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f514d546-5d01-44bc-ab77-848209685456	beyond-word2vec-embedding-words-and-phrases	null	null	https://cdn.iiit.ac.in/cdn/ltrc.iiit.ac.in/icon2017/proceedings/icon2017/pdf/W17-7526.pdf	https://cdn.iiit.ac.in/cdn/ltrc.iiit.ac.in/icon2017/proceedings/icon2017/pdf/W17-7526.pdf	Beyond Word2Vec: Embedding Words and Phrases in Same Vector Space	Word embeddings are being used for several linguistic problems and NLP tasks. Improvements in solutions to such problems are great because of the recent breakthroughs in vector representation of words and research in vector space models. However, vector embeddings of phrases keeping semantics intact with words has been challenging. We propose a novel methodology using Siamese deep neural networks to embed multi-word units and fine-tune the current state-of-the-art word embed-dings keeping both in the same vector space. We show several semantic relations between words and phrases using the embeddings generated by our system and evaluate that the similarity of words and their corresponding paraphrases are maximized using the modified embeddings.	['Manish Shrivastava', 'Vijay Prakash Dwivedi']	2017-12-18	beyond-word2vec-embedding-words-and-phrases-1	https://aclanthology.org/W17-7526	https://aclanthology.org/W17-7526.pdf	international-conference-on-natural-language	['phrase-vector-embedding']	['natural-language-processing']	[-3.25111747e-01 -2.08909005e-01 -5.69092870e-01 -4.06702161e-01 -4.02481616e-01 -4.85773206e-01 5.56243539e-01 5.35001934e-01 -9.44937527e-01 4.08799171e-01 8.41101050e-01 -3.27861309e-01 -4.93443571e-02 -9.03351605e-01 -3.30912262e-01 -4.19629097e-01 -1.28381237e-01 3.76922786e-01 1.55677378e-01 -5.36005557e-01 4.12230253e-01 5.05781293e-01 -1.21437383e+00 1.81324571e-01 5.07159948e-01 5.26813865e-01 5.61273322e-02 6.86330140e-01 -9.55729425e-01 1.11617260e-01 -5.36082208e-01 -5.60189188e-01 1.59375951e-01 -1.26902446e-01 -6.52948797e-01 -3.75320852e-01 2.61526227e-01 5.25256805e-02 -7.36963809e-01 1.13389611e+00 4.49811876e-01 5.92882872e-01 8.23404372e-01 -1.16779828e+00 -2.00264955e+00 7.74536073e-01 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0b3334fa-e439-4a1b-ac72-7bf01209b172	deshadowgan-a-deep-learning-approach-to	1910.02844	null	https://arxiv.org/abs/1910.02844v1	https://arxiv.org/pdf/1910.02844v1.pdf	DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images	Purpose: To remove retinal shadows from optical coherence tomography (OCT) images of the optic nerve head(ONH). Methods:2328 OCT images acquired through the center of the ONH using a Spectralis OCT machine for both eyes of 13 subjects were used to train a generative adversarial network (GAN) using a custom loss function. Image quality was assessed qualitatively (for artifacts) and quantitatively using the intralayer contrast: a measure of shadow visibility ranging from 0 (shadow-free) to 1 (strong shadow) and compared to compensated images. This was computed in the Retinal Nerve Fiber Layer (RNFL), the Inner Plexiform Layer (IPL), the Photoreceptor layer (PR) and the Retinal Pigment Epithelium (RPE) layers. Results: Output images had improved intralayer contrast in all ONH tissue layers. On average the intralayer contrast decreased by 33.7$\pm$6.81%, 28.8$\pm$10.4%, 35.9$\pm$13.0%, and43.0$\pm$19.5%for the RNFL, IPL, PR, and RPE layers respectively, indicating successful shadow removal across all depths. This compared to 70.3$\pm$22.7%, 33.9$\pm$11.5%, 47.0$\pm$11.2%, 26.7$\pm$19.0%for compensation. Output images were also free from artifacts commonly observed with compensation. Conclusions: DeshadowGAN significantly corrected blood vessel shadows in OCT images of the ONH. Our algorithm may be considered as a pre-processing step to improve the performance of a wide range of algorithms including those currently being used for OCT image segmentation, denoising, and classification. Translational Relevance: DeshadowGAN could be integrated to existing OCT devices to improve the diagnosis and prognosis of ocular pathologies.	['Shamira Perera', 'Xiaofei Wang', 'Zhang Liang', 'Sripad Krishna Devalla', 'Haris Cheong', 'Alexandre H. Thiery', 'Tin Aung Tun', 'Tan Hung Pham', 'Michael J. A. Girard', 'Leopold Schmetterer', 'Craig Boote', 'Aung Tin']	2019-10-07	null	null	null	null	['shadow-removal']	['computer-vision']	[ 4.43789721e-01 3.00085604e-01 4.33752477e-01 2.09378615e-01 -3.64251614e-01 -5.18939137e-01 -5.03849089e-02 -2.44027033e-01 -4.29017395e-01 1.02314985e+00 2.96498323e-03 -5.37753761e-01 2.99189061e-01 -6.58701837e-01 -5.90801120e-01 -7.92575955e-01 -9.36144218e-02 -1.40532568e-01 3.29017580e-01 3.33538264e-01 2.52332777e-01 5.32992005e-01 -1.47441900e+00 3.36387664e-01 1.27899992e+00 1.34558725e+00 -1.64702982e-01 6.96638644e-01 2.11192027e-01 2.82618940e-01 -6.43810451e-01 -2.13568985e-01 6.32971942e-01 -5.96566319e-01 -2.65346736e-01 5.13842143e-02 1.03856087e+00 -4.89902616e-01 2.88254380e-01 1.32566619e+00 6.78182840e-01 -2.61657953e-01 5.12658596e-01 -6.01590753e-01 -2.79978782e-01 -1.28407419e-01 -9.05723274e-01 2.44090214e-01 -3.31654102e-01 7.60809422e-01 3.63781244e-01 -6.14478111e-01 6.62045598e-01 5.14511585e-01 5.46296418e-01 4.32091236e-01 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6336dad1-8126-4d48-9ea8-cc8e145898af	multi-layered-semantic-representation-network	2106.11596	null	https://arxiv.org/abs/2106.11596v1	https://arxiv.org/pdf/2106.11596v1.pdf	Multi-layered Semantic Representation Network for Multi-label Image Classification	Multi-label image classification (MLIC) is a fundamental and practical task, which aims to assign multiple possible labels to an image. In recent years, many deep convolutional neural network (CNN) based approaches have been proposed which model label correlations to discover semantics of labels and learn semantic representations of images. This paper advances this research direction by improving both the modeling of label correlations and the learning of semantic representations. On the one hand, besides the local semantics of each label, we propose to further explore global semantics shared by multiple labels. On the other hand, existing approaches mainly learn the semantic representations at the last convolutional layer of a CNN. But it has been noted that the image representations of different layers of CNN capture different levels or scales of features and have different discriminative abilities. We thus propose to learn semantic representations at multiple convolutional layers. To this end, this paper designs a Multi-layered Semantic Representation Network (MSRN) which discovers both local and global semantics of labels through modeling label correlations and utilizes the label semantics to guide the semantic representations learning at multiple layers through an attention mechanism. Extensive experiments on four benchmark datasets including VOC 2007, COCO, NUS-WIDE, and Apparel show a competitive performance of the proposed MSRN against state-of-the-art models.	['Xiao Zheng', 'Linchuan Xu', 'Jun Huang', 'Hao Che', 'Xiwen Qu']	2021-06-22	null	null	null	null	['multi-label-image-classification']	['computer-vision']	[ 2.80911177e-01 -1.70894220e-01 -4.81246501e-01 -8.27465057e-01 -2.56064028e-01 -3.04118752e-01 3.96341503e-01 4.17194337e-01 -3.22956532e-01 3.26806277e-01 9.81870070e-02 2.31720537e-01 -6.49669245e-02 -6.72564268e-01 -4.87431288e-01 -5.51864803e-01 3.83599818e-01 1.64573923e-01 3.86491120e-01 -7.12207099e-03 3.23068857e-01 2.19235495e-01 -1.78385067e+00 6.10102355e-01 4.68755096e-01 1.42959714e+00 2.55097657e-01 1.16474822e-01 -4.56062496e-01 1.21850920e+00 -3.12366486e-01 -1.30044490e-01 -1.74407944e-01 -3.93565297e-01 -8.97481501e-01 2.75779873e-01 4.29199249e-01 2.31923938e-01 -1.07525341e-01 1.33814073e+00 1.45827398e-01 8.32390636e-02 6.45852268e-01 -1.33933818e+00 -1.01477528e+00 4.13112998e-01 -7.15380728e-01 -2.16289628e-02 -2.19378501e-01 -3.50218475e-01 1.30962896e+00 -7.72284806e-01 3.01802248e-01 1.43963432e+00 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5561b607-2afa-4ae0-a057-8f4182f46b2e	from-open-set-to-closed-set-supervised	2001.01886	null	https://arxiv.org/abs/2001.01886v2	https://arxiv.org/pdf/2001.01886v2.pdf	From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting	Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in [0, inf) in theory. However, collected data and labeled instances are limited in reality, which means that only a small closed set is observed. Existing methods typically model this task in a regression manner, while they are prone to suffer from an unseen scene with counts out of the scope of the closed set. In fact, counting has an interesting and exclusive property---spatially decomposable. A dense region can always be divided until sub-region counts are within the previously observed closed set. We therefore introduce the idea of spatial divide-and-conquer (S-DC) that transforms open-set counting into a closed-set problem. This idea is implemented by a novel Supervised Spatial Divide-and-Conquer Network (SS-DCNet). Thus, SS-DCNet can only learn from a closed set but generalize well to open-set scenarios via S-DC. SS-DCNet is also efficient. To avoid repeatedly computing sub-region convolutional features, S-DC is executed on the feature map instead of on the input image. We provide theoretical analyses as well as a controlled experiment on toy data, demonstrating why closed-set modeling makes sense. Extensive experiments show that SS-DCNet achieves the state-of-the-art performance. Code and models are available at: https://tinyurl.com/SS-DCNet.	['Liang Liu', 'Hao Lu', 'Chunhua Shen', 'Chengxin Liu', 'Zhiguo Cao', 'Haipeng Xiong']	2020-01-07	null	null	null	null	['object-counting']	['computer-vision']	[ 1.06482863e-01 -2.67806113e-01 -1.50305396e-02 -1.70813084e-01 -4.30095553e-01 -7.58945942e-01 5.40069103e-01 2.07218185e-01 -4.97987121e-01 5.52481353e-01 -3.62667799e-01 -3.01654696e-01 1.41559184e-01 -1.07383001e+00 -1.07599044e+00 -6.89802706e-01 -1.43982366e-01 5.48381984e-01 4.61684287e-01 1.94146380e-01 1.97621375e-01 4.24910933e-01 -1.59748781e+00 1.57793462e-01 5.87143838e-01 9.39013302e-01 3.51228654e-01 6.36122584e-01 -3.09655100e-01 9.78014708e-01 -5.17502427e-01 1.74465273e-02 4.80890185e-01 -3.23025316e-01 -5.88949144e-01 5.66638149e-02 6.75913572e-01 -4.33658868e-01 -4.71582234e-01 1.27048063e+00 1.37432907e-02 8.86950716e-02 8.84483099e-01 -1.31260681e+00 -7.62240708e-01 1.03450142e-01 -1.09660983e+00 6.09890878e-01 2.19805241e-01 2.38758460e-01 6.60768390e-01 -7.93806374e-01 4.30468023e-01 1.03688657e+00 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2c5d7877-92a7-4f22-bcb9-5f7687ccd5a3	fast-and-robust-video-based-exercise	2210.00507	null	https://arxiv.org/abs/2210.00507v1	https://arxiv.org/pdf/2210.00507v1.pdf	Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series Classifiers	Technological advancements have spurred the usage of machine learning based applications in sports science. Physiotherapists, sports coaches and athletes actively look to incorporate the latest technologies in order to further improve performance and avoid injuries. While wearable sensors are very popular, their use is hindered by constraints on battery power and sensor calibration, especially for use cases which require multiple sensors to be placed on the body. Hence, there is renewed interest in video-based data capture and analysis for sports science. In this paper, we present the application of classifying S\&C exercises using video. We focus on the popular Military Press exercise, where the execution is captured with a video-camera using a mobile device, such as a mobile phone, and the goal is to classify the execution into different types. Since video recordings need a lot of storage and computation, this use case requires data reduction, while preserving the classification accuracy and enabling fast prediction. To this end, we propose an approach named BodyMTS to turn video into time series by employing body pose tracking, followed by training and prediction using multivariate time series classifiers. We analyze the accuracy and robustness of BodyMTS and show that it is robust to different types of noise caused by either video quality or pose estimation factors. We compare BodyMTS to state-of-the-art deep learning methods which classify human activity directly from videos and show that BodyMTS achieves similar accuracy, but with reduced running time and model engineering effort. Finally, we discuss some of the practical aspects of employing BodyMTS in this application in terms of accuracy and robustness under reduced data quality and size. We show that BodyMTS achieves an average accuracy of 87\%, which is significantly higher than the accuracy of human domain experts.	['Georgiana Ifrim', 'Brian Caulfield', 'Darragh Whelan', 'Martin OReilly', 'Kevin McGuinness', 'Feiyan Hu', 'Thach Le Nguyen', 'Antonio Bevilacqua', 'Ashish Singh']	2022-10-02	null	null	null	null	['pose-tracking']	['computer-vision']	[ 2.98908323e-01 -2.89448202e-01 -3.88777852e-01 1.56573541e-02 -4.99956697e-01 -2.74894863e-01 -1.45382181e-01 2.73465425e-01 -6.81878209e-01 3.48153204e-01 -7.84663036e-02 9.72691625e-02 -1.72249451e-01 -7.20886469e-01 -7.89208353e-01 -6.06895089e-01 -1.90668985e-01 1.87037662e-01 4.89513189e-01 -7.79103860e-02 7.33661428e-02 3.35442573e-01 -1.94299555e+00 3.19006145e-01 5.54095387e-01 1.26399577e+00 3.10754012e-02 7.70480394e-01 3.11151266e-01 6.13098800e-01 -6.80370271e-01 -4.09584552e-01 2.54283309e-01 -3.88733000e-01 -5.11339724e-01 2.26636410e-01 4.85899836e-01 -1.85252637e-01 -3.75629038e-01 5.80025911e-01 8.33702266e-01 2.28431165e-01 2.06272736e-01 -1.01713455e+00 3.38575095e-01 4.49036323e-02 -3.61775219e-01 3.84398550e-01 5.96294761e-01 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187a4965-75c6-42c6-b594-6483b32a613a	entity-aware-and-motion-aware-transformers	2205.05854	null	https://arxiv.org/abs/2205.05854v1	https://arxiv.org/pdf/2205.05854v1.pdf	Entity-aware and Motion-aware Transformers for Language-driven Action Localization in Videos	Language-driven action localization in videos is a challenging task that involves not only visual-linguistic matching but also action boundary prediction. Recent progress has been achieved through aligning language query to video segments, but estimating precise boundaries is still under-explored. In this paper, we propose entity-aware and motion-aware Transformers that progressively localizes actions in videos by first coarsely locating clips with entity queries and then finely predicting exact boundaries in a shrunken temporal region with motion queries. The entity-aware Transformer incorporates the textual entities into visual representation learning via cross-modal and cross-frame attentions to facilitate attending action-related video clips. The motion-aware Transformer captures fine-grained motion changes at multiple temporal scales via integrating long short-term memory into the self-attention module to further improve the precision of action boundary prediction. Extensive experiments on the Charades-STA and TACoS datasets demonstrate that our method achieves better performance than existing methods.	['Xinxiao wu', 'Shuo Yang']	2022-05-12	null	null	null	null	['action-localization']	['computer-vision']	[ 1.55362397e-01 -4.01616454e-01 -7.44561732e-01 -2.86292106e-01 -1.07946789e+00 -3.87039810e-01 3.22840571e-01 -1.59539238e-01 -3.68897736e-01 4.19768184e-01 9.64500129e-01 4.45907474e-01 2.01076165e-01 -2.46970445e-01 -7.14072764e-01 -2.97449499e-01 -4.13844764e-01 7.02306256e-02 9.65779126e-01 2.61065483e-01 3.38092953e-01 2.22783223e-01 -1.41259027e+00 9.12507057e-01 5.28859198e-01 1.18580639e+00 2.50933945e-01 6.59406662e-01 -8.62477943e-02 1.45688832e+00 -1.84195608e-01 -1.03733554e-01 1.27002880e-01 -5.39806485e-01 -1.14096856e+00 1.89071283e-01 7.05762625e-01 -5.50854921e-01 -6.47664845e-01 7.33579278e-01 2.62467563e-01 4.23384070e-01 2.31424227e-01 -1.34826255e+00 -6.02195919e-01 4.06611472e-01 -9.05740559e-01 7.69939780e-01 7.42146075e-01 2.13539600e-01 1.08105755e+00 -9.40224588e-01 8.87250364e-01 1.30710363e+00 6.78909481e-01 3.84154201e-01 -7.98084140e-01 -5.48094749e-01 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ba2d6854-25a0-480f-a37b-dfb9c09b382d	entropy-environment-transformer-and-offline	2303.03811	null	https://arxiv.org/abs/2303.03811v1	https://arxiv.org/pdf/2303.03811v1.pdf	ENTROPY: Environment Transformer and Offline Policy Optimization	Model-based methods provide an effective approach to offline reinforcement learning (RL). They learn an environmental dynamics model from interaction experiences and then perform policy optimization based on the learned model. However, previous model-based offline RL methods lack long-term prediction capability, resulting in large errors when generating multi-step trajectories. We address this issue by developing a sequence modeling architecture, Environment Transformer, which can generate reliable long-horizon trajectories based on offline datasets. We then propose a novel model-based offline RL algorithm, ENTROPY, that learns the dynamics model and reward function by ENvironment TRansformer and performs Offline PolicY optimization. We evaluate the proposed method on MuJoCo continuous control RL environments. Results show that ENTROPY performs comparably or better than the state-of-the-art model-based and model-free offline RL methods and demonstrates more powerful long-term trajectory prediction capability compared to existing model-based offline methods.	['Shaojie Shen', 'Meixin Zhu', 'Pengqin Wang']	2023-03-07	null	null	null	null	['trajectory-prediction', 'offline-rl', 'continuous-control']	['computer-vision', 'playing-games', 'playing-games']	[-5.00916421e-01 -1.29286990e-01 -4.44251180e-01 6.12408072e-02 -6.13000989e-01 -7.89801538e-01 7.42094040e-01 6.49210960e-02 -4.93885607e-01 1.32985401e+00 7.76244700e-02 -5.89984417e-01 -2.13646129e-01 -7.00600445e-01 -8.42235923e-01 -4.48971003e-01 -5.22630632e-01 7.00419366e-01 1.11401506e-01 -5.19795954e-01 2.03838438e-01 4.82106000e-01 -1.64516449e+00 -4.27460410e-02 1.03473973e+00 6.13664150e-01 4.14080322e-01 8.64571691e-01 1.29618287e-01 1.31925607e+00 -5.13835013e-01 3.82835954e-01 3.54826063e-01 -6.48309529e-01 -5.93906224e-01 -4.67348099e-01 -6.21292949e-01 -7.78584659e-01 -6.72493339e-01 3.73797238e-01 5.77967107e-01 7.39046693e-01 4.39926386e-01 -1.30688262e+00 -1.64977923e-01 6.79855704e-01 2.84067899e-01 7.21188402e-03 5.79208732e-01 7.85530448e-01 5.39765656e-01 -2.79757380e-01 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16c8cc77-d1d6-4e67-99c8-8258443d1667	representation-learning-for-tablet-and-paper	2301.06293	null	https://arxiv.org/abs/2301.06293v1	https://arxiv.org/pdf/2301.06293v1.pdf	Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition	The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. The goal of domain adaptation (DA) is to mitigate this domain shift problem by searching for an optimal feature transformation to learn a domain-invariant representation. Such a domain shift can appear in handwriting recognition (HWR) applications where the motion pattern of the hand and with that the motion pattern of the pen is different for writing on paper and on tablet. This becomes visible in the sensor data for online handwriting (OnHW) from pens with integrated inertial measurement units. This paper proposes a supervised DA approach to enhance learning for OnHW recognition between tablet and paper data. Our method exploits loss functions such as maximum mean discrepancy and correlation alignment to learn a domain-invariant feature representation (i.e., similar covariances between tablet and paper features). We use a triplet loss that takes negative samples of the auxiliary domain (i.e., paper samples) to increase the amount of samples of the tablet dataset. We conduct an evaluation on novel sequence-based OnHW datasets (i.e., words) and show an improvement on the paper domain with an early fusion strategy by using pairwise learning.	['Christopher Mutschler', 'Bernd Bischl', 'Lucas Heublein', 'David Rügamer', 'Felix Ott']	2023-01-16	null	null	null	null	['handwriting-recognition']	['computer-vision']	[ 5.69531262e-01 -3.68091136e-01 -3.20916802e-01 -3.73778582e-01 -5.62525272e-01 -7.32320011e-01 5.21754622e-01 -2.50069082e-01 -3.31125259e-01 5.43157816e-01 7.52623975e-02 2.40793973e-02 -4.31919366e-01 -2.40847930e-01 -7.53673851e-01 -8.54114592e-01 4.29792941e-01 5.32526195e-01 -2.34226622e-02 -1.81170866e-01 4.53968316e-01 6.81614637e-01 -1.16363716e+00 2.92361796e-01 7.57277489e-01 8.12472820e-01 5.53028345e-01 7.66384304e-01 7.78188258e-02 5.04241347e-01 -8.16207290e-01 4.92874347e-02 6.39495015e-01 -5.04397452e-01 -4.64869946e-01 5.82790375e-01 6.68567359e-01 -3.17080289e-01 -5.23727536e-01 8.96459162e-01 6.98144853e-01 3.84353399e-01 8.14550340e-01 -1.01878858e+00 -6.58360600e-01 3.33805919e-01 -6.13448858e-01 -4.87862676e-02 4.64556307e-01 -2.79656976e-01 6.02693319e-01 -1.01365554e+00 9.38648403e-01 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a1ef0ced-c88a-466f-932d-094582507d68	refining-a-nearest-neighbor-graph-for-a	null	null	https://doi.org/10.1016/j.patcog.2021.107869	https://github.com/mashaan14/Spectral-Clustering/blob/master/Refining%20a%20k-nearest%20neighbor%20graph%20for%20a%20computationally%20efficient%20spectral%20clustering/PR-Preprint.pdf	Refining a -nearest neighbor graph for a computationally efficient spectral clustering	Spectral clustering became a popular choice for data clustering for its ability of uncovering clusters of different shapes. However, it is not always preferable over other clustering methods due to its computational demands. One of the effective ways to bypass these computational demands is to perform spectral clustering on a subset of points (data representatives) then generalize the clustering outcome, this is known as approximate spectral clustering (ASC). ASC uses sampling or quantization to select data representatives. This makes it vulnerable to 1) performance inconsistency (since these methods have a random step either in initialization or training), 2) local statistics loss (because the pairwise similarities are extracted from data representatives instead of data points). We proposed a refined version of -nearest neighbor graph, in which we keep data points and aggressively reduce number of edges for computational efficiency. Local statistics were exploited to keep the edges that do not violate the intra-cluster distances and nullify all other edges in the -nearest neighbor graph. We also introduced an optional step to automatically select the number of clusters . The proposed method was tested on synthetic and real datasets. Compared to ASC methods, the proposed method delivered a consistent performance despite significant reduction of edges	['Masahiro Takatsuka', 'John Stavrakakis', 'Mashaan Alshammari']	2021-02-06	null	null	null	pattern-recognition-2021-2	['graph-clustering', 'spectral-graph-clustering', 'graph-partitioning']	['graphs', 'graphs', 'graphs']	[ 7.85379112e-02 -1.86246440e-01 -1.82597954e-02 -2.82904923e-01 -5.87928116e-01 -7.61267602e-01 4.44350839e-01 4.91696447e-01 -4.63123560e-01 5.37509978e-01 6.74844980e-02 6.83926791e-02 -6.37778938e-01 -7.84218907e-01 -2.49105215e-01 -1.18989050e+00 -3.25707316e-01 5.51346123e-01 6.04796708e-01 2.89411902e-01 6.47247314e-01 7.16892838e-01 -1.73736775e+00 1.17325500e-01 1.25087380e+00 6.01338089e-01 2.78361291e-01 2.29504019e-01 -3.78167599e-01 1.05842941e-01 -3.04497182e-01 5.14669605e-02 5.41031659e-01 -3.70672911e-01 -5.84342360e-01 3.38990599e-01 -7.37302601e-02 2.82311320e-01 2.20121831e-01 1.19083691e+00 4.93025422e-01 4.20394629e-01 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ab5ce8b8-f74e-4014-8e12-2d17f74160be	motionrec-a-unified-deep-framework-for-moving	null	null	https://ieeexplore.ieee.org/abstract/document/9093324	https://openaccess.thecvf.com/content_WACV_2020/papers/Mandal_MotionRec_A_Unified_Deep_Framework_for_Moving_Object_Recognition_WACV_2020_paper.pdf	MotionRec: A Unified Deep Framework for Moving Object Recognition	In this paper we present a novel deep learning framework to perform online moving object recognition(MOR) in streaming videos. The existing methods for moving object detection (MOD) only computes class-agnostic pixel-wise binary segmentation of video frames. On the other hand, the object detection techniques do not differentiate between static and moving objects. To the best of our knowledge, this is a first attempt for simultaneous localization and classification of moving objects in a video, i.e. MOR in a single-stage deep learning framework. We achieve this by labelling axis-aligned bounding boxes for moving objects which requires less computational resources than producing pixel-level estimates. In the proposed MotionRec, both temporal and spatial features are learned using past history and current frames respectively. First, the background is estimated with a temporal depth reductionist (TDR) block. Then the estimated background, current frame and temporal median of recent observations are assimilated to encode spatiotemporal motion saliency. Moreover, feature pyramids are generated from these motion saliency maps to perform regression and classification at multiple levels of feature abstractions. MotionRec works online at inference as it requires only few past frames for MOR. Moreover, it doesn’t require predefined target initialization from user. We also annotated axis-aligned bounding boxes (42,614 objects (14,814 cars and 27,800 person) in 24,923 video frames of CDnet 2014 dataset) due to lack of available benchmark datasets for MOR. The performance is observed qualitatively and quantitatively in terms of mAP over a defined unseen test set. Experiments show that the proposed MotionRec significantly improves over strong baselines with RetinaNet architectures for MOR.	['Santosh Kumar Vipparthi', 'Mahipal Singh Saran', 'Lav Kush Kumar', 'Murari Mandal']	2020-04-14	null	null	null	wacv-2020-4	['moving-object-detection']	['computer-vision']	[ 1.32386148e-01 -3.92207503e-01 -1.64838508e-01 -8.42238218e-02 -7.54142404e-01 -3.91960442e-01 5.59085250e-01 -1.10440217e-01 -8.28585982e-01 5.95006585e-01 -1.51624752e-03 1.07949309e-01 2.27655768e-01 -5.97567976e-01 -1.03148329e+00 -7.61245251e-01 -3.25792968e-01 -1.89833969e-01 1.06216526e+00 9.04656425e-02 1.85383618e-01 4.84900296e-01 -1.87250400e+00 5.21523297e-01 5.47503889e-01 1.30074251e+00 5.43985665e-01 1.08261597e+00 2.10053816e-01 1.23914051e+00 -5.36096692e-01 -1.26033768e-01 2.96710402e-01 -4.54962283e-01 -7.38680661e-01 2.99813896e-01 7.96277463e-01 -7.65399098e-01 -5.73206246e-01 1.01164126e+00 2.58705676e-01 5.41205704e-01 4.63818163e-01 -1.22141755e+00 -3.75804007e-01 4.95991200e-01 -7.18715012e-01 1.00314355e+00 9.57980826e-02 4.30449516e-01 6.97606504e-01 -1.11851513e+00 7.53926575e-01 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365b76d2-5d6b-414d-8e18-1ac6f745b991	learning-to-branch-in-combinatorial	2307.01434	null	https://arxiv.org/abs/2307.01434v1	https://arxiv.org/pdf/2307.01434v1.pdf	Learning to Branch in Combinatorial Optimization with Graph Pointer Networks	Branch-and-bound is a typical way to solve combinatorial optimization problems. This paper proposes a graph pointer network model for learning the variable selection policy in the branch-and-bound. We extract the graph features, global features and historical features to represent the solver state. The proposed model, which combines the graph neural network and the pointer mechanism, can effectively map from the solver state to the branching variable decisions. The model is trained to imitate the classic strong branching expert rule by a designed top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. Our approach also outperforms the state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.	['Kaiwen Li', 'Xiangke Liao', 'Xin Xu', 'Ling Wang', 'Tao Zhang', 'Zhiming Zhou', 'Rui Wang']	2023-07-04	null	null	null	null	['combinatorial-optimization', 'variable-selection']	['methodology', 'methodology']	[ 1.41437026e-02 5.53512126e-02 -9.00331497e-01 -3.06716621e-01 -6.10769272e-01 -5.74692965e-01 1.92250967e-01 2.30568990e-01 -2.00032905e-01 1.02711380e+00 -6.73891246e-01 -7.45629072e-01 -5.76833308e-01 -9.82956588e-01 -6.83267355e-01 -6.48584604e-01 -6.02943659e-01 8.89469087e-01 3.85930508e-01 -6.34854361e-02 6.89462423e-01 5.93990505e-01 -1.09234428e+00 1.62756354e-01 9.54046011e-01 1.34338319e+00 5.64088114e-02 6.60784423e-01 -4.54371154e-01 8.99627268e-01 -5.75294197e-01 -2.08170593e-01 4.09859508e-01 -3.89504284e-01 -9.59120393e-01 -3.62193942e-01 2.06919506e-01 2.81738285e-02 -3.49094748e-01 1.14536369e+00 1.82403758e-01 2.56333798e-01 3.17621827e-01 -1.19889939e+00 -4.08990234e-01 7.39335239e-01 -5.80943644e-01 6.28614306e-01 4.59673673e-01 3.13300937e-01 1.05970263e+00 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7339e470-1d7d-4762-a4d4-87de7aeceb90	cross-spectral-image-reconstruction-using-a	2306.15237	null	https://arxiv.org/abs/2306.15237v1	https://arxiv.org/pdf/2306.15237v1.pdf	Cross Spectral Image Reconstruction Using a Deep Guided Neural Network	Cross spectral camera arrays, where each camera records different spectral content, are becoming increasingly popular for RGB, multispectral and hyperspectral imaging, since they are capable of a high resolution in every dimension using off-the-shelf hardware. For these, it is necessary to build an image processing pipeline to calculate a consistent image data cube, i.e., it should look like as if every camera records the scene from the center camera. Since the cameras record the scene from a different angle, this pipeline needs a reconstruction component for pixels that are not visible to peripheral cameras. For that, a novel deep guided neural network (DGNet) is presented. Since only little cross spectral data is available for training, this neural network is highly regularized. Furthermore, a new data augmentation process is introduced to generate the cross spectral content. On synthetic and real multispectral camera array data, the proposed network outperforms the state of the art by up to 2 dB in terms of PSNR on average. Besides, DGNet also tops its best competitor in terms of SSIM as well as in runtime by a factor of nearly 12. Moreover, a qualitative evaluation reveals visually more appealing results for real camera array data.	['André Kaup', 'Jürgen Seiler', 'Frank Sippel']	2023-06-27	null	null	null	null	['image-reconstruction']	['computer-vision']	[ 5.77019870e-01 -4.81718659e-01 2.36683220e-01 -1.32062986e-01 -5.54062068e-01 -5.21362364e-01 2.22594082e-01 1.15171045e-01 -6.48219585e-01 5.14671147e-01 -2.82831848e-01 -2.15743929e-01 -2.46013120e-01 -1.00064814e+00 -7.91421056e-01 -1.04108095e+00 2.60474950e-01 -1.78682730e-01 -8.91628340e-02 -2.64402423e-02 -1.02220131e-02 7.76473403e-01 -1.66118979e+00 -3.54805849e-02 8.91290307e-01 1.33723092e+00 5.79044223e-01 4.22901720e-01 1.53694972e-01 4.79989141e-01 -2.94165194e-01 -2.89689183e-01 5.47964394e-01 -1.24656722e-01 -1.88716874e-01 5.97508729e-01 8.56095314e-01 -7.37381041e-01 -2.82114834e-01 1.54896867e+00 4.77138281e-01 1.57250822e-01 1.56667277e-01 -8.34529936e-01 -5.17196834e-01 3.62652779e-01 -9.29904521e-01 -2.77813107e-01 2.08394676e-02 2.92650789e-01 8.71180773e-01 -8.17381859e-01 3.51532608e-01 5.47248363e-01 4.78180796e-01 -7.61369094e-02 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-8.57484996e-01 1.44369349e-01 2.70859599e-01 -2.75025994e-01 -4.35712308e-01 1.41845167e-01]	[10.222865104675293, -2.083768844604492]
