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paperID stringlengths 36 36	pwc_id stringlengths 8 47	arxiv_id stringlengths 6 16 ⌀	nips_id float64	url_abs stringlengths 18 329	url_pdf stringlengths 18 742	title stringlengths 8 325	abstract stringlengths 1 7.27k ⌀	authors stringlengths 2 7.06k	published stringlengths 10 10 ⌀	conference stringlengths 12 47 ⌀	conference_url_abs stringlengths 16 198 ⌀	conference_url_pdf stringlengths 27 199 ⌀	proceeding stringlengths 6 47 ⌀	taskID stringlengths 7 1.44k	areaID stringclasses 688 values	embedding stringlengths 9.26k 12.5k	umap_embedding stringlengths 29 44
fe775551-548c-4a24-9b9b-ed0822cbf437	fau-facial-expressions-valence-and-arousal-a	2002.03557	null	https://arxiv.org/abs/2002.03557v2	https://arxiv.org/pdf/2002.03557v2.pdf	Multitask Emotion Recognition with Incomplete Labels	We train a unified model to perform three tasks: facial action unit detection, expression classification, and valence-arousal estimation. We address two main challenges of learning the three tasks. First, most existing datasets are highly imbalanced. Second, most existing datasets do not contain labels for all three tasks. To tackle the first challenge, we apply data balancing techniques to experimental datasets. To tackle the second challenge, we propose an algorithm for the multitask model to learn from missing (incomplete) labels. This algorithm has two steps. We first train a teacher model to perform all three tasks, where each instance is trained by the ground truth label of its corresponding task. Secondly, we refer to the outputs of the teacher model as the soft labels. We use the soft labels and the ground truth to train the student model. We find that most of the student models outperform their teacher model on all the three tasks. Finally, we use model ensembling to boost performance further on the three tasks.	['Zhaokang Chen', 'Didan Deng', 'Bertram E. Shi']	2020-02-10	null	null	null	null	['action-unit-detection', 'facial-action-unit-detection']	['computer-vision', 'computer-vision']	[ 4.13388640e-01 6.33983687e-02 -4.82239783e-01 -6.94727480e-01 -1.11354399e+00 -4.90290552e-01 3.07921588e-01 -7.50832260e-02 -3.62599224e-01 7.61274040e-01 -9.97040793e-02 1.09824039e-01 3.37795258e-01 -2.61176497e-01 -6.23379946e-01 -8.38617086e-01 4.22032237e-01 2.69861788e-01 4.12995592e-02 3.65354754e-02 4.00895476e-02 2.65371315e-02 -1.78305376e+00 7.25791335e-01 8.31063926e-01 1.40238917e+00 -2.28570148e-01 3.78678560e-01 6.79618418e-02 1.01125455e+00 -6.02895498e-01 -4.85475779e-01 4.28300127e-02 -4.38353091e-01 -9.33593452e-01 6.90438077e-02 2.79156476e-01 -3.46760303e-01 2.94302166e-01 9.44802582e-01 5.89222133e-01 8.63560196e-03 7.03225315e-01 -1.81257105e+00 -1.34444311e-01 3.61264497e-01 -9.15938199e-01 -1.95270136e-01 1.36979938e-01 -8.55457261e-02 1.13516057e+00 -9.83783305e-01 3.37647319e-01 1.11942410e+00 3.06710780e-01 8.17233086e-01 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b4afc24a-8384-42d4-8676-91c99e7eee82	actor-identified-spatiotemporal-action	2208.1294	null	https://arxiv.org/abs/2208.12940v2	https://arxiv.org/pdf/2208.12940v2.pdf	Actor-identified Spatiotemporal Action Detection --- Detecting Who Is Doing What in Videos	The success of deep learning on video Action Recognition (AR) has motivated researchers to progressively promote related tasks from the coarse level to the fine-grained level. Compared with conventional AR which only predicts an action label for the entire video, Temporal Action Detection (TAD) has been investigated for estimating the start and end time for each action in videos. Taking TAD a step further, Spatiotemporal Action Detection (SAD) has been studied for localizing the action both spatially and temporally in videos. However, who performs the action, is generally ignored in SAD, while identifying the actor could also be important. To this end, we propose a novel task, Actor-identified Spatiotemporal Action Detection (ASAD), to bridge the gap between SAD and actor identification. In ASAD, we not only detect the spatiotemporal boundary for instance-level action but also assign the unique ID to each actor. To approach ASAD, Multiple Object Tracking (MOT) and Action Classification (AC) are two fundamental elements. By using MOT, the spatiotemporal boundary of each actor is obtained and assigned to a unique actor identity. By using AC, the action class is estimated within the corresponding spatiotemporal boundary. Since ASAD is a new task, it poses many new challenges that cannot be addressed by existing methods: i) no dataset is specifically created for ASAD, ii) no evaluation metrics are designed for ASAD, iii) current MOT performance is the bottleneck to obtain satisfactory ASAD results. To address those problems, we contribute to i) annotate a new ASAD dataset, ii) propose ASAD evaluation metrics by considering multi-label actions and actor identification, iii) improve the data association strategies in MOT to boost the MOT performance, which leads to better ASAD results. The code is available at https://github.com/fandulu/ASAD.	['Satoshi Nakamura', 'Sakriani Sakti', 'Norimichi Ukita', 'Fan Yang']	2022-08-27	null	null	null	null	['action-classification']	['computer-vision']	[ 3.74519885e-01 -3.55370194e-01 -4.18965548e-01 -1.42829835e-01 -6.25635564e-01 -3.91290277e-01 5.78882337e-01 7.43235946e-02 -2.57934868e-01 4.97623950e-01 1.94574222e-01 1.07581779e-01 -5.64546138e-02 -4.48416740e-01 -4.46403861e-01 -7.93466985e-01 6.93796203e-02 3.77889425e-02 6.24726474e-01 3.57039928e-01 1.07848816e-01 6.96082294e-01 -1.53072774e+00 4.11944747e-01 5.45949101e-01 1.19457614e+00 2.40760073e-01 4.66528326e-01 -1.73520744e-01 1.22992027e+00 -6.43857837e-01 1.52160853e-01 2.53558874e-01 -8.19954693e-01 -8.71812940e-01 2.27182806e-01 3.16397816e-01 -5.59544504e-01 -2.84117788e-01 8.74124289e-01 2.97076255e-01 2.62987643e-01 4.63117391e-01 -1.57893157e+00 1.12105235e-02 5.05181432e-01 -7.37996697e-01 4.47234333e-01 3.35907638e-01 8.32919478e-02 8.51340771e-01 -7.66133666e-01 5.51009834e-01 1.08990133e+00 3.92947614e-01 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c7010492-2407-435f-9279-15d97c18b89e	neural-math-word-problem-solver-with	null	null	https://aclanthology.org/C18-1018	https://aclanthology.org/C18-1018.pdf	Neural Math Word Problem Solver with Reinforcement Learning	Sequence-to-sequence model has been applied to solve math word problems. The model takes math problem descriptions as input and generates equations as output. The advantage of sequence-to-sequence model requires no feature engineering and can generate equations that do not exist in training data. However, our experimental analysis reveals that this model suffers from two shortcomings: (1) generate spurious numbers; (2) generate numbers at wrong positions. In this paper, we propose incorporating copy and alignment mechanism to the sequence-to-sequence model (namely CASS) to address these shortcomings. To train our model, we apply reinforcement learning to directly optimize the solution accuracy. It overcomes the {``}train-test discrepancy{''} issue of maximum likelihood estimation, which uses the surrogate objective of maximizing equation likelihood during training while the evaluation metric is solution accuracy (non-differentiable) at test time. Furthermore, to explore the effectiveness of our neural model, we use our model output as a feature and incorporate it into the feature-based model. Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.	['Chin-Yew Lin', 'Jian Yin', 'Danqing Huang', 'Jing Liu']	2018-08-01	neural-math-word-problem-solver-with-1	https://aclanthology.org/C18-1018	https://aclanthology.org/C18-1018.pdf	coling-2018-8	['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving']	['knowledge-base', 'reasoning', 'time-series']	[ 2.65792519e-01 -2.05396339e-01 9.64079425e-02 -2.59697467e-01 -7.35540926e-01 -6.61067426e-01 1.95677698e-01 -2.33424932e-01 -2.74114788e-01 1.06145656e+00 -1.55413717e-01 -5.55278957e-01 -2.82286465e-01 -9.10413921e-01 -8.71422172e-01 -1.95506200e-01 2.46559501e-01 2.73999125e-01 -1.01645000e-03 -4.72824931e-01 8.98811102e-01 3.85577470e-01 -1.52855182e+00 1.75738320e-01 1.53927732e+00 8.11964273e-01 4.61615831e-01 7.95596600e-01 -6.41975403e-01 9.52258229e-01 -8.67401063e-01 -3.64684373e-01 3.76975387e-01 -7.01258957e-01 -7.87497342e-01 -2.07988560e-01 3.27644438e-01 -4.65505034e-01 8.37231893e-03 1.18436122e+00 5.05768478e-01 1.09404668e-01 8.16288769e-01 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dee100f6-fa34-41a0-a8a9-cd8462f122c0	epileptic-seizure-prediction-using-pearson-s	2006.01359	null	https://arxiv.org/abs/2006.01359v1	https://arxiv.org/pdf/2006.01359v1.pdf	Epileptic seizure prediction using Pearson's product-moment correlation coefficient of a linear classifier from generalized Gaussian modeling	To predict an epileptic event means the ability to determine in advance the time of the seizure with the highest possible accuracy. A correct prediction benchmark for epilepsy events in clinical applications is a typical problem in biomedical signal processing that helps to an appropriate diagnosis and treatment of this disease. In this work, we use Pearson's product-moment correlation coefficient from generalized Gaussian distribution parameters coupled with a linear-based classifier to predict between seizure and non-seizure events in epileptic EEG signals. The performance in 36 epileptic events from 9 patients showing good performance with 100% of effectiveness for sensitivity and specificity greater than 83% for seizures events in all brain rhythms. Pearson's test suggests that all brain rhythms are highly correlated in non-seizure events but no during the seizure events. This suggests that our model can be scaled with the Pearson's product-moment correlation coefficient for the detection of epileptic seizures.	["Carlos D'Giano", 'Antonio Quintero-Rincon', 'Marcelo Risk']	2020-06-02	null	null	null	null	['seizure-prediction']	['medical']	[-1.97760344e-01 -1.78719521e-01 3.89770329e-01 -3.83808583e-01 -6.51625097e-01 -2.58764654e-01 2.55088449e-01 2.35397637e-01 -3.64089847e-01 9.31092322e-01 2.92490646e-02 -2.69391894e-01 -7.12457895e-01 -4.10332471e-01 -2.79090311e-02 -7.69280374e-01 -9.80192363e-01 4.01687890e-01 1.86439887e-01 -1.61162333e-03 3.46360505e-01 7.07127929e-01 -1.01698875e+00 4.33412701e-01 6.51107609e-01 1.06731057e+00 3.12681675e-01 7.54567146e-01 3.44488680e-01 6.01953387e-01 -9.76125598e-01 2.52152622e-01 1.38149172e-01 -5.82845330e-01 -2.16149494e-01 -2.63264924e-01 -8.02194715e-01 2.35399023e-01 -5.23560792e-02 9.03058231e-01 8.36320460e-01 -3.88637751e-01 1.16024494e+00 -1.30846345e+00 1.47490785e-01 4.48403686e-01 -5.09105682e-01 6.44280851e-01 5.64590752e-01 -8.39274228e-02 2.52675027e-01 -6.91062152e-01 1.47285953e-01 4.26329195e-01 7.31296062e-01 1.91675603e-01 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-1.35433340e+00 -1.39152062e+00 -1.42863225e-02 2.59585649e-01 -4.87270713e-01 -6.42271101e-01 2.54964113e-01]	[13.239418983459473, 3.5168986320495605]
69bb31cd-6b0b-4173-9bc4-3caed8b6de82	deep-structured-output-regression-learning	1607.03856	null	http://arxiv.org/abs/1607.03856v2	http://arxiv.org/pdf/1607.03856v2.pdf	Deep Structured-Output Regression Learning for Computational Color Constancy	Computational color constancy that requires esti- mation of illuminant colors of images is a fundamental yet active problem in computer vision, which can be formulated into a regression problem. To learn a robust regressor for color constancy, obtaining meaningful imagery features and capturing latent correlations across output variables play a vital role. In this work, we introduce a novel deep structured-output regression learning framework to achieve both goals simultaneously. By borrowing the power of deep convolutional neural networks (CNN) originally designed for visual recognition, the proposed framework can automatically discover strong features for white balancing over different illumination conditions and learn a multi-output regressor beyond underlying relationships between features and targets to find the complex interdependence of dif- ferent dimensions of target variables. Experiments on two public benchmarks demonstrate that our method achieves competitive performance in comparison with the state-of-the-art approaches.	['Jiri Matas', 'Joni-Kristian Kamarainen', 'Yanlin Qian', 'Ke Chen', 'Jarno Nikkanen']	2016-07-13	null	null	null	null	['color-constancy']	['computer-vision']	[ 2.00160131e-01 -7.08582163e-01 -1.51909485e-01 -4.39518601e-01 -3.70918274e-01 -5.10447264e-01 4.43877608e-01 -5.22441089e-01 -1.42153904e-01 5.57175398e-01 -8.38914141e-02 -2.63464212e-01 1.73829554e-03 -3.97184968e-01 -5.97545326e-01 -9.70043957e-01 8.85461569e-02 -3.20201755e-01 -2.27292225e-01 -2.77430058e-01 3.77642900e-01 4.38740581e-01 -1.52136958e+00 1.08230114e-01 9.60999846e-01 1.37693906e+00 -7.71836415e-02 7.40678906e-01 -1.70333058e-01 9.05026138e-01 -3.55017692e-01 -2.34254524e-01 5.87715626e-01 -6.15930498e-01 -1.00559652e-01 3.78117681e-01 7.76440144e-01 9.42180865e-03 -3.07488322e-01 1.21771514e+00 1.97925717e-01 -6.55621365e-02 8.10197473e-01 -1.55357504e+00 -1.31985366e+00 -1.42407250e-02 -1.00030792e+00 8.38982910e-02 -2.44182587e-01 2.83320963e-01 1.27356672e+00 -8.58456612e-01 -1.64385922e-02 1.19060183e+00 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68eeb2d4-7d30-4b8b-aa53-ba75a62498bd	fabric-surface-characterization-assessment-of	2003.07725	null	https://arxiv.org/abs/2003.07725v1	https://arxiv.org/pdf/2003.07725v1.pdf	Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset	Tactile sensing or fabric hand plays a critical role in an individual's decision to buy a certain fabric from the range of available fabrics for a desired application. Therefore, textile and clothing manufacturers have long been in search of an objective method for assessing fabric hand, which can then be used to engineer fabrics with a desired hand. Recognizing textures and materials in real-world images has played an important role in object recognition and scene understanding. In this paper, we explore how to computationally characterize apparent or latent properties (e.g., surface smoothness) of materials, i.e., computational material surface characterization, which moves a step further beyond material recognition. We formulate the problem as a very fine-grained texture classification problem, and study how deep learning-based texture representation techniques can help tackle the task. We introduce a new, large-scale challenging microscopic material surface dataset (CoMMonS), geared towards an automated fabric quality assessment mechanism in an intelligent manufacturing system. We then conduct a comprehensive evaluation of state-of-the-art deep learning-based methods for texture classification using CoMMonS. Additionally, we propose a multi-level texture encoding and representation network (MuLTER), which simultaneously leverages low- and high-level features to maintain both texture details and spatial information in the texture representation. Our results show that, in comparison with the state-of-the-art deep texture descriptors, MuLTER yields higher accuracy not only on our CoMMonS dataset for material characterization, but also on established datasets such as MINC-2500 and GTOS-mobile for material recognition.	['Sundaresan Jayaraman', 'Sungmee Park', 'Ghassan AlRegib', 'Anirudha Sundaresan', 'Yuting Hu', 'Zhiling Long', 'Motaz Alfarraj']	2020-03-16	null	null	null	null	['material-recognition', 'texture-classification']	['computer-vision', 'computer-vision']	[ 6.07674658e-01 -6.05755031e-01 -3.36974151e-02 -3.67405534e-01 -5.40185928e-01 -4.43968683e-01 2.16989681e-01 2.39063561e-01 1.46592394e-01 4.64205176e-01 -2.66774982e-01 -8.74861032e-02 -5.47149539e-01 -1.32008123e+00 -6.59689188e-01 -9.18280363e-01 5.55705316e-02 4.56549823e-01 -1.39406964e-01 -3.88022184e-01 3.82986158e-01 7.20623553e-01 -1.77605212e+00 7.24813342e-01 6.28087103e-01 2.06659412e+00 4.12285060e-01 5.56157589e-01 -1.47778094e-01 4.11001414e-01 -1.13564171e-01 -1.92107663e-01 7.94732720e-02 -6.77200546e-03 -9.68951523e-01 1.71145022e-01 4.41192925e-01 -3.19499016e-01 4.19469886e-02 9.36762035e-01 3.94161165e-01 -7.75209740e-02 8.41559291e-01 -5.44312060e-01 -8.88065338e-01 1.10305309e-01 -2.44667321e-01 -3.94619077e-01 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b9355416-0678-4a2d-b82e-db89e6b5356e	nlpbk-at-vlsp-2020-shared-task-compose	2101.12672	null	https://arxiv.org/abs/2101.12672v1	https://arxiv.org/pdf/2101.12672v1.pdf	NLPBK at VLSP-2020 shared task: Compose transformer pretrained models for Reliable Intelligence Identification on Social network	This paper describes our method for tuning a transformer-based pretrained model, to adaptation with Reliable Intelligence Identification on Vietnamese SNSs problem. We also proposed a model that combines bert-base pretrained models with some metadata features, such as the number of comments, number of likes, images of SNS documents,... to improved results for VLSP shared task: Reliable Intelligence Identification on Vietnamese SNSs. With appropriate training techniques, our model is able to achieve 0.9392 ROC-AUC on public test set and the final version settles at top 2 ROC-AUC (0.9513) on private test set.	['Van Nha Nguyen', 'Thanh Chinh Nguyen']	2021-01-29	null	null	null	null	['reliable-intelligence-identification']	['natural-language-processing']	[-1.27382323e-01 2.02717572e-01 -3.62126678e-01 -6.95598602e-01 -1.14689100e+00 -7.06762075e-01 6.20020270e-01 -1.50939569e-01 -6.66421413e-01 1.21439791e+00 2.90165450e-02 1.80449225e-02 -4.01346952e-01 -8.31286728e-01 -4.19236600e-01 -4.34728444e-01 -4.75125723e-02 1.30253053e+00 1.75211966e-01 -4.03774500e-01 3.16076845e-01 2.25842535e-01 -1.12670171e+00 3.40842605e-01 9.10918832e-01 1.37365901e+00 -3.62364382e-01 6.17899776e-01 1.54728200e-02 8.25517476e-01 -7.67277837e-01 -1.33768475e+00 2.72478193e-01 4.31334406e-01 -1.05396724e+00 -5.36884546e-01 5.28336406e-01 -4.26150002e-02 -1.99115705e-02 9.89203274e-01 1.92872152e-01 1.75596662e-02 8.70364130e-01 -1.64423811e+00 -7.49616563e-01 1.37105191e+00 -2.96808928e-01 3.13459545e-01 4.33990896e-01 -1.94426343e-01 1.03773487e+00 -5.98467588e-01 1.02399743e+00 9.01092172e-01 1.04270971e+00 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aaa64e0b-c267-4758-b088-1308f85f2bbc	pyramid-real-image-denoising-network	1908.00273	null	https://arxiv.org/abs/1908.00273v2	https://arxiv.org/pdf/1908.00273v2.pdf	Pyramid Real Image Denoising Network	While deep Convolutional Neural Networks (CNNs) have shown extraordinary capability of modelling specific noise and denoising, they still perform poorly on real-world noisy images. The main reason is that the real-world noise is more sophisticated and diverse. To tackle the issue of blind denoising, in this paper, we propose a novel pyramid real image denoising network (PRIDNet), which contains three stages. First, the noise estimation stage uses channel attention mechanism to recalibrate the channel importance of input noise. Second, at the multi-scale denoising stage, pyramid pooling is utilized to extract multi-scale features. Third, the stage of feature fusion adopts a kernel selecting operation to adaptively fuse multi-scale features. Experiments on two datasets of real noisy photographs demonstrate that our approach can achieve competitive performance in comparison with state-of-the-art denoisers in terms of both quantitative measure and visual perception quality. Code is available at https://github.com/491506870/PRIDNet.	['Zhuqing Jiang', 'Guodong Ju', 'Yiyun Zhao', 'Aidong Men']	2019-08-01	null	null	null	null	['noise-estimation']	['medical']	[ 1.29105344e-01 -8.03229749e-01 5.46085954e-01 -2.63439357e-01 -8.90110195e-01 -2.40830898e-01 3.07891518e-01 -1.40871495e-01 -5.66360533e-01 5.01704097e-01 4.51054782e-01 -2.85898969e-02 -4.18222062e-02 -8.26604307e-01 -4.98606592e-01 -1.05241930e+00 2.21866041e-01 -4.70995218e-01 2.97501236e-01 -3.84760618e-01 2.80707419e-01 4.50491011e-01 -1.50055552e+00 4.03667063e-01 8.19277465e-01 1.27501738e+00 3.38443071e-01 7.14396954e-01 4.19630483e-02 4.99735057e-01 -6.07846975e-01 -3.57956588e-01 5.81629634e-01 -4.58411425e-01 -1.77747324e-01 -9.09991097e-03 2.61103839e-01 -3.61116856e-01 -6.06975555e-01 1.63303840e+00 1.07447851e+00 1.03554472e-01 2.49278232e-01 -8.67907882e-01 -6.61531508e-01 4.52753544e-01 -6.28989398e-01 5.06908417e-01 8.58654454e-02 3.98120195e-01 5.93858421e-01 -9.07047570e-01 1.50382757e-01 1.30663061e+00 8.29894006e-01 3.60916585e-01 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1d840c54-aa99-4428-b852-ffee3eea76ba	attentional-feature-pair-relation-networks	1908.06255	null	https://arxiv.org/abs/1908.06255v1	https://arxiv.org/pdf/1908.06255v1.pdf	Attentional Feature-Pair Relation Networks for Accurate Face Recognition	Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and illumination is a big challenging problem for its practical application. Such variations make face recognition more difficult. In this paper, we propose a novel face recognition method, called Attentional Feature-pair Relation Network (AFRN), which represents the face by the relevant pairs of local appearance block features with their attention scores. The AFRN represents the face by all possible pairs of the 9x9 local appearance block features, the importance of each pair is considered by the attention map that is obtained from the low-rank bilinear pooling, and each pair is weighted by its corresponding attention score. To increase the accuracy, we select top-K pairs of local appearance block features as relevant facial information and drop the remaining irrelevant. The weighted top-K pairs are propagated to extract the joint feature-pair relation by using bilinear attention network. In experiments, we show the effectiveness of the proposed AFRN and achieve the outstanding performance in the 1:1 face verification and 1:N face identification tasks compared to existing state-of-the-art methods on the challenging LFW, YTF, CALFW, CPLFW, CFP, AgeDB, IJB-A, IJB-B, and IJB-C datasets.	['Bong-Nam Kang', 'Yonghyun Kim', 'Daijin Kim', 'Bongjin Jun']	2019-08-17	attentional-feature-pair-relation-networks-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Kang_Attentional_Feature-Pair_Relation_Networks_for_Accurate_Face_Recognition_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Kang_Attentional_Feature-Pair_Relation_Networks_for_Accurate_Face_Recognition_ICCV_2019_paper.pdf	iccv-2019-10	['robust-face-recognition']	['computer-vision']	[ 1.35242119e-01 -5.71604311e-01 3.41022983e-02 -5.60987234e-01 -4.44038212e-01 5.61810583e-02 4.24488276e-01 -5.83328545e-01 -3.27755153e-01 6.07647896e-01 1.32497832e-01 1.06960513e-01 -5.79562724e-01 -4.68577385e-01 -4.40839410e-01 -1.12072647e+00 -3.23137119e-02 3.23610641e-02 2.69967178e-03 -2.35950440e-01 3.75267774e-01 9.26277876e-01 -1.73625612e+00 4.10000652e-01 3.94876629e-01 1.63432682e+00 -1.82725117e-01 1.25937134e-01 8.48585088e-03 3.58593494e-01 -3.87513727e-01 -5.01995206e-01 2.83099055e-01 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7507dfca-7cae-43c7-97dc-66e0d321595a	wavemixsr-a-resource-efficient-neural-network	2307.0043	null	https://arxiv.org/abs/2307.00430v1	https://arxiv.org/pdf/2307.00430v1.pdf	WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution	Image super-resolution research recently been dominated by transformer models which need higher computational resources than CNNs due to the quadratic complexity of self-attention. We propose a new neural network -- WaveMixSR -- for image super-resolution based on WaveMix architecture which uses a 2D-discrete wavelet transform for spatial token-mixing. Unlike transformer-based models, WaveMixSR does not unroll the image as a sequence of pixels/patches. It uses the inductive bias of convolutions along with the lossless token-mixing property of wavelet transform to achieve higher performance while requiring fewer resources and training data. We compare the performance of our network with other state-of-the-art methods for image super-resolution. Our experiments show that WaveMixSR achieves competitive performance in all datasets and reaches state-of-the-art performance in the BSD100 dataset on multiple super-resolution tasks. Our model is able to achieve this performance using less training data and computational resources while maintaining high parameter efficiency compared to current state-of-the-art models.	['Amit Sethi', 'Pasunuri Prathiba', 'Akella Srinidhi', 'Pranav Jeevan']	2023-07-01	null	null	null	null	['image-super-resolution', 'super-resolution']	['computer-vision', 'computer-vision']	[ 3.80888402e-01 -5.01231700e-02 -6.08890541e-02 -9.65607613e-02 -1.28096759e+00 1.51141435e-01 4.95885670e-01 -4.79238123e-01 -4.08365548e-01 6.82886481e-01 5.31010330e-01 3.87802608e-02 1.44264564e-01 -1.01524997e+00 -8.37300062e-01 -6.00803554e-01 3.94685604e-02 1.57907590e-01 5.53123891e-01 -4.12333548e-01 8.30871332e-03 2.40829542e-01 -1.62937498e+00 1.07583869e+00 6.43620670e-01 9.45981503e-01 5.74033856e-01 5.86605847e-01 -1.88420638e-01 1.00396144e+00 -1.43642604e-01 -1.02033377e-01 3.27008903e-01 -4.71978754e-01 -8.59116256e-01 -1.94528371e-01 9.36246872e-01 -6.21476591e-01 -6.76355720e-01 1.17806768e+00 7.83727467e-01 7.14484304e-02 1.67457148e-01 -4.11276162e-01 -1.24512732e+00 8.86117041e-01 -8.13687563e-01 6.97514176e-01 8.52813199e-03 -1.31925881e-01 8.36408317e-01 -1.34482872e+00 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401345d1-308c-422b-838e-5395edfed681	learning-invariant-representations-for-2	2209.10944	null	https://arxiv.org/abs/2209.10944v1	https://arxiv.org/pdf/2209.10944v1.pdf	Learning Invariant Representations for Equivariant Neural Networks Using Orthogonal Moments	The convolutional layers of standard convolutional neural networks (CNNs) are equivariant to translation. However, the convolution and fully-connected layers are not equivariant or invariant to other affine geometric transformations. Recently, a new class of CNNs is proposed in which the conventional layers of CNNs are replaced with equivariant convolution, pooling, and batch-normalization layers. The final classification layer in equivariant neural networks is invariant to different affine geometric transformations such as rotation, reflection and translation, and the scalar value is obtained by either eliminating the spatial dimensions of filter responses using convolution and down-sampling throughout the network or average is taken over the filter responses. In this work, we propose to integrate the orthogonal moments which gives the high-order statistics of the function as an effective means for encoding global invariance with respect to rotation, reflection and translation in fully-connected layers. As a result, the intermediate layers of the network become equivariant while the classification layer becomes invariant. The most widely used Zernike, pseudo-Zernike and orthogonal Fourier-Mellin moments are considered for this purpose. The effectiveness of the proposed work is evaluated by integrating the invariant transition and fully-connected layer in the architecture of group-equivariant CNNs (G-CNNs) on rotated MNIST and CIFAR10 datasets.	['Chandan Singh', 'Jaspreet Singh']	2022-09-22	null	null	null	null	['rotated-mnist']	['computer-vision']	[-7.62506425e-02 -2.53350168e-01 2.51700908e-01 -7.16381192e-01 1.64538473e-01 -7.00397491e-01 7.74975538e-01 -3.05467546e-01 -8.82630885e-01 4.55126375e-01 2.04011664e-01 7.00281858e-02 -2.13684097e-01 -9.51503694e-01 -8.36370051e-01 -7.68567085e-01 -6.12366274e-02 -3.78685355e-01 3.24212432e-01 -3.82634491e-01 -5.85327744e-02 1.17810357e+00 -1.27000833e+00 3.33422512e-01 2.75386214e-01 1.06163764e+00 -3.72459620e-01 6.80186987e-01 2.75665224e-01 4.85971957e-01 -2.93196350e-01 -3.80264163e-01 3.64485592e-01 -2.21928194e-01 -7.30634332e-01 -2.32303903e-01 6.31361604e-01 -2.61053443e-01 -7.32151210e-01 1.42308319e+00 4.76763725e-01 5.23030043e-01 6.25430763e-01 -7.96986878e-01 -1.02113283e+00 5.36100805e-01 2.07731962e-01 4.57931280e-01 -3.18606019e-01 -1.42788827e-01 9.68480110e-01 -1.17026877e+00 4.87885624e-01 1.46425867e+00 7.18567014e-01 2.35837683e-01 -1.02607787e+00 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ec50ece5-874c-48b5-8a66-530e0f5e24cc	habitat-a-platform-for-embodied-ai-research	1904.01201	null	https://arxiv.org/abs/1904.01201v2	https://arxiv.org/pdf/1904.01201v2.pdf	Habitat: A Platform for Embodied AI Research	We present Habitat, a platform for research in embodied artificial intelligence (AI). Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation. Specifically, Habitat consists of: (i) Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, sensors, and generic 3D dataset handling. Habitat-Sim is fast -- when rendering a scene from Matterport3D, it achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. (ii) Habitat-API: a modular high-level library for end-to-end development of embodied AI algorithms -- defining tasks (e.g., navigation, instruction following, question answering), configuring, training, and benchmarking embodied agents. These large-scale engineering contributions enable us to answer scientific questions requiring experiments that were till now impracticable or 'merely' impractical. Specifically, in the context of point-goal navigation: (1) we revisit the comparison between learning and SLAM approaches from two recent works and find evidence for the opposite conclusion -- that learning outperforms SLAM if scaled to an order of magnitude more experience than previous investigations, and (2) we conduct the first cross-dataset generalization experiments {train, test} x {Matterport3D, Gibson} for multiple sensors {blind, RGB, RGBD, D} and find that only agents with depth (D) sensors generalize across datasets. We hope that our open-source platform and these findings will advance research in embodied AI.	