional type-attention LSTM to encode the entity type enhanced representations and the relation type enhanced representations, respectively. On the other hand, SDN explicitly models the interdependence between entity types and relation types via cross-type attention mechanism. In addition, we also propose a new multi-task learning strategy via modeling the interaction of two types of information. Experiments on NYT and WebNLG datasets verify the effectiveness of the proposed model, achieving state-of-the-art performance.	['Xiaodong Shi', 'Hui Wu']	null	null	null	null	emnlp-2021-11	['joint-entity-and-relation-extraction']	['natural-language-processing']	[-8.17017555e-02 2.77374446e-01 -3.40067506e-01 -5.52163839e-01 -3.56428921e-01 -4.16996539e-01 4.91265416e-01 6.43424019e-02 -5.41663289e-01 8.33165407e-01 1.99057415e-01 -3.99149835e-01 -1.22410566e-01 -9.53348696e-01 -8.07300985e-01 -4.31111306e-01 1.43207004e-02 5.02336979e-01 1.57646909e-02 -1.57788470e-01 -3.18374604e-01 2.59621263e-01 -1.22023511e+00 2.16905326e-01 1.03939390e+00 1.06587756e+00 2.05210149e-01 2.54095584e-01 -5.70848048e-01 8.26833248e-01 -3.57878596e-01 -4.61797416e-01 1.43597005e-02 8.98659322e-03 -1.14911711e+00 -1.98605180e-01 -3.68212238e-02 -1.13604687e-01 -3.84080619e-01 9.39789891e-01 5.84323585e-01 4.35259230e-02 3.48360866e-01 -1.31961799e+00 -8.95000935e-01 9.86633778e-01 -6.93668604e-01 2.77323723e-01 8.16766024e-02 -1.02037668e-01 1.25590432e+00 -8.09722304e-01 5.39484739e-01 1.23595476e+00 4.98264462e-01 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ca4f9cc7-9cf0-499a-a735-1360ab9aa46e	causal-counterfactuals-for-improving-the	2211.05551	null	https://arxiv.org/abs/2211.05551v3	https://arxiv.org/pdf/2211.05551v3.pdf	Causal Counterfactuals for Improving the Robustness of Reinforcement Learning	Reinforcement learning (RL) is used in various robotic applications. RL enables agents to learn tasks autonomously by interacting with the environment. The more critical the tasks are, the higher the demand for the robustness of the RL systems. Causal RL combines RL and causal inference to make RL more robust. Causal RL agents use a causal representation to capture the invariant causal mechanisms that can be transferred from one task to another. Currently, there is limited research in Causal RL, and existing solutions are usually not complete or feasible for real-world applications. In this work, we propose CausalCF, the first complete Causal RL solution incorporating ideas from Causal Curiosity and CoPhy. Causal Curiosity provides an approach for using interventions, and CoPhy is modified to enable the RL agent to perform counterfactuals. Causal Curiosity has been applied to robotic grasping and manipulation tasks in CausalWorld. CausalWorld provides a realistic simulation environment based on the TriFinger robot. We apply CausalCF to complex robotic tasks and show that it improves the RL agent's robustness using CausalWorld.	['Ivana Dusparic', 'Jasmina Gajcin', 'Tom He']	2022-11-02	null	null	null	null	['robotic-grasping']	['robots']	[-1.36956185e-01 5.45309007e-01 -3.51583004e-01 -8.14654082e-02 2.08047293e-02 -4.42697465e-01 9.48273301e-01 -1.24148875e-01 -3.37188616e-02 1.32073891e+00 4.11586881e-01 -3.01630288e-01 -6.81372941e-01 -5.75025678e-01 -1.01105380e+00 -7.71604598e-01 -6.52255177e-01 2.08571255e-01 4.78309058e-02 -3.35504919e-01 4.42776620e-01 5.00643075e-01 -1.50994551e+00 1.38217837e-01 6.33003771e-01 1.52536646e-01 7.38353789e-01 8.15152109e-01 3.55144560e-01 1.61142778e+00 -5.94208539e-01 5.34349263e-01 1.03714742e-01 -5.38968861e-01 -1.01111913e+00 -5.52863598e-01 -4.97253627e-01 -6.17990196e-01 -3.64178032e-01 5.30898571e-01 3.57018620e-01 1.22188292e-01 6.73656404e-01 -2.00082970e+00 -6.20112956e-01 1.24883461e+00 -3.71282876e-01 -2.86704034e-01 6.53862178e-01 4.10511285e-01 8.12920988e-01 -3.25998999e-02 6.13200009e-01 2.17834735e+00 1.90405428e-01 8.24159503e-01 -1.09529626e+00 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a54bd962-aedf-4c2f-abab-81342474a745	zero-shot-learners-for-natural-language	2210.08590	null	https://arxiv.org/abs/2210.08590v2	https://arxiv.org/pdf/2210.08590v2.pdf	Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective	We propose a new paradigm for zero-shot learners that is format agnostic, i.e., it is compatible with any format and applicable to a list of language tasks, such as text classification, commonsense reasoning, coreference resolution, and sentiment analysis. Zero-shot learning aims to train a model on a given task such that it can address new learning tasks without any additional training. Our approach converts zero-shot learning into multiple-choice tasks, avoiding problems in commonly used large-scale generative models such as FLAN. It not only adds generalization ability to models but also significantly reduces the number of parameters. Our method shares the merits of efficient training and deployment. Our approach shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as natural language inference and text classification. Our model achieves this success with only 235M parameters, which is substantially smaller than state-of-the-art models with billions of parameters. The code and pre-trained models are available at https://github.com/IDEA-CCNL/Fengshenbang-LM .	['Tetsuya Sakai', 'Jiaxing Zhang', 'Xinyu Gao', 'Ziwei Wu', 'Lin Zhang', 'Xinyu Zhu', 'Ruyi Gan', 'Junjie Wang', 'Ping Yang']	2022-10-16	null	null	null	null	['coreference-resolution']	['natural-language-processing']	[ 4.25186902e-02 2.42321342e-01 -4.47812796e-01 -5.56713998e-01 -1.05357707e+00 -3.51966828e-01 8.26845884e-01 1.40052363e-01 -5.56145966e-01 7.58099139e-01 3.87531221e-01 -4.57701355e-01 7.43225589e-02 -9.42595005e-01 -6.76788151e-01 -4.48337555e-01 5.19417346e-01 8.52831185e-01 3.39751422e-01 -5.86854517e-01 1.99731976e-01 -3.33306104e-01 -1.59956253e+00 4.06565279e-01 6.99091971e-01 8.06558192e-01 1.79241821e-01 5.82103312e-01 -5.09664118e-01 1.01614082e+00 -2.88429916e-01 -7.27734506e-01 -3.43438029e-01 -1.93445176e-01 -1.11623037e+00 -3.34355831e-01 2.67371386e-01 -1.61888719e-01 -4.77713495e-01 9.70811009e-01 5.79355478e-01 4.95621860e-01 5.92998683e-01 -1.25878036e+00 -8.13055098e-01 1.13531756e+00 -2.96276093e-01 1.77943528e-01 1.90566033e-01 3.65588255e-02 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51e1871a-f0e4-4691-9d91-b54b0843080b	unidentified-video-objects-a-benchmark-for	2104.04691	null	https://arxiv.org/abs/2104.04691v1	https://arxiv.org/pdf/2104.04691v1.pdf	Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation	Current state-of-the-art object detection and segmentation methods work well under the closed-world assumption. This closed-world setting assumes that the list of object categories is available during training and deployment. However, many real-world applications require detecting or segmenting novel objects, i.e., object categories never seen during training. In this paper, we present, UVO (Unidentified Video Objects), a new benchmark for open-world class-agnostic object segmentation in videos. Besides shifting the problem focus to the open-world setup, UVO is significantly larger, providing approximately 8 times more videos compared with DAVIS, and 7 times more mask (instance) annotations per video compared with YouTube-VOS and YouTube-VIS. UVO is also more challenging as it includes many videos with crowded scenes and complex background motions. We demonstrated that UVO can be used for other applications, such as object tracking and super-voxel segmentation, besides open-world object segmentation. We believe that UVo is a versatile testbed for researchers to develop novel approaches for open-world class-agnostic object segmentation, and inspires new research directions towards a more comprehensive video understanding beyond classification and detection.	['Du Tran', 'Heng Wang', 'Matt Feiszli', 'Weiyao Wang']	2021-04-10	null	http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Unidentified_Video_Objects_A_Benchmark_for_Dense_Open-World_Segmentation_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Unidentified_Video_Objects_A_Benchmark_for_Dense_Open-World_Segmentation_ICCV_2021_paper.pdf	iccv-2021-1	['one-shot-visual-object-segmentation']	['computer-vision']	[ 8.90807714e-03 -2.44255006e-01 -4.10840720e-01 -1.95891544e-01 -5.45877755e-01 -1.04034209e+00 1.83645170e-03 -8.67804140e-02 -3.95958722e-01 4.54506397e-01 -4.38696861e-01 -5.80175593e-02 2.44833216e-01 -5.44241667e-01 -9.72174168e-01 -4.90449160e-01 -3.74947369e-01 6.54972136e-01 1.04519939e+00 -1.83268450e-02 4.05221097e-02 4.54718024e-01 -1.79183853e+00 7.72137493e-02 6.77393913e-01 9.95690048e-01 3.82639796e-01 9.24445808e-01 -2.24615093e-02 4.47977066e-01 -6.42470062e-01 -5.06645858e-01 7.97476053e-01 1.76731884e-01 -1.05794263e+00 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52283468-f797-49b3-86eb-e60014f1e3c6	findings-of-the-shared-task-on-multimodal	null	null	https://aclanthology.org/2022.dravidianlangtech-1.39	https://aclanthology.org/2022.dravidianlangtech-1.39.pdf	Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in Dravidian Languages	This paper presents the findings of the shared task on Multimodal Sentiment Analysis and Troll meme classification in Dravidian languages held at ACL 2022. Multimodal sentiment analysis deals with the identification of sentiment from video. In addition to video data, the task requires the analysis of corresponding text and audio features for the classification of movie reviews into five classes. We created a dataset for this task in Malayalam and Tamil. The Troll meme classification task aims to classify multimodal Troll memes into two categories. This task assumes the analysis of both text and image features for making better predictions. The performance of the participating teams was analysed using the F1-score. Only one team submitted their results in the Multimodal Sentiment Analysis task, whereas we received six submissions in the Troll meme classification task. The only team that participated in the Multimodal Sentiment Analysis shared task obtained an F1-score of 0.24. In the Troll meme classification task, the winning team achieved an F1-score of 0.596.	