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paperID stringlengths 36 36	pwc_id stringlengths 8 47	arxiv_id stringlengths 6 16 ⌀	nips_id float64	url_abs stringlengths 18 329	url_pdf stringlengths 18 742	title stringlengths 8 325	abstract stringlengths 1 7.27k ⌀	authors stringlengths 2 7.06k	published stringlengths 10 10 ⌀	conference stringlengths 12 47 ⌀	conference_url_abs stringlengths 16 198 ⌀	conference_url_pdf stringlengths 27 199 ⌀	proceeding stringlengths 6 47 ⌀	taskID stringlengths 7 1.44k	areaID stringclasses 688 values	embedding stringlengths 9.26k 12.5k	umap_embedding stringlengths 29 44
d42e3176-008e-45f7-ac40-d9306e4989c4	trafficbots-towards-world-models-for	2303.04116	null	https://arxiv.org/abs/2303.04116v1	https://arxiv.org/pdf/2303.04116v1.pdf	TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction	Data-driven simulation has become a favorable way to train and test autonomous driving algorithms. The idea of replacing the actual environment with a learned simulator has also been explored in model-based reinforcement learning in the context of world models. In this work, we show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving, and based on TrafficBots we obtain a world model tailored for the planning module of autonomous vehicles. Existing data-driven traffic simulators are lacking configurability and scalability. To generate configurable behaviors, for each agent we introduce a destination as navigational information, and a time-invariant latent personality that specifies the behavioral style. To improve the scalability, we present a new scheme of positional encoding for angles, allowing all agents to share the same vectorized context and the use of an architecture based on dot-product attention. As a result, we can simulate all traffic participants seen in dense urban scenarios. Experiments on the Waymo open motion dataset show TrafficBots can simulate realistic multi-agent behaviors and achieve good performance on the motion prediction task.	['Luc van Gool', 'Fisher Yu', 'Dengxin Dai', 'Alexander Liniger', 'Zhejun Zhang']	2023-03-07	null	null	null	null	['motion-prediction']	['computer-vision']	[-4.66619372e-01 8.33950862e-02 -1.09730829e-02 -4.61525112e-01 -2.90300250e-01 -3.34423542e-01 9.79896605e-01 -3.45089048e-01 -6.57230020e-01 7.27674127e-01 1.25077069e-01 -2.10724980e-01 -1.14041433e-01 -1.15790021e+00 -7.33652592e-01 -7.51540005e-01 -1.52885169e-01 1.13478410e+00 4.93304968e-01 -8.93058181e-01 9.19498503e-02 5.22254288e-01 -1.82573545e+00 8.69869068e-02 6.90153122e-01 3.21232975e-01 8.01625848e-01 8.08522820e-01 -7.76995420e-02 8.18245649e-01 -3.93228233e-01 -2.48185664e-01 2.62792498e-01 -3.77516687e-01 -5.55749357e-01 3.92501429e-02 8.49253237e-02 -3.31331342e-01 -7.26981521e-01 5.11774480e-01 5.28026521e-01 5.49682915e-01 4.85809505e-01 -1.92951727e+00 3.35808322e-02 6.13150358e-01 1.58935219e-01 -2.78771874e-02 2.64025867e-01 8.79764974e-01 6.12705767e-01 -3.47315550e-01 1.05702972e+00 1.44718838e+00 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cb3c7c41-3ae2-4b5a-bda4-aa6124494889	raddet-range-azimuth-doppler-based-radar	2105.00363	null	https://arxiv.org/abs/2105.00363v1	https://arxiv.org/pdf/2105.00363v1.pdf	RADDet: Range-Azimuth-Doppler based Radar Object Detection for Dynamic Road Users	Object detection using automotive radars has not been explored with deep learning models in comparison to the camera based approaches. This can be attributed to the lack of public radar datasets. In this paper, we collect a novel radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-Eye-View range map. To build the dataset, we propose an instance-wise auto-annotation method. Furthermore, a novel Range-Azimuth-Doppler based multi-class object detection deep learning model is proposed. The algorithm is a one-stage anchor-based detector that generates both 3D bounding boxes and 2D bounding boxes on Range-Azimuth-Doppler and Cartesian domains, respectively. Our proposed algorithm achieves 56.3% AP with IOU of 0.3 on 3D bounding box predictions, and 51.6% with IOU of 0.5 on 2D bounding box prediction. Our dataset and the code can be found at https://github.com/ZhangAoCanada/RADDet.git.	['Robert Laganiere', 'Farzan Erlik Nowruzi', 'Ao Zhang']	2021-05-02	null	null	null	null	['radar-object-detection']	['robots']	[-2.82718718e-01 -3.72158736e-01 1.13900959e-01 -7.48543441e-01 -9.38642979e-01 -6.66626394e-01 8.04897904e-01 -4.81465161e-01 -2.57209390e-01 3.49312812e-01 3.22460383e-02 -3.63615960e-01 -3.14582139e-01 -9.15533781e-01 -5.73526740e-01 -5.53018808e-01 -1.65577576e-01 6.77909315e-01 3.24676186e-01 -2.08752453e-01 2.78939754e-01 8.75217140e-01 -1.22828722e+00 1.04426675e-01 3.91890377e-01 1.39303637e+00 -2.11445391e-01 8.48675728e-01 3.07344139e-01 5.27529359e-01 -6.81616664e-01 -4.99888510e-01 8.47332656e-01 2.61165828e-01 1.53736621e-01 -3.08514178e-01 1.01699317e+00 -7.34641016e-01 -8.04325163e-01 1.01729417e+00 4.50043499e-01 4.77358252e-02 7.68849909e-01 -1.49180496e+00 -7.04852998e-01 6.25332892e-02 -7.04181850e-01 2.76187778e-01 -1.51385769e-01 4.59896773e-02 9.92036164e-01 -1.05164385e+00 3.51265341e-01 1.29172945e+00 5.47055960e-01 1.93907857e-01 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7953fa25-e4a1-4202-bcdb-eb0d00067c02	gercct-an-annotated-corpus-for-mining	null	null	https://aclanthology.org/2022.lrec-1.658	https://aclanthology.org/2022.lrec-1.658.pdf	GerCCT: An Annotated Corpus for Mining Arguments in German Tweets on Climate Change	While the field of argument mining has grown notably in the last decade, research on the Twitter medium remains relatively understudied. Given the difficulty of mining arguments in tweets, recent work on creating annotated resources mainly utilized simplified annotation schemes that focus on single argument components, i.e., on claim or evidence. In this paper we strive to fill this research gap by presenting GerCCT, a new corpus of German tweets on climate change, which was annotated for a set of different argument components and properties. Additionally, we labelled sarcasm and toxic language to facilitate the development of tools for filtering out non-argumentative content. This, to the best of our knowledge, renders our corpus the first tweet resource annotated for argumentation, sarcasm and toxic language. We show that a comparatively complex annotation scheme can still yield promising inter-annotator agreement. We further present first good supervised classification results yielded by a fine-tuned BERT architecture.	['Manfred Stede', 'Robin Schaefer']	null	null	null	null	lrec-2022-6	['argument-mining']	['natural-language-processing']	[ 2.19109282e-01 8.55421782e-01 -3.22830290e-01 -2.44418934e-01 -8.18508565e-01 -9.87362742e-01 1.05451119e+00 1.01541221e+00 -5.62828898e-01 1.00026274e+00 6.74898326e-01 -7.47577786e-01 1.11362757e-02 -5.82225740e-01 -3.49086165e-01 -3.20728421e-01 1.84195101e-01 5.04909039e-01 1.65360391e-01 -4.27378893e-01 4.64134216e-01 -1.73500590e-02 -1.59890926e+00 5.56526601e-01 8.21668684e-01 5.91964722e-01 -4.23716873e-01 4.27295268e-01 -4.46523517e-01 1.11249173e+00 -7.58956015e-01 -9.68485773e-01 -3.70438188e-01 -4.17412341e-01 -1.41917038e+00 -3.35818976e-01 1.30035385e-01 2.12772265e-01 4.01790082e-01 8.38560343e-01 4.73290026e-01 -4.96133596e-01 3.62572074e-01 -1.07526600e+00 -1.82067513e-01 1.36239636e+00 -2.69411325e-01 1.82629049e-01 5.06044030e-01 -4.53684896e-01 1.42201674e+00 -6.11090541e-01 1.01979506e+00 1.04437900e+00 8.67104173e-01 3.93079728e-01 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984c8247-911b-4eec-9aab-58723d9e911b	prompting-and-evaluating-large-language	2305.13626	null	https://arxiv.org/abs/2305.13626v1	https://arxiv.org/pdf/2305.13626v1.pdf	Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration	Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation. However, despite their impressive capabilities, they still possess limitations, such as providing randomly-guessed answers to ambiguous queries or failing to refuse users' requests, both of which are considered aspects of a conversational agent's proactivity. This raises the question of whether LLM-based conversational systems are equipped to handle proactive dialogue problems. In this work, we conduct a comprehensive analysis of LLM-based conversational systems, specifically focusing on three aspects of proactive dialogue systems: clarification, target-guided, and non-collaborative dialogues. To trigger the proactivity of LLMs, we propose the Proactive Chain-of-Thought prompting scheme, which augments LLMs with the goal planning capability over descriptive reasoning chains. Empirical findings are discussed to promote future studies on LLM-based proactive dialogue systems.	['Tat-Seng Chua', 'Lizi Liao', 'Wenqiang Lei', 'Yang Deng']	2023-05-23	null	null	null	null	['response-generation']	['natural-language-processing']	[ 7.37831220e-02 9.35199022e-01 -3.11546717e-02 -4.79567856e-01 -5.33158660e-01 -7.09219813e-01 1.20261145e+00 -6.38001487e-02 -6.91199377e-02 8.98447335e-01 7.44642615e-01 -7.14199781e-01 -5.44641390e-02 -8.16296935e-01 3.70836049e-01 -2.52318591e-01 1.65591121e-01 7.46270180e-01 2.32848659e-01 -9.43564296e-01 8.07455897e-01 1.98061615e-01 -1.10265398e+00 8.36016297e-01 9.68263149e-01 3.12603980e-01 2.53645390e-01 9.70359623e-01 -8.68504822e-01 1.75175154e+00 -7.98128545e-01 -2.34174088e-01 -3.92078251e-01 -6.86343849e-01 -1.68048418e+00 1.24325372e-01 -2.89873838e-01 -5.25126636e-01 -9.04470775e-03 5.41387975e-01 3.76173675e-01 3.26663256e-01 4.28785324e-01 -1.45810652e+00 -3.61269116e-01 1.03930235e+00 4.21406269e-01 -5.93095012e-02 1.23482537e+00 5.60813367e-01 1.04279137e+00 -5.81788659e-01 5.14820933e-01 1.69588077e+00 4.30172354e-01 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-3.62600952e-01 -1.00530815e+00 -1.44804907e+00 -1.30695542e-02 1.50234401e-01 -6.51582927e-02 -1.11921027e-01 -3.06031168e-01 -3.12560827e-01]	[12.794414520263672, 8.032617568969727]
b9e289ed-fcd2-4e4d-9c2d-3766d2494745	learning-for-open-world-calibration-with	2305.12039	null	https://arxiv.org/abs/2305.12039v1	https://arxiv.org/pdf/2305.12039v1.pdf	Learning for Open-World Calibration with Graph Neural Networks	We tackle the problem of threshold calibration for open-world recognition by incorporating representation compactness measures into clustering. Unlike the open-set recognition which focuses on discovering and rejecting the unknown, open-world recognition learns robust representations that are generalizable to disjoint unknown classes at test time. Our proposed method is based on two key observations: (i) representation structures among neighbouring images in high dimensional visual embedding spaces have strong self-similarity which can be leveraged to encourage transferability to the open world, (ii) intra-class embedding structures can be modeled with the marginalized von Mises-Fisher (vMF) probability, whose correlation with the true positive rate is dataset-invariant. Motivated by these, we design a unified framework centered around a graph neural network (GNN) to jointly predict the pseudo-labels and the vMF concentrations which indicate the representation compactness. These predictions can be converted into statistical estimations for recognition accuracy, allowing more robust calibration of the distance threshold to achieve target utility for the open-world classes. Results on a variety of visual recognition benchmarks demonstrate the superiority of our method over traditional posthoc calibration methods for the open world, especially under distribution shift.	['Yifan Xing', 'Joseph Tighe', 'Ying Nian Wu', 'Qingming Tang', 'Tong He', 'Tianjun Xiao', 'Dongsheng An', 'Qin Zhang']	2023-05-19	null	null	null	null	['open-set-learning']	['miscellaneous']	[ 9.40925628e-02 1.40407115e-01 -4.22189087e-01 -4.48664635e-01 -7.79242277e-01 -6.73429906e-01 6.47412896e-01 1.17002062e-01 8.78468379e-02 4.60814536e-01 2.23355159e-01 -1.55162647e-01 -5.66209197e-01 -7.65344262e-01 -6.04595184e-01 -9.80116606e-01 -4.01854783e-01 4.94606465e-01 -2.83923149e-02 3.27522397e-01 1.96739778e-01 4.73701924e-01 -1.61420166e+00 6.86152978e-03 1.23624218e+00 1.22140241e+00 -2.56140321e-01 4.09636170e-01 -8.69180411e-02 4.88652110e-01 -3.12911749e-01 -5.04053056e-01 6.83509588e-01 -1.30698770e-01 -6.82142377e-01 1.41134843e-01 5.63355029e-01 2.99830794e-01 -5.04967153e-01 1.41520691e+00 4.36151296e-01 1.73420683e-01 1.37513018e+00 -1.56096327e+00 -1.24957800e+00 5.29470801e-01 -6.16557598e-01 2.93452173e-01 7.66168833e-02 2.04601675e-01 1.30748403e+00 -7.10793972e-01 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b362df8c-520d-450e-b8a8-45565084c88b	generalizable-no-reference-image-quality	null	null	https://ieeexplore.ieee.org/abstract/document/9405680	https://zhuhancheng.github.io/Hancheng_files/files/2021-TCSVT.pdf	Generalizable No-Reference Image Quality Assessment via Deep Meta-learning	Recently, researchers have shown great interest in using convolutional neural networks (CNNs) for no-reference image quality assessment (NR-IQA). Due to the lack of big training data, the efforts of existing metrics in optimizing CNN-based NR-IQA models remain limited. Furthermore, the diversity of distortions in images result in the generalization problem of NR-IQA models when trained with known distortions and tested on unseen distortions, which is an easy task for human. Hence, we propose a NR-IQA metric via deep meta-learning, which is highly generalizable in the face of unseen distortions. The fundamental idea is to learn the meta-knowledge shared by human when evaluating the quality of images with diversified distortions. Specifically, we define NR-IQA of different distortions as a series of tasks and propose a task selection strategy to build two task sets, which are characterized by synthetic to synthetic and synthetic to authentic distortions, respectively. Based on these two task sets, an optimization-based meta-learning is proposed to learn the generalized NR-IQA model, which can be directly used to evaluate the quality of images with unseen distortions. Extensive experiments demonstrate that our NR-IQA metric outperforms the state-of-the-arts in terms of both evaluation performance and generalization ability.	['and Guangming Shi', 'Weisheng Dong', 'Jinjian Wu', 'Leida Li', 'Hancheng Zhu']	2021-04-15	null	null	null	ieee-transactions-on-circuits-and-systems-for-4	['no-reference-image-quality-assessment']	['computer-vision']	[ 8.29033926e-02 -5.73279500e-01 1.25988409e-01 -3.75805974e-01 -7.29929745e-01 -1.52727976e-01 4.75906283e-01 -4.42448229e-01 -2.53021866e-01 3.30711067e-01 1.24172769e-01 -5.32944314e-02 -3.08130682e-01 -8.67506742e-01 -5.88482976e-01 -5.69129586e-01 2.53465027e-01 -9.77640525e-02 -1.15249880e-01 -3.86382878e-01 1.90721571e-01 2.75476545e-01 -1.54080153e+00 3.54698271e-01 1.01672196e+00 1.26724672e+00 1.03941292e-01 4.53549355e-01 1.92188144e-01 5.37852407e-01 -9.62240100e-01 -8.27357113e-01 4.74515766e-01 -5.68267107e-01 -6.73803270e-01 9.79582891e-02 5.22240043e-01 -5.59199929e-01 -7.72518754e-01 1.33082867e+00 8.63076389e-01 3.00158054e-01 6.37623966e-01 -1.35729086e+00 -1.19121742e+00 3.05743120e-03 -2.95043111e-01 4.11639929e-01 2.74128675e-01 5.15109003e-01 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be35f97d-d2c4-4852-b280-3260a6d9d77b	anomaly-detection-in-time-series-with-triadic	2012.04936	null	https://arxiv.org/abs/2012.04936v1	https://arxiv.org/pdf/2012.04936v1.pdf	Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification	In the time-series analysis, the time series motifs and the order patterns in time series can reveal general temporal patterns and dynamic features. Triadic Motif Field (TMF) is a simple and effective time-series image encoding method based on triadic time series motifs. Electrocardiography (ECG) signals are time-series data widely used to diagnose various cardiac anomalies. The TMF images contain the features characterizing the normal and Atrial Fibrillation (AF) ECG signals. Considering the quasi-periodic characteristics of ECG signals, the dynamic features can be extracted from the TMF images with the transfer learning pre-trained convolutional neural network (CNN) models. With the extracted features, the simple classifiers, such as the Multi-Layer Perceptron (MLP), the logistic regression, and the random forest, can be applied for accurate anomaly detection. With the test dataset of the PhysioNet Challenge 2017 database, the TMF classification model with the VGG16 transfer learning model and MLP classifier demonstrates the best performance with the 95.50% ROC-AUC and 88.43% F1 score in the AF classification. Besides, the TMF classification model can identify AF patients in the test dataset with high precision. The feature vectors extracted from the TMF images show clear patient-wise clustering with the t-distributed Stochastic Neighbor Embedding technique. Above all, the TMF classification model has very good clinical interpretability. The patterns revealed by symmetrized Gradient-weighted Class Activation Mapping have a clear clinical interpretation at the beat and rhythm levels.	['Xin Chen', 'Yadong Zhang']	2020-12-09	null	null	null	null	['ecg-classification', 'atrial-fibrillation-detection', 'electrocardiography-ecg']	['medical', 'medical', 'methodology']	[ 2.49341041e-01 -4.55490708e-01 -4.39523198e-02 -2.62410551e-01 -4.13401663e-01 -3.60378027e-01 9.09678731e-03 1.84762388e-01 -1.24229632e-01 6.48207188e-01 1.55566148e-02 -4.96127605e-01 -6.59909248e-01 -6.13428116e-01 -2.47765258e-01 -9.21024501e-01 -9.60187793e-01 1.71021909e-01 -2.96251833e-01 5.71647920e-02 1.79809198e-01 4.62796986e-01 -1.31682181e+00 6.96300805e-01 9.03442562e-01 1.49104834e+00 -2.83794254e-01 8.27682853e-01 1.59053672e-02 4.43515033e-01 -5.62830687e-01 2.11598024e-01 9.56170354e-03 -6.81410611e-01 -4.31018442e-01 -4.18890685e-01 -2.20638856e-01 1.90244451e-01 -4.38115060e-01 6.06404901e-01 9.18496668e-01 -4.49288726e-01 6.85111284e-01 -1.26774907e+00 -3.17872137e-01 3.46940309e-01 -3.76426637e-01 9.62384522e-01 3.54678661e-01 1.36112683e-02 5.97451568e-01 -8.39418113e-01 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fce1405c-01aa-4392-9331-91f83256cb7d	modeling-user-behavior-with-interaction	2207.10767	null	https://arxiv.org/abs/2207.10767v1	https://arxiv.org/pdf/2207.10767v1.pdf	Modeling User Behavior With Interaction Networks for Spam Detection	Spam is a serious problem plaguing web-scale digital platforms which facilitate user content creation and distribution. It compromises platform's integrity, performance of services like recommendation and search, and overall business. Spammers engage in a variety of abusive and evasive behavior which are distinct from non-spammers. Users' complex behavior can be well represented by a heterogeneous graph rich with node and edge attributes. Learning to identify spammers in such a graph for a web-scale platform is challenging because of its structural complexity and size. In this paper, we propose SEINE (Spam DEtection using Interaction NEtworks), a spam detection model over a novel graph framework. Our graph simultaneously captures rich users' details and behavior and enables learning on a billion-scale graph. Our model considers neighborhood along with edge types and attributes, allowing it to capture a wide range of spammers. SEINE, trained on a real dataset of tens of millions of nodes and billions of edges, achieves a high performance of 80% recall with 1% false positive rate. SEINE achieves comparable performance to the state-of-the-art techniques on a public dataset while being pragmatic to be used in a large-scale production system.	['Charles Rosenberg', 'Vishwakarma Singh', 'Manisha Srivastava', 'Prabhat Agarwal']	2022-07-21	null	null	null	null	['spam-detection']	['natural-language-processing']	[-3.45438868e-01 -1.29793912e-01 -2.34018475e-01 1.43937841e-01 -2.31497899e-01 -1.02957094e+00 7.81629503e-01 2.78911501e-01 9.78011191e-02 2.52628922e-01 -3.02010328e-02 -5.71356416e-01 4.62888293e-02 -1.14509869e+00 -3.65269244e-01 -1.71726003e-01 -4.41084802e-01 4.83410418e-01 9.63285327e-01 -6.49416864e-01 1.57305688e-01 3.75461638e-01 -1.10661519e+00 2.90570587e-01 9.52117264e-01 9.84727323e-01 -2.01925918e-01 4.25325483e-01 -1.91714048e-01 6.06837094e-01 -5.81365764e-01 -9.41223264e-01 3.58333498e-01 8.23321790e-02 -5.51071405e-01 5.41520007e-02 6.86442494e-01 -1.37436360e-01 -6.87251151e-01 1.45425081e+00 2.30355039e-01 -1.31215602e-01 4.74362463e-01 -1.53353608e+00 -5.80954790e-01 5.13332069e-01 -5.62774420e-01 2.14087948e-01 3.66964161e-01 1.89820692e-01 1.27410197e+00 -3.12691629e-01 7.21108556e-01 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8.03856194e-01 -3.32885504e-01 -1.26227427e+00 -9.85697985e-01 -1.18627429e+00 1.18261695e+00 -5.93406186e-02 -3.68526816e-01 -9.06858087e-01 -5.78445554e-01]	[7.870263576507568, 10.04216480255127]
9a4793c9-2ead-47aa-97cc-10ca2b7923e7	accelerating-inexact-hypergradient-descent	2307.00126	null	https://arxiv.org/abs/2307.00126v1	https://arxiv.org/pdf/2307.00126v1.pdf	Accelerating Inexact HyperGradient Descent for Bilevel Optimization	We present a method for solving general nonconvex-strongly-convex bilevel optimization problems. Our method -- the \emph{Restarted Accelerated HyperGradient Descent} (\texttt{RAHGD}) method -- finds an $\epsilon$-first-order stationary point of the objective with $\tilde{\mathcal{O}}(\kappa^{3.25}\epsilon^{-1.75})$ oracle complexity, where $\kappa$ is the condition number of the lower-level objective and $\epsilon$ is the desired accuracy. We also propose a perturbed variant of \texttt{RAHGD} for finding an $\big(\epsilon,\mathcal{O}(\kappa^{2.5}\sqrt{\epsilon}\,)\big)$-second-order stationary point within the same order of oracle complexity. Our results achieve the best-known theoretical guarantees for finding stationary points in bilevel optimization and also improve upon the existing upper complexity bound for finding second-order stationary points in nonconvex-strongly-concave minimax optimization problems, setting a new state-of-the-art benchmark. Empirical studies are conducted to validate the theoretical results in this paper.	['Michael I. Jordan', 'Chris Junchi Li', 'Luo Luo', 'Haikuo Yang']	2023-06-30	null	null	null	null	['bilevel-optimization']	['methodology']	[-1.70613244e-01 2.12335065e-01 -1.86669603e-01 -2.26711124e-01 -1.31444597e+00 -6.45841599e-01 -5.47863960e-01 6.69819638e-02 -5.83498955e-01 1.09801495e+00 -2.54829675e-01 -8.85229230e-01 -8.26013863e-01 -5.72279274e-01 -9.82125938e-01 -1.00072384e+00 -5.72352350e-01 3.93816710e-01 -2.70435542e-01 -4.84697461e-01 3.59771878e-01 2.12220345e-02 -1.10374582e+00 -2.49404877e-01 1.38439405e+00 1.42177570e+00 5.07622212e-02 5.31370759e-01 3.12631696e-01 2.06123665e-01 -3.58177543e-01 -5.01534343e-01 6.84414923e-01 -4.80163246e-01 -6.34193897e-01 -9.43864658e-02 3.99492115e-01 -9.45060104e-02 -6.28403351e-02 1.41457736e+00 3.41100544e-01 4.28418934e-01 3.29320192e-01 -9.67336059e-01 -4.40265745e-01 2.47484297e-01 -9.37357485e-01 3.03658247e-01 1.00153267e-01 2.16202766e-01 1.10606182e+00 -1.01383603e+00 2.36522272e-01 7.85922766e-01 8.76268744e-01 1.07382521e-01 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-4.59451824e-01 1.30433738e-01 2.36046717e-01 -5.04034281e-01 -4.22075570e-01 -3.43525529e-01]	[6.5103559494018555, 4.535711288452148]
12e95028-4250-45f5-a24e-0da5e11074e6	an-accurate-car-counting-in-aerial-images	null	null	https://link.springer.com/article/10.1007%2Fs12652-021-03377-5	https://link.springer.com/article/10.1007%2Fs12652-021-03377-5	An Accurate Car Counting in Aerial Images Based on Convolutional Neural Networks	This paper proposes a simple and effective single-shot detector model to detect and count cars in aerial images. The proposed model, called heatmap learner convolutional neural network (HLCNN), is used to predict the heatmap of target car instances. In order to learn the heatmap of the target cars, we have improved CNN architecture by adding three convolutional layers as adaptation layers instead of fully connected layers. The VGG-16 has been used as a backbone convolutional neural network in the proposed model. The proposed method successfully determines the number of cars and precisely detects the center of target cars. Experiments on the two different car datasets (PUCPR+ and CARPK) show the state-of-the-art counting and localizing performance of the proposed method in comparison with existing methods. Also, experiments have been conducted to examine the effect of data augmentation and batch normalization on the success of the proposed method. The code and data will be made available here [https://www.github.com/ekilic/Heatmap-Learner-CNN-for-Object-Counting].	['Serkan Öztürk', 'Ersin Kılıç']	2021-07-13	null	null	null	journal-of-ambient-intelligence-and-humanized-1	['object-counting']	['computer-vision']	[-1.92940027e-01 -2.89844602e-01 1.02508068e-01 -3.19110900e-01 -3.27904135e-01 -3.46827894e-01 6.47723079e-01 -1.26879737e-01 -5.32690287e-01 3.09149772e-01 -3.27214479e-01 -1.18256196e-01 4.30077463e-01 -1.04872572e+00 -6.70517564e-01 -5.43839097e-01 1.69557557e-02 2.57372111e-01 7.95889080e-01 2.60360856e-02 2.30379120e-01 5.56641698e-01 -1.51532388e+00 2.28350997e-01 4.11561280e-01 1.04794300e+00 5.03824890e-01 9.93660569e-01 -2.29745716e-01 1.28067219e+00 -5.19414842e-01 -3.89941841e-01 3.61019880e-01 -1.00126095e-01 -6.98410809e-01 -2.78775170e-02 5.50865769e-01 -6.57209992e-01 -5.78294396e-01 1.14257014e+00 4.02173579e-01 2.94530764e-02 5.92858434e-01 -1.22322750e+00 -5.07176578e-01 2.90440708e-01 -8.90925825e-01 6.07862651e-01 -2.65543938e-01 4.63409200e-02 5.28707385e-01 -8.74521971e-01 2.44193763e-01 9.99209940e-01 7.39186883e-01 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68d74fc3-31b8-4fac-95ee-4127f1cd82d1	a-comprehensive-review-of-yolo-from-yolov1-to	2304.00501	null	https://arxiv.org/abs/2304.00501v3	https://arxiv.org/pdf/2304.00501v3.pdf	A Comprehensive Review of YOLO: From YOLOv1 and Beyond	YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO's evolution, examining the innovations and contributions in each iteration from the original YOLO to YOLOv8 and YOLO-NAS. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. Finally, we summarize the essential lessons from YOLO's development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems.	['Diana Cordova-Esparza', 'Juan Terven']	2023-04-02	null	null	null	null	['real-time-object-detection']	['computer-vision']	[-3.15505594e-01 -3.10918599e-01 -4.87436056e-01 -1.49905950e-01 -1.32692814e-01 -1.75015941e-01 8.54742602e-02 -4.72692847e-01 -3.15673262e-01 3.38568509e-01 -5.60513258e-01 -3.58524740e-01 6.64657652e-02 -4.50428605e-01 -4.68626648e-01 -5.87060392e-01 -1.98953778e-01 -7.95225352e-02 6.40408576e-01 -1.92269355e-01 -1.62167922e-01 7.78259635e-01 -1.92025828e+00 -2.30120756e-02 2.79185086e-01 1.40676439e+00 1.59777299e-01 1.21928871e+00 2.52852559e-01 1.15927541e+00 -8.75110209e-01 -4.31725867e-02 3.96591008e-01 -8.60268176e-02 -1.18356489e-01 1.45827666e-01 9.96860325e-01 -3.42881352e-01 -9.44727838e-01 1.06295502e+00 5.79408944e-01 2.77369738e-01 3.58309239e-01 -1.93075097e+00 -7.10473478e-01 5.35323247e-02 -2.45984167e-01 8.06662083e-01 -3.32689464e-01 4.73023206e-01 7.59380341e-01 -1.18018317e+00 5.31193197e-01 1.16911769e+00 1.00826418e+00 6.49456441e-01 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be9e5555-dc98-4899-b44c-ee88186e4900	sgram-improving-scene-graph-parsing-via	2210.08675	null	https://arxiv.org/abs/2210.08675v1	https://arxiv.org/pdf/2210.08675v1.pdf	SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation	Scene graph is structured semantic representation that can be modeled as a form of graph from images and texts. Image-based scene graph generation research has been actively conducted until recently, whereas text-based scene graph generation research has not. In this paper, we focus on the problem of scene graph parsing from textual description of a visual scene. The core idea is to use abstract meaning representation (AMR) instead of the dependency parsing mainly used in previous studies. AMR is a graph-based semantic formalism of natural language which abstracts concepts of words in a sentence contrary to the dependency parsing which considers dependency relationships on all words in a sentence. To this end, we design a simple yet effective two-stage scene graph parsing framework utilizing abstract meaning representation, SGRAM (Scene GRaph parsing via Abstract Meaning representation): 1) transforming a textual description of an image into an AMR graph (Text-to-AMR) and 2) encoding the AMR graph into a Transformer-based language model to generate a scene graph (AMR-to-SG). Experimental results show the scene graphs generated by our framework outperforms the dependency parsing-based model by 11.61\% and the previous state-of-the-art model using a pre-trained Transformer language model by 3.78\%. Furthermore, we apply SGRAM to image retrieval task which is one of downstream tasks for scene graph, and confirm the effectiveness of scene graphs generated by our framework.	['Byoung-Tak Zhang', 'Yu-Jung Heo', 'Woo Suk Choi']	2022-10-17	null	null	null	null	['scene-graph-generation', 'dependency-parsing']	['computer-vision', 'natural-language-processing']	[ 8.52510393e-01 3.94787341e-01 2.24628061e-01 -6.43361092e-01 -5.89970648e-01 -3.83997083e-01 8.13138664e-01 1.15342043e-01 -7.92391449e-02 2.95270443e-01 4.26924139e-01 -5.02123952e-01 3.81354928e-01 -1.35695481e+00 -8.31149995e-01 -4.18033242e-01 4.83533382e-01 3.09366345e-01 4.66588974e-01 -4.00037050e-01 2.71958232e-01 2.85972178e-01 -1.34821308e+00 5.23659527e-01 3.02431256e-01 6.19085312e-01 8.79189134e-01 7.38975048e-01 -7.86609054e-01 1.21246397e+00 -7.60152102e-01 -3.77743155e-01 -4.46047820e-02 -8.48836362e-01 -9.29682910e-01 5.21370947e-01 2.93909550e-01 -2.36394763e-01 -3.77119452e-01 1.21365631e+00 1.03913032e-01 2.07037672e-01 6.53276503e-01 -1.12410927e+00 -1.03191853e+00 6.12867892e-01 -5.36285818e-01 -9.67174172e-02 5.98264277e-01 -2.69322932e-01 9.31021750e-01 -6.91625774e-01 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7.19537139e-01 2.97528833e-01 -5.96566617e-01 3.56309742e-01 -7.24788785e-01 -4.60767537e-01 9.32227314e-01 -1.97115719e-01 -3.00861001e-01 -8.70518163e-02 5.72305322e-01]	[10.49283218383789, 1.577022671699524]