['Julian Straub', 'Bhavana Jain', 'Abhishek Kadian', 'Manolis Savva', 'Yili Zhao', 'Vladlen Koltun', 'Erik Wijmans', 'Oleksandr Maksymets', 'Jia Liu', 'Dhruv Batra', 'Jitendra Malik', 'Devi Parikh']	2019-04-02	habitat-a-platform-for-embodied-ai-research-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Savva_Habitat_A_Platform_for_Embodied_AI_Research_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Savva_Habitat_A_Platform_for_Embodied_AI_Research_ICCV_2019_paper.pdf	iccv-2019-10	['pointgoal-navigation']	['robots']	[ 2.13802643e-02 -5.24783395e-02 5.55494070e-01 -1.16623513e-01 -3.43863964e-01 -8.91935229e-01 6.28627598e-01 1.88124776e-02 -8.48029494e-01 5.48801064e-01 3.76215146e-04 -5.44535220e-01 -2.82304622e-02 -7.84004033e-01 -1.08306170e+00 -6.13555193e-01 -5.86613536e-01 5.98984301e-01 2.57382810e-01 -6.38527513e-01 3.85537148e-01 6.57838464e-01 -2.26962447e+00 -6.08051680e-02 5.30054510e-01 8.53133619e-01 4.88498837e-01 1.01841497e+00 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4ac1af28-f036-4e30-a4d4-92e2b5f1d1ce	sked-sketch-guided-text-based-3d-editing	2303.10735	null	https://arxiv.org/abs/2303.10735v3	https://arxiv.org/pdf/2303.10735v3.pdf	SKED: Sketch-guided Text-based 3D Editing	Text-to-image diffusion models are gradually introduced into computer graphics, recently enabling the development of Text-to-3D pipelines in an open domain. However, for interactive editing purposes, local manipulations of content through a simplistic textual interface can be arduous. Incorporating user guided sketches with Text-to-image pipelines offers users more intuitive control. Still, as state-of-the-art Text-to-3D pipelines rely on optimizing Neural Radiance Fields (NeRF) through gradients from arbitrary rendering views, conditioning on sketches is not straightforward. In this paper, we present SKED, a technique for editing 3D shapes represented by NeRFs. Our technique utilizes as few as two guiding sketches from different views to alter an existing neural field. The edited region respects the prompt semantics through a pre-trained diffusion model. To ensure the generated output adheres to the provided sketches, we propose novel loss functions to generate the desired edits while preserving the density and radiance of the base instance. We demonstrate the effectiveness of our proposed method through several qualitative and quantitative experiments.	['Ali Mahdavi-Amiri', 'Mehdi Safaee', 'Daniel Cohen-Or', 'Or Perel', 'Aryan Mikaeili']	2023-03-19	null	null	null	null	['text-to-3d']	['computer-vision']	[ 4.09344435e-01 -6.39145914e-03 4.97422665e-01 -5.71584046e-01 -2.95464635e-01 -7.63310254e-01 9.35393870e-01 -5.99766411e-02 -5.00463396e-02 3.19478780e-01 9.61621478e-02 -1.40886664e-01 2.31481776e-01 -1.02251327e+00 -7.33565271e-01 -3.35364044e-01 2.96324402e-01 2.24161252e-01 2.83037603e-01 -2.54839480e-01 5.92250943e-01 1.04804063e+00 -1.36696184e+00 3.30865949e-01 9.49988186e-01 8.41143608e-01 2.80148625e-01 1.03368020e+00 -5.41033864e-01 7.79495299e-01 -4.72976267e-01 -4.68207151e-01 5.53944707e-01 -4.01076317e-01 -2.44091168e-01 -2.56576631e-02 9.18983102e-01 -6.84613824e-01 -2.59729296e-01 8.50352705e-01 5.42949736e-01 3.66071403e-01 9.40919340e-01 -9.55559611e-01 -1.11682272e+00 1.58923417e-01 -8.11365724e-01 -3.42456579e-01 3.33353579e-01 3.17941010e-01 7.54627943e-01 -9.43263948e-01 9.93662238e-01 1.41552639e+00 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e4531165-5f60-4c33-9a27-b93da78b2b60	scale-equivalent-distillation-for-semi	2203.12244	null	https://arxiv.org/abs/2203.12244v2	https://arxiv.org/pdf/2203.12244v2.pdf	Scale-Equivalent Distillation for Semi-Supervised Object Detection	Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based on self-training, i.e., generating hard pseudo-labels by a teacher model on unlabeled data as supervisory signals. Although they achieved certain success, the limited labeled data in semi-supervised learning scales up the challenges of object detection. We analyze the challenges these methods meet with the empirical experiment results. We find that the massive False Negative samples and inferior localization precision lack consideration. Besides, the large variance of object sizes and class imbalance (i.e., the extreme ratio between background and object) hinder the performance of prior arts. Further, we overcome these challenges by introducing a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance. SED has several appealing benefits compared to the previous works. (1) SED imposes a consistency regularization to handle the large scale variance problem. (2) SED alleviates the noise problem from the False Negative samples and inferior localization precision. (3) A re-weighting strategy can implicitly screen the potential foreground regions of the unlabeled data to reduce the effect of class imbalance. Extensive experiments show that SED consistently outperforms the recent state-of-the-art methods on different datasets with significant margins. For example, it surpasses the supervised counterpart by more than 10 mAP when using 5% and 10% labeled data on MS-COCO.	['Ping Luo', 'Yizhou Yu', 'Tianqi Wang', 'Jianyu Chen', 'Yao Mu', 'Qiushan Guo']	2022-03-23	null	http://openaccess.thecvf.com//content/CVPR2022/html/Guo_Scale-Equivalent_Distillation_for_Semi-Supervised_Object_Detection_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Guo_Scale-Equivalent_Distillation_for_Semi-Supervised_Object_Detection_CVPR_2022_paper.pdf	cvpr-2022-1	['semi-supervised-object-detection']	['computer-vision']	[ 1.67718068e-01 8.40551779e-02 -3.42027664e-01 -2.54632115e-01 -9.21506941e-01 -3.56899977e-01 5.13142943e-01 -1.70305327e-01 -4.65573639e-01 6.60825133e-01 -2.54943341e-01 -3.05018835e-02 1.98285714e-01 -4.10396695e-01 -8.80547225e-01 -9.96577203e-01 3.64966303e-01 5.18732786e-01 7.63151467e-01 1.18739396e-01 2.28976160e-02 2.44332314e-01 -1.66737974e+00 2.11250857e-01 1.11951733e+00 1.20565414e+00 4.85883087e-01 1.64951697e-01 -2.37827256e-01 8.18062603e-01 -6.83422089e-01 -2.07507819e-01 4.52008873e-01 -4.04737413e-01 -3.26958627e-01 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2810f026-d7c2-4ca7-8d8a-9a8722261cef	towards-open-vocabulary-video-instance	2304.01715	null	https://arxiv.org/abs/2304.01715v1	https://arxiv.org/pdf/2304.01715v1.pdf	Towards Open-Vocabulary Video Instance Segmentation	Video Instance Segmentation(VIS) aims at segmenting and categorizing objects in videos from a closed set of training categories, lacking the generalization ability to handle novel categories in real-world videos. To address this limitation, we make the following three contributions. First, we introduce the novel task of Open-Vocabulary Video Instance Segmentation, which aims to simultaneously segment, track, and classify objects in videos from open-set categories, including novel categories unseen during training. Second, to benchmark Open-Vocabulary VIS, we collect a Large-Vocabulary Video Instance Segmentation dataset(LV-VIS), that contains well-annotated objects from 1,212 diverse categories, significantly surpassing the category size of existing datasets by more than one order of magnitude. Third, we propose an efficient Memory-Induced Vision-Language Transformer, MindVLT, to first achieve Open-Vocabulary VIS in an end-to-end manner with near real-time inference speed. Extensive experiments on LV-VIS and four existing VIS datasets demonstrate the strong zero-shot generalization ability of MindVLT on novel categories. We will release the dataset and code to facilitate future endeavors.	['Efstratios Gavves', 'Weidi Xie', 'Yao Hu', 'Xu Tang', 'XiaoLong Jiang', 'Cilin Yan', 'Shuai Wang', 'Haochen Wang']	2023-04-04	null	null	null	null	['video-instance-segmentation']	['computer-vision']	[ 4.44124937e-01 6.35843053e-02 -4.55566853e-01 -5.42793810e-01 -9.12182510e-01 -8.57611418e-01 3.37272644e-01 -3.09173346e-01 -2.37389669e-01 5.01221836e-01 -2.12370902e-02 -1.21855438e-01 1.98996156e-01 -3.75670493e-01 -1.11695445e+00 -3.15330684e-01 -1.62862018e-01 5.05852759e-01 3.67845446e-01 2.50819981e-01 4.75000441e-02 -1.00752451e-01 -2.00302100e+00 5.10523558e-01 6.78912759e-01 1.33521187e+00 3.26458603e-01 5.85075021e-01 -1.54044256e-02 9.73655760e-01 -1.90495014e-01 -2.81807721e-01 4.82721299e-01 -3.93837422e-01 -1.06945407e+00 5.04510283e-01 1.27857387e+00 -4.55161452e-01 -5.01137972e-01 9.89897907e-01 9.15225148e-02 5.40451229e-01 6.34852886e-01 -1.25501668e+00 -7.73955405e-01 6.03420973e-01 -4.05170709e-01 5.26176393e-01 2.90030301e-01 4.03049767e-01 1.03787303e+00 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b84c96b7-c81c-4f8a-8d5c-5e3802d06e78	sound2synth-interpreting-sound-via-fm	2205.03043	null	https://arxiv.org/abs/2205.03043v2	https://arxiv.org/pdf/2205.03043v2.pdf	Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation	Synthesizer is a type of electronic musical instrument that is now widely used in modern music production and sound design. Each parameters configuration of a synthesizer produces a unique timbre and can be viewed as a unique instrument. The problem of estimating a set of parameters configuration that best restore a sound timbre is an important yet complicated problem, i.e.: the synthesizer parameters estimation problem. We proposed a multi-modal deep-learning-based pipeline Sound2Synth, together with a network structure Prime-Dilated Convolution (PDC) specially designed to solve this problem. Our method achieved not only SOTA but also the first real-world applicable results on Dexed synthesizer, a popular FM synthesizer.	['Hang Zhao', 'Jian Wu', 'Yifei Xu', 'Shengcheng Yuan', 'Yansen Jing', 'Zui Chen']	2022-05-06	null	null	null	null	['audio-signal-processing']	['audio']	[-3.82449813e-02 -7.43267894e-01 1.66545346e-01 2.41075352e-01 -7.80668736e-01 -8.42773616e-01 6.41451627e-02 -7.34024167e-01 8.32087025e-02 3.77669960e-01 1.55748963e-01 -1.32885769e-01 -2.54269123e-01 -5.41145384e-01 -6.34869337e-01 -6.80936396e-01 1.70366451e-01 5.05293846e-01 -4.58356351e-01 -4.88525808e-01 8.16684365e-02 6.17755473e-01 -1.66030443e+00 3.60224754e-01 4.95082974e-01 1.06572807e+00 2.73216307e-01 1.15815604e+00 5.87488562e-02 6.40979826e-01 -1.00730228e+00 -3.11901808e-01 3.98047447e-01 -7.02609837e-01 -5.97673118e-01 -4.73867267e-01 5.18008947e-01 -3.71350318e-01 -1.27822816e-01 1.04525352e+00 1.08749390e+00 3.61096144e-01 6.29284024e-01 -8.26195657e-01 -5.76877594e-01 1.42703545e+00 -1.94679618e-01 -9.38441381e-02 6.57580048e-02 3.31925660e-01 1.32325566e+00 -6.32694602e-01 2.36354113e-01 1.10067391e+00 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6586ddbe-0b61-40c0-9827-78586b0075d5	online-structured-sparsity-based-moving	1911.12989	null	https://arxiv.org/abs/1911.12989v3	https://arxiv.org/pdf/1911.12989v3.pdf	Online Structured Sparsity-based Moving Object Detection from Satellite Videos	Inspired by the recent developments in computer vision, low-rank and structured sparse matrix decomposition can be potentially be used for extract moving objects in satellite videos. This set of approaches seeks for rank minimization on the background that typically requires batch-based optimization over a sequence of frames, which causes delays in processing and limits their applications. To remedy this delay, we propose an Online Low-rank and Structured Sparse Decomposition (O-LSD). O-LSD reformulates the batch-based low-rank matrix decomposition with the structured sparse penalty to its equivalent frame-wise separable counterpart, which then defines a stochastic optimization problem for online subspace basis estimation. In order to promote online processing, O-LSD conducts the foreground and background separation and the subspace basis update alternatingly for every frame in a video. We also show the convergence of O-LSD theoretically. Experimental results on two satellite videos demonstrate the performance of O-LSD in term of accuracy and time consumption is comparable with the batch-based approaches with significantly reduced delay in processing.	['Junpeng Zhang', 'Xiuping Jia', 'Jocelyn Chanussot', 'Jiankun Hu']	2019-11-29	null	null	null	null	['moving-object-detection']	['computer-vision']	[ 2.26530686e-01 -6.99001253e-01 2.60212928e-01 -1.23932414e-01 -7.20436752e-01 -5.07151723e-01 2.56717354e-01 -3.83093596e-01 -2.41680518e-01 4.11969006e-01 2.39738762e-01 -1.18615545e-01 -3.08674335e-01 -1.29731104e-01 -4.91143525e-01 -1.07713974e+00 -3.32387745e-01 -8.72499570e-02 2.82209456e-01 2.30471864e-01 1.43562378e-02 4.99384165e-01 -1.45816517e+00 2.40781322e-01 9.98466730e-01 9.70608234e-01 5.54280519e-01 6.79278791e-01 1.77251831e-01 8.11718106e-01 1.42839747e-02 5.49939200e-02 6.73410714e-01 -3.33079398e-01 -3.55719239e-01 9.25482810e-01 6.58804655e-01 -6.68275535e-01 -7.67851353e-01 1.22609389e+00 3.15248072e-01 5.24435103e-01 2.40085378e-01 -9.67359245e-01 -7.35206455e-02 4.77291411e-03 -9.46356833e-01 6.13351464e-01 3.63918126e-01 -7.49442428e-02 6.30810916e-01 -1.54860651e+00 4.65679348e-01 1.35212708e+00 6.08784735e-01 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0d243c3b-df29-4361-87f1-e6f774d6ff2b	medal-deep-active-learning-sampling-method	1809.09287	null	http://arxiv.org/abs/1809.09287v2	http://arxiv.org/pdf/1809.09287v2.pdf	MedAL: Deep Active Learning Sampling Method for Medical Image Analysis	Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance.Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance. However, such large labeled datasets are costly to acquire. Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance.In this work, we propose a novel sampling method that queries the unlabeled examples that maximize the average distance to all training set examples in a learned feature space. We then extend our sampling method to define a better initial training set, without the need for a trained model, by using ORB feature descriptors. We validate MedAL on 3 medical image datasets and show that our method is robust to different dataset properties. MedAL is also efficient, achieving 80% accuracy on the task of Diabetic Retinopathy detection using only 425 labeled images, corresponding to a 32% reduction in the number of required labeled examples compared to the standard uncertainty sampling technique, and a 40% reduction compared to random sampling.	['Adrián Galdrán', 'Pedro Costa', 'Asim Smailagic', 'Susu Xu', 'Mostafa Mirshekari', 'Kartik Khandelwal', 'Hae Young Noh', 'Jonathon Fagert', 'Devesh Walawalkar']	2018-09-25	null	null	null	null	['diabetic-retinopathy-detection']	['medical']	[ 1.32496536e-01 2.79609382e-01 -2.87815005e-01 -9.08332586e-01 -1.27236700e+00 -1.91502884e-01 2.27467313e-01 4.00198400e-01 -8.89028430e-01 5.85084736e-01 -2.44342119e-01 3.84067260e-02 -2.24686041e-01 -8.73406589e-01 -6.11828268e-01 -8.84533823e-01 1.68201849e-01 7.87416279e-01 2.10754469e-01 6.39778793e-01 1.58284437e-02 3.81091535e-01 -1.48857009e+00 3.61456594e-04 1.01869476e+00 1.09286177e+00 2.02229977e-01 2.78075874e-01 -1.14349037e-01 9.38690543e-01 -7.41769910e-01 -9.05830786e-02 2.79453158e-01 -3.98085803e-01 -7.51110792e-01 6.51386738e-01 4.50019926e-01 -5.39058626e-01 8.67455974e-02 1.01745892e+00 8.42286170e-01 1.47160590e-01 8.83509457e-01 -8.08931231e-01 -3.75681251e-01 4.60701227e-01 -5.49727798e-01 -4.20755148e-02 -3.25982958e-01 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1e595f8b-878b-40cb-98cd-a6dcaa914814	evaluating-openai-s-whisper-asr-for	2305.1458	null	https://arxiv.org/abs/2305.14580v2	https://arxiv.org/pdf/2305.14580v2.pdf	Evaluating OpenAI's Whisper ASR for Punctuation Prediction and Topic Modeling of life histories of the Museum of the Person	Automatic speech recognition (ASR) systems play a key role in applications involving human-machine interactions. Despite their importance, ASR models for the Portuguese language proposed in the last decade have limitations in relation to the correct identification of punctuation marks in automatic transcriptions, which hinder the use of transcriptions by other systems, models, and even by humans. However, recently Whisper ASR was proposed by OpenAI, a general-purpose speech recognition model that has generated great expectations in dealing with such limitations. This chapter presents the first study on the performance of Whisper for punctuation prediction in the Portuguese language. We present an experimental evaluation considering both theoretical aspects involving pausing points (comma) and complete ideas (exclamation, question, and fullstop), as well as practical aspects involving transcript-based topic modeling - an application dependent on punctuation marks for promising performance. We analyzed experimental results from videos of Museum of the Person, a virtual museum that aims to tell and preserve people's life histories, thus discussing the pros and cons of Whisper in a real-world scenario. Although our experiments indicate that Whisper achieves state-of-the-art results, we conclude that some punctuation marks require improvements, such as exclamation, semicolon and colon.	['Sandra Maria Aluísio', 'Anderson Soares', 'Edresson Casanova', 'Arnaldo Candido Junior', 'Ricardo Marcacini', 'Lucas Rafael Stefanel Gris']	2023-05-23	null	null	null	null	['automatic-speech-recognition']	['speech']	[ 4.41224240e-02 1.29746526e-01 2.19005242e-01 -2.18411341e-01 -9.66754436e-01 -5.71722150e-01 8.52056265e-01 3.17104936e-01 -5.98665476e-01 7.13282883e-01 5.97929716e-01 -3.35449338e-01 3.26208584e-02 -2.37687677e-01 -3.26732934e-01 -5.82529783e-01 1.12724587e-01 6.27783060e-01 4.91987586e-01 -6.53281212e-01 4.35338229e-01 4.33557868e-01 -1.65950632e+00 6.54892683e-01 5.89124382e-01 4.89961773e-01 6.04072928e-01 8.15314412e-01 -6.90523505e-01 6.73045933e-01 -1.38316655e+00 -5.19669354e-01 -2.21337110e-01 -3.92655671e-01 -1.03980720e+00 1.73053861e-01 1.38596758e-01 2.85165727e-01 -4.40028012e-01 8.10037374e-01 5.39745331e-01 1.83677927e-01 6.29295468e-01 -9.83086586e-01 -3.74013454e-01 1.00879049e+00 -5.39437979e-02 3.07140738e-01 8.90295446e-01 -1.27544969e-01 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9.82446432e-01 1.72052652e-01 1.17866240e-01 4.27157670e-01 -6.70388341e-01 -4.20267880e-01 -1.47692990e+00 -6.54005349e-01 1.74892247e-01 1.39054805e-01 4.46075872e-02 -4.77225125e-01 2.61817634e-01]	[14.287074089050293, 6.958329200744629]
875706ea-3128-46e7-a033-81d509573d50	a-novel-twitter-sentiment-analysis-model-with	2003.08137	null	https://arxiv.org/abs/2003.08137v2	https://arxiv.org/pdf/2003.08137v2.pdf	A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency	A novel social networks sentiment analysis model is proposed based on Twitter sentiment score (TSS) for real-time prediction of the future stock market price FTSE 100, as compared with conventional econometric models of investor sentiment based on closed-end fund discount (CEFD). The proposed TSS model features a new baseline correlation approach, which not only exhibits a decent prediction accuracy, but also reduces the computation burden and enables a fast decision making without the knowledge of historical data. Polynomial regression, classification modelling and lexicon-based sentiment analysis are performed using R. The obtained TSS predicts the future stock market trend in advance by 15 time samples (30 working hours) with an accuracy of 67.22% using the proposed baseline criterion without referring to historical TSS or market data. Specifically, TSS's prediction performance of an upward market is found far better than that of a downward market. Under the logistic regression and linear discriminant analysis, the accuracy of TSS in predicting the upward trend of the future market achieves 97.87%.	['Xinyi Guo', 'Jinfeng Li']	2020-03-18	null	null	null	null	['twitter-sentiment-analysis']	['natural-language-processing']	[-6.75920129e-01 -1.22513384e-01 -2.53599346e-01 -4.43155378e-01 -2.08994791e-01 -5.61604619e-01 6.65729225e-01 2.59445995e-01 -4.46170568e-01 6.21615708e-01 -5.71154850e-03 -6.15184844e-01 -2.35265512e-02 -1.22930861e+00 -2.08838861e-02 -4.30159479e-01 -1.34090856e-01 1.89313754e-01 6.35990873e-02 -7.08438873e-01 6.73940778e-01 2.36866802e-01 -1.37069714e+00 -5.45313805e-02 3.38507444e-01 1.61444962e+00 -1.03314094e-01 3.54717702e-01 -1.36767462e-01 1.41691196e+00 -7.17064142e-01 -8.78312230e-01 6.19491756e-01 4.32190932e-02 -1.15426898e-01 -3.19171011e-01 -5.39452255e-01 -2.71076381e-01 1.78460911e-01 7.57963598e-01 -5.64702116e-02 -5.70630692e-02 5.35454452e-01 -1.12622321e+00 -5.66531956e-01 5.78336656e-01 -7.40187824e-01 2.82462031e-01 1.64131731e-01 -3.30377817e-01 1.35588276e+00 -1.00241840e+00 5.33366144e-01 6.99121475e-01 7.34627545e-01 -3.60350788e-01 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-1.28505492e+00 -6.31805360e-02 2.91989297e-01 -4.75051731e-01 -8.18421304e-01 -1.99622959e-02]	[4.481029033660889, 4.333154678344727]
64e0a369-23cd-4a61-bbb6-008aedec9156	parallel-detection-for-efficient-video	2107.12563	null	https://arxiv.org/abs/2107.12563v1	https://arxiv.org/pdf/2107.12563v1.pdf	Parallel Detection for Efficient Video Analytics at the Edge	Deep Neural Network (DNN) trained object detectors are widely deployed in many mission-critical systems for real time video analytics at the edge, such as autonomous driving and video surveillance. A common performance requirement in these mission-critical edge services is the near real-time latency of online object detection on edge devices. However, even with well-trained DNN object detectors, the online detection quality at edge may deteriorate for a number of reasons, such as limited capacity to run DNN object detection models on heterogeneous edge devices, and detection quality degradation due to random frame dropping when the detection processing rate is significantly slower than the incoming video frame rate. This paper addresses these problems by exploiting multi-model multi-device detection parallelism for fast object detection in edge systems with heterogeneous edge devices. First, we analyze the performance bottleneck of running a well-trained DNN model at edge for real time online object detection. We use the offline detection as a reference model, and examine the root cause by analyzing the mismatch among the incoming video streaming rate, video processing rate for object detection, and output rate for real time detection visualization of video streaming. Second, we study performance optimizations by exploiting multi-model detection parallelism. We show that the model-parallel detection approach can effectively speed up the FPS detection processing rate, minimizing the FPS disparity with the incoming video frame rate on heterogeneous edge devices. We evaluate the proposed approach using SSD300 and YOLOv3 on benchmark videos of different video stream rates. The results show that exploiting multi-model detection parallelism can speed up the online object detection processing rate and deliver near real-time object detection performance for efficient video analytics at edge.	['Ramana Kompella', 'Ling Liu', 'Yanzhao Wu']	2021-07-27	null	null	null	null	['real-time-object-detection']	['computer-vision']	[-5.45128575e-03 -5.15422285e-01 -3.04860294e-01 7.38161802e-02 -1.58837214e-01 -3.12002927e-01 1.11451596e-01 -2.41353661e-02 -7.53513277e-01 -1.64357036e-01 -4.53182161e-01 -5.48758268e-01 3.20130408e-01 -6.44764125e-01 -9.34314132e-01 -4.35043275e-01 -2.82230377e-01 1.46522045e-01 1.14992011e+00 2.46701762e-02 -4.90982793e-02 6.41622543e-01 -2.03140736e+00 4.27351534e-01 2.07569748e-01 1.54206514e+00 4.41020191e-01 1.40672922e+00 -3.10374964e-02 8.71236205e-01 -6.75284982e-01 5.74477427e-02 7.05274999e-01 -1.71683226e-02 1.47159874e-01 5.93349040e-02 5.70811093e-01 -1.17941213e+00 -6.82609200e-01 1.26425111e+00 5.57369769e-01 -2.16692343e-01 2.98299223e-01 -1.73180926e+00 2.62688041e-01 2.19036192e-01 -7.77434170e-01 9.34810579e-01 -1.83630720e-01 3.66928160e-01 4.34529215e-01 -7.42988408e-01 3.85175675e-01 1.08884215e+00 4.53274429e-01 6.12673283e-01 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76bcef52-6dac-46d0-bfa5-477a06676ad4	a-practical-method-for-constructing	2104.09459	null	https://arxiv.org/abs/2104.09459v1	https://arxiv.org/pdf/2104.09459v1.pdf	A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups	Symmetries and equivariance are fundamental to the generalization of neural networks on domains such as images, graphs, and point clouds. Existing work has primarily focused on a small number of groups, such as the translation, rotation, and permutation groups. In this work we provide a completely general algorithm for solving for the equivariant layers of matrix groups. In addition to recovering solutions from other works as special cases, we construct multilayer perceptrons equivariant to multiple groups that have never been tackled before, including $\mathrm{O}(1,3)$, $\mathrm{O}(5)$, $\mathrm{Sp}(n)$, and the Rubik's cube group. Our approach outperforms non-equivariant baselines, with applications to particle physics and dynamical systems. We release our software library to enable researchers to construct equivariant layers for arbitrary matrix groups.	['Andrew Gordon Wilson', 'Max Welling', 'Marc Finzi']	2021-04-19	a-practical-method-for-constructing-1	https://arxiv.org/abs/2104.09459	https://arxiv.org/pdf/2104.09459.pdf	null	['rubik-s-cube']	['graphs']	[ 2.50935167e-01 2.49906957e-01 1.41731739e-01 -1.68460950e-01 -3.77359055e-02 -6.95838451e-01 7.32449174e-01 -2.27081746e-01 -2.91145235e-01 4.31676090e-01 1.41521573e-01 -5.69822550e-01 -4.47870046e-01 -7.55445182e-01 -1.08413517e+00 -7.33241975e-01 -5.78738987e-01 5.20956635e-01 -8.78263041e-02 -5.65597713e-01 3.85012239e-01 6.61865294e-01 -1.39693213e+00 1.84044257e-01 3.59250277e-01 7.82554150e-01 -1.35532096e-01 6.75934970e-01 3.10273394e-02 6.34697735e-01 -9.52102467e-02 -2.76736677e-01 5.98399699e-01 -5.47903739e-02 -9.29368138e-01 -8.52479339e-02 1.29092669e+00 -9.48986188e-02 -5.60132027e-01 1.35289717e+00 5.36686927e-02 3.26260209e-01 8.38258505e-01 -1.38316762e+00 -9.68846858e-01 9.34374452e-01 -5.18104672e-01 1.41748458e-01 -1.17350966e-02 -1.50998518e-01 1.42067659e+00 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85bfb024-cf45-4b32-bb2c-454a02505963	ecg-arrhythmia-classification-using-a-2-d	1804.06812	null	http://arxiv.org/abs/1804.06812v1	http://arxiv.org/pdf/1804.06812v1.pdf	ECG arrhythmia classification using a 2-D convolutional neural network	In this paper, we propose an effective electrocardiogram (ECG) arrhythmia classification method using a deep two-dimensional convolutional neural network (CNN) which recently shows outstanding performance in the field of pattern recognition. Every ECG beat was transformed into a two-dimensional grayscale image as an input data for the CNN classifier. Optimization of the proposed CNN classifier includes various deep learning techniques such as batch normalization, data augmentation, Xavier initialization, and dropout. In addition, we compared our proposed classifier with two well-known CNN models; AlexNet and VGGNet. ECG recordings from the MIT-BIH arrhythmia database were used for the evaluation of the classifier. As a result, our classifier achieved 99.05% average accuracy with 97.85% average sensitivity. To precisely validate our CNN classifier, 10-fold cross-validation was performed at the evaluation which involves every ECG recording as a test data. Our experimental results have successfully validated that the proposed CNN classifier with the transformed ECG images can achieve excellent classification accuracy without any manual pre-processing of the ECG signals such as noise filtering, feature extraction, and feature reduction.	['Young-Hak Kim', 'Tae Joon Jun', 'Dohyeun Kim', 'Hoang Minh Nguyen', 'Daeyoun Kang', 'Daeyoung Kim']	2018-04-18	null	null	null	null	['arrhythmia-detection', 'electrocardiography-ecg']	['medical', 'methodology']	[ 2.61329710e-01 -3.65563244e-01 4.54742879e-01 -5.68492234e-01 -4.38538373e-01 -2.22461835e-01 -2.58640707e-01 2.78461903e-01 -8.26838911e-01 6.77547753e-01 -5.08681715e-01 -3.58087480e-01 -1.02639005e-01 -7.44878948e-01 -3.05098504e-01 -5.79425275e-01 -2.36295119e-01 3.07030566e-02 -3.56118619e-01 1.14790253e-01 9.78726074e-02 9.09511447e-01 -1.07473624e+00 2.28514001e-01 6.55147970e-01 1.47546899e+00 -4.35441256e-01 9.17904437e-01 3.94510508e-01 5.09671032e-01 -1.12133086e+00 -2.39816263e-01 3.27024370e-01 -6.24363482e-01 -4.83036190e-01 -3.11766863e-02 2.37729563e-03 -2.41947070e-01 -3.70224208e-01 8.50032926e-01 1.11628878e+00 -2.00763196e-01 3.78648371e-01 -9.02166665e-01 -2.59879559e-01 3.37877274e-01 -2.76983947e-01 7.41273344e-01 -3.26436520e-01 1.70401409e-01 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a15ad65a-0988-4b73-8f58-35fd3c748c8f	mcgnet-partial-multi-view-few-shot-learning	2105.02046	null	https://arxiv.org/abs/2105.02046v4	https://arxiv.org/pdf/2105.02046v4.pdf	Few-shot Partial Multi-view Learning	It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views; (ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness.	['Jiebo Luo', 'Richang Hong', 'Shijie Hao', 'Yanrong Guo', 'Yuan Zhou']	2021-05-05	null	null	null	null	['multi-view-learning']	['computer-vision']	[-4.44409586e-02 -3.40682417e-01 -1.33825347e-01 -3.27797055e-01 -5.27727127e-01 -2.63407081e-01 3.96613687e-01 -2.71410435e-01 -1.79364473e-01 6.47278965e-01 4.68380809e-01 1.54194638e-01 -1.83779240e-01 -6.28388345e-01 -5.51801443e-01 -7.50091076e-01 3.59618276e-01 8.80326852e-02 1.35839069e-02 -2.61777371e-01 -2.56550610e-02 1.68095976e-01 -1.76888764e+00 2.85881072e-01 8.87807190e-01 1.03786027e+00 5.14791131e-01 -7.89435804e-02 -7.82424584e-02 6.11741722e-01 -2.98229277e-01 -3.47612828e-01 4.87099826e-01 -8.47157612e-02 -2.58295953e-01 5.04613996e-01 3.98224622e-01 -5.28666675e-01 -3.75076234e-01 1.13551319e+00 6.22675598e-01 1.27727956e-01 3.33486676e-01 -1.40755618e+00 -7.42545068e-01 1.66034997e-01 -1.13473332e+00 2.19543725e-01 1.77975502e-02 -6.99211955e-02 9.25489366e-01 -1.24760413e+00 5.83904207e-01 1.20163667e+00 4.82903033e-01 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c8b40a8e-7817-46ba-92d7-dfb0a875ada2	schema-independent-relational-learning	1508.03846	null	http://arxiv.org/abs/1508.03846v2	http://arxiv.org/pdf/1508.03846v2.pdf	Schema Independent Relational Learning	Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions.	['Parisa Ataei', 'Alan Fern', 'Jose Picado', 'Arash Termehchy']	2015-08-16	null	null	null	null	['novel-concepts']	['reasoning']	[ 1.36296436e-01 1.00845210e-01 -7.40998983e-01 -8.31545711e-01 -5.93729019e-01 -7.58978784e-01 3.05662006e-01 7.07055330e-01 -2.46370584e-01 5.24330199e-01 -2.08255574e-01 -4.11343485e-01 -6.93166375e-01 -1.42433274e+00 -1.00051129e+00 -4.85704035e-01 -2.38351896e-01 1.09861934e+00 4.60098594e-01 -3.76336545e-01 -1.08222522e-01 7.09731340e-01 -1.97859371e+00 6.56400859e-01 5.36329985e-01 9.15464163e-01 -3.11070949e-01 4.34821188e-01 -4.64766115e-01 9.85869348e-01 -5.19530594e-01 -7.59555101e-01 5.35923421e-01 -2.58247018e-01 -9.86131847e-01 -1.10427432e-01 4.19865489e-01 2.40834773e-01 -4.52639878e-01 9.78886306e-01 1.15096346e-01 -3.77227589e-02 3.01336676e-01 -1.57946968e+00 -5.22729933e-01 1.10258353e+00 -2.29365170e-01 8.67325217e-02 3.93796474e-01 -4.57692474e-01 1.38448596e+00 -5.89028299e-01 7.88553774e-01 1.20958328e+00 4.28040564e-01 2.59248376e-01 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517f0635-aa52-44bd-b481-2fdc24b221c4	a-deep-moving-camera-background-model	2209.07923	null	https://arxiv.org/abs/2209.07923v1	https://arxiv.org/pdf/2209.07923v1.pdf	A Deep Moving-camera Background Model	In video analysis, background models have many applications such as background/foreground separation, change detection, anomaly detection, tracking, and more. However, while learning such a model in a video captured by a static camera is a fairly-solved task, in the case of a Moving-camera Background Model (MCBM), the success has been far more modest due to algorithmic and scalability challenges that arise due to the camera motion. Thus, existing MCBMs are limited in their scope and their supported camera-motion types. These hurdles also impeded the employment, in this unsupervised task, of end-to-end solutions based on deep learning (DL). Moreover, existing MCBMs usually model the background either on the domain of a typically-large panoramic image or in an online fashion. Unfortunately, the former creates several problems, including poor scalability, while the latter prevents the recognition and leveraging of cases where the camera revisits previously-seen parts of the scene. This paper proposes a new method, called DeepMCBM, that eliminates all the aforementioned issues and achieves state-of-the-art results. Concretely, first we identify the difficulties associated with joint alignment of video frames in general and in a DL setting in particular. Next, we propose a new strategy for joint alignment that lets us use a spatial transformer net with neither a regularization nor any form of specialized (and non-differentiable) initialization. Coupled with an autoencoder conditioned on unwarped robust central moments (obtained from the joint alignment), this yields an end-to-end regularization-free MCBM that supports a broad range of camera motions and scales gracefully. We demonstrate DeepMCBM's utility on a variety of videos, including ones beyond the scope of other methods. Our code is available at https://github.com/BGU-CS-VIL/DeepMCBM .	