['Prasanna Kumaresan', 'Arunaggiri Pandian', 'Sreelakshmi K', 'Dhanalakshmi V', 'Soman Kp', 'Bharathi B', 'Malliga Subramanian', 'Bharathi Raja Chakravarthi', 'Premjith B']	null	null	null	null	dravidianlangtech-acl-2022-5	['meme-classification']	['natural-language-processing']	[-1.11071318e-02 -2.79637098e-01 2.09349066e-01 -4.25583512e-01 -1.20454895e+00 -7.66782343e-01 6.69049442e-01 4.74828213e-01 -8.07161570e-01 3.78922969e-01 4.31990027e-01 3.87436561e-02 2.15733185e-01 -1.08736008e-01 -4.25634027e-01 -5.85024297e-01 1.84607044e-01 9.18592513e-02 -1.74496874e-01 -4.62035626e-01 7.98642755e-01 -1.74267128e-01 -1.73049831e+00 1.44476926e+00 1.22159712e-01 1.33797038e+00 3.93847190e-02 1.34986281e+00 -1.82631418e-01 1.38387263e+00 -7.62418270e-01 -6.57807767e-01 -2.02944919e-01 -5.57911932e-01 -8.11933935e-01 5.54755069e-02 7.39314318e-01 3.00558120e-01 9.91852582e-02 6.39631391e-01 6.26464427e-01 1.51090398e-01 8.79561424e-01 -1.27855909e+00 -9.15912166e-02 2.41537556e-01 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76161387-d886-4aec-84da-706d267b2146	eslam-efficient-dense-slam-system-based-on	2211.11704	null	https://arxiv.org/abs/2211.11704v2	https://arxiv.org/pdf/2211.11704v2.pdf	ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields	We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera poses in a sequential manner and incrementally reconstructs the scene representation while estimating the current camera position in the scene. We incorporate the latest advances in Neural Radiance Fields (NeRF) into a SLAM system, resulting in an efficient and accurate dense visual SLAM method. Our scene representation consists of multi-scale axis-aligned perpendicular feature planes and shallow decoders that, for each point in the continuous space, decode the interpolated features into Truncated Signed Distance Field (TSDF) and RGB values. Our extensive experiments on three standard datasets, Replica, ScanNet, and TUM RGB-D show that ESLAM improves the accuracy of 3D reconstruction and camera localization of state-of-the-art dense visual SLAM methods by more than 50%, while it runs up to 10 times faster and does not require any pre-training.	['François Fleuret', 'Camilla Carta', 'Mohammad Mahdi Johari']	2022-11-21	null	http://openaccess.thecvf.com//content/CVPR2023/html/Johari_ESLAM_Efficient_Dense_SLAM_System_Based_on_Hybrid_Representation_of_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Johari_ESLAM_Efficient_Dense_SLAM_System_Based_on_Hybrid_Representation_of_CVPR_2023_paper.pdf	cvpr-2023-1	['simultaneous-localization-and-mapping', 'camera-localization']	['computer-vision', 'computer-vision']	[ 5.69502525e-02 -3.32159132e-01 -2.39343598e-01 -6.24986708e-01 -8.07125747e-01 -7.31953502e-01 6.02836132e-01 -2.26817150e-02 -5.43958843e-01 5.60051799e-01 1.35551125e-01 -2.35812187e-01 2.43488878e-01 -7.11140931e-01 -1.16569281e+00 -1.44791618e-01 -1.90713517e-02 7.86563694e-01 1.84991062e-01 -4.17515896e-02 4.03686553e-01 8.74760628e-01 -1.47142768e+00 -1.67866468e-01 3.78561139e-01 1.10866356e+00 4.64602053e-01 8.56723487e-01 -1.14783339e-01 1.08509171e+00 -6.15562238e-02 2.30574325e-01 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895f13d8-c7f9-4104-8d53-e50d65a52bac	progression-cognition-reinforcement-learning	2306.05016	null	https://arxiv.org/abs/2306.05016v1	https://arxiv.org/pdf/2306.05016v1.pdf	Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuit	Multi-vehicle pursuit (MVP) such as autonomous police vehicles pursuing suspects is important but very challenging due to its mission and safety critical nature. While multi-agent reinforcement learning (MARL) algorithms have been proposed for MVP problem in structured grid-pattern roads, the existing algorithms use randomly training samples in centralized learning, which leads to homogeneous agents showing low collaboration performance. For the more challenging problem of pursuing multiple evading vehicles, these algorithms typically select a fixed target evading vehicle for pursuing vehicles without considering dynamic traffic situation, which significantly reduces pursuing success rate. To address the above problems, this paper proposes a Progression Cognition Reinforcement Learning with Prioritized Experience for MVP (PEPCRL-MVP) in urban multi-intersection dynamic traffic scenes. PEPCRL-MVP uses a prioritization network to assess the transitions in the global experience replay buffer according to the parameters of each MARL agent. With the personalized and prioritized experience set selected via the prioritization network, diversity is introduced to the learning process of MARL, which can improve collaboration and task related performance. Furthermore, PEPCRL-MVP employs an attention module to extract critical features from complex urban traffic environments. These features are used to develop progression cognition method to adaptively group pursuing vehicles. Each group efficiently target one evading vehicle in dynamic driving environments. Extensive experiments conducted with a simulator over unstructured roads of an urban area show that PEPCRL-MVP is superior to other state-of-the-art methods. Specifically, PEPCRL-MVP improves pursuing efficiency by 3.95% over TD3-DMAP and its success rate is 34.78% higher than that of MADDPG. Codes are open sourced.	['Lin Zhang', 'Jianhua He', 'Lei LI', 'Chen Xu', 'Qinwen Wang', 'Zhe Wang', 'Zheng Yuan', 'Yiying Yang', 'Xinhang Li']	2023-06-08	null	null	null	null	['multi-agent-reinforcement-learning']	['methodology']	[-3.61388952e-01 -9.87969115e-02 -3.27857882e-01 -1.38715636e-02 -8.67335796e-01 -8.88164937e-02 4.96215641e-01 -4.51825224e-02 -4.87785637e-01 6.17684782e-01 -2.74058562e-02 -4.55532193e-01 -4.75984335e-01 -9.17957783e-01 -6.22256458e-01 -8.99707496e-01 -3.40092212e-01 6.34965360e-01 7.26281285e-01 -6.78157091e-01 4.13327634e-01 4.99567509e-01 -1.81787646e+00 6.36783689e-02 1.12739384e+00 4.04758096e-01 8.02559793e-01 5.86036623e-01 -1.75289139e-02 7.86291659e-01 -4.77248281e-01 8.05730820e-02 2.66675204e-01 -4.19394150e-02 -3.58284861e-01 1.62754711e-02 8.31897929e-02 -2.45137155e-01 -4.84151691e-01 9.00521159e-01 4.63460475e-01 4.76024508e-01 5.73940098e-01 -2.01430178e+00 -1.04789972e-01 4.85247374e-01 -7.89771557e-01 5.54079413e-01 -6.70065954e-02 7.99286962e-01 6.57131314e-01 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a06677ff-4bcb-4fb0-81b7-199d83cacb96	escl-equivariant-self-contrastive-learning	2303.05143	null	https://arxiv.org/abs/2303.05143v1	https://arxiv.org/pdf/2303.05143v1.pdf	ESCL: Equivariant Self-Contrastive Learning for Sentence Representations	Previous contrastive learning methods for sentence representations often focus on insensitive transformations to produce positive pairs, but neglect the role of sensitive transformations that are harmful to semantic representations. Therefore, we propose an Equivariant Self-Contrastive Learning (ESCL) method to make full use of sensitive transformations, which encourages the learned representations to be sensitive to certain types of transformations with an additional equivariant learning task. Meanwhile, in order to improve practicability and generality, ESCL simplifies the implementations of traditional equivariant contrastive methods to share model parameters from the perspective of multi-task learning. We evaluate our ESCL on semantic textual similarity tasks. The proposed method achieves better results while using fewer learning parameters compared to previous methods.	['Junlan Feng', 'Chao Deng', 'Xue Han', 'Yixuan Liu', 'Jie Liu']	2023-03-09	null	null	null	null	['semantic-textual-similarity']	['natural-language-processing']	[ 3.33918780e-01 -1.09969221e-01 -1.40436471e-01 -6.93481326e-01 -8.77799988e-01 -5.22358894e-01 9.92736161e-01 2.81724781e-01 -6.19351387e-01 5.23215711e-01 4.14179057e-01 6.00415729e-02 -7.58904368e-02 -7.25184500e-01 -5.34422874e-01 -6.63995385e-01 5.04808009e-01 3.62710118e-01 2.78601855e-01 -6.40044451e-01 1.31095842e-01 1.07073948e-01 -1.12869775e+00 5.01483977e-01 8.70514989e-01 5.68136334e-01 -1.55699579e-03 1.72987282e-01 -1.45109206e-01 8.28933239e-01 -4.90669668e-01 -6.38449073e-01 1.37638509e-01 -5.27876735e-01 -8.78089905e-01 -3.07681292e-01 5.30375600e-01 1.29956920e-02 -1.93476945e-01 1.35555458e+00 4.28328961e-01 4.46033418e-01 9.27015841e-01 -1.37070072e+00 -9.36148405e-01 6.67991519e-01 -6.51918411e-01 2.88027555e-01 1.39515594e-01 -9.96382460e-02 1.31810641e+00 -1.12031603e+00 3.04647505e-01 1.59677815e+00 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c99774fd-1818-4dd5-9eb7-39ae2e7bd435	word-separation-in-continuous-sign-language	2204.00923	null	https://arxiv.org/abs/2204.00923v4	https://arxiv.org/pdf/2204.00923v4.pdf	Word separation in continuous sign language using isolated signs and post-processing	. Continuous Sign Language Recognition (CSLR) is a long challenging task in Computer Vision due to the difficulties in detecting the explicit boundaries between the words in a sign sentence. To deal with this challenge, we propose a two-stage model. In the first stage, the predictor model, which includes a combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the second stage, we apply a post-processing algorithm to the Softmax outputs obtained from the first part of the model in order to separate the isolated signs in the continuous signs. While the proposed model is trained on the isolated sign classes with similar frame numbers, it is evaluated on the continuous sign videos with a different frame length per each isolated sign class. Due to the lack of a large dataset, including both the sign sequences and the corresponding isolated signs, two public datasets in Isolated Sign Language Recognition (ISLR), RKS-PERSIANSIGN and ASLLVD, are used for evaluation. Results of the continuous sign videos confirm the efficiency of the proposed model to deal with isolated sign boundaries detection.	