Split (2)

train · 44.6k rows

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8dbfbba1-6155-4ec3-93c0-9db805674773	a-new-class-of-efficient-adaptive-filters-for	2104.09641	null	https://arxiv.org/abs/2104.09641v2	https://arxiv.org/pdf/2104.09641v2.pdf	A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling	Nonlinear models are known to provide excellent performance in real-world applications that often operate in non-ideal conditions. However, such applications often require online processing to be performed with limited computational resources. To address this problem, we propose a new class of efficient nonlinear models for online applications. The proposed algorithms are based on linear-in-the-parameters (LIP) nonlinear filters using functional link expansions. In order to make this class of functional link adaptive filters (FLAFs) efficient, we propose low-complexity expansions and frequency-domain adaptation of the parameters. Among this family of algorithms, we also define the partitioned-block frequency-domain FLAF, whose implementation is particularly suitable for online nonlinear modeling problems. We assess and compare frequency-domain FLAFs with different expansions providing the best possible tradeoff between performance and computational complexity. Experimental results prove that the proposed algorithms can be considered as an efficient and effective solution for online applications, such as the acoustic echo cancellation, even in the presence of adverse nonlinear conditions and with limited availability of computational resources.	['Aurelio Uncini', 'Amir Hussain', 'Michele Scarpiniti', 'Simone Scardapane', 'Alireza Nezamdoust', 'Danilo Comminiello']	2021-04-19	null	null	null	null	['acoustic-echo-cancellation', 'acoustic-echo-cancellation']	['medical', 'speech']	[ 2.05474272e-01 -3.68905634e-01 3.28902513e-01 -2.74199724e-01 -5.76902270e-01 -4.98440087e-01 1.13903262e-01 7.72671252e-02 -5.32026947e-01 5.40706336e-01 2.66722832e-02 -1.83255777e-01 -5.97108305e-01 -4.22495931e-01 -4.92835462e-01 -7.69153059e-01 -3.51336867e-01 8.39648247e-02 1.45619243e-01 -2.21431717e-01 9.04139802e-02 6.69474900e-01 -1.66699505e+00 -2.32747152e-01 1.15392017e+00 1.00564241e+00 2.64616907e-01 7.58236825e-01 8.67207795e-02 1.43351465e-01 -4.09443974e-01 -1.44894764e-01 5.15885353e-01 -4.01018649e-01 -1.49539495e-02 -1.11895606e-01 2.60364294e-01 -3.55835289e-01 -1.64086923e-01 1.03792441e+00 9.43333447e-01 6.72355831e-01 4.60380763e-01 -6.61437690e-01 -1.52254049e-02 3.75470489e-01 -1.14152990e-01 3.30812544e-01 2.95260102e-01 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bb6b05b1-1ecb-4680-8293-77b41af11e3e	structural-explanations-for-graph-neural	2302.02139	null	https://arxiv.org/abs/2302.02139v1	https://arxiv.org/pdf/2302.02139v1.pdf	Structural Explanations for Graph Neural Networks using HSIC	Graph neural networks (GNNs) are a type of neural model that tackle graphical tasks in an end-to-end manner. Recently, GNNs have been receiving increased attention in machine learning and data mining communities because of the higher performance they achieve in various tasks, including graph classification, link prediction, and recommendation. However, the complicated dynamics of GNNs make it difficult to understand which parts of the graph features contribute more strongly to the predictions. To handle the interpretability issues, recently, various GNN explanation methods have been proposed. In this study, a flexible model agnostic explanation method is proposed to detect significant structures in graphs using the Hilbert-Schmidt independence criterion (HSIC), which captures the nonlinear dependency between two variables through kernels. More specifically, we extend the GraphLIME method for node explanation with a group lasso and a fused lasso-based node explanation method. The group and fused regularization with GraphLIME enables the interpretation of GNNs in substructure units. Then, we show that the proposed approach can be used for the explanation of sequential graph classification tasks. Through experiments, it is demonstrated that our method can identify crucial structures in a target graph in various settings.	['Makoto Yamada', 'Ayato Toyokuni']	2023-02-04	null	null	null	null	['graph-classification']	['graphs']	[ 3.49821985e-01 5.68835557e-01 -4.81594384e-01 -4.43474442e-01 1.50664821e-01 -1.98793098e-01 3.10440809e-01 3.19808513e-01 3.52166921e-01 6.04133904e-01 1.27873316e-01 -5.28706789e-01 -6.53713882e-01 -6.91914976e-01 -8.01019847e-01 -6.84826791e-01 -3.04669976e-01 3.89829189e-01 -2.43485197e-01 -7.89631754e-02 -3.75476480e-02 3.79476130e-01 -1.01933491e+00 6.10463880e-03 1.19806981e+00 7.82921016e-01 -9.76381078e-02 3.89511049e-01 -6.46436289e-02 9.20344055e-01 1.20614216e-01 -5.04791558e-01 1.37865826e-01 -4.44289774e-01 -5.50455511e-01 1.41352981e-01 2.81174153e-01 1.52751058e-01 -5.64634442e-01 1.12132597e+00 1.14169158e-01 2.95964390e-01 6.91868246e-01 -1.62890375e+00 -9.02898848e-01 1.05277598e+00 -6.66751504e-01 7.49445185e-02 -5.43576442e-02 -2.77785510e-01 1.61425948e+00 -8.81348729e-01 3.75891745e-01 1.33426487e+00 6.57074869e-01 3.16767395e-01 -1.30218685e+00 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7cb85883-603d-4ed8-a810-faeb333ffac6	word-reordering-for-zero-shot-cross-lingual	null	null	https://aclanthology.org/2021.emnlp-main.338	https://aclanthology.org/2021.emnlp-main.338.pdf	Word Reordering for Zero-shot Cross-lingual Structured Prediction	Adapting word order from one language to another is a key problem in cross-lingual structured prediction. Current sentence encoders (e.g., RNN, Transformer with position embeddings) are usually word order sensitive. Even with uniform word form representations (MUSE, mBERT), word order discrepancies may hurt the adaptation of models. In this paper, we build structured prediction models with bag-of-words inputs, and introduce a new reordering module to organizing words following the source language order, which learns task-specific reordering strategies from a general-purpose order predictor model. Experiments on zero-shot cross-lingual dependency parsing, POS tagging, and morphological tagging show that our model can significantly improve target language performances, especially for languages that are distant from the source language.	['Xiaoling Wang', 'Yuanbin Wu', 'Fei Huang', 'Zhongqiang Huang', 'Tao Wang', 'Yong Jiang', 'Tao Ji']	null	null	null	null	emnlp-2021-11	['morphological-tagging']	['natural-language-processing']	[ 4.87127192e-02 1.63076781e-02 -6.84508324e-01 -7.52131879e-01 -7.67568588e-01 -6.28554046e-01 4.46728356e-02 4.01088566e-01 -6.21604443e-01 6.44377887e-01 7.70702600e-01 -7.46451497e-01 3.80326092e-01 -6.82413220e-01 -8.27613354e-01 -1.27919629e-01 1.27903745e-02 5.49347520e-01 3.66196901e-01 -4.69444454e-01 5.44208474e-02 -2.42916811e-02 -8.01111102e-01 5.70228994e-01 1.09395337e+00 3.29087406e-01 6.23765588e-01 6.16592586e-01 -6.29276574e-01 6.15687549e-01 -2.61405110e-01 -7.86617935e-01 3.20780501e-02 -4.27807152e-01 -9.25935566e-01 -3.24702173e-01 2.63017356e-01 -9.25234705e-02 -1.37125209e-01 9.68126893e-01 1.64286271e-01 -2.82081030e-02 4.77446944e-01 -4.10142601e-01 -1.12891865e+00 1.29667783e+00 -7.37834722e-02 4.39122289e-01 2.24899836e-02 -1.18966028e-01 1.68793273e+00 -9.15317893e-01 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0a9e1d5c-5ff8-4a99-9edd-833347621d04	gesture2path-imitation-learning-for-gesture	2209.09375	null	https://arxiv.org/abs/2209.09375v1	https://arxiv.org/pdf/2209.09375v1.pdf	Gesture2Path: Imitation Learning for Gesture-aware Navigation	As robots increasingly enter human-centered environments, they must not only be able to navigate safely around humans, but also adhere to complex social norms. Humans often rely on non-verbal communication through gestures and facial expressions when navigating around other people, especially in densely occupied spaces. Consequently, robots also need to be able to interpret gestures as part of solving social navigation tasks. To this end, we present Gesture2Path, a novel social navigation approach that combines image-based imitation learning with model-predictive control. Gestures are interpreted based on a neural network that operates on streams of images, while we use a state-of-the-art model predictive control algorithm to solve point-to-point navigation tasks. We deploy our method on real robots and showcase the effectiveness of our approach for the four gestures-navigation scenarios: left/right, follow me, and make a circle. Our experiments indicate that our method is able to successfully interpret complex human gestures and to use them as a signal to generate socially compliant trajectories for navigation tasks. We validated our method based on in-situ ratings of participants interacting with the robots.	['Sören Pirk', 'Alexander Toshev', 'Tingnan Zhang', 'Leila Takayama', 'Anthony Francis', 'Emre Fisher', 'Edward Lee', 'Catie Cuan']	2022-09-19	null	null	null	null	['social-navigation']	['robots']	[ 1.11574784e-01 3.48329186e-01 1.94824219e-01 -4.80994403e-01 -7.55496919e-02 -4.32743073e-01 8.95721018e-01 -4.44653332e-01 -7.54132688e-01 5.83895981e-01 2.20549345e-01 3.79534177e-02 -7.45564401e-02 -4.52366382e-01 -5.39635718e-01 -2.77728558e-01 -3.60132188e-01 8.20241451e-01 5.70638888e-02 -5.52806616e-01 3.69989097e-01 6.04933977e-01 -1.57178676e+00 9.08683836e-02 5.63541114e-01 5.54563284e-01 2.86073595e-01 9.10555840e-01 2.25695506e-01 8.32941234e-01 -2.50050634e-01 6.91191629e-02 1.87093690e-01 -4.69592959e-01 -6.43744588e-01 -7.10495189e-02 -2.62971312e-01 -5.92333436e-01 -1.69293761e-01 5.18782258e-01 3.90615404e-01 5.91299295e-01 1.10056984e+00 -1.58511233e+00 -2.94103742e-01 5.74956477e-01 3.78185362e-02 -6.65318429e-01 1.00581098e+00 5.18556237e-01 7.00873435e-01 -6.24426842e-01 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4fdcf9a3-bdaa-4d66-813d-1a34e359b252	large-displacement-3d-object-tracking-with	2207.12620	null	https://arxiv.org/abs/2207.12620v1	https://arxiv.org/pdf/2207.12620v1.pdf	Large-displacement 3D Object Tracking with Hybrid Non-local Optimization	Optimization-based 3D object tracking is known to be precise and fast, but sensitive to large inter-frame displacements. In this paper we propose a fast and effective non-local 3D tracking method. Based on the observation that erroneous local minimum are mostly due to the out-of-plane rotation, we propose a hybrid approach combining non-local and local optimizations for different parameters, resulting in efficient non-local search in the 6D pose space. In addition, a precomputed robust contour-based tracking method is proposed for the pose optimization. By using long search lines with multiple candidate correspondences, it can adapt to different frame displacements without the need of coarse-to-fine search. After the pre-computation, pose updates can be conducted very fast, enabling the non-local optimization to run in real time. Our method outperforms all previous methods for both small and large displacements. For large displacements, the accuracy is greatly improved ($81.7\% \;\text{v.s.}\; 19.4\%$). At the same time, real-time speed ($>$50fps) can be achieved with only CPU. The source code is available at \url{https://github.com/cvbubbles/nonlocal-3dtracking}.	['Xueying Qin', 'Fan Zhong', 'Xinran Lin', 'Xuhui Tian']	2022-07-26	null	null	null	null	['3d-object-tracking']	['computer-vision']	[-2.53640950e-01 -4.61738527e-01 6.92837453e-03 3.36232521e-02 -9.40038562e-01 -5.55368066e-01 1.32061347e-01 2.34715551e-01 -5.51708937e-01 6.06806576e-01 -4.49432641e-01 -1.36325091e-01 4.82585207e-02 -6.37215555e-01 -5.85724115e-01 -6.93677366e-01 3.70162725e-02 5.24317563e-01 8.21015120e-01 1.09057650e-01 2.72207469e-01 8.12722743e-01 -1.36428261e+00 -5.30562341e-01 7.17482448e-01 9.91515577e-01 2.94355720e-01 7.47360706e-01 -9.95479617e-03 -2.71433182e-02 -5.53320646e-01 -7.12590069e-02 2.78422594e-01 -3.41897368e-01 -2.23814577e-01 5.59959114e-02 4.51398700e-01 -2.92872518e-01 8.60131010e-02 1.16588438e+00 9.28747654e-01 2.32274145e-01 2.68941432e-01 -8.20380569e-01 2.16140017e-01 -1.96668774e-01 -9.49977994e-01 1.68579713e-01 4.44944739e-01 1.42106503e-01 3.54778498e-01 -9.96186674e-01 6.84679329e-01 9.17365789e-01 9.03101981e-01 3.85705262e-01 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-4.79862094e-01 -3.05187196e-01 1.63194418e-01 2.51278102e-01 -8.90646040e-01 -2.93435186e-01 -2.24784128e-02]	[6.955056667327881, -2.2484281063079834]
9386a8a7-1c6b-40ed-9d0a-74c1d2253239	blenderproc	1911.01911	null	https://arxiv.org/abs/1911.01911v1	https://arxiv.org/pdf/1911.01911v1.pdf	BlenderProc	BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks. These can be used in a variety of use cases including segmentation, depth, normal and pose estimation and many others. A key feature of our extension of blender is the simple to use modular pipeline, which was designed to be easily extendable. By offering standard modules, which cover a variety of scenarios, we provide a starting point on which new modules can be created.	['Mohamad Elbadrawy', 'Youssef Zidan', 'Maximilian Denninger', 'Martin Sundermeyer', 'Dmitry Olefir', 'Ahsan Lodhi', 'Harinandan Katam', 'Dominik Winkelbauer']	2019-10-25	null	null	null	null	['depth-image-estimation', '3d-object-recognition', 'surface-normals-estimation']	['computer-vision', 'computer-vision', 'computer-vision']	[-7.90089667e-02 1.64837301e-01 1.11449204e-01 -4.17436361e-01 -1.42901823e-01 -6.48985922e-01 6.43276513e-01 -7.51191005e-02 -1.10355206e-02 3.43400568e-01 -2.02711985e-01 -3.69116396e-01 2.75764465e-02 -9.66072440e-01 -5.15026569e-01 -3.32000971e-01 -1.82659671e-01 5.16808569e-01 9.10719693e-01 -4.84091610e-01 2.39544794e-01 1.13820589e+00 -1.85517180e+00 1.96838334e-01 4.07215714e-01 1.01901150e+00 2.38584712e-01 6.99081898e-01 -2.81379521e-01 5.50381899e-01 -4.84400600e-01 -4.35924292e-01 3.76579821e-01 -9.83195305e-02 -8.17351282e-01 -5.78504577e-02 4.75120842e-01 -3.50093424e-01 -1.27698341e-02 4.96298134e-01 4.82104272e-01 -1.72606651e-02 3.54488194e-01 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399ec814-a1a9-427e-9448-2b1051188906	speaker-recognition-with-two-step-multi-modal	2210.15903	null	https://arxiv.org/abs/2210.15903v1	https://arxiv.org/pdf/2210.15903v1.pdf	Speaker recognition with two-step multi-modal deep cleansing	Neural network-based speaker recognition has achieved significant improvement in recent years. A robust speaker representation learns meaningful knowledge from both hard and easy samples in the training set to achieve good performance. However, noisy samples (i.e., with wrong labels) in the training set induce confusion and cause the network to learn the incorrect representation. In this paper, we propose a two-step audio-visual deep cleansing framework to eliminate the effect of noisy labels in speaker representation learning. This framework contains a coarse-grained cleansing step to search for the peculiar samples, followed by a fine-grained cleansing step to filter out the noisy labels. Our study starts from an efficient audio-visual speaker recognition system, which achieves a close to perfect equal-error-rate (EER) of 0.01\%, 0.07\% and 0.13\% on the Vox-O, E and H test sets. With the proposed multi-modal cleansing mechanism, four different speaker recognition networks achieve an average improvement of 5.9\%. Code has been made available at: \textcolor{magenta}{\url{https://github.com/TaoRuijie/AVCleanse}}.	['Haizhou Li', 'Zhan Shi', 'Kong Aik Lee', 'Ruijie Tao']	2022-10-28	null	null	null	null	['speaker-recognition']	['speech']	[ 1.30992070e-01 -6.79761842e-02 3.24238271e-01 -5.45345187e-01 -1.24227941e+00 -2.98375696e-01 2.84738749e-01 -9.76353064e-02 -1.81352809e-01 6.13254011e-01 2.87067354e-01 -4.97952104e-02 8.15578476e-02 -4.56031770e-01 -6.07356310e-01 -9.70264256e-01 2.46531263e-01 4.42512855e-02 -1.51946247e-01 -4.35588621e-02 3.17194015e-02 1.69896141e-01 -1.76122415e+00 2.88417339e-01 7.48838305e-01 1.00084627e+00 8.75795111e-02 6.60989463e-01 -1.34375766e-01 6.43519580e-01 -9.38224912e-01 -1.25041142e-01 2.34643817e-02 -5.26132941e-01 -5.40840030e-01 -1.61252543e-01 4.62222338e-01 -1.10313281e-01 -3.36841911e-01 1.34059775e+00 8.22922945e-01 3.16507787e-01 4.63422179e-01 -1.23146582e+00 -6.20820045e-01 6.30061686e-01 -4.51516807e-01 2.81463474e-01 1.74335316e-01 1.64940417e-01 6.87540054e-01 -1.16247344e+00 1.35466859e-01 1.17029834e+00 4.43748206e-01 9.30638969e-01 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f3b723fb-2477-4f19-afd4-3e9c2bfe3976	encoder-decoder-based-convolutional-neural	2003.05586	null	https://arxiv.org/abs/2003.05586v5	https://arxiv.org/pdf/2003.05586v5.pdf	Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting	In this paper, we propose two modified neural networks based on dual path multi-scale fusion networks (SFANet) and SegNet for accurate and efficient crowd counting. Inspired by SFANet, the first model, which is named M-SFANet, is attached with atrous spatial pyramid pooling (ASPP) and context-aware module (CAN). The encoder of M-SFANet is enhanced with ASPP containing parallel atrous convolutional layers with different sampling rates and hence able to extract multi-scale features of the target object and incorporate larger context. To further deal with scale variation throughout an input image, we leverage the CAN module which adaptively encodes the scales of the contextual information. The combination yields an effective model for counting in both dense and sparse crowd scenes. Based on the SFANet decoder structure, M-SFANet's decoder has dual paths, for density map and attention map generation. The second model is called M-SegNet, which is produced by replacing the bilinear upsampling in SFANet with max unpooling that is used in SegNet. This change provides a faster model while providing competitive counting performance. Designed for high-speed surveillance applications, M-SegNet has no additional multi-scale-aware module in order to not increase the complexity. Both models are encoder-decoder based architectures and are end-to-end trainable. We conduct extensive experiments on five crowd counting datasets and one vehicle counting dataset to show that these modifications yield algorithms that could improve state-of-the-art crowd counting methods. Codes are available at https://github.com/Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting.	