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56af2c3a-8c68-4f74-8156-605a759a6547	graph-denoising-diffusion-for-inverse-protein	2306.16819	null	https://arxiv.org/abs/2306.16819v1	https://arxiv.org/pdf/2306.16819v1.pdf	Graph Denoising Diffusion for Inverse Protein Folding	Inverse protein folding is challenging due to its inherent one-to-many mapping characteristic, where numerous possible amino acid sequences can fold into a single, identical protein backbone. This task involves not only identifying viable sequences but also representing the sheer diversity of potential solutions. However, existing discriminative models, such as transformer-based auto-regressive models, struggle to encapsulate the diverse range of plausible solutions. In contrast, diffusion probabilistic models, as an emerging genre of generative approaches, offer the potential to generate a diverse set of sequence candidates for determined protein backbones. We propose a novel graph denoising diffusion model for inverse protein folding, where a given protein backbone guides the diffusion process on the corresponding amino acid residue types. The model infers the joint distribution of amino acids conditioned on the nodes' physiochemical properties and local environment. Moreover, we utilize amino acid replacement matrices for the diffusion forward process, encoding the biologically-meaningful prior knowledge of amino acids from their spatial and sequential neighbors as well as themselves, which reduces the sampling space of the generative process. Our model achieves state-of-the-art performance over a set of popular baseline methods in sequence recovery and exhibits great potential in generating diverse protein sequences for a determined protein backbone structure.	['Yu Guang Wang', 'Pietro Liò', 'Yiqing Shen', 'Bingxin Zhou', 'Kai Yi']	2023-06-29	null	null	null	null	['protein-folding']	['natural-language-processing']	[ 5.84289074e-01 5.55194952e-02 8.96727666e-03 -2.42435977e-01 -7.77362585e-01 -7.34602451e-01 4.33214784e-01 -5.57385758e-02 -6.29074126e-02 1.17218554e+00 4.58693624e-01 -2.63543814e-01 6.38098791e-02 -8.36741805e-01 -1.04529357e+00 -1.26068282e+00 2.24006012e-01 9.92424488e-01 -3.06253694e-02 -2.76716143e-01 2.16821298e-01 5.79922915e-01 -1.11245263e+00 3.16202193e-01 1.19374621e+00 2.52740294e-01 8.15928042e-01 4.82659936e-01 -3.09458584e-01 6.95160747e-01 -3.12727422e-01 -2.70280719e-01 -1.14283428e-01 -1.05873835e+00 -4.31197762e-01 -7.00857267e-02 4.33892831e-02 -9.07618627e-02 -3.46547246e-01 1.03871477e+00 4.14846003e-01 2.45341826e-02 9.76741076e-01 -6.57977045e-01 -1.27717376e+00 3.73064548e-01 -4.45930481e-01 -1.62429780e-01 4.26716775e-01 5.03324986e-01 1.05913055e+00 -9.63404477e-01 1.31328940e+00 1.21185505e+00 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f0e6e135-6b29-4231-9e74-ceaac9157a34	direct-learning-of-sparse-changes-in-markov	1304.6803	null	http://arxiv.org/abs/1304.6803v5	http://arxiv.org/pdf/1304.6803v5.pdf	Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation	We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we \emph{directly} learn the network structure change by estimating the ratio of Markov network models. This density-ratio formulation naturally allows us to introduce sparsity in the network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, which is a critical bottleneck of the naive approach, can be remarkably mitigated. We also give the dual formulation of the optimization problem, which further reduces the computation cost for large-scale Markov networks. Through experiments, we demonstrate the usefulness of our method.	['John A. Quinn', 'Taiji Suzuki', 'Michael U. Gutmann', 'Masashi Sugiyama', 'Song Liu']	2013-04-25	null	null	null	null	['density-ratio-estimation']	['methodology']	[ 3.08418602e-01 3.18081170e-01 -4.95724410e-01 -3.26082885e-01 -5.55962861e-01 -8.57867360e-01 3.99607569e-01 2.69456189e-02 -1.73967302e-01 4.66166049e-01 1.30628660e-01 -4.85089928e-01 -3.88806999e-01 -6.58668876e-01 -6.58520401e-01 -6.44914210e-01 -8.35057124e-02 3.41097832e-01 7.09369704e-02 4.63339299e-01 -3.41941006e-02 3.50746989e-01 -7.54859030e-01 2.01374758e-02 5.62914908e-01 8.83419514e-01 1.48696331e-02 7.34125972e-01 2.77032237e-02 8.48686755e-01 -2.63015330e-01 -4.76366967e-01 3.04601014e-01 -4.79316920e-01 -9.82582211e-01 3.76450449e-01 5.84450722e-01 -3.83638293e-01 -7.73896396e-01 1.45515466e+00 3.44422413e-03 2.28015199e-01 6.89283073e-01 -1.24344957e+00 -3.07579607e-01 9.53688085e-01 -9.24156189e-01 5.04238307e-01 3.57982337e-01 1.15866266e-01 1.36731958e+00 -4.66044486e-01 5.28601110e-01 1.56575382e+00 6.81959808e-01 1.92719996e-01 -1.75349653e+00 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-7.08535433e-01 3.80991727e-01 4.40327466e-01 -6.66308463e-01 -4.44424778e-01 2.37831280e-01]	[6.961027145385742, 5.446763038635254]
96c3f88d-5177-40aa-85b2-7474f6db6a28	counting-crowds-in-bad-weather	2306.01209	null	https://arxiv.org/abs/2306.01209v1	https://arxiv.org/pdf/2306.01209v1.pdf	Counting Crowds in Bad Weather	Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding. Numerous methods have been proposed and achieved state-of-the-art performance for real-world tasks. However, existing approaches do not perform well under adverse weather such as haze, rain, and snow since the visual appearances of crowds in such scenes are drastically different from those images in clear weather of typical datasets. In this paper, we propose a method for robust crowd counting in adverse weather scenarios. Instead of using a two-stage approach that involves image restoration and crowd counting modules, our model learns effective features and adaptive queries to account for large appearance variations. With these weather queries, the proposed model can learn the weather information according to the degradation of the input image and optimize with the crowd counting module simultaneously. Experimental results show that the proposed algorithm is effective in counting crowds under different weather types on benchmark datasets. The source code and trained models will be made available to the public.	['Ming-Hsuan Yang', 'Sy-Yen Kuo', 'Yuan-Chun Chiang', 'Wei-Ting Chen', 'Zhi-Kai Huang']	2023-06-02	null	null	null	null	['crowd-counting', 'image-restoration']	['computer-vision', 'computer-vision']	[ 3.97054106e-02 -6.15751445e-01 4.96609271e-01 -4.57801491e-01 -1.77185744e-01 -3.23041320e-01 5.59925437e-01 5.76367155e-02 -8.21993172e-01 7.23157763e-01 -6.38284981e-02 6.92175478e-02 4.85806137e-01 -6.49494767e-01 -4.61249888e-01 -7.16002345e-01 3.04727882e-01 4.20939058e-01 7.75367379e-01 -2.36102626e-01 5.20101964e-01 3.04788709e-01 -1.89247835e+00 -7.78757641e-03 1.06424987e+00 5.37951171e-01 4.75560188e-01 9.66168284e-01 -2.62748390e-01 9.56097305e-01 -9.68804479e-01 -4.08094347e-01 3.41686726e-01 -1.97387710e-01 -2.36907631e-01 4.66929585e-01 5.69117844e-01 -4.22238171e-01 -3.27033162e-01 1.08555281e+00 7.64279544e-01 3.34598750e-01 4.98536825e-01 -1.00784242e+00 -6.64753318e-01 -3.50927204e-01 -1.03959179e+00 6.46377385e-01 5.51775753e-01 5.21041691e-01 3.81188303e-01 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c27ab0a7-0f98-4dbe-bc92-a337ef9f29ba	biocpt-contrastive-pre-trained-transformers	2307.00589	null	https://arxiv.org/abs/2307.00589v1	https://arxiv.org/pdf/2307.00589v1.pdf	BioCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval	Information retrieval (IR) is essential in biomedical knowledge acquisition and clinical decision support. While recent progress has shown that language model encoders perform better semantic retrieval, training such models requires abundant query-article annotations that are difficult to obtain in biomedicine. As a result, most biomedical IR systems only conduct lexical matching. In response, we introduce BioCPT, a first-of-its-kind Contrastively Pre-trained Transformer model for zero-shot biomedical IR. To train BioCPT, we collected an unprecedented scale of 255 million user click logs from PubMed. With such data, we use contrastive learning to train a pair of closely-integrated retriever and re-ranker. Experimental results show that BioCPT sets new state-of-the-art performance on five biomedical IR tasks, outperforming various baselines including much larger models such as GPT-3-sized cpt-text-XL. In addition, BioCPT also generates better biomedical article and sentence representations for semantic evaluations. As such, BioCPT can be readily applied to various real-world biomedical IR tasks. BioCPT API and code are publicly available at https://github.com/ncbi/BioCPT.	['Zhiyong Lu', 'John Wilbur', 'Lana Yeganova', 'Donald C. Comeau', 'Qingyu Chen', 'Won Kim', 'Qiao Jin']	2023-07-02	null	null	null	null	['contrastive-learning', 'contrastive-learning', 'retrieval', 'semantic-retrieval', 'information-retrieval']	['computer-vision', 'methodology', 'methodology', 'natural-language-processing', 'natural-language-processing']	[ 4.38915998e-01 1.74493954e-01 -6.81323171e-01 -2.88617551e-01 -1.65164840e+00 -2.30068922e-01 3.40899229e-01 6.84701443e-01 -6.57262146e-01 8.09641778e-01 5.15907586e-01 -4.26839948e-01 -1.91647232e-01 -5.14545381e-01 -7.28838384e-01 -3.76681477e-01 3.12734872e-01 9.88065481e-01 -1.56580508e-02 -2.02671126e-01 4.07848209e-02 5.17055281e-02 -8.10446262e-01 7.61677206e-01 7.95409381e-01 8.03950369e-01 3.29792440e-01 5.22932291e-01 2.80300025e-02 6.83676779e-01 -3.45089644e-01 -6.01970553e-01 -4.29281861e-01 -1.92289993e-01 -1.13173878e+00 -9.48629379e-01 1.58029109e-01 -1.32487983e-01 -7.44185507e-01 1.00308442e+00 1.13247967e+00 -1.22531541e-01 6.85932040e-01 -5.55734694e-01 -1.23492825e+00 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1.82867303e-01 -3.01561892e-01 6.20414197e-01 4.58777547e-01 5.87012410e-01 9.42511141e-01 8.47876489e-01 6.25777841e-02 -4.38259318e-02 -5.44391751e-01 -1.28347933e+00 -1.59776315e-01 2.67154008e-01 2.07718059e-01 -2.89701343e-01 -1.56007678e-04 -7.13295937e-02]	[8.603961944580078, 8.735188484191895]
298b44c4-1979-4fc8-8203-2c72de80c506	cyber-secure-teleoperation-with-encrypted	2302.13709	null	https://arxiv.org/abs/2302.13709v1	https://arxiv.org/pdf/2302.13709v1.pdf	Cyber-Secure Teleoperation With Encrypted Four-Channel Bilateral Control	This study developed an encrypted four-channel bilateral control system that enables posture synchronization and force feedback for leader and follower robot arms. The encrypted bilateral control system communicates encrypted signals and operates with encrypted control parameters using homomorphic encryption. We created two-axis robot arms and identified them to obtain a nonlinear model consisting of a linear system and a nonlinear disturbance. Disturbance and reaction-force observers and a proportional-derivative controller were designed to construct a networked robot-manipulation system. The controllers in the constructed bilateral control system were securely implemented on dedicated computers using a controller encryption technique. The experimental results demonstrate that the developed bilateral control system can facilitate encrypted communication and controller, synchronize the posture, and feed the reaction force back. Through experimental validation, the encrypted four-channel bilateral control system enables the inheritance of control performance from the original (unencrypted) bilateral control system.	['Kiminao Kogiso', 'Kenichi Abe', 'Toru Mizuya', 'Kaoru Teranishi', 'Akane Kosugi', 'Haruki Takanashi']	2023-02-27	null	null	null	null	['robot-manipulation']	['robots']	[ 1.17719308e-01 1.49600282e-01 -1.18161105e-01 2.25883052e-01 2.25494727e-01 -1.22364378e+00 9.07326162e-01 -2.34879777e-01 -5.22651613e-01 8.07005465e-01 -2.77475476e-01 -5.73461652e-01 2.22346440e-01 -8.97232592e-01 -7.62705028e-01 -7.77734995e-01 -5.66574097e-01 -1.71434712e-02 -2.34380737e-01 -3.48461658e-01 8.21705535e-02 6.88577712e-01 -1.11255205e+00 3.51815708e-02 3.40397567e-01 1.08026075e+00 -1.20266713e-01 1.02189624e+00 8.51453543e-01 9.77288008e-01 -7.58151650e-01 -1.57200053e-01 7.64655828e-01 -5.34350201e-02 -5.19600630e-01 -6.57442570e-01 -3.73718351e-01 -9.90597844e-01 -6.70642614e-01 1.00330555e+00 3.22970271e-01 -5.92570126e-01 5.63258231e-01 -2.17230463e+00 -5.18281639e-01 5.78509748e-01 -2.75007546e-01 -9.02898729e-01 3.41421992e-01 2.27878809e-01 4.36813325e-01 8.53715613e-02 7.23995447e-01 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55ba1d76-8c3e-466c-8b81-a15d4f4ba708	deep-stable-representation-learning-on	2209.01321	null	https://arxiv.org/abs/2209.01321v1	https://arxiv.org/pdf/2209.01321v1.pdf	Deep Stable Representation Learning on Electronic Health Records	Deep learning models have achieved promising disease prediction performance of the Electronic Health Records (EHR) of patients. However, most models developed under the I.I.D. hypothesis fail to consider the agnostic distribution shifts, diminishing the generalization ability of deep learning models to Out-Of-Distribution (OOD) data. In this setting, spurious statistical correlations that may change in different environments will be exploited, which can cause sub-optimal performances of deep learning models. The unstable correlation between procedures and diagnoses existed in the training distribution can cause spurious correlation between historical EHR and future diagnosis. To address this problem, we propose to use a causal representation learning method called Causal Healthcare Embedding (CHE). CHE aims at eliminating the spurious statistical relationship by removing the dependencies between diagnoses and procedures. We introduce the Hilbert-Schmidt Independence Criterion (HSIC) to measure the degree of independence between the embedded diagnosis and procedure features. Based on causal view analyses, we perform the sample weighting technique to get rid of such spurious relationship for the stable learning of EHR across different environments. Moreover, our proposed CHE method can be used as a flexible plug-and-play module that can enhance existing deep learning models on EHR. Extensive experiments on two public datasets and five state-of-the-art baselines unequivocally show that CHE can improve the prediction accuracy of deep learning models on out-of-distribution data by a large margin. In addition, the interpretability study shows that CHE could successfully leverage causal structures to reflect a more reasonable contribution of historical records for predictions.	['Qiang Liu', 'Zhaocheng Liu', 'Yingtao Luo']	2022-09-03	null	null	null	null	['disease-prediction']	['medical']	[-6.57838881e-02 2.09310651e-01 -2.84957737e-01 -5.29228389e-01 -5.39816439e-01 -2.56003052e-01 5.05076170e-01 2.17436180e-01 -3.91506962e-02 8.00749779e-01 6.72560930e-01 -5.65966785e-01 -5.66179693e-01 -9.20029819e-01 -8.24533045e-01 -7.87936509e-01 -2.98359483e-01 2.26683319e-01 -3.50925237e-01 1.40234157e-01 -4.63978767e-01 1.06045261e-01 -8.82854581e-01 5.28263032e-01 9.25546110e-01 5.12524068e-01 -8.44234750e-02 1.77624837e-01 2.13974342e-01 1.11608839e+00 -4.33770478e-01 -3.31433594e-01 1.63727954e-01 -4.23013330e-01 -4.50741351e-01 -2.50832021e-01 -3.93504724e-02 -5.08884370e-01 -7.32202291e-01 7.89644599e-01 5.94547987e-01 -5.27949810e-01 9.13178265e-01 -1.34484124e+00 -1.05781782e+00 1.03673673e+00 -4.80258077e-01 1.68015599e-01 1.57708317e-01 2.56170154e-01 1.18576777e+00 -5.33354282e-01 5.57693183e-01 1.15976644e+00 1.00678456e+00 4.61447477e-01 -1.34120178e+00 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4028112d-f288-4af5-88a6-a7086afc2e1b	improving-molecular-pretraining-with	2209.15101	null	https://arxiv.org/abs/2209.15101v1	https://arxiv.org/pdf/2209.15101v1.pdf	Improving Molecular Pretraining with Complementary Featurizations	Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery. Recently, prosperous progress has been made in molecular pretraining with different molecular featurizations, including 1D SMILES strings, 2D graphs, and 3D geometries. However, the role of molecular featurizations with their corresponding neural architectures in molecular pretraining remains largely unexamined. In this paper, through two case studies -- chirality classification and aromatic ring counting -- we first demonstrate that different featurization techniques convey chemical information differently. In light of this observation, we propose a simple and effective MOlecular pretraining framework with COmplementary featurizations (MOCO). MOCO comprehensively leverages multiple featurizations that complement each other and outperforms existing state-of-the-art models that solely relies on one or two featurizations on a wide range of molecular property prediction tasks.	['Shu Wu', 'Qiang Liu', 'Yingze Wang', 'Yuanqi Du', 'Dingshuo Chen', 'Yanqiao Zhu']	2022-09-29	null	null	null	null	['molecular-property-prediction']	['miscellaneous']	[ 5.03698587e-01 -2.83568144e-01 -7.83912063e-01 -3.59091908e-01 -1.86521441e-01 -8.56946051e-01 6.77267432e-01 5.34506857e-01 -2.54200757e-01 1.22589231e+00 1.24249503e-01 -1.02410531e+00 -4.02058288e-02 -7.49311805e-01 -9.38545287e-01 -8.15575182e-01 -3.25295269e-01 4.30306792e-02 -3.13688070e-01 -2.74988085e-01 4.71941054e-01 8.43233883e-01 -1.23211062e+00 3.67089480e-01 9.61461723e-01 7.51392424e-01 -1.86642870e-01 4.23613280e-01 -3.36445123e-02 9.28169787e-01 -1.67076901e-01 -5.12616992e-01 2.28932127e-01 -3.12914997e-01 -8.06800008e-01 -6.49961472e-01 7.20514953e-01 -1.24877863e-01 -5.99965811e-01 8.90164018e-01 6.53006196e-01 3.80190104e-01 1.12585092e+00 -5.79981148e-01 -1.04947519e+00 7.09398925e-01 -3.98905605e-01 3.75112742e-01 4.29718941e-01 4.21468526e-01 1.19043219e+00 -7.40037382e-01 6.01217508e-01 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5.96152127e-01 -1.01808429e+00 -5.21373391e-01 -7.48440444e-01 -1.98286757e-01 3.24987739e-01 4.15446579e-01 -9.21816587e-01 -7.29877828e-03 -9.17689577e-02]	[5.109690189361572, 5.817135334014893]
d983ad64-39c6-4bb8-934b-36062f52c818	urbanbis-a-large-scale-benchmark-for-fine	2305.02627	null	https://arxiv.org/abs/2305.02627v1	https://arxiv.org/pdf/2305.02627v1.pdf	UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation	We present the UrbanBIS benchmark for large-scale 3D urban understanding, supporting practical urban-level semantic and building-level instance segmentation. UrbanBIS comprises six real urban scenes, with 2.5 billion points, covering a vast area of 10.78 square kilometers and 3,370 buildings, captured by 113,346 views of aerial photogrammetry. Particularly, UrbanBIS provides not only semantic-level annotations on a rich set of urban objects, including buildings, vehicles, vegetation, roads, and bridges, but also instance-level annotations on the buildings. Further, UrbanBIS is the first 3D dataset that introduces fine-grained building sub-categories, considering a wide variety of shapes for different building types. Besides, we propose B-Seg, a building instance segmentation method to establish UrbanBIS. B-Seg adopts an end-to-end framework with a simple yet effective strategy for handling large-scale point clouds. Compared with mainstream methods, B-Seg achieves better accuracy with faster inference speed on UrbanBIS. In addition to the carefully-annotated point clouds, UrbanBIS provides high-resolution aerial-acquisition photos and high-quality large-scale 3D reconstruction models, which shall facilitate a wide range of studies such as multi-view stereo, urban LOD generation, aerial path planning, autonomous navigation, road network extraction, and so on, thus serving as an important platform for many intelligent city applications.	['Hui Huang', 'Chi-Wing Fu', 'Ke Xie', 'Qi Zhang', 'Fuyou Xue', 'Guoqing Yang']	2023-05-04	null	null	null	null	['3d-reconstruction', 'autonomous-navigation']	['computer-vision', 'computer-vision']	[-6.79487810e-02 -8.41507167e-02 4.75786850e-02 -3.98751855e-01 -8.67860138e-01 -5.89127600e-01 6.94758654e-01 6.78398758e-02 2.33688965e-01 4.61697161e-01 -8.74430239e-02 -3.58575583e-01 -4.75290716e-02 -1.87013710e+00 -7.44069695e-01 -3.99372220e-01 5.07390946e-02 1.15012693e+00 7.07384646e-01 -4.55250263e-01 9.79858115e-02 8.65921736e-01 -1.77699780e+00 -2.85670191e-01 1.17769516e+00 9.80702519e-01 4.35589999e-01 2.75419921e-01 -1.85501993e-01 -1.66196585e-01 2.84743104e-02 -3.29610288e-01 3.43134731e-01 3.55973184e-01 -6.00963056e-01 5.41721582e-01 7.52093017e-01 -4.19316441e-01 -2.82274455e-01 1.11011910e+00 2.21719667e-01 -2.11513173e-02 4.88582402e-01 -1.16548979e+00 -1.64229602e-01 1.23000674e-01 -5.57160616e-01 -3.03747594e-01 1.77026466e-01 4.23114568e-01 1.08168745e+00 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68ecec91-e477-4e36-9afb-08a9aa16106f	attentive-fusion-enhanced-audio-visual	2008.02686	null	https://arxiv.org/abs/2008.02686v1	https://arxiv.org/pdf/2008.02686v1.pdf	Attentive Fusion Enhanced Audio-Visual Encoding for Transformer Based Robust Speech Recognition	Audio-visual information fusion enables a performance improvement in speech recognition performed in complex acoustic scenarios, e.g., noisy environments. It is required to explore an effective audio-visual fusion strategy for audiovisual alignment and modality reliability. Different from the previous end-to-end approaches where the audio-visual fusion is performed after encoding each modality, in this paper we propose to integrate an attentive fusion block into the encoding process. It is shown that the proposed audio-visual fusion method in the encoder module can enrich audio-visual representations, as the relevance between the two modalities is leveraged. In line with the transformer-based architecture, we implement the embedded fusion block using a multi-head attention based audiovisual fusion with one-way or two-way interactions. The proposed method can sufficiently combine the two streams and weaken the over-reliance on the audio modality. Experiments on the LRS3-TED dataset demonstrate that the proposed method can increase the recognition rate by 0.55%, 4.51% and 4.61% on average under the clean, seen and unseen noise conditions, respectively, compared to the state-of-the-art approach.	['Li-Rong Dai', 'JunFeng Hou', 'Jie Zhang', 'Liangfa Wei']	2020-08-06	null	null	null	null	['robust-speech-recognition']	['speech']	[ 3.28791201e-01 1.04194209e-01 3.38304430e-01 -3.28850389e-01 -1.35621345e+00 -2.69254446e-01 7.82718062e-01 2.11643443e-01 -5.02262831e-01 3.20233196e-01 4.56644654e-01 -1.22162186e-01 -6.69207647e-02 -1.98473841e-01 -6.27276659e-01 -8.27635646e-01 2.89719582e-01 -1.51739985e-01 2.27782056e-01 -5.27072996e-02 1.61205009e-01 1.06558934e-01 -2.12725568e+00 5.27397275e-01 6.22932911e-01 1.29319751e+00 4.71359521e-01 9.46036994e-01 -3.21343504e-02 6.02088988e-01 -5.70729852e-01 -3.86381119e-01 -2.18413156e-02 -1.89868703e-01 -3.80723894e-01 5.71616441e-02 5.49069345e-01 -2.49496013e-01 -3.20346504e-01 8.28601599e-01 1.00646257e+00 2.95787215e-01 4.58591700e-01 -1.18357658e+00 -3.91334862e-01 5.59555948e-01 -7.08155274e-01 1.86841339e-01 7.08735764e-01 1.42192803e-02 9.92277265e-01 -1.32788980e+00 1.68829784e-01 1.25528300e+00 2.56929934e-01 3.83332759e-01 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-2.91937441e-01 1.51762441e-01 6.96925044e-01 -1.06520116e+00 -5.10411382e-01 -2.57441942e-02]	[14.401000022888184, 5.194441795349121]
3b0f9272-b5cb-4290-8217-2c83b28b1cab	ino-at-factify-2-structure-coherence-based	2303.0151	null	https://arxiv.org/abs/2303.01510v1	https://arxiv.org/pdf/2303.01510v1.pdf	INO at Factify 2: Structure Coherence based Multi-Modal Fact Verification	This paper describes our approach to the multi-modal fact verification (FACTIFY) challenge at AAAI2023. In recent years, with the widespread use of social media, fake news can spread rapidly and negatively impact social security. Automatic claim verification becomes more and more crucial to combat fake news. In fact verification involving multiple modal data, there should be a structural coherence between claim and document. Therefore, we proposed a structure coherence-based multi-modal fact verification scheme to classify fake news. Our structure coherence includes the following four aspects: sentence length, vocabulary similarity, semantic similarity, and image similarity. Specifically, CLIP and Sentence BERT are combined to extract text features, and ResNet50 is used to extract image features. In addition, we also extract the length of the text as well as the lexical similarity. Then the features were concatenated and passed through the random forest classifier. Finally, our weighted average F1 score has reached 0.8079, achieving 2nd place in FACTIFY2.	['Tongyue Wang', 'Xi Wang', 'Zhulin Tao', 'Yinuo Zhang']	2023-03-02	null	null	null	null	['fact-verification', 'semantic-textual-similarity']	['natural-language-processing', 'natural-language-processing']	[ 7.19854310e-02 -2.00699747e-01 -3.83960843e-01 -2.40075335e-01 -1.03805208e+00 -6.88577414e-01 9.68868792e-01 6.29142702e-01 -1.88882411e-01 7.00643480e-01 6.09025300e-01 -2.49879465e-01 8.65355581e-02 -7.26179481e-01 -4.59537089e-01 -3.71855050e-01 2.93821603e-01 6.52166381e-02 3.77679706e-01 -5.02906978e-01 7.92235553e-01 1.86219573e-01 -1.22705746e+00 7.73263574e-01 7.91063368e-01 1.20051003e+00 -3.40769380e-01 2.42785916e-01 -2.25904837e-01 1.24658775e+00 -7.23107874e-01 -9.51668322e-01 8.37520417e-03 -5.50099373e-01 -1.00009382e+00 7.69491540e-03 7.20993042e-01 -1.54175684e-01 -3.91064674e-01 1.50593042e+00 3.63275945e-01 -3.43546927e-01 4.33437943e-01 -1.11799431e+00 -8.19789529e-01 7.26162493e-01 -7.07111597e-01 4.27380592e-01 6.05854511e-01 -2.88440317e-01 1.02448332e+00 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f54a97ff-2203-4405-8d5e-bbc00d047987	domain-independent-turn-level-dialogue	1908.07064	null	https://arxiv.org/abs/1908.07064v1	https://arxiv.org/pdf/1908.07064v1.pdf	Domain-Independent turn-level Dialogue Quality Evaluation via User Satisfaction Estimation	An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and rely on annotation schemes with low inter-rater reliability, limiting generalizability to conversations spanning multiple domains. To address these gaps, we created a new Response Quality annotation scheme, based on which we developed turn-level User Satisfaction metric. We introduced five new domain-independent feature sets and experimented with six machine learning models to estimate the new satisfaction metric. Using Response Quality annotation scheme, across randomly sampled single and multi-turn conversations from 26 domains, we achieved high inter-annotator agreement (Spearman's rho 0.94). The Response Quality labels were highly correlated (0.76) with explicit turn-level user ratings. Gradient boosting regression achieved best correlation of ~0.79 between predicted and annotated user satisfaction labels. Multi Layer Perceptron and Gradient Boosting regression models generalized to an unseen domain better (linear correlation 0.67) than other models. Finally, our ablation study verified that our novel features significantly improved model performance.	['Spyros Matsoukas', 'Praveen Kumar Bodigutla', 'Joshua Levy', 'Alborz Geramifard', 'Swanand Joshi', 'Longshaokan Wang', 'Kate Ridgeway']	2019-08-19	null	null	null	null	['dialogue-management']	['natural-language-processing']	[-1.44984171e-01 5.48659563e-01 -2.24500462e-01 -1.11185706e+00 -1.12248313e+00 -5.30006349e-01 3.70368689e-01 2.94143885e-01 -6.24931037e-01 1.12978947e+00 6.96369827e-01 -8.01405609e-02 1.37858471e-04 -2.42566288e-01 3.26763034e-01 5.17745130e-02 2.23296613e-01 6.58096850e-01 -1.03939220e-01 -7.59463787e-01 5.79884231e-01 -5.70498034e-02 -1.03864229e+00 7.28215098e-01 9.46864605e-01 7.63340294e-01 2.99693681e-02 1.11298716e+00 -4.13061202e-01 9.12411392e-01 -1.11368001e+00 -5.49518526e-01 -1.70360595e-01 -6.33739471e-01 -1.52247357e+00 9.12586972e-02 1.32036164e-01 -4.17474985e-01 -5.21661900e-02 2.94533938e-01 5.55200756e-01 3.66769254e-01 4.18779075e-01 -1.30904043e+00 -6.22729480e-01 4.01602864e-01 -7.36479536e-02 1.63262442e-01 1.19932556e+00 1.25510275e-01 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5d9451a4-354e-47fb-92e1-d3799c1b9575	bootstrap-aggregation-and-confidence-measures	2306.08946	null	https://arxiv.org/abs/2306.08946v1	https://arxiv.org/pdf/2306.08946v1.pdf	Bootstrap aggregation and confidence measures to improve time series causal discovery	Causal discovery methods have demonstrated the ability to identify the time series graphs representing the causal temporal dependency structure of dynamical systems. However, they do not include a measure of the confidence of the estimated links. Here, we introduce a novel bootstrap aggregation (bagging) and confidence measure method that is combined with time series causal discovery. This new method allows measuring confidence for the links of the time series graphs calculated by causal discovery methods. This is done by bootstrapping the original times series data set while preserving temporal dependencies. Next to confidence measures, aggregating the bootstrapped graphs by majority voting yields a final aggregated output graph. In this work, we combine our approach with the state-of-the-art conditional-independence-based algorithm PCMCI+. With extensive numerical experiments we empirically demonstrate that, in addition to providing confidence measures for links, Bagged-PCMCI+ improves the precision and recall of its base algorithm PCMCI+. Specifically, Bagged-PCMCI+ has a higher detection power regarding adjacencies and a higher precision in orienting contemporaneous edges while at the same time showing a lower rate of false positives. These performance improvements are especially pronounced in the more challenging settings (short time sample size, large number of variables, high autocorrelation). Our bootstrap approach can also be combined with other time series causal discovery algorithms and can be of considerable use in many real-world applications, especially when confidence measures for the links are desired.	