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178a31b6-8df8-494e-9c56-be13052a4a0a	teaser-towards-efficient-aspect-based	null	null	https://aclanthology.org/2021.ranlp-main.13	https://aclanthology.org/2021.ranlp-main.13.pdf	TEASER: Towards Efficient Aspect-based SEntiment Analysis and Recognition	Sentiment analysis aims to detect the overall sentiment, i.e., the polarity of a sentence, paragraph, or text span, without considering the entities mentioned and their aspects. Aspect-based sentiment analysis aims to extract the aspects of the given target entities and their respective sentiments. Prior works formulate this as a sequence tagging problem or solve this task using a span-based extract-then-classify framework where first all the opinion targets are extracted from the sentence, and then with the help of span representations, the targets are classified as positive, negative, or neutral. The sequence tagging problem suffers from issues like sentiment inconsistency and colossal search space. Whereas, Span-based extract-then-classify framework suffers from issues such as half-word coverage and overlapping spans. To overcome this, we propose a similar span-based extract-then-classify framework with a novel and improved heuristic. Experiments on the three benchmark datasets (Restaurant14, Laptop14, Restaurant15) show our model consistently outperforms the current state-of-the-art. Moreover, we also present a novel supervised movie reviews dataset (Movie20) and a pseudo-labeled movie reviews dataset (moviesLarge) made explicitly for this task and report the results on the novel Movie20 dataset as well.	['Radhika Mamidi', 'Srinath Nair', 'Ishan Upadhyay', 'Kartikey Pant', 'Vaibhav Bajaj']	null	null	https://aclanthology.org/2021.ranlp-1.13	https://aclanthology.org/2021.ranlp-1.13.pdf	ranlp-2021-9	['aspect-based-sentiment-analysis']	['natural-language-processing']	[ 3.13783944e-01 -3.80063243e-02 -5.00291526e-01 -6.33007407e-01 -8.14219713e-01 -1.01958048e+00 5.47994852e-01 5.58097720e-01 -4.73852426e-01 8.68423939e-01 4.46421266e-01 -2.06460461e-01 3.06615114e-01 -6.42349005e-01 -3.72204959e-01 -4.58883494e-01 3.38172555e-01 1.12610646e-01 2.51741081e-01 -4.90295351e-01 8.66937280e-01 -1.40210465e-01 -1.32339990e+00 6.97200239e-01 8.09819400e-01 1.32481420e+00 -2.05339730e-01 3.93229216e-01 -5.27347982e-01 9.92358088e-01 -6.48812652e-01 -8.77419889e-01 -6.84622349e-03 -3.06692600e-01 -6.85600817e-01 4.69705403e-01 2.51558870e-01 3.10459763e-01 5.56938350e-01 1.15202904e+00 3.44191670e-01 1.79045454e-01 8.16885948e-01 -9.69710946e-01 -5.57731628e-01 5.34137726e-01 -8.28752100e-01 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c872439d-526c-4cc4-af14-816f73880102	tensors-learning-and-kolmogorov-extension-for	1712.00205	null	http://arxiv.org/abs/1712.00205v2	http://arxiv.org/pdf/1712.00205v2.pdf	Tensors, Learning, and 'Kolmogorov Extension' for Finite-alphabet Random Vectors	Estimating the joint probability mass function (PMF) of a set of random variables lies at the heart of statistical learning and signal processing. Without structural assumptions, such as modeling the variables as a Markov chain, tree, or other graphical model, joint PMF estimation is often considered mission impossible - the number of unknowns grows exponentially with the number of variables. But who gives us the structural model? Is there a generic, `non-parametric' way to control joint PMF complexity without relying on a priori structural assumptions regarding the underlying probability model? Is it possible to discover the operational structure without biasing the analysis up front? What if we only observe random subsets of the variables, can we still reliably estimate the joint PMF of all? This paper shows, perhaps surprisingly, that if the joint PMF of any three variables can be estimated, then the joint PMF of all the variables can be provably recovered under relatively mild conditions. The result is reminiscent of Kolmogorov's extension theorem - consistent specification of lower-dimensional distributions induces a unique probability measure for the entire process. The difference is that for processes of limited complexity (rank of the high-dimensional PMF) it is possible to obtain complete characterization from only three-dimensional distributions. In fact not all three-dimensional PMFs are needed; and under more stringent conditions even two-dimensional will do. Exploiting multilinear algebra, this paper proves that such higher-dimensional PMF completion can be guaranteed - several pertinent identifiability results are derived. It also provides a practical and efficient algorithm to carry out the recovery task. Judiciously designed simulations and real-data experiments on movie recommendation and data classification are presented to showcase the effectiveness of the approach.	['Nikos Kargas', 'Xiao Fu', 'Nicholas D. Sidiropoulos']	2017-12-01	null	null	null	null	['movie-recommendation']	['miscellaneous']	[ 3.49347889e-01 1.84952512e-01 1.53926492e-03 -2.51076072e-02 -7.92197406e-01 -7.52377629e-01 3.55654627e-01 -2.63893837e-03 -3.20865899e-01 7.76501477e-01 -8.47875327e-02 -4.57754165e-01 -7.64868617e-01 -4.42225188e-01 -6.96694970e-01 -1.11875165e+00 -3.83503884e-01 7.88468838e-01 -2.79337615e-01 2.19869092e-01 2.21996848e-02 5.35616696e-01 -1.32092381e+00 -3.78946424e-01 5.01718104e-01 7.53467441e-01 2.94503927e-01 9.94356394e-01 2.66470257e-02 3.32920462e-01 -2.89281249e-01 -2.97746092e-01 4.44132656e-01 -3.29403192e-01 -4.43211555e-01 5.28668582e-01 -1.60177201e-02 3.00236251e-02 -1.39484435e-01 1.18125200e+00 9.24507827e-02 -1.42112523e-01 9.97373879e-01 -1.33018398e+00 -3.07924896e-01 3.75352204e-01 -5.53647101e-01 7.36091509e-02 3.42722476e-01 -2.53122866e-01 1.09100997e+00 -6.01942956e-01 3.08286011e-01 1.10317457e+00 6.74123466e-01 -4.21865396e-02 -1.75321412e+00 -4.28604513e-01 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7838c60e-8658-4b8c-8d09-ecd9453fca4a	towards-universal-vision-language-omni	2303.06547	null	https://arxiv.org/abs/2303.06547v1	https://arxiv.org/pdf/2303.06547v1.pdf	Towards Universal Vision-language Omni-supervised Segmentation	Existing open-world universal segmentation approaches usually leverage CLIP and pre-computed proposal masks to treat open-world segmentation tasks as proposal classification. However, 1) these works cannot handle universal segmentation in an end-to-end manner, and 2) the limited scale of panoptic datasets restricts the open-world segmentation ability on things classes. In this paper, we present Vision-Language Omni-Supervised Segmentation (VLOSS). VLOSS starts from a Mask2Former universal segmentation framework with CLIP text encoder. To improve the open-world segmentation ability, we leverage omni-supervised data (i.e., panoptic segmentation data, object detection data, and image-text pairs data) into training, thus enriching the open-world segmentation ability and achieving better segmentation accuracy. To better improve the training efficiency and fully release the power of omni-supervised data, we propose several advanced techniques, i.e., FPN-style encoder, switchable training technique, and positive classification loss. Benefiting from the end-to-end training manner with proposed techniques, VLOSS can be applied to various open-world segmentation tasks without further adaptation. Experimental results on different open-world panoptic and instance segmentation benchmarks demonstrate the effectiveness of VLOSS. Notably, with fewer parameters, our VLOSS with Swin-Tiny backbone surpasses MaskCLIP by ~2% in terms of mask AP on LVIS v1 dataset.	['WangMeng Zuo', 'Hang Xu', 'Jianhua Han', 'Jiaxi Gu', 'Bowen Dong']	2023-03-12	null	null	null	null	['panoptic-segmentation']	['computer-vision']	[ 9.86623764e-02 -1.17498092e-01 -7.06077576e-01 -4.65962172e-01 -6.78537786e-01 -7.61290789e-01 1.92876205e-01 -3.53940785e-01 -4.66774434e-01 4.04861033e-01 -2.59553760e-01 -5.16389549e-01 2.09774241e-01 -8.89828444e-01 -6.83419883e-01 -5.15604973e-01 -1.13519104e-02 6.21437788e-01 6.93099916e-01 1.76491722e-01 3.33170034e-02 4.94590029e-02 -1.23785520e+00 -1.70743585e-01 1.44713867e+00 1.26317167e+00 5.58288991e-01 5.70402324e-01 -4.52834666e-01 1.87761828e-01 -3.01183492e-01 -3.47480029e-01 9.35407221e-01 -4.73957509e-02 -6.24548137e-01 1.56295851e-01 8.52592647e-01 -4.68086064e-01 -4.95048165e-02 1.17365205e+00 4.05680418e-01 -1.78696290e-01 3.40863854e-01 -1.05034482e+00 -5.11706352e-01 7.50379562e-01 -8.28157783e-01 2.90687472e-01 -3.28038394e-01 5.75425267e-01 1.33856916e+00 -6.78951383e-01 6.36896253e-01 1.14241040e+00 7.89551795e-01 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0f6d98cc-5139-4590-8541-077465f50767	pairwise-learning-for-neural-link-prediction	2112.02936	null	https://arxiv.org/abs/2112.02936v6	https://arxiv.org/pdf/2112.02936v6.pdf	Pairwise Learning for Neural Link Prediction	In this paper, we aim at providing an effective Pairwise Learning Neural Link Prediction (PLNLP) framework. The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i.e., neighborhood encoder, link predictor, negative sampler and objective function. The framework is flexible that any generic graph neural convolution or link prediction specific neural architecture could be employed as neighborhood encoder. For link predictor, we design different scoring functions, which could be selected based on different types of graphs. In negative sampler, we provide several sampling strategies, which are problem specific. As for objective function, we propose to use an effective ranking loss, which approximately maximizes the standard ranking metric AUC. We evaluate the proposed PLNLP framework on 4 link property prediction datasets of Open Graph Benchmark, including ogbl-ddi, ogbl-collab, ogbl-ppa and ogbl-ciation2. PLNLP achieves top 1 performance on ogbl-ddi and ogbl-collab, and top 2 performance on ogbl-ciation2 only with basic neural architecture. The performance demonstrates the effectiveness of PLNLP.	['Shouzhi Chen', 'Hanjing Su', 'Yuanhang Zou', 'Litao Hong', 'Yong Zhou', 'Zhitao Wang']	2021-12-06	null	null	null	null	['link-property-prediction']	['graphs']	[-2.85192043e-01 1.98462293e-01 -9.64093983e-01 -5.10958016e-01 -6.57308519e-01 -3.62413287e-01 3.77082944e-01 2.50574708e-01 4.26218696e-02 1.28885961e+00 1.16469003e-01 -4.50523913e-01 -7.12224245e-01 -1.22582996e+00 -9.84503210e-01 -3.79378945e-01 -6.57198370e-01 8.66011262e-01 5.21690965e-01 -1.28069520e-01 -1.55973420e-01 3.24350148e-01 -9.95075762e-01 1.39762744e-01 1.01845181e+00 9.73438799e-01 5.80724999e-02 6.20728612e-01 -6.27367198e-02 7.98629642e-01 -1.16241194e-01 -7.70249724e-01 1.40880689e-01 -1.03677280e-01 -8.90816033e-01 -9.35994089e-01 7.62802541e-01 -9.74126607e-02 -7.36855030e-01 1.13152528e+00 7.00594425e-01 -1.16853513e-01 9.09522176e-01 -1.75798011e+00 -8.33234370e-01 1.18159509e+00 -5.17740786e-01 8.68865475e-02 2.61477023e-01 -3.46782386e-01 1.71398604e+00 -6.94805801e-01 7.23313689e-01 1.51540589e+00 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258016df-94df-4e7a-924a-898dac6f0b1e	probabilistic-bilevel-coreset-selection	2301.0988	null	https://arxiv.org/abs/2301.09880v1	https://arxiv.org/pdf/2301.09880v1.pdf	Probabilistic Bilevel Coreset Selection	The goal of coreset selection in supervised learning is to produce a weighted subset of data, so that training only on the subset achieves similar performance as training on the entire dataset. Existing methods achieved promising results in resource-constrained scenarios such as continual learning and streaming. However, most of the existing algorithms are limited to traditional machine learning models. A few algorithms that can handle large models adopt greedy search approaches due to the difficulty in solving the discrete subset selection problem, which is computationally costly when coreset becomes larger and often produces suboptimal results. In this work, for the first time we propose a continuous probabilistic bilevel formulation of coreset selection by learning a probablistic weight for each training sample. The overall objective is posed as a bilevel optimization problem, where 1) the inner loop samples coresets and train the model to convergence and 2) the outer loop updates the sample probability progressively according to the model's performance. Importantly, we develop an efficient solver to the bilevel optimization problem via unbiased policy gradient without trouble of implicit differentiation. We provide the convergence property of our training procedure and demonstrate the superiority of our algorithm against various coreset selection methods in various tasks, especially in more challenging label-noise and class-imbalance scenarios.	['Tong Zhang', 'Yong Lin', 'Weizhong Zhang', 'Renjie Pi', 'Xiao Zhou']	2023-01-24	null	null	null	null	['bilevel-optimization']	['methodology']	[ 2.91006386e-01 -2.06477582e-01 -6.07819259e-01 -4.50010300e-01 -9.47844923e-01 -7.03374222e-02 8.28585327e-02 2.85077631e-01 -6.16143048e-01 9.25207496e-01 -2.62220621e-01 -2.86142621e-02 -3.90445679e-01 -6.11786902e-01 -5.63232303e-01 -9.07146096e-01 1.66769460e-01 9.57680821e-01 1.34629318e-02 1.96965396e-01 2.24060088e-01 1.08085431e-01 -1.52586222e+00 8.68777931e-02 1.22221017e+00 1.22816050e+00 3.05968314e-01 3.91998470e-01 -4.10733595e-02 6.53617382e-01 -4.03712422e-01 -1.74691323e-02 2.62688428e-01 -2.35806778e-01 -7.92281806e-01 2.54389048e-01 2.90082246e-01 -9.89239067e-02 1.38075471e-01 1.01713240e+00 5.21696389e-01 1.66859731e-01 5.04040956e-01 -1.44108808e+00 1.63130481e-02 7.23593473e-01 -8.34638059e-01 1.28916532e-01 -2.03652799e-01 1.53871616e-02 1.07642984e+00 -8.03296685e-01 1.55581638e-01 9.46401358e-01 7.35867560e-01 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7a9ba174-637b-47cd-9d22-195464de271f	building-a-tocfl-learner-corpus-for-chinese	null	null	https://aclanthology.org/L18-1363	https://aclanthology.org/L18-1363.pdf	Building a TOCFL Learner Corpus for Chinese Grammatical Error Diagnosis	null	['Li-Ping Chang', 'Yuen-Hsien Tseng', 'Lung-Hao Lee']	2018-05-01	building-a-tocfl-learner-corpus-for-chinese-1	https://aclanthology.org/L18-1363	https://aclanthology.org/L18-1363.pdf	lrec-2018-5	['grammatical-error-detection']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 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68ff7c56-6f04-4de3-b70e-1ed138e1d9a7	plug-and-play-priors-for-bright-field	1512.07331	null	http://arxiv.org/abs/1512.07331v1	http://arxiv.org/pdf/1512.07331v1.pdf	Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation	Many material and biological samples in scientific imaging are characterized by non-local repeating structures. These are studied using scanning electron microscopy and electron tomography. Sparse sampling of individual pixels in a 2D image acquisition geometry, or sparse sampling of projection images with large tilt increments in a tomography experiment, can enable high speed data acquisition and minimize sample damage caused by the electron beam. In this paper, we present an algorithm for electron tomographic reconstruction and sparse image interpolation that exploits the non-local redundancy in images. We adapt a framework, termed plug-and-play (P&P) priors, to solve these imaging problems in a regularized inversion setting. The power of the P&P approach is that it allows a wide array of modern denoising algorithms to be used as a "prior model" for tomography and image interpolation. We also present sufficient mathematical conditions that ensure convergence of the P&P approach, and we use these insights to design a new non-local means denoising algorithm. Finally, we demonstrate that the algorithm produces higher quality reconstructions on both simulated and real electron microscope data, along with improved convergence properties compared to other methods.	['Charles A. Bouman', 'Lawrence F. Drummy', 'S. V. Venkatakrishnan', 'Suhas Sreehari', 'Jeffrey P. Simmons', 'Brendt Wohlberg']	2015-12-23	null	null	null	null	['electron-tomography']	['medical']	[ 6.27263963e-01 -2.83237875e-01 2.93363333e-01 -3.10544729e-01 -6.94567978e-01 -4.40573618e-02 3.83092076e-01 -3.62606436e-01 -5.46260834e-01 9.72819984e-01 -8.48188549e-02 -4.94472980e-02 -2.70088375e-01 -4.41421837e-01 -5.84948242e-01 -1.10993564e+00 2.13636994e-01 6.35184050e-01 2.34324172e-01 2.23794907e-01 4.66897815e-01 6.75493121e-01 -1.16186523e+00 7.21228197e-02 5.40403366e-01 7.82397985e-01 8.76530707e-01 4.79063660e-01 1.98435038e-01 5.16285419e-01 9.58056003e-02 1.08348079e-01 2.06271306e-01 -7.20191896e-01 -8.54017615e-01 3.42811525e-01 2.82721613e-02 -3.68659824e-01 -4.42651808e-02 1.13264596e+00 5.65395296e-01 1.80426002e-01 7.17206001e-01 -4.11866456e-01 -3.59607577e-01 -8.79823230e-03 -8.06957841e-01 2.22978994e-01 2.15248883e-01 4.45754407e-03 3.91761869e-01 -1.07736385e+00 1.01875603e+00 1.06859112e+00 9.37351584e-01 5.03947616e-01 -1.83859050e+00 -1.11265726e-01 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3.83045495e-01 -5.54641247e-01 -6.11476183e-01 4.68539476e-01]	[12.857656478881836, -2.7907793521881104]
faad786a-8596-43cc-b16c-4e05e2c4ef64	finegym-a-hierarchical-video-dataset-for-fine	2004.06704	null	https://arxiv.org/abs/2004.06704v1	https://arxiv.org/pdf/2004.06704v1.pdf	FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding	On public benchmarks, current action recognition techniques have achieved great success. However, when used in real-world applications, e.g. sport analysis, which requires the capability of parsing an activity into phases and differentiating between subtly different actions, their performances remain far from being satisfactory. To take action recognition to a new level, we develop FineGym, a new dataset built on top of gymnastic videos. Compared to existing action recognition datasets, FineGym is distinguished in richness, quality, and diversity. In particular, it provides temporal annotations at both action and sub-action levels with a three-level semantic hierarchy. For example, a "balance beam" event will be annotated as a sequence of elementary sub-actions derived from five sets: "leap-jump-hop", "beam-turns", "flight-salto", "flight-handspring", and "dismount", where the sub-action in each set will be further annotated with finely defined class labels. This new level of granularity presents significant challenges for action recognition, e.g. how to parse the temporal structures from a coherent action, and how to distinguish between subtly different action classes. We systematically investigate representative methods on this dataset and obtain a number of interesting findings. We hope this dataset could advance research towards action understanding.	['Dahua Lin', 'Dian Shao', 'Bo Dai', 'Yue Zhao']	2020-04-14	finegym-a-hierarchical-video-dataset-for-fine-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Shao_FineGym_A_Hierarchical_Video_Dataset_for_Fine-Grained_Action_Understanding_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Shao_FineGym_A_Hierarchical_Video_Dataset_for_Fine-Grained_Action_Understanding_CVPR_2020_paper.pdf	cvpr-2020-6	['action-understanding']	['computer-vision']	[ 4.04777497e-01 -8.60670954e-02 -5.35291135e-01 -4.94553834e-01 -6.13329291e-01 -7.17036843e-01 7.32006669e-01 2.01276109e-01 -1.87670380e-01 6.93833709e-01 6.78606033e-01 2.00605039e-02 -3.06565106e-01 -7.27443814e-01 -3.30100387e-01 -6.02388024e-01 -1.72306567e-01 3.82178336e-01 5.24869978e-01 -4.49601710e-01 2.73150504e-01 3.46519351e-01 -1.97209561e+00 8.75562429e-01 3.47305506e-01 9.33771372e-01 -1.72483653e-01 5.02340496e-01 -7.57207125e-02 9.83058751e-01 -6.05408847e-01 -2.73733974e-01 8.84110257e-02 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537a8738-1fa3-414c-b14f-ce24b02b09cf	counterfactual-reasoning-testing-language	2305.16572	null	https://arxiv.org/abs/2305.16572v1	https://arxiv.org/pdf/2305.16572v1.pdf	Counterfactual reasoning: Testing language models' understanding of hypothetical scenarios	Current pre-trained language models have enabled remarkable improvements in downstream tasks, but it remains difficult to distinguish effects of statistical correlation from more systematic logical reasoning grounded on the understanding of real world. We tease these factors apart by leveraging counterfactual conditionals, which force language models to predict unusual consequences based on hypothetical propositions. We introduce a set of tests from psycholinguistic experiments, as well as larger-scale controlled datasets, to probe counterfactual predictions from five pre-trained language models. We find that models are consistently able to override real-world knowledge in counterfactual scenarios, and that this effect is more robust in case of stronger baseline world knowledge -- however, we also find that for most models this effect appears largely to be driven by simple lexical cues. When we mitigate effects of both world knowledge and lexical cues to test knowledge of linguistic nuances of counterfactuals, we find that only GPT-3 shows sensitivity to these nuances, though this sensitivity is also non-trivially impacted by lexical associative factors.	['Allyson Ettinger', 'Lang Yu', 'Jiaxuan Li']	2023-05-26	null	null	null	null	['logical-reasoning']	['reasoning']	[ 8.32511634e-02 5.91213107e-01 -2.22424686e-01 -3.38560551e-01 -6.22247517e-01 -6.76489234e-01 1.21795559e+00 3.09852839e-01 -5.83106399e-01 1.28257859e+00 9.90870297e-01 -9.30694282e-01 -2.75936782e-01 -1.09932673e+00 -1.04302597e+00 -1.59902334e-01 -3.54809880e-01 2.36255527e-01 1.27470434e-01 -4.27086860e-01 5.35505891e-01 2.68533826e-01 -1.21397281e+00 4.59548414e-01 6.82212591e-01 1.69348836e-01 -1.04578048e-01 2.24170566e-01 7.59260878e-02 1.13366640e+00 -3.13663006e-01 -7.30212092e-01 2.42112935e-01 -4.53329444e-01 -8.89627099e-01 -6.27736390e-01 2.90371150e-01 -2.85341471e-01 -3.35629135e-01 9.47305024e-01 5.24628699e-01 5.80857545e-02 7.20131695e-01 -9.18098330e-01 -9.36109364e-01 1.40770888e+00 -2.39414096e-01 5.44689894e-01 7.16802061e-01 9.13259089e-01 1.22467971e+00 -4.94327843e-01 9.71615374e-01 1.84507048e+00 7.58761942e-01 1.29628941e-01 -1.63950670e+00 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7a50ba8c-0eb9-4fe3-ad32-216fc323596d	finsbd-2020-the-2nd-shared-task-on-sentence	null	null	https://aclanthology.org/2020.finnlp-1.8	https://aclanthology.org/2020.finnlp-1.8.pdf	FinSBD-2020: The 2nd Shared Task on Sentence Boundary Detection in Unstructured Text in the Financial Domain	null	['Dialekti Valsamou-Stanislawski', 'Abderrahim Ait Azzi', 'Bianca Chong', 'Willy Au']	null	null	null	null	finnlp-coling-2020-1	['boundary-detection']	['computer-vision']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 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613bde6b-68e8-41f8-8958-9c8f269dde24	celldefectnet-a-machine-designed-attention	2204.11766	null	https://arxiv.org/abs/2204.11766v1	https://arxiv.org/pdf/2204.11766v1.pdf	CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection	Photovoltaic cells are electronic devices that convert light energy to electricity, forming the backbone of solar energy harvesting systems. An essential step in the manufacturing process for photovoltaic cells is visual quality inspection using electroluminescence imaging to identify defects such as cracks, finger interruptions, and broken cells. A big challenge faced by industry in photovoltaic cell visual inspection is the fact that it is currently done manually by human inspectors, which is extremely time consuming, laborious, and prone to human error. While deep learning approaches holds great potential to automating this inspection, the hardware resource-constrained manufacturing scenario makes it challenging for deploying complex deep neural network architectures. In this work, we introduce CellDefectNet, a highly efficient attention condenser network designed via machine-driven design exploration specifically for electroluminesence-based photovoltaic cell defect detection on the edge. We demonstrate the efficacy of CellDefectNet on a benchmark dataset comprising of a diversity of photovoltaic cells captured using electroluminescence imagery, achieving an accuracy of ~86.3% while possessing just 410K parameters (~13$\times$ lower than EfficientNet-B0, respectively) and ~115M FLOPs (~12$\times$ lower than EfficientNet-B0) and ~13$\times$ faster on an ARM Cortex A-72 embedded processor when compared to EfficientNet-B0.	['Alexander Wong', 'Mohammad Javad Shafiee', 'Saeejith Nair', 'Gautam Bathla', 'Mahmoud Famouri', 'Carol Xu']	2022-04-25	null	null	null	null	['defect-detection']	['computer-vision']	[ 3.42448235e-01 -3.05338204e-01 3.60559851e-01 3.21813405e-01 -2.91690588e-01 -6.33860171e-01 -9.44436714e-02 1.74733941e-02 -2.81641781e-01 8.86410832e-01 -6.10002816e-01 -5.20510733e-01 1.56962201e-01 -8.98180604e-01 -7.64663279e-01 -1.04091394e+00 4.41121608e-01 1.84328616e-01 -2.47627825e-01 3.60242315e-02 4.89568323e-01 6.75844371e-01 -1.77221811e+00 -9.22736749e-02 1.18004870e+00 1.49606013e+00 4.76146609e-01 8.13154042e-01 2.39978939e-01 3.34668487e-01 -8.25961947e-01 1.41973188e-02 -4.38892795e-03 -1.12434655e-01 -2.77419060e-01 -1.64863691e-01 5.95836699e-01 -2.61874855e-01 -7.24229068e-02 1.06849456e+00 8.07566285e-01 -5.04635394e-01 7.88866222e-01 -1.26676726e+00 -9.72477496e-01 -2.15975255e-01 -2.23398209e-01 1.32759139e-01 1.68306485e-01 6.40569389e-01 6.72651529e-01 -8.96562755e-01 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7.78174758e-01 5.20546317e-01 -9.25001681e-01 -1.95393190e-01 -7.63278902e-01 -3.80851358e-01 -3.58895451e-01 3.67496699e-01 -4.89708781e-01 -1.81188539e-01 -9.00584534e-02]	[7.316662788391113, 1.9077208042144775]
ac98383b-1d08-4d7d-9496-062674cccc8b	e-vfia-event-based-video-frame-interpolation	2209.09359	null	https://arxiv.org/abs/2209.09359v3	https://arxiv.org/pdf/2209.09359v3.pdf	E-VFIA : Event-Based Video Frame Interpolation with Attention	Video frame interpolation (VFI) is a fundamental vision task that aims to synthesize several frames between two consecutive original video images. Most algorithms aim to accomplish VFI by using only keyframes, which is an ill-posed problem since the keyframes usually do not yield any accurate precision about the trajectories of the objects in the scene. On the other hand, event-based cameras provide more precise information between the keyframes of a video. Some recent state-of-the-art event-based methods approach this problem by utilizing event data for better optical flow estimation to interpolate for video frame by warping. Nonetheless, those methods heavily suffer from the ghosting effect. On the other hand, some of kernel-based VFI methods that only use frames as input, have shown that deformable convolutions, when backed up with transformers, can be a reliable way of dealing with long-range dependencies. We propose event-based video frame interpolation with attention (E-VFIA), as a lightweight kernel-based method. E-VFIA fuses event information with standard video frames by deformable convolutions to generate high quality interpolated frames. The proposed method represents events with high temporal resolution and uses a multi-head self-attention mechanism to better encode event-based information, while being less vulnerable to blurring and ghosting artifacts; thus, generating crispier frames. The simulation results show that the proposed technique outperforms current state-of-the-art methods (both frame and event-based) with a significantly smaller model size.	['A. Aydin Alatan', 'Ahmet Akman', 'Onur Selim Kılıç']	2022-09-19	null	null	null	null	['video-frame-interpolation']	['computer-vision']	[ 2.04215217e-02 -4.49096590e-01 1.65565044e-01 -8.00337270e-02 -3.64698112e-01 -3.79505269e-02 6.12473428e-01 -6.89844266e-02 -4.44975734e-01 9.59831834e-01 9.50061753e-02 2.92913616e-02 3.79592702e-02 -7.63413668e-01 -9.05633688e-01 -6.99046612e-01 -3.38681787e-02 -1.95893079e-01 6.06837273e-01 7.05187842e-02 2.81868994e-01 4.75057930e-01 -1.76382720e+00 5.19758344e-01 9.64312911e-01 1.00758636e+00 3.76255780e-01 7.00278580e-01 -2.28825614e-01 1.17924452e+00 -5.46410859e-01 -2.91696131e-01 1.03061832e-01 -5.93313932e-01 -5.43030560e-01 9.03279614e-03 5.51899135e-01 -8.13274980e-01 -6.38417125e-01 9.37622070e-01 1.38987869e-01 3.96042019e-01 3.23550791e-01 -1.28922844e+00 -6.69165015e-01 1.16011642e-01 -5.00812709e-01 5.16143620e-01 5.83457172e-01 2.82461286e-01 2.77989119e-01 -1.01614189e+00 6.99737549e-01 1.31441677e+00 6.27494693e-01 5.50938249e-01 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-1.77118883e-01 7.36912668e-01 6.56572700e-01 -6.86566949e-01 -2.37815291e-01 7.19217658e-01]	[10.765256881713867, -1.533995270729065]