['Sergio Escalera', 'Kourosh Kiani', 'Razieh Rastgoo']	2022-04-02	null	null	null	null	['sign-language-recognition']	['computer-vision']	[ 3.96107137e-01 -2.95169234e-01 4.81065251e-02 -3.45108479e-01 -4.75084484e-01 -2.53708720e-01 4.62287843e-01 -8.48607242e-01 -8.55004072e-01 4.55591828e-01 3.90489191e-01 -6.13715015e-02 3.73237193e-01 5.55161834e-02 -5.20352423e-01 -9.31579232e-01 2.66229391e-01 -4.54442240e-02 6.25458896e-01 -5.43332584e-02 3.14534694e-01 5.24798334e-01 -1.70541584e+00 6.34615958e-01 7.82416701e-01 1.10873616e+00 1.78551003e-01 9.54583466e-01 -1.11025244e-01 1.16449881e+00 -4.75092888e-01 -3.70856328e-03 3.76051277e-01 -7.37301707e-01 -3.55303586e-01 6.56651035e-02 9.15409207e-01 -7.23014653e-01 -6.94788754e-01 9.65207756e-01 9.13885593e-01 5.17631285e-02 6.48514986e-01 -1.20550346e+00 -3.11925560e-01 2.96147555e-01 -2.68760115e-01 1.01036914e-02 8.61075372e-02 4.38253075e-01 9.54766989e-01 -1.01392090e+00 9.12417054e-01 1.15912449e+00 4.80127990e-01 7.30407000e-01 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50b268d9-2f18-48f1-9656-a60e0ca25b94	flow-to-control-offline-reinforcement	2212.01105	null	https://arxiv.org/abs/2212.01105v1	https://arxiv.org/pdf/2212.01105v1.pdf	Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery	Offline reinforcement learning (RL) enables the agent to effectively learn from logged data, which significantly extends the applicability of RL algorithms in real-world scenarios where exploration can be expensive or unsafe. Previous works have shown that extracting primitive skills from the recurring and temporally extended structures in the logged data yields better learning. However, these methods suffer greatly when the primitives have limited representation ability to recover the original policy space, especially in offline settings. In this paper, we give a quantitative characterization of the performance of offline hierarchical learning and highlight the importance of learning lossless primitives. To this end, we propose to use a \emph{flow}-based structure as the representation for low-level policies. This allows us to represent the behaviors in the dataset faithfully while keeping the expression ability to recover the whole policy space. We show that such lossless primitives can drastically improve the performance of hierarchical policies. The experimental results and extensive ablation studies on the standard D4RL benchmark show that our method has a good representation ability for policies and achieves superior performance in most tasks.	['Chongjie Zhang', 'Qianchuan Zhao', 'Jun Yang', 'Siyuan Li', 'Wenzhe Li', 'Hao Hu', 'Yiqin Yang']	2022-12-02	null	null	null	null	['d4rl']	['robots']	[-1.01114571e-01 1.52997607e-02 -7.38199890e-01 -4.31176908e-02 -7.55325973e-01 -7.60066032e-01 6.85080767e-01 5.44022098e-02 -7.03661203e-01 1.05341268e+00 2.33583018e-01 -4.72943366e-01 -1.86012790e-01 -7.04293489e-01 -1.02578866e+00 -8.01516116e-01 -6.36867583e-01 4.38046843e-01 2.06677392e-01 -1.87688604e-01 -2.89401580e-02 6.03011549e-01 -1.69519985e+00 1.13002919e-01 8.94747138e-01 1.02276719e+00 2.26497754e-01 4.63304162e-01 -3.80604644e-03 1.39627135e+00 -5.28961301e-01 2.41347045e-01 6.36970997e-01 -3.18295598e-01 -7.89126337e-01 -2.94494275e-02 3.95340025e-01 -9.92159486e-01 -6.56387746e-01 8.83341670e-01 2.65647858e-01 5.27739406e-01 3.49027663e-01 -1.13192964e+00 -8.00959393e-02 7.33769119e-01 -3.30955476e-01 -4.98257950e-02 2.14867502e-01 4.39523250e-01 1.05808949e+00 -2.22654328e-01 6.14149451e-01 1.60915887e+00 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6c76cb1d-cb5f-4e47-a279-62eabead3134	casia-iris-africa-a-large-scale-african-iris	2302.13049	null	https://arxiv.org/abs/2302.13049v1	https://arxiv.org/pdf/2302.13049v1.pdf	CASIA-Iris-Africa: A Large-scale African Iris Image Database	Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes. Research on iris biometrics has progressed tremendously, partly due to publicly available iris databases. Various databases have been available to researchers that address pressing iris biometric challenges such as constraint, mobile, multispectral, synthetics, long-distance, contact lenses, liveness detection, etc. However, these databases mostly contain subjects of Caucasian and Asian docents with very few Africans. Despite many investigative studies on racial bias in face biometrics, very few studies on iris biometrics have been published, mainly due to the lack of racially diverse large-scale databases containing sufficient iris samples of Africans in the public domain. Furthermore, most of these databases contain a relatively small number of subjects and labelled images. This paper proposes a large-scale African database named CASIA-Iris-Africa that can be used as a complementary database for the iris recognition community to mediate the effect of racial biases on Africans. The database contains 28,717 images of 1023 African subjects (2046 iris classes) with age, gender, and ethnicity attributes that can be useful in demographically sensitive studies of Africans. Sets of specific application protocols are incorporated with the database to ensure the database's variability and scalability. Performance results of some open-source SOTA algorithms on the database are presented, which will serve as baseline performances. The relatively poor performances of the baseline algorithms on the proposed database despite better performance on other databases prove that racial biases exist in these iris recognition algorithms. The database will be made available on our website: http://www.idealtest.org.	['Zhenan Sun', 'Kunbo Zhang', 'Junxing Hu', 'Yunlong Wang', 'Jawad Muhammad']	2023-02-25	null	null	null	null	['iris-recognition']	['computer-vision']	[ 5.19047584e-03 -3.51782382e-01 -3.70043486e-01 -5.93521059e-01 -5.99780306e-02 -3.53848428e-01 3.97931248e-01 -1.64609537e-01 -2.05508590e-01 8.01097214e-01 2.04129368e-01 -3.54547679e-01 -2.46899184e-02 -5.46903908e-01 -3.87936719e-02 -9.38127279e-01 -5.23474589e-02 3.35280091e-01 -4.68473077e-01 -1.40939364e-02 5.15732110e-01 6.16545737e-01 -1.97779584e+00 2.05489069e-01 1.05991638e+00 5.07418036e-01 -7.71845520e-01 7.29325950e-01 2.77531296e-01 1.26877233e-01 -6.14732504e-01 -4.05410916e-01 6.54287279e-01 -5.81955612e-01 -6.57018363e-01 -3.30097340e-02 1.00863671e+00 -3.48798305e-01 5.21849655e-03 9.98770773e-01 1.13910198e+00 -2.64242709e-01 5.39501071e-01 -8.75206351e-01 -8.03820491e-01 8.11104849e-02 -1.24054408e+00 2.73680151e-01 4.64937985e-01 5.43642521e-01 2.97176749e-01 -4.54739749e-01 3.45278382e-01 1.18539786e+00 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958f7d85-493c-46e6-b3e9-657e9efea3e8	centerhmr-a-bottom-up-single-shot-method-for	2008.12272	null	https://arxiv.org/abs/2008.12272v4	https://arxiv.org/pdf/2008.12272v4.pdf	Monocular, One-stage, Regression of Multiple 3D People	This paper focuses on the regression of multiple 3D people from a single RGB image. Existing approaches predominantly follow a multi-stage pipeline that first detects people in bounding boxes and then independently regresses their 3D body meshes. In contrast, we propose to Regress all meshes in a One-stage fashion for Multiple 3D People (termed ROMP). The approach is conceptually simple, bounding box-free, and able to learn a per-pixel representation in an end-to-end manner. Our method simultaneously predicts a Body Center heatmap and a Mesh Parameter map, which can jointly describe the 3D body mesh on the pixel level. Through a body-center-guided sampling process, the body mesh parameters of all people in the image are easily extracted from the Mesh Parameter map. Equipped with such a fine-grained representation, our one-stage framework is free of the complex multi-stage process and more robust to occlusion. Compared with state-of-the-art methods, ROMP achieves superior performance on the challenging multi-person benchmarks, including 3DPW and CMU Panoptic. Experiments on crowded/occluded datasets demonstrate the robustness under various types of occlusion. The released code is the first real-time implementation of monocular multi-person 3D mesh regression.	['Michael J. Black', 'Yu Sun', 'Qian Bao', 'Yili Fu', 'Wu Liu', 'Tao Mei']	2020-08-27	null	http://openaccess.thecvf.com//content/ICCV2021/html/Sun_Monocular_One-Stage_Regression_of_Multiple_3D_People_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Sun_Monocular_One-Stage_Regression_of_Multiple_3D_People_ICCV_2021_paper.pdf	iccv-2021-1	['3d-depth-estimation', '3d-multi-person-pose-estimation', '3d-multi-person-mesh-recovery']	['computer-vision', 'computer-vision', 'computer-vision']	[ 7.17023462e-02 -1.10039890e-01 1.24192514e-01 -4.53763127e-01 -6.26650870e-01 -1.59142151e-01 4.12557513e-01 4.62235734e-02 -4.76269424e-01 4.04609233e-01 3.18884687e-03 4.09196138e-01 4.66584593e-01 -9.31076169e-01 -7.67005324e-01 -5.59856892e-01 2.68056065e-01 1.04214728e+00 3.95463914e-01 -2.15265721e-01 -2.69807398e-01 3.90745610e-01 -1.71167362e+00 -1.35884538e-01 7.00254440e-01 1.02048624e+00 -3.83427829e-01 6.59962952e-01 1.24790542e-01 1.33297175e-01 -4.36778426e-01 -6.76822066e-01 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260dce1f-c54c-4e68-a0b4-1e8d2fbab26c	injecting-knowledge-base-information-into-end	2107.02286	null	https://arxiv.org/abs/2107.02286v1	https://arxiv.org/pdf/2107.02286v1.pdf	Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution	We consider a joint information extraction (IE) model, solving named entity recognition, coreference resolution and relation extraction jointly over the whole document. In particular, we study how to inject information from a knowledge base (KB) in such IE model, based on unsupervised entity linking. The used KB entity representations are learned from either (i) hyperlinked text documents (Wikipedia), or (ii) a knowledge graph (Wikidata), and appear complementary in raising IE performance. Representations of corresponding entity linking (EL) candidates are added to text span representations of the input document, and we experiment with (i) taking a weighted average of the EL candidate representations based on their prior (in Wikipedia), and (ii) using an attention scheme over the EL candidate list. Results demonstrate an increase of up to 5% F1-score for the evaluated IE tasks on two datasets. Despite a strong performance of the prior-based model, our quantitative and qualitative analysis reveals the advantage of using the attention-based approach.	