['Veronika Eyring', 'Andreas Gerhardus', 'Jakob Runge', 'Kevin Debeire']	2023-06-15	null	null	null	null	['causal-discovery']	['knowledge-base']	[-8.82527307e-02 6.73649609e-02 -1.73739329e-01 1.80518582e-01 -2.39418268e-01 -5.69721222e-01 8.59798312e-01 5.10586679e-01 -8.08497425e-03 1.33474398e+00 8.71114135e-02 -5.36680222e-01 -6.81149840e-01 -1.18794334e+00 -5.70405006e-01 -7.57729471e-01 -9.34037507e-01 5.32973707e-01 5.68834186e-01 3.25956494e-02 1.49761811e-01 4.93848085e-01 -1.37717128e+00 -1.59324095e-01 1.05147028e+00 6.68719947e-01 -2.69613028e-01 5.88319123e-01 1.78132951e-01 6.39847457e-01 -5.09053946e-01 -2.30974182e-01 -1.68425247e-01 -4.88822579e-01 -4.83435899e-01 -4.36642468e-01 -1.44510880e-01 5.58449104e-02 2.42601265e-03 6.80932641e-01 6.54396042e-02 8.82703252e-03 7.27609038e-01 -1.65015936e+00 -1.54440567e-01 8.08236718e-01 -8.00386488e-01 5.02503335e-01 4.55754727e-01 -2.02253059e-01 9.45309341e-01 -6.49045527e-01 5.89719236e-01 1.17084825e+00 7.12235689e-01 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32906ccf-3d33-4569-b320-57750086b52a	benchmarking-the-combinatorial	2109.08925	null	https://arxiv.org/abs/2109.08925v2	https://arxiv.org/pdf/2109.08925v2.pdf	Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs	Complex Query Answering (CQA) is an important reasoning task on knowledge graphs. Current CQA learning models have been shown to be able to generalize from atomic operators to more complex formulas, which can be regarded as the combinatorial generalizability. In this paper, we present EFO-1-QA, a new dataset to benchmark the combinatorial generalizability of CQA models by including 301 different queries types, which is 20 times larger than existing datasets. Besides, our work, for the first time, provides a benchmark to evaluate and analyze the impact of different operators and normal forms by using (a) 7 choices of the operator systems and (b) 9 forms of complex queries. Specifically, we provide the detailed study of the combinatorial generalizability of two commonly used operators, i.e., projection and intersection, and justify the impact of the forms of queries given the canonical choice of operators. Our code and data can provide an effective pipeline to benchmark CQA models.	['Yangqiu Song', 'Hang Yin', 'ZiHao Wang']	2021-09-18	null	null	null	null	['complex-query-answering']	['knowledge-base']	[-1.14989303e-01 1.44808918e-01 -1.59230292e-01 -3.07820201e-01 -4.40156043e-01 -8.28083396e-01 8.04045379e-01 3.97282094e-01 -2.29955226e-01 4.14835215e-01 8.12021866e-02 -6.62895024e-01 -4.24208969e-01 -1.22122014e+00 -7.38897085e-01 -1.66071355e-01 -3.58646423e-01 1.04230213e+00 5.98560750e-01 -7.04365492e-01 1.13662586e-01 5.21987796e-01 -1.80180728e+00 5.50174057e-01 9.31147158e-01 1.00512719e+00 -4.11603183e-01 3.96335661e-01 -2.90262759e-01 9.84416783e-01 -7.01992810e-01 -7.41433620e-01 2.57805496e-01 -7.38471225e-02 -1.40377653e+00 -5.12485921e-01 8.23367059e-01 -2.14607373e-01 -3.07021886e-01 8.75081956e-01 2.55740315e-01 -4.23345231e-02 3.65167618e-01 -1.56215167e+00 -3.44981670e-01 8.28309655e-01 8.35017487e-02 1.52504846e-01 9.47736084e-01 2.47320816e-01 1.72056139e+00 -5.65435112e-01 7.36707926e-01 1.59393001e+00 5.39718926e-01 3.51107389e-01 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885feff6-2b32-4af2-a58e-6588e8478518	retrieval-augmented-visual-question-answering	2210.03809	null	https://arxiv.org/abs/2210.03809v2	https://arxiv.org/pdf/2210.03809v2.pdf	Retrieval Augmented Visual Question Answering with Outside Knowledge	Outside-Knowledge Visual Question Answering (OK-VQA) is a challenging VQA task that requires retrieval of external knowledge to answer questions about images. Recent OK-VQA systems use Dense Passage Retrieval (DPR) to retrieve documents from external knowledge bases, such as Wikipedia, but with DPR trained separately from answer generation, introducing a potential limit on the overall system performance. Instead, we propose a joint training scheme which includes differentiable DPR integrated with answer generation so that the system can be trained in an end-to-end fashion. Our experiments show that our scheme outperforms recent OK-VQA systems with strong DPR for retrieval. We also introduce new diagnostic metrics to analyze how retrieval and generation interact. The strong retrieval ability of our model significantly reduces the number of retrieved documents needed in training, yielding significant benefits in answer quality and computation required for training.	['Bill Byrne', 'Weizhe Lin']	2022-10-07	null	null	null	null	['passage-retrieval']	['natural-language-processing']	[-2.25036800e-01 5.14928391e-03 1.59602556e-02 -1.33887291e-01 -1.48271453e+00 -9.37728524e-01 5.41197002e-01 2.63663214e-02 -4.44839299e-01 5.43610573e-01 3.07057351e-01 -2.69285202e-01 4.65850607e-02 -8.35496187e-01 -8.82769763e-01 -4.52532709e-01 4.25985545e-01 5.44519126e-01 4.50583279e-01 -2.74704933e-01 1.38670519e-01 3.04094672e-01 -1.33828211e+00 3.86491179e-01 8.81054699e-01 1.00060666e+00 2.77750343e-01 1.12161863e+00 -3.22996259e-01 1.35448325e+00 -7.39635706e-01 -7.02103198e-01 2.16285050e-01 -2.85701632e-01 -1.17244780e+00 -2.13458791e-01 8.63013029e-01 -7.90136397e-01 -7.63992548e-01 6.19128227e-01 7.59294689e-01 3.39043558e-01 7.72609890e-01 -9.19224083e-01 -1.41571236e+00 -3.77591103e-02 -2.50344545e-01 2.60073334e-01 5.81541598e-01 4.44451243e-01 1.24917972e+00 -1.19221532e+00 9.79389668e-01 1.22785378e+00 1.75087318e-01 7.54279792e-01 -1.01354742e+00 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-1.64551120e-02 4.35550630e-01 5.91531575e-01 -3.22584629e-01 -2.25554436e-01 8.42717662e-02]	[10.864771842956543, 1.6535120010375977]
3a538d3d-99db-4255-a6ca-7e58b738981b	assorted-archetypal-and-annotated-two-million	2303.16778	null	https://arxiv.org/abs/2303.16778v1	https://arxiv.org/pdf/2303.16778v1.pdf	Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning	Cooking recipes allow individuals to exchange culinary ideas and provide food preparation instructions. Due to a lack of adequate labeled data, categorizing raw recipes found online to the appropriate food genres is a challenging task in this domain. Utilizing the knowledge of domain experts to categorize recipes could be a solution. In this study, we present a novel dataset of two million culinary recipes labeled in respective categories leveraging the knowledge of food experts and an active learning technique. To construct the dataset, we collect the recipes from the RecipeNLG dataset. Then, we employ three human experts whose trustworthiness score is higher than 86.667% to categorize 300K recipe by their Named Entity Recognition (NER) and assign it to one of the nine categories: bakery, drinks, non-veg, vegetables, fast food, cereals, meals, sides and fusion. Finally, we categorize the remaining 1900K recipes using Active Learning method with a blend of Query-by-Committee and Human In The Loop (HITL) approaches. There are more than two million recipes in our dataset, each of which is categorized and has a confidence score linked with it. For the 9 genres, the Fleiss Kappa score of this massive dataset is roughly 0.56026. We believe that the research community can use this dataset to perform various machine learning tasks such as recipe genre classification, recipe generation of a specific genre, new recipe creation, etc. The dataset can also be used to train and evaluate the performance of various NLP tasks such as named entity recognition, part-of-speech tagging, semantic role labeling, and so on. The dataset will be available upon publication: https://tinyurl.com/3zu4778y.	['Hasan Mahmud', 'Md. Kamrul Hasan', 'Md. Mohsinul Kabir', 'G. M. Shahariar', 'Nazmus Sakib']	2023-03-27	null	null	null	null	['genre-classification', 'recipe-generation', 'semantic-role-labeling', 'part-of-speech-tagging']	['computer-vision', 'miscellaneous', 'natural-language-processing', 'natural-language-processing']	[-2.72731483e-01 6.41577840e-02 -5.61405003e-01 -5.19088089e-01 -9.18441534e-01 -1.15160787e+00 1.88350901e-01 8.82828832e-01 -2.83888131e-01 6.64471567e-01 6.50494874e-01 2.17206720e-02 1.97423026e-01 -1.01243830e+00 -4.97299612e-01 -7.74097860e-01 4.32742298e-01 3.58679205e-01 -3.57713588e-02 -1.56070799e-01 1.90546677e-01 -3.31630141e-01 -1.23194420e+00 4.58073527e-01 1.12202847e+00 7.08549440e-01 2.44005173e-01 3.23662907e-01 -5.62268138e-01 8.87677073e-01 -3.46964031e-01 -6.63967669e-01 2.03651756e-01 -3.48902047e-01 -5.27668059e-01 -5.28608933e-02 2.63805152e-03 -1.57518074e-01 5.20261765e-01 1.10255063e+00 3.55089337e-01 3.87634039e-01 5.50263941e-01 -1.08527207e+00 -9.35127795e-01 1.21066010e+00 -8.75828601e-03 -4.40532118e-01 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f19dfe0d-f53f-46c6-a407-446a576a2399	seeing-in-extra-darkness-using-a-deep-red	null	null	http://openaccess.thecvf.com//content/CVPR2021/html/Xiong_Seeing_in_Extra_Darkness_Using_a_Deep-Red_Flash_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Xiong_Seeing_in_Extra_Darkness_Using_a_Deep-Red_Flash_CVPR_2021_paper.pdf	Seeing in Extra Darkness Using a Deep-Red Flash	We propose a new flash technique for low-light imaging, using deep-red light as an illuminating source. Our main observation is that in a dim environment, the human eye mainly uses rods for the perception of light, which are not sensitive to wavelengths longer than 620nm, yet the camera sensor still has a spectral response. We propose a novel modulation strategy when training a modern CNN model for guided image filtering, fusing a noisy RGB frame and a flash frame. This fusion network is further extended for video reconstruction. We have built a prototype with minor hardware adjustments and tested the new flash technique on a variety of static and dynamic scenes. The experimental results demonstrate that our method produces compelling reconstructions, even in extra dim conditions.	['Shree Nayar', 'Wolfgang Heidrich', 'Jian Wang', 'Jinhui Xiong']	2021-06-19	null	null	null	cvpr-2021-1	['video-reconstruction']	['computer-vision']	[ 4.66877073e-01 -5.44065416e-01 3.05137306e-01 -1.48753211e-01 -3.46196666e-02 -3.35470766e-01 1.91811264e-01 -8.70387495e-01 -7.14276671e-01 9.06323433e-01 2.22472772e-02 -2.27112338e-01 3.89553934e-01 -5.66536129e-01 -7.47685075e-01 -9.97602105e-01 5.24657369e-01 -6.61399603e-01 4.26889956e-01 -1.02057442e-01 9.97575819e-02 2.64090925e-01 -1.87563801e+00 2.18262151e-01 6.31468415e-01 1.08156931e+00 6.75700426e-01 9.30035949e-01 2.35600829e-01 1.05210280e+00 -7.35746384e-01 -1.13126084e-01 4.94433641e-01 -4.62494403e-01 -1.93779916e-01 9.69916135e-02 6.42381191e-01 -1.02774382e+00 -9.29429531e-01 1.11871481e+00 5.80988824e-01 2.04889715e-01 -1.16975166e-01 -5.67309022e-01 -7.91718662e-01 7.02779293e-02 -4.66025889e-01 3.83468419e-01 6.24132037e-01 6.77735686e-01 1.75821349e-01 -8.25076878e-01 3.63197416e-01 8.68401170e-01 4.36648399e-01 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66f4793b-9961-4478-a174-638037b79c65	act-the-part-learning-interaction-strategies	2105.01047	null	https://arxiv.org/abs/2105.01047v1	https://arxiv.org/pdf/2105.01047v1.pdf	Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery	People often use physical intuition when manipulating articulated objects, irrespective of object semantics. Motivated by this observation, we identify an important embodied task where an agent must play with objects to recover their parts. To this end, we introduce Act the Part (AtP) to learn how to interact with articulated objects to discover and segment their pieces. By coupling action selection and motion segmentation, AtP is able to isolate structures to make perceptual part recovery possible without semantic labels. Our experiments show AtP learns efficient strategies for part discovery, can generalize to unseen categories, and is capable of conditional reasoning for the task. Although trained in simulation, we show convincing transfer to real world data with no fine-tuning.	['Shuran Song', 'Kiana Ehsani', 'Samir Yitzhak Gadre']	2021-05-03	null	http://openaccess.thecvf.com//content/ICCV2021/html/Gadre_Act_the_Part_Learning_Interaction_Strategies_for_Articulated_Object_Part_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Gadre_Act_the_Part_Learning_Interaction_Strategies_for_Articulated_Object_Part_ICCV_2021_paper.pdf	iccv-2021-1	['motion-segmentation', 'physical-intuition']	['computer-vision', 'reasoning']	[ 3.11695933e-01 7.06123590e-01 -8.90292823e-02 -5.26729859e-02 -4.83152151e-01 -1.03787899e+00 4.52423513e-01 -1.19361408e-01 -1.32741928e-01 6.22570872e-01 1.64833888e-01 1.46948621e-01 -4.55648787e-02 -6.35714531e-01 -1.05929494e+00 -5.46511114e-01 -7.11212680e-02 8.60805213e-01 3.68376046e-01 1.01986350e-02 2.69766212e-01 6.12952769e-01 -1.59663260e+00 5.51623464e-01 5.22196352e-01 5.12392044e-01 4.93328154e-01 8.63479197e-01 1.51252588e-02 1.00234294e+00 -3.42606246e-01 -2.85387576e-01 4.31937605e-01 -4.57431048e-01 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e56971d9-8773-411a-908b-d88347c3385c	planverb-domain-independent-verbalization-and	null	null	https://ojs.aaai.org/index.php/AAAI/article/view/21204	https://ojs.aaai.org/index.php/AAAI/article/view/21204/20953	PlanVerb: Domain-Independent Verbalization and Summary of Task Plans	For users to trust planning algorithms, they must be able to understand the planner's outputs and the reasons for each action selection. This output does not tend to be user-friendly, often consisting of sequences of parametrised actions or task networks. And these may not be practical for non-expert users who may find it easier to read natural language descriptions. In this paper, we propose PlanVerb, a domain and planner-independent method for the verbalization of task plans. It is based on semantic tagging of actions and predicates. Our method can generate natural language descriptions of plans including causal explanations. The verbalized plans can be summarized by compressing the actions that act on the same parameters. We further extend the concept of verbalization space, previously applied to robot navigation, and apply it to planning to generate different kinds of plan descriptions for different user requirements. Our method can deal with PDDL and RDDL domains, provided that they are tagged accordingly. Our user survey evaluation shows that users can read our automatically generated plan descriptions and that the explanations help them answer questions about the plan.	['Andrew Coles', 'Paul Luff', 'Senka Krivic ́', 'Gerard Canal']	2022-06-28	null	null	null	proceedings-of-the-aaai-conference-on-5	['robot-navigation']	['robots']	[ 2.88151771e-01 1.13088322e+00 -1.11791417e-02 -7.40740657e-01 -3.42378110e-01 -6.52404308e-01 7.05050349e-01 3.41062576e-01 -1.91373155e-01 9.47598815e-01 8.26463521e-01 -5.48886418e-01 -3.63924235e-01 -7.90200472e-01 -2.97372490e-01 -1.30733877e-01 -1.66284159e-01 1.04578328e+00 5.56420922e-01 -5.51045477e-01 4.15915877e-01 4.31099147e-01 -1.46017230e+00 5.15025496e-01 8.67872834e-01 3.53390753e-01 6.76189601e-01 6.88172221e-01 -4.67274994e-01 1.20878696e+00 -4.75615591e-01 9.14622173e-02 -5.16116209e-02 -6.16709769e-01 -1.60781217e+00 2.88838029e-01 -4.62330848e-01 -3.46364141e-01 1.07367635e-01 7.08889008e-01 -2.81784195e-03 4.05486375e-01 7.22291410e-01 -1.41189170e+00 -2.47130439e-01 1.18911147e+00 3.92312706e-01 -3.41264337e-01 1.09771085e+00 1.41152605e-01 9.45006192e-01 -2.59846509e-01 7.76293278e-01 1.66981292e+00 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d2627b24-db3c-49dd-8153-305b889f1c46	all-in-sam-from-weak-annotation-to-pixel-wise	2307.0029	null	https://arxiv.org/abs/2307.00290v1	https://arxiv.org/pdf/2307.00290v1.pdf	All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning	The Segment Anything Model (SAM) is a recently proposed prompt-based segmentation model in a generic zero-shot segmentation approach. With the zero-shot segmentation capacity, SAM achieved impressive flexibility and precision on various segmentation tasks. However, the current pipeline requires manual prompts during the inference stage, which is still resource intensive for biomedical image segmentation. In this paper, instead of using prompts during the inference stage, we introduce a pipeline that utilizes the SAM, called all-in-SAM, through the entire AI development workflow (from annotation generation to model finetuning) without requiring manual prompts during the inference stage. Specifically, SAM is first employed to generate pixel-level annotations from weak prompts (e.g., points, bounding box). Then, the pixel-level annotations are used to finetune the SAM segmentation model rather than training from scratch. Our experimental results reveal two key findings: 1) the proposed pipeline surpasses the state-of-the-art (SOTA) methods in a nuclei segmentation task on the public Monuseg dataset, and 2) the utilization of weak and few annotations for SAM finetuning achieves competitive performance compared to using strong pixel-wise annotated data.	['Yuankai Huo', 'Yucheng Tang', 'Lucas W. Remedios', 'Shunxing Bao', 'Tianyuan Yao', 'Quan Liu', 'Ruining Deng', 'Can Cui']	2023-07-01	null	null	null	null	['zero-shot-segmentation']	['computer-vision']	[ 6.52045786e-01 4.56988990e-01 -1.26094058e-01 -3.88155103e-01 -1.12603509e+00 -4.98876333e-01 2.15607762e-01 2.67958403e-01 -7.28016734e-01 5.02223134e-01 -2.63035715e-01 -2.86487669e-01 1.56582266e-01 -5.74134707e-01 -5.40298164e-01 -8.03323448e-01 5.35682738e-01 6.82850361e-01 8.27642143e-01 -6.04204275e-02 3.35609674e-01 1.30686045e-01 -1.29211867e+00 2.46936649e-01 1.18632030e+00 8.46591473e-01 2.87178755e-01 8.31441164e-01 -2.94624239e-01 4.56007779e-01 -5.62892616e-01 -2.65242636e-01 1.55120090e-01 -5.41261256e-01 -9.74507034e-01 1.04383864e-01 2.63375968e-01 -3.97124261e-01 2.05028281e-01 1.09646022e+00 6.37104869e-01 1.96819842e-01 4.18345451e-01 -8.97774220e-01 -3.18638653e-01 7.51442373e-01 -6.44453764e-01 3.15003783e-01 -2.32252777e-02 4.56565320e-01 8.38969111e-01 -7.53601432e-01 8.14493656e-01 7.27233410e-01 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8de6816f-1a81-4c28-b0c2-b2dd02dbb169	structure-function-dynamics-hybrid-modeling	2305.03925	null	https://arxiv.org/abs/2305.03925v3	https://arxiv.org/pdf/2305.03925v3.pdf	Structure-Function Dynamics Hybrid Modeling: RNA Degradation	RNA structure and functional dynamics play fundamental roles in controlling biological systems. Molecular dynamics simulation, which can characterize interactions at an atomistic level, can advance the understanding on new drug discovery, manufacturing, and delivery mechanisms. However, it is computationally unattainable to support the development of a digital twin for enzymatic reaction network mechanism learning, and end-to-end bioprocess design and control. Thus, we create a hybrid ("mechanistic + machine learning") model characterizing the interdependence of RNA structure and functional dynamics from atomistic to macroscopic levels. To assess the proposed modeling strategy, in this paper, we consider RNA degradation which is a critical process in cellular biology that affects gene expression. The empirical study on RNA lifetime prediction demonstrates the promising performance of the proposed multi-scale bioprocess hybrid modeling strategy.	['Wandi Xu', 'Chunsheng Fang', 'Ailun Wang', 'Paul Whitford', 'Wei Xie', 'Hua Zheng']	2023-05-06	null	null	null	null	['drug-discovery']	['medical']	[ 1.69996798e-01 -3.85227263e-01 -3.34179044e-01 2.68757015e-01 1.08741699e-02 -8.87820423e-01 7.33092785e-01 4.07483190e-01 1.07967548e-01 1.15811121e+00 1.23699695e-01 -6.76392913e-01 -3.39844227e-01 -4.86181170e-01 -6.51185572e-01 -1.27459228e+00 -4.35820445e-02 3.95440578e-01 -1.54647961e-01 -5.49303591e-02 4.13466573e-01 1.02149320e+00 -1.01009738e+00 1.79893553e-01 6.15735292e-01 6.36406183e-01 9.80963483e-02 9.20750260e-01 -4.84804995e-02 7.39319146e-01 -2.75943458e-01 -7.01470781e-05 -1.52253196e-01 -7.76465952e-01 -2.97271490e-01 -2.82759607e-01 -6.13689423e-01 4.28030252e-01 -1.82964653e-01 4.35328722e-01 7.37960935e-01 4.43746476e-03 1.03304243e+00 -7.33353317e-01 -6.36987150e-01 2.80929625e-01 -2.65038073e-01 1.40858501e-01 3.16120595e-01 6.50267839e-01 6.98053837e-01 -3.28293711e-01 8.47159922e-01 1.16874135e+00 4.02545631e-01 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c4294213-c811-4cfa-afc8-b4923d536d16	text-augmentation-in-a-multi-task-view	2101.05469	null	https://arxiv.org/abs/2101.05469v1	https://arxiv.org/pdf/2101.05469v1.pdf	Text Augmentation in a Multi-Task View	Traditional data augmentation aims to increase the coverage of the input distribution by generating augmented examples that strongly resemble original samples in an online fashion where augmented examples dominate training. In this paper, we propose an alternative perspective -- a multi-task view (MTV) of data augmentation -- in which the primary task trains on original examples and the auxiliary task trains on augmented examples. In MTV data augmentation, both original and augmented samples are weighted substantively during training, relaxing the constraint that augmented examples must resemble original data and thereby allowing us to apply stronger levels of augmentation. In empirical experiments using four common data augmentation techniques on three benchmark text classification datasets, we find that the MTV leads to higher and more robust performance improvements than traditional augmentation.	['Soroush Vosoughi', 'Shiqi Xu', 'Chengyu Huang', 'Jason Wei']	2021-01-14	null	https://aclanthology.org/2021.eacl-main.252	https://aclanthology.org/2021.eacl-main.252.pdf	eacl-2021-2	['text-augmentation']	['natural-language-processing']	[ 6.74751103e-01 5.31938910e-01 -5.75001657e-01 -5.05355477e-01 -7.68851697e-01 -3.19848537e-01 7.65466332e-01 3.46077681e-01 -5.77377439e-01 1.03560746e+00 3.28937829e-01 -3.40248704e-01 1.29380286e-01 -6.23125494e-01 -7.94874489e-01 -5.00446558e-01 2.27475941e-01 7.87670851e-01 -1.23137459e-01 -3.00733298e-01 1.93483699e-02 9.05499086e-02 -1.42048955e+00 5.58496475e-01 8.56559455e-01 6.85567200e-01 -1.50574088e-01 5.35015881e-01 -3.34248871e-01 5.56231499e-01 -7.47576118e-01 -5.29736817e-01 5.72906494e-01 -2.28039965e-01 -8.01860094e-01 3.98089319e-01 5.56666970e-01 -1.49129942e-01 -1.50243789e-01 8.44516516e-01 2.56259829e-01 3.56162488e-01 5.74653447e-01 -1.42276108e+00 -1.01893508e+00 9.65781450e-01 -9.61805820e-01 1.05130434e-01 1.41275093e-01 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50be851b-c947-4203-8aae-4cffba225b2a	atomai-a-deep-learning-framework-for-analysis	2105.07485	null	https://arxiv.org/abs/2105.07485v1	https://arxiv.org/pdf/2105.07485v1.pdf	AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond	AtomAI is an open-source software package bridging instrument-specific Python libraries, deep learning, and simulation tools into a single ecosystem. AtomAI allows direct applications of the deep convolutional neural networks for atomic and mesoscopic image segmentation converting image and spectroscopy data into class-based local descriptors for downstream tasks such as statistical and graph analysis. For atomically-resolved imaging data, the output is types and positions of atomic species, with an option for subsequent refinement. AtomAI further allows the implementation of a broad range of image and spectrum analysis functions, including invariant variational autoencoders (VAEs). The latter consists of VAEs with rotational and (optionally) translational invariance for unsupervised and class-conditioned disentanglement of categorical and continuous data representations. In addition, AtomAI provides utilities for mapping structure-property relationships via im2spec and spec2im type of encoder-decoder models. Finally, AtomAI allows seamless connection to the first principles modeling with a Python interface, including molecular dynamics and density functional theory calculations on the inferred atomic position. While the majority of applications to date were based on atomically resolved electron microscopy, the flexibility of AtomAI allows straightforward extension towards the analysis of mesoscopic imaging data once the labels and feature identification workflows are established/available. The source code and example notebooks are available at https://github.com/pycroscopy/atomai.	['Sergei V. Kalinin', 'Tommy Wong', 'Ayana Ghosh', 'Maxim Ziatdinov']	2021-05-16	null	null	null	null	['materials-imaging', 'im2spec']	['computer-vision', 'computer-vision']	[ 2.19646394e-01 -3.19371700e-01 1.24313697e-01 -3.73517036e-01 -6.52151883e-01 -5.29251635e-01 5.73655307e-01 3.44793022e-01 -3.98399651e-01 8.78026068e-01 -9.49741155e-02 -5.57318330e-01 -6.29938692e-02 -8.60278487e-01 -7.25082755e-01 -1.11860466e+00 -1.83083966e-01 7.41289318e-01 -1.20841451e-01 7.89432228e-02 2.28700757e-01 9.18152690e-01 -1.52673328e+00 5.24540603e-01 3.28115612e-01 9.65010047e-01 2.85320789e-01 8.40431809e-01 -2.03993395e-01 6.97662294e-01 -7.71848410e-02 -2.33087242e-02 9.96867642e-02 -3.68091136e-01 -9.41996932e-01 -3.35153073e-01 2.71842450e-01 -7.66913891e-02 -1.64998263e-01 9.45301116e-01 7.72317648e-01 2.88129121e-01 8.33547771e-01 -8.57524812e-01 -7.80957937e-01 2.62791514e-01 -3.08357120e-01 3.92813176e-01 2.13891685e-01 7.75399864e-01 1.04787111e+00 -8.81848276e-01 9.56276298e-01 9.60216761e-01 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a50422ae-ea98-4066-9c69-43276b7e816e	learning-to-borrow-relation-representation-2	null	null	https://aclanthology.org/2022.naacl-main.209	https://aclanthology.org/2022.naacl-main.209.pdf	Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion	Prior work on integrating text corpora with knowledge graphs (KGs) to improve Knowledge Graph Embedding (KGE) have obtained good performance for entities that co-occur in sentences in text corpora. Such sentences (textual mentions of entity-pairs) are represented as Lexicalised Dependency Paths (LDPs) between two entities. However, it is not possible to represent relations between entities that do not co-occur in a single sentence using LDPs. In this paper, we propose and evaluate several methods to address this problem, where we borrow LDPs from the entity pairs that co-occur in sentences in the corpus (i.e. with mentions entity pairs) to represent entity pairs that do not co-occur in any sentence in the corpus (i.e. without mention entity pairs). We propose a supervised borrowing method, SuperBorrow, that learns to score the suitability of an LDP to represent a without-mentions entity pair using pre-trained entity embeddings and contextualised LDP representations. Experimental results show that SuperBorrow improves the link prediction performance of multiple widely-used prior KGE methods such as TransE, DistMult, ComplEx and RotatE.	['Danushka Bollegala', 'Angrosh Mandya', 'Mona Hakami', 'Huda Hakami']	null	null	null	null	naacl-2022-7	['entity-embeddings']	['methodology']	[-2.19760820e-01 6.68240845e-01 -3.42246115e-01 -1.84386283e-01 -2.16785163e-01 -6.20789170e-01 6.19674623e-01 9.08022702e-01 -5.84408581e-01 1.04627502e+00 5.18866658e-01 -4.68255907e-01 -3.91799092e-01 -1.18413055e+00 -8.39739561e-01 -1.86462298e-01 -3.92566621e-01 5.09856403e-01 5.91596484e-01 -4.25975204e-01 -8.69716927e-02 2.73744106e-01 -9.52191114e-01 1.67183980e-01 9.72535312e-01 2.75666505e-01 1.08159758e-01 4.81298596e-01 -4.97340292e-01 7.13175476e-01 -7.30955303e-01 -1.14822626e+00 -1.40950829e-01 -3.39857340e-01 -1.15216064e+00 -6.21061265e-01 4.86298829e-01 1.79738462e-01 -4.37308222e-01 9.11255658e-01 3.37208509e-01 -7.59758195e-03 9.20814812e-01 -1.42374671e+00 -9.72903728e-01 1.18567300e+00 -4.72805977e-01 4.07850116e-01 4.61983651e-01 -7.13760078e-01 1.69442356e+00 -9.75762427e-01 1.24994195e+00 1.22261453e+00 8.69784892e-01 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0a784663-8d11-4bde-bb57-112ff3eab428	topological-data-analysis-for-word-sense	2203.00565	null	https://arxiv.org/abs/2203.00565v1	https://arxiv.org/pdf/2203.00565v1.pdf	Topological Data Analysis for Word Sense Disambiguation	We develop and test a novel unsupervised algorithm for word sense induction and disambiguation which uses topological data analysis. Typical approaches to the problem involve clustering, based on simple low level features of distance in word embeddings. Our approach relies on advanced mathematical concepts in the field of topology which provides a richer conceptualization of clusters for the word sense induction tasks. We use a persistent homology barcode algorithm on the SemCor dataset and demonstrate that our approach gives low relative error on word sense induction. This shows the promise of topological algorithms for natural language processing and we advocate for future work in this promising area.	['Rishabh Choudhary', 'Mithun Bharadwaj', 'Samuel Dooley', 'Michael Rawson']	2022-03-01	null	null	null	null	['word-sense-disambiguation']	['natural-language-processing']	[ 2.45772004e-02 2.37570539e-01 -1.39476329e-01 -2.93314427e-01 -2.56400973e-01 -6.58648431e-01 7.69404948e-01 9.37904954e-01 -7.76473224e-01 5.32527268e-01 3.50801557e-01 -5.84741354e-01 -5.26163459e-01 -1.10652483e+00 -2.26078555e-01 -8.29970181e-01 -6.95294797e-01 6.91549718e-01 4.75864112e-01 -7.37777650e-01 7.52413213e-01 3.07891548e-01 -1.18162179e+00 -1.70132861e-01 9.34091866e-01 5.44084311e-01 9.99231115e-02 4.59908962e-01 -4.89334673e-01 1.25899777e-01 -2.71816790e-01 -1.42882064e-01 1.17372982e-01 -5.73963746e-02 -1.38025522e+00 -5.57053089e-01 3.31188798e-01 6.56463742e-01 -3.10299569e-03 1.18714428e+00 3.91178250e-01 5.03325939e-01 8.37785244e-01 -9.09490407e-01 -6.52871907e-01 4.77095395e-01 -3.44878197e-01 6.41470313e-01 4.77752507e-01 -5.16906857e-01 1.49556744e+00 -9.87910986e-01 9.90083456e-01 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-6.04401648e-01 -1.65987924e-01 -8.94605637e-01 -6.47626877e-01 6.06964707e-01 2.53869504e-01 -1.18708909e+00 -3.28800738e-01 9.52769667e-02]	[10.210132598876953, 8.741706848144531]