['Ken-ichi Fukui', 'Pongpisit Thanasutives', 'Boonserm Kijsirikul', 'Masayuki Numao']	2020-03-12	null	null	null	null	['object-counting']	['computer-vision']	[ 5.33438027e-02 -1.03402592e-01 2.54686594e-01 -2.79362172e-01 -6.06855452e-01 -1.88564971e-01 6.71928227e-01 -6.58131689e-02 -8.55547130e-01 6.66625738e-01 2.57920235e-01 -1.29104570e-01 5.55692613e-01 -1.15030897e+00 -8.54247689e-01 -6.02757394e-01 -9.28361043e-02 2.58120805e-01 8.39363873e-01 -2.43770123e-01 -9.62317437e-02 4.73370880e-01 -1.57038260e+00 4.55593944e-01 9.44252014e-01 9.80120063e-01 3.28129381e-01 9.67335045e-01 -2.84153283e-01 1.17812192e+00 -7.43769348e-01 -9.30033922e-01 3.31530690e-01 -1.70764312e-01 -3.43855560e-01 -2.72715956e-01 9.69244063e-01 -7.71302104e-01 -7.45477915e-01 9.88267303e-01 6.26435161e-01 -5.23374639e-02 4.88061219e-01 -1.07209468e+00 -4.01106685e-01 4.67000008e-01 -8.59082699e-01 6.56573653e-01 1.08025454e-01 3.64608586e-01 3.70696932e-01 -8.87468040e-01 1.46753237e-01 1.59462619e+00 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563947ec-f9d5-4faa-9c14-a5ece1a67cae	universal-perturbation-attack-on	2211.00366	null	https://arxiv.org/abs/2211.00366v1	https://arxiv.org/pdf/2211.00366v1.pdf	Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality Metrics	Universal adversarial perturbation attacks are widely used to analyze image classifiers that employ convolutional neural networks. Nowadays, some attacks can deceive image- and video-quality metrics. So sustainability analysis of these metrics is important. Indeed, if an attack can confuse the metric, an attacker can easily increase quality scores. When developers of image- and video-algorithms can boost their scores through detached processing, algorithm comparisons are no longer fair. Inspired by the idea of universal adversarial perturbation for classifiers, we suggest a new method to attack differentiable no-reference quality metrics through universal perturbation. We applied this method to seven no-reference image- and video-quality metrics (PaQ-2-PiQ, Linearity, VSFA, MDTVSFA, KonCept512, Nima and SPAQ). For each one, we trained a universal perturbation that increases the respective scores. We also propose a method for assessing metric stability and identify the metrics that are the most vulnerable and the most resistant to our attack. The existence of successful universal perturbations appears to diminish the metric's ability to provide reliable scores. We therefore recommend our proposed method as an additional verification of metric reliability to complement traditional subjective tests and benchmarks.	['Dmitriy Vatolin', 'Anastasia Antsiferova', 'Ekaterina Shumitskaya']	2022-11-01	null	null	null	null	['no-reference-image-quality-assessment']	['computer-vision']	[ 1.29591912e-01 -3.07222277e-01 2.24826932e-01 -1.48777202e-01 -7.28010058e-01 -8.36095393e-01 4.83423024e-01 -6.44884678e-03 -4.60000753e-01 6.29268348e-01 -3.86254668e-01 -4.88975823e-01 -1.88267633e-01 -8.43695402e-01 -7.86278367e-01 -8.29379201e-01 -3.30654591e-01 -5.01928210e-01 3.15753013e-01 -4.19632405e-01 4.24340934e-01 8.57583702e-01 -1.50436652e+00 4.10122424e-01 6.17564023e-01 1.20579600e+00 -5.53623021e-01 1.10844946e+00 3.76131684e-01 8.94567549e-01 -1.27343953e+00 -9.17530596e-01 6.14896417e-01 -2.44985327e-01 -8.50395441e-01 -4.58676308e-01 6.65927470e-01 -4.26987827e-01 -3.49887609e-01 1.46943367e+00 6.35447621e-01 -2.06618831e-01 6.05988622e-01 -1.85065281e+00 -4.94549721e-01 6.24980092e-01 -2.49513999e-01 5.20856440e-01 4.92286921e-01 6.14133775e-01 6.83341205e-01 -2.60915041e-01 4.38899577e-01 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-6.72632456e-01 -7.74894714e-01 -9.46408987e-01 -4.05235082e-01 8.02766025e-01 2.82157779e-01 -6.63372576e-01 -8.48008811e-01 6.49154931e-02]	[5.508032321929932, 7.900035858154297]
a85d73e8-a1c4-416a-b1f2-0d7da4ffbd17	real-time-physics-based-object-pose-tracking	2211.13572	null	https://arxiv.org/abs/2211.13572v2	https://arxiv.org/pdf/2211.13572v2.pdf	Real-Time Physics-Based Object Pose Tracking during Non-Prehensile Manipulation	We propose a method to track the 6D pose of an object over time, while the object is under non-prehensile manipulation by a robot. At any given time during the manipulation of the object, we assume access to the robot joint controls and an image from a camera. We use the robot joint controls to perform a physics-based prediction of how the object might be moving. We then combine this prediction with the observation coming from the camera, to estimate the object pose as accurately as possible. We use a particle filtering approach to combine the control information with the visual information. We compare the proposed method with two baselines: (i) using only an image-based pose estimation system at each time-step, and (ii) a particle filter which does not perform the computationally expensive physics predictions, but assumes the object moves with constant velocity. Our results show that making physics-based predictions is worth the computational cost, resulting in more accurate tracking, and estimating object pose even when the object is not clearly visible to the camera.	['Mehmet Dogar', 'Rafael Papallas', 'Zisong Xu']	2022-11-24	null	null	null	null	['pose-tracking']	['computer-vision']	[ 1.21570505e-01 5.60530610e-02 7.48405382e-02 1.98350519e-01 -1.17329553e-01 -5.51533341e-01 4.18292880e-01 2.26287737e-01 -7.50141621e-01 4.54705268e-01 -3.82683843e-01 5.78711592e-02 -6.16716556e-02 -6.30946219e-01 -9.99454796e-01 -5.94426394e-01 -2.08296720e-03 1.04811406e+00 7.05023944e-01 1.17728494e-01 4.82151836e-01 9.89869475e-01 -1.53556812e+00 -3.34990442e-01 3.96580130e-01 8.16474855e-01 9.20410931e-01 1.16191125e+00 3.06879997e-01 7.50068128e-01 -2.59203345e-01 6.90802187e-02 4.08362389e-01 5.25579900e-02 -5.26585102e-01 5.84630847e-01 3.23749512e-01 -4.40512419e-01 -3.96130443e-01 1.09364808e+00 1.03447944e-01 4.73047465e-01 4.07454073e-01 -9.70832467e-01 2.37027556e-01 6.79138452e-02 -6.03123426e-01 -3.65082502e-01 6.39893889e-01 3.41610163e-01 4.91439939e-01 -5.67873478e-01 1.04903924e+00 1.19386208e+00 4.77060288e-01 3.54255706e-01 -1.11518812e+00 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1.31353065e-01 4.21644956e-01 -1.10674000e+00 -3.42358977e-01 -9.80654452e-03]	[5.972313404083252, -0.8771315813064575]
00490bd7-60b7-482f-aa91-7cee16f6d420	enhancing-speech-articulation-analysis-using	2305.10775	null	https://arxiv.org/abs/2305.10775v1	https://arxiv.org/pdf/2305.10775v1.pdf	Enhancing Speech Articulation Analysis using a Geometric Transformation of the X-ray Microbeam Dataset	Accurate analysis of speech articulation is crucial for speech analysis. However, X-Y coordinates of articulators strongly depend on the anatomy of the speakers and the variability of pellet placements, and existing methods for mapping anatomical landmarks in the X-ray Microbeam Dataset (XRMB) fail to capture the entire anatomy of the vocal tract. In this paper, we propose a new geometric transformation that improves the accuracy of these measurements. Our transformation maps anatomical landmarks' X-Y coordinates along the midsagittal plane onto six relative measures: Lip Aperture (LA), Lip Protusion (LP), Tongue Body Constriction Location (TTCL), Degree (TBCD), Tongue Tip Constriction Location (TTCL) and Degree (TTCD). Our novel contribution is the extension of the palate trace towards the inferred anterior pharyngeal line, which improves measurements of tongue body constriction.	['Carol Y. Espy-Wilson', 'Mark Tiede', 'Ahmed Adel Attia']	2023-05-18	null	null	null	null	['anatomy']	['miscellaneous']	[-5.36205590e-01 1.22122020e-01 -4.48596239e-01 3.90521390e-03 -9.83533859e-01 -8.32114577e-01 3.46903920e-01 1.18564017e-01 -2.35047787e-01 2.98080832e-01 7.48997092e-01 -3.32660884e-01 -1.74659476e-01 -1.81321621e-01 -3.64355326e-01 -6.33977354e-01 2.67698139e-01 5.03947377e-01 1.63568944e-01 2.27275431e-01 3.19380969e-01 6.17787600e-01 -1.15157425e+00 -2.67698288e-01 4.21009868e-01 6.80064559e-01 3.07139039e-01 6.77053988e-01 -2.80397773e-01 -9.76936668e-02 -6.98208630e-01 -4.43479598e-01 -2.21227840e-01 -3.29549253e-01 -6.59957469e-01 -1.99368671e-01 2.01973930e-01 -3.71298015e-01 2.30283618e-01 8.29189599e-01 6.35387123e-01 -6.00869060e-02 6.05080009e-01 -7.70796657e-01 -3.39420199e-01 5.00211716e-01 -6.74315929e-01 1.79503560e-01 1.32114962e-01 -1.75026909e-01 7.18461752e-01 -7.62052596e-01 9.13563848e-01 1.03563333e+00 9.80918825e-01 8.48401308e-01 -5.26301563e-01 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27a45146-ba0a-4eb0-b65f-6b0bb8c29cb4	robustness-of-demonstration-based-learning	2210.10693	null	https://arxiv.org/abs/2210.10693v1	https://arxiv.org/pdf/2210.10693v1.pdf	Robustness of Demonstration-based Learning Under Limited Data Scenario	Demonstration-based learning has shown great potential in stimulating pretrained language models' ability under limited data scenario. Simply augmenting the input with some demonstrations can significantly improve performance on few-shot NER. However, why such demonstrations are beneficial for the learning process remains unclear since there is no explicit alignment between the demonstrations and the predictions. In this paper, we design pathological demonstrations by gradually removing intuitively useful information from the standard ones to take a deep dive of the robustness of demonstration-based sequence labeling and show that (1) demonstrations composed of random tokens still make the model a better few-shot learner; (2) the length of random demonstrations and the relevance of random tokens are the main factors affecting the performance; (3) demonstrations increase the confidence of model predictions on captured superficial patterns. We have publicly released our code at https://github.com/SALT-NLP/RobustDemo.	['Diyi Yang', 'Ruiyi Zhang', 'Yanzhe Zhang', 'Hongxin Zhang']	2022-10-19	null	null	null	null	['few-shot-ner']	['natural-language-processing']	[-1.35075748e-01 1.81155726e-01 -3.00928783e-02 -3.85941178e-01 -4.97135550e-01 -6.95156157e-01 5.93625128e-01 1.38401426e-02 -6.55721962e-01 7.82832503e-01 3.54446620e-01 -3.96856368e-01 1.39778703e-01 -4.32378173e-01 -9.00276959e-01 -5.33905983e-01 -5.31148501e-02 2.55035073e-01 4.43533570e-01 -4.48974401e-01 2.59661287e-01 3.33932549e-01 -1.41460884e+00 2.10105613e-01 8.63237798e-01 2.39360761e-02 7.52896190e-01 9.24970388e-01 -3.29835564e-01 7.79248595e-01 -6.69236600e-01 -1.64030418e-01 2.45918959e-01 -5.74043572e-01 -8.24324608e-01 -5.27426422e-01 2.72927374e-01 -4.67621893e-01 -3.40415865e-01 1.04792297e+00 4.91064012e-01 3.88976812e-01 6.05025113e-01 -1.29000759e+00 -6.21596277e-01 9.32163417e-01 -1.73120394e-01 3.90899748e-01 3.06906790e-01 6.69964075e-01 9.06643212e-01 -6.82066441e-01 8.27857912e-01 1.05273819e+00 6.49143219e-01 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4d2abcf7-0d8d-4f4b-86f2-9db715beae2b	classification-of-long-sequential-data-using	2201.02143	null	https://arxiv.org/abs/2201.02143v2	https://arxiv.org/pdf/2201.02143v2.pdf	Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks	Classification of long sequential data is an important Machine Learning task and appears in many application scenarios. Recurrent Neural Networks, Transformers, and Convolutional Neural Networks are three major techniques for learning from sequential data. Among these methods, Temporal Convolutional Networks (TCNs) which are scalable to very long sequences have achieved remarkable progress in time series regression. However, the performance of TCNs for sequence classification is not satisfactory because they use a skewed connection protocol and output classes at the last position. Such asymmetry restricts their performance for classification which depends on the whole sequence. In this work, we propose a symmetric multi-scale architecture called Circular Dilated Convolutional Neural Network (CDIL-CNN), where every position has an equal chance to receive information from other positions at the previous layers. Our model gives classification logits in all positions, and we can apply a simple ensemble learning to achieve a better decision. We have tested CDIL-CNN on various long sequential datasets. The experimental results show that our method has superior performance over many state-of-the-art approaches.	['Zhirong Yang', 'Tong Yu', 'Ruslan Khalitov', 'Lei Cheng']	2022-01-06	null	null	null	null	['long-range-modeling']	['natural-language-processing']	[ 1.50303140e-01 -5.58063805e-01 -3.06860059e-01 -2.67738372e-01 -1.27253577e-01 -4.13612843e-01 4.19477999e-01 -6.76086321e-02 -5.57576120e-01 7.70468950e-01 -1.10890511e-02 -6.14668906e-01 1.16139568e-01 -6.51163638e-01 -5.55859625e-01 -8.92618597e-01 -2.20270932e-01 2.66873538e-01 5.63841760e-01 -3.84232402e-01 2.29832605e-01 4.33851033e-01 -1.10603178e+00 7.68074572e-01 5.86950541e-01 1.24370980e+00 1.81295976e-01 6.85803950e-01 -4.15875942e-01 1.21232355e+00 -6.60267770e-01 -6.60150498e-02 -7.40126744e-02 -4.96358603e-01 -7.60348141e-01 -5.95666587e-01 -3.12526762e-01 -1.00587539e-01 -4.17176276e-01 6.83218956e-01 5.15130579e-01 -3.17694340e-03 6.36941254e-01 -1.13396144e+00 -4.07020211e-01 1.02380574e+00 -5.10906219e-01 6.19731724e-01 -1.12060167e-01 -1.41725942e-01 9.59989071e-01 -5.70237398e-01 2.90411562e-01 1.03233826e+00 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957c1301-0052-4f28-af73-8d2005f3767d	multilingual-and-cross-lingual-document	2101.11302	null	https://arxiv.org/abs/2101.11302v2	https://arxiv.org/pdf/2101.11302v2.pdf	Multilingual and cross-lingual document classification: A meta-learning approach	The great majority of languages in the world are considered under-resourced for the successful application of deep learning methods. In this work, we propose a meta-learning approach to document classification in limited-resource setting and demonstrate its effectiveness in two different settings: few-shot, cross-lingual adaptation to previously unseen languages; and multilingual joint training when limited target-language data is available during training. We conduct a systematic comparison of several meta-learning methods, investigate multiple settings in terms of data availability and show that meta-learning thrives in settings with a heterogeneous task distribution. We propose a simple, yet effective adjustment to existing meta-learning methods which allows for better and more stable learning, and set a new state of the art on several languages while performing on-par on others, using only a small amount of labeled data.	['Ekaterina Shutova', 'Pushkar Mishra', 'Helen Yannakoudakis', 'Niels van der Heijden']	2021-01-27	null	https://aclanthology.org/2021.eacl-main.168	https://aclanthology.org/2021.eacl-main.168.pdf	eacl-2021-2	['cross-lingual-document-classification']	['natural-language-processing']	[-1.68601036e-01 -4.82867420e-01 -6.33290410e-01 -2.35544771e-01 -1.41641915e+00 -5.90162516e-01 1.03096390e+00 1.09713569e-01 -9.51331437e-01 8.80142987e-01 1.34476036e-01 -2.76580602e-01 3.65078487e-02 -3.59218389e-01 -6.00907207e-01 -3.25606227e-01 1.17727995e-01 8.28476667e-01 7.45271221e-02 -3.20496261e-01 -7.87528604e-02 2.11693719e-01 -1.35081422e+00 4.87841517e-01 7.93004692e-01 3.39948267e-01 1.60385832e-01 5.87773442e-01 -4.57215101e-01 6.49177432e-01 -6.33776784e-01 -7.51305997e-01 1.39807433e-01 -1.57276578e-02 -9.76203442e-01 3.10477242e-02 7.65504897e-01 -1.23257544e-02 -1.64821520e-01 8.77377093e-01 7.89849877e-01 2.15013623e-01 7.49972165e-01 -9.37489450e-01 -6.63272858e-01 8.06679726e-01 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e4bb81a1-8479-4e77-89f9-4c84d92e23c7	zero-shot-anomaly-detection-with-pre-trained	2306.09269	null	https://arxiv.org/abs/2306.09269v1	https://arxiv.org/pdf/2306.09269v1.pdf	Zero-Shot Anomaly Detection with Pre-trained Segmentation Models	This technical report outlines our submission to the zero-shot track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge. Building on the performance of the WINCLIP framework, we aim to enhance the system's localization capabilities by integrating zero-shot segmentation models. In addition, we perform foreground instance segmentation which enables the model to focus on the relevant parts of the image, thus allowing the models to better identify small or subtle deviations. Our pipeline requires no external data or information, allowing for it to be directly applied to new datasets. Our team (Variance Vigilance Vanguard) ranked third in the zero-shot track of the VAND challenge, and achieve an average F1-max score of 81.5/24.2 at a sample/pixel level on the VisA dataset.	