['Oren Freifeld', 'Ron Shapira Weber', 'Guy Erez']	2022-09-16	null	null	null	null	['video-background-subtraction']	['computer-vision']	[ 1.09563850e-01 -5.15044868e-01 -1.93452947e-02 -2.01351568e-02 -6.02046609e-01 -5.17193377e-01 6.59445286e-01 -3.43043625e-01 -3.72556388e-01 5.48298538e-01 -1.36607990e-01 -2.86137342e-01 1.30439429e-02 -4.14073080e-01 -7.80215681e-01 -9.39020216e-01 1.17069818e-01 1.53687507e-01 5.74406207e-01 -7.13787600e-02 -5.10800146e-02 6.08923793e-01 -1.48035169e+00 -2.94908155e-02 4.65284139e-01 9.05169308e-01 4.05406058e-01 8.46619129e-01 -1.05184652e-01 8.86803687e-01 -3.83738518e-01 -5.94940126e-01 4.89339352e-01 -4.09862548e-01 -5.75656712e-01 5.25801420e-01 7.81525433e-01 -4.64974701e-01 -5.74869633e-01 1.07789969e+00 2.90760547e-01 2.61687428e-01 3.02730292e-01 -1.28183234e+00 -5.07392138e-02 -5.32544926e-02 -6.62752926e-01 4.61810946e-01 2.11077124e-01 7.08816499e-02 6.73746228e-01 -8.16284418e-01 6.65523231e-01 1.02659285e+00 7.58165717e-01 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2e5be8ee-d10b-4305-a498-5f6b06e6ad63	mdi-a-flexible-random-forest-based-feature	2307.01932	null	https://arxiv.org/abs/2307.01932v1	https://arxiv.org/pdf/2307.01932v1.pdf	MDI+: A Flexible Random Forest-Based Feature Importance Framework	Mean decrease in impurity (MDI) is a popular feature importance measure for random forests (RFs). We show that the MDI for a feature $X_k$ in each tree in an RF is equivalent to the unnormalized $R^2$ value in a linear regression of the response on the collection of decision stumps that split on $X_k$. We use this interpretation to propose a flexible feature importance framework called MDI+. Specifically, MDI+ generalizes MDI by allowing the analyst to replace the linear regression model and $R^2$ metric with regularized generalized linear models (GLMs) and metrics better suited for the given data structure. Moreover, MDI+ incorporates additional features to mitigate known biases of decision trees against additive or smooth models. We further provide guidance on how practitioners can choose an appropriate GLM and metric based upon the Predictability, Computability, Stability framework for veridical data science. Extensive data-inspired simulations show that MDI+ significantly outperforms popular feature importance measures in identifying signal features. We also apply MDI+ to two real-world case studies on drug response prediction and breast cancer subtype classification. We show that MDI+ extracts well-established predictive genes with significantly greater stability compared to existing feature importance measures. All code and models are released in a full-fledged python package on Github.	['Bin Yu', 'Tiffany M. Tang', 'Yan Shuo Tan', 'Ana M. Kenney', 'Abhineet Agarwal']	2023-07-04	null	null	null	null	['drug-response-prediction', 'feature-importance']	['medical', 'methodology']	[ 6.79458082e-01 -6.19826093e-02 -5.97149014e-01 -6.98367298e-01 -8.96907270e-01 -2.60658622e-01 4.51707691e-01 1.74752533e-01 -2.88738385e-02 1.10612869e+00 1.46263823e-01 -4.72370774e-01 -7.33103812e-01 -7.97829628e-01 -5.64734221e-01 -9.86119509e-01 -3.41745675e-01 2.05493525e-01 -1.78003341e-01 1.17935807e-01 3.85856628e-01 7.29341030e-01 -1.44613242e+00 1.09036781e-01 7.66222715e-01 1.07084644e+00 -2.09922209e-01 4.45330709e-01 2.07656473e-01 3.60653818e-01 -1.79193616e-01 -1.91908419e-01 1.55671090e-01 -3.16119432e-01 -8.70222688e-01 -3.30230772e-01 2.35847205e-01 4.20028955e-01 1.42478213e-01 7.45995343e-01 4.25403535e-01 -1.61258280e-01 1.13307142e+00 -1.41263425e+00 -3.86382937e-01 6.17286205e-01 -5.93569577e-01 1.96279675e-01 1.10520005e-01 1.20563045e-01 1.39184928e+00 -1.01389372e+00 6.47528470e-01 1.05197132e+00 8.88236761e-01 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-9.23722923e-01 -1.07789361e+00 -2.34966492e-03 2.33031854e-01 -9.05392885e-01 -6.95830762e-01 -1.60645917e-01]	[7.970056533813477, 4.8347344398498535]
fbef7e97-c8df-4ff7-94b3-49fbe779749b	composition-loss-for-counting-density-map	1808.0105	null	http://arxiv.org/abs/1808.01050v1	http://arxiv.org/pdf/1808.01050v1.pdf	Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds	With multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd analysis is emerging as a new frontier in computer vision. In particular, counting in highly dense crowds is a challenging problem with far-reaching applicability in crowd safety and management, as well as gauging political significance of protests and demonstrations. In this paper, we propose a novel approach that simultaneously solves the problems of counting, density map estimation and localization of people in a given dense crowd image. Our formulation is based on an important observation that the three problems are inherently related to each other making the loss function for optimizing a deep CNN decomposable. Since localization requires high-quality images and annotations, we introduce UCF-QNRF dataset that overcomes the shortcomings of previous datasets, and contains 1.25 million humans manually marked with dot annotations. Finally, we present evaluation measures and comparison with recent deep CNN networks, including those developed specifically for crowd counting. Our approach significantly outperforms state-of-the-art on the new dataset, which is the most challenging dataset with the largest number of crowd annotations in the most diverse set of scenes.	['Somaya Al-Maadeed', 'Dong Zhang', 'Nasir Rajpoot', 'Kishan Athrey', 'Muhmmad Tayyab', 'Mubarak Shah', 'Haroon Idrees']	2018-08-02	composition-loss-for-counting-density-map-1	http://openaccess.thecvf.com/content_ECCV_2018/html/Haroon_Idrees_Composition_Loss_for_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Haroon_Idrees_Composition_Loss_for_ECCV_2018_paper.pdf	eccv-2018-9	['visual-crowd-analysis']	['computer-vision']	[-4.42997545e-01 -4.88288313e-01 4.00560707e-01 -1.21274471e-01 -4.85530198e-01 -5.46338379e-01 8.30243647e-01 3.41489315e-01 -1.26199484e+00 1.05583894e+00 3.53566617e-01 2.07138266e-02 4.44284081e-01 -5.98888397e-01 -6.12472951e-01 -4.19355214e-01 -2.36074366e-02 9.54263389e-01 5.72387099e-01 -2.69946575e-01 1.38140693e-01 4.84103501e-01 -1.41522944e+00 -3.35806720e-02 6.67922020e-01 8.78583372e-01 5.72903454e-02 7.60178149e-01 -1.13459058e-01 1.36314607e+00 -9.77713704e-01 -9.46297467e-01 6.25797957e-02 7.86143690e-02 -7.09656477e-01 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d1f0d348-6e60-4c59-bb9c-7f9205fa24bf	training-data-efficient-image-transformers	2012.12877	null	https://arxiv.org/abs/2012.12877v2	https://arxiv.org/pdf/2012.12877v2.pdf	Training data-efficient image transformers & distillation through attention	Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images using an expensive infrastructure, thereby limiting their adoption. In this work, we produce a competitive convolution-free transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external data. More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation token ensuring that the student learns from the teacher through attention. We show the interest of this token-based distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and models.	['Hervé Jégou', 'Alexandre Sablayrolles', 'Francisco Massa', 'Matthijs Douze', 'Matthieu Cord', 'Hugo Touvron']	2020-12-23	null	null	null	null	['document-image-classification', 'document-layout-analysis']	['computer-vision', 'computer-vision']	[ 2.34593064e-01 3.67680103e-01 1.85660437e-01 -1.97937518e-01 -4.65994984e-01 -5.83199620e-01 7.53893197e-01 -2.02098072e-01 -6.46512449e-01 2.99466491e-01 -3.13293189e-01 -8.19244921e-01 3.88325512e-01 -8.56782317e-01 -1.27496338e+00 -4.41025078e-01 1.25978678e-01 3.47002685e-01 4.47841942e-01 -1.15955554e-01 -2.37410106e-02 3.42426240e-01 -1.54339623e+00 3.87325704e-01 7.76209891e-01 1.23301685e+00 4.57709759e-01 8.93923044e-01 2.41444428e-02 1.29691756e+00 -6.40593171e-01 -7.46876478e-01 3.66367072e-01 6.05266914e-02 -1.10699570e+00 5.89336343e-02 1.11432636e+00 -7.00709403e-01 -3.48069847e-01 8.76473725e-01 2.58122265e-01 -5.18301845e-01 4.39572692e-01 -1.46477902e+00 -9.29288566e-01 6.93531990e-01 -2.74731100e-01 9.46998745e-02 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67434265-2201-4393-a5db-90ee4ac29bec	model-based-offline-reinforcement-learning-1	2301.11426	null	https://arxiv.org/abs/2301.11426v1	https://arxiv.org/pdf/2301.11426v1.pdf	Model-based Offline Reinforcement Learning with Local Misspecification	We present a model-based offline reinforcement learning policy performance lower bound that explicitly captures dynamics model misspecification and distribution mismatch and we propose an empirical algorithm for optimal offline policy selection. Theoretically, we prove a novel safe policy improvement theorem by establishing pessimism approximations to the value function. Our key insight is to jointly consider selecting over dynamics models and policies: as long as a dynamics model can accurately represent the dynamics of the state-action pairs visited by a given policy, it is possible to approximate the value of that particular policy. We analyze our lower bound in the LQR setting and also show competitive performance to previous lower bounds on policy selection across a set of D4RL tasks.	['Emma Brunskill', 'Allen Nie', 'Yannis Flet-Berliac', 'Kefan Dong']	2023-01-26	null	null	null	null	['d4rl']	['robots']	[-3.38917136e-01 1.64111659e-01 -8.93953800e-01 1.50079802e-01 -9.77066159e-01 -9.39390123e-01 2.65531123e-01 1.30724818e-01 -6.54147625e-01 1.08761990e+00 1.11392133e-01 -6.77896500e-01 -4.57097292e-01 -3.66424859e-01 -9.60482359e-01 -6.94450676e-01 -4.61771578e-01 7.22069502e-01 4.68878150e-02 -1.91959277e-01 -6.85102716e-02 6.69588089e-01 -1.07146621e+00 -1.77564994e-01 6.19377613e-01 9.01195586e-01 3.21338624e-01 1.03527713e+00 2.98100740e-01 8.00497472e-01 -7.35165000e-01 1.87835887e-01 4.64136124e-01 -4.02892798e-01 -6.45187497e-01 3.51303034e-02 7.00802431e-02 -8.11727524e-01 -6.78689301e-01 9.31254864e-01 4.58678931e-01 4.71658826e-01 3.79230857e-01 -1.39798665e+00 -3.13024670e-01 9.18864846e-01 -3.99290472e-01 4.79033709e-01 -2.29962990e-02 5.36494970e-01 9.15115356e-01 3.07377756e-01 5.46488047e-01 1.47337949e+00 2.67286628e-01 8.00219178e-01 -1.45165384e+00 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b39b6e50-3dfc-4a8f-8c7a-ba0f2087cb16	a-configuration-space-decomposition-scheme	1911.08581	null	https://arxiv.org/abs/1911.08581v1	https://arxiv.org/pdf/1911.08581v1.pdf	A Configuration-Space Decomposition Scheme for Learning-based Collision Checking	Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, which train a classifier to distinguish collision free subspace from in-collision subspace in C. In this paper, we propose a novel configuration space decomposition method and show two nice properties resulted from this decomposition. Using these two properties, we build a composite classifier that works compatibly with previous machine learning methods by using them as the elementary classifiers. Experimental results are presented, showing that our composite classifier outperforms state-of-the-art single classifier methods by a large margin. A real application of motion planning in a multi-robot system in plant phenotyping using three UR5 robotic arms is also presented.	['Yong-Jin Liu', 'Yiheng Han', 'Wang Zhao', 'Ran Yi', 'Jia Pan', 'Zipeng Ye']	2019-11-17	null	null	null	null	['plant-phenotyping']	['computer-vision']	[ 1.52633205e-01 1.40437752e-01 -4.85023677e-01 7.63437226e-02 -6.95805103e-02 -5.59109688e-01 4.98010457e-01 4.79009040e-02 -3.10063586e-02 5.18500686e-01 -5.30544341e-01 -4.45051044e-01 -6.89183354e-01 -6.80044889e-01 -5.51070213e-01 -1.06552088e+00 -2.08869979e-01 9.40236866e-01 4.33445007e-01 -4.55680281e-01 3.78595263e-01 9.45180595e-01 -1.51871943e+00 6.14796579e-02 7.27796078e-01 4.31787282e-01 7.55477846e-01 6.52642369e-01 1.76118717e-01 4.56585884e-01 -1.56383798e-01 5.00624239e-01 2.61972517e-01 -3.21290165e-01 -1.17666674e+00 3.10815245e-01 -2.11049840e-01 2.29414806e-01 1.50807619e-01 6.55333579e-01 2.74416715e-01 7.76062310e-02 1.01199663e+00 -1.49843252e+00 7.73851350e-02 6.19334936e-01 -4.64713573e-01 -7.38842547e-01 4.89433765e-01 -1.53437793e-01 6.73394740e-01 -6.69271827e-01 1.08766448e+00 1.30241692e+00 7.22010612e-01 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-1.06615984e+00 -6.03734076e-01 8.19305256e-02 -3.13339941e-02 -1.00978422e+00 -1.09926276e-01 3.24517265e-02]	[4.876671314239502, 1.266391396522522]
3116a91b-ae33-4d1b-a05d-10fb24d7bd2b	characterization-of-lung-nodule-malignancy	1609.06668	null	http://arxiv.org/abs/1609.06668v1	http://arxiv.org/pdf/1609.06668v1.pdf	Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features	Computed tomography imaging is a standard modality for detecting and assessing lung cancer. In order to evaluate the malignancy of lung nodules, clinical practice often involves expert qualitative ratings on several criteria describing a nodule's appearance and shape. Translating these features for computer-aided diagnostics is challenging due to their subjective nature and the difficulties in gaining a complete description. In this paper, we propose a computerized approach to quantitatively evaluate both appearance distinctions and 3D surface variations. Nodule shape was modeled and parameterized using spherical harmonics, and appearance features were extracted using deep convolutional neural networks. Both sets of features were combined to estimate the nodule malignancy using a random forest classifier. The proposed algorithm was tested on the publicly available Lung Image Database Consortium dataset, achieving high accuracy. By providing lung nodule characterization, this method can provide a robust alternative reference opinion for lung cancer diagnosis.	['Daniel J. Mollura', 'Ulas Bagci', 'Mingchen Gao', 'Ziyue Xu', 'Aaron Wu', 'Mario Buty']	2016-09-21	null	null	null	null	['lung-cancer-diagnosis']	['medical']	[ 1.43751279e-01 2.55852610e-01 -3.98265153e-01 -3.16324174e-01 -9.82202411e-01 -6.23822510e-01 4.77057785e-01 2.72908628e-01 -1.94761589e-01 2.60155529e-01 1.20463610e-01 -4.59596306e-01 -2.03472357e-02 -6.86487317e-01 -5.12131974e-02 -6.91732049e-01 1.20887928e-01 9.67142582e-01 3.48477006e-01 2.77361244e-01 -1.42729774e-01 1.00035977e+00 -1.19174445e+00 3.62257600e-01 6.37971580e-01 1.35519171e+00 3.16763073e-01 7.51508713e-01 -4.71759364e-02 6.38221920e-01 3.05813015e-03 -9.15819779e-02 3.47966999e-01 -4.29117382e-01 -7.65260696e-01 3.65060896e-01 3.97320241e-01 -5.48655212e-01 -6.88466653e-02 6.63230598e-01 3.62491935e-01 -4.98829126e-01 1.24914742e+00 -7.00040758e-01 -3.16029966e-01 1.59681007e-01 -1.03587866e-01 2.01613590e-01 5.96340485e-02 2.01488018e-01 9.20208693e-01 -1.00618207e+00 3.36521417e-01 5.94897568e-01 8.57105076e-01 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0c6329b7-68b7-4b1c-87bf-fe1e240a0393	hypertransformer-a-textural-and-spectral	2203.02503	null	https://arxiv.org/abs/2203.02503v3	https://arxiv.org/pdf/2203.02503v3.pdf	HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening	Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high spectral and spatial resolution. Existing pansharpening approaches neglect using an attention mechanism to transfer HR texture features from PAN to LR-HSI features, resulting in spatial and spectral distortions. In this paper, we present a novel attention mechanism for pansharpening called HyperTransformer, in which features of LR-HSI and PAN are formulated as queries and keys in a transformer, respectively. HyperTransformer consists of three main modules, namely two separate feature extractors for PAN and HSI, a multi-head feature soft attention module, and a spatial-spectral feature fusion module. Such a network improves both spatial and spectral quality measures of the pansharpened HSI by learning cross-feature space dependencies and long-range details of PAN and LR-HSI. Furthermore, HyperTransformer can be utilized across multiple spatial scales at the backbone for obtaining improved performance. Extensive experiments conducted on three widely used datasets demonstrate that HyperTransformer achieves significant improvement over the state-of-the-art methods on both spatial and spectral quality measures. Implementation code and pre-trained weights can be accessed at https://github.com/wgcban/HyperTransformer.	['Vishal M. Patel', 'Wele Gedara Chaminda Bandara']	2022-03-04	null	http://openaccess.thecvf.com//content/CVPR2022/html/Bandara_HyperTransformer_A_Textural_and_Spectral_Feature_Fusion_Transformer_for_Pansharpening_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Bandara_HyperTransformer_A_Textural_and_Spectral_Feature_Fusion_Transformer_for_Pansharpening_CVPR_2022_paper.pdf	cvpr-2022-1	['pansharpening']	['computer-vision']	[ 7.90252149e-01 -5.33044696e-01 2.90648602e-02 -2.44097084e-01 -1.26991081e+00 -5.14630318e-01 3.71060371e-01 -3.49735588e-01 3.52534764e-02 4.78805751e-01 3.13848406e-01 8.71883333e-03 -4.74290520e-01 -1.22110176e+00 -5.48286796e-01 -1.18854272e+00 3.85235846e-01 -1.76645473e-01 -4.94968332e-02 -3.00527900e-01 3.40795927e-02 7.10414708e-01 -1.57793617e+00 3.69480133e-01 1.37087333e+00 1.21186459e+00 5.33564627e-01 6.33419573e-01 5.20095706e-01 3.67932826e-01 -2.06947058e-01 -6.69671362e-03 6.14482343e-01 -2.82343537e-01 -6.07130527e-01 2.64623404e-01 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1b9e56b1-3c7f-4ba4-b442-20c05bd8a61e	analysis-of-benfords-law-for-no-reference	null	null	https://www.mdpi.com/2079-9292/10/19/2378	https://www.mdpi.com/2079-9292/10/19/2378	Analysis of Benford’s Law for No-Reference Quality Assessment of Natural, Screen-Content, and Synthetic Images	With the tremendous growth and usage of digital images, no-reference image quality assessment is becoming increasingly important. This paper presents in-depth analysis of Benford’s law inspired first digit distribution feature vectors for no-reference quality assessment of natural, screen-content, and synthetic images in various viewpoints. Benford’s law makes a prediction for the probability distribution of first digits in natural datasets. It has been applied among others for detecting fraudulent income tax returns, detecting scientific fraud, election forensics, and image forensics. In particular, our analysis is based on first digit distributions in multiple domains (wavelet coefficients, DCT coefficients, singular values, etc.) as feature vectors and the extracted features are mapped onto image quality scores. Extensive experiments have been carried out on seven large image quality benchmark databases. It has been demonstrated that first digit distributions are quality-aware features, and it is possible to reach or outperform the state-of-the-art with them.	['Domonkos Varga']	2021-09-21	null	null	null	electronics-2021-9	['no-reference-image-quality-assessment', 'image-forensics']	['computer-vision', 'computer-vision']	[ 1.76732749e-01 -5.38157523e-01 -2.41107166e-01 -2.24345669e-01 -8.82211208e-01 -3.97583902e-01 7.34831989e-01 4.23288137e-01 -3.86800736e-01 4.41320807e-01 1.77402824e-01 -1.49276406e-01 -2.66094565e-01 -7.79512346e-01 -3.37097019e-01 -4.33429182e-01 -6.25555664e-02 -1.14695445e-01 2.26229414e-01 -6.96145520e-02 9.91499901e-01 7.37131715e-01 -1.69431949e+00 2.92787015e-01 6.96610034e-01 1.28145230e+00 -2.22797513e-01 7.84319103e-01 2.91710682e-02 8.93786848e-01 -8.47119570e-01 -1.19678164e+00 2.95543462e-01 -3.98870111e-01 -6.01789773e-01 2.35273212e-01 5.20386815e-01 -5.45613587e-01 -5.33193707e-01 1.50736916e+00 3.78529876e-01 -3.35339069e-01 9.54130352e-01 -1.22899890e+00 -1.00798881e+00 -1.30419940e-01 -1.02527368e+00 5.53004980e-01 7.01865673e-01 2.07238972e-01 8.48911047e-01 -7.92206764e-01 7.75498211e-01 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49229b18-b4b0-4c74-b664-d627d95db8b3	approximate-fpga-based-lstms-under	1801.0219	null	http://arxiv.org/abs/1801.02190v1	http://arxiv.org/pdf/1801.02190v1.pdf	Approximate FPGA-based LSTMs under Computation Time Constraints	Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM) networks have demonstrated state-of-the-art accuracy in several emerging Artificial Intelligence tasks. However, the models are becoming increasingly demanding in terms of computational and memory load. Emerging latency-sensitive applications including mobile robots and autonomous vehicles often operate under stringent computation time constraints. In this paper, we address the challenge of deploying computationally demanding LSTMs at a constrained time budget by introducing an approximate computing scheme that combines iterative low-rank compression and pruning, along with a novel FPGA-based LSTM architecture. Combined in an end-to-end framework, the approximation method's parameters are optimised and the architecture is configured to address the problem of high-performance LSTM execution in time-constrained applications. Quantitative evaluation on a real-life image captioning application indicates that the proposed methods required up to 6.5x less time to achieve the same application-level accuracy compared to a baseline method, while achieving an average of 25x higher accuracy under the same computation time constraints.	['Christos-Savvas Bouganis', 'Alexandros Kouris', 'Michalis Rizakis', 'Stylianos I. Venieris']	2018-01-07	null	null	null	null	['low-rank-compression']	['computer-code']	[ 5.70437491e-01 -3.28749418e-02 -7.50122443e-02 -3.54246795e-01 -9.23313737e-01 2.75457576e-02 5.27498722e-01 -6.78459108e-02 -7.57354259e-01 4.02663022e-01 -3.38854402e-01 -5.82673967e-01 -5.91032282e-02 -4.94907886e-01 -8.91779542e-01 -5.91097176e-01 -1.14426101e-02 5.88330865e-01 6.44679815e-02 2.19218917e-02 3.35349441e-01 3.69754732e-01 -1.72257543e+00 5.28028786e-01 6.51679933e-01 1.58143449e+00 6.32455111e-01 6.27492785e-01 -6.76216781e-02 7.76319623e-01 -5.25219202e-01 -1.98829800e-01 1.94512233e-01 2.71034449e-01 -5.23637116e-01 -2.77667880e-01 8.10677767e-01 -4.93537486e-01 -4.16526735e-01 8.76398504e-01 6.20906055e-01 9.59542766e-02 1.34387612e-01 -8.56893063e-01 -2.23695971e-02 4.55656260e-01 -3.45111728e-01 3.15616548e-01 -2.38789514e-01 -4.40106653e-02 6.34955883e-01 -9.89477813e-01 2.23054782e-01 9.77404773e-01 8.01728725e-01 3.17982256e-01 -9.31193650e-01 -7.16943204e-01 1.83793828e-01 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c1df2e0b-210d-46bf-b9cf-14d8410cb80a	diagnosis-of-covid-19-using-chest-x-ray	null	null	https://link.springer.com/article/10.1007/s12065-021-00679-7	https://link.springer.com/content/pdf/10.1007/s12065-021-00679-7.pdf	Diagnosis of COVID-19 using chest X-ray images based on modified DarkCovidNet model	Coronavirus disease, also known as COVID-19, is an infectious disease caused by SARS-CoV-2. It has a direct impact on the upper and lower respiratory tract and threatened the health of many people around the world. The latest statistics show that the number of people diagnosed with COVID-19 is growing exponentially. Diagnosing positive cases of COVID-19 is important for preventing further spread of the disease. Currently, Coronavirus is a serious threat to scientists, medical experts and researchers around the world from its detection to its treatment. It is currently detected using reverse transcription polymerase chain reaction (RT-PCR) analysis at the most test centers around the world. Yet, knowing the reliability of a deep learning based medical diagnosis is important for doctors to build confidence in the technology and improve treatment. The goal of this study is to develop a model that automatically identifies COVID-19 by using chest X-ray images. To achieve this, we modified the DarkCovidNet model which is based on a convolutional neural network (CNN) and plotted the experimental results for two scenarios: binary classification (COVID-19 versus No-findings) and multi-class classification (COVID-19 versus pneumonia versus No-findings). The model is trained on more than 10 thousand X-ray images and achieved an average accuracy of 99.53% and 94.18% for binary and multi-class classification, respectively. Therefore, the proposed method demonstrates the effectiveness of COVID-19 detection using X-ray images. Our model can be used to test the patient via cloud and also be used in situations where RT-PCR tests and other options aren't available.	['Om Prakash Verma & Tarun Kumar Sharma', 'Vimal Kumar Shrivastava', 'Semagn Sisay Teferi', 'Tensaie Melkamu Demissie', 'Abdulhakim Edao Sirko', 'Dawit Kiros Redie']	2022-03-09	null	null	null	journal-2022-3	['covid-19-detection']	['medical']	[-3.68422866e-02 -6.87021971e-01 1.35972217e-01 -1.32143766e-01 -2.54061639e-01 -6.22660160e-01 1.36526423e-02 3.20897400e-01 -5.77715993e-01 6.83842123e-01 -3.32262278e-01 -7.29575455e-01 2.37441272e-03 -8.09804916e-01 -3.93739760e-01 -6.96346641e-01 -1.88112870e-01 7.76440442e-01 3.69241685e-02 7.46234432e-02 -2.60367393e-01 1.06696200e+00 -1.15144265e+00 4.06596303e-01 7.30330825e-01 1.00770843e+00 4.79465485e-01 1.05534005e+00 2.14073256e-01 4.65611160e-01 -7.20900118e-01 5.68316765e-02 8.10842514e-02 -2.57575274e-01 -3.98878694e-01 -5.39352298e-01 1.73756219e-02 -6.77510023e-01 1.58523932e-01 6.44122899e-01 5.73246896e-01 -3.02749932e-01 8.32102656e-01 -1.05646670e+00 -3.35280418e-01 -4.17040348e-01 -4.16912079e-01 5.29910207e-01 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91d038f7-db43-4c9f-a875-0d10232de9e1	fully-transformer-networks-for-semantic	2106.04108	null	https://arxiv.org/abs/2106.04108v3	https://arxiv.org/pdf/2106.04108v3.pdf	Fully Transformer Networks for Semantic Image Segmentation	Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies. Recent progress has demonstrated that combining such Transformers with CNN-based semantic image segmentation models is very promising. However, it is not well studied yet on how well a pure Transformer based approach can achieve for image segmentation. In this work, we explore a novel framework for semantic image segmentation, which is encoder-decoder based Fully Transformer Networks (FTN). Specifically, we first propose a Pyramid Group Transformer (PGT) as the encoder for progressively learning hierarchical features, meanwhile reducing the computation complexity of the standard Visual Transformer (ViT). Then, we propose a Feature Pyramid Transformer (FPT) to fuse semantic-level and spatial-level information from multiple levels of the PGT encoder for semantic image segmentation. Surprisingly, this simple baseline can achieve better results on multiple challenging semantic segmentation and face parsing benchmarks, including PASCAL Context, ADE20K, COCOStuff, and CelebAMask-HQ. The source code will be released on https://github.com/BR-IDL/PaddleViT.	['Guodong Guo', 'Shengwei Tian', 'Fangjian Lin', 'Tianyi Wu', 'Sitong Wu']	2021-06-08	null	null	null	null	['face-parsing']	['computer-vision']	[ 3.23335350e-01 2.72633523e-01 -4.11380231e-02 -7.26834297e-01 -8.53974283e-01 -5.56382775e-01 4.75924641e-01 -2.45003968e-01 -1.23532861e-01 2.27533922e-01 -2.37117466e-02 -3.51375908e-01 2.10654899e-01 -8.36567998e-01 -9.44865644e-01 -5.30916631e-01 3.87205482e-01 4.88031805e-01 5.61350226e-01 -1.36069536e-01 3.37615027e-03 1.96577892e-01 -1.41796029e+00 5.24128377e-01 8.44007611e-01 1.33989465e+00 3.13466191e-01 5.42311788e-01 -4.03826118e-01 9.21161830e-01 -3.85169923e-01 -7.95698643e-01 -5.72447143e-02 -4.77133036e-01 -1.13565195e+00 6.76247776e-02 6.28743768e-01 -2.53655642e-01 -8.33349824e-02 1.04976845e+00 3.21193248e-01 -3.66172731e-01 3.29722643e-01 -1.23955643e+00 -6.34448290e-01 8.17306280e-01 -5.81896901e-01 -4.99121360e-02 8.27930123e-02 -9.83176474e-03 1.09440732e+00 -7.72971272e-01 4.82219458e-01 1.69603920e+00 7.87236869e-01 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45751355-c0a7-471e-a9af-3ab742db45e7	low-complexity-multidimensional-dct	2306.11724	null	https://arxiv.org/abs/2306.11724v1	https://arxiv.org/pdf/2306.11724v1.pdf	Low-complexity Multidimensional DCT Approximations	In this paper, we introduce low-complexity multidimensional discrete cosine transform (DCT) approximations. Three dimensional DCT (3D DCT) approximations are formalized in terms of high-order tensor theory. The formulation is extended to higher dimensions with arbitrary lengths. Several multiplierless $8\times 8\times 8$ approximate methods are proposed and the computational complexity is discussed for the general multidimensional case. The proposed methods complexity cost was assessed, presenting considerably lower arithmetic operations when compared with the exact 3D DCT. The proposed approximations were embedded into 3D DCT-based video coding scheme and a modified quantization step was introduced. The simulation results showed that the approximate 3D DCT coding methods offer almost identical output visual quality when compared with exact 3D DCT scheme. The proposed 3D approximations were also employed as a tool for visual tracking. The approximate 3D DCT-based proposed system performs similarly to the original exact 3D DCT-based method. In general, the suggested methods showed competitive performance at a considerably lower computational cost.	['F. M. Bayer', 'R. J. Cintra', 'V. A. Coutinho']	2023-06-20	null	null	null	null	['visual-tracking', 'quantization']	['computer-vision', 'methodology']	[-2.15418592e-01 -3.27560633e-01 -8.81685019e-02 1.78292133e-02 -4.16244149e-01 -4.47609723e-01 7.01308608e-01 -2.84509584e-02 -3.40502530e-01 3.03431451e-01 1.93997249e-01 -4.99756366e-01 8.00631419e-02 -1.96239114e-01 -1.59259215e-01 -5.49217939e-01 -4.58465308e-01 1.59752354e-01 3.23790193e-01 -1.47599548e-01 5.51498950e-01 6.81615472e-01 -1.55002201e+00 1.59445271e-01 4.06822085e-01 1.26015890e+00 3.96910682e-02 1.12416184e+00 4.82421042e-03 3.51567268e-01 -2.68361598e-01 -5.53344965e-01 6.23937905e-01 -3.00062090e-01 -4.49046075e-01 5.60340047e-01 4.84929383e-01 -7.05463827e-01 -3.92815500e-01 1.11402702e+00 2.82286435e-01 -2.31680106e-02 1.01184189e+00 -1.16733408e+00 -6.18002057e-01 -1.51921421e-01 -9.10616159e-01 1.93169013e-01 5.88186443e-01 -3.85225624e-01 6.52335346e-01 -1.20687187e+00 4.56388980e-01 1.23623371e+00 8.66934001e-01 7.69060403e-02 -1.00296962e+00 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-3.85411195e-02 6.31583929e-01 -6.16443396e-01 -3.33492905e-01 5.67805529e-01]	[11.440278053283691, -2.190136671066284]