68241f40-b2c3-4247-b717-a494bfe5bff8	towards-shared-datasets-for-normalization	null	null	https://aclanthology.org/L14-1574	https://aclanthology.org/L14-1574.pdf	Towards Shared Datasets for Normalization Research	In this paper we present a Dutch and English dataset that can serve as a gold standard for evaluating text normalization approaches. With the combination of text messages, message board posts and tweets, these datasets represent a variety of user generated content. All data was manually normalized to their standard form using newly-developed guidelines. We perform automatic lexical normalization experiments on these datasets using statistical machine translation techniques. We focus on both the word and character level and find that we can improve the BLEU score with ca. 20{\%} for both languages. In order for this user generated content data to be released publicly to the research community some issues first need to be resolved. These are discussed in closer detail by focussing on the current legislation and by investigating previous similar data collection projects. With this discussion we hope to shed some light on various difficulties researchers are facing when trying to share social media data.	["V{\\'e}ronique Hoste", "Orph{\\'e}e De Clercq", 'Sarah Schulz', 'Bart Desmet']	2014-05-01	null	null	null	lrec-2014-5	['lexical-normalization']	['natural-language-processing']	[ 4.25664276e-01 9.97374877e-02 -4.03332829e-01 -5.36867380e-01 -9.85587299e-01 -6.58689082e-01 8.23867202e-01 1.01744771e+00 -1.15417993e+00 9.61717069e-01 8.45309973e-01 -3.42180699e-01 1.07164010e-01 -6.94171607e-01 -1.19089104e-01 -1.65457845e-01 4.82782215e-01 4.03071016e-01 3.23069543e-02 -6.69939935e-01 4.61583763e-01 3.65269691e-01 -1.33494115e+00 3.94604981e-01 5.52402973e-01 8.10983405e-02 -1.93692803e-01 5.77273905e-01 -4.82712209e-01 7.36178458e-01 -8.28206122e-01 -9.17723894e-01 2.67544150e-01 -2.71354109e-01 -1.00487721e+00 -2.99293220e-01 4.15506989e-01 -4.25610915e-02 7.05087837e-03 1.24637103e+00 6.98263884e-01 2.25227311e-01 4.30245429e-01 -9.75796998e-01 -2.50638664e-01 9.93471563e-01 -4.09309775e-01 5.01177013e-01 5.80574095e-01 -8.34182277e-02 1.04183125e+00 -4.37871963e-01 1.01484740e+00 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3e6fdec0-3923-4c3f-ac90-20955a9fce0a	whats-wrong-with-hebrew-nlp-and-how-to-make	1908.05453	null	https://arxiv.org/abs/1908.05453v1	https://arxiv.org/pdf/1908.05453v1.pdf	What's Wrong with Hebrew NLP? And How to Make it Right	For languages with simple morphology, such as English, automatic annotation pipelines such as spaCy or Stanford's CoreNLP successfully serve projects in academia and the industry. For many morphologically-rich languages (MRLs), similar pipelines show sub-optimal performance that limits their applicability for text analysis in research and the industry.The sub-optimal performance is mainly due to errors in early morphological disambiguation decisions, which cannot be recovered later in the pipeline, yielding incoherent annotations on the whole. In this paper we describe the design and use of the Onlp suite, a joint morpho-syntactic parsing framework for processing Modern Hebrew texts. The joint inference over morphology and syntax substantially limits error propagation, and leads to high accuracy. Onlp provides rich and expressive output which already serves diverse academic and commercial needs. Its accompanying online demo further serves educational activities, introducing Hebrew NLP intricacies to researchers and non-researchers alike.	['Amit Seker', 'Reut Tsarfaty', 'Stav Klein', 'Shoval Sadde']	2019-08-15	whats-wrong-with-hebrew-nlp-and-how-to-make-1	https://aclanthology.org/D19-3044	https://aclanthology.org/D19-3044.pdf	ijcnlp-2019-11	['morphological-disambiguation']	['natural-language-processing']	[-1.35728538e-01 1.39190882e-01 -7.73941651e-02 -3.59114230e-01 -1.09243309e+00 -1.07926762e+00 3.17790270e-01 6.92306459e-01 -6.65926576e-01 7.11807311e-01 2.84465313e-01 -6.04649127e-01 -9.69044119e-02 -5.94118237e-01 -1.27322942e-01 -2.75600731e-01 2.03263745e-01 7.23473728e-01 1.68654978e-01 -2.04134136e-01 2.71914512e-01 5.74818075e-01 -1.22999489e+00 2.32598364e-01 8.86724710e-01 1.32325590e-01 3.08681279e-01 6.05706632e-01 -6.12689972e-01 3.69988978e-01 -5.93632221e-01 -9.51324880e-01 -3.94990928e-02 -1.23299114e-01 -1.04244983e+00 -4.02375042e-01 5.56750357e-01 1.35789737e-01 5.11474498e-02 1.21177411e+00 5.18037140e-01 3.19141662e-03 1.90326318e-01 -6.61947250e-01 -6.55199468e-01 1.06670225e+00 -2.99164534e-01 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79f48f01-79f0-44df-aa44-da7fa89aa298	what-makes-a-question-inquisitive-a-study-on-1	null	null	https://aclanthology.org/2022.starsem-1.22	https://aclanthology.org/2022.starsem-1.22.pdf	“What makes a question inquisitive?” A Study on Type-Controlled Inquisitive Question Generation	We propose a type-controlled framework for inquisitive question generation. We annotate an inquisitive question dataset with question types, train question type classifiers, and finetune models for type-controlled question generation. Empirical results demonstrate that we can generate a variety of questions that adhere to specific types while drawing from the source texts. We also investigate strategies for selecting a single question from a generated set, considering both an informative vs. inquisitive question classifier and a pairwise ranker trained from a small set of expert annotations. Question selection using the pairwise ranker yields strong results in automatic and manual evaluation. Our human evaluation assesses multiple aspects of the generated questions, finding that the ranker chooses questions with the best syntax (4.59), semantics (4.37), and inquisitiveness (3.92) on a scale of 1-5, even rivaling the performance of human-written questions.	['Kevin Gimpel', 'Debanjan Ghosh', 'Lingyu Gao']	null	null	null	null	sem-naacl-2022-7	['question-generation', 'question-selection']	['natural-language-processing', 'natural-language-processing']	[ 1.98731348e-01 6.68856382e-01 2.42458880e-01 -4.97768313e-01 -1.62802064e+00 -1.12620115e+00 8.23159099e-01 2.82094419e-01 -4.30857211e-01 7.57517934e-01 5.64491212e-01 -5.09655535e-01 -2.93917537e-01 -6.12339258e-01 -3.29079747e-01 -4.05317023e-02 5.45232832e-01 7.76339293e-01 5.39418042e-01 -5.29155433e-01 5.21553040e-01 -2.03525260e-01 -1.53423905e+00 7.15988398e-01 1.41275954e+00 1.06620026e+00 1.30778477e-01 1.05143857e+00 -5.27782202e-01 1.16848409e+00 -1.23388278e+00 -7.66123235e-01 -9.32357535e-02 -7.97437847e-01 -1.40881336e+00 -5.57901822e-02 8.24694932e-01 -1.01147324e-01 4.20073688e-01 5.97844183e-01 3.25854301e-01 2.45964900e-01 9.26814437e-01 -9.66399968e-01 -9.74195600e-01 7.44236410e-01 3.32021117e-01 2.95411736e-01 1.07276475e+00 2.17064068e-01 1.52598941e+00 -8.37366939e-01 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1.42501947e-02 8.17323327e-01 1.68965325e-01 -1.12749195e+00 -1.30330110e+00 -3.49488445e-02 6.54827535e-01 3.35693836e-01 -2.63089627e-01 -7.25625515e-01 8.15916657e-02]	[11.62270736694336, 8.181865692138672]
61677587-795e-48d6-932f-e22f7a64d186	coherent-comments-generation-for-chinese	null	null	https://aclanthology.org/P19-1479	https://aclanthology.org/P19-1479.pdf	Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model	Automatic article commenting is helpful in encouraging user engagement on online news platforms. However, the news documents are usually too long for models under traditional encoder-decoder frameworks, which often results in general and irrelevant comments. In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. By organizing the article into graph structure, our model can better understand the internal structure of the article and the connection between topics, which makes it better able to generate coherent and informative comments. We collect and release a large scale news-comment corpus from a popular Chinese online news platform Tencent Kuaibao. Extensive experiment results show that our model can generate much more coherent and informative comments compared with several strong baseline models.	['Xu sun', 'Yunfang Wu', 'Yancheng He', 'Jingjing Xu', 'Wei Li', 'ShengLi Yan']	2019-07-01	null	null	null	acl-2019-7	['graph-to-sequence']	['natural-language-processing']	[ 9.69235078e-02 8.46421242e-01 -5.37507772e-01 -3.00992221e-01 -8.17204475e-01 -4.55087662e-01 7.32998133e-01 2.62470216e-01 1.01125956e-01 8.35946321e-01 1.43717802e+00 -4.37116414e-01 7.50444770e-01 -5.91071665e-01 -6.03501379e-01 -3.52348015e-02 2.44284853e-01 3.52966636e-01 6.19572662e-02 -6.50428295e-01 3.01179230e-01 -6.64145768e-01 -9.42653179e-01 7.65506744e-01 1.10653663e+00 5.76375872e-02 4.22637463e-01 7.57623672e-01 -5.08394599e-01 9.10768926e-01 -6.84797704e-01 -7.66862571e-01 -2.75068343e-01 -1.09752357e+00 -7.71926641e-01 4.04547215e-01 1.96787462e-01 -3.00339580e-01 -3.43463689e-01 9.90881324e-01 3.73656720e-01 1.77280847e-02 6.86131477e-01 -9.19903576e-01 -1.03897285e+00 1.61830366e+00 -5.65478206e-01 -2.64896918e-02 6.24467075e-01 -2.96146899e-01 1.61799061e+00 -6.98668957e-01 9.33597445e-01 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e24ea9b7-aba3-45a6-a283-64f6f7687c7d	shadingnet-image-intrinsics-by-fine-grained	1912.04023	null	https://arxiv.org/abs/1912.04023v3	https://arxiv.org/pdf/1912.04023v3.pdf	ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition	In general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.	['Theo Gevers', 'Hoang-An Le', 'Sezer Karaoglu', 'Anil S. Baslamisli', 'Partha Das']	2019-12-09	null	null	null	null	['intrinsic-image-decomposition']	['computer-vision']	[ 4.50922877e-01 -3.61864924e-01 6.67786598e-01 -6.95001841e-01 -4.22351241e-01 -4.35488671e-01 7.06948161e-01 -3.76976430e-01 -2.81361956e-02 7.47954309e-01 3.01150918e-01 -1.02688290e-01 1.91549361e-01 -9.85108018e-01 -6.48525357e-01 -1.15650344e+00 1.71586022e-01 1.64455563e-01 1.56765133e-01 -3.63321632e-01 7.41172880e-02 4.92362648e-01 -2.02885771e+00 4.29975927e-01 1.19245827e+00 8.65620911e-01 4.38052624e-01 8.77416372e-01 -2.05054253e-01 7.54076838e-01 -5.09355485e-01 3.04537475e-01 8.33470583e-01 -2.91879445e-01 -3.12861174e-01 4.10016179e-01 1.23030603e+00 -5.15187919e-01 -1.34796649e-01 1.00422072e+00 6.33587360e-01 8.92041773e-02 3.28398943e-01 -9.29004490e-01 -6.26791239e-01 -1.16354287e-01 -8.70324314e-01 -7.43634552e-02 -1.25701958e-03 5.85936606e-01 8.52091849e-01 -9.09167171e-01 1.66382372e-01 1.40583634e+00 5.68053067e-01 2.17084408e-01 -1.55077970e+00 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3.49971831e-01 7.32101679e-01 -5.63865244e-01 -7.77593017e-01 1.51917025e-01]	[9.876137733459473, -2.9776949882507324]
47d40e17-55e8-448b-ac03-771ae5a8615a	per-example-gradient-regularization-improves	2303.1794	null	https://arxiv.org/abs/2303.17940v1	https://arxiv.org/pdf/2303.17940v1.pdf	Per-Example Gradient Regularization Improves Learning Signals from Noisy Data	Gradient regularization, as described in \citet{barrett2021implicit}, is a highly effective technique for promoting flat minima during gradient descent. Empirical evidence suggests that this regularization technique can significantly enhance the robustness of deep learning models against noisy perturbations, while also reducing test error. In this paper, we explore the per-example gradient regularization (PEGR) and present a theoretical analysis that demonstrates its effectiveness in improving both test error and robustness against noise perturbations. Specifically, we adopt a signal-noise data model from \citet{cao2022benign} and show that PEGR can learn signals effectively while suppressing noise. In contrast, standard gradient descent struggles to distinguish the signal from the noise, leading to suboptimal generalization performance. Our analysis reveals that PEGR penalizes the variance of pattern learning, thus effectively suppressing the memorization of noises from the training data. These findings underscore the importance of variance control in deep learning training and offer useful insights for developing more effective training approaches.	['Difan Zou', 'Yuan Cao', 'Xuran Meng']	2023-03-31	null	null	null	null	['memorization']	['natural-language-processing']	[ 1.07521698e-01 -4.02255774e-01 5.57687581e-02 -3.42956692e-01 -7.74518788e-01 -4.86162215e-01 1.86584353e-01 7.36958459e-02 -5.82279682e-01 7.47088373e-01 -7.86036849e-02 -3.97025764e-01 -2.95021027e-01 -5.56837022e-01 -1.11828911e+00 -9.45128202e-01 1.66920319e-01 -4.10497725e-01 -2.71577686e-02 -1.99097306e-01 4.55475807e-01 5.33563972e-01 -1.42235601e+00 -4.31850441e-02 7.67176092e-01 9.51031685e-01 8.29854980e-02 4.32415158e-01 2.05861494e-01 8.16098034e-01 -9.99061048e-01 -2.51098424e-01 3.30251187e-01 -6.10604584e-01 -2.38237292e-01 -1.46612287e-01 6.66233003e-01 -9.30697545e-02 -5.20067811e-01 1.18124974e+00 1.01775682e+00 3.87597144e-01 4.70139116e-01 -6.97712064e-01 -6.44022524e-01 4.38283950e-01 -6.62881374e-01 6.74208522e-01 -2.09487006e-01 3.13430160e-01 8.91269028e-01 -8.74570668e-01 2.44437367e-01 8.52506459e-01 8.57948542e-01 8.36483181e-01 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690cc5fd-423c-4dbc-9f5e-8e542c52f4d3	body-size-and-depth-disambiguation-in-multi	2111.01884	null	https://arxiv.org/abs/2111.01884v2	https://arxiv.org/pdf/2111.01884v2.pdf	Body Size and Depth Disambiguation in Multi-Person Reconstruction from Single Images	We address the problem of multi-person 3D body pose and shape estimation from a single image. While this problem can be addressed by applying single-person approaches multiple times for the same scene, recent works have shown the advantages of building upon deep architectures that simultaneously reason about all people in the scene in a holistic manner by enforcing, e.g., depth order constraints or minimizing interpenetration among reconstructed bodies. However, existing approaches are still unable to capture the size variability of people caused by the inherent body scale and depth ambiguity. In this work, we tackle this challenge by devising a novel optimization scheme that learns the appropriate body scale and relative camera pose, by enforcing the feet of all people to remain on the ground floor. A thorough evaluation on MuPoTS-3D and 3DPW datasets demonstrates that our approach is able to robustly estimate the body translation and shape of multiple people while retrieving their spatial arrangement, consistently improving current state-of-the-art, especially in scenes with people of very different heights	['Francesc Moreno-Noguer', 'Alberto Sanfeliu', 'Antonio Agudo', 'Adria Ruiz', 'Nicolas Ugrinovic']	2021-11-02	null	null	null	null	['3d-multi-person-mesh-recovery']	['computer-vision']	[-6.56956658e-02 -2.80247480e-02 3.97307336e-01 -3.00668091e-01 -3.28155786e-01 -4.23451751e-01 4.64376390e-01 3.26403789e-02 -4.42653507e-01 4.04079765e-01 2.47093171e-01 4.89867359e-01 9.26065817e-03 -6.53371155e-01 -5.75688660e-01 -4.65484500e-01 1.17702357e-01 1.20750749e+00 1.86689645e-01 -2.63758153e-01 -9.17059034e-02 4.84194040e-01 -1.64945447e+00 -2.74441034e-01 4.68989044e-01 6.97501898e-01 -5.77369072e-02 7.70212054e-01 3.91515166e-01 3.57504934e-01 -5.94374955e-01 -5.80466449e-01 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42cd3201-ba31-42e8-8562-abba03dbe22d	skill-based-reinforcement-learning-with	2210.07426	null	https://arxiv.org/abs/2210.07426v4	https://arxiv.org/pdf/2210.07426v4.pdf	Skill-Based Reinforcement Learning with Intrinsic Reward Matching	While unsupervised skill discovery has shown promise in autonomously acquiring behavioral primitives, there is still a large methodological disconnect between task-agnostic skill pretraining and downstream, task-aware finetuning. We present Intrinsic Reward Matching (IRM), which unifies these two phases of learning via the $\textit{skill discriminator}$, a pretraining model component often discarded during finetuning. Conventional approaches finetune pretrained agents directly at the policy level, often relying on expensive environment rollouts to empirically determine the optimal skill. However, often the most concise yet complete description of a task is the reward function itself, and skill learning methods learn an $\textit{intrinsic}$ reward function via the discriminator that corresponds to the skill policy. We propose to leverage the skill discriminator to $\textit{match}$ the intrinsic and downstream task rewards and determine the optimal skill for an unseen task without environment samples, consequently finetuning with greater sample-efficiency. Furthermore, we generalize IRM to sequence skills for complex, long-horizon tasks and demonstrate that IRM enables us to utilize pretrained skills far more effectively than previous skill selection methods on both the Fetch tabletop and Franka Kitchen robot manipulation benchmarks.	['Pieter Abbeel', 'Amber Xie', 'Ademi Adeniji']	2022-10-14	null	null	null	null	['robot-manipulation']	['robots']	[ 4.31039512e-01 -6.05636276e-02 -1.55618057e-01 -6.46688789e-02 -8.05144310e-01 -9.34109569e-01 4.83066767e-01 1.58092435e-02 -9.38079357e-01 1.05863202e+00 -4.41415235e-02 -2.60383099e-01 -5.96536219e-01 -4.48843777e-01 -7.68431306e-01 -7.82842219e-01 -2.66175628e-01 6.74036801e-01 8.87136087e-02 -3.10007691e-01 4.96517569e-01 1.77346691e-01 -1.70713007e+00 -3.18909973e-01 1.07412112e+00 8.80567670e-01 6.41487896e-01 8.65166664e-01 4.50117946e-01 6.72621608e-01 -4.58227515e-01 2.21759588e-01 6.21824026e-01 -4.37595546e-01 -9.51519012e-01 -1.45436391e-01 3.12867045e-01 -4.76680994e-01 -1.12242751e-01 1.11936510e+00 3.33993673e-01 6.62708163e-01 5.53492486e-01 -1.15004659e+00 -3.42274040e-01 8.35416496e-01 -2.01082870e-01 4.54195559e-01 1.26377046e-01 7.60233700e-01 1.13511205e+00 -3.74975353e-01 2.94346720e-01 1.02189338e+00 2.92269588e-01 6.86796844e-01 -1.52115083e+00 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-5.67984760e-01 4.84861046e-01 5.63938916e-01 -1.08335698e+00 -1.40572399e-01 -1.57034799e-01]	[4.1045145988464355, 1.6055314540863037]
73f2fb92-2f53-4695-852a-d518f816e85b	neuro-causal-factor-analysis	2305.19802	null	https://arxiv.org/abs/2305.19802v1	https://arxiv.org/pdf/2305.19802v1.pdf	Neuro-Causal Factor Analysis	Factor analysis (FA) is a statistical tool for studying how observed variables with some mutual dependences can be expressed as functions of mutually independent unobserved factors, and it is widely applied throughout the psychological, biological, and physical sciences. We revisit this classic method from the comparatively new perspective given by advancements in causal discovery and deep learning, introducing a framework for Neuro-Causal Factor Analysis (NCFA). Our approach is fully nonparametric: it identifies factors via latent causal discovery methods and then uses a variational autoencoder (VAE) that is constrained to abide by the Markov factorization of the distribution with respect to the learned graph. We evaluate NCFA on real and synthetic data sets, finding that it performs comparably to standard VAEs on data reconstruction tasks but with the advantages of sparser architecture, lower model complexity, and causal interpretability. Unlike traditional FA methods, our proposed NCFA method allows learning and reasoning about the latent factors underlying observed data from a justifiably causal perspective, even when the relations between factors and measurements are highly nonlinear.	['Liam Solus', 'Bryon Aragam', 'MingYu Liu', 'Alex Markham']	2023-05-31	null	null	null	null	['causal-discovery']	['knowledge-base']	[ 6.15471266e-02 2.21518204e-01 -4.24133837e-01 -2.39974350e-01 1.39356017e-01 -3.73095304e-01 1.16284060e+00 -1.66409597e-01 3.84669825e-02 8.70507538e-01 9.57983911e-01 -2.87069410e-01 -7.74452150e-01 -7.53786445e-01 -8.79444122e-01 -9.16586936e-01 -3.15184474e-01 6.33823276e-01 -3.23975354e-01 7.89339095e-02 -1.12196073e-01 3.39909077e-01 -1.20123279e+00 6.34701848e-02 5.99453390e-01 4.06612068e-01 4.47951108e-02 4.15064126e-01 1.28198594e-01 1.18341815e+00 -1.42402381e-01 -4.30745840e-01 -2.74692744e-01 -5.20872891e-01 -6.72494352e-01 -1.29209131e-01 1.70318373e-02 -2.12111980e-01 -8.94355893e-01 7.69154370e-01 -1.53677136e-01 1.80208132e-01 9.71406877e-01 -1.46666491e+00 -1.15843797e+00 1.02029002e+00 -3.49734843e-01 1.75941184e-01 2.91436791e-01 -1.38235599e-01 1.26352346e+00 -6.89577341e-01 5.59824407e-01 1.71306658e+00 4.70521867e-01 3.66794854e-01 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697dc2fc-564d-4049-bfe7-a3f4bcb11f61	multilevel-sentence-embeddings-for	2305.05748	null	https://arxiv.org/abs/2305.05748v1	https://arxiv.org/pdf/2305.05748v1.pdf	Multilevel Sentence Embeddings for Personality Prediction	Representing text into a multidimensional space can be done with sentence embedding models such as Sentence-BERT (SBERT). However, training these models when the data has a complex multilevel structure requires individually trained class-specific models, which increases time and computing costs. We propose a two step approach which enables us to map sentences according to their hierarchical memberships and polarity. At first we teach the upper level sentence space through an AdaCos loss function and then finetune with a novel loss function mainly based on the cosine similarity of intra-level pairs. We apply this method to three different datasets: two weakly supervised Big Five personality dataset obtained from English and Japanese Twitter data and the benchmark MNLI dataset. We show that our single model approach performs better than multiple class-specific classification models.	['Masashi Morita', 'Akira Yuasa', 'Paolo Tirotta']	2023-05-09	null	null	null	null	['sentence-embeddings', 'sentence-embeddings']	['methodology', 'natural-language-processing']	[ 4.50876355e-02 1.69442683e-01 -2.71285534e-01 -7.90676296e-01 -6.81491315e-01 -4.98197556e-01 6.86963081e-01 6.10209703e-01 -5.65576017e-01 7.06259310e-01 2.96959609e-01 -1.86033875e-01 -1.81671064e-02 -9.58527744e-01 -4.62205142e-01 -4.50531781e-01 1.42000571e-01 5.54748893e-01 8.60045105e-02 -3.34692568e-01 2.73327947e-01 1.39227465e-01 -1.29002702e+00 4.58143979e-01 9.53134239e-01 7.90912509e-01 -1.73199996e-02 7.23375380e-01 -5.11254907e-01 6.75052822e-01 -5.67463636e-01 -7.02052355e-01 -7.45239332e-02 -3.51664245e-01 -8.56298685e-01 8.90331566e-02 5.13415039e-01 2.45008767e-01 -3.25079560e-01 1.14320922e+00 4.11944062e-01 4.24239114e-02 1.00189447e+00 -1.03433323e+00 -8.98261130e-01 6.48445189e-01 -3.94567817e-01 8.03078115e-02 3.32438737e-01 -3.46899390e-01 1.26093686e+00 -9.68869686e-01 5.47790825e-01 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5.21949708e-01 1.09195805e+00 -7.13347614e-01 -1.01817894e+00 -1.20774448e+00 -4.95851159e-01 6.29604757e-01 1.83215827e-01 -5.85337222e-01 -3.71237755e-01 -2.05745727e-01]	[10.845069885253906, 8.641457557678223]
9b4c68b3-d0e8-4e45-bbdf-f4faaa7a41d6	predicting-epileptic-seizures-using	null	null	https://www.medrxiv.org/content/10.1101/19000430v1	https://www.medrxiv.org/content/medrxiv/early/2019/06/25/19000430.full.pdf	Predicting epileptic seizures using nonnegative matrix factorization	This paper presents a procedure for the patient-specific prediction of epileptic seizures. To this end, a combination of nonnegative matrix factorization (NMF) and smooth basis functions with robust regression is applied to power spectra of intracranial electroencephalographic (iEEG) signals. The resulting time and frequency components capture the dominant information from power spectra, while removing outliers and noise. This makes it possible to detect structure in preictal states, which is used for classification. Linear support vector machines (SVM) with L1 regularization are used to select and weigh the contributions from different number of not equally informative channels among patients. Due to class imbalance in data, synthetic minority over-sampling technique (SMOTE) is applied. The resulting method yields a computationally and conceptually simple, interpretable model of EEG signals of preictal and interictal states, which shows a good performance for the task of seizure prediction.	['Olivera Stojanović', 'Gordon Pipa']	2019-06-25	null	null	null	medrxiv-plos-one-under-review-2019-6	['seizure-prediction', 'epilepsy-prediction']	['medical', 'medical']	[ 3.95657122e-01 -2.65388280e-01 8.03627223e-02 -2.21212685e-01 -4.61275399e-01 -3.13274413e-01 2.42872700e-01 2.23254561e-01 -2.18273804e-01 1.00570869e+00 3.25091034e-01 -6.92463443e-02 -5.50157666e-01 -8.37354213e-02 -9.03169736e-02 -1.05311918e+00 -6.27108991e-01 1.25726655e-01 -3.13889176e-01 -4.10555676e-02 3.96466732e-01 5.84050596e-01 -1.59136641e+00 5.56935906e-01 9.85736609e-01 8.68299544e-01 2.52849013e-01 4.41995591e-01 1.68525144e-01 6.99677348e-01 -7.47706950e-01 2.94517189e-01 1.45465657e-01 -4.99437302e-01 -4.29170579e-01 1.28700316e-01 -3.24604869e-01 5.06444097e-01 -1.65088698e-01 1.31371140e+00 4.83549327e-01 1.19588803e-02 9.58765149e-01 -1.39488208e+00 -6.79583028e-02 5.29175162e-01 -5.90542793e-01 7.63524771e-01 4.38576341e-01 -4.10356104e-01 6.12484753e-01 -8.79757285e-01 1.76771119e-01 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8.13129187e-01 -9.28184867e-01 -3.03472489e-01 -1.09138811e+00 -1.18388093e+00 3.64861906e-01 5.69488883e-01 -7.04733253e-01 -9.33290601e-01 3.29322964e-02]	[13.163576126098633, 3.493694305419922]
971c5e68-8014-47b2-bdde-0441f72b9ea1	graph-neural-networks-for-human-aware-social	1909.09003	null	https://arxiv.org/abs/1909.09003v3	https://arxiv.org/pdf/1909.09003v3.pdf	Graph Neural Networks for Human-aware Social Navigation	Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to navigate avoiding going through the personal spaces of the people surrounding them. Complying with social rules such as not getting in the middle of human-to-human and human-to-object interactions is also important. This paper suggests using Graph Neural Networks to model how inconvenient the presence of a robot would be in a particular scenario according to learned human conventions so that it can be used by path planning algorithms. To do so, we propose two ways of modelling social interactions using graphs and benchmark them with different Graph Neural Networks using the SocNav1 dataset. We achieve close-to-human performance in the dataset and argue that, in addition to promising results, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered and easily embedded in code, in comparison with model-based approaches. The code used to train and test the resulting graph neural network is available in a public repository.	['Ronit R. Jorvekar', 'Pablo Bustos', 'Diego R. Faria', 'Pilar Bachiller', 'Luis J. Manso']	2019-09-19	null	null	null	null	['social-navigation']	['robots']	[-1.60816193e-01 6.98899388e-01 2.99375266e-01 -2.84818649e-01 5.01014769e-01 -2.02477112e-01 6.44373536e-01 1.44107014e-01 -7.85525382e-01 9.85738337e-01 1.38577700e-01 -4.29042071e-01 -5.54494083e-01 -7.85727799e-01 -6.04253054e-01 -3.24239582e-01 -3.95104289e-01 1.10961401e+00 4.50564981e-01 -7.39845335e-01 -4.19891886e-02 5.88040292e-01 -1.58133864e+00 -3.33710939e-01 6.12388134e-01 4.32349056e-01 3.56117547e-01 6.72663629e-01 2.44648531e-01 8.83701086e-01 -3.08836997e-01 -1.12641811e-01 2.11385384e-01 -4.09462929e-01 -9.20512259e-01 -1.23167913e-02 -1.59437746e-01 -3.37478489e-01 -2.58291811e-01 8.19606066e-01 3.51466984e-01 4.91715610e-01 9.19668794e-01 -1.46218348e+00 1.32769113e-03 5.30286312e-01 3.05595815e-01 -3.62695813e-01 6.63388908e-01 1.69205919e-01 7.07776487e-01 -9.93271768e-02 9.02296603e-01 1.25105393e+00 6.80384398e-01 4.27070767e-01 -9.17699933e-01 -2.50985354e-01 1.55428112e-01 3.76003891e-01 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9a2bf920-fa19-41ef-8f7d-1c82d8c3a051	proximal-policy-optimization-algorithms	1707.06347	null	http://arxiv.org/abs/1707.06347v2	http://arxiv.org/pdf/1707.06347v2.pdf	Proximal Policy Optimization Algorithms	We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically). Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.	['John Schulman', 'Filip Wolski', 'Alec Radford', 'Oleg Klimov', 'Prafulla Dhariwal']	2017-07-20	null	null	null	null	['dota-2']	['playing-games']	[-2.8391546e-01 -1.3733596e-01 -7.2922450e-01 -1.1217749e-01 -6.1764562e-01 -4.6537629e-01 6.7141330e-01 4.0485926e-02 -1.0181148e+00 1.5481156e+00 1.8266396e-01 -5.7111293e-01 -1.5423280e-01 -5.0031793e-01 -7.9695308e-01 -7.1073341e-01 -4.0857351e-01 5.3257394e-01 2.5887477e-01 -4.6801910e-01 5.4791409e-01 3.2126388e-01 -1.2974865e+00 -2.7752379e-01 1.0387509e+00 9.2446023e-01 1.8972783e-01 5.3967708e-01 2.0414166e-01 8.6760932e-01 -5.0272417e-01 7.1433149e-02 2.0901141e-01 -3.7085703e-01 -8.5696572e-01 -1.4147612e-01 -1.8398507e-01 -5.7835799e-01 -2.0697816e-01 8.5264546e-01 7.5873941e-01 6.0963535e-01 4.1145280e-01 -1.1907367e+00 -1.9241430e-01 6.0003227e-01 -5.7324415e-01 2.0467044e-01 4.6048161e-01 4.5350513e-01 8.4606439e-01 -6.8757898e-01 6.0380483e-01 1.3980541e+00 6.7934549e-01 7.5887936e-01 -1.3111724e+00 -4.3364635e-01 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7973c2c5-f087-4f25-8096-91f826e5db4f	uni-qsar-an-auto-ml-tool-for-molecular	2304.12239	null	https://arxiv.org/abs/2304.12239v1	https://arxiv.org/pdf/2304.12239v1.pdf	Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction	Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in drug discovery. However, most DL based QSAR models are restricted to limited labeled data to achieve better performance, and also are sensitive to model scale and hyper-parameters. In this paper, we propose Uni-QSAR, a powerful Auto-ML tool for molecule property prediction tasks. Uni-QSAR combines molecular representation learning (MRL) of 1D sequential tokens, 2D topology graphs, and 3D conformers with pretraining models to leverage rich representation from large-scale unlabeled data. Without any manual fine-tuning or model selection, Uni-QSAR outperforms SOTA in 21/22 tasks of the Therapeutic Data Commons (TDC) benchmark under designed parallel workflow, with an average performance improvement of 6.09\%. Furthermore, we demonstrate the practical usefulness of Uni-QSAR in drug discovery domains.	['Linfeng Zhang', 'Guolin Ke', 'Hang Zheng', 'Hongshuai Wang', 'Guojiang Zhao', 'Xiaohong Ji', 'Zhifeng Gao']	2023-04-24	null	null	null	null	['drug-discovery', 'molecular-property-prediction']	['medical', 'miscellaneous']	[ 1.56085372e-01 -3.27488452e-01 -7.66958714e-01 -2.84946591e-01 -1.14859605e+00 -6.10542774e-01 2.51882404e-01 7.11508453e-01 -9.71606523e-02 1.31251359e+00 -8.22927250e-05 -7.35953033e-01 -3.51871014e-01 -6.09505951e-01 -8.93347263e-01 -8.80181551e-01 -5.65515757e-01 8.40771437e-01 1.91582069e-02 -5.35580777e-02 1.37124166e-01 9.24916029e-01 -4.60901529e-01 5.42015374e-01 9.54652607e-01 7.53074050e-01 8.92997459e-02 3.10201228e-01 1.82313088e-03 8.36452067e-01 -4.67865974e-01 5.31148314e-02 -6.33142740e-02 -3.30204874e-01 -8.15599918e-01 -5.79991996e-01 5.25974691e-01 -2.70723961e-02 -3.32249790e-01 6.09700322e-01 1.05492353e+00 4.55259375e-04 8.95128131e-01 -6.75238550e-01 -6.25935137e-01 4.47248369e-01 -6.25477254e-01 3.32960874e-01 5.32225609e-01 2.52108365e-01 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8.13509285e-01 5.24012268e-01 6.36642754e-01 -2.46488884e-01 -4.12193507e-01 -1.00411797e+00 -6.82860851e-01 -1.08162627e-01 3.35439146e-01 -6.05028331e-01 2.86662310e-01 -8.72789919e-02]	[5.151066780090332, 5.853537559509277]