['Bernhard Kainz', 'Johanna P. Müller', 'James Batten', 'Matthew Baugh']	2023-06-15	null	null	null	null	['zero-shot-segmentation', 'anomaly-detection']	['computer-vision', 'methodology']	[ 7.20108375e-02 -4.04294617e-02 1.26708955e-01 -5.08860312e-02 -6.11318052e-01 -5.70719838e-01 6.67243302e-01 4.08634037e-01 -5.42434990e-01 7.20684379e-02 -2.07238510e-01 -1.92575261e-01 1.58513084e-01 -2.80111343e-01 -6.08725727e-01 -2.34567046e-01 -1.16537780e-01 6.58716932e-02 9.41873610e-01 7.24489316e-02 3.45890850e-01 5.44578135e-01 -1.92238593e+00 4.02984440e-01 6.22907698e-01 9.92140770e-01 -1.62191138e-01 9.76265252e-01 -1.09749630e-01 6.33337200e-01 -5.77093124e-01 -2.15418637e-01 4.82892722e-01 -2.54526705e-01 -5.38106382e-01 -7.48104602e-02 1.13476896e+00 -3.69183898e-01 -2.34950408e-01 1.16356814e+00 5.23672223e-01 2.68896282e-01 4.39573646e-01 -1.54152203e+00 -1.72472820e-01 1.52472019e-01 -8.12063336e-01 1.15327787e+00 1.30566776e-01 7.33579755e-01 9.16806042e-01 -1.09801543e+00 7.87458420e-01 8.61808479e-01 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2ab996e2-d6ce-4370-ba6b-be76e6d4502e	neural-relation-extraction-with-selective	null	null	https://aclanthology.org/p16-1200	https://aclanthology.org/p16-1200.pdf	Neural Relation Extraction with Selective Attention over Instances	null	['Huanbo Luan', 'Yankai Lin', 'Maosong Sun', 'Zhiyuan Liu', 'Shiqi Shen']	2016-08-01	neural-relation-extraction-with-selective-1	https://aclanthology.org/P16-1200	https://aclanthology.org/P16-1200.pdf	acl-2016-8	['relationship-extraction-distant-supervised']	['natural-language-processing']	[-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01 -8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01 -5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01 -2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01 -7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302e-01 8.47113907e-01 6.29996777e-01 -1.62287033e+00 5.77558100e-01 6.81926727e-01 5.49597681e-01 5.66466339e-02 1.04480565e+00 6.13576770e-02 1.59847176e+00 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73fdbca0-1497-4f0a-9c2e-07f1cbba48f6	a-fully-end-to-end-deep-learning-approach-for	1703.04699	null	http://arxiv.org/abs/1703.04699v1	http://arxiv.org/pdf/1703.04699v1.pdf	A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition	This paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.) in a scene is important for many tasks, e.g. robotic interventions in nuclear decommissioning. Previous work on 3D semantic reconstruction has predominantly focused on recognition of everyday domestic objects (tables, chairs etc.), whereas previous work on material recognition has largely been confined to single 2D images without any 3D reconstruction. Meanwhile, most 3D semantic reconstruction methods rely on computationally expensive post-processing, using Fully-Connected Conditional Random Fields (CRFs), to achieve consistent segmentations. In contrast, we propose a deep learning method which performs 3D reconstruction while simultaneously recognising different types of materials and labelling them at the pixel level. Unlike previous methods, we propose a fully end-to-end approach, which does not require hand-crafted features or CRF post-processing. Instead, we use only learned features, and the CRF segmentation constraints are incorporated inside the fully end-to-end learned system. We present the results of experiments, in which we trained our system to perform real-time 3D semantic reconstruction for 23 different materials in a real-world application. The run-time performance of the system can be boosted to around 10Hz, using a conventional GPU, which is enough to achieve real-time semantic reconstruction using a 30fps RGB-D camera. To the best of our knowledge, this work is the first real-time end-to-end system for simultaneous 3D reconstruction and material recognition.	['Cheng Zhao', 'Rustam Stolkin', 'Li Sun']	2017-03-14	null	null	null	null	['material-recognition']	['computer-vision']	[ 4.74794000e-01 4.62097228e-02 2.53968269e-01 -4.00875241e-01 -7.45622694e-01 -5.23694456e-01 6.63667858e-01 1.80092677e-01 -4.09565210e-01 1.53985888e-01 -3.84737372e-01 -4.30036098e-01 1.81362540e-01 -7.89297581e-01 -9.71397519e-01 -4.62118655e-01 2.97489107e-01 9.74765897e-01 5.05615354e-01 1.74358904e-01 2.49940470e-01 9.48433936e-01 -1.62923741e+00 2.53407270e-01 1.95848599e-01 1.30303931e+00 7.85840333e-01 7.12786198e-01 -1.71208516e-01 4.03836191e-01 -2.34369725e-01 3.40366475e-02 4.93966281e-01 -1.02763139e-02 -9.60537732e-01 7.46118724e-01 3.15306097e-01 -3.80743742e-01 -2.35719115e-01 8.10249209e-01 3.93049091e-01 6.66465163e-02 8.39436769e-01 -7.86110222e-01 7.75304288e-02 1.03776278e-02 -4.16400194e-01 -4.27696407e-01 6.93115592e-01 6.69094920e-02 4.43863541e-01 -8.57662976e-01 5.70205331e-01 1.19077373e+00 7.00156868e-01 4.06672359e-01 -1.12086785e+00 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-7.04057157e-01 3.96779180e-01 -5.31661436e-02 -9.91525412e-01 -2.97859926e-02 2.29902640e-01]	[8.276694297790527, -2.702944755554199]
7af74764-25cd-4382-ac79-066d94b65bd5	a-time-vertex-signal-processing-framework	1705.02307	null	http://arxiv.org/abs/1705.02307v1	http://arxiv.org/pdf/1705.02307v1.pdf	A Time-Vertex Signal Processing Framework	An emerging way to deal with high-dimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This work aims to elevate the notion of joint harmonic analysis to a full-fledged framework denoted as Time-Vertex Signal Processing, that links together the time-domain signal processing techniques with the new tools of graph signal processing. This entails three main contributions: (a) We provide a formal motivation for harmonic time-vertex analysis as an analysis tool for the state evolution of simple Partial Differential Equations on graphs. (b) We improve the accuracy of joint filtering operators by up-to two orders of magnitude. (c) Using our joint filters, we construct time-vertex dictionaries analyzing the different scales and the local time-frequency content of a signal. The utility of our tools is illustrated in numerous applications and datasets, such as dynamic mesh denoising and classification, still-video inpainting, and source localization in seismic events. Our results suggest that joint analysis of time-vertex signals can bring benefits to regression and learning.	['Nathanaël Perraudin', 'Andreas Loukas', 'Benjamin Ricaud', 'Francesco Grassi']	2017-05-05	null	null	null	null	['video-inpainting']	['computer-vision']	[ 3.29907835e-01 7.53323957e-02 5.07466257e-01 3.37086990e-03 -8.34405243e-01 -3.79627854e-01 4.32120115e-01 1.91994369e-01 6.88977027e-03 4.00166005e-01 1.85846865e-01 4.19613495e-02 -6.77206874e-01 -7.83569276e-01 -6.31187439e-01 -9.39267755e-01 -7.00106382e-01 -2.75025889e-02 1.09929167e-01 -4.90378171e-01 1.84241645e-02 7.45658338e-01 -1.30623555e+00 9.05380398e-02 4.56125826e-01 9.42496657e-01 -3.03537756e-01 9.74925041e-01 1.42382324e-01 6.31743729e-01 -3.97605479e-01 -1.67755261e-01 1.21733963e-01 -5.52834392e-01 -6.18062735e-01 2.33627126e-01 2.01571271e-01 2.22570561e-02 -6.58511043e-01 8.97194743e-01 4.34358120e-01 2.56477505e-01 4.41851258e-01 -1.10209715e+00 -3.60518694e-01 4.77032334e-01 -5.26520371e-01 2.58459002e-01 4.44156229e-01 -2.67224789e-01 8.45923007e-01 -7.53287852e-01 6.73396647e-01 1.15151739e+00 1.19276369e+00 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5e06b808-c0d5-4fbe-8e3e-6e5a863fb694	a-generic-diffusion-based-approach-for-3d	2210.05669	null	https://arxiv.org/abs/2210.05669v2	https://arxiv.org/pdf/2210.05669v2.pdf	A generic diffusion-based approach for 3D human pose prediction in the wild	Predicting 3D human poses in real-world scenarios, also known as human pose forecasting, is inevitably subject to noisy inputs arising from inaccurate 3D pose estimations and occlusions. To address these challenges, we propose a diffusion-based approach that can predict given noisy observations. We frame the prediction task as a denoising problem, where both observation and prediction are considered as a single sequence containing missing elements (whether in the observation or prediction horizon). All missing elements are treated as noise and denoised with our conditional diffusion model. To better handle long-term forecasting horizon, we present a temporal cascaded diffusion model. We demonstrate the benefits of our approach on four publicly available datasets (Human3.6M, HumanEva-I, AMASS, and 3DPW), outperforming the state-of-the-art. Additionally, we show that our framework is generic enough to improve any 3D pose prediction model as a pre-processing step to repair their inputs and a post-processing step to refine their outputs. The code is available online: \url{https://github.com/vita-epfl/DePOSit}.	['Alexandre Alahi', 'Taylor Mordan', 'Sara Rajabzadeh', 'Yasamin Medghalchi', 'Mohammadreza Mofayezi', 'Ali Rasekh', 'Saeed Saadatnejad']	2022-10-11	null	null	null	null	['human-pose-forecasting']	['computer-vision']	[ 1.09907992e-01 2.03172311e-01 3.41595143e-01 -4.46701556e-01 -7.47907758e-01 -4.22461241e-01 6.14142954e-01 -9.07047391e-02 -4.54297543e-01 5.69104493e-01 4.22037572e-01 8.56190920e-02 1.94723457e-01 -5.80456078e-01 -9.80084360e-01 -4.25371081e-01 -3.17792371e-02 7.39365935e-01 3.23923260e-01 -3.22518289e-01 -2.11761057e-01 2.40578681e-01 -1.52996850e+00 2.63765454e-01 6.09995067e-01 1.09103656e+00 1.73198283e-02 8.64491463e-01 4.22307998e-01 5.70321798e-01 -4.18266028e-01 -5.56616068e-01 3.54461998e-01 -1.60038128e-01 -6.52340412e-01 1.90389112e-01 3.03293437e-01 -4.61241961e-01 -4.66544509e-01 6.93683386e-01 6.95670187e-01 2.32288763e-01 5.39328158e-01 -1.12391806e+00 -3.83129716e-01 2.31514107e-02 -2.97982335e-01 -3.59013267e-02 6.43674791e-01 3.35809261e-01 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0b2d0687-74b8-49ad-a905-c18f76ef2434	insurance-contract-for-high-renewable-energy	2209.10363	null	https://arxiv.org/abs/2209.10363v1	https://arxiv.org/pdf/2209.10363v1.pdf	Insurance Contract for High Renewable Energy Integration	The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium fee to the utility, so that he will get compensated in case his demand is not fully satisfied. A proper insurance design needs to resolve the following two challenges: (i) users' reliability preference is private information; and (ii) the insurance design is tightly coupled with the renewable energy investment decision. To address these challenges, we adopt the contract theory to elicit users' private reliability preferences, and we study how the utility can jointly optimize the insurance contract and the planning of renewable energy. A key analytical challenge is that the joint optimization of the insurance design and the planning of renewables is non-convex. We resolve this difficulty by revealing important structural properties of the optimal solution, using the help of two benchmark problems: the no-insurance benchmark and the social-optimum benchmark. Compared with the no-insurance benchmark, we prove that the social cost and users' total energy cost are always no larger under the optimal contract. Simulation results show that the largest benefit of the insurance contract is achieved at a medium electricity-bill price together with a low type heterogeneity and a high renewable uncertainty.	['Xiaojun Lin', 'Jianwei Huang', 'Hao Wang', 'Dongwei Zhao']	2022-09-21	null	null	null	null	['total-energy']	['miscellaneous']	[-3.09114724e-01 4.42095876e-01 -4.98737007e-01 1.26798809e-01 -7.96043217e-01 -7.32051194e-01 -8.50122944e-02 -2.30281129e-02 8.20175856e-02 1.28693998e+00 2.43080959e-01 -3.56517971e-01 -3.11316758e-01 -1.12547207e+00 -3.75221848e-01 -1.05380070e+00 -2.14463659e-03 1.44158930e-01 -6.06309712e-01 -2.65067548e-01 -5.76088913e-02 1.40933171e-01 -1.13918805e+00 -5.60682356e-01 1.56584787e+00 1.47131681e+00 2.90636361e-01 -1.60408437e-01 3.33409220e-01 7.20233619e-02 -5.22345006e-01 -8.69646072e-02 3.90463769e-01 -3.23905110e-01 -2.88120121e-01 -8.91964808e-02 -1.25350237e+00 -4.73394841e-01 2.62963563e-01 1.34502089e+00 5.83462119e-01 2.70944387e-02 4.87575293e-01 -1.61796188e+00 -7.93560386e-01 7.85878003e-01 -5.51088393e-01 -2.13148981e-01 3.15742850e-01 -1.25627339e-01 1.15380120e+00 -2.86405832e-01 1.64198026e-01 5.57261944e-01 2.73504317e-01 6.09827459e-01 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c446dac0-691a-40a1-9d05-a30efdf4db23	remote-photoplethysmography-correspondence	null	null	http://openaccess.thecvf.com/content_ECCV_2018/html/Siqi_Liu_Remote_Photoplethysmography_Correspondence_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Siqi_Liu_Remote_Photoplethysmography_Correspondence_ECCV_2018_paper.pdf	Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection	3D mask face presentation attack, as a new challenge in face recognition, has been attracting increasing attention. Recently, remote Photoplethysmography (rPPG) is employed as an intrinsic liveness cue which is independent of the mask appearance. Although existing rPPG-based methods achieve promising results on both intra and cross dataset scenarios, they may not be robust enough when rPPG signals are contaminated by noise. In this paper, we propose a new liveness feature, called rPPG correspondence feature (CFrPPG) to precisely identify the heartbeat vestige from the observed noisy rPPG signals. To further overcome the global interferences, we propose a novel learning strategy which incorporates the global noise within the CFrPPG feature. Extensive experiments indicate that the proposed feature not only outperforms the state-of-the-art rPPG based methods on 3D mask attacks but also be able to handle the practical scenarios with dim light and camera motion.	['Si-Qi Liu', 'Xiangyuan Lan', 'Pong C. Yuen']	2018-09-01	null	null	null	eccv-2018-9	['face-presentation-attack-detection']	['computer-vision']	[ 3.41124356e-01 -4.34803754e-01 2.16104146e-02 -2.26807147e-01 -6.33614838e-01 -4.65366453e-01 4.63479966e-01 -5.08476794e-01 -3.75801474e-02 4.83003616e-01 5.19165024e-02 3.92638259e-02 2.61602476e-02 -2.72288859e-01 -2.99078703e-01 -1.02707744e+00 8.74078348e-02 -4.07294959e-01 3.57381180e-02 1.70271188e-01 2.49787793e-01 7.72594512e-01 -1.63883579e+00 -4.87567298e-02 5.95315874e-01 1.18062866e+00 -1.80836484e-01 4.55317646e-01 8.01933631e-02 1.70142964e-01 -7.82168567e-01 -2.93170124e-01 5.65592349e-01 -6.36267960e-01 -6.55019656e-02 2.64010638e-01 4.16457325e-01 -2.19843239e-01 -3.79015386e-01 9.71147001e-01 1.12938595e+00 -2.18903020e-01 2.05290467e-01 -1.16354883e+00 -1.13392487e-01 1.49420816e-02 -9.39549804e-01 5.12968361e-01 5.88761747e-01 2.66551942e-01 2.42944121e-01 -1.01030409e+00 3.73642921e-01 1.07808006e+00 5.96257329e-01 7.97805786e-01 -1.24967945e+00 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-9.84935582e-01 4.04851139e-01 4.08079058e-01 -6.64327681e-01 -8.94363105e-01 -6.25648201e-02]	[13.732780456542969, 2.421980857849121]
73e73c2f-73a8-4a46-bb55-6a74255034a0	multimodal-performers-for-genomic-selection	null	null	https://doi.org/10.1016/j.atech.2021.100017	https://doi.org/10.1016/j.atech.2021.100017	Multimodal Performers for Genomic Selection and Crop Yield Prediction	Working towards optimal crop yields is a crucial step towards securing a stable food supply for the world. To this end, approaches to model and predict crop yields can help speed up research and reduce costs. However, crop yield prediction is very challenging due to the dependencies on factors such as genotype and environmental factors. In this paper we introduce a performer-based deep learning framework for crop yield prediction using single nucleotide polymorphisms and weather data. We compare the proposed models with traditional Bayesian-based methods and traditional neural network architectures on the task of predicting barley yields across 8 different locations in Norway for the years 2017 and 2018. We show that the performer-based models significantly outperform the traditional approaches, achieving an R score of 0.820 and a root mean squared error of 69.05, compared to 0.807 and 71.63, and 0.076 and 149.78 for the best traditional neural network and traditional Bayesian approach respectively. Furthermore, we show that visualizing the self-attention maps of a Multimodal Performer network indicates that the model makes meaningful connections between genotype and weather data that can be used by the breeder to inform breeding decisions and shorten breeding cycle length. The performer-based models can also be applied to other types of genomic selection such as salmon breeding for increased Omega-3 fatty acid production or similar animal husbandry applications. The code is available at: https://github.com/haakom/pay-attention-to-genomic-selection.	['Keith L. Downing', 'Muath Alsheikh', 'Stein Bergersen', 'Susanne Windju', 'Håkon Måløy']	2021-10-20	null	null	null	smart-agricultural-technology-2021-10	['crop-yield-prediction', 'crop-yield-prediction']	['computer-vision', 'miscellaneous']	[-1.46993071e-01 -6.75465018e-02 -3.26176196e-01 -5.37150085e-01 -2.03155205e-01 -5.41820467e-01 3.36685367e-02 5.75034976e-01 -7.66119640e-03 6.98769808e-01 7.85294697e-02 -4.99079943e-01 -3.70061904e-01 -1.16631782e+00 -9.65239346e-01 -1.00693357e+00 -4.11475152e-01 1.35479137e-01 -3.06467891e-01 -5.13951182e-01 9.74575728e-02 2.98227429e-01 -1.76021004e+00 -2.72058137e-02 8.92427742e-01 8.16238403e-01 7.24127293e-01 7.48009920e-01 7.71693736e-02 -8.59174058e-02 -4.32800651e-01 -3.61241519e-01 -7.81333447e-02 -3.73719662e-01 -1.23646855e-01 -6.98495984e-01 9.06342864e-02 -3.24969292e-01 1.19874246e-01 1.14252710e+00 7.99762666e-01 -4.48558107e-02 6.71357095e-01 -1.09729612e+00 -1.19533169e+00 1.23713613e+00 -9.78929162e-01 -2.37605438e-01 -1.80773616e-01 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6e0d02d4-1411-4470-8f8c-fcf596248751	improving-self-organizing-maps-with	2009.02174	null	https://arxiv.org/abs/2009.02174v1	https://arxiv.org/pdf/2009.02174v1.pdf	Improving Self-Organizing Maps with Unsupervised Feature Extraction	The Self-Organizing Map (SOM) is a brain-inspired neural model that is very promising for unsupervised learning, especially in embedded applications. However, it is unable to learn efficient prototypes when dealing with complex datasets. We propose in this work to improve the SOM performance by using extracted features instead of raw data. We conduct a comparative study on the SOM classification accuracy with unsupervised feature extraction using two different approaches: a machine learning approach with Sparse Convolutional Auto-Encoders using gradient-based learning, and a neuroscience approach with Spiking Neural Networks using Spike Timing Dependant Plasticity learning. The SOM is trained on the extracted features, then very few labeled samples are used to label the neurons with their corresponding class. We investigate the impact of the feature maps, the SOM size and the labeled subset size on the classification accuracy using the different feature extraction methods. We improve the SOM classification by +6.09\% and reach state-of-the-art performance on unsupervised image classification.	['Benoit Miramond', 'Laurent Rodriguez', 'Lyes Khacef']	2020-09-04	null	null	null	null	['unsupervised-image-classification', 'unsupervised-mnist']	['computer-vision', 'methodology']	[ 3.26039493e-01 -1.17140241e-01 9.14728194e-02 -4.52323169e-01 -1.24714095e-02 -2.84083128e-01 7.25781024e-01 3.00229698e-01 -9.17891860e-01 8.35653245e-01 6.18799180e-02 2.34657481e-01 -2.77847677e-01 -9.00451958e-01 -7.27454364e-01 -1.09410417e+00 -4.91020717e-02 4.60101992e-01 7.35641003e-01 9.70185548e-02 7.25157738e-01 6.62177086e-01 -2.24461007e+00 7.15845942e-01 6.08346760e-01 1.26909959e+00 4.23175275e-01 3.03501368e-01 -3.98346812e-01 8.50111961e-01 -4.54539657e-01 5.09273946e-01 2.54847437e-01 -5.25447428e-01 -3.80898625e-01 -1.23583907e-02 -1.11208335e-01 3.33202600e-01 -2.67738461e-01 8.14581096e-01 5.70470095e-01 -3.69983837e-02 1.00740647e+00 -1.09025776e+00 -1.12314165e-01 6.26178026e-01 -2.03244900e-03 5.08644044e-01 -2.37501279e-01 -2.33403267e-03 2.68129379e-01 -9.09457624e-01 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8.14372718e-01 -1.05540907e+00 -1.12668969e-01 -9.38628078e-01 -8.89358819e-01 1.45020470e-01 -1.13717400e-01 -8.32734466e-01 -5.15768945e-01 2.96377599e-01]	[8.210295677185059, 2.7004010677337646]