dd4e1e8b-78c8-486e-9ebf-b89641e16316	alexa-teacher-model-pretraining-and	2206.07808	null	https://arxiv.org/abs/2206.07808v1	https://arxiv.org/pdf/2206.07808v1.pdf	Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems	We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system. Though we train using 70% spoken-form data, our teacher models perform comparably to XLM-R and mT5 when evaluated on the written-form Cross-lingual Natural Language Inference (XNLI) corpus. We perform a second stage of pretraining on our teacher models using in-domain data from our system, improving error rates by 3.86% relative for intent classification and 7.01% relative for slot filling. We find that even a 170M-parameter model distilled from our Stage 2 teacher model has 2.88% better intent classification and 7.69% better slot filling error rates when compared to the 2.3B-parameter teacher trained only on public data (Stage 1), emphasizing the importance of in-domain data for pretraining. When evaluated offline using labeled NLU data, our 17M-parameter Stage 2 distilled model outperforms both XLM-R Base (85M params) and DistillBERT (42M params) by 4.23% to 6.14%, respectively. Finally, we present results from a full virtual assistant experimentation platform, where we find that models trained using our pretraining and distillation pipeline outperform models distilled from 85M-parameter teachers by 3.74%-4.91% on an automatic measurement of full-system user dissatisfaction.	['Prem Natarajan', 'Gokhan Tur', 'Shuai Zheng', 'Haiyang Yu', 'Pan Wei', 'Fabian Triefenbach', 'Liz Tan', 'Mukund Harakere Sridhar', 'Saleh Soltan', 'Anjali Shenoy', 'Andy Rosenbaum', 'Stephen Rawls', 'Chandana Satya Prakash', 'Charith Peris', 'Enrico Palumbo', 'Gokmen Oz', 'Karolina Owczarzak', 'Pradeep Natarajan', 'Alessandro Manzotti', 'Jianhua Lu', 'Beiye Liu', 'Haidar Khan', 'Kevin Martin Jose', 'Jonathan Hueser', 'Wael Hamza', 'Dilek Hakkani-Tur', 'Thomas Gueudre', 'Karthik Gopalakrishnan', 'Turan Gojayev', 'Satyam Dwivedi', 'Anurag Dwarakanath', 'Luoxin Chen', 'Amit Chauhan', 'Rakesh Chada', 'Jin Cao', 'Claudio Delli Bovi', 'Abhishek Bhagia', 'Davide Bernardi', 'Konstantine Arkoudas', 'Shankar Ananthakrishnan', 'Jack FitzGerald']	2022-06-15	null	null	null	null	['cross-lingual-natural-language-inference', 'xlm-r', 'slot-filling']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 5.85867427e-02 9.28379118e-01 -4.84033048e-01 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8abad33f-69ad-4098-82e0-72e0626ae18c	mesh-interest-point-detection-based-on	1604.08806	null	http://arxiv.org/abs/1604.08806v3	http://arxiv.org/pdf/1604.08806v3.pdf	Mesh Interest Point Detection Based on Geometric Measures and Sparse Refinement	Three dimensional (3D) interest point detection plays a fundamental role in 3D computer vision and graphics. In this paper, we introduce a new method for detecting mesh interest points based on geometric measures and sparse refinement (GMSR). The key point of our approach is to calculate the 3D interest point response function using two intuitive and effective geometric properties of the local surface on a 3D mesh model, namely Euclidean distances between the neighborhood vertices to the tangent plane of a vertex and the angles of normal vectors of them. The response function is defined in multi-scale space and can be utilized to effectively distinguish 3D interest points from edges and flat areas. Those points with local maximal 3D interest point response value are selected as the candidates of 3D interest points. Finally, we utilize an $\ell_0$ norm based optimization method to refine the candidates of 3D interest points by constraining its quality and quantity. Numerical experiments demonstrate that our proposed GMSR based 3D interest point detector outperforms current several state-of-the-art methods for different kinds of 3D mesh models.	['Yipeng Liu', 'Ce Zhu', 'Xinyu Lin']	2016-04-29	null	null	null	null	['interest-point-detection']	['computer-vision']	[-1.39481887e-01 -2.64694184e-01 -1.14158466e-02 1.24395952e-01 -6.43281817e-01 -4.03975584e-02 4.64448512e-01 4.02774572e-01 -1.09475657e-01 7.59943053e-02 -2.04804912e-01 6.29575849e-02 -5.98102845e-02 -9.48359549e-01 -4.22514617e-01 -3.60980898e-01 -3.19890350e-01 6.64927721e-01 6.71273470e-01 -2.05410331e-01 8.72201622e-01 1.21267319e+00 -1.47072983e+00 -3.49311590e-01 5.73309481e-01 1.12137139e+00 -6.91044256e-02 3.39453161e-01 -1.16527818e-01 -2.97015846e-01 -3.82733554e-01 4.75102156e-01 4.83371377e-01 -1.77401051e-01 -5.24365127e-01 1.82582542e-01 3.08643728e-01 -7.72809237e-02 2.79914558e-01 1.12126589e+00 5.20322621e-01 2.10944057e-01 1.03755903e+00 -1.00449228e+00 -1.06102884e-01 -4.27845389e-01 -1.14211607e+00 7.22055584e-02 5.70102453e-01 -3.63892317e-01 5.97577393e-01 -1.57329559e+00 9.11432862e-01 1.34840643e+00 6.98476136e-01 1.05313368e-01 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b5481244-0741-48be-b251-1645c63b631c	empirical-evaluation-of-leveraging-named	1904.10195	null	http://arxiv.org/abs/1904.10195v1	http://arxiv.org/pdf/1904.10195v1.pdf	Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis	Social media reflects the public attitudes towards specific events. Events are often related to persons, locations or organizations, the so-called Named Entities. This can define Named Entities as sentiment-bearing components. In this paper, we dive beyond Named Entities recognition to the exploitation of sentiment-annotated Named Entities in Arabic sentiment analysis. Therefore, we develop an algorithm to detect the sentiment of Named Entities based on the majority of attitudes towards them. This enabled tagging Named Entities with proper tags and, thus, including them in a sentiment analysis framework of two models: supervised and lexicon-based. Both models were applied on datasets of multi-dialectal content. The results revealed that Named Entities have no considerable impact on the supervised model, while employing them in the lexicon-based model improved the classification performance and outperformed most of the baseline systems.	['Ismail Babaoglu', 'Mourad Gridach', 'Hatem Haddad', 'Hala Mulki']	2019-04-23	null	null	null	null	['arabic-sentiment-analysis']	['natural-language-processing']	[-4.82842118e-01 1.31894946e-01 1.81733407e-02 -5.27316868e-01 -3.04828674e-01 -8.88690710e-01 6.67533219e-01 5.61747015e-01 -8.25069010e-01 7.03212678e-01 6.18764043e-01 -9.34460089e-02 3.50763708e-01 -1.07451010e+00 -2.72856086e-01 -5.63676655e-01 1.80120394e-01 1.88588873e-01 2.62009025e-01 -5.94396234e-01 5.26570976e-01 4.37200487e-01 -1.32761323e+00 4.50762302e-01 8.63166928e-01 8.82001698e-01 -1.25204459e-01 2.02916652e-01 -7.05046237e-01 1.09878242e+00 -7.59445906e-01 -8.69470119e-01 -2.70657510e-01 -2.19592571e-01 -6.68298542e-01 1.67503953e-01 -2.68948406e-01 2.99623817e-01 5.33993542e-01 9.32289898e-01 3.71265531e-01 -6.41234368e-02 8.36188793e-01 -6.87861860e-01 -4.16810274e-01 6.90168142e-01 -2.92900294e-01 -1.47069842e-01 4.75291729e-01 -7.47449875e-01 1.04247880e+00 -1.15737307e+00 8.24658990e-01 9.02583599e-01 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-4.00559008e-01 1.00977845e-01 -1.01926732e+00 -6.86941445e-01 4.71454382e-01 -1.09091684e-01 -3.68911147e-01 -4.57120806e-01 2.80759424e-01]	[11.05174732208252, 6.9406867027282715]
0ac4ac1f-b11d-43ea-8694-7ac5fc37906b	smac-symbiotic-multi-agent-construction	2010.08473	null	https://arxiv.org/abs/2010.08473v1	https://arxiv.org/pdf/2010.08473v1.pdf	SMAC: Symbiotic Multi-Agent Construction	We present a novel concept of a heterogeneous, distributed platform for autonomous 3D construction. The platform is composed of two types of robots acting in a coordinated and complementary fashion: (i) A collection of communicating smart construction blocks behaving as a form of growable smart matter, and capable of planning and monitoring their own state and the construction progress; and (ii) A team of inchworm-shaped builder robots designed to navigate and modify the 3D structure, following the guidance of the smart blocks. We describe the design of the hardware and introduce algorithms for navigation and construction that support a wide class of 3D structures. We demonstrate the capabilities of our concept and characterize its performance through simulations and real-robot experiments.	['Carlo Pinciroli', 'Gregory Lewin', 'Hannan Liang', 'Josue Contreras', 'Trevor Rizzo', 'Neel Dhanaraj', 'Caleb Wagner']	2020-10-16	null	null	null	null	['smac-1', 'smac']	['playing-games', 'playing-games']	[-2.80717641e-01 6.26170516e-01 4.23132598e-01 -4.50779125e-03 1.57057658e-01 -8.18447948e-01 4.98746336e-01 -1.72944427e-01 4.77714807e-01 3.22463930e-01 8.28290880e-02 -3.37725058e-02 -3.14432919e-01 -1.28869104e+00 -6.96875036e-01 -7.92325437e-01 -6.44329965e-01 1.23791838e+00 6.73728347e-01 -8.63581240e-01 1.15223207e-01 6.89605117e-01 -1.81016326e+00 -2.44936004e-01 2.60117173e-01 1.01521277e+00 7.63609588e-01 6.42679930e-01 3.90101492e-01 8.53204846e-01 -1.46673471e-01 5.79037189e-01 3.76794875e-01 9.41109881e-02 -9.85891342e-01 4.05552208e-01 -5.52460730e-01 -2.24655405e-01 -2.63377298e-02 3.10019553e-01 5.83927929e-01 -2.49659717e-01 3.73760879e-01 -1.13947463e+00 -3.10452580e-01 7.15669155e-01 2.18899623e-01 -9.09739375e-01 1.03783560e+00 1.23800538e-01 5.35913944e-01 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6faf8e1b-93aa-4436-b340-dfd66631209e	one-shot-learning-based-drivers-head-movement	2306.05291	null	https://arxiv.org/abs/2306.05291v1	https://arxiv.org/pdf/2306.05291v1.pdf	One shot learning based drivers head movement identification using a millimetre wave radar sensor	Concentration of drivers on traffic is a vital safety issue; thus, monitoring a driver being on road becomes an essential requirement. The key purpose of supervision is to detect abnormal behaviours of the driver and promptly send warnings to him her for avoiding incidents related to traffic accidents. In this paper, to meet the requirement, based on radar sensors applications, the authors first use a small sized millimetre wave radar installed at the steering wheel of the vehicle to collect signals from different head movements of the driver. The received signals consist of the reflection patterns that change in response to the head movements of the driver. Then, in order to distinguish these different movements, a classifier based on the measured signal of the radar sensor is designed. However, since the collected data set is not large, in this paper, the authors propose One shot learning to classify four cases of driver's head movements. The experimental results indicate that the proposed method can classify the four types of cases according to the various head movements of the driver with a high accuracy reaching up to 100. In addition, the classification performance of the proposed method is significantly better than that of the convolutional neural network model.	['Yong Hwa Kim', 'Tien Tung Nguyen', 'Seongwook Lee', 'Hong Nhung Nguyen']	2023-05-31	null	null	null	null	['one-shot-learning']	['methodology']	[ 2.20722705e-01 -2.64977902e-01 1.20497495e-01 -4.89531219e-01 -1.25788167e-01 1.51970237e-01 2.86042690e-01 -3.95941168e-01 -6.08716488e-01 4.16534215e-01 -1.21634662e-01 -3.62458915e-01 -3.47452819e-01 -7.62266040e-01 -1.05569750e-01 -1.03726006e+00 5.04075170e-01 -1.07472427e-01 4.81900901e-01 -3.24667990e-01 4.86819267e-01 7.84151137e-01 -2.06979942e+00 7.03154951e-02 6.56158030e-01 1.18748462e+00 1.85904250e-01 4.10462290e-01 4.46380582e-03 6.39219105e-01 -5.80218613e-01 2.38448039e-01 1.42601356e-01 -3.78540576e-01 1.85035244e-01 4.74204607e-02 1.18909568e-01 -2.44011879e-01 -4.27593857e-01 1.12168276e+00 5.39580464e-01 2.31836766e-01 5.56973279e-01 -1.14131784e+00 8.74882340e-02 6.62796106e-03 -6.55907512e-01 6.09482408e-01 1.31688798e-02 2.03796625e-01 3.63292396e-01 -5.86036205e-01 -7.68380240e-02 7.82753468e-01 2.67424852e-01 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06d2c7e5-97a5-4efb-b9eb-f1ca6e382295	invalidator-automated-patch-correctness	2301.01113	null	https://arxiv.org/abs/2301.01113v2	https://arxiv.org/pdf/2301.01113v2.pdf	Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning	Automated program repair (APR) faces the challenge of test overfitting, where generated patches pass validation tests but fail to generalize. Existing methods for patch assessment involve generating new tests or manual inspection, which can be time-consuming or biased. In this paper, we propose a novel technique, INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR leverages program invariants to reason about program semantics while also capturing program syntax through language semantics learned from a large code corpus using a pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that an APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains erroneous behaviors from the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is threefold. First, INVALIDATOR leverages both semantic and syntactic reasoning to enhance its discriminative capability. Second, INVALIDATOR does not require new test cases to be generated, but instead only relies on the current test suite and uses invariant inference to generalize program behaviors. Third, INVALIDATOR is fully automated. Experimental results demonstrate that INVALIDATOR outperforms existing methods in terms of Accuracy and F-measure, correctly identifying 79% of overfitting patches and detecting 23% more overfitting patches than the best baseline.	['Quyet-Thang Huynh', 'Bui Quang-Huy', 'Nhat-Hoa Tran', 'David Lo', 'Xuan Bach D. Le', 'Duc-Minh Luong', 'Thanh Le-Cong']	2023-01-03	null	null	null	null	['program-repair', 'program-repair']	['computer-code', 'reasoning']	[ 2.42302611e-01 1.29720539e-01 -5.90147257e-01 -2.14162394e-01 -1.19907749e+00 -8.95584464e-01 -1.01527400e-01 3.98429841e-01 6.08556390e-01 4.21873927e-01 -2.04730377e-01 -7.81538785e-01 2.85615921e-01 -9.27081943e-01 -1.15522194e+00 1.02350004e-01 -7.13139102e-02 4.53098044e-02 5.93976319e-01 7.77349994e-02 4.46343035e-01 -1.43242076e-01 -1.80442381e+00 6.42740965e-01 1.45673907e+00 6.06248200e-01 -5.34089953e-02 8.10869634e-01 9.85120982e-02 7.50345051e-01 -7.76056767e-01 -3.19400549e-01 8.09243545e-02 -7.14785755e-01 -9.37789202e-01 1.72815286e-02 6.91903949e-01 -2.58916557e-01 4.04678673e-01 1.34094417e+00 -2.04228789e-01 -4.73698825e-01 6.47518337e-02 -1.40430939e+00 -4.60160971e-01 6.23537660e-01 -4.10130471e-01 -1.99476127e-02 7.25085139e-01 4.44160521e-01 1.24559498e+00 -5.93640745e-01 4.83005375e-01 8.63937736e-01 9.78949368e-01 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d63c59b2-ce11-4d39-9cd9-a747f345dff0	fpcc-net-fast-point-cloud-clustering-for	2012.14618	null	https://arxiv.org/abs/2012.14618v5	https://arxiv.org/pdf/2012.14618v5.pdf	FPCC: Fast Point Cloud Clustering based Instance Segmentation for Industrial Bin-picking	Instance segmentation is an important pre-processing task in numerous real-world applications, such as robotics, autonomous vehicles, and human-computer interaction. Compared with the rapid development of deep learning for two-dimensional (2D) image tasks, deep learning-based instance segmentation of 3D point cloud still has a lot of room for development. In particular, distinguishing a large number of occluded objects of the same class is a highly challenging problem, which is seen in a robotic bin-picking. In a usual bin-picking scene, many identical objects are stacked together and the model of the objects is known. Thus, the semantic information can be ignored; instead, the focus in the bin-picking is put on the segmentation of instances. Based on this task requirement, we propose a Fast Point Cloud Clustering (FPCC) for instance segmentation of bin-picking scene. FPCC includes a network named FPCC-Net and a fast clustering algorithm. FPCC-net has two subnets, one for inferring the geometric centers for clustering and the other for describing features of each point. FPCC-Net extracts features of each point and infers geometric center points of each instance simultaneously. After that, the proposed clustering algorithm clusters the remaining points to the closest geometric center in feature embedding space. Experiments show that FPCC also surpasses the existing works in bin-picking scenes and is more computationally efficient. Our code and data are available at https://github.com/xyjbaal/FPCC.	['Kazuhiro Kosuge', 'Fangzhou Lin', 'Diyi Liu', 'Shogo Arai', 'Yajun Xu']	2020-12-29	null	null	null	null	['3d-instance-segmentation-1']	['computer-vision']	[-2.17398852e-01 -1.43781304e-01 -2.96946727e-02 -4.08331394e-01 -4.07986313e-01 -4.34149861e-01 4.37948406e-01 3.23509395e-01 -3.06310356e-01 4.81531136e-02 -4.13112760e-01 -1.91735089e-01 -2.90362328e-01 -9.06557679e-01 -7.84727871e-01 -8.67833734e-01 -7.41330385e-02 9.88699555e-01 4.88542974e-01 5.00938632e-02 3.28849226e-01 8.57506990e-01 -1.50952387e+00 -8.50507021e-02 6.97639227e-01 1.22752976e+00 6.34298027e-01 2.19047174e-01 -5.74051261e-01 -8.87130946e-02 -3.99598151e-01 -1.21641964e-01 4.79697049e-01 5.83797060e-02 -5.69232166e-01 4.05957550e-01 1.47659078e-01 -2.29642794e-01 -1.86606497e-01 1.18039083e+00 1.31109610e-01 2.57204860e-01 6.23785436e-01 -1.75949669e+00 -2.56541044e-01 3.95964026e-01 -8.45452726e-01 -1.54504329e-01 -1.78106889e-01 1.51752606e-02 7.55167961e-01 -8.52422535e-01 2.45024338e-01 1.33914661e+00 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c9ec1740-b1de-414b-9560-3b12847e9844	codekgc-code-language-model-for-generative	2304.09048	null	https://arxiv.org/abs/2304.09048v1	https://arxiv.org/pdf/2304.09048v1.pdf	CodeKGC: Code Language Model for Generative Knowledge Graph Construction	Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks. Intuitively, we address the task of generative knowledge graph construction with code language model: given a code-format natural language input, the target is to generate triples which can be represented as code completion tasks. Specifically, we develop schema-aware prompts that effectively utilize the semantic structure within the knowledge graph. As code inherently possesses structure, such as class and function definitions, it serves as a useful model for prior semantic structural knowledge. Furthermore, we employ a rationale-enhanced generation method to boost the performance. Rationales provide intermediate steps, thereby improving knowledge extraction abilities. Experimental results indicate that the proposed approach can obtain better performance on benchmark datasets compared with baselines. Code and datasets are available in https://github.com/zjunlp/DeepKE/tree/main/example/llm.	['Ningyu Zhang', 'Huajun Chen', 'Wei Guo', 'Feiyu Xiong', 'Yinuo Jiang', 'Jing Chen', 'Zhen Bi']	2023-04-18	null	null	null	null	['graph-construction']	['graphs']	[ 9.64119807e-02 5.80611587e-01 -4.69838083e-01 -4.42798346e-01 -7.06175804e-01 -7.48394191e-01 5.65307915e-01 8.73880833e-03 4.15869832e-01 5.99384725e-01 4.76558417e-01 -6.66935861e-01 1.21188767e-01 -1.08178186e+00 -9.99815643e-01 -1.05908372e-01 1.86771989e-01 2.79118389e-01 8.05815607e-02 -1.54883996e-01 2.29687661e-01 -3.74923646e-01 -1.28594112e+00 6.38168633e-01 1.22393775e+00 5.47170162e-01 4.12876427e-01 2.70031273e-01 -6.45063221e-01 1.43138146e+00 -1.79631650e-01 -8.39856803e-01 -3.21528725e-02 -5.79145014e-01 -1.27094746e+00 1.68034213e-03 -4.36981320e-02 -2.09275395e-01 -2.28597075e-01 1.16989863e+00 -9.45288464e-02 -1.57776430e-01 4.34107929e-01 -1.37205982e+00 -1.10833716e+00 1.51267862e+00 -4.34556782e-01 -1.17393218e-01 6.13243818e-01 1.26402348e-01 1.29007900e+00 -1.04041946e+00 6.65249646e-01 1.14892673e+00 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f76a75ab-defd-44d7-b261-6dd6ae8c64bf	region-adaptive-texture-enhancement-for	2005.12486	null	https://arxiv.org/abs/2005.12486v1	https://arxiv.org/pdf/2005.12486v1.pdf	Region-adaptive Texture Enhancement for Detailed Person Image Synthesis	The ability to produce convincing textural details is essential for the fidelity of synthesized person images. However, existing methods typically follow a ``warping-based'' strategy that propagates appearance features through the same pathway used for pose transfer. However, most fine-grained features would be lost due to down-sampling, leading to over-smoothed clothes and missing details in the output images. In this paper we presents RATE-Net, a novel framework for synthesizing person images with sharp texture details. The proposed framework leverages an additional texture enhancing module to extract appearance information from the source image and estimate a fine-grained residual texture map, which helps to refine the coarse estimation from the pose transfer module. In addition, we design an effective alternate updating strategy to promote mutual guidance between two modules for better shape and appearance consistency. Experiments conducted on DeepFashion benchmark dataset have demonstrated the superiority of our framework compared with existing networks.	['Zhanning Gao', 'Xuansong Xie', 'Xinfeng Zhang', 'Wen Gao', 'Shanshe Wang', 'Lingbo Yang', 'Siwei Ma', 'Peiran Ren', 'Pan Wang']	2020-05-26	null	null	null	null	['pose-transfer']	['computer-vision']	[ 4.15982127e-01 1.62964761e-01 2.57263094e-01 -4.48176265e-01 -3.37814420e-01 -3.71872514e-01 6.05591834e-01 -4.52698886e-01 4.74397019e-02 7.42079556e-01 3.31357628e-01 4.09402072e-01 4.50727418e-02 -8.99242043e-01 -8.31756890e-01 -8.01193416e-01 4.17196721e-01 9.78169031e-03 1.64613664e-01 -3.30773979e-01 -4.82762083e-02 2.60010600e-01 -1.39549851e+00 3.49618524e-01 9.76554334e-01 9.69017506e-01 1.58638824e-02 3.25969547e-01 1.17288105e-01 6.20233834e-01 -4.33690190e-01 -7.76023388e-01 4.50642437e-01 -3.99039090e-01 -5.35330534e-01 4.65553939e-01 9.17688310e-01 -6.37526572e-01 -5.56747973e-01 1.08780062e+00 3.93474013e-01 2.17555821e-01 6.11561775e-01 -9.69864428e-01 -8.78037930e-01 2.53920555e-01 -8.99537325e-01 -3.50822002e-01 4.85907435e-01 4.01188433e-01 6.31632745e-01 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b70530fb-b260-42c9-94d6-e7d014d82940	tab2kg-semantic-table-interpretation-with	2302.01150	null	https://arxiv.org/abs/2302.01150v1	https://arxiv.org/pdf/2302.01150v1.pdf	Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles	Tabular data plays an essential role in many data analytics and machine learning tasks. Typically, tabular data does not possess any machine-readable semantics. In this context, semantic table interpretation is crucial for making data analytics workflows more robust and explainable. This article proposes Tab2KG - a novel method that targets at the interpretation of tables with previously unseen data and automatically infers their semantics to transform them into semantic data graphs. We introduce original lightweight semantic profiles that enrich a domain ontology's concepts and relations and represent domain and table characteristics. We propose a one-shot learning approach that relies on these profiles to map a tabular dataset containing previously unseen instances to a domain ontology. In contrast to the existing semantic table interpretation approaches, Tab2KG relies on the semantic profiles only and does not require any instance lookup. This property makes Tab2KG particularly suitable in the data analytics context, in which data tables typically contain new instances. Our experimental evaluation on several real-world datasets from different application domains demonstrates that Tab2KG outperforms state-of-the-art semantic table interpretation baselines.	['Elena Demidova', 'Simon Gottschalk']	2023-02-02	null	null	null	null	['one-shot-learning']	['methodology']	[ 3.00638109e-01 7.17331529e-01 -4.61593568e-01 -7.18235195e-01 -5.82971156e-01 -7.34868169e-01 4.56932068e-01 1.11548007e+00 1.83089226e-01 8.45411420e-01 8.50626752e-02 -3.04248661e-01 -4.52216297e-01 -1.38674629e+00 -1.05956829e+00 -1.07324801e-01 7.28405192e-02 1.17885959e+00 5.86550593e-01 -3.66784632e-01 3.68653610e-02 -8.21250230e-02 -2.12999511e+00 9.33681905e-01 9.80773628e-01 1.29328430e+00 -9.64299813e-02 -6.51094988e-02 -1.17789626e+00 9.61916029e-01 -4.87693131e-01 -8.33520412e-01 2.12376073e-01 -1.19059747e-02 -1.05432796e+00 -4.38020565e-02 3.39164466e-01 2.03555584e-01 6.19490221e-02 1.14778352e+00 -1.78426191e-01 1.12131834e-01 1.37020871e-01 -1.65384042e+00 -7.73313403e-01 9.54896390e-01 1.06478587e-01 -1.37078911e-01 4.73185122e-01 -4.14592803e-01 1.19915771e+00 -5.85320115e-01 9.70096052e-01 1.47853339e+00 6.38561130e-01 5.51502168e-01 -1.16300094e+00 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1.06094491e+00 1.64985042e-02 -9.58785832e-01 6.67888224e-02 2.86089957e-01]	[9.342069625854492, 8.00100040435791]
f266d564-6895-4ef7-8052-096632485d82	cellular-segmentation-and-composition-in	2203.02510	null	https://arxiv.org/abs/2203.02510v1	https://arxiv.org/pdf/2203.02510v1.pdf	Cellular Segmentation and Composition in Routine Histology Images using Deep Learning	Identification and quantification of nuclei in colorectal cancer haematoxylin \& eosin (H\&E) stained histology images is crucial to prognosis and patient management. In computational pathology these tasks are referred to as nuclear segmentation, classification and composition and are used to extract meaningful interpretable cytological and architectural features for downstream analysis. The CoNIC challenge poses the task of automated nuclei segmentation, classification and composition into six different types of nuclei from the largest publicly known nuclei dataset - Lizard. In this regard, we have developed pipelines for the prediction of nuclei segmentation using HoVer-Net and ALBRT for cellular composition. On testing on the preliminary test set, HoVer-Net achieved a PQ of 0.58, a PQ+ of 0.58 and finally a mPQ+ of 0.35. For the prediction of cellular composition with ALBRT on the preliminary test set, we achieved an overall $R^2$ score of 0.53, consisting of 0.84 for lymphocytes, 0.70 for epithelial cells, 0.70 for plasma and .060 for eosinophils.	['Adam Shephard', 'Manahil Raza', 'Srijay Deshpande', 'Raja Muhammad Saad Bashir', 'Muhammad Dawood']	2022-03-04	null	null	null	null	['nuclear-segmentation']	['medical']	[-2.14269999e-02 2.78307050e-01 8.95233974e-02 -3.52758467e-02 -8.94590616e-01 -7.22749054e-01 3.46354663e-01 9.61738050e-01 -7.79383957e-01 8.22689414e-01 -7.70203546e-02 -3.40038151e-01 -1.23122232e-02 -8.35382879e-01 -2.35309768e-02 -1.20450008e+00 -3.87871545e-03 9.30263460e-01 1.38199121e-01 1.71997979e-01 2.89154887e-01 7.02621877e-01 -1.20469213e+00 2.93584198e-01 4.24494296e-01 7.89565742e-01 -1.78650379e-01 1.43579793e+00 7.66400099e-02 7.07488716e-01 -2.39504263e-01 -3.24418575e-01 -8.76738727e-02 -2.76759207e-01 -9.38469589e-01 -8.76169875e-02 1.95008013e-02 8.86683259e-03 1.74124241e-01 8.42425883e-01 7.28092551e-01 -2.19071701e-01 1.06018627e+00 -8.49925578e-01 2.22294152e-01 5.80915451e-01 -5.99334300e-01 4.82078731e-01 5.49999103e-02 4.14291650e-01 1.01492572e+00 -6.68959677e-01 9.79531884e-01 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69125399-4a06-428a-8ae0-73286eeba4cb	synthesizing-diverse-human-motions-in-3d	2305.12411	null	https://arxiv.org/abs/2305.12411v2	https://arxiv.org/pdf/2305.12411v2.pdf	Synthesizing Diverse Human Motions in 3D Indoor Scenes	We present a novel method for populating 3D indoor scenes with virtual humans that can navigate the environment and interact with objects in a realistic manner. Existing approaches rely on high-quality training sequences that capture a diverse range of human motions in 3D scenes. However, such motion data is costly, difficult to obtain and can never cover the full range of plausible human-scene interactions in complex indoor environments. To address these challenges, we propose a reinforcement learning-based approach to learn policy networks that predict latent variables of a powerful generative motion model that is trained on a large-scale motion capture dataset (AMASS). For navigating in a 3D environment, we propose a scene-aware policy training scheme with a novel collision avoidance reward function. Combined with the powerful generative motion model, we can synthesize highly diverse human motions navigating 3D indoor scenes, meanwhile effectively avoiding obstacles. For detailed human-object interactions, we carefully curate interaction-aware reward functions by leveraging a marker-based body representation and the signed distance field (SDF) representation of the 3D scene. With a number of important training design schemes, our method can synthesize realistic and diverse human-object interactions (e.g.,~sitting on a chair and then getting up) even for out-of-distribution test scenarios with different object shapes, orientations, starting body positions, and poses. Experimental results demonstrate that our approach outperforms state-of-the-art human-scene interaction synthesis frameworks in terms of both motion naturalness and diversity. Video results are available on the project page: https://zkf1997.github.io/DIMOS.	['Siyu Tang', 'Thabo Beeler', 'Shaofei Wang', 'Yan Zhang', 'Kaifeng Zhao']	2023-05-21	null	null	null	null	['human-object-interaction-detection']	['computer-vision']	[-4.74822409e-02 -2.25558370e-01 8.22947174e-02 -1.93549737e-01 -4.39173818e-01 -4.26365018e-01 4.72052604e-01 -5.37395358e-01 -2.95720100e-01 6.83723509e-01 2.84808964e-01 -1.91134661e-01 -4.67107669e-02 -7.67559111e-01 -9.34664309e-01 -4.94903058e-01 -1.25358239e-01 6.63960576e-01 3.19831014e-01 -4.08676773e-01 -2.07360134e-01 5.69705784e-01 -1.59700751e+00 -1.20673947e-01 7.45296180e-01 5.03279150e-01 6.28038585e-01 1.12731957e+00 3.08438122e-01 6.88011408e-01 -5.39270937e-01 -6.00453839e-02 3.75029504e-01 -3.83496225e-01 -5.26525617e-01 2.32876763e-01 2.32516035e-01 -6.33882105e-01 -5.29940784e-01 4.27914649e-01 8.04708540e-01 6.46062493e-01 4.56295490e-01 -1.32492399e+00 -1.72109783e-01 1.26477219e-02 -3.26384425e-01 -1.18528448e-01 1.00794458e+00 6.91609859e-01 4.60006982e-01 -6.44856632e-01 9.84718204e-01 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69f6a9e0-96c6-424c-a6e9-16a1f7dc655f	fast-and-accurate-intrinsic-symmetry	1807.10162	null	http://arxiv.org/abs/1807.10162v4	http://arxiv.org/pdf/1807.10162v4.pdf	Fast and Accurate Intrinsic Symmetry Detection	In computer vision and graphics, various types of symmetries are extensively studied since symmetry present in objects is a fundamental cue for understanding the shape and the structure of objects. In this work, we detect the intrinsic reflective symmetry in triangle meshes where we have to find the intrinsically symmetric point for each point of the shape. We establish correspondences between functions defined on the shapes by extending the functional map framework and then recover the point-to-point correspondences. Previous approaches using the functional map for this task find the functional correspondences matrix by solving a non-linear optimization problem which makes them slow. In this work, we propose a closed form solution for this matrix which makes our approach faster. We find the closed-form solution based on our following results. If the given shape is intrinsically symmetric, then the shortest length geodesic between two intrinsically symmetric points is also intrinsically symmetric. If an eigenfunction of the Laplace-Beltrami operator for the given shape is an even (odd) function, then its restriction on the shortest length geodesic between two intrinsically symmetric points is also an even (odd) function. The sign of a low-frequency eigenfunction is the same on the neighboring points. Our method is invariant to the ordering of the eigenfunctions and has the least time complexity. We achieve the best performance on the SCAPE dataset and comparable performance with the state-of-the-art methods on the TOSCA dataset.	['Rajendra Nagar', 'Shanmuganathan Raman']	2018-07-26	fast-and-accurate-intrinsic-symmetry-1	http://openaccess.thecvf.com/content_ECCV_2018/html/Rajendra_Nagar_Fast_and_Accurate_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Rajendra_Nagar_Fast_and_Accurate_ECCV_2018_paper.pdf	eccv-2018-9	['symmetry-detection']	['computer-vision']	[ 1.51997998e-01 -8.39298666e-02 4.73640710e-02 -1.11286789e-01 -1.50738969e-01 -8.12904894e-01 3.40021253e-01 -6.17297813e-02 7.51874992e-04 8.40623453e-02 -6.12331070e-02 -1.08923770e-01 -2.97432929e-01 -8.96881342e-01 -8.27002704e-01 -6.82201385e-01 -6.23112954e-02 6.67199016e-01 4.31240499e-01 -3.06806952e-01 5.71286440e-01 8.14988554e-01 -1.44014740e+00 2.32017741e-01 6.49143517e-01 9.68802273e-01 -5.58819957e-02 4.87200320e-01 8.31145570e-02 -3.32050562e-01 -3.37142381e-03 -8.77988860e-02 7.45234847e-01 -4.84986961e-01 -9.61271882e-01 1.24192648e-01 6.93302214e-01 2.23693907e-01 5.37067419e-04 1.15042889e+00 3.58779430e-02 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3071d261-748f-4646-857d-958029fc0154	decomposed-meta-learning-for-few-shot-named	2204.05751	null	https://arxiv.org/abs/2204.05751v2	https://arxiv.org/pdf/2204.05751v2.pdf	Decomposed Meta-Learning for Few-Shot Named Entity Recognition	Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples. In this paper, we present a decomposed meta-learning approach which addresses the problem of few-shot NER by sequentially tackling few-shot span detection and few-shot entity typing using meta-learning. In particular, we take the few-shot span detection as a sequence labeling problem and train the span detector by introducing the model-agnostic meta-learning (MAML) algorithm to find a good model parameter initialization that could fast adapt to new entity classes. For few-shot entity typing, we propose MAML-ProtoNet, i.e., MAML-enhanced prototypical networks to find a good embedding space that can better distinguish text span representations from different entity classes. Extensive experiments on various benchmarks show that our approach achieves superior performance over prior methods.	