554ee5bb-ca5e-4bc6-850d-85985289930d	brouhaha-multi-task-training-for-voice	2210.13248	null	https://arxiv.org/abs/2210.13248v3	https://arxiv.org/pdf/2210.13248v3.pdf	Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation	Most automatic speech processing systems register degraded performance when applied to noisy or reverberant speech. But how can one tell whether speech is noisy or reverberant? We propose Brouhaha, a neural network jointly trained to extract speech/non-speech segments, speech-to-noise ratios, and C50room acoustics from single-channel recordings. Brouhaha is trained using a data-driven approach in which noisy and reverberant audio segments are synthesized. We first evaluate its performance and demonstrate that the proposed multi-task regime is beneficial. We then present two scenarios illustrating how Brouhaha can be used on naturally noisy and reverberant data: 1) to investigate the errors made by a speaker diarization model (pyannote.audio); and 2) to assess the reliability of an automatic speech recognition model (Whisper from OpenAI). Both our pipeline and a pretrained model are open source and shared with the speech community.	['Hervé Bredin', 'Emmanuel Dupoux', 'Alejandrina Cristia', 'Elika Bergelson', 'Morgane Rivière', 'Jade Copet', 'Alodie Boissonnet', 'Hadrien Titeux', 'Marianne Métais', 'Marvin Lavechin']	2022-10-24	null	null	null	null	['activity-detection']	['computer-vision']	[ 3.00376415e-01 -1.04300067e-01 7.97796428e-01 -4.54064846e-01 -1.62273347e+00 -5.48927307e-01 4.21333045e-01 -1.55694753e-01 -3.63685876e-01 3.77949744e-01 5.75404346e-01 -5.63516855e-01 2.12825209e-01 -1.15014814e-01 -6.20469928e-01 -8.39450836e-01 7.88646713e-02 1.99847668e-01 4.84664738e-02 -8.83045420e-02 -2.06593424e-01 2.51177758e-01 -1.64710808e+00 5.38937807e-01 4.86179650e-01 9.10272360e-01 4.82715040e-01 1.51827598e+00 3.35947484e-01 5.15786469e-01 -1.30318177e+00 -5.05670672e-04 1.83143169e-01 -5.56758046e-01 -5.26600242e-01 -1.16410442e-01 4.05787975e-01 -3.57515782e-01 -4.42938283e-02 9.10108387e-01 1.27941120e+00 2.58255392e-01 3.97591144e-01 -7.45054007e-01 -2.78644860e-01 1.15107036e+00 1.55129239e-01 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1.14544056e-01 1.17625809e+00 9.12124395e-01 1.95683673e-01 1.00346744e+00 7.56107986e-01 -1.04485941e+00 -4.49838012e-01 -1.58816099e+00 -4.73330885e-01 -6.96016848e-02 1.05261004e+00 5.59701771e-02 -9.27323997e-01 8.76048859e-03]	[14.837136268615723, 6.092375755310059]
133586ac-b660-405c-9d12-a8181c06a7b9	high-resolution-cloud-removal-with-multi	2301.03432	null	https://arxiv.org/abs/2301.03432v1	https://arxiv.org/pdf/2301.03432v1.pdf	High-Resolution Cloud Removal with Multi-Modal and Multi-Resolution Data Fusion: A New Baseline and Benchmark	In this paper, we introduce Planet-CR, a benchmark dataset for high-resolution cloud removal with multi-modal and multi-resolution data fusion. Planet-CR is the first public dataset for cloud removal to feature globally sampled high resolution optical observations, in combination with paired radar measurements as well as pixel-level land cover annotations. It provides solid basis for exhaustive evaluation in terms of generating visually pleasing textures and semantically meaningful structures. With this dataset, we consider the problem of cloud removal in high resolution optical remote sensing imagery by integrating multi-modal and multi-resolution information. Existing multi-modal data fusion based methods, which assume the image pairs are aligned pixel-to-pixel, are hence not appropriate for this problem. To this end, we design a new baseline named Align-CR to perform the low-resolution SAR image guided high-resolution optical image cloud removal. It implicitly aligns the multi-modal and multi-resolution data during the reconstruction process to promote the cloud removal performance. The experimental results demonstrate that the proposed Align-CR method gives the best performance in both visual recovery quality and semantic recovery quality. The project is available at https://github.com/zhu-xlab/Planet-CR, and hope this will inspire future research.	['Xiao Xiang Zhu', 'Wen Yang', 'Patrick Ebel', 'Yilei Shi', 'Fang Xu']	2023-01-09	null	null	null	null	['cloud-removal']	['computer-vision']	[ 4.34545487e-01 -8.57589662e-01 9.25383121e-02 -1.44989714e-01 -1.38365507e+00 -7.15019882e-01 4.88627166e-01 -1.20396987e-01 4.48415689e-02 6.64960921e-01 -1.37266787e-02 -1.80534005e-01 -2.49005765e-01 -1.18520749e+00 -3.45822513e-01 -9.14406300e-01 1.24957316e-01 1.48813546e-01 -3.78638902e-03 -3.41006428e-01 1.07596569e-01 8.91948700e-01 -1.90169597e+00 4.23997551e-01 1.38900435e+00 7.21341074e-01 8.18637490e-01 4.73780185e-01 2.12899432e-01 4.93395537e-01 3.17188613e-02 2.15896040e-01 5.65565646e-01 -2.62062430e-01 -4.54499006e-01 2.95271426e-01 8.96146894e-01 -2.45685801e-01 5.53901419e-02 1.47060227e+00 5.69745481e-01 -1.25836618e-02 5.17386913e-01 -7.93355107e-01 -7.58449912e-01 2.14459258e-03 -1.07391942e+00 2.63045549e-01 1.17749581e-02 7.60114938e-02 8.57173979e-01 -1.26690722e+00 4.98360664e-01 1.14277315e+00 5.87907135e-01 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-9.84081402e-02 -9.76007044e-01 -1.15901887e+00 4.08037677e-02 8.41620088e-01 -6.30750000e-01 -3.54520649e-01 -1.01278469e-01]	[9.895493507385254, -1.8516641855239868]
08253bbc-fb10-457c-b5e8-f956ae46fbb8	graph-community-detection-from-coarse	2102.13135	null	https://arxiv.org/abs/2102.13135v1	https://arxiv.org/pdf/2102.13135v1.pdf	Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model	We study the problem of community recovery from coarse measurements of a graph. In contrast to the problem of community recovery of a fully observed graph, one often encounters situations when measurements of a graph are made at low-resolution, each measurement integrating across multiple graph nodes. Such low-resolution measurements effectively induce a coarse graph with its own communities. Our objective is to develop conditions on the graph structure, the quantity, and properties of measurements, under which we can recover the community organization in this coarse graph. In this paper, we build on the stochastic block model by mathematically formalizing the coarsening process, and characterizing its impact on the community members and connections. Through this novel setup and modeling, we characterize an error bound for community recovery. The error bound yields simple and closed-form asymptotic conditions to achieve the perfect recovery of the coarse graph communities.	['Stark C. Draper', 'Gautam Dasarathy', 'Nafiseh Ghoroghchian']	2021-02-25	null	null	null	null	['stochastic-block-model']	['graphs']	[ 4.05496001e-01 3.52161348e-01 1.49921298e-01 2.32365847e-01 -5.79354942e-01 -8.03743005e-01 4.00065601e-01 4.84784126e-01 6.28985241e-02 7.20341325e-01 1.98372364e-01 1.49497427e-02 -3.71617317e-01 -1.08464646e+00 -7.32975066e-01 -8.19448411e-01 -4.42519337e-01 6.55420840e-01 1.18359715e-01 1.34890079e-01 1.24574326e-01 4.43953037e-01 -6.35262191e-01 -3.08784813e-01 6.37866914e-01 2.29323223e-01 1.05789408e-01 1.14416552e+00 3.04276049e-01 6.08383536e-01 -5.63905716e-01 -1.18392192e-01 3.77857625e-01 -3.67543429e-01 -7.07566738e-01 7.97809899e-01 1.97322443e-01 -1.43095076e-01 -6.13137305e-01 1.49441504e+00 2.65670538e-01 -3.95859987e-01 6.88095927e-01 -1.25152206e+00 -5.60328245e-01 1.05622756e+00 -9.45413232e-01 1.54459298e-01 6.75436914e-01 -3.84147227e-01 1.10515964e+00 -5.67771614e-01 8.00037324e-01 1.00939572e+00 8.78129482e-01 7.98247978e-02 -1.89667594e+00 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b4c44a79-9dd9-4af4-a17b-538034095ebd	high-order-joint-embedding-for-multi-level	2111.05265	null	https://arxiv.org/abs/2111.05265v1	https://arxiv.org/pdf/2111.05265v1.pdf	High-order joint embedding for multi-level link prediction	Link prediction infers potential links from observed networks, and is one of the essential problems in network analyses. In contrast to traditional graph representation modeling which only predicts two-way pairwise relations, we propose a novel tensor-based joint network embedding approach on simultaneously encoding pairwise links and hyperlinks onto a latent space, which captures the dependency between pairwise and multi-way links in inferring potential unobserved hyperlinks. The major advantage of the proposed embedding procedure is that it incorporates both the pairwise relationships and subgroup-wise structure among nodes to capture richer network information. In addition, the proposed method introduces a hierarchical dependency among links to infer potential hyperlinks, and leads to better link prediction. In theory we establish the estimation consistency for the proposed embedding approach, and provide a faster convergence rate compared to link prediction utilizing pairwise links or hyperlinks only. Numerical studies on both simulation settings and Facebook ego-networks indicate that the proposed method improves both hyperlink and pairwise link prediction accuracy compared to existing link prediction algorithms.	['Annie Qu', 'Yubai Yuan']	2021-11-07	null	null	null	null	['network-embedding']	['methodology']	[-1.26428977e-01 5.01093566e-01 -7.35821128e-01 -2.10506558e-01 1.04886949e-01 -5.26798964e-01 4.80661690e-01 4.09318566e-01 2.17407271e-01 7.72783935e-01 4.70059365e-01 -2.45855615e-01 -9.06544149e-01 -1.25965941e+00 -2.64976859e-01 -4.08958942e-01 -7.51957417e-01 7.23807752e-01 4.04330909e-01 7.60059757e-03 -1.10400617e-01 2.63849676e-01 -8.02179575e-01 -4.19027805e-01 9.20339108e-01 4.37410802e-01 -2.59800225e-01 4.88528162e-01 -1.91111818e-01 6.17861331e-01 1.05759524e-01 -9.89503801e-01 1.64906353e-01 1.45672020e-02 -5.80977261e-01 -3.44787203e-02 1.86069027e-01 -3.50154400e-01 -1.02061498e+00 9.31983411e-01 1.72068506e-01 -2.05246583e-01 6.03188813e-01 -1.67909539e+00 -6.02382720e-01 1.04104078e+00 -6.75135195e-01 2.16284633e-01 6.28353417e-01 -3.02407533e-01 1.74116039e+00 -6.35051668e-01 7.10593581e-01 1.26930916e+00 8.71606290e-01 -7.57443830e-02 -1.73075545e+00 -7.52960026e-01 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a4b99d93-79f1-4c8c-9499-fe56a55b52a2	zaebuc-an-annotated-arabic-english-bilingual	null	null	https://aclanthology.org/2022.lrec-1.9	https://aclanthology.org/2022.lrec-1.9.pdf	ZAEBUC: An Annotated Arabic-English Bilingual Writer Corpus	We present ZAEBUC, an annotated Arabic-English bilingual writer corpus comprising short essays by first-year university students at Zayed University in the United Arab Emirates. We describe and discuss the various guidelines and pipeline processes we followed to create the annotations and quality check them. The annotations include spelling and grammar correction, morphological tokenization, Part-of-Speech tagging, lemmatization, and Common European Framework of Reference (CEFR) ratings. All of the annotations are done on Arabic and English texts using consistent guidelines as much as possible, with tracked alignments among the different annotations, and to the original raw texts. For morphological tokenization, POS tagging, and lemmatization, we use existing automatic annotation tools followed by manual correction. We also present various measurements and correlations with preliminary insights drawn from the data and annotations. The publicly available ZAEBUC corpus and its annotations are intended to be the stepping stones for additional annotations.	['David Palfreyman', 'Nizar Habash']	null	null	null	null	lrec-2022-6	['lemmatization']	['natural-language-processing']	[-1.65046826e-01 3.88331935e-02 -1.68223634e-01 -3.50138903e-01 -1.05446529e+00 -1.35399282e+00 6.12103164e-01 5.38056910e-01 -5.33469200e-01 8.78618240e-01 6.10686481e-01 -5.11892259e-01 3.06589250e-02 -3.47341418e-01 -2.10017357e-02 -2.71704942e-01 5.06638288e-01 6.65649116e-01 2.53408346e-02 -6.21278346e-01 8.70231926e-01 4.02161330e-01 -7.30793655e-01 3.31941783e-01 1.16734707e+00 1.50375441e-01 2.71387666e-01 6.52349293e-01 -1.84377477e-01 5.60014248e-01 -7.67365277e-01 -1.15863848e+00 1.00439914e-01 -3.51911545e-01 -1.40131855e+00 5.80806248e-02 2.08673075e-01 -1.00194074e-01 9.18420553e-02 1.02044761e+00 3.06487232e-01 1.00025095e-01 4.19173002e-01 -5.35095394e-01 -8.93085301e-01 1.06054580e+00 -4.78736013e-01 2.46130034e-01 5.39010942e-01 -3.07461143e-01 1.29423547e+00 -1.18921816e+00 1.00741446e+00 8.82320881e-01 8.63487244e-01 3.96797508e-01 -4.85254019e-01 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c9cf7d36-b33e-4d79-bf5f-d3b6d1b2384b	high-fidelity-and-low-latency-universal	2105.09856	null	https://arxiv.org/abs/2105.09856v2	https://arxiv.org/pdf/2105.09856v2.pdf	High-Fidelity and Low-Latency Universal Neural Vocoder based on Multiband WaveRNN with Data-Driven Linear Prediction for Discrete Waveform Modeling	This paper presents a novel high-fidelity and low-latency universal neural vocoder framework based on multiband WaveRNN with data-driven linear prediction for discrete waveform modeling (MWDLP). MWDLP employs a coarse-fine bit WaveRNN architecture for 10-bit mu-law waveform modeling. A sparse gated recurrent unit with a relatively large size of hidden units is utilized, while the multiband modeling is deployed to achieve real-time low-latency usage. A novel technique for data-driven linear prediction (LP) with discrete waveform modeling is proposed, where the LP coefficients are estimated in a data-driven manner. Moreover, a novel loss function using short-time Fourier transform (STFT) for discrete waveform modeling with Gumbel approximation is also proposed. The experimental results demonstrate that the proposed MWDLP framework generates high-fidelity synthetic speech for seen and unseen speakers and/or language on 300 speakers training data including clean and noisy/reverberant conditions, where the number of training utterances is limited to 60 per speaker, while allowing for real-time low-latency processing using a single core of $\sim\!$ 2.1--2.7 GHz CPU with $\sim\!$ 0.57--0.64 real-time factor including input/output and feature extraction.	['Tomoki Toda', 'Patrick Lumban Tobing']	2021-05-20	null	null	null	null	['low-latency-processing']	['robots']	[-0.04011761 -0.3480215 0.21987627 -0.42548573 -1.249539 -0.164571 0.0638475 -0.15842183 -0.29150656 0.61534995 0.15199703 -0.39354962 0.10809544 -0.6892487 -0.5744783 -0.66178805 -0.22979672 -0.04587458 -0.04006032 -0.06412769 -0.4290126 0.38129145 -1.7463744 0.30054596 0.45907134 1.243617 0.42128268 1.286622 0.41461694 0.5932524 -0.87642676 -0.09961298 0.31096286 -0.43713632 0.20782556 -0.3494284 0.29340735 -0.4561057 -0.5519979 0.7852216 1.282782 0.44984576 0.3787759 -0.91013384 -0.24672471 0.50475657 -0.39081606 0.38417107 0.02217137 0.21400735 0.92869025 -0.9695389 0.04952816 1.1933118 0.8450685 0.27436754 -1.0382612 -1.0563607 -0.2976332 0.28792986 -1.7785333 -0.76272357 0.9472518 -0.02393281 1.4995905 0.38037965 0.36819333 1.1180544 0.370411 0.38261116 0.7510554 -0.40615723 0.22487617 -0.01310757 0.18148719 0.5851825 -0.18839711 0.24415627 -0.8773439 -0.38250017 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c5def424-737e-4b55-88ce-b4a5f90f2e78	a-data-augmentation-method-for-fully	2202.06344	null	https://arxiv.org/abs/2202.06344v2	https://arxiv.org/pdf/2202.06344v2.pdf	A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation	Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatment and monitoring of disease. In this paper, an augmentation method, called TensorMixup, was proposed and applied to the three dimensional U-Net architecture for brain tumor segmentation. The main ideas included that first, two image patches with size of 128 in three dimensions were selected according to glioma information of ground truth labels from the magnetic resonance imaging data of any two patients with the same modality. Next, a tensor in which all elements were independently sampled from Beta distribution was used to mix the image patches. Then the tensor was mapped to a matrix which was used to mix the one-hot encoded labels of the above image patches. Therefore, a new image and its one-hot encoded label were synthesized. Finally, the new data was used to train the model which could be used to segment glioma. The experimental results show that the mean accuracy of Dice scores are 91.32%, 85.67%, and 82.20% respectively on the whole tumor, tumor core, and enhancing tumor segmentation, which proves that the proposed TensorMixup is feasible and effective for brain tumor segmentation.	['Hongbing Xiao', 'Yarong Ji', 'Yu Wang']	2022-02-13	null	null	null	null	['brain-tumor-segmentation']	['medical']	[ 1.44849062e-01 1.91459246e-02 -4.76670787e-02 -2.60868371e-01 -6.35316610e-01 -1.08083703e-01 1.44991621e-01 -3.98638211e-02 -5.89142025e-01 5.24579406e-01 2.90814400e-01 -1.29011273e-01 -1.08489081e-01 -6.83233857e-01 -1.34391293e-01 -1.29665732e+00 1.58739135e-01 4.79586542e-01 2.12970793e-01 1.62075773e-01 2.34294608e-01 2.99876362e-01 -8.77765059e-01 2.72971302e-01 9.97032404e-01 1.19281614e+00 4.64397818e-01 1.94777325e-01 -3.87945980e-01 6.34303987e-01 -3.78370672e-01 4.42506224e-02 2.13909939e-01 -4.19152170e-01 -7.82733619e-01 3.63856792e-01 7.43501354e-03 -2.66486287e-01 -2.78731436e-01 1.37912297e+00 4.51081157e-01 1.19879153e-02 8.97611439e-01 -8.88730526e-01 -2.47994408e-01 6.63615763e-01 -9.86187875e-01 6.49594590e-02 -3.36642742e-01 -1.19451776e-01 5.06379843e-01 -6.22693539e-01 5.70002258e-01 8.87885809e-01 3.44757199e-01 3.84036452e-01 -8.24492395e-01 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-7.30901301e-01 2.99868733e-01 4.31227982e-02 -6.56933308e-01 -2.97992587e-01 -2.56938905e-01]	[14.480636596679688, -2.430408000946045]
0c1979c3-019c-45c0-956f-10fb3d39fc88	improving-auto-encoders-self-supervised-image	2012.03322	null	https://arxiv.org/abs/2012.03322v2	https://arxiv.org/pdf/2012.03322v2.pdf	A Pseudo-labelling Auto-Encoder for unsupervised image classification	In this paper, we introduce a unique variant of the denoising Auto-Encoder and combine it with the perceptual loss to classify images in an unsupervised manner. The proposed method, called Pseudo Labelling, consists of first applying a randomly sampled set of data augmentation transformations to each training image. As a result, each initial image can be considered as a pseudo-label to its corresponding augmented ones. Then, an Auto-Encoder is used to learn the mapping between each set of the augmented images and its corresponding pseudo-label. Furthermore, the perceptual loss is employed to take into consideration the existing dependencies between the pixels in the same neighbourhood of an image. This combination encourages the encoder to output richer encodings that are highly informative of the input's class. Consequently, the Auto-Encoder's performance on unsupervised image classification is improved in terms of stability, accuracy and consistency across all tested datasets. Previous state-of-the-art accuracy on the MNIST, CIFAR-10 and SVHN datasets is improved by 0.3\%, 3.11\% and 9.21\% respectively.	['Abdelhakim Saim', 'Rachid Deriche', 'Karim Atif', 'Aymene Mohammed Bouayed']	2020-12-06	null	null	null	null	['self-supervised-image-classification', 'unsupervised-image-classification']	['computer-vision', 'computer-vision']	[ 6.76602721e-01 2.61128485e-01 -3.36715346e-03 -6.68519557e-01 -4.71471697e-01 -2.20528767e-01 5.77839971e-01 3.03096265e-01 -6.83368027e-01 8.22570384e-01 -1.37959063e-01 1.83413640e-01 3.08068879e-02 -8.52626264e-01 -8.20641398e-01 -1.05900860e+00 2.20985889e-01 2.86127806e-01 6.44070506e-02 1.50388628e-01 -6.81675598e-02 2.53355682e-01 -1.66198123e+00 3.50004315e-01 8.32797170e-01 1.23182833e+00 3.64690006e-01 3.99109721e-01 -3.71416621e-02 8.37990582e-01 -5.27840197e-01 -4.12895203e-01 2.01384217e-01 -5.56913376e-01 -6.76402450e-01 5.12513578e-01 4.40277249e-01 -4.52528559e-02 -1.83588281e-01 1.31683207e+00 2.58039743e-01 2.12274358e-01 8.02154601e-01 -1.07814991e+00 -7.98040569e-01 6.32122278e-01 -5.61425865e-01 8.66215304e-02 -1.83562830e-01 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24cd634e-6d5c-4f0d-85e9-b60b9f8e8242	prottrans-towards-cracking-the-language-of	2007.06225	null	https://arxiv.org/abs/2007.06225v3	https://arxiv.org/pdf/2007.06225v3.pdf	ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing	Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models taken from NLP. These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids. The LMs were trained on the Summit supercomputer using 5616 GPUs and TPU Pod up-to 1024 cores. Dimensionality reduction revealed that the raw protein LM-embeddings from unlabeled data captured some biophysical features of protein sequences. We validated the advantage of using the embeddings as exclusive input for several subsequent tasks. The first was a per-residue prediction of protein secondary structure (3-state accuracy Q3=81%-87%); the second were per-protein predictions of protein sub-cellular localization (ten-state accuracy: Q10=81%) and membrane vs. water-soluble (2-state accuracy Q2=91%). For the per-residue predictions the transfer of the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without using evolutionary information thereby bypassing expensive database searches. Taken together, the results implied that protein LMs learned some of the grammar of the language of life. To facilitate future work, we released our models at https://github.com/agemagician/ProtTrans.	['Tom Gibbs', 'Tamas Feher', 'Llion Jones', 'Ahmed Elnaggar', 'Yu Wang', 'Michael Heinzinger', 'Martin Steinegger', 'Ghalia Rihawi', 'Debsindhu Bhowmik', 'Christian Dallago', 'Burkhard Rost', 'Christoph Angerer']	2020-07-13	null	null	null	null	['protein-secondary-structure-prediction']	['medical']	[ 9.69559327e-02 1.93302780e-01 -7.31314123e-02 -2.43327826e-01 -6.20832026e-01 -6.04579687e-01 4.82293576e-01 3.69912833e-01 -6.04687274e-01 1.22648740e+00 1.30865470e-01 -7.73962557e-01 1.09202951e-01 -4.61140335e-01 -1.14649212e+00 -9.92220163e-01 -9.44289938e-02 7.19111264e-01 -1.74727831e-02 -1.73208460e-01 2.93569565e-02 5.59974492e-01 -1.32648265e+00 5.14299989e-01 6.06927395e-01 8.17792535e-01 5.11800408e-01 1.02240646e+00 -2.47520551e-01 5.00169039e-01 -6.73931390e-02 -4.77907211e-01 1.13976486e-01 -1.86000932e-02 -6.23717964e-01 -6.22861266e-01 1.53401643e-01 2.44112145e-02 -2.92824298e-01 6.78218842e-01 6.35746777e-01 -2.53760695e-01 8.41966689e-01 -8.15295398e-01 -1.04673195e+00 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ee722a42-99ae-49c9-9f12-2550b3ddcf1a	learning-bloch-simulations-for-mr	2008.04139	null	https://arxiv.org/abs/2008.04139v2	https://arxiv.org/pdf/2008.04139v2.pdf	Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks	Magnetic resonance fingerprinting (MRF) enables fast and multiparametric MR imaging. Despite fast acquisition, the state-of-the-art reconstruction of MRF based on dictionary matching is slow and lacks scalability. To overcome these limitations, neural network (NN) approaches estimating MR parameters from fingerprints have been proposed recently. Here, we revisit NN-based MRF reconstruction to jointly learn the forward process from MR parameters to fingerprints and the backward process from fingerprints to MR parameters by leveraging invertible neural networks (INNs). As a proof-of-concept, we perform various experiments showing the benefit of learning the forward process, i.e., the Bloch simulations, for improved MR parameter estimation. The benefit especially accentuates when MR parameter estimation is difficult due to MR physical restrictions. Therefore, INNs might be a feasible alternative to the current solely backward-based NNs for MRF reconstruction.	['Benjamin Marty', 'Olivier Scheidegger', 'Fabian Balsiger', 'Mauricio Reyes', 'Alain Jungo']	2020-08-10	null	null	null	null	['magnetic-resonance-fingerprinting']	['medical']	[ 5.76626539e-01 -5.01335859e-02 -3.59092891e-01 -4.82022494e-01 -7.99477637e-01 -2.30209395e-01 3.63172859e-01 1.12004648e-03 -5.51863849e-01 6.84116840e-01 1.99413046e-01 -4.67401505e-01 -4.44585979e-01 -5.37470579e-01 -9.78705764e-01 -8.65913212e-01 -3.73652250e-01 4.92959321e-01 -7.42832478e-03 8.19495693e-02 1.52854666e-01 8.37060153e-01 -1.06630802e+00 2.74073780e-01 7.14105844e-01 8.76934648e-01 5.37140191e-01 5.15953064e-01 -1.51087977e-02 9.16539788e-01 -9.50173289e-02 5.77249639e-02 2.48531535e-01 -3.41359526e-01 -7.47365177e-01 -5.55012882e-01 4.54788387e-01 -7.65596151e-01 -7.44638264e-01 9.38699961e-01 9.22006369e-01 2.53023118e-01 5.40509522e-01 -4.63279158e-01 -5.70442021e-01 7.04449475e-01 -3.78649741e-01 4.55837816e-01 1.79707617e-01 -1.57797515e-01 4.35955793e-01 -9.66459334e-01 8.36166382e-01 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d38978b8-9884-4794-9cde-33ad14292803	continual-semantic-segmentation-with	2304.05015	null	https://arxiv.org/abs/2304.05015v1	https://arxiv.org/pdf/2304.05015v1.pdf	Continual Semantic Segmentation with Automatic Memory Sample Selection	Continual Semantic Segmentation (CSS) extends static semantic segmentation by incrementally introducing new classes for training. To alleviate the catastrophic forgetting issue in CSS, a memory buffer that stores a small number of samples from the previous classes is constructed for replay. However, existing methods select the memory samples either randomly or based on a single-factor-driven handcrafted strategy, which has no guarantee to be optimal. In this work, we propose a novel memory sample selection mechanism that selects informative samples for effective replay in a fully automatic way by considering comprehensive factors including sample diversity and class performance. Our mechanism regards the selection operation as a decision-making process and learns an optimal selection policy that directly maximizes the validation performance on a reward set. To facilitate the selection decision, we design a novel state representation and a dual-stage action space. Our extensive experiments on Pascal-VOC 2012 and ADE 20K datasets demonstrate the effectiveness of our approach with state-of-the-art (SOTA) performance achieved, outperforming the second-place one by 12.54% for the 6stage setting on Pascal-VOC 2012.	['Jun Liu', 'Simon See', 'Jianxiong Yin', 'Tianrun Chen', 'Lanyun Zhu']	2023-04-11	null	http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_Continual_Semantic_Segmentation_With_Automatic_Memory_Sample_Selection_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_Continual_Semantic_Segmentation_With_Automatic_Memory_Sample_Selection_CVPR_2023_paper.pdf	cvpr-2023-1	['continual-semantic-segmentation']	['computer-vision']	[ 4.45506990e-01 -3.44580449e-02 -5.77183068e-01 -6.38914168e-01 -9.37157452e-01 -4.21572208e-01 4.14674044e-01 -1.08669242e-02 -8.73313963e-01 8.82181466e-01 -2.02962682e-01 -2.39923924e-01 2.16665372e-01 -8.00888121e-01 -9.34960961e-01 -7.29073882e-01 3.04777116e-01 5.79247594e-01 7.57878423e-01 1.59565076e-01 4.60687816e-01 2.55193740e-01 -1.80664206e+00 3.70900542e-01 1.17844391e+00 1.05618906e+00 5.74224770e-01 6.57396317e-01 -2.83890694e-01 5.62664092e-01 -9.14702952e-01 -2.75903702e-01 1.08826384e-01 -4.92031604e-01 -9.59301770e-01 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c5d67d23-5067-4bba-b743-657f692b2393	metadata-based-raw-reconstruction-via	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Li_Metadata-Based_RAW_Reconstruction_via_Implicit_Neural_Functions_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Metadata-Based_RAW_Reconstruction_via_Implicit_Neural_Functions_CVPR_2023_paper.pdf	Metadata-Based RAW Reconstruction via Implicit Neural Functions	Many low-level computer vision tasks are desirable to utilize the unprocessed RAW image as input, which remains the linear relationship between pixel values and scene radiance. Recent works advocate to embed the RAW image samples into sRGB images at capture time, and reconstruct the RAW from sRGB by these metadata when needed. However, there still exist some limitations on taking full use of the metadata. In this paper, instead of following the perspective of sRGB-to-RAW mapping, we reformulate the problem as mapping the 2D coordinates of the metadata to its RAW values conditioned on the corresponding sRGB values. With this novel formulation, we propose to reconstruct the RAW image with an implicit neural function, which achieves significant performance improvement (more than 10dB average PSNR) only with the uniform sampling. Compared with most deep learning-based approaches, our method is trained in a self-supervised way that requiring no pre-training on different camera ISPs. We perform further experiments to demonstrate the effectiveness of our method, and show that our framework is also suitable for the task of guided super-resolution.	['Qinmin Yang', 'Qi Ye', 'Huijie Qiao', 'Leyi Li']	2023-01-01	null	null	null	cvpr-2023-1	['raw-reconstruction']	['computer-vision']	[ 5.72202742e-01 -1.01441421e-01 3.04227434e-02 -4.80762869e-01 -7.97878742e-01 -1.65117905e-01 3.61608744e-01 -5.19943774e-01 -4.64444876e-01 7.40596950e-01 2.15715513e-01 -4.27132919e-02 -1.53563228e-02 -1.00615573e+00 -9.41868007e-01 -9.05910432e-01 2.67026603e-01 -1.70274958e-01 2.20176190e-01 -2.87098140e-02 2.14991450e-01 4.70273376e-01 -1.56454897e+00 2.49744102e-01 8.91692221e-01 1.38130534e+00 5.48825681e-01 6.28412724e-01 -1.96575582e-01 1.08183253e+00 -4.43691462e-01 -8.15433338e-02 7.17137575e-01 -3.65579873e-01 -6.55435622e-01 3.11819494e-01 7.82242239e-01 -9.08447146e-01 -5.27283251e-01 1.32311285e+00 2.73756325e-01 1.44113600e-01 1.36292487e-01 -7.17611551e-01 -9.61442173e-01 2.53027618e-01 -7.21769869e-01 7.14050382e-02 1.12115428e-01 1.01813070e-01 7.23465085e-01 -8.19914103e-01 3.46684277e-01 9.35523450e-01 7.36128509e-01 2.18960360e-01 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68a79e1f-b54e-42d3-909a-9e1ec8bb80ec	mri-multi-modal-3d-human-pose-estimation	2210.08394	null	https://arxiv.org/abs/2210.08394v1	https://arxiv.org/pdf/2210.08394v1.pdf	mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors	The ability to estimate 3D human body pose and movement, also known as human pose estimation (HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) radars, and wearable inertial sensors. Despite previous efforts on datasets and benchmarks for HPE, few dataset exploits multiple modalities and focuses on home-based health monitoring. To bridge the gap, we present mRI, a multi-modal 3D human pose estimation dataset with mmWave, RGB-D, and Inertial Sensors. Our dataset consists of over 160k synchronized frames from 20 subjects performing rehabilitation exercises and supports the benchmarks of HPE and action detection. We perform extensive experiments using our dataset and delineate the strength of each modality. We hope that the release of mRI can catalyze the research in pose estimation, multi-modal learning, and action understanding, and more importantly facilitate the applications of home-based health monitoring.	['Umit Ogras', 'Yin Li', 'Sizhe An']	2022-10-15	null	null	null	null	['action-understanding', '3d-human-pose-estimation']	['computer-vision', 'computer-vision']	[ 1.56119168e-01 4.90693115e-02 -2.69281268e-01 -9.24786478e-02 -8.85933042e-01 1.32272132e-02 -2.26748064e-01 -3.16538244e-01 -5.09915531e-01 5.21649539e-01 9.71893072e-01 2.90504068e-01 -7.92128891e-02 -5.86685300e-01 -3.58367920e-01 -4.83277172e-01 -5.33365250e-01 4.00657713e-01 5.18129161e-03 -3.86732012e-01 -2.46261626e-01 2.66801089e-01 -1.38911033e+00 2.15401068e-01 5.49199246e-02 9.83933687e-01 -2.00135291e-01 6.34750128e-01 8.70635867e-01 3.75498563e-01 -5.14061809e-01 1.11696966e-01 2.64239341e-01 -4.47232336e-01 -7.16990411e-01 1.32573217e-01 2.55620211e-01 -5.88658631e-01 -5.46360850e-01 2.98924893e-01 1.60586524e+00 1.40610129e-01 1.20582856e-01 -1.06347382e+00 -1.22260287e-01 8.70418176e-02 -5.59262693e-01 2.36566380e-01 1.29096496e+00 3.33576918e-01 3.74207884e-01 -5.29368520e-01 5.96487045e-01 8.83539855e-01 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-4.09062505e-01 -1.02033818e+00 -7.38493383e-01 4.70113724e-01 2.54132599e-01 -8.49876165e-01 -7.92969391e-02 -2.29609292e-03]	[7.154171943664551, 0.11492877453565598]