db162006-5ec8-4539-8cf0-7cdf186c339a	distributed-learning-via-filtered	2007.09392	null	https://arxiv.org/abs/2007.09392v1	https://arxiv.org/pdf/2007.09392v1.pdf	Distributed Learning via Filtered Hyperinterpolation on Manifolds	Learning mappings of data on manifolds is an important topic in contemporary machine learning, with applications in astrophysics, geophysics, statistical physics, medical diagnosis, biochemistry, 3D object analysis. This paper studies the problem of learning real-valued functions on manifolds through filtered hyperinterpolation of input-output data pairs where the inputs may be sampled deterministically or at random and the outputs may be clean or noisy. Motivated by the problem of handling large data sets, it presents a parallel data processing approach which distributes the data-fitting task among multiple servers and synthesizes the fitted sub-models into a global estimator. We prove quantitative relations between the approximation quality of the learned function over the entire manifold, the type of target function, the number of servers, and the number and type of available samples. We obtain the approximation rates of convergence for distributed and non-distributed approaches. For the non-distributed case, the approximation order is optimal.	['Guido Montúfar', 'Yu Guang Wang']	2020-07-18	null	null	null	null	['geophysics']	['miscellaneous']	[-3.99761200e-01 -7.35596791e-02 1.02571048e-01 -1.27288878e-01 -8.51986349e-01 -4.26605374e-01 3.47773492e-01 3.95654291e-01 -4.28365648e-01 8.74772668e-01 -2.79587567e-01 -1.72630996e-01 -3.71743053e-01 -8.63647640e-01 -1.07069635e+00 -8.74019027e-01 -3.35898757e-01 7.93602884e-01 1.51543040e-02 2.78762549e-01 -1.06109761e-01 8.51115823e-01 -1.40382147e+00 -2.16359496e-01 7.06801653e-01 1.06232536e+00 3.19511592e-02 9.73621011e-01 -2.40633667e-01 1.69614375e-01 -4.37546968e-01 -1.51113287e-01 4.97525841e-01 -4.87775147e-01 -6.03550732e-01 1.99495733e-01 3.11195284e-01 2.08974019e-01 -2.21085072e-01 1.36023653e+00 4.56095994e-01 2.52781957e-01 1.03647220e+00 -1.42410827e+00 -2.38962218e-01 2.13616565e-01 -5.48914969e-01 2.16870561e-01 -2.62305200e-01 -4.25631776e-02 4.60729182e-01 -8.37634742e-01 3.07267994e-01 1.18195951e+00 5.78419089e-01 3.77717465e-01 -1.39174390e+00 -2.02378720e-01 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cbc55542-e214-431d-a3c7-3ca0f3acf9b9	swapped-face-detection-using-deep-learning	1909.04217	null	https://arxiv.org/abs/1909.04217v1	https://arxiv.org/pdf/1909.04217v1.pdf	Swapped Face Detection using Deep Learning and Subjective Assessment	The tremendous success of deep learning for imaging applications has resulted in numerous beneficial advances. Unfortunately, this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent. Transferring one person's face from a source image to a target image of another person, while keeping the image photo-realistic overall has become increasingly easy and automatic, even for individuals without much knowledge of image processing. In this study, we use deep transfer learning for face swapping detection, showing true positive rates >96% with very few false alarms. Distinguished from existing methods that only provide detection accuracy, we also provide uncertainty for each prediction, which is critical for trust in the deployment of such detection systems. Moreover, we provide a comparison to human subjects. To capture human recognition performance, we build a website to collect pairwise comparisons of images from human subjects. Based on these comparisons, images are ranked from most real to most fake. We compare this ranking to the outputs from our automatic model, showing good, but imperfect, correspondence with linear correlations >0.75. Overall, the results show the effectiveness of our method. As part of this study, we create a novel, publicly available dataset that is, to the best of our knowledge, the largest public swapped face dataset created using still images. Our goal of this study is to inspire more research in the field of image forensics through the creation of a public dataset and initial analysis.	['Zohreh Raziei', 'Michael Hahsler', 'Paul Krueger', 'Eric C. Larson', 'Eli V. Olinick', 'Xinyi Ding']	2019-09-10	null	null	null	null	['image-forensics']	['computer-vision']	[ 9.60420966e-02 9.00320858e-02 2.52936333e-01 -5.30101657e-01 -8.50365698e-01 -7.66491234e-01 4.63564813e-01 -3.33971232e-01 -3.19304675e-01 5.97343385e-01 -9.67871174e-02 -1.89131573e-01 2.27856308e-01 -5.98321676e-01 -7.86318421e-01 -5.38545489e-01 -5.71733415e-02 3.82626355e-01 -4.84604724e-02 7.25221708e-02 2.97359377e-01 7.29846179e-01 -1.20704472e+00 5.45778036e-01 2.96963602e-01 8.90999734e-01 -3.95527124e-01 3.75028193e-01 5.12322068e-01 5.52445292e-01 -7.23535597e-01 -1.23312330e+00 6.62540734e-01 -3.52624536e-01 -6.54316783e-01 2.31294960e-01 9.48955953e-01 -9.45098698e-01 -3.38852286e-01 9.32586253e-01 7.48903990e-01 -2.77118981e-01 2.47316971e-01 -1.50511134e+00 -8.13833714e-01 2.04792082e-01 -7.95867503e-01 8.50791186e-02 3.63667071e-01 6.98578477e-01 6.75889075e-01 -7.22038686e-01 6.99043214e-01 1.23124576e+00 8.52775156e-01 6.63475811e-01 -1.13204646e+00 -1.16283655e+00 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1.40053183e-01 -5.31474829e-01 -5.08432269e-01 4.44154888e-02]	[12.740562438964844, 1.0409080982208252]
562fda9f-ee37-4a74-a3e2-476316341ffd	instructions-as-backdoors-backdoor	2305.14710	null	https://arxiv.org/abs/2305.14710v1	https://arxiv.org/pdf/2305.14710v1.pdf	Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models	Instruction-tuned models are trained on crowdsourcing datasets with task instructions to achieve superior performance. However, in this work we raise security concerns about this training paradigm. Our studies demonstrate that an attacker can inject backdoors by issuing very few malicious instructions among thousands of gathered data and control model behavior through data poisoning, without even the need of modifying data instances or labels themselves. Through such instruction attacks, the attacker can achieve over 90% attack success rate across four commonly used NLP datasets, and cause persistent backdoors that are easily transferred to 15 diverse datasets zero-shot. In this way, the attacker can directly apply poisoned instructions designed for one dataset on many other datasets. Moreover, the poisoned model cannot be cured by continual learning. Lastly, instruction attacks show resistance to existing inference-time defense. These findings highlight the need for more robust defenses against data poisoning attacks in instructiontuning models and underscore the importance of ensuring data quality in instruction crowdsourcing.	['Muhao Chen', 'Chaowei Xiao', 'Fei Wang', 'Mingyu Derek Ma', 'Jiashu Xu']	2023-05-24	null	null	null	null	['data-poisoning']	['adversarial']	[ 2.73047872e-02 -8.94258618e-02 -4.99171674e-01 3.04116547e-04 -9.45981324e-01 -1.40637362e+00 5.51354468e-01 1.80312917e-01 -6.88893199e-01 8.21821809e-01 1.35155367e-02 -7.43145227e-01 5.03467679e-01 -6.91650569e-01 -1.18560278e+00 -6.51869059e-01 4.74881351e-01 2.71317542e-01 4.80389357e-01 -4.06345487e-01 4.49256331e-01 6.01013541e-01 -1.25509381e+00 9.13125202e-02 9.39964235e-01 5.80785200e-02 -6.04649782e-01 8.33307624e-01 1.03938885e-01 1.13137853e+00 -1.38236749e+00 -8.40630174e-01 3.45340908e-01 9.21318084e-02 -5.44704974e-01 -4.16575760e-01 6.39464736e-01 -1.07142484e+00 -7.34574199e-01 1.08025002e+00 5.26602507e-01 -3.81155685e-02 1.38381258e-01 -1.88345444e+00 -1.02157307e+00 5.61557889e-01 -3.32379103e-01 2.59071052e-01 5.20830274e-01 9.73725557e-01 5.25517225e-01 -3.83474678e-01 2.93488413e-01 1.15214276e+00 1.71344832e-01 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-4.52255845e-01 -1.66072801e-01 4.68450367e-01 4.60901856e-01 -4.59646583e-01 -6.72028780e-01 -1.33604929e-01]	[5.976677894592285, 7.808117866516113]
7b59f1df-80e6-4fb0-a553-66dc1adc58a1	consistency-based-semi-supervised-learning	null	null	http://papers.nips.cc/paper/9259-consistency-based-semi-supervised-learning-for-object-detection	http://papers.nips.cc/paper/9259-consistency-based-semi-supervised-learning-for-object-detection.pdf	Consistency-based Semi-supervised Learning for Object detection	Making a precise annotation in a large dataset is crucial to the performance of object detection. While the object detection task requires a huge number of annotated samples to guarantee its performance, placing bounding boxes for every object in each sample is time-consuming and costs a lot. To alleviate this problem, we propose a Consistency-based Semi-supervised learning method for object Detection (CSD), which is a way of using consistency constraints as a tool for enhancing detection performance by making full use of available unlabeled data. Specifically, the consistency constraint is applied not only for object classification but also for the localization. We also proposed Background Elimination (BE) to avoid the negative effect of the predominant backgrounds on the detection performance. We have evaluated the proposed CSD both in single-stage and two-stage detectors and the results show the effectiveness of our method.	['Jeesoo Kim', 'Nojun Kwak', 'Seungeui Lee', 'Jisoo Jeong']	2019-12-01	null	null	null	neurips-2019-12	['semi-supervised-object-detection']	['computer-vision']	[ 1.28676474e-01 -2.44659469e-01 1.42895579e-01 -4.27117437e-01 -5.17850459e-01 -3.67329299e-01 3.46529365e-01 3.13142657e-01 -6.24751329e-01 4.85982984e-01 -3.77180248e-01 -2.01213226e-01 2.97712088e-01 -7.88252771e-01 -5.50376773e-01 -8.45599174e-01 3.65032315e-01 1.79226086e-01 1.03552353e+00 2.84093142e-01 7.80650824e-02 6.21395290e-01 -1.44495094e+00 1.43854152e-02 8.41063976e-01 9.86371636e-01 6.69894695e-01 3.81490588e-01 -3.97109807e-01 6.66241884e-01 -5.15911818e-01 -1.20221145e-01 5.54413557e-01 -3.46282870e-01 -3.29077035e-01 5.38610399e-01 1.41805574e-01 -4.78896737e-01 4.84306626e-02 1.25135303e+00 3.21194440e-01 -8.26381221e-02 6.57921851e-01 -1.23161006e+00 8.65495577e-02 2.45863155e-01 -1.03474498e+00 2.81120002e-01 -2.58528054e-01 -3.66127975e-02 5.66821933e-01 -9.06606615e-01 2.41557777e-01 9.22156334e-01 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bac627e2-eb02-4969-93a0-fc5cc5673336	assessing-the-use-of-prosody-in-constituency	2106.07794	null	https://arxiv.org/abs/2106.07794v1	https://arxiv.org/pdf/2106.07794v1.pdf	Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts	This work explores constituency parsing on automatically recognized transcripts of conversational speech. The neural parser is based on a sentence encoder that leverages word vectors contextualized with prosodic features, jointly learning prosodic feature extraction with parsing. We assess the utility of the prosody in parsing on imperfect transcripts, i.e. transcripts with automatic speech recognition (ASR) errors, by applying the parser in an N-best reranking framework. In experiments on Switchboard, we obtain 13-15% of the oracle N-best gain relative to parsing the 1-best ASR output, with insignificant impact on word recognition error rate. Prosody provides a significant part of the gain, and analyses suggest that it leads to more grammatical utterances via recovering function words.	['Mari Ostendorf', 'Trang Tran']	2021-06-14	null	null	null	null	['constituency-parsing']	['natural-language-processing']	[ 4.99822170e-01 9.46202755e-01 -2.03145549e-01 -6.86646461e-01 -1.40060115e+00 -8.13168705e-01 7.51649961e-02 8.12638253e-02 -3.67862225e-01 5.71875393e-01 1.21274281e+00 -5.42470753e-01 2.80635655e-01 -4.43606138e-01 -4.80613947e-01 -2.81674892e-01 -7.98381567e-02 2.43247181e-01 -1.39669269e-01 -3.72049093e-01 -1.31010544e-03 1.32414149e-02 -9.09281671e-01 7.69895256e-01 2.60250032e-01 7.44705915e-01 5.42517975e-02 1.08411396e+00 -4.02215570e-01 9.33265269e-01 -9.05964792e-01 -5.60482085e-01 2.08434641e-01 -5.22828281e-01 -1.10344946e+00 1.41561016e-01 2.18739316e-01 -4.07773525e-01 -1.62143752e-01 6.86417878e-01 2.11164996e-01 7.52626732e-02 2.17750743e-01 -4.87514645e-01 -3.17881644e-01 1.31261218e+00 -3.32047045e-02 5.03067136e-01 7.20728695e-01 8.76725614e-02 2.03334475e+00 -6.89266324e-01 6.22156262e-01 1.54007304e+00 5.65890074e-01 6.70517981e-01 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b2aecdee-60b2-485e-a942-5f8c4e4cd40b	massively-multi-lingual-event-understanding	2305.10561	null	https://arxiv.org/abs/2305.10561v1	https://arxiv.org/pdf/2305.10561v1.pdf	Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search	In this paper, we present ISI-Clear, a state-of-the-art, cross-lingual, zero-shot event extraction system and accompanying user interface for event visualization & search. Using only English training data, ISI-Clear makes global events available on-demand, processing user-supplied text in 100 languages ranging from Afrikaans to Yiddish. We provide multiple event-centric views of extracted events, including both a graphical representation and a document-level summary. We also integrate existing cross-lingual search algorithms with event extraction capabilities to provide cross-lingual event-centric search, allowing English-speaking users to search over events automatically extracted from a corpus of non-English documents, using either English natural language queries (e.g. cholera outbreaks in Iran) or structured queries (e.g. find all events of type Disease-Outbreak with agent cholera and location Iran).	['Elizabeth Boschee', 'Steven Fincke', 'Joel Barry', 'Shantanu Agarwal', 'Chris Jenkins']	2023-05-17	null	null	null	null	['event-extraction']	['natural-language-processing']	[-3.27955902e-01 -2.71145314e-01 -1.15540020e-01 -1.09139338e-01 -1.27915597e+00 -8.97395849e-01 8.93505573e-01 1.24427474e+00 -6.46387458e-01 7.21867800e-01 7.55629897e-01 -5.78719914e-01 -4.39225227e-01 -9.14902866e-01 -1.05247460e-01 -3.96425366e-01 -5.68051100e-01 7.30994046e-01 1.47790164e-01 -2.35063732e-01 1.91041663e-01 6.09581828e-01 -1.52260089e+00 5.27282298e-01 6.42030001e-01 3.50975126e-01 3.30552459e-01 7.53769100e-01 -4.62401778e-01 6.21001124e-01 -9.67742085e-01 2.18577478e-02 -3.95109743e-01 -2.63160199e-01 -7.44349837e-01 -6.38584733e-01 -3.63020480e-01 1.28734753e-01 -1.37647629e-01 5.84197819e-01 7.16604531e-01 -5.28776646e-02 5.99595904e-01 -1.05123639e+00 -2.36162588e-01 6.63518786e-01 -1.72067761e-01 7.27003515e-01 1.09791100e+00 -1.49228266e-02 5.63381135e-01 -1.16347671e+00 1.51299679e+00 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59226799-fe22-49ec-baf3-b7dcb0f269d4	deep-semantic-multimodal-hashing-network-for	1901.02662	null	https://arxiv.org/abs/1901.02662v3	https://arxiv.org/pdf/1901.02662v3.pdf	Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals	Hashing has been widely applied to multimodal retrieval on large-scale multimedia data due to its efficiency in computation and storage. In this article, we propose a novel deep semantic multimodal hashing network (DSMHN) for scalable image-text and video-text retrieval. The proposed deep hashing framework leverages 2-D convolutional neural networks (CNN) as the backbone network to capture the spatial information for image-text retrieval, while the 3-D CNN as the backbone network to capture the spatial and temporal information for video-text retrieval. In the DSMHN, two sets of modality-specific hash functions are jointly learned by explicitly preserving both intermodality similarities and intramodality semantic labels. Specifically, with the assumption that the learned hash codes should be optimal for the classification task, two stream networks are jointly trained to learn the hash functions by embedding the semantic labels on the resultant hash codes. Moreover, a unified deep multimodal hashing framework is proposed to learn compact and high-quality hash codes by exploiting the feature representation learning, intermodality similarity-preserving learning, semantic label-preserving learning, and hash function learning with different types of loss functions simultaneously. The proposed DSMHN method is a generic and scalable deep hashing framework for both image-text and video-text retrievals, which can be flexibly integrated with different types of loss functions. We conduct extensive experiments for both single modal- and cross-modal-retrieval tasks on four widely used multimodal-retrieval data sets. Experimental results on both image-text- and video-text-retrieval tasks demonstrate that the DSMHN significantly outperforms the state-of-the-art methods.	