044b4ffa-6e82-4147-9add-5e52a902f82a	horizon-lines-in-the-wild	1604.02129	null	http://arxiv.org/abs/1604.02129v2	http://arxiv.org/pdf/1604.02129v2.pdf	Horizon Lines in the Wild	The horizon line is an important contextual attribute for a wide variety of image understanding tasks. As such, many methods have been proposed to estimate its location from a single image. These methods typically require the image to contain specific cues, such as vanishing points, coplanar circles, and regular textures, thus limiting their real-world applicability. We introduce a large, realistic evaluation dataset, Horizon Lines in the Wild (HLW), containing natural images with labeled horizon lines. Using this dataset, we investigate the application of convolutional neural networks for directly estimating the horizon line, without requiring any explicit geometric constraints or other special cues. An extensive evaluation shows that using our CNNs, either in isolation or in conjunction with a previous geometric approach, we achieve state-of-the-art results on the challenging HLW dataset and two existing benchmark datasets.	['Menghua Zhai', 'Scott Workman', 'Nathan Jacobs']	2016-04-07	null	null	null	null	['horizon-line-estimation']	['computer-vision']	[ 3.47629815e-01 6.19439296e-02 -6.94430992e-02 -4.86860335e-01 -5.27842224e-01 -6.01762176e-01 9.16921079e-01 2.29362801e-01 -4.35876817e-01 2.68553287e-01 -1.82753503e-01 -2.06320956e-01 3.15880850e-02 -7.90523112e-01 -9.35822606e-01 -3.51399601e-01 -1.10160872e-01 2.93917030e-01 6.45655453e-01 -2.79869586e-01 3.86185408e-01 5.90416193e-01 -1.43845975e+00 1.80467099e-01 4.64883059e-01 1.41579676e+00 6.10985421e-02 4.51799542e-01 1.53439224e-01 4.34448868e-01 -4.86039370e-01 -4.31625634e-01 5.24624348e-01 1.45424232e-01 -6.31100059e-01 3.10617238e-01 1.00625575e+00 -5.41632295e-01 -2.68202245e-01 7.73897231e-01 3.16311926e-01 6.79019094e-02 5.40265501e-01 -9.68198180e-01 -4.50089186e-01 2.47998476e-01 -8.45172465e-01 -2.21978575e-01 3.92995059e-01 4.04130220e-02 9.39596355e-01 -8.41095865e-01 7.25730240e-01 1.08002174e+00 9.97687101e-01 -4.87851202e-02 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-2.85853744e-01 -7.53117919e-01 -3.88519913e-01 7.09546149e-01 2.88467228e-01 -8.30763757e-01 -1.50387794e-01 3.58298905e-02]	[8.249381065368652, -1.9886410236358643]
5377532f-71cc-4ca3-8725-aed0c83930a6	efficient-annotation-and-learning-for-3d-hand	2206.02257	null	https://arxiv.org/abs/2206.02257v3	https://arxiv.org/pdf/2206.02257v3.pdf	Efficient Annotation and Learning for 3D Hand Pose Estimation: A Survey	In this survey, we present a systematic review of 3D hand pose estimation from the perspective of efficient annotation and learning. 3D hand pose estimation has been an important research area owing to its potential to enable various applications, such as video understanding, AR/VR, and robotics. However, the performance of models is tied to the quality and quantity of annotated 3D hand poses. Under the status quo, acquiring such annotated 3D hand poses is challenging, e.g., due to the difficulty of 3D annotation and the presence of occlusion. To reveal this problem, we review the pros and cons of existing annotation methods classified as manual, synthetic-model-based, hand-sensor-based, and computational approaches. Additionally, we examine methods for learning 3D hand poses when annotated data are scarce, including self-supervised pretraining, semi-supervised learning, and domain adaptation. Based on the study of efficient annotation and learning, we further discuss limitations and possible future directions in this field.	['Yoichi Sato', 'Ryosuke Furuta', 'Takehiko Ohkawa']	2022-06-05	null	null	null	null	['3d-hand-pose-estimation', '3d-hand-pose-estimation']	['computer-vision', 'graphs']	[ 3.57313678e-02 -5.62417544e-02 -5.36703348e-01 -7.28666931e-02 -5.12256086e-01 -7.43616104e-01 -3.33227031e-02 -1.59069479e-01 -3.15111428e-01 6.25324607e-01 2.96452701e-01 -2.08545546e-03 -7.36669078e-02 -1.13926068e-01 -5.41298151e-01 -4.40427572e-01 7.86528811e-02 8.65537524e-01 2.11063087e-01 -7.73873329e-02 3.11246991e-01 9.75432873e-01 -1.60614765e+00 -3.61188471e-01 4.15318638e-01 7.84215629e-01 4.62558508e-01 4.56371754e-01 -9.78290737e-02 2.89663941e-01 -6.80940986e-01 -7.47120380e-02 3.30619991e-01 -1.27936721e-01 -9.42744613e-01 2.74323851e-01 2.85594851e-01 -6.81294858e-01 -2.97481596e-01 7.25958109e-01 1.10744715e+00 1.42842904e-01 6.89278424e-01 -1.22090638e+00 -2.02156883e-03 7.70942345e-02 -4.43770170e-01 -1.07529663e-01 8.30845416e-01 1.80879645e-02 6.70317113e-01 -8.86714697e-01 1.03876984e+00 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83e6e5f7-1e59-4a65-a3cf-21c6480b748e	logical-form-generation-via-multi-task	null	null	https://aclanthology.org/2022.coling-1.145	https://aclanthology.org/2022.coling-1.145.pdf	Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases	Question answering over knowledge bases (KBQA) for complex questions is a challenging task in natural language processing. Recently, generation-based methods that translate natural language questions to executable logical forms have achieved promising performance. These methods use auxiliary information to augment the logical form generation of questions with unseen KB items or novel combinations, but the noise introduced can also leads to more incorrect results. In this work, we propose GMT-KBQA, a Generation-based KBQA method via Multi-Task learning, to better retrieve and utilize auxiliary information. GMT-KBQA first obtains candidate entities and relations through dense retrieval, and then introduces a multi-task model which jointly learns entity disambiguation, relation classification, and logical form generation. Experimental results show that GMT-KBQA achieves state-of-the-art results on both ComplexWebQuestions and WebQuestionsSP datasets. Furthermore, the detailed evaluation demonstrates that GMT-KBQA benefits from the auxiliary tasks and has a strong generalization capability.	['Yuzhong Qu', 'Yiheng Shu', 'Xuan Wu', 'Xixin Hu']	null	null	null	null	coling-2022-10	['relation-classification', 'entity-disambiguation']	['natural-language-processing', 'natural-language-processing']	[-1.27955243e-01 3.24528068e-01 -1.49293423e-01 -3.74899805e-01 -1.51359749e+00 -6.50115967e-01 2.92609304e-01 2.38458946e-01 -3.31058770e-01 1.35047948e+00 1.48784757e-01 -4.38948095e-01 -4.09894288e-01 -1.33603251e+00 -8.49371016e-01 -7.73951560e-02 2.49342412e-01 1.17835557e+00 5.68378508e-01 -8.43217194e-01 -2.04252869e-01 -6.64297566e-02 -1.17649877e+00 8.59256864e-01 1.52342463e+00 1.03836405e+00 -1.31340966e-01 4.70880955e-01 -7.05441713e-01 1.08343995e+00 -6.23350382e-01 -1.04220164e+00 -1.17344400e-02 -8.52610022e-02 -1.48336375e+00 -5.81444681e-01 6.50889516e-01 -4.25791383e-01 -2.18535140e-01 7.77194023e-01 3.69650185e-01 2.49089420e-01 4.65035111e-01 -1.20263457e+00 -1.09027481e+00 7.91025519e-01 -5.04688546e-02 1.69920295e-01 1.04012716e+00 -1.32571399e-01 1.67874801e+00 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5.97611964e-01 4.64443564e-01 2.90674865e-01 3.30920219e-01 -7.70866334e-01 -1.16631079e+00 -1.33056507e-01 3.69740367e-01 2.54229546e-01 -4.21554416e-01 -8.55090693e-02 -1.53763011e-01]	[10.63388442993164, 7.938277721405029]
67f848ed-14f9-4624-af61-660e3221566f	intrinsic-dimensionality-explains-the	2012.13255	null	https://arxiv.org/abs/2012.13255v1	https://arxiv.org/pdf/2012.13255v1.pdf	Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning	Although pretrained language models can be fine-tuned to produce state-of-the-art results for a very wide range of language understanding tasks, the dynamics of this process are not well understood, especially in the low data regime. Why can we use relatively vanilla gradient descent algorithms (e.g., without strong regularization) to tune a model with hundreds of millions of parameters on datasets with only hundreds or thousands of labeled examples? In this paper, we argue that analyzing fine-tuning through the lens of intrinsic dimension provides us with empirical and theoretical intuitions to explain this remarkable phenomenon. We empirically show that common pre-trained models have a very low intrinsic dimension; in other words, there exists a low dimension reparameterization that is as effective for fine-tuning as the full parameter space. For example, by optimizing only 200 trainable parameters randomly projected back into the full space, we can tune a RoBERTa model to achieve 90\% of the full parameter performance levels on MRPC. Furthermore, we empirically show that pre-training implicitly minimizes intrinsic dimension and, perhaps surprisingly, larger models tend to have lower intrinsic dimension after a fixed number of pre-training updates, at least in part explaining their extreme effectiveness. Lastly, we connect intrinsic dimensionality with low dimensional task representations and compression based generalization bounds to provide intrinsic-dimension-based generalization bounds that are independent of the full parameter count.	['Sonal Gupta', 'Luke Zettlemoyer', 'Armen Aghajanyan']	2020-12-22	null	https://aclanthology.org/2021.acl-long.568	https://aclanthology.org/2021.acl-long.568.pdf	acl-2021-5	['paraphrase-identification']	['natural-language-processing']	[-1.89553797e-01 1.33900359e-01 -4.14858520e-01 -3.11181694e-01 -7.63474107e-01 -7.74174750e-01 6.93499386e-01 -3.70226474e-03 -6.67643964e-01 6.83420122e-01 3.37751627e-01 -3.79001200e-01 -3.54036629e-01 -6.94403231e-01 -7.13655949e-01 -6.23538256e-01 2.10985821e-02 7.68815041e-01 -2.16257825e-01 -5.13079524e-01 3.16034883e-01 3.98574084e-01 -1.35351372e+00 2.70311609e-02 7.64058530e-01 4.50090826e-01 7.81318620e-02 7.21078515e-01 -1.67769894e-01 2.42087647e-01 -3.28339159e-01 -3.53689849e-01 2.96324700e-01 -1.53725967e-01 -8.54882121e-01 -9.59664360e-02 3.94956648e-01 -2.32787982e-01 -2.98216105e-01 8.36766303e-01 2.58524597e-01 2.75874436e-01 8.50916445e-01 -6.20567620e-01 -9.00259495e-01 1.02407110e+00 -4.53762889e-01 4.16419566e-01 -1.93713263e-01 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ce851b5e-e9b0-40ec-a913-5d0eecdba1f2	ratatouille-a-tool-for-novel-recipe	2206.08267	null	https://arxiv.org/abs/2206.08267v1	https://arxiv.org/pdf/2206.08267v1.pdf	Ratatouille: A tool for Novel Recipe Generation	Due to availability of a large amount of cooking recipes online, there is a growing interest in using this as data to create novel recipes. Novel Recipe Generation is a problem in the field of Natural Language Processing in which our main interest is to generate realistic, novel cooking recipes. To come up with such novel recipes, we trained various Deep Learning models such as LSTMs and GPT-2 with a large amount of recipe data. We present Ratatouille (https://cosylab.iiitd.edu.in/ratatouille2/), a web based application to generate novel recipes.	['Ganesh Bagler', 'Aakanksha Saini', 'Sritanaya Tatipamala', 'Minnet Khan', 'Vijay Ponnaganti', 'Pallab Chakraborty', 'Mansi Goel']	2022-05-10	null	null	null	null	['recipe-generation']	['miscellaneous']	[-2.00442180e-01 -1.65296923e-02 4.67090338e-01 -3.22072744e-01 -3.80855381e-01 -6.24211490e-01 3.49881917e-01 2.34154135e-01 -1.28692165e-01 7.37969697e-01 5.91871798e-01 -1.64425597e-01 4.63343769e-01 -1.45185769e+00 -1.04413188e+00 -5.01522005e-01 -3.30585726e-02 -8.50183610e-03 -3.62424701e-01 -8.45558763e-01 1.54437840e-01 -5.74252754e-02 -1.11654317e+00 6.94072068e-01 9.19544935e-01 2.67582953e-01 5.16423643e-01 9.43749130e-01 -5.53374112e-01 8.80031466e-01 -3.39991271e-01 -3.37643415e-01 4.41353917e-01 -8.14443171e-01 -5.21331489e-01 -2.93280482e-01 1.66744441e-01 -3.82676005e-01 -1.53710306e-01 9.90120530e-01 6.29320621e-01 8.88331234e-01 2.42757604e-01 -1.08585095e+00 -1.26890874e+00 1.47935104e+00 1.13209583e-01 -1.48867309e-01 5.01021564e-01 5.05096436e-01 7.45467901e-01 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55da0d99-6cbf-467b-9777-9678563c3094	a-multiresolution-clinical-decision-support	1512.08051	null	http://arxiv.org/abs/1512.08051v1	http://arxiv.org/pdf/1512.08051v1.pdf	A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours	Tissue texture is known to exhibit a heterogeneous or non-stationary nature, therefore using a single resolution approach for optimum classification might not suffice. A clinical decision support system that exploits the subband textural fractal characteristics for best bases selection of meningioma brain histopathological image classification is proposed. Each subband is analysed using its fractal dimension instead of energy, which has the advantage of being less sensitive to image intensity and abrupt changes in tissue texture. The most significant subband that best identifies texture discontinuities will be chosen for further decomposition, and its fractal characteristics would represent the optimal feature vector for classification. The performance was tested using the support vector machine (SVM), Bayesian and k-nearest neighbour (kNN) classifiers and a leave-one-patient-out method was employed for validation. Our method outperformed the classical energy based selection approaches, achieving for SVM, Bayesian and kNN classifiers an overall classification accuracy of 94.12%, 92.50% and 79.70%, as compared to 86.31%, 83.19% and 51.63% for the co-occurrence matrix, and 76.01%, 73.50% and 50.69% for the energy texture signatures, respectively. These results indicate the potential usefulness as a decision support system that could complement radiologists diagnostic capability to discriminate higher order statistical textural information, for which it would be otherwise difficult via ordinary human vision.	['Omar S. Al-Kadi']	2015-12-25	null	null	null	null	['histopathological-image-classification']	['medical']	[ 4.19413030e-01 -1.36187430e-02 -6.69950768e-02 -1.15348868e-01 -5.05707324e-01 -1.51712433e-01 5.46154857e-01 6.73296094e-01 -5.88298202e-01 8.11536849e-01 -1.05118513e-01 -1.65088579e-01 -8.37690949e-01 -8.34155738e-01 1.33821741e-01 -1.43584406e+00 -3.99730891e-01 3.72957259e-01 4.98136550e-01 -2.05964535e-01 5.76281250e-01 6.64957702e-01 -1.82606030e+00 6.97185099e-01 8.11144173e-01 1.43371999e+00 4.17853892e-01 7.00134873e-01 -1.06252231e-01 6.69117033e-01 -3.79928231e-01 -1.23357987e-02 -2.68346071e-01 -2.88844228e-01 -7.03072131e-01 -1.41938135e-01 -1.59396857e-01 2.22799718e-01 3.46057845e-04 9.85605597e-01 4.41497117e-01 1.87561080e-01 1.38555348e+00 -3.96484792e-01 -3.41872483e-01 1.37960464e-01 -6.44328237e-01 6.75632179e-01 2.44439676e-01 -2.77156144e-01 2.84537554e-01 -5.70582211e-01 5.12394845e-01 6.39985800e-01 6.75456166e-01 3.55221212e-01 -1.10245693e+00 -2.34325141e-01 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1.76583409e-01 3.59850168e-01 -1.05318415e+00 -3.67388934e-01 1.17882647e-01]	[15.056410789489746, -2.6725783348083496]
a7489a91-0203-454e-890d-66d36f47bc91	construction-of-hierarchical-structured	null	null	https://aclanthology.org/2022.dialdoc-1.9	https://aclanthology.org/2022.dialdoc-1.9.pdf	Construction of Hierarchical Structured Knowledge-based Recommendation Dialogue Dataset and Dialogue System	We work on a recommendation dialogue system to help a user understand the appealing points of some target (e.g., a movie). In such dialogues, the recommendation system needs to utilize structured external knowledge to make informative and detailed recommendations. However, there is no dialogue dataset with structured external knowledge designed to make detailed recommendations for the target. Therefore, we construct a dialogue dataset, Japanese Movie Recommendation Dialogue (JMRD), in which the recommender recommends one movie in a long dialogue (23 turns on average). The external knowledge used in this dataset is hierarchically structured, including title, casts, reviews, and plots. Every recommender’s utterance is associated with the external knowledge related to the utterance. We then create a movie recommendation dialogue system that considers the structure of the external knowledge and the history of the knowledge used. Experimental results show that the proposed model is superior in knowledge selection to the baseline models.	['Sadao Kurohashi', 'Ribeka Tanaka', 'Takashi Kodama']	null	null	null	null	dialdoc-acl-2022-5	['movie-recommendation']	['miscellaneous']	[-3.50767612e-01 3.99618566e-01 -3.47481340e-01 -7.96001732e-01 -3.87003094e-01 -8.63668382e-01 5.58470547e-01 -1.04278862e-01 -1.34284467e-01 5.71548641e-01 8.04325044e-01 -2.42133602e-01 4.84052561e-02 -7.50447512e-01 -1.00438990e-01 -3.07868004e-01 6.11327648e-01 4.40235585e-01 4.91167545e-01 -6.81735218e-01 6.95713639e-01 -2.71441452e-02 -1.07533598e+00 6.40187263e-01 7.73557365e-01 9.77019548e-01 6.73460782e-01 6.33974731e-01 -5.08672714e-01 1.06929386e+00 -5.69594026e-01 -7.31301665e-01 -1.70876309e-01 -7.28722036e-01 -1.12027025e+00 2.92626709e-01 -1.71371162e-01 -7.73649335e-01 -2.49949977e-01 6.76038206e-01 3.26827437e-01 9.36658800e-01 9.46556032e-01 -4.08549428e-01 -8.04328978e-01 1.19978738e+00 9.78106409e-02 7.00186938e-02 5.73649526e-01 -4.68047529e-01 1.25905681e+00 -9.57084239e-01 8.04627478e-01 1.08364379e+00 1.40869766e-01 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8296c44c-0b8e-403c-ae3f-92833d1ae93d	adaptively-learning-facial-expression	null	null	https://ieeexplore.ieee.org/abstract/document/9321757	https://ieeexplore.ieee.org/abstract/document/9321757	Adaptively learning facial expression representation via cf labels and distillation.	Facial expression recognition is of significant importance in criminal investigation and digital entertainment. Under unconstrained conditions, existing expression datasets are highly class-imbalanced, and the similarity between expressions is high. Previous methods tend to improve the performance of facial expression recognition through deeper or wider network structures, resulting in increased storage and computing costs. In this paper, we propose a new adaptive supervised objective named AdaReg loss, re-weighting category importance coefficients to address this class imbalance and increasing the discrimination power of expression representations. Inspired by human beings’ cognitive mode, an innovative coarse-fine (C-F) labels strategy is designed to guide the model from easy to difficult to classify highly similar representations. On this basis, we propose a novel training framework named the emotional education mechanism (EEM) to transfer knowledge, composed of a knowledgeable teacher network (KTN) and a self-taught student network (STSN). Specifically, KTN integrates the outputs of coarse and fine streams, learning expression representations from easy to difficult. Under the supervision of the pre-trained KTN and existing learning experience, STSN can maximize the potential performance and compress the original KTN. Extensive experiments on public benchmarks demonstrate that the proposed method achieves superior performance compared to current state-of-the-art frameworks with 88.07% on RAF-DB, 63.97% on AffectNet and 90.49% on FERPlus.	['Hangyu Li; Nannan Wang; Xinpeng Ding; Xi Yang; Xinbo Gao']	2021-01-13	null	null	null	ieee-transactions-on-image-processing-2021-1	['facial-expression-recognition']	['computer-vision']	[ 1.91924259e-01 -9.36975703e-02 -3.36674899e-01 -8.25826526e-01 -3.80620658e-02 1.49258614e-01 2.21206099e-01 -3.02008241e-01 -4.97766793e-01 7.07448483e-01 -4.55137268e-02 1.98568434e-01 -3.66756953e-02 -8.12365115e-01 -2.80991256e-01 -7.73973465e-01 4.04080451e-02 7.91949853e-02 -2.61515468e-01 -4.01436210e-01 1.31465733e-01 5.53839505e-01 -1.59431529e+00 5.71133554e-01 9.42885399e-01 1.72937584e+00 -2.81074643e-01 4.97190021e-02 -4.05990064e-01 1.14628613e+00 -6.46080315e-01 -7.35410452e-01 -8.35439116e-02 -3.26748997e-01 -7.62382030e-01 2.41643861e-02 4.20748107e-02 -3.57806802e-01 -1.61125019e-01 1.16166437e+00 6.81220949e-01 1.95347190e-01 5.61974406e-01 -1.45859432e+00 -6.23766243e-01 3.10816497e-01 -8.22502017e-01 1.60297945e-01 4.51217964e-02 -6.92443997e-02 5.10823131e-01 -9.07886684e-01 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3b5d12b2-f37c-4943-b367-77a47134f2aa	inner-ear-augmented-metal-artifact-reduction	2104.1251	null	https://arxiv.org/abs/2104.12510v1	https://arxiv.org/pdf/2104.12510v1.pdf	Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks	Metal Artifacts creates often difficulties for a high quality visual assessment of post-operative imaging in {c}omputed {t}omography (CT). A vast body of methods have been proposed to tackle this issue, but {these} methods were designed for regular CT scans and their performance is usually insufficient when imaging tiny implants. In the context of post-operative high-resolution {CT} imaging, we propose a 3D metal {artifact} reduction algorithm based on a generative adversarial neural network. It is based on the simulation of physically realistic CT metal artifacts created by cochlea implant electrodes on preoperative images. The generated images serve to train a 3D generative adversarial networks for artifacts reduction. The proposed approach was assessed qualitatively and quantitatively on clinical conventional and cone-beam CT of cochlear implant postoperative images. These experiments show that the proposed method {outperforms other} general metal artifact reduction approaches.	['Delingette Herve', 'Guevara Nicolas', 'Raffaelli Charles', 'Gnansia Dan', 'Demarcy Thomas', 'Vandersteen Clair', 'Wang Zihao']	2021-04-26	null	null	null	null	['metal-artifact-reduction']	['medical']	[ 5.08559167e-01 4.02013272e-01 8.53904009e-01 9.52445995e-03 -1.12494099e+00 -2.97804266e-01 1.15522027e-01 -1.79932103e-01 -4.76625741e-01 4.70113426e-01 4.66129333e-02 -2.64815778e-01 -2.74161547e-01 -3.47772628e-01 -7.20485449e-01 -6.59096420e-01 -2.63710972e-03 6.89402521e-01 1.10719815e-01 7.04797134e-02 1.22439094e-01 6.77874506e-01 -1.29330134e+00 3.29797179e-01 7.23401666e-01 1.10153103e+00 3.11101109e-01 6.19003773e-01 3.26143891e-01 4.54988390e-01 -7.16044068e-01 -4.15293783e-01 3.77842188e-01 -4.61296201e-01 -4.58458662e-01 1.92820847e-01 3.52530897e-01 -3.41273516e-01 -4.06544000e-01 1.23207331e+00 1.07013440e+00 -4.29733276e-01 9.16277647e-01 -8.88722599e-01 -6.04402423e-01 6.73833072e-01 -2.23494813e-01 2.97066122e-01 1.12485424e-01 9.91985649e-02 -2.71270815e-02 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3.63605052e-01 -2.03993231e-01 1.03927565e+00 5.85093021e-01 -7.95352340e-01 -4.61966574e-01 -1.14531457e+00 -9.59024489e-01 3.58278483e-01 1.80849820e-01 -6.17376745e-01 -4.65174794e-01 1.63979709e-01]	[13.449530601501465, -2.555616855621338]
94c5b379-146d-4ece-bd49-420208faeb08	object-discovery-via-cohesion-measurement	1704.08944	null	http://arxiv.org/abs/1704.08944v1	http://arxiv.org/pdf/1704.08944v1.pdf	Object Discovery via Cohesion Measurement	Color and intensity are two important components in an image. Usually, groups of image pixels, which are similar in color or intensity, are an informative representation for an object. They are therefore particularly suitable for computer vision tasks, such as saliency detection and object proposal generation. However, image pixels, which share a similar real-world color, may be quite different since colors are often distorted by intensity. In this paper, we reinvestigate the affinity matrices originally used in image segmentation methods based on spectral clustering. A new affinity matrix, which is robust to color distortions, is formulated for object discovery. Moreover, a Cohesion Measurement (CM) for object regions is also derived based on the formulated affinity matrix. Based on the new Cohesion Measurement, a novel object discovery method is proposed to discover objects latent in an image by utilizing the eigenvectors of the affinity matrix. Then we apply the proposed method to both saliency detection and object proposal generation. Experimental results on several evaluation benchmarks demonstrate that the proposed CM based method has achieved promising performance for these two tasks.	['Xuelong. Li', 'Wan-Lei Zhao', 'Yan Yan', 'Hanzi Wang', 'Guanjun Guo']	2017-04-28	null	null	null	null	['object-proposal-generation']	['computer-vision']	[ 4.67317134e-01 -2.73100048e-01 -1.12262316e-01 -3.95275801e-01 -3.82037222e-01 -2.10581541e-01 2.60888606e-01 4.52843994e-01 -4.40203458e-01 4.65921134e-01 -7.38790724e-03 1.14072755e-01 -2.66394675e-01 -7.26910889e-01 -5.98850191e-01 -8.86329234e-01 2.51814872e-01 2.33205911e-02 7.62217999e-01 -4.95418124e-02 6.32991433e-01 1.50499225e-01 -1.76128888e+00 -5.85761443e-02 1.27167356e+00 9.71291840e-01 6.01123154e-01 -1.39436871e-02 -2.66227275e-01 4.12101775e-01 -5.14594018e-01 7.51147494e-02 1.28540099e-01 -5.12778521e-01 -6.90893173e-01 5.79031467e-01 4.16505150e-02 1.40646890e-01 4.28293616e-01 1.62739444e+00 1.06676295e-01 3.97106349e-01 6.49644315e-01 -1.20980179e+00 -5.57803810e-01 6.63919985e-01 -1.04695320e+00 1.58861518e-01 2.19611779e-01 -2.17718765e-01 9.83811855e-01 -9.14927363e-01 4.22493428e-01 1.31401825e+00 -3.15030143e-02 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-7.33997405e-01 -8.96995783e-01 6.67567492e-01 -2.32977159e-02 -8.79637063e-01 -1.33831665e-01 2.55603522e-01]	[9.820380210876465, -0.6004092693328857]
4066b942-021c-45d3-987d-a48b83a83c62	energy-based-models-for-atomic-resolution-1	2004.13167	null	https://arxiv.org/abs/2004.13167v1	https://arxiv.org/pdf/2004.13167v1.pdf	Energy-based models for atomic-resolution protein conformations	We propose an energy-based model (EBM) of protein conformations that operates at atomic scale. The model is trained solely on crystallized protein data. By contrast, existing approaches for scoring conformations use energy functions that incorporate knowledge of physical principles and features that are the complex product of several decades of research and tuning. To evaluate the model, we benchmark on the rotamer recovery task, the problem of predicting the conformation of a side chain from its context within a protein structure, which has been used to evaluate energy functions for protein design. The model achieves performance close to that of the Rosetta energy function, a state-of-the-art method widely used in protein structure prediction and design. An investigation of the model's outputs and hidden representations finds that it captures physicochemical properties relevant to protein energy.	['Rob Fergus', 'Alexander Rives', 'Yilun Du', 'Jerry Ma', 'Joshua Meier']	2020-04-27	null	https://openreview.net/forum?id=S1e_9xrFvS	https://openreview.net/pdf?id=S1e_9xrFvS	iclr-2020-1	['protein-design']	['medical']	[ 1.98381767e-01 5.40216826e-02 -2.56462246e-01 -5.60416579e-01 -8.70926023e-01 -6.00059927e-01 3.27553630e-01 4.24453557e-01 -3.65297973e-01 1.03816843e+00 3.81101668e-01 -6.97400570e-01 2.84798384e-01 -5.28544664e-01 -1.03890395e+00 -9.80683863e-01 -2.11722478e-01 4.83226150e-01 1.37915760e-02 -4.57152188e-01 5.27823627e-01 6.46080196e-01 -1.24604774e+00 5.53733170e-01 5.87807775e-01 7.05867469e-01 2.96539783e-01 4.85087097e-01 -1.41355786e-02 5.50529182e-01 -3.36615771e-01 -2.66827065e-02 -2.24850029e-01 -6.23528719e-01 -1.02138019e+00 -6.21370256e-01 2.60405570e-01 1.63689330e-01 -4.67005037e-02 5.65397978e-01 6.05290592e-01 1.50631845e-01 1.04948986e+00 -1.71229541e-01 -9.00483668e-01 -4.97059263e-02 -8.05838779e-02 5.96700571e-02 7.02353597e-01 4.05620128e-01 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4a441619-ee12-46f2-98d6-ce66418124d7	unsupervised-question-answering-via-answer	2208.10813	null	https://arxiv.org/abs/2208.10813v1	https://arxiv.org/pdf/2208.10813v1.pdf	Unsupervised Question Answering via Answer Diversifying	Unsupervised question answering is an attractive task due to its independence on labeled data. Previous works usually make use of heuristic rules as well as pre-trained models to construct data and train QA models. However, most of these works regard named entity (NE) as the only answer type, which ignores the high diversity of answers in the real world. To tackle this problem, we propose a novel unsupervised method by diversifying answers, named DiverseQA. Specifically, the proposed method is composed of three modules: data construction, data augmentation and denoising filter. Firstly, the data construction module extends the extracted named entity into a longer sentence constituent as the new answer span to construct a QA dataset with diverse answers. Secondly, the data augmentation module adopts an answer-type dependent data augmentation process via adversarial training in the embedding level. Thirdly, the denoising filter module is designed to alleviate the noise in the constructed data. Extensive experiments show that the proposed method outperforms previous unsupervised models on five benchmark datasets, including SQuADv1.1, NewsQA, TriviaQA, BioASQ, and DuoRC. Besides, the proposed method shows strong performance in the few-shot learning setting.	