['Chin-Yew Lin', 'Tiejun Zhao', 'Qianhui Wu', 'Huiqiang Jiang', 'Tingting Ma']	2022-04-12	null	https://aclanthology.org/2022.findings-acl.124	https://aclanthology.org/2022.findings-acl.124.pdf	findings-acl-2022-5	['few-shot-ner', 'entity-typing']	['natural-language-processing', 'natural-language-processing']	[-1.42951738e-02 -8.44538510e-02 -3.25589895e-01 -3.67355675e-01 -9.81061995e-01 -4.93228853e-01 4.00027156e-01 2.63312548e-01 -6.77524269e-01 5.44247985e-01 1.03432439e-01 -9.20389965e-02 8.35031085e-03 -8.59643757e-01 -4.95576531e-01 -2.65897572e-01 -5.65277562e-02 4.74360645e-01 2.75082409e-01 -2.91689605e-01 1.44474924e-01 5.43319285e-01 -1.27050948e+00 4.90301579e-01 8.96605432e-01 4.31971937e-01 1.56603783e-01 7.99753428e-01 -8.45604300e-01 7.78962016e-01 -6.94915831e-01 -7.10948527e-01 -1.12103634e-01 -4.22590494e-01 -1.09467351e+00 -3.43414783e-01 2.99856871e-01 -1.98769756e-02 -3.32853377e-01 9.29906726e-01 7.07045078e-01 5.71323574e-01 6.76054597e-01 -1.11756194e+00 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f41080a0-aff0-416b-a573-0f4b03d53c9a	un-likelihood-training-for-interpretable	2207.00282	null	https://arxiv.org/abs/2207.00282v2	https://arxiv.org/pdf/2207.00282v2.pdf	(Un)likelihood Training for Interpretable Embedding	Cross-modal representation learning has become a new normal for bridging the semantic gap between text and visual data. Learning modality agnostic representations in a continuous latent space, however, is often treated as a black-box data-driven training process. It is well-known that the effectiveness of representation learning depends heavily on the quality and scale of training data. For video representation learning, having a complete set of labels that annotate the full spectrum of video content for training is highly difficult if not impossible. These issues, black-box training and dataset bias, make representation learning practically challenging to be deployed for video understanding due to unexplainable and unpredictable results. In this paper, we propose two novel training objectives, likelihood and unlikelihood functions, to unroll semantics behind embeddings while addressing the label sparsity problem in training. The likelihood training aims to interpret semantics of embeddings beyond training labels, while the unlikelihood training leverages prior knowledge for regularization to ensure semantically coherent interpretation. With both training objectives, a new encoder-decoder network, which learns interpretable cross-modal representation, is proposed for ad-hoc video search. Extensive experiments on TRECVid and MSR-VTT datasets show the proposed network outperforms several state-of-the-art retrieval models with a statistically significant performance margin.	['Zhijian Hou', 'Wing-Kwong Chan', 'Chong-Wah Ngo', 'Jiaxin Wu']	2022-07-01	null	null	null	null	['ad-hoc-video-search']	['computer-vision']	[ 3.50716561e-01 -1.97189406e-01 -8.17545712e-01 -5.08558095e-01 -9.41528261e-01 -6.11919641e-01 6.80909872e-01 2.75193844e-02 -3.32689047e-01 4.55375075e-01 4.91405785e-01 -2.45897487e-01 -1.55279428e-01 -2.23099977e-01 -7.63043463e-01 -5.92539132e-01 3.56391184e-02 3.48969162e-01 -3.11585933e-01 9.01594833e-02 3.12762409e-02 3.05546098e-03 -1.64367127e+00 6.87310815e-01 5.84940314e-01 1.24706757e+00 5.45187555e-02 3.42994601e-01 -1.70850813e-01 9.11317766e-01 -2.43290842e-01 -2.55629957e-01 1.30895823e-01 -1.83847308e-01 -6.20593965e-01 2.88454473e-01 7.09259927e-01 -2.86707550e-01 -6.09156370e-01 9.63736475e-01 2.32881576e-01 6.17228486e-02 1.09635389e+00 -1.49929452e+00 -1.22447681e+00 4.26673472e-01 -7.71709502e-01 1.49410382e-01 3.43218654e-01 -2.81503946e-02 1.37038827e+00 -1.21033692e+00 5.05410373e-01 1.32563782e+00 4.68851149e-01 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d2e449af-552d-4ea6-aa72-cd23593bd243	csclog-a-component-subsequence-correlation	2307.03359	null	https://arxiv.org/abs/2307.03359v1	https://arxiv.org/pdf/2307.03359v1.pdf	CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection Method	Anomaly detection based on system logs plays an important role in intelligent operations, which is a challenging task due to the extremely complex log patterns. Existing methods detect anomalies by capturing the sequential dependencies in log sequences, which ignore the interactions of subsequences. To this end, we propose CSCLog, a Component Subsequence Correlation-Aware Log anomaly detection method, which not only captures the sequential dependencies in subsequences, but also models the implicit correlations of subsequences. Specifically, subsequences are extracted from log sequences based on components and the sequential dependencies in subsequences are captured by Long Short-Term Memory Networks (LSTMs). An implicit correlation encoder is introduced to model the implicit correlations of subsequences adaptively. In addition, Graph Convolution Networks (GCNs) are employed to accomplish the information interactions of subsequences. Finally, attention mechanisms are exploited to fuse the embeddings of all subsequences. Extensive experiments on four publicly available log datasets demonstrate the effectiveness of CSCLog, outperforming the best baseline by an average of 7.41% in Macro F1-Measure.	['Feifei Li', 'Dachao Fu', 'Xu Wang', 'Chaodu Song', 'Ling Chen']	2023-07-07	null	null	null	null	['anomaly-detection']	['methodology']	[-2.29213014e-02 -4.11239058e-01 -1.78757645e-02 -2.55381376e-01 5.52457012e-02 -2.55381733e-01 3.56356949e-01 7.08720386e-01 -2.39125401e-01 6.43649548e-02 4.20913219e-01 -4.78704274e-01 3.22780833e-02 -4.58639473e-01 -9.39361870e-01 -4.95403647e-01 -6.37420058e-01 -3.89288515e-02 2.97006458e-01 -2.40107253e-01 4.47251290e-01 3.91639918e-01 -1.29296756e+00 2.71726608e-01 7.56298304e-01 1.23394907e+00 -2.53595505e-03 8.14554632e-01 -5.86249948e-01 1.12969708e+00 -5.30743420e-01 3.31284013e-03 -1.27765238e-01 -5.43569207e-01 -3.62141132e-01 -2.69218031e-02 4.30577435e-02 -5.49849391e-01 -8.94203484e-01 1.08987343e+00 5.03633954e-02 1.70681864e-01 2.70416290e-01 -9.98990953e-01 -7.58072257e-01 6.99872673e-01 -6.50490940e-01 8.45826507e-01 2.30029166e-01 3.61355752e-01 1.20705771e+00 -7.63438523e-01 -1.19769201e-01 1.07315707e+00 4.52411503e-01 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e24167cb-f8f3-49ee-9548-2c38b06a8448	rf-based-fall-monitoring-using-convolutional	null	null	https://doi.org/10.1145/3264947	http://people.csail.mit.edu/yonglong/yonglong/rffall.pdf	RF-Based Fall Monitoring Using Convolutional Neural Networks	Falls are the top reason for fatal and non-fatal injuries among seniors. Existing solutions are based on wearable fall-alert sensors, but medical research has shown that they are ineffective, mostly because seniors do not wear them. These revelations have led to new passive sensors that infer falls by analyzing Radio Frequency (RF) signals in homes. Seniors can go about their lives as usual without the need to wear any device. While passive monitoring has made major advances, current approaches still cannot deal with the complexities of real-world scenarios. They typically train and test their classifiers on the same people in the same environments, and cannot generalize to new people or new environments. Further, they cannot separate motions from different people and can easily miss a fall in the presence of other motions. To overcome these limitations, we introduce Aryokee, an RF-based fall detection system that uses convolutional neural networks governed by a state machine. Aryokee works with new people and environments unseen in the training set. It also separates different sources of motion to increase robustness. Results from testing Aryokee with over 140 people performing 40 types of activities in 57 different environments show a recall of 94% and a precision of 92% in detecting falls.	['Dina Katabi', 'Chen-Yu Hsu', 'Guang-He Lee', 'Yonglong Tian', 'Hao He']	2018-09-01	null	null	null	proceedings-of-the-acm-on-interactive-mobile	['rf-based-pose-estimation']	['computer-vision']	[ 3.80331054e-02 -3.09270173e-01 -6.32843226e-02 -3.58702302e-01 -5.45344472e-01 -1.67539805e-01 -2.45566994e-01 -1.17684109e-02 -8.31705093e-01 1.01128292e+00 5.47908604e-01 -1.47447854e-01 -3.11169773e-02 -9.97981071e-01 -4.23561573e-01 -2.66015917e-01 -2.18495235e-01 2.00092316e-01 8.61674726e-01 -4.87348527e-01 -3.68478268e-01 3.33666503e-01 -1.70906687e+00 5.35122693e-01 6.69882238e-01 5.29608011e-01 -1.16546877e-01 6.89980090e-01 5.04946887e-01 4.30714905e-01 -9.48200583e-01 1.97676599e-01 1.24880418e-01 -3.06796998e-01 -4.06675220e-01 -5.96289039e-01 4.19460803e-01 -8.85905147e-01 -8.11182261e-01 2.87171453e-01 9.41099524e-01 2.38657385e-01 3.37438166e-01 -1.16591573e+00 -2.52513289e-01 1.57177851e-01 -2.51672417e-01 7.12133110e-01 9.79466796e-01 1.04120083e-01 2.20326379e-01 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8.81280959e-01 -4.09429334e-02 -8.65746319e-01 -1.80446252e-01 -1.05779541e+00 -4.47654039e-01 3.27015877e-01 6.27047777e-01 -9.04390514e-01 -3.20998847e-01 3.35968584e-02]	[7.197625160217285, 0.5851206183433533]
fcc7c05f-1b16-4836-99da-5ae63cc00f4a	cascaded-deep-monocular-3d-human-pose-1	2006.07778	null	https://arxiv.org/abs/2006.07778v3	https://arxiv.org/pdf/2006.07778v3.pdf	Cascaded deep monocular 3D human pose estimation with evolutionary training data	End-to-end deep representation learning has achieved remarkable accuracy for monocular 3D human pose estimation, yet these models may fail for unseen poses with limited and fixed training data. This paper proposes a novel data augmentation method that: (1) is scalable for synthesizing massive amount of training data (over 8 million valid 3D human poses with corresponding 2D projections) for training 2D-to-3D networks, (2) can effectively reduce dataset bias. Our method evolves a limited dataset to synthesize unseen 3D human skeletons based on a hierarchical human representation and heuristics inspired by prior knowledge. Extensive experiments show that our approach not only achieves state-of-the-art accuracy on the largest public benchmark, but also generalizes significantly better to unseen and rare poses. Code, pre-trained models and tools are available at this HTTPS URL.	['Kwang-Ting Cheng', 'Chi-Keung Tang', 'Yu-Wing Tai', 'Lei Ke', 'Shichao Li', 'Kevin Pratama']	2020-06-14	cascaded-deep-monocular-3d-human-pose	http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Cascaded_Deep_Monocular_3D_Human_Pose_Estimation_With_Evolutionary_Training_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Cascaded_Deep_Monocular_3D_Human_Pose_Estimation_With_Evolutionary_Training_CVPR_2020_paper.pdf	cvpr-2020-6	['monocular-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation']	['computer-vision', 'computer-vision']	[ 4.99725789e-02 1.48735285e-01 -2.88600236e-01 -1.97695211e-01 -8.02347720e-01 -3.08539987e-01 3.58874232e-01 -5.93068659e-01 -3.52051765e-01 7.61594951e-01 6.44427657e-01 6.32199571e-02 3.69238853e-01 -3.98153514e-01 -1.00154030e+00 -1.15067407e-01 -1.30196661e-01 1.08095348e+00 1.04733348e-01 -1.56712070e-01 -3.51311982e-01 5.96498132e-01 -1.43631828e+00 6.79560900e-02 1.80495352e-01 7.84335911e-01 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8259321d-c68f-4cd1-ae81-2c50f1dd91eb	object-driven-active-mapping-for-more	2012.01788	null	https://arxiv.org/abs/2012.01788v3	https://arxiv.org/pdf/2012.01788v3.pdf	Object SLAM-Based Active Mapping and Robotic Grasping	This paper presents the first active object mapping framework for complex robotic manipulation and autonomous perception tasks. The framework is built on an object SLAM system integrated with a simultaneous multi-object pose estimation process that is optimized for robotic grasping. Aiming to reduce the observation uncertainty on target objects and increase their pose estimation accuracy, we also design an object-driven exploration strategy to guide the object mapping process, enabling autonomous mapping and high-level perception. Combining the mapping module and the exploration strategy, an accurate object map that is compatible with robotic grasping can be generated. Additionally, quantitative evaluations also indicate that the proposed framework has a very high mapping accuracy. Experiments with manipulation (including object grasping and placement) and augmented reality significantly demonstrate the effectiveness and advantages of our proposed framework.	['Zhiqiang Deng', 'Xinggang Hu', 'Wenkai Sun', 'Sonya Coleman', 'Xin Chen', 'Delong Zhu', 'Yunzhou Zhang', 'Yanmin Wu']	2020-12-03	null	null	null	null	['object-slam']	['computer-vision']	[-1.02777466e-01 7.25988373e-02 -1.65414855e-01 -3.97821993e-01 -2.80569315e-01 -4.94228512e-01 3.68868172e-01 -1.65890902e-02 -1.45257249e-01 4.09583360e-01 -2.46728197e-01 3.07208419e-01 -8.01086247e-01 -8.41425121e-01 -6.99520767e-01 -4.27432209e-01 -2.11284563e-01 9.46752131e-01 3.76041740e-01 -1.17735989e-01 6.07143581e-01 9.70606267e-01 -1.75171280e+00 -7.02062473e-02 9.62297142e-01 1.11852622e+00 1.44068849e+00 2.73879766e-01 -2.37004578e-01 3.81358057e-01 -2.17319161e-01 3.58504057e-01 5.30431628e-01 5.62996805e-01 -6.16528332e-01 1.58167064e-01 -9.62281302e-02 -8.21557641e-01 -4.46875542e-02 1.02474177e+00 2.89140940e-01 3.26319113e-02 3.37019056e-01 -1.36977303e+00 -4.19331372e-01 7.17157364e-01 -1.33406341e-01 -5.56230128e-01 4.06835556e-01 2.73554260e-03 6.02628052e-01 -1.04446340e+00 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1903a1a4-5863-4c2f-9738-5eaf997bcb04	meta-voice-fast-few-shot-style-transfer-for	2111.07218	null	https://arxiv.org/abs/2111.07218v1	https://arxiv.org/pdf/2111.07218v1.pdf	Meta-Voice: Fast few-shot style transfer for expressive voice cloning using meta learning	The task of few-shot style transfer for voice cloning in text-to-speech (TTS) synthesis aims at transferring speaking styles of an arbitrary source speaker to a target speaker's voice using very limited amount of neutral data. This is a very challenging task since the learning algorithm needs to deal with few-shot voice cloning and speaker-prosody disentanglement at the same time. Accelerating the adaptation process for a new target speaker is of importance in real-world applications, but even more challenging. In this paper, we approach to the hard fast few-shot style transfer for voice cloning task using meta learning. We investigate the model-agnostic meta-learning (MAML) algorithm and meta-transfer a pre-trained multi-speaker and multi-prosody base TTS model to be highly sensitive for adaptation with few samples. Domain adversarial training mechanism and orthogonal constraint are adopted to disentangle speaker and prosody representations for effective cross-speaker style transfer. Experimental results show that the proposed approach is able to conduct fast voice cloning using only 5 samples (around 12 second speech data) from a target speaker, with only 100 adaptation steps. Audio samples are available online.	['Dong Yu', 'Dan Su', 'Songxiang Liu']	2021-11-14	null	null	null	null	['voice-cloning']	['speech']	[ 5.52625656e-01 7.59155676e-02 -1.19581111e-01 -2.72632360e-01 -1.33381212e+00 -4.51906592e-01 5.62180340e-01 -4.53681886e-01 -3.79378766e-01 7.98224330e-01 2.75858968e-01 -2.31637537e-01 4.62338239e-01 -2.30750412e-01 -5.86847842e-01 -7.79572964e-01 3.61123651e-01 5.41877151e-01 9.38642547e-02 -4.00455832e-01 -1.12238742e-01 4.67702955e-01 -1.38249242e+00 3.84969920e-01 7.54229069e-01 7.57848084e-01 4.78939414e-01 1.10803783e+00 -3.62635374e-01 4.81420904e-01 -8.35288107e-01 -3.22137922e-01 4.17790301e-02 -8.97549450e-01 -7.50864983e-01 4.30248119e-02 3.41074556e-01 -1.04059927e-01 1.30066514e-01 1.04708052e+00 1.17409790e+00 3.49719405e-01 8.00190330e-01 -1.20738530e+00 -3.46295863e-01 6.87204421e-01 -2.42019653e-01 3.40381145e-01 1.58116922e-01 9.06256363e-02 6.45873308e-01 -1.00928664e+00 3.14321548e-01 1.50045753e+00 4.82485622e-01 1.19775486e+00 -1.17157257e+00 -1.03097713e+00 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c1b7cb3d-d19d-49ab-8c5d-1ab4daa102a7	safe-exploration-in-linear-equality	null	null	https://openreview.net/forum?id=5vjyt5JHmaU	https://openreview.net/pdf?id=5vjyt5JHmaU	Safe Exploration in Linear Equality Constraint	With the extensive research and application, some shortcomings of reinforcement learning methods are gradually revealed. One of the considerable problems is that it is difficult for reinforcement learning methods to strictly satisfy the constraints. In this paper, a Singular Value Decomposition-based non-training method called 'Action Decomposition Regular' is proposed to achieve safe exploration. By adopting linear dynamics model, our method decomposes the action space into a constraint dimension and a free dimension for separate control, making policy strictly satisfy the linear equality constraint without limiting the exploration region. In addition, we show how our method should be used when the action space is limited and convex, which makes the method more suitable for real-world scenarios. Finally, we show the effectiveness of our method in a physically-based environment and prevail where reward shaping fails.	['Jinwei Liu', 'Wang Yao', 'Zijia Niu', 'Xiaohu Jia']	2021-09-29	null	null	null	null	['safe-exploration']	['robots']	[-6.58722371e-02 1.34189636e-01 -5.65103590e-01 2.96522379e-01 -1.26738427e-02 -3.92778248e-01 2.57577360e-01 -3.76951396e-01 -5.36739349e-01 1.35181999e+00 3.76760960e-02 -3.30190569e-01 -4.15642083e-01 -7.32397199e-01 -4.01386440e-01 -1.10029233e+00 -1.48127764e-01 -9.66542438e-02 2.24208593e-01 -6.86394393e-01 4.46890354e-01 2.95239568e-01 -1.37121809e+00 -5.19607902e-01 1.22762704e+00 7.97611415e-01 4.45516288e-01 8.37547109e-02 1.32577628e-01 5.54891467e-01 -4.23236370e-01 4.37694728e-01 5.65634429e-01 -5.85660100e-01 -5.93608499e-01 3.52106988e-01 -5.09433627e-01 -5.61816692e-01 -2.77683675e-01 1.15839696e+00 6.09565437e-01 6.32735848e-01 3.50519896e-01 -1.14637876e+00 -4.41417605e-01 3.09258789e-01 -7.76882291e-01 1.60416976e-01 1.32016331e-01 4.18670326e-01 6.14944756e-01 -1.94201082e-01 4.03487623e-01 1.23501658e+00 3.21971148e-01 7.75669396e-01 -9.31838155e-01 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6.02735355e-02 2.68100560e-01 -1.33306527e+00 -4.35879409e-01 -4.65077013e-01]	[4.291292190551758, 2.15521502494812]
b39dc31b-1996-4d5d-bec8-8d892e9bb2cc	perceiving-the-world-question-guided	2204.09597	null	https://arxiv.org/abs/2204.09597v2	https://arxiv.org/pdf/2204.09597v2.pdf	Perceiving the World: Question-guided Reinforcement Learning for Text-based Games	Text-based games provide an interactive way to study natural language processing. While deep reinforcement learning has shown effectiveness in developing the game playing agent, the low sample efficiency and the large action space remain to be the two major challenges that hinder the DRL from being applied in the real world. In this paper, we address the challenges by introducing world-perceiving modules, which automatically decompose tasks and prune actions by answering questions about the environment. We then propose a two-phase training framework to decouple language learning from reinforcement learning, which further improves the sample efficiency. The experimental results show that the proposed method significantly improves the performance and sample efficiency. Besides, it shows robustness against compound error and limited pre-training data.	['Chengqi Zhang', 'Joey Tianyi Zhou', 'Yali Du', 'Ling Chen', 'Meng Fang', 'Yunqiu Xu']	2022-03-20	null	https://aclanthology.org/2022.acl-long.41	https://aclanthology.org/2022.acl-long.41.pdf	acl-2022-5	['text-based-games']	['playing-games']	[-6.40298799e-02 -1.27686873e-01 5.19077405e-02 -1.38596511e-02 -6.66970372e-01 -5.60161233e-01 5.91352761e-01 -9.48412642e-02 -9.10914302e-01 6.60173595e-01 -9.00482107e-03 -4.93348420e-01 -2.86644381e-02 -1.11874676e+00 -3.62147689e-01 -4.94950533e-01 1.21363133e-01 3.65941554e-01 5.08940399e-01 -4.12523091e-01 3.04712206e-01 1.74707934e-01 -1.64779449e+00 8.76965076e-02 1.11646438e+00 7.32314110e-01 6.14732921e-01 6.97767794e-01 -3.22645158e-01 1.35011125e+00 -6.62104070e-01 -9.64493230e-02 2.42576659e-01 -4.79508430e-01 -7.51205564e-01 -1.51995644e-01 -4.35698986e-01 -6.14288151e-01 -2.96472877e-01 1.13451147e+00 5.11886716e-01 4.47179675e-01 1.11363098e-01 -1.10980892e+00 -3.37537706e-01 5.24412513e-01 -3.50897014e-01 8.28811601e-02 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891d50e8-64cd-431f-b2e1-55f84ba9c25b	the-first-proven-performance-guarantees-for	2305.13459	null	https://arxiv.org/abs/2305.13459v2	https://arxiv.org/pdf/2305.13459v2.pdf	The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem	The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most prominent algorithms to solve multi-objective optimization problems. Recently, the first mathematical runtime guarantees have been obtained for this algorithm, however only for synthetic benchmark problems. In this work, we give the first proven performance guarantees for a classic optimization problem, the NP-complete bi-objective minimum spanning tree problem. More specifically, we show that the NSGA-II with population size $N \ge 4((n-1) w_{\max} + 1)$ computes all extremal points of the Pareto front in an expected number of $O(m^2 n w_{\max} \log(n w_{\max}))$ iterations, where $n$ is the number of vertices, $m$ the number of edges, and $w_{\max}$ is the maximum edge weight in the problem instance. This result confirms, via mathematical means, the good performance of the NSGA-II observed empirically. It also shows that mathematical analyses of this algorithm are not only possible for synthetic benchmark problems, but also for more complex combinatorial optimization problems. As a side result, we also obtain a new analysis of the performance of the global SEMO algorithm on the bi-objective minimum spanning tree problem, which improves the previous best result by a factor of $\|F\|$, the number of extremal points of the Pareto front, a set that can be as large as $n w_{\max}$. The main reason for this improvement is our observation that both multi-objective evolutionary algorithms find the different extremal points in parallel rather than sequentially, as assumed in the previous proofs.	['Simon Wietheger', 'Yakob Kahane', 'Benjamin Hebras', 'Benjamin Doerr', 'Sacha Cerf']	2023-05-22	null	null	null	null	['combinatorial-optimization']	['methodology']	[ 4.83320326e-01 7.76615217e-02 5.96661605e-02 1.16958961e-01 -5.21977961e-01 -6.60224915e-01 -3.99968386e-01 4.07356203e-01 -5.98030567e-01 1.02173674e+00 -6.50884926e-01 -3.62349033e-01 -1.05125868e+00 -1.13699162e+00 -7.95533717e-01 -1.07503939e+00 -6.54469967e-01 7.39888728e-01 1.54883951e-01 -3.87813479e-01 4.68153924e-01 4.37322199e-01 -2.06781793e+00 -3.48152190e-01 1.08497310e+00 1.15628469e+00 9.68174487e-02 7.52025664e-01 -1.37386695e-01 -1.80945098e-01 -6.87529624e-01 -3.91034126e-01 3.81122023e-01 -5.34978390e-01 -8.56142223e-01 -2.49204226e-03 -1.08854108e-01 2.69081712e-01 3.32074314e-01 1.29317403e+00 4.68955070e-01 2.64464468e-01 3.16270828e-01 -1.60349250e+00 2.46264925e-03 7.25090086e-01 -9.96934414e-01 -1.24494366e-01 1.23571791e-01 3.03435400e-02 1.10962808e+00 -9.74944755e-02 6.39311254e-01 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1101a9b7-5830-4ffe-af75-ee78a3c570e3	glt-t-global-local-transformer-for-3d-siamese	2304.00242	null	https://arxiv.org/abs/2304.00242v1	https://arxiv.org/pdf/2304.00242v1.pdf	GLT-T++: Global-Local Transformer for 3D Siamese Tracking with Ranking Loss	Siamese trackers based on 3D region proposal network (RPN) have shown remarkable success with deep Hough voting. However, using a single seed point feature as the cue for voting fails to produce high-quality 3D proposals. Additionally, the equal treatment of seed points in the voting process, regardless of their significance, exacerbates this limitation. To address these challenges, we propose a novel transformer-based voting scheme to generate better proposals. Specifically, a global-local transformer (GLT) module is devised to integrate object- and patch-aware geometric priors into seed point features, resulting in robust and accurate cues for offset learning of seed points. To train the GLT module, we introduce an importance prediction branch that learns the potential importance weights of seed points as a training constraint. Incorporating this transformer-based voting scheme into 3D RPN, a novel Siamese method dubbed GLT-T is developed for 3D single object tracking on point clouds. Moreover, we identify that the highest-scored proposal in the Siamese paradigm may not be the most accurate proposal, which limits tracking performance. Towards this concern, we approach the binary score prediction task as a ranking problem, and design a target-aware ranking loss and a localization-aware ranking loss to produce accurate ranking of proposals. With the ranking losses, we further present GLT-T++, an enhanced version of GLT-T. Extensive experiments on multiple benchmarks demonstrate that our GLT-T and GLT-T++ outperform state-of-the-art methods in terms of tracking accuracy while maintaining a real-time inference speed. The source code will be made available at https://github.com/haooozi/GLT-T.	['Jing Zhang', 'Mingyu Gao', 'Xudong Lv', 'Yuxiang Yang', 'Zhiwei He', 'Jiahao Nie']	2023-04-01	null	null	null	null	['3d-single-object-tracking']	['computer-vision']	[-2.60981232e-01 -1.67448923e-01 -3.28884840e-01 -3.37548077e-01 -9.45643127e-01 -6.12284005e-01 6.46510303e-01 2.18179282e-02 -4.19879615e-01 3.75976533e-01 -7.04126209e-02 -2.37485711e-02 -1.17719807e-01 -6.37556136e-01 -8.86138499e-01 -5.81319332e-01 2.78495601e-04 5.71771383e-01 7.19866753e-01 -3.70251350e-02 3.01423639e-01 6.79998815e-01 -1.42515683e+00 -2.02875301e-01 8.78291607e-01 1.21570742e+00 1.95864469e-01 1.57468125e-01 7.22037479e-02 2.72068560e-01 -6.26891434e-01 -4.05544937e-01 8.04515183e-01 3.51229161e-02 -1.21100908e-02 -3.72980624e-01 1.10269237e+00 -3.18696916e-01 -1.87734485e-01 9.95635748e-01 5.60849190e-01 1.56839490e-02 6.65985107e-01 -1.43108273e+00 -2.97270596e-01 4.01087224e-01 -8.10521483e-01 -1.36597231e-02 3.00660711e-02 3.31747562e-01 1.18101001e+00 -1.26628816e+00 4.63299334e-01 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890bbbc2-7beb-434f-811f-b65bd9566a61	heart-sound-classification-considering	2106.01865	null	https://arxiv.org/abs/2106.01865v1	https://arxiv.org/pdf/2106.01865v1.pdf	Heart Sound Classification Considering Additive Noise and Convolutional Distortion	Cardiac auscultation is an essential point-of-care method used for the early diagnosis of heart diseases. Automatic analysis of heart sounds for abnormality detection is faced with the challenges of additive noise and sensor-dependent degradation. This paper aims to develop methods to address the cardiac abnormality detection problem when both types of distortions are present in the cardiac auscultation sound. We first mathematically analyze the effect of additive and convolutional noise on short-term filterbank-based features and a Convolutional Neural Network (CNN) layer. Based on the analysis, we propose a combination of linear and logarithmic spectrogram-image features. These 2D features are provided as input to a residual CNN network (ResNet) for heart sound abnormality detection. Experimental validation is performed on an open-access heart sound abnormality detection dataset involving noisy recordings obtained from multiple stethoscope sensors. The proposed method achieves significantly improved results compared to the conventional approaches, with an area under the ROC (receiver operating characteristics) curve (AUC) of 91.36%, F-1 score of 84.09%, and Macc (mean of sensitivity and specificity) of 85.08%. We also show that the proposed method shows the best mean accuracy across different source domains including stethoscope and noise variability, demonstrating its effectiveness in different recording conditions. The proposed combination of linear and logarithmic features along with the ResNet classifier effectively minimizes the impact of background noise and sensor variability for classifying phonocardiogram (PCG) signals. The proposed method paves the way towards developing computer-aided cardiac auscultation systems in noisy environments using low-cost stethoscopes.	['Taufiq Hasan', 'Ian Mclane', 'Md. Istiaq Ansari', 'Farhat Binte Azam']	2021-06-03	null	null	null	null	['sound-classification']	['audio']	[ 4.05843467e-01 -3.54680657e-01 5.47082365e-01 -6.39029872e-03 -7.24424958e-01 -4.50265855e-01 -1.67599529e-01 2.65003979e-01 -3.74814332e-01 3.45939100e-01 7.93425832e-03 -5.19746900e-01 -2.40365118e-01 -3.89519483e-01 -1.86793238e-01 -6.51554465e-01 -4.47959363e-01 -2.79514611e-01 5.03837503e-02 1.37875333e-01 -6.40804172e-02 5.08760750e-01 -1.44402623e+00 -2.43241200e-03 6.60308361e-01 1.28834796e+00 5.34419939e-02 1.47822368e+00 5.54247260e-01 3.51831704e-01 -1.08643031e+00 1.40488595e-01 2.61533767e-01 -8.49339664e-01 -2.51051843e-01 -2.16767192e-01 2.32973412e-01 -2.66401321e-01 3.31952982e-02 8.38507533e-01 1.33933854e+00 -8.27678218e-02 4.16851372e-01 -5.05034626e-01 -1.37199700e-01 3.74776483e-01 6.01832522e-03 9.16662633e-01 2.28382409e-01 1.86620340e-01 6.29446089e-01 -7.03346193e-01 -6.22665994e-02 6.13300383e-01 1.31451654e+00 1.26391649e-01 -8.98926556e-01 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4.16432053e-01 9.72669601e-01 -1.55753523e-01 -5.22180557e-01 3.50451916e-01]	[14.271682739257812, 3.2607057094573975]