b9b02b72-0259-464e-a3b1-41fa70f1f104	quickprobs-2-towards-rapid-construction-of	1512.07437	null	http://arxiv.org/abs/1512.07437v2	http://arxiv.org/pdf/1512.07437v2.pdf	QuickProbs 2: towards rapid construction of high-quality alignments of large protein families	Increasing size of sequence databases caused by the development of high throughput sequencing, poses multiple alignment algorithms to face one of the greatest challenges yet. As we show, well-established techniques employed for increasing alignment quality, i.e., refinement and consistency, are ineffective when large protein families are of interest. We present QuickProbs 2, an algorithm for multiple sequence alignment. Based on probabilistic models, equipped with novel column-oriented refinement and selective consistency, it offers outstanding accuracy. When analysing hundreds of sequences, QuickProbs 2 is significantly better than Clustal Omega, the previous leader for processing numerous protein families. In the case of smaller sets, for which consistency-based methods are the best performing, QuickProbs 2 is also superior to the competitors. Due to computational scalability of selective consistency and utilisation of massively parallel architectures, presented algorithm is comparable to Clustal Omega in terms of execution time, and orders of magnitude faster than full consistency approaches, like MSAProbs or PicXAA. All these make QuickProbs 2 a useful tool for aligning families ranging from few, to hundreds of proteins. QuickProbs 2 is available at https://github.com/refresh-bio/QuickProbs.	[]	2016-08-30	null	null	null	null	['multiple-sequence-alignment']	['medical']	[ 3.65937978e-01 -3.33628595e-01 -1.80165708e-01 -1.39673382e-01 -7.82812893e-01 -6.32026255e-01 3.16809267e-01 6.86883986e-01 -5.45801282e-01 1.31017458e+00 -3.30628365e-01 -4.26441669e-01 -3.88911188e-01 -4.90579277e-01 -4.90458786e-01 -1.01611149e+00 -4.34596799e-02 1.27475393e+00 6.82021558e-01 -3.17915589e-01 5.35927355e-01 5.82619131e-01 -1.55207121e+00 3.22138548e-01 9.94794309e-01 4.90172118e-01 7.12874591e-01 6.68028951e-01 -3.13993156e-01 1.30832016e-01 -2.29380831e-01 -3.64935905e-01 -8.03440139e-02 -3.64632636e-01 -1.07346022e+00 -4.23631042e-01 2.12515801e-01 1.51530549e-01 6.65651500e-01 8.07817042e-01 5.23192585e-01 -2.52360612e-01 5.24067700e-01 -9.99871194e-01 1.30249590e-01 3.20025563e-01 -5.61262667e-01 3.10842842e-01 6.54327989e-01 1.82152420e-01 1.01800656e+00 -8.06815505e-01 9.30477977e-01 1.03813493e+00 8.32919061e-01 2.33074069e-01 -1.58859694e+00 -3.81913096e-01 -4.44338024e-01 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77b0c695-5e46-4b52-b773-21e923cb1605	a-high-performance-cnn-method-for-offline	1812.11489	null	https://arxiv.org/abs/1812.11489v2	https://arxiv.org/pdf/1812.11489v2.pdf	A High-Performance CNN Method for Offline Handwritten Chinese Character Recognition and Visualization	Recent researches introduced fast, compact and efficient convolutional neural networks (CNNs) for offline handwritten Chinese character recognition (HCCR). However, many of them did not address the problem of network interpretability. We propose a new architecture of a deep CNN with high recognition performance which is capable of learning deep features for visualization. A special characteristic of our model is the bottleneck layers which enable us to retain its expressiveness while reducing the number of multiply-accumulate operations and the required storage. We introduce a modification of global weighted average pooling (GWAP) - global weighted output average pooling (GWOAP). This paper demonstrates how they allow us to calculate class activation maps (CAMs) in order to indicate the most relevant input character image regions used by our CNN to identify a certain class. Evaluating on the ICDAR-2013 offline HCCR competition dataset, we show that our model enables a relative 0.83% error reduction while having 49% fewer parameters and the same computational cost compared to the current state-of-the-art single-network method trained only on handwritten data. Our solution outperforms even recent residual learning approaches.	['Zhiqiang You', 'Pavlo Melnyk', 'Keqin Li']	2018-12-30	null	null	null	null	['offline-handwritten-chinese-character', 'offline-handwritten-chinese-character']	['computer-vision', 'natural-language-processing']	[ 1.23448670e-01 -1.86452851e-01 2.18067113e-02 -4.42109823e-01 -2.29702711e-01 -4.11901325e-01 3.80179435e-01 7.56242592e-03 -7.26188838e-01 5.36595464e-01 -1.86119959e-01 -5.03275275e-01 -4.20160824e-03 -8.21048915e-01 -7.01192439e-01 -6.30306363e-01 -1.87294669e-02 1.43449428e-02 3.33939642e-01 -7.56099150e-02 5.16712964e-01 1.28315747e+00 -1.15352094e+00 7.13251412e-01 6.54674649e-01 1.25196683e+00 2.85591513e-01 8.95862520e-01 1.01206210e-02 1.29990983e+00 -1.07082748e+00 -5.13770700e-01 8.58443528e-02 -7.54159316e-02 -7.98523724e-01 -9.75737199e-02 4.58745837e-01 -3.99035513e-01 -8.50010395e-01 6.94436789e-01 5.60722589e-01 1.77221432e-01 4.43907499e-01 -8.43086600e-01 -1.05443370e+00 7.95113444e-01 -1.62939668e-01 1.48325548e-01 -1.72631577e-01 -7.58727044e-02 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2.65811980e-01 7.60056317e-01 4.29073304e-01 -8.45244944e-01 -5.85498333e-01 -1.04767537e+00 -5.27403414e-01 7.10972399e-02 2.84153610e-01 9.96930823e-02 8.55257642e-03 7.15640709e-02]	[11.760517120361328, 2.6227502822875977]
833c9062-deb8-427f-a67e-a9e56f2ef28c	person-recognition-in-personal-photo-1	1509.03502	null	http://arxiv.org/abs/1509.03502v2	http://arxiv.org/pdf/1509.03502v2.pdf	Person Recognition in Personal Photo Collections	Recognising persons in everyday photos presents major challenges (occluded faces, different clothing, locations, etc.) for machine vision. We propose a convnet based person recognition system on which we provide an in-depth analysis of informativeness of different body cues, impact of training data, and the common failure modes of the system. In addition, we discuss the limitations of existing benchmarks and propose more challenging ones. Our method is simple and is built on open source and open data, yet it improves the state of the art results on a large dataset of social media photos (PIPA).	['Seong Joon Oh', 'Rodrigo Benenson', 'Mario Fritz', 'Bernt Schiele']	2015-09-11	person-recognition-in-personal-photo-2	http://openaccess.thecvf.com/content_iccv_2015/html/Oh_Person_Recognition_in_ICCV_2015_paper.html	http://openaccess.thecvf.com/content_iccv_2015/papers/Oh_Person_Recognition_in_ICCV_2015_paper.pdf	iccv-2015-12	['person-recognition']	['computer-vision']	[ 2.23077741e-02 -1.42501146e-01 2.10232243e-01 -6.02668941e-01 7.57381395e-02 -2.88268209e-01 7.35166073e-01 -5.98561108e-01 -5.46547472e-01 5.01763284e-01 5.86373746e-01 4.74858701e-01 -9.85219851e-02 -4.39194173e-01 -4.89826173e-01 -4.02901471e-01 -1.96048930e-01 3.18391293e-01 2.34312378e-03 -2.05342054e-01 -2.41744384e-01 3.01602125e-01 -1.58387923e+00 3.67374927e-01 1.54214278e-01 8.88492882e-01 -5.23470223e-01 7.20188797e-01 5.38689494e-01 5.17637730e-01 -5.75578451e-01 -1.15683424e+00 5.84610224e-01 -2.19011921e-02 -7.02689946e-01 4.74355131e-01 1.41555321e+00 -5.30980289e-01 -6.96685553e-01 8.87666821e-01 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2531ebf2-5a32-4192-ac61-77a9e55a2fcf	event-based-tracking-of-human-hands	2304.06534	null	https://arxiv.org/abs/2304.06534v1	https://arxiv.org/pdf/2304.06534v1.pdf	Event-based tracking of human hands	This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space).	['Pedro Neto', 'Mohammad Safeea', 'Laura Duarte']	2023-04-13	null	null	null	null	['dynamic-time-warping']	['time-series']	[ 4.20570970e-01 -3.48536879e-01 3.04824561e-01 6.15141541e-03 -1.47218227e-01 -5.97270191e-01 3.44371259e-01 3.31194699e-01 -1.14822614e+00 6.36179030e-01 -2.68161995e-04 3.80260348e-01 -2.16083467e-01 -6.63055062e-01 -3.03117275e-01 -6.57630086e-01 -3.49877387e-01 2.29077071e-01 6.53356612e-01 1.31567687e-01 1.08101308e-01 1.08786964e+00 -2.04078388e+00 1.90392360e-01 5.52106276e-03 8.47364843e-01 5.09366155e-01 1.11709607e+00 2.38781780e-01 7.73700058e-01 -9.29647684e-01 2.90866196e-01 3.71715993e-01 -2.40885541e-01 -2.99506813e-01 3.63957658e-02 1.73901796e-01 -5.19877315e-01 -2.04014108e-01 6.13246441e-01 9.92421091e-01 1.35119274e-01 3.03467304e-01 -1.14851213e+00 3.72618526e-01 -1.08940631e-01 -4.50390995e-01 4.40227747e-01 1.16096067e+00 4.96826053e-01 1.78030431e-01 -7.32001543e-01 1.17733276e+00 1.22658205e+00 5.22067249e-01 4.12971020e-01 -1.26883650e+00 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868f2421-7796-4c31-9fac-4395f1f20cd4	earthmapper-a-tool-box-for-the-semantic	1804.00292	null	http://arxiv.org/abs/1804.00292v1	http://arxiv.org/pdf/1804.00292v1.pdf	EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery	Deep learning continues to push state-of-the-art performance for the semantic segmentation of color (i.e., RGB) imagery; however, the lack of annotated data for many remote sensing sensors (i.e. hyperspectral imagery (HSI)) prevents researchers from taking advantage of this recent success. Since generating sensor specific datasets is time intensive and cost prohibitive, remote sensing researchers have embraced deep unsupervised feature extraction. Although these methods have pushed state-of-the-art performance on current HSI benchmarks, many of these tools are not readily accessible to many researchers. In this letter, we introduce a software pipeline, which we call EarthMapper, for the semantic segmentation of non-RGB remote sensing imagery. It includes self-taught spatial-spectral feature extraction, various standard and deep learning classifiers, and undirected graphical models for post-processing. We evaluated EarthMapper on the Indian Pines and Pavia University datasets and have released this code for public use.	['Christopher Kanan', 'Utsav B. Gewali', 'Ronald Kemker']	2018-04-01	null	null	null	null	['segmentation-of-remote-sensing-imagery', 'the-semantic-segmentation-of-remote-sensing']	['miscellaneous', 'miscellaneous']	[ 5.59196174e-01 -2.94635266e-01 1.27133623e-01 -6.64106905e-01 -1.04629207e+00 -8.51798952e-01 2.58456349e-01 2.30745245e-02 -4.29508120e-01 6.25020444e-01 -2.69067675e-01 -9.96240735e-01 -3.31609994e-01 -1.19225419e+00 -5.79747260e-01 -8.02339554e-01 -3.37624431e-01 4.27785546e-01 8.11926126e-02 3.90374996e-02 1.45686567e-01 8.51853848e-01 -1.68922675e+00 3.91414389e-02 9.99340057e-01 8.66972327e-01 1.05424203e-01 8.81463289e-01 -1.65111259e-01 3.47438991e-01 -1.51427090e-01 1.26952171e-01 5.25811017e-01 -3.05174023e-01 -9.13991511e-01 1.06438853e-01 6.12989783e-01 -5.06786466e-01 -7.91856572e-02 1.13530350e+00 3.68402332e-01 1.94289431e-01 6.48466349e-01 -1.35863662e+00 -6.25538588e-01 7.24619627e-01 -8.66800666e-01 1.01317681e-01 -2.73086458e-01 1.69509768e-01 1.12261760e+00 -6.48426175e-01 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cc97f892-4bf8-4a24-a59c-27526589161c	emergence-of-a-phonological-bias-in-chatgpt	2305.15929	null	https://arxiv.org/abs/2305.15929v2	https://arxiv.org/pdf/2305.15929v2.pdf	Emergence of a phonological bias in ChatGPT	Current large language models, such as OpenAI's ChatGPT, have captured the public's attention because how remarkable they are in the use of language. Here, I demonstrate that ChatGPT displays phonological biases that are a hallmark of human language processing. More concretely, just like humans, ChatGPT has a consonant bias. That is, the chatbot has a tendency to use consonants over vowels to identify words. This is observed across languages that differ in their relative distribution of consonants and vowels such as English and Spanish. Despite the differences in how current artificial intelligence language models are trained to process linguistic stimuli and how human infants acquire language, such training seems to be enough for the emergence of a phonological bias in ChatGPT	['Juan Manuel Toro']	2023-05-25	null	null	null	null	['chatbot', 'chatbot']	['methodology', 'natural-language-processing']	[-2.35875428e-01 1.67267203e-01 -1.63363963e-01 -2.14825854e-01 -7.78711438e-02 -8.17022800e-01 6.13825023e-01 3.44818026e-01 -5.17999768e-01 1.37162387e-01 5.71287334e-01 -6.00652277e-01 3.67708266e-01 -8.38010907e-01 -5.13109207e-01 -3.45926851e-01 1.52110577e-01 5.04267752e-01 1.66256577e-01 -5.23785233e-01 4.05915290e-01 -1.13637835e-01 -1.24382555e+00 2.71571934e-01 9.19798017e-01 1.31305814e-01 4.73857671e-01 4.30247813e-01 -5.11508346e-01 7.66690612e-01 -3.12748104e-01 -4.41884100e-01 -1.39478259e-02 -3.06589246e-01 -9.53676939e-01 -4.27906990e-01 1.93106323e-01 -1.40862823e-01 -8.85175169e-02 9.10526872e-01 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59736214-2d42-4af0-a15d-8874cdc9a392	an-evaluation-of-large-scale-methods-for	1708.02898	null	http://arxiv.org/abs/1708.02898v1	http://arxiv.org/pdf/1708.02898v1.pdf	An evaluation of large-scale methods for image instance and class discovery	This paper aims at discovering meaningful subsets of related images from large image collections without annotations. We search groups of images related at different levels of semantic, i.e., either instances or visual classes. While k-means is usually considered as the gold standard for this task, we evaluate and show the interest of diffusion methods that have been neglected by the state of the art, such as the Markov Clustering algorithm. We report results on the ImageNet and the Paris500k instance dataset, both enlarged with images from YFCC100M. We evaluate our methods with a labelling cost that reflects how much effort a human would require to correct the generated clusters. Our analysis highlights several properties. First, when powered with an efficient GPU implementation, the cost of the discovery process is small compared to computing the image descriptors, even for collections as large as 100 million images. Second, we show that descriptions selected for instance search improve the discovery of object classes. Third, the Markov Clustering technique consistently outperforms other methods; to our knowledge it has never been considered in this large scale scenario.	['Hervé Jégou', 'Matthijs Douze', 'Jeff Johnson']	2017-08-09	null	null	null	null	['instance-search']	['computer-vision']	[ 2.12376472e-02 4.76526879e-02 3.26494388e-02 -2.18166918e-01 -9.20791209e-01 -8.03762197e-01 7.95677602e-01 6.69537723e-01 -7.67029941e-01 5.72092175e-01 -6.46545887e-02 1.01485960e-01 -3.46588910e-01 -5.64648211e-01 -6.58505738e-01 -6.94232523e-01 -2.25045487e-01 1.05518293e+00 7.35778272e-01 2.47709215e-01 5.27576745e-01 4.11062449e-01 -2.17707014e+00 3.17732632e-01 4.64329988e-01 9.16733205e-01 4.34973598e-01 4.57506776e-01 -5.60287461e-02 6.83647990e-01 -6.12753570e-01 -4.69883651e-01 2.34693617e-01 -1.64984912e-01 -1.24159133e+00 4.41025972e-01 5.59462726e-01 4.99277152e-02 6.52683377e-02 1.16966081e+00 3.87460947e-01 2.64445484e-01 6.85603201e-01 -1.31556678e+00 -1.91161335e-01 6.19280398e-01 -5.63231409e-01 1.52207151e-01 2.33777687e-01 -8.60766023e-02 8.28391910e-01 -6.82132006e-01 1.18963408e+00 1.18434811e+00 5.20741284e-01 1.65132716e-01 -1.30247211e+00 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8.00435901e-01 -1.45393565e-01 -6.30734026e-01 -3.34474325e-01 3.35527390e-01]	[9.450721740722656, 1.0346708297729492]
11fa7de5-efd0-48d7-b0d5-00e888284d64	an-event-calculus-production-rule-system-for	1512.04358	null	http://arxiv.org/abs/1512.04358v2	http://arxiv.org/pdf/1512.04358v2.pdf	An Event Calculus Production Rule System for Reasoning in Dynamic and Uncertain Domains	Action languages have emerged as an important field of Knowledge Representation for reasoning about change and causality in dynamic domains. This article presents Cerbere, a production system designed to perform online causal, temporal and epistemic reasoning based on the Event Calculus. The framework implements the declarative semantics of the underlying logic theories in a forward-chaining rule-based reasoning system, coupling the high expressiveness of its formalisms with the efficiency of rule-based systems. To illustrate its applicability, we present both the modeling of benchmark problems in the field, as well as its utilization in the challenging domain of smart spaces. A hybrid framework that combines logic-based with probabilistic reasoning has been developed, that aims to accommodate activity recognition and monitoring tasks in smart spaces. Under consideration in Theory and Practice of Logic Programming (TPLP)	['Yacine Amirat', 'Abdelghani Chibani', 'Dimitris Plexousakis', 'Theodore Patkos']	2015-12-14	null	null	null	null	['epistemic-reasoning']	['miscellaneous']	[-3.99378352e-02 6.36272371e-01 -1.59523800e-01 -3.11706632e-01 5.71353212e-02 -5.46142876e-01 1.35751283e+00 2.43224859e-01 -1.24622323e-02 9.81559396e-01 3.72097552e-01 -4.26031500e-01 -9.47605908e-01 -1.43797612e+00 -4.93041098e-01 -2.68041015e-01 -4.44834411e-01 5.37296414e-01 6.73608303e-01 -3.82693022e-01 -1.99866742e-01 4.97096956e-01 -1.96615791e+00 4.93410975e-01 5.93936265e-01 1.14605761e+00 -2.59342730e-01 3.82646024e-01 -1.62426651e-01 1.65271986e+00 -1.47537710e-02 -2.23402888e-01 -2.25142002e-01 -1.18829325e-01 -1.00585485e+00 -4.75533187e-01 -2.74750888e-01 -1.00262858e-01 -2.59862542e-01 6.58667088e-01 -1.21783830e-01 3.76654238e-01 3.59764338e-01 -1.65301204e+00 -1.36124298e-01 1.11389101e+00 6.05515957e-01 -1.07048079e-01 1.07791638e+00 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9728496b-9723-4da7-8a38-d5f556361c97	slotdiffusion-object-centric-generative	2305.11281	null	https://arxiv.org/abs/2305.11281v1	https://arxiv.org/pdf/2305.11281v1.pdf	SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models	Object-centric learning aims to represent visual data with a set of object entities (a.k.a. slots), providing structured representations that enable systematic generalization. Leveraging advanced architectures like Transformers, recent approaches have made significant progress in unsupervised object discovery. In addition, slot-based representations hold great potential for generative modeling, such as controllable image generation and object manipulation in image editing. However, current slot-based methods often produce blurry images and distorted objects, exhibiting poor generative modeling capabilities. In this paper, we focus on improving slot-to-image decoding, a crucial aspect for high-quality visual generation. We introduce SlotDiffusion -- an object-centric Latent Diffusion Model (LDM) designed for both image and video data. Thanks to the powerful modeling capacity of LDMs, SlotDiffusion surpasses previous slot models in unsupervised object segmentation and visual generation across six datasets. Furthermore, our learned object features can be utilized by existing object-centric dynamics models, improving video prediction quality and downstream temporal reasoning tasks. Finally, we demonstrate the scalability of SlotDiffusion to unconstrained real-world datasets such as PASCAL VOC and COCO, when integrated with self-supervised pre-trained image encoders.	['Animesh Garg', 'Igor Gilitschenski', 'Wuyue Lu', 'Jingyu Hu', 'Ziyi Wu']	2023-05-18	null	null	null	null	['object-discovery', 'unsupervised-object-segmentation', 'video-prediction', 'systematic-generalization']	['computer-vision', 'computer-vision', 'computer-vision', 'reasoning']	[ 1.82555839e-01 3.71627688e-01 -4.27678764e-01 -4.02298421e-01 -5.25562882e-01 -4.22471255e-01 1.18610954e+00 -2.54063845e-01 7.69591331e-02 5.38852155e-01 3.34473997e-01 1.24196394e-03 6.95758015e-02 -9.24917638e-01 -1.25970125e+00 -6.55350685e-01 5.41035309e-02 7.43459582e-01 2.59589434e-01 3.76397967e-02 1.69186015e-02 2.42880255e-01 -1.66986215e+00 6.60700798e-01 8.29553068e-01 8.89968753e-01 6.28917396e-01 8.70041311e-01 -3.29508662e-01 1.25495601e+00 -5.22404850e-01 -3.51670921e-01 -1.25750929e-01 -4.27463353e-01 -9.16582346e-01 6.46419168e-01 4.23630357e-01 -5.97088456e-01 -5.85310221e-01 7.34834969e-01 1.26312539e-01 1.18372396e-01 8.30288529e-01 -1.52945077e+00 -1.37337935e+00 8.22989464e-01 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9607d92c-f830-4063-ba9b-51e98c5b3edc	design-of-quantum-optical-experiments-with	2109.13273	null	https://arxiv.org/abs/2109.13273v3	https://arxiv.org/pdf/2109.13273v3.pdf	Design of quantum optical experiments with logic artificial intelligence	Logic Artificial Intelligence (AI) is a subfield of AI where variables can take two defined arguments, True or False, and are arranged in clauses that follow the rules of formal logic. Several problems that span from physical systems to mathematical conjectures can be encoded into these clauses and solved by checking their satisfiability (SAT). In contrast to machine learning approaches where the results can be approximations or local minima, Logic AI delivers formal and mathematically exact solutions to those problems. In this work, we propose the use of logic AI for the design of optical quantum experiments. We show how to map into a SAT problem the experimental preparation of an arbitrary quantum state and propose a logic-based algorithm, called Klaus, to find an interpretable representation of the photonic setup that generates it. We compare the performance of Klaus with the state-of-the-art algorithm for this purpose based on continuous optimization. We also combine both logic and numeric strategies to find that the use of logic AI significantly improves the resolution of this problem, paving the path to developing more formal-based approaches in the context of quantum physics experiments.	['Alán Aspuru-Guzik', 'Mario Krenn', 'Alba Cervera-Lierta']	2021-09-27	null	null	null	null	['formal-logic']	['reasoning']	[ 4.60269481e-01 5.42831540e-01 1.35665596e-01 -4.04429942e-01 -3.51529956e-01 -7.86455870e-01 4.56130385e-01 9.90760922e-02 -9.27945301e-02 1.01243675e+00 -4.19851154e-01 -8.13955009e-01 -6.67015254e-01 -1.25181651e+00 -7.89379478e-01 -6.44971907e-01 -4.05155532e-02 9.02000308e-01 1.36715904e-01 -2.72443891e-01 5.30608714e-01 5.06038129e-01 -1.50400960e+00 4.51682150e-01 6.61435246e-01 1.12648869e+00 -3.87372315e-01 6.15600884e-01 -2.96629101e-01 9.70245898e-01 -2.56444901e-01 -2.70232499e-01 3.45728815e-01 -6.57306492e-01 -1.28663397e+00 -2.69926280e-01 1.41046822e-01 1.86344564e-01 -1.64898992e-01 1.32653499e+00 -2.20295027e-01 -2.21029684e-01 3.75638723e-01 -1.32509816e+00 -6.63473010e-01 5.81540585e-01 1.33178845e-01 -2.86050409e-01 5.75327158e-01 3.29325378e-01 1.42962563e+00 -8.22710991e-02 6.95945919e-01 1.16277623e+00 2.25641474e-01 3.60714167e-01 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-2.02847049e-01 -3.90608609e-01 -2.48331800e-01 3.65356058e-01 -3.88039082e-01 -3.78336519e-01 -1.76223069e-01]	[8.583711624145508, 6.811710834503174]
9534b3a7-e411-49fa-b742-e45201d904a1	all-around-real-label-supervision-cyclic	2109.1393	null	https://arxiv.org/abs/2109.13930v2	https://arxiv.org/pdf/2109.13930v2.pdf	All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation	Semi-supervised learning has substantially advanced medical image segmentation since it alleviates the heavy burden of acquiring the costly expert-examined annotations. Especially, the consistency-based approaches have attracted more attention for their superior performance, wherein the real labels are only utilized to supervise their paired images via supervised loss while the unlabeled images are exploited by enforcing the perturbation-based \textit{"unsupervised"} consistency without explicit guidance from those real labels. However, intuitively, the expert-examined real labels contain more reliable supervision signals. Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training? To this end, we discard the previous perturbation-based consistency but absorb the essence of non-parametric prototype learning. Based on the prototypical network, we then propose a novel cyclic prototype consistency learning (CPCL) framework, which is constructed by a labeled-to-unlabeled (L2U) prototypical forward process and an unlabeled-to-labeled (U2L) backward process. Such two processes synergistically enhance the segmentation network by encouraging more discriminative and compact features. In this way, our framework turns previous \textit{"unsupervised"} consistency into new \textit{"supervised"} consistency, obtaining the \textit{"all-around real label supervision"} property of our method. Extensive experiments on brain tumor segmentation from MRI and kidney segmentation from CT images show that our CPCL can effectively exploit the unlabeled data and outperform other state-of-the-art semi-supervised medical image segmentation methods.	['Raymond Kai-yu Tong', 'Yefeng Zheng', 'Kai Ma', 'Jie Luo', 'Jiangpeng Yan', 'Lequan Yu', 'Donghuan Lu', 'Yixin Wang', 'Zhe Xu']	2021-09-28	null	null	null	null	['semi-supervised-medical-image-segmentation']	['computer-vision']	[ 5.26601493e-01 4.64595467e-01 -6.37751937e-01 -6.77325964e-01 -8.58235121e-01 -2.95654535e-01 4.28077012e-01 6.94248006e-02 -3.73605967e-01 7.55401969e-01 -1.78672716e-01 -3.34127963e-01 -3.58254552e-01 -3.69038403e-01 -5.93552530e-01 -1.29642296e+00 2.57821739e-01 5.76466978e-01 1.80008024e-01 9.21010077e-02 -6.45909682e-02 1.98111147e-01 -1.09500694e+00 -3.02953776e-02 1.27394783e+00 9.95100260e-01 2.71636426e-01 -1.32524043e-01 -9.42795351e-02 9.36792195e-01 -8.19009612e-04 -8.75390992e-02 2.45933279e-01 -6.26698256e-01 -7.99126446e-01 5.48975825e-01 1.42618403e-01 1.89092045e-03 -5.17523549e-02 1.55359972e+00 3.53210360e-01 -5.27083548e-03 6.92006886e-01 -1.19472396e+00 -5.57424903e-01 6.68248117e-01 -9.23365951e-01 -5.61213456e-02 -7.04273283e-02 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2bede581-e153-40ff-908d-5039fc6c3aa7	pre-training-graph-neural-networks	1905.12265	null	https://arxiv.org/abs/1905.12265v3	https://arxiv.org/pdf/1905.12265v3.pdf	Strategies for Pre-training Graph Neural Networks	Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. An effective approach to this challenge is to pre-train a model on related tasks where data is abundant, and then fine-tune it on a downstream task of interest. While pre-training has been effective in many language and vision domains, it remains an open question how to effectively use pre-training on graph datasets. In this paper, we develop a new strategy and self-supervised methods for pre-training Graph Neural Networks (GNNs). The key to the success of our strategy is to pre-train an expressive GNN at the level of individual nodes as well as entire graphs so that the GNN can learn useful local and global representations simultaneously. We systematically study pre-training on multiple graph classification datasets. We find that naive strategies, which pre-train GNNs at the level of either entire graphs or individual nodes, give limited improvement and can even lead to negative transfer on many downstream tasks. In contrast, our strategy avoids negative transfer and improves generalization significantly across downstream tasks, leading up to 9.4% absolute improvements in ROC-AUC over non-pre-trained models and achieving state-of-the-art performance for molecular property prediction and protein function prediction.	['Jure Leskovec', 'Vijay Pande', 'Percy Liang', 'Bowen Liu', 'Marinka Zitnik', 'Joseph Gomes', 'Weihua Hu']	2019-05-29	strategies-for-pre-training-graph-neural	https://openreview.net/forum?id=HJlWWJSFDH	https://openreview.net/pdf?id=HJlWWJSFDH	iclr-2020-1	['protein-function-prediction']	['medical']	[ 6.98298931e-01 1.98444575e-01 -2.44531885e-01 -5.72228014e-01 -6.60675228e-01 -6.89624310e-01 5.40894210e-01 6.76515877e-01 -4.96006966e-01 7.45760381e-01 -1.85860917e-01 -6.19610012e-01 -1.56327337e-01 -9.63977337e-01 -8.46789002e-01 -6.51015282e-01 -1.84806466e-01 6.79796875e-01 2.28326023e-01 -3.41239423e-01 -5.11814356e-02 5.40206552e-01 -1.11149824e+00 2.90460736e-01 9.53796804e-01 7.97080994e-01 1.13198861e-01 6.17972672e-01 -7.96366259e-02 5.17740965e-01 -4.39172328e-01 -3.76281619e-01 1.53504491e-01 -4.65145886e-01 -9.60132122e-01 -2.22111046e-01 4.25475687e-01 1.88617229e-01 -1.79663375e-01 1.08367169e+00 5.80309749e-01 1.51074260e-01 9.46515083e-01 -1.00244057e+00 -9.19456840e-01 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2cb58307-4505-41f8-b3e0-281f8248112f	dpccn-densely-connected-pyramid-complex	2112.1352	null	https://arxiv.org/abs/2112.13520v2	https://arxiv.org/pdf/2112.13520v2.pdf	DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation And Extraction	In recent years, a number of time-domain speech separation methods have been proposed. However, most of them are very sensitive to the environments and wide domain coverage tasks. In this paper, from the time-frequency domain perspective, we propose a densely-connected pyramid complex convolutional network, termed DPCCN, to improve the robustness of speech separation under complicated conditions. Furthermore, we generalize the DPCCN to target speech extraction (TSE) by integrating a new specially designed speaker encoder. Moreover, we also investigate the robustness of DPCCN to unsupervised cross-domain TSE tasks. A Mixture-Remix approach is proposed to adapt the target domain acoustic characteristics for fine-tuning the source model. We evaluate the proposed methods not only under noisy and reverberant in-domain condition, but also in clean but cross-domain conditions. Results show that for both speech separation and extraction, the DPCCN-based systems achieve significantly better performance and robustness than the currently dominating time-domain methods, especially for the cross-domain tasks. Particularly, we find that the Mixture-Remix fine-tuning with DPCCN significantly outperforms the TD-SpeakerBeam for unsupervised cross-domain TSE, with around 3.5 dB SISNR improvement on target domain test set, without any source domain performance degradation.	