['Xian-Ling Mao', 'Zewen Chi', 'Heyan Huang', 'Yuxiang Nie']	2022-08-23	null	https://aclanthology.org/2022.coling-1.149	https://aclanthology.org/2022.coling-1.149.pdf	coling-2022-10	['triviaqa']	['miscellaneous']	[ 4.61444929e-02 -2.38173688e-03 1.78687990e-01 -5.56186795e-01 -9.08758938e-01 -3.29357356e-01 3.68903577e-01 7.96849951e-02 -6.39494181e-01 7.92016387e-01 5.95770717e-01 -9.42788199e-02 8.88636708e-02 -1.22488868e+00 -2.83022970e-01 -6.43293083e-01 7.51653075e-01 4.61659133e-01 5.23689806e-01 -6.09797776e-01 2.72847325e-01 -1.56263560e-01 -1.35262585e+00 1.05453268e-01 1.40889478e+00 8.93614948e-01 3.61600704e-02 3.96943241e-01 -8.62511873e-01 1.12502742e+00 -7.40664542e-01 -9.66813624e-01 1.49079576e-01 -8.23107243e-01 -8.62939358e-01 2.43559927e-01 2.56083850e-02 -2.23391488e-01 -3.65803212e-01 9.00794685e-01 9.40183401e-01 5.15330195e-01 3.35738271e-01 -8.59164953e-01 -8.77256751e-01 3.96190137e-01 -2.30767697e-01 2.16799811e-01 3.14990133e-01 3.15429926e-01 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6a5b80ad-8380-434c-aed4-1cc5dfb05b5d	shakkil-an-automatic-diacritization-system	null	null	https://aclanthology.org/W17-1311	https://aclanthology.org/W17-1311.pdf	SHAKKIL: An Automatic Diacritization System for Modern Standard Arabic Texts	This paper sheds light on a system that would be able to diacritize Arabic texts automatically (SHAKKIL). In this system, the diacritization problem will be handled through two levels; morphological and syntactic processing levels. The adopted morphological disambiguation algorithm depends on four layers; Uni-morphological form layer, rule-based morphological disambiguation layer, statistical-based disambiguation layer and Out Of Vocabulary (OOV) layer. The adopted syntactic disambiguation algorithms is concerned with detecting the case ending diacritics depending on a rule based approach simulating the shallow parsing technique. This will be achieved using an annotated corpus for extracting the Arabic linguistic rules, building the language models and testing the system output. This system is considered as a good trial of the interaction between rule-based approach and statistical approach, where the rules can help the statistics in detecting the right diacritization and vice versa. At this point, the morphological Word Error Rate (WER) is 4.56{\%} while the morphological Diacritic Error Rate (DER) is 1.88{\%} and the syntactic WER is 9.36{\%}. The best WER is 14.78{\%} compared to the best-published results, of (Abandah, 2015); 11.68{\%}, (Rashwan, et al., 2015); 12.90{\%} and (Metwally, Rashwan, {\&} Atiya, 2016); 13.70{\%}.	['Sameh Alansary', 'Amany Fashwan']	2017-04-01	null	null	null	ws-2017-4	['morphological-disambiguation']	['natural-language-processing']	[-1.17136724e-01 3.40924352e-01 2.63476104e-01 -1.59091994e-01 -3.21300119e-01 -6.04413807e-01 6.17329061e-01 7.65077412e-01 -6.45769536e-01 7.05681980e-01 -1.09399088e-01 -7.72091627e-01 -4.70276177e-01 -1.06741726e+00 -2.69485801e-01 -6.67607844e-01 6.21232670e-03 6.61211014e-01 2.89787591e-01 -6.90157890e-01 4.49718177e-01 6.72765017e-01 -1.69076264e+00 2.52534628e-01 9.90349054e-01 5.35402417e-01 1.77186906e-01 6.69558167e-01 -3.81747812e-01 4.52730477e-01 -9.01701450e-01 -3.46683145e-01 7.55747184e-02 -4.08062488e-01 -1.07681894e+00 -2.21543938e-01 -8.88341442e-02 -1.51245501e-02 4.69917864e-01 1.23245430e+00 2.94540435e-01 1.35831490e-01 1.10383022e+00 -5.60248613e-01 -4.38123941e-01 8.12143862e-01 -3.35201621e-01 1.67976618e-01 5.23320496e-01 -1.60168484e-01 7.73832500e-01 -8.16577554e-01 7.00214803e-01 1.09867501e+00 1.78132534e-01 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d8f40842-559e-4333-a866-c0ffa2b352d3	constrained-few-shot-class-incremental	2203.16588	null	https://arxiv.org/abs/2203.16588v1	https://arxiv.org/pdf/2203.16588v1.pdf	Constrained Few-shot Class-incremental Learning	Continually learning new classes from fresh data without forgetting previous knowledge of old classes is a very challenging research problem. Moreover, it is imperative that such learning must respect certain memory and computational constraints such as (i) training samples are limited to only a few per class, (ii) the computational cost of learning a novel class remains constant, and (iii) the memory footprint of the model grows at most linearly with the number of classes observed. To meet the above constraints, we propose C-FSCIL, which is architecturally composed of a frozen meta-learned feature extractor, a trainable fixed-size fully connected layer, and a rewritable dynamically growing memory that stores as many vectors as the number of encountered classes. C-FSCIL provides three update modes that offer a trade-off between accuracy and compute-memory cost of learning novel classes. C-FSCIL exploits hyperdimensional embedding that allows to continually express many more classes than the fixed dimensions in the vector space, with minimal interference. The quality of class vector representations is further improved by aligning them quasi-orthogonally to each other by means of novel loss functions. Experiments on the CIFAR100, miniImageNet, and Omniglot datasets show that C-FSCIL outperforms the baselines with remarkable accuracy and compression. It also scales up to the largest problem size ever tried in this few-shot setting by learning 423 novel classes on top of 1200 base classes with less than 1.6% accuracy drop. Our code is available at https://github.com/IBM/constrained-FSCIL.	['Abbas Rahimi', 'Abu Sebastian', 'Luca Benini', 'Giovanni Cherubini', 'Geethan Karunaratne', 'Michael Hersche']	2022-03-30	null	http://openaccess.thecvf.com//content/CVPR2022/html/Hersche_Constrained_Few-Shot_Class-Incremental_Learning_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Hersche_Constrained_Few-Shot_Class-Incremental_Learning_CVPR_2022_paper.pdf	cvpr-2022-1	['few-shot-class-incremental-learning']	['methodology']	[ 1.65991649e-01 7.24022975e-03 -4.08124536e-01 -3.61621737e-01 -5.52125156e-01 -5.37178278e-01 4.10781443e-01 3.08374286e-01 -6.77330315e-01 8.12709510e-01 -2.25925326e-01 -7.10907355e-02 -2.34069094e-01 -9.56312239e-01 -8.01800311e-01 -8.10159683e-01 -2.48158127e-01 4.21275973e-01 4.99753654e-01 7.70319477e-02 1.03873901e-01 6.25468314e-01 -1.82540822e+00 2.79795676e-01 5.73297799e-01 1.18138075e+00 1.28430933e-01 5.63211441e-01 -1.56739876e-01 4.69810307e-01 -3.37523848e-01 -3.28142822e-01 3.70746166e-01 1.62132178e-02 -7.87481606e-01 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59b2f1e4-0a8f-4a09-9842-74ba83b04fc9	multi-feature-data-fusion-based-load	2301.13774	null	https://arxiv.org/abs/2301.13774v1	https://arxiv.org/pdf/2301.13774v1.pdf	Multi Feature Data Fusion-Based Load Forecasting of Electric Vehicle Charging Stations Using a Deep Learning Model	We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data as multiple inputs to a Long Short-Term Memory (LSTM) model to achieve a robust prediction of charging loads. Weather conditions are significant influencers of the behavior of EV drivers and their driving patterns. These behavioral and driving patterns affect the charging patterns of the drivers. Rather than one prediction (step, model, or variables) made by conventional LSTM models, three charging load (energy demand) predictions of EVs were made depending on different multi-feature inputs. Data fusion was used to combine and optimize the different charging load prediction results. The performance of the final implemented model was evaluated by the mean absolute prediction error of the forecast. The implemented model had a prediction error of 3.29%. This prediction error was lower than initial prediction results by the LSTM model. The numerical results indicate an improvement in the performance of the EV load forecast, indicating that the proposed model could be used to optimize and improve EV load forecasts for electric vehicle charging stations to meet the energy requirements of EVs.	['Ameena S. Al Sumaiti', 'Zhibo Zhang', 'Prince Aduama']	2023-01-31	null	null	null	null	['load-forecasting']	['miscellaneous']	[-3.66637588e-01 -2.93518215e-01 5.82871288e-02 -8.44540238e-01 -1.74887076e-01 -2.86341697e-01 7.91635573e-01 1.40475318e-01 -3.26310724e-01 7.50119805e-01 6.62532449e-02 -5.31782985e-01 -2.24532232e-01 -1.04709649e+00 -7.04872787e-01 -8.02812755e-01 -2.00832427e-01 6.01746380e-01 -3.01035583e-01 -4.76398021e-01 1.22308359e-01 7.30564177e-01 -1.98655713e+00 4.30026025e-01 9.91435468e-01 1.18874896e+00 8.53977203e-01 4.84844416e-01 -5.16784489e-01 6.27912223e-01 -6.05055928e-01 3.60129327e-01 -8.42140093e-02 2.25959823e-01 -1.73374623e-01 -7.58804560e-01 -3.19381714e-01 -1.77729666e-01 6.35816306e-02 7.21245229e-01 3.36277455e-01 2.92592436e-01 5.39444923e-01 -1.40687644e+00 -1.47497609e-01 6.12705290e-01 2.59815007e-01 1.35887787e-01 -1.90000489e-01 -1.93817034e-01 2.10592195e-01 -7.08632052e-01 -5.01634851e-02 9.12289321e-01 7.43683875e-01 1.34637251e-01 -8.23162556e-01 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437377e8-569f-4362-a68f-1d1d9dd25518	a-novel-light-field-coding-scheme-based-on	2210.01447	null	https://arxiv.org/abs/2210.01447v2	https://arxiv.org/pdf/2210.01447v2.pdf	A Novel Light Field Coding Scheme Based on Deep Belief Network & Weighted Binary Images for Additive Layered Displays	Light-field displays create an immersive experience by providing binocular depth sensation and motion parallax. Stacking light attenuating layers is one approach to implement a light field display with a broader depth of field, wide viewing angles and high resolution. Due to the transparent holographic optical element (HOE) layers, additive layered displays can be integrated into augmented reality (AR) wearables to overlay virtual objects onto the real world, creating a seamless mixed reality (XR) experience. This paper proposes a novel framework for light field representation and coding that utilizes Deep Belief Network (DBN) and weighted binary images suitable for additive layered displays. The weighted binary representation of layers makes the framework more flexible for adaptive bitrate encoding. The framework effectively captures intrinsic redundancies in the light field data, and thus provides a scalable solution for light field coding suitable for XR display applications. The latent code is encoded by H.265 codec generating a rate-scalable bit-stream. We achieve adaptive bitrate decoding by varying the number of weighted binary images and the H.265 quantization parameter, while maintaining an optimal reconstruction quality. The framework is tested on real and synthetic benchmark datasets, and the results validate the rate-scalable property of the proposed scheme.	['Mansi Sharma', 'Sally Khaidem']	2022-10-04	null	null	null	null	['mixed-reality']	['computer-vision']	[ 5.47734022e-01 -3.32449198e-01 -1.23727195e-01 -1.92096919e-01 -2.04450503e-01 -1.88267678e-01 2.06739604e-01 -4.30424482e-01 -1.93177402e-01 6.93221569e-01 5.16176999e-01 9.39990976e-04 -1.39817461e-01 -9.06114757e-01 -4.25507754e-01 -8.30449820e-01 1.21055387e-01 -6.41459882e-01 4.30144727e-01 -6.58306330e-02 4.27133292e-01 4.06336635e-01 -2.05418468e+00 4.95519102e-01 9.41081285e-01 1.51360214e+00 1.13422847e+00 8.37177515e-01 -7.71317780e-02 1.14231861e+00 -1.62776589e-01 -3.20248842e-01 3.72188270e-01 7.46507198e-02 2.34664872e-01 -7.58861238e-03 6.84840679e-01 -1.04023612e+00 -7.03674912e-01 1.19081843e+00 7.79878676e-01 -3.77714634e-02 8.62851813e-02 -7.71802485e-01 -1.06143463e+00 -1.68076485e-01 -5.48671067e-01 1.70043737e-01 8.50683331e-01 2.64176041e-01 5.39530337e-01 -8.48442078e-01 5.12591839e-01 1.07482374e+00 2.79876202e-01 6.43680573e-01 -9.73221421e-01 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4c5a9282-ae75-48db-9750-a2bfa6e1a178	evaluation-guidelines-to-deal-with-implicit	null	null	https://aclanthology.org/2021.unimplicit-1.3	https://aclanthology.org/2021.unimplicit-1.3.pdf	Evaluation Guidelines to Deal with Implicit Phenomena to Assess Factuality in Data-to-Text Generation	Data-to-text generation systems are trained on large datasets, such as WebNLG, Ro-toWire, E2E or DART. Beyond traditional token-overlap evaluation metrics (BLEU or METEOR), a key concern faced by recent generators is to control the factuality of the generated text with respect to the input data specification. We report on our experience when developing an automatic factuality evaluation system for data-to-text generation that we are testing on WebNLG and E2E data. We aim to prepare gold data annotated manually to identify cases where the text communicates more information than is warranted based on the in-put data (extra) or fails to communicate data that is part of the input (missing). While analyzing reference (data, text) samples, we encountered a range of systematic uncertainties that are related to cases on implicit phenomena in text, and the nature of non-linguistic knowledge we expect to be involved when assessing factuality. We derive from our experience a set of evaluation guidelines to reach high inter-annotator agreement on such cases.	['Michael Elhadad', 'Roy Eisenstadt']	null	null	null	null	acl-unimplicit-2021-8	['data-to-text-generation']	['natural-language-processing']	[ 1.82669014e-01 8.91272843e-01 1.48823380e-01 -4.57491487e-01 -1.29280543e+00 -8.34040344e-01 1.25638866e+00 5.51374614e-01 -4.38237965e-01 1.45102787e+00 9.16826844e-01 -5.64461291e-01 -8.05105940e-02 -6.77745819e-01 -5.51540315e-01 -5.92910983e-02 5.48497379e-01 9.19073522e-01 -6.29236400e-02 -2.74500519e-01 4.28989559e-01 -6.58180267e-02 -1.44438016e+00 6.91908598e-01 1.17041147e+00 4.65300053e-01 -2.46984586e-02 5.91464043e-01 -7.11395562e-01 9.73717928e-01 -1.26577485e+00 -6.30343735e-01 2.46174284e-03 -6.83168530e-01 -1.37418187e+00 -7.91145861e-02 3.77333939e-01 2.32053787e-01 3.41361493e-01 9.24967289e-01 6.25902653e-01 -1.54619813e-01 9.30976272e-01 -1.25333047e+00 -5.51194489e-01 1.34394634e+00 1.96412086e-01 1.44311920e-01 6.31480396e-01 2.59327322e-01 8.98284376e-01 -8.47436666e-01 1.08584630e+00 1.28912532e+00 4.48256046e-01 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add9762b-3762-45fe-82e5-d9fdcc6a7f44	causally-aware-intraoperative-imputation-for	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Li_Causally-Aware_Intraoperative_Imputation_for_Overall_Survival_Time_Prediction_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Causally-Aware_Intraoperative_Imputation_for_Overall_Survival_Time_Prediction_CVPR_2023_paper.pdf	Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction	Previous efforts in vision community are mostly made on learning good representations from visual patterns. Beyond this, this paper emphasizes the high-level ability of causal reasoning. We thus present a case study of solving the challenging task of Overall Survival (OS) time in primary liver cancers. Critically, the prediction of OS time at the early stage remains challenging, due to the unobvious image patterns of reflecting the OS. To this end, we propose a causal inference system by leveraging the intraoperative attributes and the correlation among them, as an intermediate supervision to bridge the gap between the images and the final OS. Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction. We present a novel Causally-aware Intraoperative Imputation Model (CAWIM) that can sequentially predict each attribute using its parent nodes in the estimated causal graph. To determine the causal directions, we propose a splitting-voting mechanism, which votes for the direction for each pair of adjacent nodes among multiple predictions obtained via causal discovery from heterogeneity. The practicability and effectiveness of our method are demonstrated by the promising result on liver cancer dataset of 361 patients with long-term observations.	['Yanwei Fu', 'Xiuzhong Yao', 'Dingxia Liu', 'Jiejun Chen', 'Qiaole Dong', 'Lingjie Kong', 'Litian Liang', 'Xuelin Qian', 'Xiang Li']	2023-01-01	null	null	null	cvpr-2023-1	['causal-inference', 'causal-discovery', 'causal-inference']	['knowledge-base', 'knowledge-base', 'miscellaneous']	[ 1.72196582e-01 4.62741941e-01 -8.52885723e-01 -5.04554510e-01 -5.67308009e-01 -1.41586557e-01 6.07279062e-01 3.08165282e-01 1.39049321e-01 8.59986901e-01 8.35714817e-01 -5.35753369e-01 -5.75226247e-01 -6.14658535e-01 -8.02388012e-01 -9.34765160e-01 -3.21127027e-01 1.99016362e-01 -3.06574047e-01 2.80305207e-01 1.64422020e-01 2.07458854e-01 -9.46030021e-01 4.89182949e-01 9.32791054e-01 9.39385831e-01 2.10867692e-02 5.71931839e-01 1.16195023e-01 1.44319010e+00 -7.44594932e-02 -3.49435776e-01 -1.07248202e-01 -5.73786139e-01 -6.56609535e-01 -5.96937612e-02 9.78854671e-02 -3.85734916e-01 -6.29735947e-01 6.74235821e-01 1.76796481e-01 -6.73777163e-01 9.26008284e-01 -1.62660635e+00 -5.41673243e-01 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6216a043-cf80-4f2e-a441-af33faef3e19	enhancing-robustness-of-pre-trained-language	null	null	https://openreview.net/forum?id=3FIjaX458P	https://openreview.net/pdf?id=3FIjaX458P	Enhancing Robustness of Pre-trained Language Model with Lexical Simplification	For both human readers and pre-trained language models (PrLMs), lexical diversity may lead to confusion and inaccuracy when understanding the underlying semantic meanings of given sentences. By substituting complex words with simple alternatives, lexical simplification (LS) is a recognized method to reduce such lexical diversity. In this paper, we leverage a novel improved LS approach which can enhance robustness of PrLMs, resulting in improved performances in downstream tasks. A rule-based simplification process is applied to a given sentence. PrLMs are encouraged to predict the real label of the given sentence with auxiliary inputs from the simplified version. Using strong PrLMs (BERT and ELECTRA) as baselines, our approach can still further improve the performance in various text classification tasks.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['lexical-simplification']	['natural-language-processing']	[ 5.63163579e-01 4.06696230e-01 -9.62480828e-02 -6.03349209e-01 -6.86124504e-01 -3.39438438e-01 6.28353417e-01 5.29517293e-01 -7.15617657e-01 7.77268410e-01 5.61017990e-01 -3.10969055e-01 4.06122476e-01 -6.56511128e-01 -4.53077585e-01 -2.34770238e-01 7.52756715e-01 1.82515189e-01 4.51145172e-02 -5.04003167e-01 3.02439928e-01 1.28870383e-01 -1.26804101e+00 7.09910750e-01 1.31121409e+00 4.41898853e-01 5.56863308e-01 3.60691756e-01 -6.77221596e-01 8.84705722e-01 -7.90358961e-01 -8.23182285e-01 2.43025497e-02 -5.12098789e-01 -8.30762744e-01 -2.13850155e-01 1.51322827e-01 -5.71904555e-02 -1.69309407e-01 1.02810729e+00 4.59719121e-01 3.83167833e-01 7.66114414e-01 -6.52019203e-01 -6.74800694e-01 1.35016763e+00 -4.93736178e-01 1.46244630e-01 3.71347904e-01 -3.43158394e-02 1.13733828e+00 -1.03136790e+00 5.83422005e-01 1.78712118e+00 7.68302739e-01 6.49495125e-01 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94a0779f-9501-4288-827a-5324a07312c2	open-domain-question-answering-over-virtual-1	null	null	https://openreview.net/forum?id=tKTRPNNc7A3	https://openreview.net/pdf?id=tKTRPNNc7A3	Open Domain Question Answering over Virtual Documents: A Unified Approach for Data and Text	Due to its potential for a universal interface over both data and text, data-to-text generation is becoming increasingly popular. However, few prior work has focused on its application to downstream tasks, e.g. using the converted data for grounding or reasoning. In this work, we bridge this gap and use the data-to-text method as a means for encoding structured knowledge for knowledge-intensive applications, i.e. open-domain question answering (ODQA). Specifically, we propose a verbalizer-retriever-reader framework for ODQA over data and text where verbalized tables from Wikipedia and graphs from Wikidata are used as augmented knowledge sources. We show that our Unified Data and Text QA, UDT-QA. can effectively benefit from the expanded knowledge index, leading to large gains over text-only baselines. Notably, our approach sets the single-model state-of-the-art on Natural Questions. Furthermore, our analyses indicate that verbalized knowledge is preferred for answer reasoning for both adapted and hot-swap settings.	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013b84f8-2f16-4b8e-9a53-e14652108a37	forecasting-crude-oil-price-using-event	2111.09111	null	https://arxiv.org/abs/2111.09111v1	https://arxiv.org/pdf/2111.09111v1.pdf	Forecasting Crude Oil Price Using Event Extraction	Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, and political events. Most previous research treats crude oil price forecasting as a time series or econometric variable prediction problem. Although recently there have been researches considering the effects of real-time news events, most of these works mainly use raw news headlines or topic models to extract text features without profoundly exploring the event information. In this study, a novel crude oil price forecasting framework, AGESL, is proposed to deal with this problem. In our approach, an open domain event extraction algorithm is utilized to extract underlying related events, and a text sentiment analysis algorithm is used to extract sentiment from massive news. Then a deep neural network integrating the news event features, sentimental features, and historical price features is built to predict future crude oil prices. Empirical experiments are performed on West Texas Intermediate (WTI) crude oil price data, and the results show that our approach obtains superior performance compared with several benchmark methods.	['Xiaohong Huang', 'Jiangwei Liu']	2021-11-14	null	null	null	null	['topic-models']	['natural-language-processing']	[-3.74092042e-01 -4.59938109e-01 -2.56253392e-01 -3.32156181e-01 -4.35521632e-01 -3.80881488e-01 8.78436625e-01 1.21207476e-01 -3.63378555e-01 6.43771291e-01 7.72555351e-01 -3.76550734e-01 7.94583857e-02 -1.36869621e+00 -3.17909688e-01 -7.52530634e-01 -3.61484855e-01 7.64469579e-02 -5.79973981e-02 -3.68649364e-01 5.92032790e-01 3.10365826e-01 -1.12264776e+00 -1.10321030e-01 6.85022056e-01 1.21217382e+00 5.68967722e-02 1.34395152e-01 -5.41408777e-01 1.09329402e+00 -7.06893146e-01 -3.33299756e-01 3.59969646e-01 -1.56484529e-01 -4.34569925e-01 -1.16008602e-01 -6.88666463e-01 -5.58749318e-01 -5.21041036e-01 1.29412282e+00 4.42759693e-01 -3.07516754e-02 4.06444788e-01 -8.41994464e-01 -8.17268252e-01 1.10825980e+00 -9.12314534e-01 5.94972789e-01 -1.30991144e-02 -1.45963311e-01 1.18183720e+00 -1.12159562e+00 2.95634419e-01 1.06995881e+00 3.58991146e-01 -2.57259846e-01 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-8.66364181e-01 3.03742737e-01 2.81358302e-01 -4.93106008e-01 -5.21713376e-01 1.23783283e-01]	[4.420652389526367, 4.2763800621032715]
678fb346-a9a0-4c60-920e-ce7a31bbffb4	artificial-color-constancy-via-googlenet-with	1811.08456	null	https://arxiv.org/abs/1811.08456v2	https://arxiv.org/pdf/1811.08456v2.pdf	Artificial Color Constancy via GoogLeNet with Angular Loss Function	Color Constancy is the ability of the human visual system to perceive colors unchanged independently of the illumination. Giving a machine this feature will be beneficial in many fields where chromatic information is used. Particularly, it significantly improves scene understanding and object recognition. In this paper, we propose transfer learning-based algorithm, which has two main features: accuracy higher than many state-of-the-art algorithms and simplicity of implementation. Despite the fact that GoogLeNet was used in the experiments, given approach may be applied to any CNN. Additionally, we discuss design of a new loss function oriented specifically to this problem, and propose a few the most suitable options.	['Oleksii Sidorov']	2018-11-20	null	null	null	null	['color-constancy']	['computer-vision']	[ 5.03537850e-03 -5.00522137e-01 -8.85299221e-02 -4.23331022e-01 -3.18547860e-02 -4.67326164e-01 5.57179928e-01 -3.03572983e-01 -6.40250027e-01 8.65176499e-01 -5.70556045e-01 -3.12129378e-01 4.81350161e-02 -6.60258174e-01 -5.96019506e-01 -8.07550311e-01 1.14856593e-01 -1.32655606e-01 3.69419664e-01 -2.92547107e-01 4.59897757e-01 8.43563855e-01 -1.80004275e+00 8.98709223e-02 9.07557368e-01 1.21266592e+00 3.25431287e-01 5.16265392e-01 -2.38146245e-01 7.82267153e-01 -5.67837238e-01 -3.31347317e-01 4.38607782e-01 -6.27096415e-01 -6.39217138e-01 1.67258009e-02 7.21374512e-01 1.37952762e-02 3.46264802e-02 1.22042727e+00 2.90899694e-01 4.00017530e-01 6.31091595e-01 -1.39493823e+00 -9.60468709e-01 -1.58926964e-01 -5.50662279e-01 2.37883776e-01 1.74679130e-01 7.74253309e-02 8.49391103e-01 -8.12808216e-01 4.21839714e-01 9.84307885e-01 5.34776628e-01 6.48215473e-01 -9.73471344e-01 -5.25267065e-01 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0301ecb9-45bd-4dcb-a920-2475e17da4d5	augmenting-neural-response-generation-with	1811.01063	null	https://arxiv.org/abs/1811.01063v2	https://arxiv.org/pdf/1811.01063v2.pdf	Augmenting Neural Response Generation with Context-Aware Topical Attention	Sequence-to-Sequence (Seq2Seq) models have witnessed a notable success in generating natural conversational exchanges. Notwithstanding the syntactically well-formed responses generated by these neural network models, they are prone to be acontextual, short and generic. In this work, we introduce a Topical Hierarchical Recurrent Encoder Decoder (THRED), a novel, fully data-driven, multi-turn response generation system intended to produce contextual and topic-aware responses. Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation. To train our model, we provide a clean and high-quality conversational dataset mined from Reddit comments. We evaluate THRED on two novel automated metrics, dubbed Semantic Similarity and Response Echo Index, as well as with human evaluation. Our experiments demonstrate that the proposed model is able to generate more diverse and contextually relevant responses compared to the strong baselines.	['Osmar Zaiane', 'Nouha Dziri', 'Ehsan Kamalloo', 'Kory W. Mathewson']	2018-11-02	augmenting-neural-response-generation-with-1	https://aclanthology.org/W19-4103	https://aclanthology.org/W19-4103.pdf	ws-2019-8	['open-domain-dialog']	['natural-language-processing']	[ 5.24143934e-01 4.15004641e-01 2.23528758e-01 -7.27570832e-01 -1.31208181e+00 -6.03216410e-01 1.12676144e+00 -1.55145526e-01 -1.90623134e-01 1.22605586e+00 1.11177826e+00 -4.05688062e-02 2.63666362e-01 -6.45854175e-01 -3.52290481e-01 -3.34902942e-01 3.80100489e-01 8.01121473e-01 -6.65505007e-02 -9.27252948e-01 4.48747009e-01 -1.78090364e-01 -1.34648550e+00 1.06909013e+00 1.04631412e+00 5.65603375e-01 1.77743003e-01 1.06838572e+00 -1.83330476e-01 1.27150118e+00 -9.85311568e-01 -6.98896646e-01 -3.19993228e-01 -1.07432830e+00 -1.32047987e+00 -1.40764296e-01 1.90161139e-01 -2.20373526e-01 -2.08565399e-01 2.83655524e-01 8.52952778e-01 4.03560430e-01 5.93641579e-01 -8.45488131e-01 -6.97145522e-01 1.01155782e+00 5.66936173e-02 -2.45991368e-02 1.07259858e+00 2.30374381e-01 1.22980201e+00 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ffdee8cf-80bd-45fe-80eb-46c6837bf457	bi-lstm-neural-networks-for-chinese	null	null	https://aclanthology.org/W16-4919	https://aclanthology.org/W16-4919.pdf	Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis	Grammatical Error Diagnosis for Chinese has always been a challenge for both foreign learners and NLP researchers, for the variousity of grammar and the flexibility of expression. In this paper, we present a model based on Bidirectional Long Short-Term Memory(Bi-LSTM) neural networks, which treats the task as a sequence labeling problem, so as to detect Chinese grammatical errors, to identify the error types and to locate the error positions. In the corpora of this year{'}s shared task, there can be multiple errors in a single offset of a sentence, to address which, we simutaneously train three Bi-LSTM models sharing word embeddings which label Missing, Redundant and Selection errors respectively. We regard word ordering error as a special kind of word selection error which is longer during training phase, and then separate them by length during testing phase. In NLP-TEA 3 shared task for Chinese Grammatical Error Diagnosis(CGED), Our system achieved relatively high F1 for all the three levels in the traditional Chinese track and for the detection level in the Simpified Chinese track.	['Shen Huang', 'Houfeng Wang']	2016-12-01	null	null	null	ws-2016-12	['grammatical-error-detection']	['natural-language-processing']	[-4.56489511e-02 -2.07171783e-01 3.82555991e-01 -5.02986789e-01 -6.53885663e-01 -2.05225706e-01 -3.93295914e-01 1.68688774e-01 -7.50573814e-01 8.49041998e-01 2.38037631e-01 -7.69122839e-01 2.03135282e-01 -5.88728845e-01 -6.63504541e-01 -2.92050868e-01 2.88144685e-02 4.76279318e-01 1.73548430e-01 -2.08696380e-01 4.25702304e-01 3.51836532e-02 -1.03307819e+00 5.93237400e-01 1.62241161e+00 6.53489888e-01 8.00146878e-01 7.06202745e-01 -6.63612306e-01 7.67583728e-01 -9.79442537e-01 -4.82761294e-01 -5.34173250e-01 -4.95029926e-01 -1.23603272e+00 -4.69112486e-01 1.55473188e-01 -2.30721205e-01 3.24576497e-02 1.38298631e+00 7.59303629e-01 -4.58617993e-02 3.36281627e-01 -7.78985262e-01 -1.15525079e+00 8.58752906e-01 -3.89582030e-02 5.59108317e-01 2.04773992e-01 -3.97352457e-01 8.64698887e-01 -1.15237713e+00 6.12619936e-01 1.20623553e+00 8.95719528e-01 9.01212692e-01 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b5fd0148-32c8-4842-b3ef-f856c1a4b1a4	cooperative-task-and-motion-planning-for	2203.02475	null	https://arxiv.org/abs/2203.02475v1	https://arxiv.org/pdf/2203.02475v1.pdf	Cooperative Task and Motion Planning for Multi-Arm Assembly Systems	Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs. However, effectively planning for these systems in a manner that ensures each robot is simultaneously productive, and not idle, is challenging due to (1) the close proximity that the robots must operate in to manipulate the structure and (2) the inherent structural partial orderings on when each part can be installed. In this paper, we present a task and motion planning framework that jointly plans safe, low-makespan plans for a team of robots to assemble complex spatial structures. Our framework takes a hierarchical approach that, at the high level, uses Mixed-integer Linear Programs to compute an abstract plan comprised of an allocation of robots to tasks subject to precedence constraints and, at the low level, builds on a state-of-the-art algorithm for Multi-Agent Path Finding to plan collision-free robot motions that realize this abstract plan. Critical to our approach is the inclusion of certain collision constraints and movement durations during high-level planning, which better informs the search for abstract plans that are likely to be both feasible and low-makespan while keeping the search tractable. We demonstrate our planning system on several challenging assembly domains with several (sometimes heterogeneous) robots with grippers or suction plates for assembling structures with up to 23 objects involving Lego bricks, bars, plates, or irregularly shaped blocks.	