61e41678-b564-4bdc-a981-cedd67abc1f8	new-frontiers-in-graph-autoencoders-joint	2211.08972	null	https://arxiv.org/abs/2211.08972v1	https://arxiv.org/pdf/2211.08972v1.pdf	New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction	Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction (LP). Their performances are less impressive on community detection (CD), where they are often outperformed by simpler alternatives such as the Louvain method. It is still unclear to what extent one can improve CD with GAE and VGAE, especially in the absence of node features. It is moreover uncertain whether one could do so while simultaneously preserving good performances on LP in a multi-task setting. In this workshop paper, summarizing results from our journal publication (Salha-Galvan et al. 2022), we show that jointly addressing these two tasks with high accuracy is possible. For this purpose, we introduce a community-preserving message passing scheme, doping our GAE and VGAE encoders by considering both the initial graph and Louvain-based prior communities when computing embedding spaces. Inspired by modularity-based clustering, we further propose novel training and optimization strategies specifically designed for joint LP and CD. We demonstrate the empirical effectiveness of our approach, referred to as Modularity-Aware GAE and VGAE, on various real-world graphs.	['Michalis Vazirgiannis', 'Romain Hennequin', 'George Dasoulas', 'Johannes F. Lutzeyer', 'Guillaume Salha-Galvan']	2022-11-16	null	null	null	null	['community-detection']	['graphs']	[-1.86144397e-01 3.65507096e-01 8.27059075e-02 3.66126060e-01 -2.47177690e-01 -4.78107929e-01 6.99967980e-01 5.59650421e-01 -2.61165679e-01 6.97341263e-01 2.84201473e-01 -2.52412647e-01 -3.88606191e-01 -1.06701279e+00 -8.60517263e-01 -6.41488969e-01 -4.87673759e-01 5.48699439e-01 8.37058350e-02 -1.64487839e-01 -9.85186249e-02 3.60693544e-01 -1.34283257e+00 4.21380736e-02 8.97746086e-01 3.45425487e-01 1.05510980e-01 8.83560300e-01 5.01629524e-03 8.14589083e-01 -4.31842655e-01 -9.05636609e-01 -1.42273251e-02 -3.02356243e-01 -8.72442901e-01 2.51741022e-01 1.53490290e-01 2.67759450e-02 -5.50973892e-01 8.98361862e-01 3.17937911e-01 -5.91637231e-02 8.73051643e-01 -1.39290845e+00 -8.51174533e-01 9.27881002e-01 -5.49650013e-01 4.67766300e-02 8.54557380e-02 -3.22706789e-01 1.69836068e+00 -6.81065142e-01 9.97812510e-01 1.06739223e+00 9.97749507e-01 3.03006202e-01 -1.59574378e+00 -2.32630923e-01 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cc0db26e-a445-4f2d-a2ad-9956d7d7dde4	graph-based-aspect-representation-learning	null	null	https://aclanthology.org/2020.textgraphs-1.2	https://aclanthology.org/2020.textgraphs-1.2.pdf	Graph-based Aspect Representation Learning for Entity Resolution	Entity Resolution (ER) identifies records that refer to the same real-world entity. Deep learning approaches improved the generalization ability of entity matching models, but hardly overcame the impact of noisy or incomplete data sources. In real scenes, an entity usually consists of multiple semantic facets, called aspects. In this paper, we focus on entity augmentation, namely retrieving the values of missing aspects. The relationship between aspects is naturally suitable to be represented by a knowledge graph, where entity augmentation can be modeled as a link prediction problem. Our paper proposes a novel graph-based approach to solve entity augmentation. Specifically, we apply a dedicated random walk algorithm, which uses node types to limit the traversal length, and encodes graph structure into low-dimensional embeddings. Thus, the missing aspects could be retrieved by a link prediction model. Furthermore, the augmented aspects with fixed orders are served as the input of a deep Siamese BiLSTM network for entity matching. We compared our method with state-of-the-art methods through extensive experiments on downstream ER tasks. According to the experiment results, our model outperforms other methods on evaluation metrics (accuracy, precision, recall, and f1-score) to a large extent, which demonstrates the effectiveness of our method.	['Bin Gu', 'Xiangnan He', 'Yufan Huang', 'Dingxian Wang', 'Yuchen Guo', 'Zhenqi Zhao']	null	null	null	null	coling-textgraphs-2020-12	['entity-resolution']	['natural-language-processing']	[-2.55250931e-01 4.14825201e-01 -3.93146425e-01 -2.82044470e-01 -5.15212476e-01 -2.34057009e-01 5.61519504e-01 5.17066777e-01 -5.72213113e-01 6.56283617e-01 4.31505054e-01 -5.78308702e-02 -2.85326153e-01 -1.34994209e+00 -1.05915558e+00 -3.37015837e-01 -4.90682013e-02 7.17275620e-01 2.53790021e-01 -2.69442201e-01 -1.37002766e-01 2.05226809e-01 -1.14929295e+00 4.00665365e-02 1.05492532e+00 8.14335644e-01 9.53992158e-02 -1.19444415e-01 -6.78812504e-01 5.88279307e-01 -4.91805226e-01 -9.18792486e-01 -1.10270254e-01 -8.78952146e-02 -6.47055387e-01 -3.41430902e-01 1.53045535e-01 -2.20992312e-01 -8.46405029e-01 1.16267538e+00 4.57645863e-01 1.93367496e-01 3.57242733e-01 -1.35547674e+00 -1.19818139e+00 9.80759859e-01 -5.31351328e-01 9.06159058e-02 2.75252491e-01 -1.82937101e-01 1.31694019e+00 -9.08642054e-01 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b7c75cd1-4e0d-4567-b59d-8ddba8de40a9	using-machine-learning-methods-for-automation	2306.09775	null	https://arxiv.org/abs/2306.09775v1	https://arxiv.org/pdf/2306.09775v1.pdf	Using Machine Learning Methods for Automation of Size Grid Building and Management	Fashion apparel companies require planning for the next season, a year in advance for supply chain management. This study focuses on size selection decision making for Levi Strauss. Currently, the region and planning group level size grids are built and managed manually. The company suffers from the workload it creates for sizing, merchant and planning teams. This research is aiming to answer two research questions: "Which sizes should be available to the planners under each size grid name for the next season(s)?" and "Which sizes should be adopted for each planning group for the next season(s)?". We approach to the problem with a classification model, which is one of the popular models used in machine learning. With this research, a more automated process was created by using machine learning techniques. A decrease in workload of the teams in the company is expected after it is put into practice. Unlike many studies in the state of art for fashion and apparel industry, this study focuses on sizes where the stock keeping unit represents a product with a certain size.	['Filipa Peleja', 'Dries Benoit', 'Salim Yunus']	2023-06-16	null	null	null	null	['management']	['miscellaneous']	[-1.61082953e-01 2.90456321e-02 -3.78352404e-01 -3.82979155e-01 1.38331190e-01 -4.52796698e-01 4.23442051e-02 5.22351444e-01 -2.90093511e-01 7.19697833e-01 -1.49158090e-02 -3.00979912e-01 -4.66223210e-01 -1.12825704e+00 -4.42123920e-01 -5.71050882e-01 7.88328201e-02 1.22384477e+00 -1.95854008e-01 -3.65016550e-01 7.57079184e-01 6.52591884e-01 -1.25534117e+00 3.36889625e-01 4.41961944e-01 1.14189577e+00 7.22080588e-01 2.18065798e-01 -2.72091240e-01 9.83513534e-01 -4.45066988e-01 -1.54527605e-01 7.96773672e-01 -5.41663349e-01 -8.05099726e-01 7.85994709e-01 2.64286324e-02 -1.29681835e-02 5.21465421e-01 7.91879356e-01 -3.84921534e-03 1.32583618e-01 7.67353058e-01 -1.43786108e+00 -1.94268018e-01 1.22633147e+00 -4.46203947e-01 1.34953693e-01 2.69460857e-01 1.12654455e-02 9.81249869e-01 -4.83711541e-01 7.51082242e-01 9.54887092e-01 2.98503339e-01 -2.82230973e-01 -1.14099216e+00 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cbafa84d-a8fa-4f82-aba6-f34f81c0488a	adaptive-action-supervision-in-reinforcement	2305.13030	null	https://arxiv.org/abs/2305.13030v2	https://arxiv.org/pdf/2305.13030v2.pdf	Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations	Modeling of real-world biological multi-agents is a fundamental problem in various scientific and engineering fields. Reinforcement learning (RL) is a powerful framework to generate flexible and diverse behaviors in cyberspace; however, when modeling real-world biological multi-agents, there is a domain gap between behaviors in the source (i.e., real-world data) and the target (i.e., cyberspace for RL), and the source environment parameters are usually unknown. In this paper, we propose a method for adaptive action supervision in RL from real-world demonstrations in multi-agent scenarios. We adopt an approach that combines RL and supervised learning by selecting actions of demonstrations in RL based on the minimum distance of dynamic time warping for utilizing the information of the unknown source dynamics. This approach can be easily applied to many existing neural network architectures and provide us with an RL model balanced between reproducibility as imitation and generalization ability to obtain rewards in cyberspace. In the experiments, using chase-and-escape and football tasks with the different dynamics between the unknown source and target environments, we show that our approach achieved a balance between the reproducibility and the generalization ability compared with the baselines. In particular, we used the tracking data of professional football players as expert demonstrations in football and show successful performances despite the larger gap between behaviors in the source and target environments than the chase-and-escape task.	['Yoshinobu Kawahara', 'Naoya Takeishi', 'Hiroshi Nakahara', 'Atom Scott', 'Kazushi Tsutsui', 'Keisuke Fujii']	2023-05-22	null	null	null	null	['dynamic-time-warping']	['time-series']	[-1.38344780e-01 -2.61844575e-01 7.60667911e-03 2.60608107e-01 -2.13473141e-01 -7.13100314e-01 6.11101747e-01 -3.36169332e-01 -8.42478871e-01 1.14864147e+00 -4.78918672e-01 5.82147799e-02 -3.40281785e-01 -4.68513429e-01 -9.03286636e-01 -1.15338218e+00 -3.88565898e-01 4.59091246e-01 4.84631598e-01 -6.53644145e-01 1.18352018e-01 5.78569055e-01 -1.45920193e+00 -1.29702166e-01 9.30185854e-01 4.10504788e-01 5.89223623e-01 8.22469175e-01 4.35385734e-01 6.68993473e-01 -9.42251980e-01 3.60525638e-01 4.32773262e-01 -6.71623170e-01 -2.69050539e-01 -2.27193326e-01 -2.93487132e-01 -9.71299335e-02 -4.55560297e-01 8.41557384e-01 5.12452900e-01 5.15083551e-01 5.22676766e-01 -1.73125863e+00 -5.09832263e-01 4.40951854e-01 -3.31711650e-01 2.03283370e-01 1.94355458e-01 8.38297606e-01 4.17714387e-01 -6.86902478e-02 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2ba23d17-20d0-4011-802a-7ad1120daf38	portmanteauing-features-for-scene-text	2211.05036	null	https://arxiv.org/abs/2211.05036v1	https://arxiv.org/pdf/2211.05036v1.pdf	Portmanteauing Features for Scene Text Recognition	Scene text images have different shapes and are subjected to various distortions, e.g. perspective distortions. To handle these challenges, the state-of-the-art methods rely on a rectification network, which is connected to the text recognition network. They form a linear pipeline which uses text rectification on all input images, even for images that can be recognized without it. Undoubtedly, the rectification network improves the overall text recognition performance. However, in some cases, the rectification network generates unnecessary distortions on images, resulting in incorrect predictions in images that would have otherwise been correct without it. In order to alleviate the unnecessary distortions, the portmanteauing of features is proposed. The portmanteau feature, inspired by the portmanteau word, is a feature containing information from both the original text image and the rectified image. To generate the portmanteau feature, a non-linear input pipeline with a block matrix initialization is presented. In this work, the transformer is chosen as the recognition network due to its utilization of attention and inherent parallelism, which can effectively handle the portmanteau feature. The proposed method is examined on 6 benchmarks and compared with 13 state-of-the-art methods. The experimental results show that the proposed method outperforms the state-of-the-art methods on various of the benchmarks.	['Joo Hwee Lim', 'Jung-jae Kim', 'Adams Wai-Kin Kong', 'Ernest Yu Kai Chew', 'Yew Lee Tan']	2022-11-09	null	null	null	null	['scene-text-recognition']	['computer-vision']	[ 6.31160438e-01 -4.02528763e-01 1.54470280e-01 -2.92116404e-01 -1.17913492e-01 -1.81761086e-01 7.80225217e-01 -2.47029915e-01 -3.70867908e-01 2.51900285e-01 1.36603564e-01 -2.69584246e-02 1.36028096e-01 -7.15768516e-01 -6.73042238e-01 -7.78416693e-01 8.82630169e-01 3.05367678e-01 2.83741623e-01 -2.99495161e-01 7.34693527e-01 3.40231478e-01 -1.59042180e+00 7.79211164e-01 7.42407799e-01 1.07412469e+00 3.29837322e-01 4.54504311e-01 -4.92766231e-01 6.48286462e-01 -5.77919781e-01 -4.62891310e-01 3.77099693e-01 -4.17641550e-01 -4.32219476e-01 2.32950181e-01 5.83330810e-01 -4.77300614e-01 -6.29845381e-01 1.18038857e+00 1.99690968e-01 1.75699979e-01 7.27046788e-01 -9.64872360e-01 -8.83944750e-01 5.45356989e-01 -8.41650426e-01 1.78853169e-01 3.14791888e-01 3.54886726e-02 8.31110060e-01 -1.40536368e+00 4.62890297e-01 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c1c04ff7-46b5-4d66-a334-60843c1c8a6c	pre-trained-models-or-feature-engineering-the	null	null	https://aclanthology.org/2022.osact-1.5	https://aclanthology.org/2022.osact-1.5.pdf	Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic	The usage of social media platforms has resulted in the proliferation of work on Arabic Natural Language Processing (ANLP), including the development of resources. There is also an increased interest in processing Arabic dialects and a number of models and algorithms have been utilised for the purpose of Dialectal Arabic Natural Language Processing (DANLP). In this paper, we conduct a comparison study between some of the most well-known and most commonly used methods in NLP in order to test their performance on different corpora and two NLP tasks: Dialect Identification and Sentiment Analysis. In particular, we compare three general classes of models: a) traditional Machine Learning models with features, b) classic Deep Learning architectures (LSTMs) with pre-trained word embeddings and lastly c) different Bidirectional Encoder Representations from Transformers (BERT) models such as (Multilingual-BERT, Ara-BERT, and Twitter-Arabic-BERT). The results of the comparison show that using feature-based classification can still compete with BERT models in these dialectal Arabic contexts. The use of transformer models have the ability to outperform traditional Machine Learning approaches, depending on the type of text they have been trained on, in contrast to classic Deep Learning models like LSTMs which do not perform well on the tasks	['Simon Dobnik', 'Stergios Chatzikyriakidis', 'Kathrein Abu Kwaik']	null	null	null	null	osact-lrec-2022-6	['dialect-identification']	['natural-language-processing']	[-4.11867917e-01 -1.19419225e-01 2.10652515e-01 -4.66377467e-01 -3.06725532e-01 -6.89335525e-01 1.07968676e+00 7.28403449e-01 -8.28166842e-01 3.95896584e-01 4.36047375e-01 -4.36278135e-01 7.00727701e-02 -1.11899686e+00 -3.48383993e-01 -6.07600212e-01 -2.42645890e-01 7.94313312e-01 -2.13662572e-02 -1.17387569e+00 2.21210152e-01 5.20335138e-01 -1.33251381e+00 5.95071197e-01 7.19288409e-01 8.96330237e-01 -1.17149517e-01 4.79075968e-01 -5.56127787e-01 8.36315870e-01 -4.63638812e-01 -7.06999719e-01 -6.04377761e-02 -1.37875810e-01 -1.17236245e+00 -2.79744297e-01 -5.22074066e-02 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1658c0ed-7d43-4c8c-8652-732114104f66	learning-multi-scale-deep-features-for-high	1611.03591	null	http://arxiv.org/abs/1611.03591v1	http://arxiv.org/pdf/1611.03591v1.pdf	Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification	In this paper, we propose a multi-scale deep feature learning method for high-resolution satellite image classification. Specifically, we firstly warp the original satellite image into multiple different scales. The images in each scale are employed to train a deep convolutional neural network (DCNN). However, simultaneously training multiple DCNNs is time-consuming. To address this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since different SPP-nets have the same number of parameters, which share the identical initial values, and only fine-tuning the parameters in fully-connected layers ensures the effectiveness of each network, thereby greatly accelerating the training process. Then, the multi-scale satellite images are fed into their corresponding SPP-nets respectively to extract multi-scale deep features. Finally, a multiple kernel learning method is developed to automatically learn the optimal combination of such features. Experiments on two difficult datasets show that the proposed method achieves favorable performance compared to other state-of-the-art methods.	['Zhi Li', 'Renlong Hang', 'Qingshan Liu', 'Huihui Song']	2016-11-11	null	null	null	null	['satellite-image-classification']	['computer-vision']	[ 2.72955024e-03 -6.01735234e-01 5.69465756e-02 -4.10297155e-01 -6.67883992e-01 -3.34283978e-01 2.34810919e-01 -1.80756509e-01 -7.84400165e-01 4.94867802e-01 -1.29028887e-01 -2.77431915e-03 -2.06837267e-01 -1.20603228e+00 -6.47838056e-01 -9.39669907e-01 -3.35390359e-01 -2.23922685e-01 5.74833393e-01 -1.00276047e-05 5.75738288e-02 8.70263636e-01 -1.33449686e+00 1.39038906e-01 8.44417930e-01 1.12559378e+00 4.77759421e-01 3.40633512e-01 -2.90425378e-03 5.91630399e-01 -4.20048058e-01 1.18456803e-01 3.22026789e-01 3.90432663e-02 -5.64954042e-01 1.96216017e-01 2.64049053e-01 -6.53166831e-01 -5.97242534e-01 1.29715657e+00 4.35496092e-01 3.35507661e-01 2.95073479e-01 -8.56739879e-01 -7.49328136e-01 4.15964693e-01 -8.43815029e-01 5.22413969e-01 -4.37026739e-01 2.77493745e-02 8.95064712e-01 -6.79247260e-01 8.18778053e-02 1.21411026e+00 7.06973374e-01 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a230dc40-c232-4540-a5d7-570fd2402d3b	seastar-vertex-centric-programming-for-graph	null	null	https://dl.acm.org/doi/10.1145/3447786.3456247	https://dl.acm.org/doi/pdf/10.1145/3447786.3456247	Seastar: vertex-centric programming for graph neural networks	Graph neural networks (GNNs) have achieved breakthrough performance in graph analytics such as node classification, link prediction and graph clustering. Many GNN training frameworks have been developed, but they are usually designed as a set of manually written, GNN-specific operators plugged into existing deep learning systems, which incurs high memory consumption, poor data locality, and large semantic gap between algorithm design and implementation. This paper proposes the Seastar system, which presents a vertex-centric programming model for GNN training on GPU and provides idiomatic python constructs to enable easy development of novel homogeneous and heterogeneous GNN models. We also propose novel optimizations to produce highly efficient fused GPU kernels for forward and backward passes in GNN training. Compared with the state-of-the art GNN systems, DGL and PyG, Seastar achieves better usability, up to 2 and 8 times less memory consumption, and 14 and 3 times faster execution, respectively.	['Fan Yu', 'James Cheng', 'Chenguang Zheng', 'Boyang Li', 'Tatiana Jin', 'Zhenkun Cai', 'Kaihao Ma', 'Yidi Wu']	2021-04-21	null	null	null	proceedings-of-the-sixteenth-european	['graph-clustering']	['graphs']	[-5.45588791e-01 -1.54297084e-01 -3.02658647e-01 -2.00812921e-01 3.73940431e-02 -3.83403689e-01 4.15190488e-01 3.93546134e-01 -1.89488590e-01 2.06604078e-01 -4.20556515e-01 -9.26401675e-01 3.83554921e-02 -1.49897075e+00 -5.24306953e-01 -4.36414778e-01 -2.16705844e-01 6.42540812e-01 5.44302642e-01 -2.47807845e-01 -2.39501894e-01 6.05296969e-01 -1.36334419e+00 3.18359844e-02 9.09051776e-01 6.60351992e-01 -1.96198188e-02 1.00578296e+00 -4.27935928e-01 9.30014074e-01 -2.77842641e-01 -7.52019405e-01 3.45126480e-01 -9.28121805e-02 -5.99862516e-01 -5.28526127e-01 3.89771223e-01 -1.57188922e-01 -6.74835503e-01 9.64927971e-01 5.44095814e-01 -6.62263185e-02 7.83343613e-02 -1.69528604e+00 -4.48935866e-01 9.56851602e-01 -6.31091654e-01 -3.45359817e-02 -2.36283652e-02 1.82084441e-01 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d5e56cc7-fc0a-4ee7-99a8-dc9110e99c55	coevolution-of-camouflage	2304.11793	null	https://arxiv.org/abs/2304.11793v2	https://arxiv.org/pdf/2304.11793v2.pdf	Coevolution of Camouflage	Camouflage in nature seems to arise from competition between predator and prey. To survive, predators must find prey, and prey must avoid being found. This work simulates an abstract model of that adversarial relationship. It looks at crypsis through evolving prey camouflage patterns (as color textures) in competition with evolving predator vision. During their "lifetime" predators learn to better locate camouflaged prey. The environment for this 2D simulation is provided by a set of photographs, typically of natural scenes. This model is based on two evolving populations, one of prey and another of predators. Mutual conflict between these populations can produce both effective prey camouflage and predators skilled at "breaking" camouflage. The result is an open source artificial life model to help study camouflage in nature, and the perceptual phenomenon of camouflage more generally.	['Craig Reynolds']	2023-04-24	null	null	null	null	['artificial-life']	['miscellaneous']	[ 1.73777919e-02 -3.82464468e-01 6.21480465e-01 6.24078512e-01 6.85719669e-01 -1.02320421e+00 5.44834614e-01 -4.84588534e-01 -6.61697328e-01 8.88921976e-01 -1.01465508e-01 9.84024554e-02 5.19307077e-01 -6.85870886e-01 -6.19938076e-01 -1.10153437e+00 -4.21991944e-01 1.61362402e-02 5.10086298e-01 -4.43531662e-01 2.28649095e-01 5.14407575e-01 -1.83292782e+00 -1.75527304e-01 3.51986080e-01 5.37338853e-01 3.87830526e-01 1.19544721e+00 1.76956326e-01 4.74908859e-01 -1.18669701e+00 -4.84818637e-01 9.19335485e-01 -9.33908582e-01 1.69970527e-01 -5.44903129e-02 6.72535449e-02 -1.06642298e-01 -2.14666530e-01 1.50557494e+00 2.77428031e-01 -3.23384821e-01 7.10201859e-01 -1.34810793e+00 -1.29472136e+00 2.28972077e-01 -4.14468646e-01 4.34656769e-01 2.61662215e-01 1.07229745e+00 2.82100469e-01 -4.75104362e-01 4.48210090e-01 1.45621741e+00 5.94521224e-01 1.25029266e+00 -1.32283199e+00 -7.14336991e-01 -2.38189280e-01 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a8f9c2f9-723d-45d7-9743-252e7384b243	implicit-bias-of-sgd-in-l-2-regularized	2305.16038	null	https://arxiv.org/abs/2305.16038v1	https://arxiv.org/pdf/2305.16038v1.pdf	Implicit bias of SGD in $L_{2}$-regularized linear DNNs: One-way jumps from high to low rank	The $L_{2}$-regularized loss of Deep Linear Networks (DLNs) with more than one hidden layers has multiple local minima, corresponding to matrices with different ranks. In tasks such as matrix completion, the goal is to converge to the local minimum with the smallest rank that still fits the training data. While rank-underestimating minima can easily be avoided since they do not fit the data, gradient descent might get stuck at rank-overestimating minima. We show that with SGD, there is always a probability to jump from a higher rank minimum to a lower rank one, but the probability of jumping back is zero. More precisely, we define a sequence of sets $B_{1}\subset B_{2}\subset\cdots\subset B_{R}$ so that $B_{r}$ contains all minima of rank $r$ or less (and not more) that are absorbing for small enough ridge parameters $\lambda$ and learning rates $\eta$: SGD has prob. 0 of leaving $B_{r}$, and from any starting point there is a non-zero prob. for SGD to go in $B_{r}$.	['Arthur Jacot', 'Zihan Wang']	2023-05-25	null	null	null	null	['matrix-completion']	['methodology']	[-1.49089769e-01 5.06244957e-01 -5.15111051e-02 -3.87356400e-01 -1.07482648e+00 -3.78552079e-01 -1.19100526e-01 1.40080407e-01 -6.29835606e-01 8.91114175e-01 -3.67960222e-02 -3.99744123e-01 -6.49823964e-01 -8.87524545e-01 -9.36568677e-01 -9.80253220e-01 -7.40405500e-01 5.07174850e-01 -1.21932983e-01 -1.97344989e-01 -2.16278613e-01 1.94309250e-01 -1.19127488e+00 1.86642036e-01 6.26236379e-01 1.11877608e+00 -1.57747958e-02 3.25094342e-01 -1.42612532e-01 5.10828614e-01 -3.90456319e-01 -4.33382243e-01 8.58376145e-01 -3.98730844e-01 -6.38761580e-01 -4.46534514e-01 6.91709518e-01 -1.34177163e-01 -2.34327704e-01 1.44808912e+00 2.73247808e-01 3.79211277e-01 5.69742262e-01 -9.16444898e-01 -3.31354469e-01 8.86893511e-01 -9.84200835e-01 -2.17932954e-01 -2.41765812e-01 -3.69244143e-02 1.52370989e+00 -9.60416436e-01 4.11819518e-01 1.09542680e+00 8.37844133e-01 5.82231402e-01 -1.63856828e+00 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21a3c6a2-51dd-463a-895e-52cd5165ff52	multi-modal-relational-graph-for-cross-modal	null	null	http://openaccess.thecvf.com//content/CVPR2021/html/Zeng_Multi-Modal_Relational_Graph_for_Cross-Modal_Video_Moment_Retrieval_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Zeng_Multi-Modal_Relational_Graph_for_Cross-Modal_Video_Moment_Retrieval_CVPR_2021_paper.pdf	Multi-Modal Relational Graph for Cross-Modal Video Moment Retrieval	Given an untrimmed video and a query sentence, cross-modal video moment retrieval aims to rank a video moment from pre-segmented video moment candidates that best matches the query sentence. Pioneering work typically learns the representations of the textual and visual content separately and then obtains the interactions or alignments between different modalities. However, the task of cross-modal video moment retrieval is not yet thoroughly addressed as it needs to further identify the fine-grained differences of video moment candidates with high repeatability and similarity. Moveover, the relation among objects in both video and query sentence is intuitive and efficient for understanding semantics but is rarely considered. Toward this end, we contribute a multi-modal relational graph to capture the interactions among objects from the visual and textual content to identify the differences among similar video moment candidates. Specifically, we first introduce a visual relational graph and a textual relational graph to form relation-aware representations via message propagation. Thereafter, a multi-task pre-training is designed to capture domain-specific knowledge about objects and relations, enhancing the structured visual representation after explicitly defined relation. Finally, the graph matching and boundary regression are employed to perform the cross-modal retrieval. We conduct extensive experiments on two datasets about daily activities and cooking activities, demonstrating significant improvements over state-of-the-art solutions.	['Zheng Qin', 'Zhou Zhao', 'Meng Liu', 'Xiaochi Wei', 'Da Cao', 'Yawen Zeng']	2021-06-19	null	null	null	cvpr-2021-1	['moment-retrieval']	['computer-vision']	[ 1.65455416e-01 -4.57341939e-01 -5.76530874e-01 -3.64739269e-01 -9.88478780e-01 -6.03122771e-01 7.22388208e-01 5.95634818e-01 -7.54187778e-02 9.39509496e-02 4.53276426e-01 2.70437002e-01 -4.03142571e-01 -4.89546746e-01 -7.39842772e-01 -3.60891074e-01 -3.20454240e-01 1.88175887e-01 3.49344879e-01 -1.27454132e-01 2.27080643e-01 1.13218352e-01 -1.63821948e+00 7.11860359e-01 4.85041380e-01 1.21536529e+00 3.17278892e-01 6.21924400e-01 -2.03807682e-01 1.07760608e+00 -3.02374154e-01 -2.31431261e-01 -8.86632726e-02 -5.82130969e-01 -8.70123148e-01 5.66845477e-01 6.84866726e-01 -2.44972721e-01 -8.00368905e-01 9.90762949e-01 1.99561492e-01 4.94415283e-01 6.49243653e-01 -1.32179630e+00 -7.86181986e-01 6.46750629e-01 -7.47945070e-01 4.18785721e-01 9.89849150e-01 -1.25838071e-01 1.17033923e+00 -6.90259993e-01 9.41580713e-01 1.24701715e+00 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8dbdf16a-ff1f-4d51-815d-4a2fd990096c	neuse-neural-se-3-equivariant-embedding-for	2303.07308	null	https://arxiv.org/abs/2303.07308v2	https://arxiv.org/pdf/2303.07308v2.pdf	NeuSE: Neural SE(3)-Equivariant Embedding for Consistent Spatial Understanding with Objects	We present NeuSE, a novel Neural SE(3)-Equivariant Embedding for objects, and illustrate how it supports object SLAM for consistent spatial understanding with long-term scene changes. NeuSE is a set of latent object embeddings created from partial object observations. It serves as a compact point cloud surrogate for complete object models, encoding full shape information while transforming SE(3)-equivariantly in tandem with the object in the physical world. With NeuSE, relative frame transforms can be directly derived from inferred latent codes. Our proposed SLAM paradigm, using NeuSE for object shape and pose characterization, can operate independently or in conjunction with typical SLAM systems. It directly infers SE(3) camera pose constraints that are compatible with general SLAM pose graph optimization, while also maintaining a lightweight object-centric map that adapts to real-world changes. Our approach is evaluated on synthetic and real-world sequences featuring changed objects and shows improved localization accuracy and change-aware mapping capability, when working either standalone or jointly with a common SLAM pipeline.	['John J. Leonard', 'Joshua B. Tenenbaum', 'Kurran Singh', 'Yilun Du', 'Jiahui Fu']	2023-03-13	null	null	null	null	['object-slam']	['computer-vision']	[-8.60901643e-03 -4.63612638e-02 2.39990093e-02 -5.88930726e-01 -4.27142799e-01 -7.87584424e-01 9.74852324e-01 1.17709942e-01 -3.40479612e-01 3.48715752e-01 1.39748991e-01 2.02491462e-01 -3.08956534e-01 -4.88077670e-01 -1.03764737e+00 -4.11667019e-01 -1.93223551e-01 1.04577446e+00 3.50193888e-01 -4.09522951e-02 1.80019855e-01 1.08015168e+00 -1.54169309e+00 -2.56267428e-01 3.77278656e-01 6.70123219e-01 5.64178526e-01 8.55833411e-01 5.74875399e-02 6.03984773e-01 -5.77239506e-02 6.90887868e-02 4.70914066e-01 1.81685060e-01 -4.49445903e-01 1.98731020e-01 1.07762837e+00 -1.10432558e-01 -6.61519647e-01 7.93211639e-01 9.43666250e-02 7.08793327e-02 4.17332977e-01 -1.59921813e+00 -6.96141660e-01 6.59252778e-02 -1.55031038e-02 -1.12819485e-01 4.76446211e-01 2.01379016e-01 9.11080718e-01 -1.20013201e+00 9.61538255e-01 1.32048404e+00 9.80423391e-01 8.87455195e-02 -1.54077542e+00 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-6.11530602e-01 4.36538845e-01 6.79209590e-01 -1.10006118e+00 -2.28690699e-01 7.60037005e-02]	[7.396956920623779, -2.331049680709839]