ea7b8333-a799-434c-8659-f547eeb7411f	inesc-id-at-semeval-2016-task-4-a-reducing	null	null	https://aclanthology.org/S16-1036	https://aclanthology.org/S16-1036.pdf	INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words	null	["M{\\'a}rio J. Silva", 'Ramon Astudillo', 'Wang Ling', 'Silvio Amir', 'Isabel Trancoso']	2016-06-01	null	null	null	semeval-2016-6	['twitter-sentiment-analysis']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 5.37373662e-01 4.49454933e-02 1.08379531e+00 -1.42548037e+00 -5.91619551e-01 6.10313773e-01 -4.01565880e-02 1.21627934e-01 1.03678203e+00 -5.39447069e-01 1.19919395e+00 -1.13026452e+00 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ea7505c6-44f8-44ef-adb9-19a3ba1be534	on-the-role-of-bidirectionality-in-language	2205.11726	null	https://arxiv.org/abs/2205.11726v2	https://arxiv.org/pdf/2205.11726v2.pdf	On the Role of Bidirectionality in Language Model Pre-Training	Prior work on language model pre-training has explored different architectures and learning objectives, but differences in data, hyperparameters and evaluation make a principled comparison difficult. In this work, we focus on bidirectionality as a key factor that differentiates existing approaches, and present a comprehensive study of its role in next token prediction, text infilling, zero-shot priming and fine-tuning. We propose a new framework that generalizes prior approaches, including fully unidirectional models like GPT, fully bidirectional models like BERT, and hybrid models like CM3 and prefix LM. Our framework distinguishes between two notions of bidirectionality (bidirectional context and bidirectional attention) and allows us to control each of them separately. We find that the optimal configuration is largely application-dependent (e.g., bidirectional attention is beneficial for fine-tuning and infilling, but harmful for next token prediction and zero-shot priming). We train models with up to 6.7B parameters, and find differences to remain consistent at scale. While prior work on scaling has focused on left-to-right autoregressive models, our results suggest that this approach comes with some trade-offs, and it might be worthwhile to develop very large bidirectional models.	['Ves Stoyanov', 'Luke Zettlemoyer', 'Naman Goyal', 'Jingfei Du', 'Mikel Artetxe']	2022-05-24	null	null	null	null	['text-infilling']	['natural-language-processing']	[ 3.12541574e-02 -2.19535977e-02 -5.39150774e-01 -2.10616082e-01 -6.68060541e-01 -8.63044858e-01 1.09060550e+00 5.77851338e-03 -5.86628914e-01 5.13999939e-01 6.52723789e-01 -9.78370368e-01 2.27600727e-02 -6.15325689e-01 -6.05444729e-01 -4.40677166e-01 -2.83956602e-02 4.13012803e-01 3.87122601e-01 -5.93294084e-01 3.62892717e-01 2.81657219e-01 -1.05889535e+00 4.99034971e-01 4.48933482e-01 4.41225350e-01 2.32813194e-01 1.03483772e+00 -3.83223444e-01 8.20217609e-01 -4.51378971e-01 -5.00579476e-01 1.47822246e-01 -1.71014339e-01 -1.13868749e+00 -4.10725713e-01 2.50697941e-01 -2.41428196e-01 -2.46869504e-01 4.35290486e-01 7.91160107e-01 9.00199711e-02 6.58536494e-01 -1.03351831e+00 -1.16529703e+00 1.06110060e+00 -4.67178702e-01 5.45726657e-01 -1.52379349e-01 4.06704158e-01 1.18552756e+00 -9.35238540e-01 4.41050023e-01 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3.29791456e-01 -1.04652834e+00 -7.66322017e-01 -1.18825603e+00 -3.95908803e-02 2.71890849e-01 7.32024908e-02 -4.62688506e-01 -3.93749960e-02 -5.37050031e-02]	[10.818471908569336, 8.48800277709961]
3fe3bea7-0c89-4f4d-8922-134bd0b6b0fe	1d-convolutional-neural-network-models-for	1903.01552	null	http://arxiv.org/abs/1903.01552v1	http://arxiv.org/pdf/1903.01552v1.pdf	1D Convolutional Neural Network Models for Sleep Arousal Detection	Sleep arousals transition the depth of sleep to a more superficial stage. The occurrence of such events is often considered as a protective mechanism to alert the body of harmful stimuli. Thus, accurate sleep arousal detection can lead to an enhanced understanding of the underlying causes and influencing the assessment of sleep quality. Previous studies and guidelines have suggested that sleep arousals are linked mainly to abrupt frequency shifts in EEG signals, but the proposed rules are shown to be insufficient for a comprehensive characterization of arousals. This study investigates the application of five recent convolutional neural networks (CNNs) for sleep arousal detection and performs comparative evaluations to determine the best model for this task. The investigated state-of-the-art CNN models have originally been designed for image or speech processing. A detailed set of evaluations is performed on the benchmark dataset provided by PhysioNet/Computing in Cardiology Challenge 2018, and the results show that the best 1D CNN model has achieved an average of 0.31 and 0.84 for the area under the precision-recall and area under the ROC curves, respectively.	['Simo Särkkä', 'Ali Bahrami Rad', 'Serkan Kiranyaz', 'Moncef Gabbouj', 'Morteza Zabihi']	2019-03-01	null	null	null	null	['sleep-quality-prediction', 'sleep-arousal-detection']	['medical', 'medical']	[ 1.84711143e-01 -1.46552473e-01 -1.21120207e-01 -3.57097924e-01 -2.05804422e-01 -1.17302142e-01 3.97996694e-01 7.23103702e-01 -7.34348357e-01 6.99965715e-01 1.65372938e-02 -5.42554669e-02 -1.29415274e-01 -2.82283604e-01 -1.21244989e-01 -7.73314357e-01 -3.14515412e-01 2.99126823e-02 -1.41025502e-02 -1.52952433e-01 2.06043079e-01 3.47679496e-01 -1.58436227e+00 2.17776239e-01 8.29969943e-01 1.44637108e+00 -5.50673120e-02 7.90586531e-01 5.28729819e-02 1.54288471e-01 -9.25348759e-01 -9.82375666e-02 -8.98217633e-02 -5.34297884e-01 -3.89345556e-01 -5.91401935e-01 1.69725254e-01 1.98022440e-01 7.30272790e-04 9.62626636e-01 8.15950215e-01 -5.11681847e-02 3.80306572e-01 -9.86043394e-01 -1.24316528e-01 1.91835374e-01 1.45006865e-01 1.20410585e+00 2.71739334e-01 2.31929228e-01 5.19168317e-01 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da257d69-2bbd-4931-8ced-3de610bd41bf	ao14eae3ec-aa1i-cc2ii12a14c3-the-duplex-model	null	null	https://aclanthology.org/O17-1017	https://aclanthology.org/O17-1017.pdf	基於雙工音高感知模型之神經網路旋律抽取演算法 (The duplex model of pitch perception inspired neural network for melody extraction) [In Chinese]	null	['Tai-Shih Chi', 'Hsin Chou']	2017-11-01	the-duplex-model-of-pitch-perception-inspired	https://aclanthology.org/O17-1017	https://aclanthology.org/O17-1017.pdf	roclingijclclp-2017-11	['melody-extraction']	['music']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 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28e7fa42-9546-4f0c-a1c7-29dd6501ddaf	automatic-language-identification-using-deep	null	null	https://ieeexplore.ieee.org/document/6854622	https://static.googleusercontent.com/media/research.google.com/ru//pubs/archive/42538.pdf	AUTOMATIC LANGUAGE IDENTIFICATION USING DEEP NEURAL NETWORKS	This work studies the use of deep neural networks (DNNs) to address automatic language identification (LID). Motivated by their recent success in acoustic modelling, we adapt DNNs to the problem of identifying the language of a given spoken utterance from short-term acoustic features. The proposed approach is compared to state-of-the-art i-vector based acoustic systems on two different datasets: Google 5M LID corpus and NIST LRE 2009. Results show how LID can largely benefit from using DNNs, especially when a large amount of training data is available. We found relative improvements up to 70%, in Cavg, over the baseline system.	['Pedro Moreno', 'Joaquin Gonzalez-Rodriguez', 'David Martinez', 'Oldrich Plchot', 'Javier Gonzalez-Dominguez', 'Ignacio Lopez-Moreno']	2014-05-04	null	null	null	null	['acoustic-modelling']	['speech']	[-2.19788522e-01 -2.13151380e-01 4.76819165e-02 -5.16715884e-01 -1.21175432e+00 -5.39495826e-01 7.14897096e-01 -4.41576779e-01 -9.49070275e-01 2.97820985e-01 5.66505492e-01 -4.52132493e-01 3.66608471e-01 -4.15224470e-02 -5.47134876e-01 -4.49609190e-01 1.19335152e-01 6.07031524e-01 -3.00990760e-01 -2.07187291e-02 2.65737884e-02 4.10320371e-01 -1.47800195e+00 -7.86916465e-02 1.55352995e-01 1.14203227e+00 3.21507812e-01 1.03223121e+00 -2.95771599e-01 6.18561924e-01 -6.73441827e-01 -1.49693638e-01 -2.15363130e-02 -6.97969869e-02 -8.76229048e-01 -3.60124797e-01 6.64773047e-01 -5.67338169e-01 -4.93210196e-01 9.54277456e-01 1.03458571e+00 3.54188681e-01 6.75676465e-01 -7.87936985e-01 -7.48490334e-01 8.51829767e-01 3.43533419e-02 2.35311761e-01 2.89402425e-01 1.03028178e-01 1.21079147e+00 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7938e6c1-f550-42b5-9968-1695c90b548b	siammask-a-framework-for-fast-online-object	2207.02088	null	https://arxiv.org/abs/2207.02088v1	https://arxiv.org/pdf/2207.02088v1.pdf	SiamMask: A Framework for Fast Online Object Tracking and Segmentation	In this paper we introduce SiamMask, a framework to perform both visual object tracking and video object segmentation, in real-time, with the same simple method. We improve the offline training procedure of popular fully-convolutional Siamese approaches by augmenting their losses with a binary segmentation task. Once the offline training is completed, SiamMask only requires a single bounding box for initialization and can simultaneously carry out visual object tracking and segmentation at high frame-rates. Moreover, we show that it is possible to extend the framework to handle multiple object tracking and segmentation by simply re-using the multi-task model in a cascaded fashion. Experimental results show that our approach has high processing efficiency, at around 55 frames per second. It yields real-time state-of-the-art results on visual-object tracking benchmarks, while at the same time demonstrating competitive performance at a high speed for video object segmentation benchmarks.	['Philip H. S. Torr', 'Luca Bertinetto', 'Li Zhang', 'Qiang Wang', 'Weiming Hu']	2022-07-05	null	null	null	null	['visual-object-tracking']	['computer-vision']	[ 1.52179867e-01 -2.96301991e-01 -2.50636578e-01 -1.91456363e-01 -9.71541047e-01 -8.30020130e-01 3.87024999e-01 8.34202915e-02 -8.71536970e-01 2.79297709e-01 -6.80499434e-01 -2.15578586e-01 4.10991371e-01 -3.73227298e-01 -1.14834261e+00 -5.97580492e-01 2.66316589e-02 7.79344201e-01 9.66312051e-01 3.37973177e-01 -4.82763862e-03 7.32040584e-01 -1.41483092e+00 2.10539743e-01 3.66591871e-01 1.12265730e+00 4.24284339e-02 1.20697451e+00 -5.94884753e-02 7.59702265e-01 -4.24720943e-01 -7.22861350e-01 5.13313591e-01 -9.80111808e-02 -8.66202712e-01 5.32808721e-01 1.06965351e+00 -5.67596138e-01 -3.35132152e-01 1.07192791e+00 1.47263497e-01 8.07554796e-02 2.26795420e-01 -1.32858145e+00 -1.40198827e-01 3.88111025e-01 -7.66609967e-01 2.39736676e-01 -6.65871352e-02 2.67748684e-01 8.46968949e-01 -8.47775996e-01 5.85288644e-01 1.26314795e+00 8.03488135e-01 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55076973-4bdd-4bdc-8afe-066442d8227d	class-incremental-novel-class-discovery	2207.08605	null	https://arxiv.org/abs/2207.08605v1	https://arxiv.org/pdf/2207.08605v1.pdf	Class-incremental Novel Class Discovery	We study the new task of class-incremental Novel Class Discovery (class-iNCD), which refers to the problem of discovering novel categories in an unlabelled data set by leveraging a pre-trained model that has been trained on a labelled data set containing disjoint yet related categories. Apart from discovering novel classes, we also aim at preserving the ability of the model to recognize previously seen base categories. Inspired by rehearsal-based incremental learning methods, in this paper we propose a novel approach for class-iNCD which prevents forgetting of past information about the base classes by jointly exploiting base class feature prototypes and feature-level knowledge distillation. We also propose a self-training clustering strategy that simultaneously clusters novel categories and trains a joint classifier for both the base and novel classes. This makes our method able to operate in a class-incremental setting. Our experiments, conducted on three common benchmarks, demonstrate that our method significantly outperforms state-of-the-art approaches. Code is available at https://github.com/OatmealLiu/class-iNCD	['Elisa Ricci', 'Nicu Sebe', 'Zhun Zhong', 'Mingxuan Liu', 'Subhankar Roy']	2022-07-18	null	null	null	null	['novel-class-discovery', 'novel-class-discovery']	['computer-vision', 'methodology']	[ 4.26705718e-01 3.27677131e-01 -2.01222152e-01 -4.95770097e-01 -6.75264657e-01 -6.11922503e-01 8.10418785e-01 5.14915407e-01 -3.78654420e-01 7.15239167e-01 -2.14302972e-01 -2.21542180e-01 -2.22101703e-01 -6.36367202e-01 -7.82039046e-01 -6.75486088e-01 -3.05106014e-01 6.89750314e-01 5.01991272e-01 3.01212102e-01 3.20038646e-01 5.41717887e-01 -2.21523261e+00 6.16627514e-01 5.41337252e-01 6.97297096e-01 5.49577847e-02 6.51285052e-01 3.11924722e-02 8.26384783e-01 -2.65314192e-01 -1.26147076e-01 3.04063529e-01 -4.42182392e-01 -1.14135396e+00 4.10503417e-01 4.34274465e-01 5.53801097e-02 -2.78516740e-01 7.74391234e-01 2.24260136e-01 5.33815563e-01 5.68055272e-01 -1.25661457e+00 -4.99941707e-01 6.07686639e-01 -3.02096397e-01 3.09381664e-01 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d082d03c-1c85-4041-b39b-ef80a5368f40	quantifying-character-similarity-with-vision	2305.14672	null	https://arxiv.org/abs/2305.14672v1	https://arxiv.org/pdf/2305.14672v1.pdf	Quantifying Character Similarity with Vision Transformers	Record linkage is a bedrock of quantitative social science, as analyses often require linking data from multiple, noisy sources. Off-the-shelf string matching methods are widely used, as they are straightforward and cheap to implement and scale. Not all character substitutions are equally probable, and for some settings there are widely used handcrafted lists denoting which string substitutions are more likely, that improve the accuracy of string matching. However, such lists do not exist for many settings, skewing research with linked datasets towards a few high-resource contexts that are not representative of the diversity of human societies. This study develops an extensible way to measure character substitution costs for OCR'ed documents, by employing large-scale self-supervised training of vision transformers (ViT) with augmented digital fonts. For each language written with the CJK script, we contrastively learn a metric space where different augmentations of the same character are represented nearby. In this space, homoglyphic characters - those with similar appearance such as ``O'' and ``0'' - have similar vector representations. Using the cosine distance between characters' representations as the substitution cost in an edit distance matching algorithm significantly improves record linkage compared to other widely used string matching methods, as OCR errors tend to be homoglyphic in nature. Homoglyphs can plausibly capture character visual similarity across any script, including low-resource settings. We illustrate this by creating homoglyph sets for 3,000 year old ancient Chinese characters, which are highly pictorial. Fascinatingly, a ViT is able to capture relationships in how different abstract concepts were conceptualized by ancient societies, that have been noted in the archaeological literature.	['Melissa Dell', 'Shao-Yu Jheng', 'Abhishek Arora', 'Xinmei Yang']	2023-05-24	null	null	null	null	['optical-character-recognition']	['computer-vision']	[ 3.80733907e-01 -3.78549844e-01 -9.82649848e-02 -4.65855241e-01 -4.72155184e-01 -9.14532304e-01 4.80800003e-01 6.08829141e-01 -7.03133106e-01 6.42497897e-01 5.04646122e-01 -8.91302675e-02 -2.14594185e-01 -1.02816927e+00 -7.77417600e-01 -2.82850236e-01 -2.28930898e-02 4.98535246e-01 -1.94379255e-01 -3.97009760e-01 4.11478966e-01 3.02780002e-01 -1.68989778e+00 3.72390538e-01 7.96053231e-01 2.53885925e-01 2.92116225e-01 5.72196126e-01 -3.63969922e-01 2.38766626e-01 -6.05814040e-01 -1.27539051e+00 2.58880436e-01 -3.73233885e-01 -4.64942962e-01 -6.34348989e-01 1.36514819e+00 -8.53019059e-02 -4.70308870e-01 1.11100733e+00 5.99960268e-01 -2.60745108e-01 6.88896537e-01 -1.08452332e+00 -1.36249936e+00 9.43182766e-01 -6.77944481e-01 1.64719701e-01 6.96220160e-01 1.69136867e-01 1.37243330e+00 -4.73727971e-01 9.81084526e-01 1.52614832e+00 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959808d2-db87-4305-a3ec-d7e07c475db5	unsupervised-hebbian-learning-on-point-sets	2207.12323	null	https://arxiv.org/abs/2207.12323v1	https://arxiv.org/pdf/2207.12323v1.pdf	Unsupervised Hebbian Learning on Point Sets in StarCraft II	Learning the evolution of real-time strategy (RTS) game is a challenging problem in artificial intelligent (AI) system. In this paper, we present a novel Hebbian learning method to extract the global feature of point sets in StarCraft II game units, and its application to predict the movement of the points. Our model includes encoder, LSTM, and decoder, and we train the encoder with the unsupervised learning method. We introduce the concept of neuron activity aware learning combined with k-Winner-Takes-All. The optimal value of neuron activity is mathematically derived, and experiments support the effectiveness of the concept over the downstream task. Our Hebbian learning rule benefits the prediction with lower loss compared to self-supervised learning. Also, our model significantly saves the computational cost such as activations and FLOPs compared to a frame-based approach.	['Saibal Mukhopadhyay', 'Saurabh Dash', 'Harshit Kumar', 'Beomseok Kang']	2022-07-13	null	null	null	null	['starcraft-ii']	['playing-games']	[ 4.88201529e-02 4.29424196e-02 -1.00945070e-01 -1.06581777e-01 -1.13026887e-01 -1.87597424e-01 4.25052285e-01 -1.68504938e-01 -1.03983188e+00 8.07144701e-01 -4.41379920e-02 1.25404358e-01 -2.54225194e-01 -9.26183283e-01 -8.48753452e-01 -1.00917530e+00 -2.98593521e-01 1.13135271e-01 7.62773097e-01 -6.58275247e-01 5.72428226e-01 3.53628248e-01 -1.45024610e+00 3.61953616e-01 3.82408857e-01 1.31541824e+00 7.25316942e-01 9.52333868e-01 -2.51350760e-01 1.76398098e+00 -9.80604947e-01 -2.51267135e-01 4.52364981e-01 -5.01036823e-01 -6.78510725e-01 -2.95544982e-01 -5.23205936e-01 -1.97338954e-01 -8.61033261e-01 6.44568026e-01 6.86875820e-01 3.07745814e-01 4.89922017e-01 -1.21761143e+00 -2.02861205e-01 5.63953817e-01 -3.34898144e-01 7.71322489e-01 -2.56480426e-01 2.32457936e-01 8.84037971e-01 -4.37995195e-01 4.88122314e-01 6.43458724e-01 6.07578933e-01 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b7c335d9-b839-4421-a10f-0e09db0d55c5	decipherment	null	null	https://aclanthology.org/P13-5003	https://aclanthology.org/P13-5003.pdf	Decipherment	null	['Kevin Knight']	2013-08-01	null	null	null	acl-2013-8	['decipherment']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 5.37373662e-01 4.49454933e-02 1.08379531e+00 -1.42548037e+00 -5.91619551e-01 6.10313773e-01 -4.01565880e-02 1.21627934e-01 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e5463ecf-5281-49cf-9044-ea3034832607	sahaayak-2023-the-multi-domain-bilingual	2307.00021	null	https://arxiv.org/abs/2307.00021v1	https://arxiv.org/pdf/2307.00021v1.pdf	SAHAAYAK 2023 -- the Multi Domain Bilingual Parallel Corpus of Sanskrit to Hindi for Machine Translation	The data article presents the large bilingual parallel corpus of low-resourced language pair Sanskrit-Hindi, named SAHAAYAK 2023. The corpus contains total of 1.5M sentence pairs between Sanskrit and Hindi. To make the universal usability of the corpus and to make it balanced, data from multiple domain has been incorporated into the corpus that includes, News, Daily conversations, Politics, History, Sport, and Ancient Indian Literature. The multifaceted approach has been adapted to make a sizable multi-domain corpus of low-resourced languages like Sanskrit. Our development approach is spanned from creating a small hand-crafted dataset to applying a wide range of mining, cleaning, and verification. We have used the three-fold process of mining: mining from machine-readable sources, mining from non-machine readable sources, and collation from existing corpora sources. Post mining, the dedicated pipeline for normalization, alignment, and corpus cleaning is developed and applied to the corpus to make it ready to use on machine translation algorithms.	['Jitendra Nasariwala', 'Vishvajitsinh Bakrola']	2023-06-27	null	null	null	null	['machine-translation']	['natural-language-processing']	[ 1.41454324e-01 -1.46702990e-01 1.80832040e-03 -4.28106815e-01 -1.16163957e+00 -9.68347311e-01 8.16527188e-01 1.20050497e-01 -5.63569546e-01 1.14313233e+00 6.67032719e-01 -4.39535052e-01 4.46924008e-02 -4.11939830e-01 -4.68834341e-01 -2.17245087e-01 4.03421819e-01 1.04509795e+00 3.39885317e-02 -7.24236250e-01 6.18584514e-01 1.00029528e-01 -8.70573163e-01 3.87482256e-01 8.00702095e-01 1.10763781e-01 5.12162209e-01 4.20036525e-01 -3.37611556e-01 6.15995586e-01 -4.50430632e-01 -6.84749007e-01 6.59537435e-01 -6.32337272e-01 -1.31670725e+00 -1.90367848e-01 8.63141045e-02 1.19619317e-01 3.24583650e-02 8.10216844e-01 6.23285472e-01 -1.86008155e-01 1.73257023e-01 -7.97489941e-01 -7.14934349e-01 1.07486904e+00 -7.15382814e-01 3.76398087e-01 5.52735031e-01 -2.50828981e-01 9.08778429e-01 -8.99307251e-01 1.21683490e+00 9.49300647e-01 4.40320104e-01 2.17898369e-01 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699861ef-fe95-447b-ac48-2dda2678fe26	contrastive-label-disambiguation-for-partial	null	null	https://openreview.net/forum?id=EhYjZy6e1gJ	https://openreview.net/pdf?id=EhYjZy6e1gJ	Contrastive Label Disambiguation for Partial Label Learning	Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. In this work, we bridge the gap by addressing two key research challenges in PLL---representation learning and label disambiguation---in one coherent framework. Specifically, our proposed framework PiCO consists of a contrastive learning module along with a novel class prototype-based label disambiguation algorithm. PiCO produces closely aligned representations for examples from the same classes and facilitates label disambiguation. Theoretically, we show that these two components are mutually beneficial, and can be rigorously justified from an expectation-maximization (EM) algorithm perspective. Extensive experiments demonstrate that PiCO significantly outperforms the current state-of-the-art approaches in PLL and even achieves comparable results to fully supervised learning.	['Junbo Zhao', 'Gang Chen', 'Gang Niu', 'Lei Feng', 'Sharon Li', 'Ruixuan Xiao', 'Haobo Wang']	2021-09-29	null	null	null	iclr-2022-4	['partial-label-learning', 'pico']	['methodology', 'natural-language-processing']	[ 6.16738856e-01 3.47415924e-01 -7.27793992e-01 -4.27610666e-01 -1.00226986e+00 -5.65276086e-01 5.91238022e-01 4.68326002e-01 -3.49324852e-01 8.30488861e-01 -1.89440444e-01 -1.34066911e-02 -2.31262863e-01 -4.40333456e-01 -2.33305633e-01 -7.75173724e-01 2.04416201e-01 8.99109364e-01 -3.32134068e-02 1.12187825e-01 1.47630155e-01 2.96624571e-01 -1.78975916e+00 1.11419223e-01 8.51791859e-01 1.11724341e+00 1.54413238e-01 -9.49058402e-03 -5.34642994e-01 9.78302479e-01 -4.03768152e-01 -3.10088754e-01 1.80430695e-01 -1.40769675e-01 -1.11593997e+00 3.39609057e-01 5.87238252e-01 3.29096437e-01 2.27398753e-01 1.10902750e+00 5.24173141e-01 8.95399675e-02 9.52789962e-01 -1.49501550e+00 -2.50316232e-01 5.78892767e-01 -6.24990284e-01 -2.50498503e-01 2.53806084e-01 -4.61945027e-01 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0fe199b0-82c3-483a-a933-a8945ff7cd0a	a-data-centric-solution-to-nonhomogeneous	2304.07874	null	https://arxiv.org/abs/2304.07874v2	https://arxiv.org/pdf/2304.07874v2.pdf	A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer	Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset introduced by NTIRE challenges. One of the reasons for such failures is that non-homogeneous haze does not obey one of the assumptions that is required for modeling homogeneous haze. In addition, a large number of pairs of non-homogeneous hazy image and the clean counterpart is required using traditional end-to-end training approaches, while NH-HAZE23 dataset is of limited quantities. Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing approach that reduces the distribution gaps between the target dataset and the augmented one. This finding indeed aligns with the essence of data-centric AI. With a novel network architecture and a principled data-preprocessing approach that systematically enhances data quality, we present an innovative dehazing method. Specifically, we apply RGB-channel-wise transformations on the augmented datasets, and incorporate the state-of-the-art transformers as the backbone in the two-branch framework. We conduct extensive experiments and ablation study to demonstrate the effectiveness of our proposed method.	['Jun Chen', 'Zijun Wu', 'Liangyan Li', 'Huan Liu', 'Yangyi Liu']	2023-04-16	null	null	null	null	['image-dehazing']	['computer-vision']	[ 3.30584764e-01 -7.70732835e-02 3.05903614e-01 -3.14973503e-01 -5.68442941e-01 -1.45334512e-01 4.58523482e-01 -2.66946882e-01 -4.50774729e-01 5.87615192e-01 2.49600355e-02 -1.51674673e-01 -3.48542184e-01 -1.10571063e+00 -7.99040854e-01 -1.30200219e+00 2.26613939e-01 5.54773919e-02 1.98996425e-01 -6.40198290e-01 4.21427377e-02 3.59404534e-01 -1.91419029e+00 8.96390527e-02 1.24530506e+00 1.17837811e+00 1.45399332e-01 4.68678206e-01 4.18444462e-02 6.60325706e-01 -8.14986646e-01 -3.45924646e-01 8.63295972e-01 -6.16884410e-01 -4.10377085e-01 5.92422225e-02 9.43646431e-01 -3.63461554e-01 -4.62403148e-01 1.09230447e+00 4.90800381e-01 2.26361960e-01 6.28220320e-01 -1.42894340e+00 -9.25602019e-01 1.68415174e-01 -6.54399097e-01 3.71312834e-02 -2.92619377e-01 2.46003166e-01 6.66092396e-01 -8.57303917e-01 1.86126649e-01 7.41887927e-01 5.05325317e-01 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c4074bc9-7ea8-4ea8-a92e-36c0a82c0b62	explainable-fmri-based-brain-decoding-via	2210.05713	null	https://arxiv.org/abs/2210.05713v1	https://arxiv.org/pdf/2210.05713v1.pdf	Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network	Brain decoding, aiming to identify the brain states using neural activity, is important for cognitive neuroscience and neural engineering. However, existing machine learning methods for fMRI-based brain decoding either suffer from low classification performance or poor explainability. Here, we address this issue by proposing a biologically inspired architecture, Spatial Temporal-pyramid Graph Convolutional Network (STpGCN), to capture the spatial-temporal graph representation of functional brain activities. By designing multi-scale spatial-temporal pathways and bottom-up pathways that mimic the information process and temporal integration in the brain, STpGCN is capable of explicitly utilizing the multi-scale temporal dependency of brain activities via graph, thereby achieving high brain decoding performance. Additionally, we propose a sensitivity analysis method called BrainNetX to better explain the decoding results by automatically annotating task-related brain regions from the brain-network standpoint. We conduct extensive experiments on fMRI data under 23 cognitive tasks from Human Connectome Project (HCP) S1200. The results show that STpGCN significantly improves brain decoding performance compared to competing baseline models; BrainNetX successfully annotates task-relevant brain regions. Post hoc analysis based on these regions further validates that the hierarchical structure in STpGCN significantly contributes to the explainability, robustness and generalization of the model. Our methods not only provide insights into information representation in the brain under multiple cognitive tasks but also indicate a bright future for fMRI-based brain decoding.	