['Brian C. Williams', 'Sven Koenig', 'Caitlin Mueller', 'Andreas Hofmann', 'Chuchu Fan', 'Dawei Sun', 'Caelan Garrett', 'Yijiang Huang', 'Jiaoyang Li', 'Jingkai Chen']	2022-03-04	null	null	null	null	['multi-agent-path-finding']	['playing-games']	[ 2.29013234e-01 3.54044557e-01 -8.06553811e-02 1.31260514e-01 -2.55478501e-01 -9.92075384e-01 9.25221220e-02 4.56842482e-01 1.38590336e-01 1.00461042e+00 -2.86087424e-01 -3.13228220e-01 -1.05963194e+00 -7.73795307e-01 -6.59813106e-01 -4.68623519e-01 -5.63503385e-01 1.21190250e+00 3.27767491e-01 -4.65708196e-01 3.82504702e-01 1.01564705e+00 -1.46088398e+00 1.04345709e-01 5.41141152e-01 7.54180253e-01 1.00389397e+00 7.26083934e-01 2.56309271e-01 6.34852648e-01 -3.74019384e-01 3.21288019e-01 3.85302722e-01 9.22705308e-02 -1.23062849e+00 6.97909832e-01 -4.72889543e-01 -1.63636938e-01 3.53726178e-01 4.86471862e-01 -1.40449807e-01 2.29864120e-01 6.58922136e-01 -1.80795324e+00 -1.89384490e-01 5.26701808e-01 -5.49913466e-01 -3.94102424e-01 4.73027647e-01 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b7fcbf43-2839-49dc-ae7e-4000744b7a91	predicting-the-future-a-jointly-learnt-model-1	1912.07148	null	https://arxiv.org/abs/1912.07148v1	https://arxiv.org/pdf/1912.07148v1.pdf	Predicting the Future: A Jointly Learnt Model for Action Anticipation	Inspired by human neurological structures for action anticipation, we present an action anticipation model that enables the prediction of plausible future actions by forecasting both the visual and temporal future. In contrast to current state-of-the-art methods which first learn a model to predict future video features and then perform action anticipation using these features, the proposed framework jointly learns to perform the two tasks, future visual and temporal representation synthesis, and early action anticipation. The joint learning framework ensures that the predicted future embeddings are informative to the action anticipation task. Furthermore, through extensive experimental evaluations we demonstrate the utility of using both visual and temporal semantics of the scene, and illustrate how this representation synthesis could be achieved through a recurrent Generative Adversarial Network (GAN) framework. Our model outperforms the current state-of-the-art methods on multiple datasets: UCF101, UCF101-24, UT-Interaction and TV Human Interaction.	['Harshala Gammulle', 'Clinton Fookes', 'Sridha Sridharan', 'Simon Denman']	2019-12-16	predicting-the-future-a-jointly-learnt-model	http://openaccess.thecvf.com/content_ICCV_2019/html/Gammulle_Predicting_the_Future_A_Jointly_Learnt_Model_for_Action_Anticipation_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Gammulle_Predicting_the_Future_A_Jointly_Learnt_Model_for_Action_Anticipation_ICCV_2019_paper.pdf	iccv-2019-10	['action-anticipation']	['computer-vision']	[ 4.74690586e-01 6.92130685e-01 -5.74285798e-02 -4.26834434e-01 -4.17035133e-01 -2.44699255e-01 1.36533642e+00 -5.12419462e-01 -2.06580162e-01 6.77526534e-01 9.15754616e-01 7.17637539e-02 2.26929069e-01 -4.75867689e-01 -9.30740774e-01 -3.40836316e-01 -3.76558155e-01 2.76316971e-01 4.02473286e-02 5.16277775e-02 4.85910736e-02 3.68332744e-01 -1.67935729e+00 8.14354897e-01 4.33805585e-01 9.42362905e-01 2.20560044e-01 8.67457509e-01 3.32749426e-01 1.49346089e+00 -1.75182149e-01 -4.28267598e-01 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c3607514-25bf-4122-abb7-82d67bfc7104	knowledge-and-data-driven-services-for-energy	2103.07248	null	https://arxiv.org/abs/2103.07248v1	https://arxiv.org/pdf/2103.07248v1.pdf	Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks	The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resources (e.g. renewable energy generators, electric vehicles) and internet-connected sensing and control devices (e.g. smart heating and cooling) require new tools to support accurate, datadriven decision making. Modelling the effect of such growing complexity in the electrical grid is possible in principle using state-of-the-art power-power flow models. In practice, the detailed information needed for these physical simulations may be unknown or prohibitively expensive to obtain. Hence, datadriven approaches to power systems modelling, including feedforward neural networks and auto-encoders, have been studied to leverage the increasing availability of sensor data, but have seen limited practical adoption due to lack of transparency and inefficiencies on large-scale problems. Our work addresses this gap by proposing a data- and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs). The model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality when compared with traditional machine-learning models with similar accuracy. Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services for a distribution system operator participating in an energy flexibility market.	['Seshu Tirupathi', 'Mark Purcell', 'Robert Gormally', 'Bradley Eck', 'Francesco Fusco']	2021-03-12	null	null	null	null	['physical-simulations']	['miscellaneous']	[-2.87081778e-01 2.18555629e-01 -2.97929257e-01 -2.24697903e-01 -2.06020996e-01 -7.25283325e-01 8.06922972e-01 1.92570686e-01 2.48643294e-01 1.10988796e+00 -7.33369738e-02 -6.32491291e-01 -6.67327642e-01 -1.28555191e+00 -5.16413808e-01 -8.78779829e-01 -4.34992015e-01 7.76422679e-01 -4.74074632e-01 -2.07854155e-02 -3.01609579e-02 6.65148497e-01 -1.14161503e+00 -4.58788931e-01 8.49206269e-01 1.11781466e+00 2.64937580e-01 4.40458715e-01 -5.83482720e-02 5.67956507e-01 -3.84088755e-01 4.31190521e-01 2.93398827e-01 -4.77289379e-01 -4.21799570e-01 -3.63854855e-01 -6.12419009e-01 -4.53199208e-01 -4.59921926e-01 1.23852372e+00 4.35981363e-01 1.23134732e-01 5.05359054e-01 -1.56892538e+00 -5.04508197e-01 9.94241655e-01 -2.63158649e-01 4.73454222e-02 -1.19069539e-01 3.62605691e-01 9.68673646e-01 -2.70417482e-01 -1.39254762e-03 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93fbf9ea-de41-49a4-b60a-f8168d17f3b5	learning-efficient-explainable-and	2101.07429	null	https://arxiv.org/abs/2101.07429v1	https://arxiv.org/pdf/2101.07429v1.pdf	Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification	Automatic pulmonary nodules classification is significant for early diagnosis of lung cancers. Recently, deep learning techniques have enabled remarkable progress in this field. However, these deep models are typically of high computational complexity and work in a black-box manner. To combat these challenges, in this work, we aim to build an efficient and (partially) explainable classification model. Specially, we use \emph{neural architecture search} (NAS) to automatically search 3D network architectures with excellent accuracy/speed trade-off. Besides, we use the convolutional block attention module (CBAM) in the networks, which helps us understand the reasoning process. During training, we use A-Softmax loss to learn angularly discriminative representations. In the inference stage, we employ an ensemble of diverse neural networks to improve the prediction accuracy and robustness. We conduct extensive experiments on the LIDC-IDRI database. Compared with previous state-of-the-art, our model shows highly comparable performance by using less than 1/40 parameters. Besides, empirical study shows that the reasoning process of learned networks is in conformity with physicians' diagnosis. Related code and results have been released at: https://github.com/fei-hdu/NAS-Lung.	['Weidong Han', 'Fei Gao', 'Fuhao Shen', 'Hanliang Jiang']	2021-01-19	null	null	null	null	['pulmonary-nodules-classification', 'lung-nodule-classification']	['medical', 'medical']	[-1.35094807e-01 2.81119794e-01 -4.81489748e-01 -4.00175333e-01 -7.11763978e-01 -1.08923621e-01 1.82457328e-01 -3.71706694e-01 -1.19740717e-01 3.59094799e-01 1.14880867e-01 -6.55388474e-01 -2.95241505e-01 -6.67830288e-01 -6.73325777e-01 -6.82447255e-01 4.33639467e-01 4.83666599e-01 2.73845047e-01 1.47180498e-01 -1.45820588e-01 4.81564134e-01 -9.89217162e-01 5.00959337e-01 9.16746736e-01 1.14623904e+00 3.54242951e-01 5.64072132e-01 1.97872743e-02 1.21003342e+00 -9.30362791e-02 -5.09953082e-01 1.46841444e-02 -3.27742338e-01 -1.03776610e+00 -1.29323557e-01 8.71513784e-02 -3.81372213e-01 -6.89542294e-01 9.04245794e-01 5.47528386e-01 -1.56844124e-01 7.27073014e-01 -8.49227130e-01 -7.39192784e-01 4.73287076e-01 -4.18330878e-01 2.10433900e-01 -1.32893860e-01 7.77386576e-02 1.05364656e+00 -1.00988030e+00 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ca631b2c-16d6-45cb-a89a-e6a209b29aee	chatgpt-vision-and-challenges	2305.15323	null	https://arxiv.org/abs/2305.15323v1	https://arxiv.org/pdf/2305.15323v1.pdf	ChatGPT: Vision and Challenges	Artificial intelligence (AI) and machine learning have changed the nature of scientific inquiry in recent years. Of these, the development of virtual assistants has accelerated greatly in the past few years, with ChatGPT becoming a prominent AI language model. In this study, we examine the foundations, vision, research challenges of ChatGPT. This article investigates into the background and development of the technology behind it, as well as its popular applications. Moreover, we discuss the advantages of bringing everything together through ChatGPT and Internet of Things (IoT). Further, we speculate on the future of ChatGPT by considering various possibilities for study and development, such as energy-efficiency, cybersecurity, enhancing its applicability to additional technologies (Robotics and Computer Vision), strengthening human-AI communications, and bridging the technological gap. Finally, we discuss the important ethics and current trends of ChatGPT.	['Rupinder Kaur', 'Sukhpal Singh Gill']	2023-05-08	null	null	null	null	['ethics']	['miscellaneous']	[-1.29167646e-01 5.67642689e-01 -3.34644943e-01 6.79619983e-02 4.85394657e-01 -4.04374152e-01 7.14725494e-01 -1.58996448e-01 -3.70838463e-01 7.06463754e-01 2.26366609e-01 -4.30317640e-01 -2.10789770e-01 -9.54125822e-01 -2.87559837e-01 -5.81085443e-01 2.04070985e-01 4.19793695e-01 -4.21061143e-02 -2.81623572e-01 1.49197519e-01 9.69306752e-02 -1.66806233e+00 -1.89831838e-01 1.09671009e+00 8.87186348e-01 4.78596151e-01 3.93240809e-01 -3.61106455e-01 9.49280620e-01 -7.46278048e-01 -6.11368597e-01 -4.04675044e-02 -1.87088832e-01 -1.20877194e+00 -3.26823860e-01 -4.29700553e-01 -1.43542662e-01 -4.06603068e-01 7.86547840e-01 3.13894808e-01 -2.59041926e-03 1.30679131e-01 -1.87757504e+00 -1.12838387e+00 7.98922539e-01 -1.99655548e-01 -4.18914288e-01 5.13467968e-01 2.83835679e-01 7.37360895e-01 -1.88281715e-01 8.02374601e-01 1.39282823e+00 4.07253474e-01 7.06012964e-01 -4.15834546e-01 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2944ba86-4799-49ef-bdb5-f8d8d0d10131	rolling-shutter-camera-synchronization-with	1902.11084	null	http://arxiv.org/abs/1902.11084v1	http://arxiv.org/pdf/1902.11084v1.pdf	Rolling Shutter Camera Synchronization with Sub-millisecond Accuracy	A simple method for synchronization of video streams with a precision better than one millisecond is proposed. The method is applicable to any number of rolling shutter cameras and when a few photographic flashes or other abrupt lighting changes are present in the video. The approach exploits the rolling shutter sensor property that every sensor row starts its exposure with a small delay after the onset of the previous row. The cameras may have different frame rates and resolutions, and need not have overlapping fields of view. The method was validated on five minutes of four streams from an ice hockey match. The found transformation maps events visible in all cameras to a reference time with a standard deviation of the temporal error in the range of 0.3 to 0.5 milliseconds. The quality of the synchronization is demonstrated on temporally and spatially overlapping images of a fast moving puck observed in two cameras.	['Jiri Matas', 'Matej Smid']	2019-02-28	null	null	null	null	['video-synchronization']	['computer-vision']	[ 4.55751002e-01 -5.20542324e-01 6.88881949e-02 -3.44865881e-02 -3.20943624e-01 -7.27997482e-01 6.53257012e-01 3.21711779e-01 -7.96173096e-01 6.56122148e-01 -3.84647280e-01 1.32128909e-01 1.27448022e-01 -3.69007498e-01 -7.55404949e-01 -4.70866323e-01 -2.98693299e-01 5.08300737e-02 9.68681514e-01 4.70568873e-02 1.86678678e-01 6.93990409e-01 -1.57321417e+00 1.65747240e-01 1.10454656e-01 9.59062755e-01 3.27925414e-01 1.09327030e+00 4.60743606e-01 6.94474518e-01 -7.69719064e-01 7.36197233e-02 5.78862429e-01 -3.74642998e-01 -3.48744132e-02 2.80518949e-01 5.70208669e-01 -7.63634861e-01 -5.85706234e-01 5.92823446e-01 -1.22525757e-02 5.81878908e-02 3.92575115e-02 -1.24419832e+00 3.87974262e-01 -1.76463977e-01 -7.75588810e-01 6.12702131e-01 9.38022017e-01 1.38757616e-01 -4.52760048e-02 -3.67253274e-01 9.18292344e-01 4.15332168e-01 7.63296723e-01 2.60099974e-02 -9.67523932e-01 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2.27719709e-01 6.11959279e-01 -2.25221485e-01 -5.37561953e-01 8.42459142e-01]	[8.71487808227539, -1.9046692848205566]
4be7c459-449e-4f30-8323-feafddce5351	enhancing-early-lung-cancer-detection-on	2208.14742	null	https://arxiv.org/abs/2208.14742v1	https://arxiv.org/pdf/2208.14742v1.pdf	Enhancing Early Lung Cancer Detection on Chest Radiographs with AI-assistance: A Multi-Reader Study	Objectives: The present study evaluated the impact of a commercially available explainable AI algorithm in augmenting the ability of clinicians to identify lung cancer on chest X-rays (CXR). Design: This retrospective study evaluated the performance of 11 clinicians for detecting lung cancer from chest radiographs, with and without assistance from a commercially available AI algorithm (red dot, Behold.ai) that predicts suspected lung cancer from CXRs. Clinician performance was evaluated against clinically confirmed diagnoses. Setting: The study analysed anonymised patient data from an NHS hospital; the dataset consisted of 400 chest radiographs from adult patients (18 years and above) who had a CXR performed in 2020, with corresponding clinical text reports. Participants: A panel of readers consisting of 11 clinicians (consultant radiologists, radiologist trainees and reporting radiographers) participated in this study. Main outcome measures: Overall accuracy, sensitivity, specificity and precision for detecting lung cancer on CXRs by clinicians, with and without AI input. Agreement rates between clinicians and performance standard deviation were also evaluated, with and without AI input. Results: The use of the AI algorithm by clinicians led to an improved overall performance for lung tumour detection, achieving an overall increase of 17.4% of lung cancers being identified on CXRs which would have otherwise been missed, an overall increase in detection of smaller tumours, a 24% and 13% increased detection of stage 1 and stage 2 lung cancers respectively, and standardisation of clinician performance. Conclusions: This study showed great promise in the clinical utility of AI algorithms in improving early lung cancer diagnosis and promoting health equity through overall improvement in reader performances, without impacting downstream imaging resources.	['Simon Rasalingham', 'George Pearse', 'Jordan Smith', 'Liliana Garcia-Mondragon', 'Paul Williams', 'Tom Naunton Morgan', 'Qaiser Malik', 'Amanda Stockham', 'Stephanie Patterson', 'Jackson J. Pat', 'James Hoare', 'David Doyne', 'Richard Dittrich', 'Matthew Tam', 'Tom Dyer', 'Nicole Tay', 'Gaetan Dissez']	2022-08-31	null	null	null	null	['lung-cancer-diagnosis']	['medical']	[ 4.17726129e-01 4.10857320e-01 -3.15309554e-01 -1.18911207e-01 -1.50892305e+00 -7.67514467e-01 2.63844490e-01 4.96615499e-01 -6.35769367e-01 6.27503097e-01 5.15111506e-01 -1.13858223e+00 -6.75552905e-01 -5.94659686e-01 -2.97522873e-01 -7.11567819e-01 3.60816747e-01 1.16619158e+00 3.53324205e-01 8.15052986e-01 -3.03101867e-01 8.43256474e-01 -6.49678349e-01 6.96037173e-01 3.11742485e-01 6.58789277e-01 3.34738016e-01 1.40855730e+00 2.79001147e-01 1.60087311e+00 -3.26310366e-01 -3.74486387e-01 4.70357597e-01 -7.01244950e-01 -7.91855216e-01 -2.37521902e-02 3.10108244e-01 -5.82236528e-01 -3.41222316e-01 -1.29738569e-01 7.45354652e-01 -3.52531105e-01 8.78606021e-01 -6.24368131e-01 -3.88403803e-01 5.72819471e-01 -1.01808719e-01 6.35199606e-01 3.20448786e-01 7.99508691e-01 8.29644084e-01 -5.60021937e-01 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13ff6911-0f5a-452e-bccc-72e03a8327c3	eeg-synthetic-data-generation-using	2303.06068	null	https://arxiv.org/abs/2303.06068v1	https://arxiv.org/pdf/2303.06068v1.pdf	EEG Synthetic Data Generation Using Probabilistic Diffusion Models	Electroencephalography (EEG) plays a significant role in the Brain Computer Interface (BCI) domain, due to its non-invasive nature, low cost, and ease of use, making it a highly desirable option for widespread adoption by the general public. This technology is commonly used in conjunction with deep learning techniques, the success of which is largely dependent on the quality and quantity of data used for training. To address the challenge of obtaining sufficient EEG data from individual participants while minimizing user effort and maintaining accuracy, this study proposes an advanced methodology for data augmentation: generating synthetic EEG data using denoising diffusion probabilistic models. The synthetic data are generated from electrode-frequency distribution maps (EFDMs) of emotionally labeled EEG recordings. To assess the validity of the synthetic data generated, both a qualitative and a quantitative comparison with real EEG data were successfully conducted. This study opens up the possibility for an open\textendash source accessible and versatile toolbox that can process and generate data in both time and frequency dimensions, regardless of the number of channels involved. Finally, the proposed methodology has potential implications for the broader field of neuroscience research by enabling the creation of large, publicly available synthetic EEG datasets without privacy concerns.	['Francesco Fumagalli', 'Cesare M. Dalbagno', 'Giulio Tosato']	2023-03-06	null	null	null	null	['synthetic-data-generation', 'eeg', 'synthetic-data-generation', 'eeg']	['medical', 'methodology', 'miscellaneous', 'time-series']	[ 2.98308372e-01 -1.50449753e-01 4.98837352e-01 -3.36527050e-01 -4.85426903e-01 -2.83281654e-01 3.54396492e-01 3.83363038e-01 -6.62592292e-01 1.09727049e+00 -2.62281727e-02 2.95618977e-02 -2.91114479e-01 -6.21072888e-01 -4.79655057e-01 -9.60066617e-01 -1.59223944e-01 9.57579166e-02 -4.57132667e-01 1.85303181e-01 2.78083622e-01 6.50312424e-01 -1.59627926e+00 3.03888358e-02 1.17600369e+00 1.15158057e+00 2.59139985e-01 -6.20919541e-02 3.62201124e-01 7.46255666e-02 -7.77263343e-01 -4.57635671e-01 1.10475002e-02 -2.68242776e-01 -3.03319544e-01 -4.20942232e-02 -2.58530289e-01 -7.14893118e-02 -1.63122803e-01 1.14094341e+00 8.60027194e-01 1.22048959e-01 6.12990439e-01 -1.37109184e+00 -5.35857201e-01 9.20637622e-02 -4.37895417e-01 2.24530309e-01 3.13813031e-01 1.08844660e-01 4.33001995e-01 -7.74981916e-01 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4.54744309e-01 -9.81583774e-01 -1.49027258e-01 -1.19407809e+00 -8.10904086e-01 3.72904718e-01 1.71061512e-02 -8.93386126e-01 -2.03576803e-01 -1.17283147e-02]	[13.158992767333984, 3.3995816707611084]
875127b1-529f-4bcb-b7d9-1f7f795af58f	deep-linear-networks-dynamics-low-rank-biases	2106.15933	null	https://arxiv.org/abs/2106.15933v2	https://arxiv.org/pdf/2106.15933v2.pdf	Saddle-to-Saddle Dynamics in Deep Linear Networks: Small Initialization Training, Symmetry, and Sparsity	The dynamics of Deep Linear Networks (DLNs) is dramatically affected by the variance $\sigma^2$ of the parameters at initialization $\theta_0$. For DLNs of width $w$, we show a phase transition w.r.t. the scaling $\gamma$ of the variance $\sigma^2=w^{-\gamma}$ as $w\to\infty$: for large variance ($\gamma<1$), $\theta_0$ is very close to a global minimum but far from any saddle point, and for small variance ($\gamma>1$), $\theta_0$ is close to a saddle point and far from any global minimum. While the first case corresponds to the well-studied NTK regime, the second case is less understood. This motivates the study of the case $\gamma \to +\infty$, where we conjecture a Saddle-to-Saddle dynamics: throughout training, gradient descent visits the neighborhoods of a sequence of saddles, each corresponding to linear maps of increasing rank, until reaching a sparse global minimum. We support this conjecture with a theorem for the dynamics between the first two saddles, as well as some numerical experiments.	['Berfin Şimşek', 'Clément Hongler', 'Franck Gabriel', 'François Ged', 'Arthur Jacot']	2021-06-30	null	null	null	null	['l2-regularization']	['methodology']	[-2.69956768e-01 3.18866551e-01 -9.39975455e-02 9.21790488e-03 -4.37300980e-01 -4.24385577e-01 1.73857868e-01 1.46842986e-01 -6.89297616e-01 7.33819187e-01 -2.43715674e-01 -4.32197273e-01 -7.48786986e-01 -8.41770947e-01 -9.93147194e-01 -1.25013292e+00 -8.52231324e-01 4.27396983e-01 2.74377197e-01 -5.82713962e-01 6.72451556e-02 2.07017958e-01 -1.15329909e+00 -2.09728763e-01 6.93737090e-01 1.30011451e+00 -2.00649008e-01 4.43515897e-01 -3.06720704e-01 1.25130206e-01 -3.85688633e-01 -2.79257566e-01 7.75182545e-01 -6.59378171e-01 -4.54252332e-01 -4.67449009e-01 3.91285241e-01 3.54147494e-01 -1.29488930e-01 1.70266259e+00 2.66962379e-01 2.49682918e-01 6.25554264e-01 -1.07552218e+00 -3.85236889e-01 7.79683709e-01 -9.33337927e-01 4.90500927e-01 -2.80938476e-01 7.43776038e-02 8.70671928e-01 -5.80773473e-01 6.82910919e-01 8.11339200e-01 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7e2bfb10-0bad-4ff8-a00e-97235662b96c	dual-stream-time-delay-neural-network-with	2303.1102	null	https://arxiv.org/abs/2303.11020v2	https://arxiv.org/pdf/2303.11020v2.pdf	Dual-stream Time-Delay Neural Network with Dynamic Global Filter for Speaker Verification	The time-delay neural network (TDNN) is one of the state-of-the-art models for text-independent speaker verification. However, it is difficult for conventional TDNN to capture global context that has been proven critical for robust speaker representations and long-duration speaker verification in many recent works. Besides, the common solutions, e.g., self-attention, have quadratic complexity for input tokens, which makes them computationally unaffordable when applied to the feature maps with large sizes in TDNN. To address these issues, we propose the Global Filter for TDNN, which applies log-linear complexity FFT/IFFT and a set of differentiable frequency-domain filters to efficiently model the long-term dependencies in speech. Besides, a dynamic filtering strategy, and a sparse regularization method are specially designed to enhance the performance of the global filter and prevent it from overfitting. Furthermore, we construct a dual-stream TDNN (DS-TDNN), which splits the basic channels for complexity reduction and employs the global filter to increase recognition performance. Experiments on Voxceleb and SITW databases show that the DS-TDNN achieves approximate 10% improvement with a decline over 28% and 15% in complexity and parameters compared with the ECAPA-TDNN. Besides, it has the best trade-off between efficiency and effectiveness compared with other popular baseline systems when facing long-duration speech. Finally, visualizations and a detailed ablation study further reveal the advantages of the DS-TDNN.	['Xiaodan Lin', 'Yangfu Li']	2023-03-20	null	null	null	null	['text-independent-speaker-verification', 'speaker-verification']	['speech', 'speech']	[-7.16058165e-02 -3.39386225e-01 2.20938735e-02 -4.96912032e-01 -7.90561140e-01 -2.97998846e-01 2.82099247e-01 -3.66368771e-01 -4.07599479e-01 3.90171289e-01 4.27876800e-01 -5.33446133e-01 -7.72270858e-02 -9.82560813e-02 -3.75764906e-01 -8.83906901e-01 -2.56604124e-02 -2.11698234e-01 -6.81543946e-02 -2.41583362e-02 -6.72407374e-02 3.15014631e-01 -1.49466169e+00 -9.29124653e-02 1.18964362e+00 1.15528941e+00 2.68909991e-01 3.83363962e-01 -6.24764487e-02 3.69156510e-01 -8.49790454e-01 -3.01261038e-01 6.93968013e-02 -3.40581983e-01 -2.82796115e-01 -2.33274773e-01 3.97558421e-01 -3.49855483e-01 -6.55324459e-01 9.95595038e-01 1.01522195e+00 3.56417209e-01 4.36746329e-01 -1.07277715e+00 -5.57210565e-01 7.16498077e-01 -4.82618779e-01 4.81253266e-01 7.02632889e-02 3.67306359e-02 8.92379999e-01 -1.09216273e+00 -6.10613264e-02 1.46210539e+00 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8aefd15d-b744-4448-80ac-670d508e0456	fault-detection-and-localization-in-active	2204.0569	null	https://arxiv.org/abs/2204.05690v1	https://arxiv.org/pdf/2204.05690v1.pdf	Fault Detection and Localization in Active Distribution Networks using Optimally Placed Phasor Measurements Units	This paper introduces an algorithm able to detect and localize the occurrance of a fault in an Active Distribution Network, using the measurements collected by Phasor Measurement Units (PMUs). First, a basic algorithm that works under the assumption that all grid buses are equipped with a PMU is designed. Then, formal observability conditions that allow detection and localization with a reduced number of PMUs are provided. Based on these conditions, the algorithm is extended to perform correctly when not all network buses are monitored. Moreover, an Optimal Positioning Algorithm, always based on the observability conditions, is designed. This algorithm allows the user to customize the fault localization resolution. The approach is validated through simulations carried out on a benchmark active distribution network.	['Federico Silvestro', 'Giacomo-Piero Schiapparelli', 'Bruno Gabriele', "Fabio D'Agostino", 'Francesco Conte']	2022-04-12	null	null	null	null	['fault-localization']	['computer-code']	[-2.21916869e-01 3.25888023e-02 9.52363536e-02 9.08819884e-02 -1.62752554e-01 -7.86148846e-01 4.03425872e-01 7.15936720e-01 1.53445944e-01 1.04811895e+00 -5.71375787e-01 -1.27935663e-01 -5.01445055e-01 -1.08656085e+00 -2.93851376e-01 -8.28427315e-01 -5.36357462e-01 5.73994398e-01 1.67724475e-01 -1.68325529e-01 5.73777966e-02 1.00475645e+00 -9.14828539e-01 -4.75678414e-01 1.04192042e+00 7.00415432e-01 3.30356181e-01 3.23073596e-01 5.77941656e-01 4.66780573e-01 -1.39561105e+00 5.83930910e-01 2.47359663e-01 -4.46331590e-01 -6.39562428e-01 4.51096416e-01 -5.32024801e-01 -3.67475301e-01 -6.54400885e-02 1.37100077e+00 3.41884255e-01 1.97247192e-01 6.40953600e-01 -1.42315459e+00 3.17956716e-01 8.81716728e-01 -1.45871341e-01 4.23425108e-01 7.59439707e-01 -9.68214124e-02 9.01137054e-01 -4.33801502e-01 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6d94cba8-a180-462e-8b81-3fb886bdf06a	protein-complex-invariant-embedding-with	2305.0948	null	https://arxiv.org/abs/2305.09480v3	https://arxiv.org/pdf/2305.09480v3.pdf	Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer	Antibodies are crucial proteins produced by the immune system in response to foreign substances or antigens. The specificity of an antibody is determined by its complementarity-determining regions (CDRs), which are located in the variable domains of the antibody chains and form the antigen-binding site. Previous studies have utilized complex techniques to generate CDRs, but they suffer from inadequate geometric modeling. Moreover, the common iterative refinement strategies lead to an inefficient inference. In this paper, we propose a \textit{simple yet effective} model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner. To achieve this, we decouple the antibody CDR design problem into two stages: (i) geometric modeling of protein complex structures and (ii) sequence-structure co-learning. We develop a novel macromolecular structure invariant embedding, typically for protein complexes, that captures both intra- and inter-component interactions among the backbone atoms, including C$\alpha$, N, C, and O atoms, to achieve comprehensive geometric modeling. Then, we introduce a simple cross-gate MLP for sequence-structure co-learning, allowing sequence and structure representations to implicitly refine each other. This enables our model to design desired sequences and structures in a one-shot manner. Extensive experiments are conducted to evaluate our results at both the sequence and structure level, which demonstrate that our model achieves superior performance compared to the state-of-the-art antibody CDR design methods.	['Stan Z. Li', 'Zhangyang Gao', 'Cheng Tan']	2023-04-21	null	null	null	null	['specificity']	['natural-language-processing']	[ 3.89909387e-01 -2.61673242e-01 -1.55583382e-01 -2.95253485e-01 -4.72334474e-01 -6.95713103e-01 2.41073206e-01 3.04640502e-01 -1.84119582e-01 8.89230847e-01 5.43305054e-02 -6.99237883e-01 2.29644701e-01 -6.89417601e-01 -9.37636018e-01 -9.67485070e-01 7.76952365e-03 5.84386468e-01 8.33440870e-02 -3.37865084e-01 3.95647109e-01 8.67327213e-01 -1.21275663e+00 3.02540630e-01 9.33036029e-01 4.94900227e-01 3.23911875e-01 5.84774852e-01 -4.29443419e-01 8.43903899e-01 -3.06429505e-01 -2.09523767e-01 -9.72359478e-02 -5.57966828e-01 -6.09914303e-01 -1.86461583e-01 1.11259744e-01 3.50280106e-02 -1.55706719e-01 7.65391767e-01 5.80769300e-01 -3.65298018e-02 9.39034283e-01 -5.07031322e-01 -9.88790810e-01 9.28388759e-02 -7.37351716e-01 -1.80603057e-01 5.34551620e-01 3.63363743e-01 1.11797500e+00 -9.78718579e-01 8.43870759e-01 1.35647035e+00 6.97783172e-01 6.65182889e-01 -1.67886782e+00 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3.10834706e-01 4.03130800e-01 -7.92894602e-01 2.14169934e-01 -2.40663469e-01]	[4.746662616729736, 5.65386438369751]
7129cb15-ad1f-44b0-aad5-241814e79bd0	190511807	1905.11807	null	https://arxiv.org/abs/1905.11807v1	https://arxiv.org/pdf/1905.11807v1.pdf	Artificial Consciousness and Security	This paper describes a possible way to improve computer security by implementing a program which implements the following three features related to a weak notion of artificial consciousness: (partial) self-monitoring, ability to compute the truth of quantifier-free propositions and the ability to communicate with the user. The integrity of the program could be enhanced by using a trusted computing approach, that is to say a hardware module that is at the root of a chain of trust. This paper outlines a possible approach but does not refer to an implementation (which would need further work), but the author believes that an implementation using current processors, a debugger, a monitoring program and a trusted processing module is currently possible.	['Andrew Powell']	2019-05-11	null	null	null	null	['computer-security']	['miscellaneous']	[-1.54824957e-01 4.26495463e-01 2.55371273e-01 -4.28842723e-01 -1.33376449e-01 -6.10983670e-01 9.32207704e-01 4.91600305e-01 -7.55399823e-01 6.56784475e-01 -1.22233313e-02 -7.92284071e-01 2.26039529e-01 -1.09637260e+00 -3.83178681e-01 -4.12813693e-01 -2.48525083e-01 2.34337121e-01 8.74620736e-01 -6.53334618e-01 3.20302248e-01 4.45661873e-01 -1.42059255e+00 1.08964965e-01 4.60254699e-01 7.36064732e-01 -4.79831070e-01 6.68225765e-01 1.87661737e-01 1.00347829e+00 -4.82848138e-01 -2.59576350e-01 1.43525019e-01 -3.47850293e-01 -8.68422449e-01 -2.12049693e-01 -2.41020784e-01 -4.17278528e-01 3.01590234e-01 1.30089569e+00 -1.57877043e-01 -3.79080653e-01 -1.94458496e-02 -1.44329500e+00 5.61080277e-02 6.94885135e-01 8.29575881e-02 -7.76933655e-02 5.92703879e-01 2.81490833e-01 2.64903069e-01 7.28188977e-02 3.20509315e-01 9.48856831e-01 4.83816803e-01 3.16959202e-01 -1.02094710e+00 -3.99783939e-01 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-1.60491377e-01 1.21808305e-01 -1.17408752e-01 -3.90224308e-01 -1.08440451e-01]	[5.791286945343018, 4.796272277832031]