['Chris Develder', 'Thomas Demeester', 'Johannes Deleu', 'Klim Zaporojets', 'Severine Verlinden']	2021-07-05	null	https://aclanthology.org/2021.findings-acl.171	https://aclanthology.org/2021.findings-acl.171.pdf	findings-acl-2021-8	['joint-entity-and-relation-extraction']	['natural-language-processing']	[ 1.41697705e-01 1.09544420e+00 -4.06409264e-01 7.14150146e-02 -9.55725670e-01 -5.01050532e-01 9.29069579e-01 6.49066806e-01 -7.50149727e-01 1.17666221e+00 7.90913284e-01 2.89114006e-02 -5.54919064e-01 -8.08020711e-01 -1.05651379e+00 -2.95898557e-01 -8.05491582e-02 9.25948679e-01 3.87684941e-01 -2.15770096e-01 1.50317684e-01 4.34220910e-01 -1.41707158e+00 2.81354427e-01 1.20786786e+00 5.91197133e-01 2.02512756e-01 4.13265795e-01 -6.85305119e-01 1.10849464e+00 -6.75225258e-01 -8.52883220e-01 -3.74501437e-01 -1.13696329e-01 -1.51946151e+00 -4.38121080e-01 3.84068280e-01 2.36864954e-01 -4.52316105e-01 1.13497210e+00 4.15133715e-01 1.41692907e-01 7.88955688e-01 -1.07589483e+00 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8f9b9392-38fe-4a3c-8d2f-ef1185861924	achieving-stable-subspace-clustering-by-post	1605.08680	null	http://arxiv.org/abs/1605.08680v1	http://arxiv.org/pdf/1605.08680v1.pdf	Achieving stable subspace clustering by post-processing generic clustering results	We propose an effective subspace selection scheme as a post-processing step to improve results obtained by sparse subspace clustering (SSC). Our method starts by the computation of stable subspaces using a novel random sampling scheme. Thus constructed preliminary subspaces are used to identify the initially incorrectly clustered data points and then to reassign them to more suitable clusters based on their goodness-of-fit to the preliminary model. To improve the robustness of the algorithm, we use a dominant nearest subspace classification scheme that controls the level of sensitivity against reassignment. We demonstrate that our algorithm is convergent and superior to the direct application of a generic alternative such as principal component analysis. On several popular datasets for motion segmentation and face clustering pervasively used in the sparse subspace clustering literature the proposed method is shown to reduce greatly the incidence of clustering errors while introducing negligible disturbance to the data points already correctly clustered.	['Duc-Son Pham', 'Svetha Venkatesh', 'Ognjen Arandjelovic']	2016-05-27	null	null	null	null	['face-clustering']	['computer-vision']	[ 4.21304822e-01 -3.24321032e-01 1.07932970e-01 -1.94687277e-01 -8.50424409e-01 -8.42159748e-01 5.40890217e-01 4.19923291e-02 -3.48634183e-01 3.95710468e-01 2.31172875e-01 -3.66402157e-02 -4.65301394e-01 -2.78791010e-01 -3.87780726e-01 -1.08234107e+00 8.18867683e-02 6.76400721e-01 2.25466952e-01 1.76712930e-01 2.77557582e-01 9.60590959e-01 -1.61350572e+00 6.89870268e-02 9.17479873e-01 4.84856963e-01 -2.71058325e-02 4.26698238e-01 2.14851294e-02 5.14543019e-02 -3.22298020e-01 1.91825643e-01 4.18603033e-01 -5.75631738e-01 -5.50197303e-01 6.78604186e-01 3.54367584e-01 1.55660734e-01 1.42187133e-01 1.12179136e+00 4.94203180e-01 4.71410334e-01 8.11902821e-01 -1.08677840e+00 3.47777665e-01 2.13693947e-01 -6.66865349e-01 6.33544698e-02 4.06649798e-01 -2.26137668e-01 4.73963231e-01 -1.11864126e+00 7.79680073e-01 1.14489102e+00 6.83301270e-01 3.64304423e-01 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-7.05067039e-01 6.70867383e-01 1.27169877e-01 -5.37579179e-01 -4.57363248e-01 4.62142974e-01]	[7.708306312561035, 4.452266216278076]
ff757cbc-ba96-4e35-9dea-5d0096ca3838	exploring-partial-intrinsic-and-extrinsic	2003.02294	null	https://arxiv.org/abs/2003.02294v2	https://arxiv.org/pdf/2003.02294v2.pdf	Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging	We present a novel methodology to detect imperfect bilateral symmetry in CT of human anatomy. In this paper, the structurally symmetric nature of the pelvic bone is explored and is used to provide interventional image augmentation for treatment of unilateral fractures in patients with traumatic injuries. The mathematical basis of our solution is on the incorporation of attributes and characteristics that satisfy the properties of intrinsic and extrinsic symmetry and are robust to outliers. In the first step, feature points that satisfy intrinsic symmetry are automatically detected in the M\"obius space defined on the CT data. These features are then pruned via a two-stage RANSAC to attain correspondences that satisfy also the extrinsic symmetry. Then, a disparity function based on Tukey's biweight robust estimator is introduced and minimized to identify a symmetry plane parametrization that yields maximum contralateral similarity. Finally, a novel regularization term is introduced to enhance similarity between bone density histograms across the partial symmetry plane, relying on the important biological observation that, even if injured, the dislocated bone segments remain within the body. Our extensive evaluations on various cases of common fracture types demonstrate the validity of the novel concepts and the robustness and accuracy of the proposed method.	['Greg Osgood', 'Mathias Unberath', 'Javad Fotouhi', 'Mehran Armand', 'Giacomo Taylor', 'Nassir Navab', 'Alex Johnson', 'Sing Chun Lee']	2020-03-04	null	null	null	null	['novel-concepts']	['reasoning']	[ 2.82653153e-01 2.18263134e-01 -2.44135395e-01 -2.11820185e-01 -5.22305429e-01 -8.92864764e-02 1.31301254e-01 2.93280691e-01 -4.65042114e-01 5.91566682e-01 1.15984075e-01 1.88211903e-01 -6.31795526e-01 -5.27594745e-01 -5.88711560e-01 -8.33200812e-01 -1.86441779e-01 7.97047019e-01 2.45982736e-01 -1.32484660e-01 5.25044203e-01 8.72182667e-01 -1.72083151e+00 -6.72516823e-02 9.23994243e-01 8.70829880e-01 -1.12153152e-02 2.39039123e-01 1.61363453e-01 1.69870630e-01 -1.61210418e-01 -3.24767441e-01 6.08599305e-01 -4.36777443e-01 -7.69136906e-01 3.90772521e-01 2.33591393e-01 -1.61429182e-01 -4.92940806e-02 9.69734311e-01 5.78466058e-01 2.39728317e-01 1.05878496e+00 -9.85678077e-01 8.69220676e-05 2.17083022e-01 -8.88176858e-01 1.72518790e-01 3.40951890e-01 -2.04561725e-01 8.50689471e-01 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b4c1364f-e1ce-4297-b480-dbc86722fb17	gabor-barcodes-for-medical-image-retrieval	1605.04478	null	http://arxiv.org/abs/1605.04478v1	http://arxiv.org/pdf/1605.04478v1.pdf	Gabor Barcodes for Medical Image Retrieval	In recent years, advances in medical imaging have led to the emergence of massive databases, containing images from a diverse range of modalities. This has significantly heightened the need for automated annotation of the images on one side, and fast and memory-efficient content-based image retrieval systems on the other side. Binary descriptors have recently gained more attention as a potential vehicle to achieve these goals. One of the recently introduced binary descriptors for tagging of medical images are Radon barcodes (RBCs) that are driven from Radon transform via local thresholding. Gabor transform is also a powerful transform to extract texture-based information. Gabor features have exhibited robustness against rotation, scale, and also photometric disturbances, such as illumination changes and image noise in many applications. This paper introduces Gabor Barcodes (GBCs), as a novel framework for the image annotation. To find the most discriminative GBC for a given query image, the effects of employing Gabor filters with different parameters, i.e., different sets of scales and orientations, are investigated, resulting in different barcode lengths and retrieval performances. The proposed method has been evaluated on the IRMA dataset with 193 classes comprising of 12,677 x-ray images for indexing, and 1,733 x-rays images for testing. A total error score as low as $351$ ($\approx 80\%$ accuracy for the first hit) was achieved.	