f1145e32-a01e-4230-afe8-acbf10951099	automatic-design-of-semantic-similarity	2307.00925	null	https://arxiv.org/abs/2307.00925v1	https://arxiv.org/pdf/2307.00925v1.pdf	Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolution	Semantic similarity measures are widely used in natural language processing to catalyze various computer-related tasks. However, no single semantic similarity measure is the most appropriate for all tasks, and researchers often use ensemble strategies to ensure performance. This research work proposes a method for automatically designing semantic similarity ensembles. In fact, our proposed method uses grammatical evolution, for the first time, to automatically select and aggregate measures from a pool of candidates to create an ensemble that maximizes correlation to human judgment. The method is evaluated on several benchmark datasets and compared to state-of-the-art ensembles, showing that it can significantly improve similarity assessment accuracy and outperform existing methods in some cases. As a result, our research demonstrates the potential of using grammatical evolution to automatically compare text and prove the benefits of using ensembles for semantic similarity tasks.	['Jorge Martinez-Gil']	2023-07-03	null	null	null	null	['semantic-textual-similarity', 'semantic-similarity']	['natural-language-processing', 'natural-language-processing']	[ 3.68341833e-01 -3.75507087e-01 2.86228567e-01 -3.86365175e-01 -3.60761464e-01 -3.33216935e-01 6.94126546e-01 6.19301975e-01 -4.65733081e-01 5.70602894e-01 1.14164211e-01 1.37654275e-01 -4.16853637e-01 -1.02411366e+00 1.41470850e-01 -4.21372294e-01 3.84958208e-01 5.01814663e-01 1.98581159e-01 -6.28299594e-01 1.00363457e+00 8.69355872e-02 -2.19529223e+00 1.77562743e-01 1.68487704e+00 7.19190121e-01 3.42296273e-01 1.04845025e-01 -4.76552427e-01 2.89523393e-01 -7.34705806e-01 -5.71351111e-01 6.09509386e-02 -7.53358245e-01 -8.51828218e-01 -3.53194803e-01 2.34726146e-01 4.58858669e-01 2.50497639e-01 1.23548782e+00 7.52740204e-01 5.20138144e-01 7.91021109e-01 -9.86962974e-01 -6.62659228e-01 6.56158268e-01 -1.45321980e-01 2.04210162e-01 6.87261045e-01 -1.60274476e-01 1.12732375e+00 -6.73614860e-01 4.10555810e-01 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01e51062-4783-460c-9fb8-fa13f2fd5f1b	active-universal-domain-adaptation	null	null	http://openaccess.thecvf.com//content/ICCV2021/html/Ma_Active_Universal_Domain_Adaptation_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Ma_Active_Universal_Domain_Adaptation_ICCV_2021_paper.pdf	Active Universal Domain Adaptation	Most unsupervised domain adaptation methods rely on rich prior knowledge about the source-target label set relationship, and they cannot recognize categories beyond the source classes, which limits their applicability in practical scenarios. This paper proposes a new paradigm for unsupervised domain adaptation, termed as Active Universal Domain Adaptation (AUDA), which removes all label set assumptions and aims for not only recognizing target samples from source classes but also inferring those from target-private classes by using active learning to annotate a small budget of target data. For AUDA, it is challenging to jointly adapt the model to the target domain and select informative target samples for annotations under a large domain gap and significant semantic shift. To address the problems, we propose an Active Universal Adaptation Network (AUAN). Specifically, we first introduce Adversarial and Diverse Curriculum Learning (ADCL), which progressively aligns source and target domains to classify whether target samples are from source classes. Then, we propose a Clustering Non-transferable Gradient Embedding (CNTGE) strategy, which utilizes the clues of transferability, diversity, and uncertainty to annotate target informative sample, making it possible to infer labels for target samples of target-private classes. Finally, we propose to jointly train ADCL and CNTGE with target supervision to promote domain adaptation and target-private class recognition. Extensive experiments demonstrate that the proposed AUDA model equipped with ADCL and CNTGE achieves significant results on four popular benchmarks.	['Changsheng Xu', 'Junyu Gao', 'Xinhong Ma']	2021-01-01	null	null	null	iccv-2021-1	['universal-domain-adaptation']	['computer-vision']	[ 4.95484471e-01 1.77848563e-01 -6.83245361e-01 -5.07469893e-01 -9.81056035e-01 -8.15212429e-01 4.33111161e-01 1.08243123e-01 -2.49315485e-01 8.69216502e-01 4.83475588e-02 -3.96917900e-03 -5.46863489e-02 -8.44918907e-01 -6.79952085e-01 -9.04791951e-01 2.51986355e-01 5.68928242e-01 3.73166859e-01 -3.65642644e-02 -2.17778385e-01 3.54465395e-01 -1.11364257e+00 1.92608729e-01 1.22115719e+00 1.05711687e+00 1.05025291e-01 9.65811759e-02 -3.10767382e-01 6.20610118e-01 -5.59404612e-01 -3.13116461e-01 2.09149644e-01 -4.78037804e-01 -7.16000438e-01 1.36107892e-01 1.69401422e-01 -1.97116464e-01 -8.72747302e-02 1.03200734e+00 4.13338512e-01 2.13480130e-01 1.08520353e+00 -1.37788987e+00 -9.07024443e-01 4.79968548e-01 -3.89961004e-01 6.28009439e-02 6.55063689e-02 2.65205652e-01 9.77021217e-01 -9.95312393e-01 4.71158385e-01 1.12503839e+00 5.45129418e-01 9.09407854e-01 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17b532ed-5318-4c90-9fdf-bc31bad56aab	learning-a-general-clause-to-clause	2208.13549	null	https://arxiv.org/abs/2208.13549v2	https://arxiv.org/pdf/2208.13549v2.pdf	Learning a General Clause-to-Clause Relationships for Enhancing Emotion-Cause Pair Extraction	Emotion-cause pair extraction (ECPE) is an emerging task aiming to extract potential pairs of emotions and corresponding causes from documents. Previous approaches have focused on modeling the pair-to-pair relationship and achieved promising results. However, the clause-to-clause relationship, which fundamentally symbolizes the underlying structure of a document, has still been in its research infancy. In this paper, we define a novel clause-to-clause relationship. To learn it applicably, we propose a general clause-level encoding model named EA-GAT comprising E-GAT and Activation Sort. E-GAT is designed to aggregate information from different types of clauses; Activation Sort leverages the individual emotion/cause prediction and the sort-based mapping to propel the clause to a more favorable representation. Since EA-GAT is a clause-level encoding model, it can be broadly integrated with any previous approach. Experimental results show that our approach has a significant advantage over all current approaches on the Chinese and English benchmark corpus, with an average of $2.1\%$ and $1.03\%$.	['Xiang Li', 'Xinyu Yang', 'Hang Chen']	2022-08-29	null	null	null	null	['emotion-cause-pair-extraction']	['natural-language-processing']	[ 2.31925547e-01 2.87869334e-01 -4.71511126e-01 -9.13356066e-01 -1.06470346e+00 -5.13305187e-01 4.88638103e-01 4.33631361e-01 9.72292423e-02 6.13225996e-01 3.54098350e-01 3.60177383e-02 -7.25759938e-02 -8.00328553e-01 -3.65381777e-01 -4.93272930e-01 -3.86419833e-01 2.74383724e-01 -3.58228564e-01 -3.16432148e-01 2.20806003e-01 -5.77198938e-02 -1.65571523e+00 8.11854482e-01 8.79764318e-01 1.30700743e+00 -2.18976930e-01 2.04476908e-01 -5.65091789e-01 1.17869818e+00 -5.38889408e-01 -6.82766914e-01 -1.70773208e-01 -5.93740165e-01 -9.57044423e-01 -2.27097079e-01 -2.56311774e-01 1.63696110e-01 3.85605603e-01 9.43357050e-01 3.64528120e-01 -1.73373744e-01 6.56460881e-01 -1.40529573e+00 -4.29903209e-01 9.83157992e-01 -6.21542990e-01 -4.18827720e-02 7.73667336e-01 -5.64787745e-01 1.70248878e+00 -9.11681175e-01 6.21518850e-01 1.14569283e+00 3.93316329e-01 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633c7235-3815-44bc-b9a5-85f92d3202e7	complex-relation-extraction-challenges-and	2012.04821	null	https://arxiv.org/abs/2012.04821v1	https://arxiv.org/pdf/2012.04821v1.pdf	Complex Relation Extraction: Challenges and Opportunities	Relation extraction aims to identify the target relations of entities in texts. Relation extraction is very important for knowledge base construction and text understanding. Traditional binary relation extraction, including supervised, semi-supervised and distant supervised ones, has been extensively studied and significant results are achieved. In recent years, many complex relation extraction tasks, i.e., the variants of simple binary relation extraction, are proposed to meet the complex applications in practice. However, there is no literature to fully investigate and summarize these complex relation extraction works so far. In this paper, we first report the recent progress in traditional simple binary relation extraction. Then we summarize the existing complex relation extraction tasks and present the definition, recent progress, challenges and opportunities for each task.	['Yanghua Xiao', 'Li Wang', 'Deqing Yang', 'Qiao Cheng', 'Qiaoben Bao', 'Haiyun Jiang']	2020-12-09	null	null	null	null	['binary-relation-extraction']	['natural-language-processing']	[ 2.34979123e-01 6.16600096e-01 -8.23764622e-01 -2.66334236e-01 -1.38748825e-01 -4.15906608e-01 8.07258427e-01 7.07452655e-01 -2.06521481e-01 1.41522872e+00 -8.03763717e-02 -5.89746773e-01 -3.48977447e-01 -1.13507342e+00 2.71662660e-02 -3.37235838e-01 -2.62677252e-01 8.30693126e-01 3.09932798e-01 -2.21767351e-01 -4.77382503e-02 5.97184598e-01 -1.25244427e+00 2.19337314e-01 8.36460888e-01 8.95778656e-01 -3.91125232e-01 4.63205874e-01 -4.96465057e-01 1.15874910e+00 -7.43128896e-01 -8.74337316e-01 -3.29694569e-01 -3.97973567e-01 -1.49689543e+00 -2.25989565e-01 -5.21315277e-01 2.04779148e-01 -3.98596197e-01 9.08591330e-01 2.28209049e-01 -1.52972266e-01 8.71105909e-01 -1.40706968e+00 -3.96973252e-01 1.34448075e+00 -5.01337171e-01 4.77055341e-01 6.40934408e-01 -7.57971168e-01 1.24545908e+00 -6.57710612e-01 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7.20608532e-01 3.24277610e-01 -2.07447812e-01 3.26710671e-01 -1.21188641e+00 -5.86591184e-01 1.25130326e-01 -1.22694187e-01 -1.01120722e+00 1.71381142e-02 9.30920690e-02]	[9.153907775878906, 8.709939956665039]
2283d8a5-4180-4890-b72a-f21ad2497089	modeling-4d-fmri-data-via-spatio-temporal	1805.12564	null	http://arxiv.org/abs/1805.12564v3	http://arxiv.org/pdf/1805.12564v3.pdf	Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)	Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis. Inspired by the recent success in applying deep learning for functional brain decoding and encoding, in this work we propose a spatio-temporal convolutional neural network (ST-CNN)to jointly learn the spatial and temporal patterns of targeted network from the training data and perform automatic, pin-pointing functional network identification. The proposed ST-CNN is evaluated by the task of identifying the Default Mode Network (DMN) from fMRI data. Results show that while the framework is only trained on one fMRI dataset,it has the sufficient generalizability to identify the DMN from different populations of data as well as different cognitive tasks. Further investigation into the results show that the superior performance of ST-CNN is driven by the jointly-learning scheme, which capture the intrinsic relationship between the spatial and temporal characteristic of DMN and ensures the accurate identification.	['Wei zhang', 'Yu Zhao', 'Mo Zhang', 'Tianming Liu', 'Shijie Zhao', 'Quanzheng Li', 'Milad Makkie', 'Xiang Li']	2018-05-31	null	null	null	null	['brain-decoding', 'brain-decoding']	['medical', 'miscellaneous']	[ 2.27836475e-01 -3.39688361e-01 1.24526015e-02 -4.83315349e-01 -1.12285160e-01 -3.44296128e-01 5.42018294e-01 -2.36170873e-01 -5.10631144e-01 2.84280658e-01 1.80591360e-01 -5.37427031e-02 -6.58135235e-01 -2.87390292e-01 -4.57586259e-01 -7.97165155e-01 -4.72135007e-01 2.99657941e-01 2.25207508e-01 2.26623878e-01 2.46583164e-01 7.99972177e-01 -1.23904228e+00 4.58101422e-01 6.81068838e-01 1.24155974e+00 4.87557471e-01 1.19979367e-01 1.04666561e-01 6.02914453e-01 -2.43183300e-01 1.49354771e-01 2.01976568e-01 -4.20297742e-01 -8.50961566e-01 -1.15690738e-01 1.05032414e-01 -1.72070801e-01 -5.64178228e-01 1.02195799e+00 8.20653439e-01 1.48510337e-01 6.85078025e-01 -7.68117249e-01 -3.70269030e-01 4.28806484e-01 -2.21431300e-01 9.40557241e-01 -1.87502310e-01 3.65358353e-01 8.31179500e-01 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7084d139-de17-48de-86e1-b60e47b18c45	demograsp-few-shot-learning-for-robotic	2112.02849	null	https://arxiv.org/abs/2112.02849v1	https://arxiv.org/pdf/2112.02849v1.pdf	DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration	The ability to successfully grasp objects is crucial in robotics, as it enables several interactive downstream applications. To this end, most approaches either compute the full 6D pose for the object of interest or learn to predict a set of grasping points. While the former approaches do not scale well to multiple object instances or classes yet, the latter require large annotated datasets and are hampered by their poor generalization capabilities to new geometries. To overcome these shortcomings, we propose to teach a robot how to grasp an object with a simple and short human demonstration. Hence, our approach neither requires many annotated images nor is it restricted to a specific geometry. We first present a small sequence of RGB-D images displaying a human-object interaction. This sequence is then leveraged to build associated hand and object meshes that represent the depicted interaction. Subsequently, we complete missing parts of the reconstructed object shape and estimate the relative transformation between the reconstruction and the visible object in the scene. Finally, we transfer the a-priori knowledge from the relative pose between object and human hand with the estimate of the current object pose in the scene into necessary grasping instructions for the robot. Exhaustive evaluations with Toyota's Human Support Robot (HSR) in real and synthetic environments demonstrate the applicability of our proposed methodology and its advantage in comparison to previous approaches.	['Benjamin Busam', 'Nassir Navab', 'Sven Meie', 'Lorenzo Garattoni', 'Luca Minciullo', 'Fabian Manhardt', 'Pengyuan Wang']	2021-12-06	null	null	null	null	['robotic-grasping']	['robots']	[ 1.91504180e-01 2.30627641e-01 1.85710728e-01 -2.47338921e-01 -3.98566306e-01 -7.02721179e-01 4.49068427e-01 8.55817944e-02 -1.49227053e-01 4.70847219e-01 -5.90690255e-01 2.93332078e-02 -4.83782738e-01 -6.26646996e-01 -8.38747978e-01 -6.17782593e-01 -1.36851713e-01 1.09255219e+00 4.17861938e-01 -2.71082252e-01 2.56525278e-01 9.91204202e-01 -1.72697902e+00 6.34158179e-02 7.34982312e-01 9.89515722e-01 8.74166071e-01 4.58134294e-01 5.45509495e-02 3.20164502e-01 -2.81580031e-01 -7.47895893e-03 4.55086708e-01 4.65872586e-02 -7.74947047e-01 5.50170600e-01 -2.81881914e-03 -6.07946634e-01 -1.07687473e-01 7.03657568e-01 1.59109429e-01 1.80050477e-01 6.81035399e-01 -1.30168962e+00 -2.39387285e-02 4.39726979e-01 -3.77365589e-01 -5.16117096e-01 7.01542199e-01 1.79519802e-01 5.62605202e-01 -9.64225292e-01 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8a9f0107-aba4-4895-949d-27bc20ebeec1	can-transfer-entropy-infer-causality-in	1901.07589	null	https://arxiv.org/abs/1901.07589v2	https://arxiv.org/pdf/1901.07589v2.pdf	Can Transfer Entropy Infer Information Flow in Neuronal Circuits for Cognitive Processing?	To infer information flow in any network of agents, it is important first and foremost to establish causal temporal relations between the nodes. Practical and automated methods that can infer causality are difficult to find, and the subject of ongoing research. While Shannon information only detects correlation, there are several information-theoretic notions of "directed information" that have successfully detected causality in some systems, in particular in the neuroscience community. However, recent work has shown that some directed information measures can sometimes inadequately estimate the extent of causal relations, or even fail to identify existing cause-effect relations between components of systems, especially if neurons contribute in a cryptographic manner to influence the effector neuron. Here, we test how often cryptographic logic emerges in an evolutionary process that generates artificial neural circuits for two fundamental cognitive tasks: motion detection and sound localization. We also test whether activity time-series recorded from behaving digital brains can infer information flow using the transfer entropy concept, when compared to a ground-truth model of causal influence constructed from connectivity and circuit logic. Our results suggest that transfer entropy will sometimes fail to infer causality when it exists, and sometimes suggest a causal connection when there is none. However, the extent of incorrect inference strongly depends on the cognitive task considered. These results emphasize the importance of understanding the fundamental logic processes that contribute to information flow in cognitive processing, and quantifying their relevance in any given nervous system.	['Ali Tehrani-Saleh', 'Christoph Adami']	2019-01-22	null	null	null	null	['motion-detection']	['computer-vision']	[ 6.40227020e-01 1.08128726e-01 1.36255309e-01 8.73584747e-02 3.04952651e-01 -8.98423731e-01 9.51879680e-01 3.83005828e-01 -2.23910108e-01 9.40318346e-01 2.58353680e-01 -5.51633775e-01 -7.69146323e-01 -8.98930967e-01 -5.45842528e-01 -6.55962825e-01 -4.50458884e-01 1.70971170e-01 2.82517046e-01 -1.79176763e-01 6.59900784e-01 3.25203776e-01 -1.34862697e+00 7.86981955e-02 5.98941505e-01 7.18908310e-01 1.92410663e-01 9.52732801e-01 1.21348001e-01 1.55210233e+00 -6.58595383e-01 -2.33809471e-01 -2.08522797e-01 -1.18931067e+00 -9.74183559e-01 -4.80643690e-01 -1.16937764e-01 -1.46018758e-01 -2.88487434e-01 1.00596833e+00 3.72589342e-02 -2.88834989e-01 8.43568504e-01 -1.40964520e+00 -5.83550811e-01 8.60604823e-01 -7.59618506e-02 7.68557072e-01 5.15213013e-01 2.22034782e-01 1.20929337e+00 -1.26788303e-01 6.34606361e-01 1.18583095e+00 4.86477971e-01 2.43635982e-01 -1.51351023e+00 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a4d1968c-bd9d-433b-b49e-cf24e86f1676	advanced-customer-activity-prediction-based	1904.07687	null	https://arxiv.org/abs/1904.07687v4	https://arxiv.org/pdf/1904.07687v4.pdf	Advanced Customer Activity Prediction based on Deep Hierarchic Encoder-Decoders	Product recommender systems and customer profiling techniques have always been a priority in online retail. Recent machine learning research advances and also wide availability of massive parallel numerical computing has enabled various approaches and directions of recommender systems advancement. Worth to mention is the fact that in past years multiple traditional "offline" retail business are gearing more and more towards employing inferential and even predictive analytics both to stock-related problems such as predictive replenishment but also to enrich customer interaction experience. One of the most important areas of recommender systems research and development is that of Deep Learning based models which employ representational learning to model consumer behavioral patterns. Current state of the art in Deep Learning based recommender systems uses multiple approaches ranging from already classical methods such as the ones based on learning product representation vector, to recurrent analysis of customer transactional time-series and up to generative models based on adversarial training. Each of these methods has multiple advantages and inherent weaknesses such as inability of understanding the actual user-journey, ability to propose only single product recommendation or top-k product recommendations without prediction of actual next-best-offer. In our work we will present a new and innovative architectural approach of applying state-of-the-art hierarchical multi-module encoder-decoder architecture in order to solve several of current state-of-the-art recommender systems issues. Our approach will also produce by-products such as product need-based segmentation and customer behavioral segmentation - all in an end-to-end trainable approach. Finally, we will present a couple methods that solve known retail & distribution pain-points based on the proposed architecture.	['Laurentiu Piciu', 'Andrei Damian', 'Sergiu Turlea', 'Nicolae Tapus']	2019-04-11	null	null	null	null	['activity-prediction', 'product-recommendation', 'activity-prediction']	['computer-vision', 'miscellaneous', 'time-series']	[ 3.70702855e-02 -1.16286930e-02 -1.86983332e-01 -7.17437565e-01 -6.38876557e-01 -4.15835619e-01 3.55457217e-01 1.86738089e-01 1.87491323e-03 1.80362865e-01 1.31393313e-01 -4.93749797e-01 -5.14481425e-01 -8.16616833e-01 -4.64085639e-01 -3.97236735e-01 -1.88744500e-01 9.93156135e-01 -3.86231124e-01 -1.03389633e+00 4.85772043e-01 4.87667173e-01 -1.82025027e+00 6.76680684e-01 4.64462191e-01 1.45400691e+00 7.65382797e-02 6.71694636e-01 -1.67776853e-01 8.11534226e-01 -2.63946831e-01 -9.18820500e-01 6.00434005e-01 -1.86440066e-01 -4.27240700e-01 -1.13456085e-01 5.86220846e-02 -3.02558571e-01 -1.50773093e-01 5.71260333e-01 5.88078022e-01 5.36790490e-01 7.22735047e-01 -9.26535785e-01 -1.13817656e+00 1.10230982e+00 -4.08549219e-01 3.21637899e-01 2.98946381e-01 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383fcdfc-0177-4b83-aa3f-b6c94074c409	an-end-to-end-review-of-gaze-estimation-and	2307.00122	null	https://arxiv.org/abs/2307.00122v1	https://arxiv.org/pdf/2307.00122v1.pdf	An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices	In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state of the art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This paper aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.	['Juan Ye', 'Mohamed Khamis', 'Shijing He', 'Yaxiong Lei']	2023-06-30	null	null	null	null	['gaze-estimation']	['computer-vision']	[ 3.26641589e-01 5.92880696e-03 -3.79460216e-01 -2.83484161e-01 -1.72941655e-01 -3.31238538e-01 3.11773121e-01 -3.89271796e-01 -3.89515996e-01 5.51252961e-01 -7.54644349e-02 -2.04770073e-01 -1.41045690e-01 -2.55429116e-03 -1.27317369e-01 -5.48902810e-01 1.70767412e-01 -6.08260408e-02 -2.15098247e-01 6.50534332e-02 7.64344454e-01 1.81121945e-01 -2.22666883e+00 5.77507541e-02 8.20304632e-01 1.16583896e+00 1.76431671e-01 9.16352451e-01 -1.44259378e-01 5.24256170e-01 -3.38981688e-01 -6.43155932e-01 -3.23274672e-01 -3.01395327e-01 -7.76775479e-01 -2.59293050e-01 7.82810569e-01 -5.04675329e-01 3.48644465e-01 7.82034039e-01 7.32733130e-01 2.94878427e-02 2.92017281e-01 -1.67633712e+00 -5.46721697e-01 -2.81032145e-01 -9.32512879e-01 5.11834681e-01 7.88430333e-01 1.86173767e-01 7.35585392e-01 -7.54959583e-01 2.14377165e-01 9.17182565e-01 5.62281013e-01 8.71208370e-01 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-3.21718276e-01 -1.02515973e-01 7.78066695e-01 -7.77205944e-01 -1.43449664e-01 3.49513084e-01]	[14.10536003112793, 0.11554199457168579]
004b9e85-9c7d-4830-af9c-8f554eb1bc43	detecting-histologic-glioblastoma-regions-of	2302.00669	null	https://arxiv.org/abs/2302.00669v2	https://arxiv.org/pdf/2302.00669v2.pdf	Detecting Histologic & Clinical Glioblastoma Patterns of Prognostic Relevance	Glioblastoma is the most common and aggressive malignant adult tumor of the central nervous system, with a grim prognosis and heterogeneous morphologic and molecular profiles. Since adopting the current standard-of-care treatment 18 years ago, no substantial prognostic improvement has been noticed. Accurate prediction of patient overall survival (OS) from histopathology whole slide images (WSI) integrated with clinical data using advanced computational methods could optimize clinical decision-making and patient management. Here, we focus on identifying prognostically relevant glioblastoma characteristics from H&E stained WSI & clinical data relating to OS. The exact approach for WSI capitalizes on the comprehensive curation of apparent artifactual content and an interpretability mechanism via a weakly supervised attention-based multiple-instance learning algorithm that further utilizes clustering to constrain the search space. The automatically placed pat- terns of high diagnostic value classify each WSI as representative of short or long-survivors. Further assessment of the prognostic relevance of the associated clinical patient data is performed both in isolation and in an integrated manner, using XGBoost and SHapley Additive exPlanations (SHAP). Identifying tumor morphological & clinical patterns associated with short and long OS will enable the clinical neuropathologist to provide additional relevant prognostic information to the treating team and suggest avenues of biological investigation for understanding and potentially treating glioblastoma.	['Sharath Chandra Guntuku', 'Garv Mehdiratta', 'Sunny Rai', 'Spyridon Bakas', 'MacLean P. Nasrallah', 'Shubham Innani', 'Bhakti Baheti']	2023-02-01	null	null	null	null	['whole-slide-images', 'multiple-instance-learning']	['computer-vision', 'methodology']	[ 5.19603074e-01 1.29112720e-01 -3.36725086e-01 -2.31298149e-01 -1.12993383e+00 -1.58871099e-01 2.96651810e-01 8.99797320e-01 -6.29364729e-01 1.02004898e+00 5.18299162e-01 -5.86693466e-01 -8.14753830e-01 -3.64298254e-01 7.78454728e-03 -1.33554840e+00 -1.50960684e-01 8.47080827e-01 -3.11631739e-01 -2.87573412e-02 3.94891530e-01 6.91636026e-01 -1.19000101e+00 2.21147805e-01 1.04694426e+00 8.08149457e-01 6.55754566e-01 7.67114103e-01 -1.56297460e-02 6.19917691e-01 -3.13637763e-01 -1.80981178e-02 -3.31410408e-01 -2.67623186e-01 -7.82443821e-01 1.85810439e-02 -2.34900638e-01 8.65480900e-02 1.11178653e-02 7.92259455e-01 6.64754689e-01 -2.36547858e-01 9.46111500e-01 -1.22712016e+00 -1.55823603e-01 5.00433743e-01 -3.34818810e-01 5.46556115e-01 8.22824761e-02 3.88847589e-01 9.61911917e-01 -5.74911177e-01 8.51772785e-01 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82fda4e4-ff1c-420b-aa83-a2d7ee5a9b87	few-shot-3d-point-cloud-semantic-segmentation-1	2303.15654	null	https://arxiv.org/abs/2303.15654v1	https://arxiv.org/pdf/2303.15654v1.pdf	Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network	3D point cloud semantic segmentation aims to group all points into different semantic categories, which benefits important applications such as point cloud scene reconstruction and understanding. Existing supervised point cloud semantic segmentation methods usually require large-scale annotated point clouds for training and cannot handle new categories. While a few-shot learning method was proposed recently to address these two problems, it suffers from high computational complexity caused by graph construction and inability to learn fine-grained relationships among points due to the use of pooling operations. In this paper, we further address these problems by developing a new multi-layer transformer network for few-shot point cloud semantic segmentation. In the proposed network, the query point cloud features are aggregated based on the class-specific support features in different scales. Without using pooling operations, our method makes full use of all pixel-level features from the support samples. By better leveraging the support features for few-shot learning, the proposed method achieves the new state-of-the-art performance, with 15\% less inference time, over existing few-shot 3D point cloud segmentation models on the S3DIS dataset and the ScanNet dataset.	['Song Wang', 'Ziyu Zhao', 'Xinyi Wu', 'Zhenyao Wu', 'Canyu Zhang']	2023-03-28	null	null	null	null	['point-cloud-segmentation', 'graph-construction']	['computer-vision', 'graphs']	[ 1.22103058e-01 -4.56792749e-02 -3.90979171e-01 -6.41311467e-01 -7.92597651e-01 -1.16567016e-01 3.77211571e-01 4.14984465e-01 -3.04580688e-01 6.86073527e-02 -3.88925761e-01 -8.21141340e-03 -2.78354347e-01 -1.15050340e+00 -8.41525793e-01 -5.28071821e-01 -9.24989432e-02 7.25923181e-01 9.32634175e-01 4.29589823e-02 3.81535679e-01 6.80359662e-01 -1.85866535e+00 4.26639318e-02 9.46149051e-01 1.32209814e+00 4.65170562e-01 5.42740375e-02 -9.84335124e-01 2.94793963e-01 -2.82672226e-01 -2.14690603e-02 4.75225687e-01 1.59273624e-01 -7.16064692e-01 3.15799326e-01 5.60454667e-01 -3.53359699e-01 -7.63055608e-02 1.30514526e+00 3.49342257e-01 4.55687612e-01 4.78553385e-01 -1.41073179e+00 -3.37332278e-01 3.13361913e-01 -7.51815856e-01 1.54646352e-01 -8.95304605e-02 5.10976948e-02 1.09641194e+00 -1.01765239e+00 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71d8b7dd-8c9b-488d-9e0f-0f54919c2442	deep-insights-of-learning-based-micro	2210.04935	null	https://arxiv.org/abs/2210.04935v1	https://arxiv.org/pdf/2210.04935v1.pdf	Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs	Micro expression recognition (MER) is a very challenging area of research due to its intrinsic nature and fine-grained changes. In the literature, the problem of MER has been solved through handcrafted/descriptor-based techniques. However, in recent times, deep learning (DL) based techniques have been adopted to gain higher performance for MER. Also, rich survey articles on MER are available by summarizing the datasets, experimental settings, conventional and deep learning methods. In contrast, these studies lack the ability to convey the impact of network design paradigms and experimental setting strategies for DL-based MER. Therefore, this paper aims to provide a deep insight into the DL-based MER frameworks with a perspective on promises in network model designing, experimental strategies, challenges, and research needs. Also, the detailed categorization of available MER frameworks is presented in various aspects of model design and technical characteristics. Moreover, an empirical analysis of the experimental and validation protocols adopted by MER methods is presented. The challenges mentioned earlier and network design strategies may assist the affective computing research community in forging ahead in MER research. Finally, we point out the future directions, research needs, and draw our conclusions.	['Girdhari Singh', 'Santosh Kumar Vipparthi', 'Monu Verma']	2022-10-10	null	null	null	null	['micro-expression-recognition']	['computer-vision']	[-1.25815555e-01 -9.75702628e-02 -4.98672813e-01 -5.65164626e-01 7.32416511e-02 -3.15520585e-01 2.06059843e-01 -1.40765170e-02 -2.66556680e-01 5.44868946e-01 -1.23633511e-01 2.67981917e-01 -2.80614078e-01 -7.13706672e-01 2.68605072e-02 -8.50619435e-01 -2.25499392e-01 1.05772614e-01 -5.31996310e-01 -5.38860977e-01 -6.73563639e-03 9.68131125e-01 -1.49264121e+00 1.02030344e-01 1.33840799e-01 1.44813335e+00 -2.72806436e-01 2.55770892e-01 -1.47252709e-01 9.85712230e-01 -6.68611944e-01 -8.18619251e-01 -7.91443214e-02 -3.17285120e-01 -5.88553548e-01 1.38197653e-02 -1.42677307e-01 -2.59777248e-01 -5.33268929e-01 8.19640875e-01 1.00494099e+00 1.94434926e-01 5.74699879e-01 -1.73314202e+00 -5.15349686e-01 5.57535946e-01 -3.62301022e-01 1.19074360e-01 2.18063161e-01 -9.49619785e-02 1.07610536e+00 -9.22743618e-01 5.03564894e-01 1.10113633e+00 7.25851595e-01 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b39d5b8d-4888-43f9-9156-918403bb4be3	time-and-cost-efficient-bathymetric-mapping	2210.10263	null	https://arxiv.org/abs/2210.10263v1	https://arxiv.org/pdf/2210.10263v1.pdf	Time and Cost-Efficient Bathymetric Mapping System using Sparse Point Cloud Generation and Automatic Object Detection	Generating 3D point cloud (PC) data from noisy sonar measurements is a problem that has potential applications for bathymetry mapping, artificial object inspection, mapping of aquatic plants and fauna as well as underwater navigation and localization of vehicles such as submarines. Side-scan sonar sensors are available in inexpensive cost ranges, especially in fish-finders, where the transducers are usually mounted to the bottom of a boat and can approach shallower depths than the ones attached to an Uncrewed Underwater Vehicle (UUV) can. However, extracting 3D information from side-scan sonar imagery is a difficult task because of its low signal-to-noise ratio and missing angle and depth information in the imagery. Since most algorithms that generate a 3D point cloud from side-scan sonar imagery use Shape from Shading (SFS) techniques, extracting 3D information is especially difficult when the seafloor is smooth, is slowly changing in depth, or does not have identifiable objects that make acoustic shadows. This paper introduces an efficient algorithm that generates a sparse 3D point cloud from side-scan sonar images. This computation is done in a computationally efficient manner by leveraging the geometry of the first sonar return combined with known positions provided by GPS and down-scan sonar depth measurement at each data point. Additionally, this paper implements another algorithm that uses a Convolutional Neural Network (CNN) using transfer learning to perform object detection on side-scan sonar images collected in real life and generated with a simulation. The algorithm was tested on both real and synthetic images to show reasonably accurate anomaly detection and classification.	['Jaejeong Shin', 'Peter Ifju', 'Andrew Ortega', 'Antonio Diaz', 'Ruoyao Qin', 'Andres Pulido']	2022-10-19	null	null	null	null	['point-cloud-generation']	['computer-vision']	[ 2.09506318e-01 -1.32926971e-01 9.48726296e-01 -3.98872018e-01 -3.12127322e-01 -6.10678136e-01 2.90374070e-01 2.94528324e-02 -7.74388313e-01 3.93694282e-01 -3.03473592e-01 -2.56305993e-01 -1.12647846e-01 -9.66327965e-01 -7.39991724e-01 -7.43876576e-01 -6.72046006e-01 5.59819579e-01 4.12284434e-01 -7.51263499e-01 2.53422707e-01 7.61938751e-01 -1.76252687e+00 -4.17409092e-01 6.91602826e-01 9.15805697e-01 6.15224838e-01 9.04186368e-01 -5.98975755e-02 -1.62632659e-01 -4.32731569e-01 1.43844321e-01 6.99719489e-01 -2.13647515e-01 9.93570238e-02 -1.40242800e-01 7.45919168e-01 -6.67432845e-01 1.08015470e-01 1.07771432e+00 4.93155688e-01 1.36852011e-01 5.99176645e-01 -6.81020617e-01 3.03475380e-01 1.49362579e-01 -4.92154568e-01 1.03794284e-01 1.13302976e-01 -2.04162017e-01 4.55589890e-01 -9.42975461e-01 3.48014496e-02 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a5c3abe4-9c13-4d94-8f51-d171012f39ad	physics-informed-machine-learning-with	2206.10718	null	https://arxiv.org/abs/2206.10718v1	https://arxiv.org/pdf/2206.10718v1.pdf	Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management	Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. The heterogeneity typically requires high-fidelity physics-based models to make predictions on CO$_2$ fate. Furthermore, characterizing the heterogeneity accurately is fraught with parametric uncertainty. Accounting for both, heterogeneity and uncertainty, makes this a computationally-intensive problem challenging for current reservoir simulators. To tackle this, we use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization at critical reservoir locations. We use DPFEHM framework, which has trustworthy physics based on the standard two-point flux finite volume discretization and is also automatically differentiable like machine learning models. Our physics-informed machine learning framework uses convolutional neural networks to learn an appropriate extraction rate based on the permeability field. We also perform a hyperparameter search to improve the model's accuracy. Training and testing scenarios are executed to evaluate the feasibility of using physics-informed machine learning to manage reservoir pressures. We constructed and tested a sufficiently accurate simulator that is 400000 times faster than the underlying physics-based simulator, allowing for near real-time analysis and robust uncertainty quantification.	