['Jan Cernocky', 'Lukas Burget', 'Yanhua Long', 'Jiangyu Han']	2021-12-27	null	null	null	null	['speech-extraction']	['speech']	[ 8.30669329e-02 -5.28452992e-01 2.11500287e-01 -1.66651145e-01 -1.02709007e+00 -6.26663327e-01 3.77408773e-01 -2.82299578e-01 -2.08361298e-01 6.64519489e-01 3.91804546e-01 -1.87965825e-01 -3.94673467e-01 -1.37096882e-01 -3.05259794e-01 -1.09564304e+00 4.06650230e-02 -7.34334886e-02 1.56041235e-01 -2.78411865e-01 -1.69742644e-01 5.02862036e-01 -1.35110044e+00 -1.21357553e-01 1.32195449e+00 8.95734131e-01 4.33609694e-01 6.76783800e-01 2.57978171e-01 1.77251235e-01 -1.13524628e+00 -2.15286329e-01 1.80224210e-01 -3.87635887e-01 -4.53485250e-02 1.06931537e-01 9.38482732e-02 6.25425950e-02 -5.27256906e-01 1.20020747e+00 1.18425846e+00 2.97003418e-01 6.51106477e-01 -8.23976159e-01 -5.83239496e-01 6.68332100e-01 -5.65115273e-01 3.41417998e-01 -1.81636840e-01 1.26463920e-01 5.23030519e-01 -1.06644630e+00 -1.36478469e-01 1.15918756e+00 6.38913035e-01 2.97187597e-01 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cf76ea85-be00-43d2-95ce-c8f8220945c2	syntax-guided-contrastive-learning-for-pre	null	null	https://aclanthology.org/2022.findings-acl.191	https://aclanthology.org/2022.findings-acl.191.pdf	Syntax-guided Contrastive Learning for Pre-trained Language Model	Syntactic information has been proved to be useful for transformer-based pre-trained language models. Previous studies often rely on additional syntax-guided attention components to enhance the transformer, which require more parameters and additional syntactic parsing in downstream tasks. This increase in complexity severely limits the application of syntax-enhanced language model in a wide range of scenarios. In order to inject syntactic knowledge effectively and efficiently into pre-trained language models, we propose a novel syntax-guided contrastive learning method which does not change the transformer architecture. Based on constituency and dependency structures of syntax trees, we design phrase-guided and tree-guided contrastive objectives, and optimize them in the pre-training stage, so as to help the pre-trained language model to capture rich syntactic knowledge in its representations. Experimental results show that our contrastive method achieves consistent improvements in a variety of tasks, including grammatical error detection, entity tasks, structural probing and GLUE. Detailed analysis further verifies that the improvements come from the utilization of syntactic information, and the learned attention weights are more explainable in terms of linguistics.	['Hua Wu', 'Xinyan Xiao', 'Wang Lijie', 'Shuai Zhang']	null	null	null	null	findings-acl-2022-5	['grammatical-error-detection']	['natural-language-processing']	[ 6.78483173e-02 4.67748225e-01 -1.65390685e-01 -7.58682132e-01 -5.25556028e-01 -3.01109672e-01 1.16096959e-01 1.35166213e-01 -4.65263993e-01 3.67112011e-01 6.57128870e-01 -6.65870965e-01 2.57117212e-01 -8.27800989e-01 -6.44622743e-01 -3.38740379e-01 9.89030078e-02 3.36594552e-01 1.38497993e-01 -3.96986008e-01 1.72189236e-01 5.49285933e-02 -1.16792011e+00 5.90892911e-01 1.29144001e+00 7.50261545e-01 7.82179534e-01 1.98089421e-01 -7.80364513e-01 8.04884017e-01 -3.27329814e-01 -6.76828444e-01 -2.22232789e-01 -2.58061945e-01 -9.20780241e-01 -3.06510925e-01 -1.97772846e-01 -2.03816026e-01 9.54483002e-02 1.07769942e+00 1.46063238e-01 -1.26713336e-01 3.42420220e-01 -5.47043741e-01 -1.05028319e+00 1.43810213e+00 -2.49755964e-01 3.51048023e-01 1.20794967e-01 -6.90477490e-02 1.67332542e+00 -1.12154531e+00 3.18253517e-01 1.73058796e+00 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80cf9479-2ea0-4fa3-8d6c-eddc27a0d249	seeing-the-advantage-visually-grounding-word	2202.10292	null	https://arxiv.org/abs/2202.10292v1	https://arxiv.org/pdf/2202.10292v1.pdf	Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge	Distributional semantic models capture word-level meaning that is useful in many natural language processing tasks and have even been shown to capture cognitive aspects of word meaning. The majority of these models are purely text based, even though the human sensory experience is much richer. In this paper we create visually grounded word embeddings by combining English text and images and compare them to popular text-based methods, to see if visual information allows our model to better capture cognitive aspects of word meaning. Our analysis shows that visually grounded embedding similarities are more predictive of the human reaction times in a large priming experiment than the purely text-based embeddings. The visually grounded embeddings also correlate well with human word similarity ratings. Importantly, in both experiments we show that the grounded embeddings account for a unique portion of explained variance, even when we include text-based embeddings trained on huge corpora. This shows that visual grounding allows our model to capture information that cannot be extracted using text as the only source of information.	['Mirjam Ernestus', 'Stefan L. Frank', 'Danny Merkx']	2022-02-21	null	https://aclanthology.org/2022.cmcl-1.1	https://aclanthology.org/2022.cmcl-1.1.pdf	cmcl-acl-2022-5	['learning-semantic-representations', 'word-similarity', 'grounded-language-learning']	['methodology', 'natural-language-processing', 'natural-language-processing']	[-1.20111212e-01 8.11759755e-03 -7.92988762e-02 -2.25032181e-01 -4.03830588e-01 -7.53670275e-01 1.05023432e+00 8.58164608e-01 -8.76982987e-01 2.05212787e-01 9.66884434e-01 -3.04238856e-01 8.79034773e-02 -8.73646438e-01 -2.74808109e-01 -5.32081127e-01 -1.46868797e-02 1.47352904e-01 9.93339866e-02 -4.68028784e-01 5.83760083e-01 3.09248030e-01 -1.47895551e+00 3.79940301e-01 2.38410130e-01 6.11046910e-01 4.61567998e-01 6.24955773e-01 -5.35338163e-01 6.43011391e-01 -5.33248067e-01 -1.41939208e-01 1.80517375e-01 -3.69594663e-01 -6.92256808e-01 -2.22976491e-01 6.53137326e-01 9.86367464e-03 -2.34354272e-01 8.66275907e-01 5.13799071e-01 3.70743275e-01 7.50941634e-01 -1.17421877e+00 -1.32943046e+00 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e6dfa26a-4062-45d9-9680-da468493fd39	a-novel-approach-for-automatic-acoustic	null	null	https://ieeexplore.ieee.org/abstract/document/7178320	https://ieeexplore.ieee.org/abstract/document/7178320	A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks	Acoustic novelty detection aims at identifying abnormal/novel acoustic signals which differ from the reference/normal data that the system was trained with. In this paper we present a novel unsupervised approach based on a denoising autoencoder. In our approach auditory spectral features are processed by a denoising autoencoder with bidirectional Long Short-Term Memory recurrent neural networks. We use the reconstruction error between the input and the output of the autoencoder as activation signal to detect novel events. The autoencoder is trained on a public database which contains recordings of typical in-home situations such as talking, watching television, playing and eating. The evaluation was performed on more than 260 different abnormal events. We compare results with state-of-the-art methods and we conclude that our novel approach significantly outperforms existing methods by achieving up to 93.4% F-Measure.	['Erik Marchi ; Fabio Vesperini ; Florian Eyben ; Stefano Squartini ; Björn Schuller']	2015-08-06	null	null	null	2015-ieee-international-conference-on-1	['acoustic-novelty-detection']	['audio']	[ 2.33820736e-01 -2.86580138e-02 8.09935808e-01 -2.66105324e-01 -8.41069102e-01 5.36850188e-03 2.66210020e-01 2.94460088e-01 -6.23285830e-01 3.94838929e-01 4.60512668e-01 2.54085928e-01 1.16052464e-01 -6.96775019e-01 -8.29324126e-01 -5.90534925e-01 -2.37600073e-01 1.70519575e-02 3.04182738e-01 -2.81945974e-01 -8.20274577e-02 7.83531666e-02 -1.99691415e+00 7.41228223e-01 1.05571948e-01 1.11651146e+00 1.15367152e-01 1.11662698e+00 1.28340721e-01 6.22086883e-01 -9.15430605e-01 -2.67614067e-01 -3.80809456e-02 -5.69846094e-01 -5.88901460e-01 -3.05423141e-01 1.25043159e-02 -3.49789798e-01 -4.25520927e-01 1.09541583e+00 9.52185214e-01 6.41880095e-01 3.29037577e-01 -6.17466152e-01 -5.61214507e-01 8.39911759e-01 3.41051459e-01 7.49745131e-01 6.26207829e-01 -7.74905235e-02 7.74587870e-01 -1.08310580e+00 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5540e62e-cfbe-4aeb-8add-1a4888c810df	body-meshes-as-points	2105.02467	null	https://arxiv.org/abs/2105.02467v2	https://arxiv.org/pdf/2105.02467v2.pdf	Body Meshes as Points	We consider the challenging multi-person 3D body mesh estimation task in this work. Existing methods are mostly two-stage based--one stage for person localization and the other stage for individual body mesh estimation, leading to redundant pipelines with high computation cost and degraded performance for complex scenes (e.g., occluded person instances). In this work, we present a single-stage model, Body Meshes as Points (BMP), to simplify the pipeline and lift both efficiency and performance. In particular, BMP adopts a new method that represents multiple person instances as points in the spatial-depth space where each point is associated with one body mesh. Hinging on such representations, BMP can directly predict body meshes for multiple persons in a single stage by concurrently localizing person instance points and estimating the corresponding body meshes. To better reason about depth ordering of all the persons within the same scene, BMP designs a simple yet effective inter-instance ordinal depth loss to obtain depth-coherent body mesh estimation. BMP also introduces a novel keypoint-aware augmentation to enhance model robustness to occluded person instances. Comprehensive experiments on benchmarks Panoptic, MuPoTS-3D and 3DPW clearly demonstrate the state-of-the-art efficiency of BMP for multi-person body mesh estimation, together with outstanding accuracy. Code can be found at: https://github.com/jfzhang95/BMP.	['Jiashi Feng', 'Xuecheng Nie', 'Jun Hao Liew', 'Dongdong Yu', 'Jianfeng Zhang']	2021-05-06	null	http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Body_Meshes_as_Points_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Body_Meshes_as_Points_CVPR_2021_paper.pdf	cvpr-2021-1	['3d-pose-estimation', '3d-multi-person-pose-estimation']	['computer-vision', 'computer-vision']	[-6.58624172e-02 3.63563932e-02 2.88838660e-03 -2.15071291e-01 -9.23863649e-01 -1.63761288e-01 2.13351741e-01 1.59145087e-01 -1.04243927e-01 2.66484946e-01 8.35725293e-02 5.13701439e-01 2.38928407e-01 -1.02841115e+00 -6.96888387e-01 -3.69870484e-01 -7.38701075e-02 1.02613139e+00 6.31429911e-01 -8.15877318e-02 -2.25694627e-01 3.67860109e-01 -1.62577689e+00 2.35304475e-01 6.68242335e-01 8.56430829e-01 -1.63508728e-01 7.08749950e-01 2.55243834e-02 1.65372044e-01 -4.94462281e-01 -7.40400374e-01 4.75733131e-01 -1.55798644e-01 -7.39842594e-01 3.57313246e-01 9.63381350e-01 -5.10749221e-01 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36a02863-6189-4c1d-bfa2-f1ce0553f0cd	large-scale-multi-class-and-hierarchical	null	null	https://aclanthology.org/C16-1051	https://aclanthology.org/C16-1051.pdf	Large-scale Multi-class and Hierarchical Product Categorization for an E-commerce Giant	In order to organize the large number of products listed in e-commerce sites, each product is usually assigned to one of the multi-level categories in the taxonomy tree. It is a time-consuming and difficult task for merchants to select proper categories within thousands of options for the products they sell. In this work, we propose an automatic classification tool to predict the matching category for a given product title and description. We used a combination of two different neural models, i.e., deep belief nets and deep autoencoders, for both titles and descriptions. We implemented a selective reconstruction approach for the input layer during the training of the deep neural networks, in order to scale-out for large-sized sparse feature vectors. GPUs are utilized in order to train neural networks in a reasonable time. We have trained our models for around 150 million products with a taxonomy tree with at most 5 levels that contains 28,338 leaf categories. Tests with millions of products show that our first predictions matches 81{\%} of merchants{'} assignments, when {``}others{''} categories are excluded.	['Koji Murakami', 'Ali Cevahir']	2016-12-01	large-scale-multi-class-and-hierarchical-1	https://aclanthology.org/C16-1051	https://aclanthology.org/C16-1051.pdf	coling-2016-12	['product-categorization']	['miscellaneous']	[-2.74570018e-01 -8.28945488e-02 -1.61922842e-01 -7.76275694e-01 -1.44083023e-01 -7.03346252e-01 1.06134832e-01 5.06302893e-01 -2.21573666e-01 3.52684349e-01 1.32597148e-01 -4.79143441e-01 -6.90774992e-02 -1.30278349e+00 -7.52003193e-01 -1.84075221e-01 -6.76592141e-02 9.37177062e-01 9.10832733e-02 -1.31030202e-01 2.31853679e-01 4.62668210e-01 -2.00755572e+00 8.96620095e-01 5.91438830e-01 1.67490232e+00 1.93922177e-01 2.41483554e-01 -6.24940217e-01 8.55281651e-01 -3.27998281e-01 -1.04800415e+00 5.18328011e-01 2.23942343e-02 -7.15961695e-01 2.74616927e-01 5.27852356e-01 -5.10593653e-01 -1.40832603e-01 1.29030573e+00 -7.40561634e-02 1.01132095e-01 7.63627172e-01 -1.02106631e+00 -5.48966527e-01 9.61619735e-01 -3.91970605e-01 -9.97706726e-02 1.53950527e-01 -1.96855187e-01 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cc91abf3-1048-4cc0-beae-fb656c5970ba	calibrated-rgb-d-salient-object-detection	null	null	http://openaccess.thecvf.com//content/CVPR2021/html/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Ji_Calibrated_RGB-D_Salient_Object_Detection_CVPR_2021_paper.pdf	Calibrated RGB-D Salient Object Detection	Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD). This naturally leads to the incorporation of depth information in addition to the conventional RGB image as input, known as RGB-D SOD or depth-aware SOD. Meanwhile, this emerging line of research has been considerably hindered by the noise and ambiguity that prevail in raw depth images. To address the aforementioned issues, we propose a Depth Calibration and Fusion (DCF) framework that contains two novel components: 1) a learning strategy to calibrate the latent bias in the original depth maps towards boosting the SOD performance; 2) a simple yet effective cross reference module to fuse features from both RGB and depth modalities. Extensive empirical experiments demonstrate that the proposed approach achieves superior performance against 27 state-of-the-art methods. Moreover, the proposed depth calibration strategy as a preprocessing step, can be further applied to existing cutting-edge RGB-D SOD models and noticeable improvements are achieved.	['Li Cheng', 'Huchuan Lu', 'Yefeng Zheng', 'Kai Ma', 'Qi Bi', 'Shunyu Yao', 'Yongri Piao', 'Miao Zhang', 'Shuang Yu', 'Jingjing Li', 'Wei Ji']	2021-06-19	null	null	null	cvpr-2021-1	['rgb-d-salient-object-detection', 'thermal-image-segmentation']	['computer-vision', 'computer-vision']	[ 5.68181336e-01 1.51086897e-02 6.71049878e-02 -2.93340594e-01 -6.90101504e-01 -2.25563660e-01 6.55073762e-01 1.51442856e-01 -3.71999413e-01 5.18082678e-01 1.79286122e-01 -6.38321191e-02 -3.88736948e-02 -7.61611283e-01 -3.38237464e-01 -1.01816797e+00 5.23806274e-01 -1.20258540e-01 8.05155873e-01 -2.70738542e-01 3.06712449e-01 7.60217488e-01 -1.97306693e+00 1.20806508e-02 8.77292633e-01 1.28608358e+00 3.19510728e-01 4.25285310e-01 -2.64193326e-01 7.54688501e-01 -1.87848121e-01 -4.86127794e-01 4.63728487e-01 -2.84585446e-01 -4.61095929e-01 4.78200346e-01 5.76647639e-01 -4.93315667e-01 -3.12720001e-01 1.20921695e+00 5.89648068e-01 4.44101728e-02 3.35049987e-01 -1.14343023e+00 -3.00018549e-01 -2.06841201e-01 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6e6776f0-ab58-443c-9378-ca605d59ccd0	sigan-siamese-generative-adversarial-network	1807.0837	null	http://arxiv.org/abs/1807.08370v1	http://arxiv.org/pdf/1807.08370v1.pdf	SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination	Despite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the identities of hallucinated HR faces, making the HR faces poorly recognizable. To address this problem, we propose a Siamese GAN (SiGAN) to reconstruct HR faces that visually resemble their corresponding identities. On top of a Siamese network, the proposed SiGAN consists of a pair of two identical generators and one discriminator. We incorporate reconstruction error and identity label information in the loss function of SiGAN in a pairwise manner. By iteratively optimizing the loss functions of the generator pair and discriminator of SiGAN, we cannot only achieve photo-realistic face reconstruction, but also ensures the reconstructed information is useful for identity recognition. Experimental results demonstrate that SiGAN significantly outperforms existing face hallucination GANs in objective face verification performance, while achieving photo-realistic reconstruction. Moreover, for input LR faces from unknown identities who are not included in training, SiGAN can still do a good job.	['Weng-Tai Su', 'Chia-Wen Lin', 'Chih-Chung Hsu', 'Gene Cheung']	2018-07-22	null	null	null	null	['face-hallucination']	['computer-vision']	[ 2.89247513e-01 4.12901700e-01 3.55371177e-01 -3.94226998e-01 -9.22284365e-01 -4.86392349e-01 4.07432795e-01 -9.85216737e-01 2.06643149e-01 8.58473957e-01 1.90564170e-01 2.33082786e-01 4.03218120e-01 -8.20788383e-01 -8.64749134e-01 -9.59314883e-01 4.45962816e-01 3.89786333e-01 -5.91384470e-01 -4.35185619e-03 -3.44756305e-01 7.53680050e-01 -1.46430039e+00 2.94694811e-01 1.00261140e+00 1.03156030e+00 -7.41435662e-02 1.96906000e-01 3.44527602e-01 8.06393087e-01 -7.52542675e-01 -7.55261242e-01 6.23922288e-01 -9.81226802e-01 -2.67317444e-01 5.34769446e-02 7.44205892e-01 -5.23845434e-01 -7.23947644e-01 1.06713831e+00 5.64899206e-01 9.64957625e-02 6.65718138e-01 -1.52724719e+00 -1.38215482e+00 3.43516499e-01 -5.85836291e-01 -5.60890317e-01 4.32259500e-01 2.69716293e-01 5.26291490e-01 -1.03711927e+00 5.08237243e-01 1.53138745e+00 6.68889225e-01 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f58deb3c-43ad-4e61-9460-66ecaa258fb1	190503277	1905.03277	null	https://arxiv.org/abs/1905.03277v2	https://arxiv.org/pdf/1905.03277v2.pdf	Handheld Multi-Frame Super-Resolution	Compared to DSLR cameras, smartphone cameras have smaller sensors, which limits their spatial resolution; smaller apertures, which limits their light gathering ability; and smaller pixels, which reduces their signal-to noise ratio. The use of color filter arrays (CFAs) requires demosaicing, which further degrades resolution. In this paper, we supplant the use of traditional demosaicing in single-frame and burst photography pipelines with a multiframe super-resolution algorithm that creates a complete RGB image directly from a burst of CFA raw images. We harness natural hand tremor, typical in handheld photography, to acquire a burst of raw frames with small offsets. These frames are then aligned and merged to form a single image with red, green, and blue values at every pixel site. This approach, which includes no explicit demosaicing step, serves to both increase image resolution and boost signal to noise ratio. Our algorithm is robust to challenging scene conditions: local motion, occlusion, or scene changes. It runs at 100 milliseconds per 12-megapixel RAW input burst frame on mass-produced mobile phones. Specifically, the algorithm is the basis of the Super-Res Zoom feature, as well as the default merge method in Night Sight mode (whether zooming or not) on Google's flagship phone.	['Chia-Kai Liang', 'Ignacio Garcia-Dorado', 'Marc Levoy', 'Manfred Ernst', 'Bartlomiej Wronski', 'Peyman Milanfar', 'Michael Krainin', 'Damien Kelly']	2019-05-08	null	null	null	null	['multi-frame-super-resolution']	['computer-vision']	[ 6.35734439e-01 -5.09493947e-01 3.46826375e-01 -1.87354863e-01 -6.98448181e-01 -7.76745617e-01 3.19766998e-01 -3.74556810e-01 -5.94304144e-01 6.07253134e-01 1.37704104e-01 -3.26087058e-01 3.58274639e-01 -7.47030914e-01 -6.48918211e-01 -8.41354012e-01 5.19638002e-01 -3.64890188e-01 5.88646472e-01 -5.21828569e-02 3.82487811e-02 4.68288571e-01 -1.71334982e+00 1.87172368e-01 9.23153520e-01 9.80551958e-01 4.82664019e-01 9.43483531e-01 1.49319574e-01 7.45469451e-01 -5.91709733e-01 -1.30927011e-01 4.86418992e-01 -4.72565979e-01 -1.77061051e-01 4.10199612e-01 8.99897873e-01 -9.76891994e-01 -3.35436016e-01 9.43215072e-01 4.47625548e-01 1.55285820e-01 -1.30440131e-01 -9.09855783e-01 -2.06133425e-01 -2.53714979e-01 -7.96803594e-01 2.81088054e-01 5.37404716e-01 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f057cafc-5508-4a82-9ff7-1a662f64e25a	duformer-a-novel-architecture-for-power-line	2304.05821	null	https://arxiv.org/abs/2304.05821v1	https://arxiv.org/pdf/2304.05821v1.pdf	DUFormer: A Novel Architecture for Power Line Segmentation of Aerial Images	Power lines pose a significant safety threat to unmanned aerial vehicles (UAVs) operating at low altitudes. However, detecting power lines in aerial images is challenging due to the small size of the foreground data (i.e., power lines) and the abundance of background information. To address this challenge, we propose DUFormer, a semantic segmentation algorithm designed specifically for power line detection in aerial images. We assume that performing sufficient feature extraction with a convolutional neural network (CNN) that has a strong inductive bias is beneficial for training an efficient Transformer model. To this end, we propose a heavy token encoder responsible for overlapping feature re-mining and tokenization. The encoder comprises a pyramid CNN feature extraction module and a power line feature enhancement module. Following sufficient feature extraction for power lines, the feature fusion is carried out, and then the Transformer block is used for global modeling. The final segmentation result is obtained by fusing local and global features in the decode head. Additionally, we demonstrate the significance of the joint multi-weight loss function in power line segmentation. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance in power line segmentation on the publicly available TTPLA dataset.	['Jia Xu', 'ZhenPeng Bian', 'Yong Deng', 'Feng Qiao', 'Ting Li', 'Jianshu Chao', 'Qiang Zhang', 'Deyu An']	2023-04-12	null	null	null	null	['line-detection']	['computer-vision']	[ 3.03198993e-01 -2.86950707e-01 -2.85320163e-01 -9.53614637e-02 -4.82194662e-01 -8.92548203e-01 3.49908993e-02 -8.12566802e-02 -1.06879413e-01 4.21837986e-01 -5.40092289e-01 -2.78042555e-01 -9.15641114e-02 -1.01877952e+00 -5.17634273e-01 -8.12293649e-01 -2.84608006e-01 -1.92910418e-01 2.18187630e-01 3.77129056e-02 6.66892081e-02 9.83381212e-01 -1.24923670e+00 1.05012298e-01 1.23286831e+00 1.48713493e+00 4.50770138e-03 6.44037008e-01 1.81742489e-01 9.99314308e-01 -9.54780161e-01 -2.17671484e-01 5.74770570e-01 -3.69322598e-01 -8.18165243e-01 6.13477945e-01 4.87459213e-01 -7.49034584e-01 -5.13040304e-01 1.37257266e+00 4.26906168e-01 5.02010658e-02 3.53219926e-01 -1.72379720e+00 5.52022569e-02 7.90147424e-01 -1.14988959e+00 3.74632448e-01 2.73377039e-02 2.08645891e-02 1.22475362e+00 -5.34221828e-01 1.05085127e-01 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a13e3bb5-46d9-467e-93a2-af40bde83d32	3d-gans-and-latent-space-a-comprehensive	2304.03932	null	https://arxiv.org/abs/2304.03932v1	https://arxiv.org/pdf/2304.03932v1.pdf	3D GANs and Latent Space: A comprehensive survey	Generative Adversarial Networks (GANs) have emerged as a significant player in generative modeling by mapping lower-dimensional random noise to higher-dimensional spaces. These networks have been used to generate high-resolution images and 3D objects. The efficient modeling of 3D objects and human faces is crucial in the development process of 3D graphical environments such as games or simulations. 3D GANs are a new type of generative model used for 3D reconstruction, point cloud reconstruction, and 3D semantic scene completion. The choice of distribution for noise is critical as it represents the latent space. Understanding a GAN's latent space is essential for fine-tuning the generated samples, as demonstrated by the morphing of semantically meaningful parts of images. In this work, we explore the latent space and 3D GANs, examine several GAN variants and training methods to gain insights into improving 3D GAN training, and suggest potential future directions for further research.	['Subhankar Mishra', 'Satya Pratheek Tata']	2023-04-08	null	null	null	null	['point-cloud-reconstruction', '3d-semantic-scene-completion']	['computer-vision', 'computer-vision']	[ 2.64741659e-01 2.36618087e-01 1.65187821e-01 -1.52710050e-01 -5.85347354e-01 -6.67464495e-01 7.61810005e-01 -8.32577169e-01 4.02647197e-01 5.09582222e-01 3.28957677e-01 -1.92245990e-01 2.11124718e-01 -1.21892846e+00 -6.37848735e-01 -7.28820503e-01 3.34713459e-01 7.49719024e-01 -1.84936449e-01 -4.37037386e-02 -1.39827937e-01 1.09851658e+00 -1.38920212e+00 5.02367914e-02 6.21828616e-01 7.60810494e-01 8.01503807e-02 7.87774205e-01 -3.54094982e-01 4.53036606e-01 -8.69469047e-01 -5.31513453e-01 4.19119745e-01 -8.63699436e-01 -1.56287938e-01 3.51328284e-01 1.58934966e-01 -4.14454222e-01 -1.45036459e-01 8.07865500e-01 5.55472136e-01 1.85113475e-02 1.12241352e+00 -1.30984128e+00 -7.90779412e-01 7.76116177e-02 -4.35936093e-01 -4.79129523e-01 2.65325606e-01 2.57399917e-01 3.59093964e-01 -7.56702006e-01 7.46032715e-01 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e979dc1e-8eef-4172-9bc3-3cca9417eb94	gashis-transformer-a-multi-scale-visual	2104.14528	null	https://arxiv.org/abs/2104.14528v7	https://arxiv.org/pdf/2104.14528v7.pdf	GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection	In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer images. GasHis-Transformer model consists of two key modules designed to extract global and local information using a position-encoded transformer model and a convolutional neural network with local convolution, respectively. A publicly available hematoxylin and eosin (H&E) stained gastric histopathological image dataset is used in the experiment. Furthermore, a Dropconnect based lightweight network is proposed to reduce the model size and training time of GasHis-Transformer for clinical applications with improved confidence. Moreover, a series of contrast and extended experiments verify the robustness, extensibility and stability of GasHis-Transformer. In conclusion, GasHis-Transformer demonstrates high global detection performance and shows its significant potential in GHID task.	['Shiliang Ai', 'Changhao Sun', 'Hongzan Sun', 'Md Rahaman', 'Xiaoyan Li', 'Ge Wang', 'Yixin Li', 'Marcin Grzegorzek', 'Wanli Liu', 'Weiming Hu', 'Chen Li', 'HaoYuan Chen']	2021-04-29	null	null	null	null	['histopathological-image-classification']	['medical']	[-2.20166475e-01 9.09903571e-02 -3.56657393e-02 2.57103652e-01 -9.71374154e-01 -3.32191885e-01 1.17480956e-01 3.64142060e-01 -4.48727220e-01 2.32305393e-01 -4.09065634e-02 -5.12811601e-01 2.14272976e-01 -9.55789804e-01 -1.43972859e-01 -1.24434829e+00 -2.95713961e-01 2.20915806e-02 5.24438262e-01 -1.73275545e-01 -2.28566110e-01 3.76203060e-01 -9.74533856e-01 4.18787569e-01 6.13467515e-01 1.23199785e+00 4.30056155e-01 7.60221004e-01 4.94332582e-01 9.07626092e-01 -1.26231194e-01 -2.22619504e-01 7.68419355e-02 -1.57472804e-01 -7.72659361e-01 -1.67935148e-01 -7.41288648e-04 -7.09957004e-01 -2.84378678e-01 9.21926498e-01 8.05822432e-01 -5.96396267e-01 4.88335371e-01 -9.19374287e-01 -3.94019604e-01 1.72005564e-01 -5.49026549e-01 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6e431ae6-460c-4361-af5d-384758af3120	topic-preserving-synthetic-news-generation-an	2010.16324	null	https://arxiv.org/abs/2010.16324v1	https://arxiv.org/pdf/2010.16324v1.pdf	Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach	Nowadays, there exist powerful language models such as OpenAI's GPT-2 that can generate readable text and can be fine-tuned to generate text for a specific domain. Considering GPT-2, it cannot directly generate synthetic news with respect to a given topic and the output of the language model cannot be explicitly controlled. In this paper, we study the novel problem of topic-preserving synthetic news generation. We propose a novel deep reinforcement learning-based method to control the output of GPT-2 with respect to a given news topic. When generating text using GPT-2, by default, the most probable word is selected from the vocabulary. Instead of selecting the best word each time from GPT-2's output, an RL agent tries to select words that optimize the matching of a given topic. In addition, using a fake news detector as an adversary, we investigate generating realistic news using our proposed method. In this paper, we consider realistic news as news that cannot be easily detected by a fake news classifier. Experimental results demonstrate the effectiveness of the proposed framework on generating topic-preserving news content than state-of-the-art baselines.	['Huan Liu', 'Kai Shu', 'Ahmadreza Mosallanezhad']	2020-10-30	null	null	null	null	['news-generation']	['natural-language-processing']	[ 2.34644815e-01 5.88796198e-01 -1.39215127e-01 1.08265337e-02 -1.03192651e+00 -6.37190104e-01 1.01249230e+00 -4.60976437e-02 -4.37153816e-01 1.02798522e+00 3.70519370e-01 -2.38094762e-01 6.18425608e-01 -1.39214277e+00 -1.23736680e+00 -5.84901571e-01 4.54535097e-01 9.01935399e-01 1.74324647e-01 -5.50845981e-01 2.71987587e-01 1.31229758e-01 -1.05096972e+00 4.55100179e-01 9.29463685e-01 4.66389060e-01 3.09915721e-01 6.07969105e-01 -1.79053694e-01 6.04804814e-01 -1.49214482e+00 -5.25350094e-01 2.40404353e-01 -8.77645493e-01 -4.85082269e-01 -4.37197573e-02 2.13733584e-01 -4.78508830e-01 -5.23694575e-01 1.11683476e+00 6.66854739e-01 1.66751042e-01 7.79662192e-01 -1.32184899e+00 -9.29469883e-01 1.28012598e+00 -2.44211987e-01 -5.78875653e-03 2.19986275e-01 3.26392770e-01 8.41158450e-01 -4.26670969e-01 9.32878375e-01 1.60481405e+00 1.95518080e-02 1.01227081e+00 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2.34923333e-01 3.93732190e-01 -1.59272298e-01 -4.61049348e-01 -1.10767134e-01]	[11.818798065185547, 9.187715530395508]