['Hamid. R. Tizhoosh', 'Ershad Banijamali', 'Mina Nouredanesh']	2016-05-14	null	null	null	null	['medical-image-retrieval', 'medical-image-retrieval']	['computer-vision', 'medical']	[ 2.94423074e-01 -4.08439845e-01 -1.61286723e-02 -3.09393853e-01 -1.22677815e+00 -2.81028986e-01 5.02272308e-01 6.67236269e-01 -6.13302290e-01 6.44746184e-01 -1.25723600e-01 7.55383149e-02 -6.16751432e-01 -9.30913925e-01 -1.84304953e-01 -1.18847120e+00 -1.21446103e-01 2.95743912e-01 6.14482880e-01 9.98966172e-02 6.11432672e-01 8.28747749e-01 -1.73015451e+00 2.18516916e-01 5.15801013e-01 1.29048622e+00 3.37224573e-01 5.02702713e-01 -1.28709093e-01 5.32470405e-01 -6.49187386e-01 -2.96468467e-01 -4.65447754e-02 -3.22851866e-01 -6.87241852e-01 2.18254879e-01 -1.04101144e-01 6.24596961e-02 -2.07585886e-01 1.04072046e+00 6.89724445e-01 1.00521207e-01 8.18665981e-01 -4.14840639e-01 -5.23720980e-01 6.47140369e-02 -5.46238720e-01 4.97898817e-01 2.51252621e-01 -2.25467354e-01 6.51129186e-01 -8.53623807e-01 6.23778343e-01 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d1be6093-ce6b-4bb2-ae50-2c9684352c5f	improving-distantly-supervised-relation-3	2102.01156	null	https://arxiv.org/abs/2102.01156v1	https://arxiv.org/pdf/2102.01156v1.pdf	Improving Distantly-Supervised Relation Extraction through BERT-based Label & Instance Embeddings	Distantly-supervised relation extraction (RE) is an effective method to scale RE to large corpora but suffers from noisy labels. Existing approaches try to alleviate noise through multi-instance learning and by providing additional information, but manage to recognize mainly the top frequent relations, neglecting those in the long-tail. We propose REDSandT (Relation Extraction with Distant Supervision and Transformers), a novel distantly-supervised transformer-based RE method, that manages to capture a wider set of relations through highly informative instance and label embeddings for RE, by exploiting BERT's pre-trained model, and the relationship between labels and entities, respectively. We guide REDSandT to focus solely on relational tokens by fine-tuning BERT on a structured input, including the sub-tree connecting an entity pair and the entities' types. Using the extracted informative vectors, we shape label embeddings, which we also use as attention mechanism over instances to further reduce noise. Finally, we represent sentences by concatenating relation and instance embeddings. Experiments in the NYT-10 dataset show that REDSandT captures a broader set of relations with higher confidence, achieving state-of-the-art AUC (0.424).	['Grigorios Tsoumakas', 'Despina Christou']	2021-02-01	null	null	null	null	['relationship-extraction-distant-supervised']	['natural-language-processing']	[-0.05814383 0.76173955 -0.67103297 -0.52072775 -0.77769005 -0.59545517 0.68617904 0.6001395 -0.37177533 0.6760644 0.5912221 -0.20800243 -0.4148364 -0.9910722 -0.44869545 -0.51412034 -0.18972565 1.007577 0.275104 -0.2853538 -0.3379627 0.4536053 -1.208219 0.35862443 0.7359901 1.1937264 -0.38421777 0.33863842 -0.34467843 1.1097414 -0.57797873 -1.0432872 0.00811551 -0.08584403 -1.3934304 -0.15577197 0.12583697 -0.01118672 -0.48729044 0.6224229 0.2475987 0.17251298 0.87220657 -1.109194 -0.9527804 1.2928964 -0.39097744 0.30707768 0.31316966 -0.46147442 1.8484123 -1.145031 0.8174138 1.3847419 0.6703126 0.3583985 -1.3651167 -0.5623686 0.18823878 0.28570947 -1.500161 -0.3214031 0.5244659 -0.24486652 1.6031137 0.47272012 0.37797412 1.0234938 -0.29210505 0.67202395 0.6013186 -0.5644798 -0.19992247 0.17049676 0.5039821 0.45283097 0.2151135 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92bea0a2-f37c-460e-8d98-5c457cdcde9f	tuning-deep-active-learning-for-semantic-role	null	null	https://aclanthology.org/2021.iwcs-1.20	https://aclanthology.org/2021.iwcs-1.20.pdf	Tuning Deep Active Learning for Semantic Role Labeling	Active learning has been shown to reduce annotation requirements for numerous natural language processing tasks, including semantic role labeling (SRL). SRL involves labeling argument spans for potentially multiple predicates in a sentence, which makes it challenging to aggregate the numerous decisions into a single score for determining new instances to annotate. In this paper, we apply two ways of aggregating scores across multiple predicates in order to choose query sentences with two methods of estimating model certainty: using the neural network’s outputs and using dropout-based Bayesian Active Learning by Disagreement. We compare these methods with three passive baselines — random sentence selection, random whole-document selection, and selecting sentences with the most predicates — and analyse the effect these strategies have on the learning curve with respect to reducing the number of annotated sentences and predicates to achieve high performance.	['Martha Palmer', 'Skatje Myers']	null	null	null	null	iwcs-acl-2021-6	['semantic-role-labeling']	['natural-language-processing']	[ 7.12544441e-01 8.83002520e-01 -5.05435169e-01 -8.04672599e-01 -1.65498090e+00 -8.88431668e-01 6.96510434e-01 9.21019018e-01 -1.06877732e+00 1.09139824e+00 6.00846648e-01 -1.85093477e-01 -1.21681355e-01 -5.93791425e-01 -6.00548506e-01 -6.28295302e-01 2.19725683e-01 8.44225347e-01 6.95510745e-01 2.26246864e-01 1.42232001e-01 1.54795378e-01 -1.09509337e+00 6.40518725e-01 8.48592818e-01 6.88421369e-01 1.57289894e-03 4.07854915e-01 -4.01986003e-01 1.28506231e+00 -8.65313649e-01 -5.52700341e-01 4.76585180e-02 -2.29668751e-01 -1.19585645e+00 -2.25829288e-01 3.28685522e-01 -9.02830437e-02 3.45026225e-01 7.75363207e-01 4.74076331e-01 5.05560815e-01 6.00979149e-01 -7.64787555e-01 -2.69777775e-02 1.13827014e+00 -2.75858670e-01 3.60427439e-01 6.37662292e-01 1.91335425e-01 1.59178722e+00 -6.31315410e-01 7.06906974e-01 1.57890511e+00 3.30509841e-01 5.16566098e-01 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9b5b3341-3d1a-40ad-a48f-430266ed7c20	self-organization-preserved-graph-structure	2301.00015	null	https://arxiv.org/abs/2301.00015v1	https://arxiv.org/pdf/2301.00015v1.pdf	Self-organization Preserved Graph Structure Learning with Principle of Relevant Information	Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption cannot always be satisfied, as real-world graphs are always incomplete, noisy, or redundant. How to reveal the inherent graph structure in a unified way remains under-explored. We proposed PRI-GSL, a Graph Structure Learning framework guided by the Principle of Relevant Information, providing a simple and unified framework for identifying the self-organization and revealing the hidden structure. PRI-GSL learns a structure that contains the most relevant yet least redundant information quantified by von Neumann entropy and Quantum Jensen-Shannon divergence. PRI-GSL incorporates the evolution of quantum continuous walk with graph wavelets to encode node structural roles, showing in which way the nodes interplay and self-organize with the graph structure. Extensive experiments demonstrate the superior effectiveness and robustness of PRI-GSL.	['Philip S. Yu', 'Hao Peng', 'Xingcheng Fu', 'Beining Yang', 'JianXin Li', 'Qingyun Sun']	2022-12-30	null	null	null	null	['graph-structure-learning']	['graphs']	[ 2.56924003e-01 5.87408662e-01 -1.71825811e-01 -4.78781722e-02 3.55146490e-02 -6.85395122e-01 4.49465007e-01 4.39493418e-01 1.75964877e-01 6.91711068e-01 1.22697778e-01 -2.33448774e-01 -5.12873471e-01 -1.39303088e+00 -7.52810121e-01 -1.16844964e+00 -9.12493289e-01 2.80381471e-01 1.63071588e-01 -4.17229235e-01 6.84097270e-03 2.94187188e-01 -1.22193003e+00 8.48148689e-02 4.72314984e-01 5.94893575e-01 1.50275216e-01 7.96403229e-01 -2.95661241e-02 1.09647572e+00 -1.94901437e-01 -4.33426321e-01 2.10714355e-01 -8.11793864e-01 -9.09085453e-01 -1.36327118e-01 -9.27472264e-02 4.42614593e-02 -1.00664496e+00 1.35835326e+00 1.42974511e-01 -3.19525689e-01 3.36910367e-01 -1.30265653e+00 -8.27808857e-01 1.14944160e+00 -1.69132769e-01 3.74412507e-01 4.38698620e-01 1.29300386e-01 1.72765374e+00 -1.13773227e-01 9.99248803e-01 1.09965503e+00 8.14634562e-01 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894dbd3d-d9a4-4d42-8aab-36bb36e14a38	dounseen-zero-shot-object-detection-for	2304.02833	null	https://arxiv.org/abs/2304.02833v1	https://arxiv.org/pdf/2304.02833v1.pdf	DoUnseen: Zero-Shot Object Detection for Robotic Grasping	How can we segment varying numbers of objects where each specific object represents its own separate class? To make the problem even more realistic, how can we add and delete classes on the fly without retraining? This is the case of robotic applications where no datasets of the objects exist or application that includes thousands of objects (E.g., in logistics) where it is impossible to train a single model to learn all of the objects. Most current research on object segmentation for robotic grasping focuses on class-level object segmentation (E.g., box, cup, bottle), closed sets (specific objects of a dataset; for example, YCB dataset), or deep learning-based template matching. In this work, we are interested in open sets where the number of classes is unknown, varying, and without pre-knowledge about the objects' types. We consider each specific object as its own separate class. Our goal is to develop a zero-shot object detector that requires no training and can add any object as a class just by capturing a few images of the object. Our main idea is to break the segmentation pipelines into two steps by combining unseen object segmentation networks cascaded by zero-shot classifiers. We evaluate our zero-shot object detector on unseen datasets and compare it to a trained Mask R-CNN on those datasets. The results show that the performance varies from practical to unsuitable depending on the environment setup and the objects being handled. The code is available in our DoUnseen library repository.	['Moritz Roidl', 'Anas Gouda']	2023-04-06	null	null	null	null	['template-matching', 'zero-shot-object-detection', 'robotic-grasping']	['computer-vision', 'computer-vision', 'robots']	[ 4.23515588e-01 1.77755266e-01 3.39444540e-02 -4.41876113e-01 -3.39094460e-01 -8.69292438e-01 3.08600307e-01 1.90524444e-01 -4.20020193e-01 3.30784678e-01 -6.69301212e-01 -1.72179982e-01 -3.70899625e-02 -8.68502438e-01 -1.14761424e+00 -6.06937170e-01 1.67647570e-01 9.65363860e-01 9.92772460e-01 -2.57501960e-01 1.03373431e-01 8.08322310e-01 -1.88088262e+00 3.20425302e-01 5.71905315e-01 1.07201970e+00 6.50610149e-01 7.59246826e-01 -3.40161383e-01 3.05836827e-01 -7.59002507e-01 -9.55197439e-02 8.17683458e-01 -4.54594865e-02 -8.68718386e-01 3.91200662e-01 4.62385833e-01 -5.28272510e-01 8.01859051e-02 1.06818867e+00 1.43851757e-01 1.06161699e-01 6.83525443e-01 -1.35750318e+00 -3.17890346e-01 7.50451744e-01 -3.87623608e-01 3.82372700e-02 -1.38642579e-01 4.39895123e-01 4.84702587e-01 -6.16658866e-01 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51cd3cab-6755-445f-97f4-0042f3f254a5	sustainability-of-collusion-and-market	2105.02094	null	https://arxiv.org/abs/2105.02094v1	https://arxiv.org/pdf/2105.02094v1.pdf	Sustainability of Collusion and Market Transparency in a Sequential Search Market: a Generalization	The present work generalizes the analytical results of Petrikaite (2016) to a market where more than two firms interact. As a consequence, for a generic number of firms in the oligopoly model described by Janssen et al (2005), the relationship between the critical discount factor which sustains the monopoly collusive allocation and the share of perfectly informed buyers is non-monotonic, reaching a unique internal point of minimum. The first section locates the work within the proper economic framework. The second section hosts the analytical computations and the mathematical reasoning needed to derive the desired generalization, which mainly relies on the Leibniz rule for the differentiation under the integral sign and the Bounded Convergence Theorem.	