['Quanying Liu', 'Mo Wang', 'Zhichao Liang', 'Youzhi Qu', 'Ziyuan Ye']	2022-10-08	null	null	null	null	['brain-decoding', 'brain-decoding']	['medical', 'miscellaneous']	[ 3.43532920e-01 2.56832123e-01 1.44093603e-01 -4.42697257e-01 1.18412107e-01 -3.12192708e-01 5.45760572e-01 -7.29580820e-02 -1.97102502e-02 5.59359312e-01 5.77546835e-01 -3.55701774e-01 -4.92481440e-01 -6.34539902e-01 -6.95481300e-01 -3.77334893e-01 -4.08314288e-01 1.08462483e-01 -2.35117972e-02 -1.25106052e-01 2.77681470e-01 4.88470405e-01 -1.11772799e+00 5.94125867e-01 1.05290937e+00 8.13228846e-01 3.78123075e-01 2.84732968e-01 -7.92505965e-03 5.20578980e-01 -2.48368263e-01 -1.38209924e-01 3.92509997e-02 -8.12015355e-01 -8.25751960e-01 -1.67078033e-01 -5.13892695e-02 1.07617274e-01 -5.53747296e-01 1.09468210e+00 4.97350484e-01 4.42217439e-02 6.81235611e-01 -1.06816590e+00 -7.39785850e-01 6.72410965e-01 -3.12816381e-01 6.82004869e-01 2.10216835e-01 3.95379424e-01 8.94876182e-01 -5.47920823e-01 5.18758237e-01 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8a41afaa-b344-4c17-88e9-13bbb8d2e67f	heartbeat-heart-beat-estimation-through	1810.08554	null	http://arxiv.org/abs/1810.08554v2	http://arxiv.org/pdf/1810.08554v2.pdf	HeartBEAT: Heart Beat Estimation through Adaptive Tracking	In this paper, we propose an algorithm denoted as HeartBEAT that tracks heart rate from wrist-type photoplethysmography (PPG) signals and simultaneously recorded three-axis acceleration data. HeartBEAT contains three major parts: spectrum estimation of PPG signals and acceleration data, elimination of motion artifacts in PPG signals using recursive least Square (RLS) adaptive filters, and auxiliary heuristics. We tested HeartBEAT on the 22 datasets provided in the 2015 IEEE Signal Processing Cup. The first ten datasets were recorded from subjects performing forearm and upper-arm exercises, jumping, or pushing-up. The last twelve datasets were recorded from subjects running on tread mills. The experimental results were compared to the ground truth heart rate, which comes from simultaneously recorded electrocardiogram (ECG) signals. Compared to state-of-the-art algorithms, HeartBEAT not only produces comparable Pearson's correlation and mean absolute error, but also higher Spearman's rho and Kendall's tau.	['Ghassan AlRegib', 'Dogancan Temel', 'Huijie Pan']	2018-10-19	null	null	null	null	['photoplethysmography-ppg']	['medical']	[ 3.51584285e-01 -1.52877467e-02 -9.32956953e-03 -1.65248722e-01 -4.47399348e-01 -3.43538672e-01 -2.03481525e-01 -3.44350278e-01 -3.66326183e-01 7.75391459e-01 1.96928665e-01 -7.75590912e-02 -2.84276038e-01 -2.42250219e-01 -1.44605646e-02 -4.75318015e-01 -7.02950239e-01 -1.68596506e-02 -1.73501790e-01 1.00801565e-01 2.94671059e-01 2.12491766e-01 -9.12755966e-01 -2.72698849e-01 6.54834747e-01 1.34964406e+00 -5.75399339e-01 1.13791013e+00 4.65743631e-01 5.33977509e-01 -7.21757591e-01 2.84567401e-02 4.48521107e-01 -9.77003574e-01 -4.30601805e-01 -1.82397723e-01 -5.13652712e-02 -1.09472267e-01 -2.24453181e-01 6.28531754e-01 1.10828316e+00 -1.78721040e-01 2.12070137e-01 -1.21996117e+00 3.06733279e-03 4.09386992e-01 -7.09645152e-01 4.39376712e-01 7.80468524e-01 2.97690392e-01 4.31029022e-01 -5.20165563e-01 3.90366971e-01 5.65001965e-01 1.54621315e+00 3.03129524e-01 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1407cc7c-36e5-4bd9-b175-3d560ee36df9	adaptive-multi-stage-density-ratio-estimation	2209.08739	null	https://arxiv.org/abs/2209.08739v1	https://arxiv.org/pdf/2209.08739v1.pdf	Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model	This paper studies the fundamental problem of learning energy-based model (EBM) in the latent space of the generator model. Learning such prior model typically requires running costly Markov Chain Monte Carlo (MCMC). Instead, we propose to use noise contrastive estimation (NCE) to discriminatively learn the EBM through density ratio estimation between the latent prior density and latent posterior density. However, the NCE typically fails to accurately estimate such density ratio given large gap between two densities. To effectively tackle this issue and learn more expressive prior models, we develop the adaptive multi-stage density ratio estimation which breaks the estimation into multiple stages and learn different stages of density ratio sequentially and adaptively. The latent prior model can be gradually learned using ratio estimated in previous stage so that the final latent space EBM prior can be naturally formed by product of ratios in different stages. The proposed method enables informative and much sharper prior than existing baselines, and can be trained efficiently. Our experiments demonstrate strong performances in image generation and reconstruction as well as anomaly detection.	['Tian Han', 'Zhisheng Xiao']	2022-09-19	null	null	null	null	['density-ratio-estimation']	['methodology']	[ 2.25720838e-01 -2.37699285e-01 -1.02943033e-01 -3.11900437e-01 -1.23995078e+00 -2.47427821e-01 9.15352106e-01 -2.44409487e-01 -4.11583990e-01 6.78075969e-01 2.27481022e-01 -1.12921976e-01 1.67051539e-01 -7.95304716e-01 -8.76739919e-01 -8.69180977e-01 1.55373439e-01 7.73149371e-01 2.49185175e-01 3.34699363e-01 2.27221921e-01 3.48447531e-01 -1.22701025e+00 -1.91599861e-01 9.30526257e-01 9.10712421e-01 5.78550160e-01 8.50077212e-01 -2.52805561e-01 9.65443194e-01 -5.26240587e-01 -1.55856058e-01 1.15782641e-01 -7.78795421e-01 -5.33136249e-01 1.10731043e-01 2.03733832e-01 -6.54468238e-01 -3.16327631e-01 1.32671368e+00 4.40997630e-01 3.22870493e-01 1.44947124e+00 -1.09872854e+00 -6.09637141e-01 3.59690577e-01 -1.14683795e+00 3.78775209e-01 8.43248889e-02 6.04443848e-02 7.56803513e-01 -1.14461219e+00 2.56557167e-01 1.45639396e+00 5.30807614e-01 4.19365764e-01 -1.33687878e+00 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2.26224080e-01 3.41258258e-01 -7.47875750e-01 -3.75892669e-01 8.29510838e-02]	[7.079527854919434, 3.8064730167388916]
b4060cd3-a811-458f-afb0-270015c85451	a-hybrid-statistical-machine-learning	2212.02255	null	https://arxiv.org/abs/2212.02255v1	https://arxiv.org/pdf/2212.02255v1.pdf	A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Study	We apply classical statistical methods in conjunction with the state-of-the-art machine learning techniques to develop a hybrid interpretable model to analyse 454,897 online customers' behavior for a particular product category at the largest online retailer in China, that is JD. While most mere machine learning methods are plagued by the lack of interpretability in practice, our novel hybrid approach will address this practical issue by generating explainable output. This analysis involves identifying what features and characteristics have the most significant impact on customers' purchase behavior, thereby enabling us to predict future sales with a high level of accuracy, and identify the most impactful variables. Our results reveal that customers' product choice is insensitive to the promised delivery time, but this factor significantly impacts customers' order quantity. We also show that the effectiveness of various discounting methods depends on the specific product and the discount size. We identify product classes for which certain discounting approaches are more effective and provide recommendations on better use of different discounting tools. Customers' choice behavior across different product classes is mostly driven by price, and to a lesser extent, by customer demographics. The former finding asks for exercising care in deciding when and how much discount should be offered, whereas the latter identifies opportunities for personalized ads and targeted marketing. Further, to curb customers' batch ordering behavior and avoid the undesirable Bullwhip effect, JD should improve its logistics to ensure faster delivery of orders.	['Foaad Iravani', 'Ali Eshragh', 'Kasun Bandara', 'Saed Alizami']	2022-12-01	null	null	null	null	['marketing']	['miscellaneous']	[-3.15176815e-01 -6.68688938e-02 -8.15097213e-01 -7.73698926e-01 -4.00407195e-01 -6.21653259e-01 2.41786823e-01 6.16685569e-01 -2.96431720e-01 1.85985744e-01 3.25033724e-01 -6.49554968e-01 -4.60357338e-01 -8.70143414e-01 -4.47137594e-01 -7.17248440e-01 -5.70415109e-02 5.81156850e-01 -4.13336426e-01 -5.49183607e-01 5.44910252e-01 4.64839429e-01 -1.32359767e+00 2.37343311e-01 9.44674492e-01 1.17002916e+00 1.88635647e-01 1.29064724e-01 -1.32543504e-01 5.71031451e-01 -5.10846496e-01 -7.46280551e-01 4.20611262e-01 -2.66367733e-01 -4.89441957e-03 2.78592974e-01 -3.06569666e-01 -7.09293067e-01 1.54649273e-01 6.12503052e-01 6.17015846e-02 -1.88793868e-01 7.91049957e-01 -1.25682986e+00 -9.76968467e-01 1.01709950e+00 -3.29703718e-01 1.55665636e-01 1.54663116e-01 2.54217349e-02 1.46923959e+00 -3.17976326e-01 3.25708419e-01 1.15336430e+00 4.25381690e-01 9.79908481e-02 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fa1bbd51-d3fa-418e-b5dc-e87b0f651a8a	inductive-biases-for-deep-learning-of-higher	2011.15091	null	https://arxiv.org/abs/2011.15091v4	https://arxiv.org/pdf/2011.15091v4.pdf	Inductive Biases for Deep Learning of Higher-Level Cognition	A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics). If that hypothesis was correct, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behavior of complex systems like brains, and substantial computation might be needed to simulate human-like intelligence. This hypothesis would suggest that studying the kind of inductive biases that humans and animals exploit could help both clarify these principles and provide inspiration for AI research and neuroscience theories. Deep learning already exploits several key inductive biases, and this work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities in terms of flexible out-of-distribution and systematic generalization, which is currently an area where a large gap exists between state-of-the-art machine learning and human intelligence.	['Yoshua Bengio', 'Anirudh Goyal']	2020-11-30	null	null	null	null	['systematic-generalization']	['reasoning']	[ 3.65642607e-02 4.10567284e-01 6.55806139e-02 -4.26009417e-01 6.42789841e-01 -5.42460442e-01 8.73284459e-01 3.44196916e-01 -5.63049376e-01 6.62570894e-01 -8.48670527e-02 -5.11341453e-01 -4.01789874e-01 -1.05773640e+00 -4.69908208e-01 -5.05497277e-01 -2.71000981e-01 6.16369724e-01 3.39351624e-01 -6.00743234e-01 7.38396645e-01 6.12003624e-01 -1.88052726e+00 3.32079351e-01 1.10862505e+00 6.46246731e-01 5.09769678e-01 4.67361450e-01 -2.07397431e-01 9.70608115e-01 -3.77389491e-01 -4.45838630e-01 4.72361408e-02 -6.23138011e-01 -8.94780159e-01 -3.73132616e-01 3.93730886e-02 2.81773973e-02 3.24463956e-02 1.02162158e+00 1.50318652e-01 1.95328295e-01 7.00314939e-01 -8.48586798e-01 -8.78207505e-01 5.93285799e-01 -1.56640872e-01 2.23303482e-01 1.98714375e-01 3.55439633e-01 9.28033829e-01 -2.17045277e-01 3.21492970e-01 1.36977947e+00 5.52725196e-01 7.14916527e-01 -1.08432543e+00 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3d7b7d38-938b-4f45-88ac-aa2a8a642104	new-sqrt-n-consistent-numerically-stable	2302.08097	null	https://arxiv.org/abs/2302.08097v1	https://arxiv.org/pdf/2302.08097v1.pdf	New $\sqrt{n}$-consistent, numerically stable higher-order influence function estimators	Higher-Order Influence Functions (HOIFs) provide a unified theory for constructing rate-optimal estimators for a large class of low-dimensional (smooth) statistical functionals/parameters (and sometimes even infinite-dimensional functions) that arise in substantive fields including epidemiology, economics, and the social sciences. Since the introduction of HOIFs by Robins et al. (2008), they have been viewed mostly as a theoretical benchmark rather than a useful tool for statistical practice. Works aimed to flip the script are scant, but a few recent papers Liu et al. (2017, 2021b) make some partial progress. In this paper, we take a fresh attempt at achieving this goal by constructing new, numerically stable HOIF estimators (or sHOIF estimators for short with ``s'' standing for ``stable'') with provable statistical, numerical, and computational guarantees. This new class of sHOIF estimators (up to the 2nd order) was foreshadowed in synthetic experiments conducted by Liu et al. (2020a).	['Chang Li', 'Lin Liu']	2023-02-16	null	null	null	null	['epidemiology']	['medical']	[ 1.50856510e-01 1.77169248e-01 -5.14597178e-01 -1.42919749e-01 -8.38022113e-01 -4.20634955e-01 7.42313683e-01 2.46994883e-01 -5.51095188e-01 1.22524858e+00 1.80809513e-01 -3.01794589e-01 -4.15008426e-01 -5.80754161e-01 -6.63784266e-01 -9.71040964e-01 -5.47082841e-01 1.64523408e-01 1.46671310e-01 1.37008473e-01 2.71632433e-01 4.50722009e-01 -1.57987952e+00 -5.09847939e-01 1.00211287e+00 6.64324939e-01 -2.06972718e-01 5.65373182e-01 2.53169179e-01 2.23677561e-01 -1.49450511e-01 -6.31035566e-01 1.82356834e-01 -5.81866741e-01 -6.32395685e-01 -1.98669404e-01 1.72598064e-01 -3.96893471e-02 2.82149520e-02 1.18359482e+00 4.98926699e-01 9.35538113e-02 9.07716811e-01 -1.30121362e+00 -5.52523792e-01 6.76266849e-01 -6.74217641e-01 4.29020897e-02 1.10087164e-01 1.84932917e-01 1.06865656e+00 -8.88096988e-01 5.65813065e-01 1.17297685e+00 8.56147707e-01 5.49481452e-01 -1.13817322e+00 -5.46576858e-01 -1.43105626e-01 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87284432-fcd0-470d-aff7-b710588782d5	deepir-a-deep-semantics-driven-framework-for	1811.07793	null	https://arxiv.org/abs/1811.07793v3	https://arxiv.org/pdf/1811.07793v3.pdf	DeepIR: A Deep Semantics Driven Framework for Image Retargeting	We present \emph{Deep Image Retargeting} (\emph{DeepIR}), a coarse-to-fine framework for content-aware image retargeting. Our framework first constructs the semantic structure of input image with a deep convolutional neural network. Then a uniform re-sampling that suits for semantic structure preserving is devised to resize feature maps to target aspect ratio at each feature layer. The final retargeting result is generated by coarse-to-fine nearest neighbor field search and step-by-step nearest neighbor field fusion. We empirically demonstrate the effectiveness of our model with both qualitative and quantitative results on widely used RetargetMe dataset.	['Zhibo Chen', 'Jianxin Lin', 'Tiankuang Zhou']	2018-11-19	null	null	null	null	['image-retargeting']	['computer-vision']	[ 3.9855194e-01 2.4554588e-01 -2.0565777e-01 -5.1070559e-01 -8.7386918e-01 -7.8282112e-01 6.4252639e-01 3.7249871e-02 -5.5402535e-01 6.0099012e-01 7.8794557e-01 1.6252303e-01 -1.6197388e-01 -9.3626338e-01 -7.1352410e-01 -3.4156826e-01 6.2736863e-01 -1.4382219e-01 4.3176609e-01 -4.5399922e-01 5.7745838e-01 7.0686799e-01 -1.3633299e+00 6.6334689e-01 3.7861329e-01 9.1044813e-01 2.4761398e-01 5.1632899e-01 -6.9719769e-02 7.2111380e-01 -4.0854385e-01 -3.5026670e-01 5.4710084e-01 -2.5583383e-01 -1.1190535e+00 3.1824872e-02 6.9604635e-01 -3.2232124e-01 -7.4544585e-01 1.3775030e+00 7.8416562e-01 5.5607855e-01 5.7228959e-01 -7.5611681e-01 -1.4467919e+00 7.9962933e-01 -8.4379411e-01 7.0656341e-01 2.3221263e-01 1.3927005e-01 7.7644068e-01 -1.1224040e+00 9.3543798e-01 1.2138792e+00 5.7255656e-01 2.6118231e-01 -1.3431536e+00 -6.6212565e-01 1.5215178e-01 1.9361487e-01 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8e3ef591-97e0-4719-8516-c73617b9eb7e	ra-unet-a-hybrid-deep-attention-aware-network	1811.01328	null	http://arxiv.org/abs/1811.01328v1	http://arxiv.org/pdf/1811.01328v1.pdf	RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans	Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, 3D networks have some drawbacks due to their high cost on computational resources. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, named RA-UNet, to precisely extract the liver volume of interests (VOI) and segment tumors from the liver VOI. The proposed network has a basic architecture as a 3D U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention modules are stacked so that the attention-aware features change adaptively as the network goes "very deep" and this is made possible by residual learning. This is the first work that an attention residual mechanism is used to process medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and the 3DIRCADb dataset. The results show that our architecture outperforms other state-of-the-art methods. We also extend our RA-UNet to brain tumor segmentation on the BraTS2018 and BraTS2017 datasets, and the results indicate that RA-UNet achieves good performance on a brain tumor segmentation task as well.	['Qiangguo Jin', 'Zhaopeng Meng', 'Leyi Wei', 'Ran Su', 'Changming Sun']	2018-11-04	null	null	null	null	['deep-attention', 'deep-attention']	['computer-vision', 'natural-language-processing']	[-2.26472139e-01 3.52122545e-01 -3.26247305e-01 -3.68006796e-01 -6.50180042e-01 1.01583009e-03 3.72740328e-01 -3.60725485e-02 -3.95539194e-01 3.17841113e-01 3.54265541e-01 -1.98069438e-01 1.43689722e-01 -6.00628674e-01 -4.78518575e-01 -8.45156312e-01 -2.78120309e-01 7.67537892e-01 4.23976302e-01 5.98269068e-02 -1.43856227e-01 6.67081535e-01 -7.55004048e-01 1.09593451e-01 1.00915897e+00 1.13838375e+00 3.54619712e-01 4.49761719e-01 -4.56985354e-01 8.03390861e-01 -2.34374449e-01 1.45975038e-01 3.08454454e-01 -4.05493438e-01 -1.19168591e+00 3.46148610e-01 8.01882744e-02 -3.10459614e-01 -3.28660995e-01 1.05079794e+00 5.34698904e-01 -4.15842891e-01 8.12592030e-01 -9.65143561e-01 -6.63850188e-01 7.96133041e-01 -7.66815066e-01 4.56506521e-01 -1.71111271e-01 1.59171477e-01 5.71195006e-01 -8.64374816e-01 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f53a637f-82ca-4ad9-8699-30666fb78115	semantic-vad-low-latency-voice-activity	2305.1245	null	https://arxiv.org/abs/2305.12450v1	https://arxiv.org/pdf/2305.12450v1.pdf	Semantic VAD: Low-Latency Voice Activity Detection for Speech Interaction	For speech interaction, voice activity detection (VAD) is often used as a front-end. However, traditional VAD algorithms usually need to wait for a continuous tail silence to reach a preset maximum duration before segmentation, resulting in a large latency that affects user experience. In this paper, we propose a novel semantic VAD for low-latency segmentation. Different from existing methods, a frame-level punctuation prediction task is added to the semantic VAD, and the artificial endpoint is included in the classification category in addition to the often-used speech presence and absence. To enhance the semantic information of the model, we also incorporate an automatic speech recognition (ASR) related semantic loss. Evaluations on an internal dataset show that the proposed method can reduce the average latency by 53.3% without significant deterioration of character error rate in the back-end ASR compared to the traditional VAD approach.	['Li-Rong Dai', 'Jie Zhang', 'Shiliang Zhang', 'Qian Chen', 'Lingyun Zuo', 'Yuchun Shu', 'Mohan Shi']	2023-05-21	null	null	null	null	['activity-detection']	['computer-vision']	[ 3.57233942e-01 -1.59728050e-01 1.53453082e-01 -4.21391964e-01 -8.36001039e-01 -5.82547486e-01 2.73936801e-02 2.36026451e-01 -4.86892104e-01 3.85399193e-01 9.41592678e-02 -6.95888996e-01 4.53325689e-01 -3.31969708e-01 -3.02393138e-01 -5.36742985e-01 5.40247917e-01 1.95065483e-01 7.35681891e-01 1.47005513e-01 7.48191029e-02 4.28032458e-01 -1.46402597e+00 2.88162053e-01 1.10540605e+00 1.16734552e+00 7.08132863e-01 7.38466859e-01 -6.44314051e-01 4.11390990e-01 -1.22681665e+00 1.29646286e-01 -4.37514707e-02 -5.98601937e-01 -5.40678203e-01 2.04811350e-01 -5.36291525e-02 -3.57952356e-01 -1.23277925e-01 9.16993856e-01 8.39072824e-01 3.23342174e-01 2.88857639e-01 -1.00393474e+00 3.47215503e-01 6.10238135e-01 -3.32356215e-01 2.56457061e-01 4.59628284e-01 1.35382608e-01 6.94859862e-01 -7.20538855e-01 2.98029453e-01 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6.97951913e-01 -2.93744236e-01 -1.84816226e-01 -1.07475698e+00 -4.57161456e-01 1.04490623e-01 5.05752921e-01 -3.40400368e-01 -6.45441592e-01 -6.46349937e-02]	[14.687544822692871, 6.529976844787598]
618d25b3-e5cb-4841-998e-562e8942fe0c	dynamic-mlp-for-mri-reconstruction	2301.08868	null	https://arxiv.org/abs/2301.08868v2	https://arxiv.org/pdf/2301.08868v2.pdf	Computationally Efficient 3D MRI Reconstruction with Adaptive MLP	Compared with 2D MRI, 3D MRI provides superior volumetric spatial resolution and signal-to-noise ratio. However, it is more challenging to reconstruct 3D MRI images. Current methods are mainly based on convolutional neural networks (CNN) with small kernels, which are difficult to scale up to have sufficient fitting power for 3D MRI reconstruction due to the large image size and GPU memory constraint. Furthermore, MRI reconstruction is a deconvolution problem, which demands long-distance information that is difficult to capture by CNNs with small convolution kernels. The multi-layer perceptron (MLP) can model such long-distance information, but it requires a fixed input size. In this paper, we proposed Recon3DMLP, a hybrid of CNN modules with small kernels for low-frequency reconstruction and adaptive MLP (dMLP) modules with large kernels to boost the high-frequency reconstruction, for 3D MRI reconstruction. We further utilized the circular shift operation based on MRI physics such that dMLP accepts arbitrary image size and can extract global information from the entire FOV. We also propose a GPU memory efficient data fidelity module that can reduce $>$50$\%$ memory. We compared Recon3DMLP with other CNN-based models on a high-resolution (HR) 3D MRI dataset. Recon3DMLP improves HR 3D reconstruction and outperforms several existing CNN-based models under similar GPU memory consumption, which demonstrates that Recon3DMLP is a practical solution for HR 3D MRI reconstruction.	['Chi Zhang', 'Eric Z. Chen', 'Shanhui Sun', 'Terrence Chen', 'Yikang Liu', 'Xiao Chen']	2023-01-21	null	null	null	null	['mri-reconstruction']	['computer-vision']	[-8.06633160e-02 -1.51467845e-01 -2.33887248e-02 -1.95688367e-01 -6.52002692e-01 1.11469679e-01 -2.66559273e-02 7.80044720e-02 -6.10558093e-01 3.21702272e-01 2.40760222e-01 -3.36988658e-01 -2.28173956e-01 -1.04578698e+00 -7.76947021e-01 -8.46034110e-01 -2.38758087e-01 1.61956683e-01 5.32945991e-01 1.60242036e-01 -8.19372535e-02 9.17421043e-01 -1.08936381e+00 3.43760878e-01 6.17539048e-01 1.11739945e+00 7.64818132e-01 5.22731841e-01 1.03117544e-02 7.71410823e-01 -1.01198569e-01 2.10176766e-01 7.19137862e-02 -3.30525711e-02 -7.41137564e-01 -2.65709579e-01 -1.80893741e-03 -7.00163901e-01 -8.19092929e-01 8.33963394e-01 1.03932106e+00 -2.06974987e-03 3.97756994e-01 -4.88451600e-01 -6.32328629e-01 4.41123366e-01 -7.88944185e-01 7.26226807e-01 -1.06783576e-01 1.42938823e-01 -6.55333251e-02 -8.69876623e-01 3.65783274e-01 7.17147052e-01 1.04109073e+00 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16c96ab3-005b-424d-b0c7-08d2ab65de47	synthetic-data-augmentation-using-gan-for-1	2212.09317	null	https://arxiv.org/abs/2212.09317v1	https://arxiv.org/pdf/2212.09317v1.pdf	Synthetic Data Augmentation Using GAN For Improved Automated Visual Inspection	Quality control is a crucial activity performed by manufacturing companies to ensure their products conform to the requirements and specifications. The introduction of artificial intelligence models enables to automate the visual quality inspection, speeding up the inspection process and ensuring all products are evaluated under the same criteria. In this research, we compare supervised and unsupervised defect detection techniques and explore data augmentation techniques to mitigate the data imbalance in the context of automated visual inspection. Furthermore, we use Generative Adversarial Networks for data augmentation to enhance the classifiers' discriminative performance. Our results show that state-of-the-art unsupervised defect detection does not match the performance of supervised models but can be used to reduce the labeling workload by more than 50%. Furthermore, the best classification performance was achieved considering GAN-based data generation with AUC ROC scores equal to or higher than 0,9898, even when increasing the dataset imbalance by leaving only 25\% of the images denoting defective products. We performed the research with real-world data provided by Philips Consumer Lifestyle BV.	['Dunja Mladenić', 'Blaž Fortuna', 'Erik Koehorst', 'Spyros Theodoropoulos', 'Patrik Zajec', 'Jože M. Rožanec']	2022-12-19	null	null	null	null	['defect-detection']	['computer-vision']	[ 4.44901735e-01 4.98480290e-01 1.99065119e-01 -4.29246247e-01 -2.71255255e-01 -3.96587312e-01 1.99898139e-01 5.67529678e-01 -9.97927114e-02 4.13448691e-01 -2.82483369e-01 1.88264437e-03 -2.10628688e-01 -9.36652362e-01 -5.64054608e-01 -6.00851238e-01 2.41336510e-01 4.44835454e-01 -6.35613501e-02 -3.60524608e-03 2.54384726e-01 6.32559121e-01 -1.93547916e+00 4.30571705e-01 1.25724685e+00 1.10155904e+00 1.78023484e-02 5.35004616e-01 2.35400326e-03 6.51237667e-01 -1.01545680e+00 -3.07539970e-01 3.49128872e-01 -4.00166631e-01 -5.95797181e-01 7.80044198e-01 1.27915189e-01 -7.45160729e-02 3.39989126e-01 1.21186483e+00 1.95973277e-01 -2.61891842e-01 6.80179477e-01 -1.36445403e+00 -7.17787445e-01 9.52678770e-02 -4.36876386e-01 -1.88269198e-01 2.59118170e-01 3.88282180e-01 8.04890275e-01 -3.72975856e-01 4.94681537e-01 8.00725579e-01 3.66717190e-01 2.82623500e-01 -1.38272822e+00 -1.70391589e-01 -1.83284029e-01 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9b8d4181-0eee-4ace-8b1b-547adb12a24e	greenkgc-a-lightweight-knowledge-graph	2208.09137	null	https://arxiv.org/abs/2208.09137v1	https://arxiv.org/pdf/2208.09137v1.pdf	GreenKGC: A Lightweight Knowledge Graph Completion Method	Knowledge graph completion (KGC) aims to discover missing relationships between entities in knowledge graphs (KGs). Most prior KGC work focuses on learning representations for entities and relations. Yet, a higher-dimensional embedding space is usually required for a better reasoning capability, which leads to a larger model size and hinders applicability to real-world problems (e.g., large-scale KGs or mobile/edge computing). A lightweight modularized KGC solution, called GreenKGC, is proposed in this work to address this issue. GreenKGC consists of three modules: 1) representation learning, 2) feature pruning, and 3) decision learning. In Module 1, we leverage existing KG embedding models to learn high-dimensional representations for entities and relations. In Module 2, the KG is partitioned into several relation groups followed by a feature pruning process to find the most discriminant features for each relation group. Finally, a classifier is assigned to each relation group to cope with low-dimensional triple features for KGC tasks in Module 3. We evaluate the performance of GreenKGC on four widely used link prediction datasets and observe that GreenKGC can achieve comparable or even better performance against original high-dimensional embeddings with a much smaller model size. Furthermore, we experiment on two triple classification datasets to demonstrate that the same methodology can generalize to more tasks.	['C. -C. Jay Kuo', 'Bin Wang', 'Xiou Ge', 'Yun-Cheng Wang']	2022-08-19	null	null	null	null	['triple-classification']	['graphs']	[-1.72290921e-01 3.98731083e-01 -5.35611749e-01 -7.76917636e-02 -3.08329195e-01 -2.66894370e-01 3.65044564e-01 6.71417773e-01 -7.76639357e-02 4.91258889e-01 1.57290995e-01 -4.63865370e-01 -5.92762828e-01 -1.24495435e+00 -4.64810669e-01 -5.27642787e-01 -5.29555857e-01 4.49825257e-01 1.84507638e-01 -1.81448609e-01 -1.46283954e-01 3.68015528e-01 -1.33483803e+00 1.50943130e-01 1.04365849e+00 1.02222073e+00 -1.06032319e-01 2.46394724e-01 -1.83228403e-01 7.28026927e-01 -1.06471471e-01 -7.95310795e-01 2.04425603e-01 2.07304712e-02 -1.02311313e+00 -2.21214712e-01 -1.08810877e-02 6.66646883e-02 -4.72327620e-01 9.11535561e-01 2.22368315e-01 1.71129107e-01 7.86313772e-01 -1.53473771e+00 -6.65102243e-01 7.93238401e-01 -4.09143001e-01 -9.38871056e-02 3.77273202e-01 -3.94675702e-01 1.44028473e+00 -1.03038526e+00 5.85936189e-01 1.14896905e+00 7.19462037e-01 2.41649941e-01 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bef75037-e1f1-492b-9a16-70d37b27d089	wave-propagation-of-visual-stimuli-in-focus	2006.11035	null	https://arxiv.org/abs/2006.11035v1	https://arxiv.org/pdf/2006.11035v1.pdf	Wave Propagation of Visual Stimuli in Focus of Attention	Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to most relevant locations in the visual field. While current computational models keep improving their predictive ability thanks to the increasing availability of data, they still struggle approximating the effectiveness and efficiency exhibited by foveated animals. In this paper, we present a biologically-plausible computational model of focus of attention that exhibits spatiotemporal locality and that is very well-suited for parallel and distributed implementations. Attention emerges as a wave propagation process originated by visual stimuli corresponding to details and motion information. The resulting field obeys the principle of "inhibition of return" so as not to get stuck in potential holes. An accurate experimentation of the model shows that it achieves top level performance in scanpath prediction tasks. This can easily be understood at the light of a theoretical result that we establish in the paper, where we prove that as the velocity of wave propagation goes to infinity, the proposed model reduces to recently proposed state of the art gravitational models of focus of attention.	