50d818b2-ecbb-4b47-b04f-3ed995441f68	deep-online-correction-for-monocular-visual	2103.10029	null	https://arxiv.org/abs/2103.10029v1	https://arxiv.org/pdf/2103.10029v1.pdf	Deep Online Correction for Monocular Visual Odometry	In this work, we propose a novel deep online correction (DOC) framework for monocular visual odometry. The whole pipeline has two stages: First, depth maps and initial poses are obtained from convolutional neural networks (CNNs) trained in self-supervised manners. Second, the poses predicted by CNNs are further improved by minimizing photometric errors via gradient updates of poses during inference phases. The benefits of our proposed method are twofold: 1) Different from online-learning methods, DOC does not need to calculate gradient propagation for parameters of CNNs. Thus, it saves more computation resources during inference phases. 2) Unlike hybrid methods that combine CNNs with traditional methods, DOC fully relies on deep learning (DL) frameworks. Though without complex back-end optimization modules, our method achieves outstanding performance with relative transform error (RTE) = 2.0% on KITTI Odometry benchmark for Seq. 09, which outperforms traditional monocular VO frameworks and is comparable to hybrid methods.	['Qian Zhang', 'Hongmei Zhu', 'Wenming Meng', 'Xinggang Wang', 'Wei Sui', 'Jiaxin Zhang']	2021-03-18	null	null	null	null	['monocular-visual-odometry']	['robots']	[-2.52029538e-01 1.97418705e-01 -1.32279903e-01 -4.45757687e-01 -3.15995812e-01 -4.29181665e-01 5.28169870e-01 -9.38310623e-02 -7.16943145e-01 7.20021963e-01 -6.11340255e-02 -1.51397824e-01 3.64379525e-01 -7.32451200e-01 -9.86205459e-01 -3.08242410e-01 3.53767216e-01 5.68542004e-01 4.83853728e-01 -1.10771999e-01 2.44248569e-01 5.75706244e-01 -1.46209288e+00 -6.00651622e-01 9.20781076e-01 1.17581439e+00 1.51849285e-01 5.88213503e-01 1.25232965e-01 1.12556899e+00 -2.41685480e-01 -3.64503086e-01 4.19071585e-01 -9.43356231e-02 -5.90579510e-01 -9.40170363e-02 7.93652177e-01 -7.39665806e-01 -7.66326606e-01 1.27206159e+00 7.59559333e-01 1.06377294e-02 3.79569322e-01 -9.30732906e-01 -4.07533675e-01 3.65034789e-02 -4.28557217e-01 -2.60727137e-01 2.22488612e-01 3.34147483e-01 8.20904553e-01 -1.11895633e+00 8.49221051e-01 1.10952795e+00 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cde2016d-337c-461e-b0ff-e5a35bbe4fbd	deep-attention-guided-graph-clustering-with	2111.05548	null	https://arxiv.org/abs/2111.05548v3	https://arxiv.org/pdf/2111.05548v3.pdf	Deep Attention-guided Graph Clustering with Dual Self-supervision	Existing deep embedding clustering works only consider the deepest layer to learn a feature embedding and thus fail to well utilize the available discriminative information from cluster assignments, resulting performance limitation. To this end, we propose a novel method, namely deep attention-guided graph clustering with dual self-supervision (DAGC). Specifically, DAGC first utilizes a heterogeneity-wise fusion module to adaptively integrate the features of an auto-encoder and a graph convolutional network in each layer and then uses a scale-wise fusion module to dynamically concatenate the multi-scale features in different layers. Such modules are capable of learning a discriminative feature embedding via an attention-based mechanism. In addition, we design a distribution-wise fusion module that leverages cluster assignments to acquire clustering results directly. To better explore the discriminative information from the cluster assignments, we develop a dual self-supervision solution consisting of a soft self-supervision strategy with a triplet Kullback-Leibler divergence loss and a hard self-supervision strategy with a pseudo supervision loss. Extensive experiments validate that our method consistently outperforms state-of-the-art methods on six benchmark datasets. Especially, our method improves the ARI by more than 18.14% over the best baseline.	['Junhui Hou', 'Yuheng Jia', 'Hui Liu', 'Zhihao Peng']	2021-11-10	null	null	null	null	['deep-attention', 'graph-clustering', 'deep-attention']	['computer-vision', 'graphs', 'natural-language-processing']	[-2.87487149e-01 -9.75076780e-02 -1.88361153e-01 -6.03404582e-01 -7.40697324e-01 -3.36033374e-01 5.70127547e-01 1.99552178e-01 -3.98120433e-01 9.47697088e-02 1.88011989e-01 6.48052767e-02 -5.25946915e-02 -6.69561923e-01 -8.28518450e-01 -1.04133677e+00 -1.12905018e-01 3.62735629e-01 1.57598719e-01 4.98834401e-02 -3.11204642e-02 -6.99947681e-03 -1.16979957e+00 1.13242500e-01 1.07225573e+00 9.99059319e-01 4.84362721e-01 1.10206924e-01 -5.06954454e-02 4.95018631e-01 -1.51271671e-01 -1.96173072e-01 2.29458824e-01 -3.55852991e-01 -5.19689739e-01 2.26649940e-01 4.74382341e-02 -2.32576624e-01 -6.01063073e-01 1.12784660e+00 4.60473597e-01 1.78348467e-01 4.71860290e-01 -1.20073116e+00 -9.44059253e-01 5.77986658e-01 -8.79371166e-01 -1.41464807e-02 3.46536860e-02 3.93201739e-01 1.26574445e+00 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ea689694-d979-4e31-845a-9fad226852f5	learning-rich-features-from-rgb-d-images-for	1407.5736	null	http://arxiv.org/abs/1407.5736v1	http://arxiv.org/pdf/1407.5736v1.pdf	Learning Rich Features from RGB-D Images for Object Detection and Segmentation	In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an average precision of 37.3%, which is a 56% relative improvement over existing methods. We then focus on the task of instance segmentation where we label pixels belonging to object instances found by our detector. For this task, we propose a decision forest approach that classifies pixels in the detection window as foreground or background using a family of unary and binary tests that query shape and geocentric pose features. Finally, we use the output from our object detectors in an existing superpixel classification framework for semantic scene segmentation and achieve a 24% relative improvement over current state-of-the-art for the object categories that we study. We believe advances such as those represented in this paper will facilitate the use of perception in fields like robotics.	['Pablo Arbeláez', 'Ross Girshick', 'Saurabh Gupta', 'Jitendra Malik']	2014-07-22	null	null	null	null	['object-detection-in-indoor-scenes']	['computer-vision']	[ 5.65896332e-01 3.20006490e-01 -9.43490341e-02 -5.91868043e-01 -7.51039863e-01 -6.37377501e-01 3.64489377e-01 2.64960945e-01 -5.55685401e-01 1.46181673e-01 -2.64683455e-01 -2.11437955e-01 3.79783392e-01 -1.01860476e+00 -9.15201008e-01 -6.72709942e-01 -9.21071991e-02 4.58943069e-01 7.56086946e-01 8.79228339e-02 5.54450452e-01 5.96119046e-01 -2.03826642e+00 2.87527949e-01 2.97263324e-01 1.38068438e+00 2.81832725e-01 1.13156128e+00 -1.29096480e-02 4.54167187e-01 -4.83079493e-01 1.54594302e-01 4.98972803e-01 -3.07233948e-02 -9.47866619e-01 3.43291968e-01 1.03936565e+00 -6.83371902e-01 -9.62527394e-02 9.94250059e-01 3.97447139e-01 -1.29368261e-01 8.01940441e-01 -1.24022019e+00 -3.68921727e-01 1.35458156e-01 -8.51602137e-01 5.08661151e-01 2.06936806e-01 6.08121939e-02 9.19358969e-01 -8.93738449e-01 6.29649282e-01 1.27648306e+00 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a62721dd-ff72-4c8a-9812-dd3b848e82ee	aggression-identification-using-deep-learning	null	null	https://aclanthology.org/W18-4418	https://aclanthology.org/W18-4418.pdf	Aggression Identification Using Deep Learning and Data Augmentation	Social media platforms allow users to share and discuss their opinions online. However, a minority of user posts is aggressive, thereby hinders respectful discussion, and {---} at an extreme level {---} is liable to prosecution. The automatic identification of such harmful posts is important, because it can support the costly manual moderation of online discussions. Further, the automation allows unprecedented analyses of discussion datasets that contain millions of posts. This system description paper presents our submission to the First Shared Task on Aggression Identification. We propose to augment the provided dataset to increase the number of labeled comments from 15,000 to 60,000. Thereby, we introduce linguistic variety into the dataset. As a consequence of the larger amount of training data, we are able to train a special deep neural net, which generalizes especially well to unseen data. To further boost the performance, we combine this neural net with three logistic regression classifiers trained on character and word n-grams, and hand-picked syntactic features. This ensemble is more robust than the individual single models. Our team named {``}Julian{''} achieves an F1-score of 60{\%} on both English datasets, 63{\%} on the Hindi Facebook dataset, and 38{\%} on the Hindi Twitter dataset.	['Julian Risch', 'Ralf Krestel']	2018-08-01	null	null	null	coling-2018-8	['aggression-identification']	['natural-language-processing']	[-2.83272296e-01 3.20110500e-01 -5.93113750e-02 -4.73459244e-01 -7.73846686e-01 -4.96777713e-01 5.03327250e-01 6.52441680e-01 -9.44583237e-01 9.81453598e-01 3.79143834e-01 -4.82755542e-01 8.21515322e-02 -7.46933520e-01 -2.17023298e-01 -4.33734834e-01 1.61098123e-01 4.17048931e-01 -1.50560737e-01 -5.30713558e-01 4.52568263e-01 3.47965181e-01 -1.37607300e+00 3.89691383e-01 8.24973702e-01 8.37667763e-01 -4.36755955e-01 5.37917733e-01 -1.87364131e-01 8.91087234e-01 -7.60165155e-01 -9.39250469e-01 -5.95722906e-02 -1.13956675e-01 -8.84176254e-01 -2.88398743e-01 7.17532992e-01 -5.26757300e-01 -2.24184811e-01 9.18423831e-01 6.21926844e-01 2.13862956e-01 6.34111762e-01 -1.12380314e+00 -5.29909253e-01 1.07084227e+00 -4.35526818e-01 4.53256905e-01 3.03152114e-01 9.77298766e-02 1.12936020e+00 -6.53876901e-01 6.52955651e-01 1.04160810e+00 8.08569014e-01 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e90e6f4a-4426-4549-9670-66c12fcc345b	unsupervised-3d-learning-for-shape-analysis	2008.01068	null	https://arxiv.org/abs/2008.01068v2	https://arxiv.org/pdf/2008.01068v2.pdf	Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination	Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic network for various shape analysis tasks with competitive performance to supervised methods. In this paper, we propose an unsupervised method for learning a generic and efficient shape encoding network for different shape analysis tasks. The key idea of our method is to jointly encode and learn shape and point features from unlabeled 3D point clouds. For this purpose, we adapt HR-Net to octree-based convolutional neural networks for jointly encoding shape and point features with fused multiresolution subnetworks and design a simple-yet-efficient Multiresolution Instance Discrimination (MID) loss for jointly learning the shape and point features. Our network takes a 3D point cloud as input and output both shape and point features. After training, the network is concatenated with simple task-specific back-end layers and fine-tuned for different shape analysis tasks. We evaluate the efficacy and generality of our method and validate our network and loss design with a set of shape analysis tasks, including shape classification, semantic shape segmentation, as well as shape registration tasks. With simple back-ends, our network demonstrates the best performance among all unsupervised methods and achieves competitive performance to supervised methods, especially in tasks with a small labeled dataset. For fine-grained shape segmentation, our method even surpasses existing supervised methods by a large margin.	['Qian-Fang Zou', 'Yu-Qi Yang', 'Peng-Shuai Wang', 'Yang Liu', 'Xin Tong', 'Zhirong Wu']	2020-08-03	null	null	null	null	['3d-point-cloud-linear-classification']	['computer-vision']	[-4.27666958e-03 -3.54979024e-03 -3.40912268e-02 -8.00182343e-01 -8.73647630e-01 -7.78825343e-01 4.71658826e-01 3.04694057e-01 -3.23955029e-01 1.32655680e-01 -1.78194761e-01 -1.33867621e-01 -2.48188078e-01 -9.53510165e-01 -6.84023023e-01 -5.31055093e-01 -1.59980189e-02 9.96573150e-01 3.37554753e-01 -3.64409131e-03 -7.01513563e-05 1.32392693e+00 -1.39659846e+00 1.88818559e-01 7.09612608e-01 1.25687838e+00 6.23325892e-02 3.50213945e-01 -4.42032337e-01 2.83540845e-01 -5.58715202e-02 -1.53349027e-01 4.56092775e-01 3.71360511e-01 -1.01480865e+00 3.37812990e-01 7.47052729e-01 -2.26198703e-01 -1.39172941e-01 6.67316854e-01 8.54790986e-01 1.49993584e-01 8.21833849e-01 -1.05897725e+00 -6.10771179e-01 2.18580127e-01 -4.63666648e-01 -1.04900800e-01 -1.49664432e-01 1.16842955e-01 8.95354688e-01 -1.14616275e+00 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a5b812ee-e992-466a-a0d4-1af1c06c717d	unsupervised-word-influencer-networks-from	null	null	https://aclanthology.org/W18-3109	https://aclanthology.org/W18-3109.pdf	Unsupervised Word Influencer Networks from News Streams	In this paper, we propose a new unsupervised learning framework to use news events for predicting trends in stock prices. We present Word Influencer Networks (WIN), a graph framework to extract longitudinal temporal relationships between any pair of informative words from news streams. Using the temporal occurrence of words, WIN measures how the appearance of one word in a news stream influences the emergence of another set of words in the future. The latent word-word influencer relationships in WIN are the building blocks for causal reasoning and predictive modeling. We demonstrate the efficacy of WIN by using it for unsupervised extraction of latent features for stock price prediction and obtain 2 orders lower prediction error compared to a similar causal graph based method. WIN discovered influencer links from seemingly unrelated words from topics like politics to finance. WIN also validated 67{\%} of the causal evidence found manually in the text through a direct edge and the rest 33{\%} through a path of length 2.	['an', 'Sun Chakraborty', 'Ananth Balashankar', 'Lakshminarayanan Subramanian']	2018-07-01	null	null	null	ws-2018-7	['relationship-extraction-distant-supervised', 'stock-price-prediction']	['natural-language-processing', 'time-series']	[-3.85041326e-01 3.50024968e-01 -7.49542713e-01 -1.65031314e-01 -8.98453686e-03 -5.78480542e-01 1.15074408e+00 5.48935592e-01 4.90034148e-02 8.63335073e-01 9.38091755e-01 -5.93695283e-01 -5.15445948e-01 -1.39910889e+00 -7.52446175e-01 -3.46807897e-01 -8.86311889e-01 3.33202213e-01 4.90547061e-01 -2.95348853e-01 3.47646117e-01 1.93289235e-01 -1.12029970e+00 2.40442380e-01 1.74691007e-01 4.96598691e-01 -2.65785009e-01 3.48941356e-01 -4.78268594e-01 1.37797379e+00 -3.16519737e-01 -6.87058449e-01 1.02710985e-01 -4.97780710e-01 -4.88760024e-01 -2.93473095e-01 -1.43425956e-01 -4.89541665e-02 -6.08598173e-01 8.74646902e-01 -1.42788008e-01 -1.39843985e-01 8.57378125e-01 -1.33318233e+00 -5.60064256e-01 1.69879961e+00 -6.30359530e-01 8.13882530e-01 3.58876348e-01 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be37f5d1-c65d-4fe3-866d-c26f111204a5	evm-cnn-real-time-contactless-heart-rate	2212.13843	null	https://arxiv.org/abs/2212.13843v1	https://arxiv.org/pdf/2212.13843v1.pdf	EVM-CNN: Real-Time Contactless Heart Rate Estimation from Facial Video	With the increase in health consciousness, noninvasive body monitoring has aroused interest among researchers. As one of the most important pieces of physiological information, researchers have remotely estimated the heart rate (HR) from facial videos in recent years. Although progress has been made over the past few years, there are still some limitations, like the processing time increasing with accuracy and the lack of comprehensive and challenging datasets for use and comparison. Recently, it was shown that HR information can be extracted from facial videos by spatial decomposition and temporal filtering. Inspired by this, a new framework is introduced in this paper to remotely estimate the HR under realistic conditions by combining spatial and temporal filtering and a convolutional neural network. Our proposed approach shows better performance compared with the benchmark on the MMSE-HR dataset in terms of both the average HR estimation and short-time HR estimation. High consistency in short-time HR estimation is observed between our method and the ground truth.	['Abdulmotaleb El Saddik', 'Haiwei Dong', 'Juan Arteaga-Falconi', 'Yang Liu', 'Ying Qiu']	2022-12-25	null	null	null	null	['heart-rate-estimation']	['medical']	[ 1.76968709e-01 -4.06415164e-02 2.06657276e-02 -4.53522831e-01 -2.47914955e-01 2.69879159e-02 2.52424300e-01 -6.45266175e-02 -5.01875579e-01 8.87449920e-01 1.77707836e-01 4.81602162e-01 -1.17060855e-01 -4.34825391e-01 -7.40715489e-02 -7.70922601e-01 -3.01906556e-01 -6.65703833e-01 -2.59840608e-01 6.71083108e-02 -5.35822846e-02 5.31950593e-01 -1.86071444e+00 -2.09801704e-01 8.92321050e-01 1.49183404e+00 -1.78133398e-01 3.34432513e-01 4.28843141e-01 5.43209136e-01 -5.10926545e-01 -9.65068266e-02 3.13420892e-01 -8.86047602e-01 -4.96660709e-01 -4.20303941e-02 4.57768589e-01 -3.93136352e-01 -6.08674586e-01 8.04189205e-01 1.02232897e+00 2.96165377e-01 4.39633615e-03 -1.15453398e+00 -8.87837335e-02 9.83937979e-02 -5.72198093e-01 2.87837595e-01 5.16037524e-01 9.59365815e-02 3.22136849e-01 -7.03021467e-01 3.34679037e-01 9.32671189e-01 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698b4ee7-deae-4e29-bb14-5c67c58b79e0	semi-supervised-meta-learning-for-cross	null	null	https://aclanthology.org/2021.metanlp-1.8	https://aclanthology.org/2021.metanlp-1.8.pdf	Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification	Meta learning aims to optimize the model’s capability to generalize to new tasks and domains. Lacking a data-efficient way to create meta training tasks has prevented the application of meta-learning to the real-world few shot learning scenarios. Recent studies have proposed unsupervised approaches to create meta-training tasks from unlabeled data for free, e.g., the SMLMT method (Bansal et al., 2020a) constructs unsupervised multi-class classification tasks from the unlabeled text by randomly masking words in the sentence and let the meta learner choose which word to fill in the blank. This study proposes a semi-supervised meta-learning approach that incorporates both the representation power of large pre-trained language models and the generalization capability of prototypical networks enhanced by SMLMT. The semi-supervised meta training approach avoids overfitting prototypical networks on a small number of labeled training examples and quickly learns cross-domain task-specific representation only from a few supporting examples. By incorporating SMLMT with prototypical networks, the meta learner generalizes better to unseen domains and gains higher accuracy on out-of-scope examples without the heavy lifting of pre-training. We observe significant improvement in few-shot generalization after training only a few epochs on the intent classification tasks evaluated in a multi-domain setting.	['Jiong Zhang', 'Yue Li']	null	null	null	null	acl-metanlp-2021-8	['cross-domain-few-shot']	['computer-vision']	[ 4.39352185e-01 3.50302637e-01 -5.32915294e-01 -5.83720148e-01 -7.10054874e-01 -2.75145561e-01 7.80120671e-01 1.48541734e-01 -7.61479080e-01 9.34908628e-01 1.21886924e-01 -2.88031757e-01 -1.14649303e-01 -7.99323976e-01 -5.92957020e-01 -3.00047040e-01 2.25517422e-01 6.73277438e-01 1.11277819e-01 -5.97689927e-01 -3.08954623e-03 -3.08851935e-02 -1.76463878e+00 8.65955532e-01 1.09492195e+00 6.12727344e-01 4.73345309e-01 4.23242003e-01 -4.74474996e-01 8.88176680e-01 -7.23464906e-01 -3.60583276e-01 1.63166851e-01 -5.13322592e-01 -7.68097162e-01 1.13906786e-01 5.50540566e-01 -1.30597979e-01 -1.92379922e-01 8.09734583e-01 4.54287589e-01 6.57931924e-01 9.11985636e-01 -9.96236742e-01 -7.97125101e-01 9.98387456e-01 -3.21419209e-01 4.45592403e-01 -1.33798659e-01 2.18629763e-01 6.86409473e-01 -1.03014064e+00 7.63891220e-01 1.01807988e+00 7.65612781e-01 1.11805558e+00 -1.23014617e+00 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5115de40-d8b6-44f1-8c09-080aa4b78443	combining-public-human-activity-recognition	2306.13735	null	https://arxiv.org/abs/2306.13735v1	https://arxiv.org/pdf/2306.13735v1.pdf	Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity	The use of supervised learning for Human Activity Recognition (HAR) on mobile devices leads to strong classification performances. Such an approach, however, requires large amounts of labeled data, both for the initial training of the models and for their customization on specific clients (whose data often differ greatly from the training data). This is actually impractical to obtain due to the costs, intrusiveness, and time-consuming nature of data annotation. Moreover, even with the help of a significant amount of labeled data, model deployment on heterogeneous clients faces difficulties in generalizing well on unseen data. Other domains, like Computer Vision or Natural Language Processing, have proposed the notion of pre-trained models, leveraging large corpora, to reduce the need for annotated data and better manage heterogeneity. This promising approach has not been implemented in the HAR domain so far because of the lack of public datasets of sufficient size. In this paper, we propose a novel strategy to combine publicly available datasets with the goal of learning a generalized HAR model that can be fine-tuned using a limited amount of labeled data on an unseen target domain. Our experimental evaluation, which includes experimenting with different state-of-the-art neural network architectures, shows that combining public datasets can significantly reduce the number of labeled samples required to achieve satisfactory performance on an unseen target domain.	['Claudio Bettini', 'Philippe Lalanda', 'François Portet', 'Gabriele Civitarese', 'Sannara Ek', 'Riccardo Presotto']	2023-06-23	null	null	null	null	['activity-recognition', 'human-activity-recognition', 'human-activity-recognition']	['computer-vision', 'computer-vision', 'time-series']	[ 1.27840176e-01 5.70328496e-02 -2.20719248e-01 -3.98959458e-01 -8.74939382e-01 -3.76175314e-01 3.81308019e-01 -3.98269035e-02 -4.97510254e-01 8.70887935e-01 -7.02824667e-02 -1.44747958e-01 -9.37830433e-02 -4.96255934e-01 -5.26091874e-01 -5.40673912e-01 2.56387860e-01 8.13363791e-01 4.09658015e-01 2.99657509e-02 -2.04951763e-01 5.51245332e-01 -1.72106516e+00 3.96648377e-01 5.86510301e-01 1.02382040e+00 3.08282763e-01 3.00179064e-01 -1.43848374e-01 8.57236624e-01 -7.67847180e-01 -2.54321605e-01 1.44134730e-01 -3.78463298e-01 -7.48337626e-01 4.13543373e-01 2.06220746e-01 -2.24398732e-01 1.96550056e-01 4.95746136e-01 6.07798398e-01 1.57774836e-01 4.12841856e-01 -1.19470716e+00 -3.49089235e-01 4.57194626e-01 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f1437fb7-308f-4e45-b003-e526ddca1215	robust-subspace-segmentation-with-block	null	null	http://openaccess.thecvf.com/content_cvpr_2014/html/Feng_Robust_Subspace_Segmentation_2014_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2014/papers/Feng_Robust_Subspace_Segmentation_2014_CVPR_paper.pdf	Robust Subspace Segmentation with Block-diagonal Prior	The subspace segmentation problem is addressed in this paper by effectively constructing an exactly block-diagonal sample affinity matrix. The block-diagonal structure is heavily desired for accurate sample clustering but is rather difficult to obtain. Most current state-of-the-art subspace segmentation methods (such as SSC and LRR) resort to alternative structural priors (such as sparseness and low-rankness) to construct the affinity matrix. In this work, we directly pursue the block-diagonal structure by proposing a graph Laplacian constraint based formulation, and then develop an efficient stochastic subgradient algorithm for optimization. Moreover, two new subspace segmentation methods, the block-diagonal SSC and LRR, are devised in this work. To the best of our knowledge, this is the first research attempt to explicitly pursue such a block-diagonal structure. Extensive experiments on face clustering, motion segmentation and graph construction for semi-supervised learning clearly demonstrate the superiority of our novelly proposed subspace segmentation methods.	['Zhouchen Lin', 'Jiashi Feng', 'Shuicheng Yan', 'Huan Xu']	2014-06-01	null	null	null	cvpr-2014-6	['face-clustering']	['computer-vision']	[ 3.65286827e-01 -2.06707001e-01 -4.78401542e-01 -1.71527505e-01 -7.04092801e-01 -5.56209505e-01 2.53538489e-01 -5.20339489e-01 -7.57840499e-02 4.44003075e-01 1.48171380e-01 -3.87583017e-01 -3.87096912e-01 -1.56332374e-01 -4.56810385e-01 -1.07202327e+00 1.83766410e-01 4.29291934e-01 -4.34270389e-02 2.18018219e-01 3.05790424e-01 5.61775982e-01 -1.24854743e+00 -3.01085502e-01 1.19742274e+00 4.23724681e-01 1.37914217e-03 3.04916769e-01 6.46272823e-02 2.93644547e-01 2.38919910e-02 -6.98705614e-02 2.34465599e-01 -6.62185729e-01 -6.92092240e-01 9.88050520e-01 4.56656307e-01 -1.31439790e-01 -2.66838074e-01 1.30579698e+00 3.85527760e-01 1.91300556e-01 7.96144307e-01 -1.12011874e+00 -4.86924499e-01 5.99765122e-01 -1.15258014e+00 -1.01737924e-01 3.07879180e-01 -9.96112823e-02 9.90380764e-01 -1.04990900e+00 7.63607085e-01 1.12299621e+00 3.77220541e-01 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e83c343e-f03f-4ffb-bd0b-0bd367a6d4f3	an-intelligent-non-invasive-real-time-human	2008.02567	null	https://arxiv.org/abs/2008.02567v1	https://arxiv.org/pdf/2008.02567v1.pdf	An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare	Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real time monitoring by deploying equipment on a person's body. However, putting devices on a person's body all the time make it uncomfortable and the elderly tends to forget it to wear as well in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals presents particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine learning model was able to achieve 96.70 % accuracy using the Random Forest algorithm using 10 fold cross validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90 %. The machine learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.	['Muhammad Ali Imran', 'Qammer H. Abbasi', 'Adnan Zahid', 'Kia Dashtipour', 'Syed Aziz Shah', 'William Taylor']	2020-08-06	null	null	null	null	['motion-detection']	['computer-vision']	[ 4.12195444e-01 -1.22926040e-02 -1.62042633e-01 -1.00571908e-01 -3.42968494e-01 -2.58459657e-01 -1.99600961e-02 4.16972471e-04 -3.94880861e-01 8.07409108e-01 2.53423452e-01 -2.00213611e-01 -3.46981257e-01 -8.01381052e-01 -1.18901595e-01 -8.35607886e-01 -3.26990962e-01 1.60281464e-01 4.42601472e-01 -2.88584054e-01 -3.29539984e-01 3.57639790e-01 -1.68278348e+00 1.57421112e-01 2.35368341e-01 7.35351324e-01 7.83456024e-03 8.47239554e-01 6.55750871e-01 5.84493101e-01 -7.72744715e-01 4.74787474e-01 1.68589860e-01 -4.51414675e-01 -4.79725033e-01 -1.63301438e-01 -8.75480622e-02 -2.75464684e-01 7.70782232e-02 3.92876118e-01 1.03909254e+00 7.54869170e-03 5.03930867e-01 -1.25931895e+00 2.56154388e-01 2.04914913e-01 -4.24966365e-01 5.34164369e-01 1.10667503e+00 -2.92689893e-02 9.09953192e-02 -1.04357444e-01 3.99529934e-01 6.64382994e-01 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ea20745a-f1b1-4b89-9c17-3d9d5674592a	generative-adversarial-nets-from-a-density	1610.0292	null	http://arxiv.org/abs/1610.02920v2	http://arxiv.org/pdf/1610.02920v2.pdf	Generative Adversarial Nets from a Density Ratio Estimation Perspective	Generative adversarial networks (GANs) are successful deep generative models. GANs are based on a two-player minimax game. However, the objective function derived in the original motivation is changed to obtain stronger gradients when learning the generator. We propose a novel algorithm that repeats the density ratio estimation and f-divergence minimization. Our algorithm offers a new perspective toward the understanding of GANs and is able to make use of multiple viewpoints obtained in the research of density ratio estimation, e.g. what divergence is stable and relative density ratio is useful.	