49854822-9f72-4751-844e-6fc1f2dd400f	speed-reading-learning-to-read-forbackward	null	null	https://aclanthology.org/D18-1474	https://aclanthology.org/D18-1474.pdf	Speed Reading: Learning to Read ForBackward via Shuttle	We present LSTM-Shuttle, which applies human speed reading techniques to natural language processing tasks for accurate and efficient comprehension. In contrast to previous work, LSTM-Shuttle not only reads shuttling forward but also goes back. Shuttling forward enables high efficiency, and going backward gives the model a chance to recover lost information, ensuring better prediction. We evaluate LSTM-Shuttle on sentiment analysis, news classification, and cloze on IMDB, Rotten Tomatoes, AG, and Children{'}s Book Test datasets. We show that LSTM-Shuttle predicts both better and more quickly. To demonstrate how LSTM-Shuttle actually behaves, we also analyze the shuttling operation and present a case study.	['Wei-Yun Ma', 'Tsu-Jui Fu']	2018-10-01	null	null	null	emnlp-2018-10	['news-classification']	['natural-language-processing']	[ 4.06910717e-01 2.00812697e-01 -1.22061238e-01 -5.67769587e-01 -5.93199968e-01 -4.18716192e-01 -1.22797024e-02 6.49056613e-01 -5.99048853e-01 4.48792160e-01 2.87657231e-01 -6.18035495e-01 1.95789441e-01 -7.55693018e-01 -9.64086771e-01 -7.50966296e-02 3.58321071e-02 5.10642409e-01 1.94955971e-02 -3.16181928e-01 4.06497657e-01 -3.14278960e-01 -1.22822058e+00 9.80455577e-01 9.67331707e-01 8.82956982e-01 4.22688097e-01 8.45120072e-01 -4.26886827e-01 1.49246192e+00 -6.04435563e-01 -6.68694615e-01 -2.45149747e-01 -2.79379606e-01 -1.26615119e+00 -5.51282644e-01 6.33558452e-01 -5.58256984e-01 2.26804927e-01 6.81675553e-01 2.41360202e-01 3.02507550e-01 3.23421568e-01 -7.00710475e-01 -8.21006536e-01 1.29935801e+00 -5.16500413e-01 4.13856059e-01 6.05729640e-01 -1.11247517e-01 1.07060528e+00 -8.95729125e-01 4.00318623e-01 1.23594332e+00 7.19329655e-01 5.92000306e-01 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0c761c35-0991-4bd9-bd2f-b40da4a17294	an-optimal-energy-management-algorithm	2303.04503	null	https://arxiv.org/abs/2303.04503v1	https://arxiv.org/pdf/2303.04503v1.pdf	An Optimal Energy Management Algorithm Considering Regenerative Braking and Renewable Energy for EV Charging in Railway Stations	This paper proposes a novel optimal Energy Management System (EMS) algorithm for Electric Vehicle (EV) charging in smart electric railway stations with renewable generation. As opposed to previous railway EMS methods, the proposed EMS coordinates the combined Regenerative Braking Energy (RBE), renewable generation, electric railway demand and EV charging demand at the EV parking lot of the railway station. Numerical results using a scenario-based approach on an actual railway station in Chur, Switzerland demonstrate that the proposed algorithm can effectively minimize the expected daily operating cost for the train station over an entire year.	['Gabriela Hug', 'Yannick Zwirner', 'Georgia Pierrou']	2023-03-08	null	null	null	null	['energy-management']	['time-series']	[-3.94129246e-01 5.89317456e-02 -3.52204405e-02 8.41318537e-03 -6.99288070e-01 -4.64204222e-01 4.86576080e-01 -1.49723858e-01 -5.04655778e-01 1.34402013e+00 -1.21321291e-01 -4.24492866e-01 -7.20336497e-01 -1.12943983e+00 -3.16284448e-01 -8.68584812e-01 3.83497626e-01 8.22221398e-01 -1.83367327e-01 -7.06973910e-01 3.85409296e-01 1.08135009e+00 -1.34944737e+00 -5.28553903e-01 8.74639928e-01 1.00202239e+00 8.48419726e-01 -2.43027210e-02 -8.60492699e-03 2.01665938e-01 -4.49180931e-01 -2.51295507e-01 -1.17357239e-01 -1.37367453e-02 -8.51718545e-01 -2.46613950e-01 -1.24465358e+00 -1.08438924e-01 -3.06234121e-01 6.85366511e-01 5.92969954e-01 4.36739951e-01 6.79317296e-01 -1.77392542e+00 1.50120422e-01 7.19313502e-01 -2.81060278e-01 1.38558939e-01 -2.84881592e-01 -3.19438398e-01 6.68929338e-01 -7.05554605e-01 4.68537807e-01 4.51103747e-01 2.71098077e-01 5.23719847e-01 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e592916e-9454-47c0-b4d8-e2876d6419ac	deepface-closing-the-gap-to-human-level-1	null	null	https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/	https://research.fb.com/wp-content/uploads/2016/11/deepface-closing-the-gap-to-human-level-performance-in-face-verification.pdf	DeepFace: Closing the Gap to Human-Level Performance in Face Verification	In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.	['Marc’ Aurelio Ranzato', 'Ming Yang', 'Yaniv Taigman', 'Lior Wolf']	2014-06-24	deepface-closing-the-gap-to-human-level	http://openaccess.thecvf.com/content_cvpr_2014/html/Taigman_DeepFace_Closing_the_2014_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf	conference-on-computer-vision-and-pattern	['3d-face-modeling']	['computer-vision']	[ 1.68584123e-01 2.63028771e-01 -8.03703889e-02 -1.02261591e+00 -6.34434938e-01 -5.06419718e-01 7.97125220e-01 -6.73748791e-01 -3.32115680e-01 1.92108154e-01 -9.02472585e-02 -1.87311396e-02 4.62509274e-01 -4.25593764e-01 -7.13183403e-01 -5.44128776e-01 -8.96472856e-02 7.02877641e-01 -3.85662585e-01 9.41204093e-03 1.16558662e-02 1.07844245e+00 -1.62632132e+00 4.66061622e-01 1.74289029e-02 1.42131233e+00 -6.73709869e-01 4.45245951e-01 -2.75823772e-02 5.40187001e-01 -4.10292357e-01 -9.62613106e-01 5.94064474e-01 -2.71924376e-01 -7.69319236e-01 1.69515729e-01 1.46698093e+00 -7.46325850e-01 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dbac8014-aa48-4881-aa92-317b6c1d6414	spinenetv2-automated-detection-labelling-and	2205.01683	null	https://arxiv.org/abs/2205.01683v1	https://arxiv.org/pdf/2205.01683v1.pdf	SpineNetV2: Automated Detection, Labelling and Radiological Grading Of Clinical MR Scans	This technical report presents SpineNetV2, an automated tool which: (i) detects and labels vertebral bodies in clinical spinal magnetic resonance (MR) scans across a range of commonly used sequences; and (ii) performs radiological grading of lumbar intervertebral discs in T2-weighted scans for a range of common degenerative changes. SpineNetV2 improves over the original SpineNet software in two ways: (1) The vertebral body detection stage is significantly faster, more accurate and works across a range of fields-of-view (as opposed to just lumbar scans). (2) Radiological grading adopts a more powerful architecture, adding several new grading schemes without loss in performance. A demo of the software is available at the project website: http://zeus.robots.ox.ac.uk/spinenet2/.	['Andrew Zisserman', 'Timor Kadir', 'Amir Jamaludin', 'Rhydian Windsor']	2022-05-03	null	null	null	null	['body-detection']	['computer-vision']	[ 1.23404004e-01 2.22449809e-01 -3.60036492e-01 -2.71616340e-01 -7.65965223e-01 -6.17889404e-01 3.65454704e-01 3.44930664e-02 -3.14026862e-01 6.62612736e-01 3.75655740e-01 -7.10067153e-01 -3.37340772e-01 -2.47319654e-01 -2.57877856e-01 -1.08284891e-01 -2.48859346e-01 1.26473594e+00 1.21839678e+00 -3.42916578e-01 3.93781871e-01 8.68215621e-01 -1.38713026e+00 1.23560831e-01 4.33161467e-01 9.28985953e-01 5.29689133e-01 8.78372133e-01 2.78450191e-01 1.33668613e+00 -4.37154859e-01 -9.36857332e-03 8.63113478e-02 -4.11207497e-01 -1.50000501e+00 2.17814624e-01 5.83580434e-01 -2.04501644e-01 -1.13903157e-01 9.40611720e-01 7.47295201e-01 -1.94324479e-01 5.19508660e-01 -5.35329819e-01 -1.83295295e-01 1.90081462e-01 -6.12921298e-01 9.67278004e-01 1.69330880e-01 -5.62151074e-02 3.42465073e-01 -5.01741052e-01 9.90411222e-01 9.61687386e-01 8.65387499e-01 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73019071-1f37-421b-aa29-a2d7f2f83005	do-not-trust-the-neighbors-adversarial-metric	2011.07945	null	https://arxiv.org/abs/2011.07945v1	https://arxiv.org/pdf/2011.07945v1.pdf	Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation	Scene flow is the task of estimating 3D motion vectors to individual points of a dynamic 3D scene. Motion vectors have shown to be beneficial for downstream tasks such as action classification and collision avoidance. However, data collected via LiDAR sensors and stereo cameras are computation and labor intensive to precisely annotate for scene flow. We address this annotation bottleneck on two ends. We propose a 3D scene flow benchmark and a novel self-supervised setup for training flow models. The benchmark consists of datasets designed to study individual aspects of flow estimation in progressive order of complexity, from a single object in motion to real-world scenes. Furthermore, we introduce Adversarial Metric Learning for self-supervised flow estimation. The flow model is fed with sequences of point clouds to perform flow estimation. A second model learns a latent metric to distinguish between the points translated by the flow estimations and the target point cloud. This latent metric is learned via a Multi-Scale Triplet loss, which uses intermediary feature vectors for the loss calculation. We use our proposed benchmark to draw insights about the performance of the baselines and of different models when trained using our setup. We find that our setup is able to keep motion coherence and preserve local geometries, which many self-supervised baselines fail to grasp. Dealing with occlusions, on the other hand, is still an open challenge.	['Victor Zuanazzi']	2020-11-01	null	null	null	null	['scene-flow-estimation']	['computer-vision']	[ 9.79037657e-02 -3.37239176e-01 -2.86404908e-01 -9.40499380e-02 -7.13143766e-01 -8.16869974e-01 7.08420217e-01 -3.82712148e-02 -3.60182464e-01 5.82026958e-01 2.35243812e-01 3.39278881e-03 1.24229401e-01 -6.94741666e-01 -6.87271118e-01 -5.55728495e-01 -1.63408920e-01 6.54119611e-01 4.66585994e-01 -2.70576403e-02 4.22301233e-01 8.76025677e-01 -1.40449393e+00 1.11634314e-01 5.31916380e-01 8.36488426e-01 -6.82346970e-02 1.12980580e+00 -1.08016267e-01 1.00241792e+00 -4.16329026e-01 -2.97488093e-01 7.74468064e-01 -2.36420542e-01 -1.09137309e+00 5.72862998e-02 1.13434863e+00 -5.78339398e-01 -6.55979156e-01 6.67230844e-01 2.98556298e-01 4.37141389e-01 7.75782466e-01 -1.58242548e+00 1.32816479e-01 -1.92523584e-01 -4.53310519e-01 4.11911339e-01 5.25264800e-01 6.20134294e-01 1.11747026e+00 -7.38478422e-01 1.06981921e+00 1.34903944e+00 6.18821979e-01 5.53175211e-01 -1.25264490e+00 -2.77344555e-01 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448db99b-7d7f-4ff3-b126-4565649de7fc	wabert-a-low-resource-end-to-end-model-for	2204.10461	null	https://arxiv.org/abs/2204.10461v1	https://arxiv.org/pdf/2204.10461v1.pdf	WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment	Historically lower-level tasks such as automatic speech recognition (ASR) and speaker identification are the main focus in the speech field. Interest has been growing in higher-level spoken language understanding (SLU) tasks recently, like sentiment analysis (SA). However, improving performances on SLU tasks remains a big challenge. Basically, there are two main methods for SLU tasks: (1) Two-stage method, which uses a speech model to transfer speech to text, then uses a language model to get the results of downstream tasks; (2) One-stage method, which just fine-tunes a pre-trained speech model to fit in the downstream tasks. The first method loses emotional cues such as intonation, and causes recognition errors during ASR process, and the second one lacks necessary language knowledge. In this paper, we propose the Wave BERT (WaBERT), a novel end-to-end model combining the speech model and the language model for SLU tasks. WaBERT is based on the pre-trained speech and language model, hence training from scratch is not needed. We also set most parameters of WaBERT frozen during training. By introducing WaBERT, audio-specific information and language knowledge are integrated in the short-time and low-resource training process to improve results on the dev dataset of SLUE SA tasks by 1.15% of recall score and 0.82% of F1 score. Additionally, we modify the serial Continuous Integrate-and-Fire (CIF) mechanism to achieve the monotonic alignment between the speech and text modalities.	['Yafeng Deng', 'Zijian Chen', 'Yingfang Yang', 'Ruizhuo Xu', 'Jianfei Song', 'Lin Yao']	2022-04-22	null	null	null	null	['speaker-identification']	['speech']	[ 1.08988620e-01 9.54642668e-02 1.29725903e-01 -5.62769592e-01 -1.03257620e+00 -3.42233002e-01 4.17623043e-01 1.00017473e-01 -5.16260743e-01 3.90220582e-01 2.90802866e-01 -3.10407341e-01 4.51917946e-01 -5.42881668e-01 -6.04951739e-01 -5.75299382e-01 4.03654486e-01 2.18493268e-01 4.05708641e-01 -4.78035867e-01 -9.52691212e-02 9.84557420e-02 -1.52833962e+00 4.78052467e-01 7.99086750e-01 1.11260068e+00 4.27538872e-01 7.41862893e-01 -5.41700721e-01 7.36846209e-01 -5.97964048e-01 -1.97807997e-01 -1.93918616e-01 -6.61311746e-01 -7.02407002e-01 5.99177205e-04 -3.63759726e-01 2.36934759e-02 3.59106846e-02 7.81621695e-01 8.48666787e-01 5.13162971e-01 4.25297737e-01 -8.70937049e-01 -2.79810578e-01 8.59296799e-01 -3.14239830e-01 -3.58141549e-02 3.33125472e-01 -7.60168210e-02 8.54524791e-01 -1.15617728e+00 2.88066775e-01 1.29815817e+00 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8.91171455e-01 -8.06129813e-01 -3.69808733e-01 -1.24653244e+00 -5.36007881e-01 4.22205403e-02 3.94101530e-01 -4.11221892e-01 -5.61198592e-01 -2.83249505e-02]	[14.39553165435791, 6.682505130767822]
f76aefd7-4a5d-4cfb-8736-59fc16ea7c03	conditional-generation-of-temporally-ordered	2012.15786	null	https://arxiv.org/abs/2012.15786v2	https://arxiv.org/pdf/2012.15786v2.pdf	Conditional Generation of Temporally-ordered Event Sequences	Models of narrative schema knowledge have proven useful for a range of event-related tasks, but they typically do not capture the temporal relationships between events. We propose a single model that addresses both temporal ordering, sorting given events into the order they occurred, and event infilling, predicting new events which fit into an existing temporally-ordered sequence. We use a BART-based conditional generation model that can capture both temporality and common event co-occurrence, meaning it can be flexibly applied to different tasks in this space. Our model is trained as a denoising autoencoder: we take temporally-ordered event sequences, shuffle them, delete some events, and then attempt to recover the original event sequence. This task teaches the model to make inferences given incomplete knowledge about the events in an underlying scenario. On the temporal ordering task, we show that our model is able to unscramble event sequences from existing datasets without access to explicitly labeled temporal training data, outperforming both a BERT-based pairwise model and a BERT-based pointer network. On event infilling, human evaluation shows that our model is able to generate events that fit better temporally into the input events when compared to GPT-2 story completion models.	['Greg Durrett', 'Nathanael Chambers', 'Shih-ting Lin']	2020-12-31	null	https://aclanthology.org/2021.acl-long.555	https://aclanthology.org/2021.acl-long.555.pdf	acl-2021-5	['story-completion']	['natural-language-processing']	[ 4.04212534e-01 1.64006576e-01 -1.35780245e-01 -4.41044331e-01 -7.49878168e-01 -7.99655318e-01 1.07145560e+00 4.34695959e-01 -3.41336101e-01 8.04000139e-01 1.03566217e+00 -3.21710348e-01 -4.51528728e-01 -1.09221172e+00 -9.23596740e-01 -2.45079458e-01 -4.12157148e-01 9.00529087e-01 3.34618777e-01 -2.37880588e-01 1.27831772e-01 2.90387601e-01 -1.49508071e+00 8.66888881e-01 3.90648574e-01 4.75407511e-01 1.33215770e-01 7.72010982e-01 1.07130796e-01 1.57116151e+00 -6.06086493e-01 -4.49834436e-01 -5.38334176e-02 -6.78330064e-01 -1.06707203e+00 -2.34223634e-01 -6.92070723e-02 -5.96988261e-01 -6.49799407e-01 2.12057903e-01 1.75562069e-01 6.17170274e-01 7.99620867e-01 -1.17820001e+00 -4.27319854e-01 1.33636951e+00 -1.01419948e-02 4.24434453e-01 7.56946623e-01 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7dd3ce3c-5943-4d5e-b1bf-7f67f710742a	cross-lingual-word-representations-induction	null	null	https://aclanthology.org/D17-3007	https://aclanthology.org/D17-3007.pdf	Cross-Lingual Word Representations: Induction and Evaluation	In recent past, NLP as a field has seen tremendous utility of distributional word vector representations as features in downstream tasks. The fact that these word vectors can be trained on unlabeled monolingual corpora of a language makes them an inexpensive resource in NLP. With the increasing use of monolingual word vectors, there is a need for word vectors that can be used as efficiently across multiple languages as monolingually. Therefore, learning bilingual and multilingual word embeddings/vectors is currently an important research topic. These vectors offer an elegant and language-pair independent way to represent content across different languages.This tutorial aims to bring NLP researchers up to speed with the current techniques in cross-lingual word representation learning. We will first discuss how to induce cross-lingual word representations (covering both bilingual and multilingual ones) from various data types and resources (e.g., parallel data, comparable data, non-aligned monolingual data in different languages, dictionaries and theasuri, or, even, images, eye-tracking data). We will then discuss how to evaluate such representations, intrinsically and extrinsically. We will introduce researchers to state-of-the-art methods for constructing cross-lingual word representations and discuss their applicability in a broad range of downstream NLP applications.We will deliver a detailed survey of the current methods, discuss best training and evaluation practices and use-cases, and provide links to publicly available implementations, datasets, and pre-trained models.	["Ivan Vuli{\\'c}", 'Anders S{\\o}gaard', 'Manaal Faruqui']	2017-09-01	null	null	null	emnlp-2017-9	['multilingual-word-embeddings']	['methodology']	[-2.88672686e-01 -4.58790749e-01 -8.58831465e-01 -3.95912975e-01 -1.10226679e+00 -1.07000613e+00 7.70701170e-01 2.16229856e-01 -8.23372066e-01 6.29550695e-01 5.38805664e-01 -5.94218969e-01 3.47554058e-01 -4.47698921e-01 -4.44434106e-01 -4.58147585e-01 2.42547736e-01 5.53112209e-01 -2.65824735e-01 -4.75424290e-01 1.85965419e-01 4.88433748e-01 -1.35987055e+00 9.15057212e-02 6.20243967e-01 3.11010808e-01 4.83973801e-01 3.23568970e-01 -7.24750161e-01 7.42296502e-02 -4.86055285e-01 -6.01056695e-01 2.15854287e-01 -1.65366650e-01 -6.68500483e-01 -2.31790289e-01 6.43162847e-01 2.65056491e-01 -2.93946058e-01 1.06330919e+00 8.04318726e-01 1.25789285e-01 8.11631143e-01 -9.45857346e-01 -1.53641462e+00 7.37544119e-01 -6.15733325e-01 4.75019008e-01 4.46175754e-01 5.82273602e-02 1.44127786e+00 -1.37718832e+00 9.61687446e-01 1.24613714e+00 5.65213323e-01 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035e64e8-1f20-4a5d-868a-c00ceb83f1e0	edu-level-extractive-summarization-with	2210.04029	null	https://arxiv.org/abs/2210.04029v2	https://arxiv.org/pdf/2210.04029v2.pdf	EDU-level Extractive Summarization with Varying Summary Lengths	Extractive models usually formulate text summarization as extracting fixed top-$k$ salient sentences from the document as a summary. Few works exploited extracting finer-grained Elementary Discourse Unit (EDU) with little analysis and justification for the extractive unit selection. Further, the selection strategy of the fixed top-$k$ salient sentences fits the summarization need poorly, as the number of salient sentences in different documents varies and therefore a common or best $k$ does not exist in reality. To fill these gaps, this paper first conducts the comparison analysis of oracle summaries based on EDUs and sentences, which provides evidence from both theoretical and experimental perspectives to justify and quantify that EDUs make summaries with higher automatic evaluation scores than sentences. Then, considering this merit of EDUs, this paper further proposes an EDU-level extractive model with Varying summary Lengths and develops the corresponding learning algorithm. EDU-VL learns to encode and predict probabilities of EDUs in the document, generate multiple candidate summaries with varying lengths based on various $k$ values, and encode and score candidate summaries, in an end-to-end training manner. Finally, EDU-VL is experimented on single and multi-document benchmark datasets and shows improved performances on ROUGE scores in comparison with state-of-the-art extractive models, and further human evaluation suggests that EDU-constituent summaries maintain good grammaticality and readability.	['Xiao-jun Zeng', 'Goran Nenadic', 'Shengzhong Mao', 'Jiayu Shang', 'Ching-Hsun Tseng', 'Yuping Wu']	2022-10-08	null	null	null	null	['extractive-summarization']	['natural-language-processing']	[ 0.29974878 0.39896974 -0.56888777 -0.30636337 -1.3180888 -0.50043786 0.52390987 0.65909445 -0.3117039 1.0618869 1.0377425 0.01391945 -0.32654142 -0.7376737 -0.519611 -0.40102535 0.00793976 0.39165115 0.15295109 -0.11853825 0.9829436 -0.02206064 -1.490732 0.4705737 1.5159812 0.40349564 0.46432623 1.0471166 -0.29652455 0.68906647 -1.3113542 -0.538138 -0.20580232 -0.74285805 -1.0252146 0.13948138 0.624076 -0.43277463 -0.24305223 0.90047574 0.69054383 0.27375045 0.9987477 -0.58194053 -0.8164004 1.3322142 -0.30656385 0.33559972 0.6318 -0.07341863 1.6305076 -0.8093516 0.5347693 1.1063048 0.22321588 0.40798536 -0.71486086 -0.13122332 0.27124384 0.05524278 -0.9422632 -0.3519054 0.8909797 -0.02368596 1.3175819 0.6932389 0.6375295 1.0072472 0.45791212 1.3899814 0.57777035 -0.6013435 0.15360183 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e53fadfd-6180-48e0-aaa7-f7d8d9d8029e	narrative-modeling-with-memory-chains-and	1805.06122	null	http://arxiv.org/abs/1805.06122v1	http://arxiv.org/pdf/1805.06122v1.pdf	Narrative Modeling with Memory Chains and Semantic Supervision	Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. Inspired by previous studies on ROC Story Cloze Test, we propose a novel method, tracking various semantic aspects with external neural memory chains while encouraging each to focus on a particular semantic aspect. Evaluated on the task of story ending prediction, our model demonstrates superior performance to a collection of competitive baselines, setting a new state of the art.	['Timothy Baldwin', 'Fei Liu', 'Trevor Cohn']	2018-05-16	narrative-modeling-with-memory-chains-and-1	https://aclanthology.org/P18-2045	https://aclanthology.org/P18-2045.pdf	acl-2018-7	['cloze-test']	['natural-language-processing']	[ 3.28187317e-01 2.18995541e-01 -5.50787210e-01 -3.98050010e-01 -6.03360534e-01 -7.08946764e-01 1.02325547e+00 2.30648667e-01 -2.69719064e-01 5.88116050e-01 1.34772980e+00 1.57455638e-01 1.61410511e-01 -8.89320433e-01 -6.71630025e-01 -5.16416356e-02 2.77433187e-01 6.49011016e-01 1.96586668e-01 -5.33552110e-01 5.99394977e-01 -1.85420349e-01 -1.35785091e+00 8.44896376e-01 2.87458390e-01 7.37052202e-01 6.36578500e-02 4.81199533e-01 -7.89990947e-02 1.76212084e+00 -7.36628592e-01 -6.82353914e-01 -3.44456106e-01 -7.27477610e-01 -1.12504661e+00 -1.75118282e-01 3.28536808e-01 -4.56032276e-01 -6.82416856e-01 6.85186982e-01 2.90512532e-01 6.30303621e-01 8.50149632e-01 -8.34629297e-01 -9.95767295e-01 1.19982922e+00 -1.75993070e-01 4.57820088e-01 1.02599967e+00 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3bf98cde-fcb9-47a9-8107-77f41e51615a	generative-category-level-shape-and-pose	2210.01112	null	https://arxiv.org/abs/2210.01112v2	https://arxiv.org/pdf/2210.01112v2.pdf	Generative Category-Level Shape and Pose Estimation with Semantic Primitives	Empowering autonomous agents with 3D understanding for daily objects is a grand challenge in robotics applications. When exploring in an unknown environment, existing methods for object pose estimation are still not satisfactory due to the diversity of object shapes. In this paper, we propose a novel framework for category-level object shape and pose estimation from a single RGB-D image. To handle the intra-category variation, we adopt a semantic primitive representation that encodes diverse shapes into a unified latent space, which is the key to establish reliable correspondences between observed point clouds and estimated shapes. Then, by using a SIM(3)-invariant shape descriptor, we gracefully decouple the shape and pose of an object, thus supporting latent shape optimization of target objects in arbitrary poses. Extensive experiments show that the proposed method achieves SOTA pose estimation performance and better generalization in the real-world dataset. Code and video are available at https://zju3dv.github.io/gCasp.	['Guofeng Zhang', 'Zhaopeng Cui', 'Tao Kong', 'Qihang Zhang', 'Zhichao Ye', 'Yifeng Li', 'Guanglin Li']	2022-10-03	null	null	null	null	['6d-pose-estimation-using-rgbd']	['computer-vision']	[-2.51980931e-01 -2.17692077e-01 -7.76625723e-02 -4.52116072e-01 -5.15439332e-01 -7.96832025e-01 6.13840401e-01 -1.39687464e-01 -1.39763728e-01 1.79923669e-01 -8.07559874e-04 2.08153650e-01 -1.85099095e-01 -6.21384501e-01 -6.53301835e-01 -7.83152580e-01 3.00825536e-01 9.92420256e-01 3.08315367e-01 -8.30323994e-02 1.73271596e-01 8.46893132e-01 -1.52036631e+00 -2.33465046e-01 6.84369862e-01 1.14774072e+00 5.88108122e-01 1.20711647e-01 -5.04851118e-02 5.68934157e-02 -2.51394629e-01 -4.71008532e-02 4.87127990e-01 3.34762186e-01 -3.26407701e-01 6.40126646e-01 3.54792595e-01 -4.46035802e-01 -3.07065129e-01 1.24877667e+00 1.65910840e-01 -3.85061663e-04 7.41026580e-01 -1.48117340e+00 -4.71576810e-01 1.07692950e-03 -3.83331150e-01 -3.72368485e-01 4.02861387e-01 1.41090930e-01 8.04323077e-01 -1.10407722e+00 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7be1c37b-4790-49c1-bb03-5426cc13e292	improving-graph-representation-for-point	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Improving_Graph_Representation_for_Point_Cloud_Segmentation_via_Attentive_Filtering_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Improving_Graph_Representation_for_Point_Cloud_Segmentation_via_Attentive_Filtering_CVPR_2023_paper.pdf	Improving Graph Representation for Point Cloud Segmentation via Attentive Filtering	Recently, self-attention networks achieve impressive performance in point cloud segmentation due to their superiority in modeling long-range dependencies. However, compared to self-attention mechanism, we find graph convolutions show a stronger ability in capturing local geometry information with less computational cost. In this paper, we employ a hybrid architecture design to construct our Graph Convolution Network with Attentive Filtering (AF-GCN), which takes advantage of both graph convolution and self-attention mechanism. We adopt graph convolutions to aggregate local features in the shallow encoder stages, while in the deeper stages, we propose a self-attention-like module named Graph Attentive Filter (GAF) to better model long-range contexts from distant neighbors. Besides, to further improve graph representation for point cloud segmentation, we employ a Spatial Feature Projection (SFP) module for graph convolutions which helps to handle spatial variations of unstructured point clouds. Finally, a graph-shared down-sampling and up-sampling strategy is introduced to make full use of the graph structures in point cloud processing. We conduct extensive experiments on multiple datasets including S3DIS, ScanNetV2, Toronto-3D, and ShapeNetPart. Experimental results show our AF-GCN obtains competitive performance.	['Ge Li', 'Wei Gao', 'Thomas H. Li', 'Zhiyi Pan', 'Nan Zhang']	2023-01-01	null	null	null	cvpr-2023-1	['point-cloud-segmentation']	['computer-vision']	[-3.06788385e-01 1.37804687e-01 1.71065435e-01 -6.13537610e-01 -1.37738794e-01 -1.35799035e-01 1.90036729e-01 9.59453732e-03 -5.98961823e-02 2.01879278e-01 1.21075593e-01 -4.63963002e-01 -4.04546736e-03 -1.36995316e+00 -1.07270980e+00 -3.62134963e-01 -2.35799044e-01 2.29894802e-01 4.02297556e-01 -1.36249036e-01 1.05755188e-01 1.00998628e+00 -1.08259177e+00 1.94653064e-01 1.05529666e+00 9.71973121e-01 5.35447776e-01 5.37608385e-01 -6.97540760e-01 6.67486191e-01 -1.69391721e-01 -4.01156545e-01 2.41259247e-01 7.40014985e-02 -8.01014543e-01 1.94831729e-01 5.17478645e-01 -4.65196073e-01 -7.03753769e-01 1.16138840e+00 3.89566928e-01 3.03281993e-01 1.86332852e-01 -1.16286731e+00 -1.15447021e+00 6.09941542e-01 -6.72616959e-01 3.03891957e-01 6.31823242e-02 4.25996751e-01 1.02767611e+00 -8.72133911e-01 3.01342785e-01 1.60323465e+00 7.10985482e-01 1.89722419e-01 -7.29894400e-01 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73b5f8d8-6b03-41ff-8ff4-8ecd3c495607	centralised-rehearsal-of-decentralised	2305.18875	null	https://arxiv.org/abs/2305.18875v2	https://arxiv.org/pdf/2305.18875v2.pdf	Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility	This paper investigates how deep multi-agent reinforcement learning can enable the scalable and privacy-preserving coordination of residential energy flexibility. The coordination of distributed resources such as electric vehicles and heating will be critical to the successful integration of large shares of renewable energy in our electricity grid and, thus, to help mitigate climate change. The pre-learning of individual reinforcement learning policies can enable distributed control with no sharing of personal data required during execution. However, previous approaches for multi-agent reinforcement learning-based distributed energy resources coordination impose an ever greater training computational burden as the size of the system increases. We therefore adopt a deep multi-agent actor-critic method which uses a \emph{centralised but factored critic} to rehearse coordination ahead of execution. Results show that coordination is achieved at scale, with minimal information and communication infrastructure requirements, no interference with daily activities, and privacy protection. Significant savings are obtained for energy users, the distribution network and greenhouse gas emissions. Moreover, training times are nearly 40 times shorter than with a previous state-of-the-art reinforcement learning approach without the factored critic for 30 homes.	['Malcolm McCulloch', 'Thomas Morstyn', 'Bei Peng', 'Flora Charbonnier']	2023-05-30	null	null	null	null	['multi-agent-reinforcement-learning']	['methodology']	[-6.54405594e-01 4.07559961e-01 -1.45743743e-01 -2.46326357e-01 -6.32374406e-01 -7.66378641e-01 3.91873538e-01 2.12469697e-01 -5.63149810e-01 1.43740129e+00 -4.16462608e-02 -2.24129125e-01 -2.02135652e-01 -1.21226442e+00 -3.62943739e-01 -1.06653297e+00 -1.45351514e-01 5.11735141e-01 -3.82832140e-01 -1.06921107e-01 -4.70032543e-01 4.61708754e-01 -1.07006669e+00 -4.69142526e-01 7.05489039e-01 9.87215340e-01 -1.54226527e-01 6.26057744e-01 5.03469586e-01 8.65494668e-01 -8.08230877e-01 2.31684014e-01 5.97298384e-01 -1.59607768e-01 -6.92929924e-01 4.88991179e-02 -3.22721243e-01 -1.10905111e+00 -1.68344557e-01 8.60252321e-01 9.18087780e-01 4.48638856e-01 1.95360154e-01 -1.90044522e+00 -3.34741235e-01 7.22200990e-01 -3.90232831e-01 -3.42518330e-01 1.48758203e-01 4.32418823e-01 9.90061283e-01 2.34298632e-01 9.73942280e-02 5.65304518e-01 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d017d610-8938-4ba8-aa94-da62b8115f70	pubchemqc-b3lyp-6-31g-pm6-dataset-the	2305.18454	null	https://arxiv.org/abs/2305.18454v1	https://arxiv.org/pdf/2305.18454v1.pdf	PubChemQC B3LYP/6-31G//PM6 dataset: the Electronic Structures of 86 Million Molecules using B3LYP/6-31G calculations	This article presents the "PubChemQC B3LYP/6-31G//PM6" dataset, containing electronic properties of 85,938,443 molecules. It includes orbitals, orbital energies, total energies, dipole moments, and other relevant properties. The dataset encompasses a wide range of molecules, from essential compounds to biomolecules up to 1000 molecular weight, covering 94.0% of the original PubChem Compound catalog (as of August 29, 2016). The electronic properties were calculated using the B3LYP/6-31G and PM6 methods. The dataset is available in three formats: (i) GAMESS quantum chemistry program files, (ii) selected JSON output files, and (iii) a PostgreSQL database, enabling researchers to query molecular properties. Five sub-datasets offer more specific data. The first two subsets include molecules with C, H, O, and N, under 300 and 500 molecular weight respectively. The third and fourth subsets contain C, H, N, O, P, S, F, and Cl, under 300 and 500 molecular weight respectively. The fifth subset includes C, H, N, O, P, S, F, Cl, Na, K, Mg, and Ca, under 500 molecular weight. Coefficients of determination ranged from 0.892 (CHON500) to 0.803 (whole) for the HOMO-LUMO energy gap. These findings represent extensive investigations and can be utilized for drug discovery, material science, and other applications. The datasets are available under the Creative Commons Attribution 4.0 International license at https://nakatamaho.riken.jp/pubchemqc.riken.jp/b3lyp_pm6_datasets.html.	['Toshiyuki Maeda', 'Maho Nakata']	2023-05-29	null	null	null	null	['drug-discovery']	['medical']	[-3.82272489e-02 -3.93858761e-01 -6.12946272e-01 2.71900445e-01 -5.73231101e-01 -4.24258620e-01 2.08477050e-01 8.54802132e-01 -1.31595537e-01 1.49875522e+00 1.45113289e-01 -4.43638206e-01 2.00667642e-02 -9.56843257e-01 -4.27248120e-01 -1.27402604e+00 -2.73394346e-01 8.12582001e-02 1.54355407e-01 -1.59552053e-01 6.58796251e-01 7.36371696e-01 -1.40408194e+00 2.36984968e-01 1.15987194e+00 1.02082372e+00 2.49002293e-01 1.60958320e-01 1.69994295e-01 3.99420023e-01 -2.92279601e-01 -2.08998144e-01 -5.64327538e-02 -3.86828423e-01 -7.56534100e-01 -4.07885283e-01 -5.26488423e-02 -5.99706024e-02 -1.63369119e-01 1.19018936e+00 7.63558030e-01 1.79969624e-01 9.17725623e-01 -9.45384324e-01 -9.35939848e-01 3.94286960e-02 -6.46055877e-01 7.34453872e-02 7.33251631e-01 5.47369063e-01 1.04647136e+00 -9.97010469e-01 7.19977915e-01 8.91086519e-01 2.33792394e-01 2.78899550e-01 -1.03580594e+00 -1.08589447e+00 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-3.57225374e-03 7.84789920e-02 -7.32988358e-01 -3.13968450e-01 -2.00809568e-01]	[5.070320129394531, 5.486292839050293]