['Lapo Faggi', 'Marco Gori', 'Dario Zanca', 'Alessandro Betti', 'Stefano Melacci']	2020-06-19	null	null	null	null	['scanpath-prediction']	['computer-vision']	[ 2.17481971e-01 6.17871396e-02 1.27023518e-01 1.48814648e-01 2.92032301e-01 -3.62561345e-01 6.12030685e-01 3.20202410e-01 -5.49788237e-01 3.98112416e-01 3.42936993e-01 -3.53776366e-02 -3.97975534e-01 -8.16154957e-01 -7.12093949e-01 -7.10525036e-01 -2.95090288e-01 2.65666187e-01 7.18276978e-01 -3.79138857e-01 8.33390772e-01 7.25588024e-01 -1.95192015e+00 3.47353444e-02 5.47709644e-01 5.76167941e-01 6.34915471e-01 8.53426456e-01 3.33336860e-01 9.27842379e-01 -3.52093488e-01 8.62961486e-02 1.46876752e-01 -6.07439280e-01 -9.75209117e-01 -3.37546140e-01 2.62194991e-01 2.09156409e-01 -3.66789013e-01 9.56499457e-01 5.64799190e-01 4.59211528e-01 6.18051469e-01 -9.22609806e-01 -9.19296086e-01 4.69231308e-02 -5.13511062e-01 1.06176281e+00 2.49539807e-01 3.08375627e-01 9.01042163e-01 -9.03463781e-01 1.01416767e+00 9.02404249e-01 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544d65ab-214f-4a04-b35e-8d7c62cd3da9	wsgat-weighted-and-signed-graph-attention	2109.11519	null	https://arxiv.org/abs/2109.11519v1	https://arxiv.org/pdf/2109.11519v1.pdf	wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction	Graph Neural Networks (GNNs) have been widely used to learn representations on graphs and tackle many real-world problems from a wide range of domains. In this paper we propose wsGAT, an extension of the Graph Attention Network (GAT) layers, meant to address the lack of GNNs that can handle graphs with signed and weighted links, which are ubiquitous, for instance, in trust and correlation networks. We first evaluate the performance of our proposal by comparing against GCNII in the weighed link prediction task, and against SGCN in the link sign prediction task. After that, we combine the two tasks and show their performance on predicting the signed weight of links, and their existence. Our results on real-world networks show that models with wsGAT layers outperform the ones with GCNII and SGCN layers, and that there is no loss in performance when signed weights are predicted.	['Giuseppe Mangioni', 'Marco Grassia']	2021-09-21	null	null	null	null	['link-sign-prediction']	['graphs']	[-1.33093372e-01 5.30782104e-01 -3.39752585e-01 -2.51423836e-01 3.58032823e-01 -2.39118606e-01 7.97471642e-01 2.47660220e-01 -1.93279326e-01 6.98504806e-01 1.63104400e-01 -4.52019006e-01 -4.55787748e-01 -1.14338505e+00 -5.90326965e-01 -3.04293722e-01 -6.65138543e-01 6.13449872e-01 6.19389236e-01 -5.00129580e-01 -2.12861553e-01 4.81972128e-01 -9.91852999e-01 1.09301113e-01 7.26723850e-01 1.00119472e+00 -3.05862248e-01 4.94930595e-01 -8.33488107e-02 1.02566183e+00 -3.27229679e-01 -1.03581321e+00 1.68653682e-01 -1.96812034e-01 -7.80160725e-01 -3.72143805e-01 4.77699757e-01 -7.56731704e-02 -6.45274282e-01 1.07270479e+00 2.99168080e-01 -7.02965334e-02 6.86564267e-01 -1.69296217e+00 -9.05879498e-01 1.25726640e+00 -4.97679204e-01 2.20754147e-01 2.92799741e-01 -7.30244219e-02 1.74806738e+00 -5.56061327e-01 8.19001436e-01 1.48442733e+00 1.09773600e+00 3.29909056e-01 -1.13783848e+00 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e165915e-65aa-435b-a6ac-6f12a665f1ff	a-self-paced-regularization-framework-for-1	1804.07759	null	http://arxiv.org/abs/1804.07759v2	http://arxiv.org/pdf/1804.07759v2.pdf	A Self-paced Regularization Framework for Partial-Label Learning	Partial label learning (PLL) aims to solve the problem where each training instance is associated with a set of candidate labels, one of which is the correct label. Most PLL algorithms try to disambiguate the candidate label set, by either simply treating each candidate label equally or iteratively identifying the true label. Nonetheless, existing algorithms usually treat all labels and instances equally, and the complexities of both labels and instances are not taken into consideration during the learning stage. Inspired by the successful application of self-paced learning strategy in machine learning field, we integrate the self-paced regime into the partial label learning framework and propose a novel Self-Paced Partial-Label Learning (SP-PLL) algorithm, which could control the learning process to alleviate the problem by ranking the priorities of the training examples together with their candidate labels during each learning iteration. Extensive experiments and comparisons with other baseline methods demonstrate the effectiveness and robustness of the proposed method.	['Congyang Lang', 'Songhe Feng', 'Gengyu Lyu']	2018-04-20	null	null	null	null	['partial-label-learning']	['methodology']	[ 3.90188456e-01 8.84344131e-02 -7.24605322e-01 -5.06151915e-01 -6.11524880e-01 -6.06207430e-01 6.22265577e-01 3.93046856e-01 -6.26912057e-01 8.08192134e-01 -1.15294665e-01 -8.11409876e-02 -3.08878213e-01 -6.50504231e-01 -2.02140138e-01 -9.10438895e-01 1.56169966e-01 7.66600668e-01 1.65656894e-01 3.91239554e-01 3.03931594e-01 1.00769512e-01 -1.73701000e+00 8.69584903e-02 1.01887751e+00 9.49382901e-01 1.92825526e-01 1.59313411e-01 -5.26750922e-01 1.04421437e+00 -4.81848150e-01 -2.87188981e-02 2.54901826e-01 -4.67826396e-01 -9.09675121e-01 2.36190081e-01 3.52301419e-01 2.08633274e-01 2.05118924e-01 1.00999653e+00 4.51907724e-01 1.66316435e-01 6.50541425e-01 -1.39897144e+00 -8.16810429e-02 4.01921928e-01 -7.00986564e-01 2.17362225e-01 5.06881215e-02 -2.19763890e-01 1.16848671e+00 -8.50348830e-01 4.42032605e-01 1.10920024e+00 5.91276050e-01 5.52613258e-01 -1.24476242e+00 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5.11787117e-01 3.15081656e-01 -8.84800971e-01 -3.33929837e-01 1.75164312e-01]	[9.435420989990234, 4.004659652709961]
700f3962-d9f1-4373-9c8c-7c3d59f926cb	hdr-reconstruction-from-bracketed-exposures	2203.14825	null	https://arxiv.org/abs/2203.14825v1	https://arxiv.org/pdf/2203.14825v1.pdf	HDR Reconstruction from Bracketed Exposures and Events	Reconstruction of high-quality HDR images is at the core of modern computational photography. Significant progress has been made with multi-frame HDR reconstruction methods, producing high-resolution, rich and accurate color reconstructions with high-frequency details. However, they are still prone to fail in dynamic or largely over-exposed scenes, where frame misalignment often results in visible ghosting artifacts. Recent approaches attempt to alleviate this by utilizing an event-based camera (EBC), which measures only binary changes of illuminations. Despite their desirable high temporal resolution and dynamic range characteristics, such approaches have not outperformed traditional multi-frame reconstruction methods, mainly due to the lack of color information and low-resolution sensors. In this paper, we propose to leverage both bracketed LDR images and simultaneously captured events to obtain the best of both worlds: high-quality RGB information from bracketed LDRs and complementary high frequency and dynamic range information from events. We present a multi-modal end-to-end learning-based HDR imaging system that fuses bracketed images and event modalities in the feature domain using attention and multi-scale spatial alignment modules. We propose a novel event-to-image feature distillation module that learns to translate event features into the image-feature space with self-supervision. Our framework exploits the higher temporal resolution of events by sub-sampling the input event streams using a sliding window, enriching our combined feature representation. Our proposed approach surpasses SoTA multi-frame HDR reconstruction methods using synthetic and real events, with a 2dB and 1dB improvement in PSNR-L and PSNR-mu on the HdM HDR dataset, respectively.	['Eduardo Perez-Pellitero', 'Ales Leonardis', 'Sibi Catley-Chandar', 'Richard Shaw']	2022-03-28	null	null	null	null	['hdr-reconstruction']	['computer-vision']	[ 4.36206430e-01 -5.00606060e-01 1.70333177e-01 -3.93017262e-01 -1.05079544e+00 -1.89700171e-01 5.00596225e-01 -1.13726981e-01 -3.75902385e-01 8.01550329e-01 2.99011499e-01 3.94166201e-01 -2.47105211e-02 -8.06306481e-01 -9.32760596e-01 -8.06828499e-01 1.40972942e-01 -1.41506821e-01 3.36580336e-01 -1.92806363e-01 2.44687852e-02 5.10809898e-01 -2.07775354e+00 3.42611849e-01 6.85850084e-01 1.24108028e+00 4.68747646e-01 8.52745950e-01 2.64601171e-01 1.21396244e+00 -2.11599544e-01 -1.42305225e-01 4.31955785e-01 -5.42047918e-01 -3.72907162e-01 2.53568083e-01 6.44711912e-01 -9.14701939e-01 -8.77795041e-01 7.80525744e-01 6.77070618e-01 1.82642758e-01 1.76471826e-02 -9.93527055e-01 -5.68937659e-01 -2.05701411e-01 -7.27194726e-01 2.83432633e-01 7.32097387e-01 3.71686190e-01 8.87228429e-01 -8.69477510e-01 7.61080742e-01 1.10303521e+00 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-1.17902197e-01 -5.48991859e-01 -7.74027884e-01 7.84347653e-01 9.08364236e-01 -5.76938331e-01 -5.86360872e-01 4.46812510e-01]	[10.678972244262695, -2.1089367866516113]
7c192e3c-104c-4505-a7b4-29f46e69dc8d	conditional-generative-adversarial-nets	1411.1784	null	https://arxiv.org/abs/1411.1784v1	https://arxiv.org/pdf/1411.1784v1.pdf	Conditional Generative Adversarial Nets	Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.	['Mehdi Mirza', 'Simon Osindero']	2014-11-06	null	null	null	null	['human-action-generation']	['computer-vision']	[ 6.33001089e-01 8.05798292e-01 1.82189375e-01 -6.17966712e-01 -8.40683937e-01 -1.05257285e+00 1.07076502e+00 -5.44104874e-01 -2.70775914e-01 1.13464224e+00 8.20564851e-02 -2.56044298e-01 2.46151671e-01 -1.15411949e+00 -8.63825500e-01 -7.77367294e-01 1.57222256e-01 9.31713998e-01 -1.02729686e-01 -1.66241273e-01 -2.10051298e-01 5.39775312e-01 -1.16385901e+00 4.49383259e-01 3.87246668e-01 4.78648454e-01 -2.44311899e-01 8.10652435e-01 -6.67914078e-02 8.90442908e-01 -1.03137100e+00 -7.04552889e-01 2.61094689e-01 -6.05665088e-01 -9.51997697e-01 1.01350702e-01 2.90251940e-01 -2.71748245e-01 -3.74478638e-01 1.17748022e+00 4.58296955e-01 1.12399586e-01 1.18537712e+00 -1.49224424e+00 -1.16514671e+00 1.04260778e+00 3.56418461e-01 -2.65321314e-01 2.04474613e-01 -1.37939557e-01 6.66994452e-01 -6.17825925e-01 7.99995601e-01 1.23999608e+00 6.79826736e-01 1.33371270e+00 -1.50333762e+00 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ba967527-f9fe-4b56-812a-eb86a664658c	robust-automated-human-activity-recognition	1607.04867	null	http://arxiv.org/abs/1607.04867v2	http://arxiv.org/pdf/1607.04867v2.pdf	Robust Automated Human Activity Recognition and its Application to Sleep Research	Human Activity Recognition (HAR) is a powerful tool for understanding human behaviour. Applying HAR to wearable sensors can provide new insights by enriching the feature set in health studies, and enhance the personalisation and effectiveness of health, wellness, and fitness applications. Wearable devices provide an unobtrusive platform for user monitoring, and due to their increasing market penetration, feel intrinsic to the wearer. The integration of these devices in daily life provide a unique opportunity for understanding human health and wellbeing. This is referred to as the "quantified self" movement. The analyses of complex health behaviours such as sleep, traditionally require a time-consuming manual interpretation by experts. This manual work is necessary due to the erratic periodicity and persistent noisiness of human behaviour. In this paper, we present a robust automated human activity recognition algorithm, which we call RAHAR. We test our algorithm in the application area of sleep research by providing a novel framework for evaluating sleep quality and examining the correlation between the aforementioned and an individual's physical activity. Our results improve the state-of-the-art procedure in sleep research by 15 percent for area under ROC and by 30 percent for F1 score on average. However, application of RAHAR is not limited to sleep analysis and can be used for understanding other health problems such as obesity, diabetes, and cardiac diseases.	['Jaideep Srivastava', 'Luis Fernandes-Luque', 'Ferda Ofli', 'Aarti Sathyanarayana', 'Ahmed Elmagarmid', 'Shahrad Taheri', 'Teresa Arora']	2016-07-17	null	null	null	null	['sleep-quality-prediction']	['medical']	[ 3.11745703e-01 -2.09240645e-01 -5.62582016e-01 -9.13997293e-02 -2.06269830e-01 -2.03352839e-01 1.27821907e-01 4.04034197e-01 -4.52698588e-01 7.70518363e-01 3.67769361e-01 -1.55097455e-01 -2.27765247e-01 -6.95635319e-01 -4.76346351e-02 -8.27798605e-01 -7.62271211e-02 -1.48548678e-01 8.72821137e-02 5.25162332e-02 -2.19958797e-01 2.18403339e-01 -1.74268699e+00 -1.98914558e-01 7.42808044e-01 1.03506720e+00 -1.07106365e-01 5.91224313e-01 3.08149099e-01 1.45720571e-01 -7.18360782e-01 6.81535080e-02 -2.21176043e-01 -8.92112494e-01 -2.44394690e-01 -3.66595984e-02 -1.36587590e-01 1.12207323e-01 3.59098822e-01 6.31947339e-01 5.58326840e-01 3.60812068e-01 4.19067852e-02 -1.01801300e+00 -1.39114738e-01 -7.49015808e-03 -1.83618248e-01 4.94419098e-01 8.97964716e-01 1.34999007e-01 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bafb1f04-1efb-4440-9449-0c5e0c1d35b6	breaking-trade-offs-in-speech-separation-with	2211.06493	null	https://arxiv.org/abs/2211.06493v2	https://arxiv.org/pdf/2211.06493v2.pdf	Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts	Employing a monaural speech separation (SS) model as a front-end for automatic speech recognition (ASR) involves balancing two kinds of trade-offs. First, while a larger model improves the SS performance, it also requires a higher computational cost. Second, an SS model that is more optimized for handling overlapped speech is likely to introduce more processing artifacts in non-overlapped-speech regions. In this paper, we address these trade-offs with a sparsely-gated mixture-of-experts (MoE) architecture. Comprehensive evaluation results obtained using both simulated and real meeting recordings show that our proposed sparsely-gated MoE SS model achieves superior separation capabilities with less speech distortion, while involving only a marginal run-time cost increase.	['Takuya Yoshioka', 'Naoyuki Kanda', 'Jian Wu', 'Yu Shi', 'Zhuo Chen', 'Xiaofei Wang']	2022-11-11	null	null	null	null	['speech-separation']	['speech']	[ 1.65283516e-01 -1.44711480e-01 3.24231148e-01 -3.88725728e-01 -1.25295889e+00 -1.70152932e-01 1.65200219e-01 -1.13327548e-01 -1.76076889e-01 3.91023904e-01 4.87824887e-01 -2.72565663e-01 7.41668642e-02 -6.11995794e-02 -4.62496608e-01 -6.37359142e-01 5.83905503e-02 -6.76687658e-02 3.05763930e-01 -1.26798138e-01 8.98367465e-02 4.33897048e-01 -1.71172500e+00 3.48945916e-01 1.22625577e+00 1.00984979e+00 5.65521657e-01 6.95485175e-01 -3.74314860e-02 5.53336680e-01 -8.39917243e-01 -1.18642092e-01 2.93013781e-01 -3.64894748e-01 -2.14353636e-01 2.91331202e-01 2.67082542e-01 1.12809949e-01 -3.61977756e-01 1.05219305e+00 9.38918054e-01 4.52400595e-01 4.58691984e-01 -7.68988132e-01 -1.21358857e-01 5.65739751e-01 -6.28419459e-01 3.48011136e-01 2.49152496e-01 1.00171573e-01 7.73523748e-01 -1.12980390e+00 -2.40170807e-01 1.14747298e+00 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27c3ace4-8246-4607-af2f-9dc2b296ab22	robust-contrastive-language-image-pretraining	2303.06854	null	https://arxiv.org/abs/2303.06854v1	https://arxiv.org/pdf/2303.06854v1.pdf	Robust Contrastive Language-Image Pretraining against Adversarial Attacks	Contrastive vision-language representation learning has achieved state-of-the-art performance for zero-shot classification, by learning from millions of image-caption pairs crawled from the internet. However, the massive data that powers large multimodal models such as CLIP, makes them extremely vulnerable to various types of adversarial attacks, including targeted and backdoor data poisoning attacks. Despite this vulnerability, robust contrastive vision-language pretraining against adversarial attacks has remained unaddressed. In this work, we propose RoCLIP, the first effective method for robust pretraining {and fine-tuning} multimodal vision-language models. RoCLIP effectively breaks the association between poisoned image-caption pairs by considering a pool of random examples, and (1) matching every image with the text that is most similar to its caption in the pool, and (2) matching every caption with the image that is most similar to its image in the pool. Our extensive experiments show that our method renders state-of-the-art targeted data poisoning and backdoor attacks ineffective during pre-training or fine-tuning of CLIP. In particular, RoCLIP decreases the poison and backdoor attack success rates down to 0\% during pre-training and 1\%-4\% during fine-tuning, and effectively improves the model's performance.	['Baharan Mirzasoleiman', 'Wenhan Yang']	2023-03-13	null	null	null	null	['data-poisoning', 'backdoor-attack']	['adversarial', 'adversarial']	[ 3.11091896e-02 -1.54998749e-01 -1.91147611e-01 2.04643682e-01 -1.25540090e+00 -1.17710185e+00 6.69570446e-01 1.87383324e-03 -7.00412393e-01 4.92095381e-01 -1.22226000e-01 -3.48367423e-01 4.12501276e-01 -6.37369275e-01 -1.28401554e+00 -6.90542042e-01 5.16114384e-02 5.29512525e-01 4.19113487e-01 -3.27100277e-01 -1.66113693e-02 4.58941221e-01 -1.22808588e+00 4.72181201e-01 5.53294063e-01 9.93490577e-01 -1.65352941e-01 9.51017022e-01 4.12028804e-02 1.06801414e+00 -7.03516364e-01 -1.17399335e+00 4.15523469e-01 -3.11571211e-01 -5.39299011e-01 -1.61477908e-01 9.64727938e-01 -6.41094565e-01 -9.94127631e-01 1.51353610e+00 6.09191298e-01 -3.34105790e-01 5.08940279e-01 -1.83044112e+00 -9.62650299e-01 4.77910668e-01 -7.11795449e-01 1.10236123e-01 2.26084396e-01 8.47322941e-01 6.95325315e-01 -8.38388383e-01 4.91575956e-01 1.33150482e+00 5.18618166e-01 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7abe72b4-3f19-4dfa-835c-93b22d723495	rotateqvs-representing-temporal-information	2203.07993	null	https://arxiv.org/abs/2203.07993v2	https://arxiv.org/pdf/2203.07993v2.pdf	RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion	Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention. In TKG, relation patterns inherent with temporality are required to be studied for representation learning and reasoning across temporal facts. However, existing methods can hardly model temporal relation patterns, nor can capture the intrinsic connections between relations when evolving over time, lacking of interpretability. In this paper, we propose a novel temporal modeling method which represents temporal entities as Rotations in Quaternion Vector Space (RotateQVS) and relations as complex vectors in Hamilton's quaternion space. We demonstrate our method can model key patterns of relations in TKG, such as symmetry, asymmetry, inverse, and can further capture time-evolved relations by theory. Empirically, we show that our method can boost the performance of link prediction tasks over four temporal knowledge graph benchmarks.	['Aiping Li', 'Yitong Li', 'Ye Wang', 'Kai Chen']	2022-03-15	null	https://aclanthology.org/2022.acl-long.402	https://aclanthology.org/2022.acl-long.402.pdf	acl-2022-5	['temporal-knowledge-graph-completion']	['knowledge-base']	[-4.33165550e-01 9.52180997e-02 -8.06420982e-01 -1.10748984e-01 5.29671371e-01 -6.08111620e-01 9.76600111e-01 4.89540577e-01 -2.67034527e-02 7.68462002e-01 3.60966831e-01 -6.47167027e-01 -6.95969403e-01 -1.04049885e+00 -6.48789585e-01 -3.55888039e-01 -7.08873451e-01 4.24468309e-01 4.48821157e-01 -7.86702216e-01 -7.86396116e-02 4.27534968e-01 -9.89983499e-01 -2.68065352e-02 6.47042334e-01 6.27545595e-01 -5.30138016e-01 5.10539830e-01 3.09983473e-02 1.28384101e+00 -4.38084275e-01 -7.43237972e-01 -9.75438133e-02 -4.80038077e-01 -1.18305242e+00 -5.64731538e-01 -2.05193341e-01 1.64205268e-01 -1.24293101e+00 6.41499043e-01 -4.70399968e-02 2.98656106e-01 8.01020443e-01 -1.53369713e+00 -9.61068332e-01 7.56556988e-01 -5.35732329e-01 7.12372124e-01 6.02745831e-01 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d99507bd-2dee-490b-a693-7c788721c18c	can-audio-captions-be-evaluated-with-image	2110.04684	null	https://arxiv.org/abs/2110.04684v2	https://arxiv.org/pdf/2110.04684v2.pdf	Can Audio Captions Be Evaluated with Image Caption Metrics?	Automated audio captioning aims at generating textual descriptions for an audio clip. To evaluate the quality of generated audio captions, previous works directly adopt image captioning metrics like SPICE and CIDEr, without justifying their suitability in this new domain, which may mislead the development of advanced models. This problem is still unstudied due to the lack of human judgment datasets on caption quality. Therefore, we firstly construct two evaluation benchmarks, AudioCaps-Eval and Clotho-Eval. They are established with pairwise comparison instead of absolute rating to achieve better inter-annotator agreement. Current metrics are found in poor correlation with human annotations on these datasets. To overcome their limitations, we propose a metric named FENSE, where we combine the strength of Sentence-BERT in capturing similarity, and a novel Error Detector to penalize erroneous sentences for robustness. On the newly established benchmarks, FENSE outperforms current metrics by 14-25% accuracy. Code, data and web demo available at: https://github.com/blmoistawinde/fense	['Kenny Q. Zhu', 'Mengyue Wu', 'Zeyu Xie', 'Xuenan Xu', 'Zhiling Zhang', 'Zelin Zhou']	2021-10-10	null	null	null	null	['audio-captioning']	['audio']	[ 2.41722777e-01 4.82344627e-03 6.67817593e-02 -3.28374207e-01 -1.24931276e+00 -5.90698242e-01 6.03640437e-01 3.49281251e-01 -3.49743336e-01 7.94442117e-01 6.14465356e-01 1.45453006e-01 7.76907578e-02 -3.19998056e-01 -5.11632204e-01 -2.41182223e-01 1.24335639e-01 1.04336858e-01 9.68787521e-02 -1.07326388e-01 3.76817226e-01 -2.08281592e-01 -1.64773047e+00 5.78629017e-01 8.77967417e-01 1.16512465e+00 8.62478763e-02 6.75154567e-01 -8.69977009e-03 7.82805920e-01 -7.88936079e-01 -5.21739304e-01 -5.98380379e-02 -6.97714865e-01 -7.45390952e-01 -1.42311230e-01 3.55767310e-01 -1.48907542e-01 -3.15894298e-02 1.01895678e+00 8.11007917e-01 -2.22357258e-01 4.03626204e-01 -1.58231080e+00 -7.27601171e-01 9.01461065e-01 -2.03460455e-01 1.61792919e-01 9.97654915e-01 3.58623534e-01 1.20958579e+00 -9.00977671e-01 6.18953705e-01 1.05824375e+00 6.85058057e-01 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-3.87690514e-01 -1.30204570e+00 -1.16891690e-01 9.08924758e-01 1.69671670e-01 -5.88250279e-01 -6.11531258e-01 1.75602034e-01]	[15.259384155273438, 4.872631072998047]
1303246e-65e2-40ba-8225-9736a5b81046	comparing-knowledge-based-reinforcement	1901.04626	null	https://arxiv.org/abs/1901.04626v2	https://arxiv.org/pdf/1901.04626v2.pdf	Comparing Knowledge-based Reinforcement Learning to Neural Networks in a Strategy Game	The paper reports on an experiment, in which a Knowledge-Based Reinforcement Learning (KB-RL) method was compared to a Neural Network (NN) approach in solving a classical Artificial Intelligence (AI) task. In contrast to NNs, which require a substantial amount of data to learn a good policy, the KB-RL method seeks to encode human knowledge into the solution, considerably reducing the amount of data needed for a good policy. By means of Reinforcement Learning (RL), KB-RL learns to optimize the model and improves the output of the system. Furthermore, KB-RL offers the advantage of a clear explanation of the taken decisions as well as transparent reasoning behind the solution. The goal of the reported experiment was to examine the performance of the KB-RL method in contrast to the Neural Network and to explore the capabilities of KB-RL to deliver a strong solution for the AI tasks. The results show that, within the designed settings, KB-RL outperformed the NN, and was able to learn a better policy from the available amount of data. These results support the opinion that Artificial Intelligence can benefit from the discovery and study of alternative approaches, potentially extending the frontiers of AI.	['Liudmyla Nechepurenko', 'Viktor Voss', 'Vyacheslav Gritsenko']	2019-01-15	null	null	null	null	['game-of-go']	['playing-games']	[ 5.16687483e-02 8.48503768e-01 -2.71501839e-01 -1.69824034e-01 -2.45314792e-01 -3.19635600e-01 7.41002917e-01 1.89897329e-01 -6.80449188e-01 1.23074985e+00 -7.55383596e-02 -4.85668302e-01 -6.24239504e-01 -9.59977210e-01 -7.21026242e-01 -7.64404953e-01 -1.37263536e-03 6.80091918e-01 -7.56188557e-02 -4.11008209e-01 5.48070014e-01 5.89667261e-01 -1.51757157e+00 1.05187334e-01 6.55859172e-01 1.10825956e+00 4.14994866e-01 4.23246324e-01 -9.74345058e-02 1.24932706e+00 -6.18501127e-01 -3.80711779e-02 4.07763153e-01 -3.62528920e-01 -1.04102862e+00 -2.79903591e-01 -3.42975371e-02 -1.46135956e-01 -1.20622657e-01 6.57752097e-01 4.17616814e-01 4.08044368e-01 4.57894683e-01 -1.01151633e+00 -5.11665523e-01 5.44997811e-01 1.05710745e-01 1.68059021e-01 5.34569800e-01 5.06762803e-01 8.37061286e-01 -2.85634100e-01 7.19843209e-01 1.33300424e+00 2.82165498e-01 7.53726065e-01 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-4.95094270e-01 -2.10159225e-03 2.84045219e-01 -7.46241510e-01 -2.92872787e-01 -2.08582550e-01]	[4.227792263031006, 1.6675138473510742]