['Zechao Li', 'Lu Jin', 'Jinhui Tang']	2019-01-09	null	null	null	null	['video-text-retrieval']	['computer-vision']	[-3.66488606e-01 -5.40086925e-01 -5.84869862e-01 -3.16275418e-01 -1.41382158e+00 -3.15623134e-01 5.15317440e-01 1.11167721e-01 -4.27356571e-01 2.01311082e-01 2.05512673e-01 2.98084050e-01 -2.31086701e-01 -6.30348980e-01 -7.46510863e-01 -1.01413023e+00 -2.53165543e-01 3.88087839e-01 1.28465056e-01 -2.66712494e-02 1.85642481e-01 3.52975577e-01 -1.77136600e+00 2.62090445e-01 1.71125978e-01 1.59812391e+00 1.88050985e-01 4.02034372e-01 2.10712641e-01 5.82192004e-01 2.16344874e-02 -2.64839083e-01 3.17484200e-01 4.08181846e-02 -5.43805480e-01 -2.16929287e-01 5.52206993e-01 -8.65312457e-01 -1.30936170e+00 8.16839576e-01 8.13975692e-01 3.77904683e-01 6.50962532e-01 -1.53761756e+00 -9.24655676e-01 2.50803642e-02 -4.36191410e-01 -1.59429446e-01 4.00849611e-01 2.88130529e-02 1.36967278e+00 -1.16625619e+00 5.29807508e-01 1.42206442e+00 6.42085433e-01 2.98271596e-01 -8.18476021e-01 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f54dde82-0d13-4f4a-8577-b3fc9b0e4a5e	an-lmi-framework-for-contraction-based	2301.08398	null	https://arxiv.org/abs/2301.08398v1	https://arxiv.org/pdf/2301.08398v1.pdf	An LMI Framework for Contraction-based Nonlinear Control Design by Derivatives of Gaussian Process Regression	Contraction theory formulates the analysis of nonlinear systems in terms of Jacobian matrices. Although this provides the potential to develop a linear matrix inequality (LMI) framework for nonlinear control design, conditions are imposed not on controllers but on their partial derivatives, which makes control design challenging. In this paper, we illustrate this so-called integrability problem can be solved by a non-standard use of Gaussian process regression (GPR) for parameterizing controllers and then establish an LMI framework of contraction-based control design for nonlinear discrete-time systems, as an easy-to-implement tool. Later on, we consider the case where the drift vector fields are unknown and employ GPR for functional fitting as its standard use. GPR describes learning errors in terms of probability, and thus we further discuss how to incorporate stochastic learning errors into the proposed LMI framework.	['Kenji Kashima', 'Yu Kawano']	2023-01-20	null	null	null	null	['gpr', 'gpr']	['computer-vision', 'miscellaneous']	[ 2.09338918e-01 1.28605485e-01 4.90230098e-02 5.17877042e-01 -5.20254195e-01 -6.22783482e-01 2.90486157e-01 -2.90515304e-01 -3.52586061e-01 1.04957175e+00 -7.13223696e-01 -4.21568125e-01 -6.09315872e-01 -3.38162959e-01 -7.86501467e-01 -1.21629858e+00 1.89811364e-01 3.45541447e-01 -1.82010829e-01 -9.58187580e-02 4.09621894e-02 5.70531487e-01 -9.89261389e-01 -8.24677408e-01 8.22533846e-01 7.46945620e-01 2.19876811e-01 6.88384652e-01 4.12409872e-01 5.17034113e-01 -5.32481492e-01 9.35586840e-02 2.62366742e-01 -3.37989450e-01 -1.98220104e-01 2.43583277e-01 -1.98270440e-01 1.78859308e-01 -1.26191244e-01 1.31856954e+00 5.80885589e-01 3.80236447e-01 9.06959713e-01 -1.27683651e+00 -5.57712793e-01 1.71179265e-01 -2.95827270e-01 -2.29170188e-01 -1.67218134e-01 1.27486289e-02 3.95276248e-01 -8.19935143e-01 4.10761982e-01 1.01487339e+00 7.32512534e-01 6.35175884e-01 -1.34674096e+00 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-1.60073698e-01 4.20595139e-01 -9.23028827e-01 -8.32510710e-01 1.46322861e-01]	[5.286634922027588, 2.602344274520874]
09f5cda3-e88c-4d97-880f-df23b1a2f354	biologically-inspired-sleep-algorithm-for-1	null	null	https://openreview.net/forum?id=r1xGnA4Kvr	https://openreview.net/pdf?id=r1xGnA4Kvr	Biologically inspired sleep algorithm for increased generalization and adversarial robustness in deep neural networks	Current artificial neural networks (ANNs) can perform and excel at a variety of tasks ranging from image classification to spam detection through training on large datasets of labeled data. While the trained network may perform well on similar testing data, inputs that differ even slightly from the training data may trigger unpredictable behavior. Due to this limitation, it is possible to design inputs with very small perturbations that can result in misclassification. These adversarial attacks present a security risk to deployed ANNs and indicate a divergence between how ANNs and humans perform classification. Humans are robust at behaving in the presence of noise and are capable of correctly classifying objects that are noisy, blurred, or otherwise distorted. It has been hypothesized that sleep promotes generalization of knowledge and improves robustness against noise in animals and humans. In this work, we utilize a biologically inspired sleep phase in ANNs and demonstrate the benefit of sleep on defending against adversarial attacks as well as in increasing ANN classification robustness. We compare the sleep algorithm's performance on various robustness tasks with two previously proposed adversarial defenses - defensive distillation and fine-tuning. We report an increase in robustness after sleep phase to adversarial attacks as well as to general image distortions for three datasets: MNIST, CUB200, and a toy dataset. Overall, these results demonstrate the potential for biologically inspired solutions to solve existing problems in ANNs and guide the development of more robust, human-like ANNs.	['Ramyaa Ramyaa', 'Giri Krishnan', 'Timothy Tadros', 'Maxim Bazhenov']	2020-05-01	null	null	null	iclr-2020-1	['spam-detection']	['natural-language-processing']	[ 5.50668955e-01 -7.38743320e-02 5.46736300e-01 -3.14108849e-01 2.24221200e-01 -1.03314543e+00 4.58127677e-01 2.47510592e-03 -6.14175439e-01 8.21732223e-01 -1.56553879e-01 -3.15561861e-01 -8.22368264e-02 -7.54909754e-01 -8.04690421e-01 -9.34253037e-01 -1.75818786e-01 1.26834482e-01 2.83285588e-01 -5.24992526e-01 3.00749511e-01 1.06316566e+00 -1.47905779e+00 2.12241918e-01 6.48324728e-01 7.90730655e-01 -2.25693956e-01 7.99981296e-01 5.79629421e-01 6.00583613e-01 -1.55356026e+00 -3.54346067e-01 5.37162602e-01 -3.04253459e-01 -5.56104362e-01 -3.59089762e-01 3.25055182e-01 1.74093544e-01 -3.59730929e-01 1.02574801e+00 6.73637211e-01 3.22506815e-01 4.37371463e-01 -1.34797382e+00 -8.82781327e-01 3.07143688e-01 7.08342046e-02 7.50130236e-01 7.40859061e-02 6.56551182e-01 1.00731000e-01 -7.35449567e-02 6.78333789e-02 1.26059842e+00 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62da7eae-290d-4ec8-a49f-a6f172a6211c	hybrik-transformer	2302.04774	null	https://arxiv.org/abs/2302.04774v4	https://arxiv.org/pdf/2302.04774v4.pdf	3D Human Pose and Shape Estimation via HybrIK-Transformer	HybrIK relies on a combination of analytical inverse kinematics and deep learning to produce more accurate 3D pose estimation from 2D monocular images. HybrIK has three major components: (1) pretrained convolution backbone, (2) deconvolution to lift 3D pose from 2D convolution features, (3) analytical inverse kinematics pass correcting deep learning prediction using learned distribution of plausible twist and swing angles. In this paper we propose an enhancement of the 2D to 3D lifting module, replacing deconvolution with Transformer, resulting in accuracy and computational efficiency improvement relative to the original HybrIK method. We demonstrate our results on commonly used H36M, PW3D, COCO and HP3D datasets. Our code is publicly available https://github.com/boreshkinai/hybrik-transformer.	['Boris N. Oreshkin']	2023-02-09	null	null	null	null	['3d-pose-estimation', '3d-human-pose-estimation', '3d-human-pose-and-shape-estimation', 'monocular-3d-human-pose-estimation']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[-4.95406210e-01 -1.35973776e-02 1.56911779e-02 -2.77105629e-01 -5.69344461e-01 -5.77987134e-01 5.54828286e-01 -5.52683592e-01 -2.71328181e-01 7.84751236e-01 4.68398869e-01 -3.78604084e-01 -1.65182903e-01 -5.44613421e-01 -1.16629231e+00 -3.94118726e-01 -1.95034161e-01 6.65817559e-01 -3.44512425e-02 -4.76871252e-01 2.63277292e-01 5.24069548e-01 -1.10043359e+00 1.74799547e-01 4.03291285e-01 1.11504066e+00 1.03347912e-01 8.89421463e-01 4.95184630e-01 6.18890226e-01 1.35902107e-01 -9.26940888e-02 5.33614457e-01 1.35492776e-02 -1.02843714e+00 -1.33076742e-01 2.77416289e-01 -6.83054686e-01 -4.16517019e-01 4.24423635e-01 7.54468322e-01 -8.02142918e-02 4.45790917e-01 -1.13807738e+00 -7.22654164e-01 2.41144836e-01 -5.03114581e-01 -1.06142581e-01 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424a0f54-e964-4391-ad71-c42cf3e7ec70	an-empirical-study-for-vietnamese	2010.09623	null	https://arxiv.org/abs/2010.09623v2	https://arxiv.org/pdf/2010.09623v2.pdf	An Empirical Study for Vietnamese Constituency Parsing with Pre-training	In this work, we use a span-based approach for Vietnamese constituency parsing. Our method follows the self-attention encoder architecture and a chart decoder using a CKY-style inference algorithm. We present analyses of the experiment results of the comparison of our empirical method using pre-training models XLM-Roberta and PhoBERT on both Vietnamese datasets VietTreebank and NIIVTB1. The results show that our model with XLM-Roberta archived the significantly F1-score better than other pre-training models, VietTreebank at 81.19% and NIIVTB1 at 85.70%.	['Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen', 'Duc-Vu Nguyen', 'Xuan-Thien Pham', 'Tuan-Vi Tran']	2020-10-19	null	null	null	null	['constituency-parsing', 'vietnamese-parsing', 'vietnamese-datasets']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[-2.22259209e-01 7.47898161e-01 -6.41252577e-01 -7.49791324e-01 -1.16487610e+00 -8.62621725e-01 4.51352328e-01 8.91852379e-03 -6.25944316e-01 1.32716525e+00 7.42515385e-01 -9.87263918e-01 4.97202754e-01 -8.90915155e-01 -8.73135388e-01 -1.30952671e-01 2.02334478e-01 6.09083891e-01 4.34611104e-02 -5.76685250e-01 2.70035088e-01 1.10301645e-02 -6.63016856e-01 6.02684975e-01 5.76361299e-01 4.12049085e-01 9.97146685e-03 1.28314662e+00 -4.62735295e-02 9.35437143e-01 -8.10747564e-01 -1.31402540e+00 -1.92399118e-02 -3.58173400e-01 -1.40285027e+00 -7.43417382e-01 8.41671765e-01 -4.37532902e-01 -4.02313858e-01 6.14498556e-01 3.11465323e-01 -1.73583388e-01 4.89613473e-01 -5.99282622e-01 -8.36053193e-01 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3a28f9af-e5bc-47dc-bfb0-f35f45628568	mask-is-all-you-need-rethinking-mask-r-cnn	2109.03426	null	https://arxiv.org/abs/2109.03426v1	https://arxiv.org/pdf/2109.03426v1.pdf	Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detection	Due to the large success in object detection and instance segmentation, Mask R-CNN attracts great attention and is widely adopted as a strong baseline for arbitrary-shaped scene text detection and spotting. However, two issues remain to be settled. The first is dense text case, which is easy to be neglected but quite practical. There may exist multiple instances in one proposal, which makes it difficult for the mask head to distinguish different instances and degrades the performance. In this work, we argue that the performance degradation results from the learning confusion issue in the mask head. We propose to use an MLP decoder instead of the "deconv-conv" decoder in the mask head, which alleviates the issue and promotes robustness significantly. And we propose instance-aware mask learning in which the mask head learns to predict the shape of the whole instance rather than classify each pixel to text or non-text. With instance-aware mask learning, the mask branch can learn separated and compact masks. The second is that due to large variations in scale and aspect ratio, RPN needs complicated anchor settings, making it hard to maintain and transfer across different datasets. To settle this issue, we propose an adaptive label assignment in which all instances especially those with extreme aspect ratios are guaranteed to be associated with enough anchors. Equipped with these components, the proposed method named MAYOR achieves state-of-the-art performance on five benchmarks including DAST1500, MSRA-TD500, ICDAR2015, CTW1500, and Total-Text.	['Weiping Wang', 'Hongbin Wang', 'Ning Jiang', 'Zhihong Tian', 'Dayan Wu', 'Youhui Guo', 'Yu Zhou', 'Xugong Qin']	2021-09-08	null	null	null	null	['scene-text-detection']	['computer-vision']	[ 3.45470130e-01 -1.10098319e-02 -1.24465756e-01 -2.34505758e-01 -6.11786783e-01 -4.19473559e-01 2.91770428e-01 -8.69456008e-02 -3.38934928e-01 4.12628084e-01 -2.41605893e-01 -2.87419349e-01 1.53490096e-01 -6.40487373e-01 -6.46247864e-01 -8.53476524e-01 5.95796943e-01 5.85796714e-01 6.02719069e-01 1.12822898e-01 2.53158629e-01 3.08802992e-01 -1.29362059e+00 4.11978275e-01 1.17222869e+00 1.11160743e+00 6.07683957e-01 1.09288223e-01 -5.49466908e-01 5.91070831e-01 -8.21989715e-01 -4.95036066e-01 3.08604360e-01 -2.31371462e-01 -5.08081496e-01 4.33263779e-01 3.49876314e-01 -2.46350244e-01 -2.27269873e-01 1.11132360e+00 5.42448401e-01 -8.68699998e-02 6.48299575e-01 -9.45708215e-01 -2.80854702e-01 7.90443718e-01 -1.35757887e+00 8.22117403e-02 -2.17128456e-01 2.05354363e-01 9.14595664e-01 -8.74645233e-01 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5a1ad1cd-72a4-44d8-b247-9ecc093ef9f4	regulation-of-mouse-learning-and-mood-by-the	2306.14556	null	https://arxiv.org/abs/2306.14556v1	https://arxiv.org/pdf/2306.14556v1.pdf	Regulation of Mouse Learning and Mood by the Anti-Inflammatory Cytokine Interleukin-10	Major depressive disorder is a widespread mood disorder. One of the most debilitating symptoms patients often experience is cognitive impairment. Recent findings suggest that inflammation is associated with depression and impaired cognition. Pro-inflammatory cytokines are elevated in the blood of depressed patients and impair learning and memory processes, suggesting that an anti-inflammatory approach might be beneficial for both depression and cognition. Utilizing the learned helplessness paradigm, we first established a mouse model of depression in which learning and memory are impaired. We found that learned helplessness (LH) impaired novel object recognition (NOR) and spatial working memory. LH mice also exhibited reduced hippocampal dendritic spine density and increased microglial activation compared to non-shocked (NS) mice or mice that were subjected to the learned helpless paradigm but did not exhibit learned helplessness (non-learned helpless, or NLH). These effects were mediated by microglia, as treatment with PLX5622, which depletes microglia and macrophages, restored learning and memory and hippocampal dendritic spine density in LH mice. However, PLX5622 also impaired learning and memory and reduced hippocampal dendritic spine density in NLH mice, suggesting that microglia in NLH mice are involved in the production of molecules that promote learning and memory. We found that microglial interleukin (IL)-10 levels were reduced in LH mice and IL-10 administration was sufficient to restore NOR, spatial working memory, and hippocampal dendritic spine density in LH mice, and in NLH mice treated with PLX5622, consistent with a pro-cognitive role for IL-10. Altogether, these data demonstrate the critical role of IL-10 in promoting learning and memory after learned helplessness.	['Ryan Joseph Worthen']	2023-06-26	null	null	null	null	['object-recognition']	['computer-vision']	[-1.50313556e-01 -9.87913609e-01 -1.12711981e-01 1.16485812e-01 -2.78651923e-01 -1.88132301e-01 1.38520315e-01 1.06830454e+00 -1.07811964e+00 7.88451433e-01 2.77966391e-02 2.50557177e-02 -4.84955013e-02 -9.26106036e-01 -5.32202721e-01 -9.93739605e-01 1.09167434e-01 3.74576539e-01 -1.51438221e-01 -1.93646371e-01 3.48609686e-01 8.99709880e-01 -1.08963108e+00 2.12292179e-01 8.59660685e-01 3.47407013e-01 5.17865002e-01 3.67120326e-01 -2.83374906e-01 3.37577611e-01 -2.83096910e-01 4.77699131e-01 -1.22871548e-01 -3.66783351e-01 -2.47180313e-01 -1.67055786e-01 -2.22182840e-01 -3.62690032e-01 2.59136915e-01 6.40888214e-01 7.70681560e-01 1.98714092e-01 6.38395429e-01 -1.31957066e+00 9.91648287e-02 -5.01820028e-01 -7.27692783e-01 7.49005377e-01 -2.50145160e-02 -2.72988170e-01 -1.67372435e-01 -1.09079707e+00 1.86726615e-01 1.34566045e+00 3.48418534e-01 6.13276541e-01 -1.86150181e+00 -1.00929844e+00 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9de14740-3938-4692-a79b-76ffc4d3e580	bilingual-correspondence-recursive	null	null	https://aclanthology.org/D15-1146	https://aclanthology.org/D15-1146.pdf	Bilingual Correspondence Recursive Autoencoder for Statistical Machine Translation	null	['Deyi Xiong', 'Biao Zhang', 'Yang Liu', 'Min Zhang', 'Junfeng Yao', 'Jinsong Su']	2015-09-01	null	null	null	emnlp-2015-9	['learning-semantic-representations']	['methodology']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 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f08fa905-4e3d-4e50-8d8b-6e794a8b7da0	many-languages-one-parser	1602.01595	null	http://arxiv.org/abs/1602.01595v4	http://arxiv.org/pdf/1602.01595v4.pdf	Many Languages, One Parser	We train one multilingual model for dependency parsing and use it to parse sentences in several languages. The parsing model uses (i) multilingual word clusters and embeddings; (ii) token-level language information; and (iii) language-specific features (fine-grained POS tags). This input representation enables the parser not only to parse effectively in multiple languages, but also to generalize across languages based on linguistic universals and typological similarities, making it more effective to learn from limited annotations. Our parser's performance compares favorably to strong baselines in a range of data scenarios, including when the target language has a large treebank, a small treebank, or no treebank for training.	['Chris Dyer', 'Miguel Ballesteros', 'Waleed Ammar', 'Noah A. Smith', 'George Mulcaire']	2016-02-04	many-languages-one-parser-1	https://aclanthology.org/Q16-1031	https://aclanthology.org/Q16-1031.pdf	tacl-2016-1	['cross-lingual-zero-shot-dependency-parsing']	['natural-language-processing']	[-4.05858159e-01 2.27594785e-02 -5.26651919e-01 -5.78456342e-01 -1.18577147e+00 -1.07516742e+00 3.85012627e-01 5.70756435e-01 -8.68068278e-01 8.07673216e-01 6.81157291e-01 -9.21938479e-01 4.72183347e-01 -7.33018339e-01 -4.14694250e-01 -2.22633764e-01 -2.25068033e-01 5.01699686e-01 1.77729875e-01 -3.47654969e-01 -1.07875504e-01 4.33374077e-01 -7.18450010e-01 9.71906856e-02 9.10597384e-01 3.36116374e-01 6.08672023e-01 6.41950130e-01 -6.30080342e-01 8.10765207e-01 -5.09172678e-01 -4.42907244e-01 1.22121811e-01 -9.37651470e-02 -1.06005645e+00 -4.08203006e-01 2.62067676e-01 7.88779631e-02 8.29038769e-03 1.03233492e+00 1.76052883e-01 -1.01273112e-01 2.96243668e-01 -4.24557239e-01 -8.58745992e-01 9.96725738e-01 -4.04107988e-01 4.01268929e-01 4.23931241e-01 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045b07b0-66ac-45df-b0f9-8261469b7d7c	early-icu-mortality-prediction-and-survival	2109.03048	null	https://arxiv.org/abs/2109.03048v1	https://arxiv.org/pdf/2109.03048v1.pdf	Early ICU Mortality Prediction and Survival Analysis for Respiratory Failure	Respiratory failure is the one of major causes of death in critical care unit. During the outbreak of COVID-19, critical care units experienced an extreme shortage of mechanical ventilation because of respiratory failure related syndromes. To help this, the early mortality risk prediction in patients who suffer respiratory failure can provide timely support for clinical treatment and resource management. In the study, we propose a dynamic modeling approach for early mortality risk prediction of the respiratory failure patients based on the first 24 hours ICU physiological data. Our proposed model is validated on the eICU collaborate database. We achieved a high AUROC performance (80-83%) and significantly improved AUCPR 4% on Day 5 since ICU admission, compared to the state-of-art prediction models. In addition, we illustrated that the survival curve includes the time-varying information for the early ICU admission survival analysis.	