16fe0955-7556-4082-979c-d13c2ffd7227	few-shot-video-object-detection	2104.14805	null	https://arxiv.org/abs/2104.14805v3	https://arxiv.org/pdf/2104.14805v3.pdf	Few-Shot Video Object Detection	We introduce Few-Shot Video Object Detection (FSVOD) with three contributions to real-world visual learning challenge in our highly diverse and dynamic world: 1) a large-scale video dataset FSVOD-500 comprising of 500 classes with class-balanced videos in each category for few-shot learning; 2) a novel Tube Proposal Network (TPN) to generate high-quality video tube proposals for aggregating feature representation for the target video object which can be highly dynamic; 3) a strategically improved Temporal Matching Network (TMN+) for matching representative query tube features with better discriminative ability thus achieving higher diversity. Our TPN and TMN+ are jointly and end-to-end trained. Extensive experiments demonstrate that our method produces significantly better detection results on two few-shot video object detection datasets compared to image-based methods and other naive video-based extensions. Codes and datasets are released at \url{https://github.com/fanq15/FewX}.	['Yu-Wing Tai', 'Chi-Keung Tang', 'Qi Fan']	2021-04-30	null	null	null	null	['few-shot-video-object-detection']	['computer-vision']	[-1.12482414e-01 -5.44572234e-01 -5.32003939e-01 -1.53210074e-01 -1.00760412e+00 -4.22390312e-01 4.07685429e-01 -5.75218618e-01 -2.66271263e-01 2.79930055e-01 4.54966843e-01 4.18132395e-02 2.45969161e-01 -3.27252358e-01 -9.10802722e-01 -3.01849574e-01 -4.84598905e-01 3.02430749e-01 9.60773647e-01 1.24108367e-01 1.17640108e-01 1.75011680e-01 -1.68176937e+00 6.48078382e-01 1.92945033e-01 1.33908761e+00 5.69533825e-01 1.02553952e+00 1.51816025e-01 1.22106075e+00 -1.38747379e-01 -1.88551426e-01 8.16901922e-01 -3.88486922e-01 -5.45250654e-01 5.01646638e-01 1.07717752e+00 -8.77947152e-01 -1.00721395e+00 8.24099004e-01 6.67696178e-01 4.05290842e-01 6.51421130e-01 -1.46221256e+00 -2.93071568e-01 5.14356196e-01 -9.34079945e-01 1.01452434e+00 5.20012379e-01 8.46468508e-01 1.00443316e+00 -1.19640815e+00 1.16287851e+00 1.30758345e+00 5.51424921e-01 8.18913937e-01 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-5.57249129e-01 5.17243922e-01 3.00825387e-01 -7.62017131e-01 -2.66759396e-01 1.58040106e-01]	[8.790526390075684, 0.8481943011283875]
0c01620f-7aa0-441e-8755-ea86b942ced6	stitch-it-in-time-gan-based-facial-editing-of	2201.08361	null	https://arxiv.org/abs/2201.08361v2	https://arxiv.org/pdf/2201.08361v2.pdf	Stitch it in Time: GAN-Based Facial Editing of Real Videos	The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing. However, replicating their success with videos has proven challenging. Sets of high-quality facial videos are lacking, and working with videos introduces a fundamental barrier to overcome - temporal coherency. We propose that this barrier is largely artificial. The source video is already temporally coherent, and deviations from this state arise in part due to careless treatment of individual components in the editing pipeline. We leverage the natural alignment of StyleGAN and the tendency of neural networks to learn low frequency functions, and demonstrate that they provide a strongly consistent prior. We draw on these insights and propose a framework for semantic editing of faces in videos, demonstrating significant improvements over the current state-of-the-art. Our method produces meaningful face manipulations, maintains a higher degree of temporal consistency, and can be applied to challenging, high quality, talking head videos which current methods struggle with.	['Daniel Cohen-Or', 'Amit H. Bermano', 'Rinon Gal', 'Ron Mokady', 'Rotem Tzaban']	2022-01-20	null	null	null	null	['facial-editing']	['computer-vision']	[ 6.24168336e-01 2.74416387e-01 7.26189315e-02 -4.39829767e-01 -4.91195828e-01 -6.78663909e-01 8.45047593e-01 -8.59578013e-01 -4.79617640e-02 6.68156862e-01 6.47175252e-01 2.61772245e-01 -3.60400341e-02 -4.08688724e-01 -9.27201569e-01 -5.62298656e-01 -2.03953639e-01 7.80621469e-02 -2.63368428e-01 -2.22193778e-01 -2.72545904e-01 4.29021984e-01 -1.58705080e+00 4.22416419e-01 4.02922034e-01 6.84539199e-01 -1.66844934e-01 6.49127722e-01 1.96731210e-01 1.03186047e+00 -4.63787079e-01 -6.04342401e-01 4.75639433e-01 -7.12344110e-01 -7.08965838e-01 3.07495862e-01 8.82787108e-01 -8.76651824e-01 -7.11825252e-01 9.45718169e-01 2.88279057e-01 1.56549916e-01 5.64795494e-01 -1.54673469e+00 -6.50999367e-01 3.13898116e-01 -4.44063544e-01 -6.96665868e-02 4.29629803e-01 3.90643984e-01 8.86342883e-01 -7.89374530e-01 1.04686427e+00 1.52674830e+00 8.56433630e-01 9.22708929e-01 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-3.34037393e-01 1.00855649e+00 4.96665865e-01 -7.68010795e-01 -5.05048096e-01 6.81228101e-01]	[12.585700988769531, -0.3156823515892029]
804d321e-5d24-4211-90cf-e0d42b2a18bb	real-time-multi-object-tracking-based-on-bi	2303.08444	null	https://arxiv.org/abs/2303.08444v1	https://arxiv.org/pdf/2303.08444v1.pdf	Real-time Multi-Object Tracking Based on Bi-directional Matching	In recent years, anchor-free object detection models combined with matching algorithms are used to achieve real-time muti-object tracking and also ensure high tracking accuracy. However, there are still great challenges in multi-object tracking. For example, when most part of a target is occluded or the target just disappears from images temporarily, it often leads to tracking interruptions for most of the existing tracking algorithms. Therefore, this study offers a bi-directional matching algorithm for multi-object tracking that makes advantage of bi-directional motion prediction information to improve occlusion handling. A stranded area is used in the matching algorithm to temporarily store the objects that fail to be tracked. When objects recover from occlusions, our method will first try to match them with objects in the stranded area to avoid erroneously generating new identities, thus forming a more continuous trajectory. Experiments show that our approach can improve the multi-object tracking performance in the presence of occlusions. In addition, this study provides an attentional up-sampling module that not only assures tracking accuracy but also accelerates training speed. In the MOT17 challenge, the proposed algorithm achieves 63.4% MOTA, 55.3% IDF1, and 20.1 FPS tracking speed.	['Zehua Zeng', 'Huilan Luo']	2023-03-15	null	null	null	null	['motion-prediction', 'occlusion-handling', 'real-time-multi-object-tracking']	['computer-vision', 'computer-vision', 'computer-vision']	[-3.28768045e-02 -7.01674998e-01 -3.06396514e-01 1.09024987e-01 -3.30447704e-01 -4.28172737e-01 1.22237787e-01 3.73795666e-02 -4.43299323e-01 6.76302850e-01 -3.54646474e-01 -1.21685781e-01 2.06931204e-01 -7.64294744e-01 -8.59163463e-01 -7.46490836e-01 -3.57243605e-02 5.12367666e-01 1.23651218e+00 2.31129289e-01 -1.36972945e-02 6.38180852e-01 -1.76597142e+00 9.80007574e-02 7.29056299e-01 9.96182740e-01 4.30944800e-01 5.95909774e-01 -2.47859895e-01 3.61744791e-01 -8.19786966e-01 -8.01750347e-02 3.14675629e-01 8.78799930e-02 -2.66303420e-01 -6.45788088e-02 7.77089298e-01 -5.76875389e-01 -1.88739240e-01 9.63584006e-01 4.29282248e-01 2.82007381e-02 2.29802519e-01 -1.48820150e+00 -1.55851409e-01 2.63011903e-01 -9.36562836e-01 3.85918856e-01 1.31276384e-01 2.67419934e-01 4.14800763e-01 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97d41055-ba38-4778-9bbd-a88caabd086c	action-attending-graphic-neural-network	1711.06427	null	http://arxiv.org/abs/1711.06427v1	http://arxiv.org/pdf/1711.06427v1.pdf	Action-Attending Graphic Neural Network	The motion analysis of human skeletons is crucial for human action recognition, which is one of the most active topics in computer vision. In this paper, we propose a fully end-to-end action-attending graphic neural network (A$^2$GNN) for skeleton-based action recognition, in which each irregular skeleton is structured as an undirected attribute graph. To extract high-level semantic representation from skeletons, we perform the local spectral graph filtering on the constructed attribute graphs like the standard image convolution operation. Considering not all joints are informative for action analysis, we design an action-attending layer to detect those salient action units (AUs) by adaptively weighting skeletal joints. Herein the filtering responses are parameterized into a weighting function irrelevant to the order of input nodes. To further encode continuous motion variations, the deep features learnt from skeletal graphs are gathered along consecutive temporal slices and then fed into a recurrent gated network. Finally, the spectral graph filtering, action-attending and recurrent temporal encoding are integrated together to jointly train for the sake of robust action recognition as well as the intelligibility of human actions. To evaluate our A$^2$GNN, we conduct extensive experiments on four benchmark skeleton-based action datasets, including the large-scale challenging NTU RGB+D dataset. The experimental results demonstrate that our network achieves the state-of-the-art performances.	['Jian Yang', 'Zhen Cui', 'Wenming Zheng', 'Rongrong Ji', 'Chunyan Xu', 'Chaolong Li']	2017-11-17	null	null	null	null	['action-analysis']	['computer-vision']	[ 6.15968525e-01 1.35527298e-01 -1.54267490e-01 -2.89413452e-01 -5.09949088e-01 1.39253572e-01 2.93922991e-01 -5.16237676e-01 -3.52668315e-01 2.16657028e-01 6.60399735e-01 2.34235480e-01 -1.38031960e-01 -5.93663275e-01 -6.68528140e-01 -8.44508708e-01 -1.15129486e-01 -6.17629960e-02 5.56470811e-01 -1.15017660e-01 1.29424378e-01 4.34146404e-01 -1.48870385e+00 5.86004734e-01 4.99740392e-01 1.22326374e+00 8.27283487e-02 4.71804172e-01 -8.32237527e-02 1.26955593e+00 -3.41144204e-01 -2.19196472e-02 1.95407700e-02 -7.70959020e-01 -7.89585054e-01 6.60302579e-01 2.39205226e-01 -4.34818983e-01 -8.00465107e-01 1.02211738e+00 5.70608199e-01 5.39089501e-01 4.71730888e-01 -8.62675428e-01 -6.36573195e-01 5.09556711e-01 -7.46000051e-01 3.89117956e-01 4.73009229e-01 5.85384429e-01 1.10130894e+00 -7.91093886e-01 6.27912581e-01 1.56087554e+00 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79a140aa-7dff-4857-b571-4178ace25c4d	grace-generation-using-associated-code-edits	2305.14129	null	https://arxiv.org/abs/2305.14129v2	https://arxiv.org/pdf/2305.14129v2.pdf	GrACE: Generation using Associated Code Edits	Developers expend a significant amount of time in editing code for a variety of reasons such as bug fixing or adding new features. Designing effective methods to predict code edits has been an active yet challenging area of research due to the diversity of code edits and the difficulty of capturing the developer intent. In this work, we address these challenges by endowing pre-trained large language models (LLMs) of code with the knowledge of prior, relevant edits. The generative capability of the LLMs helps address the diversity in code changes and conditioning code generation on prior edits helps capture the latent developer intent. We evaluate two well-known LLMs, Codex and CodeT5, in zero-shot and fine-tuning settings respectively. In our experiments with two datasets, the knowledge of prior edits boosts the performance of the LLMs significantly and enables them to generate 29% and 54% more correctly edited code in top-1 suggestions relative to the current state-of-the-art symbolic and neural approaches, respectively.	['Ashish Tiwari', 'Gustavo Soares', 'Arjun Radhakrishna', 'Aditya Kanade', 'Sumit Gulwani', 'Saikat Chakraborty', 'Yasharth Bajpai', 'Avishree Khare', 'Priyanshu Gupta']	2023-05-23	null	null	null	null	['code-generation']	['computer-code']	[ 2.34392419e-01 2.06327051e-01 -1.76281929e-01 -5.06458580e-01 -6.58903420e-01 -5.38075566e-01 4.68191087e-01 3.97803158e-01 1.77582100e-01 2.68199623e-01 2.33985886e-01 -2.15360820e-01 -3.43000442e-02 -4.28925574e-01 -8.76842082e-01 2.02532277e-01 -4.70052548e-02 7.78723657e-02 1.36414468e-01 -2.74441183e-01 7.05093801e-01 -2.10946247e-01 -1.63632309e+00 4.17254716e-01 1.43626857e+00 4.19964343e-01 6.22742057e-01 7.91573882e-01 -3.78884017e-01 1.08533084e+00 -4.33112979e-01 -6.64701581e-01 7.02687278e-02 -3.38212997e-01 -6.57060862e-01 -3.32107902e-01 4.75876063e-01 -2.01895311e-01 3.78700979e-02 1.16797042e+00 2.37753242e-01 -8.48489702e-02 6.02618277e-01 -1.01115716e+00 -1.06883371e+00 1.01056623e+00 -6.87503874e-01 9.87013206e-02 5.30912697e-01 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d1c73a1b-1e6b-4d1a-b7ea-1fb18f945c80	sentiment-predictability-for-stocks	1712.05785	null	http://arxiv.org/abs/1712.05785v2	http://arxiv.org/pdf/1712.05785v2.pdf	Sentiment Predictability for Stocks	In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific convolutional architectures.	['Xingyou Song', 'Jordan Prosky', 'Michael Zhao', 'Andrew Tan']	2017-12-15	null	null	null	null	['stock-market-prediction']	['time-series']	[-5.33682406e-01 -1.65437222e-01 -2.96776444e-01 -6.55681789e-01 -7.77437761e-02 -5.84465206e-01 6.37225688e-01 4.56510514e-01 -4.53489184e-01 9.20250177e-01 6.33985579e-01 -6.15067720e-01 4.01741594e-01 -1.06560838e+00 -1.70538977e-01 -8.97031948e-02 -3.04512411e-01 -4.31490950e-02 3.16331498e-02 -6.12935483e-01 5.99892557e-01 5.22262752e-01 -1.10623038e+00 4.64512110e-01 -3.02001387e-02 1.89682961e+00 -7.17549324e-01 6.05607450e-01 -6.79447591e-01 1.97500980e+00 -8.15610588e-01 -6.56295300e-01 -7.87580237e-02 1.27805546e-01 -5.33437669e-01 -4.83879417e-01 -2.74508506e-01 -2.65945822e-01 -4.33508635e-01 9.01527405e-01 2.53138721e-01 1.31354347e-01 3.18964183e-01 -9.79169190e-01 -8.43640804e-01 1.19804049e+00 -4.31124419e-01 6.58844411e-01 1.96741506e-01 -1.98215649e-01 1.17807496e+00 -9.26764727e-01 4.85549539e-01 8.16481352e-01 9.01031792e-01 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45f98af3-2753-4bb1-ab1c-cc8d91d5dcbe	a-survey-on-deep-learning-and-explainability	2010.10563	null	https://arxiv.org/abs/2010.10563v2	https://arxiv.org/pdf/2010.10563v2.pdf	A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images	Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context, we survey works in the area of automatic report generation from medical images, with emphasis on methods using deep neural networks, with respect to: (1) Datasets, (2) Architecture Design, (3) Explainability and (4) Evaluation Metrics. Our survey identifies interesting developments, but also remaining challenges. Among them, the current evaluation of generated reports is especially weak, since it mostly relies on traditional Natural Language Processing (NLP) metrics, which do not accurately capture medical correctness.	['Daniel Capurro', 'Claudia Prieto', 'Cristian Tejos', 'Marcelo andía', 'Sergio Uribe', 'Cecilia Besa', 'Alvaro Soto', 'Denis Parra', 'Pablo Pino', 'Pablo Messina']	2020-10-20	null	null	null	null	['medical-report-generation']	['medical']	[ 4.80596572e-01 5.53383708e-01 -1.82931855e-01 -3.74630094e-01 -7.30172753e-01 -2.69046694e-01 4.66274321e-01 7.60496378e-01 -2.71868438e-01 9.88269746e-01 2.96943188e-01 -2.92917937e-01 -2.79723287e-01 -8.08455944e-01 -4.04399097e-01 -3.23866159e-01 -5.54945767e-02 6.32433534e-01 -3.29766601e-01 1.09426342e-01 2.07753167e-01 4.12596881e-01 -1.43773055e+00 5.74742138e-01 7.86651194e-01 1.07515335e+00 -9.84752625e-02 8.56034398e-01 -1.54478833e-01 1.41253436e+00 -9.11331236e-01 -6.89777672e-01 -4.10544574e-01 -8.00726056e-01 -9.58313704e-01 2.96102583e-01 4.31007802e-01 -2.73071945e-01 -9.16244611e-02 1.07395875e+00 3.53758514e-01 -3.56176168e-01 7.69272983e-01 -1.05466783e+00 -9.38507318e-01 7.02558279e-01 -2.00024843e-01 3.14657718e-01 3.13931435e-01 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4d413151-3357-4edc-8277-e92f2603fb12	deep-evolution-for-facial-emotion-recognition	2009.14194	null	https://arxiv.org/abs/2009.14194v2	https://arxiv.org/pdf/2009.14194v2.pdf	Deep Evolution for Facial Emotion Recognition	Deep facial expression recognition faces two challenges that both stem from the large number of trainable parameters: long training times and a lack of interpretability. We propose a novel method based on evolutionary algorithms, that deals with both challenges by massively reducing the number of trainable parameters, whilst simultaneously retaining classification performance, and in some cases achieving superior performance. We are robustly able to reduce the number of parameters on average by 95% (e.g. from 2M to 100k parameters) with no loss in classification accuracy. The algorithm learns to choose small patches from the image, relative to the nose, which carry the most important information about emotion, and which coincide with typical human choices of important features. Our work implements a novel form attention and shows that evolutionary algorithms are a valuable addition to machine learning in the deep learning era, both for reducing the number of parameters for facial expression recognition and for providing interpretable features that can help reduce bias.	['Emmanuel Dufourq', 'Bruce A. Bassett']	2020-09-29	null	null	null	null	['facial-emotion-recognition']	['computer-vision']	[ 4.52788830e-01 1.72789231e-01 1.30399242e-01 -6.74205542e-01 -6.41383290e-01 -5.36477983e-01 2.32159525e-01 -2.26014689e-01 -6.77395165e-01 6.69122279e-01 -2.69183189e-01 3.61495353e-02 -4.00030881e-01 -4.54271734e-01 -5.71965158e-01 -9.22605991e-01 -1.06444836e-01 5.45401275e-01 -4.31826681e-01 -3.70890886e-01 2.62940258e-01 8.36384892e-01 -2.06946921e+00 2.19526902e-01 7.73862123e-01 1.43389904e+00 -1.92881197e-01 3.47453803e-01 6.70262203e-02 7.75008261e-01 -5.14291346e-01 -7.09000647e-01 -1.62745845e-02 -3.38061512e-01 -7.21916497e-01 4.49583046e-02 4.55780745e-01 -8.94922316e-02 1.89631671e-01 1.00756621e+00 6.35888577e-01 2.40558848e-01 6.06507599e-01 -1.29032886e+00 -2.24789768e-01 9.58038419e-02 -4.75149542e-01 8.68637636e-02 -8.91252905e-02 1.06012933e-01 9.84013796e-01 -7.88660645e-01 5.86088598e-01 1.16045105e+00 7.44924188e-01 9.86191094e-01 -1.10043561e+00 -7.30910778e-01 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3.67759138e-01 2.21338958e-01 -7.59659111e-01 -4.12677079e-01 9.19189230e-02]	[13.488551139831543, 1.6357522010803223]
25e06e33-c68d-468d-8acb-bbaeec94872b	generating-fast-and-slow-scene-decomposition	2203.11194	null	https://arxiv.org/abs/2203.11194v3	https://arxiv.org/pdf/2203.11194v3.pdf	Test-time Adaptation with Slot-Centric Models	Current visual detectors, though impressive within their training distribution, often fail to parse out-of-distribution scenes into their constituent entities. Recent test-time adaptation methods use auxiliary self-supervised losses to adapt the network parameters to each test example independently and have shown promising results towards generalization outside the training distribution for the task of image classification. In our work, we find evidence that these losses are insufficient for the task of scene decomposition, without also considering architectural inductive biases. Recent slot-centric generative models attempt to decompose scenes into entities in a self-supervised manner by reconstructing pixels. Drawing upon these two lines of work, we propose Slot-TTA, a semi-supervised slot-centric scene decomposition model that at test time is adapted per scene through gradient descent on reconstruction or cross-view synthesis objectives. We evaluate Slot-TTA across multiple input modalities, images or 3D point clouds, and show substantial out-of-distribution performance improvements against state-of-the-art supervised feed-forward detectors, and alternative test-time adaptation methods.	['Katerina Fragkiadaki', 'Thomas Kipf', 'Gaurav Aggarwal', 'Mehdi S. M. Sajjadi', 'Sjoerd van Steenkiste', 'Sujoy Paul', 'Deepak Pathak', 'Anirudh Goyal', 'Mihir Prabhudesai']	2022-03-21	null	null	null	null	['scene-segmentation', 'semi-supervised-instance-segmentation']	['computer-vision', 'computer-vision']	[ 3.26651365e-01 1.97175145e-01 -7.24878237e-02 -7.57393241e-01 -8.70787442e-01 -6.22769356e-01 8.63268971e-01 -2.01081783e-01 -2.25424901e-01 3.79188210e-01 9.32134595e-03 -2.56305784e-01 1.14120618e-01 -7.58960545e-01 -1.15955877e+00 -5.04939914e-01 2.50817299e-01 1.02834749e+00 4.10164207e-01 2.74607092e-01 -5.36738411e-02 5.07050574e-01 -1.49423099e+00 6.89352930e-01 6.45099223e-01 8.68494272e-01 2.18166620e-01 7.23431587e-01 -1.93267584e-01 9.66884136e-01 -3.35118949e-01 -3.95879507e-01 2.83177853e-01 -4.44612622e-01 -6.20232284e-01 5.26328683e-01 1.03457499e+00 -2.19056815e-01 -3.64192069e-01 9.69151795e-01 3.68565857e-01 -4.83879074e-02 9.42366004e-01 -1.14924347e+00 -8.21533322e-01 3.13951075e-01 -4.66282010e-01 1.29281297e-01 -1.54465698e-02 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a214b09d-a438-469e-86fc-95ce0a4411b3	image-reconstruction-algorithms-in-radio	2202.12959	null	https://arxiv.org/abs/2202.12959v2	https://arxiv.org/pdf/2202.12959v2.pdf	Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers	We introduce a new class of iterative image reconstruction algorithms for radio interferometry, at the interface of convex optimization and deep learning, inspired by plug-and-play methods. The approach consists in learning a prior image model by training a deep neural network (DNN) as a denoiser, and substituting it for the handcrafted proximal regularization operator of an optimization algorithm. The proposed AIRI (``AI for Regularization in radio-interferometric Imaging'') framework, for imaging complex intensity structure with diffuse and faint emission from visibility data, inherits the robustness and interpretability of optimization, and the learning power and speed of networks. Our approach relies on three steps. Firstly, we design a low dynamic range training database from optical intensity images. Secondly, we train a DNN denoiser at a noise level inferred from the signal-to-noise ratio of the data. We use training losses enhanced with a nonexpansiveness term ensuring algorithm convergence, and including on-the-fly database dynamic range enhancement via exponentiation. Thirdly, we plug the learned denoiser into the forward-backward optimization algorithm, resulting in a simple iterative structure alternating a denoising step with a gradient-descent data-fidelity step. We have validated AIRI against CLEAN, optimization algorithms of the SARA family, and a DNN trained to reconstruct the image directly from visibility data. Simulation results show that AIRI is competitive in imaging quality with SARA and its unconstrained forward-backward-based version uSARA, while providing significant acceleration. CLEAN remains faster but offers lower quality. The end-to-end DNN offers further acceleration, but with far lower quality than AIRI.	['Yves Wiaux', 'Chao Tang', 'Arwa Dabbech', 'Matthieu Terris']	2022-02-25	null	null	null	null	['radio-interferometry']	['miscellaneous']	[ 4.68710631e-01 -8.01118165e-02 5.94570577e-01 -4.74890441e-01 -9.35867012e-01 -2.72660047e-01 3.92650843e-01 -7.87552714e-01 -7.26220846e-01 7.07916439e-01 2.08794370e-01 -3.07520598e-01 -6.64543033e-01 -6.47884369e-01 -9.58946347e-01 -1.19965363e+00 -2.24959806e-01 4.32757616e-01 -4.26639497e-01 -3.48358303e-01 -1.99660063e-01 6.77420497e-01 -1.37796664e+00 -1.91446155e-01 9.92179096e-01 1.18540764e+00 3.13175440e-01 8.72786820e-01 4.23231125e-01 9.74257588e-01 -3.52013469e-01 -1.90244555e-01 7.87904859e-01 -5.36203742e-01 -4.67855573e-01 2.46834174e-01 5.46991229e-01 -5.11622250e-01 -6.37899876e-01 1.14600945e+00 6.35923028e-01 1.22971632e-01 2.08988756e-01 -5.76999843e-01 -4.88910049e-01 2.32764885e-01 -4.75823313e-01 6.55359477e-02 -8.38880241e-02 3.81305486e-01 9.07367826e-01 -9.74559844e-01 5.60822427e-01 8.69600773e-01 1.03506017e+00 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b52c7d4a-3abd-4a78-a340-7617d842b526	deep-exhaustive-model-for-nested-named-entity	null	null	https://aclanthology.org/D18-1309	https://aclanthology.org/D18-1309.pdf	Deep Exhaustive Model for Nested Named Entity Recognition	We propose a simple deep neural model for nested named entity recognition (NER). Most NER models focused on flat entities and ignored nested entities, which failed to fully capture underlying semantic information in texts. The key idea of our model is to enumerate all possible regions or spans as potential entity mentions and classify them with deep neural networks. To reduce the computational costs and capture the information of the contexts around the regions, the model represents the regions using the outputs of shared underlying bidirectional long short-term memory. We evaluate our exhaustive model on the GENIA and JNLPBA corpora in biomedical domain, and the results show that our model outperforms state-of-the-art models on nested and flat NER, achieving 77.1{\%} and 78.4{\%} respectively in terms of F-score, without any external knowledge resources.	['Mohammad Golam Sohrab', 'Makoto Miwa']	2018-10-01	null	null	null	emnlp-2018-10	['nested-named-entity-recognition']	['natural-language-processing']	[-3.88845235e-01 4.90220010e-01 -1.84725076e-01 -3.43066216e-01 -6.98118329e-01 -5.69422007e-01 3.15782130e-01 4.99421924e-01 -1.01961923e+00 9.67349172e-01 5.50512910e-01 -2.88267285e-01 -5.09048346e-03 -1.09278226e+00 -7.60036945e-01 -3.11518192e-01 -2.22984955e-01 6.11070991e-01 1.33639783e-01 -1.53573513e-01 -5.48428558e-02 3.52725685e-01 -4.99755859e-01 5.22144377e-01 8.37843359e-01 7.17579246e-01 1.59959465e-01 4.97352332e-01 -5.77867210e-01 8.82150471e-01 -6.75531924e-01 -7.83510506e-01 -2.31850833e-01 -8.43279809e-02 -1.33157229e+00 -7.59926438e-01 -1.28952026e-01 -1.48730248e-01 -7.07175016e-01 1.01669085e+00 7.34443784e-01 2.62155473e-01 5.19280136e-01 -4.04103011e-01 -9.63534653e-01 1.13951194e+00 -1.54589102e-01 3.79087776e-01 8.37356374e-02 -4.66756433e-01 1.11472964e+00 -8.66328597e-01 1.03525782e+00 8.45381975e-01 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623c0500-f68b-4791-a383-1580d7f6cb20	task-space-control-of-robot-manipulators	2302.04163	null	https://arxiv.org/abs/2302.04163v1	https://arxiv.org/pdf/2302.04163v1.pdf	Task Space Control of Robot Manipulators based on Visual SLAM	This paper aims to address the open problem of designing a globally stable vision-based controller for robot manipulators. Accordingly, based on a hybrid mechanism, this paper proposes a novel task-space control law attained by taking the gradient of a potential function in SE(3). The key idea is to employ the Visual Simultaneous Localization and Mapping (VSLAM) algorithm to estimate a robot pose. The estimated robot pose is then used in the proposed hybrid controller as feedback information. Invoking Barbalats lemma and Lyapunov's stability theorem, it is guaranteed that the resulting closed-loop system is globally asymptotically stable, which is the main accomplishment of the proposed structure. Simulation studies are conducted on a six degrees of freedom (6-DOF) robot manipulator to demonstrate the effectiveness and validate the performance of the proposed VSLAM-based control scheme.	['Jouni Mattila', 'Seyed Hamed Hashemi']	2023-02-08	null	null	null	null	['simultaneous-localization-and-mapping']	['computer-vision']	[ 3.75587530e-02 1.73294380e-01 -2.08034426e-01 4.25286204e-01 -1.10843100e-01 -5.82285345e-01 3.80922109e-01 -2.55028993e-01 -3.31790775e-01 6.51145637e-01 -4.97834474e-01 -3.99203569e-01 -5.82831621e-01 -1.71366587e-01 -5.10181546e-01 -8.77102137e-01 3.02946776e-01 -1.41167983e-01 -5.90943620e-02 -3.58922362e-01 5.55616736e-01 7.49133766e-01 -8.71077240e-01 -1.06711137e+00 8.23272228e-01 6.90140724e-01 9.42201138e-01 5.03537357e-01 5.64444602e-01 4.29019660e-01 -1.88792214e-01 4.85927254e-01 4.97361392e-01 -3.12329143e-01 -4.03055131e-01 7.02605128e-01 -1.40119061e-01 -2.31323063e-01 -3.38197678e-01 1.39798903e+00 3.59629720e-01 4.78339881e-01 6.72852039e-01 -1.22507143e+00 -3.24123293e-01 -1.24366311e-02 -6.86133981e-01 -6.17835000e-02 9.28013995e-02 2.05313787e-01 5.20680845e-01 -9.51845586e-01 6.30692005e-01 1.28712702e+00 3.86875927e-01 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9a4346cd-8834-4aa6-864b-bca733cfc284	aqua-a-benchmarking-tool-for-label-quality	2306.09467	null	https://arxiv.org/abs/2306.09467v1	https://arxiv.org/pdf/2306.09467v1.pdf	AQuA: A Benchmarking Tool for Label Quality Assessment	Machine learning (ML) models are only as good as the data they are trained on. But recent studies have found datasets widely used to train and evaluate ML models, e.g. ImageNet, to have pervasive labeling errors. Erroneous labels on the train set hurt ML models' ability to generalize, and they impact evaluation and model selection using the test set. Consequently, learning in the presence of labeling errors is an active area of research, yet this field lacks a comprehensive benchmark to evaluate these methods. Most of these methods are evaluated on a few computer vision datasets with significant variance in the experimental protocols. With such a large pool of methods and inconsistent evaluation, it is also unclear how ML practitioners can choose the right models to assess label quality in their data. To this end, we propose a benchmarking environment AQuA to rigorously evaluate methods that enable machine learning in the presence of label noise. We also introduce a design space to delineate concrete design choices of label error detection models. We hope that our proposed design space and benchmark enable practitioners to choose the right tools to improve their label quality and that our benchmark enables objective and rigorous evaluation of machine learning tools facing mislabeled data.	['Artur Dubrawski', 'Chalisa Udompanyawit', 'Arvind Srinivasan', 'Arjun Choudhry', 'Vedant Sanil', 'Mononito Goswami']	2023-06-15	null	null	null	null	['benchmarking', 'benchmarking']	['miscellaneous', 'robots']	[ 2.82480359e-01 -1.61073133e-02 -5.42636693e-01 -7.67297387e-01 -9.35509980e-01 -8.40097129e-01 3.88354093e-01 7.01121315e-02 -5.30483961e-01 6.34282470e-01 -5.69975257e-01 -5.18183172e-01 9.21495035e-02 -3.89145344e-01 -7.05182850e-01 -3.38837862e-01 4.57650900e-01 5.69132388e-01 7.66952187e-02 3.88914764e-01 3.37205261e-01 3.08856517e-01 -1.82272530e+00 3.97857279e-01 9.54380631e-01 1.17757618e+00 -1.61221191e-01 4.50952619e-01 -1.27672300e-01 1.22471058e+00 -8.56650352e-01 -5.21390617e-01 2.23781541e-01 -2.70939887e-01 -1.02350616e+00 7.53252879e-02 1.04710102e+00 -1.25962317e-01 3.56952965e-01 1.04339576e+00 6.28944635e-01 -2.97823101e-01 7.75829375e-01 -1.76409316e+00 -6.73802912e-01 5.24059713e-01 -4.26434353e-02 -1.72813553e-02 -7.09830821e-02 5.19907951e-01 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2.73271531e-01 4.50349271e-01 5.00393391e-01 -1.72818646e-01 -9.18791056e-01 -1.12246180e+00 -3.38348061e-01 8.82583857e-01 1.13104515e-01 -9.09302652e-01 -5.83949149e-01 7.00001493e-02]	[9.39620590209961, 4.035164833068848]