97fb5984-b32e-4348-ad5a-86e2d6e54a75	from-isolated-islands-to-pangea-unifying	2304.00553	null	https://arxiv.org/abs/2304.00553v2	https://arxiv.org/pdf/2304.00553v2.pdf	From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding	Action understanding matters and attracts attention. It can be formed as the mapping from the action physical space to the semantic space. Typically, researchers built action datasets according to idiosyncratic choices to define classes and push the envelope of benchmarks respectively. Thus, datasets are incompatible with each other like "Isolated Islands" due to semantic gaps and various class granularities, e.g., do housework in dataset A and wash plate in dataset B. We argue that a more principled semantic space is an urgent need to concentrate the community efforts and enable us to use all datasets together to pursue generalizable action learning. To this end, we design a Poincare action semantic space given verb taxonomy hierarchy and covering massive actions. By aligning the classes of previous datasets to our semantic space, we gather (image/video/skeleton/MoCap) datasets into a unified database in a unified label system, i.e., bridging "isolated islands" into a "Pangea". Accordingly, we propose a bidirectional mapping model between physical and semantic space to fully use Pangea. In extensive experiments, our system shows significant superiority, especially in transfer learning. Code and data will be made publicly available.	['Cewu Lu', 'Xudong Lu', 'Jingru Tan', 'Yixing Li', 'Junyi Zhang', 'Yikun Ji', 'Yiming Dou', 'Xinpeng Liu', 'Xiaoqian Wu', 'Yong-Lu Li']	2023-04-02	null	null	null	null	['action-understanding']	['computer-vision']	[ 5.32528125e-02 -5.96412830e-02 -5.31374514e-01 -3.76444906e-01 -3.85827214e-01 -5.39975762e-01 7.18081951e-01 -3.26659709e-01 -1.88121438e-01 5.78935802e-01 5.82599998e-01 7.10519031e-04 -4.98281896e-01 -1.04750144e+00 -5.94856620e-01 -6.87493563e-01 6.15232527e-01 1.22521199e-01 5.18509924e-01 -3.12392294e-01 3.48662257e-01 -5.43199927e-02 -1.48292077e+00 4.50723261e-01 7.88729608e-01 1.02287591e+00 3.27009439e-01 -1.30307674e-01 -3.35695475e-01 8.10005665e-01 -1.82003096e-01 -4.14261460e-01 3.95599544e-01 -6.57707989e-01 -1.26709139e+00 1.81392297e-01 3.58020067e-01 -1.19054146e-01 -4.93815929e-01 1.31305599e+00 7.05714896e-02 1.07821070e-01 4.25306976e-01 -1.62670422e+00 -1.02000284e+00 7.11387277e-01 -2.66117334e-01 -1.42165393e-01 4.35753614e-01 3.01970482e-01 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53f78a09-fe91-4c7c-bbde-4b42f8f3857c	deepfake-detection-with-deep-learning	2304.03698	null	https://arxiv.org/abs/2304.03698v1	https://arxiv.org/pdf/2304.03698v1.pdf	Deepfake Detection with Deep Learning: Convolutional Neural Networks versus Transformers	The rapid evolvement of deepfake creation technologies is seriously threating media information trustworthiness. The consequences impacting targeted individuals and institutions can be dire. In this work, we study the evolutions of deep learning architectures, particularly CNNs and Transformers. We identified eight promising deep learning architectures, designed and developed our deepfake detection models and conducted experiments over well-established deepfake datasets. These datasets included the latest second and third generation deepfake datasets. We evaluated the effectiveness of our developed single model detectors in deepfake detection and cross datasets evaluations. We achieved 88.74%, 99.53%, 97.68%, 99.73% and 92.02% accuracy and 99.95%, 100%, 99.88%, 99.99% and 97.61% AUC, in the detection of FF++ 2020, Google DFD, Celeb-DF, Deeper Forensics and DFDC deepfakes, respectively. We also identified and showed the unique strengths of CNNs and Transformers models and analysed the observed relationships among the different deepfake datasets, to aid future developments in this area.	['Vrizlynn L. L. Thing']	2023-04-07	null	null	null	null	['face-swapping']	['computer-vision']	[-9.46133256e-01 -3.98048788e-01 -1.25767305e-01 4.28160615e-02 -4.15843934e-01 -9.59476173e-01 1.12743926e+00 2.10295413e-02 -4.25367147e-01 6.04447067e-01 9.09355357e-02 -2.68464327e-01 -1.73610225e-01 -1.06959772e+00 -4.90195572e-01 -3.91130239e-01 -2.50687420e-01 4.22574341e-01 3.10721040e-01 8.46255850e-03 3.92440587e-01 6.96541011e-01 -1.14827156e+00 4.95544165e-01 5.01996517e-01 1.29533231e+00 -6.35446668e-01 9.57472205e-01 3.76789756e-02 1.03900397e+00 -1.03300536e+00 -1.32826138e+00 1.98345408e-01 3.90778273e-01 -8.59392583e-01 -6.94523633e-01 6.30812824e-01 -6.86333477e-01 -6.31991327e-01 1.04894495e+00 8.70927453e-01 -6.45555437e-01 5.61961532e-01 -1.37382233e+00 -1.15528703e+00 6.43840671e-01 -9.05163765e-01 8.31282973e-01 -6.79796711e-02 2.54360557e-01 6.05910718e-01 -8.50506961e-01 4.84622985e-01 1.16484332e+00 1.33650506e+00 4.83723998e-01 -4.51028079e-01 -1.27823198e+00 -7.47989774e-01 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d331861f-6c76-435e-9ab2-85ad158ecf5f	lime-live-intrinsic-material-estimation	1801.01075	null	http://arxiv.org/abs/1801.01075v2	http://arxiv.org/pdf/1801.01075v2.pdf	LIME: Live Intrinsic Material Estimation	We present the first end to end approach for real time material estimation for general object shapes with uniform material that only requires a single color image as input. In addition to Lambertian surface properties, our approach fully automatically computes the specular albedo, material shininess, and a foreground segmentation. We tackle this challenging and ill posed inverse rendering problem using recent advances in image to image translation techniques based on deep convolutional encoder decoder architectures. The underlying core representations of our approach are specular shading, diffuse shading and mirror images, which allow to learn the effective and accurate separation of diffuse and specular albedo. In addition, we propose a novel highly efficient perceptual rendering loss that mimics real world image formation and obtains intermediate results even during run time. The estimation of material parameters at real time frame rates enables exciting mixed reality applications, such as seamless illumination consistent integration of virtual objects into real world scenes, and virtual material cloning. We demonstrate our approach in a live setup, compare it to the state of the art, and demonstrate its effectiveness through quantitative and qualitative evaluation.	['Hans-Peter Seidel', 'Michael Zollhoefer', 'Maxim Maximov', 'Avishek Chatterjee', 'Abhimitra Meka', 'Christian Theobalt', 'Christian Richardt']	2018-01-03	lime-live-intrinsic-material-estimation-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Meka_LIME_Live_Intrinsic_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Meka_LIME_Live_Intrinsic_CVPR_2018_paper.pdf	cvpr-2018-6	['foreground-segmentation']	['computer-vision']	[ 7.47783363e-01 -1.99857205e-01 6.78177238e-01 -3.10446799e-01 -7.16480255e-01 -7.49375045e-01 6.74797297e-01 -3.97609055e-01 -8.65637138e-02 5.15418649e-01 -2.96044558e-01 -9.70095098e-02 3.17196518e-01 -8.97544742e-01 -1.10543442e+00 -5.72941184e-01 1.95926756e-01 6.96901262e-01 3.88272226e-01 -2.17216879e-01 1.71694770e-01 7.54685521e-01 -1.91714251e+00 4.10336882e-01 9.48935866e-01 1.21507478e+00 2.87240475e-01 1.19711375e+00 -2.29591802e-01 1.06105959e+00 -4.08636957e-01 -2.57432550e-01 5.25507867e-01 -2.61917084e-01 -6.09401941e-01 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c7ae10ed-fb5e-4d85-b132-bb880a6eed5d	reconstructing-the-image-scanning-microscopy	2211.1251	null	https://arxiv.org/abs/2211.12510v1	https://arxiv.org/pdf/2211.12510v1.pdf	Reconstructing the Image Scanning Microscopy Dataset: an Inverse Problem	Confocal laser-scanning microscopy (CLSM) is one of the most popular optical architectures for fluorescence imaging. In CLSM, a focused laser beam excites the fluorescence emission from a specific specimen position. Some actuators scan the probed region across the sample and a photodetector collects a single intensity value for each scan point, building a two-dimensional image pixel-by-pixel. Recently, new fast single-photon array detectors have allowed the recording of a full bi-dimensional image of the probed region for each scan point, transforming CLSM into image scanning microscopy (ISM). This latter offers significant improvements over traditional imaging but requires an optimal processing tool to extract a super-resolved image from the four-dimensional dataset. Here we describe the image formation process in ISM from a statistical point of view, and we use the Bayesian framework to formulate a multi-image deconvolution problem. Notably, the single-photon detector suffers exclusively from the photon shot noise, enabling the development of an effective likelihood model. We derive an iterative likelihood maximization algorithm and test it on experimental and simulated data. Furthermore, we demonstrate that the ISM dataset is redundant, enabling the possibility of obtaining reconstruction sampled at twice the scanning step. Our results prove that in ISM, under appropriate conditions, the Nyquist-Shannon sampling criterium is effectively relaxed. This finding can be exploited to speed up the acquisition process by a factor of four, further improving the versatility of ISM systems.	['Giuseppe Vicidomini', 'Marco Castello', 'Alessandro Zunino']	2022-11-22	null	null	null	null	['image-deconvolution']	['computer-vision']	[ 9.06160235e-01 -3.02281320e-01 1.83848023e-01 -1.75580420e-02 -7.08302379e-01 -5.45049906e-01 2.66212344e-01 -5.12864925e-02 -1.05866241e+00 8.13848555e-01 -5.82517326e-01 -7.34103844e-03 -8.62468854e-02 -6.45141304e-01 -6.55068934e-01 -1.40209961e+00 3.58082294e-01 5.64772010e-01 1.68370768e-01 6.87908173e-01 4.03233528e-01 9.45428610e-01 -1.50456595e+00 -1.50744557e-01 4.35554683e-01 7.93870866e-01 7.60037124e-01 7.49882042e-01 -1.53999820e-01 2.18019173e-01 -3.49563301e-01 -2.50351150e-03 -2.19579693e-02 -5.34667730e-01 -6.12199187e-01 3.78795445e-01 -1.19669423e-01 -4.82212245e-01 4.54935543e-02 1.10142529e+00 5.27185500e-01 -1.26889810e-01 6.83728635e-01 -5.70917547e-01 3.21821719e-02 -1.11796387e-01 -7.41641939e-01 1.35692209e-01 4.09029126e-01 3.32679629e-01 5.77616513e-01 -9.73138213e-01 8.09656739e-01 8.27051640e-01 1.56759143e-01 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-4.08785492e-01 -5.77606857e-01 -1.06386518e+00 -1.54741609e-03 6.77369416e-01 -3.35073203e-01 -7.40843594e-01 1.81824118e-01]	[12.837176322937012, -2.788691759109497]
99122de3-2c7e-4c34-ab0b-446763a09276	adaptive-experimental-design-and	2210.14369	null	https://arxiv.org/abs/2210.14369v1	https://arxiv.org/pdf/2210.14369v1.pdf	Adaptive Experimental Design and Counterfactual Inference	Adaptive experimental design methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods. This paper shares lessons learned regarding the challenges and pitfalls of naively using adaptive experimentation systems in industrial settings where non-stationarity is prevalent, while also providing perspectives on the proper objectives and system specifications in these settings. We developed an adaptive experimental design framework for counterfactual inference based on these experiences, and tested it in a commercial environment.	['Lalit Jain', 'Houssam Nassif', 'Arick Chen', 'Sergio Gamez', 'Tanner Fiez']	2022-10-25	null	null	null	null	['counterfactual-inference']	['miscellaneous']	[ 3.58593792e-01 -1.62059635e-01 -5.74851573e-01 -2.93745905e-01 -2.46629730e-01 -6.86666369e-01 3.75126690e-01 -3.76483142e-01 -3.43269706e-01 1.20324564e+00 -2.97979087e-01 -1.24674666e+00 -5.96133411e-01 -4.54421967e-01 -6.69836819e-01 -3.59976172e-01 -3.40892136e-01 2.66500205e-01 -1.11641616e-01 1.64503455e-01 6.58376276e-01 4.33099508e-01 -1.75855696e+00 -2.05226377e-01 4.05066252e-01 6.32699013e-01 -3.79289448e-01 7.14234710e-01 5.10197818e-01 5.36166370e-01 -9.71440017e-01 -2.01209247e-01 4.92532164e-01 -6.94221735e-01 -4.64908391e-01 2.21251380e-02 2.09241703e-01 -8.02265704e-01 1.23696022e-01 9.39015210e-01 7.02937245e-01 2.99678259e-02 1.03186771e-01 -1.76800954e+00 -1.18469439e-01 1.05965900e+00 -6.89314842e-01 4.07166928e-01 4.86729413e-01 8.01609218e-01 8.40252995e-01 -6.10738061e-02 3.40281397e-01 1.37240791e+00 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c922b686-036d-40cf-8b1c-8f2dfb6b29fa	acne-severity-grading-on-face-images-via	null	null	https://ieeexplore.ieee.org/document/9995101	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9995101	Acne Severity Grading on Face Images via Extraction and Guidance of Prior Knowledge	Acne Vulgaris seriously affects people’s daily life. In this paper, we propose a face acne grading framework which is a new paradigm to solve the image classification problem where the number and type of small objects are the evidence. This framework includes two components: prior knowledge extraction and prior knowledge guided network. The prior knowledge extraction uses an excellent segmentation method to predict the lesion areas as prior knowledge. The prior knowledge guided network fuses the prior knowledge and its corresponding image to grade the severity. The experiment results demonstrate that our framework achieves the state-of-the-art and diagnosis level of dermatologists.	['Xue Cheng', 'Jing Yang', 'Haiyan You', 'Xiguang Liu', 'Yi Guan', 'Zhaoyang Ma', 'Dongxin Chen', 'Jingchi Jiang', 'Yi Lin']	2023-01-02	null	null	null	ieee-international-conference-on-6	['acne-severity-grading']	['medical']	[ 2.15460956e-01 4.65281084e-02 -6.25567019e-01 -4.11561400e-01 -3.50794554e-01 -1.81840375e-01 1.71271190e-01 -2.46006146e-01 -7.56512508e-02 6.08265340e-01 -1.63409352e-01 7.86439627e-02 -1.31521255e-01 -9.38684583e-01 -9.35265943e-02 -7.21027374e-01 3.71054858e-01 4.33211714e-01 5.38403690e-01 -1.99421644e-02 3.92484307e-01 6.91903293e-01 -1.52527547e+00 6.45215511e-01 1.26642358e+00 1.41991258e+00 8.23014677e-02 4.61786360e-01 -2.79370248e-01 5.06382048e-01 -4.96069998e-01 -3.94429743e-01 2.36661434e-02 -2.22789869e-01 -7.62161672e-01 4.15016115e-01 5.93251050e-01 -3.24962974e-01 -2.23791059e-02 1.27084684e+00 3.33572447e-01 -4.09143955e-01 1.16557670e+00 -1.33088958e+00 -7.95471787e-01 -1.63663343e-01 -8.67137074e-01 3.85231256e-01 5.27204312e-02 3.00692767e-01 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b3dded28-2437-4cc4-8d2f-1afc0422db01	context-aware-video-reconstruction-for	2205.12912	null	https://arxiv.org/abs/2205.12912v1	https://arxiv.org/pdf/2205.12912v1.pdf	Context-Aware Video Reconstruction for Rolling Shutter Cameras	With the ubiquity of rolling shutter (RS) cameras, it is becoming increasingly attractive to recover the latent global shutter (GS) video from two consecutive RS frames, which also places a higher demand on realism. Existing solutions, using deep neural networks or optimization, achieve promising performance. However, these methods generate intermediate GS frames through image warping based on the RS model, which inevitably result in black holes and noticeable motion artifacts. In this paper, we alleviate these issues by proposing a context-aware GS video reconstruction architecture. It facilitates the advantages such as occlusion reasoning, motion compensation, and temporal abstraction. Specifically, we first estimate the bilateral motion field so that the pixels of the two RS frames are warped to a common GS frame accordingly. Then, a refinement scheme is proposed to guide the GS frame synthesis along with bilateral occlusion masks to produce high-fidelity GS video frames at arbitrary times. Furthermore, we derive an approximated bilateral motion field model, which can serve as an alternative to provide a simple but effective GS frame initialization for related tasks. Experiments on synthetic and real data show that our approach achieves superior performance over state-of-the-art methods in terms of objective metrics and subjective visual quality. Code is available at \url{https://github.com/GitCVfb/CVR}.	['Mingyi He', 'Qi Liu', 'Zhiyuan Zhang', 'Yuchao Dai', 'Bin Fan']	2022-05-25	null	http://openaccess.thecvf.com//content/CVPR2022/html/Fan_Context-Aware_Video_Reconstruction_for_Rolling_Shutter_Cameras_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Fan_Context-Aware_Video_Reconstruction_for_Rolling_Shutter_Cameras_CVPR_2022_paper.pdf	cvpr-2022-1	['video-reconstruction', 'motion-compensation']	['computer-vision', 'computer-vision']	[ 8.51704627e-02 -5.23624241e-01 -2.00704873e-01 -2.02436849e-01 -4.68561590e-01 -2.80791432e-01 4.66901422e-01 -5.09172440e-01 -2.69411206e-01 7.06484675e-01 2.00634763e-01 -2.87592053e-01 2.21941620e-01 -5.64178109e-01 -6.75656796e-01 -7.91904211e-01 4.54420745e-01 -3.91812146e-01 2.22819820e-01 -3.76867913e-02 2.21829489e-01 4.59374368e-01 -1.16045237e+00 4.60495129e-02 8.79338801e-01 1.05990934e+00 4.66217309e-01 5.09506822e-01 7.55723119e-02 8.56581986e-01 -4.28898394e-01 -2.08629861e-01 4.44617420e-01 -5.49318016e-01 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4b57de34-1321-4649-b015-c76cde908c77	towards-automatic-short-answer-assessment-for	null	null	https://aclanthology.org/2022.bea-1.30	https://aclanthology.org/2022.bea-1.30.pdf	Towards Automatic Short Answer Assessment for Finnish as a Paraphrase Retrieval Task	Automatic grouping of textual answers has the potential of allowing batch grading, but is challenging because the answers, especially longer essays, have many claims. To explore the feasibility of grouping together answers based on their semantic meaning, this paper investigates the grouping of short textual answers, proxies of single claims. This is approached as a paraphrase identification task, where neural and non-neural sentence embeddings and a paraphrase identification model are tested. These methods are evaluated on a dataset consisting of over 4000 short textual answers from various disciplines. The results map out the suitable question types for the paraphrase identification model and those for the neural and non-neural methods.	['Filip Ginter', 'Jenna Kanerva', 'Li-Hsin Chang']	null	null	null	null	naacl-bea-2022-7	['paraphrase-identification']	['natural-language-processing']	[-6.50768876e-02 1.88717812e-01 -2.01246560e-01 -5.08291721e-01 -8.20967376e-01 -8.78515124e-01 5.56571186e-01 5.54519713e-01 -4.62452143e-01 4.92921770e-01 7.94120848e-01 -5.83985984e-01 -4.28575456e-01 -5.93379557e-01 -2.62351245e-01 -1.80108830e-01 7.36145616e-01 4.24630344e-01 1.26240224e-01 -8.70702565e-02 9.04837430e-01 3.09873819e-01 -1.44271183e+00 6.40294135e-01 1.09157658e+00 9.67067659e-01 4.01402451e-02 7.65518129e-01 -5.49125671e-01 1.33757210e+00 -9.25322473e-01 -9.98260736e-01 4.04104143e-02 -6.44814491e-01 -1.11784148e+00 -2.75575548e-01 1.16942906e+00 -1.00937746e-01 -3.35363328e-01 8.98072064e-01 6.50571883e-01 3.25123668e-01 9.58008289e-01 -9.30363595e-01 -1.35322237e+00 7.44354486e-01 -3.14682536e-02 4.78020579e-01 8.11328232e-01 -2.02557608e-01 1.53543556e+00 -9.96920109e-01 4.75018442e-01 9.66522574e-01 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b357adcb-88f0-4307-907c-3be2e21faec3	machine-learning-based-assessment-of-energy	2111.08295	null	https://arxiv.org/abs/2111.08295v1	https://arxiv.org/pdf/2111.08295v1.pdf	Machine Learning-Based Assessment of Energy Behavior of RC Shear Walls	Current seismic design codes primarily rely on the strength and displacement capacity of structural members and do not account for the influence of the ground motion duration or the hysteretic behavior characteristics. The energy-based approach serves as a supplemental index to response quantities and includes the effect of repeated loads in seismic performance. The design philosophy suggests that the seismic demands are met by the energy dissipation capacity of the structural members. Therefore, the energy dissipation behavior of the structural members should be well understood to achieve an effective energy-based design approach. This study focuses on the energy dissipation capacity of reinforced concrete (RC) shear walls that are widely used in high seismic regions as they provide significant stiffness and strength to resist lateral forces. A machine learning (Gaussian Process Regression (GPR))-based predictive model for energy dissipation capacity of shear walls is developed as a function of wall design parameters. Eighteen design parameters are shown to influence energy dissipation, whereas the most important ones are determined by applying sequential backward elimination and by using feature selection methods to reduce the complexity of the predictive model. The ability of the proposed model to make robust and accurate predictions is validated based on novel data with a prediction accuracy (the ratio of predicted/actual values) of around 1.00 and a coefficient of determination (R2) of 0.93. The outcomes of this study are believed to contribute to the energy-based approach by (i) defining the most influential wall properties on the seismic energy dissipation capacity of shear walls and (ii) providing predictive models that can enable comparisons of different wall design configurations to achieve higher energy dissipation capacity.	['Zeynep Tuna Deger', 'Fatih Sutcu', 'Gulsen Taskin Kaya', 'Berkay Topaloglu']	2021-11-16	null	null	null	null	['gpr', 'gpr']	['computer-vision', 'miscellaneous']	[-3.69268917e-02 -2.06003323e-01 4.97054867e-02 5.65789863e-02 -5.04952133e-01 -1.83435515e-01 3.38396490e-01 5.47630131e-01 -2.23649517e-01 5.38346648e-01 4.08214390e-01 -2.87884504e-01 -8.78490925e-01 -9.37717378e-01 -2.93311238e-01 -1.11083674e+00 -3.87570888e-01 4.91501987e-02 4.25641596e-01 -3.72641206e-01 6.47763193e-01 8.14984143e-01 -1.39436615e+00 5.21372743e-02 5.06141126e-01 7.51348674e-01 4.46347296e-01 3.89288247e-01 3.67686421e-01 6.25055730e-01 -2.87937045e-01 3.45146328e-01 -3.46069932e-01 -1.87512442e-01 -9.59287524e-01 -1.41808420e-01 -8.61110151e-01 -5.08965313e-01 1.93250880e-01 1.98447153e-01 6.86609030e-01 5.86980581e-01 1.06327713e+00 -2.48552203e-01 -2.11131558e-01 6.64354503e-01 -3.86771798e-01 5.22294044e-02 3.01825941e-01 -1.48201108e-01 9.28416073e-01 -1.08544445e+00 1.15525603e-01 8.38941872e-01 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65944591-73f0-4765-ae5b-fbf09eabd793	a-fault-localization-and-debugging-support	2103.02386	null	https://arxiv.org/abs/2103.02386v1	https://arxiv.org/pdf/2103.02386v1.pdf	A Fault Localization and Debugging Support Framework driven by Bug Tracking Data	Fault localization has been determined as a major resource factor in the software development life cycle. Academic fault localization techniques are mostly unknown and unused in professional environments. Although manual debugging approaches can vary significantly depending on bug type (e.g. memory bugs or semantic bugs), these differences are not reflected in most existing fault localization tools. Little research has gone into automated identification of bug types to optimize the fault localization process. Further, existing fault localization techniques leverage on historical data only for augmentation of suspiciousness rankings. This thesis aims to provide a fault localization framework by combining data from various sources to help developers in the fault localization process. To achieve this, a bug classification schema is introduced, benchmarks are created, and a novel fault localization method based on historical data is proposed.	['Thomas Hirsch']	2021-03-03	null	null	null	null	['fault-localization']	['computer-code']	[-4.02020603e-01 -3.86294186e-01 -2.69956082e-01 -2.71023899e-01 -3.52048934e-01 -6.11541808e-01 4.11518570e-03 5.75778782e-01 3.98824543e-01 5.94639361e-01 -2.54387796e-01 -4.03730810e-01 -5.75623035e-01 -6.37940526e-01 -3.75570863e-01 -6.33960404e-03 -5.03499694e-02 -8.43945611e-03 2.96857685e-01 -9.53378826e-02 1.02600265e+00 1.65816024e-01 -1.76101840e+00 2.19872847e-01 1.04118192e+00 4.43099290e-01 2.20488667e-01 6.33996367e-01 -2.05275178e-01 8.71484399e-01 -1.16115236e+00 1.05288724e-04 -4.32104021e-02 -4.90422249e-01 -9.27483141e-01 7.50404075e-02 1.91271305e-01 -2.11279884e-01 2.02890247e-01 1.28067267e+00 4.24566269e-02 -3.73609692e-01 4.80776355e-02 -1.71566474e+00 -4.87928092e-01 7.77855039e-01 -2.71725208e-01 4.73805279e-01 8.96412492e-01 -8.68021175e-02 9.32533681e-01 -8.10750663e-01 7.41781592e-01 8.38638008e-01 7.55223513e-01 2.16198608e-01 -9.66594279e-01 -4.09163207e-01 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0f2874c2-3515-4ab6-9158-17dcb0bcfcc8	a-multi-purpose-audio-visual-corpus-for-multi	2301.1018	null	https://arxiv.org/abs/2301.10180v1	https://arxiv.org/pdf/2301.10180v1.pdf	A Multi-Purpose Audio-Visual Corpus for Multi-Modal Persian Speech Recognition: the Arman-AV Dataset	In recent years, significant progress has been made in automatic lip reading. But these methods require large-scale datasets that do not exist for many low-resource languages. In this paper, we have presented a new multipurpose audio-visual dataset for Persian. This dataset consists of almost 220 hours of videos with 1760 corresponding speakers. In addition to lip reading, the dataset is suitable for automatic speech recognition, audio-visual speech recognition, and speaker recognition. Also, it is the first large-scale lip reading dataset in Persian. A baseline method was provided for each mentioned task. In addition, we have proposed a technique to detect visemes (a visual equivalent of a phoneme) in Persian. The visemes obtained by this method increase the accuracy of the lip reading task by 7% relatively compared to the previously proposed visemes, which can be applied to other languages as well.	['Nasser Mozayani', 'Mohammad Reza Mohammadi', 'Hossein Zeinali', 'Ali Lashini', 'Samin Heydarian', 'Javad Peymanfard']	2023-01-21	null	null	null	null	['speaker-recognition', 'audio-visual-speech-recognition']	['speech', 'speech']	[ 1.40765309e-01 3.37662771e-02 -3.39439034e-01 -8.33312608e-03 -1.17746139e+00 -2.17424765e-01 6.78792894e-01 -5.16875237e-02 -4.25256670e-01 8.28141153e-01 5.57684183e-01 -1.19926833e-01 4.36710477e-01 -3.41377467e-01 -4.18969780e-01 -7.39988148e-01 4.60181355e-01 3.75733674e-01 3.16914320e-01 1.89556271e-01 4.39540565e-01 5.31802773e-01 -2.16638088e+00 1.52228013e-01 8.77922654e-01 7.56311595e-01 6.27272069e-01 5.88922381e-01 -2.84859598e-01 2.72750616e-01 -7.26699352e-01 -2.42385983e-01 -7.37641230e-02 -3.60484987e-01 -7.27561891e-01 2.26694271e-01 8.38663518e-01 -2.84151465e-01 -8.84048939e-02 9.44483757e-01 1.17918313e+00 1.44201979e-01 7.44474471e-01 -1.19298017e+00 -5.78115642e-01 5.65063238e-01 -6.50849760e-01 -1.72868639e-01 5.66611826e-01 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b6d9d9bd-697b-4285-92a3-a444c2e98295	vector-quantized-semantic-communication	2209.11519	null	https://arxiv.org/abs/2209.11519v2	https://arxiv.org/pdf/2209.11519v2.pdf	Vector Quantized Semantic Communication System	Although analog semantic communication systems have received considerable attention in the literature, there is less work on digital semantic communication systems. In this paper, we develop a deep learning (DL)-enabled vector quantized (VQ) semantic communication system for image transmission, named VQ-DeepSC. Specifically, we propose a convolutional neural network (CNN)-based transceiver to extract multi-scale semantic features of images and introduce multi-scale semantic embedding spaces to perform semantic feature quantization, rendering the data compatible with digital communication systems. Furthermore, we employ adversarial training to improve the quality of received images by introducing a PatchGAN discriminator. Experimental results demonstrate that the proposed VQ-DeepSC is more robustness than BPG in digital communication systems and has comparable MS-SSIM performance to the DeepJSCC method.	['Xiaoming Tao', 'Gregory Slabaugh', 'Zhijin Qin', 'Huiqiang Xie', 'Qifan Fu']	2022-09-23	null	null	null	null	['ms-ssim']	['computer-vision']	[ 4.95120555e-01 1.09259717e-01 -5.69332913e-02 -3.55968833e-01 -6.67302251e-01 -3.83335888e-01 5.03420889e-01 -2.81047523e-01 -1.93735257e-01 6.13309383e-01 2.50754923e-01 -3.79068345e-01 -8.27682465e-02 -1.13142049e+00 -6.29303932e-01 -6.42294228e-01 8.00478309e-02 -4.47226405e-01 7.13561326e-02 -4.94701922e-01 1.18582696e-01 3.81726027e-01 -7.20343590e-01 1.31070800e-02 5.56730986e-01 1.78249466e+00 2.27806374e-01 6.33535445e-01 -1.00346781e-01 6.21198237e-01 -1.08984005e+00 -6.55992985e-01 2.64457941e-01 -7.03873456e-01 -4.84654725e-01 -6.79096878e-02 8.42819922e-04 -4.74802971e-01 -1.19165015e+00 1.50854099e+00 7.78551042e-01 -5.10474920e-01 5.13319135e-01 -1.38988423e+00 -1.13456941e+00 5.18394768e-01 -6.17160974e-03 2.26865727e-02 3.75762492e-01 -3.82610522e-02 6.85689628e-01 -4.26658630e-01 1.83897704e-01 1.52618861e+00 5.94575465e-01 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1b9c82bb-4ad2-45e6-b434-08b1eceb6c6b	neural-semi-markov-crf-for-monolingual-word	2106.02569	null	https://arxiv.org/abs/2106.02569v2	https://arxiv.org/pdf/2106.02569v2.pdf	Neural semi-Markov CRF for Monolingual Word Alignment	Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc. In this paper, we present a novel neural semi-Markov CRF alignment model, which unifies word and phrase alignments through variable-length spans. We also create a new benchmark with human annotations that cover four different text genres to evaluate monolingual word alignment models in more realistic settings. Experimental results show that our proposed model outperforms all previous approaches for monolingual word alignment as well as a competitive QA-based baseline, which was previously only applied to bilingual data. Our model demonstrates good generalizability to three out-of-domain datasets and shows great utility in two downstream applications: automatic text simplification and sentence pair classification tasks.	['Wei Xu', 'Chao Jiang', 'Wuwei Lan']	2021-06-04	null	https://aclanthology.org/2021.acl-long.531	https://aclanthology.org/2021.acl-long.531.pdf	acl-2021-5	['sentence-pair-classification']	['natural-language-processing']	[ 6.03534102e-01 3.77810746e-02 -2.51633108e-01 -4.65116888e-01 -1.22403324e+00 -5.96916676e-01 6.88650608e-01 1.50880232e-01 -5.92247367e-01 1.22006333e+00 5.75064540e-01 -6.71002030e-01 4.26032305e-01 -4.64553446e-01 -6.91348255e-01 -3.57700020e-01 7.45920539e-01 1.05901337e+00 -3.56108010e-01 -7.58400023e-01 3.21951807e-01 1.55693039e-01 -8.40371490e-01 3.14994782e-01 1.45188618e+00 -1.84604853e-01 4.14946318e-01 5.81189752e-01 -2.51014829e-01 4.19628978e-01 -9.25003290e-01 -9.39640641e-01 2.01572672e-01 -5.41902840e-01 -1.08350992e+00 -3.75136405e-01 9.14229810e-01 -7.76020288e-02 -5.60608543e-02 1.10161436e+00 8.67700994e-01 1.03825450e-01 7.72437692e-01 -6.74756944e-01 -9.82893467e-01 1.08581293e+00 -5.30445695e-01 2.91818976e-01 7.33024657e-01 -1.21638529e-01 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40fc8c70-a5dd-46be-8fba-dc7f94a60315	evaluation-of-dynamic-causal-modelling-and	2306.15859	null	https://arxiv.org/abs/2306.15859v1	https://arxiv.org/pdf/2306.15859v1.pdf	Evaluation of dynamic causal modelling and Bayesian model selection using simulations of networks of spiking neurons	Inferring the mechanisms underlying physiological and pathological processes in the brain from recorded electrical activity is challenging. Bayesian model selection and dynamic causal modelling aim to identify likely biophysical models to explain data and to fit the model parameters. Here, we use data generated by simulations to investigate the effectiveness of Bayesian model selection and dynamic causal modelling when applied at steady state in the frequency domain to identify and fit Jansen-Rit models. We first investigate the impact of the necessary assumption of linearity on the dynamics of the Jansen-Rit model. We then apply dynamic causal modelling and Bayesian model selection to data generated from simulations of linear neural mass models, non-linear neural mass models, and networks of discrete spiking neurons. Action potentials are a characteristic feature of neuronal dynamics but have not previously been explicitly included in simulations used to test Bayesian model selection or dynamic causal modelling. We find that the assumption of linearity abolishes the qualitative transitions seen as a function of the connectivity parameter in the original Jansen-Rit model. As with previous work, we find that the recovery procedures are effective when applied to data from linear Jansen-Rit neural mass models, however, when applying them to non-linear neural mass models and networks of discrete spiking neurons we find that their effectiveness is significantly reduced, suggesting caution is required when applying these methods.	['Matthew G. Thomas']	2023-06-28	null	null	null	null	['model-selection']	['methodology']	[ 6.17134929e-01 -7.26686567e-02 2.09118709e-01 1.39794827e-01 4.48861942e-02 -3.33966136e-01 8.83858323e-01 2.59566102e-02 -6.62295640e-01 1.02317131e+00 5.73828742e-02 -5.97996414e-01 -7.19671309e-01 -3.90205622e-01 -8.52814078e-01 -1.05392337e+00 -3.32415909e-01 4.19183314e-01 5.07016361e-01 2.16815948e-01 4.65942144e-01 5.74573696e-01 -1.33455253e+00 -1.25789316e-02 5.52600205e-01 4.49924201e-01 2.24172294e-01 7.12895095e-01 2.32794315e-01 4.30598676e-01 -4.95375186e-01 3.18845540e-01 -6.29330985e-03 -7.11033523e-01 -5.08511961e-01 -4.43327636e-01 4.15489711e-02 4.54409085e-02 -4.66455311e-01 8.29645455e-01 6.47614956e-01 -7.27163479e-02 1.11932909e+00 -1.09590900e+00 -4.20169234e-02 8.77966762e-01 -3.48788321e-01 6.68401539e-01 -2.12421939e-01 2.74987251e-01 3.65173668e-01 -2.64827549e-01 5.81277907e-01 1.29619920e+00 8.39742362e-01 3.97472471e-01 -2.15489745e+00 -9.33115482e-01 8.96673575e-02 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-1.56457350e-01 1.52346432e-01 -7.67495513e-01 -9.97895002e-01 1.91033632e-01]	[7.873845100402832, 3.036752223968506]