['Hari Viswanathan', 'Dylan Robert Harp', "Daniel O'Malley", 'Aleksandra Pachalieva']	2022-06-21	null	null	null	null	['physics-informed-machine-learning']	['graphs']	[-3.33608925e-01 -6.38901070e-02 9.71905217e-02 3.22630256e-02 -7.43888438e-01 -4.51350808e-01 5.84974885e-01 4.98139203e-01 -1.15445167e-01 1.07807553e+00 -1.15783051e-01 -7.78327942e-01 -2.31211215e-01 -1.21286714e+00 -1.09170318e+00 -6.49950981e-01 -7.58050621e-01 8.07509422e-01 1.14871249e-01 -2.45323941e-01 5.39796293e-01 8.12812090e-01 -1.42893755e+00 6.05029017e-02 1.20879936e+00 1.01615989e+00 1.94898978e-01 6.86267376e-01 -2.37487659e-01 4.93756115e-01 -1.59229085e-01 5.12361646e-01 3.00205588e-01 1.22168921e-02 -6.06190264e-01 -6.25665069e-01 -1.50504634e-01 -5.08922100e-01 -1.01347543e-01 7.87372828e-01 3.49357337e-01 1.21046841e-01 1.08897483e+00 -7.47910380e-01 -1.27052665e-01 5.24505138e-01 -4.23971593e-01 1.14119500e-01 -2.45992020e-01 5.14982700e-01 5.13405204e-01 -9.71650839e-01 -1.07990004e-01 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2153ea79-4f44-4581-b49b-9b101586756d	deep-mds-framework-for-recovering-the-3d	2210.15200	null	https://arxiv.org/abs/2210.15200v1	https://arxiv.org/pdf/2210.15200v1.pdf	Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Image	In this paper, a low parameter deep learning framework utilizing the Non-metric Multi-Dimensional scaling (NMDS) method, is proposed to recover the 3D shape of 2D landmarks on a human face, in a single input image. Hence, NMDS approach is used for the first time to establish a mapping from a 2D landmark space to the corresponding 3D shape space. A deep neural network learns the pairwise dissimilarity among 2D landmarks, used by NMDS approach, whose objective is to learn the pairwise 3D Euclidean distance of the corresponding 2D landmarks on the input image. This scheme results in a symmetric dissimilarity matrix, with the rank larger than 2, leading the NMDS approach toward appropriately recovering the 3D shape of corresponding 2D landmarks. In the case of posed images and complex image formation processes like perspective projection which causes occlusion in the input image, we consider an autoencoder component in the proposed framework, as an occlusion removal part, which turns different input views of the human face into a profile view. The results of a performance evaluation using different synthetic and real-world human face datasets, including Besel Face Model (BFM), CelebA, CoMA - FLAME, and CASIA-3D, indicates the comparable performance of the proposed framework, despite its small number of training parameters, with the related state-of-the-art and powerful 3D reconstruction methods from the literature, in terms of efficiency and accuracy.	['Zohreh Azimifar', 'Shima Kamyab']	2022-10-27	null	null	null	null	['face-model']	['computer-vision']	[-1.90087646e-01 3.48086916e-02 4.19597656e-01 -4.01706815e-01 -1.54099271e-01 -1.51384294e-01 7.69252717e-01 -4.04924065e-01 -3.40417236e-01 3.16841573e-01 1.69963434e-01 1.75786287e-01 -3.75911891e-01 -7.75947988e-01 -6.14078224e-01 -8.63421500e-01 -7.92325195e-03 6.95851088e-01 -4.75071728e-01 -8.72000530e-02 1.79497048e-01 1.16644645e+00 -1.81183231e+00 -2.80931979e-01 1.96483552e-01 1.09612787e+00 -1.19249091e-01 7.72134438e-02 -6.51628003e-02 2.74002422e-02 -3.69757831e-01 -5.64602256e-01 8.12802136e-01 -1.17528126e-01 -2.98529238e-01 2.47338310e-01 7.40777254e-01 -5.17835259e-01 -3.67977947e-01 9.42454040e-01 7.74175823e-01 1.06943980e-01 1.04230130e+00 -1.18602300e+00 -8.92087877e-01 -1.30402729e-01 -9.41330314e-01 -1.30775496e-01 5.01235187e-01 -3.83226901e-01 4.89504039e-01 -1.43884254e+00 7.02295840e-01 1.60006535e+00 7.28045225e-01 4.91263330e-01 -1.01508784e+00 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7.15907395e-01 4.41615850e-01 -1.14363539e+00 -4.85864371e-01 -7.83509687e-02]	[13.211397171020508, 0.3007567226886749]
5d4af61e-f502-48be-9c1c-44aa282397aa	exbrainable-an-open-source-gui-for-cnn-based	2201.04065	null	https://arxiv.org/abs/2201.04065v1	https://arxiv.org/pdf/2201.04065v1.pdf	ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation	We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding. Available functions include model training, evaluation, and parameter visualization in terms of temporal and spatial representations. We demonstrate these functions using a well-studied public dataset of motor-imagery EEG and compare the results with existing knowledge of neuroscience. The primary objective of ExBrainable is to provide a fast, simplified, and user-friendly solution of EEG decoding for investigators across disciplines to leverage cutting-edge methods in brain/neuroscience research.	['Chun-Shu Wei', 'Jian-Xue Huang', 'Chia-Ying Hsieh', 'Ya-Lin Huang']	2022-01-10	null	null	null	null	['eeg-decoding', 'eeg-decoding']	['medical', 'time-series']	[-2.11562306e-01 -5.49043775e-01 4.08607692e-01 -5.15649438e-01 -1.46269664e-01 -3.76638025e-01 3.15487385e-01 -2.67089218e-01 -5.89179337e-01 8.00136626e-01 7.52555206e-02 -7.47267425e-01 -2.34478787e-01 -1.45750031e-01 -7.01154709e-01 -3.92752528e-01 -6.53739512e-01 2.05769405e-01 -3.48321766e-01 -3.24172266e-02 3.33992422e-01 7.64804006e-01 -1.30796123e+00 4.61669803e-01 6.16150379e-01 1.04525495e+00 4.99208868e-01 6.19481623e-01 4.67678726e-01 2.65037477e-01 -8.49868417e-01 1.95440844e-01 -2.80991793e-01 -2.85264194e-01 -5.95881164e-01 -6.79388404e-01 -9.76603329e-02 -3.00725460e-01 -3.92525911e-01 9.37881112e-01 8.22610021e-01 1.83574967e-02 4.77252245e-01 -1.14982259e+00 -6.38005972e-01 2.61635333e-01 -1.13144979e-01 9.71635878e-01 1.47227511e-01 5.02652466e-01 4.24857959e-02 -6.19480014e-01 5.17431617e-01 7.53838301e-01 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5ea28d69-95ae-4023-ba8e-90d74981c68f	nanoflow-scalable-normalizing-flows-with	2006.06280	null	https://arxiv.org/abs/2006.06280v4	https://arxiv.org/pdf/2006.06280v4.pdf	NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity	Normalizing flows (NFs) have become a prominent method for deep generative models that allow for an analytic probability density estimation and efficient synthesis. However, a flow-based network is considered to be inefficient in parameter complexity because of reduced expressiveness of bijective mapping, which renders the models unfeasibly expensive in terms of parameters. We present an alternative parameterization scheme called NanoFlow, which uses a single neural density estimator to model multiple transformation stages. Hence, we propose an efficient parameter decomposition method and the concept of flow indication embedding, which are key missing components that enable density estimation from a single neural network. Experiments performed on audio and image models confirm that our method provides a new parameter-efficient solution for scalable NFs with significant sublinear parameter complexity.	['Sang-gil Lee', 'Sungwon Kim', 'Sungroh Yoon']	2020-06-11	null	http://proceedings.neurips.cc/paper/2020/hash/a1c3ae6c49a89d92aef2d423dadb477f-Abstract.html	http://proceedings.neurips.cc/paper/2020/file/a1c3ae6c49a89d92aef2d423dadb477f-Paper.pdf	neurips-2020-12	['normalising-flows']	['methodology']	[-1.88564345e-01 4.17531170e-02 -3.60001802e-01 -1.34652749e-01 -5.15458882e-01 -4.51704741e-01 5.99490404e-01 -3.51274282e-01 -3.11880022e-01 9.46278751e-01 1.20682627e-01 -2.81572819e-01 -1.26156777e-01 -9.79262888e-01 -7.59641588e-01 -8.36161017e-01 1.53995484e-01 3.81231695e-01 -2.47516483e-02 1.53937489e-01 -2.22468320e-02 6.62879169e-01 -1.23612678e+00 -4.49546814e-01 8.71061742e-01 1.11022282e+00 1.53665647e-01 4.31916386e-01 -4.30934995e-01 6.94939137e-01 -5.12308478e-01 -6.83345437e-01 1.11975357e-01 -5.00918865e-01 -3.49013835e-01 -2.54641026e-01 4.60263193e-01 -8.75873387e-01 -4.98746157e-01 1.14491332e+00 3.82537186e-01 -2.93770749e-02 1.10208941e+00 -1.41450620e+00 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5e5b1284-5a19-48eb-af7c-c4922a2cf115	out-of-distribution-detection-with-distance	2002.03328	null	https://arxiv.org/abs/2002.03328v5	https://arxiv.org/pdf/2002.03328v5.pdf	Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models	Recent research has revealed that deep generative models including flow-based models and Variational Autoencoders may assign higher likelihoods to out-of-distribution (OOD) data than in-distribution (ID) data. However, we cannot sample OOD data from the model. This counterintuitive phenomenon has not been satisfactorily explained and brings obstacles to OOD detection with flow-based models. In this paper, we prove theorems to investigate the Kullback-Leibler divergence in flow-based model and give two explanations for the above phenomenon. Based on our theoretical analysis, we propose a new method \PADmethod\ to leverage KL divergence and local pixel dependence of representations to perform anomaly detection. Experimental results on prevalent benchmarks demonstrate the effectiveness and robustness of our method. For group anomaly detection, our method achieves 98.1\% AUROC on average with a small batch size of 5. On the contrary, the baseline typicality test-based method only achieves 64.6\% AUROC on average due to its failure on challenging problems. Our method also outperforms the state-of-the-art method by 9.1\% AUROC. For point-wise anomaly detection, our method achieves 90.7\% AUROC on average and outperforms the baseline by 5.2\% AUROC. Besides, our method has the least notable failures and is the most robust one.	['Hongmei Wei', 'Kenli Li', 'Zhiming Liu', 'Ji Wang', 'Zhenbang Chen', 'Wanwei Liu', 'Jialu Pan', 'Yufeng Zhang']	2020-02-09	null	null	null	null	['group-anomaly-detection']	['methodology']	[-3.60033691e-01 -2.13570625e-01 -8.38241801e-02 2.33691055e-02 -6.62953436e-01 -3.95588070e-01 6.15020216e-01 3.48158143e-02 -1.45393968e-01 5.50745845e-01 -5.62467128e-02 -5.07552922e-01 -1.37412354e-01 -7.88452446e-01 -6.05627477e-01 -8.08933377e-01 -3.42616707e-01 2.43411422e-01 2.59869903e-01 1.21014453e-01 3.06136757e-01 3.84099275e-01 -1.51609290e+00 -2.17127651e-01 1.09588909e+00 1.22448993e+00 -2.76640445e-01 7.45603144e-01 -2.27311581e-01 7.04326749e-01 -8.52261066e-01 -3.46087664e-01 4.46534932e-01 -6.45124972e-01 -3.39517534e-01 2.26829704e-02 8.26617122e-01 -5.54576397e-01 -6.13611519e-01 1.05075312e+00 4.70469803e-01 3.15065056e-01 1.06995988e+00 -1.54986835e+00 -7.31586099e-01 1.93146691e-02 -7.55563498e-01 7.85210550e-01 -2.52237823e-02 3.44981194e-01 1.17375410e+00 -9.53715026e-01 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132eaafc-3c33-4ea5-9337-bfd74e51fc69	generating-multiple-choice-questions-for	2303.07069	null	https://arxiv.org/abs/2303.07069v1	https://arxiv.org/pdf/2303.07069v1.pdf	Generating multiple-choice questions for medical question answering with distractors and cue-masking	Medical multiple-choice question answering (MCQA) is particularly difficult. Questions may describe patient symptoms and ask for the correct diagnosis, which requires domain knowledge and complex reasoning. Standard language modeling pretraining alone is not sufficient to achieve the best results. \citet{jin2020disease} showed that focusing masked language modeling on disease name prediction when using medical encyclopedic paragraphs as input leads to considerable MCQA accuracy improvement. In this work, we show that (1) fine-tuning on generated MCQA dataset outperforms the masked language modeling based objective and (2) correctly masking the cues to the answers is critical for good performance. We release new pretraining datasets and achieve state-of-the-art results on 4 MCQA datasets, notably +5.7\% with base-size model on MedQA-USMLE.	['Marie-Francine Moens', 'Kanimozhi Uma', 'Damien Sileo']	2023-03-13	null	null	null	null	['multiple-choice-qa']	['natural-language-processing']	[ 1.16069885e-02 4.50210214e-01 -3.20618868e-01 -4.20133680e-01 -1.90533769e+00 -4.03068423e-01 1.22358315e-01 4.50769067e-01 -4.67672974e-01 1.02633595e+00 6.69242799e-01 -5.71369767e-01 -3.74197245e-01 -6.90168440e-01 -6.19148731e-01 -1.27165541e-01 1.76351443e-01 1.06359565e+00 2.56905645e-01 -4.43403304e-01 -2.31448844e-01 -5.47138266e-02 -9.11181867e-01 1.20777214e+00 1.29755068e+00 8.10430467e-01 2.09714808e-02 9.49139833e-01 -5.64916134e-01 1.39089680e+00 -5.41417658e-01 -6.31816804e-01 -1.68701068e-01 -6.91147387e-01 -1.12190402e+00 -3.61590356e-01 5.25737941e-01 -5.14469370e-02 -1.20126799e-01 5.28846979e-01 6.65708661e-01 -2.99037695e-01 6.34134352e-01 -7.92109489e-01 -7.72049725e-01 6.75221622e-01 -3.35440263e-02 3.08201522e-01 5.55652976e-01 2.81842917e-01 1.28264380e+00 -6.77288532e-01 7.49987543e-01 1.21852934e+00 6.43945873e-01 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0b5c7936-4b13-4acd-af8c-2adf2d353467	an-emg-gesture-recognition-system-with	1802.10237	null	http://arxiv.org/abs/1802.10237v2	http://arxiv.org/pdf/1802.10237v2.pdf	An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier	EMG-based gesture recognition shows promise for human-machine interaction. Systems are often afflicted by signal and electrode variability which degrades performance over time. We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm. EMG electrodes are fabricated on a flexible substrate and interfaced to a custom wireless device for 64-channel signal acquisition and streaming. We use brain-inspired high-dimensional (HD) computing for processing EMG features in one-shot learning. The HD algorithm is tolerant to noise and electrode misplacement and can quickly learn from few gestures without gradient descent or back-propagation. We achieve an average classification accuracy of 96.64% for five gestures, with only 7% degradation when training and testing across different days. Our system maintains this accuracy when trained with only three trials of gestures; it also demonstrates comparable accuracy with the state-of-the-art when trained with one trial.	['Luca Benini', 'Fred Burghardt', 'Natasha Yamamoto', 'Simone Benatti', 'Jonathan Ting', 'Jan M. Rabaey', 'Alisha Menon', 'Ali Moin', 'Abbas Rahimi', 'Yasser Khan', 'Senam Tamakloe', 'Andy Zhou', 'Ana C. Arias']	2018-02-28	null	null	null	null	['emg-gesture-recognition']	['medical']	[ 7.50415027e-01 -2.93900400e-01 -1.59479193e-02 -2.39085674e-01 -1.28570390e+00 -2.97490478e-01 -1.81346878e-01 -3.77254516e-01 -9.33723748e-01 5.04562855e-01 -5.04449755e-02 4.86809671e-01 -5.37950173e-02 1.04017928e-01 -6.90323174e-01 -6.30326033e-01 -5.27874768e-01 1.79167420e-01 2.50142395e-01 2.99058318e-01 4.67864089e-02 2.86551267e-01 -1.51702845e+00 4.12710905e-01 2.90514499e-01 1.29691029e+00 2.71495461e-01 8.43953252e-01 4.79665369e-01 2.11382285e-01 -9.04917777e-01 1.01170756e-01 3.13582569e-01 -3.55356753e-01 -1.61379933e-01 -2.18034282e-01 2.76805282e-01 -5.53234994e-01 -3.35779727e-01 5.31120956e-01 1.29200280e+00 -3.06135297e-01 5.27749956e-01 -1.04401481e+00 5.95901348e-02 4.42825228e-01 -4.38072592e-01 2.38575652e-01 5.90826571e-01 3.16413403e-01 3.76558483e-01 -1.06280565e+00 6.25980020e-01 6.15574539e-01 1.16458654e+00 9.89637494e-01 -1.37218869e+00 -9.04869497e-01 -2.65933692e-01 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-1.73610628e-01 9.91573557e-02]	[6.808749198913574, 0.1495652049779892]
307beb6e-b789-40c1-a866-1e3392fa10da	self-constrained-inference-optimization-on	2207.02425	null	https://arxiv.org/abs/2207.02425v1	https://arxiv.org/pdf/2207.02425v1.pdf	Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation	We observe that human poses exhibit strong group-wise structural correlation and spatial coupling between keypoints due to the biological constraints of different body parts. This group-wise structural correlation can be explored to improve the accuracy and robustness of human pose estimation. In this work, we develop a self-constrained prediction-verification network to characterize and learn the structural correlation between keypoints during training. During the inference stage, the feedback information from the verification network allows us to perform further optimization of pose prediction, which significantly improves the performance of human pose estimation. Specifically, we partition the keypoints into groups according to the biological structure of human body. Within each group, the keypoints are further partitioned into two subsets, high-confidence base keypoints and low-confidence terminal keypoints. We develop a self-constrained prediction-verification network to perform forward and backward predictions between these keypoint subsets. One fundamental challenge in pose estimation, as well as in generic prediction tasks, is that there is no mechanism for us to verify if the obtained pose estimation or prediction results are accurate or not, since the ground truth is not available. Once successfully learned, the verification network serves as an accuracy verification module for the forward pose prediction. During the inference stage, it can be used to guide the local optimization of the pose estimation results of low-confidence keypoints with the self-constrained loss on high-confidence keypoints as the objective function. Our extensive experimental results on benchmark MS COCO and CrowdPose datasets demonstrate that the proposed method can significantly improve the pose estimation results.	['Zhihai He', 'Zeng Li', 'Shuoshuo Chen', 'Zhehan Kan']	2022-07-06	null	null	null	null	['inference-optimization', 'multi-person-pose-estimation']	['audio', 'computer-vision']	[-2.42127895e-01 1.35565653e-01 -3.30793470e-01 -3.38129610e-01 -5.77846110e-01 -3.89306456e-01 1.75293639e-01 2.13544935e-01 -3.57977837e-01 6.71723962e-01 1.14349894e-01 3.65902752e-01 -2.27149665e-01 -6.17353857e-01 -9.45346534e-01 -6.00253880e-01 -3.52519631e-01 7.12356865e-01 4.21405733e-01 -2.54541814e-01 3.40454723e-03 4.41528231e-01 -1.43393695e+00 -1.20118715e-01 6.93536103e-01 1.18736851e+00 6.76945820e-02 4.69349325e-01 6.54366076e-01 2.58166075e-01 -4.47829753e-01 -2.39554122e-01 2.94836253e-01 -1.71540409e-01 -5.30683875e-01 -3.48197877e-01 4.10175383e-01 -1.35206133e-01 -5.51367104e-02 8.90130222e-01 7.08473921e-01 3.21964860e-01 3.57831776e-01 -1.29853249e+00 1.94252133e-01 2.79657632e-01 -4.02725101e-01 -1.38982147e-01 7.06963599e-01 1.65459260e-01 9.98950005e-01 -1.03360951e+00 6.64480865e-01 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ff6ad74a-eae9-4007-861a-56bc2ed37bda	guided-slot-attention-for-unsupervised-video	2303.08314	null	https://arxiv.org/abs/2303.08314v1	https://arxiv.org/pdf/2303.08314v1.pdf	Guided Slot Attention for Unsupervised Video Object Segmentation	Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However, the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue, we propose a guided slot attention network to reinforce spatial structural information and obtain better foreground--background separation. The foreground and background slots, which are initialized with query guidance, are iteratively refined based on interactions with template information. Furthermore, to improve slot--template interaction and effectively fuse global and local features in the target and reference frames, K-nearest neighbors filtering and a feature aggregation transformer are introduced. The proposed model achieves state-of-the-art performance on two popular datasets. Additionally, we demonstrate the robustness of the proposed model in challenging scenes through various comparative experiments.	['Sangyoun Lee', 'Jungho Lee', 'Chaewon Park', 'Dogyoon Lee', 'Suhwan Cho', 'Minhyeok Lee']	2023-03-15	null	null	null	null	['video-object-segmentation', 'video-semantic-segmentation', 'unsupervised-video-object-segmentation']	['computer-vision', 'computer-vision', 'computer-vision']	[ 0.3250427 -0.3860547 -0.22344491 -0.44157916 -0.66072166 -0.31183377 0.22643471 -0.11953242 -0.45530334 0.53283226 0.1297529 0.02806459 -0.08414364 -0.5188712 -0.51505 -0.8900051 0.18748954 0.01905362 1.0383058 0.1544415 0.30088368 0.46071634 -1.4619257 0.4471816 0.9595632 1.0703572 0.6234672 0.41571346 -0.38461497 0.92432415 -0.66461825 -0.06896529 0.26687703 -0.3704165 -0.66933835 0.74369407 0.6100017 -0.50341994 -0.46751088 1.2854192 0.24512073 0.4964162 0.2464346 -1.0898501 -0.3868533 0.44461006 -0.850736 0.97322243 0.01361611 0.12953825 0.8353882 -0.99853635 0.5148477 1.1785346 0.04600498 0.27119055 -1.0130477 -0.49191448 0.856283 0.5341719 -1.4517386 -0.34442192 0.94439125 -0.27785978 0.5293903 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7b9562ab-a8c0-4dc6-8165-28d8ac29b8f5	weakly-supervised-action-localization-with-2	2004.00163	null	https://arxiv.org/abs/2004.00163v2	https://arxiv.org/pdf/2004.00163v2.pdf	Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning	Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label. It can be solved under the Multiple Instance Learning (MIL) framework, where a bag (video) contains multiple instances (action segments). Since only the bag's label is known, the main challenge is assigning which key instances within the bag to trigger the bag's label. Most previous models use attention-based approaches applying attentions to generate the bag's representation from instances, and then train it via the bag's classification. These models, however, implicitly violate the MIL assumption that instances in negative bags should be uniformly negative. In this work, we explicitly model the key instances assignment as a hidden variable and adopt an Expectation-Maximization (EM) framework. We derive two pseudo-label generation schemes to model the E and M process and iteratively optimize the likelihood lower bound. We show that our EM-MIL approach more accurately models both the learning objective and the MIL assumptions. It achieves state-of-the-art performance on two standard benchmarks, THUMOS14 and ActivityNet1.2.	['Huijuan Xu', 'Fang Wan', 'Zhekun Luo', 'Baifeng Shi', 'Devin Guillory', 'Wei Ke', 'Trevor Darrell']	2020-03-31	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6965_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740715.pdf	eccv-2020-8	['weakly-supervised-action-localization']	['computer-vision']	[ 5.83578467e-01 3.03741872e-01 -8.17897499e-01 -5.57008684e-01 -1.23141336e+00 -3.60974193e-01 4.99867171e-01 -1.14321388e-01 -4.33280766e-01 8.10601890e-01 2.24349812e-01 4.43622768e-02 2.78292060e-01 -5.00834882e-01 -1.11428082e+00 -9.28041160e-01 -4.82742637e-02 4.78269637e-01 1.71747997e-01 5.06790221e-01 1.19831435e-01 -5.11143319e-02 -1.38919508e+00 7.16034174e-01 4.52421427e-01 1.10968363e+00 1.86728835e-01 7.37677813e-01 -1.06291123e-01 1.63850200e+00 -6.70166731e-01 -2.27642789e-01 2.14985207e-01 -6.88609600e-01 -1.09350610e+00 6.35356009e-01 5.02174437e-01 -5.34867942e-01 -3.53394091e-01 8.98259699e-01 1.43431842e-01 2.92884290e-01 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351973fc-39d6-4aab-960b-c28a61ecd821	interpretable-summaries-of-black-box-incident	2108.03013	null	https://arxiv.org/abs/2108.03013v1	https://arxiv.org/pdf/2108.03013v1.pdf	Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery	The need of predictive maintenance comes with an increasing number of incidents reported by monitoring systems and equipment/software users. In the front line, on-call engineers (OCEs) have to quickly assess the degree of severity of an incident and decide which service to contact for corrective actions. To automate these decisions, several predictive models have been proposed, but the most efficient models are opaque (say, black box), strongly limiting their adoption. In this paper, we propose an efficient black box model based on 170K incidents reported to our company over the last 7 years and emphasize on the need of automating triage when incidents are massively reported on thousands of servers running our product, an ERP. Recent developments in eXplainable Artificial Intelligence (XAI) help in providing global explanations to the model, but also, and most importantly, with local explanations for each model prediction/outcome. Sadly, providing a human with an explanation for each outcome is not conceivable when dealing with an important number of daily predictions. To address this problem, we propose an original data-mining method rooted in Subgroup Discovery, a pattern mining technique with the natural ability to group objects that share similar explanations of their black box predictions and provide a description for each group. We evaluate this approach and present our preliminary results which give us good hope towards an effective OCE's adoption. We believe that this approach provides a new way to address the problem of model agnostic outcome explanation.	['Mehdi Kaytoue', 'Céline Robardet', 'Marc Plantevit', 'Anes Bendimerad', 'Youcef Remil']	2021-08-06	null	null	null	null	['subgroup-discovery']	['methodology']	[ 2.13953018e-01 4.56138939e-01 -6.63439482e-02 -6.37025595e-01 -5.91417924e-02 -1.62552238e-01 1.01608515e-01 5.90353966e-01 1.73269287e-01 7.41141438e-01 9.63319372e-03 -6.02687299e-01 -1.03200936e+00 -7.92133927e-01 -2.05996230e-01 -3.73440236e-01 -2.09617272e-01 1.02423847e+00 3.41140516e-02 -3.45783770e-01 6.06088638e-01 7.61728227e-01 -1.76832163e+00 8.80800128e-01 8.42875123e-01 1.10814071e+00 1.75829068e-01 4.96751249e-01 -1.01329975e-01 1.07424092e+00 -7.01026261e-01 -2.70589828e-01 1.66139916e-01 -2.74165154e-01 -1.01853704e+00 3.48436236e-01 -2.60269642e-01 -8.55144113e-02 3.70068550e-01 4.24509317e-01 -7.14446511e-03 -6.81681931e-02 5.49957812e-01 -1.69505286e+00 -1.70395538e-01 5.14639854e-01 -2.24453077e-01 2.81044990e-01 5.28189719e-01 -6.25759661e-02 9.14902389e-01 -6.17696345e-01 5.06621480e-01 8.27956021e-01 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11da22ec-a946-41f0-8b63-927adba81448	wdr-face-the-first-database-for-studying-face	2101.03826	null	https://arxiv.org/abs/2101.03826v1	https://arxiv.org/pdf/2101.03826v1.pdf	WDR FACE: The First Database for Studying Face Detection in Wide Dynamic Range	Currently, face detection approaches focus on facial information by varying specific parameters including pose, occlusion, lighting, background, race, and gender. These studies only utilized the information obtained from low dynamic range images, however, face detection in wide dynamic range (WDR) scenes has received little attention. To our knowledge, there is no publicly available WDR database for face detection research. To facilitate and support future face detection research in the WDR field, we propose the first WDR database for face detection, called WDR FACE, which contains a total of 398 16-bit megapixel grayscale wide dynamic range images collected from 29 subjects. These WDR images (WDRIs) were taken in eight specific WDR scenes. The dynamic range of 90% images surpasses 60,000:1, and that of 70% images exceeds 65,000:1. Furthermore, we show the effect of different face detection procedures on the WDRIs in our database. This is done with 25 different tone mapping operators and five different face detectors. We provide preliminary experimental results of face detection on this unique WDR database.	['Orly Yadid-Pecht', 'Svetlana Yanushkevich', 'Kenneth Kam Fai Lai', 'Mengchen Lin', 'Jie Yang', 'Ziyi Liu']	2021-01-11	null	null	null	null	['tone-mapping']	['computer-vision']	[ 1.62443310e-01 -7.60589063e-01 -3.33018675e-02 -4.90577400e-01 -3.82166713e-01 -3.55062127e-01 2.22633064e-01 -8.37279856e-01 -4.48711336e-01 5.09480178e-01 -1.02842197e-01 -9.63164866e-02 1.47666216e-01 -6.72424495e-01 -1.59680814e-01 -6.16512656e-01 -1.60005361e-01 -8.07102025e-02 1.99140698e-01 -4.23459142e-01 1.24011308e-01 1.17438614e+00 -1.87217140e+00 3.05181354e-01 1.47727966e-01 8.10895801e-01 3.34479809e-02 7.69480944e-01 4.91257489e-01 3.28321546e-01 -7.75093734e-01 -3.55948746e-01 6.44295633e-01 -3.89671385e-01 -1.86678499e-01 2.22835869e-01 8.52333188e-01 -8.38638484e-01 -6.75207794e-01 1.17078936e+00 9.57590461e-01 9.73759405e-03 4.05443847e-01 -1.11127889e+00 -9.87708688e-01 3.06163132e-01 -1.28017867e+00 8.03464532e-01 6.45995557e-01 2.61809349e-01 2.86162853e-01 -1.26400840e+00 6.14096284e-01 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1.84644714e-01 8.88538539e-01 -5.57094038e-01 -3.41741264e-01 -9.68956888e-01 -1.16653240e+00 2.31143758e-01 2.94086576e-01 -9.72486734e-01 -3.10416132e-01 2.05088496e-01]	[13.23963737487793, 0.7978472709655762]