02609003-d21f-4760-9de2-6285c276b8d8	heart-rate-estimation-from-face-videos-for	2006.00825	null	https://arxiv.org/abs/2006.00825v1	https://arxiv.org/pdf/2006.00825v1.pdf	Heart Rate Estimation from Face Videos for Student Assessment: Experiments on edBB	In this study we estimate the heart rate from face videos for student assessment. This information could be very valuable to track their status along time and also to estimate other data such as their attention level or the presence of stress that may be caused by cheating attempts. The recent edBBplat, a platform for student behavior modelling in remote education, is considered in this study1. This platform permits to capture several signals from a set of sensors that capture biometric and behavioral data: RGB and near infrared cameras, microphone, EEG band, mouse, smartwatch, and keyboard, among others. In the experimental framework of this study, we focus on the RGB and near-infrared video sequences for performing heart rate estimation applying remote photoplethysmography techniques. The experiments include behavioral and physiological data from 25 different students completing a collection of tasks related to e-learning. Our proposed face heart rate estimation approach is compared with the heart rate provided by the smartwatch, achieving very promising results for its future deployment in e-learning applications.	['Javier Hernandez-Ortega', 'Ruben Tolosana', 'Roberto Daza', 'Julian Fierrez', 'Aythami Morales']	2020-06-01	null	null	null	null	['heart-rate-estimation']	['medical']	[-1.80269882e-01 -1.08210199e-01 2.67734319e-01 -4.13982034e-01 -6.84229508e-02 -4.57858622e-01 -1.71265051e-01 1.79159790e-01 -4.05704796e-01 6.61144853e-01 -1.45951018e-01 -4.91937548e-02 -1.64413527e-01 -4.65804040e-01 -1.77939713e-01 -7.86840439e-01 4.37937409e-01 -3.02566528e-01 -3.62834036e-01 6.00378923e-02 6.36508107e-01 7.88419604e-01 -2.11572289e+00 -1.24377139e-01 6.62393689e-01 9.10052359e-01 -1.51905149e-01 1.01481450e+00 2.13101164e-01 8.17133486e-01 -9.46166098e-01 -2.10846942e-02 -3.41599762e-01 -4.49653178e-01 -2.75181442e-01 -2.81886995e-01 6.27824664e-01 -3.95311683e-01 -5.81097149e-04 8.22770655e-01 1.14277279e+00 3.44144821e-01 -1.49491668e-01 -1.33932805e+00 1.12588607e-01 3.31973672e-01 -2.96721101e-01 6.66226327e-01 1.00870812e+00 1.17534727e-01 -3.73235308e-02 -4.64147896e-01 1.05980873e-01 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7.80661166e-01 -7.11792886e-01 6.00912757e-02 -1.23040760e+00 -9.47180331e-01 2.47888640e-01 2.20733672e-01 -3.27610224e-01 -2.95494288e-01 1.51753291e-01]	[13.600967407226562, 2.7526755332946777]
4ce5c463-422c-412a-998b-9de606821f93	evolutionary-multi-objective-algorithms-for	2303.01695	null	https://arxiv.org/abs/2303.01695v1	https://arxiv.org/pdf/2303.01695v1.pdf	Evolutionary Multi-Objective Algorithms for the Knapsack Problems with Stochastic Profits	Evolutionary multi-objective algorithms have been widely shown to be successful when utilized for a variety of stochastic combinatorial optimization problems. Chance constrained optimization plays an important role in complex real-world scenarios, as it allows decision makers to take into account the uncertainty of the environment. We consider a version of the knapsack problem with stochastic profits to guarantee a certain level of confidence in the profit of the solutions. We introduce the multi-objective formulations of the profit chance constrained knapsack problem and design three bi-objective fitness evaluation methods that work independently of the specific confidence level required. We evaluate our approaches using well-known multi-objective evolutionary algorithms GSEMO and NSGA-II. In addition, we introduce a filtering method for GSEMO that improves the quality of the final population by periodically removing certain solutions from the interim populations based on their confidence level. We show the effectiveness of our approaches on several benchmarks for both settings where the knapsack items have fixed uniform uncertainties and uncertainties that are positively correlated with the expected profit of an item.	['Frank Neumann', 'Aneta Neumann', 'Kokila Perera']	2023-03-03	null	null	null	null	['combinatorial-optimization']	['methodology']	[ 1.40742078e-01 -2.59841889e-01 -4.18001376e-02 -2.33212382e-01 -5.04947722e-01 -6.96848214e-01 -8.39030668e-02 4.69638169e-01 -6.05826974e-01 1.22398698e+00 -3.28920960e-01 -3.06203868e-02 -9.77634728e-01 -9.70153809e-01 -8.27058733e-01 -1.05463672e+00 -2.80658733e-02 8.16513181e-01 1.28023490e-01 -2.10926831e-01 5.31616509e-01 3.25837791e-01 -1.68479669e+00 -5.75780496e-02 1.23977840e+00 1.21895254e+00 6.06105253e-02 5.77380776e-01 1.84736073e-01 -2.52092659e-01 -9.68969345e-01 -3.98620129e-01 3.01381320e-01 -1.14488788e-01 -1.56484023e-01 1.02386121e-02 -5.95640123e-01 3.43742251e-01 8.36987257e-01 1.03964603e+00 5.43456137e-01 3.80461365e-01 6.02966249e-01 -1.58780885e+00 -1.90532863e-01 8.26414406e-01 -5.83117545e-01 2.40415949e-02 1.49365693e-01 -7.66088143e-02 7.35073984e-01 -3.60448986e-01 2.30583653e-01 9.18021619e-01 3.24428916e-01 1.11049727e-01 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101e2196-6eed-45b6-b401-bcfb57cc1da2	differentiable-spike-rethinking-gradient	null	null	http://proceedings.neurips.cc/paper/2021/hash/c4ca4238a0b923820dcc509a6f75849b-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/c4ca4238a0b923820dcc509a6f75849b-Paper.pdf	Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks	Spiking Neural Networks (SNNs) have emerged as a biology-inspired method mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy efficiency of inference on neuromorphic hardware. However, it also causes an intrinsic disadvantage in training high-performing SNNs from scratch since the discrete spike prohibits the gradient calculation. To overcome this issue, the surrogate gradient (SG) approach has been proposed as a continuous relaxation. Yet the heuristic choice of SG leaves it vacant how the SG benefits the SNN training. In this work, we first theoretically study the gradient descent problem in SNN training and introduce finite difference gradient to quantitatively analyze the training behavior of SNN. Based on the introduced finite difference gradient, we propose a new family of Differentiable Spike (Dspike) functions that can adaptively evolve during training to find the optimal shape and smoothness for gradient estimation. Extensive experiments over several popular network structures show that training SNN with Dspike consistently outperforms the state-of-the-art training methods. For example, on the CIFAR10-DVS classification task, we can train a spiking ResNet-18 and achieve 75.4% top-1 accuracy with 10 time steps.	['Shi Gu', 'Yongqing Hai', 'Shikuang Deng', 'Shanghang Zhang', 'Yufei Guo', 'Yuhang Li']	2021-12-01	null	https://openreview.net/forum?id=H4e7mBnC9f0	https://openreview.net/pdf?id=H4e7mBnC9f0	neurips-2021-12	['event-data-classification']	['computer-vision']	[ 2.44827494e-01 -2.14996964e-01 2.44479761e-01 -3.21669400e-01 4.65703830e-02 -2.38692015e-01 3.99722755e-01 -3.20796192e-01 -7.85526693e-01 1.07942319e+00 -5.18497825e-01 -1.03569575e-01 3.11065884e-03 -8.32096398e-01 -1.09764874e+00 -9.00087774e-01 -1.01312995e-01 -1.86732307e-01 5.57307899e-01 -3.56229097e-01 5.77253044e-01 5.99064887e-01 -1.56695390e+00 -5.74948527e-02 1.08257234e+00 1.24566233e+00 5.08381903e-01 2.07708716e-01 -1.96193591e-01 5.64887583e-01 -7.06420898e-01 -2.22062990e-01 4.81712788e-01 -6.74492002e-01 -1.42506897e-01 -5.90355515e-01 -7.51800602e-03 2.02402085e-01 -4.75174725e-01 1.20671058e+00 7.45143592e-01 2.01003277e-03 4.62418646e-01 -1.23395586e+00 -4.52962458e-01 7.12134898e-01 -1.28346384e-01 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70d61ba9-1e67-49cb-a3a2-22411e4dc0c3	fine-grained-visual-classification-using-self	2205.10529	null	https://arxiv.org/abs/2205.10529v1	https://arxiv.org/pdf/2205.10529v1.pdf	Fine-Grained Visual Classification using Self Assessment Classifier	Extracting discriminative features plays a crucial role in the fine-grained visual classification task. Most of the existing methods focus on developing attention or augmentation mechanisms to achieve this goal. However, addressing the ambiguity in the top-k prediction classes is not fully investigated. In this paper, we introduce a Self Assessment Classifier, which simultaneously leverages the representation of the image and top-k prediction classes to reassess the classification results. Our method is inspired by continual learning with coarse-grained and fine-grained classifiers to increase the discrimination of features in the backbone and produce attention maps of informative areas on the image. In practice, our method works as an auxiliary branch and can be easily integrated into different architectures. We show that by effectively addressing the ambiguity in the top-k prediction classes, our method achieves new state-of-the-art results on CUB200-2011, Stanford Dog, and FGVC Aircraft datasets. Furthermore, our method also consistently improves the accuracy of different existing fine-grained classifiers with a unified setup.	['Anh Nguyen', 'Quang D. Tran', 'Erman Tjiputra', 'Huy Tran', 'Tuong Do']	2022-05-21	null	null	null	null	['fine-grained-image-classification']	['computer-vision']	[-4.22012098e-02 -2.50332236e-01 -3.66856515e-01 -5.23400426e-01 -8.34496796e-01 -6.74537182e-01 5.67985177e-01 8.92581791e-02 -5.62724061e-02 4.61175591e-01 2.69125909e-01 -2.01416723e-02 -1.38965368e-01 -6.14237309e-01 -8.20260763e-01 -6.56164587e-01 3.04329604e-01 3.35717827e-01 6.00519180e-01 -9.30971131e-02 2.43894666e-01 6.28258884e-01 -1.86592579e+00 7.46149242e-01 7.88589776e-01 1.67920077e+00 2.06977502e-01 4.68498111e-01 3.81406844e-02 8.08695138e-01 -4.75175202e-01 -3.74320358e-01 1.84706405e-01 -8.39606002e-02 -1.05565488e+00 1.34421676e-01 9.41082597e-01 -4.76727873e-01 -1.42481970e-02 1.03934193e+00 1.18250296e-01 -7.86720514e-02 8.11499059e-01 -1.25794864e+00 -6.67749703e-01 3.45590532e-01 -6.09802663e-01 4.97211695e-01 -2.35765636e-01 1.11614630e-01 1.21146476e+00 -9.90048945e-01 2.10353836e-01 1.23290944e+00 6.80878580e-01 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1cb5b281-ac50-4e9e-80fa-403b05614bcf	graph-neural-networks-and-representation	2208.11203	null	https://arxiv.org/abs/2208.11203v1	https://arxiv.org/pdf/2208.11203v1.pdf	Graph Neural Networks and Representation Embedding for Table Extraction in PDF Documents	Tables are widely used in several types of documents since they can bring important information in a structured way. In scientific papers, tables can sum up novel discoveries and summarize experimental results, making the research comparable and easily understandable by scholars. Several methods perform table analysis working on document images, losing useful information during the conversion from the PDF files since OCR tools can be prone to recognition errors, in particular for text inside tables. The main contribution of this work is to tackle the problem of table extraction, exploiting Graph Neural Networks. Node features are enriched with suitably designed representation embeddings. These representations help to better distinguish not only tables from the other parts of the paper, but also table cells from table headers. We experimentally evaluated the proposed approach on a new dataset obtained by merging the information provided in the PubLayNet and PubTables-1M datasets.	['Simone Marinai', 'Emanuele Vivoli', 'Andrea Gemelli']	2022-08-23	null	null	null	null	['table-extraction']	['miscellaneous']	[ 7.43714571e-02 1.67977646e-01 -2.61123091e-01 -6.23840466e-02 -3.15575361e-01 -7.90928185e-01 7.02146232e-01 1.28175938e+00 -2.87470698e-01 1.14697552e+00 2.37615302e-01 -2.40310520e-01 -4.76357430e-01 -1.35474360e+00 -6.71326101e-01 -3.58845234e-01 -5.77058531e-02 4.78480726e-01 2.59640627e-02 -1.11145407e-01 5.65784395e-01 8.80863011e-01 -1.66173196e+00 4.55232948e-01 7.66323626e-01 1.18195331e+00 4.10756059e-02 4.02373374e-01 -8.91433179e-01 1.06097758e+00 -9.33037102e-01 -8.57480168e-01 -4.89765918e-03 -4.96027768e-02 -5.67451894e-01 -4.59002331e-02 6.19172812e-01 -5.30261686e-03 -4.68485147e-01 1.29879773e+00 1.52012154e-01 1.84952170e-02 9.61191297e-01 -1.25970995e+00 -7.97494650e-01 1.06883681e+00 -2.76687413e-01 1.65246323e-01 4.57597256e-01 -5.19065440e-01 1.08017397e+00 -6.78811073e-01 1.18283451e+00 1.31405377e+00 3.58414769e-01 1.57221537e-02 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7fd55761-4c2a-457b-ab83-5e6711811f88	leveraging-smartphone-sensors-for-detecting	2208.01876	null	https://arxiv.org/abs/2208.01876v1	https://arxiv.org/pdf/2208.01876v1.pdf	Leveraging Smartphone Sensors for Detecting Abnormal Gait for Smart Wearable Mobile Technologies	Walking is one of the most common modes of terrestrial locomotion for humans. Walking is essential for humans to perform most kinds of daily activities. When a person walks, there is a pattern in it, and it is known as gait. Gait analysis is used in sports and healthcare. We can analyze this gait in different ways, like using video captured by the surveillance cameras or depth image cameras in the lab environment. It also can be recognized by wearable sensors. e.g., accelerometer, force sensors, gyroscope, flexible goniometer, magneto resistive sensors, electromagnetic tracking system, force sensors, and electromyography (EMG). Analysis through these sensors required a lab condition, or users must wear these sensors. For detecting abnormality in gait action of a human, we need to incorporate the sensors separately. We can know about one's health condition by abnormal human gait after detecting it. Understanding a regular gait vs. abnormal gait may give insights to the health condition of the subject using the smart wearable technologies. Therefore, in this paper, we proposed a way to analyze abnormal human gait through smartphone sensors. Though smart devices like smartphones and smartwatches are used by most of the person nowadays. So, we can track down their gait using sensors of these intelligent wearable devices.	['Ahmed Al Marouf', 'Md Shahriar Tasjid']	2022-08-03	null	null	null	null	['electromyography-emg']	['medical']	[ 1.51237011e-01 -3.50344747e-01 -1.80901721e-01 1.05928935e-01 3.96253586e-01 -1.88019246e-01 -2.13702247e-01 -2.20099941e-01 -3.94743532e-01 6.54686630e-01 3.20484310e-01 3.74846533e-02 2.33110234e-01 -1.09118080e+00 -4.35912162e-01 -5.30124724e-01 -1.42349349e-02 -1.85275570e-01 7.40261078e-01 -4.31253731e-01 2.78167665e-01 1.36301443e-01 -1.84045911e+00 -9.52227861e-02 7.25421369e-01 8.08852315e-01 2.87704647e-01 5.96052170e-01 2.45608360e-01 8.08155984e-02 -4.32694584e-01 6.96157143e-02 -1.18629411e-01 -5.26800871e-01 -2.87933201e-01 1.25478402e-01 -4.74901974e-01 -3.98490250e-01 1.05114855e-01 9.05035317e-01 6.30118787e-01 -2.61333752e-02 3.78772050e-01 -1.06337833e+00 -1.94645897e-01 6.76781982e-02 -6.28403783e-01 2.64149755e-01 1.07352257e+00 3.97586435e-01 1.58464108e-02 -3.73540998e-01 3.24570835e-01 9.22904134e-01 9.44327652e-01 4.93688077e-01 -5.16801357e-01 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148819fa-c85c-4ffa-b01f-26bd6f1f8fbf	refined-plane-segmentation-for-cuboid-shaped	2003.1287	null	https://arxiv.org/abs/2003.12870v1	https://arxiv.org/pdf/2003.12870v1.pdf	Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection	Recent advances in the area of plane segmentation from single RGB images show strong accuracy improvements and now allow a reliable segmentation of indoor scenes into planes. Nonetheless, fine-grained details of these segmentation masks are still lacking accuracy, thus restricting the usability of such techniques on a larger scale in numerous applications, such as inpainting for Augmented Reality use cases. We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image. This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects. Our approach is motivated by logistics, where this assumption is valid and refined planes can be used to perform robust object detection without the need for supervised learning. Results for two baselines and our approach are reported on our own dataset, which we made publicly available. The results show a consistent improvement over the state-of-the-art. The influence of the prior segmentation and the edge detection is investigated and finally, areas for future research are proposed.	['Kai Furmans', 'Laura Dörr', 'Niels Ole Salscheider', 'Alexander Naumann']	2020-03-28	null	null	null	null	['robust-object-detection']	['computer-vision']	[ 6.47636771e-01 2.35277683e-01 1.78838581e-01 -5.14436126e-01 -7.42348075e-01 -4.61277664e-01 3.88680965e-01 1.96137633e-02 -5.31881452e-01 6.77176416e-01 -1.28073439e-01 -1.12156026e-01 -1.40050054e-02 -8.54627013e-01 -8.56348097e-01 -4.58031476e-01 7.38393236e-03 3.20703059e-01 7.06105769e-01 -8.21089223e-02 2.97226757e-01 7.97136724e-01 -1.66580307e+00 3.60821664e-01 6.76516294e-01 1.14537251e+00 1.83016077e-01 5.54190814e-01 -4.62040715e-02 5.14639430e-02 -5.05333483e-01 -4.46484566e-01 5.84590316e-01 -2.75514483e-01 -6.28743112e-01 7.37759233e-01 6.95661843e-01 -3.09233665e-01 3.84376012e-02 8.73080552e-01 1.47948042e-01 1.83569878e-01 4.03469443e-01 -9.32433784e-01 7.42186010e-02 -1.32662207e-01 -6.67388022e-01 -1.20084800e-01 4.70174044e-01 -2.43118629e-02 6.74295545e-01 -6.84262693e-01 8.85372818e-01 8.41776013e-01 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2.38299996e-01 -7.03698695e-01 -4.14348155e-01 2.99545914e-01 1.82933226e-01 -7.66679823e-01 -6.49423227e-02 2.97195256e-01]	[8.694480895996094, -2.718822717666626]
82c6de55-91b3-4fd9-81ff-68391ee7567c	bits-and-pieces-understanding-information	2008.09535	null	https://arxiv.org/abs/2008.09535v2	https://arxiv.org/pdf/2008.09535v2.pdf	Bits and Pieces: Understanding Information Decomposition from Part-whole Relationships and Formal Logic	Partial information decomposition (PID) seeks to decompose the multivariate mutual information that a set of source variables contains about a target variable into basic pieces, the so called "atoms of information". Each atom describes a distinct way in which the sources may contain information about the target. In this paper we show, first, that the entire theory of partial information decomposition can be derived from considerations of elementary parthood relationships between information contributions. This way of approaching the problem has the advantage of directly characterizing the atoms of information, instead of taking an indirect approach via the concept of redundancy. Secondly, we describe several intriguing links between PID and formal logic. In particular, we show how to define a measure of PID based on the information provided by certain statements about source realizations. Furthermore, we show how the mathematical lattice structure underlying PID theory can be translated into an isomorphic structure of logical statements with a particularly simple ordering relation: logical implication. The conclusion to be drawn from these considerations is that there are three isomorphic "worlds" of partial information decomposition, i.e. three equivalent ways to mathematically describe the decomposition of the information carried by a set of sources about a target: the world of parthood relationships, the world of logical statements, and the world of antichains that was utilized by Williams and Beer in their original exposition of PID theory. We additionally show how the parthood perspective provides a systematic way to answer a type of question that has been much discussed in the PID field: whether a partial information decomposition can be uniquely determined based on concepts other than redundant information.	['Michael Wibral', 'Abdullah Makkeh', 'Aaron J. Gutknecht']	2020-08-21	null	null	null	null	['formal-logic']	['reasoning']	[ 1.95968315e-01 6.25845253e-01 -3.21374238e-01 -3.20936620e-01 -2.32225284e-01 -7.67251551e-01 9.61620450e-01 3.72607172e-01 -6.83114454e-02 6.82010472e-01 4.31924790e-01 -4.48440790e-01 -7.05452979e-01 -1.06074023e+00 -3.32897723e-01 -8.12239707e-01 -1.68731198e-01 6.94698036e-01 2.58441597e-01 -5.22803724e-01 3.34846556e-01 4.63193744e-01 -1.55877709e+00 3.40920955e-01 5.46543241e-01 8.71670365e-01 -1.11869723e-01 3.51946294e-01 -5.55132270e-01 1.22316515e+00 -3.79065692e-01 -3.35718334e-01 1.51011482e-01 -7.91114628e-01 -1.25109148e+00 -7.78344944e-02 -2.88873762e-01 6.24609962e-02 -3.00223511e-02 1.22487199e+00 -2.39720672e-01 -3.39057505e-01 7.36797392e-01 -1.42799366e+00 -4.39165622e-01 9.78796661e-01 -2.95787036e-01 1.11216821e-01 7.42130637e-01 -3.27504128e-01 1.37481952e+00 -4.15203720e-01 6.61263883e-01 1.43910027e+00 3.79895419e-01 1.78561881e-01 -1.49474216e+00 -2.07508445e-01 -7.96326622e-02 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50a7bf86-7370-435d-885a-031138fbbf13	from-word-models-to-world-models-translating	2306.12672	null	https://arxiv.org/abs/2306.12672v2	https://arxiv.org/pdf/2306.12672v2.pdf	From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought	How does language inform our downstream thinking? In particular, how do humans make meaning from language--and how can we leverage a theory of linguistic meaning to build machines that think in more human-like ways? In this paper, we propose rational meaning construction, a computational framework for language-informed thinking that combines neural language models with probabilistic models for rational inference. We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought (PLoT)--a general-purpose symbolic substrate for generative world modeling. Our architecture integrates two computational tools that have not previously come together: we model thinking with probabilistic programs, an expressive representation for commonsense reasoning; and we model meaning construction with large language models (LLMs), which support broad-coverage translation from natural language utterances to code expressions in a probabilistic programming language. We illustrate our framework through examples covering four core domains from cognitive science: probabilistic reasoning, logical and relational reasoning, visual and physical reasoning, and social reasoning. In each, we show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings, while Bayesian inference with the generated programs supports coherent and robust commonsense reasoning. We extend our framework to integrate cognitively-motivated symbolic modules (physics simulators, graphics engines, and planning algorithms) to provide a unified commonsense thinking interface from language. Finally, we explore how language can drive the construction of world models themselves. We hope this work will provide a roadmap towards cognitive models and AI systems that synthesize the insights of both modern and classical computational perspectives.	['Joshua B. Tenenbaum', 'Jacob Andreas', 'Vikash K. Mansinghka', 'Noah D. Goodman', 'Alexander K. Lew', 'Gabriel Grand', 'Lionel Wong']	2023-06-22	null	null	null	null	['bayesian-inference', 'probabilistic-programming', 'relational-reasoning']	['methodology', 'methodology', 'natural-language-processing']	[ 1.16152711e-01 9.48243916e-01 2.97606569e-02 -6.96002901e-01 -5.15900433e-01 -6.77919865e-01 1.12674820e+00 7.38869160e-02 9.19247493e-02 2.56199598e-01 8.57650876e-01 -8.07045817e-01 -2.27384150e-01 -1.49316502e+00 -6.50634885e-01 -1.44388020e-01 2.86210358e-01 9.14122343e-01 1.47638038e-01 -5.34437656e-01 4.11882043e-01 2.77895927e-01 -1.39084649e+00 5.99538803e-01 8.63227069e-01 3.20595115e-01 1.60289988e-01 5.72391868e-01 -5.74289441e-01 1.61311710e+00 -2.65816730e-02 -4.70095098e-01 -3.27490777e-01 -3.97781461e-01 -1.13969004e+00 -5.87528169e-01 -2.70512164e-01 -2.76326612e-02 -3.84418294e-02 1.11198246e+00 -1.05555952e-01 1.31366909e-01 7.60243833e-01 -1.17552280e+00 -1.45813334e+00 1.37171125e+00 9.47069675e-02 -2.31732130e-01 8.69975150e-01 6.15832686e-01 8.64477813e-01 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4.69543934e-01 -6.06373250e-01 -7.08644986e-01 -6.94060028e-01 -4.44317251e-01 -2.01863885e-01 -7.24809095e-02 -2.34973535e-01 -5.41609049e-01 -1.27532110e-01 4.45723176e-01]	[9.221184730529785, 7.233150005340576]
48f0b3b6-3325-4041-ae80-4af1c8b00924	armbench-an-object-centric-benchmark-dataset	2303.16382	null	https://arxiv.org/abs/2303.16382v1	https://arxiv.org/pdf/2303.16382v1.pdf	ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation	This paper introduces Amazon Robotic Manipulation Benchmark (ARMBench), a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. Automation of operations in modern warehouses requires a robotic manipulator to deal with a wide variety of objects, unstructured storage, and dynamically changing inventory. Such settings pose challenges in perceiving the identity, physical characteristics, and state of objects during manipulation. Existing datasets for robotic manipulation consider a limited set of objects or utilize 3D models to generate synthetic scenes with limitation in capturing the variety of object properties, clutter, and interactions. We present a large-scale dataset collected in an Amazon warehouse using a robotic manipulator performing object singulation from containers with heterogeneous contents. ARMBench contains images, videos, and metadata that corresponds to 235K+ pick-and-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com	['Manikantan Nambi', 'Felipe Polido', 'Tyler Garaas', 'Vikedo Terhuja', 'Shiyang Lu', 'Fan Wang', 'Chaitanya Mitash']	2023-03-29	null	null	null	null	['defect-detection']	['computer-vision']	[-5.66906929e-02 -6.21392429e-01 2.62280367e-02 -3.01249772e-01 -2.38221258e-01 -1.06210172e+00 1.39952347e-01 3.80307466e-01 -4.32475321e-02 2.49265842e-02 -3.37893933e-01 1.28184959e-01 -3.54038566e-01 -4.95141566e-01 -1.02869332e+00 -4.81528938e-01 -2.37403542e-01 8.98706615e-01 3.05634856e-01 -3.77048463e-01 2.18719393e-01 1.04454291e+00 -1.48408234e+00 6.59356952e-01 2.25516379e-01 1.44389713e+00 7.25720167e-01 7.55922675e-01 -2.55408064e-02 4.66934800e-01 -9.07578588e-01 -2.02317879e-01 8.42626035e-01 2.48224884e-01 -4.08910811e-01 7.23017037e-01 2.24742353e-01 -7.39011705e-01 -4.98934835e-01 8.83991420e-01 2.01809376e-01 2.44031295e-01 6.43521190e-01 -1.78879690e+00 -7.26144850e-01 5.10811985e-01 -4.78117079e-01 1.13795258e-01 3.54387790e-01 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a417a357-e8bc-4f8f-88fb-3ac424882b43	scandeval-a-benchmark-for-scandinavian	2304.00906	null	https://arxiv.org/abs/2304.00906v1	https://arxiv.org/pdf/2304.00906v1.pdf	ScandEval: A Benchmark for Scandinavian Natural Language Processing	This paper introduces a Scandinavian benchmarking platform, ScandEval, which can benchmark any pretrained model on four different tasks in the Scandinavian languages. The datasets used in two of the tasks, linguistic acceptability and question answering, are new. We develop and release a Python package and command-line interface, scandeval, which can benchmark any model that has been uploaded to the Hugging Face Hub, with reproducible results. Using this package, we benchmark more than 100 Scandinavian or multilingual models and present the results of these in an interactive online leaderboard, as well as provide an analysis of the results. The analysis shows that there is substantial cross-lingual transfer among the Mainland Scandinavian languages (Danish, Swedish and Norwegian), with limited cross-lingual transfer between the group of Mainland Scandinavian languages and the group of Insular Scandinavian languages (Icelandic and Faroese). The benchmarking results also show that the investment in language technology in Norway, Sweden and Denmark has led to language models that outperform massively multilingual models such as XLM-RoBERTa and mDeBERTaV3. We release the source code for both the package and leaderboard.	['Dan Saattrup Nielsen']	2023-04-03	null	null	null	null	['cross-lingual-transfer', 'linguistic-acceptability']	['natural-language-processing', 'natural-language-processing']	[-8.84962261e-01 2.14172378e-01 1.65541083e-01 -5.45675635e-01 -1.09545279e+00 -1.06710196e+00 6.16711020e-01 1.39175355e-01 -9.99427855e-01 8.16092670e-01 6.53104007e-01 -8.98938239e-01 1.48841947e-01 -2.77363211e-01 -7.93796778e-01 -1.00696586e-01 1.27268359e-01 8.88115168e-01 1.61351323e-01 -9.13074553e-01 -3.88448417e-01 -4.01175350e-01 -1.18984544e+00 7.54397750e-01 9.48587894e-01 3.57815444e-01 9.68763307e-02 6.88062966e-01 -2.72162586e-01 7.81612635e-01 -5.69691718e-01 -9.86765027e-01 2.26437420e-01 -2.25069612e-01 -1.18324757e+00 -7.39376366e-01 6.86693251e-01 9.69204381e-02 1.94634512e-01 6.61613762e-01 8.16334605e-01 -7.60887051e-03 2.37941191e-01 -6.28568709e-01 -3.55020970e-01 1.10563529e+00 8.56820494e-02 6.81341067e-02 7.44689524e-01 1.16813980e-01 1.14469922e+00 -9.34255600e-01 9.74419653e-01 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72ae9124-123e-4673-a847-faa283beb9b9	non-linear-phase-retrieval-algorithms-for-x	2305.00334	null	https://arxiv.org/abs/2305.00334v1	https://arxiv.org/pdf/2305.00334v1.pdf	Non-Linear Phase-Retrieval Algorithms for X-ray Propagation-Based Phase-Contrast Tomography	X-ray phase-contrast tomography (XPCT) is widely used for high-contrast 3D micron-scale imaging using nearly monochromatic X-rays at synchrotron beamlines. XPCT enables an order of magnitude improvement in image contrast of the reconstructed material interfaces with low X-ray absorption contrast. The dominant approaches to 3D reconstruction using XPCT relies on the use of phase-retrieval algorithms that make one or more limiting approximations for the experimental configuration and material properties. Since many experimental scenarios violate such approximations, the resulting reconstructions contain blur, artifacts, or other quantitative inaccuracies. Our solution to this problem is to formulate new iterative non-linear phase-retrieval (NLPR) algorithms that avoid such limiting approximations. Compared to the widely used state-of-the-art approaches, we show that our proposed algorithms result in sharp and quantitatively accurate reconstruction with reduced artifacts. Unlike existing NLPR algorithms, our approaches avoid the laborious manual tuning of regularization hyper-parameters while still achieving the stated goals. As an alternative to regularization, we propose explicit constraints on the material properties to constrain the solution space and solve the phase-retrieval problem. These constraints are easily user-configurable since they follow directly from the imaged object's dimensions and material properties.	['Dilworth Parkinson', 'Jefferson A. Cuadra', 'Venkatesh Sridhar', 'Jean-Baptiste Forien', 'K. Aditya Mohan']	2023-04-29	null	null	null	null	['3d-reconstruction']	['computer-vision']	[ 5.88373542e-01 -5.85859716e-02 1.87687144e-01 -1.96073949e-01 -9.53264952e-01 -1.22297809e-01 4.05787885e-01 -4.62801792e-02 -5.46075165e-01 7.40080237e-01 -2.26877749e-01 -2.01643094e-01 -3.69075090e-01 -7.14738667e-01 -3.57097328e-01 -1.02202296e+00 2.35040024e-01 8.85812938e-01 5.41628897e-01 2.53200740e-01 5.61118066e-01 7.82818198e-01 -1.29704666e+00 -1.41923986e-02 6.51524007e-01 1.05440593e+00 4.05724794e-01 4.75050449e-01 5.18228486e-02 5.04987180e-01 -8.94609541e-02 2.20445380e-01 1.63175493e-01 -6.42792761e-01 -7.89814770e-01 3.73831749e-01 1.39277443e-01 -3.74770522e-01 -1.08489305e-01 1.10663986e+00 3.55975509e-01 -1.07993530e-02 8.50486517e-01 -6.51487172e-01 -3.77463073e-01 -1.74669236e-01 -9.16339159e-01 7.36582130e-02 5.09385288e-01 2.14988708e-01 5.63636839e-01 -7.89510369e-01 8.32929492e-01 7.60968089e-01 6.19984746e-01 5.61146736e-01 -1.39607680e+00 -3.61765772e-01 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99adbda4-b322-4291-b603-bcfaf0f84ab1	analysis-of-convolutional-neural-networks-for	1708.03273	null	http://arxiv.org/abs/1708.03273v1	http://arxiv.org/pdf/1708.03273v1.pdf	Analysis of Convolutional Neural Networks for Document Image Classification	Convolutional Neural Networks (CNNs) are state-of-the-art models for document image classification tasks. However, many of these approaches rely on parameters and architectures designed for classifying natural images, which differ from document images. We question whether this is appropriate and conduct a large empirical study to find what aspects of CNNs most affect performance on document images. Among other results, we exceed the state-of-the-art on the RVL-CDIP dataset by using shear transform data augmentation and an architecture designed for a larger input image. Additionally, we analyze the learned features and find evidence that CNNs trained on RVL-CDIP learn region-specific layout features.	['Chris Tensmeyer', 'Tony Martinez']	2017-08-10	null	null	null	null	['document-image-classification']	['computer-vision']	[ 1.61037296e-01 -1.53781369e-01 -4.86287653e-01 -4.39492047e-01 -2.57245332e-01 -9.26545084e-01 9.08691525e-01 8.56694132e-02 -4.42486912e-01 6.29100651e-02 3.08473825e-01 -7.40330815e-01 -9.48267058e-02 -8.63219440e-01 -9.26278830e-01 -2.76386082e-01 -2.03706650e-03 3.15717518e-01 4.67859171e-02 -2.05595747e-01 7.56113648e-01 8.41373861e-01 -1.24162483e+00 1.12041891e+00 3.58728260e-01 1.02134705e+00 -1.33738324e-01 1.09714293e+00 -4.54024702e-01 8.64312112e-01 -1.00442123e+00 -2.16614738e-01 1.91024840e-01 -3.12137038e-01 -1.00693452e+00 1.17734604e-01 1.20721805e+00 -3.20435435e-01 -4.68250543e-01 4.74325716e-01 3.38110536e-01 -2.75436472e-02 8.32107663e-01 -1.05620933e+00 -1.57453156e+00 5.57612479e-01 -5.15275836e-01 4.30607647e-01 -3.68779339e-02 1.76506579e-01 9.69930649e-01 -9.09254313e-01 8.55553687e-01 1.25930715e+00 1.04563749e+00 1.74264044e-01 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-1.13841319e+00 -4.51694787e-01 5.15763402e-01 1.23399198e-01 -2.42885649e-01 -6.44473825e-03 2.34993652e-01]	[11.516401290893555, 2.6286261081695557]