8a11704a-d527-4012-9a31-05ff75e70233	05-petabyte-simulation-of-a-45-qubit-quantum	1704.01127	null	https://arxiv.org/abs/1704.01127v2	https://arxiv.org/pdf/1704.01127v2.pdf	0.5 Petabyte Simulation of a 45-Qubit Quantum Circuit	Near-term quantum computers will soon reach sizes that are challenging to directly simulate, even when employing the most powerful supercomputers. Yet, the ability to simulate these early devices using classical computers is crucial for calibration, validation, and benchmarking. In order to make use of the full potential of systems featuring multi- and many-core processors, we use automatic code generation and optimization of compute kernels, which also enables performance portability. We apply a scheduling algorithm to quantum supremacy circuits in order to reduce the required communication and simulate a 45-qubit circuit on the Cori II supercomputer using 8,192 nodes and 0.5 petabytes of memory. To our knowledge, this constitutes the largest quantum circuit simulation to this date. Our highly-tuned kernels in combination with the reduced communication requirements allow an improvement in time-to-solution over state-of-the-art simulations by more than an order of magnitude at every scale.	['Thomas Häner', 'Damian S. Steiger']	2017-04-04	null	null	null	null	['image-outpainting', 'image-relighting']	['computer-vision', 'computer-vision']	[ 1.09856658e-01 -2.68754750e-01 4.44856614e-01 -9.05737206e-02 -7.65473187e-01 -7.81581283e-01 4.59001839e-01 3.44711930e-01 -6.32941604e-01 9.33086276e-01 -5.22028983e-01 -9.51150179e-01 -5.07377796e-02 -1.09642851e+00 -4.05970335e-01 -7.61952400e-01 -1.96243554e-01 8.28902245e-01 2.66858518e-01 -5.36720932e-01 4.06048536e-01 5.84952414e-01 -1.39479280e+00 7.03339800e-02 7.91134834e-01 7.14750767e-01 -1.29113495e-01 8.02458167e-01 4.11896765e-01 2.84958601e-01 -3.59599799e-01 -1.25681058e-01 2.29759201e-01 -6.28024876e-01 -8.81821215e-01 -7.72345066e-01 -9.07465070e-02 -1.97121322e-01 -7.38806963e-01 1.03826654e+00 6.03832841e-01 1.53484881e-01 1.73060343e-01 -8.37421238e-01 3.91159505e-02 5.92016578e-01 -8.56300443e-02 2.95339227e-01 1.56925365e-01 5.44624031e-01 8.02868724e-01 -1.81975607e-02 6.42001688e-01 6.80103242e-01 4.85177577e-01 3.03903729e-01 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d254d099-caf9-4a45-8a22-6c521f001b71	self-supervised-deformation-modeling-for	1911.00735	null	https://arxiv.org/abs/1911.00735v2	https://arxiv.org/pdf/1911.00735v2.pdf	Self-supervised Deformation Modeling for Facial Expression Editing	Recent advances in deep generative models have demonstrated impressive results in photo-realistic facial image synthesis and editing. Facial expressions are inherently the result of muscle movement. However, existing neural network-based approaches usually only rely on texture generation to edit expressions and largely neglect the motion information. In this work, we propose a novel end-to-end network that disentangles the task of facial editing into two steps: a " "motion-editing" step and a "texture-editing" step. In the "motion-editing" step, we explicitly model facial movement through image deformation, warping the image into the desired expression. In the "texture-editing" step, we generate necessary textures, such as teeth and shading effects, for a photo-realistic result. Our physically-based task-disentanglement system design allows each step to learn a focused task, removing the need of generating texture to hallucinate motion. Our system is trained in a self-supervised manner, requiring no ground truth deformation annotation. Using Action Units [8] as the representation for facial expression, our method improves the state-of-the-art facial expression editing performance in both qualitative and quantitative evaluations.	['Dimitris Samaras', 'Zhixin Shu', 'ShahRukh Athar']	2019-11-02	null	null	null	null	['facial-editing']	['computer-vision']	[ 3.02447021e-01 2.71543771e-01 7.95044154e-02 -6.91341579e-01 -5.78928709e-01 -3.26205820e-01 6.41444266e-01 -7.11556792e-01 -1.36285588e-01 6.09296679e-01 2.60554463e-01 1.46761805e-01 5.47768354e-01 -7.78714001e-01 -9.24984097e-01 -7.66882420e-01 4.38275605e-01 2.65868008e-01 -3.21876526e-01 -3.48288596e-01 -2.40947798e-01 6.71706378e-01 -1.32748413e+00 3.55721831e-01 6.08834565e-01 9.04743314e-01 -1.16188250e-01 6.57784760e-01 -1.35780737e-01 8.84861648e-01 -5.02611101e-01 -4.27427053e-01 1.09365918e-01 -8.09088171e-01 -7.02011526e-01 4.14621651e-01 5.78780532e-01 -6.64262712e-01 -3.93087357e-01 9.07994688e-01 6.13551438e-01 1.05257928e-01 5.80700517e-01 -1.24414194e+00 -9.95214164e-01 4.05155085e-02 -7.55711317e-01 -6.42544806e-01 2.98726946e-01 2.32943684e-01 4.87826645e-01 -1.09162831e+00 9.55985487e-01 1.41130841e+00 5.12755036e-01 1.22311497e+00 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5dc7e187-b0a2-48c3-a55f-4bd75bd6346f	lightts-lightweight-time-series	2302.12721	null	https://arxiv.org/abs/2302.12721v1	https://arxiv.org/pdf/2302.12721v1.pdf	LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Version	Due to the sweeping digitalization of processes, increasingly vast amounts of time series data are being produced. Accurate classification of such time series facilitates decision making in multiple domains. State-of-the-art classification accuracy is often achieved by ensemble learning where results are synthesized from multiple base models. This characteristic implies that ensemble learning needs substantial computing resources, preventing their use in resource-limited environments, such as in edge devices. To extend the applicability of ensemble learning, we propose the LightTS framework that compresses large ensembles into lightweight models while ensuring competitive accuracy. First, we propose adaptive ensemble distillation that assigns adaptive weights to different base models such that their varying classification capabilities contribute purposefully to the training of the lightweight model. Second, we propose means of identifying Pareto optimal settings w.r.t. model accuracy and model size, thus enabling users with a space budget to select the most accurate lightweight model. We report on experiments using 128 real-world time series sets and different types of base models that justify key decisions in the design of LightTS and provide evidence that LightTS is able to outperform competitors.	['Christian S. Jensen', 'Chenjuan Guo', 'Tung Kieu', 'Bin Yang', 'Miao Zhang', 'David Campos']	2023-02-24	null	null	null	null	['time-series-classification']	['time-series']	[ 3.53386849e-01 -5.96291125e-01 -1.15183592e-01 -4.06467915e-01 -6.14545286e-01 -5.93324959e-01 6.04995191e-01 3.11843395e-01 -3.43734175e-01 6.58771813e-01 -2.29146689e-01 -4.96420443e-01 -6.38977945e-01 -6.56899095e-01 -2.16436356e-01 -7.16941953e-01 -3.57431471e-01 6.30318224e-01 -2.08915338e-01 -1.69418320e-01 4.62149471e-01 4.82007295e-01 -1.92862976e+00 4.04089481e-01 1.17867553e+00 1.33466744e+00 -8.20958689e-02 9.66020584e-01 -1.14402257e-01 4.97222751e-01 -8.90934348e-01 -3.36011857e-01 4.57742035e-01 -2.74254888e-01 -1.78874522e-01 -2.97088474e-01 2.02198982e-01 -6.23479001e-02 1.15480728e-01 3.41544688e-01 6.24055743e-01 1.74559832e-01 6.65774465e-01 -1.62588060e+00 -4.63532731e-02 6.71802938e-01 -2.16029376e-01 4.56230253e-01 -5.08911768e-03 2.30535999e-01 9.06010509e-01 -5.42348385e-01 1.72560558e-01 8.66658092e-01 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-9.71618235e-01 -9.82340336e-01 -2.94482797e-01 2.04183117e-01 1.23235047e-01 -7.58986175e-01 -3.27803284e-01 1.35914817e-01]	[7.269883632659912, 3.0792715549468994]
6399a3c1-98e2-448a-86bb-95381979334b	bronchoscopic-video-synchronization-for	2303.11258	null	https://arxiv.org/abs/2303.11258v1	https://arxiv.org/pdf/2303.11258v1.pdf	Bronchoscopic video synchronization for interactive multimodal inspection of bronchial lesions	With lung cancer being the most fatal cancer worldwide, it is important to detect the disease early. A potentially effective way of detecting early cancer lesions developing along the airway walls (epithelium) is bronchoscopy. To this end, developments in bronchoscopy offer three promising noninvasive modalities for imaging bronchial lesions: white-light bronchoscopy (WLB), autofluorescence bronchoscopy (AFB), and narrow-band imaging (NBI). While these modalities give complementary views of the airway epithelium, the physician must manually inspect each video stream produced by a given modality to locate the suspect cancer lesions. Unfortunately, no effort has been made to rectify this situation by providing efficient quantitative and visual tools for analyzing these video streams. This makes the lesion search process extremely time-consuming and error-prone, thereby making it impractical to utilize these rich data sources effectively. We propose a framework for synchronizing multiple bronchoscopic videos to enable an interactive multimodal analysis of bronchial lesions. Our methods first register the video streams to a reference 3D chest computed-tomography (CT) scan to produce multimodal linkages to the airway tree. Our methods then temporally correlate the videos to one another to enable synchronous visualization of the resulting multimodal data set. Pictorial and quantitative results illustrate the potential of the methods.	['William E. Higgins', 'Rebecca Bascom', 'Jennifer Toth', 'Danish Ahmad', 'Patrick D. Byrnes', 'Qi Chang']	2023-03-20	null	null	null	null	['video-synchronization']	['computer-vision']	[ 3.94335926e-01 -2.39211395e-01 -4.41920668e-01 2.30426550e-01 -8.79249454e-01 -9.28599358e-01 4.59255099e-01 3.13190550e-01 -2.46523291e-01 3.86110216e-01 -3.83877382e-02 -7.54049659e-01 4.90993224e-02 -6.91471517e-01 -2.03587219e-01 -8.41039240e-01 1.49745777e-01 3.01564515e-01 7.67941773e-01 2.73009956e-01 -8.30455944e-02 9.60165441e-01 -1.21016622e+00 4.54292983e-01 4.10845101e-01 5.86002648e-01 6.36964619e-01 1.32771516e+00 -3.54421675e-01 6.46235883e-01 -4.70593303e-01 -2.34343231e-01 2.42776796e-02 -6.04670703e-01 -7.29789317e-01 2.82428116e-01 3.58882189e-01 -5.55418730e-01 -2.34183148e-01 8.85912001e-01 2.51327932e-01 -1.52877957e-01 5.17669678e-01 -8.30969870e-01 1.47181362e-01 -1.97723687e-01 -5.73549986e-01 6.74818456e-01 7.00866401e-01 1.63022906e-01 5.19027650e-01 -7.30720758e-01 8.55517745e-01 9.13301229e-01 4.72296745e-01 7.50190735e-01 -8.83078933e-01 -2.04633847e-01 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4.93181169e-01 5.85423768e-01 -3.08161855e-01 -5.84249735e-01 4.76216227e-01]	[15.268305778503418, -2.1613807678222656]
e2024bf1-86ff-48e8-a5aa-f8aa94641e3f	visual-attention-consistency-under-image	null	null	http://openaccess.thecvf.com/content_CVPR_2019/html/Guo_Visual_Attention_Consistency_Under_Image_Transforms_for_Multi-Label_Image_Classification_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Guo_Visual_Attention_Consistency_Under_Image_Transforms_for_Multi-Label_Image_Classification_CVPR_2019_paper.pdf	Visual Attention Consistency Under Image Transforms for Multi-Label Image Classification	Human visual perception shows good consistency for many multi-label image classification tasks under certain spatial transforms, such as scaling, rotation, flipping and translation. This has motivated the data augmentation strategy widely used in CNN classifier training -- transformed images are included for training by assuming the same class labels as their original images. In this paper, we further propose the assumption of perceptual consistency of visual attention regions for classification under such transforms, i.e., the attention region for a classification follows the same transform if the input image is spatially transformed. While the attention regions of CNN classifiers can be derived as an attention heatmap in middle layers of the network, we find that their consistency under many transforms are not preserved. To address this problem, we propose a two-branch network with an original image and its transformed image as inputs and introduce a new attention consistency loss that measures the attention heatmap consistency between two branches. This new loss is then combined with multi-label image classification loss for network training. Experiments on three datasets verify the superiority of the proposed network by achieving new state-of-the-art classification performance.	[' Song Wang', ' Hongkai Yu', ' Xiaochuan Fan', ' Kang Zheng', 'Hao Guo']	2019-06-01	null	null	null	cvpr-2019-6	['multi-label-image-classification']	['computer-vision']	[ 5.33910871e-01 4.82426994e-02 -2.49011397e-01 -6.93898559e-01 -3.21652949e-01 -4.06635553e-01 3.83365482e-01 1.45504355e-01 -5.37589788e-01 5.25264621e-01 -2.18036294e-01 -1.24962211e-01 5.24736680e-02 -5.46841502e-01 -8.87655616e-01 -8.20677221e-01 6.10355794e-01 -1.92560442e-02 3.37189883e-01 1.19134583e-01 1.94419518e-01 2.75951445e-01 -1.53579986e+00 4.67969358e-01 7.98422158e-01 1.36530972e+00 1.44224957e-01 3.34648669e-01 -6.96902648e-02 7.99091458e-01 -4.68821108e-01 -4.24818516e-01 2.67921656e-01 -5.81948400e-01 -1.14801753e+00 7.37194270e-02 8.45965326e-01 -4.34383340e-02 4.13912274e-02 1.34663844e+00 2.95372456e-01 2.02839941e-01 7.09335744e-01 -1.62580776e+00 -1.23893547e+00 2.43206099e-01 -7.49205709e-01 1.97259292e-01 -1.01165049e-01 -1.78072929e-01 1.06085896e+00 -8.07617128e-01 4.42962319e-01 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294bbfdc-22da-4da1-83da-2f3d21ff6b9c	qasr-qcri-aljazeera-speech-resource-a-large-1	null	null	https://aclanthology.org/2021.acl-long.177	https://aclanthology.org/2021.acl-long.177.pdf	QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus	We introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. This multi-dialect speech dataset contains 2,000 hours of speech sampled at 16kHz crawled from Aljazeera news channel. The dataset is released with lightly supervised transcriptions, aligned with the audio segments. Unlike previous datasets, QASR contains linguistically motivated segmentation, punctuation, speaker information among others. QASR is suitable for training and evaluating speech recognition systems, acoustics- and/or linguistics- based Arabic dialect identification, punctuation restoration, speaker identification, speaker linking, and potentially other NLP modules for spoken data. In addition to QASR transcription, we release a dataset of 130M words to aid in designing and training a better language model. We show that end-to-end automatic speech recognition trained on QASR reports a competitive word error rate compared to the previous MGB-2 corpus. We report baseline results for downstream natural language processing tasks such as named entity recognition using speech transcript. We also report the first baseline for Arabic punctuation restoration. We make the corpus available for the research community.	['Ahmed Ali', 'Shammur Absar Chowdhury', 'Amir Hussein', 'Hamdy Mubarak']	2021-08-01	null	null	null	acl-2021-5	['dialect-identification', 'punctuation-restoration', 'speaker-identification']	['natural-language-processing', 'natural-language-processing', 'speech']	[ 1.51301488e-01 2.76677907e-01 1.35009795e-01 -7.74584293e-01 -1.69526029e+00 -6.91843510e-01 3.03057432e-01 6.69192374e-02 -3.79202753e-01 3.39824855e-01 7.23991990e-01 -5.85101128e-01 2.33218700e-01 -2.15697512e-01 -5.46874106e-01 -6.29608691e-01 -1.18805356e-01 7.36952603e-01 9.59389210e-02 -6.50598526e-01 1.19688615e-01 2.02215210e-01 -1.10128140e+00 5.68462908e-01 7.99875200e-01 6.66569352e-01 4.33984488e-01 8.36972356e-01 -6.82600914e-03 6.85743988e-01 -9.70297217e-01 -4.76915240e-01 -1.99772626e-01 -4.28019762e-01 -1.30148804e+00 3.74070078e-01 3.16299379e-01 -2.02905715e-01 -1.80250049e-01 6.75989747e-01 7.23455012e-01 9.06274468e-02 4.44207460e-01 -8.22874129e-01 -5.77439904e-01 1.37796247e+00 -5.52254505e-02 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3ff837ac-1d63-48c3-9396-3e60d6db1212	photon-field-networks-for-dynamic-real-time	2304.07338	null	https://arxiv.org/abs/2304.07338v1	https://arxiv.org/pdf/2304.07338v1.pdf	Photon Field Networks for Dynamic Real-Time Volumetric Global Illumination	Volume data is commonly found in many scientific disciplines, like medicine, physics, and biology. Experts rely on robust scientific visualization techniques to extract valuable insights from the data. Recent years have shown path tracing to be the preferred approach for volumetric rendering, given its high levels of realism. However, real-time volumetric path tracing often suffers from stochastic noise and long convergence times, limiting interactive exploration. In this paper, we present a novel method to enable real-time global illumination for volume data visualization. We develop Photon Field Networks -- a phase-function-aware, multi-light neural representation of indirect volumetric global illumination. The fields are trained on multi-phase photon caches that we compute a priori. Training can be done within seconds, after which the fields can be used in various rendering tasks. To showcase their potential, we develop a custom neural path tracer, with which our photon fields achieve interactive framerates even on large datasets. We conduct in-depth evaluations of the method's performance, including visual quality, stochastic noise, inference and rendering speeds, and accuracy regarding illumination and phase function awareness. Results are compared to ray marching, path tracing and photon mapping. Our findings show that Photon Field Networks can faithfully represent indirect global illumination across the phase spectrum while exhibiting less stochastic noise and rendering at a significantly faster rate than traditional methods.	['Kwan-Liu Ma', 'Qi Wu', 'David Bauer']	2023-04-14	null	null	null	null	['data-visualization', 'data-visualization']	['methodology', 'miscellaneous']	[ 1.30257383e-01 -4.16645557e-01 4.41571563e-01 -3.79252076e-01 -6.52426600e-01 -5.89582741e-01 4.96995091e-01 3.17709655e-01 -3.70422184e-01 7.18463719e-01 -1.72541335e-01 -7.65402973e-01 1.40098721e-01 -1.08452356e+00 -6.72278702e-01 -4.60182488e-01 -4.08360362e-01 3.95617545e-01 4.79006261e-01 -1.23107294e-02 2.03169286e-01 9.67988312e-01 -1.65720356e+00 2.69543022e-01 7.96716154e-01 9.29456532e-01 -1.43212089e-02 1.02060783e+00 -5.07331729e-01 5.54176688e-01 -6.50646567e-01 5.82640618e-02 1.30053192e-01 -2.69819587e-01 -5.49535155e-01 -6.21483088e-01 5.69691837e-01 -6.88471496e-01 -1.82306767e-02 5.18688798e-01 5.91481268e-01 3.61486524e-01 4.17658627e-01 -9.80196238e-01 -1.53291658e-01 -1.99186519e-01 -6.32787168e-01 2.97638118e-01 4.65959668e-01 7.10933685e-01 4.27796185e-01 -8.05555642e-01 8.25355232e-01 1.34775770e+00 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49084ef2-a50f-4b43-bb06-fc5ec231fe85	being-right-for-whose-right-reasons	2306.00639	null	https://arxiv.org/abs/2306.00639v1	https://arxiv.org/pdf/2306.00639v1.pdf	Being Right for Whose Right Reasons?	Explainability methods are used to benchmark the extent to which model predictions align with human rationales i.e., are 'right for the right reasons'. Previous work has failed to acknowledge, however, that what counts as a rationale is sometimes subjective. This paper presents what we think is a first of its kind, a collection of human rationale annotations augmented with the annotators demographic information. We cover three datasets spanning sentiment analysis and common-sense reasoning, and six demographic groups (balanced across age and ethnicity). Such data enables us to ask both what demographics our predictions align with and whose reasoning patterns our models' rationales align with. We find systematic inter-group annotator disagreement and show how 16 Transformer-based models align better with rationales provided by certain demographic groups: We find that models are biased towards aligning best with older and/or white annotators. We zoom in on the effects of model size and model distillation, finding -- contrary to our expectations -- negative correlations between model size and rationale agreement as well as no evidence that either model size or model distillation improves fairness.	['Anders Søgaard', 'Laura Cabello', 'Terne Sasha Thorn Jakobsen']	2023-06-01	null	null	null	null	['sentiment-analysis', 'common-sense-reasoning']	['natural-language-processing', 'reasoning']	[-1.66459121e-02 8.71536851e-01 -4.75363165e-01 -7.18609035e-01 -3.57822180e-01 -5.86747468e-01 6.56214654e-01 5.60056925e-01 -4.24781352e-01 7.33954370e-01 1.27233911e+00 -4.24903959e-01 -1.20355397e-01 -1.82627410e-01 8.19548368e-02 -2.62913764e-01 8.80415618e-01 5.84082544e-01 -3.27524811e-01 -2.24583209e-01 6.80315614e-01 -3.97711128e-01 -1.17862177e+00 4.15941745e-01 1.10677302e+00 3.90334547e-01 -5.85497737e-01 3.82468194e-01 2.64795609e-02 1.14891553e+00 -3.10593963e-01 -1.32568252e+00 5.41167371e-02 -2.50377685e-01 -8.62572491e-01 -5.31682849e-01 7.47657895e-01 1.15701355e-01 2.16699257e-01 7.13459194e-01 5.27663827e-01 -3.17468137e-01 1.12786102e+00 -1.45253968e+00 -1.14203656e+00 1.15152609e+00 -6.64061069e-01 1.25776321e-01 5.14762521e-01 1.69250801e-01 1.40047526e+00 -5.11735141e-01 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8e06ab4f-3288-4730-89f9-553388a7585f	learning-generative-structure-prior-for-blind	2303.14726	null	https://arxiv.org/abs/2303.14726v1	https://arxiv.org/pdf/2303.14726v1.pdf	Learning Generative Structure Prior for Blind Text Image Super-resolution	Blind text image super-resolution (SR) is challenging as one needs to cope with diverse font styles and unknown degradation. To address the problem, existing methods perform character recognition in parallel to regularize the SR task, either through a loss constraint or intermediate feature condition. Nonetheless, the high-level prior could still fail when encountering severe degradation. The problem is further compounded given characters of complex structures, e.g., Chinese characters that combine multiple pictographic or ideographic symbols into a single character. In this work, we present a novel prior that focuses more on the character structure. In particular, we learn to encapsulate rich and diverse structures in a StyleGAN and exploit such generative structure priors for restoration. To restrict the generative space of StyleGAN so that it obeys the structure of characters yet remains flexible in handling different font styles, we store the discrete features for each character in a codebook. The code subsequently drives the StyleGAN to generate high-resolution structural details to aid text SR. Compared to priors based on character recognition, the proposed structure prior exerts stronger character-specific guidance to restore faithful and precise strokes of a designated character. Extensive experiments on synthetic and real datasets demonstrate the compelling performance of the proposed generative structure prior in facilitating robust text SR.	['Chen Change Loy', 'WangMeng Zuo', 'Xiaoming Li']	2023-03-26	null	http://openaccess.thecvf.com//content/CVPR2023/html/Li_Learning_Generative_Structure_Prior_for_Blind_Text_Image_Super-Resolution_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Learning_Generative_Structure_Prior_for_Blind_Text_Image_Super-Resolution_CVPR_2023_paper.pdf	cvpr-2023-1	['image-super-resolution']	['computer-vision']	[ 1.01766670e+00 -1.89931870e-01 2.97678821e-02 -2.18933627e-01 -5.56673646e-01 -6.79750562e-01 5.92757463e-01 -5.58638871e-01 1.03555247e-01 7.23519623e-01 5.79805017e-01 -6.56203367e-03 1.96633279e-01 -6.13609552e-01 -5.96976757e-01 -9.50855672e-01 6.36056066e-01 5.56326434e-02 9.97444391e-02 -3.29621792e-01 5.70694029e-01 7.42014289e-01 -1.56549394e+00 5.69724262e-01 1.24545825e+00 6.09304428e-01 6.06106281e-01 7.56319106e-01 -3.36779237e-01 6.98260307e-01 -8.43101680e-01 -4.09208536e-01 4.34650391e-01 -5.43851435e-01 -2.54415184e-01 6.38094306e-01 7.29693472e-01 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eda7b0da-993c-4b74-b30d-ca03729bc9e9	hey-human-if-your-facial-emotions-are	2008.07426	null	https://arxiv.org/abs/2008.07426v1	https://arxiv.org/pdf/2008.07426v1.pdf	Hey Human, If your Facial Emotions are Uncertain, You Should Use Bayesian Neural Networks!	Facial emotion recognition is the task to classify human emotions in face images. It is a difficult task due to high aleatoric uncertainty and visual ambiguity. A large part of the literature aims to show progress by increasing accuracy on this task, but this ignores the inherent uncertainty and ambiguity in the task. In this paper we show that Bayesian Neural Networks, as approximated using MC-Dropout, MC-DropConnect, or an Ensemble, are able to model the aleatoric uncertainty in facial emotion recognition, and produce output probabilities that are closer to what a human expects. We also show that calibration metrics show strange behaviors for this task, due to the multiple classes that can be considered correct, which motivates future work. We believe our work will motivate other researchers to move away from Classical and into Bayesian Neural Networks.	['Matias Valdenegro-Toro', 'Maryam Matin']	2020-08-17	null	null	null	null	['facial-emotion-recognition']	['computer-vision']	[ 1.09568425e-01 2.69867957e-01 1.80848598e-01 -1.14925742e+00 -5.93398750e-01 -2.85485983e-01 4.50979263e-01 -4.88355011e-01 -4.18220878e-01 8.55812609e-01 -1.12168752e-01 -1.58808772e-02 -3.95482183e-02 -2.54631639e-01 -6.41186118e-01 -7.72683561e-01 8.27706605e-02 4.59738582e-01 -2.09429041e-01 2.69392729e-01 3.25209312e-02 6.29843056e-01 -1.72827697e+00 3.28930348e-01 5.29386103e-01 1.20878339e+00 -5.35965443e-01 7.27763355e-01 -6.03708997e-02 7.01330364e-01 -5.70848763e-01 -9.02863085e-01 -5.73176891e-02 -5.06515503e-01 -5.23895144e-01 4.46869247e-02 7.62339830e-01 -2.60827661e-01 6.84012100e-02 1.40823424e+00 3.06461602e-01 5.22490703e-02 1.17907894e+00 -1.61836207e+00 -5.82500577e-01 3.68092984e-01 -5.27634144e-01 -9.36266035e-02 -1.44466534e-01 -9.23139080e-02 7.77186036e-01 -8.04107308e-01 3.45780283e-01 1.70412910e+00 7.23579824e-01 1.05111527e+00 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557fb0f2-0f27-4c23-8493-9b9519cb840e	weakly-supervised-anomaly-detection-in-the	2305.03761	null	https://arxiv.org/abs/2305.03761v1	https://arxiv.org/pdf/2305.03761v1.pdf	Weakly-Supervised Anomaly Detection in the Milky Way	Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches. To this end, we use Classification Without Labels (CWoLa), a weakly-supervised anomaly detection method, to identify cold stellar streams within the more than one billion Milky Way stars observed by the Gaia satellite. CWoLa operates without the use of labeled streams or knowledge of astrophysical principles. Instead, we train a classifier to distinguish between mixed samples for which the proportions of signal and background samples are unknown. This computationally lightweight strategy is able to detect both simulated streams and the known stream GD-1 in data. Originally designed for high-energy collider physics, this technique may have broad applicability within astrophysics as well as other domains interested in identifying localized anomalies.	['Jack H. Collins', 'Matthew R. Buckley', 'David Shih', 'Benjamin Nachman', 'Sowmya Thanvantri', 'Mariel Pettee']	2023-05-05	null	null	null	null	['supervised-anomaly-detection']	['computer-vision']	[ 1.10047296e-01 -3.68508130e-01 6.86674118e-02 -1.67397156e-01 -3.05404752e-01 -6.27206504e-01 1.35575879e+00 6.04483902e-01 -3.28110635e-01 5.62284350e-01 -5.49947202e-01 -7.80477464e-01 1.16131552e-01 -7.57302046e-01 -4.72197652e-01 -1.14101589e+00 -1.83433712e-01 8.74955535e-01 5.39557934e-01 1.24333590e-01 4.22937721e-01 9.37084198e-01 -1.74826252e+00 4.55971450e-01 5.08719206e-01 1.24039018e+00 -2.13487282e-01 8.76649261e-01 -4.85981554e-01 8.28218758e-01 -5.96714139e-01 3.03943872e-01 3.35375011e-01 -7.54421115e-01 -3.75041634e-01 -2.72178590e-01 5.28417885e-01 1.20234653e-01 -1.94581151e-01 1.06201172e+00 1.48477197e-01 -1.14976972e-01 7.84118772e-01 -1.29520357e+00 3.75468135e-01 1.10354774e-01 -5.35703599e-01 1.14182830e+00 -1.06572350e-02 5.73049843e-01 7.17890978e-01 -7.78656960e-01 3.17812979e-01 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4180d9d8-3ed9-45d3-b405-fa6f34d428e1	a-multimodal-sensor-fusion-framework-robust	2210.10972	null	https://arxiv.org/abs/2210.10972v2	https://arxiv.org/pdf/2210.10972v2.pdf	A Multimodal Sensor Fusion Framework Robust to Missing Modalities for Person Recognition	Utilizing the sensor characteristics of the audio, visible camera, and thermal camera, the robustness of person recognition can be enhanced. Existing multimodal person recognition frameworks are primarily formulated assuming that multimodal data is always available. In this paper, we propose a novel trimodal sensor fusion framework using the audio, visible, and thermal camera, which addresses the missing modality problem. In the framework, a novel deep latent embedding framework, termed the AVTNet, is proposed to learn multiple latent embeddings. Also, a novel loss function, termed missing modality loss, accounts for possible missing modalities based on the triplet loss calculation while learning the individual latent embeddings. Additionally, a joint latent embedding utilizing the trimodal data is learnt using the multi-head attention transformer, which assigns attention weights to the different modalities. The different latent embeddings are subsequently used to train a deep neural network. The proposed framework is validated on the Speaking Faces dataset. A comparative analysis with baseline algorithms shows that the proposed framework significantly increases the person recognition accuracy while accounting for missing modalities.	['Yasutomo Kawanishi', 'Vijay John']	2022-10-20	null	null	null	null	['person-recognition']	['computer-vision']	[ 3.01834166e-01 -2.35324010e-01 1.14743806e-01 -5.75675130e-01 -9.02201533e-01 -6.70270994e-02 6.48550451e-01 -2.81640142e-01 -2.73928523e-01 4.63416874e-01 5.27350247e-01 4.45795625e-01 -5.16561838e-03 -4.11125064e-01 -5.11882126e-01 -9.95226085e-01 5.33845544e-01 -9.89039913e-02 -5.38253427e-01 2.70802945e-01 -7.36416578e-02 1.46019459e-01 -1.77342296e+00 2.70190686e-01 7.93360353e-01 1.33913529e+00 -1.45548046e-01 2.85038561e-01 1.79675728e-01 5.75551033e-01 -3.43874723e-01 -6.56142354e-01 1.09760068e-01 -9.38756168e-02 -9.66995135e-02 3.02553684e-01 6.91460192e-01 -6.86983407e-01 -5.43593526e-01 9.17818010e-01 8.21317971e-01 1.88471854e-01 5.20690501e-01 -1.59037113e+00 -6.85552478e-01 4.31179404e-01 -4.54439908e-01 -2.84199923e-01 7.37470984e-01 4.91580516e-02 9.13712859e-01 -1.00080979e+00 -1.85002834e-01 1.48487115e+00 6.34629786e-01 5.94555140e-01 -1.08503914e+00 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-5.42185903e-01 4.96416658e-01 5.68823516e-01 -1.01782644e+00 -4.94341791e-01 5.87308183e-02]	[13.225430488586426, 4.921758651733398]