922fc2cb-f1f0-4067-a233-d0ab789c2b2e	2305-14386	2305.14386	null	https://arxiv.org/abs/2305.14386v1	https://arxiv.org/pdf/2305.14386v1.pdf	Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation	In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models. Our approach is designed to consider the student model's weaknesses and foster a tailored learning experience by generating targeted exercises aligned with educational science principles, such as knowledge tracing and personalized learning. Concretely, we let GPT-3 be a math tutor and run two steps iteratively: 1) assessing the student model's current learning status on a GPT-generated exercise book, and 2) improving the student model by training it with tailored exercise samples generated by GPT-3. Experimental results reveal that our approach outperforms LLMs (e.g., GPT-3 and PaLM) in accuracy across three distinct benchmarks while employing significantly fewer parameters. Furthermore, we provide a comprehensive analysis of the various components within our methodology to substantiate their efficacy.	['Ashwin Kaylan', 'Xiangliang Zhang', 'Peter Clark', 'Tanmay Rajpurohit', 'Wenhao Yu', 'Zhenwen Liang']	2023-05-22	null	null	null	null	['math-word-problem-solving', 'knowledge-tracing', 'math-word-problem-solving', 'math-word-problem-solving']	['knowledge-base', 'miscellaneous', 'reasoning', 'time-series']	[ 1.95947126e-01 2.29217276e-01 -2.58670717e-01 -1.57395303e-01 -8.12785327e-01 -9.60997045e-01 3.83895129e-01 3.89855534e-01 -3.04239392e-01 5.98344207e-01 4.28726338e-02 -1.04049361e+00 -4.64837551e-01 -1.31308103e+00 -7.98291028e-01 5.19424044e-02 3.19320232e-01 4.04505372e-01 3.90027732e-01 -3.21170568e-01 4.37010109e-01 4.52629596e-01 -1.64827919e+00 1.55158073e-01 1.62464333e+00 5.10377347e-01 1.68239936e-01 1.02124703e+00 -6.55100107e-01 1.51124716e+00 -8.05704176e-01 -6.80965841e-01 -1.37434795e-01 -6.55075312e-01 -1.29214799e+00 -3.24457824e-01 7.44494855e-01 -2.19886735e-01 -1.36256039e-01 8.47353876e-01 2.48252168e-01 5.42186737e-01 4.41066772e-01 -9.24878597e-01 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9d323f76-b661-40df-993e-5c83ab051b89	a-single-image-dehazing-technique-using-the	null	null	https://ieeexplore.ieee.org/document/9458242	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9458242	A Single Image Dehazing Technique Using the Dual Transmission Maps Strategy and Gradient-Domain Guided Image Filtering	In this paper, a single image dehazing technique using dual transmission maps strategy and gradient-domain guided image filtering is presented. A new strategy is adopted to compute the dual transmission maps using the dark channel and atmospheric light. Further, the transmission maps are refined to remove any remaining ill effects using the gradient-domain-guided filter. Finally, using the dark channel, atmospheric light, and refined transmission map, the haze-free image is obtained. The dual transmission maps strategy not only removes halo artifacts and reduces the saturation but also ensures the natural appearance in the recovered images. Furthermore, the proposed scheme is evaluated using a wide range of images and compared with state-of-the-art schemes. The comparison shows the superiority of the proposed technique in terms of recovering haze-free images.	['A. Ullah and E. Elbasi', 'M. Imran', 'S. M. Ehsan']	2021-07-17	null	null	null	journal-2021-7	['image-dehazing']	['computer-vision']	[ 4.00193006e-01 -6.01149261e-01 7.96301007e-01 -3.14958543e-02 -2.20337614e-01 -7.98237398e-02 4.25179899e-01 -3.97268355e-01 -4.39967841e-01 8.98756981e-01 -4.03910391e-02 6.63505122e-03 -1.58257574e-01 -9.16657269e-01 -2.51373559e-01 -1.37866890e+00 -4.04264554e-02 -4.72215921e-01 7.80439317e-01 -4.90397364e-01 4.45000112e-01 2.06464276e-01 -1.72354293e+00 2.25112643e-02 1.32719588e+00 9.68708634e-01 6.03571653e-01 6.56246006e-01 1.11623667e-01 6.88235164e-01 -6.32856607e-01 -6.09996216e-03 6.15498602e-01 -5.06529570e-01 -2.41465017e-01 4.36777472e-01 5.29935956e-01 -6.94027305e-01 -3.16287279e-01 1.42157638e+00 3.40981752e-01 5.23792386e-01 3.45176846e-01 -6.46031320e-01 -7.73563862e-01 -5.43183684e-01 -8.50395858e-01 3.18107754e-01 1.22811049e-01 2.29441002e-01 1.87430769e-01 -1.10347223e+00 4.18669343e-01 1.00992465e+00 2.61594951e-01 1.43192425e-01 -9.37874675e-01 -6.63093686e-01 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0033596c-ac93-48ed-b71a-53208359c5c8	enhance-enriching-health-data-by-annotations	2107.12734	null	https://arxiv.org/abs/2107.12734v2	https://arxiv.org/pdf/2107.12734v2.pdf	ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification	We present ENHANCE, an open dataset with multiple annotations to complement the existing ISIC and PH2 skin lesion classification datasets. This dataset contains annotations of visual ABC (asymmetry, border, colour) features from non-expert annotation sources: undergraduate students, crowd workers from Amazon MTurk and classic image processing algorithms. In this paper we first analyse the correlations between the annotations and the diagnostic label of the lesion, as well as study the agreement between different annotation sources. Overall we find weak correlations of non-expert annotations with the diagnostic label, and low agreement between different annotation sources. We then study multi-task learning (MTL) with the annotations as additional labels, and show that non-expert annotations can improve (ensembles of) state-of-the-art convolutional neural networks via MTL. We hope that our dataset can be used in further research into multiple annotations and/or MTL. All data and models are available on Github: https://github.com/raumannsr/ENHANCE.	['Veronika Cheplygina', 'Josien P. W. Pluim', 'Max Joosten', 'Gerard Schouten', 'Ralf Raumanns']	2021-07-27	null	null	null	null	['skin-lesion-classification']	['medical']	[ 8.92293304e-02 1.75475180e-01 -2.64212549e-01 -2.39569157e-01 -9.62058127e-01 -1.03559470e+00 5.71181536e-01 5.32693565e-01 -6.00200891e-01 5.47780633e-01 1.29655540e-01 2.77462490e-02 -1.48021847e-01 -3.28106016e-01 -3.68848622e-01 -6.77240968e-01 2.42445111e-01 4.70255047e-01 7.60933936e-01 -5.37714846e-02 2.65473485e-01 3.00741613e-01 -1.42931974e+00 1.01046705e+00 5.87689817e-01 1.23979986e+00 -3.99519578e-02 7.84220994e-01 5.33798039e-02 9.43062603e-01 -4.36438829e-01 -7.35096455e-01 9.16605219e-02 -5.94806578e-03 -1.25079191e+00 -1.08852416e-01 8.86098742e-01 1.52863443e-01 1.31600853e-02 1.00825787e+00 8.66445303e-01 -4.18270707e-01 7.41179466e-01 -1.38904262e+00 -9.50933158e-01 2.04879612e-01 -8.80774558e-01 2.82672584e-01 2.44880185e-01 2.14342102e-01 8.33844841e-01 -7.29949951e-01 9.98724401e-01 9.13496673e-01 1.15022361e+00 5.63158512e-01 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4acb536c-2a26-4d6f-a139-6c42f615885d	eusdisparser-improving-an-under-resourced	null	null	https://aclanthology.org/W19-2709	https://aclanthology.org/W19-2709.pdf	EusDisParser: improving an under-resourced discourse parser with cross-lingual data	Development of discourse parsers to annotate the relational discourse structure of a text is crucial for many downstream tasks. However, most of the existing work focuses on English, assuming a quite large dataset. Discourse data have been annotated for Basque, but training a system on these data is challenging since the corpus is very small. In this paper, we create the first demonstrator based on RST for Basque, and we investigate the use of data in another language to improve the performance of a Basque discourse parser. More precisely, we build a monolingual system using the small set of data available and investigate the use of multilingual word embeddings to train a system for Basque using data annotated for another language. We found that our approach to building a system limited to the small set of data available for Basque allowed us to get an improvement over previous approaches making use of many data annotated in other languages. At best, we get 34.78 in F1 for the full discourse structure. More data annotation is necessary in order to improve the results obtained with these techniques. We also describe which relations match with the gold standard, in order to understand these results.	["Chlo{\\'e} Braud", 'Mikel Iruskieta']	2019-06-01	null	null	null	ws-2019-6	['multilingual-word-embeddings']	['methodology']	[-1.81237325e-01 7.28531837e-01 5.57918698e-02 -4.11790371e-01 -9.30762053e-01 -8.60814035e-01 9.82861817e-01 5.29202580e-01 -7.01155782e-01 1.11867988e+00 7.57769823e-01 -4.21433032e-01 2.08557293e-01 -7.29247153e-01 -5.53616822e-01 -4.30265903e-01 1.44028133e-02 8.97635996e-01 6.74888372e-01 -8.64172876e-01 2.25620866e-01 5.20651191e-02 -1.19912505e+00 7.36757755e-01 6.94026768e-01 1.61597416e-01 5.85184515e-01 7.56338477e-01 -3.15137267e-01 1.14338756e+00 -1.04278445e+00 -4.04208750e-01 -1.72665268e-01 -5.57238817e-01 -1.52675855e+00 -3.44504207e-01 2.85196245e-01 -1.55436501e-01 3.57755576e-03 5.75798213e-01 4.18969095e-01 1.64424941e-01 5.02888381e-01 -7.24408805e-01 -5.13990879e-01 1.11737692e+00 3.76923871e-03 5.08251846e-01 7.08862066e-01 -4.87388015e-01 1.08639538e+00 -4.24803615e-01 1.20416307e+00 1.37877607e+00 3.54160368e-01 6.08059525e-01 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5.83713233e-01 8.92469287e-02 -6.24874234e-01 4.92194891e-02 2.82472879e-01]	[10.768061637878418, 9.513350486755371]
a2c7aed5-adda-4cb9-b032-09b583372b06	implicit-feedback-deep-collaborative	2009.0895	null	https://arxiv.org/abs/2009.08950v2	https://arxiv.org/pdf/2009.08950v2.pdf	Implicit Feedback Deep Collaborative Filtering Product Recommendation System	In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the sparse customer purchasing behaviours. The latent factors are used to generalize the purchasing pattern of the customers and to provide product recommendations. CF with Neural Collaborative Filtering(NCF) was shown to produce the highest Normalized Discounted Cumulative Gain (NDCG) performance on the real-world proprietary dataset provided by a large parts supply company. Different hyperparameters were tested using Bayesian Optimization (BO) for applicability in the CF framework. External data sources like click-data and metrics like Clickthrough Rate (CTR) were reviewed for potential extensions to the work presented. The work shown in this paper provides techniques the Company can use to provide product recommendations to enhance revenues, attract new customers, and gain advantages over competitors.	['Yuri Lawryshyn', 'Deepa Kundur', 'Karthik Raja Kalaiselvi Bhaskar']	2020-09-08	null	null	null	null	['product-recommendation']	['miscellaneous']	[ 4.70125377e-02 -1.63473248e-01 -6.90302610e-01 -9.80174303e-01 -2.28263170e-01 -3.42306942e-01 4.78928715e-01 1.80929840e-01 -2.43047073e-01 6.78891838e-01 4.61216241e-01 -5.22276759e-01 -7.30656683e-01 -7.58310080e-01 -1.04499944e-01 -5.81866682e-01 -3.29137176e-01 4.45619315e-01 -2.57996917e-01 -2.64510423e-01 8.35529864e-01 3.45031947e-01 -1.70952332e+00 8.09831917e-01 7.79081047e-01 9.35265481e-01 5.70068061e-01 5.84960461e-01 -1.97153926e-01 7.39833936e-02 -5.95781982e-01 -3.92959327e-01 6.25046790e-01 -1.22337043e-01 -6.84593394e-02 9.75947231e-02 3.68278354e-01 -2.78081954e-01 1.21948466e-01 7.22758293e-01 4.22070742e-01 7.44582593e-01 4.91704136e-01 -9.97583926e-01 -1.08610058e+00 9.89209294e-01 -1.81447640e-01 3.95352036e-01 4.53350335e-01 -2.43734017e-01 1.16311896e+00 -9.43643689e-01 5.53008556e-01 1.30039811e+00 5.50614059e-01 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-1.16060996e+00 -9.62791443e-01 1.49943620e-01 -4.05119002e-01 -6.53994203e-01 -5.16965985e-01 -5.43278456e-02]	[10.007655143737793, 5.7537150382995605]
d8109521-21d5-4583-b53d-37b14ce6479f	real-word-error-correction-with-trigrams	2302.04096	null	https://arxiv.org/abs/2302.04096v1	https://arxiv.org/pdf/2302.04096v1.pdf	Real-Word Error Correction with Trigrams: Correcting Multiple Errors in a Sentence	Spelling correction is a fundamental task in Text Mining. In this study, we assess the real-word error correction model proposed by Mays, Damerau and Mercer and describe several drawbacks of the model. We propose a new variation which focuses on detecting and correcting multiple real-word errors in a sentence, by manipulating a Probabilistic Context-Free Grammar (PCFG) to discriminate between items in the search space. We test our approach on the Wall Street Journal corpus and show that it outperforms Hirst and Budanitsky's WordNet-based method and Wilcox-O'Hearn, Hirst, and Budanitsky's fixed windows size method.-O'Hearn, Hirst, and Budanitsky's fixed windows size method.	['Seyed MohammadSadegh Dashti']	2023-02-07	null	null	null	null	['spelling-correction']	['natural-language-processing']	[ 1.33783549e-01 -1.18352294e-01 -3.98343243e-02 -2.39406079e-01 -6.96848571e-01 -2.60478884e-01 3.93198401e-01 8.36310387e-01 -9.63231087e-01 1.05292201e+00 1.69906661e-01 -6.95884049e-01 -5.45513034e-01 -6.56329155e-01 -2.11975887e-01 -6.95759207e-02 1.28248528e-01 4.28015321e-01 6.08578980e-01 -3.68941724e-01 1.03783393e+00 1.92626283e-01 -1.58586633e+00 1.17526151e-01 1.13032019e+00 3.57220054e-01 6.31968975e-01 9.77948368e-01 -4.90684927e-01 3.88942778e-01 -7.74855673e-01 -4.14535701e-01 -1.10485934e-01 -4.08435762e-01 -9.27347660e-01 -6.18977487e-01 3.49358171e-01 4.03824985e-01 1.33461937e-01 1.19421411e+00 3.24953675e-01 3.71100754e-01 6.46791875e-01 -6.04226708e-01 -5.63129723e-01 7.39251971e-01 -3.61655772e-01 6.23579621e-01 5.49035251e-01 -2.31670856e-01 1.11164379e+00 -8.78856003e-01 7.15501249e-01 1.13981175e+00 8.29110324e-01 6.25429273e-01 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c86f7ce9-f99b-4777-9984-8c0f4b89ee9e	is-it-possible-not-to-cheat-on-the-turing	2206.14672	null	https://arxiv.org/abs/2206.14672v4	https://arxiv.org/pdf/2206.14672v4.pdf	Not Cheating on the Turing Test: Towards Grounded Language Learning in Artificial Intelligence	Recent hype surrounding the increasing sophistication of language processing models has renewed optimism regarding machines achieving a human-like command of natural language. Research in the area of natural language understanding (NLU) in artificial intelligence claims to have been making great strides in this area, however, the lack of conceptual clarity/consistency in how 'understanding' is used in this and other disciplines makes it difficult to discern how close we actually are. In this interdisciplinary research thesis, I integrate insights from cognitive science/psychology, philosophy of mind, and cognitive linguistics, and evaluate it against a critical review of current approaches in NLU to explore the basic requirements--and remaining challenges--for developing artificially intelligent systems with human-like capacities for language use and comprehension.	['Lize Alberts']	2022-06-29	null	null	null	null	['grounded-language-learning']	['natural-language-processing']	[ 3.54529798e-01 6.08104408e-01 -1.97544217e-01 -4.20493722e-01 1.02586458e-02 -6.38191879e-01 7.14903772e-01 6.38046086e-01 -3.46164912e-01 2.55951554e-01 3.98118049e-01 -1.01891756e+00 -1.75832957e-01 -6.90466285e-01 -2.64528215e-01 -1.83084402e-02 3.70335191e-01 2.74724036e-01 -1.85180813e-01 -6.25024319e-01 6.87608898e-01 2.46724576e-01 -1.40956008e+00 8.02437291e-02 1.02132630e+00 1.76296294e-01 4.12313640e-01 4.78266180e-01 -5.71299314e-01 1.25830472e+00 -3.51633638e-01 -2.32417792e-01 -3.07271034e-01 -6.42809868e-01 -1.46994948e+00 1.15218438e-01 -2.12242231e-01 -9.72260535e-02 -6.13161027e-02 9.07788277e-01 -2.40786821e-02 -4.64076474e-02 3.73476505e-01 -7.92288423e-01 -1.01231825e+00 5.20266533e-01 -1.30326584e-01 2.87915707e-01 8.97384763e-01 1.85298175e-01 7.57986963e-01 -3.28628629e-01 5.97177744e-01 1.72057438e+00 3.75812948e-01 7.88346350e-01 -1.16034031e+00 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8deb673f-143b-473e-83b4-dc920cae584e	attribute-based-chinese-named-entity	null	null	https://aclanthology.org/W12-6324	https://aclanthology.org/W12-6324.pdf	Attribute based Chinese Named Entity Recognition and Disambiguation	null	['Zhenni Huang', 'Wei Han', 'Guang Liu', 'Yuzhao Mao']	2012-12-01	attribute-based-chinese-named-entity-1	https://aclanthology.org/W12-6324	https://aclanthology.org/W12-6324.pdf	ws-2012-12	['chinese-named-entity-recognition']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 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abfdc6d5-0f5f-4d3b-803f-855b7802f4cc	large-scale-learning-on-non-homophilous	2110.14446	null	https://arxiv.org/abs/2110.14446v1	https://arxiv.org/pdf/2110.14446v1.pdf	Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods	Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other. Recently, new Graph Neural Networks (GNNs) have been developed that move beyond the homophily regime; however, their evaluation has often been conducted on small graphs with limited application domains. We collect and introduce diverse non-homophilous datasets from a variety of application areas that have up to 384x more nodes and 1398x more edges than prior datasets. We further show that existing scalable graph learning and graph minibatching techniques lead to performance degradation on these non-homophilous datasets, thus highlighting the need for further work on scalable non-homophilous methods. To address these concerns, we introduce LINKX -- a strong simple method that admits straightforward minibatch training and inference. Extensive experimental results with representative simple methods and GNNs across our proposed datasets show that LINKX achieves state-of-the-art performance for learning on non-homophilous graphs. Our codes and data are available at https://github.com/CUAI/Non-Homophily-Large-Scale.	['Ser-Nam Lim', 'Omkar Bhalerao', 'Vaishnavi Gupta', 'Sijia Linda Huang', 'Xiuyu Li', 'Felix Hohne', 'Derek Lim']	2021-10-27	null	http://proceedings.neurips.cc/paper/2021/hash/ae816a80e4c1c56caa2eb4e1819cbb2f-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/ae816a80e4c1c56caa2eb4e1819cbb2f-Paper.pdf	neurips-2021-12	['node-classification-on-non-homophilic']	['graphs']	[-1.87386498e-01 3.34427267e-01 -6.08152628e-01 -2.35434428e-01 -9.71537679e-02 -4.79865402e-01 5.73491454e-01 3.32637519e-01 1.28255352e-01 9.17236328e-01 -2.65524298e-01 -7.41807401e-01 -2.48579741e-01 -1.12538934e+00 -8.37506175e-01 -5.05868435e-01 -8.13753486e-01 8.13698411e-01 4.75601137e-01 -1.38273714e-02 -2.06892058e-01 2.84683108e-01 -8.84666741e-01 -1.03924863e-01 6.00029051e-01 5.68464875e-01 -2.12782949e-01 7.72557259e-01 1.46600872e-01 8.71133149e-01 -1.94945887e-01 -5.73056519e-01 4.90451038e-01 -2.21593767e-01 -8.06767821e-01 -1.69252351e-01 7.95849502e-01 2.94325780e-03 -8.86317909e-01 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b8de7f9f-3aab-4232-aa66-37d734bec43f	exploring-efficient-volumetric-medical-image	2010.06163	null	https://arxiv.org/abs/2010.06163v2	https://arxiv.org/pdf/2010.06163v2.pdf	Bridging 2D and 3D Segmentation Networks for Computation Efficient Volumetric Medical Image Segmentation: An Empirical Study of 2.5D Solutions	Recently, deep convolutional neural networks have achieved great success for medical image segmentation. However, unlike segmentation of natural images, most medical images such as MRI and CT are volumetric data. In order to make full use of volumetric information, 3D CNNs are widely used. However, 3D CNNs suffer from higher inference time and computation cost, which hinders their further clinical applications. Additionally, with the increased number of parameters, the risk of overfitting is higher, especially for medical images where data and annotations are expensive to acquire. To issue this problem, many 2.5D segmentation methods have been proposed to make use of volumetric spatial information with less computation cost. Despite these works lead to improvements on a variety of segmentation tasks, to the best of our knowledge, there has not previously been a large-scale empirical comparison of these methods. In this paper, we aim to present a review of the latest developments of 2.5D methods for volumetric medical image segmentation. Additionally, to compare the performance and effectiveness of these methods, we provide an empirical study of these methods on three representative segmentation tasks involving different modalities and targets. Our experimental results highlight that 3D CNNs may not always be the best choice. Despite all these 2.5D methods can bring performance gains to 2D baseline, not all the methods hold the benefits on different datasets. We hope the results and conclusions of our study will prove useful for the community on exploring and developing efficient volumetric medical image segmentation methods.	['Jicong Zhang', 'Le Ding', 'Qingcheng Liao', 'Yichi Zhang']	2020-10-13	null	null	null	null	['volumetric-medical-image-segmentation']	['medical']	[ 8.42434242e-02 1.97113752e-01 -3.02746445e-01 -4.24773872e-01 -6.44400656e-01 -2.62697786e-01 1.74036846e-01 3.45743269e-01 -5.73426008e-01 6.36735260e-01 -1.44224539e-01 -4.51176077e-01 5.96024059e-02 -8.59675467e-01 -4.31563199e-01 -6.12644672e-01 -3.23836386e-01 5.84212184e-01 5.52686334e-01 8.83216634e-02 4.98252809e-02 8.61816406e-01 -1.17051339e+00 -1.65508352e-02 7.72746503e-01 1.19983542e+00 2.37954333e-01 2.36186087e-01 -3.56026471e-01 2.63379306e-01 -4.30628628e-01 -1.80730984e-01 3.62432122e-01 -3.46961230e-01 -9.88776505e-01 1.77244782e-01 3.38232666e-01 -6.84924424e-01 -1.32308558e-01 8.62707675e-01 8.06341410e-01 6.27016416e-03 6.43430173e-01 -9.21104491e-01 -4.07997400e-01 3.37557942e-01 -7.03281343e-01 4.69051600e-01 1.02700070e-02 1.04699947e-01 4.97886807e-01 -6.67967319e-01 5.68026662e-01 8.98609161e-01 9.12442684e-01 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16dc90f8-d8d9-4d9a-b214-2eb544090b7b	variance-covariance-regularization-improves	2306.13292	null	https://arxiv.org/abs/2306.13292v1	https://arxiv.org/pdf/2306.13292v1.pdf	Variance-Covariance Regularization Improves Representation Learning	Transfer learning has emerged as a key approach in the machine learning domain, enabling the application of knowledge derived from one domain to improve performance on subsequent tasks. Given the often limited information about these subsequent tasks, a strong transfer learning approach calls for the model to capture a diverse range of features during the initial pretraining stage. However, recent research suggests that, without sufficient regularization, the network tends to concentrate on features that primarily reduce the pretraining loss function. This tendency can result in inadequate feature learning and impaired generalization capability for target tasks. To address this issue, we propose Variance-Covariance Regularization (VCR), a regularization technique aimed at fostering diversity in the learned network features. Drawing inspiration from recent advancements in the self-supervised learning approach, our approach promotes learned representations that exhibit high variance and minimal covariance, thus preventing the network from focusing solely on loss-reducing features. We empirically validate the efficacy of our method through comprehensive experiments coupled with in-depth analytical studies on the learned representations. In addition, we develop an efficient implementation strategy that assures minimal computational overhead associated with our method. Our results indicate that VCR is a powerful and efficient method for enhancing transfer learning performance for both supervised learning and self-supervised learning, opening new possibilities for future research in this domain.	['Yann Lecun', 'Yubei Chen', 'Ravid Shwartz-Ziv', 'Jiachen Zhu']	2023-06-23	null	null	null	null	['self-supervised-learning', 'transfer-learning']	['computer-vision', 'miscellaneous']	[ 4.12173390e-01 -4.40424532e-02 -2.83337474e-01 -5.05201697e-01 -5.18060565e-01 -3.14030528e-01 4.59888309e-01 1.98236987e-01 -4.98404980e-01 9.10470903e-01 -9.42092538e-02 -1.56520113e-01 -4.29727852e-01 -8.63969445e-01 -7.64349520e-01 -6.59429371e-01 6.26884922e-02 4.84656654e-02 7.67802522e-02 -2.50055939e-01 2.28395477e-01 6.16209865e-01 -1.57520700e+00 1.50982574e-01 1.15977430e+00 1.00036502e+00 3.10781628e-01 3.68785337e-02 -6.59413636e-02 7.18231559e-01 -4.44297671e-01 -3.16438615e-01 2.09345296e-01 -4.85090166e-01 -6.69428587e-01 2.10454520e-02 3.45292896e-01 7.48997414e-03 -1.39876366e-01 9.15468812e-01 4.40949976e-01 4.97424573e-01 8.78345132e-01 -1.01059461e+00 -7.19212949e-01 3.88547391e-01 -4.69013959e-01 2.57246286e-01 -3.29123177e-02 8.06800053e-02 1.06930923e+00 -8.67888689e-01 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91a8da5e-7081-438b-b3e3-c17d8315f5a5	efficient-and-accurate-monitoring-of-the	1706.08088	null	http://arxiv.org/abs/1706.08088v1	http://arxiv.org/pdf/1706.08088v1.pdf	Efficient and accurate monitoring of the depth information in a Wireless Multimedia Sensor Network based surveillance	Wireless Multimedia Sensor Network (WMSN) is a promising technology capturing rich multimedia data like audio and video, which can be useful to monitor an environment under surveillance. However, many scenarios in real time monitoring requires 3D depth information. In this research work, we propose to use the disparity map that is computed from two or multiple images, in order to monitor the depth information in an object or event under surveillance using WMSN. Our system is based on distributed wireless sensors allowing us to notably reduce the computational time needed for 3D depth reconstruction, thus permitting the success of real time solutions. Each pair of sensors will capture images for a targeted place/object and will operate a Stereo Matching in order to create a Disparity Map. Disparity maps will give us the ability to decrease traffic on the bandwidth, because they are of low size. This will increase WMSN lifetime. Any event can be detected after computing the depth value for the target object in the scene, and also 3D scene reconstruction can be achieved with a disparity map and some reference(s) image(s) taken by the node(s).	['Rony Darazi', 'Anthony Tannoury', 'Christophe Guyeux', 'Abdallah Makhoul']	2017-06-25	null	null	null	null	['3d-scene-reconstruction']	['computer-vision']	[ 8.01336706e-01 5.72316535e-02 1.37308657e-01 -2.59481758e-01 -2.28726357e-01 -3.76769990e-01 3.79475683e-01 4.31508303e-01 -6.86455965e-01 4.06131595e-01 -2.39557952e-01 1.07134625e-01 -1.11616261e-01 -1.53837800e+00 -3.36558998e-01 -9.73408163e-01 -4.38794255e-01 5.37358187e-02 1.04162180e+00 3.66592743e-02 2.27564991e-01 6.45395041e-01 -1.83597004e+00 8.86092857e-02 2.12673292e-01 1.60982144e+00 7.56070554e-01 7.08932221e-01 -1.81581810e-01 5.71769893e-01 -7.37039447e-01 1.11090392e-01 3.66510779e-01 -3.00929639e-02 -2.76134163e-01 2.62562707e-02 9.81482640e-02 -8.12716067e-01 -9.22789648e-02 1.01709855e+00 6.05800867e-01 -1.34221613e-01 3.58770877e-01 -1.20906675e+00 6.32743120e-01 3.23047519e-01 -8.42413664e-01 1.66166797e-01 8.85644913e-01 -5.00831828e-02 6.70849234e-02 -8.90937150e-02 6.50738060e-01 1.14214540e+00 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9dae68fe-3b8c-441e-a6a9-a8ad12154bc7	point-spread-function-estimation-for-blind	2112.11004	null	https://arxiv.org/abs/2112.11004v1	https://arxiv.org/pdf/2112.11004v1.pdf	Point spread function estimation for blind image deblurring problems based on framelet transform	One of the most important issues in the image processing is the approximation of the image that has been lost due to the blurring process. These types of matters are divided into non-blind and blind problems. The second type of problem is more complex in terms of calculations than the first problems due to the unknown of original image and point spread function estimation. In the present paper, an algorithm based on coarse-to-fine iterative by $l_0-\alpha l_1$ regularization and framelet transform is introduced to approximate the spread function estimation. Framelet transfer improves the restored kernel due to the decomposition of the kernel to different frequencies. Also in the proposed model fraction gradient operator is used instead of ordinary gradient operator. The proposed method is investigated on different kinds of images such as text, face, natural. The output of the proposed method reflects the effectiveness of the proposed algorithm in restoring the images from blind problems.	['Reza Parvaz']	2021-12-21	null	null	null	null	['blind-image-deblurring']	['computer-vision']	[-6.13888167e-02 -3.17041993e-01 5.78326583e-01 -1.15505278e-01 1.49757624e-01 -1.54848188e-01 2.67733753e-01 -2.68465310e-01 -4.66781795e-01 1.05113220e+00 5.81822574e-01 1.06845617e-01 -4.47522223e-01 -3.54250342e-01 -2.77523488e-01 -5.24839818e-01 2.54777938e-01 2.41332818e-02 8.93476531e-02 -2.05316275e-01 6.25767589e-01 4.37259883e-01 -1.67145836e+00 3.02747991e-02 9.94778693e-01 7.78103411e-01 3.80353332e-01 5.92037022e-01 -2.64163673e-01 8.16584826e-01 -4.89770591e-01 9.43336170e-03 4.06869918e-01 -7.52728283e-01 -5.17118812e-01 3.30080926e-01 1.33833766e-01 -5.63830316e-01 -4.96215299e-02 1.41407633e+00 6.55319035e-01 4.18407470e-01 1.01471043e+00 -4.95054394e-01 -7.60598242e-01 1.69770683e-05 -9.98190284e-01 5.10457993e-01 4.52822685e-01 -1.29263088e-01 -4.15821299e-02 -9.41212952e-01 3.91718864e-01 1.38607991e+00 7.13211656e-01 2.94929743e-01 -9.93989289e-01 -2.57732958e-01 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6.24086380e-01 6.70755029e-01 -5.91489196e-01 -8.38648736e-01 2.67158508e-01]	[11.599422454833984, -2.660295009613037]