['Chun-An Chou', 'Yilin Yin']	2021-09-06	null	null	null	null	['icu-mortality', 'respiratory-failure']	['medical', 'medical']	[ 4.46145721e-02 -5.93847454e-01 -9.12195072e-03 5.39103448e-02 -9.37339440e-02 -3.40577692e-01 -2.82755464e-01 4.36533540e-01 -5.77084541e-01 8.96432638e-01 -3.94396074e-02 -7.11261034e-01 -8.04784656e-01 -4.11182225e-01 4.37339395e-02 -7.55783260e-01 -3.78505588e-01 9.62169051e-01 1.45423546e-01 2.35668957e-01 8.66432711e-02 7.69145429e-01 -9.34939921e-01 2.22334504e-01 7.17906177e-01 8.21690738e-01 2.33266726e-01 9.61910009e-01 2.32206836e-01 8.47403944e-01 -3.51302564e-01 3.37042361e-01 1.68482766e-01 -7.08029926e-01 -4.26371545e-01 -6.70162797e-01 -8.58687401e-01 -5.93777835e-01 -9.76974592e-02 5.07940687e-02 9.29384291e-01 -5.46214618e-02 1.01505172e+00 -1.40320730e+00 4.00891691e-01 4.02157903e-01 3.48644853e-02 4.59635854e-01 1.09375156e-02 2.32793674e-01 3.29748809e-01 -7.99854636e-01 7.03862309e-02 5.76091707e-01 7.32566476e-01 8.17782938e-01 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0c5ecb62-e3e4-4f6a-bd0d-195d8d5e24b9	augmenting-dl-with-adversarial-training-for	null	null	https://dl.acm.org/doi/abs/10.1145/3386580	https://dl.acm.org/doi/abs/10.1145/3386580	Augmenting DL with Adversarial Training for Robust Prediction of Epilepsy Seizures	Epilepsy is a chronic medical condition that involves abnormal brain activity causing patients to lose control of awareness or motor activity. As a result, detection of pre-ictal states, before the onset of a seizure, can be lifesaving. The problem is challenging because it is difficult to discern between electroencephalogram signals in pre-ictal states versus signals in normal inter-ictal states. There are three key challenges that have not been addressed previously: (1) the inconsistent performance of prediction models across patients, (2) the lack of perfect prediction to protect patients from any episode, and (3) the limited amount of pre-ictal labeled data for advancing machine learning methods. This article addresses these limitations through a novel approach that uses adversarial examples with optimized tuning of a combined convolutional neural network and gated recurrent unit. Compared to the state of the art, the results showed an improvement of 3x in model robustness as measured in reduced variations and superior accuracy of the area under the curve, with an average increase of 6.7%. The proposed method also exhibited superior performance with other advances in the field of machine learning and customized for epilepsy prediction including data augmentation with Gaussian noise and multitask learning.	['Hazem', 'Mohhamad; Hajj', 'Reem; Dhaybi', 'Marc; Mahmoud', 'Amir; Djandji', 'Hussein']	2020-06-01	null	null	null	null	['epilepsy-prediction']	['medical']	[ 5.13494253e-01 -6.49822503e-03 2.35007018e-01 -7.55110383e-02 -8.53148937e-01 -3.10019076e-01 2.39343569e-01 2.11541712e-01 -3.06877285e-01 9.91021633e-01 1.27163097e-01 -3.94965649e-01 -1.82612389e-01 -2.29331061e-01 -5.05119264e-01 -8.02588761e-01 -5.26680887e-01 1.80968717e-01 -3.29306524e-04 -7.72978738e-02 2.20844224e-01 4.63469505e-01 -1.15511990e+00 4.43531781e-01 7.72858918e-01 1.14332271e+00 2.55538728e-02 3.99175316e-01 3.28093022e-01 6.13051116e-01 -8.21291327e-01 3.18175286e-01 1.38654977e-01 -4.52824742e-01 -4.73692834e-01 -3.39566439e-01 -1.41401738e-01 -8.46177414e-02 -2.13784307e-01 9.05192435e-01 9.76939857e-01 -3.79891902e-01 5.07713020e-01 -1.16225779e+00 -3.15104067e-01 3.56331736e-01 -3.38679552e-01 5.53313613e-01 2.43585899e-01 5.32556921e-02 2.98259765e-01 -4.43111509e-01 7.56036416e-02 4.41223264e-01 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4.62651253e-01 -4.96050000e-01 -3.77382398e-01 -1.23384035e+00 -6.98134601e-01 1.90324828e-01 5.28537929e-01 -6.15428746e-01 -6.79711461e-01 -1.72526196e-01]	[13.243879318237305, 3.526808261871338]
1a95340a-4928-4059-b122-c9636384262d	unified-chinese-license-plate-detection-and	2205.03582	null	https://arxiv.org/abs/2205.03582v1	https://arxiv.org/pdf/2205.03582v1.pdf	Unified Chinese License Plate Detection and Recognition with High Efficiency	Recently, deep learning-based methods have reached an excellent performance on License Plate (LP) detection and recognition tasks. However, it is still challenging to build a robust model for Chinese LPs since there are not enough large and representative datasets. In this work, we propose a new dataset named Chinese Road Plate Dataset (CRPD) that contains multi-objective Chinese LP images as a supplement to the existing public benchmarks. The images are mainly captured with electronic monitoring systems with detailed annotations. To our knowledge, CRPD is the largest public multi-objective Chinese LP dataset with annotations of vertices. With CRPD, a unified detection and recognition network with high efficiency is presented as the baseline. The network is end-to-end trainable with totally real-time inference efficiency (30 fps with 640p). The experiments on several public benchmarks demonstrate that our method has reached competitive performance. The code and dataset will be publicly available at https://github.com/yxgong0/CRPD.	['Mei Xie', 'Zheng Ma', 'Zhiwei Xie', 'Peicheng Wu', 'Xinchen Lu', 'Shuai Tao', 'Linjie Deng', 'Yanxiang Gong']	2022-05-07	null	null	null	null	['license-plate-detection']	['computer-vision']	[-5.68660498e-01 -5.95152497e-01 -2.43945807e-01 -1.78049371e-01 -1.26615608e+00 -4.57426995e-01 2.00334042e-01 -6.63274705e-01 -1.98984072e-01 4.94959205e-01 -2.58638233e-01 -2.89574713e-01 5.07976711e-01 -7.12327540e-01 -9.69659925e-01 -5.79013467e-01 4.41518366e-01 4.69998151e-01 7.03738093e-01 1.34653971e-01 3.79842937e-01 5.81309497e-01 -1.28830373e+00 3.74536127e-01 7.65256822e-01 1.10995746e+00 2.24051237e-01 4.89342481e-01 8.50281790e-02 7.10373461e-01 -3.60574841e-01 -7.57088900e-01 5.01763165e-01 3.31393182e-01 -6.09512448e-01 2.39622831e-01 6.29866779e-01 -5.06413341e-01 -7.83055007e-01 1.01600266e+00 6.85264289e-01 -1.10032953e-01 4.22696918e-01 -1.35995030e+00 -9.94608402e-01 1.68147162e-01 -9.97502208e-01 1.41009465e-02 -2.57064495e-02 3.03956062e-01 9.62985933e-01 -1.34735930e+00 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e277abb7-b05a-42d8-addd-cbd97e78d307	monograspnet-6-dof-grasping-with-a-single-rgb	2209.13036	null	https://arxiv.org/abs/2209.13036v2	https://arxiv.org/pdf/2209.13036v2.pdf	MonoGraspNet: 6-DoF Grasping with a Single RGB Image	6-DoF robotic grasping is a long-lasting but unsolved problem. Recent methods utilize strong 3D networks to extract geometric grasping representations from depth sensors, demonstrating superior accuracy on common objects but perform unsatisfactorily on photometrically challenging objects, e.g., objects in transparent or reflective materials. The bottleneck lies in that the surface of these objects can not reflect back accurate depth due to the absorption or refraction of light. In this paper, in contrast to exploiting the inaccurate depth data, we propose the first RGB-only 6-DoF grasping pipeline called MonoGraspNet that utilizes stable 2D features to simultaneously handle arbitrary object grasping and overcome the problems induced by photometrically challenging objects. MonoGraspNet leverages keypoint heatmap and normal map to recover the 6-DoF grasping poses represented by our novel representation parameterized with 2D keypoints with corresponding depth, grasping direction, grasping width, and angle. Extensive experiments in real scenes demonstrate that our method can achieve competitive results in grasping common objects and surpass the depth-based competitor by a large margin in grasping photometrically challenging objects. To further stimulate robotic manipulation research, we additionally annotate and open-source a multi-view and multi-scene real-world grasping dataset, containing 120 objects of mixed photometric complexity with 20M accurate grasping labels.	['Benjamin Busam', 'Nassir Navab', 'Federico Tombari', 'Fabian Manhardt', 'Yan Di', 'HyunJun Jung', 'Shun-Cheng Wu', 'Dianye Huang', 'Guangyao Zhai']	2022-09-26	null	null	null	null	['robotic-grasping']	['robots']	[-7.40901008e-02 -6.34297878e-02 1.25173077e-01 -3.30250531e-01 -5.99270880e-01 -8.85840595e-01 1.73809230e-01 -1.48701981e-01 9.49097648e-02 6.47215992e-02 -4.09517884e-02 2.12053210e-01 -3.44960541e-01 -6.71769202e-01 -1.03666759e+00 -7.16409385e-01 -1.70171589e-01 7.02503264e-01 2.65547186e-01 -2.16987863e-01 3.56851071e-01 9.17821229e-01 -1.72840118e+00 4.52335984e-01 4.40545708e-01 1.23973012e+00 5.54621398e-01 3.68806362e-01 -1.71188727e-01 2.11132482e-01 -2.87139714e-01 -2.52802432e-01 8.53116035e-01 5.64086854e-01 -4.95603919e-01 -1.10225193e-01 8.09321105e-01 -1.09976482e+00 -5.33969045e-01 8.00973058e-01 3.36469710e-01 -3.55195999e-01 6.03592038e-01 -1.44667864e+00 -7.71999300e-01 4.88466620e-01 -6.22919083e-01 -5.09766698e-01 5.72098851e-01 3.50789666e-01 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f1d0c130-ad2d-45b6-8f72-00a58cf18846	synthetic-traffic-generation-with-wasserstein	null	null	https://ieeexplore.ieee.org/document/10001157	https://ieeexplore.ieee.org/document/10001157	Synthetic Traffic Generation with Wasserstein Generative Adversarial Networks	Network traffic data are critical for network research. With the help of synthetic traffic, researchers can readily generate data for network simulation and performance evaluation. However, the state-of-the-art traffic generators are either too simple to generate realistic traffic or require the implementation of original applications and user operations. We propose Synthetic PAcket Traffic Generative Adversarial Networks (SPATGAN) that are capable of generating synthetic traffic. The framework includes a server agent and a client agent, which transmit synthetic packets to each other and take the opponent's synthetic packets as conditional labels for the built-in Timing Synthesis Generative Adversarial Networks (TSynGAN) and a Packet Synthesis Generative Adversarial Networks (PSynGAN) to generate synthetic traffic. The evaluations demonstrate that the proposed framework can generate traffic whose distribution resembles real traffic distribution.	['Chih–Yu Wang', 'Po–Yu Chou', 'Yu–Ying Chen', 'Chao–Lun Wu']	2022-12-05	null	null	null	ieee-global-communications-conference-2022-12	['synthetic-data-generation', 'synthetic-data-generation', 'intelligent-communication']	['medical', 'miscellaneous', 'time-series']	[ 1.95926011e-01 -1.27640992e-01 7.79486671e-02 -1.63831905e-01 -1.51694998e-01 -6.48763835e-01 6.46473467e-01 -8.69004071e-01 -2.98684705e-02 1.16226161e+00 -5.31661630e-01 -9.33561146e-01 2.93600261e-01 -1.37161517e+00 -5.47652006e-01 -5.82557738e-01 1.07910074e-01 7.77747333e-01 7.85790443e-01 -2.51595676e-01 4.90141520e-03 7.89976895e-01 -1.11241639e+00 2.19801545e-01 8.01311672e-01 6.97940648e-01 -1.55444190e-01 1.04820037e+00 -4.98983175e-01 9.12636220e-01 -1.46936584e+00 -5.65008461e-01 4.84688044e-01 -6.92563236e-01 -3.46500009e-01 -2.10786268e-01 -3.58933210e-01 -5.08175135e-01 -8.50913703e-01 7.15908408e-01 4.50639188e-01 -1.75309703e-01 4.65659678e-01 -2.39551687e+00 -4.36583728e-01 8.91946971e-01 -2.21323654e-01 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1.57460868e-01 3.14374119e-01 3.90996784e-01 5.63154519e-01 3.39767188e-01 1.08616865e+00 1.21315110e+00 -5.65427899e-01 -7.32432306e-02 -6.84568822e-01 -4.94984925e-01 2.67377168e-01 -9.42785889e-02 -4.85352039e-01 -3.10704291e-01 -5.44581749e-02]	[5.382530212402344, 7.479783058166504]
f5164fe8-1939-4ca1-af8c-66021265c343	agmn-association-graph-based-graph-matching	2301.04733	null	https://arxiv.org/abs/2301.04733v1	https://arxiv.org/pdf/2301.04733v1.pdf	AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms	Semantic labeling of coronary arterial segments in invasive coronary angiography (ICA) is important for automated assessment and report generation of coronary artery stenosis in the computer-aided diagnosis of coronary artery disease (CAD). Inspired by the training procedure of interventional cardiologists for interpreting the structure of coronary arteries, we propose an association graph-based graph matching network (AGMN) for coronary arterial semantic labeling. We first extract the vascular tree from invasive coronary angiography (ICA) and convert it into multiple individual graphs. Then, an association graph is constructed from two individual graphs where each vertex represents the relationship between two arterial segments. Using the association graph, the AGMN extracts the vertex features by the embedding module, aggregates the features from adjacent vertices and edges by graph convolution network, and decodes the features to generate the semantic mappings between arteries. By learning the mapping of arterial branches between two individual graphs, the unlabeled arterial segments are classified by the labeled segments to achieve semantic labeling. A dataset containing 263 ICAs was employed to train and validate the proposed model, and a five-fold cross-validation scheme was performed. Our AGMN model achieved an average accuracy of 0.8264, an average precision of 0.8276, an average recall of 0.8264, and an average F1-score of 0.8262, which significantly outperformed existing coronary artery semantic labeling methods. In conclusion, we have developed and validated a new algorithm with high accuracy, interpretability, and robustness for coronary artery semantic labeling on ICAs.	['Weihua Zhou', 'Guang-Uei Hung', 'Drew Pienta', 'Michele Esposito', 'Jingfeng Jiang', 'Zhihui Xu', 'Chen Zhao']	2023-01-11	null	null	null	null	['graph-matching']	['graphs']	[ 1.97482944e-01 5.20551264e-01 -3.57557386e-01 -5.01747310e-01 -6.19696975e-01 -7.06066728e-01 -8.73706788e-02 3.18369180e-01 4.17294241e-02 4.18231696e-01 -6.39022663e-02 -9.00510192e-01 -1.72886327e-01 -1.05281866e+00 -2.37932801e-01 -3.91907483e-01 -2.13331982e-01 6.47141576e-01 1.75938725e-01 3.50541443e-01 -3.81941050e-02 6.62465811e-01 -8.72305453e-01 2.39142597e-01 8.85615289e-01 1.16726995e+00 -1.31700560e-01 6.52223766e-01 -5.98809004e-01 6.88105345e-01 -3.88210267e-01 -5.01058817e-01 1.94606200e-01 -8.23419511e-01 -8.76295447e-01 4.56655741e-01 2.91161779e-02 1.00604661e-01 -3.99544209e-01 1.09567451e+00 4.52636868e-01 -3.10338736e-01 5.92145026e-01 -9.70194221e-01 -8.26425374e-01 5.39446294e-01 -4.55964237e-01 2.80366480e-01 -2.28531539e-01 8.51776171e-03 1.07164752e+00 -5.92561126e-01 6.35680199e-01 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fba39afd-1c12-41f3-9dc7-da6fc0217920	vision-transformers-and-yolov5-based-driver	2209.01401	null	https://arxiv.org/abs/2209.01401v1	https://arxiv.org/pdf/2209.01401v1.pdf	Vision Transformers and YoloV5 based Driver Drowsiness Detection Framework	Human drivers have distinct driving techniques, knowledge, and sentiments due to unique driving traits. Driver drowsiness has been a serious issue endangering road safety; therefore, it is essential to design an effective drowsiness detection algorithm to bypass road accidents. Miscellaneous research efforts have been approached the problem of detecting anomalous human driver behaviour to examine the frontal face of the driver and automobile dynamics via computer vision techniques. Still, the conventional methods cannot capture complicated driver behaviour features. However, with the origin of deep learning architectures, a substantial amount of research has also been executed to analyze and recognize driver's drowsiness using neural network algorithms. This paper introduces a novel framework based on vision transformers and YoloV5 architectures for driver drowsiness recognition. A custom YoloV5 pre-trained architecture is proposed for face extraction with the aim of extracting Region of Interest (ROI). Owing to the limitations of previous architectures, this paper introduces vision transformers for binary image classification which is trained and validated on a public dataset UTA-RLDD. The model had achieved 96.2\% and 97.4\% as it's training and validation accuracies respectively. For the further evaluation, proposed framework is tested on a custom dataset of 39 participants in various light circumstances and achieved 95.5\% accuracy. The conducted experimentations revealed the significant potential of our framework for practical applications in smart transportation systems.	