16bf4ac8-0800-4d87-980f-ba5672a2c67b	cochlscene-acquisition-of-acoustic-scene-data	2211.02289	null	https://arxiv.org/abs/2211.02289v1	https://arxiv.org/pdf/2211.02289v1.pdf	CochlScene: Acquisition of acoustic scene data using crowdsourcing	This paper describes a pipeline for collecting acoustic scene data by using crowdsourcing. The detailed process of crowdsourcing is explained, including planning, validation criteria, and actual user interfaces. As a result of data collection, we present CochlScene, a novel dataset for acoustic scene classification. Our dataset consists of 76k samples collected from 831 participants in 13 acoustic scenes. We also propose a manual data split of training, validation, and test sets to increase the reliability of the evaluation results. Finally, we provide a baseline system for future research.	['Jeongsoo Park', 'Il-Young Jeong']	2022-11-04	null	null	null	null	['scene-classification']	['computer-vision']	[ 3.73401940e-02 -1.38177291e-01 7.90474117e-01 -1.00401413e+00 -1.01118767e+00 -7.78093100e-01 3.79778206e-01 1.98092356e-01 -7.85634339e-01 2.80637890e-01 4.40953732e-01 1.99043900e-01 4.64695156e-01 -2.89622962e-01 -5.20902872e-01 -4.42096889e-01 -9.43809003e-02 3.64601672e-01 8.40308785e-01 -2.59202659e-01 1.40528709e-01 2.35176757e-01 -1.56746614e+00 4.69916731e-01 1.71345636e-01 9.93625224e-01 1.23853557e-01 1.24493337e+00 1.48804858e-01 6.85498238e-01 -1.33903122e+00 -5.32838225e-01 2.33102039e-01 -2.69673496e-01 -7.21221209e-01 1.09314986e-01 6.26587212e-01 -3.58641207e-01 -2.20629454e-01 5.50775945e-01 1.37520051e+00 6.05199754e-01 1.57661483e-01 -1.54273748e+00 -5.04560709e-01 4.47868198e-01 4.13267575e-02 1.35836080e-01 8.81231904e-01 3.21366131e-01 8.48046720e-01 -1.08561695e+00 5.32904901e-02 1.03610098e+00 7.81121492e-01 7.66363740e-01 -5.95891297e-01 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3.51597875e-01 3.98485750e-01 -4.35193896e-01 -3.09500575e-01 2.33127177e-01]	[14.929625511169434, 5.246989727020264]
8b7e387d-6b4f-4508-9e41-da3d1a1316d8	emowoz-a-large-scale-corpus-and-labelling	2109.04919	null	https://arxiv.org/abs/2109.04919v2	https://arxiv.org/pdf/2109.04919v2.pdf	EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems	The ability to recognise emotions lends a conversational artificial intelligence a human touch. While emotions in chit-chat dialogues have received substantial attention, emotions in task-oriented dialogues remain largely unaddressed. This is despite emotions and dialogue success having equally important roles in a natural system. Existing emotion-annotated task-oriented corpora are limited in size, label richness, and public availability, creating a bottleneck for downstream tasks. To lay a foundation for studies on emotions in task-oriented dialogues, we introduce EmoWOZ, a large-scale manually emotion-annotated corpus of task-oriented dialogues. EmoWOZ is based on MultiWOZ, a multi-domain task-oriented dialogue dataset. It contains more than 11K dialogues with more than 83K emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine dialogues within the same set of domains to sufficiently cover the space of various emotions that can happen during the lifetime of a data-driven dialogue system. To the best of our knowledge, this is the first large-scale open-source corpus of its kind. We propose a novel emotion labelling scheme, which is tailored to task-oriented dialogues. We report a set of experimental results to show the usability of this corpus for emotion recognition and state tracking in task-oriented dialogues.	['Milica Gašić', 'Carel van Niekerk', 'Michael Heck', 'Hsien-Chin Lin', 'Christian Geishauser', 'Nurul Lubis', 'Shutong Feng']	2021-09-10	null	https://aclanthology.org/2022.lrec-1.436	https://aclanthology.org/2022.lrec-1.436.pdf	lrec-2022-6	['emotion-recognition-in-conversation']	['natural-language-processing']	[-6.25991002e-02 5.39509356e-01 1.20173305e-01 -6.93981826e-01 -4.64695752e-01 -8.45685542e-01 6.14486992e-01 2.35277295e-01 -3.79102588e-01 7.74267614e-01 4.97175455e-01 -1.74436316e-01 3.25437039e-01 -2.77434975e-01 2.56312102e-01 -2.79819638e-01 -1.09139688e-01 7.16199577e-01 8.07117950e-03 -8.67353201e-01 8.47910270e-02 1.13208257e-01 -1.42168915e+00 5.47598720e-01 4.30339962e-01 9.79908884e-01 -5.26673496e-02 8.92081439e-01 -3.07579160e-01 1.17694366e+00 -9.29871798e-01 -5.69326043e-01 -2.02037320e-01 -5.96873164e-01 -1.66005850e+00 2.52689362e-01 -3.59778166e-01 1.18652843e-02 4.00338881e-02 5.03662825e-01 7.70808220e-01 4.79885250e-01 5.36420941e-01 -1.58321464e+00 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be87cd25-6237-482f-a356-85ebaaafd788	selective-structured-state-spaces-for-long	2303.14526	null	https://arxiv.org/abs/2303.14526v1	https://arxiv.org/pdf/2303.14526v1.pdf	Selective Structured State-Spaces for Long-Form Video Understanding	Effective modeling of complex spatiotemporal dependencies in long-form videos remains an open problem. The recently proposed Structured State-Space Sequence (S4) model with its linear complexity offers a promising direction in this space. However, we demonstrate that treating all image-tokens equally as done by S4 model can adversely affect its efficiency and accuracy. To address this limitation, we present a novel Selective S4 (i.e., S5) model that employs a lightweight mask generator to adaptively select informative image tokens resulting in more efficient and accurate modeling of long-term spatiotemporal dependencies in videos. Unlike previous mask-based token reduction methods used in transformers, our S5 model avoids the dense self-attention calculation by making use of the guidance of the momentum-updated S4 model. This enables our model to efficiently discard less informative tokens and adapt to various long-form video understanding tasks more effectively. However, as is the case for most token reduction methods, the informative image tokens could be dropped incorrectly. To improve the robustness and the temporal horizon of our model, we propose a novel long-short masked contrastive learning (LSMCL) approach that enables our model to predict longer temporal context using shorter input videos. We present extensive comparative results using three challenging long-form video understanding datasets (LVU, COIN and Breakfast), demonstrating that our approach consistently outperforms the previous state-of-the-art S4 model by up to 9.6% accuracy while reducing its memory footprint by 23%.	['Raffay Hamid', 'Mohamed Omar', 'Linda Liu', 'Xiang Yu', 'Pichao Wang', 'Wentao Zhu', 'Jue Wang']	2023-03-25	null	http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Selective_Structured_State-Spaces_for_Long-Form_Video_Understanding_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Selective_Structured_State-Spaces_for_Long-Form_Video_Understanding_CVPR_2023_paper.pdf	cvpr-2023-1	['video-understanding']	['computer-vision']	[ 4.54865575e-01 -1.73725933e-01 -2.51771182e-01 -1.90893546e-01 -6.92167103e-01 -4.19872403e-01 6.21565640e-01 -3.38954240e-01 -5.57332754e-01 6.08543217e-01 4.08688523e-02 -2.36746535e-01 1.39031082e-01 -2.09298879e-01 -9.74217176e-01 -6.75115347e-01 -4.53970246e-02 3.72426659e-02 5.24073422e-01 5.30569255e-02 3.70003402e-01 4.77231331e-02 -1.65632272e+00 5.42926133e-01 1.16012001e+00 1.01041758e+00 7.82897770e-01 6.32241070e-01 -1.18459716e-01 1.16533983e+00 -4.42093313e-01 -3.01916122e-01 3.04720789e-01 -4.28032875e-01 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3ca3885a-15a1-4bd7-9503-6f13ff212070	concept-identification-of-directly-and	2107.00955	null	https://arxiv.org/abs/2107.00955v1	https://arxiv.org/pdf/2107.00955v1.pdf	Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons	Unsupervised concept identification through clustering, i.e., identification of semantically related words and phrases, is a common approach to identify contextual primitives employed in various use cases, e.g., text dimension reduction, i.e., replace words with the concepts to reduce the vocabulary size, summarization, and named entity resolution. We demonstrate the first results of an unsupervised approach for the identification of groups of persons as actors extracted from a set of related articles. Specifically, the approach clusters mentions of groups of persons that act as non-named entity actors in the texts, e.g., "migrant families" = "asylum-seekers." Compared to our baseline, the approach keeps the mentions of the geopolitical entities separated, e.g., "Iran leaders" != "European leaders," and clusters (in)directly related mentions with diverse wording, e.g., "American officials" = "Trump Administration."	['Bela Gipp', 'Karsten Donnay', 'Felix Hamborg', 'Anastasia Zhukova']	2021-07-02	null	null	null	null	['entity-resolution']	['natural-language-processing']	[-2.58196235e-01 3.72587413e-01 -1.10035583e-01 -1.01966068e-01 -4.59635466e-01 -7.91291773e-01 8.43774259e-01 8.85687888e-01 -7.27139950e-01 7.90452600e-01 1.08634508e+00 -1.48973376e-01 -1.22429915e-01 -7.87353337e-01 -3.01264450e-02 -9.10045445e-01 1.76491529e-01 7.06883788e-01 -5.28832555e-01 -2.54255742e-01 7.11117446e-01 3.87860447e-01 -1.28640342e+00 1.32927015e-01 8.88644040e-01 2.72263914e-01 -6.48116469e-02 1.93892904e-02 -7.07143545e-01 6.77114546e-01 -9.29765224e-01 -4.93192166e-01 -3.05237234e-01 -3.43912244e-01 -1.02592611e+00 2.12944821e-01 1.38553977e-01 8.50304291e-02 -1.40614688e-01 1.30235529e+00 2.92425662e-01 3.83791536e-01 1.14725387e+00 -8.93335521e-01 -6.99625552e-01 1.29646838e+00 -7.35047162e-01 2.70021975e-01 4.92981434e-01 -6.05775476e-01 1.22916806e+00 -1.04929006e+00 1.09003425e+00 1.64945114e+00 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687187a8-99f2-4a69-8766-0ddeaaf8c643	facial-action-units-detection-aided-by-global	2210.13718	null	https://arxiv.org/abs/2210.13718v1	https://arxiv.org/pdf/2210.13718v1.pdf	Facial Action Units Detection Aided by Global-Local Expression Embedding	Since Facial Action Unit (AU) annotations require domain expertise, common AU datasets only contain a limited number of subjects. As a result, a crucial challenge for AU detection is addressing identity overfitting. We find that AUs and facial expressions are highly associated, and existing facial expression datasets often contain a large number of identities. In this paper, we aim to utilize the expression datasets without AU labels to facilitate AU detection. Specifically, we develop a novel AU detection framework aided by the Global-Local facial Expressions Embedding, dubbed GLEE-Net. Our GLEE-Net consists of three branches to extract identity-independent expression features for AU detection. We introduce a global branch for modeling the overall facial expression while eliminating the impacts of identities. We also design a local branch focusing on specific local face regions. The combined output of global and local branches is firstly pre-trained on an expression dataset as an identity-independent expression embedding, and then finetuned on AU datasets. Therefore, we significantly alleviate the issue of limited identities. Furthermore, we introduce a 3D global branch that extracts expression coefficients through 3D face reconstruction to consolidate 2D AU descriptions. Finally, a Transformer-based multi-label classifier is employed to fuse all the representations for AU detection. Extensive experiments demonstrate that our method significantly outperforms the state-of-the-art on the widely-used DISFA, BP4D and BP4D+ datasets.	['Xin Yu', 'Zhigang Deng', 'Wei Chen', 'Yu Ding', 'Lincheng Li', 'Wei zhang', 'Zhipeng Hu']	2022-10-25	null	null	null	null	['3d-face-reconstruction', 'face-reconstruction']	['computer-vision', 'computer-vision']	[ 1.43446892e-01 -2.37222388e-01 -9.61817801e-02 -5.51079750e-01 -8.00017059e-01 -4.62279677e-01 3.94599259e-01 -5.76706946e-01 3.09690982e-02 3.80179375e-01 6.86980709e-02 4.09241199e-01 4.09758985e-01 -6.51700199e-01 -3.83421361e-01 -8.67152035e-01 3.19652706e-01 -1.28523976e-01 -4.21447903e-01 -3.95483762e-01 -4.30756986e-01 5.56000412e-01 -1.66763270e+00 3.19785833e-01 5.12948155e-01 1.72058403e+00 -6.16879046e-01 1.08448066e-01 3.58484983e-02 8.56472611e-01 -4.15948987e-01 -6.15973771e-01 4.72298533e-01 -6.50967896e-01 -3.98303270e-01 4.31088924e-01 5.47538102e-01 -7.21211851e-01 -2.34457389e-01 1.06918132e+00 6.70276403e-01 -1.07825339e-01 6.85139537e-01 -1.56359839e+00 -7.15806782e-01 -8.11949596e-02 -9.82866764e-01 -2.87016004e-01 3.95655930e-01 1.12056136e-01 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474ae113-3db5-4a50-8341-bd4046d709e6	qrrt-quality-biased-incremental-rrt-for	2101.02635	null	https://arxiv.org/abs/2101.02635v1	https://arxiv.org/pdf/2101.02635v1.pdf	qRRT: Quality-Biased Incremental RRT for Optimal Motion Planning in Non-Holonomic Systems	This paper presents a sampling-based method for optimal motion planning in non-holonomic systems in the absence of known cost functions. It uses the principle of learning through experience to deduce the cost-to-go of regions within the workspace. This cost information is used to bias an incremental graph-based search algorithm that produces solution trajectories. Iterative improvement of cost information and search biasing produces solutions that are proven to be asymptotically optimal. The proposed framework builds on incremental Rapidly-exploring Random Trees (RRT) for random sampling-based search and Reinforcement Learning (RL) to learn workspace costs. A series of experiments were performed to evaluate and demonstrate the performance of the proposed method.	['Suril V. Shah', 'Balaraman Ravindran', 'Francis James', 'Nahas Pareekutty']	2021-01-07	null	null	null	null	['optimal-motion-planning']	['robots']	[ 6.34669587e-02 2.87485331e-01 -4.55703348e-01 3.28857116e-02 -4.69411135e-01 -3.84037256e-01 4.18806762e-01 9.44451522e-03 -3.81978393e-01 1.31496227e+00 -1.79218687e-02 -4.90534812e-01 -7.18628466e-01 -5.61898887e-01 -5.76945186e-01 -6.05935931e-01 -5.81979692e-01 4.27206010e-01 1.31664053e-01 -4.29353386e-01 8.48699331e-01 7.64120758e-01 -1.07588112e+00 -5.69679439e-01 1.07123899e+00 6.03526711e-01 8.71503949e-01 7.46406615e-01 3.87650102e-01 3.84893209e-01 -3.64892125e-01 5.10560632e-01 7.70195067e-01 -4.77633625e-01 -8.50402296e-01 2.50197291e-01 -3.09794456e-01 -8.33218098e-02 -1.50688112e-01 8.99489641e-01 4.69153196e-01 7.75793314e-01 6.21243000e-01 -9.91646647e-01 -1.97635423e-02 2.44798958e-01 -2.62392431e-01 2.51166672e-01 4.39160258e-01 5.02217174e-01 5.64739823e-01 -6.97299957e-01 9.21177745e-01 1.31277132e+00 4.60942179e-01 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94c52a51-2358-40ad-8167-c666f96d53f8	extending-the-adverbial-coverage-of-a-french	null	null	https://aclanthology.org/L12-1564	https://aclanthology.org/L12-1564.pdf	Extending the adverbial coverage of a French morphological lexicon	We present an extension of the adverbial entries of the French morphological lexicon DELA (Dictionnaires Electroniques du LADL / LADL electronic dictionaries). Adverbs were extracted from LGLex, a NLP-oriented syntactic resource for French, which in its turn contains all adverbs extracted from the Lexicon-Grammar tables of both simple adverbs ending in -ment (i.e., '-ly') and compound adverbs. This work exploits fine-grained linguistic information provided in existing resources. The resulting resource is reviewed in order to delete duplicates and is freely available under the LGPL-LR license.	['Matthieu Constant', 'Claude Martineau', 'Elsa Tolone', 'Stavroula Voyatzi']	2012-05-01	null	null	null	lrec-2012-5	['prepositional-phrase-attachment']	['natural-language-processing']	[-3.43962610e-01 3.02506000e-01 -5.72085619e-01 -2.45190397e-01 -7.30438828e-01 -1.25365853e+00 1.55747667e-01 9.77357149e-01 -6.45530641e-01 1.19954073e+00 5.40130019e-01 -4.31104600e-01 -3.91254500e-02 -8.07840288e-01 -6.71903729e-01 -1.68900281e-01 3.07944596e-01 3.90387267e-01 3.68980646e-01 -7.01046348e-01 1.19239591e-01 5.47044277e-01 -9.57764804e-01 6.43985271e-01 5.64329028e-01 1.00751841e+00 3.03963125e-01 2.95971036e-02 -2.67623067e-01 4.72299844e-01 -3.62721473e-01 -9.61537898e-01 1.12349026e-01 -4.40883130e-01 -8.71190310e-01 -2.99610525e-01 -7.57980719e-02 2.55297750e-01 7.03206810e-04 1.03907979e+00 2.88465582e-02 3.78504507e-02 3.71833175e-01 -4.94781017e-01 -7.45922089e-01 1.41805482e+00 1.58775598e-01 7.18033433e-01 7.71288753e-01 -2.91512221e-01 1.55683637e+00 -1.16977799e+00 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173afcc8-d767-4ba8-9c6d-e908d7c654de	instance-adaptive-self-training-for	2008.12197	null	https://arxiv.org/abs/2008.12197v1	https://arxiv.org/pdf/2008.12197v1.pdf	Instance Adaptive Self-Training for Unsupervised Domain Adaptation	The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such a problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in balancing scalability and performance. In this paper, we propose an instance adaptive self-training framework for UDA on the task of semantic segmentation. To effectively improve the quality of pseudo-labels, we develop a novel pseudo-label generation strategy with an instance adaptive selector. Besides, we propose the region-guided regularization to smooth the pseudo-label region and sharpen the non-pseudo-label region. Our method is so concise and efficient that it is easy to be generalized to other unsupervised domain adaptation methods. Experiments on 'GTA5 to Cityscapes' and 'SYNTHIA to Cityscapes' demonstrate the superior performance of our approach compared with the state-of-the-art methods.	['Jiaqi Zou', 'Shanghang Zhang', 'Chuang Zhu', 'Ke Mei']	2020-08-27	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5406_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710409.pdf	eccv-2020-8	['synthetic-to-real-translation']	['computer-vision']	[ 2.33960286e-01 1.45690441e-01 -3.37807536e-01 -7.73240685e-01 -8.77068818e-01 -5.65643609e-01 4.38295096e-01 -1.31869316e-01 -5.03448248e-01 7.29389131e-01 -1.96250334e-01 -1.44607082e-01 -5.81020750e-02 -6.63014293e-01 -5.75785220e-01 -8.02519083e-01 4.63771969e-01 7.94316411e-01 5.16525269e-01 -4.16396819e-02 9.26973578e-03 2.10241064e-01 -1.40456498e+00 1.70632347e-01 1.34574664e+00 8.77490163e-01 3.05584639e-01 1.73355699e-01 -4.58282918e-01 3.96590650e-01 -5.11941910e-01 -1.94796592e-01 2.12833822e-01 -6.24114931e-01 -1.22616458e+00 6.06811345e-01 3.49326432e-01 2.52914578e-02 2.29808062e-01 1.17930400e+00 4.80156332e-01 2.89218128e-01 9.33974445e-01 -1.15755486e+00 -5.51291525e-01 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1fa70813-47e4-42f5-a03b-e590095de452	graph-convolutional-network-with-sequential	null	null	https://openreview.net/forum?id=Skz-3j05tm	https://openreview.net/pdf?id=Skz-3j05tm	Graph Convolutional Network with Sequential Attention For Goal-Oriented Dialogue Systems	Domain specific goal-oriented dialogue systems typically require modeling three types of inputs, viz., (i) the knowledge-base associated with the domain, (ii) the history of the conversation, which is a sequence of utterances and (iii) the current utterance for which the response needs to be generated. While modeling these inputs, current state-of-the-art models such as Mem2Seq typically ignore the rich structure inherent in the knowledge graph and the sentences in the conversation context. Inspired by the recent success of structure-aware Graph Convolutional Networks (GCNs) for various NLP tasks such as machine translation, semantic role labeling and document dating, we propose a memory augmented GCN for goal-oriented dialogues. Our model exploits (i) the entity relation graph in a knowledge-base and (ii) the dependency graph associated with an utterance to compute richer representations for words and entities. Further, we take cognizance of the fact that in certain situations, such as, when the conversation is in a code-mixed language, dependency parsers may not be available. We show that in such situations we could use the global word co-occurrence graph and use it to enrich the representations of utterances. We experiment with the modified DSTC2 dataset and its recently released code-mixed versions in four languages and show that our method outperforms existing state-of-the-art methods, using a wide range of evaluation metrics.	['Suman Banerjee', 'Mitesh M. Khapra']	2019-05-01	graph-convolutional-network-with-sequential-1	https://aclanthology.org/Q19-1034	https://aclanthology.org/Q19-1034.pdf	iclr-2019-5	['document-dating', 'goal-oriented-dialogue-systems']	['natural-language-processing', 'natural-language-processing']	[ 2.61019528e-01 5.15927434e-01 -8.37097466e-02 -4.16930497e-01 -3.56968701e-01 -7.53882766e-01 7.85269558e-01 5.46843648e-01 -4.46462601e-01 7.68209994e-01 8.74011695e-01 -5.28988957e-01 1.13758720e-01 -8.62114072e-01 -4.83622581e-01 -9.82168019e-02 -2.44319409e-01 7.07372546e-01 2.35484898e-01 -8.93603623e-01 2.49491125e-01 1.24054551e-02 -1.12436402e+00 5.38799465e-01 7.00442314e-01 8.16020012e-01 3.33710462e-01 8.78618479e-01 -6.53940439e-01 1.35532987e+00 -6.07001483e-01 -3.86142850e-01 -1.92211151e-01 -7.06379652e-01 -1.45437181e+00 1.90399274e-01 -1.33865535e-01 -3.16566080e-01 -4.75013405e-01 9.46538806e-01 1.90384224e-01 5.20499706e-01 2.98710257e-01 -1.05714524e+00 -5.10674298e-01 1.10085011e+00 8.39947350e-03 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d4f235ac-8c22-4aca-920c-3a85965d1a00	diverse-sequential-subset-selection-for	null	null	http://papers.nips.cc/paper/5413-diverse-sequential-subset-selection-for-supervised-video-summarization	http://papers.nips.cc/paper/5413-diverse-sequential-subset-selection-for-supervised-video-summarization.pdf	Diverse Sequential Subset Selection for Supervised Video Summarization	Video summarization is a challenging problem with great application potential. Whereas prior approaches, largely unsupervised in nature, focus on sampling useful frames and assembling them as summaries, we consider video summarization as a supervised subset selection problem. Our idea is to teach the system to learn from human-created summaries how to select informative and diverse subsets, so as to best meet evaluation metrics derived from human-perceived quality. To this end, we propose the sequential determinantal point process (seqDPP), a probabilistic model for diverse sequential subset selection. Our novel seqDPP heeds the inherent sequential structures in video data, thus overcoming the deficiency of the standard DPP, which treats video frames as randomly permutable items. Meanwhile, seqDPP retains the power of modeling diverse subsets, essential for summarization. Our extensive results of summarizing videos from 3 datasets demonstrate the superior performance of our method, compared to not only existing unsupervised methods but also naive applications of the standard DPP model.	['Wei-Lun Chao', 'Boqing Gong', 'Kristen Grauman', 'Fei Sha']	2014-12-01	null	null	null	neurips-2014-12	['supervised-video-summarization']	['computer-vision']	[ 5.53770006e-01 -2.88154110e-02 -5.29314101e-01 -3.09152603e-01 -9.01830256e-01 -6.14107668e-01 5.43522537e-01 1.23175852e-01 -4.64541279e-02 8.37515354e-01 9.40883160e-01 1.98285282e-01 -1.07858635e-01 -4.40768898e-01 -6.84684038e-01 -7.83163249e-01 4.21322882e-02 1.53883323e-01 3.59488308e-01 1.14942335e-01 5.44477463e-01 3.97720151e-02 -1.75248754e+00 3.17051977e-01 1.23253441e+00 5.65626502e-01 2.52447814e-01 6.46021307e-01 4.24147956e-02 1.09840536e+00 -6.47203386e-01 -2.08101764e-01 -5.63133061e-02 -9.48935866e-01 -6.33361876e-01 5.26415706e-01 5.23471236e-01 -5.39262116e-01 -5.38811326e-01 1.01568615e+00 5.41680336e-01 4.09507513e-01 8.15003872e-01 -1.28875029e+00 -4.62514222e-01 8.21623027e-01 -5.62352717e-01 1.03168704e-01 7.80575991e-01 4.02106903e-02 1.37962699e+00 -7.27864623e-01 7.36704350e-01 1.11312950e+00 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138783d1-4b4f-4bb8-829e-a532c67543e2	slotformer-unsupervised-visual-dynamics	2210.05861	null	https://arxiv.org/abs/2210.05861v2	https://arxiv.org/pdf/2210.05861v2.pdf	SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models	Understanding dynamics from visual observations is a challenging problem that requires disentangling individual objects from the scene and learning their interactions. While recent object-centric models can successfully decompose a scene into objects, modeling their dynamics effectively still remains a challenge. We address this problem by introducing SlotFormer -- a Transformer-based autoregressive model operating on learned object-centric representations. Given a video clip, our approach reasons over object features to model spatio-temporal relationships and predicts accurate future object states. In this paper, we successfully apply SlotFormer to perform video prediction on datasets with complex object interactions. Moreover, the unsupervised SlotFormer's dynamics model can be used to improve the performance on supervised downstream tasks, such as Visual Question Answering (VQA), and goal-conditioned planning. Compared to past works on dynamics modeling, our method achieves significantly better long-term synthesis of object dynamics, while retaining high quality visual generation. Besides, SlotFormer enables VQA models to reason about the future without object-level labels, even outperforming counterparts that use ground-truth annotations. Finally, we show its ability to serve as a world model for model-based planning, which is competitive with methods designed specifically for such tasks.	['Animesh Garg', 'Thomas Kipf', 'Klaus Greff', 'Nikita Dvornik', 'Ziyi Wu']	2022-10-12	null	null	null	null	['video-prediction']	['computer-vision']	[ 3.92614231e-02 2.58592755e-01 -4.35993612e-01 -2.79429555e-01 -5.86577952e-01 -6.22594357e-01 9.46380615e-01 -4.75925878e-02 8.71664360e-02 2.02365085e-01 6.28351867e-01 -1.55420065e-01 -2.67799236e-02 -6.64955318e-01 -9.53913987e-01 -5.41216612e-01 -8.76825899e-02 1.01966381e+00 3.60368192e-01 -9.92719010e-02 4.75831479e-02 3.91504109e-01 -1.58792937e+00 6.53443873e-01 4.17347193e-01 8.67841542e-01 7.22579539e-01 9.81250644e-01 -8.18424523e-02 1.60633194e+00 -5.24233207e-02 -1.78390574e-02 -4.94518355e-02 -3.72282445e-01 -1.09440613e+00 5.67547321e-01 5.72765768e-01 -4.83693987e-01 -7.95351267e-01 5.30445516e-01 -2.57368028e-01 3.30492377e-01 7.69270301e-01 -1.42521739e+00 -7.38915682e-01 4.10790980e-01 -8.04778337e-02 1.31902441e-01 3.03881347e-01 6.02610886e-01 1.34294212e+00 -6.50924206e-01 1.10609913e+00 1.60659814e+00 1.38091843e-03 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a6a78fa2-ea75-4cea-80e2-388b5dc60d10	fedbone-towards-large-scale-federated-multi	2306.17465	null	https://arxiv.org/abs/2306.17465v1	https://arxiv.org/pdf/2306.17465v1.pdf	FedBone: Towards Large-Scale Federated Multi-Task Learning	Heterogeneous federated multi-task learning (HFMTL) is a federated learning technique that combines heterogeneous tasks of different clients to achieve more accurate, comprehensive predictions. In real-world applications, visual and natural language tasks typically require large-scale models to extract high-level abstract features. However, large-scale models cannot be directly applied to existing federated multi-task learning methods. Existing HFML methods also disregard the impact of gradient conflicts on multi-task optimization during the federated aggregation process. In this work, we propose an innovative framework called FedBone, which enables the construction of large-scale models with better generalization from the perspective of server-client split learning and gradient projection. We split the entire model into two components: a large-scale general model (referred to as the general model) on the cloud server and multiple task-specific models (referred to as the client model) on edge clients, solving the problem of insufficient computing power on edge clients. The conflicting gradient projection technique is used to enhance the generalization of the large-scale general model between different tasks. The proposed framework is evaluated on two benchmark datasets and a real ophthalmic dataset. Comprehensive results demonstrate that FedBone efficiently adapts to heterogeneous local tasks of each client and outperforms existing federated learning algorithms in most dense prediction and classification tasks with off-the-shelf computational resources on the client side.	