['Kotaro Nakayama', 'Yutaka Matsuo', 'Masahiro Suzuki', 'Masatoshi Uehara', 'Issei Sato']	2016-10-10	null	null	null	null	['density-ratio-estimation']	['methodology']	[-1.44628629e-01 2.80520737e-01 -1.62823871e-01 -2.62206882e-01 -7.85881996e-01 -4.68605101e-01 6.96573198e-01 -6.11371338e-01 -1.90827399e-01 1.30720115e+00 1.26389295e-01 -2.44677700e-02 8.94836709e-02 -1.12819648e+00 -7.00054824e-01 -9.87230599e-01 3.02688867e-01 6.95616841e-01 -2.09145233e-01 -3.04655284e-01 8.57195482e-02 4.26290125e-01 -1.21331918e+00 -3.01418781e-01 9.63421822e-01 7.50856936e-01 6.42030537e-02 8.97587836e-01 -1.64478064e-01 1.02606666e+00 -8.17352951e-01 -7.30803907e-01 3.54499727e-01 -1.15300751e+00 -5.77450156e-01 -1.89058438e-01 1.13198809e-01 -6.17770612e-01 -1.61173135e-01 1.36066496e+00 5.11458814e-01 1.35344312e-01 1.19604218e+00 -1.60338593e+00 -9.34297502e-01 4.03486282e-01 -4.12841260e-01 2.32599393e-01 1.07070006e-01 1.34069368e-01 7.46234834e-01 -5.10097802e-01 4.31843847e-01 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7ed4e999-0833-41d2-8ea2-95b631ae16f2	combining-shallow-and-deep-representations	null	null	https://aclanthology.org/2021.alta-1.7	https://aclanthology.org/2021.alta-1.7.pdf	Combining Shallow and Deep Representations for Text-Pair Classification	Text-pair classification is the task of determining the class relationship between two sentences. It is embedded in several tasks such as paraphrase identification and duplicate question detection. Contemporary methods use fine-tuned transformer encoder semantic representations of the classification token in the text-pair sequence from the transformer’s final layer for class prediction. However, research has shown that earlier parts of the network learn shallow features, such as syntax and structure, which existing methods do not directly exploit. We propose a novel convolution-based decoder for transformer-based architecture that maximizes the use of encoder hidden features for text-pair classification. Our model exploits hidden representations within transformer-based architecture. It outperforms a transformer encoder baseline on average by 50% (relative F1-score) on six datasets from the medical, software engineering, and open-domains. Our work shows that transformer-based models can improve text-pair classification by modifying the fine-tuning step to exploit shallow features while improving model generalization, with only a slight reduction in efficiency.	['Zhenchang Xing', 'Sarvnaz Karimi', 'Vincent Nguyen']	null	null	null	null	alta-2021-12	['paraphrase-identification']	['natural-language-processing']	[ 5.58151066e-01 3.86847258e-01 -1.35632798e-01 -7.74543405e-01 -1.12377524e+00 -5.15743196e-01 2.91074842e-01 3.83035690e-01 -2.94712842e-01 3.43877524e-01 4.20097739e-01 -5.83397388e-01 2.08440855e-01 -7.47092426e-01 -8.19240510e-01 1.55788586e-02 3.10436368e-01 3.40076149e-01 2.66730785e-01 -1.63292900e-01 4.10382956e-01 -2.50637740e-01 -1.43458104e+00 1.30133653e+00 7.05786169e-01 1.09887886e+00 3.23127657e-01 8.26887548e-01 -3.64714235e-01 1.12365603e+00 -4.98093098e-01 -7.93294072e-01 2.36053672e-02 -6.10219002e-01 -1.38722825e+00 -4.52564985e-01 6.12062693e-01 -5.01625121e-01 -4.63234097e-01 7.95059383e-01 4.15132076e-01 -4.15685326e-01 3.99853647e-01 -1.09151936e+00 -8.62779379e-01 8.22824538e-01 -8.99795815e-02 3.94914448e-01 4.68441457e-01 -2.65402466e-01 1.55054772e+00 -8.96933019e-01 4.61505026e-01 1.16950679e+00 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6e4e2223-ffaa-4fb6-a65e-6369740a0695	graph-neural-network-for-video-query-based	2007.09877	null	https://arxiv.org/abs/2007.09877v2	https://arxiv.org/pdf/2007.09877v2.pdf	Graph Neural Network for Video Relocalization	In this paper, we focus on video relocalization task, which uses a query video clip as input to retrieve a semantic relative video clip in another untrimmed long video. we find that in video relocalization datasets, there exists a phenomenon showing that there does not exist consistent relationship between feature similarity by frame and feature similarity by video, which affects the feature fusion among frames. However, existing video relocalization methods do not fully consider it. Taking this phenomenon into account, in this article, we treat video features as a graph by concatenating the query video feature and proposal video feature along time dimension, where each timestep is treated as a node, each row of the feature matrix is treated as feature of each node. Then, with the power of graph neural networks, we propose a Multi-Graph Feature Fusion Module to fuse the relation feature of this graph. After evaluating our method on ActivityNet v1.2 dataset and Thumos14 dataset, we find that our proposed method outperforms the state of art methods.	['Yuan Zhou', 'Mingfei Wang', 'Ruolin Wang', 'Shuwei Huo']	2020-07-20	null	null	null	null	['moment-retrieval']	['computer-vision']	[ 4.50539067e-02 -4.82650608e-01 -3.67835641e-01 -2.34815016e-01 -2.67676324e-01 -3.76460940e-01 4.43892717e-01 2.07701176e-01 -4.96160179e-01 5.28095245e-01 5.11244714e-01 1.51648447e-01 -1.89839303e-01 -4.95879680e-01 -7.44076431e-01 -6.90907896e-01 7.01681301e-02 -2.60819465e-01 6.20788932e-01 5.05656824e-02 3.35243613e-01 2.47520506e-01 -1.32532179e+00 2.71655142e-01 3.03661972e-01 1.04879868e+00 1.95604548e-01 4.04816628e-01 -4.45577409e-03 1.01454437e+00 -5.89696705e-01 -3.96770276e-02 2.26641074e-01 -5.64176261e-01 -8.43212008e-01 1.66551322e-01 6.38318360e-01 -4.28592086e-01 -8.49818468e-01 1.33188212e+00 4.13568705e-01 4.95082408e-01 5.12874901e-01 -1.59351146e+00 -7.56911993e-01 7.89853334e-01 -7.31796920e-01 8.00038636e-01 7.47671008e-01 -1.58819944e-01 9.36770618e-01 -7.55447924e-01 1.08246326e+00 1.21390700e+00 3.85477483e-01 2.54236981e-02 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-7.97477603e-01 8.68156254e-01 2.75511980e-01 -8.60813797e-01 -3.42833012e-01 4.16430026e-01]	[9.829684257507324, 0.7595773339271545]
6f1e5f54-905e-4b0b-82d0-94244933748d	guess-where-actor-supervision-for	1804.01824	null	http://arxiv.org/abs/1804.01824v1	http://arxiv.org/pdf/1804.01824v1.pdf	Guess Where? Actor-Supervision for Spatiotemporal Action Localization	This paper addresses the problem of spatiotemporal localization of actions in videos. Compared to leading approaches, which all learn to localize based on carefully annotated boxes on training video frames, we adhere to a weakly-supervised solution that only requires a video class label. We introduce an actor-supervised architecture that exploits the inherent compositionality of actions in terms of actor transformations, to localize actions. We make two contributions. First, we propose actor proposals derived from a detector for human and non-human actors intended for images, which is linked over time by Siamese similarity matching to account for actor deformations. Second, we propose an actor-based attention mechanism that enables the localization of the actions from action class labels and actor proposals and is end-to-end trainable. Experiments on three human and non-human action datasets show actor supervision is state-of-the-art for weakly-supervised action localization and is even competitive to some fully-supervised alternatives.	['Victor Escorcia', 'Cuong D. Dao', 'Cees Snoek', 'Mihir Jain', 'Bernard Ghanem']	2018-04-05	null	null	null	null	['weakly-supervised-action-localization']	['computer-vision']	[ 1.49958283e-01 4.42047030e-01 -4.08405662e-01 -4.22841042e-01 -7.55065262e-01 -4.65859830e-01 9.05020595e-01 -1.60428405e-01 -5.55268943e-01 3.59800190e-01 5.58181345e-01 4.24313664e-01 2.04995289e-01 -1.11921299e-02 -1.00871181e+00 -5.96979260e-01 -3.94924521e-01 5.18303633e-01 6.17550194e-01 1.17315151e-01 6.39745295e-02 3.78268927e-01 -1.20969176e+00 6.10095322e-01 3.20863783e-01 9.01487470e-01 -6.29849285e-02 6.53514028e-01 3.36247146e-01 1.50862253e+00 -5.40683456e-02 -3.35905045e-01 4.93824273e-01 -8.09502840e-01 -1.09839296e+00 5.17458916e-01 7.82787263e-01 -4.86041099e-01 -5.93331099e-01 8.55544806e-01 5.79669848e-02 2.33556613e-01 6.47084296e-01 -1.38550806e+00 -5.63886523e-01 5.46378314e-01 -4.20281380e-01 2.48901412e-01 6.10746205e-01 3.26160789e-01 1.11417735e+00 -1.00973499e+00 1.02840185e+00 1.36780369e+00 6.78264081e-01 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01b3ed21-6cff-43e9-a63d-f06331bf488b	divgan-towards-diverse-paraphrase-generation	null	null	https://aclanthology.org/2020.findings-emnlp.218	https://aclanthology.org/2020.findings-emnlp.218.pdf	DivGAN: Towards Diverse Paraphrase Generation via Diversified Generative Adversarial Network	Paraphrases refer to texts that convey the same meaning with different expression forms. Traditional seq2seq-based models on paraphrase generation mainly focus on the fidelity while ignoring the diversity of outputs. In this paper, we propose a deep generative model to generate diverse paraphrases. We build our model based on the conditional generative adversarial network, and propose to incorporate a simple yet effective diversity loss term into the model in order to improve the diversity of outputs. The proposed diversity loss maximizes the ratio of pairwise distance between the generated texts and their corresponding latent codes, forcing the generator to focus more on the latent codes and produce diverse samples. Experimental results on benchmarks of paraphrase generation show that our proposed model can generate more diverse paraphrases compared with baselines.	['Xiaojun Wan', 'Yue Cao']	2020-11-01	null	null	null	findings-of-the-association-for-computational	['paraphrase-generation', 'paraphrase-generation']	['computer-code', 'natural-language-processing']	[ 2.84119487e-01 7.48779103e-02 -9.81456265e-02 -3.87725383e-01 -8.76644731e-01 -7.89812505e-01 5.86905360e-01 -2.83315897e-01 5.24465479e-02 8.87836277e-01 8.54704857e-01 -1.67851578e-02 3.75420153e-01 -8.93705904e-01 -8.63438427e-01 -4.93380129e-01 6.60525143e-01 1.86660454e-01 -3.32935959e-01 -3.48360449e-01 4.09845948e-01 1.07765503e-01 -1.07115412e+00 7.77782679e-01 1.27813172e+00 2.83856213e-01 1.57625467e-01 6.11626744e-01 -2.41953880e-01 7.77125657e-01 -1.14370263e+00 -7.24031091e-01 2.85190046e-01 -1.20486808e+00 -6.24743640e-01 -2.38255009e-01 3.29172611e-01 -3.66372168e-01 -5.76336145e-01 1.07059574e+00 6.97038233e-01 1.10157952e-01 7.81461060e-01 -1.12532771e+00 -1.20757759e+00 9.05115962e-01 -4.10576701e-01 1.06791325e-01 7.90894151e-01 2.58058429e-01 1.13806605e+00 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67a9b2df-d564-4fd6-9e79-7c5e658a543d	sparsistent-model-discovery	2106.11936	null	https://arxiv.org/abs/2106.11936v2	https://arxiv.org/pdf/2106.11936v2.pdf	Sparsistent Model Discovery	Discovering the partial differential equations underlying spatio-temporal datasets from very limited and highly noisy observations is of paramount interest in many scientific fields. However, it remains an open question to know when model discovery algorithms based on sparse regression can actually recover the underlying physical processes. In this work, we show the design matrices used to infer the equations by sparse regression can violate the irrepresentability condition (IRC) of the Lasso, even when derived from analytical PDE solutions (i.e. without additional noise). Sparse regression techniques which can recover the true underlying model under violated IRC conditions are therefore required, leading to the introduction of the randomised adaptive Lasso. We show once the latter is integrated within the deep learning model discovery framework DeepMod, a wide variety of nonlinear and chaotic canonical PDEs can be recovered: (1) up to $\mathcal{O}(2)$ higher noise-to-sample ratios than state-of-the-art algorithms, (2) with a single set of hyperparameters, which paves the road towards truly automated model discovery.	['Remy Kusters', 'Gert-Jan Both', 'Georges Tod']	2021-06-22	sparsistent-model-discovery-1	https://openreview.net/forum?id=WNTscnQd1s	https://openreview.net/pdf?id=WNTscnQd1s	null	['model-discovery']	['miscellaneous']	[ 1.78759217e-01 -1.98402748e-01 3.60230297e-01 1.61916539e-01 -7.65906274e-01 -5.54010093e-01 5.60635507e-01 -5.06998189e-02 1.91008560e-02 9.80987549e-01 -1.00390971e-01 -2.18882263e-01 -6.59612358e-01 -5.05385756e-01 -6.82340026e-01 -1.29264772e+00 -3.11325282e-01 7.73074508e-01 -2.33547434e-01 3.28760631e-02 1.57932401e-01 7.54428923e-01 -1.36936235e+00 -2.18496099e-01 8.19880009e-01 1.08024752e+00 -1.46035478e-02 5.13352275e-01 2.39976749e-01 7.92401552e-01 4.77124639e-02 1.76788494e-01 3.69463146e-01 -5.37955701e-01 -4.03239965e-01 -6.91153556e-02 1.12702362e-01 -1.02695614e-01 -5.12663901e-01 8.61317456e-01 4.38512176e-01 3.38932514e-01 9.11630988e-01 -8.01281750e-01 -4.54382569e-01 1.00733407e-01 -4.25673604e-01 2.38853544e-01 -1.53038755e-01 2.31193155e-01 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6f3c5525-2511-49ab-bb8f-7e54e833b898	in-domain-self-supervised-learning-can-lead	2307.01645	null	https://arxiv.org/abs/2307.01645v1	https://arxiv.org/pdf/2307.01645v1.pdf	In-Domain Self-Supervised Learning Can Lead to Improvements in Remote Sensing Image Classification	Self-supervised learning (SSL) has emerged as a promising approach for remote sensing image classification due to its ability to leverage large amounts of unlabeled data. In contrast to traditional supervised learning, SSL aims to learn representations of data without the need for explicit labels. This is achieved by formulating auxiliary tasks that can be used to create pseudo-labels for the unlabeled data and learn pre-trained models. The pre-trained models can then be fine-tuned on downstream tasks such as remote sensing image scene classification. The paper analyzes the effectiveness of SSL pre-training using Million AID - a large unlabeled remote sensing dataset on various remote sensing image scene classification datasets as downstream tasks. More specifically, we evaluate the effectiveness of SSL pre-training using the iBOT framework coupled with Vision transformers (ViT) in contrast to supervised pre-training of ViT using the ImageNet dataset. The comprehensive experimental work across 14 datasets with diverse properties reveals that in-domain SSL leads to improved predictive performance of models compared to the supervised counterparts.	['Dragi Kocev', 'Nikola Simidjievski', 'Ivan Kitanovski', 'Ivica Dimitrovski']	2023-07-04	null	null	null	null	['self-supervised-learning', 'scene-classification', 'remote-sensing-image-classification']	['computer-vision', 'computer-vision', 'miscellaneous']	[ 6.67931199e-01 9.79174823e-02 -3.54498833e-01 -7.50199080e-01 -5.83125532e-01 -6.42900467e-01 7.51163781e-01 -3.13695855e-02 -3.89770180e-01 7.30641305e-01 -3.68985301e-03 -6.06857061e-01 -1.30215079e-01 -8.86806130e-01 -6.42635465e-01 -6.42329872e-01 -1.11773377e-02 4.90486979e-01 -4.28901874e-02 -3.89807224e-02 1.17512709e-02 5.96314490e-01 -1.72291946e+00 2.17529222e-01 8.76624942e-01 8.80270720e-01 3.90478343e-01 4.13815349e-01 -1.61584452e-01 1.24575102e+00 -1.16849869e-01 2.57141709e-01 5.68576097e-01 -2.54801005e-01 -8.35779607e-01 4.73326236e-01 4.62232172e-01 -3.07914466e-01 -2.27697901e-02 8.77239347e-01 2.99168646e-01 1.07272916e-01 8.02091956e-01 -1.01579130e+00 -6.73031569e-01 7.27695882e-01 -3.17456245e-01 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f4454001-2192-4a7b-8972-02b8ccbe6b8b	road-planning-for-slums-via-deep	2305.1306	null	https://arxiv.org/abs/2305.13060v3	https://arxiv.org/pdf/2305.13060v3.pdf	Road Planning for Slums via Deep Reinforcement Learning	Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate road infrastructure within slums, and road planning for slums is critical to the sustainable development of cities. Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs. In this paper, we present a deep reinforcement learning based approach to automatically layout roads for slums. We propose a generic graph model to capture the topological structure of a slum, and devise a novel graph neural network to select locations for the planned roads. Through masked policy optimization, our model can generate road plans that connect places in a slum at minimal construction costs. Extensive experiments on real-world slums in different countries verify the effectiveness of our model, which can significantly improve accessibility by 14.3% against existing baseline methods. Further investigations on transferring across different tasks demonstrate that our model can master road planning skills in simple scenarios and adapt them to much more complicated ones, indicating the potential of applying our model in real-world slum upgrading. The code and data are available at https://github.com/tsinghua-fib-lab/road-planning-for-slums.	['Yong Li', 'Depeng Jin', 'Jingtao Ding', 'Hongyuan Su', 'Yu Zheng']	2023-05-22	null	null	null	null	['blocking']	['natural-language-processing']	[-9.81178805e-02 3.64866614e-01 -3.42845798e-01 -1.52672395e-01 -3.94031167e-01 -2.76016116e-01 4.66849357e-01 1.05587542e-01 -1.07138596e-01 9.60527480e-01 4.53273594e-01 -9.87263381e-01 -1.45762235e-01 -1.64886224e+00 -6.23471677e-01 -3.20803642e-01 -1.95278630e-01 5.86329758e-01 2.22921327e-01 -8.23994577e-01 1.85208306e-01 7.07861900e-01 -1.32525516e+00 -4.16031927e-01 1.49565005e+00 1.28414378e-01 5.75386345e-01 4.79356229e-01 -9.42539498e-02 2.32893988e-01 -1.52109504e-01 -1.28825232e-01 4.46482748e-01 -7.57920370e-03 -8.62433195e-01 8.89965426e-03 6.48248076e-01 -5.79680562e-01 -7.81250000e-01 7.22638190e-01 6.66869879e-01 2.49802187e-01 5.91804743e-01 -1.22529769e+00 -8.08316410e-01 7.98805714e-01 -3.81633043e-01 3.06919441e-02 3.61294746e-01 2.08810627e-01 1.11434591e+00 -5.30244470e-01 3.55904400e-01 1.19473076e+00 6.72451079e-01 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e2cc4094-d085-43ba-b902-4a237c068292	trecvid-2019-an-evaluation-campaign-to	2009.09984	null	https://arxiv.org/abs/2009.09984v1	https://arxiv.org/pdf/2009.09984v1.pdf	TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval	The TREC Video Retrieval Evaluation (TRECVID) 2019 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in research and development of content-based exploitation and retrieval of information from digital video via open, metrics-based evaluation. Over the last nineteen years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID has been funded by NIST (National Institute of Standards and Technology) and other US government agencies. In addition, many organizations and individuals worldwide contribute significant time and effort. TRECVID 2019 represented a continuation of four tasks from TRECVID 2018. In total, 27 teams from various research organizations worldwide completed one or more of the following four tasks: 1. Ad-hoc Video Search (AVS) 2. Instance Search (INS) 3. Activities in Extended Video (ActEV) 4. Video to Text Description (VTT) This paper is an introduction to the evaluation framework, tasks, data, and measures used in the workshop.	['Lukas Diduch', 'Keith Curtis', 'Jesse Zhang', 'Asad A. Butt', 'Andrew Delgado', 'Wessel Kraaij', 'Afzal Godil', 'Yooyoung Lee', 'George Awad', 'Eliot Godard', 'Yvette Graham', 'Jonathan Fiscus', 'Georges Quenot', 'Alan F. Smeaton']	2020-09-21	null	null	null	null	['instance-search', 'ad-hoc-video-search']	['computer-vision', 'computer-vision']	[ 2.39875048e-01 -7.84159064e-01 -1.42734334e-01 -3.61434191e-01 -1.64870095e+00 -9.96105790e-01 9.12525833e-01 5.18966794e-01 -9.82486010e-01 3.67331117e-01 4.79906201e-01 1.09346069e-01 -1.38503641e-01 -9.06991065e-02 -2.65230715e-01 -2.91968495e-01 -1.63017854e-01 2.27206782e-01 5.87519050e-01 -6.14844486e-02 8.88450682e-01 1.72414616e-01 -2.02564645e+00 6.49816394e-01 2.67048478e-01 1.24485183e+00 -7.10801333e-02 1.11162007e+00 1.16235204e-01 7.58893073e-01 -4.15399015e-01 -5.61219871e-01 9.48914886e-02 -2.11061507e-01 -1.19778204e+00 -2.29049474e-01 8.54570329e-01 -4.33452934e-01 -2.52488345e-01 8.80041778e-01 6.65165961e-01 4.14761931e-01 7.87051260e-01 -1.12699568e+00 -4.02255505e-01 -4.85453084e-02 -4.65300888e-01 6.46342158e-01 1.08641517e+00 -1.00902347e-02 1.12373424e+00 -9.56050158e-01 9.45876479e-01 1.03200948e+00 3.58715206e-01 1.59747943e-01 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-5.84286928e-01 4.48273182e-01 -5.11374846e-02 -6.49476886e-01 -3.15254033e-01 3.00675988e-01]	[10.471197128295898, 0.6665819883346558]
704e135c-c5f6-4624-a70b-6bff16ffce60	jnr-joint-based-neural-rig-representation-for	2007.06755	null	https://arxiv.org/abs/2007.06755v3	https://arxiv.org/pdf/2007.06755v3.pdf	JNR: Joint-based Neural Rig Representation for Compact 3D Face Modeling	In this paper, we introduce a novel approach to learn a 3D face model using a joint-based face rig and a neural skinning network. Thanks to the joint-based representation, our model enjoys some significant advantages over prior blendshape-based models. First, it is very compact such that we are orders of magnitude smaller while still keeping strong modeling capacity. Second, because each joint has its semantic meaning, interactive facial geometry editing is made easier and more intuitive. Third, through skinning, our model supports adding mouth interior and eyes, as well as accessories (hair, eye glasses, etc.) in a simpler, more accurate and principled way. We argue that because the human face is highly structured and topologically consistent, it does not need to be learned entirely from data. Instead we can leverage prior knowledge in the form of a human-designed 3D face rig to reduce the data dependency, and learn a compact yet strong face model from only a small dataset (less than one hundred 3D scans). To further improve the modeling capacity, we train a skinning weight generator through adversarial learning. Experiments on fitting high-quality 3D scans (both neutral and expressive), noisy depth images, and RGB images demonstrate that its modeling capacity is on-par with state-of-the-art face models, such as FLAME and Facewarehouse, even though the model is 10 to 20 times smaller. This suggests broad value in both graphics and vision applications on mobile and edge devices.	['HsiangTao Wu', 'Mitch Rundle', 'Noranart Vesdapunt', 'Baoyuan Wang']	2020-07-14	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2989_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123630375.pdf	eccv-2020-8	['3d-face-modeling']	['computer-vision']	[-3.69218038e-03 7.43370771e-01 1.16244763e-01 -2.73275673e-01 -4.89927173e-01 -6.44699454e-01 4.85476702e-01 -7.33513653e-01 2.98071429e-02 3.83287877e-01 1.33491354e-02 -1.75687388e-01 2.92750925e-01 -6.90621912e-01 -8.66888881e-01 -2.96274692e-01 1.89034715e-01 5.02370059e-01 3.29101309e-02 -2.45203465e-01 -1.87441990e-01 1.14236927e+00 -1.63986051e+00 -1.36165664e-01 4.95436490e-01 1.20353878e+00 -4.48883146e-01 3.86293948e-01 8.86859279e-03 1.85012847e-01 -2.14663178e-01 -6.87850296e-01 6.92416489e-01 -1.80433586e-01 -3.98427933e-01 3.63518834e-01 9.47395563e-01 -7.34715581e-01 -2.14811146e-01 6.46037996e-01 4.63175327e-01 -1.78058907e-01 6.01854742e-01 -1.28355408e+00 -3.70825678e-01 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2be97b01-6d41-4573-afae-22024defd908	equipocket-an-e-3-equivariant-geometric-graph	2302.12177	null	https://arxiv.org/abs/2302.12177v1	https://arxiv.org/pdf/2302.12177v1.pdf	EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction	Predicting the binding sites of the target proteins plays a fundamental role in drug discovery. Most existing deep-learning methods consider a protein as a 3D image by spatially clustering its atoms into voxels and then feed the voxelized protein into a 3D CNN for prediction. However, the CNN-based methods encounter several critical issues: 1) defective in representing irregular protein structures; 2) sensitive to rotations; 3) insufficient to characterize the protein surface; 4) unaware of data distribution shift. To address the above issues, this work proposes EquiPocket, an E(3)-equivariant Graph Neural Network (GNN) for binding site prediction. In particular, EquiPocket consists of three modules: the first one to extract local geometric information for each surface atom, the second one to model both the chemical and spatial structure of the protein, and the last one to capture the geometry of the surface via equivariant message passing over the surface atoms. We further propose a dense attention output layer to better alleviate the data distribution shift effect incurred by the variable protein size. Extensive experiments on several representative benchmarks demonstrate the superiority of our framework to the state-of-the-art methods.	['Zhaohan Ding', 'Ye Yuan', 'Zhewei Wei', 'Wenbing Huang', 'Yang Zhang']	2023-02-23	null	null	null	null	['drug-discovery']	['medical']	[ 1.10450082e-01 1.20079324e-01 -5.93789369e-02 -2.52092123e-01 -4.27203566e-01 -2.50749797e-01 1.87144950e-01 2.51118451e-01 -1.35933444e-01 5.73657751e-01 6.77968562e-02 -3.68928432e-01 1.57973289e-01 -7.34227359e-01 -1.26055801e+00 -1.05001426e+00 -6.66393116e-02 7.92034268e-01 3.54760975e-01 -1.20110735e-01 2.34756082e-01 8.75990510e-01 -9.05777514e-01 2.84884453e-01 8.65620673e-01 9.73306417e-01 3.50041717e-01 5.09598255e-02 -3.28313440e-01 5.05906343e-01 -1.47328198e-01 -9.81489755e-03 1.27230614e-01 -2.67495304e-01 -7.71125019e-01 -2.17644200e-02 2.85579175e-01 -1.14223763e-01 -3.23303193e-01 1.17151451e+00 4.34768111e-01 5.05120829e-02 9.34208393e-01 -6.23695731e-01 -9.12466824e-01 2.08224773e-01 -9.12814140e-01 2.93192156e-02 3.97272147e-02 3.14328820e-01 8.39680254e-01 -1.11804020e+00 8.15042615e-01 1.33750594e+00 6.13638520e-01 3.66437733e-01 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b59502b2-d107-4dc0-a2e8-db708afb9fbc	dtw-k-means-clustering-for-fault-detection-in	2306.08003	null	https://arxiv.org/abs/2306.08003v1	https://arxiv.org/pdf/2306.08003v1.pdf	DTW k-means clustering for fault detection in photovoltaic modules	The increase in the use of photovoltaic (PV) energy in the world has shown that the useful life and maintenance of a PV plant directly depend on theability to quickly detect severe faults on a PV plant. To solve this problem of detection, data based approaches have been proposed in the literature.However, these previous solutions consider only specific behavior of one or few faults. Most of these approaches can be qualified as supervised, requiring an enormous labelling effort (fault types clearly identified in each technology). In addition, most of them are validated in PV cells or one PV module. That is hardly applicable in large-scale PV plants considering their complexity. Alternatively, some unsupervised well-known approaches based on data try to detect anomalies but are not able to identify precisely the type of fault. The most performant of these methods do manage to efficiently group healthy panels and separate them from faulty panels. In that way, this article presents an unsupervised approach called DTW K-means. This approach takes advantages of both the dynamic time warping (DWT) metric and the Kmeans clustering algorithm as a data-driven approach. The results of this mixed method in a PV string are compared to diagnostic labels established by visual inspection of the panels.	['Corinne Alonso', 'Marko Pavlov', 'Audine Subias', 'Louise Travé-Massuyès', 'Edgar Hernando Sepúlveda Oviedo']	2023-06-13	null	null	null	null	['clustering', 'fault-detection', 'dynamic-time-warping']	['methodology', 'miscellaneous', 'time-series']	[ 1.61280125e-01 -8.53162557e-02 1.89933032e-01 5.80486879e-02 -2.88971961e-01 -1.03375387e+00 5.75324774e-01 5.68342030e-01 2.58883506e-01 7.53339589e-01 -3.62795055e-01 -1.60249338e-01 -6.75369143e-01 -9.38410103e-01 -1.15244046e-01 -1.19762278e+00 -9.11004767e-02 5.02446711e-01 5.46212077e-01 -1.19071975e-02 4.21623915e-01 9.39687133e-01 -1.88486624e+00 1.15007326e-01 1.35006642e+00 9.22981024e-01 5.64422667e-01 5.60657024e-01 -1.94981545e-01 3.67886573e-01 -9.67960656e-01 4.01887178e-01 2.81043574e-02 -6.72214329e-01 -5.28834820e-01 2.81422168e-01 2.39349045e-02 1.18668631e-01 4.07394528e-01 1.04624641e+00 3.85228604e-01 -3.13539654e-01 8.87542129e-01 -1.45363486e+00 -8.04968178e-02 2.46215880e-01 -3.06639224e-01 2.14274496e-01 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4.98455554e-01 9.29912329e-01 9.31237876e-01 3.94251719e-02 7.69813776e-01 1.03050435e+00 -8.26125264e-01 -2.02192232e-01 -1.07818878e+00 -9.84584630e-01 3.60300213e-01 3.87332812e-02 -7.40367770e-01 -5.49262166e-01 1.61965564e-01]	[6.886842250823975, 2.252988576889038]
5d62480d-be15-4c73-924e-ec343be0785e	point-4d-transformer-networks-for-spatio	null	null	http://openaccess.thecvf.com//content/CVPR2021/html/Fan_Point_4D_Transformer_Networks_for_Spatio-Temporal_Modeling_in_Point_Cloud_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Fan_Point_4D_Transformer_Networks_for_Spatio-Temporal_Modeling_in_Point_Cloud_CVPR_2021_paper.pdf	Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos	Point cloud videos exhibit irregularities and lack of order along the spatial dimension where points emerge inconsistently across different frames. To capture the dynamics in point cloud videos, point tracking is usually employed. However, as points may flow in and out across frames, computing accurate point trajectories is extremely difficult. Moreover, tracking usually relies on point colors and thus may fail to handle colorless point clouds. In this paper, to avoid point tracking, we propose a novel Point 4D Transformer (P4Transformer) network to model raw point cloud videos. Specifically, P4Transformer consists of (i) a point 4D convolution to embed the spatio-temporal local structures presented in a point cloud video and (ii) a transformer to capture the appearance and motion information across the entire video by performing self-attention on the embedded local features. In this fashion, related or similar local areas are merged with attention weight rather than by explicit tracking. Extensive experiments, including 3D action recognition and 4D semantic segmentation, on four benchmarks demonstrate the effectiveness of our P4Transformer for point cloud video modeling.	['Mohan Kankanhalli', 'Yi Yang', 'Hehe Fan']	2021-06-19	null	null	null	cvpr-2021-1	['3d-human-action-recognition']	['computer-vision']	[-1.41106740e-01 -5.49220324e-01 6.61548749e-02 -1.02368630e-01 -3.06105465e-01 -7.11519122e-01 4.97306734e-01 1.17010228e-01 -7.30714276e-02 5.60889654e-02 -4.05433744e-01 -1.51610643e-01 2.03235313e-01 -6.12341940e-01 -1.22145212e+00 -5.63955903e-01 -1.02855667e-01 3.16552520e-01 5.28602898e-01 7.83077478e-02 2.60279417e-01 1.06643128e+00 -1.23136568e+00 1.82671204e-01 8.31348836e-01 1.20216131e+00 3.29418004e-01 4.38222677e-01 -6.29699767e-01 6.20324075e-01 -4.85267490e-01 -2.26562291e-01 4.92988914e-01 6.42991289e-02 -5.76152444e-01 6.88961983e-01 7.60748208e-01 -5.32096386e-01 -6.02443933e-01 1.14323866e+00 -2.60500401e-01 3.59443694e-01 5.18915176e-01 -1.44780815e+00 -8.05503130e-01 1.73009280e-02 -1.09176278e+00 3.35568488e-01 2.80148178e-01 5.17666638e-01 6.54820621e-01 -1.08994365e+00 5.61450183e-01 1.48482001e+00 6.76257312e-01 4.52743024e-01 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a046d8fa-8130-4c1b-8de7-4706755027bc	prediction-of-properties-of-metal-alloy	2109.09394	null	https://arxiv.org/abs/2109.09394v1	https://arxiv.org/pdf/2109.09394v1.pdf	Prediction of properties of metal alloy materials based on machine learning	Density functional theory and its optimization algorithm are the main methods to calculate the properties in the field of materials. Although the calculation results are accurate, it costs a lot of time and money. In order to alleviate this problem, we intend to use machine learning to predict material properties. In this paper, we conduct experiments on atomic volume, atomic energy and atomic formation energy of metal alloys, using the open quantum material database. Through the traditional machine learning models, deep learning network and automated machine learning, we verify the feasibility of machine learning in material property prediction. The experimental results show that the machine learning can predict the material properties accurately.	['Jie Hu', 'Yan Yang', 'Yongquan Jiang', 'Houchen Zuo']	2021-09-20	null	null	null	null	['formation-energy']	['miscellaneous']	[-4.99365330e-01 -3.21259975e-01 -2.95248628e-01 -3.10275525e-01 -4.05393660e-01 4.75457191e-01 2.90203486e-02 -4.93174717e-02 -2.36796439e-01 1.12488663e+00 -1.33079752e-01 -7.25972801e-02 -5.61293736e-02 -1.34950054e+00 -4.40644652e-01 -1.01748908e+00 1.56916767e-01 5.75594664e-01 1.67308092e-01 -3.65571529e-01 6.09524667e-01 4.58361298e-01 -1.54003406e+00 3.28682065e-01 9.75068927e-01 1.51337636e+00 1.46903694e-01 -4.62703668e-02 -5.38851731e-02 9.58634377e-01 -7.88208395e-02 -1.10711388e-01 1.54589802e-01 -1.51911601e-01 -9.48177636e-01 -3.06819975e-01 -5.40465832e-01 -4.75354224e-01 -5.23541451e-01 1.06292939e+00 4.88418519e-01 1.49068624e-01 1.31705117e+00 -1.16806734e+00 -1.16292858e+00 7.64680028e-01 -2.70334184e-01 -2.21706986e-01 2.82997817e-01 1.06890805e-01 9.95042026e-01 -7.86479592e-01 1.29324779e-01 8.73228550e-01 6.59588099e-01 5.36523938e-01 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-1.13119912e+00 2.79839575e-01 -2.22159043e-01 -9.74396050e-01 7.21110329e-02 -3.95180643e-01]	[5.29987096786499, 5.423282623291016]