['Mallikharjuna Rao K', 'Jai Vardhan', 'Kundrapu Supriya', 'Ghanta Sai Krishna']	2022-09-03	null	null	null	null	['miscellaneous']	['miscellaneous']	[ 2.03913003e-02 5.03907204e-02 2.21113046e-03 -4.00276899e-01 -1.97318524e-01 -3.33891600e-01 4.40627843e-01 -2.81758100e-01 -2.52806932e-01 4.18224275e-01 -1.35764465e-01 -4.15126354e-01 -2.09453419e-01 -5.79138696e-01 -2.40688264e-01 -7.48287737e-01 3.81246984e-01 -2.73601443e-01 1.68588758e-01 -4.24595118e-01 3.96423399e-01 8.05617988e-01 -2.01451635e+00 3.95532288e-02 6.21948004e-01 1.17820418e+00 -2.44945139e-01 8.80426288e-01 4.08187240e-01 7.82839715e-01 -3.90682280e-01 -3.50926936e-01 5.42950809e-01 -7.22320052e-03 -2.46772796e-01 9.03638601e-02 5.79025507e-01 -3.50042492e-01 -5.12201726e-01 8.19751501e-01 6.59847558e-01 1.49290994e-01 5.81838250e-01 -1.78475177e+00 -6.95277929e-01 -3.35081160e-01 -6.60749137e-01 7.43533134e-01 -4.53752503e-02 5.17098188e-01 3.51009935e-01 -6.39613450e-01 -4.96201143e-02 8.95444214e-01 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8fb8c129-9d21-4812-8464-f3d35c1eda6c	dag-matters-gflownets-enhanced-explainer-for	2303.02448	null	https://arxiv.org/abs/2303.02448v1	https://arxiv.org/pdf/2303.02448v1.pdf	DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks	Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over the years. Existing literature mainly focus on selecting a subgraph, through combinatorial optimization, to provide faithful explanations. However, the exponential size of candidate subgraphs limits the applicability of state-of-the-art methods to large-scale GNNs. We enhance on this through a different approach: by proposing a generative structure -- GFlowNets-based GNN Explainer (GFlowExplainer), we turn the optimization problem into a step-by-step generative problem. Our GFlowExplainer aims to learn a policy that generates a distribution of subgraphs for which the probability of a subgraph is proportional to its' reward. The proposed approach eliminates the influence of node sequence and thus does not need any pre-training strategies. We also propose a new cut vertex matrix to efficiently explore parent states for GFlowNets structure, thus making our approach applicable in a large-scale setting. We conduct extensive experiments on both synthetic and real datasets, and both qualitative and quantitative results show the superiority of our GFlowExplainer.	['Yan Pang', 'Jianye Hao', 'Zhigang Li', 'Yinchuan Li', 'Wenqian Li']	2023-03-04	null	null	null	null	['combinatorial-optimization']	['methodology']	[ 2.90884495e-01 8.59647512e-01 -3.76316667e-01 -3.62002462e-01 -3.27017993e-01 -4.11925256e-01 6.46309853e-01 -5.18974587e-02 1.94289200e-02 8.91622365e-01 1.31018624e-01 -6.90723360e-01 -1.88413620e-01 -1.08306170e+00 -1.18176162e+00 -5.93111694e-01 -4.27880958e-02 4.86397624e-01 1.99543372e-01 -5.61031746e-03 4.03868854e-02 2.97639221e-01 -1.16576147e+00 -2.31971033e-02 9.75588322e-01 4.84705031e-01 3.13241124e-01 3.33517700e-01 -4.56945188e-02 6.55434608e-01 -2.61410058e-01 -6.03021324e-01 3.07556480e-01 -6.22291803e-01 -6.91532195e-01 2.31077090e-01 4.17543799e-02 3.45475078e-02 -5.12416482e-01 1.12327945e+00 2.24450201e-01 4.13302369e-02 5.03292799e-01 -1.44362628e+00 -6.94609642e-01 1.29741526e+00 -5.65092683e-01 2.73695067e-02 -1.45033717e-01 2.19512254e-01 1.46030891e+00 -4.06480491e-01 7.27868021e-01 1.29720211e+00 4.43304569e-01 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-6.35778010e-01 -6.35032713e-01 -5.89722335e-01 -4.43321727e-02 -1.18411286e-02 -6.53744340e-01 -3.71653229e-01 -3.54853719e-01]	[7.436333179473877, 6.238124847412109]
312aea8f-aa35-4068-994f-672039100f8c	defending-water-treatment-networks-exploiting	2008.12618	null	https://arxiv.org/abs/2008.12618v1	https://arxiv.org/pdf/2008.12618v1.pdf	Defending Water Treatment Networks: Exploiting Spatio-temporal Effects for Cyber Attack Detection	While Water Treatment Networks (WTNs) are critical infrastructures for local communities and public health, WTNs are vulnerable to cyber attacks. Effective detection of attacks can defend WTNs against discharging contaminated water, denying access, destroying equipment, and causing public fear. While there are extensive studies in WTNs attack detection, they only exploit the data characteristics partially to detect cyber attacks. After preliminary exploring the sensing data of WTNs, we find that integrating spatio-temporal knowledge, representation learning, and detection algorithms can improve attack detection accuracy. To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatio-temporal characteristics of cyber attacks in WTNs. In particular, we propose a spatio-temporal representation framework specially tailored to cyber attacks after separating the sensing data of WTNs into a sequence of time segments. This framework has two key components. The first component is a temporal embedding module to preserve temporal patterns within a time segment by projecting the time segment of a sensor into a temporal embedding vector. We then construct Spatio-Temporal Graphs (STGs), where a node is a sensor and an attribute is the temporal embedding vector of the sensor, to describe the state of the WTNs. The second component is a spatial embedding module, which learns the final fused embedding of the WTNs from STGs. In addition, we devise an improved one class-SVM model that utilizes a new designed pairwise kernel to detect cyber attacks. The devised pairwise kernel augments the distance between normal and attack patterns in the fused embedding space. Finally, we conducted extensive experimental evaluations with real-world data to demonstrate the effectiveness of our framework.	['Leilei Sun', 'Jingbo Zhou', 'Pengyang Wang', 'Bowen Du', 'Yanjie Fu', 'Dongjie Wang']	2020-08-26	null	null	null	null	['cyber-attack-detection']	['miscellaneous']	[ 2.40810484e-01 -1.56634152e-01 4.82461005e-02 2.00591311e-01 -2.22266600e-01 -6.79908872e-01 6.36050045e-01 6.48072124e-01 -2.19449610e-01 1.37869362e-02 1.22625388e-01 -5.23931205e-01 -3.08429062e-01 -1.27982807e+00 -4.75179374e-01 -1.13388765e+00 -5.95531166e-01 -4.66654301e-01 3.96857440e-01 -1.86070070e-01 1.76310420e-01 8.20804477e-01 -7.68183768e-01 8.30640271e-02 7.30127275e-01 1.05937505e+00 -3.60907108e-01 4.55582231e-01 -1.71367899e-02 6.40061080e-01 -5.58302462e-01 1.90515637e-01 3.94179285e-01 -1.75677180e-01 -4.57147121e-01 -8.84758607e-02 -2.27739289e-01 -1.00203641e-01 -8.14661801e-01 1.15268648e+00 1.37803718e-01 8.25937539e-02 4.27754700e-01 -1.71858561e+00 -4.46844578e-01 4.53717977e-01 -4.57122058e-01 4.08446610e-01 3.81050825e-01 4.45708722e-01 7.88569510e-01 -3.97346824e-01 1.48992702e-01 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-5.37279665e-01 -6.74944282e-01 -8.13825846e-01 6.83308363e-01 7.34294116e-01 -4.45605040e-01 -8.27154934e-01 -3.80981445e-01]	[7.195394992828369, 2.704976797103882]
dee0ba74-d430-4a53-b22a-87a7a4f89306	graph-neural-networks-for-improved-el-nino	2012.01598	null	https://arxiv.org/abs/2012.01598v3	https://arxiv.org/pdf/2012.01598v3.pdf	Graph Neural Networks for Improved El Niño Forecasting	Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which are difficult to interpret and can fail to model large-scale atmospheric patterns called teleconnections. Hence, we propose the application of spatiotemporal Graph Neural Networks (GNN) to forecast ENSO at long lead times, finer granularity and improved predictive skill than current state-of-the-art methods. The explicit modeling of information flow via edges may also allow for more interpretable forecasts. Preliminary results are promising and outperform state-of-the art systems for projections 1 and 3 months ahead.	['Björn Lütjens', 'Salomey Osei', 'Willa Potosnak', 'Ernest Pokropek', 'Arthur Fender C. Bucker', 'Emma Erickson', 'Salva Rühling Cachay']	2020-12-02	null	null	null	null	['spatio-temporal-forecasting']	['time-series']	[-5.66077292e-01 1.07695274e-01 -2.07666948e-01 -3.50882977e-01 6.89952374e-01 -5.22301853e-01 1.11191523e+00 1.96924210e-01 2.10387528e-01 8.99110496e-01 4.06619042e-01 -1.33671248e+00 -3.61542255e-01 -1.40513730e+00 -4.09559995e-01 -3.42852235e-01 -9.46890950e-01 5.87237835e-01 -4.60805707e-02 -9.20120656e-01 -2.79028594e-01 7.46943057e-01 -1.01462018e+00 -4.75266166e-02 8.08849514e-01 7.31854796e-01 -3.20945650e-01 8.07470322e-01 -4.07730192e-01 8.61597300e-01 -2.63095081e-01 7.10357651e-02 7.81713575e-02 -5.38535237e-01 -5.47037661e-01 -3.01740021e-01 2.63863772e-01 -2.26468503e-01 -5.90976894e-01 8.24077666e-01 1.08827449e-01 2.15491533e-01 4.22274292e-01 -1.06751573e+00 -9.33480322e-01 7.32516229e-01 -3.98856133e-01 7.76133597e-01 -1.24137178e-01 1.66700691e-01 1.01908076e+00 -5.35256624e-01 7.20070958e-01 1.21976650e+00 1.23354161e+00 1.61449928e-02 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5e70f77b-018d-4c51-abe7-2989f20fae70	a-novel-speech-feature-fusion-algorithm-for	2212.00329	null	https://arxiv.org/abs/2212.00329v1	https://arxiv.org/pdf/2212.00329v1.pdf	A Novel Speech Feature Fusion Algorithm for Text-Independent Speaker Recognition	A novel speech feature fusion algorithm with independent vector analysis (IVA) and parallel convolutional neural network (PCNN) is proposed for text-independent speaker recognition. Firstly, some different feature types, such as the time domain (TD) features and the frequency domain (FD) features, can be extracted from a speaker's speech, and the TD and the FD features can be considered as the linear mixtures of independent feature components (IFCs) with an unknown mixing system. To estimate the IFCs, the TD and the FD features of the speaker's speech are concatenated to build the TD and the FD feature matrix, respectively. Then, a feature tensor of the speaker's speech is obtained by paralleling the TD and the FD feature matrix. To enhance the dependence on different feature types and remove the redundancies of the same feature type, the independent vector analysis (IVA) can be used to estimate the IFC matrices of TD and FD features with the feature tensor. The IFC matrices are utilized as the input of the PCNN to extract the deep features of the TD and FD features, respectively. The deep features can be integrated to obtain the fusion feature of the speaker's speech. Finally, the fusion feature of the speaker's speech is employed as the input of a deep convolutional neural network (DCNN) classifier for speaker recognition. The experimental results show the effectiveness and performances of the proposed speaker recognition system.	['Ye Zhang', 'Chengben Xu', 'Biao Ma']	2022-12-01	null	null	null	null	['speaker-recognition', 'text-independent-speaker-recognition']	['speech', 'speech']	[-1.09303348e-01 -6.43639982e-01 2.31212720e-01 -5.77543795e-01 -5.01170933e-01 -2.83244699e-01 4.34875727e-01 -3.67838591e-01 -2.59042114e-01 1.01468183e-01 3.99156034e-01 -1.19544432e-01 -2.42969275e-01 -4.72469151e-01 -2.24217877e-01 -1.26087296e+00 3.23199318e-03 -4.24702205e-02 -2.94795215e-01 -2.49547556e-01 8.93117115e-02 4.74603891e-01 -1.95988750e+00 2.01360449e-01 7.04323232e-01 1.55646217e+00 4.49062884e-02 6.45668685e-01 -4.79830205e-01 6.72857106e-01 -8.68029654e-01 2.58156002e-01 6.78825798e-03 -1.54204905e-01 -3.91867340e-01 3.46989602e-01 -2.81294346e-01 -5.36202669e-01 -4.81777757e-01 7.97149360e-01 5.93837917e-01 4.93391216e-01 5.62699854e-01 -1.30755031e+00 -4.78362411e-01 5.91633320e-01 -2.18598023e-01 3.15656513e-01 1.98295906e-01 -1.58352062e-01 6.13530636e-01 -1.33228087e+00 6.75807893e-03 1.62507367e+00 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613070e2-1f67-4870-98e5-e68d45d60777	acsc-automatic-calibration-for-non-repetitive	2011.08516	null	https://arxiv.org/abs/2011.08516v1	https://arxiv.org/pdf/2011.08516v1.pdf	ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems	Recently, the rapid development of Solid-State LiDAR (SSL) enables low-cost and efficient obtainment of 3D point clouds from the environment, which has inspired a large quantity of studies and applications. However, the non-uniformity of its scanning pattern, and the inconsistency of the ranging error distribution bring challenges to its calibration task. In this paper, we proposed a fully automatic calibration method for the non-repetitive scanning SSL and camera systems. First, a temporal-spatial-based geometric feature refinement method is presented, to extract effective features from SSL point clouds; then, the 3D corners of the calibration target (a printed checkerboard) are estimated with the reflectance distribution of points. Based on the above, a target-based extrinsic calibration method is finally proposed. We evaluate the proposed method on different types of LiDAR and camera sensor combinations in real conditions, and achieve accuracy and robustness calibration results. The code is available at https://github.com/HViktorTsoi/ACSC.git .	['Dian Liu', 'Yunxiang He', 'Zhenchao Ouyang', 'Jianwei Niu', 'Jiahe Cui']	2020-11-17	null	null	null	null	['camera-auto-calibration', '3d-geometry-perception']	['computer-vision', 'computer-vision']	[ 1.62116051e-01 -6.40360117e-01 1.60797179e-01 -5.10141790e-01 -5.96055508e-01 -5.07107198e-01 3.44847053e-01 -2.04744875e-01 -1.36900619e-01 3.86063993e-01 -4.46263731e-01 -1.22497544e-01 -2.12376580e-01 -7.82559097e-01 -5.62103808e-01 -5.30459106e-01 3.43335420e-01 5.58536887e-01 5.12484848e-01 1.11179732e-01 4.23780769e-01 9.25335586e-01 -1.62904131e+00 -5.25985837e-01 1.16178334e+00 1.14236748e+00 4.70681250e-01 8.85914117e-02 -2.12369934e-01 -2.14416042e-01 -9.83836949e-02 4.72524716e-03 3.96120191e-01 -4.70913574e-02 2.13693138e-02 6.84802281e-03 3.93043399e-01 -2.29530320e-01 5.01731448e-02 1.01660395e+00 4.02515262e-01 -1.67203501e-01 6.78128958e-01 -1.31325948e+00 -2.99235761e-01 -1.21797867e-01 -9.30074155e-01 -5.29672384e-01 2.53930837e-01 -8.15790743e-02 4.31917071e-01 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e4f6de7a-e953-40e8-a015-16ab02987bc8	structured-aspect-extraction	null	null	https://aclanthology.org/C16-1219	https://aclanthology.org/C16-1219.pdf	Structured Aspect Extraction	Aspect extraction identifies relevant features from a textual description of an entity, e.g., a phone, and is typically targeted to product descriptions, reviews, and other short texts as an enabling task for, e.g., opinion mining and information retrieval. Current aspect extraction methods mostly focus on aspect terms and often neglect interesting modifiers of the term or embed them in the aspect term without proper distinction. Moreover, flat syntactic structures are often assumed, resulting in inaccurate extractions of complex aspects. This paper studies the problem of structured aspect extraction, a variant of traditional aspect extraction aiming at a fine-grained extraction of complex (i.e., hierarchical) aspects. We propose an unsupervised and scalable method for structured aspect extraction consisting of statistical noun phrase clustering, cPMI-based noun phrase segmentation, and hierarchical pattern induction. Our evaluation shows a substantial improvement over existing methods in terms of both quality and computational efficiency.	['Omer Gunes', 'Giorgio Orsi', 'Tim Furche']	2016-12-01	structured-aspect-extraction-1	https://aclanthology.org/C16-1219	https://aclanthology.org/C16-1219.pdf	coling-2016-12	['aspect-extraction']	['natural-language-processing']	[ 3.07085097e-01 1.71467051e-01 -5.05887926e-01 -4.25755054e-01 -7.30680406e-01 -9.13061559e-01 6.39193714e-01 7.71830976e-01 -3.29967231e-01 5.44156015e-01 3.31199467e-01 -5.78044116e-01 9.76778939e-02 -8.99185240e-01 -2.49018237e-01 -5.81234097e-01 2.88293958e-01 6.26697183e-01 3.53775546e-02 9.51717049e-03 4.20388460e-01 2.84551620e-01 -1.44536078e+00 1.96199998e-01 6.59359515e-01 8.11615944e-01 -3.50480936e-02 2.36586824e-01 -8.01261842e-01 2.87585795e-01 -8.05432081e-01 -7.28990674e-01 -1.89820677e-01 -1.03611439e-01 -8.40591490e-01 5.84204674e-01 -2.98784733e-01 2.28768755e-02 3.79147112e-01 1.00291657e+00 6.33903816e-02 -8.12041610e-02 7.60957181e-01 -1.07227564e+00 -9.50535238e-02 9.31014419e-01 -7.25027144e-01 -8.27927217e-02 4.44443315e-01 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1c2b55b0-2748-4c64-878d-67c9c2eb05b1	egmm-an-evidential-version-of-the-gaussian	2010.01333	null	https://arxiv.org/abs/2010.01333v3	https://arxiv.org/pdf/2010.01333v3.pdf	EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering	The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference. In this paper, we propose a new model-based clustering algorithm, called EGMM (evidential GMM), in the theoretical framework of belief functions to better characterize cluster-membership uncertainty. With a mass function representing the cluster membership of each object, the evidential Gaussian mixture distribution composed of the components over the powerset of the desired clusters is proposed to model the entire dataset. The parameters in EGMM are estimated by a specially designed Expectation-Maximization (EM) algorithm. A validity index allowing automatic determination of the proper number of clusters is also provided. The proposed EGMM is as simple as the classical GMM, but can generate a more informative evidential partition for the considered dataset. The synthetic and real dataset experiments show that the proposed EGMM performs better than other representative clustering algorithms. Besides, its superiority is also demonstrated by an application to multi-modal brain image segmentation.	['Quan Pan', 'Zhun-Ga Liu', 'Thierry Denoeux', 'Lianmeng Jiao']	2020-10-03	null	null	null	null	['brain-image-segmentation']	['medical']	[-2.29019284e-01 2.10502326e-01 1.16453506e-01 -2.68704355e-01 -5.79721808e-01 -1.62956312e-01 7.09418774e-01 1.85236156e-01 -4.37982827e-01 4.34723586e-01 -2.83754408e-01 5.11699654e-02 -6.82050943e-01 -4.58515465e-01 -2.70680487e-01 -1.34166420e+00 -7.35405907e-02 9.15330827e-01 -1.02023268e-02 4.14772838e-01 4.07761067e-01 6.32915974e-01 -1.73238909e+00 -1.11202732e-01 1.35777080e+00 9.11157548e-01 5.94180405e-01 2.41831422e-01 -5.21141626e-02 3.71448338e-01 -7.85777748e-01 -2.49610826e-01 -1.52146414e-01 -3.42589319e-01 -5.23274302e-01 7.48920262e-01 -4.08838391e-01 1.23887040e-01 3.47755551e-01 1.27544463e+00 3.09778690e-01 6.05423927e-01 1.33914590e+00 -1.29311144e+00 -3.11001688e-01 7.73108661e-01 -5.54901302e-01 -2.17578426e-01 -7.89381638e-02 -3.63487571e-01 4.16087568e-01 -8.47855628e-01 2.07342923e-01 1.20976532e+00 1.18246011e-01 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3.07291925e-01 -2.40757540e-01 -8.97273958e-01 -5.57420552e-01 1.37291327e-01]	[7.3129377365112305, 4.335756778717041]