['Giuseppe Puleio', 'Jacopo De Tullio']	2021-05-05	null	null	null	null	['mathematical-reasoning']	['natural-language-processing']	[-5.54777026e-01 5.20069361e-01 -5.06724954e-01 4.15126711e-01 2.02189922e-01 -1.18895459e+00 4.08714682e-01 1.12545133e-01 -5.12432694e-01 7.96581924e-01 -6.24990165e-01 -4.47325677e-01 -6.49266958e-01 -6.73083782e-01 -3.87788743e-01 -1.04697740e+00 -2.79321492e-01 2.26320252e-01 1.43705800e-01 -2.63330162e-01 2.04179391e-01 6.17284596e-01 -1.10146570e+00 -3.50865901e-01 8.64240408e-01 1.39909053e+00 7.66686127e-02 5.12129128e-01 1.93442047e-01 4.32038426e-01 -3.75992775e-01 -9.37398255e-01 1.01932347e+00 -1.40263647e-01 -3.21820796e-01 1.03161886e-01 -5.57547033e-01 -5.17790616e-01 -3.79156359e-02 1.18569732e+00 1.84910133e-01 2.56686568e-01 7.89369524e-01 -1.64020777e+00 -6.79140091e-01 7.79829741e-01 -4.68828022e-01 4.07553017e-01 -1.93761781e-01 -1.55262098e-01 1.03893626e+00 -6.68271542e-01 6.63893461e-01 9.35574710e-01 1.96943060e-01 2.43652344e-01 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09c0c12e-214b-408f-a70c-e6479baf8df2	toward-dnn-of-luts-learning-efficient-image	2303.14506	null	https://arxiv.org/abs/2303.14506v1	https://arxiv.org/pdf/2303.14506v1.pdf	Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up Tables	The widespread usage of high-definition screens on edge devices stimulates a strong demand for efficient image restoration algorithms. The way of caching deep learning models in a look-up table (LUT) is recently introduced to respond to this demand. However, the size of a single LUT grows exponentially with the increase of its indexing capacity, which restricts its receptive field and thus the performance. To overcome this intrinsic limitation of the single-LUT solution, we propose a universal method to construct multiple LUTs like a neural network, termed MuLUT. Firstly, we devise novel complementary indexing patterns, as well as a general implementation for arbitrary patterns, to construct multiple LUTs in parallel. Secondly, we propose a re-indexing mechanism to enable hierarchical indexing between cascaded LUTs. Finally, we introduce channel indexing to allow cross-channel interaction, enabling LUTs to process color channels jointly. In these principled ways, the total size of MuLUT is linear to its indexing capacity, yielding a practical solution to obtain superior performance with the enlarged receptive field. We examine the advantage of MuLUT on various image restoration tasks, including super-resolution, demosaicing, denoising, and deblocking. MuLUT achieves a significant improvement over the single-LUT solution, e.g., up to 1.1dB PSNR for super-resolution and up to 2.8dB PSNR for grayscale denoising, while preserving its efficiency, which is 100$\times$ less in energy cost compared with lightweight deep neural networks. Our code and trained models are publicly available at https://github.com/ddlee-cn/MuLUT.	['Zhiwei Xiong', 'Zhen Cheng', 'Chang Chen', 'Jiacheng Li']	2023-03-25	null	null	null	null	['demosaicking']	['computer-vision']	[ 2.08348170e-01 -4.47046041e-01 3.74830030e-02 -1.66001171e-01 -7.44575381e-01 -2.71970838e-01 1.99029781e-02 -1.06456399e-01 -5.40629625e-01 4.80704159e-01 2.53939211e-01 -4.11627561e-01 1.93619832e-01 -9.88915026e-01 -8.12594712e-01 -7.10234642e-01 1.53949365e-01 -4.66445982e-01 4.81596053e-01 -5.52966213e-03 2.00377718e-01 6.58798039e-01 -1.52577734e+00 3.02064240e-01 6.35492861e-01 1.33388853e+00 5.40271580e-01 5.54212809e-01 -1.15280442e-01 7.35244095e-01 -4.35856074e-01 -3.59923631e-01 3.52190256e-01 -2.92687863e-01 -5.36275744e-01 -7.21455291e-02 5.47723413e-01 -8.63744736e-01 -7.88403511e-01 1.20313442e+00 7.79008269e-01 -4.61723320e-02 1.99703015e-02 -6.59461856e-01 -7.78343976e-01 3.84607106e-01 -6.92388117e-01 3.78487974e-01 -1.86088040e-01 -3.99789661e-02 7.34724641e-01 -7.26038992e-01 3.97296965e-01 1.20137751e+00 6.49922490e-01 6.79754198e-01 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efd1bf90-5d18-449a-bfc0-0442a3d89379	human-action-recognition-without-human	1608.07876	null	http://arxiv.org/abs/1608.07876v1	http://arxiv.org/pdf/1608.07876v1.pdf	Human Action Recognition without Human	The objective of this paper is to evaluate "human action recognition without human". Motion representation is frequently discussed in human action recognition. We have examined several sophisticated options, such as dense trajectories (DT) and the two-stream convolutional neural network (CNN). However, some features from the background could be too strong, as shown in some recent studies on human action recognition. Therefore, we considered whether a background sequence alone can classify human actions in current large-scale action datasets (e.g., UCF101). In this paper, we propose a novel concept for human action analysis that is named "human action recognition without human". An experiment clearly shows the effect of a background sequence for understanding an action label.	['Hirokatsu Kataoka', 'Soma Shirakabe', 'Yun He', 'Yutaka Satoh']	2016-08-29	null	null	null	null	['action-analysis']	['computer-vision']	[ 5.08819044e-01 -2.55873144e-01 -3.38935703e-01 -2.09694415e-01 -1.11475803e-01 -1.28311306e-01 7.94548392e-01 -4.88078505e-01 -6.52527690e-01 8.52685571e-01 5.57677627e-01 -1.62229255e-01 3.30600470e-01 -7.45891333e-01 -5.05174935e-01 -8.95510793e-01 2.19816074e-01 -2.98275828e-01 5.95730007e-01 -2.27253661e-01 3.55669886e-01 5.92126608e-01 -1.71634579e+00 8.52860928e-01 4.64105964e-01 8.53137851e-01 -9.06589702e-02 7.60772645e-01 -2.25468874e-01 1.63672709e+00 -8.61860096e-01 5.01611978e-02 2.84088433e-01 -9.35102284e-01 -1.05395770e+00 3.03763688e-01 1.55634448e-01 -6.06121302e-01 -6.27129853e-01 9.90241289e-01 4.00631100e-01 4.34975863e-01 4.85735238e-01 -1.48056543e+00 -4.31087017e-01 4.85230893e-01 -3.66374880e-01 6.13397956e-01 5.49634695e-01 4.67389435e-01 4.09230381e-01 -4.42812055e-01 8.01881373e-01 1.33506441e+00 6.40417993e-01 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-1.57704428e-01 -1.12773895e+00 -5.79613984e-01 7.42050290e-01 3.24271098e-02 -7.69545317e-01 -4.02271509e-01 4.52601612e-01]	[8.153359413146973, 0.5474671125411987]
769d69bc-375e-4ab0-b8ba-f3a64f9d8a38	data-driven-modeling-of-time-domain-induced	2107.14796	null	https://arxiv.org/abs/2107.14796v1	https://arxiv.org/pdf/2107.14796v1.pdf	Data-driven modeling of time-domain induced polarization	We present a novel approach for data-driven modeling of the time-domain induced polarization (IP) phenomenon using variational autoencoders (VAE). VAEs are Bayesian neural networks that aim to learn a latent statistical distribution to encode extensive data sets as lower dimension representations. We collected 1 600 319 IP decay curves in various regions of Canada, the United States and Kazakhstan, and compiled them to train a deep VAE. The proposed deep learning approach is strictly unsupervised and data-driven: it does not require manual processing or ground truth labeling of IP data. Moreover, our VAE approach avoids the pitfalls of IP parametrization with the empirical Cole-Cole and Debye decomposition models, simple power-law models, or other sophisticated mechanistic models. We demonstrate four applications of VAEs to model and process IP data: (1) representative synthetic data generation, (2) unsupervised Bayesian denoising and data uncertainty estimation, (3) quantitative evaluation of the signal-to-noise ratio, and (4) automated outlier detection. We also interpret the IP compilation's latent representation and reveal a strong correlation between its first dimension and the average chargeability of IP decays. Finally, we experiment with varying VAE latent space dimensions and demonstrate that a single real-valued scalar parameter contains sufficient information to encode our extensive IP data compilation. This new finding suggests that modeling time-domain IP data using mathematical models governed by more than one free parameter is ambiguous, whereas modeling only the average chargeability is justified. A pre-trained implementation of our model -- readily applicable to new IP data from any geolocation -- is available as open-source Python code for the applied geophysics community.	