3df1a8d1-721f-4686-90d9-e8d055ba705c	deep-ensembling-for-perceptual-image-quality	2305.09141	null	https://arxiv.org/abs/2305.09141v1	https://arxiv.org/pdf/2305.09141v1.pdf	Deep Ensembling for Perceptual Image Quality Assessment	Blind image quality assessment is a challenging task particularly due to the unavailability of reference information. Training a deep neural network requires a large amount of training data which is not readily available for image quality. Transfer learning is usually opted to overcome this limitation and different deep architectures are used for this purpose as they learn features differently. After extensive experiments, we have designed a deep architecture containing two CNN architectures as its sub-units. Moreover, a self-collected image database BIQ2021 is proposed with 12,000 images having natural distortions. The self-collected database is subjectively scored and is used for model training and validation. It is demonstrated that synthetic distortion databases cannot provide generalization beyond the distortion types used in the database and they are not ideal candidates for general-purpose image quality assessment. Moreover, a large-scale database of 18.75 million images with synthetic distortions is used to pretrain the model and then retrain it on benchmark databases for evaluation. Experiments are conducted on six benchmark databases three of which are synthetic distortion databases (LIVE, CSIQ and TID2013) and three are natural distortion databases (LIVE Challenge Database, CID2013 and KonIQ-10 k). The proposed approach has provided a Pearson correlation coefficient of 0.8992, 0.8472 and 0.9452 subsequently and Spearman correlation coefficient of 0.8863, 0.8408 and 0.9421. Moreover, the performance is demonstrated using perceptually weighted rank correlation to indicate the perceptual superiority of the proposed approach. Multiple experiments are conducted to validate the generalization performance of the proposed model by training on different subsets of the databases and validating on the test subset of BIQ2021 database.	['Atif Khan', 'Abdul Rauf Bhatti', 'H. M. Shahzad Asif', 'Nisar Ahmed']	2023-05-16	null	null	null	null	['blind-image-quality-assessment', 'image-quality-assessment']	['computer-vision', 'computer-vision']	[-2.09240556e-01 -5.13881624e-01 2.03322142e-01 -4.81536478e-01 -6.31667852e-01 -3.44263166e-01 4.57920671e-01 -1.00344382e-01 -6.34628534e-01 7.97270775e-01 3.89427431e-02 -1.68999508e-01 -2.36445650e-01 -8.68034303e-01 -6.07948244e-01 -5.51851153e-01 -2.37966329e-01 5.72594889e-02 6.60289377e-02 -1.59451216e-01 2.45853290e-01 5.05954742e-01 -1.66272593e+00 1.70767337e-01 1.05473554e+00 1.23959780e+00 2.93277711e-01 7.71617711e-01 3.90727699e-01 4.14802998e-01 -1.08777905e+00 -5.14260828e-01 6.82199776e-01 -2.94898868e-01 -5.27681470e-01 1.51477382e-01 4.34304088e-01 -6.55342519e-01 -4.72428560e-01 1.24996901e+00 9.43952799e-01 2.45263577e-01 5.03556073e-01 -1.18048096e+00 -9.49755192e-01 3.21587361e-02 -4.33840126e-01 4.47124183e-01 1.49459526e-01 4.09069300e-01 7.10400760e-01 -8.88038695e-01 3.60327423e-01 1.05312896e+00 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c8fdf614-8df0-429c-ba87-09e3a5be3a65	good-examples-make-a-faster-learner-simple	2110.08454	null	https://arxiv.org/abs/2110.08454v3	https://arxiv.org/pdf/2110.08454v3.pdf	Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER	Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. Similar attempts have been made on named entity recognition (NER) which manually design templates to predict entity types for every text span in a sentence. However, such methods may suffer from error propagation induced by entity span detection, high cost due to enumeration of all possible text spans, and omission of inter-dependencies among token labels in a sentence. Here we present a simple demonstration-based learning method for NER, which lets the input be prefaced by task demonstrations for in-context learning. We perform a systematic study on demonstration strategy regarding what to include (entity examples, with or without surrounding context), how to select the examples, and what templates to use. Results on in-domain learning and domain adaptation show that the model's performance in low-resource settings can be largely improved with a suitable demonstration strategy (e.g., a 4-17% improvement on 25 train instances). We also find that good demonstration can save many labeled examples and consistency in demonstration contributes to better performance.	['Xiang Ren', 'Toshiyuki Sekiya', 'Ryosuke Mitani', 'Takashi Shibuya', 'Xinyu Feng', 'Kangmin Tan', 'Akshen Kadakia', 'Jay Pujara', 'Mahak Agarwal', 'Dong-Ho Lee']	2021-10-16	null	https://aclanthology.org/2022.acl-long.192	https://aclanthology.org/2022.acl-long.192.pdf	acl-2022-5	['few-shot-text-classification']	['natural-language-processing']	[-1.88267939e-02 -4.90594395e-02 -3.88014689e-02 -6.18988514e-01 -1.05154133e+00 -6.75222635e-01 5.16389489e-01 2.53457427e-01 -1.01835573e+00 9.32328880e-01 3.70798320e-01 -4.05516177e-01 2.70258579e-02 -4.77055788e-01 -3.27465326e-01 -2.43097454e-01 9.62578654e-02 3.35755259e-01 4.16713119e-01 -1.73452348e-01 2.57144839e-01 3.10636252e-01 -1.38366652e+00 2.86420017e-01 8.12799513e-01 4.29514319e-01 3.58731240e-01 8.18141401e-01 -5.69510102e-01 9.55485523e-01 -1.15913475e+00 -2.99876958e-01 -9.40927044e-02 -4.00733560e-01 -1.08805871e+00 -1.41900197e-01 5.84367812e-01 -3.94083947e-01 -2.99859166e-01 6.59199834e-01 8.75846803e-01 5.32397509e-01 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441283a4-5949-435e-af1f-90e888ee5859	on-board-change-detection-for-resource	2305.10119	null	https://arxiv.org/abs/2305.10119v1	https://arxiv.org/pdf/2305.10119v1.pdf	On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites	The amount of data generated by Earth observation satellites can be enormous, which poses a great challenge to the satellite-to-ground connections with limited rate. This paper considers problem of efficient downlink communication of multi-spectral satellite images for Earth observation using change detection. The proposed method for image processing consists of the joint design of cloud removal and change encoding, which can be seen as an instance of semantic communication, as it encodes important information, such as changed multi-spectral pixels (MPs), while aiming to minimize energy consumption. It comprises a three-stage end-to-end scoring mechanism that determines the importance of each MP before deciding its transmission. Specifically, the sensing image is (1) standardized, (2) passed through a high-performance cloud filtering via the Cloud-Net model, and (3) passed to the proposed scoring algorithm that uses Change-Net to identify MPs that have a high likelihood of being changed, compress them and forward the result to the ground station. The experimental results indicate that the proposed framework is effective in optimizing energy usage while preserving high-quality data transmission in satellite-based Earth observation applications.	['Petar Popovski', 'Eva Lagunas', 'Shashi Raj Pandey', 'Israel Leyva-Mayorga', 'Thinh Q. Dinh', 'Van-Phuc Bui']	2023-05-17	null	null	null	null	['cloud-removal', 'change-detection']	['computer-vision', 'computer-vision']	[ 5.87985039e-01 -4.18012500e-01 -5.42259729e-03 -2.55803168e-01 -3.24836314e-01 -5.12073815e-01 3.30171824e-01 2.06472680e-01 -4.16904002e-01 5.18092871e-01 1.62239417e-01 -2.87221551e-01 -4.42487180e-01 -1.13167810e+00 -3.51346046e-01 -1.12015212e+00 -4.62821394e-01 2.90366337e-02 1.70800999e-01 -1.70369089e-01 2.32276544e-01 5.26150644e-01 -1.42921042e+00 -1.80395886e-01 7.43040085e-01 1.31119168e+00 6.62567556e-01 7.02968359e-01 3.45606029e-01 4.44263279e-01 -2.33038172e-01 3.04310769e-01 3.55480999e-01 -4.32145447e-01 -7.32090473e-01 3.71592224e-01 -2.57542759e-01 -3.72669816e-01 -2.14358675e-03 1.45448649e+00 6.76845193e-01 1.96266875e-01 3.63594800e-01 -9.12080348e-01 4.46752412e-03 2.49401778e-01 -5.82589388e-01 7.24086344e-01 -1.20747350e-01 5.59097715e-02 7.28267491e-01 -6.34445071e-01 4.61892605e-01 8.74776244e-01 2.70246804e-01 -9.44320336e-02 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23eaf275-375e-4985-908a-f7e76ffc6cbf	opengait-revisiting-gait-recognition-toward	2211.06597	null	https://arxiv.org/abs/2211.06597v3	https://arxiv.org/pdf/2211.06597v3.pdf	OpenGait: Revisiting Gait Recognition Toward Better Practicality	Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importantly, we also find that some conclusions drawn from indoor datasets cannot be generalized to real applications. Therefore, the primary goal of this paper is to present a comprehensive benchmark study for better practicality rather than only a particular model for better performance. To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait. Based on OpenGait, we deeply revisit the recent development of gait recognition by re-conducting the ablative experiments. Encouragingly,we detect some unperfect parts of certain prior woks, as well as new insights. Inspired by these discoveries, we develop a structurally simple, empirically powerful, and practically robust baseline model, GaitBase. Experimentally, we comprehensively compare GaitBase with many current gait recognition methods on multiple public datasets, and the results reflect that GaitBase achieves significantly strong performance in most cases regardless of indoor or outdoor situations. Code is available at https://github.com/ShiqiYu/OpenGait.	['Shiqi Yu', 'Yongzhen Huang', 'Saihui Hou', 'Chuanfu Shen', 'Junhao Liang', 'Chao Fan']	2022-11-12	null	null	null	null	['gait-recognition']	['computer-vision']	[-1.17321700e-01 -6.81662202e-01 -3.48819315e-01 -1.89679921e-01 -5.63528478e-01 -4.95761842e-01 3.03418070e-01 -2.81893194e-01 -2.58871228e-01 8.37594330e-01 2.77229875e-01 -1.69184521e-01 -2.53589869e-01 -6.62542224e-01 -3.66014898e-01 -8.74200881e-01 -3.43641788e-01 4.58850153e-02 2.89828569e-01 -2.03810051e-01 2.60250747e-01 2.07718730e-01 -1.64932787e+00 -1.89665794e-01 8.30355465e-01 8.19599271e-01 -1.06528714e-01 6.62461340e-01 3.85238409e-01 4.80619580e-01 -3.84752929e-01 -4.69864070e-01 7.03874677e-02 -3.22844118e-01 -7.13963330e-01 -2.06487998e-01 2.21599355e-01 -2.59458631e-01 -6.17225230e-01 7.68556416e-01 7.48171806e-01 1.14861257e-01 4.83333886e-01 -1.50299633e+00 -7.28968740e-01 2.29566947e-01 -4.34126765e-01 3.64213377e-01 6.25862300e-01 3.48020375e-01 1.10712719e+00 -7.31482208e-01 2.89477170e-01 7.40569055e-01 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473af198-c3b9-42ae-bb72-a4e92af6110a	shi-he-jian-dong-ren-shi-yong-zhi-yu-yin-1	null	null	https://aclanthology.org/2019.rocling-1.16	https://aclanthology.org/2019.rocling-1.16.pdf	適合漸凍人使用之語音轉換系統初步研究(Deep Neural-Network Bandwidth Extension and Denoising Voice Conversion System for ALS Patients)	null	['Daniel Hládek', 'Matúš Pleva', 'Yuan-Fu Liao', 'Bai-Hong Huang']	null	null	null	null	rocling-2019-10	['bandwidth-extension', 'bandwidth-extension']	['audio', 'speech']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 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d23ab62d-eea2-487d-bc95-a2d59b959d0e	functional-object-oriented-network-1	1905.00502	null	https://arxiv.org/abs/1905.00502v4	https://arxiv.org/pdf/1905.00502v4.pdf	Task Planning with a Weighted Functional Object-Oriented Network	In reality, there is still much to be done for robots to be able to perform manipulation actions with full autonomy. Complicated manipulation tasks, such as cooking, may still require a person to perform some actions that are very risky for a robot to perform. On the other hand, some other actions may be very risky for a human with physical disabilities to perform. Therefore, it is necessary to balance the workload of a robot and a human based on their limitations while minimizing the effort needed from a human in a collaborative robot (cobot) set-up. This paper proposes a new version of our functional object-oriented network (FOON) that integrates weights in its functional units to reflect a robot's chance of successfully executing an action of that functional unit. The paper also presents a task planning algorithm for the weighted FOON to allocate manipulation action load to the robot and human to achieve optimal performance while minimizing human effort. Through a number of experiments, this paper shows several successful cases in which using the proposed weighted FOON and the task planning algorithm allow a robot and a human to successfully complete complicated tasks together with higher success rates than a robot doing them alone.	['Yu Sun', 'David Paulius', 'Kelvin Sheng Pei Dong']	2019-05-01	null	null	null	null	['robot-task-planning']	['robots']	[ 2.13779986e-01 7.50887215e-01 2.49583393e-01 -4.77500260e-01 2.99305022e-01 2.72682286e-04 2.92355008e-03 -6.50725663e-02 -7.03046799e-01 8.55951786e-01 -1.93323329e-01 9.90025997e-02 -6.13924086e-01 -7.31025279e-01 -3.37493449e-01 -5.63335121e-01 -2.08198905e-01 6.15477800e-01 4.57715601e-01 -4.37180012e-01 2.28364438e-01 6.86357379e-01 -1.54491556e+00 -2.17546552e-01 8.93501878e-01 4.58591253e-01 1.28236377e+00 3.72094631e-01 1.21854499e-01 1.00319040e+00 -6.79908216e-01 1.14196107e-01 4.63460177e-01 -3.90658915e-01 -8.69238913e-01 4.44920748e-01 -5.68129897e-01 -5.01677692e-01 -1.70143172e-01 8.38290274e-01 3.36135268e-01 4.85190392e-01 8.46355915e-01 -1.78337312e+00 1.26441270e-01 7.16028452e-01 -8.08049887e-02 -5.16628921e-01 4.05705988e-01 2.36036569e-01 4.64452565e-01 -5.46412989e-02 5.15473843e-01 1.32009923e+00 1.71925858e-01 3.43315393e-01 -7.58594632e-01 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5382bc92-6d2c-4fe9-8c1a-e6288f5a3f3c	fooling-neural-network-interpretations-via	1902.02041	null	https://arxiv.org/abs/1902.02041v3	https://arxiv.org/pdf/1902.02041v3.pdf	Fooling Neural Network Interpretations via Adversarial Model Manipulation	We ask whether the neural network interpretation methods can be fooled via adversarial model manipulation, which is defined as a model fine-tuning step that aims to radically alter the explanations without hurting the accuracy of the original models, e.g., VGG19, ResNet50, and DenseNet121. By incorporating the interpretation results directly in the penalty term of the objective function for fine-tuning, we show that the state-of-the-art saliency map based interpreters, e.g., LRP, Grad-CAM, and SimpleGrad, can be easily fooled with our model manipulation. We propose two types of fooling, Passive and Active, and demonstrate such foolings generalize well to the entire validation set as well as transfer to other interpretation methods. Our results are validated by both visually showing the fooled explanations and reporting quantitative metrics that measure the deviations from the original explanations. We claim that the stability of neural network interpretation method with respect to our adversarial model manipulation is an important criterion to check for developing robust and reliable neural network interpretation method.	['Juyeon Heo', 'Taesup Moon', 'Sunghwan Joo']	2019-02-06	fooling-neural-network-interpretations-via-1	http://papers.nips.cc/paper/8558-fooling-neural-network-interpretations-via-adversarial-model-manipulation	http://papers.nips.cc/paper/8558-fooling-neural-network-interpretations-via-adversarial-model-manipulation.pdf	neurips-2019-12	['network-interpretation']	['computer-vision']	[ 4.65297431e-01 9.70655084e-01 2.16846973e-01 -3.18409592e-01 -2.79425263e-01 -8.44804168e-01 8.50627422e-01 -3.59197348e-01 -3.07523519e-01 8.81110191e-01 -1.14995539e-01 -2.96129704e-01 -9.71111357e-02 -5.51843822e-01 -1.18431067e+00 -5.47082424e-01 2.78252810e-01 4.42158759e-01 4.32799071e-01 -4.40963447e-01 4.94435489e-01 4.18790549e-01 -1.22481740e+00 -4.70051877e-02 1.22362363e+00 8.52139831e-01 -1.11471981e-01 6.36580348e-01 2.55595177e-01 8.91436338e-01 -9.92376149e-01 -5.74956596e-01 1.70257851e-01 -4.81105477e-01 -9.77469087e-01 -2.92260021e-01 7.14719355e-01 -3.88841093e-01 -3.61303747e-01 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de7b23e9-ed5b-4469-827f-92f84ac89c5b	hegel-a-novel-dataset-for-geo-location-from	2307.00509	null	https://arxiv.org/abs/2307.00509v1	https://arxiv.org/pdf/2307.00509v1.pdf	HeGeL: A Novel Dataset for Geo-Location from Hebrew Text	The task of textual geolocation - retrieving the coordinates of a place based on a free-form language description - calls for not only grounding but also natural language understanding and geospatial reasoning. Even though there are quite a few datasets in English used for geolocation, they are currently based on open-source data (Wikipedia and Twitter), where the location of the described place is mostly implicit, such that the location retrieval resolution is limited. Furthermore, there are no datasets available for addressing the problem of textual geolocation in morphologically rich and resource-poor languages, such as Hebrew. In this paper, we present the Hebrew Geo-Location (HeGeL) corpus, designed to collect literal place descriptions and analyze lingual geospatial reasoning. We crowdsourced 5,649 literal Hebrew place descriptions of various place types in three cities in Israel. Qualitative and empirical analysis show that the data exhibits abundant use of geospatial reasoning and requires a novel environmental representation.	['Reut Tsarfaty', 'Sagi Dalyot', 'Itzhak Omer', 'Itai Mondshine', 'Tal Bauman', 'Tzuf Paz-Argaman']	2023-07-02	null	null	null	null	['retrieval', 'natural-language-understanding']	['methodology', 'natural-language-processing']	[-5.81489503e-01 1.76840078e-03 -4.30660546e-01 -3.80428582e-01 -9.00734425e-01 -8.95189881e-01 1.03758526e+00 7.60676920e-01 -8.62310708e-01 9.53097641e-01 1.11981618e+00 -4.38796103e-01 -2.13993728e-01 -1.24212921e+00 -5.32548726e-01 -3.56530219e-01 1.89479843e-01 6.31152749e-01 1.06851600e-01 -5.80405712e-01 5.24788141e-01 4.86494452e-01 -1.57059062e+00 3.26057337e-02 7.31928289e-01 5.68250418e-01 3.43027920e-01 1.03402987e-01 -5.41774750e-01 7.79984295e-01 -2.76352376e-01 -2.04756409e-01 -5.00669964e-02 -5.46330400e-02 -9.19730186e-01 -5.44901848e-01 2.39650920e-01 -1.58552900e-01 -2.80379087e-01 9.50665593e-01 6.38721704e-01 3.77428502e-01 8.47820640e-01 -1.05534744e+00 -1.07445061e+00 7.07705855e-01 -1.81763247e-01 1.37223229e-01 7.86171317e-01 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2e785919-99aa-4988-9754-a6d948f0afdc	contact-graspnet-efficient-6-dof-grasp	2103.14127	null	https://arxiv.org/abs/2103.14127v1	https://arxiv.org/pdf/2103.14127v1.pdf	Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes	Grasping unseen objects in unconstrained, cluttered environments is an essential skill for autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning, existing approaches often consist of complex sequential pipelines that possess several potential failure points and run-times unsuitable for closed-loop grasping. Therefore, we propose an end-to-end network that efficiently generates a distribution of 6-DoF parallel-jaw grasps directly from a depth recording of a scene. Our novel grasp representation treats 3D points of the recorded point cloud as potential grasp contacts. By rooting the full 6-DoF grasp pose and width in the observed point cloud, we can reduce the dimensionality of our grasp representation to 4-DoF which greatly facilitates the learning process. Our class-agnostic approach is trained on 17 million simulated grasps and generalizes well to real world sensor data. In a robotic grasping study of unseen objects in structured clutter we achieve over 90% success rate, cutting the failure rate in half compared to a recent state-of-the-art method.	['Dieter Fox', 'Rudolph Triebel', 'Arsalan Mousavian', 'Martin Sundermeyer']	2021-03-25	null	null	null	null	['grasp-generation']	['computer-vision']	[-2.67038252e-02 -7.66360909e-02 2.25977093e-01 -2.86121905e-01 -7.98239052e-01 -1.00435293e+00 1.67765826e-01 8.77446458e-02 -2.12688789e-01 2.18578592e-01 -1.58741996e-01 -1.38111010e-01 -3.76246750e-01 -5.55927813e-01 -1.26155448e+00 -5.21183550e-01 -5.62414944e-01 1.16719842e+00 3.58276874e-01 -1.04718491e-01 3.79178375e-01 9.28644538e-01 -1.61937773e+00 3.11872870e-01 5.09076953e-01 1.00781345e+00 7.82035589e-01 7.74171054e-01 -2.80116051e-02 2.14217361e-02 -5.18535137e-01 9.22012329e-02 6.70625806e-01 5.09745955e-01 -6.54644847e-01 1.02890804e-01 4.49634671e-01 -8.57603073e-01 -4.05799717e-01 7.12864757e-01 4.61477995e-01 -1.31459758e-01 6.15317762e-01 -1.13421333e+00 -3.90976161e-01 4.48248118e-01 -3.42702091e-01 -4.22715485e-01 4.11132246e-01 4.85663474e-01 6.74426734e-01 -8.10184300e-01 7.31937528e-01 1.55248523e+00 4.29645687e-01 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ca5793c0-271d-497b-aa21-da4e7ff0a3d9	large-sequence-models-for-sequential-decision	2306.13945	null	https://arxiv.org/abs/2306.13945v1	https://arxiv.org/pdf/2306.13945v1.pdf	Large Sequence Models for Sequential Decision-Making: A Survey	Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e.g., GPT-3 and Swin Transformer. Although originally designed for prediction problems, it is natural to inquire about their suitability for sequential decision-making and reinforcement learning problems, which are typically beset by long-standing issues involving sample efficiency, credit assignment, and partial observability. In recent years, sequence models, especially the Transformer, have attracted increasing interest in the RL communities, spawning numerous approaches with notable effectiveness and generalizability. This survey presents a comprehensive overview of recent works aimed at solving sequential decision-making tasks with sequence models such as the Transformer, by discussing the connection between sequential decision-making and sequence modeling, and categorizing them based on the way they utilize the Transformer. Moreover, this paper puts forth various potential avenues for future research intending to improve the effectiveness of large sequence models for sequential decision-making, encompassing theoretical foundations, network architectures, algorithms, and efficient training systems. As this article has been accepted by the Frontiers of Computer Science, here is an early version, and the most up-to-date version can be found at https://journal.hep.com.cn/fcs/EN/10.1007/s11704-023-2689-5	['Weinan Zhang', 'Haifeng Zhang', 'Jun Wang', 'Luo Mai', 'Ying Wen', 'Yaodong Yang', 'Hanjing Wang', 'Runji Lin', 'Muning Wen']	2023-06-24	null	null	null	null	['decision-making']	['reasoning']	[ 3.45870465e-01 4.30317596e-02 -5.53548634e-01 -2.68797994e-01 -3.91234815e-01 -3.26263815e-01 3.78917038e-01 1.68740675e-01 -4.52877820e-01 6.41010642e-01 -2.14338139e-01 -6.62433088e-01 -1.45241916e-01 -7.55663157e-01 -2.90912539e-01 -7.65837967e-01 -1.29468426e-01 6.58425570e-01 9.29068625e-02 -2.12493256e-01 1.84094697e-01 4.69154924e-01 -1.45833492e+00 1.72511548e-01 8.89113665e-01 1.21939576e+00 6.12159550e-01 4.25640821e-01 6.75045745e-03 1.09540761e+00 -4.04309779e-01 -6.77571893e-01 3.72764915e-02 -5.64130247e-01 -7.84215152e-01 -3.00506413e-01 -3.62192720e-01 -8.55515003e-02 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a26ac538-e362-4385-863e-538cd64f529e	coarse-to-fine-seam-estimation-for-image	1805.09578	null	http://arxiv.org/abs/1805.09578v1	http://arxiv.org/pdf/1805.09578v1.pdf	Coarse-to-fine Seam Estimation for Image Stitching	Seam-cutting and seam-driven techniques have been proven effective for handling imperfect image series in image stitching. Generally, seam-driven is to utilize seam-cutting to find a best seam from one or finite alignment hypotheses based on a predefined seam quality metric. However, the quality metrics in most methods are defined to measure the average performance of the pixels on the seam without considering the relevance and variance among them. This may cause that the seam with the minimal measure is not optimal (perception-inconsistent) in human perception. In this paper, we propose a novel coarse-to-fine seam estimation method which applies the evaluation in a different way. For pixels on the seam, we develop a patch-point evaluation algorithm concentrating more on the correlation and variation of them. The evaluations are then used to recalculate the difference map of the overlapping region and reestimate a stitching seam. This evaluation-reestimation procedure iterates until the current seam changes negligibly comparing with the previous seams. Experiments show that our proposed method can finally find a nearly perception-consistent seam after several iterations, which outperforms the conventional seam-cutting and other seam-driven methods.	['Yifang Xu', 'Tianli Liao', 'Jing Chen']	2018-05-24	null	null	null	null	['image-stitching']	['computer-vision']	[ 5.19569576e-01 -3.91844332e-01 1.99904040e-01 -2.67409563e-01 -6.99013829e-01 -3.05933923e-01 2.82002181e-01 6.99853105e-03 -2.25081086e-01 4.01320994e-01 6.09741770e-02 1.67145655e-01 -3.23158592e-01 -6.85730219e-01 -5.83175898e-01 -8.31438839e-01 2.73304909e-01 8.75668749e-02 7.42952824e-01 -4.18525696e-01 9.47396398e-01 1.00815125e-01 -1.65867519e+00 1.23505138e-01 1.30619395e+00 8.31517756e-01 4.51963067e-01 4.28731233e-01 -1.64668053e-01 -9.71029792e-03 -6.28353834e-01 -1.92095652e-01 4.32186872e-01 -7.54697978e-01 -5.20087719e-01 4.02429283e-01 5.37929773e-01 -2.26770997e-01 3.91478837e-01 1.31493223e+00 1.99324533e-01 2.09355637e-01 4.15249437e-01 -9.98807907e-01 -3.33785832e-01 2.04991609e-01 -1.09117091e+00 2.35840306e-02 3.81182641e-01 -6.46609142e-02 6.03702247e-01 -9.54126537e-01 4.58258480e-01 1.13754547e+00 7.83964097e-01 1.63741812e-01 -1.05285203e+00 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5.55791199e-01 3.15174788e-01 -5.70310652e-01 -5.99121571e-01 5.87099697e-03]	[9.489852905273438, -2.2210853099823]
2a9f0b67-2a92-42cb-a8c5-85be8947eeae	the-monitor-model-and-its-misconceptions-a	2210.14367	null	https://arxiv.org/abs/2210.14367v2	https://arxiv.org/pdf/2210.14367v2.pdf	The Monitor Model and its Misconceptions: A Clarification	Horizontal (automatic) and vertical (control) processes have been observed and reported for a long time in translation production. Schaeffer and Carl's Monitor Model integrates these two processes into one framework, assuming that priming mechanisms underlie horizontal/automatic processes, while vertical/monitoring processes implement consciously accessible control mechanisms. The Monitor Model has been criticized in various ways and several misconceptions have accumulated over the past years. In this chapter, I update the Monitor Model with additional evidence and argue that it is compatible with an enactivist approach to cognition. I address several misconceptions related to the Monitor Model.	['Michael Carl']	2022-10-25	null	null	null	null	['misconceptions']	['miscellaneous']	[ 1.78090125e-01 3.02475899e-01 -5.84515572e-01 -1.78562433e-01 -1.11342020e-01 -9.74194467e-01 1.33203030e+00 6.75933838e-01 -2.26986229e-01 2.68009037e-01 7.12846637e-01 -9.92304921e-01 -1.16120480e-01 -3.07928830e-01 -7.38361031e-02 -2.53598422e-01 5.19669890e-01 3.25372964e-01 7.06480145e-02 -2.62780339e-01 1.07279694e+00 5.16387939e-01 -1.37216961e+00 2.82646388e-01 8.96891415e-01 9.92681235e-02 1.73637033e-01 5.79556584e-01 -2.34144479e-01 1.05941463e+00 -7.18912184e-01 -5.61736166e-01 -3.13269436e-01 -8.58072340e-01 -1.07733536e+00 9.58618671e-02 -7.45182335e-02 1.31483868e-01 4.63428013e-02 1.13011646e+00 2.65337080e-01 -1.22926906e-01 3.25731605e-01 -8.62959027e-01 -1.08172035e+00 6.55598462e-01 -3.07594717e-01 7.28933632e-01 5.90780437e-01 1.56981528e-01 6.69548690e-01 -8.26202035e-01 5.89655936e-01 1.64007378e+00 3.45870048e-01 5.52626610e-01 -1.55458105e+00 -3.86079013e-01 4.71870720e-01 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