e063e00d-04d4-4d9d-808c-fb875caba203	face-recognition-using-synthetic-face-data	2305.10079	null	https://arxiv.org/abs/2305.10079v1	https://arxiv.org/pdf/2305.10079v1.pdf	Face Recognition Using Synthetic Face Data	In the field of deep learning applied to face recognition, securing large-scale, high-quality datasets is vital for attaining precise and reliable results. However, amassing significant volumes of high-quality real data faces hurdles such as time limitations, financial burdens, and privacy issues. Furthermore, prevalent datasets are often impaired by racial biases and annotation inaccuracies. In this paper, we underscore the promising application of synthetic data, generated through rendering digital faces via our computer graphics pipeline, in achieving competitive results with the state-of-the-art on synthetic data across multiple benchmark datasets. By finetuning the model,we obtain results that rival those achieved when training with hundreds of thousands of real images (98.7% on LFW [1]). We further investigate the contribution of adding intra-class variance factors (e.g., makeup, accessories, haircuts) on model performance. Finally, we reveal the sensitivity of pre-trained face recognition models to alternating specific parts of the face by leveraging the granular control capability in our platform.	['Orly Zvitia', 'Max Kogan', 'Vladimir Loginov', 'Alexey Gruzdev', 'Omer Granoviter']	2023-05-17	null	null	null	null	['face-recognition']	['computer-vision']	[ 2.67443001e-01 -8.77811201e-03 2.35869467e-01 -8.46733689e-01 -7.87515581e-01 -4.85571474e-01 7.06345975e-01 -3.99768084e-01 -4.09898579e-01 7.22833693e-01 3.83455269e-02 -1.45536378e-01 3.08087338e-02 -7.79898167e-01 -9.61677432e-01 -3.82230192e-01 1.18938342e-01 2.46853799e-01 -5.34683526e-01 1.66523992e-03 1.56443521e-01 8.84480655e-01 -1.84603941e+00 3.87164563e-01 6.88891709e-01 1.16871488e+00 -6.08126640e-01 8.27474371e-02 2.64358614e-02 3.68770152e-01 -9.59145606e-01 -8.41468215e-01 8.16906631e-01 -4.14222740e-02 -1.77467346e-01 2.62924582e-01 1.44201088e+00 -8.37926507e-01 -2.00850099e-01 8.03076863e-01 6.63374484e-01 -2.54299909e-01 6.27994895e-01 -1.49105287e+00 -7.00351119e-01 1.01301074e-01 -7.79423058e-01 -1.15430400e-01 8.80135223e-02 5.13405502e-01 5.40800393e-01 -1.12621748e+00 6.03246450e-01 1.42790616e+00 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1dbaedc7-6c52-4e69-a19c-511190a6a3e0	word-embeddings-for-banking-industry	2306.01807	null	https://arxiv.org/abs/2306.01807v1	https://arxiv.org/pdf/2306.01807v1.pdf	Word Embeddings for Banking Industry	Applications of Natural Language Processing (NLP) are plentiful, from sentiment analysis to text classification. Practitioners rely on static word embeddings (e.g. Word2Vec or GloVe) or static word representation from contextual models (e.g. BERT or ELMo) to perform many of these NLP tasks. These widely available word embeddings are built from large amount of text, so they are likely to have captured most of the vocabulary in different context. However, how well would they capture domain-specific semantics and word relatedness? This paper explores this idea by creating a bank-specific word embeddings and evaluates them against other sources of word embeddings such as GloVe and BERT. Not surprising that embeddings built from bank-specific corpora does a better job of capturing the bank-specific semantics and word relatedness. This finding suggests that bank-specific word embeddings could be a good stand-alone source or a complement to other widely available embeddings when performing NLP tasks specific to the banking industry.	['Avnish Patel']	2023-06-02	null	null	null	null	['word-embeddings', 'sentiment-analysis']	['methodology', 'natural-language-processing']	[-5.82421601e-01 -7.22870976e-02 -3.93419832e-01 -5.67952216e-01 -4.30916339e-01 -8.09412420e-01 8.26373816e-01 7.98631072e-01 -8.37555408e-01 4.82295394e-01 9.47592199e-01 -5.80382884e-01 -3.24400561e-03 -9.71416712e-01 -3.04158814e-02 -5.25051236e-01 1.82368606e-01 5.31123519e-01 6.87041581e-02 -8.36975574e-01 5.50907135e-01 4.83247817e-01 -1.13994157e+00 3.47763970e-02 3.57033640e-01 5.93493283e-01 1.48186952e-01 5.69858313e-01 -8.76757503e-01 3.10103416e-01 -5.31035006e-01 -4.99042422e-01 1.70794681e-01 8.35810006e-02 -7.89938927e-01 -3.36092353e-01 -3.67012247e-02 5.25797904e-02 -1.34346291e-01 7.62199223e-01 6.18366897e-01 3.02816093e-01 5.79405427e-01 -9.13281024e-01 -1.28784120e+00 5.61705351e-01 -3.07456374e-01 5.40705681e-01 4.09870356e-01 1.27093717e-01 1.57621694e+00 -1.06046605e+00 5.36708415e-01 1.25581074e+00 7.01004505e-01 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72a5962e-9eb4-44e0-8c75-13238b89130e	asdot-any-shot-data-to-text-generation-with	2210.04325	null	https://arxiv.org/abs/2210.04325v3	https://arxiv.org/pdf/2210.04325v3.pdf	ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models	Data-to-text generation is challenging due to the great variety of the input data in terms of domains (e.g., finance vs sports) or schemata (e.g., diverse predicates). Recent end-to-end neural methods thus require substantial training examples to learn to disambiguate and describe the data. Yet, real-world data-to-text problems often suffer from various data-scarce issues: one may have access to only a handful of or no training examples, and/or have to rely on examples in a different domain or schema. To fill this gap, we propose Any-Shot Data-to-Text (ASDOT), a new approach flexibly applicable to diverse settings by making efficient use of any given (or no) examples. ASDOT consists of two steps, data disambiguation and sentence fusion, both of which are amenable to be solved with off-the-shelf pretrained language models (LMs) with optional finetuning. In the data disambiguation stage, we employ the prompted GPT-3 model to understand possibly ambiguous triples from the input data and convert each into a short sentence with reduced ambiguity. The sentence fusion stage then uses an LM like T5 to fuse all the resulting sentences into a coherent paragraph as the final description. We evaluate extensively on various datasets in different scenarios, including the zero-/few-/full-shot settings, and generalization to unseen predicates and out-of-domain data. Experimental results show that ASDOT consistently achieves significant improvement over baselines, e.g., a 30.81 BLEU gain on the DART dataset under the zero-shot setting.	['Zhiting Hu', 'Eric P. Xing', 'Yucheng Zhou', 'Zhengzhong Liu', 'Jiannan Xiang']	2022-10-09	null	null	null	null	['data-to-text-generation']	['natural-language-processing']	[ 5.31163216e-01 1.95165589e-01 -1.81257412e-01 -5.63623905e-01 -1.33051550e+00 -7.76945591e-01 6.96105242e-01 4.92009163e-01 -4.92520630e-01 1.04044044e+00 2.60787189e-01 -3.51417512e-01 -1.26459211e-01 -7.24859715e-01 -7.43835568e-01 -2.97405243e-01 4.44079280e-01 1.02879536e+00 8.40392411e-02 -7.05197871e-01 -4.95601296e-02 -1.26185313e-01 -1.58146954e+00 4.95771438e-01 1.10176075e+00 9.50402558e-01 4.66315717e-01 5.32726347e-01 -6.97524965e-01 3.31880033e-01 -8.72881949e-01 -6.09322846e-01 3.25068891e-01 -3.44589829e-01 -8.66546154e-01 1.02916226e-01 4.85673964e-01 7.77257513e-03 6.99053258e-02 8.85182798e-01 8.25699627e-01 2.94676036e-01 3.27868134e-01 -1.01812851e+00 -6.22288465e-01 1.03062665e+00 -4.14785743e-01 1.58615947e-01 5.99413097e-01 1.02187142e-01 1.15934050e+00 -1.16230881e+00 7.45149612e-01 1.47289526e+00 3.25138599e-01 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36a4b8d6-763a-42e8-af36-6ced43d0fe43	efficient-vertical-federated-learning-method	null	null	https://ieeexplore.ieee.org/abstract/document/9930870	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9930870	Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares Solution	Integrating data from multiple parties to achieve cross-institutional machine learning is an important trend in Industry 4.0 era. However, the privacy risks from sharing data pose a significant challenge to data integration. To integrate data without sharing data and meet large-scale samples' modeling needs, we propose two vertical federation learning algorithms for ridge regression via least-squares solution for two-party and multi-party scenarios, respectively. Compared with the state-of-the-art algorithms, our algorithms only need one round of calculation for the optimization instead of iteration. Furthermore, our algorithms can effectively handle large-scale samples due to the number of cryptographic operations in our algorithms being independent of the number of samples. Through our proposed the matrix secure agent computing theory and $\delta$ -data indistinguishability theory, we provide quantitative theoretical guarantees for the security of our algorithms. Our algorithms satisfy complete data indistinguishability under the “semi-honest” assumption and the quantitative security under the “malicious” assumption. The experiments show that our proposed algorithm takes only about 400 seconds to handle up to 9.6 million large-scale samples, while the state-of-the-art algorithms take close to 1000 seconds to handle every 1000 samples, which embodies the advantage of our algorithms in handling large-scale samples.δ -data indistinguishability theory, we provide quantitative theoretical guarantees for the security of our algorithms. Our algorithms satisfy complete data indistinguishability under the “semi-honest” assumption and the quantitative security under the “malicious” assumption. The experiments show that our proposed algorithm takes only about 400 seconds to handle up to 9.6 million large-scale samples, while the state-of-the-art algorithms take close to 1000 seconds to handle every 1000 samples, which embodies the advantage of our algorithms in handling large-scale samples.	['Jiayin Li', 'Kun Guo', 'Zhiyong Yu', 'Ximeng Liu', 'Jianping Cai']	2022-10-26	null	null	null	ieee-transactions-on-emerging-topics-in	['data-integration']	['knowledge-base']	[-3.15218657e-01 -1.99056625e-01 -4.08944279e-01 -2.96772450e-01 -1.12120461e+00 -9.20162678e-01 1.90237120e-01 9.96019468e-02 -5.90087056e-01 5.25849998e-01 -4.54597712e-01 -5.18234313e-01 -2.17082694e-01 -7.48569012e-01 -9.46960926e-01 -7.99512565e-01 -2.96904713e-01 4.73300308e-01 -1.99575260e-01 2.00137449e-03 -8.28254372e-02 3.18365425e-01 -1.04016376e+00 1.91757515e-01 5.19589961e-01 1.38407254e+00 -6.75219834e-01 2.79394746e-01 4.87179831e-02 5.36282837e-01 -2.76899070e-01 -7.08673596e-01 1.19594491e+00 -4.62855212e-02 -7.00409591e-01 -1.64857700e-01 2.40388975e-01 -4.17102665e-01 -2.52583206e-01 1.40995026e+00 1.96292117e-01 -5.42909384e-01 1.43091232e-01 -2.01115227e+00 -6.95899963e-01 8.43526363e-01 -8.49817395e-01 -5.93337476e-01 1.43249994e-02 2.67283529e-01 1.05077183e+00 -3.78563046e-01 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63645777-50cc-4c10-bd54-d8e3e4e3aad0	sft-kd-recon-learning-a-student-friendly	2304.05057	null	https://arxiv.org/abs/2304.05057v1	https://arxiv.org/pdf/2304.05057v1.pdf	SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image Reconstruction	Deep cascaded architectures for magnetic resonance imaging (MRI) acceleration have shown remarkable success in providing high-quality reconstruction. However, as the number of cascades increases, the improvements in reconstruction tend to become marginal, indicating possible excess model capacity. Knowledge distillation (KD) is an emerging technique to compress these models, in which a trained deep teacher network is used to distill knowledge to a smaller student network such that the student learns to mimic the behavior of the teacher. Most KD methods focus on effectively training the student with a pre-trained teacher unaware of the student model. We propose SFT-KD-Recon, a student-friendly teacher training approach along with the student as a prior step to KD to make the teacher aware of the structure and capacity of the student and enable aligning the representations of the teacher with the student. In SFT, the teacher is jointly trained with the unfolded branch configurations of the student blocks using three loss terms - teacher-reconstruction loss, student-reconstruction loss, and teacher-student imitation loss, followed by KD of the student. We perform extensive experiments for MRI acceleration in 4x and 5x under-sampling on the brain and cardiac datasets on five KD methods using the proposed approach as a prior step. We consider the DC-CNN architecture and setup teacher as D5C5 (141765 parameters), and student as D3C5 (49285 parameters), denoting a compression of 2.87:1. Results show that (i) our approach consistently improves the KD methods with improved reconstruction performance and image quality, and (ii) the student distilled using our approach is competitive with the teacher, with the performance gap reduced from 0.53 dB to 0.03 dB.	['Mohanasankar Sivaprakasam', 'Keerthi Ram', 'Rahul G S', 'Mohammad Al Fahim', 'Sriprabha Ramanarayanan', 'Matcha Naga Gayathri']	2023-04-11	null	null	null	null	['image-reconstruction']	['computer-vision']	[ 1.07162483e-01 3.47505718e-01 -1.01399757e-01 -3.10354978e-01 -7.07187831e-01 -1.81412175e-01 2.92582035e-01 -2.79282425e-02 -5.91584802e-01 4.89639759e-01 2.84530252e-01 -4.72093284e-01 -1.21354144e-02 -4.74410236e-01 -1.04387987e+00 -9.32706535e-01 -1.98744133e-01 4.69218314e-01 3.69081855e-01 5.87935634e-02 -1.99515119e-01 5.84990740e-01 -8.16215813e-01 3.26835960e-01 8.10615242e-01 1.00045860e+00 4.27926809e-01 7.09225893e-01 3.35212052e-01 1.16608322e+00 -4.53878075e-01 -3.89329493e-01 2.43254483e-01 -3.89980853e-01 -1.02703583e+00 6.54019862e-02 2.40516439e-01 -7.15766072e-01 -8.21821988e-01 9.70286667e-01 8.23089421e-01 -3.28095979e-04 4.98479307e-01 -7.92418540e-01 -2.51360744e-01 8.55661333e-01 -9.38899100e-01 5.88795960e-01 -4.88073647e-01 2.51219541e-01 4.48916376e-01 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485ea4f8-7e77-45e9-a864-a7f73491d3a8	an-adaptive-simulated-annealing-based-machine	2212.11892	null	https://arxiv.org/abs/2212.11892v1	https://arxiv.org/pdf/2212.11892v1.pdf	An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations	Patient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. Different tools are used for patient triage and one of the most common ones is the emergency severity index (ESI), which has a scale of five levels, where level 1 is the most urgent and level 5 is the least urgent. This paper proposes a framework for utilizing machine learning to develop an e-triage tool that can be used at EDs. A large retrospective dataset of ED patient visits is obtained from the electronic health record of a healthcare provider in the Midwest of the US for three years. However, the main challenge of using machine learning algorithms is that most of them have many parameters and without optimizing these parameters, developing a high-performance model is not possible. This paper proposes an approach to optimize the hyperparameters of machine learning. The metaheuristic optimization algorithms simulated annealing (SA) and adaptive simulated annealing (ASA) are proposed to optimize the parameters of extreme gradient boosting (XGB) and categorical boosting (CaB). The newly proposed algorithms are SA-XGB, ASA-XGB, SA-CaB, ASA-CaB. Grid search (GS), which is a traditional approach used for machine learning fine-tunning is also used to fine-tune the parameters of XGB and CaB, which are named GS-XGB and GS-CaB. The six algorithms are trained and tested using eight data groups obtained from the feature selection phase. The results show ASA-CaB outperformed all the proposed algorithms with accuracy, precision, recall, and f1 of 83.3%, 83.2%, 83.3%, 83.2%, respectively.	['Dursun Delen', 'Mohammad Firouz', 'Mohammed Al-Maamari', 'Abdulaziz Ahmed']	2022-12-22	null	null	null	null	['metaheuristic-optimization']	['methodology']	[ 7.01962709e-02 -4.53464359e-01 3.78102474e-02 -3.49361926e-01 -5.75186610e-01 -2.58995861e-01 -6.65525498e-04 7.92376220e-01 -5.09025693e-01 9.25586879e-01 2.83725232e-01 -5.80871165e-01 -9.40973461e-01 -5.60523689e-01 -8.53972360e-02 -8.41690481e-01 -1.19597219e-01 7.88686514e-01 -1.37449829e-02 -2.93236166e-01 5.57511091e-01 4.59004611e-01 -1.60080075e+00 5.90699017e-01 1.16651428e+00 1.10167730e+00 6.43051788e-02 7.86646068e-01 -5.50707541e-02 2.45500207e-01 -4.04889226e-01 -3.53219509e-02 5.02463102e-01 -6.12100124e-01 -6.92829967e-01 -3.28645796e-01 -3.39301407e-01 1.76515773e-01 5.36109626e-01 3.50552112e-01 8.64037812e-01 2.80103862e-01 6.98865354e-01 -1.26656830e+00 2.58151978e-01 1.66255087e-01 -5.01183271e-01 6.04054332e-01 3.82648528e-01 4.35411707e-02 5.00881851e-01 -3.14458281e-01 1.21678591e-01 1.20451701e+00 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-3.85665357e-01 -5.83497703e-01 -9.09504116e-01 -2.18995109e-01 4.15345162e-01 2.31914282e-01 -9.74404752e-01 -4.86792982e-01 -2.01470897e-01]	[8.453534126281738, 4.792398452758789]
964edbc9-66c4-4a11-9227-ff7c763152a3	comparing-rule-based-and-deep-learning-models	1703.08705	null	http://arxiv.org/abs/1703.08705v1	http://arxiv.org/pdf/1703.08705v1.pdf	Comparing Rule-Based and Deep Learning Models for Patient Phenotyping	Objective: We investigate whether deep learning techniques for natural language processing (NLP) can be used efficiently for patient phenotyping. Patient phenotyping is a classification task for determining whether a patient has a medical condition, and is a crucial part of secondary analysis of healthcare data. We assess the performance of deep learning algorithms and compare them with classical NLP approaches. Materials and Methods: We compare convolutional neural networks (CNNs), n-gram models, and approaches based on cTAKES that extract pre-defined medical concepts from clinical notes and use them to predict patient phenotypes. The performance is tested on 10 different phenotyping tasks using 1,610 discharge summaries extracted from the MIMIC-III database. Results: CNNs outperform other phenotyping algorithms in all 10 tasks. The average F1-score of our model is 76 (PPV of 83, and sensitivity of 71) with our model having an F1-score up to 37 points higher than alternative approaches. We additionally assess the interpretability of our model by presenting a method that extracts the most salient phrases for a particular prediction. Conclusion: We show that NLP methods based on deep learning improve the performance of patient phenotyping. Our CNN-based algorithm automatically learns the phrases associated with each patient phenotype. As such, it reduces the annotation complexity for clinical domain experts, who are normally required to develop task-specific annotation rules and identify relevant phrases. Our method performs well in terms of both performance and interpretability, which indicates that deep learning is an effective approach to patient phenotyping based on clinicians' notes.	['Leo Anthony Celi', 'John Foote Jr.', 'Franck Dernoncourt', 'David W. Grant', 'Yeran Li', 'Edward T. Moseley', 'Joy T. Wu', 'Jonathan Welt', 'Eric T. Carlson', 'Sebastian Gehrmann', 'Patrick D. Tyler']	2017-03-25	null	null	null	null	['patient-phenotyping']	['medical']	[ 3.10097575e-01 4.36746091e-01 -2.94580072e-01 -4.40905720e-01 -9.37629759e-01 -4.48914737e-01 -1.01125814e-01 1.00174999e+00 -5.30228496e-01 8.28060448e-01 5.22227705e-01 -5.85292220e-01 -4.42227453e-01 -6.86058402e-01 -4.39470708e-01 -5.75334311e-01 -1.24564976e-01 1.15998423e+00 -5.75329602e-01 2.18273237e-01 -1.92068890e-01 5.02288938e-01 -1.01923597e+00 7.91448534e-01 8.24200451e-01 1.00793493e+00 -1.36393920e-01 7.87308395e-01 -8.61669779e-02 8.88683856e-01 -7.18233824e-01 -4.99583483e-01 -6.56086951e-02 -2.55188763e-01 -9.53045130e-01 -3.00700009e-01 7.23417699e-02 -1.87127814e-01 2.86143124e-01 8.82988393e-01 8.66411626e-01 -4.77912962e-01 5.98633051e-01 -7.49278426e-01 -3.58363211e-01 7.32044220e-01 5.61072491e-02 2.63425887e-01 5.42825282e-01 4.58654612e-01 1.24891293e+00 -4.93028194e-01 5.04136384e-01 8.58543634e-01 1.23596334e+00 5.05348325e-01 -1.32124984e+00 -5.72044313e-01 -2.68476486e-01 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4.32395220e-01 -1.86938047e-01 -2.33541895e-02 2.68530667e-01]	[8.062433242797852, 7.078860759735107]
8c998420-1431-4fd4-a8ec-b4ccf6b38a48	image-shape-manipulation-from-a-single	2109.06151	null	https://arxiv.org/abs/2109.06151v3	https://arxiv.org/pdf/2109.06151v3.pdf	Image Shape Manipulation from a Single Augmented Training Sample	In this paper, we present DeepSIM, a generative model for conditional image manipulation based on a single image. We find that extensive augmentation is key for enabling single image training, and incorporate the use of thin-plate-spline (TPS) as an effective augmentation. Our network learns to map between a primitive representation of the image to the image itself. The choice of a primitive representation has an impact on the ease and expressiveness of the manipulations and can be automatic (e.g. edges), manual (e.g. segmentation) or hybrid such as edges on top of segmentations. At manipulation time, our generator allows for making complex image changes by modifying the primitive input representation and mapping it through the network. Our method is shown to achieve remarkable performance on image manipulation tasks.	['Yedid Hoshen', 'Nir Zabari', 'Eliahu Horwitz', 'Yael Vinker']	2021-09-13	null	http://openaccess.thecvf.com//content/ICCV2021/html/Vinker_Image_Shape_Manipulation_From_a_Single_Augmented_Training_Sample_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Vinker_Image_Shape_Manipulation_From_a_Single_Augmented_Training_Sample_ICCV_2021_paper.pdf	iccv-2021-1	['sketch-to-image-translation']	['computer-vision']	[ 6.95301712e-01 4.39012825e-01 -1.89183224e-02 -2.07753018e-01 -3.82277429e-01 -8.75714362e-01 9.91031945e-01 -3.85965407e-01 -3.88977349e-01 4.54809934e-01 -5.94461784e-02 -4.93888229e-01 2.86178827e-01 -9.38352644e-01 -1.16982365e+00 -5.47447264e-01 -1.87182648e-03 4.54360098e-01 2.51276344e-01 -3.58290911e-01 2.43889257e-01 7.74153531e-01 -1.36383915e+00 1.28098875e-01 5.93354702e-01 7.42696524e-01 4.06420261e-01 9.50238287e-01 -1.23696774e-01 2.24152192e-01 -7.79075027e-01 -3.31765860e-01 6.75358534e-01 -2.88641542e-01 -7.11908102e-01 5.44676900e-01 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85fa9172-c98b-4057-a7b7-d88f3ce1c341	change-detection-needs-change-information	2304.12639	null	https://arxiv.org/abs/2304.12639v1	https://arxiv.org/pdf/2304.12639v1.pdf	Change detection needs change information: improving deep 3D point cloud change detection	Change detection is an important task to rapidly identify modified areas, in particular when multi-temporal data are concerned. In landscapes with complex geometry such as urban environment, vertical information turn out to be a very useful knowledge not only to highlight changes but also to classify them into different categories. In this paper, we focus on change segmentation directly using raw 3D point clouds (PCs), to avoid any loss of information due to rasterization processes. While deep learning has recently proved its effectiveness for this particular task by encoding the information through Siamese networks, we investigate here the idea of also using change information in early steps of deep networks. To do this, we first propose to provide the Siamese KPConv State-of-The-Art (SoTA) network with hand-crafted features and especially a change-related one. This improves the mean of Intersection over Union (IoU) over classes of change by 4.70\%. Considering that the major improvement was obtained thanks to the change-related feature, we propose three new architectures to address 3D PCs change segmentation: OneConvFusion, Triplet KPConv, and Encoder Fusion SiamKPConv. All the three networks take into account change information in early steps and outperform SoTA methods. In particular, the last network, entitled Encoder Fusion SiamKPConv, overtakes SoTA with more than 5% of mean of IoU over classes of change emphasizing the value of having the network focus on change information for change detection task.	['Sébastien Lefèvre', 'Thomas Corpetti', 'Iris de Gélis']	2023-04-25	null	null	null	null	['change-detection']	['computer-vision']	[ 1.90322503e-01 -1.77930892e-01 1.83628544e-01 -3.01919878e-01 -2.99398333e-01 -6.02905631e-01 1.07674611e+00 4.70190406e-01 -8.66595924e-01 6.78650081e-01 -5.98034337e-02 -2.43595153e-01 -3.53761226e-01 -1.17431915e+00 -9.00501907e-01 -6.43929183e-01 -3.70645911e-01 4.08016235e-01 6.22124970e-01 -6.03416264e-01 5.87329753e-02 1.16431606e+00 -1.79853487e+00 8.12996104e-02 1.04985464e+00 1.02566707e+00 1.43160775e-01 4.91950631e-01 -2.89059311e-01 1.71900287e-01 -3.31082582e-01 -1.74269751e-01 4.24167067e-01 7.51197431e-03 -6.74377263e-01 -2.62835026e-01 6.87418520e-01 -1.49153024e-01 -1.92239713e-02 1.03326666e+00 4.67443913e-01 1.02931634e-01 6.42456830e-01 -9.34819818e-01 7.06892237e-02 5.26353538e-01 -6.69994473e-01 6.33593261e-01 -3.97418030e-02 2.28063598e-01 9.42337930e-01 -6.94357395e-01 9.19095278e-01 1.03142083e+00 8.62996221e-01 -2.55288005e-01 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bafd5637-8333-42b9-b1dc-9b80de3d68da	inverse-path-tracing-for-joint-material-and	1903.07145	null	http://arxiv.org/abs/1903.07145v1	http://arxiv.org/pdf/1903.07145v1.pdf	Inverse Path Tracing for Joint Material and Lighting Estimation	Modern computer vision algorithms have brought significant advancement to 3D geometry reconstruction. However, illumination and material reconstruction remain less studied, with current approaches assuming very simplified models for materials and illumination. We introduce Inverse Path Tracing, a novel approach to jointly estimate the material properties of objects and light sources in indoor scenes by using an invertible light transport simulation. We assume a coarse geometry scan, along with corresponding images and camera poses. The key contribution of this work is an accurate and simultaneous retrieval of light sources and physically based material properties (e.g., diffuse reflectance, specular reflectance, roughness, etc.) for the purpose of editing and re-rendering the scene under new conditions. To this end, we introduce a novel optimization method using a differentiable Monte Carlo renderer that computes derivatives with respect to the estimated unknown illumination and material properties. This enables joint optimization for physically correct light transport and material models using a tailored stochastic gradient descent.	['Matthias Nießner', 'Tzu-Mao Li', 'Dejan Azinović', 'Anton Kaplanyan']	2019-03-17	null	null	null	null	['lighting-estimation']	['computer-vision']	[ 6.27840757e-01 -6.54314280e-01 8.01988542e-01 -3.89941752e-01 -4.45283502e-01 -4.66484487e-01 6.56253040e-01 1.05846375e-02 -1.88175544e-01 8.11500072e-01 -2.04455405e-01 1.94080211e-02 -2.14209035e-01 -8.84941876e-01 -6.39916003e-01 -8.21315825e-01 4.27099109e-01 6.78011894e-01 -7.37121701e-02 1.12557001e-02 3.43924791e-01 1.04418182e+00 -1.78167558e+00 -1.55924857e-01 8.89795542e-01 6.18032098e-01 2.60587662e-01 9.58205283e-01 -2.33255565e-01 4.42200363e-01 5.08146873e-03 -2.09710836e-01 2.87058383e-01 -2.56885946e-01 -4.43320036e-01 4.26046103e-01 5.35077512e-01 -5.71801782e-01 -5.97140454e-02 9.34623659e-01 3.42366427e-01 4.55490023e-01 8.70978236e-01 -7.01795995e-01 -3.82243067e-01 -3.13974977e-01 -6.75555348e-01 -4.38836843e-01 7.38472581e-01 2.00435266e-01 5.34786582e-01 -9.67486799e-01 5.28032124e-01 1.05100131e+00 6.65307581e-01 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-1.98524460e-01 -8.33716571e-01 3.33729625e-01 4.75172877e-01 -7.91073263e-01 -5.54332137e-01 1.93066567e-01]	[9.736343383789062, -3.0645880699157715]
f85fcf0e-bcf3-4d46-85ad-28f35e1250da	panoramic-image-reflection-removal	null	null	http://openaccess.thecvf.com//content/CVPR2021/html/Hong_Panoramic_Image_Reflection_Removal_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Hong_Panoramic_Image_Reflection_Removal_CVPR_2021_paper.pdf	Panoramic Image Reflection Removal	This paper studies the problem of panoramic image reflection removal, aiming at reliving the content ambiguity between reflection and transmission scenes. Although a partial view of the reflection scene is included in the panoramic image, it cannot be utilized directly due to its misalignment with the reflection-contaminated image. We propose a two-step approach to solve this problem, by first accomplishing geometric and photometric alignment for the reflection scene via a coarse-to-fine strategy, and then restoring the transmission scene via a recovery network. The proposed method is trained with a synthetic dataset and verified quantitatively with a real panoramic image dataset. The effectiveness of the proposed method is validated by the significant performance advantage over single image-based reflection removal methods and generalization capacity to limited-FoV scenarios captured by conventional camera or mobile phone users.	['Boxin Shi', 'Alex C. Kot', 'Xudong Jiang', 'Lingran Zhao', 'Qian Zheng', 'Yuchen Hong']	2021-06-19	null	null	null	cvpr-2021-1	['reflection-removal']	['computer-vision']	[ 1.11995411e+00 -2.04042464e-01 4.27955300e-01 -1.08646020e-01 -6.38516128e-01 -2.76443332e-01 6.76742256e-01 -9.03585374e-01 -1.90139338e-01 5.08654058e-01 3.22745919e-01 -2.45344311e-01 -3.24073911e-01 -8.69416595e-01 -6.50527954e-01 -9.08952773e-01 6.57085001e-01 -1.05782673e-01 9.58145782e-03 -2.66489685e-01 2.77075022e-01 4.26898897e-01 -1.44825745e+00 -1.55854169e-02 8.28121305e-01 6.81353927e-01 5.88968337e-01 5.45356512e-01 3.19696218e-01 5.93039155e-01 -4.15619910e-01 -1.85182970e-02 7.81657457e-01 -5.88538706e-01 -3.52058709e-01 5.89235842e-01 8.10420394e-01 -7.94225514e-01 -5.18779337e-01 1.19244838e+00 2.38647640e-01 3.77856903e-02 3.17322195e-01 -7.57418275e-01 -3.43876094e-01 -4.69070449e-02 -8.21909130e-01 -1.86428219e-01 5.38593113e-01 -2.30047673e-01 4.58723873e-01 -9.43930805e-01 4.11732435e-01 8.67859721e-01 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26282afa-61bd-4aed-90a5-fb4bdb2871b0	stereoscene-bev-assisted-stereo-matching	2303.13959	null	https://arxiv.org/abs/2303.13959v2	https://arxiv.org/pdf/2303.13959v2.pdf	StereoScene: BEV-Assisted Stereo Matching Empowers 3D Semantic Scene Completion	3D semantic scene completion (SSC) is an ill-posed task that requires inferring a dense 3D scene from incomplete observations. Previous methods either explicitly incorporate 3D geometric input or rely on learnt 3D prior behind monocular RGB images. However, 3D sensors such as LiDAR are expensive and intrusive while monocular cameras face challenges in modeling precise geometry due to the inherent ambiguity. In this work, we propose StereoScene for 3D Semantic Scene Completion (SSC), which explores taking full advantage of light-weight camera inputs without resorting to any external 3D sensors. Our key insight is to leverage stereo matching to resolve geometric ambiguity. To improve its robustness in unmatched areas, we introduce bird's-eye-view (BEV) representation to inspire hallucination ability with rich context information. On top of the stereo and BEV representations, a mutual interactive aggregation (MIA) module is carefully devised to fully unleash their power. Specifically, a Bi-directional Interaction Transformer (BIT) augmented with confidence re-weighting is used to encourage reliable prediction through mutual guidance while a Dual Volume Aggregation (DVA) module is designed to facilitate complementary aggregation. Experimental results on SemanticKITTI demonstrate that the proposed StereoScene outperforms the state-of-the-art camera-based methods by a large margin with a relative improvement of 26.9% in geometry and 38.6% in semantic.	['Dalong Du', 'Hang Xiao', 'James Okae', 'Yunpeng Zhang', 'Xiaoefeng Wang', 'Zheng Zhu', 'Wenjun Zeng', 'Xin Jin', 'Yasheng Sun', 'Bohan Li']	2023-03-24	null	null	null	null	['3d-semantic-scene-completion', 'stereo-matching-1']	['computer-vision', 'computer-vision']	[ 2.55650729e-01 1.44281015e-01 1.57156155e-01 -4.91346538e-01 -6.05938256e-01 -4.23573852e-01 5.38603127e-01 -3.12373608e-01 -1.27255693e-01 3.30500931e-01 3.44553769e-01 -1.80280685e-01 7.82052428e-02 -6.89704895e-01 -7.67463863e-01 -5.92861414e-01 4.20712203e-01 2.19885543e-01 3.18428010e-01 -2.26118460e-01 2.85548925e-01 4.03068423e-01 -1.62592137e+00 1.23884998e-01 1.11287510e+00 1.09973621e+00 7.91491568e-01 2.23070398e-01 -1.61562070e-01 5.41299284e-01 -6.84464797e-02 -1.04409702e-01 6.30845010e-01 4.81516682e-02 -2.98774362e-01 4.54584628e-01 5.23365617e-01 -6.13326788e-01 -4.28544819e-01 1.04001307e+00 3.08463395e-01 1.44539043e-01 4.03924763e-01 -1.15113151e+00 -3.07820946e-01 -2.50349343e-01 -8.48201334e-01 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3c73c41a-591b-4e4d-ba73-73ba0ee70cf4	astra-a-novel-algorithm-level-approach-to	2209.01685	null	https://arxiv.org/abs/2209.01685v1	https://arxiv.org/pdf/2209.01685v1.pdf	ASTra: A Novel Algorithm-Level Approach to Imbalanced Classification	We propose a novel output layer activation function, which we name ASTra (Asymmetric Sigmoid Transfer function), which makes the classification of minority examples, in scenarios of high imbalance, more tractable. We combine this with a loss function that helps to effectively target minority misclassification. These two methods can be used together or separately, with their combination recommended for the most severely imbalanced cases. The proposed approach is tested on datasets with IRs from 588.24 to 4000 and very few minority examples (in some datasets, as few as five). Results using neural networks with from two to 12 hidden units are demonstrated to be comparable to, or better than, equivalent results obtained in a recent study that deployed a wide range of complex, hybrid data-level ensemble classifiers.	