['Wuliang Huang', 'Chenlong Gao', 'Qian Chen', 'Xinlong Jiang', 'Teng Zhang', 'Yiqiang Chen']	2023-06-30	null	null	null	null	['multi-task-learning']	['methodology']	[-1.42425790e-01 -5.32503903e-01 -1.45219713e-01 -4.16373670e-01 -8.41451108e-01 -1.56270742e-01 3.07936370e-01 -3.54064971e-01 -6.36139289e-02 6.61860168e-01 -1.09125584e-01 -1.12481110e-01 -2.66811609e-01 -5.44154823e-01 -5.21874428e-01 -9.22824562e-01 1.91056579e-01 6.70435309e-01 3.46783131e-01 1.44362822e-01 -1.91935807e-01 2.71735162e-01 -1.96963918e+00 1.21462798e+00 9.92049396e-01 1.50700307e+00 5.98488629e-01 2.49382347e-01 -4.41337049e-01 9.21019197e-01 -3.99179965e-01 -5.13335288e-01 3.63675714e-01 2.79583931e-01 -5.80592215e-01 -7.22713321e-02 4.74404752e-01 -1.13939233e-01 3.03494304e-01 7.57796228e-01 6.78892016e-01 1.64125878e-02 2.95119405e-01 -1.79396856e+00 -2.11195379e-01 2.81398445e-01 -3.12963724e-01 5.03759719e-02 -9.24960375e-02 -8.54500383e-02 7.04019547e-01 -1.38382709e+00 6.18615210e-01 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1299f1a6-3f58-4da9-b8d3-6db0040d8d9a	towards-complete-view-and-high-level-pose	2209.11577	null	https://arxiv.org/abs/2209.11577v1	https://arxiv.org/pdf/2209.11577v1.pdf	Towards Complete-View and High-Level Pose-based Gait Recognition	The model-based gait recognition methods usually adopt the pedestrian walking postures to identify human beings. However, existing methods did not explicitly resolve the large intra-class variance of human pose due to camera views changing. In this paper, we propose to generate multi-view pose sequences for each single-view pose sample by learning full-rank transformation matrices via lower-upper generative adversarial network (LUGAN). By the prior of camera imaging, we derive that the spatial coordinates between cross-view poses satisfy a linear transformation of a full-rank matrix, thereby, this paper employs the adversarial training to learn transformation matrices from the source pose and target views to obtain the target pose sequences. To this end, we implement a generator composed of graph convolutional (GCN) layers, fully connected (FC) layers and two-branch convolutional (CNN) layers: GCN layers and FC layers encode the source pose sequence and target view, then CNN branches learn a lower triangular matrix and an upper triangular matrix, respectively, finally they are multiplied to formulate the full-rank transformation matrix. For the purpose of adversarial training, we further devise a condition discriminator that distinguishes whether the pose sequence is true or generated. To enable the high-level correlation learning, we propose a plug-and-play module, named multi-scale hypergraph convolution (HGC), to replace the spatial graph convolutional layer in baseline, which could simultaneously model the joint-level, part-level and body-level correlations. Extensive experiments on two large gait recognition datasets, i.e., CASIA-B and OUMVLP-Pose, demonstrate that our method outperforms the baseline model and existing pose-based methods by a large margin.	['Zhenyu He', 'Yunqi He', 'Tingyang Xu', 'Yongyong Chen', 'Honghu Pan']	2022-09-23	null	null	null	null	['gait-recognition']	['computer-vision']	[ 1.03010952e-01 -1.82054833e-01 1.58000216e-01 -8.20026472e-02 -4.38125342e-01 -4.63768095e-01 3.68589014e-01 -1.06511676e+00 -4.15923670e-02 6.79010749e-01 2.52549052e-01 2.14956641e-01 3.51777285e-01 -9.94511902e-01 -9.24319327e-01 -8.46356690e-01 -1.35653401e-02 3.55182916e-01 1.23652264e-01 -3.34109128e-01 -3.26216042e-01 2.14046165e-01 -1.21623480e+00 1.89755693e-01 6.19298279e-01 7.45520473e-01 -3.04958135e-01 8.40341568e-01 5.41316628e-01 7.06019640e-01 -6.76517606e-01 -7.13864267e-01 4.15686369e-01 -7.21376359e-01 -3.61141682e-01 3.70892763e-01 5.36525965e-01 -5.78990161e-01 -9.74762142e-01 1.07269382e+00 7.84503102e-01 -4.59828321e-03 6.97623491e-01 -1.65069997e+00 -8.23853612e-01 1.55179054e-01 -7.19482601e-01 -1.32033965e-02 7.65450954e-01 6.80695176e-01 4.78128284e-01 -6.42453313e-01 7.35717952e-01 1.51897752e+00 9.45386410e-01 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499a0040-58ed-4123-8b9f-62c36ab836f9	mdgcf-multi-dependency-graph-collaborative	null	null	https://doi.org/10.1145/3511808.3557390	https://dl.acm.org/doi/pdf/10.1145/3511808.3557390	MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies	Due to the success of graph convolutional networks (GCNs) in effectively extracting features in non-Euclidean spaces, GCNs has become the rising star in implicit collaborative filtering. Existing works, while encouraging, typically adopt simple aggregation operation on the user-item bipartite graph to model user and item representations, but neglect to mine the sufficient dependencies between nodes, e.g., the relationships between users/items and their neighbors (or congeners), resulting in inadequate graph representation learning. To address these problems, we propose a novel Multi-Dependency Graph Collaborative Filtering (MDGCF) model, which mines the neighborhood- and homogeneous-level dependencies to enhance the representation power of graph-based CF models. Specifically, for neighborhood-level dependencies, we explicitly consider both popularity score and preference correlation by designing a joint neighborhood-level dependency weight, based on which we construct a neighborhood-level dependencies graph to capture higher-order interaction features. Besides, by adaptively mining the homogeneous-level dependencies among users and items, we construct two homogeneous graphs, based on which we further aggregate features from homogeneous users and items to supplement their representations, respectively. Extensive experiments on three real-world benchmark datasets demonstrate the effectiveness of the proposed MDGCF. Further experiments reveal that our model can capture rich dependencies between nodes for explaining user behaviors.	['Chaoyang Wang', 'Jianjun Li', 'Zhiqiang Guo', 'GuoHui Li']	2022-10-17	null	null	null	cikm-2022-10	['graph-representation-learning', 'collaborative-filtering']	['methodology', 'miscellaneous']	[-2.46444300e-01 -3.30899149e-01 -3.56883913e-01 -4.56709325e-01 2.37310156e-01 -4.08162475e-01 3.38236064e-01 6.17970347e-01 -1.02508157e-01 3.18616986e-01 4.86565828e-01 -3.44816595e-01 -6.58703864e-01 -1.20456135e+00 -4.23878103e-01 -5.08800030e-01 -4.65173662e-01 -4.86119166e-02 2.03773737e-01 -2.81385541e-01 3.67483981e-02 1.27409831e-01 -1.22667289e+00 1.85424685e-01 1.22726202e+00 9.24484491e-01 1.17087208e-01 5.78970164e-02 -3.69290978e-01 4.51804310e-01 -1.67289957e-01 -3.99878979e-01 1.90917596e-01 -4.15113240e-01 -3.86091262e-01 2.35076621e-01 -2.63098460e-02 -2.05723301e-01 -6.84071898e-01 1.14670777e+00 1.80000693e-01 3.91984761e-01 5.41547298e-01 -1.06567729e+00 -1.18831396e+00 8.92817557e-01 -6.90677881e-01 3.01028103e-01 4.81903553e-01 5.47997765e-02 1.61436605e+00 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41dc646b-00cd-4ac1-be8e-48835ada54cb	more-synergy-less-redundancy-exploiting-joint	2307.00651	null	https://arxiv.org/abs/2307.00651v1	https://arxiv.org/pdf/2307.00651v1.pdf	More Synergy, Less Redundancy: Exploiting Joint Mutual Information for Self-Supervised Learning	Self-supervised learning (SSL) is now a serious competitor for supervised learning, even though it does not require data annotation. Several baselines have attempted to make SSL models exploit information about data distribution, and less dependent on the augmentation effect. However, there is no clear consensus on whether maximizing or minimizing the mutual information between representations of augmentation views practically contribute to improvement or degradation in performance of SSL models. This paper is a fundamental work where, we investigate role of mutual information in SSL, and reformulate the problem of SSL in the context of a new perspective on mutual information. To this end, we consider joint mutual information from the perspective of partial information decomposition (PID) as a key step in \textbf{reliable multivariate information measurement}. PID enables us to decompose joint mutual information into three important components, namely, unique information, redundant information and synergistic information. Our framework aims for minimizing the redundant information between views and the desired target representation while maximizing the synergistic information at the same time. Our experiments lead to a re-calibration of two redundancy reduction baselines, and a proposal for a new SSL training protocol. Extensive experimental results on multiple datasets and two downstream tasks show the effectiveness of this framework.	['Donald A. Adjeroh', 'Gianfranco Doretto', 'Salman Mohamadi']	2023-07-02	null	null	null	null	['self-supervised-learning']	['computer-vision']	[ 4.06920880e-01 3.71032685e-01 -4.16070938e-01 -6.07789099e-01 -7.44300485e-01 -4.73221809e-01 7.24938571e-01 1.78938225e-01 -4.86040823e-02 6.17155254e-01 5.09958565e-01 5.27794808e-02 -5.49237728e-01 -5.07046282e-01 -5.35226822e-01 -7.72014499e-01 3.14509541e-01 2.21766442e-01 -1.91930249e-01 -5.17618544e-02 2.02228114e-01 3.27104717e-01 -1.63124502e+00 3.69433731e-01 7.53828824e-01 8.37650180e-01 2.50120193e-01 1.40297920e-01 -2.04427466e-01 9.53750014e-01 -4.43555534e-01 -1.69443339e-01 2.64177203e-01 -6.33416116e-01 -8.26654851e-01 3.69132996e-01 1.97129726e-01 1.91597968e-01 1.13312509e-02 9.78572369e-01 4.42445338e-01 -5.64844534e-02 8.14016044e-01 -1.52623093e+00 -3.52504611e-01 6.98560357e-01 -6.29640520e-01 8.86033550e-02 2.59297252e-01 -1.61339477e-01 1.31449151e+00 -1.02437711e+00 6.27986789e-01 1.28745592e+00 6.61776483e-01 3.86368185e-01 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bfbba482-1709-43d1-8a6d-79447a159752	cont-contrastive-neural-text-generation	2205.14690	null	https://arxiv.org/abs/2205.14690v4	https://arxiv.org/pdf/2205.14690v4.pdf	CoNT: Contrastive Neural Text Generation	Recently, contrastive learning attracts increasing interests in neural text generation as a new solution to alleviate the exposure bias problem. It introduces a sequence-level training signal which is crucial to generation tasks that always rely on auto-regressive decoding. However, previous methods using contrastive learning in neural text generation usually lead to inferior performance. In this paper, we analyse the underlying reasons and propose a new Contrastive Neural Text generation framework, CoNT. CoNT addresses bottlenecks that prevent contrastive learning from being widely adopted in generation tasks from three aspects -- the construction of contrastive examples, the choice of the contrastive loss, and the strategy in decoding. We validate CoNT on five generation tasks with ten benchmarks, including machine translation, summarization, code comment generation, data-to-text generation and commonsense generation. Experimental results show that CoNT clearly outperforms the conventional training framework on all the ten benchmarks with a convincing margin. Especially, CoNT surpasses previous the most competitive contrastive learning method for text generation, by 1.50 BLEU on machine translation and 1.77 ROUGE-1 on summarization, respectively. It achieves new state-of-the-art on summarization, code comment generation (without external data) and data-to-text generation.	['Xuanjing Huang', 'Xipeng Qiu', 'Lingpeng Kong', 'Kai Lv', 'Jiangtao Feng', 'Chenxin An']	2022-05-29	null	null	null	null	['code-comment-generation', 'comment-generation', 'data-to-text-generation']	['computer-code', 'natural-language-processing', 'natural-language-processing']	[ 6.05091393e-01 1.76770046e-01 -1.59543872e-01 -4.15003076e-02 -1.27584553e+00 -4.08193707e-01 1.07064426e+00 -2.94383280e-02 -4.12947565e-01 1.41283941e+00 6.23942554e-01 -5.68392515e-01 3.81639421e-01 -7.62476861e-01 -8.47579062e-01 -6.58962727e-01 6.01403058e-01 5.79308867e-01 -2.44381025e-01 -5.74441016e-01 6.34191036e-01 -1.93558961e-01 -1.13525856e+00 4.27975655e-01 1.38295686e+00 6.38105333e-01 2.88021863e-01 8.72993648e-01 -2.76965618e-01 7.56065190e-01 -1.20255470e+00 -8.18291783e-01 -1.57119364e-01 -1.11965370e+00 -9.02285159e-01 -3.40573370e-01 3.94302070e-01 -1.64974496e-01 -3.87982354e-02 8.66284370e-01 1.09606290e+00 5.42322434e-02 1.17450023e+00 -9.71410513e-01 -1.07284582e+00 1.24560094e+00 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fb23f412-8ad3-4e49-b4ca-59a760074568	unsupervised-domain-adaptation-on-person-re	2301.12439	null	https://arxiv.org/abs/2301.12439v1	https://arxiv.org/pdf/2301.12439v1.pdf	Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning	Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generated labels to train the model in the target domain. However, these homogeneous networks identify people in approximate subspaces and equally exchange their knowledge with others or their mean net to improve their ability, inevitably limiting the scope of available knowledge and putting them into the same mistake. This paper proposes a Dual-level Asymmetric Mutual Learning method (DAML) to learn discriminative representations from a broader knowledge scope with diverse embedding spaces. Specifically, two heterogeneous networks mutually learn knowledge from asymmetric subspaces through the pseudo label generation in a hard distillation manner. The knowledge transfer between two networks is based on an asymmetric mutual learning manner. The teacher network learns to identify both the target and source domain while adapting to the target domain distribution based on the knowledge of the student. Meanwhile, the student network is trained on the target dataset and employs the ground-truth label through the knowledge of the teacher. Extensive experiments in Market-1501, CUHK-SYSU, and MSMT17 public datasets verified the superiority of DAML over state-of-the-arts.	['Rongrong Ji', 'Yongjian Wu', 'Liujuan Cao', 'Qixiang Ye', 'Pingyang Dai', 'Jiahan Li', 'Qiong Wu']	2023-01-29	null	null	null	null	['person-re-identification']	['computer-vision']	[ 3.72415222e-02 -1.30553972e-02 -2.56102890e-01 -5.49984872e-01 -3.46278787e-01 -4.87962306e-01 4.39460307e-01 -3.03749889e-01 -7.38574684e-01 1.08693743e+00 6.47680238e-02 2.75796086e-01 1.40737757e-01 -9.04664397e-01 -5.47987103e-01 -8.62208068e-01 5.45528650e-01 6.63912952e-01 7.04031661e-02 1.31235182e-01 -2.82790154e-01 7.67913833e-02 -1.27694786e+00 1.80422530e-01 1.09321368e+00 7.89454043e-01 1.77824609e-02 1.11682430e-01 -8.14264715e-02 9.16216552e-01 -3.89426678e-01 -8.22934508e-01 5.56670606e-01 -4.68393952e-01 -7.38139987e-01 1.24892950e-01 4.76870954e-01 -3.32116723e-01 -8.54281962e-01 1.25788760e+00 7.45402038e-01 2.27003485e-01 9.53168690e-01 -1.43838537e+00 -1.15956163e+00 4.10758615e-01 -6.20047390e-01 1.31652178e-02 -5.35568446e-02 1.84844807e-01 5.99955082e-01 -7.64953732e-01 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69f46333-05a5-491b-ae0d-3bc160aa5c7f	dataset-and-baseline-for-automatic-student	null	null	https://aclanthology.org/2022.lrec-1.219	https://aclanthology.org/2022.lrec-1.219.pdf	Dataset and Baseline for Automatic Student Feedback Analysis	In this paper, we present a student feedback corpus, which contains 3000 instances of feedback written by university students. This dataset has been annotated for aspect terms, opinion terms, polarities of the opinion terms towards targeted aspects, document-level opinion polarities and sentence separations. We develop a hierarchical taxonomy for aspect categorization, which covers all the areas of the teaching-learning process. We annotated both implicit and explicit aspects using this taxonomy. Annotation methodology, difficulties faced during the annotation, and the details about the aspect term categorization have been discussed in detail. This annotated corpus can be used for Aspect Extraction, Aspect Level Sentiment Analysis, and Document Level Sentiment Analysis. Also the baseline results for all three tasks are given in the paper.	['Surangika Ranathunga', 'Hashan Maduwantha', 'Kushan Chamindu', 'Missaka Herath']	null	null	null	null	lrec-2022-6	['aspect-extraction']	['natural-language-processing']	[-4.58995700e-02 6.54402018e-01 -4.39914972e-01 -6.59465551e-01 -5.16958535e-01 -1.15418482e+00 6.97591007e-01 1.12171543e+00 -3.74669135e-01 5.96228480e-01 7.32387602e-01 -7.37028897e-01 -1.16835438e-01 -7.93556869e-01 -4.71182540e-02 -6.01107538e-01 2.69752651e-01 4.46944773e-01 1.71329334e-01 -5.90284824e-01 9.04841602e-01 1.93542540e-01 -1.70414209e+00 7.98632503e-01 8.46878231e-01 9.50538516e-01 -4.92101699e-01 8.71495843e-01 -7.97130644e-01 8.21339309e-01 -1.17861879e+00 -9.28992808e-01 -4.36241508e-01 -2.68590271e-01 -1.18588626e+00 4.25516129e-01 6.23886704e-01 3.55827451e-01 7.24222660e-01 1.06677747e+00 5.47939956e-01 2.57051289e-01 1.05021203e+00 -1.02551162e+00 -6.86970413e-01 6.92387581e-01 -3.02438676e-01 2.97614574e-01 8.22683096e-01 -5.45425057e-01 1.36343098e+00 -8.15356553e-01 7.05574274e-01 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17d1e310-156a-44eb-a947-6b512f23a17b	co-teaching-for-unsupervised-domain	2204.01210	null	https://arxiv.org/abs/2204.01210v2	https://arxiv.org/pdf/2204.01210v2.pdf	Co-Teaching for Unsupervised Domain Adaptation and Expansion	Unsupervised Domain Adaptation (UDA) is known to trade a model's performance on a source domain for improving its performance on a target domain. To resolve the issue, Unsupervised Domain Expansion (UDE) has been proposed recently to adapt the model for the target domain as UDA does, and in the meantime maintain its performance on the source domain. For both UDA and UDE, a model tailored to a given domain, let it be the source or the target domain, is assumed to well handle samples from the given domain. We question the assumption by reporting the existence of cross-domain visual ambiguity: Due to the lack of a crystally clear boundary between the two domains, samples from one domain can be visually close to the other domain. We exploit this finding and accordingly propose in this paper Co-Teaching (CT) that consists of knowledge distillation based CT (kdCT) and mixup based CT (miCT). Specifically, kdCT transfers knowledge from a leader-teacher network and an assistant-teacher network to a student network, so the cross-domain visual ambiguity will be better handled by the student. Meanwhile, miCT further enhances the generalization ability of the student. Comprehensive experiments on two image-classification benchmarks and two driving-scene-segmentation benchmarks justify the viability of the proposed method.	['Xirong Li', 'Qijie Wei', 'Kaibin Tian']	2022-04-04	null	null	null	null	['scene-segmentation', 'unsupervised-domain-expansion']	['computer-vision', 'methodology']	[ 2.01276451e-01 3.68727565e-01 -1.35527849e-01 -3.81261826e-01 -3.81163478e-01 -6.27729893e-01 4.21054989e-01 2.00096384e-01 -2.70970851e-01 7.67284870e-01 -3.22602987e-01 -3.49718809e-01 -2.52075523e-01 -8.76241088e-01 -6.76260948e-01 -9.06751454e-01 5.21192133e-01 6.98132098e-01 7.89165020e-01 -1.82957232e-01 1.04650892e-02 4.52114880e-01 -1.50676787e+00 1.70614034e-01 1.42147136e+00 7.02268243e-01 5.56541026e-01 1.31560534e-01 -5.24172068e-01 5.17072737e-01 -7.08383679e-01 -2.83976316e-01 2.60813057e-01 -5.12779653e-01 -9.29507375e-01 4.21024978e-01 3.12323511e-01 2.11908687e-02 -7.36861005e-02 1.08770704e+00 2.30558023e-01 2.39642709e-01 7.62258232e-01 -1.35890937e+00 -5.30768394e-01 3.60852867e-01 -6.84389353e-01 1.45870432e-01 6.15415769e-03 -4.64741141e-02 6.01187289e-01 -7.61511385e-01 8.13235283e-01 9.51662064e-01 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3292874f-9b6f-4ee4-833d-dccf52d17ac2	continuous-sign-language-recognition-through-1	null	null	https://www.mdpi.com/1424-8220/21/7/2437	https://www.mdpi.com/1424-8220/21/7/2437	Continuous Sign Language Recognition through a Context-Aware Generative Adversarial Network	Continuous sign language recognition is a weakly supervised task dealing with the identification of continuous sign gestures from video sequences, without any prior knowledge about the temporal boundaries between consecutive signs. Most of the existing methods focus mainly on the extraction of spatio-temporal visual features without exploiting text or contextual information to further improve the recognition accuracy. Moreover, the ability of deep generative models to effectively model data distribution has not been investigated yet in the field of sign language recognition. To this end, a novel approach for context-aware continuous sign language recognition using a generative adversarial network architecture, named as Sign Language Recognition Generative Adversarial Network (SLRGAN), is introduced. The proposed network architecture consists of a generator that recognizes sign language glosses by extracting spatial and temporal features from video sequences, as well as a discriminator that evaluates the quality of the generator’s predictions by modeling text information at the sentence and gloss levels. The paper also investigates the importance of contextual information on sign language conversations for both Deaf-to-Deaf and Deaf-to-hearing communication. Contextual information, in the form of hidden states extracted from the previous sentence, is fed into the bidirectional long short-term memory module of the generator to improve the recognition accuracy of the network. At the final stage, sign language translation is performed by a transformer network, which converts sign language glosses to natural language text. Our proposed method achieved word error rates of 23.4%, 2.1% and 2.26% on the RWTH-Phoenix-Weather-2014 and the Chinese Sign Language (CSL) and Greek Sign Language (GSL) Signer Independent (SI) datasets, respectively	['Petros Daras', 'Kosmas Dimitropoulos', 'Ilias Papastratis']	2021-04-01	null	null	null	null	['sign-language-translation']	['computer-vision']	[ 4.98224854e-01 -2.08080038e-01 1.55186981e-01 -4.36380625e-01 -7.78739512e-01 -4.34220731e-01 8.61113012e-01 -1.01338995e+00 -5.99875033e-01 6.05889916e-01 4.82277155e-01 -2.22437367e-01 6.91633150e-02 -6.56603336e-01 -5.74918985e-01 -9.75864232e-01 1.28559530e-01 3.22480559e-01 1.26873667e-04 -1.21019170e-01 -1.10438429e-01 4.46472019e-01 -1.63330591e+00 4.06281620e-01 7.92141497e-01 7.97569752e-01 1.36542274e-02 1.06880653e+00 -5.97836040e-02 1.14456606e+00 -6.76986992e-01 -2.78676033e-01 2.53450692e-01 -9.37919736e-01 -4.40957397e-01 -8.34369063e-02 2.81575799e-01 -6.41590595e-01 -6.86071336e-01 7.40675211e-01 9.44415867e-01 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ec5fa778-752b-4ab0-805e-de57008f949e	analytical-engines-with-context-rich	2212.07517	null	https://arxiv.org/abs/2212.07517v1	https://arxiv.org/pdf/2212.07517v1.pdf	Analytical Engines With Context-Rich Processing: Towards Efficient Next-Generation Analytics	As modern data pipelines continue to collect, produce, and store a variety of data formats, extracting and combining value from traditional and context-rich sources such as strings, text, video, audio, and logs becomes a manual process where such formats are unsuitable for RDBMS. To tap into the dark data, domain experts analyze and extract insights and integrate them into the data repositories. This process can involve out-of-DBMS, ad-hoc analysis, and processing resulting in ETL, engineering effort, and suboptimal performance. While AI systems based on ML models can automate the analysis process, they often further generate context-rich answers. Using multiple sources of truth, for either training the models or in the form of knowledge bases, further exacerbates the problem of consolidating the data of interest. We envision an analytical engine co-optimized with components that enable context-rich analysis. Firstly, as the data from different sources or resulting from model answering cannot be cleaned ahead of time, we propose using online data integration via model-assisted similarity operations. Secondly, we aim for a holistic pipeline cost- and rule-based optimization across relational and model-based operators. Thirdly, with increasingly heterogeneous hardware and equally heterogeneous workloads ranging from traditional relational analytics to generative model inference, we envision a system that just-in-time adapts to the complex analytical query requirements. To solve increasingly complex analytical problems, ML offers attractive solutions that must be combined with traditional analytical processing and benefit from decades of database community research to achieve scalability and performance effortless for the end user.	['Anastasia Ailamaki', 'Viktor Sanca']	2022-12-14	null	null	null	null	['data-integration']	['knowledge-base']	[-1.79250482e-02 -1.51851878e-01 -6.50647134e-02 -3.48960221e-01 -8.49723339e-01 -6.73250139e-01 1.90760374e-01 6.99200571e-01 -2.14082494e-01 1.72430947e-01 -4.63538663e-03 -6.23148561e-01 -2.32398942e-01 -9.95354831e-01 -5.51827908e-01 9.47300047e-02 -2.60682218e-02 9.07508552e-01 4.78889376e-01 -2.38955989e-01 2.77365953e-01 5.98481417e-01 -1.85534453e+00 5.66701353e-01 7.99989343e-01 1.29611278e+00 1.06850527e-01 7.25818396e-01 -7.62939274e-01 1.09431863e+00 -6.91725016e-01 -4.70323980e-01 4.47089851e-01 -1.47074265e-02 -6.22403562e-01 -2.39917412e-01 2.52312589e-02 -4.46575016e-01 -5.36710210e-02 5.84047854e-01 3.03417623e-01 -2.77978450e-01 -6.77258745e-02 -1.19646430e+00 -2.52262086e-01 5.30333817e-01 -2.38778561e-01 1.31809324e-01 4.58163530e-01 6.75007522e-01 9.34038579e-01 -9.30301309e-01 7.16878772e-01 1.00313509e+00 6.23971283e-01 -2.31974319e-01 -1.25283635e+00 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9a8643c7-a3e2-4c3e-820a-b19773ed3ff4	a-generative-deep-learning-approach-to	2204.02028	null	https://arxiv.org/abs/2204.02028v2	https://arxiv.org/pdf/2204.02028v2.pdf	A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts	Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems, i.e., learning to add fine-scale structure to coarse images. Leinonen et al. (2020) previously applied a GAN to produce ensembles of reconstructed high-resolution atmospheric fields, given coarsened input data. In this paper, we demonstrate this approach can be extended to the more challenging problem of increasing the accuracy and resolution of comparatively low-resolution input from a weather forecasting model, using high-resolution radar measurements as a "ground truth". The neural network must learn to add resolution and structure whilst accounting for non-negligible forecast error. We show that GANs and VAE-GANs can match the statistical properties of state-of-the-art pointwise post-processing methods whilst creating high-resolution, spatially coherent precipitation maps. Our model compares favourably to the best existing downscaling methods in both pixel-wise and pooled CRPS scores, power spectrum information and rank histograms (used to assess calibration). We test our models and show that they perform in a range of scenarios, including heavy rainfall.	['Tim N. Palmer', 'Peter D. Dueben', 'Matthew Chantry', 'Andrew T. T. McRae', 'Lucy Harris']	2022-04-05	null	null	null	null	['weather-forecasting']	['miscellaneous']	[ 4.14854348e-01 -3.11584938e-02 4.18387204e-01 -4.00207222e-01 -9.63287055e-01 -7.77269781e-01 1.04533160e+00 -2.01302767e-01 -1.54964566e-01 1.53394997e+00 3.02790761e-01 -3.54886919e-01 -6.09366223e-02 -1.28651667e+00 -5.41818261e-01 -1.03911316e+00 -3.99179995e-01 5.36965668e-01 -3.70338589e-01 -6.50911391e-01 -2.60873765e-01 8.36610854e-01 -1.57123661e+00 -1.33614421e-01 1.13894272e+00 5.15476346e-01 6.26750886e-02 9.39675391e-01 1.90166458e-01 5.27084887e-01 -6.42936587e-01 3.66043337e-02 5.23281097e-01 -6.40832365e-01 -2.45214343e-01 -1.60415024e-01 1.01899028e+00 -3.42966259e-01 -9.33547392e-02 1.11138940e+00 5.83123028e-01 1.66294351e-01 7.85521388e-01 -7.38696516e-01 -5.77435791e-01 3.11437547e-01 -6.13210917e-01 3.35856408e-01 -2.85445035e-01 2.51439184e-01 7.51079619e-01 -7.25905001e-01 3.71482670e-01 1.12254167e+00 1.13814044e+00 1.28615290e-01 -1.81190646e+00 -8.12905729e-01 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-9.27540720e-01 2.28268847e-01 7.39085138e-01 -4.28515285e-01 -7.57572114e-01 -6.74029719e-03]	[6.601593494415283, 2.9043047428131104]