['Pierre Bérubé', 'Charles L. Bérubé']	2021-07-30	null	null	null	null	['geophysics']	['miscellaneous']	[-4.07638773e-02 -1.31831944e-01 1.87337235e-01 -3.87228936e-01 -1.02555490e+00 -4.90480840e-01 7.69843221e-01 9.95430425e-02 -3.32874835e-01 9.25307870e-01 2.04706758e-01 -4.93133605e-01 -5.02536237e-01 -8.46466780e-01 -9.42758441e-01 -1.24818528e+00 -3.36433858e-01 8.74503434e-01 1.43671244e-01 -1.35347238e-02 1.22054584e-01 7.21514583e-01 -1.50325632e+00 -1.25313208e-01 8.86383057e-01 1.23301387e+00 -2.82968014e-01 5.11885941e-01 2.18743578e-01 4.75519955e-01 -5.20255148e-01 -8.37146565e-02 8.31931755e-02 -2.34378025e-01 -3.37581098e-01 -1.65505841e-01 -1.80436280e-02 -2.35860899e-01 -4.67481464e-01 9.12787735e-01 4.97906417e-01 3.55772562e-02 1.28982604e+00 -1.23369849e+00 -3.18093121e-01 4.77503866e-01 -3.69198084e-01 2.04803631e-01 -3.81163746e-01 3.09385866e-01 8.83595347e-01 -8.21685314e-01 5.08163869e-01 8.99529338e-01 8.38821948e-01 1.62211172e-02 -1.66400826e+00 -4.84196603e-01 -4.57725435e-01 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3c1de6a4-c160-467c-a572-847eff895f92	an-empirical-assessment-of-the-qualitative	null	null	https://aclanthology.org/2021.nlp4if-1.11	https://aclanthology.org/2021.nlp4if-1.11.pdf	An Empirical Assessment of the Qualitative Aspects of Misinformation in Health News	The explosion of online health news articles runs the risk of the proliferation of low-quality information. Within the existing work on fact-checking, however, relatively little attention has been paid to medical news. We present a health news classification task to determine whether medical news articles satisfy a set of review criteria deemed important by medical experts and health care journalists. We present a dataset of 1,119 health news paired with systematic reviews. The review criteria consist of six elements that are essential to the accuracy of medical news. We then present experiments comparing the classical token-based approach with the more recent transformer-based models. Our results show that detecting qualitative lapses is a challenging task with direct ramifications in misinformation, but is an important direction to pursue beyond assigning True or False labels to short claims.	['Ritwik Banerjee', 'Qi Zhang', 'Chaoyuan Zuo']	null	null	null	null	naacl-nlp4if-2021-6	['news-classification']	['natural-language-processing']	[ 2.59134471e-01 4.54490274e-01 -1.09261787e+00 -2.83821046e-01 -1.31438673e+00 -4.47817206e-01 7.36642659e-01 1.13617826e+00 -4.64893967e-01 8.01620483e-01 8.06008875e-01 -7.90723681e-01 -1.96338937e-01 -7.76337683e-01 -8.25215578e-01 -2.05436066e-01 1.72427505e-01 3.73688757e-01 1.60584062e-01 7.94807822e-02 6.38086915e-01 -5.83962277e-02 -1.13946056e+00 7.29954600e-01 7.70044386e-01 9.68543410e-01 -4.65107322e-01 3.56668621e-01 -8.26923996e-02 1.56072903e+00 -6.05255902e-01 -9.58173215e-01 -3.15927804e-01 -6.84825718e-01 -1.15670967e+00 -7.45987371e-02 2.39635944e-01 -2.82802992e-02 1.11478299e-01 1.23260403e+00 4.17627454e-01 -4.11214888e-01 5.96973836e-01 -8.89268458e-01 -9.47711527e-01 1.14531350e+00 -3.47385228e-01 6.81839406e-01 6.45123661e-01 -3.09131205e-01 1.11005414e+00 -4.01386440e-01 1.08599758e+00 8.71048987e-01 1.10234833e+00 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270a83b3-3eed-491b-b3a3-fe7471b8d911	rapid-ai-development-cycle-for-the	2003.05037	null	https://arxiv.org/abs/2003.05037v3	https://arxiv.org/pdf/2003.05037v3.pdf	Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis	Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, including from Chinese disease-infected areas were included. We present a system that utilizes robust 2D and 3D deep learning models, modifying and adapting existing AI models and combining them with clinical understanding. We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score. The study includes a testing set of 157 international patients (China and U.S). Results: Classification results for Coronavirus vs Non-coronavirus cases per thoracic CT studies were 0.996 AUC (95%CI: 0.989-1.00) ; on datasets of Chinese control and infected patients. Possible working point: 98.2% sensitivity, 92.2% specificity. For time analysis of Coronavirus patients, the system output enables quantitative measurements for smaller opacities (volume, diameter) and visualization of the larger opacities in a slice-based heat map or a 3D volume display. Our suggested Corona score measures the progression of disease over time. Conclusion: This initial study, which is currently being expanded to a larger population, demonstrated that rapidly developed AI-based image analysis can achieve high accuracy in detection of Coronavirus as well as quantification and tracking of disease burden.	['Wenbin Ji', 'Maayan Frid-Adar', 'Ophir Gozes', 'Hayit Greenspan', 'Eliot Siegel', 'Huangqi Zhang', 'Adam Bernheim', 'Patrick D. Browning']	2020-03-10	null	null	null	null	['covid-19-image-segmentation']	['computer-vision']	[-2.13309135e-02 -5.75820506e-01 -6.70951381e-02 -1.29226789e-01 -5.25268555e-01 -8.42565238e-01 1.60851523e-01 5.37581325e-01 -3.10902923e-01 3.15324277e-01 1.18808866e-01 -6.61655426e-01 -1.76494062e-01 -6.30965769e-01 -2.72009164e-01 -6.27222538e-01 -8.40597570e-01 1.31429660e+00 -1.78693667e-01 3.28814417e-01 -1.36249274e-01 1.04804170e+00 -7.42921293e-01 4.97618228e-01 9.47725356e-01 8.29175830e-01 3.20225418e-01 1.16301620e+00 1.13346428e-01 4.41453785e-01 -6.38176262e-01 3.93862814e-01 3.70387435e-01 -3.10829163e-01 -2.13489503e-01 -1.98594213e-01 6.19595885e-01 -6.92607224e-01 9.88819003e-02 3.44772935e-01 5.29458344e-01 -6.43835783e-01 1.23391998e+00 -1.02541304e+00 -4.91357595e-01 -1.70268700e-01 -2.39370167e-01 7.27072954e-01 -5.26835583e-02 5.27693450e-01 1.65072158e-01 -2.92488307e-01 9.13781822e-01 7.89008975e-01 1.21999609e+00 2.46039614e-01 -8.31093967e-01 -7.51700163e-01 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0c3ee8bb-2a6e-4210-804d-7bb0a3d0a5d3	adaptive-background-matting-using-background	2203.05193	null	https://arxiv.org/abs/2203.05193v2	https://arxiv.org/pdf/2203.05193v2.pdf	Adaptive Background Matting Using Background Matching	Due to the difficulty of solving the matting problem, lots of methods use some kinds of assistance to acquire high quality alpha matte. Green screen matting methods rely on physical equipment. Trimap-based methods take manual interactions as external input. Background-based methods require a pre-captured, static background. The methods are not flexible and convenient enough to use widely. Trimap-free methods are flexible but not stable in complicated video applications. To be stable and flexible in real applications, we propose an adaptive background matting method. The user first captures their videos freely, moving the cameras. Then the user captures the background video afterwards, roughly covering the previous captured regions. We use dynamic background video instead of static background for accurate matting. The proposed method is convenient to use in any scenes as the static camera and background is no more the limitation. To achieve this goal, we use background matching network to find the best-matched background frame by frame from dynamic backgrounds. Then, robust semantic estimation network is used to estimate the coarse alpha matte. Finally, we crop and zoom the target region according to the coarse alpha matte, and estimate the final accurate alpha matte. In experiments, the proposed method is able to perform comparably against the state-of-the-art matting methods.	['Jinlin Liu']	2022-03-10	null	null	null	null	['image-matting']	['computer-vision']	[ 2.31280953e-01 -5.40115595e-01 2.71439217e-02 -1.38136130e-02 -2.27901965e-01 -3.68677735e-01 3.01872075e-01 -3.02691489e-01 -5.12732625e-01 6.92133367e-01 -3.22200894e-01 -4.11553308e-02 2.70935923e-01 -9.75687385e-01 -7.05851316e-01 -8.67888570e-01 4.32032049e-01 4.96132165e-01 9.07931268e-01 -2.31873706e-01 6.47973940e-02 4.34427798e-01 -1.68784916e+00 3.29437882e-01 1.07545686e+00 7.93490350e-01 7.72057891e-01 5.58219910e-01 -5.91977417e-01 9.16625619e-01 -7.00240314e-01 -3.00711185e-01 4.92075175e-01 -7.53849268e-01 -2.58427143e-01 4.87234682e-01 4.50932443e-01 -3.99264365e-01 -3.59502822e-01 1.41727638e+00 1.37311876e-01 3.18841100e-01 2.11605653e-01 -1.24967945e+00 -4.14759815e-02 3.20309877e-01 -8.98838818e-01 4.15080100e-01 4.27954227e-01 1.05054900e-01 2.26615101e-01 -8.51530135e-01 5.92009664e-01 1.22028506e+00 3.59326541e-01 5.69192648e-01 -9.37262297e-01 -6.68187737e-01 3.79519582e-01 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cf11c1ab-be79-4ebd-a1a3-a3bb05183763	on-developing-facial-stress-analysis-and	2209.07916	null	https://arxiv.org/abs/2209.07916v2	https://arxiv.org/pdf/2209.07916v2.pdf	On Developing Facial Stress Analysis and Expression Recognition Platform	This work represents the experimental and development process of system facial expression recognition and facial stress analysis algorithms for an immersive digital learning platform. The system retrieves from users web camera and evaluates it using artificial neural network (ANN) algorithms. The ANN output signals can be used to score and improve the learning process. Adapting an ANN to a new system can require a significant implementation effort or the need to repeat the ANN training. There are also limitations related to the minimum hardware required to run an ANN. To overpass these constraints, some possible implementations of facial expression recognition and facial stress analysis algorithms in real-time systems are presented. The implementation of the new solution has made it possible to improve the accuracy in the recognition of facial expressions and also to increase their response speed. Experimental results showed that using the developed algorithms allow to detect the heart rate with better rate in comparison with social equipment.	