cbf77879-58cc-4aac-a7db-76cc57fbcee7	ordinal-depth-supervision-for-3d-human-pose	1805.04095	null	http://arxiv.org/abs/1805.04095v1	http://arxiv.org/pdf/1805.04095v1.pdf	Ordinal Depth Supervision for 3D Human Pose Estimation	Our ability to train end-to-end systems for 3D human pose estimation from single images is currently constrained by the limited availability of 3D annotations for natural images. Most datasets are captured using Motion Capture (MoCap) systems in a studio setting and it is difficult to reach the variability of 2D human pose datasets, like MPII or LSP. To alleviate the need for accurate 3D ground truth, we propose to use a weaker supervision signal provided by the ordinal depths of human joints. This information can be acquired by human annotators for a wide range of images and poses. We showcase the effectiveness and flexibility of training Convolutional Networks (ConvNets) with these ordinal relations in different settings, always achieving competitive performance with ConvNets trained with accurate 3D joint coordinates. Additionally, to demonstrate the potential of the approach, we augment the popular LSP and MPII datasets with ordinal depth annotations. This extension allows us to present quantitative and qualitative evaluation in non-studio conditions. Simultaneously, these ordinal annotations can be easily incorporated in the training procedure of typical ConvNets for 3D human pose. Through this inclusion we achieve new state-of-the-art performance for the relevant benchmarks and validate the effectiveness of ordinal depth supervision for 3D human pose.	['Xiaowei Zhou', 'Georgios Pavlakos', 'Kostas Daniilidis']	2018-05-10	ordinal-depth-supervision-for-3d-human-pose-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Pavlakos_Ordinal_Depth_Supervision_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Pavlakos_Ordinal_Depth_Supervision_CVPR_2018_paper.pdf	cvpr-2018-6	['monocular-3d-human-pose-estimation']	['computer-vision']	[-1.33768901e-01 1.58299685e-01 -1.51682273e-01 -2.80557513e-01 -7.44655430e-01 -6.36120737e-01 5.99699020e-01 -2.36923695e-01 -9.15813267e-01 5.29294550e-01 2.72035480e-01 3.69408101e-01 7.73254037e-02 -1.59771353e-01 -7.28153527e-01 -2.60674953e-01 -2.67700851e-01 7.96716154e-01 4.11368966e-01 -4.68499005e-01 -2.55103350e-01 6.58410192e-01 -1.42354965e+00 5.46885394e-02 1.29183039e-01 1.02549469e+00 -3.61314528e-02 6.82663918e-01 5.86315095e-01 3.33910972e-01 -5.56627333e-01 -3.19586337e-01 5.91864049e-01 -4.68289703e-02 -7.70818830e-01 2.03377649e-01 8.73266757e-01 -6.69115901e-01 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9d8ed2f7-3b3c-4b06-86e8-01c36f488b9a	an-open-source-part-of-speech-tagger-for	null	null	https://aclanthology.org/L14-1622	https://aclanthology.org/L14-1622.pdf	An open source part-of-speech tagger for Norwegian: Building on existing language resources	This paper presents an open source part-of-speech tagger for the Norwegian language. It describes how an existing language processing library (FreeLing) was used to build a new part-of-speech tagger for this language. This part-of-speech tagger has been built on already available resources, in particular a Norwegian dictionary and gold standard corpus, which were partly customized for the purposes of this paper. The results of a careful evaluation show that this tagger yields an accuracy close to state-of-the-art taggers for other languages.	["Cristina S{\\'a}nchez Marco"]	2014-05-01	null	null	null	lrec-2014-5	['morphological-tagging']	['natural-language-processing']	[-3.74535829e-01 4.07532632e-01 1.45624861e-01 -5.04455745e-01 -9.98105109e-01 -5.91588557e-01 7.79316008e-01 4.60913628e-01 -7.68449008e-01 5.41854620e-01 6.57770574e-01 -3.73046458e-01 1.59348100e-01 -5.90367734e-01 6.20527891e-04 -2.41653457e-01 -8.02238658e-02 8.95094454e-01 7.49418318e-01 -8.01028490e-01 3.73857431e-02 2.21699730e-01 -1.50503993e+00 5.79630971e-01 3.21795136e-01 1.80842385e-01 5.67130089e-01 4.48708206e-01 -7.19458401e-01 3.61024171e-01 -6.30613923e-01 -3.27040225e-01 1.32522538e-01 -3.07649851e-01 -1.11670721e+00 -2.27726340e-01 -3.57903183e-01 2.62602121e-01 -1.58197433e-01 9.54104662e-01 4.71955806e-01 3.32565844e-01 1.88270986e-01 -5.30782759e-01 -4.18926105e-02 1.24904788e+00 4.78416026e-01 3.81638795e-01 7.95574903e-01 -5.23265123e-01 9.20250177e-01 -7.58596778e-01 8.77350986e-01 1.27007580e+00 5.57979941e-01 5.40907741e-01 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55f7121c-5fe1-4516-bdcb-f6be2064e145	a-random-forest-and-current-fault-texture	2211.03789	null	https://arxiv.org/abs/2211.03789v1	https://arxiv.org/pdf/2211.03789v1.pdf	A Random Forest and Current Fault Texture Feature-Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR	Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture feature-based method for current sensor fault diagnosis in three-phase PWM VSR systems. First, the three-phase alternating currents (ACs) of the three-phase PWM VSR are collected to extract the current fault texture features, and no additional hardware sensors are needed to avoid causing additional unstable factors. Then, the current fault texture features are adopted to train the random forest current sensor fault detection and diagnosis (CSFDD) classifier, which is a data-driven CSFDD classifier. Finally, the effectiveness of the proposed method is verified by simulation experiments. The result shows that the current sensor faults can be detected and located successfully and that it can effectively provide fault locations for maintenance personnel to keep the stable operation of the whole system.	['Ya-nan Dong', 'Quan-de Yuan', 'Yang Li', 'Xiu-hui Ni', 'Yi Zheng', 'Xiao-dong Gong', 'Lei Kou']	2022-11-08	null	null	null	null	['fault-detection']	['miscellaneous']	[ 2.79734910e-01 -7.51254082e-01 -3.06101471e-01 6.03907816e-02 -4.00448106e-02 -2.65065879e-01 2.15262443e-01 -3.49364251e-01 3.47844958e-01 6.29957020e-01 -2.92181104e-01 -3.73610586e-01 -7.29795933e-01 -7.46802032e-01 -2.12752298e-02 -1.17254925e+00 4.57484387e-02 9.37140882e-02 4.38403070e-01 -1.76828071e-01 8.00624430e-01 9.76180792e-01 -1.75751841e+00 6.27942532e-02 1.22053266e+00 1.34939718e+00 3.24664026e-01 3.00528675e-01 3.54498625e-01 6.48886740e-01 -1.25060523e+00 1.17949128e+00 -1.69733584e-01 -2.58982062e-01 -6.80869639e-01 2.43732944e-01 -6.98428690e-01 -2.79182702e-01 -7.40518749e-01 1.02867079e+00 3.90997320e-01 3.85082699e-03 7.82964885e-01 -1.97435391e+00 -1.55917183e-01 -5.77907525e-02 -5.69904447e-01 5.77870250e-01 4.11330462e-01 2.32262403e-01 2.85636187e-01 -7.04518020e-01 9.05138403e-02 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f104b05b-3b84-434c-92df-b6b18b0a028e	a-comparative-genomic-analysis-of-coronavirus	2107.06282	null	https://arxiv.org/abs/2107.06282v1	https://arxiv.org/pdf/2107.06282v1.pdf	A Comparative Genomic Analysis of Coronavirus Families Using Chaos Game Representation and Fisher-Shannon Complexity	From its first emergence in Wuhan, China in December, 2019 the COVID-19 pandemic has caused unprecedented health crisis throughout the world. The novel coronavirus disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which belongs to the coronaviridae family. In this paper, a comparative genomic analysis of eight coronaviruses namely Human coronavirus OC43 (HCoV-OC43), Human coronavirus HKU1 (HCoV-HKU1), Human coronavirus 229E (HCoV-229E), Human coronavirus NL63 (HCoV-NL63), Severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome-related coronavirus (MERS-CoV), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Bat coronavirus RaTG13 has been carried out using Chaos Game Representation and Fisher-Shannon Complexity (CGR-FSC) measure. Chaos Game Representation (CGR) is a unique alignment-free method to visualize one dimensional DNA sequence in a two-dimensional fractal-like pattern. The two-dimensional CGR pattern is then quantified by Fisher-Shannon Complexity (FSC) measure. The CGR-FSC can effectively identify the viruses uniquely and their similarity/dissimilarity can be revealed in the Fisher-Shannon Information Plane (FSIP).	['S. K. Laha']	2021-07-13	null	null	null	null	['information-plane']	['methodology']	[ 6.23356886e-02 -6.23875856e-01 2.66037226e-01 2.06645995e-01 -2.71241311e-02 -9.46883976e-01 2.16208503e-01 3.25463444e-01 -2.85010010e-01 6.28340542e-01 1.06171697e-01 -6.82931483e-01 -3.71090055e-01 -4.77860183e-01 6.88507780e-02 -6.73668444e-01 -8.44038069e-01 6.18003368e-01 -2.65051335e-01 -2.22054005e-01 1.23108529e-01 1.02512908e+00 -1.21479881e+00 -1.18059255e-01 1.08168089e+00 3.22336078e-01 6.24131799e-01 1.35799909e+00 2.39980310e-01 -1.74009442e-01 -2.85838068e-01 2.42552429e-01 1.43093660e-01 -6.95833027e-01 -2.07891211e-01 -7.65444875e-01 -5.74470282e-01 -1.40796721e-01 2.52244741e-01 7.91287482e-01 2.85596639e-01 -2.17594147e-01 1.34615302e+00 -1.58787417e+00 -1.87166914e-01 -4.10769641e-01 -4.51245338e-01 3.22012931e-01 2.95913368e-01 3.17863435e-01 4.66659606e-01 -4.88387376e-01 1.15773344e+00 1.19104087e+00 1.06477749e+00 5.36240637e-01 -1.04603577e+00 -4.74796772e-01 -6.18826270e-01 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717b3b15-3c88-4343-b3eb-3e628dd3edef	a-learning-approach-for-joint-design-of-event	2205.0707	null	https://arxiv.org/abs/2205.07070v1	https://arxiv.org/pdf/2205.07070v1.pdf	A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation	In emerging Industrial Cyber-Physical Systems (ICPSs), the joint design of communication and control sub-systems is essential, as these sub-systems are interconnected. In this paper, we study the joint design problem of an event-triggered control and an energy-efficient resource allocation in a fifth generation (5G) wireless network. We formally state the problem as a multi-objective optimization one, aiming to minimize the number of updates on the actuators' input and the power consumption in the downlink transmission. To address the problem, we propose a model-free hierarchical reinforcement learning approach \textcolor{blue}{with uniformly ultimate boundedness stability guarantee} that learns four policies simultaneously. These policies contain an update time policy on the actuators' input, a control policy, and energy-efficient sub-carrier and power allocation policies. Our simulation results show that the proposed approach can properly control a simulated ICPS and significantly decrease the number of updates on the actuators' input as well as the downlink power consumption.	['Mehdi Rasti', 'Atefeh Termehchi']	2022-05-14	null	null	null	null	['hierarchical-reinforcement-learning']	['methodology']	[ 1.31855205e-01 5.45073688e-01 -5.33749223e-01 8.08867291e-02 -3.50599498e-01 -4.77896452e-01 -1.12072192e-02 -9.83887538e-02 -4.95749433e-03 1.13497996e+00 -4.97088194e-01 -5.00369787e-01 -5.66322029e-01 -8.43394518e-01 -3.78677905e-01 -1.19655335e+00 -3.49496335e-01 6.87420368e-02 -6.53766170e-02 2.07717597e-01 -1.64702341e-01 1.96359724e-01 -7.09417105e-01 -7.15952635e-01 6.91622257e-01 1.55452037e+00 3.93434376e-01 6.24300659e-01 7.57591486e-01 4.70022649e-01 -8.58449101e-01 3.30075353e-01 2.12707147e-01 -5.55277646e-01 -4.68513638e-01 3.09847564e-01 -5.83050966e-01 -5.49555838e-01 -4.96519417e-01 9.04272676e-01 7.54027426e-01 5.85338324e-02 3.99995863e-01 -1.97599459e+00 -1.70930117e-01 6.22288048e-01 -6.54577851e-01 -7.58379400e-02 -3.75508398e-01 3.25409472e-01 6.73740327e-01 1.29176214e-01 2.53320575e-01 8.66198778e-01 -5.06426767e-02 6.98567569e-01 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d43781b4-2025-4d84-8ae2-6265fe8a472e	range-gan-range-constrained-generative	2103.0623	null	https://arxiv.org/abs/2103.06230v1	https://arxiv.org/pdf/2103.06230v1.pdf	Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis	Typical engineering design tasks require the effort to modify designs iteratively until they meet certain constraints, i.e., performance or attribute requirements. Past work has proposed ways to solve the inverse design problem, where desired designs are directly generated from specified requirements, thus avoid the trial and error process. Among those approaches, the conditional deep generative model shows great potential since 1) it works for complex high-dimensional designs and 2) it can generate multiple alternative designs given any condition. In this work, we propose a conditional deep generative model, Range-GAN, to achieve automatic design synthesis subject to range constraints. The proposed model addresses the sparse conditioning issue in data-driven inverse design problems by introducing a label-aware self-augmentation approach. We also propose a new uniformity loss to ensure generated designs evenly cover the given requirement range. Through a real-world example of constrained 3D shape generation, we show that the label-aware self-augmentation leads to an average improvement of 14% on the constraint satisfaction for generated 3D shapes, and the uniformity loss leads to a 125% average increase on the uniformity of generated shapes' attributes. This work laid the foundation for data-driven inverse design problems where we consider range constraints and there are sparse regions in the condition space.	['Faez Ahmed', 'Wei Chen', 'Amin Heyrani Nobari']	2021-03-10	null	null	null	null	['design-synthesis', '3d-shape-generation']	['adversarial', 'computer-vision']	[ 5.24266958e-01 2.84478545e-01 -1.64905399e-01 -5.22679925e-01 -5.26740253e-01 -4.12060350e-01 1.01395920e-01 -4.03667718e-01 4.29121852e-01 6.30062282e-01 4.24826384e-01 -3.08574252e-02 -3.99439633e-01 -1.07562602e+00 -6.67241693e-01 -4.02378023e-01 3.86423528e-01 7.60383904e-01 -5.20743370e-01 -3.04678738e-01 2.59367555e-01 4.92086112e-01 -1.56913245e+00 2.58993328e-01 1.02996147e+00 1.00536203e+00 8.11095387e-02 2.20784515e-01 -3.43388468e-02 2.98073024e-01 -6.40940905e-01 -1.62517149e-02 3.78111154e-01 -6.51041150e-01 -3.53673756e-01 4.43929911e-01 6.08090870e-02 -2.96655864e-01 1.01542309e-01 7.69163907e-01 7.87475586e-01 2.14819089e-02 9.74789083e-01 -1.33890069e+00 -1.19681835e+00 6.15073562e-01 -6.56393170e-01 -7.29982972e-01 3.39099795e-01 2.43079767e-01 9.95278597e-01 -1.01485336e+00 2.68459737e-01 1.18997562e+00 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b1253f22-7e1f-42bd-b0f0-eb61e6e5c1c4	dissecting-image-crops	2011.11831	null	https://arxiv.org/abs/2011.11831v4	https://arxiv.org/pdf/2011.11831v4.pdf	Dissecting Image Crops	The elementary operation of cropping underpins nearly every computer vision system, ranging from data augmentation and translation invariance to computational photography and representation learning. This paper investigates the subtle traces introduced by this operation. For example, despite refinements to camera optics, lenses will leave behind certain clues, notably chromatic aberration and vignetting. Photographers also leave behind other clues relating to image aesthetics and scene composition. We study how to detect these traces, and investigate the impact that cropping has on the image distribution. While our aim is to dissect the fundamental impact of spatial crops, there are also a number of practical implications to our work, such as revealing faulty photojournalism and equipping neural network researchers with a better understanding of shortcut learning. Code is available at https://github.com/basilevh/dissecting-image-crops.	['Carl Vondrick', 'Basile Van Hoorick']	2020-11-24	null	http://openaccess.thecvf.com//content/ICCV2021/html/Van_Hoorick_Dissecting_Image_Crops_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Van_Hoorick_Dissecting_Image_Crops_ICCV_2021_paper.pdf	iccv-2021-1	['image-forensics', 'image-cropping']	['computer-vision', 'computer-vision']	[ 7.42894292e-01 -9.73633826e-02 7.73731768e-02 -3.51101339e-01 -2.14484632e-01 -8.07654917e-01 4.75230932e-01 2.64702737e-02 -1.51081234e-01 3.46880376e-01 2.57000476e-01 -5.63003480e-01 -9.17062908e-02 -5.32861352e-01 -1.13110399e+00 -7.17061341e-01 -2.46969834e-02 -3.93679857e-01 -9.07052904e-02 -9.01110768e-02 7.39687622e-01 5.14936090e-01 -1.80502403e+00 6.67926371e-02 5.20001590e-01 5.99804044e-01 3.45977902e-01 8.61642599e-01 1.19118333e-01 6.05100155e-01 -5.18712521e-01 -4.08176005e-01 5.01538754e-01 -3.77615452e-01 -5.53103924e-01 5.90659976e-01 9.09783661e-01 -4.82088506e-01 -3.36029857e-01 1.29888749e+00 2.44801387e-01 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be22e198-62b3-43b3-9014-783dd4403619	se-gsl-a-general-and-effective-graph	2303.09778	null	https://arxiv.org/abs/2303.09778v1	https://arxiv.org/pdf/2303.09778v1.pdf	SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization	Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it is susceptible to low-quality and unreliable structure, which has been a norm rather than an exception in real-world graphs. Existing graph structure learning (GSL) frameworks still lack robustness and interpretability. This paper proposes a general GSL framework, SE-GSL, through structural entropy and the graph hierarchy abstracted in the encoding tree. Particularly, we exploit the one-dimensional structural entropy to maximize embedded information content when auxiliary neighbourhood attributes are fused to enhance the original graph. A new scheme of constructing optimal encoding trees is proposed to minimize the uncertainty and noises in the graph whilst assuring proper community partition in hierarchical abstraction. We present a novel sample-based mechanism for restoring the graph structure via node structural entropy distribution. It increases the connectivity among nodes with larger uncertainty in lower-level communities. SE-GSL is compatible with various GNN models and enhances the robustness towards noisy and heterophily structures. Extensive experiments show significant improvements in the effectiveness and robustness of structure learning and node representation learning.	['Philip S. Yu', 'Chunyang Liu', 'Jia Wu', 'JianXin Li', 'Renyu Yang', 'Xiang Huang', 'Hao Peng', 'Dongcheng Zou']	2023-03-17	null	null	null	null	['graph-structure-learning']	['graphs']	[ 2.28911802e-01 7.86708653e-01 -1.81008235e-01 -1.76560953e-01 -3.49668823e-02 -3.78411382e-01 2.26913556e-01 6.88886464e-01 2.47850977e-02 8.60909820e-01 4.02874440e-01 -5.83121590e-02 -7.88935304e-01 -1.38671327e+00 -6.42803848e-01 -9.83661711e-01 -6.16708279e-01 3.57838333e-01 -2.38636676e-02 -2.99845099e-01 1.20681107e-01 5.51580906e-01 -1.33339131e+00 3.18928100e-02 1.09640062e+00 6.50514424e-01 1.38560861e-01 5.40416002e-01 -2.69195735e-01 8.38941276e-01 -4.44689006e-01 -3.61304790e-01 2.24595040e-01 -2.79180676e-01 -8.68189156e-01 1.46136880e-01 7.42792860e-02 2.73080587e-01 -6.18903697e-01 1.43708181e+00 5.73244393e-01 -2.48663966e-02 6.27244949e-01 -1.37743151e+00 -9.89920974e-01 1.22510314e+00 -4.52954233e-01 5.57604358e-02 3.47381860e-01 -6.99931383e-02 1.38061607e+00 -4.19617355e-01 8.57417881e-01 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-5.84779561e-01 -3.77521515e-01 -7.93335259e-01 -9.30839598e-01 4.14715528e-01 2.36517489e-01 -5.92245519e-01 -7.18679070e-01 -2.26235121e-01]	[6.974442958831787, 6.156213283538818]
f529cef8-53dc-4e21-baf8-10624b241778	speech-enhancement-for-virtual-meetings-on	2302.00868	null	https://arxiv.org/abs/2302.00868v2	https://arxiv.org/pdf/2302.00868v2.pdf	Speech Enhancement for Virtual Meetings on Cellular Networks	We study speech enhancement using deep learning (DL) for virtual meetings on cellular devices, where transmitted speech has background noise and transmission loss that affects speech quality. Since the Deep Noise Suppression (DNS) Challenge dataset does not contain practical disturbance, we collect a transmitted DNS (t-DNS) dataset using Zoom Meetings over T-Mobile network. We select two baseline models: Demucs and FullSubNet. The Demucs is an end-to-end model that takes time-domain inputs and outputs time-domain denoised speech, and the FullSubNet takes time-frequency-domain inputs and outputs the energy ratio of the target speech in the inputs. The goal of this project is to enhance the speech transmitted over the cellular networks using deep learning models.	['Ojas Bhargave', 'Joseph Konan', 'Minjeong Kim', 'Kawon Lee', 'Minseon Gwak', 'Hojeong Lee']	2023-02-02	null	null	null	null	['speech-enhancement']	['speech']	[-2.04048127e-01 1.23896427e-01 2.52868712e-01 -1.10744707e-01 -7.47005343e-01 -2.99480647e-01 4.79670852e-01 -5.99385500e-01 -4.04938072e-01 8.74896228e-01 8.52484584e-01 -6.51424289e-01 7.87430350e-03 -5.49345851e-01 -2.88072079e-01 -8.54864836e-01 -6.42782375e-02 -2.18966991e-01 9.13796201e-02 -6.09874070e-01 -7.08634973e-01 4.60776597e-01 -8.50895405e-01 5.06851494e-01 5.07952750e-01 1.07946193e+00 2.34114915e-01 1.51112556e+00 1.59586184e-02 9.94144917e-01 -1.40529907e+00 -3.08397263e-01 3.30180734e-01 -4.57488000e-01 -4.33801264e-01 -2.12766156e-01 2.06321478e-01 -5.75327456e-01 -1.41255534e+00 1.03014612e+00 1.63816810e+00 1.91070676e-01 3.86170208e-01 -1.32262480e+00 -4.88458723e-01 6.37469888e-01 -5.62535971e-02 8.01048696e-01 -2.19203442e-01 2.61774600e-01 4.30083454e-01 -4.44717705e-01 4.65475142e-01 1.69333315e+00 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21fe4b73-f4af-4d3a-ae5b-a3555ef8b9f3	functional-code-building-genetic-programming	2206.04561	null	https://arxiv.org/abs/2206.04561v1	https://arxiv.org/pdf/2206.04561v1.pdf	Functional Code Building Genetic Programming	General program synthesis has become an important application area for genetic programming (GP), and for artificial intelligence more generally. Code Building Genetic Programming (CBGP) is a recently introduced GP method for general program synthesis that leverages reflection and first class specifications to support the evolution of programs that may use arbitrary data types, polymorphism, and functions drawn from existing codebases. However, neither a formal description nor a thorough benchmarking of CBGP have yet been reported. In this work, we formalize the method of CBGP using algorithms from type theory. Specially, we show that a functional programming language and a Hindley-Milner type system can be used to evolve type-safe programs using the process abstractly described in the original CBGP paper. Furthermore, we perform a comprehensive analysis of the search performance of this functional variant of CBGP compared to other contemporary GP program synthesis methods.	['Lee Spector', 'Thomas Helmuth', 'Edward Pantridge']	2022-06-09	null	null	null	null	['program-synthesis']	['computer-code']	[ 2.28953287e-01 3.11006427e-01 -2.30985835e-01 -6.12369217e-02 -2.42604718e-01 -5.43929636e-01 4.34883207e-01 2.39362702e-01 1.07855454e-01 8.23817134e-01 -4.37503517e-01 -7.33544350e-01 -7.98465833e-02 -1.38352370e+00 -8.87519896e-01 -5.60107648e-01 -2.05132067e-01 1.93604037e-01 3.36208373e-01 -6.52586341e-01 6.21337950e-01 2.08398834e-01 -2.43867278e+00 2.97969609e-01 1.23677325e+00 5.13042867e-01 7.53527656e-02 7.52654970e-01 -3.74380648e-01 1.61858559e-01 -8.15512776e-01 -6.19334280e-01 1.90016046e-01 -5.80583632e-01 -6.65877819e-01 -4.14368361e-01 -3.14212769e-01 4.35246021e-01 3.26368898e-01 1.05735791e+00 5.35231411e-01 -8.54351744e-02 2.52042413e-01 -1.70472360e+00 -7.41101742e-01 8.73750925e-01 -1.53144985e-01 -3.79072845e-01 4.42114592e-01 3.39405417e-01 7.64617085e-01 -5.22084713e-01 6.94925904e-01 1.21636617e+00 8.04295719e-01 8.98183048e-01 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4f5042be-f023-4657-8fb4-b1b7dd4c41c3	one-shot-affordance-detection	2106.14747	null	https://arxiv.org/abs/2106.14747v1	https://arxiv.org/pdf/2106.14747v1.pdf	One-Shot Affordance Detection	Affordance detection refers to identifying the potential action possibilities of objects in an image, which is an important ability for robot perception and manipulation. To empower robots with this ability in unseen scenarios, we consider the challenging one-shot affordance detection problem in this paper, i.e., given a support image that depicts the action purpose, all objects in a scene with the common affordance should be detected. To this end, we devise a One-Shot Affordance Detection (OS-AD) network that firstly estimates the purpose and then transfers it to help detect the common affordance from all candidate images. Through collaboration learning, OS-AD can capture the common characteristics between objects having the same underlying affordance and learn a good adaptation capability for perceiving unseen affordances. Besides, we build a Purpose-driven Affordance Dataset (PAD) by collecting and labeling 4k images from 31 affordance and 72 object categories. Experimental results demonstrate the superiority of our model over previous representative ones in terms of both objective metrics and visual quality. The benchmark suite is at ProjectPage.	['DaCheng Tao', 'Yang Cao', 'Jing Zhang', 'Wei Zhai', 'Hongchen Luo']	2021-06-28	null	null	null	null	['affordance-detection']	['computer-vision']	[ 1.75224438e-01 -1.37223706e-01 -1.73527479e-01 -3.37476283e-01 -1.56722471e-01 -2.74188668e-01 3.83947432e-01 -1.49886876e-01 -3.33601207e-01 1.96975738e-01 3.90270174e-01 1.84571952e-01 -3.77363175e-01 -2.75535703e-01 -7.72221804e-01 -5.21572113e-01 -2.08212450e-01 1.91731408e-01 4.12298471e-01 -3.40484768e-01 2.92605668e-01 4.71155137e-01 -1.77277613e+00 6.13214858e-02 9.83017445e-01 1.03222096e+00 8.37564290e-01 3.67545396e-01 4.07598764e-01 7.95454204e-01 -3.08778226e-01 2.23894760e-01 2.07902223e-01 1.38846477e-02 -7.32247174e-01 4.77173477e-01 4.89552647e-01 -8.68317127e-01 -4.62276578e-01 1.12098706e+00 2.94912606e-01 5.89605093e-01 6.38748646e-01 -1.68955934e+00 -9.68354642e-01 4.99795169e-01 -3.27783674e-01 4.26999509e-01 6.63306832e-01 6.99372649e-01 1.20000577e+00 -1.19514394e+00 5.59306800e-01 1.76665485e+00 -2.71205753e-01 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-1.20079231e+00 -6.61228001e-01 4.71250147e-01 6.59955561e-01 -1.21694374e+00 -1.57767639e-01 -9.67609882e-02]	[5.147587776184082, -0.09687581658363342]
6eb73d36-5dfa-4281-995f-b679568ab47c	drug-repurposing-for-cancer-an-nlp-approach	1911.07819	null	https://arxiv.org/abs/1911.07819v2	https://arxiv.org/pdf/1911.07819v2.pdf	Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies	More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of generic drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs worldwide. Evidence on the efficacy of non-cancer generic drugs being tested for cancer exists in scientific publications, but trying to manually identify and extract such evidence is intractable. In this paper, we introduce a system to automate this evidence extraction from PubMed abstracts. Our primary contribution is to define the natural language processing pipeline required to obtain such evidence, comprising the following modules: querying, filtering, cancer type entity extraction, therapeutic association classification, and study type classification. Using the subject matter expertise on our team, we create our own datasets for these specialized domain-specific tasks. We obtain promising performance in each of the modules by utilizing modern language modeling techniques and plan to treat them as baseline approaches for future improvement of individual components.	['Laura B. Kleiman', 'Prasanna Sattigeri', 'Dmitriy A. Katz-Rogozhnikov', 'Shivashankar Subramanian', 'Sushma Ravichandran', 'Pradeep Mangalath', 'Kush R. Varshney', 'Karthikeyan Natesan Ramamurthy', 'Annmarie Wang', 'Ioana Baldini']	2019-11-18	null	null	null	null	['entity-extraction']	['natural-language-processing']	[ 3.03233296e-01 1.29012123e-01 -1.04612780e+00 -1.40249774e-01 -1.36215091e+00 -8.07061374e-01 6.17720068e-01 1.11373878e+00 -4.51055795e-01 1.10509312e+00 3.28244895e-01 -8.51630330e-01 -1.94608018e-01 -5.67985713e-01 -3.23746890e-01 -4.03447986e-01 2.40700379e-01 5.70912182e-01 9.83038452e-03 3.15537214e-01 2.69759566e-01 7.90469050e-01 -1.02129781e+00 6.05261147e-01 9.86502826e-01 7.09243298e-01 1.79799739e-02 3.72487217e-01 -2.66432643e-01 5.85939467e-01 -4.97584730e-01 -1.88340679e-01 -1.49006277e-01 -2.46740729e-01 -9.03177917e-01 -2.41766155e-01 2.56673396e-01 -4.50923070e-02 -8.79488587e-02 8.02273691e-01 5.62434137e-01 -3.86869758e-01 6.03061378e-01 -7.44080186e-01 -2.93480396e-01 6.42182112e-01 -3.01980585e-01 -1.22843251e-01 7.04163909e-01 4.40750979e-02 8.29547584e-01 -7.31526017e-01 9.56512153e-01 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