311e7e0e-6dd7-44e6-aa96-3d1146d0b4ff	a-survey-of-natural-language-generation	2112.11739	null	https://arxiv.org/abs/2112.11739v2	https://arxiv.org/pdf/2112.11739v2.pdf	A Survey of Natural Language Generation	This paper offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology. This survey aims to (a) give the latest synthesis of deep learning research on the NLG core tasks, as well as the architectures adopted in the field; (b) detail meticulously and comprehensively various NLG tasks and datasets, and draw attention to the challenges in NLG evaluation, focusing on different evaluation methods and their relationships; (c) highlight some future emphasis and relatively recent research issues that arise due to the increasing synergy between NLG and other artificial intelligence areas, such as computer vision, text and computational creativity.	['Min Yang', 'Ying Shen', 'Junxin Li', 'Miaoxin Chen', 'Haifan Gong', 'Yinghui Li', 'Chenhe Dong']	2021-12-22	null	null	null	null	['data-to-text-generation']	['natural-language-processing']	[ 4.16359216e-01 5.84029794e-01 -1.25486106e-01 1.23918742e-01 -5.20885527e-01 -5.60815454e-01 1.13675642e+00 -1.77863970e-01 1.46830101e-02 1.10495448e+00 8.19040477e-01 -1.68576062e-01 1.62366658e-01 -1.12111139e+00 -2.42551133e-01 -5.83266854e-01 1.02780700e-01 7.64103293e-01 -1.09111035e+00 -5.60222328e-01 4.82312262e-01 2.38239408e-01 -1.34661865e+00 4.32018131e-01 1.25304770e+00 9.62664068e-01 1.06565818e-01 9.19909716e-01 -6.23175263e-01 7.45946229e-01 -1.24124026e+00 -7.92887866e-01 -1.34962589e-01 -8.53765547e-01 -1.19391131e+00 5.35466410e-02 4.78714257e-01 -1.91730246e-01 -1.43440410e-01 6.17776752e-01 1.04202425e+00 2.88842380e-01 9.03643072e-01 -1.29755819e+00 -1.59836006e+00 8.50350499e-01 6.35230541e-02 1.42660066e-02 5.94660938e-01 4.18583691e-01 1.13420975e+00 -1.00660193e+00 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0ed66d7f-3693-49cb-bd6f-accb9f4d2634	a-multi-perspective-architecture-for-semantic	2005.06980	null	https://arxiv.org/abs/2005.06980v1	https://arxiv.org/pdf/2005.06980v1.pdf	A Multi-Perspective Architecture for Semantic Code Search	The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories. In this paper, we propose a novel multi-perspective cross-lingual neural framework for code--text matching, inspired in part by a previous model for monolingual text-to-text matching, to capture both global and local similarities. Our experiments on the CoNaLa dataset show that our proposed model yields better performance on this cross-lingual text-to-code matching task than previous approaches that map code and text to a single joint embedding space.	['JinJun Xiong', 'Lingfei Wu', 'Julia Hockenmaier', 'Rajarshi Haldar']	2020-05-06	a-multi-perspective-architecture-for-semantic-1	https://aclanthology.org/2020.acl-main.758	https://aclanthology.org/2020.acl-main.758.pdf	acl-2020-6	['code-search', 'code-search']	['computer-code', 'computer-vision']	[-2.26196006e-01 -2.57103831e-01 -3.82348567e-01 -5.00390053e-01 -1.32361901e+00 -6.31819189e-01 6.37171507e-01 3.85106117e-01 -1.55585304e-01 -2.38710687e-01 4.25339311e-01 -2.65177220e-01 -1.12232931e-01 -4.18525457e-01 -5.65909564e-01 1.73494726e-01 1.92453012e-01 4.01321620e-01 -1.25607818e-01 -1.60759017e-01 2.37571806e-01 -1.50439590e-01 -1.44901502e+00 6.89564466e-01 8.71558070e-01 4.72184777e-01 5.43078482e-01 4.06751335e-01 -6.32741749e-01 9.16032255e-01 -6.18607625e-02 -9.28777635e-01 1.67200744e-01 -6.39041588e-02 -8.60332668e-01 -5.73934615e-01 9.36234772e-01 5.26553690e-02 -4.17304695e-01 1.45652509e+00 3.54062349e-01 -3.36444736e-01 5.48782289e-01 -1.30418909e+00 -1.34924221e+00 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9fd256e0-2c2f-45c2-b0f0-0cf0e63a458d	accurate-brain-extraction-using-active-shape	1802.01268	null	https://arxiv.org/abs/1802.01268v3	https://arxiv.org/pdf/1802.01268v3.pdf	ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks	Brain extraction (skull stripping) is a challenging problem in neuroimaging. It is due to the variability in conditions from data acquisition or abnormalities in images, making brain morphology and intensity characteristics changeable and complicated. In this paper, we propose an algorithm for skull stripping in Magnetic Resonance Imaging (MRI) scans, namely ASMCNN, by combining the Active Shape Model (ASM) and Convolutional Neural Network (CNN) for taking full of their advantages to achieve remarkable results. Instead of working with 3D structures, we process 2D image sequences in the sagittal plane. First, we divide images into different groups such that, in each group, shapes and structures of brain boundaries have similar appearances. Second, a modified version of ASM is used to detect brain boundaries by utilizing prior knowledge of each group. Finally, CNN and post-processing methods, including Conditional Random Field (CRF), Gaussian processes, and several special rules are applied to refine the segmentation contours. Experimental results show that our proposed method outperforms current state-of-the-art algorithms by a significant margin in all experiments.	['Pham T. Bao', 'Thu Nguyen', 'Mai T. N. Truong', 'Nguyen A. Triet', 'Khanh T. Tran', 'Duy M. Nguyen', 'Duy H. M. Nguyen', 'Binh T. Nguyen']	2018-02-05	null	null	null	null	['skull-stripping']	['medical']	[ 4.68424618e-01 8.17197859e-02 2.44280428e-01 -5.26924074e-01 -1.88908145e-01 -1.56377196e-01 2.72944301e-01 2.63283923e-02 -7.46529222e-01 6.12476707e-01 -6.91170618e-02 3.56911346e-02 -9.17693898e-02 -5.52041471e-01 -3.27116191e-01 -8.31889629e-01 -1.90395698e-01 4.79378134e-01 6.41328573e-01 2.60802120e-01 3.13887358e-01 9.58095670e-01 -1.10923624e+00 -1.60532966e-01 1.21396756e+00 8.94371569e-01 3.80667895e-01 2.09802151e-01 -3.34906012e-01 3.72661501e-01 -2.79288024e-01 -1.74073309e-01 2.46328652e-01 -3.04724246e-01 -8.08485627e-01 4.46988821e-01 2.32080817e-02 -2.02208921e-01 -3.78369093e-02 1.43153274e+00 4.66755360e-01 2.00151458e-01 8.79862905e-01 -8.32680106e-01 -4.54653144e-01 4.96710420e-01 -1.07597280e+00 3.71501625e-01 -1.89084098e-01 -2.18515825e-02 -8.58564451e-02 -9.10525084e-01 5.10834932e-01 1.01424253e+00 5.95549583e-01 5.24538279e-01 -1.07053339e+00 -9.28358912e-01 6.58614933e-02 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ffc79d9c-cef0-4528-b2bb-988d0b51d7a8	m2fpa-a-multi-yaw-multi-pitch-high-quality	1904.00168	null	https://arxiv.org/abs/1904.00168v2	https://arxiv.org/pdf/1904.00168v2.pdf	M2FPA: A Multi-Yaw Multi-Pitch High-Quality Database and Benchmark for Facial Pose Analysis	Facial images in surveillance or mobile scenarios often have large view-point variations in terms of pitch and yaw angles. These jointly occurred angle variations make face recognition challenging. Current public face databases mainly consider the case of yaw variations. In this paper, a new large-scale Multi-yaw Multi-pitch high-quality database is proposed for Facial Pose Analysis (M2FPA), including face frontalization, face rotation, facial pose estimation and pose-invariant face recognition. It contains 397,544 images of 229 subjects with yaw, pitch, attribute, illumination and accessory. M2FPA is the most comprehensive multi-view face database for facial pose analysis. Further, we provide an effective benchmark for face frontalization and pose-invariant face recognition on M2FPA with several state-of-the-art methods, including DR-GAN, TP-GAN and CAPG-GAN. We believe that the new database and benchmark can significantly push forward the advance of facial pose analysis in real-world applications. Moreover, a simple yet effective parsing guided discriminator is introduced to capture the local consistency during GAN optimization. Extensive quantitative and qualitative results on M2FPA and Multi-PIE demonstrate the superiority of our face frontalization method. Baseline results for both face synthesis and face recognition from state-of-theart methods demonstrate the challenge offered by this new database.	['Pei-Pei Li', 'Yibo Hu', 'Xiang Wu', 'Zhenan Sun', 'Ran He']	2019-03-30	null	null	null	null	['robust-face-recognition']	['computer-vision']	[-1.88035429e-01 -5.29001094e-02 -1.54125169e-02 -6.98585451e-01 -8.82647455e-01 -4.83425319e-01 3.96640271e-01 -1.30098832e+00 3.38412791e-01 3.68994325e-01 1.82859063e-01 3.65127504e-01 2.06352845e-01 -5.37770689e-01 -6.18938327e-01 -1.17618775e+00 1.75464094e-01 3.47047031e-01 -5.82198739e-01 -2.26075262e-01 -2.86842704e-01 1.15599084e+00 -1.76562166e+00 7.37758726e-02 2.16251537e-01 1.27230966e+00 -3.32505554e-01 2.46540904e-01 5.06958365e-01 -2.67114997e-01 -4.88856941e-01 -9.80858862e-01 5.13019085e-01 -2.11391985e-01 -2.44767413e-01 5.09829879e-01 1.12924755e+00 -5.34880042e-01 -2.12267354e-01 7.48773992e-01 1.05083323e+00 -2.39573449e-01 4.89774972e-01 -1.41464448e+00 -2.95395404e-01 -1.57418564e-01 -9.62072730e-01 -2.41466433e-01 5.22053838e-01 3.92684788e-01 3.15345347e-01 -1.47689927e+00 6.97539866e-01 1.75970209e+00 7.72175789e-01 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d62004b7-406f-4748-9735-0bb9a98d3143	dtp-net-a-convolutional-neural-network-model	null	null	https://www.sciencedirect.com/science/article/pii/S0010482522006047	https://doi.org/10.1016/j.compbiomed.2022.105852	DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images	Highly focused images of skin captured with ordinary cameras, called macro-images, are extensively used in dermatology. Being highly focused views, the macro-images contain only lesions and background regions. Hence, the localization of lesions on the macro-images is a simple thresholding problem. However, algorithms that offer an accurate estimate of threshold and retain consistent performance on different dermatological macro-images are rare. A deep learning model, termed ‘Deep Threshold Prediction Network (DTP-Net)’, is proposed in this paper to address this issue. For training the model, grayscale versions of the macro-images are fed as input to the model, and the corresponding gray-level threshold values at which the Dice similarity index (DSI) between the segmented and the ground-truth images are maximized are defined as the targets. The DTP-Net exhibited the least value of root mean square error for the predicted threshold compared with 11 state-of-the-art threshold estimation algorithms (such as Otsu’s thresholding, Valley emphasized otsu’s thresholding, Isodata thresholding, Histogram slope difference distribution-based thresholding, Minimum error thresholding, Poisson’s distribution-based minimum error thresholding, Kapur’s maximum entropy thresholding, Entropy-weighted otsu’s thresholding, Minimum cross-entropy thresholding, Type-2 fuzzy-based thresholding, and Fuzzy entropy thresholding). The DTP-Net could learn the difference between the lesion and background in the intensity space and accurately predict the threshold that separates the lesion from the background. The proposed DTP-Net can be integrated into the segmentation module in automated tools that detect skin cancer from dermatological macro-images.	['Malaya Kumar Nath', 'M Vipin Das', 'Justin Joseph', 'Vipin Venugopal']	2022-07-12	null	null	null	computers-in-biology-and-medicine-2022-7	['skin-lesion-segmentation']	['medical']	[ 4.48472142e-01 5.51320538e-02 -1.90890536e-01 -4.31912839e-01 -5.42635262e-01 -3.42061967e-01 -2.00792849e-01 3.70903850e-01 -6.10101819e-01 5.29241383e-01 -4.57369208e-01 9.36562642e-02 -1.56989187e-01 -9.04680908e-01 -2.72473991e-01 -1.05948317e+00 2.15853781e-01 1.69521406e-01 6.00764036e-01 2.52570331e-01 3.10608983e-01 4.68287081e-01 -1.43298566e+00 6.42046213e-01 1.06654692e+00 1.43885767e+00 1.40434906e-01 6.72375798e-01 -2.08040118e-01 3.88283432e-01 -4.47285533e-01 -4.23047513e-01 1.27565920e-01 -4.72271025e-01 -3.12509596e-01 1.11734487e-01 5.12402833e-01 -3.01226020e-01 7.66473785e-02 1.35460353e+00 4.82952982e-01 -3.37940216e-01 9.82322454e-01 -9.02573407e-01 -4.74607974e-01 -7.80263245e-02 -8.71432602e-01 1.92997783e-01 1.55189425e-01 1.59360692e-01 4.58421022e-01 -5.54857314e-01 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163a1a88-76ad-4640-a114-1aa072a8d814	hd-bind-encoding-of-molecular-structure-with	2303.15604	null	https://arxiv.org/abs/2303.15604v1	https://arxiv.org/pdf/2303.15604v1.pdf	HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations	Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis. Traditional methods for identifying ``hit'' molecules from a large collection of potential drug-like candidates have relied on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug to its protein target. A major drawback of the approaches is that they require exceptional computing capabilities to consider for even relatively small collections of molecules. Hyperdimensional Computing (HDC) is a recently proposed learning paradigm that is able to leverage low-precision binary vector arithmetic to build efficient representations of the data that can be obtained without the need for gradient-based optimization approaches that are required in many conventional machine learning and deep learning approaches. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated for a range of application areas. We consider existing HDC approaches for molecular property classification and introduce two novel encoding algorithms that leverage the extended connectivity fingerprint (ECFP) algorithm. We show that HDC-based inference methods are as much as 90 times more efficient than more complex representative machine learning methods and achieve an acceleration of nearly 9 orders of magnitude as compared to inference with molecular docking. We demonstrate multiple approaches for the encoding of molecular data for HDC and examine their relative performance on a range of challenging molecular property prediction and drug-protein binding classification tasks. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools.	['Tajana S. Rosing', 'Niema Moshiri', 'Weihong Xu', 'Jaeyoung Kang', 'Behnam Khaleghi', 'Xiaohua Zhang', 'Jonathan E. Allen', 'Derek Jones']	2023-03-27	null	null	null	null	['molecular-docking', 'molecular-property-prediction']	['medical', 'miscellaneous']	[ 4.69701231e-01 -4.49184030e-01 -6.37516856e-01 -3.36709052e-01 -1.08750629e+00 -6.23599529e-01 5.78476429e-01 8.74159217e-01 -3.56295317e-01 1.22735071e+00 -3.57311219e-01 -8.36088181e-01 -3.40544999e-01 -8.13238382e-01 -7.86630094e-01 -8.84908557e-01 -5.02924800e-01 6.36619329e-01 1.55505642e-01 1.65908728e-02 6.09380960e-01 9.03223515e-01 -1.38969564e+00 3.05703521e-01 7.45071769e-01 8.63071680e-01 1.33743314e-02 5.93286335e-01 9.84439552e-02 3.32951784e-01 -4.95467186e-01 -2.35569701e-01 -2.71922387e-02 -2.72265971e-01 -7.49489844e-01 -6.87243760e-01 7.20023513e-01 -5.85358702e-02 -2.41819955e-02 7.15173781e-01 8.38305891e-01 2.01764122e-01 9.47151065e-01 -6.12563550e-01 -3.16127837e-01 1.79234579e-01 -2.92643040e-01 1.34566456e-01 6.00187361e-01 1.08420238e-01 9.64148641e-01 -9.02096748e-01 6.32913351e-01 8.66673648e-01 7.78121889e-01 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00efcb8a-c83f-402b-ab25-b6beb35e8bfd	post-training-model-quantization-using-gans	2305.06052	null	https://arxiv.org/abs/2305.06052v1	https://arxiv.org/pdf/2305.06052v1.pdf	Post-training Model Quantization Using GANs for Synthetic Data Generation	Quantization is a widely adopted technique for deep neural networks to reduce the memory and computational resources required. However, when quantized, most models would need a suitable calibration process to keep their performance intact, which requires data from the target domain, such as a fraction of the dataset used in model training and model validation (i.e. calibration dataset). In this study, we investigate the use of synthetic data as a substitute for the calibration with real data for the quantization method. We propose a data generation method based on Generative Adversarial Networks that are trained prior to the model quantization step. We compare the performance of models quantized using data generated by StyleGAN2-ADA and our pre-trained DiStyleGAN, with quantization using real data and an alternative data generation method based on fractal images. Overall, the results of our experiments demonstrate the potential of leveraging synthetic data for calibration during the quantization process. In our experiments, the percentage of accuracy degradation of the selected models was less than 0.6%, with our best performance achieved on MobileNetV2 (0.05%). The code is available at: https://github.com/ThanosM97/gsoc2022-openvino	['Raymond Lo', 'Zhuo Wu', 'Alexander Kozlov', 'Adrian Boguszewski', 'Mansi Sharma', 'Athanasios Masouris']	2023-05-10	null	null	null	null	['synthetic-data-generation', 'synthetic-data-generation']	['medical', 'miscellaneous']	[ 2.63045073e-01 3.01698565e-01 6.33196309e-02 -2.39695713e-01 -7.47666597e-01 -5.64934850e-01 7.41026282e-01 -9.55176875e-02 -7.03111410e-01 8.43812883e-01 -2.18505070e-01 -3.75562608e-01 4.46292818e-01 -1.05297136e+00 -9.98142779e-01 -6.28078759e-01 2.26962775e-01 4.06602055e-01 1.13491505e-01 -1.23556815e-01 3.90444621e-02 3.74791533e-01 -1.32227206e+00 1.85058475e-01 9.67219293e-01 1.18909693e+00 1.51341736e-01 7.34404266e-01 1.90261751e-02 6.65721893e-01 -1.16540205e+00 -5.80538213e-01 4.81063902e-01 -5.52301407e-01 -4.15531665e-01 -7.66014606e-02 4.49105382e-01 -6.19122088e-01 -3.78998876e-01 1.21155643e+00 6.69745505e-01 -1.08773775e-01 5.32058060e-01 -1.43443871e+00 -5.07641733e-01 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91cd5b67-acbe-4f27-99bb-61c13930ed0c	prokaryotic-genome-editing-based-on-the	2305.05093	null	https://arxiv.org/abs/2305.05093v1	https://arxiv.org/pdf/2305.05093v1.pdf	Prokaryotic genome editing based on the subtype I-B-Svi CRISPR-Cas system	Type I CRISPR-Cas systems are the most common among six types of CRISPR-Cas systems, however, non-self-targeting genome editing based on a single Cas3 of type I CRISPR-Cas systems has not been reported. Here, we present the subtype I-B-Svi CRISPR-Cas system (with three confirmed CRISPRs and a cas gene cluster) and genome editing based on this system found in Streptomyces virginiae IBL14. Importantly, like the animal-derived bacterial protein SpCas9 (1368 amino-acids), the single, compact, non-animal-derived bacterial protein SviCas3 (771 amino-acids) can also direct template-based microbial genome editing through the target cell's own homology-directed repair system, which breaks the view that the genome editing based on type I CRISPR-Cas systems requires a full Cascade. Notably, no off-target changes or indel-formation were detected in the analysis of potential off-target sites. This discovery broadens our understanding of the diversity of type I CRISPR-Cas systems and will facilitate new developments in genome editing tools.	['Xue Li', 'Yan Sun', 'Su-Li Cao', 'Qing-Yang Liu', 'Ting-Ting Xia', 'Xing-Wang Yang', 'Yan Zhang', 'Cai-Hua Qiu', 'Xin Xu', 'De-Xiang Yong', 'Wang-Yu Tong']	2023-05-08	null	null	null	null	['type']	['speech']	[ 1.08717310e+00 -2.43343581e-02 8.49313214e-02 3.54010224e-01 -3.75775278e-01 -1.64748490e+00 4.55698192e-01 6.64083600e-01 -1.57412440e-01 8.82690012e-01 -2.55621105e-01 -6.92467451e-01 -1.52628243e-01 -6.34277701e-01 -1.28488266e+00 -8.66682291e-01 2.15968322e-02 -2.44588912e-01 5.17404795e-01 -5.28676987e-01 7.09181249e-01 5.50174534e-01 -1.16225040e+00 4.76700425e-01 1.19316614e+00 6.10344447e-02 9.50381219e-01 8.91025364e-01 -1.55493960e-01 3.01106423e-01 -7.32712746e-01 1.05120331e-01 1.53017774e-01 -1.05161953e+00 1.11134939e-01 -1.07820332e+00 -2.55966276e-01 -1.56445205e-01 3.39239717e-01 6.86890006e-01 3.48722339e-01 -1.79404706e-01 8.34584713e-01 -8.65314841e-01 -9.22974586e-01 1.08432405e-01 -2.33075261e-01 -4.62573498e-01 7.77602196e-01 6.28231466e-02 1.52403623e-01 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5706794f-be75-4ccd-aece-d59388c9a725	interpretable-deep-learning-based-forensic	2112.00849	null	https://arxiv.org/abs/2112.00849v2	https://arxiv.org/pdf/2112.00849v2.pdf	Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition	Iris recognition of living individuals is a mature biometric modality that has been adopted globally from governmental ID programs, border crossing, voter registration and de-duplication, to unlocking mobile phones. On the other hand, the possibility of recognizing deceased subjects with their iris patterns has emerged recently. In this paper, we present an end-to-end deep learning-based method for postmortem iris segmentation and recognition with a special visualization technique intended to support forensic human examiners in their efforts. The proposed postmortem iris segmentation approach outperforms the state of the art and in addition to iris annulus, as in case of classical iris segmentation methods - detects abnormal regions caused by eye decomposition processes, such as furrows or irregular specular highlights present on the drying and wrinkling cornea. The method was trained and validated with data acquired from 171 cadavers, kept in mortuary conditions, and tested on subject-disjoint data acquired from 259 deceased subjects. To our knowledge, this is the largest corpus of data used in postmortem iris recognition research to date. The source code of the proposed method are offered with the paper. The test data will be available through the National Archive of Criminal Justice Data (NACJD) archives.	['Eric Benjamin', 'Dennis Chute', 'Patrick Flynn', 'Kevin Bowyer', 'Adam Czajka', 'Aidan Boyd', 'Andrey Kuehlkamp']	2021-12-01	null	null	null	null	['iris-segmentation']	['medical']	[ 1.46216610e-02 1.16022199e-01 7.28532001e-02 -9.56130251e-02 -3.75407040e-01 -3.67274433e-01 3.65315914e-01 1.73079699e-01 -5.10257542e-01 5.29228032e-01 1.25780672e-01 -3.68306190e-01 -2.77029127e-01 -2.33439058e-01 -1.50536686e-01 -7.26421833e-01 -6.21065795e-02 5.68660438e-01 -3.70068252e-01 3.19142729e-01 6.27899408e-01 9.34081435e-01 -1.69417739e+00 2.99257003e-02 9.20780361e-01 8.07111740e-01 -6.88515544e-01 5.90574682e-01 3.02983314e-01 4.04368252e-01 -6.49011195e-01 -7.56787717e-01 4.63241696e-01 -4.22807813e-01 -8.94402862e-01 2.20031843e-01 1.05645466e+00 -6.67898595e-01 -1.74206749e-01 8.63124371e-01 8.00722957e-01 -1.95792094e-02 6.13189518e-01 -5.12024939e-01 -5.84103763e-01 9.00386721e-02 -1.02080393e+00 5.64694583e-01 4.48313236e-01 4.17836070e-01 3.23640078e-01 -5.99919975e-01 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80ecccbc-0940-47be-9899-c9761ea9c098	towards-a-common-understanding-of	2305.16768	null	https://arxiv.org/abs/2305.16768v1	https://arxiv.org/pdf/2305.16768v1.pdf	Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Review	In recent years, pre-trained Multilingual Language Models (MLLMs) have shown a strong ability to transfer knowledge across different languages. However, given that the aspiration for such an ability has not been explicitly incorporated in the design of the majority of MLLMs, it is challenging to obtain a unique and straightforward explanation for its emergence. In this review paper, we survey literature that investigates different factors contributing to the capacity of MLLMs to perform zero-shot cross-lingual transfer and subsequently outline and discuss these factors in detail. To enhance the structure of this review and to facilitate consolidation with future studies, we identify five categories of such factors. In addition to providing a summary of empirical evidence from past studies, we identify consensuses among studies with consistent findings and resolve conflicts among contradictory ones. Our work contextualizes and unifies existing research streams which aim at explaining the cross-lingual potential of MLLMs. This review provides, first, an aligned reference point for future research and, second, guidance for a better-informed and more efficient way of leveraging the cross-lingual capacity of MLLMs.	['Shohreh Haddadan', 'Siwen Guo', 'Fred Philippy']	2023-05-26	null	null	null	null	['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer']	['natural-language-processing', 'natural-language-processing']	[-8.82262066e-02 6.78481981e-02 -8.55870008e-01 -4.19540018e-01 -1.13733101e+00 -9.15680289e-01 7.02928841e-01 1.82287797e-01 -6.78328812e-01 7.40531266e-01 3.04988950e-01 -9.68744278e-01 -1.97880380e-02 -2.68715411e-01 -7.66973913e-01 -2.38847002e-01 1.49859428e-01 1.63701773e-01 -1.99791491e-01 -1.00444727e-01 1.46598116e-01 3.49624068e-01 -1.49264765e+00 2.44578272e-01 1.08479404e+00 1.44893780e-01 5.13154924e-01 1.01630315e-01 -3.42986703e-01 6.64157629e-01 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67d69db5-7081-4b81-a1da-7881ca571051	high-frequency-stereo-matching-network	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_High-Frequency_Stereo_Matching_Network_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_High-Frequency_Stereo_Matching_Network_CVPR_2023_paper.pdf	High-Frequency Stereo Matching Network	In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo. However, most of these methods lose information during the iterative process, making it difficult to generate more detailed difference maps that take full advantage of high-frequency information. We propose the Decouple module to alleviate the problem of data coupling and allow features containing subtle details to transfer across the iterations which proves to alleviate the problem significantly in the ablations. To further capture high-frequency details, we propose a Normalization Refinement module that unifies the disparities as a proportion of the disparities over the width of the image, which address the problem of module failure in cross-domain scenarios. Further, with the above improvements, the ResNet-like feature extractor that has not been changed for years becomes a bottleneck. Towards this end, we proposed a multi-scale and multi-stage feature extractor that introduces the channel-wise self-attention mechanism which greatly addresses this bottleneck. Our method (DLNR) ranks 1st on the Middlebury leaderboard, significantly outperforming the next best method by 13.04%. Our method also achieves SOTA performance on the KITTI-2015 benchmark for D1-fg.	['Yong Zhao', 'Yitong Yang', 'Jie Chen', 'Yongjun Zhang', 'Huizhou Zhou', 'Haoliang Zhao']	2023-01-01	null	null	null	cvpr-2023-1	['stereo-matching-1']	['computer-vision']	[-4.83657643e-02 -1.55301765e-01 1.16051726e-01 -3.66390616e-01 -6.79802835e-01 -4.19723690e-01 7.16565967e-01 -2.12747931e-01 -5.07551968e-01 5.75088143e-01 5.92450202e-01 -5.18578663e-02 1.39121786e-01 -6.92886829e-01 -6.48633659e-01 -5.45286298e-01 2.68723935e-01 -8.21498558e-02 5.07051885e-01 -5.26980102e-01 3.39041322e-01 3.34656298e-01 -1.79146528e+00 4.48875755e-01 1.10105407e+00 9.05402958e-01 2.66781807e-01 3.35670292e-01 -1.22576177e-01 6.64786875e-01 -3.06532055e-01 -3.87036562e-01 8.12410176e-01 -2.28241399e-01 -7.14044571e-01 -9.20956284e-02 1.16552126e+00 -5.39277315e-01 -6.48129702e-01 1.07795262e+00 7.31909871e-01 -1.28063634e-01 2.93433458e-01 -1.08130682e+00 -3.56348127e-01 2.70503342e-01 -1.08383548e+00 2.63473153e-01 1.30338997e-01 2.75814205e-01 9.86936271e-01 -1.20819771e+00 6.78880453e-01 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848da995-7eec-45de-855d-2ecf650b61e3	surgical-phase-and-instrument-recognition-how	2306.16879	null	https://arxiv.org/abs/2306.16879v1	https://arxiv.org/pdf/2306.16879v1.pdf	Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits	Purpose: The development of machine learning models for surgical workflow and instrument recognition from temporal data represents a challenging task due to the complex nature of surgical workflows. In particular, the imbalanced distribution of data is one of the major challenges in the domain of surgical workflow recognition. In order to obtain meaningful results, careful partitioning of data into training, validation, and test sets, as well as the selection of suitable evaluation metrics are crucial. Methods: In this work, we present an openly available web-based application that enables interactive exploration of dataset partitions. The proposed visual framework facilitates the assessment of dataset splits for surgical workflow recognition, especially with regard to identifying sub-optimal dataset splits. Currently, it supports visualization of surgical phase and instrument annotations. Results: In order to validate the dedicated interactive visualizations, we use a dataset split of the Cholec80 dataset. This dataset split was specifically selected to reflect a case of strong data imbalance. Using our software, we were able to identify phases, phase transitions, and combinations of surgical instruments that were not represented in one of the sets. Conclusion: In order to obtain meaningful results in highly unbalanced class distributions, special care should be taken with respect to the selection of an appropriate split. Interactive data visualization represents a promising approach for the assessment of machine learning datasets. The source code is available at https://github.com/Cardio-AI/endovis-ml	['Sandy Engelhardt', 'Bernhard Preim', 'Ivo Wolf', 'Benedikt Mayer', 'Lalith Sharan', 'Georgii Kostiuchik']	2023-06-29	null	null	null	null	['instrument-recognition', 'data-visualization', 'data-visualization']	['audio', 'methodology', 'miscellaneous']	[ 3.37429121e-02 -2.11915374e-01 -2.55617857e-01 -1.76613584e-01 -4.23391312e-01 -6.86329126e-01 1.09246731e-01 1.03310955e+00 -4.49393988e-01 5.46833217e-01 4.83836792e-02 -6.74498320e-01 -6.73232019e-01 -4.53243434e-01 -8.47211033e-02 -7.95109272e-01 -1.65312603e-01 7.99596131e-01 -7.62552768e-02 4.38058861e-02 5.17271101e-01 1.08571625e+00 -1.66916811e+00 5.25402963e-01 8.13260436e-01 7.91035652e-01 2.62418956e-01 6.69600427e-01 -4.73367363e-01 4.11561221e-01 -8.64752293e-01 2.11423635e-01 2.15080947e-01 -6.10217631e-01 -5.71276367e-01 -8.47727731e-02 1.01184830e-01 1.64977729e-01 4.98811305e-01 5.06274402e-01 6.90110147e-01 -2.34534785e-01 6.09994173e-01 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54cf08de-de0e-46c7-8fd0-7ef6c64197c2	context-dependent-domain-adversarial-neural	null	null	https://www.isca-speech.org/archive/interspeech_2020/lian20b_interspeech.html	https://www.isca-speech.org/archive/pdfs/interspeech_2020/lian20b_interspeech.pdf	Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition	Emotion recognition remains a complex task due to speaker variations and low-resource training samples. To address these difficulties, we focus on the domain adversarial neural networks (DANN) for emotion recognition. The primary task is to predict emotion labels. The secondary task is to learn a common representation where speaker identities can not be distinguished. By using this approach, we bring the representations of different speakers closer. Meanwhile, through using the unlabeled data in the training process, we alleviate the impact of low-resource training samples. In the meantime, prior work found that contextual information and multimodal features are important for emotion recognition. However, previous DANN-based approaches ignore this information, thus limiting their performance. In this paper, we propose the context-dependent domain adversarial neural network for multimodal emotion recognition. To verify the effectiveness of our proposed method, we conduct experiments on the benchmark dataset IEMOCAP. Experimental results demonstrate that the proposed method shows an absolute improvement of 3.48% over state-of-the-art strategies.	['Rongjun Li', 'Zhanlei Yang', 'Jian Huang', 'Bin Liu', 'JianHua Tao', 'Zheng Lian']	2020-10-28	null	null	null	interspeech-2020-10	['multimodal-emotion-recognition', 'multimodal-emotion-recognition']	['computer-vision', 'speech']	[ 2.23906547e-01 -2.94316024e-01 9.05084312e-02 -5.65513968e-01 -7.16059446e-01 -5.69790602e-01 3.92473400e-01 -3.87156576e-01 -3.72302175e-01 7.41708279e-01 2.43203178e-01 6.79118261e-02 3.97167981e-01 -3.28974962e-01 -3.78134221e-01 -8.95515025e-01 3.33583266e-01 2.31573619e-02 -2.84956187e-01 -1.75235093e-01 -4.91754115e-02 4.09230232e-01 -1.38668287e+00 3.34514201e-01 9.73436177e-01 1.30425024e+00 -3.46859366e-01 2.44036943e-01 -3.90701562e-01 7.90995538e-01 -8.47695172e-01 -5.42391300e-01 9.76988208e-03 -6.17979825e-01 -5.98421872e-01 8.22025016e-02 1.18386477e-01 -2.23198190e-01 -2.53196269e-01 1.17589474e+00 7.02292979e-01 3.86494488e-01 8.12887788e-01 -1.48470485e+00 -6.41755939e-01 3.85249972e-01 -3.91396582e-01 -1.33848682e-01 2.31530875e-01 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83e24dad-bf68-425c-89ad-6b2721b1cd74	using-integrated-gradients-to-explain	2106.07349	null	https://arxiv.org/abs/2106.07349v2	https://arxiv.org/pdf/2106.07349v2.pdf	Using Integrated Gradients and Constituency Parse Trees to explain Linguistic Acceptability learnt by BERT	Linguistic Acceptability is the task of determining whether a sentence is grammatical or ungrammatical. It has applications in several use cases like Question-Answering, Natural Language Generation, Neural Machine Translation, where grammatical correctness is crucial. In this paper we aim to understand the decision-making process of BERT (Devlin et al., 2019) in distinguishing between Linguistically Acceptable sentences (LA) and Linguistically Unacceptable sentences (LUA). We leverage Layer Integrated Gradients Attribution Scores (LIG) to explain the Linguistic Acceptability criteria that are learnt by BERT on the Corpus of Linguistic Acceptability (CoLA) (Warstadt et al., 2018) benchmark dataset. Our experiments on 5 categories of sentences lead to the following interesting findings: 1) LIG for LA are significantly smaller in comparison to LUA, 2) There are specific subtrees of the Constituency Parse Tree (CPT) for LA and LUA which contribute larger LIG, 3) Across the different categories of sentences we observed around 88% to 100% of the Correctly classified sentences had positive LIG, indicating a strong positive relationship to the prediction confidence of the model, and 4) Around 43% of the Misclassified sentences had negative LIG, which we believe can become correctly classified sentences if the LIG are parameterized in the loss function of the model.	