['Anastasiia Archangelskaya', 'Dmitrii Grigorev', 'Sergei Nikolaev', 'Fabio Cacciatori']	2022-09-16	null	null	null	null	['facial-expression-recognition']	['computer-vision']	[ 9.25133377e-02 -2.28435490e-02 2.61953115e-01 -7.01148450e-01 4.94488746e-01 -2.19310746e-01 9.93510475e-04 -1.90052472e-03 -7.12125421e-01 5.13440609e-01 -4.37652618e-01 8.93937889e-03 -5.13466671e-02 -7.71153927e-01 -1.56768113e-01 -3.83784413e-01 -4.12260108e-02 5.05330153e-02 -5.50817922e-02 -4.02299494e-01 1.68814063e-01 1.04823971e+00 -2.42930102e+00 1.36848658e-01 3.96167040e-01 8.57924461e-01 2.24156063e-02 9.65128005e-01 -1.17829114e-01 8.29246938e-01 -8.08231771e-01 2.06930470e-02 1.83823623e-03 -4.11873937e-01 -4.79780078e-01 2.55160220e-02 2.00303614e-01 -5.49145579e-01 2.94572741e-01 1.05940640e+00 6.21645451e-01 2.53822833e-01 4.08475637e-01 -1.48061562e+00 5.54631874e-02 -1.77535102e-01 -1.36957377e-01 2.31528014e-01 7.61917889e-01 -1.93401784e-01 -2.00227439e-01 -6.82566345e-01 4.86740917e-01 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56d88bd1-b34b-41db-88c8-725a04586e89	human-saliency-driven-patch-based-matching	2208.03138	null	https://arxiv.org/abs/2208.03138v1	https://arxiv.org/pdf/2208.03138v1.pdf	Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition	Forensic iris recognition, as opposed to live iris recognition, is an emerging research area that leverages the discriminative power of iris biometrics to aid human examiners in their efforts to identify deceased persons. As a machine learning-based technique in a predominantly human-controlled task, forensic recognition serves as "back-up" to human expertise in the task of post-mortem identification. As such, the machine learning model must be (a) interpretable, and (b) post-mortem-specific, to account for changes in decaying eye tissue. In this work, we propose a method that satisfies both requirements, and that approaches the creation of a post-mortem-specific feature extractor in a novel way employing human perception. We first train a deep learning-based feature detector on post-mortem iris images, using annotations of image regions highlighted by humans as salient for their decision making. In effect, the method learns interpretable features directly from humans, rather than purely data-driven features. Second, regional iris codes (again, with human-driven filtering kernels) are used to pair detected iris patches, which are translated into pairwise, patch-based comparison scores. In this way, our method presents human examiners with human-understandable visual cues in order to justify the identification decision and corresponding confidence score. When tested on a dataset of post-mortem iris images collected from 259 deceased subjects, the proposed method places among the three best iris matchers, demonstrating better results than the commercial (non-human-interpretable) VeriEye approach. We propose a unique post-mortem iris recognition method trained with human saliency to give fully-interpretable comparison outcomes for use in the context of forensic examination, achieving state-of-the-art recognition performance.	['Adam Czajka', 'Kevin Bowyer', 'Andrey Kuehlkamp', 'Daniel Moreira', 'Aidan Boyd']	2022-08-03	null	null	null	null	['iris-recognition']	['computer-vision']	[ 4.79766041e-01 1.69537112e-01 -1.16062872e-01 -4.01390761e-01 -8.09846938e-01 -6.25330389e-01 5.27217567e-01 4.11134422e-01 -5.35845399e-01 1.68919742e-01 1.40511826e-01 -4.99149144e-01 -2.98307598e-01 -2.13489547e-01 -4.50456113e-01 -7.24107146e-01 1.10165156e-01 4.05699164e-01 -3.96946877e-01 2.56984353e-01 6.18829250e-01 8.86250615e-01 -1.62500334e+00 3.40680212e-01 8.75197709e-01 9.33313191e-01 -5.21927178e-01 7.39852011e-01 5.05824268e-01 7.35608459e-01 -4.97722417e-01 -8.35702598e-01 4.80902731e-01 -5.42648435e-01 -7.46928453e-01 3.91331047e-01 1.01035023e+00 -3.74016047e-01 6.27116263e-02 9.12099540e-01 5.12039542e-01 -1.85827255e-01 6.51474714e-01 -6.08437836e-01 -7.42427826e-01 -3.13669406e-02 -9.12000895e-01 5.61444163e-01 4.94006902e-01 8.18885684e-01 7.61564195e-01 -8.18288088e-01 4.05062616e-01 8.21518004e-01 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5de0881c-9a97-44a5-a766-747440d8de19	msmix-an-interpolation-based-text-data	2305.19617	null	https://arxiv.org/abs/2305.19617v1	https://arxiv.org/pdf/2305.19617v1.pdf	MSMix:An Interpolation-Based Text Data Augmentation Method Manifold Swap Mixup	To solve the problem of poor performance of deep neural network models due to insufficient data, a simple yet effective interpolation-based data augmentation method is proposed: MSMix (Manifold Swap Mixup). This method feeds two different samples to the same deep neural network model, and then randomly select a specific layer and partially replace hidden features at that layer of one of the samples by the counterpart of the other. The mixed hidden features are fed to the model and go through the rest of the network. Two different selection strategies are also proposed to obtain richer hidden representation. Experiments are conducted on three Chinese intention recognition datasets, and the results show that the MSMix method achieves better results than other methods in both full-sample and small-sample configurations.	['Zheqian Chen', 'Haitao Wang', 'Mao Ye']	2023-05-31	null	null	null	null	['intent-detection']	['natural-language-processing']	[ 1.65366456e-01 1.87352002e-01 -4.16170895e-01 -2.58552104e-01 -1.08445197e-01 3.99675548e-01 4.05830473e-01 -6.22463644e-01 -4.03985918e-01 7.58348346e-01 4.12375271e-01 -1.66902065e-01 2.42202654e-01 -6.85247958e-01 -4.62406605e-01 -1.04931319e+00 1.73881546e-01 1.94565207e-01 9.61379111e-02 7.17868954e-02 6.06655031e-02 1.82388704e-02 -1.25304854e+00 2.89704084e-01 1.08276558e+00 9.73493338e-01 3.67647171e-01 5.93486503e-02 -2.37602621e-01 9.29694653e-01 -3.56648922e-01 -1.17958583e-01 4.08898771e-01 -6.10400796e-01 -5.70668995e-01 2.63015687e-01 1.29857704e-01 -5.87021053e-01 -6.72733486e-01 8.32928777e-01 2.82217920e-01 3.93392026e-01 2.22917363e-01 -1.25954580e+00 -9.60722446e-01 8.76192570e-01 -4.13468778e-01 -1.16322488e-01 -1.82171062e-01 -1.48990244e-01 5.23461163e-01 -1.11079991e+00 3.51463377e-01 1.12692380e+00 7.23888814e-01 6.20909929e-01 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49f5c6d7-d8a1-4c95-9119-9053a0212f38	sok-privacy-preserving-deep-learning-with	2112.12855	null	https://arxiv.org/abs/2112.12855v2	https://arxiv.org/pdf/2112.12855v2.pdf	SoK: Privacy-preserving Deep Learning with Homomorphic Encryption	Outsourced computation for neural networks allows users access to state of the art models without needing to invest in specialized hardware and know-how. The problem is that the users lose control over potentially privacy sensitive data. With homomorphic encryption (HE) computation can be performed on encrypted data without revealing its content. In this systematization of knowledge, we take an in-depth look at approaches that combine neural networks with HE for privacy preservation. We categorize the changes to neural network models and architectures to make them computable over HE and how these changes impact performance. We find numerous challenges to HE based privacy-preserving deep learning such as computational overhead, usability, and limitations posed by the encryption schemes.	['Peizhao Hu', 'Daniel Takabi', 'Robert Podschwadt']	2021-12-23	null	null	null	null	['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning']	['methodology', 'natural-language-processing']	[ 1.16285659e-01 2.97233671e-01 4.67470437e-02 -9.11795318e-01 -5.71251392e-01 -9.80393231e-01 1.95077449e-01 5.82033535e-03 -1.23423040e+00 5.35631418e-01 2.64699720e-02 -3.37698877e-01 9.14862100e-03 -1.01011491e+00 -9.58103895e-01 -6.86420858e-01 -1.90891296e-01 -1.48915485e-01 -9.45808366e-02 -1.70420930e-01 -3.01428229e-01 8.00308585e-01 -1.53978169e+00 6.09019458e-01 -6.44894838e-02 1.14390051e+00 -2.43751258e-01 5.27729392e-01 3.12759548e-01 8.13577175e-01 -5.58329582e-01 -1.18249333e+00 9.17681992e-01 3.22132468e-01 -1.02026057e+00 -7.34813631e-01 7.23394036e-01 -1.04709268e+00 -8.66122425e-01 1.33481085e+00 2.76786417e-01 -1.11545347e-01 -1.10742949e-01 -1.81569672e+00 -6.33342862e-01 5.90725303e-01 3.36740315e-01 -1.55862495e-01 -3.92914563e-01 6.40084594e-02 6.66164398e-01 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725ef0a7-c805-451a-b8f1-5afe1119d595	provably-efficient-offline-reinforcement-1	2306.08364	null	https://arxiv.org/abs/2306.08364v1	https://arxiv.org/pdf/2306.08364v1.pdf	Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources	Existing theoretical studies on offline reinforcement learning (RL) mostly consider a dataset sampled directly from the target task. In practice, however, data often come from several heterogeneous but related sources. Motivated by this gap, this work aims at rigorously understanding offline RL with multiple datasets that are collected from randomly perturbed versions of the target task instead of from itself. An information-theoretic lower bound is derived, which reveals a necessary requirement on the number of involved sources in addition to that on the number of data samples. Then, a novel HetPEVI algorithm is proposed, which simultaneously considers the sample uncertainties from a finite number of data samples per data source and the source uncertainties due to a finite number of available data sources. Theoretical analyses demonstrate that HetPEVI can solve the target task as long as the data sources collectively provide a good data coverage. Moreover, HetPEVI is demonstrated to be optimal up to a polynomial factor of the horizon length. Finally, the study is extended to offline Markov games and offline robust RL, which demonstrates the generality of the proposed designs and theoretical analyses.	['Jing Yang', 'Cong Shen', 'Wei Xiong', 'Chengshuai Shi']	2023-06-14	null	null	null	null	['offline-rl']	['playing-games']	[ 9.66121182e-02 3.84790719e-01 -3.75080943e-01 2.43407011e-01 -1.30736780e+00 -7.58547783e-01 8.72574151e-02 3.11422557e-01 -3.53582442e-01 1.10653591e+00 -7.82550275e-02 -2.62107756e-02 -6.86696112e-01 -6.46892786e-01 -1.09374642e+00 -9.02709961e-01 -2.16698214e-01 4.87715811e-01 -1.73926219e-01 -6.31925166e-02 -1.26755998e-01 1.05477244e-01 -1.39340842e+00 -5.72600365e-01 9.50730383e-01 1.46420825e+00 5.16010284e-01 4.43594098e-01 3.65038306e-01 8.29865634e-01 -8.66145790e-01 -7.96809345e-02 7.25563526e-01 -2.65879542e-01 -2.20981166e-01 3.85516524e-01 -5.47527015e-01 -6.20261848e-01 -3.14186096e-01 1.18093133e+00 6.02889299e-01 4.46523577e-01 4.01440591e-01 -1.59593666e+00 -1.04741216e-01 7.38908589e-01 -6.07655883e-01 2.39625946e-03 -5.64415641e-02 1.83006138e-01 7.17418730e-01 -4.53532815e-01 4.28370863e-01 9.67854738e-01 2.88317412e-01 3.75079691e-01 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