['Denise Gorse', 'David Twomey']	2022-09-04	null	null	null	null	['imbalanced-classification']	['miscellaneous']	[ 3.76573503e-01 2.80594677e-01 -2.51552820e-01 -4.97540861e-01 -3.94388944e-01 -3.51673067e-01 5.19442856e-01 5.18824220e-01 -7.24764526e-01 1.17219841e+00 -2.57701278e-01 -5.93287766e-01 -3.45723897e-01 -8.55395555e-01 -4.64302808e-01 -7.55677640e-01 -6.70036748e-02 4.13757920e-01 2.07942780e-02 -2.39861324e-01 3.04319084e-01 5.96233428e-01 -1.95427561e+00 6.23478949e-01 1.12775791e+00 1.08578193e+00 -5.73587775e-01 4.98651296e-01 -6.82732183e-03 6.96696818e-01 -1.15446770e+00 -3.71431142e-01 2.58297354e-01 -1.00680396e-01 -3.64488512e-01 -2.72444129e-01 5.68744302e-01 -6.51808307e-02 2.96056420e-01 3.93549383e-01 7.79192746e-01 8.19735005e-02 7.81823516e-01 -1.24671853e+00 -2.45608002e-01 5.27850091e-01 -3.98053199e-01 4.35725212e-01 3.30096595e-02 -4.50632237e-02 5.06960511e-01 -7.91002572e-01 2.64773935e-01 9.94154274e-01 1.23894811e+00 2.97341883e-01 -1.09233809e+00 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e038945d-23e2-41a7-8315-103cb613fa60	a-bayesian-treatment-of-real-to-sim-for	2112.05068	null	https://arxiv.org/abs/2112.05068v1	https://arxiv.org/pdf/2112.05068v1.pdf	A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation	Deformable object manipulation remains a challenging task in robotics research. Conventional techniques for parameter inference and state estimation typically rely on a precise definition of the state space and its dynamics. While this is appropriate for rigid objects and robot states, it is challenging to define the state space of a deformable object and how it evolves in time. In this work, we pose the problem of inferring physical parameters of deformable objects as a probabilistic inference task defined with a simulator. We propose a novel methodology for extracting state information from image sequences via a technique to represent the state of a deformable object as a distribution embedding. This allows to incorporate noisy state observations directly into modern Bayesian simulation-based inference tools in a principled manner. Our experiments confirm that we can estimate posterior distributions of physical properties, such as elasticity, friction and scale of highly deformable objects, such as cloth and ropes. Overall, our method addresses the real-to-sim problem probabilistically and helps to better represent the evolution of the state of deformable objects.	['Jeannette Bohg', 'Fabio Ramos', 'Dieter Fox', 'Priya Sundaresan', 'Jingyun Yang', 'Rika Antonova']	2021-12-09	null	null	null	null	['deformable-object-manipulation']	['robots']	[ 1.24113886e-02 -1.17881931e-01 -9.80062559e-02 -6.22876473e-02 -2.29403615e-01 -7.53927410e-01 6.47255003e-01 -2.11838841e-01 -3.15847486e-01 7.75799572e-01 -1.83827907e-01 6.23492673e-02 -3.53195250e-01 -6.66437685e-01 -9.09297585e-01 -1.01030421e+00 -1.41687049e-02 1.06753981e+00 6.47619605e-01 3.21126916e-03 1.80204943e-01 1.00192869e+00 -1.32454813e+00 -5.56449354e-01 2.99989969e-01 5.82163751e-01 3.69720519e-01 1.07587707e+00 3.31592590e-01 2.23955512e-01 -3.20938766e-01 -5.21374196e-02 1.02496952e-01 2.14338303e-02 -6.35694206e-01 9.57753435e-02 1.56955048e-01 -6.54826701e-01 -5.56706011e-01 9.27306533e-01 9.40938890e-02 3.66284698e-01 1.12201500e+00 -1.19582665e+00 -3.25343817e-01 3.59676391e-01 -1.14133596e-01 -1.69114888e-01 3.18560332e-01 2.59182751e-01 6.53524101e-01 -2.77922302e-01 7.37719834e-01 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fbcd5ea8-4540-4fd6-8de2-b000beb6a9ff	knowledge-driven-answer-generation-for	2104.06892	null	https://arxiv.org/abs/2104.06892v1	https://arxiv.org/pdf/2104.06892v1.pdf	Knowledge-driven Answer Generation for Conversational Search	The conversational search paradigm introduces a step change over the traditional search paradigm by allowing users to interact with search agents in a multi-turn and natural fashion. The conversation flows naturally and is usually centered around a target field of knowledge. In this work, we propose a knowledge-driven answer generation approach for open-domain conversational search, where a conversation-wide entities' knowledge graph is used to bias search-answer generation. First, a conversation-specific knowledge graph is extracted from the top passages retrieved with a Transformer-based re-ranker. The entities knowledge-graph is then used to bias a search-answer generator Transformer towards information rich and concise answers. This conversation specific bias is computed by identifying the most relevant passages according to the most salient entities of that particular conversation. Experiments show that the proposed approach successfully exploits entities knowledge along the conversation, and outperforms a set of baselines on the search-answer generation task.	['João Magalhães', 'David Semedo', 'Rafael Ferreira', 'Mariana Leite']	2021-04-14	null	null	null	null	['conversational-search']	['natural-language-processing']	[ 2.59398311e-01 6.05747759e-01 -4.84520316e-01 -1.50809869e-01 -1.17097712e+00 -7.45409131e-01 1.14935458e+00 2.12417096e-01 -3.81369978e-01 8.95622194e-01 9.23567235e-01 -1.72620475e-01 -2.32374325e-01 -8.45449030e-01 -2.38437667e-01 -1.59072146e-01 2.09104344e-01 1.13896215e+00 5.04060328e-01 -8.30682456e-01 6.73762202e-01 -1.06951915e-01 -1.41272295e+00 6.36084735e-01 1.17694759e+00 7.62299418e-01 3.49723518e-01 7.82666862e-01 -7.75541723e-01 9.73197103e-01 -7.22274363e-01 -7.03683138e-01 -2.80088753e-01 -7.92170227e-01 -1.73489952e+00 -2.39188492e-01 8.01076517e-02 5.39809950e-02 -1.87599406e-01 6.68711782e-01 5.81030607e-01 6.81561947e-01 6.59097970e-01 -1.03854561e+00 -4.41693872e-01 9.45027649e-01 1.46399468e-01 5.31129062e-01 1.13105977e+00 -1.30119011e-01 1.64628947e+00 -1.07922065e+00 1.02981758e+00 1.47319925e+00 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20fae2f8-bd94-4e72-a1fc-8a172d1e79f0	cross-attention-is-not-enough-incongruity	2305.13583	null	https://arxiv.org/abs/2305.13583v2	https://arxiv.org/pdf/2305.13583v2.pdf	Cross-Attention is Not Enough: Incongruity-Aware Hierarchical Multimodal Sentiment Analysis and Emotion Recognition	Fusing multiple modalities for affective computing tasks has proven effective for performance improvement. However, how multimodal fusion works is not well understood, and its use in the real world usually results in large model sizes. In this work, on sentiment and emotion analysis, we first analyze how the salient affective information in one modality can be affected by the other in crossmodal attention. We find that inter-modal incongruity exists at the latent level due to crossmodal attention. Based on this finding, we propose a lightweight model via Hierarchical Crossmodal Transformer with Modality Gating (HCT-MG), which determines a primary modality according to its contribution to the target task and then hierarchically incorporates auxiliary modalities to alleviate inter-modal incongruity and reduce information redundancy. The experimental evaluation on three benchmark datasets: CMU-MOSI, CMU-MOSEI, and IEMOCAP verifies the efficacy of our approach, showing that it: 1) achieves better performance than prior work as well as manual selection of the primary modality; 2) can recognize hard samples whose emotions are hard to tell; 3) mitigates the inter-modal incongruity at the latent level when modalities have mismatched affective tendencies; 4) reduces model size to less than 1M parameters while outperforming existing models of similar sizes.	['Catherine Lai', 'Peter Bell', 'Yuanchao Li', 'Yaoting Wang']	2023-05-23	null	null	null	null	['multimodal-sentiment-analysis', 'sentiment-analysis', 'multimodal-sentiment-analysis']	['computer-vision', 'natural-language-processing', 'natural-language-processing']	[ 2.25527465e-01 -9.24005955e-02 -6.10041954e-02 -3.72160822e-01 -6.95297062e-01 -3.96200776e-01 5.22680521e-01 2.57044703e-01 -3.24523926e-01 5.37455142e-01 5.05497754e-01 1.48770258e-01 6.39774799e-02 -3.86102796e-01 -3.87904376e-01 -7.70271122e-01 3.67923737e-01 2.48404264e-01 -1.19798847e-01 -4.89130735e-01 1.35640547e-01 -1.87803328e-01 -1.73447049e+00 7.38265216e-01 1.04978716e+00 1.34855378e+00 -1.39768990e-02 1.49262041e-01 -1.58973694e-01 8.55683506e-01 -3.41816694e-01 -6.96809173e-01 -1.67715669e-01 -4.85252649e-01 -8.76094699e-01 -1.12816446e-01 4.83734906e-02 8.40215161e-02 9.73445699e-02 8.95959854e-01 6.72347486e-01 1.11908622e-01 5.82569897e-01 -1.41859925e+00 -4.65979725e-01 6.73147619e-01 -6.99966013e-01 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bdd726f9-e8db-4bbf-be91-963d3daa6837	an-order-invariant-and-interpretable	2302.06243	null	https://arxiv.org/abs/2302.06243v1	https://arxiv.org/pdf/2302.06243v1.pdf	An Order-Invariant and Interpretable Hierarchical Dilated Convolution Neural Network for Chemical Fault Detection and Diagnosis	Fault detection and diagnosis is significant for reducing maintenance costs and improving health and safety in chemical processes. Convolution neural network (CNN) is a popular deep learning algorithm with many successful applications in chemical fault detection and diagnosis tasks. However, convolution layers in CNN are very sensitive to the order of features, which can lead to instability in the processing of tabular data. Optimal order of features result in better performance of CNN models but it is expensive to seek such optimal order. In addition, because of the encapsulation mechanism of feature extraction, most CNN models are opaque and have poor interpretability, thus failing to identify root-cause features without human supervision. These difficulties inevitably limit the performance and credibility of CNN methods. In this paper, we propose an order-invariant and interpretable hierarchical dilated convolution neural network (HDLCNN), which is composed by feature clustering, dilated convolution and the shapley additive explanations (SHAP) method. The novelty of HDLCNN lies in its capability of processing tabular data with features of arbitrary order without seeking the optimal order, due to the ability to agglomerate correlated features of feature clustering and the large receptive field of dilated convolution. Then, the proposed method provides interpretability by including the SHAP values to quantify feature contribution. Therefore, the root-cause features can be identified as the features with the highest contribution. Computational experiments are conducted on the Tennessee Eastman chemical process benchmark dataset. Compared with the other methods, the proposed HDLCNN-SHAP method achieves better performance on processing tabular data with features of arbitrary order, detecting faults, and identifying the root-cause features.	['Hongwei Wang', 'Min Wang', 'Peng Peng', 'Mengxuan Li']	2023-02-13	null	null	null	null	['fault-detection']	['miscellaneous']	[ 2.19089314e-01 -1.30437806e-01 3.17489058e-01 -1.57341763e-01 2.69593596e-01 -3.53466958e-01 1.47068188e-01 2.76501924e-01 2.88713863e-03 5.65034926e-01 -8.48395228e-02 -3.57136935e-01 -7.96636105e-01 -8.65736604e-01 -5.19276917e-01 -9.14242566e-01 -8.22912231e-02 2.65841544e-01 -1.37436688e-01 -3.74936238e-02 4.17710960e-01 7.36083210e-01 -1.57962513e+00 7.10054159e-01 1.08252740e+00 1.30450988e+00 2.35622928e-01 1.76860407e-01 -1.44240022e-01 8.21934283e-01 -6.42549753e-01 -9.25019942e-03 1.45267472e-01 -4.62942392e-01 -9.41334844e-01 1.35135576e-01 -2.53656894e-01 -1.20106354e-01 -7.40271509e-02 1.15941954e+00 3.42723340e-01 -7.23367848e-04 7.52350211e-01 -1.35395324e+00 -8.05925846e-01 5.26693225e-01 -2.40269601e-01 1.63755998e-01 1.55068608e-02 7.99453035e-02 7.99760699e-01 -9.69464421e-01 9.12321582e-02 1.43316901e+00 6.81995153e-01 2.35457003e-01 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5acc523e-314f-4202-aa92-03ffceb166ba	a-multimodal-dataset-for-deception-detection	null	null	https://aclanthology.org/L14-1673	https://aclanthology.org/L14-1673.pdf	A Multimodal Dataset for Deception Detection	This paper presents the construction of a multimodal dataset for deception detection, including physiological, thermal, and visual responses of human subjects under three deceptive scenarios. We present the experimental protocol, as well as the data acquisition process. To evaluate the usefulness of the dataset for the task of deception detection, we present a statistical analysis of the physiological and thermal modalities associated with the deceptive and truthful conditions. Initial results show that physiological and thermal responses can differentiate between deceptive and truthful states.	["Ver{\\'o}nica P{\\'e}rez-Rosas", 'Mihai Burzo', 'Alexis Narvaez', 'Rada Mihalcea']	2014-05-01	null	null	null	lrec-2014-5	['deception-detection']	['miscellaneous']	[-5.19216731e-02 -4.57418144e-01 3.17204982e-01 -7.92507946e-01 -4.21847731e-01 -8.37349892e-01 7.77193189e-01 -3.25635560e-02 -3.69089335e-01 7.80353487e-01 2.20658854e-01 2.93544456e-02 2.03143939e-01 2.66638130e-01 -5.92270680e-03 -7.27503419e-01 -4.02194215e-03 -3.46981019e-01 -6.34679794e-01 1.65193319e-01 8.81028295e-01 6.97803676e-01 -1.39779305e+00 6.41808510e-01 7.13305175e-01 1.60794699e+00 -7.94602633e-01 1.01115358e+00 5.60516834e-01 4.51290607e-01 -1.08167231e+00 -4.08034623e-01 2.34758966e-02 -5.88232994e-01 -6.85561061e-01 3.99445817e-02 7.64254928e-01 -7.14433551e-01 -5.47432721e-01 7.14931726e-01 7.78509259e-01 1.12727530e-01 7.05705225e-01 -1.59946799e+00 -8.78914118e-01 -4.54678386e-01 7.17837736e-02 4.11286771e-01 1.12166464e+00 5.60574889e-01 2.18140289e-01 -9.44671273e-01 -5.46486769e-03 1.20067978e+00 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b6c1a0ce-2e27-4284-a50a-6e5a6b6c92c3	depth-infused-binaural-audio-generation-using	2108.04906	null	https://arxiv.org/abs/2108.04906v1	https://arxiv.org/pdf/2108.04906v1.pdf	Depth Infused Binaural Audio Generation using Hierarchical Cross-Modal Attention	Binaural audio gives the listener the feeling of being in the recording place and enhances the immersive experience if coupled with AR/VR. But the problem with binaural audio recording is that it requires a specialized setup which is not possible to fabricate within handheld devices as compared to traditional mono audio that can be recorded with a single microphone. In order to overcome this drawback, prior works have tried to uplift the mono recorded audio to binaural audio as a post processing step conditioning on the visual input. But all the prior approaches missed other most important information required for the task, i.e. distance of different sound producing objects from the recording setup. In this work, we argue that the depth map of the scene can act as a proxy for encoding distance information of objects in the scene and show that adding depth features along with image features improves the performance both qualitatively and quantitatively. We propose a novel encoder-decoder architecture, where we use a hierarchical attention mechanism to encode the image and depth feature extracted from individual transformer backbone, with audio features at each layer of the decoder.	['Gaurav Sharma', 'Neeraj Matiyali', 'Siddharth Srivastava', 'Kranti Kumar Parida']	2021-08-10	null	null	null	null	['audio-generation']	['audio']	[ 4.06435370e-01 -1.75535023e-01 6.31931126e-01 -2.59217620e-01 -7.79231608e-01 -5.65243125e-01 3.71121615e-01 1.81351498e-01 -3.69741142e-01 2.55934834e-01 4.60035443e-01 1.27238765e-01 -3.64821292e-02 -6.95758402e-01 -9.42329347e-01 -6.43422425e-01 1.72359362e-01 7.51783475e-02 4.76425856e-01 -7.24228323e-02 3.69694352e-01 4.00023460e-01 -2.26187372e+00 5.81177711e-01 2.73292959e-01 1.23122442e+00 7.33478487e-01 1.11262870e+00 1.43015921e-01 7.10242271e-01 -8.13223302e-01 2.11859606e-02 2.05778137e-01 -3.73204947e-01 -3.84441227e-01 -1.49284035e-01 7.13353634e-01 -3.71693373e-01 -3.57046336e-01 9.25612271e-01 8.84338915e-01 1.62671372e-01 4.49173987e-01 -9.09678042e-01 -3.75057369e-01 4.79262829e-01 -3.74695867e-01 2.64152795e-01 8.29376221e-01 -1.25035942e-01 9.45746243e-01 -8.94580483e-01 4.91961509e-01 8.89403701e-01 4.29546505e-01 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5e180310-8930-41fb-9dc4-482169e685bd	duta-vc-a-duration-aware-typical-to-atypical	2306.10588	null	https://arxiv.org/abs/2306.10588v1	https://arxiv.org/pdf/2306.10588v1.pdf	DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model	We present a novel typical-to-atypical voice conversion approach (DuTa-VC), which (i) can be trained with nonparallel data (ii) first introduces diffusion probabilistic model (iii) preserves the target speaker identity (iv) is aware of the phoneme duration of the target speaker. DuTa-VC consists of three parts: an encoder transforms the source mel-spectrogram into a duration-modified speaker-independent mel-spectrogram, a decoder performs the reverse diffusion to generate the target mel-spectrogram, and a vocoder is applied to reconstruct the waveform. Objective evaluations conducted on the UASpeech show that DuTa-VC is able to capture severity characteristics of dysarthric speech, reserves speaker identity, and significantly improves dysarthric speech recognition as a data augmentation. Subjective evaluations by two expert speech pathologists validate that DuTa-VC can preserve the severity and type of dysarthria of the target speakers in the synthesized speech.	['Laureano Moro-Velazquez', 'Najim Dehak', 'Becky Lammers', 'Myra Sydnor', 'Jesus Villalba', 'Thomas Thebaud', 'Helin Wang']	2023-06-18	null	null	null	null	['voice-conversion', 'voice-conversion']	['audio', 'speech']	[ 1.95593163e-01 2.09726825e-01 1.70588002e-01 -7.63857961e-02 -1.17806196e+00 -5.07015169e-01 1.35166287e-01 -4.48093206e-01 -1.19647786e-01 5.62688887e-01 8.91159296e-01 -4.23365295e-01 2.30024233e-02 -3.07226866e-01 -3.55366588e-01 -6.91340268e-01 1.14916921e-01 4.49749619e-01 2.51473606e-01 -1.63419873e-01 -7.76416510e-02 4.97419298e-01 -1.47967756e+00 4.24836367e-01 8.98347318e-01 6.25249267e-01 6.23005748e-01 1.09841621e+00 1.09504566e-01 7.26947427e-01 -9.56492186e-01 -1.36106133e-01 9.26740617e-02 -8.82096350e-01 -7.06049502e-01 2.49717832e-01 2.25823373e-01 -4.63716537e-01 -7.37902582e-01 1.10782230e+00 9.43429530e-01 -7.47160539e-02 5.66810846e-01 -8.37182462e-01 -9.04141009e-01 8.82907152e-01 6.09296523e-02 4.25268501e-01 3.03861976e-01 1.75172687e-01 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c695caac-eb3e-4e04-b7ff-c0f021dd53b3	on-robustness-of-prompt-based-semantic	2301.12868	null	https://arxiv.org/abs/2301.12868v3	https://arxiv.org/pdf/2301.12868v3.pdf	On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex	Semantic parsing is a technique aimed at constructing a structured representation of the meaning of a natural-language question. Recent advancements in few-shot language models trained on code have demonstrated superior performance in generating these representations compared to traditional unimodal language models, which are trained on downstream tasks. Despite these advancements, existing fine-tuned neural semantic parsers are susceptible to adversarial attacks on natural-language inputs. While it has been established that the robustness of smaller semantic parsers can be enhanced through adversarial training, this approach is not feasible for large language models in real-world scenarios, as it requires both substantial computational resources and expensive human annotation on in-domain semantic parsing data. This paper presents the first empirical study on the adversarial robustness of a large prompt-based language model of code, \codex. Our results demonstrate that the state-of-the-art (SOTA) code-language models are vulnerable to carefully crafted adversarial examples. To address this challenge, we propose methods for improving robustness without the need for significant amounts of labeled data or heavy computational resources.	['Fatemeh Shiri', 'Gholamreza Haffari', 'Weiqing Wang', 'Yuan-Fang Li', 'Yujin Huang', 'Zhuang Li', 'Terry Yue Zhuo']	2023-01-30	null	null	null	null	['semantic-parsing']	['natural-language-processing']	[ 4.15724248e-01 7.88385868e-01 1.75106451e-01 -4.43185240e-01 -1.35391164e+00 -8.89422596e-01 5.26401103e-01 1.26108736e-01 -1.47013918e-01 4.10971552e-01 3.11943442e-01 -7.47316539e-01 4.88483131e-01 -8.75070572e-01 -1.11831033e+00 -1.36051044e-01 -1.34562543e-02 1.19587503e-01 3.88184428e-01 -5.18794537e-01 2.37817138e-01 6.42287806e-02 -1.06433773e+00 2.93274194e-01 8.04904640e-01 3.97940099e-01 -9.66844186e-02 9.08277273e-01 -4.40212607e-01 1.16291964e+00 -8.86768043e-01 -8.66547048e-01 2.16103807e-01 -4.01486456e-01 -9.46329534e-01 -4.56615627e-01 3.84868354e-01 -1.64840072e-01 -4.43463296e-01 1.48027778e+00 3.77297252e-01 -6.29186705e-02 3.72200012e-01 -1.14979017e+00 -1.26116443e+00 9.56920147e-01 -1.41145959e-01 1.11977987e-01 3.97714198e-01 3.47578466e-01 1.01349604e+00 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ca060c59-7417-4cae-9efa-72625f09901f	negation-scope-detection-for-twitter	null	null	https://aclanthology.org/W15-2914	https://aclanthology.org/W15-2914.pdf	Negation Scope Detection for Twitter Sentiment Analysis	null	['Bj{\\"o}rn Gamb{\\"a}ck', 'J{\\o}rgen Faret', 'Lars Bungum', 'Johan Reitan']	2015-09-01	null	null	null	ws-2015-9	['twitter-sentiment-analysis', 'negation-detection']	['natural-language-processing', '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 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43fde8f4-5bac-4842-8206-fcd601afe822	affinity-aware-graph-networks	2206.11941	null	https://arxiv.org/abs/2206.11941v1	https://arxiv.org/pdf/2206.11941v1.pdf	Affinity-Aware Graph Networks	Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data. Owing to the relatively limited number of message passing steps they perform -- and hence a smaller receptive field -- there has been significant interest in improving their expressivity by incorporating structural aspects of the underlying graph. In this paper, we explore the use of affinity measures as features in graph neural networks, in particular measures arising from random walks, including effective resistance, hitting and commute times. We propose message passing networks based on these features and evaluate their performance on a variety of node and graph property prediction tasks. Our architecture has lower computational complexity, while our features are invariant to the permutations of the underlying graph. The measures we compute allow the network to exploit the connectivity properties of the graph, thereby allowing us to outperform relevant benchmarks for a wide variety of tasks, often with significantly fewer message passing steps. On one of the largest publicly available graph regression datasets, OGB-LSC-PCQM4Mv1, we obtain the best known single-model validation MAE at the time of writing.	['Sreenivas Gollapudi', 'Petar Veličković', 'Ira Ktena', 'Ali Kemal Sinop', 'Ameya Velingker']	2022-06-23	null	null	null	null	['graph-property-prediction', 'graph-regression']	['graphs', 'graphs']	[ 1.15244448e-01 1.34279191e-01 -4.77987498e-01 -2.61984169e-01 7.98630640e-02 -4.68517601e-01 6.52688682e-01 8.17869604e-01 -5.74631155e-01 5.93710721e-01 -1.60411485e-02 -6.82753921e-01 -4.97994810e-01 -1.25613701e+00 -7.88159251e-01 -4.11020964e-01 -7.24559963e-01 5.60216486e-01 3.61732185e-01 -4.66755748e-01 2.58502215e-01 8.30706000e-01 -1.24812174e+00 5.03856093e-02 3.71800959e-01 8.91912699e-01 -3.72436009e-02 7.71757185e-01 1.01583473e-01 1.05492592e+00 -2.73994595e-01 -4.07555968e-01 2.52361167e-02 -1.44418627e-01 -9.67418253e-01 -4.03712481e-01 3.62177610e-01 8.41454118e-02 -1.00349569e+00 7.57082224e-01 2.70689905e-01 3.84290606e-01 6.82965457e-01 -1.22289503e+00 -5.93767822e-01 9.17795599e-01 -3.32824081e-01 4.64560896e-01 2.66340505e-02 -6.61411434e-02 1.67804837e+00 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e3fd38c2-08a4-4649-bddf-80d64c999155	learning-graph-embeddings-for-open-world	2105.01017	null	https://arxiv.org/abs/2105.01017v3	https://arxiv.org/pdf/2105.01017v3.pdf	Learning Graph Embeddings for Open World Compositional Zero-Shot Learning	Compositional Zero-Shot learning (CZSL) aims to recognize unseen compositions of state and object visual primitives seen during training. A problem with standard CZSL is the assumption of knowing which unseen compositions will be available at test time. In this work, we overcome this assumption operating on the open world setting, where no limit is imposed on the compositional space at test time, and the search space contains a large number of unseen compositions. To address this problem, we propose a new approach, Compositional Cosine Graph Embeddings (Co-CGE), based on two principles. First, Co-CGE models the dependency between states, objects and their compositions through a graph convolutional neural network. The graph propagates information from seen to unseen concepts, improving their representations. Second, since not all unseen compositions are equally feasible, and less feasible ones may damage the learned representations, Co-CGE estimates a feasibility score for each unseen composition, using the scores as margins in a cosine similarity-based loss and as weights in the adjacency matrix of the graphs. Experiments show that our approach achieves state-of-the-art performances in standard CZSL while outperforming previous methods in the open world scenario.	['Zeynep Akata', 'Yongqin Xian', 'Muhammad Ferjad Naeem', 'Massimiliano Mancini']	2021-05-03	null	null	null	null	['compositional-zero-shot-learning']	['computer-vision']	[ 3.22376788e-02 6.98385388e-02 -2.15730499e-02 1.92715794e-01 -1.08164161e-01 -7.08867788e-01 5.83404422e-01 3.76107514e-01 -2.30833203e-01 2.54570305e-01 1.43205151e-01 -8.50885808e-02 2.61783917e-02 -1.09952199e+00 -8.72708678e-01 -6.71278298e-01 -1.65497258e-01 7.34061420e-01 5.46187699e-01 -1.71056524e-01 8.19371864e-02 4.49412912e-01 -1.70190084e+00 4.13475066e-01 6.03971899e-01 1.00822163e+00 1.16242208e-01 5.42077601e-01 -1.33200526e-01 7.71409214e-01 -3.80582094e-01 -6.33548617e-01 4.07818586e-01 -3.39889646e-01 -6.74900889e-01 7.49298036e-02 6.72852337e-01 -1.51652545e-01 -5.22536278e-01 1.35450375e+00 2.42506996e-01 4.15813178e-01 8.09214175e-01 -1.65354836e+00 -1.10588193e+00 6.25908971e-01 -1.97615623e-01 1.59880430e-01 3.91241610e-01 3.02025318e-01 1.47542572e+00 -8.88541400e-01 9.77086544e-01 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6.17900908e-01 -6.27335012e-01 -6.27582371e-01 -5.22807717e-01 -6.61258876e-01 8.92489195e-01 3.37648809e-01 -8.51049602e-01 -4.75585431e-01 -7.98270777e-02]	[10.247544288635254, 2.2520911693573]
79b1e219-1583-4155-ba10-f5ca9c483432	bulk-production-augmentation-towards	2103.02198	null	https://arxiv.org/abs/2103.02198v1	https://arxiv.org/pdf/2103.02198v1.pdf	Bulk Production Augmentation Towards Explainable Melanoma Diagnosis	Although highly accurate automated diagnostic techniques for melanoma have been reported, the realization of a system capable of providing diagnostic evidence based on medical indices remains an open issue because of difficulties in obtaining reliable training data. In this paper, we propose bulk production augmentation (BPA) to generate high-quality, diverse pseudo-skin tumor images with the desired structural malignant features for additional training images from a limited number of labeled images. The proposed BPA acts as an effective data augmentation in constructing the feature detector for the atypical pigment network (APN), which is a key structure in melanoma diagnosis. Experiments show that training with images generated by our BPA largely boosts the APN detection performance by 20.0 percentage points in the area under the receiver operating characteristic curve, which is 11.5 to 13.7 points higher than that of conventional CycleGAN-based augmentations in AUC.	['Hitoshi Iyatomi', 'Masaru Tanaka', 'Noriko Umegaki-Arao', 'Quan Huu Cap', 'Kasumi Obi']	2021-03-03	null	null	null	null	['melanoma-diagnosis']	['computer-vision']	[ 1.00018704e+00 3.07411373e-01 -2.93345541e-01 -2.86071226e-02 -9.71988976e-01 -2.99691379e-01 5.16014695e-01 7.01257139e-02 -4.29056108e-01 8.14633250e-01 -9.60895717e-02 -4.23127145e-01 1.27919286e-01 -6.91991985e-01 -4.01836187e-01 -1.14233851e+00 2.45132491e-01 1.71986476e-01 1.11043490e-01 -5.49723953e-03 8.13539624e-02 5.44166744e-01 -1.41423285e+00 2.97116339e-01 1.32144797e+00 1.04494023e+00 2.91769914e-02 9.53163385e-01 -1.29929677e-01 4.47839856e-01 -7.40081012e-01 -4.49660957e-01 2.02874750e-01 -7.22777545e-01 -5.16634524e-01 4.55460459e-01 5.47356963e-01 -2.67852068e-01 1.13526620e-01 1.08281493e+00 3.89422834e-01 -4.96403456e-01 8.78043354e-01 -1.00396132e+00 -3.87655109e-01 3.65525633e-01 -9.12412822e-01 -1.08189091e-01 5.48993647e-02 1.85552180e-01 6.58980191e-01 -7.51699626e-01 5.79987168e-01 5.38136780e-01 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6b0ac06c-2648-4e4c-9ebd-6e909ce961e4	recovering-compressed-images-for-automatic	2003.03028	null	https://arxiv.org/abs/2003.03028v1	https://arxiv.org/pdf/2003.03028v1.pdf	Recovering compressed images for automatic crack segmentation using generative models	In a structural health monitoring (SHM) system that uses digital cameras to monitor cracks of structural surfaces, techniques for reliable and effective data compression are essential to ensure a stable and energy efficient crack images transmission in wireless devices, e.g., drones and robots with high definition cameras installed. Compressive sensing (CS) is a signal processing technique that allows accurate recovery of a signal from a sampling rate much smaller than the limitation of the Nyquist sampling theorem. The conventional CS method is based on the principle that, through a regularized optimization, the sparsity property of the original signals in some domain can be exploited to get the exact reconstruction with a high probability. However, the strong assumption of the signals being highly sparse in an invertible space is relatively hard for real crack images. In this paper, we present a new approach of CS that replaces the sparsity regularization with a generative model that is able to effectively capture a low dimension representation of targeted images. We develop a recovery framework for automatic crack segmentation of compressed crack images based on this new CS method and demonstrate the remarkable performance of the method taking advantage of the strong capability of generative models to capture the necessary features required in the crack segmentation task even the backgrounds of the generated images are not well reconstructed. The superior performance of our recovery framework is illustrated by comparing with three existing CS algorithms. Furthermore, we show that our framework is extensible to other common problems in automatic crack segmentation, such as defect recovery from motion blurring and occlusion.	['Haoyu Zhang', 'Stephen Wu', 'Hui Li', 'Yong Huang']	2020-03-06	null	null	null	null	['crack-segmentation']	['computer-vision']	[ 9.52173412e-01 -6.35302439e-02 2.60576189e-01 1.86326280e-01 -5.32293677e-01 -9.22495499e-02 5.89591824e-02 -1.55214384e-01 -1.82007939e-01 6.01958692e-01 -1.20539092e-01 1.11874126e-01 -3.77025366e-01 -9.75921750e-01 -6.28583789e-01 -9.68977511e-01 1.39857888e-01 1.12828009e-01 3.33603770e-01 -2.06168875e-01 3.10091883e-01 5.20984113e-01 -1.84879637e+00 6.28378242e-03 8.90533626e-01 9.19272482e-01 6.13651752e-01 5.71877718e-01 4.02926803e-01 6.44108713e-01 -4.17327791e-01 3.47935021e-01 1.27497325e-02 -5.49169362e-01 -5.27107358e-01 4.85154420e-01 -3.06703821e-02 -3.32306147e-01 -3.79904598e-01 1.09094405e+00 2.74908394e-01 -1.65549412e-01 6.54101610e-01 -8.81216764e-01 -1.36409312e-01 2.98049152e-01 -5.76176405e-01 -3.58795784e-02 6.51860118e-01 -1.78334862e-01 4.99396026e-01 -1.00026155e+00 6.79285467e-01 7.01311648e-01 7.33533919e-01 3.21294665e-01 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