['Hari Prasad Timmapathini', 'Anmol Nayak']	2021-06-01	null	https://aclanthology.org/2021.icon-main.11	https://aclanthology.org/2021.icon-main.11.pdf	icon-2021-12	['linguistic-acceptability']	['natural-language-processing']	[ 9.09828469e-02 6.51303411e-01 1.31352693e-01 -8.50712061e-01 -8.62314105e-01 -7.11847007e-01 5.48193872e-01 4.55241770e-01 -2.76581347e-01 6.83163643e-01 3.46775681e-01 -7.60175943e-01 8.89701098e-02 -6.98715448e-01 -8.81063700e-01 -2.88779110e-01 -3.40216048e-02 2.61029392e-01 -3.81196737e-02 -3.24650198e-01 1.34967491e-01 1.05241671e-01 -1.39008665e+00 7.61396408e-01 1.29661179e+00 1.22911608e+00 -1.37100056e-01 6.33230865e-01 -1.44198328e-01 9.75570560e-01 -4.08321679e-01 -8.56972635e-01 5.82283027e-02 -4.43165392e-01 -1.15173876e+00 -3.99896801e-01 7.86428690e-01 1.74251154e-01 4.75891590e-01 1.26183677e+00 8.29997286e-02 -7.48903081e-02 8.95804584e-01 -9.48873043e-01 -6.93380892e-01 9.43713367e-01 -1.79773420e-01 2.03481495e-01 5.53469479e-01 3.07657838e-01 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58d9c0be-9808-4002-a3b1-97d89829c63c	why-a-naive-way-to-combine-symbolic-and	null	null	https://openreview.net/forum?id=JQHqeGx6qFw	https://openreview.net/pdf?id=JQHqeGx6qFw	Why a Naive Way to Combine Symbolic and Latent Knowledge Base Completion Works Surprisingly Well	We compare a rule-based approach for knowledge graph completion against current state-of-the-art, which is based on embbedings. Instead of focusing on aggregated metrics, we look at several examples that illustrate essential differences between symbolic and latent approaches. Based on our insights, we construct a simple method to combine the outcome of rule-based and latent approaches in a post-processing step. Our method improves the results constantly for each model and dataset used in our experiments.	['Heiner Stuckenschmidt', 'Patrick Betz', 'Christian Meilicke']	2021-06-22	null	null	null	akbc-2021-10	['knowledge-base-completion', 'knowledge-base-completion']	['graphs', 'knowledge-base']	[ 1.46928802e-01 4.44351226e-01 -4.32579070e-01 -1.86347976e-01 -3.88758540e-01 -6.16403818e-01 1.08323514e+00 4.56576943e-01 -1.61296979e-01 5.92316210e-01 2.31934413e-01 -4.11590904e-01 -7.57371485e-01 -9.51447785e-01 -4.94743675e-01 -2.92194970e-02 -2.94499815e-01 5.29186904e-01 5.31929493e-01 -2.25864023e-01 1.94366351e-01 3.95651877e-01 -1.67670405e+00 5.24449289e-01 8.59463394e-01 7.48985708e-01 -3.44045907e-01 2.88264602e-01 -2.15134516e-01 1.38670301e+00 -2.95356631e-01 -4.77182209e-01 5.65611050e-02 -2.05007672e-01 -1.06263292e+00 -2.20528096e-01 2.56294847e-01 1.72859840e-02 -3.75795096e-01 7.69481361e-01 5.07855564e-02 2.32332557e-01 5.88563502e-01 -1.27179945e+00 -3.60406488e-01 1.04527283e+00 6.32471815e-02 -1.85334504e-01 9.90228415e-01 -6.89355470e-03 1.05504799e+00 -9.86662805e-01 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25426063-8675-471f-9213-c2add18336d0	one-class-slab-support-vector-machine	1608.01026	null	http://arxiv.org/abs/1608.01026v1	http://arxiv.org/pdf/1608.01026v1.pdf	One-Class Slab Support Vector Machine	This work introduces the one-class slab SVM (OCSSVM), a one-class classifier that aims at improving the performance of the one-class SVM. The proposed strategy reduces the false positive rate and increases the accuracy of detecting instances from novel classes. To this end, it uses two parallel hyperplanes to learn the normal region of the decision scores of the target class. OCSSVM extends one-class SVM since it can scale and learn non-linear decision functions via kernel methods. The experiments on two publicly available datasets show that OCSSVM can consistently outperform the one-class SVM and perform comparable to or better than other state-of-the-art one-class classifiers.	['Joao Hespanha', 'Walter Scheirer', 'Victor Fragoso', 'Matthew Turk']	2016-08-02	null	null	null	null	['one-class-classifier']	['methodology']	[ 7.35575482e-02 -1.86911765e-02 -7.67751575e-01 -5.46645224e-01 -4.56867307e-01 -3.52805734e-01 4.79887187e-01 1.22593239e-01 -2.23885536e-01 1.01658106e+00 -7.82595813e-01 -5.95311999e-01 -7.38846837e-03 -6.10976934e-01 -4.65604514e-01 -9.85587716e-01 5.44748222e-03 3.78284216e-01 1.10589075e+00 9.26988199e-02 3.39235842e-01 7.28589535e-01 -1.63780391e+00 8.59600186e-01 9.15854394e-01 1.23498058e+00 -3.00453037e-01 5.56364059e-01 -5.91141805e-02 5.21379709e-01 -8.01904142e-01 -3.04714870e-02 1.41384870e-01 -3.83134857e-02 -7.60754049e-01 -2.22024977e-01 4.60379153e-01 8.92398059e-02 -2.26837680e-01 9.47071910e-01 -1.60103127e-01 1.63044512e-01 1.07836628e+00 -1.70957780e+00 -5.53365529e-01 -2.73812208e-02 -4.45747823e-01 4.72852767e-01 1.54411808e-01 -6.27667382e-02 7.74715781e-01 -9.90627527e-01 3.37631941e-01 9.62961376e-01 9.34793353e-01 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ec98155b-e904-44bc-b82c-004cc0066af9	computing-with-subjectivity-lexicons	null	null	https://aclanthology.org/2020.lrec-1.400	https://aclanthology.org/2020.lrec-1.400.pdf	Computing with Subjectivity Lexicons	In this paper, we introduce a new set of lexicons for expressing subjectivity in text documents written in Brazilian Portuguese. Besides the non-English idiom, in contrast to other subjectivity lexicons available, these lexicons represent different subjectivity dimensions (other than sentiment) and are more compact in number of terms. This last feature was designed intentionally to leverage the power of word embedding techniques, i.e., with the words mapped to an embedding space and the appropriate distance measures, we can easily capture semantically related words to the ones in the lexicons. Thus, we do not need to build comprehensive vocabularies and can focus on the most representative words for each lexicon dimension. We showcase the use of these lexicons in three highly non-trivial tasks: (1) Automated Essay Scoring in the Presence of Biased Ratings, (2) Subjectivity Bias in Brazilian Presidential Elections and (3) Fake News Classification Based on Text Subjectivity. All these tasks involve text documents written in Portuguese.	['ro', 'Le Balby Marinho', 'Claudio E. C. Campelo', 'Allan Sales', 'Roberta Viola', 'Caio L. M. Jeronimo', 'Adriano Veloso']	2020-05-01	null	null	null	lrec-2020-5	['automated-essay-scoring', 'news-classification']	['natural-language-processing', 'natural-language-processing']	[-3.57232302e-01 3.91276240e-01 -6.02716386e-01 -1.78222045e-01 -2.71618754e-01 -9.21456218e-01 1.12862039e+00 6.34011626e-01 -4.98660654e-01 7.05359876e-01 9.10907209e-01 -2.03677833e-01 -8.82877037e-02 -9.01145101e-01 7.44548813e-02 -3.31284016e-01 5.27405560e-01 4.45270330e-01 3.82794277e-03 -9.10631418e-01 6.20645285e-01 1.83794647e-01 -1.48856676e+00 5.32431826e-02 6.71612382e-01 9.49452043e-01 -2.38103151e-01 9.54383388e-02 -2.44442195e-01 1.16033220e+00 -7.85236061e-01 -9.52011645e-01 -1.05900817e-01 -4.29225504e-01 -9.01283801e-01 -2.22434308e-02 2.38646910e-01 1.91808894e-01 3.99147607e-02 1.25403392e+00 2.75074303e-01 -7.21167773e-02 9.67981875e-01 -7.50488579e-01 -1.09336340e+00 6.91104949e-01 -2.72985101e-01 2.50534415e-01 5.90631068e-01 -4.48486954e-01 1.61073208e+00 -9.39323902e-01 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dd4c2eb1-1b01-4ab5-88ae-41c84d2f551f	fibinet-improving-fibinet-by-greatly-reducing	2209.05016	null	https://arxiv.org/abs/2209.05016v1	https://arxiv.org/pdf/2209.05016v1.pdf	FiBiNet++:Improving FiBiNet by Greatly Reducing Model Size for CTR Prediction	Click-Through Rate(CTR) estimation has become one of the most fundamental tasks in many real-world applications and various deep models have been proposed to resolve this problem. Some research has proved that FiBiNet is one of the best performance models and outperforms all other models on Avazu dataset.However, the large model size of FiBiNet hinders its wider applications.In this paper, we propose a novel FiBiNet++ model to redesign FiBiNet's model structure ,which greatly reducess model size while further improves its performance.Extensive experiments on three public datasets show that FiBiNet++ effectively reduces non-embedding model parameters of FiBiNet by 12x to 16x on three datasets and has comparable model size with DNN model which is the smallest one among deep CTR models.On the other hand, FiBiNet++ leads to significant performance improvements compared to state-of-the-art CTR methods,including FiBiNet.	['Junlin Zhang', 'PengTao Zhang']	2022-09-12	null	null	null	null	['click-through-rate-prediction']	['miscellaneous']	[-3.69038403e-01 -4.82030034e-01 -2.76253641e-01 -4.18947376e-02 -5.81979752e-01 -2.68506467e-01 6.63768470e-01 -3.09669793e-01 -6.43704891e-01 5.11268616e-01 4.24033105e-01 -4.19330269e-01 9.42392722e-02 -6.98939264e-01 -2.45639727e-01 -5.76325655e-01 3.43052119e-01 3.33172977e-01 5.38987041e-01 -3.07441473e-01 3.57542008e-01 -1.74001157e-01 -1.21248543e+00 8.28011930e-02 5.88314712e-01 1.00691223e+00 3.44509125e-01 3.93898934e-01 -1.67916894e-01 8.47344816e-01 -4.88607258e-01 -7.26853192e-01 2.48176530e-01 -1.03378229e-01 -3.16432983e-01 -8.63109410e-01 1.55270875e-01 -6.44237697e-01 -9.59635854e-01 8.23400140e-01 8.51970136e-01 6.99412227e-02 6.69051647e-01 -1.10920131e+00 -8.47734928e-01 8.29636097e-01 -1.01287878e+00 4.62332875e-01 -1.93450198e-01 -1.78103466e-02 1.47334921e+00 -8.68034780e-01 2.14831650e-01 1.44955480e+00 6.60241485e-01 3.74975979e-01 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-5.62065780e-01 1.49047256e-01 1.16685346e-01 -8.15283418e-01 -3.69865268e-01 -3.33759993e-01]	[10.158974647521973, 5.568612575531006]
b7be5475-e882-45dd-b03b-820fc52c977c	reconfigurable-and-intelligent-ultra-wideband	2008.06085	null	https://arxiv.org/abs/2008.06085v1	https://arxiv.org/pdf/2008.06085v1.pdf	Reconfigurable and Intelligent Ultra-Wideband Angular Sensing: Prototype Design and Validation	The emergence of beyond-licensed spectrum sharing in FR1 (0.45-6 GHz) and FR2 (24 - 52 GHz) along with the multi-antenna narrow-beam based directional transmissions demand a wideband spectrum sensing in temporal as well as spatial domains. We referred to it as ultra-wideband angular spectrum sensing (UWAS), and it consists of digitization followed by characterization of the wideband spectrum. In this paper, we design and develop state-of-the-art UWAS prototype using USRPs and LabVIEW NXG for the validation in the real-radio environment. Since 5G is expected to co-exist with LTE, the transmitter generates the multi-directional multi-user wideband traffic via LTE specific single carrier frequency division multiple access (SC-FDMA) approach. At the receiver, the first step of wideband spectrum digitization is accomplished using a novel approach of integrating sparse antenna-array with reconfigurable sub-Nyquist sampling (SNS). The reconfigurable SNS allows the digitization of non-contiguous spectrum via low-rate analog-to-digital converters, but it needs intelligence to choose the frequency bands for digitization. We explore the multi-play multi-armed bandit based learning algorithm to embed intelligence. Compared to previous works, the proposed characterization (frequency band status and direction-of-arrival estimation) approach does not need prior knowledge of received signal distribution. The detailed experimental results for various spectrum statistics, power gains and antenna array arrangements along with lower complexity validate the functional correctness, superiority and feasibility of the proposed UWAS over state-of-the-art approaches.	['Bhavani Shankar Mysore Rama Rao', 'Mohammad Alaee-Kerahroodi', 'Sumit J. Darak', 'Himani Joshi']	2020-08-13	null	null	null	null	['direction-of-arrival-estimation']	['audio']	[ 3.44132066e-01 -1.04242742e-01 -2.98149556e-01 8.82793739e-02 -9.11662877e-01 -7.80031025e-01 2.44146362e-02 -6.74486339e-01 5.77188432e-02 1.08312762e+00 9.96305346e-02 -6.53976202e-01 -1.13262057e+00 -8.78284812e-01 -3.36892396e-01 -8.14759076e-01 -4.05832082e-01 2.45007992e-01 -3.40308666e-01 -2.84401923e-01 -2.28776842e-01 6.68461323e-01 -1.22444880e+00 3.48008603e-01 6.67329490e-01 1.70226753e+00 2.70322859e-01 7.30607867e-01 2.62106061e-01 4.04448807e-01 -7.05838740e-01 1.85494572e-01 6.29368961e-01 -4.89133775e-01 -2.25026920e-01 -2.21261363e-02 -3.69021446e-01 -3.42405140e-01 -3.57339270e-02 8.91570866e-01 7.04097569e-01 -7.21614584e-02 5.32220423e-01 -9.60425317e-01 -1.73562586e-01 6.19488895e-01 -5.70266008e-01 2.94242412e-01 5.24480224e-01 -2.60785997e-01 6.82594299e-01 -4.22291517e-01 1.55878738e-01 9.01252866e-01 8.00452948e-01 -3.74513805e-01 -8.43698025e-01 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9d308940-55d5-49f3-8dc8-681ce08be245	unifying-cardiovascular-modelling-with-deep	2101.08477	null	https://arxiv.org/abs/2101.08477v3	https://arxiv.org/pdf/2101.08477v3.pdf	Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment	Sepsis is a potentially life threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the patient's state to guide the management of intravenous fluids and vasopressors, among other interventions. Despite decades of research, there's still debate among experts on optimal treatment. Here, we combine for the first time, distributional deep reinforcement learning with mechanistic physiological models to find personalized sepsis treatment strategies. Our method handles partial observability by leveraging known cardiovascular physiology, introducing a novel physiology-driven recurrent autoencoder, and quantifies the uncertainty of its own results. Moreover, we introduce a framework for uncertainty aware decision support with humans in the loop. We show that our method learns physiologically explainable, robust policies that are consistent with clinical knowledge. Further our method consistently identifies high risk states that lead to death, which could potentially benefit from more frequent vasopressor administration, providing valuable guidance for future research	['David Swigon', 'Christopher James Langmead', 'Gilles Clermont', 'Thesath Nanayakkara']	2021-01-21	null	null	null	null	['distributional-reinforcement-learning', 'clinical-knowledge']	['methodology', 'miscellaneous']	[-4.63400222e-02 -1.92114115e-01 -9.39146504e-02 -6.10829815e-02 -1.84975177e-01 -4.46798444e-01 3.10422853e-02 7.95910537e-01 -4.15065259e-01 1.01665032e+00 5.31050980e-01 -5.41994750e-01 -3.94656539e-01 -6.14295781e-01 -5.43919742e-01 -9.27162051e-01 -3.22020859e-01 6.96192026e-01 -4.81889784e-01 -9.08059031e-02 -2.27129292e-02 6.06613874e-01 -8.64074528e-01 -5.02797589e-02 6.64159000e-01 9.45023000e-01 -1.47896826e-01 7.71445394e-01 3.51901501e-01 1.03358746e+00 -3.41628700e-01 1.92005053e-01 1.97946861e-01 -6.01782024e-01 -2.79293090e-01 -4.41690207e-01 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fee78bbd-1f2b-48aa-987c-df963a371ab8	debiased-learning-from-naturally-imbalanced	2201.01490	null	https://arxiv.org/abs/2201.01490v2	https://arxiv.org/pdf/2201.01490v2.pdf	Debiased Learning from Naturally Imbalanced Pseudo-Labels	Pseudo-labels are confident predictions made on unlabeled target data by a classifier trained on labeled source data. They are widely used for adapting a model to unlabeled data, e.g., in a semi-supervised learning setting. Our key insight is that pseudo-labels are naturally imbalanced due to intrinsic data similarity, even when a model is trained on balanced source data and evaluated on balanced target data. If we address this previously unknown imbalanced classification problem arising from pseudo-labels instead of ground-truth training labels, we could remove model biases towards false majorities created by pseudo-labels. We propose a novel and effective debiased learning method with pseudo-labels, based on counterfactual reasoning and adaptive margins: The former removes the classifier response bias, whereas the latter adjusts the margin of each class according to the imbalance of pseudo-labels. Validated by extensive experimentation, our simple debiased learning delivers significant accuracy gains over the state-of-the-art on ImageNet-1K: 26% for semi-supervised learning with 0.2% annotations and 9% for zero-shot learning. Our code is available at: https://github.com/frank-xwang/debiased-pseudo-labeling.	['Stella X. Yu', 'Long Lian', 'Zhirong Wu', 'Xudong Wang']	2022-01-05	null	http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Debiased_Learning_From_Naturally_Imbalanced_Pseudo-Labels_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Debiased_Learning_From_Naturally_Imbalanced_Pseudo-Labels_CVPR_2022_paper.pdf	cvpr-2022-1	['semi-supervised-image-classification']	['computer-vision']	[ 4.20355469e-01 6.80236220e-01 -9.62696731e-01 -8.29918325e-01 -1.03658926e+00 -5.15333593e-01 4.88041729e-01 2.35132948e-01 -2.32551366e-01 1.02986383e+00 2.31162965e-01 -1.52906477e-01 2.89565772e-01 -4.45300788e-01 -9.37841356e-01 -7.33672082e-01 4.29634333e-01 7.26803184e-01 1.56837478e-02 8.19192603e-02 5.85286170e-02 -2.39667147e-01 -1.46523702e+00 4.35092986e-01 9.57693398e-01 1.16156554e+00 -6.31688595e-01 3.00345957e-01 4.54526618e-02 1.28554404e+00 -5.25150061e-01 -7.24286020e-01 5.01631677e-01 -5.36012292e-01 -8.13637257e-01 2.30601683e-01 5.27323604e-01 -2.47964039e-01 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6f5fd993-d877-4205-a235-b8d51764ebab	may-the-force-be-with-your-copy-mechanism-1	2112.10360	null	https://arxiv.org/abs/2112.10360v1	https://arxiv.org/pdf/2112.10360v1.pdf	May the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generation	Recent neural sequence-to-sequence models with a copy mechanism have achieved remarkable progress in various text generation tasks. These models addressed out-of-vocabulary problems and facilitated the generation of rare words. However, the identification of the word which needs to be copied is difficult, as observed by prior copy models, which suffer from incorrect generation and lacking abstractness. In this paper, we propose a novel supervised approach of a copy network that helps the model decide which words need to be copied and which need to be generated. Specifically, we re-define the objective function, which leverages source sequences and target vocabularies as guidance for copying. The experimental results on data-to-text generation and abstractive summarization tasks verify that our approach enhances the copying quality and improves the degree of abstractness.	['Yeonsoo Lee', 'Hyungjong Noh', 'Jeong-in Hwang', 'Sanghyuk Choi']	2021-12-20	may-the-force-be-with-your-copy-mechanism	https://arxiv.org/abs/2112.10360	https://arxiv.org/pdf/2112.10360.pdf	arxiv-2021-12	['data-to-text-generation']	['natural-language-processing']	[ 7.07693994e-01 2.39743322e-01 -3.65085602e-01 -2.11625323e-01 -6.15747809e-01 -5.09889960e-01 8.56546462e-01 1.80029646e-01 -2.40120023e-01 1.07763708e+00 8.21169257e-01 -9.22871009e-02 8.87894779e-02 -8.34888816e-01 -7.73647189e-01 -5.35359740e-01 5.49995899e-01 5.68925619e-01 -1.11308672e-01 -5.66128790e-01 6.49674118e-01 -8.92042965e-02 -1.43870616e+00 2.48668104e-01 1.37915492e+00 4.09488440e-01 5.74042320e-01 5.95742106e-01 -5.11139452e-01 5.51732838e-01 -1.08057547e+00 -3.74872237e-01 4.94295210e-02 -1.08315647e+00 -6.93059683e-01 -1.21430129e-01 4.90257829e-01 -6.03220701e-01 -4.03074622e-01 1.03502095e+00 6.90004349e-01 1.07178673e-01 1.00376904e+00 -1.05144787e+00 -1.14319682e+00 1.27366459e+00 -2.74355024e-01 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c2e94760-9a1e-4e0b-af5e-32d15d3b9005	deception-detection-for-the-russian-language	null	null	https://aclanthology.org/W17-7701	https://aclanthology.org/W17-7701.pdf	Deception Detection for the Russian Language: Lexical and Syntactic Parameters	The field of automated deception detection in written texts is methodologically challenging. Different linguistic levels (lexics, syntax and semantics) are basically used for different types of English texts to reveal if they are truthful or deceptive. Such parameters as POS tags and POS tags n-grams, punctuation marks, sentiment polarity of words, psycholinguistic features, fragments of syntaÑ�tic structures are taken into consideration. The importance of different types of parameters was not compared for the Russian language before and should be investigated before moving to complex models and higher levels of linguistic processing. On the example of the Russian Deception Bank Corpus we estimate the impact of three groups of features (POS features including bigrams, sentiment and psycholinguistic features, syntax and readability features) on the successful deception detection and find out that POS features can be used for binary text classification, but the results should be double-checked and, if possible, improved.	['Olga Litvinova', 'Dina Pisarevskaya', 'Tatiana Litvinova']	2017-09-01	null	null	null	ranlp-2017-9	['deception-detection']	['miscellaneous']	[-1.24173567e-01 -1.47700921e-01 -1.00922897e-01 -6.20715916e-01 -3.11936855e-01 -8.75366986e-01 9.64465678e-01 7.54911304e-01 -8.31175804e-01 7.45318770e-01 5.70883930e-01 -7.39659011e-01 -1.40686601e-01 -4.07943815e-01 -1.78709358e-01 -5.35727561e-01 1.24094471e-01 2.93099046e-01 4.49031740e-02 -3.22664976e-01 1.17424512e+00 8.52398813e-01 -1.49389923e+00 4.05139893e-01 9.63124633e-01 6.78211570e-01 -1.62767991e-01 6.64190888e-01 -1.47982061e-01 9.96115208e-01 -9.87468541e-01 -8.69105279e-01 -1.90693259e-01 -5.49116015e-01 -9.63354826e-01 6.30924180e-02 7.58574456e-02 -4.69311699e-02 1.66114513e-02 1.42969072e+00 3.00319672e-01 -2.07386300e-01 1.09285188e+00 -6.87757492e-01 -6.81161821e-01 6.46594524e-01 -6.94416016e-02 5.97367346e-01 6.76790833e-01 9.54280868e-02 8.12917233e-01 -7.66580939e-01 5.55820644e-01 1.29362452e+00 4.22866344e-01 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107b3508-27b9-4e63-a87b-edf72b73105f	towards-fully-automated-deep-learning-based	2212.07497	null	https://arxiv.org/abs/2212.07497v1	https://arxiv.org/pdf/2212.07497v1.pdf	Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?	State-of-the-art brain tumor segmentation is based on deep learning models applied to multi-modal MRIs. Currently, these models are trained on images after a preprocessing stage that involves registration, interpolation, brain extraction (BE, also known as skull-stripping) and manual correction by an expert. However, for clinical practice, this last step is tedious and time-consuming and, therefore, not always feasible, resulting in skull-stripping faults that can negatively impact the tumor segmentation quality. Still, the extent of this impact has never been measured for any of the many different BE methods available. In this work, we propose an automatic brain tumor segmentation pipeline and evaluate its performance with multiple BE methods. Our experiments show that the choice of a BE method can compromise up to 15.7% of the tumor segmentation performance. Moreover, we propose training and testing tumor segmentation models on non-skull-stripped images, effectively discarding the BE step from the pipeline. Our results show that this approach leads to a competitive performance at a fraction of the time. We conclude that, in contrast to the current paradigm, training tumor segmentation models on non-skull-stripped images can be the best option when high performance in clinical practice is desired.	['Danilo Silva', 'Guilherme de Souza e Cassia', 'Bruno Machado Pacheco']	2022-12-14	null	null	null	null	['tumor-segmentation', 'brain-tumor-segmentation', 'skull-stripping']	['computer-vision', 'medical', 'medical']	[ 3.15623969e-01 2.74233311e-01 2.58535475e-01 -2.38422960e-01 -9.75581229e-01 -3.23219806e-01 5.15828788e-01 5.14665365e-01 -8.89169872e-01 6.04227185e-01 -1.96756229e-01 -5.11816204e-01 5.07506020e-02 -6.19108558e-01 -5.96606255e-01 -8.39473486e-01 2.09761679e-01 1.00212264e+00 6.92735195e-01 -4.24383953e-03 7.99169466e-02 8.46493185e-01 -1.22185147e+00 1.85069591e-01 9.02770400e-01 8.10546517e-01 2.79316485e-01 5.12250304e-01 -3.61277282e-01 3.23915064e-01 -4.95128542e-01 -3.60598177e-01 1.68134615e-01 -2.77328789e-01 -9.47607338e-01 1.41361237e-01 2.79426485e-01 -2.82047510e-01 2.08584890e-01 9.47820961e-01 3.34695399e-01 -3.63014638e-01 6.21613681e-01 -8.00819397e-01 1.67417765e-01 4.55052942e-01 -5.96276462e-01 1.64300531e-01 -1.49808750e-01 1.72727630e-01 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8e6d1063-228f-45b5-8c6c-9a750e2982a8	unbiased-heterogeneous-scene-graph-generation	2212.00443	null	https://arxiv.org/abs/2212.00443v4	https://arxiv.org/pdf/2212.00443v4.pdf	Unbiased Heterogeneous Scene Graph Generation with Relation-aware Message Passing Neural Network	Recent scene graph generation (SGG) frameworks have focused on learning complex relationships among multiple objects in an image. Thanks to the nature of the message passing neural network (MPNN) that models high-order interactions between objects and their neighboring objects, they are dominant representation learning modules for SGG. However, existing MPNN-based frameworks assume the scene graph as a homogeneous graph, which restricts the context-awareness of visual relations between objects. That is, they overlook the fact that the relations tend to be highly dependent on the objects with which the relations are associated. In this paper, we propose an unbiased heterogeneous scene graph generation (HetSGG) framework that captures relation-aware context using message passing neural networks. We devise a novel message passing layer, called relation-aware message passing neural network (RMP), that aggregates the contextual information of an image considering the predicate type between objects. Our extensive evaluations demonstrate that HetSGG outperforms state-of-the-art methods, especially outperforming on tail predicate classes.	['Chanyoung Park', 'Jinyoung Moon', 'Kibum Kim', 'Kanghoon Yoon']	2022-12-01	null	null	null	null	['scene-graph-generation']	['computer-vision']	[ 4.11666423e-01 2.36783594e-01 -3.02498806e-02 -4.32375371e-01 -1.45203024e-01 -8.11238308e-03 8.40404153e-01 4.36295569e-01 -6.70690686e-02 3.60875815e-01 2.22491547e-01 -1.59116462e-01 -2.04911739e-01 -1.40471244e+00 -9.70679164e-01 -5.66313028e-01 -2.79157132e-01 2.61105835e-01 5.35980165e-01 -2.02524304e-01 -3.48719805e-02 3.79273117e-01 -1.70940363e+00 7.25568175e-01 6.23757303e-01 1.05971205e+00 4.12965685e-01 6.65281534e-01 -2.81540036e-01 1.52495515e+00 -5.70556760e-01 -4.70175683e-01 -6.45539165e-02 -3.75913262e-01 -8.68772686e-01 2.25772321e-01 7.08737016e-01 -2.16044143e-01 -8.71363342e-01 1.08185565e+00 1.71899781e-01 2.57198453e-01 5.30464947e-01 -1.58398700e+00 -1.05598247e+00 8.63714337e-01 -2.83591926e-01 1.03062645e-01 2.16898024e-01 -4.81255539e-02 1.41945553e+00 -8.07920635e-01 8.87377858e-01 1.51094925e+00 2.66977400e-01 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65a61b2e-4c84-401b-b1cf-8249a743eda8	pointclimb-an-exemplar-free-point-cloud-class	2304.06775	null	https://arxiv.org/abs/2304.06775v1	https://arxiv.org/pdf/2304.06775v1.pdf	PointCLIMB: An Exemplar-Free Point Cloud Class Incremental Benchmark	Point clouds offer comprehensive and precise data regarding the contour and configuration of objects. Employing such geometric and topological 3D information of objects in class incremental learning can aid endless application in 3D-computer vision. Well known 3D-point cloud class incremental learning methods for addressing catastrophic forgetting generally entail the usage of previously encountered data, which can present difficulties in situations where there are restrictions on memory or when there are concerns about the legality of the data. Towards this we pioneer to leverage exemplar free class incremental learning on Point Clouds. In this paper we propose PointCLIMB: An exemplar Free Class Incremental Learning Benchmark. We focus on a pragmatic perspective to consider novel classes for class incremental learning on 3D point clouds. We setup a benchmark for 3D Exemplar free class incremental learning. We investigate performance of various backbones on 3D-Exemplar Free Class Incremental Learning framework. We demonstrate our results on ModelNet40 dataset.	['Uma Mudenagudi', 'Ramesh Ashok Tabib', 'Tejas Anvekar', 'Shivanand Kundargi']	2023-04-13	null	null	null	null	['class-incremental-learning']	['computer-vision']	[-6.22804761e-02 -1.30080134e-01 -1.89372495e-01 -2.33686835e-01 -4.80876744e-01 -8.20908129e-01 9.07493055e-01 6.35206163e-01 -3.91627580e-01 5.62449396e-01 -6.43483341e-01 -5.30979097e-01 -3.62220734e-01 -6.70568168e-01 -1.21017301e+00 -3.81309122e-01 -6.17450953e-01 9.84636903e-01 6.80469453e-01 -1.00193426e-01 6.56397223e-01 1.36597323e+00 -2.06581640e+00 3.75251025e-01 6.70617044e-01 6.78656280e-01 1.32617176e-01 6.73531413e-01 -6.23261631e-01 1.15235522e-01 -4.66833770e-01 -1.56722069e-01 7.05835462e-01 4.94944304e-01 -6.10771775e-01 3.14999670e-02 1.04797149e+00 -1.70686379e-01 1.35043368e-01 7.25334585e-01 4.86020565e-01 1.07791379e-01 6.31557107e-01 -1.72017670e+00 -2.24301323e-01 2.10373461e-01 -4.58478987e-01 3.22272778e-01 2.73937017e-01 3.43687773e-01 4.81307626e-01 -1.45084167e+00 9.51989353e-01 1.05271065e+00 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e7735b9e-8136-4893-968c-f92d727dc182	conversational-search-with-mixed-initiative	2112.07308	null	https://arxiv.org/abs/2112.07308v2	https://arxiv.org/pdf/2112.07308v2.pdf	Conversational Search with Mixed-Initiative -- Asking Good Clarification Questions backed-up by Passage Retrieval	We deal with the scenario of conversational search, where user queries are under-specified or ambiguous. This calls for a mixed-initiative setup. User-asks (queries) and system-answers, as well as system-asks (clarification questions) and user response, in order to clarify her information needs. We focus on the task of selecting the next clarification question, given the conversation context. Our method leverages passage retrieval from a background content to fine-tune two deep-learning models for ranking candidate clarification questions. We evaluated our method on two different use-cases. The first is an open domain conversational search in a large web collection. The second is a task-oriented customer-support setup. We show that our method performs well on both use-cases.	['David Konopnicki', 'Asaf Yehudai', 'Doron Cohen', 'Yosi Mass']	2021-12-14	null	null	null	null	['conversational-search']	['natural-language-processing']	[ 2.86104202e-01 2.45348126e-01 -7.65507594e-02 -4.38275337e-01 -1.52952170e+00 -7.98799753e-01 9.25914168e-01 4.17055130e-01 -5.42740166e-01 7.24385619e-01 8.00774574e-01 -6.00188851e-01 -2.37633273e-01 -3.72107267e-01 -7.42561966e-02 5.38908057e-02 4.07324791e-01 1.16097438e+00 2.06448004e-01 -6.36813283e-01 5.52522361e-01 -1.28164124e-02 -1.24725783e+00 1.02902031e+00 8.70810151e-01 7.25709498e-01 6.23831153e-01 1.15255046e+00 -5.56098998e-01 7.78383553e-01 -4.76734221e-01 -4.53339964e-01 -1.28273368e-01 -3.22286934e-01 -1.80684018e+00 9.88717899e-02 3.94471288e-01 -5.23708940e-01 -2.29719821e-02 4.27301109e-01 6.50424182e-01 3.93392771e-01 3.65643591e-01 -1.02091062e+00 -3.52044851e-01 8.45947742e-01 1.58261448e-01 4.21335131e-01 8.29617500e-01 1.69993564e-01 1.61345136e+00 -6.88373566e-01 5.55964112e-01 1.33069229e+00 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