6d84a493-6a20-4347-b46d-07d0fe6cc266	transmomo-invariance-driven-unsupervised	2003.14401	null	https://arxiv.org/abs/2003.14401v2	https://arxiv.org/pdf/2003.14401v2.pdf	TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting	We present a lightweight video motion retargeting approach TransMoMo that is capable of transferring motion of a person in a source video realistically to another video of a target person. Without using any paired data for supervision, the proposed method can be trained in an unsupervised manner by exploiting invariance properties of three orthogonal factors of variation including motion, structure, and view-angle. Specifically, with loss functions carefully derived based on invariance, we train an auto-encoder to disentangle the latent representations of such factors given the source and target video clips. This allows us to selectively transfer motion extracted from the source video seamlessly to the target video in spite of structural and view-angle disparities between the source and the target. The relaxed assumption of paired data allows our method to be trained on a vast amount of videos needless of manual annotation of source-target pairing, leading to improved robustness against large structural variations and extreme motion in videos. We demonstrate the effectiveness of our method over the state-of-the-art methods. Code, model and data are publicly available on our project page (https://yzhq97.github.io/transmomo).	['Zhuoqian Yang', 'Wayne Wu', 'Wentao Zhu', 'Qiang Zhou', 'Chen Qian', 'Bolei Zhou', 'Chen Change Loy']	2020-03-31	transmomo-invariance-driven-unsupervised-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Yang_TransMoMo_Invariance-Driven_Unsupervised_Video_Motion_Retargeting_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_TransMoMo_Invariance-Driven_Unsupervised_Video_Motion_Retargeting_CVPR_2020_paper.pdf	cvpr-2020-6	['motion-retargeting']	['computer-vision']	[ 2.12261543e-01 -1.24044232e-01 -3.10432673e-01 -9.87907127e-02 -7.54492581e-01 -7.74039030e-01 4.22845572e-01 -6.28210008e-01 -2.65495658e-01 5.01226366e-01 3.59625101e-01 1.29255325e-01 1.68509096e-01 -3.65465224e-01 -9.13525343e-01 -8.08237851e-01 -1.40237153e-01 -1.25902101e-01 3.36066574e-01 8.58719200e-02 1.08603045e-01 2.55261868e-01 -1.42082524e+00 2.27178127e-01 4.11119342e-01 7.29623556e-01 1.11254536e-01 1.00928986e+00 5.85999846e-01 8.50300491e-01 -3.30647081e-01 -3.00983489e-01 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ace2c9a1-014b-4f7f-ae26-d32679c074b6	boltzmann-tuning-of-generative-models	2104.05252	null	https://arxiv.org/abs/2104.05252v1	https://arxiv.org/pdf/2104.05252v1.pdf	Boltzmann Tuning of Generative Models	The paper focuses on the a posteriori tuning of a generative model in order to favor the generation of good instances in the sense of some external differentiable criterion. The proposed approach, called Boltzmann Tuning of Generative Models (BTGM), applies to a wide range of applications. It covers conditional generative modelling as a particular case, and offers an affordable alternative to rejection sampling. The contribution of the paper is twofold. Firstly, the objective is formalized and tackled as a well-posed optimization problem; a practical methodology is proposed to choose among the candidate criteria representing the same goal, the one best suited to efficiently learn a tuned generative model. Secondly, the merits of the approach are demonstrated on a real-world application, in the context of robust design for energy policies, showing the ability of BTGM to sample the extreme regions of the considered criteria.	['Michele Sebag', 'Victor Berger']	2021-04-12	null	null	null	null	['robust-design']	['miscellaneous']	[ 3.76580179e-01 4.23809260e-01 -6.82186931e-02 -5.93163520e-02 -9.81302142e-01 -1.14242941e-01 8.43904734e-01 6.75380602e-03 -5.55163920e-01 9.72086549e-01 -1.46962792e-01 2.56086327e-02 -7.17365980e-01 -9.39561129e-01 -4.87575114e-01 -1.26393986e+00 1.89952776e-01 8.58881295e-01 -2.37980098e-01 -1.73810989e-01 3.30507457e-01 5.94966471e-01 -1.69816351e+00 -2.66552985e-01 1.17439914e+00 7.43093371e-01 2.60009617e-01 2.62761950e-01 2.74725050e-01 3.75517718e-02 -6.06499195e-01 -1.89095214e-01 -3.63673158e-02 -5.74181080e-01 -5.22802055e-01 1.52748510e-01 -1.74965516e-01 4.15762365e-01 5.97612977e-01 1.00482082e+00 7.08994269e-01 5.34783602e-01 1.35255933e+00 -8.48219275e-01 -2.56408309e-03 4.50591773e-01 9.04341936e-02 -1.37633681e-02 5.72679490e-02 7.53357038e-02 9.56706464e-01 -8.00544977e-01 2.70574123e-01 1.05050302e+00 3.13230306e-02 5.24799645e-01 -1.75831449e+00 -2.10426852e-01 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1.82617549e-02 -9.81432676e-01 -3.82137954e-01 -4.68249261e-01]	[6.094314098358154, 3.6384620666503906]
2a148dfd-039b-4cad-8636-f939f9e7d5c7	evaluating-multilingual-bert-for-estonian	2010.00454	null	https://arxiv.org/abs/2010.00454v2	https://arxiv.org/pdf/2010.00454v2.pdf	Evaluating Multilingual BERT for Estonian	Recently, large pre-trained language models, such as BERT, have reached state-of-the-art performance in many natural language processing tasks, but for many languages, including Estonian, BERT models are not yet available. However, there exist several multilingual BERT models that can handle multiple languages simultaneously and that have been trained also on Estonian data. In this paper, we evaluate four multilingual models -- multilingual BERT, multilingual distilled BERT, XLM and XLM-RoBERTa -- on several NLP tasks including POS and morphological tagging, NER and text classification. Our aim is to establish a comparison between these multilingual BERT models and the existing baseline neural models for these tasks. Our results show that multilingual BERT models can generalise well on different Estonian NLP tasks outperforming all baselines models for POS and morphological tagging and text classification, and reaching the comparable level with the best baseline for NER, with XLM-RoBERTa achieving the highest results compared with other multilingual models.	['Kairit Sirts', 'Claudia Kittask', 'Kirill Milintsevich']	2020-10-01	null	null	null	null	['morphological-tagging']	['natural-language-processing']	[-5.91351151e-01 1.02411538e-01 -1.38555050e-01 -3.76064837e-01 -1.04182601e+00 -9.12714005e-01 7.70953357e-01 6.74416780e-01 -1.12607408e+00 9.64092374e-01 4.03614134e-01 -5.52382827e-01 1.24034442e-01 -3.91289115e-01 -4.31266904e-01 -1.43315375e-01 1.64939631e-02 1.12727439e+00 3.00514817e-01 -3.04616213e-01 -1.23323761e-01 2.54779249e-01 -5.16710579e-01 5.84431350e-01 8.35692823e-01 4.30686831e-01 4.57999140e-01 6.53082669e-01 -5.81769586e-01 8.93375695e-01 -6.16975844e-01 -9.75484133e-01 -1.34269744e-01 1.49551854e-01 -9.53699589e-01 -9.71452355e-01 3.85297835e-01 2.49977648e-01 -2.42890030e-01 7.96783149e-01 7.63296902e-01 -5.39686568e-02 7.10432708e-01 -7.43367076e-01 -7.26213336e-01 1.39166451e+00 -3.24883193e-01 3.31632972e-01 3.38407815e-01 -2.38572240e-01 1.12788212e+00 -9.10775304e-01 8.30977261e-01 1.61787581e+00 1.18510091e+00 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e8717dad-cf8f-43c7-8a37-e3b4a98dc544	an-interpretable-federated-learning-based	2201.03134	null	https://arxiv.org/abs/2201.03134v1	https://arxiv.org/pdf/2201.03134v1.pdf	An Interpretable Federated Learning-based Network Intrusion Detection Framework	Learning-based Network Intrusion Detection Systems (NIDSs) are widely deployed for defending various cyberattacks. Existing learning-based NIDS mainly uses Neural Network (NN) as a classifier that relies on the quality and quantity of cyberattack data. Such NN-based approaches are also hard to interpret for improving efficiency and scalability. In this paper, we design a new local-global computation paradigm, FEDFOREST, a novel learning-based NIDS by combining the interpretable Gradient Boosting Decision Tree (GBDT) and Federated Learning (FL) framework. Specifically, FEDFOREST is composed of multiple clients that extract local cyberattack data features for the server to train models and detect intrusions. A privacy-enhanced technology is also proposed in FEDFOREST to further defeat the privacy of the FL systems. Extensive experiments on 4 cyberattack datasets of different tasks demonstrate that FEDFOREST is effective, efficient, interpretable, and extendable. FEDFOREST ranks first in the collaborative learning and cybersecurity competition 2021 for Chinese college students.	['Jialiang Lu', 'Han Qiu', 'Song Li', 'Tian Dong']	2022-01-10	null	null	null	null	['network-intrusion-detection']	['miscellaneous']	[-1.41676188e-01 -4.49566454e-01 -4.54293936e-01 -7.68126726e-01 -1.46758318e-01 -8.61836076e-01 5.01231611e-01 5.90294041e-02 -3.36677700e-01 7.63314664e-01 -2.81410366e-01 -1.14732730e+00 -3.04539472e-01 -8.82766664e-01 -4.32835668e-01 -5.08416235e-01 1.00421727e-01 1.04801003e-02 2.95885921e-01 -1.80169940e-01 4.07423019e-01 8.98822844e-01 -1.19614458e+00 7.58037388e-01 7.80743957e-01 1.53703058e+00 -7.78381646e-01 7.68302560e-01 -1.36433452e-01 1.00777042e+00 -6.66615725e-01 -1.00050330e+00 6.49609208e-01 -1.40885338e-01 -7.47346997e-01 -7.80080497e-01 2.38525689e-01 -5.13255119e-01 -2.16181725e-01 9.53028202e-01 7.48457685e-02 -7.54123032e-02 2.76142776e-01 -2.02346897e+00 -5.02330422e-01 3.21308047e-01 -3.03391844e-01 3.38657111e-01 8.66543874e-02 1.39883369e-01 8.09248984e-01 -4.12616789e-01 3.14741224e-01 1.38371003e+00 7.54583001e-01 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27f348ae-8285-45fd-8f8e-d04f44319b5b	novel-deep-learning-framework-for-bovine-iris	2212.11439	null	https://arxiv.org/abs/2212.11439v1	https://arxiv.org/pdf/2212.11439v1.pdf	Novel Deep Learning Framework For Bovine Iris Segmentation	Iris segmentation is the initial step to identify biometric of animals to establish a traceability system of livestock. In this study, we propose a novel deep learning framework for pixel-wise segmentation with minimum use of annotation labels using BovineAAEyes80 public dataset. In the experiment, U-Net with VGG16 backbone was selected as the best combination of encoder and decoder model, demonstrating a 99.50% accuracy and a 98.35% Dice coefficient score. Remarkably, the selected model accurately segmented corrupted images even without proper annotation data. This study contributes to the advancement of the iris segmentation and the development of a reliable DNNs training framework.	['Sang-Hee Lee', 'Mira Park', 'Heemoon Yoon']	2022-12-22	null	null	null	null	['iris-segmentation']	['medical']	[ 3.17474663e-01 -8.22101533e-02 -2.84544677e-01 -7.59686470e-01 -1.76093012e-01 -2.49596983e-01 3.81102003e-02 -1.98212609e-01 -4.43438381e-01 6.47490919e-01 -2.28657991e-01 -1.89947486e-01 8.27179253e-02 -7.16241062e-01 -6.88352406e-01 -7.24402606e-01 3.06333870e-01 1.68555945e-01 -2.13934228e-01 1.49382144e-01 2.79514402e-01 4.39121902e-01 -1.44267786e+00 1.07579559e-01 1.27477074e+00 1.17575371e+00 -7.18125179e-02 4.19933200e-01 2.16351803e-02 5.63204110e-01 -5.96162140e-01 -7.67788470e-01 5.12188911e-01 -6.55809760e-01 -7.27150917e-01 8.75219181e-02 7.64293969e-01 -6.61872923e-01 -1.88556299e-01 1.34904516e+00 7.55468965e-01 -1.41908973e-01 4.85583484e-01 -7.55755246e-01 -8.46409202e-01 7.33410180e-01 -9.14854228e-01 2.45106474e-01 -3.37003499e-01 4.44006473e-01 6.66494548e-01 -1.03793822e-01 5.73075056e-01 8.20971012e-01 5.63487768e-01 6.47464454e-01 -1.10486627e+00 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1.46249205e-01 3.55779111e-01 -1.70783669e-01 2.08314642e-01 4.48820978e-01]	[3.7500877380371094, -3.6304140090942383]
301158ed-aeaa-49c2-af24-117f0c1e17ae	building-a-parallel-corpus-and-training	2301.02773	null	https://arxiv.org/abs/2301.02773v1	https://arxiv.org/pdf/2301.02773v1.pdf	Building a Parallel Corpus and Training Translation Models Between Luganda and English	Neural machine translation (NMT) has achieved great successes with large datasets, so NMT is more premised on high-resource languages. This continuously underpins the low resource languages such as Luganda due to the lack of high-quality parallel corpora, so even 'Google translate' does not serve Luganda at the time of this writing. In this paper, we build a parallel corpus with 41,070 pairwise sentences for Luganda and English which is based on three different open-sourced corpora. Then, we train NMT models with hyper-parameter search on the dataset. Experiments gave us a BLEU score of 21.28 from Luganda to English and 17.47 from English to Luganda. Some translation examples show high quality of the translation. We believe that our model is the first Luganda-English NMT model. The bilingual dataset we built will be available to the public.	['Heeyoul Choi', 'Daniela N. Rim', 'Richard Kimera']	2023-01-07	null	null	null	null	['nmt']	['computer-code']	[-1.15477175e-01 -5.06153964e-02 -4.58012611e-01 -2.12616041e-01 -1.15518105e+00 -6.74801648e-01 7.86106765e-01 -3.85590643e-01 -5.07309854e-01 1.15841472e+00 4.89122957e-01 -8.81218493e-01 3.12134862e-01 -5.12170196e-01 -8.02064598e-01 -5.11884689e-01 3.38977963e-01 1.08407378e+00 -3.58573467e-01 -5.68929017e-01 3.64410058e-02 -3.82633172e-02 -6.40908539e-01 3.95712733e-01 9.13838327e-01 1.65609136e-01 5.13863444e-01 4.58319694e-01 -1.27753079e-01 1.99498042e-01 -5.46866238e-01 -7.31347859e-01 6.37645543e-01 -8.80827487e-01 -9.95951653e-01 -3.93608838e-01 3.16623271e-01 -2.48505890e-01 -2.74955146e-02 9.82303321e-01 6.74371541e-01 -4.46077824e-01 3.06531698e-01 -8.27486992e-01 -8.86354268e-01 1.06711411e+00 -5.46603024e-01 3.61223668e-02 8.63479897e-02 1.30263910e-01 1.12745965e+00 -1.02050483e+00 1.13530993e+00 1.12073612e+00 5.24634063e-01 6.54744029e-01 -8.48533750e-01 -6.60138726e-01 -4.73743111e-01 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4ed7b02a-dea0-49d7-89d8-95d7a2ec1ab6	a-multi-stage-memory-augmented-neural-network	null	null	https://aclanthology.org/W18-2603	https://aclanthology.org/W18-2603.pdf	A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension	Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. However, most of these models suffer in reasoning over long documents. In this work, we propose a novel Memory Augmented Machine Comprehension Network (MAMCN) to address long-range dependencies present in machine reading comprehension. We perform extensive experiments to evaluate proposed method with the renowned benchmark datasets such as SQuAD, QUASAR-T, and TriviaQA. We achieve the state of the art performance on both the document-level (QUASAR-T, TriviaQA) and paragraph-level (SQuAD) datasets compared to all the previously published approaches.	['Haejun Lee', 'Seohyun Back', 'Sathish Reddy Indurthi', 'Seunghak Yu']	2018-07-01	null	null	null	ws-2018-7	['triviaqa']	['miscellaneous']	[ 3.43110770e-01 2.78179310e-02 4.00613993e-01 -4.98440474e-01 -9.83685255e-01 -4.81716365e-01 7.86666811e-01 6.72153831e-01 -7.36548185e-01 7.26135850e-01 6.16839230e-01 -7.01337755e-01 -2.97477841e-01 -8.08694303e-01 -7.02378035e-01 -2.07327425e-01 2.20507145e-01 7.38657355e-01 3.46104801e-01 -4.16663736e-01 6.95032954e-01 -5.21154515e-02 -1.20649374e+00 5.52289367e-01 1.37329340e+00 8.89670253e-01 3.27212900e-01 1.04355907e+00 -1.99021310e-01 1.12398970e+00 -5.82755625e-01 -5.06962836e-01 -4.12371039e-01 -3.74449283e-01 -1.38149464e+00 -5.77584922e-01 7.34185755e-01 -2.07766503e-01 3.36578153e-02 7.70691931e-01 4.25000578e-01 3.85244101e-01 6.83962882e-01 -5.38806796e-01 -9.12224829e-01 6.57526731e-01 -5.34229636e-01 6.63203835e-01 7.42303848e-01 -1.77880704e-01 1.12340569e+00 -8.48435462e-01 1.35764927e-01 1.53751922e+00 3.59468132e-01 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ed493d8c-e317-4bb8-9996-d1fc8acdc0c6	road-reality-oriented-adaptation-for-semantic	1711.11556	null	http://arxiv.org/abs/1711.11556v2	http://arxiv.org/pdf/1711.11556v2.pdf	ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes	Exploiting synthetic data to learn deep models has attracted increasing attention in recent years. However, the intrinsic domain difference between synthetic and real images usually causes a significant performance drop when applying the learned model to real world scenarios. This is mainly due to two reasons: 1) the model overfits to synthetic images, making the convolutional filters incompetent to extract informative representation for real images; 2) there is a distribution difference between synthetic and real data, which is also known as the domain adaptation problem. To this end, we propose a new reality oriented adaptation approach for urban scene semantic segmentation by learning from synthetic data. First, we propose a target guided distillation approach to learn the real image style, which is achieved by training the segmentation model to imitate a pretrained real style model using real images. Second, we further take advantage of the intrinsic spatial structure presented in urban scene images, and propose a spatial-aware adaptation scheme to effectively align the distribution of two domains. These two modules can be readily integrated with existing state-of-the-art semantic segmentation networks to improve their generalizability when adapting from synthetic to real urban scenes. We evaluate the proposed method on Cityscapes dataset by adapting from GTAV and SYNTHIA datasets, where the results demonstrate the effectiveness of our method.	['Luc van Gool', 'Yuhua Chen', 'Wen Li']	2017-11-30	road-reality-oriented-adaptation-for-semantic-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_ROAD_Reality_Oriented_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_ROAD_Reality_Oriented_CVPR_2018_paper.pdf	cvpr-2018-6	['synthetic-to-real-translation']	['computer-vision']	[ 3.88758957e-01 1.64149746e-01 1.05187513e-01 -5.62844932e-01 -5.58182001e-01 -4.84766632e-01 6.99467719e-01 -4.26882505e-01 -4.95173514e-01 6.68235481e-01 8.00106674e-02 6.64947331e-02 1.44231126e-01 -1.04034042e+00 -9.75733221e-01 -5.39191246e-01 5.59728980e-01 4.96572584e-01 6.17450178e-01 -4.09620434e-01 1.56706482e-01 3.58526021e-01 -1.43431282e+00 2.82481849e-01 1.50362337e+00 7.55350113e-01 6.43659651e-01 1.18401773e-01 -4.76620615e-01 5.15893996e-01 -5.76506019e-01 -1.53976724e-01 4.83069688e-01 -5.49326241e-01 -8.56509864e-01 5.22736907e-01 1.69602707e-01 -2.38040477e-01 -3.67351353e-01 1.34525752e+00 2.75188118e-01 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7459761c-b33b-46df-a1b1-a726c8f9d889	3-dimensional-dense-reconstruction-a-review	2304.09371	null	https://arxiv.org/abs/2304.09371v1	https://arxiv.org/pdf/2304.09371v1.pdf	3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset	3D dense reconstruction refers to the process of obtaining the complete shape and texture features of 3D objects from 2D planar images. 3D reconstruction is an important and extensively studied problem, but it is far from being solved. This work systematically introduces classical methods of 3D dense reconstruction based on geometric and optical models, as well as methods based on deep learning. It also introduces datasets for deep learning and the performance and advantages and disadvantages demonstrated by deep learning methods on these datasets.	['Yangming Li']	2023-04-19	null	null	null	null	['3d-reconstruction']	['computer-vision']	[-2.28264779e-01 -9.73479077e-02 1.56807527e-01 -3.85820985e-01 -5.50216138e-01 4.28831438e-03 6.67965889e-01 -3.09092581e-01 -6.62317351e-02 3.97921681e-01 2.63746470e-01 5.73425181e-02 -7.39279315e-02 -7.95285046e-01 -7.86961675e-01 -6.34683967e-01 -2.88089991e-01 1.14957905e+00 2.73415521e-02 1.62058517e-01 2.23537609e-01 1.31305373e+00 -1.41791904e+00 1.87599257e-01 1.75321233e-02 1.32942152e+00 2.23580033e-01 2.52042294e-01 -4.83758509e-01 6.45348012e-01 -2.13757247e-01 -3.31186615e-02 2.98479587e-01 -1.83313210e-02 -7.79709816e-01 5.05032003e-01 5.33292949e-01 -8.13740194e-01 -5.69856465e-01 5.51432312e-01 6.41505599e-01 -1.16236463e-01 1.02026379e+00 -5.29811263e-01 -8.08505476e-01 -1.67995617e-01 -6.42149389e-01 6.51526228e-02 7.57032275e-01 -1.70934737e-01 5.34551501e-01 -1.35820067e+00 9.15629923e-01 1.36571693e+00 8.19637656e-01 4.37998354e-01 -1.15112388e+00 -2.55223542e-01 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be889c32-d404-4d37-8eeb-cab01f1867f4	coupled-physics-informed-neural-networks-for	2301.08618	null	https://arxiv.org/abs/2301.08618v3	https://arxiv.org/pdf/2301.08618v3.pdf	Solving PDEs with Unmeasurable Source Terms Using Coupled Physics-Informed Neural Network with Recurrent Prediction for Soft Sensors	Partial differential equations (PDEs) are a model candidate for soft sensors in industrial processes with spatiotemporal dependence. Although physics-informed neural networks (PINNs) are a promising machine learning method for solving PDEs, they are infeasible for the nonhomogeneous PDEs with unmeasurable source terms. To this end, a coupled PINN (CPINN) with a recurrent prediction (RP) learning strategy (CPINN- RP) is proposed. First, CPINN composed of NetU and NetG is proposed. NetU is for approximating PDEs solutions and NetG is for regularizing the training of NetU. The two networks are integrated into a data-physics-hybrid loss function. Then, we theoretically prove that the proposed CPINN has a satisfying approximation capability for solutions to nonhomogeneous PDEs with unmeasurable source terms. Besides the theoretical aspects, we propose a hierarchical training strategy to optimize and couple NetU and NetG. Secondly, NetU-RP is proposed for compensating information loss in data sampling to improve the prediction performance, in which RP is the recurrently delayed outputs of well-trained CPINN and hard sensors. Finally, the artificial and practical datasets are used to verify the feasibility and effectiveness of CPINN-RP for soft sensors.	['Xi-Ming Sun', 'Pan Qin', 'Aina Wang']	2023-01-20	null	null	null	null	['sensor-modeling']	['computer-vision']	[-4.26744372e-02 2.26160914e-01 -2.93587483e-02 8.40021006e-04 -3.37122113e-01 4.78793234e-02 8.32192376e-02 -1.29520744e-01 1.90017730e-01 8.44135880e-01 -2.77563959e-01 -1.56694353e-01 -6.25648677e-01 -8.74850035e-01 -9.84368443e-01 -9.36510980e-01 1.83182538e-01 1.98223993e-01 2.22100258e-01 -3.13464962e-02 -4.27707881e-02 5.15155494e-01 -1.16715109e+00 -2.04621702e-01 1.04859984e+00 1.69956028e+00 3.78982186e-01 3.45365345e-01 2.10226059e-01 9.86418843e-01 -5.36378510e-02 4.76942621e-02 4.07624185e-01 -2.25383177e-01 -2.65918583e-01 -2.19429016e-01 -1.15708478e-01 -3.32245193e-02 -6.75800800e-01 1.02079737e+00 6.38187051e-01 5.81947327e-01 7.07189202e-01 -1.04056776e+00 -6.99879527e-01 4.76504862e-01 -3.26769114e-01 -1.33680895e-01 -2.29203761e-01 3.15218329e-01 5.77415824e-01 -7.09797084e-01 2.81374216e-01 1.25232267e+00 1.03856492e+00 4.49160814e-01 -8.37932408e-01 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-1.14532866e-01 5.23727775e-01 -7.49936223e-01 -6.23533607e-01 -1.37739316e-01]	[6.619194507598877, 3.5012874603271484]
db717a1c-cab6-40cd-9d70-c0488cf74919	localization-of-deep-inpainting-using-high	null	null	http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Localization_of_Deep_Inpainting_Using_High-Pass_Fully_Convolutional_Network_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Localization_of_Deep_Inpainting_Using_High-Pass_Fully_Convolutional_Network_ICCV_2019_paper.pdf	Localization of Deep Inpainting Using High-Pass Fully Convolutional Network	Image inpainting has been substantially improved with deep learning in the past years. Deep inpainting can fill image regions with plausible contents, which are not visually apparent. Although inpainting is originally designed to repair images, it can even be used for malicious manipulations, e.g., removal of specific objects. Therefore, it is necessary to identify the presence of inpainting in an image. This paper presents a method to locate the regions manipulated by deep inpainting. The proposed method employs a fully convolutional network that is based on high-pass filtered image residuals. Firstly, we analyze and observe that the inpainted regions are more distinguishable from the untouched ones in the residual domain. Hence, a high-pass pre-filtering module is designed to get image residuals for enhancing inpainting traces. Then, a feature extraction module, which learns discriminative features from image residuals, is built with four concatenated ResNet blocks. The learned feature maps are finally enlarged by an up-sampling module, so that a pixel-wise inpainting localization map is obtained. The whole network is trained end-to-end with a loss addressing the class imbalance. Extensive experimental results evaluated on both synthetic and realistic images subjected to deep inpainting have shown the effectiveness of the proposed method.	[' Jiwu Huang', 'Haodong Li']	2019-10-01	null	null	null	iccv-2019-10	['image-manipulation-detection']	['computer-vision']	[ 5.94706595e-01 3.34118456e-02 1.52453342e-02 -8.72203484e-02 -5.61281025e-01 -2.96691898e-02 2.64922112e-01 -2.46240478e-02 -1.43720314e-01 7.31291234e-01 8.76154304e-02 1.24972060e-01 2.85998642e-01 -8.30696642e-01 -1.07692885e+00 -9.77980852e-01 2.14364961e-01 -3.01262736e-01 6.04097843e-02 -1.13282464e-01 4.06422287e-01 5.22278666e-01 -1.28937018e+00 4.04750735e-01 9.23183680e-01 1.06772125e+00 4.48388845e-01 3.79252434e-01 -4.52204980e-02 1.14756680e+00 -7.81553626e-01 -1.76863328e-01 4.24664170e-01 -5.65086246e-01 -4.30161506e-01 5.82760513e-01 4.49856520e-01 -7.17967629e-01 -8.54460418e-01 1.27755868e+00 2.93487579e-01 2.03027297e-02 3.13794553e-01 -9.61675763e-01 -8.13748658e-01 3.07243645e-01 -1.04557633e+00 2.62631387e-01 2.06896856e-01 4.22464788e-01 4.69824910e-01 -9.62907314e-01 5.70178270e-01 1.38164985e+00 5.43795288e-01 3.10328633e-01 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-6.28700435e-01 7.75336385e-01 6.74250960e-01 -3.93049508e-01 -5.75772703e-01 2.37150759e-01]	[11.4028902053833, -1.2865592241287231]
0bb366e0-948f-4316-b09f-7bce9bd99afc	embedded-deep-regularized-block-hsic	2106.02106	null	https://arxiv.org/abs/2106.02106v1	https://arxiv.org/pdf/2106.02106v1.pdf	Embedded Deep Regularized Block HSIC Thermomics for Early Diagnosis of Breast Cancer	Thermography has been used extensively as a complementary diagnostic tool in breast cancer detection. Among thermographic methods matrix factorization (MF) techniques show an unequivocal capability to detect thermal patterns corresponding to vasodilation in cancer cases. One of the biggest challenges in such techniques is selecting the best representation of the thermal basis. In this study, an embedding method is proposed to address this problem and Deep-semi-nonnegative matrix factorization (Deep-SemiNMF) for thermography is introduced, then tested for 208 breast cancer screening cases. First, we apply Deep-SemiNMF to infrared images to extract low-rank thermal representations for each case. Then, we embed low-rank bases to obtain one basis for each patient. After that, we extract 300 thermal imaging features, called thermomics, to decode imaging information for the automatic diagnostic model. We reduced the dimensionality of thermomics by spanning them onto Hilbert space using RBF kernel and select the three most efficient features using the block Hilbert Schmidt Independence Criterion Lasso (block HSIC Lasso). The preserved thermal heterogeneity successfully classified asymptomatic versus symptomatic patients applying a random forest model (cross-validated accuracy of 71.36% (69.42%-73.3%)).	['Xavier P. V. Maldague', 'Hossein Memarzadeh Sharifipour', 'Bardia Yousefi']	2021-06-03	null	null	null	null	['breast-cancer-detection', 'breast-cancer-detection']	['knowledge-base', 'medical']	[ 2.95231849e-01 -1.38319820e-01 -4.04258966e-01 -1.95911750e-01 -8.17283511e-01 -4.03444409e-01 7.22247139e-02 -3.42748135e-01 -2.37529054e-01 3.48050654e-01 4.92410213e-01 -2.93265879e-01 -3.68529975e-01 -5.05546868e-01 -7.76295364e-02 -1.44789839e+00 -2.28089824e-01 2.43576363e-01 -5.82229555e-01 -1.65910646e-02 1.69684365e-01 3.12600672e-01 -1.21810615e+00 6.84859753e-01 1.32354021e+00 8.28465879e-01 -2.90753804e-02 7.86416590e-01 1.02504916e-01 6.08895183e-01 -1.04581296e-01 -5.86248524e-02 1.71516776e-01 -5.66172302e-01 -6.87348008e-01 -1.75853059e-01 5.97578175e-02 -2.46802390e-01 -2.88277179e-01 6.82674170e-01 5.85956454e-01 3.34944278e-02 1.09303510e+00 -8.24107528e-01 -6.22688770e-01 2.83948660e-01 -8.47320497e-01 6.89283088e-02 1.13219477e-01 -3.63253653e-01 6.43503964e-01 -6.78847790e-01 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