616340a6-70c0-4c4c-abff-71886d8afed9	exploiting-personalized-invariance-for-better	2211.11243	null	https://arxiv.org/abs/2211.11243v1	https://arxiv.org/pdf/2211.11243v1.pdf	Exploiting Personalized Invariance for Better Out-of-distribution Generalization in Federated Learning	Recently, data heterogeneity among the training datasets on the local clients (a.k.a., Non-IID data) has attracted intense interest in Federated Learning (FL), and many personalized federated learning methods have been proposed to handle it. However, the distribution shift between the training dataset and testing dataset on each client is never considered in FL, despite it being general in real-world scenarios. We notice that the distribution shift (a.k.a., out-of-distribution generalization) problem under Non-IID federated setting becomes rather challenging due to the entanglement between personalized and spurious information. To tackle the above problem, we elaborate a general dual-regularized learning framework to explore the personalized invariance, compared with the exsiting personalized federated learning methods which are regularized by a single baseline (usually the global model). Utilizing the personalized invariant features, the developed personalized models can efficiently exploit the most relevant information and meanwhile eliminate spurious information so as to enhance the out-of-distribution generalization performance for each client. Both the theoretical analysis on convergence and OOD generalization performance and the results of extensive experiments demonstrate the superiority of our method over the existing federated learning and invariant learning methods, in diverse out-of-distribution and Non-IID data cases.	['Jie Zhang', 'Song Guo', 'Xueyang Tang']	2022-11-21	null	null	null	null	['personalized-federated-learning']	['methodology']	[-3.37563396e-01 -4.56153542e-01 -5.36406398e-01 -4.67414469e-01 -8.83648694e-01 -7.25663424e-01 4.25957114e-01 -3.62067401e-01 8.49262029e-02 7.06449628e-01 1.83407947e-01 -1.86276630e-01 -7.81974792e-01 -5.68342209e-01 -7.48499990e-01 -1.20191872e+00 -2.07034275e-01 4.67706263e-01 -8.51489455e-02 4.03465889e-02 -1.58270568e-01 5.65906703e-01 -1.51045084e+00 4.48780626e-01 1.01478064e+00 1.32776153e+00 -7.34168068e-02 -1.45770935e-03 -5.11822402e-01 8.24352562e-01 -5.17146468e-01 -5.20959675e-01 6.34492338e-01 -3.31578851e-01 -5.06293833e-01 1.30130664e-01 6.45562589e-01 -3.70931059e-01 -8.43638599e-01 1.19196761e+00 6.43093467e-01 9.90569144e-02 5.52071571e-01 -1.49091482e+00 -9.68181610e-01 6.13135397e-01 -5.84666789e-01 3.43008071e-01 -6.46893680e-02 1.54325843e-01 8.56876493e-01 -8.32746625e-01 4.36214387e-01 1.16049004e+00 5.71912169e-01 3.61499071e-01 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c158a2b9-e869-430b-b028-e99197a8f36c	icsvr-investigating-compositional-and	2306.16533	null	https://arxiv.org/abs/2306.16533v1	https://arxiv.org/pdf/2306.16533v1.pdf	ICSVR: Investigating Compositional and Semantic Understanding in Video Retrieval Models	Video retrieval (VR) involves retrieving the ground truth video from the video database given a text caption or vice-versa. The two important components of compositionality: objects \& attributes and actions are joined using correct semantics to form a proper text query. These components (objects \& attributes, actions and semantics) each play an important role to help distinguish among videos and retrieve the correct ground truth video. However, it is unclear what is the effect of these components on the video retrieval performance. We therefore, conduct a systematic study to evaluate the compositional and semantic understanding of video retrieval models on standard benchmarks such as MSRVTT, MSVD and DIDEMO. The study is performed on two categories of video retrieval models: (i) which are pre-trained on video-text pairs and fine-tuned on downstream video retrieval datasets (Eg. Frozen-in-Time, Violet, MCQ etc.) (ii) which adapt pre-trained image-text representations like CLIP for video retrieval (Eg. CLIP4Clip, XCLIP, CLIP2Video etc.). Our experiments reveal that actions and semantics play a minor role compared to objects \& attributes in video understanding. Moreover, video retrieval models that use pre-trained image-text representations (CLIP) have better semantic and compositional understanding as compared to models pre-trained on video-text data.	['Vasudev Lal', 'Avinash Madasu']	2023-06-28	null	null	null	null	['video-retrieval', 'video-understanding', 'retrieval']	['computer-vision', 'computer-vision', 'methodology']	[ 2.18694672e-01 -5.27654409e-01 -4.35676962e-01 -2.04282179e-01 -6.90953374e-01 -7.81682730e-01 8.16862822e-01 -1.13976829e-01 -3.17131996e-01 3.75957936e-01 2.98055440e-01 1.97358839e-02 -3.08244795e-01 -5.09888172e-01 -9.33991015e-01 -4.53304410e-01 -2.83018090e-02 4.44351792e-01 4.66085494e-01 -1.55174345e-01 3.70604187e-01 1.37278482e-01 -2.09412646e+00 6.78300202e-01 4.53817457e-01 1.21284318e+00 4.23929244e-01 8.00386727e-01 -3.69766653e-01 1.27739716e+00 -5.40207922e-01 -3.01667541e-01 2.21087769e-01 -5.43553650e-01 -1.04614961e+00 5.16304262e-02 7.13452518e-01 -6.27662659e-01 -8.08574557e-01 1.00284171e+00 3.04475576e-01 3.39444399e-01 8.65534246e-01 -1.47973847e+00 -8.14940214e-01 5.68475544e-01 -1.52439669e-01 5.95510960e-01 8.91238749e-01 -4.98733111e-02 1.07758260e+00 -8.22753966e-01 1.09241664e+00 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28b221c0-1aa2-412f-91b3-955830e263f8	two-dimensional-deep-regression-for-early	2111.08069	null	https://arxiv.org/abs/2111.08069v1	https://arxiv.org/pdf/2111.08069v1.pdf	Two-dimensional Deep Regression for Early Yield Prediction of Winter Wheat	Crop yield prediction is one of the tasks of Precision Agriculture that can be automated based on multi-source periodic observations of the fields. We tackle the yield prediction problem using a Convolutional Neural Network (CNN) trained on data that combines radar satellite imagery and on-ground information. We present a CNN architecture called Hyper3DNetReg that takes in a multi-channel input image and outputs a two-dimensional raster, where each pixel represents the predicted yield value of the corresponding input pixel. We utilize radar data acquired from the Sentinel-1 satellites, while the on-ground data correspond to a set of six raster features: nitrogen rate applied, precipitation, slope, elevation, topographic position index (TPI), and aspect. We use data collected during the early stage of the winter wheat growing season (March) to predict yield values during the harvest season (August). We present experiments over four fields of winter wheat and show that our proposed methodology yields better results than five compared methods, including multiple linear regression, an ensemble of feedforward networks using AdaBoost, a stacked autoencoder, and two other CNN architectures.	['John W. Sheppard', 'Giorgio Morales']	2021-11-15	null	null	null	null	['crop-yield-prediction', 'crop-yield-prediction']	['computer-vision', 'miscellaneous']	[ 2.90693223e-01 -1.53683409e-01 -1.35966703e-01 -4.80233699e-01 1.05343349e-01 -5.36086380e-01 2.58280426e-01 4.65835989e-01 -2.41590098e-01 8.28302264e-01 3.66936699e-02 -7.83381999e-01 -3.16545933e-01 -1.70816410e+00 -7.69951701e-01 -7.83144832e-01 -6.71011508e-01 -5.84084056e-02 -7.09893554e-02 -8.28137994e-01 -1.98426381e-01 9.14170325e-01 -1.76570714e+00 1.15836136e-01 6.23801947e-01 1.53593314e+00 2.91644752e-01 8.36008012e-01 1.71548665e-01 2.32847348e-01 -1.75592318e-01 2.35985667e-01 4.54971462e-01 -7.76287122e-03 -2.85178095e-01 -1.33793533e-01 2.17036501e-01 -7.08655477e-01 -2.72096917e-02 9.32191074e-01 2.44849518e-01 -3.13031107e-01 5.59541285e-01 -9.94205296e-01 -7.07782209e-01 6.05758369e-01 -6.43543720e-01 2.48717051e-02 -4.90652710e-01 -9.80291292e-02 5.92925251e-01 -3.49021405e-01 1.99226379e-01 9.04169083e-01 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4e52bbec-c887-4c5a-a5a4-fcf78a3701de	gans-in-computer-vision-ebook	null	null	https://github.com/The-AI-Summer/GANs-in-Computer-Vision	https://theaisummer.com/gans-computer-vision-ebook/	GANs in computer vision ebook	In this article-series we are reviewing the most fundamental works of Generative Adversarial Networks in Computer Vision. We start from the very beginning from concepts such as generative learning, adversarial learning. We provide some code and illustrations for educational purposes. The goal is to focus on the intuition of the models, by tackling the multiple problems that arise when training a GAN. We have thoroughly analyzed more than 20 papers in 6 different articles in a chronological order. We will continue to update the GAN series, based on the newer publications or older ones that we skipped. We do hope that this series will provide you a big overview of the field, so that you will not need to read all the literature by yourself, independent of your background on GANs.	['Nikolas Adaloglou', 'Sergios Karagianakos']	2020-06-10	null	null	null	ebook-2020-6	['video-to-video-synthesis']	['computer-vision']	[ 3.30719441e-01 7.07845688e-01 4.09259871e-02 -1.90143138e-01 -5.87271750e-01 -6.87720537e-01 5.77389300e-01 -8.65124762e-01 -6.04841299e-03 9.58141744e-01 -8.05311427e-02 -4.02639747e-01 -4.79490235e-02 -7.64624596e-01 -5.90713382e-01 -9.79692936e-01 1.77216493e-02 1.65349960e-01 -2.93983221e-01 -4.98114794e-01 1.21329941e-01 3.64911973e-01 -1.41399932e+00 4.53843102e-02 8.54828358e-01 5.17605841e-01 -1.56009600e-01 9.58735466e-01 -5.99250421e-02 1.07685900e+00 -1.02265239e+00 -1.12327254e+00 1.84019417e-01 -1.02283370e+00 -7.72321165e-01 1.73433330e-02 4.14892405e-01 -2.81626999e-01 -3.40482086e-01 1.02225840e+00 6.44240856e-01 -1.75090581e-01 8.03576708e-01 -1.37485015e+00 -9.84348238e-01 6.89686835e-01 -3.04057896e-01 2.66492069e-01 4.30326276e-02 6.86589554e-02 5.64050972e-01 -4.85817939e-01 4.73717928e-01 8.80329847e-01 9.79872644e-01 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30d81e0e-f2f2-452e-8a53-c7ea4116e27e	adversarial-evasion-attacks-practicality-in	2306.05494	null	https://arxiv.org/abs/2306.05494v1	https://arxiv.org/pdf/2306.05494v1.pdf	Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning	Machine Learning (ML) has become ubiquitous, and its deployment in Network Intrusion Detection Systems (NIDS) is inevitable due to its automated nature and high accuracy in processing and classifying large volumes of data. However, ML has been found to have several flaws, on top of them are adversarial attacks, which aim to trick ML models into producing faulty predictions. While most adversarial attack research focuses on computer vision datasets, recent studies have explored the practicality of such attacks against ML-based network security entities, especially NIDS. This paper presents two distinct contributions: a taxonomy of practicality issues associated with adversarial attacks against ML-based NIDS and an investigation of the impact of continuous training on adversarial attacks against NIDS. Our experiments indicate that continuous re-training, even without adversarial training, can reduce the effect of adversarial attacks. While adversarial attacks can harm ML-based NIDSs, our aim is to highlight that there is a significant gap between research and real-world practicality in this domain which requires attention.	['Ashraf Matrawy', 'Mohamed el Shehaby']	2023-06-08	null	null	null	null	['adversarial-attack', 'network-intrusion-detection']	['adversarial', 'miscellaneous']	[ 1.52751803e-01 1.49321124e-01 -4.46675569e-02 -3.58179748e-01 -2.11574882e-01 -9.33944046e-01 7.08947659e-01 -3.44279036e-02 -3.80149901e-01 5.85933089e-01 -5.51653266e-01 -8.97215724e-01 -7.54077965e-03 -9.41339195e-01 -6.42755210e-01 -3.01748604e-01 -4.17122006e-01 4.30430323e-01 2.52634853e-01 -1.76485285e-01 1.82911754e-01 1.19946468e+00 -9.63170588e-01 1.06634423e-01 5.58466733e-01 7.53122330e-01 -9.63949025e-01 8.23971689e-01 -8.05807412e-02 1.10557902e+00 -1.18404865e+00 -9.93555486e-01 6.80945516e-01 -7.67420977e-02 -7.36068368e-01 -4.40606803e-01 6.17611289e-01 -4.29631323e-01 -4.60984081e-01 1.05037618e+00 3.60276759e-01 -2.35881478e-01 5.31991243e-01 -2.19673228e+00 -4.47031677e-01 3.81598085e-01 -2.75033087e-01 3.09405982e-01 1.35882333e-01 6.01471007e-01 8.07515204e-01 -2.73427516e-01 4.42739636e-01 1.46428442e+00 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6d9913a8-8164-43d6-9253-6faa68540b89	improving-ctc-based-speech-recognition-via	2203.03582	null	https://arxiv.org/abs/2203.03582v1	https://arxiv.org/pdf/2203.03582v1.pdf	Improving CTC-based speech recognition via knowledge transferring from pre-trained language models	Recently, end-to-end automatic speech recognition models based on connectionist temporal classification (CTC) have achieved impressive results, especially when fine-tuned from wav2vec2.0 models. Due to the conditional independence assumption, CTC-based models are always weaker than attention-based encoder-decoder models and require the assistance of external language models (LMs). To solve this issue, we propose two knowledge transferring methods that leverage pre-trained LMs, such as BERT and GPT2, to improve CTC-based models. The first method is based on representation learning, in which the CTC-based models use the representation produced by BERT as an auxiliary learning target. The second method is based on joint classification learning, which combines GPT2 for text modeling with a hybrid CTC/attention architecture. Experiment on AISHELL-1 corpus yields a character error rate (CER) of 4.2% on the test set. When compared to the vanilla CTC-based models fine-tuned from the wav2vec2.0 models, our knowledge transferring method reduces CER by 16.1% relatively without external LMs.	['Pengyuan Zhang', 'Ji Xu', 'Gaofeng Cheng', 'Long Ma', 'Yike Zhang', 'Songjun Cao', 'Keqi Deng']	2022-02-22	null	null	null	null	['auxiliary-learning']	['methodology']	[ 2.19363526e-01 2.35245749e-01 -1.41064599e-01 -1.77656457e-01 -9.95123208e-01 -2.17067048e-01 5.61785102e-01 -1.68127760e-01 -7.12338448e-01 6.74950182e-01 3.76722246e-01 -6.33322060e-01 4.13908541e-01 -4.28515464e-01 -6.16323352e-01 -6.17347062e-01 4.49068785e-01 4.79078263e-01 4.62100744e-01 -1.97164431e-01 4.86938320e-02 -1.04992718e-01 -9.92209256e-01 4.38737839e-01 9.88344133e-01 9.69531953e-01 4.91614699e-01 9.11054552e-01 -4.71186399e-01 1.03740513e+00 -6.47329092e-01 -5.57204783e-01 -3.10017377e-01 -3.60661894e-01 -8.08232963e-01 -4.55250084e-01 -1.78394690e-02 -1.68162286e-01 -7.88230658e-01 6.99660242e-01 6.20374978e-01 3.23580116e-01 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23023824-304d-4e65-88f6-44ccb8f0e3f7	few-shot-fine-tuning-vs-in-context-learning-a	2305.16938	null	https://arxiv.org/abs/2305.16938v2	https://arxiv.org/pdf/2305.16938v2.pdf	Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation	Few-shot fine-tuning and in-context learning are two alternative strategies for task adaptation of pre-trained language models. Recently, in-context learning has gained popularity over fine-tuning due to its simplicity and improved out-of-domain generalization, and because extensive evidence shows that fine-tuned models pick up on spurious correlations. Unfortunately, previous comparisons of the two approaches were done using models of different sizes. This raises the question of whether the observed weaker out-of-domain generalization of fine-tuned models is an inherent property of fine-tuning or a limitation of the experimental setup. In this paper, we compare the generalization of few-shot fine-tuning and in-context learning to challenge datasets, while controlling for the models used, the number of examples, and the number of parameters, ranging from 125M to 30B. Our results show that fine-tuned language models can in fact generalize well out-of-domain. We find that both approaches generalize similarly; they exhibit large variation and depend on properties such as model size and the number of examples, highlighting that robust task adaptation remains a challenge.	['Yanai Elazar', 'Dietrich Klakow', 'Shauli Ravfogel', 'Tiago Pimentel', 'Marius Mosbach']	2023-05-26	null	null	null	null	['domain-generalization']	['methodology']	[-1.19887851e-01 -3.80633324e-01 -3.25336546e-01 -5.08939445e-01 -8.30195069e-01 -7.09440947e-01 9.08615232e-01 1.90092012e-01 -8.21734428e-01 8.08804750e-01 3.24841648e-01 -1.67002961e-01 -2.70345181e-01 -5.68879366e-01 -6.08002543e-01 -4.32962328e-01 1.86281782e-02 5.86354911e-01 6.29300356e-01 -4.31083083e-01 3.05338979e-01 2.60828763e-01 -1.20956683e+00 1.53597161e-01 6.83832109e-01 4.79087174e-01 1.23276822e-01 6.61422551e-01 -3.24847490e-01 2.67119169e-01 -7.64595032e-01 -2.38988698e-01 1.91270411e-01 -2.95335859e-01 -6.47192717e-01 -2.51281410e-01 5.72103739e-01 -1.22332154e-03 -1.79780945e-01 8.75340819e-01 5.63978136e-01 6.75433338e-01 8.57214689e-01 -8.95196557e-01 -9.74486947e-01 5.42166770e-01 -3.26267481e-01 7.84645200e-01 -3.58719490e-02 2.23844454e-01 8.84203911e-01 -9.26168919e-01 5.28069735e-01 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eb564e12-0bb8-458b-8e51-004a32832e15	bright-graph-neural-networks-in-real-time	2205.13084	null	https://arxiv.org/abs/2205.13084v2	https://arxiv.org/pdf/2205.13084v2.pdf	BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection	Detecting fraudulent transactions is an essential component to control risk in e-commerce marketplaces. Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inference with graph neural networks (GNNs), which is useful to catch multihop risk propagation in a transaction graph. However, two challenges arise in the implementation of GNNs in production. First, future information in a dynamic graph should not be considered in message passing to predict the past. Second, the latency of graph query and GNN model inference is usually up to hundreds of milliseconds, which is costly for some critical online services. To tackle these challenges, we propose a Batch and Real-time Inception GrapH Topology (BRIGHT) framework to conduct an end-to-end GNN learning that allows efficient online real-time inference. BRIGHT framework consists of a graph transformation module (Two-Stage Directed Graph) and a corresponding GNN architecture (Lambda Neural Network). The Two-Stage Directed Graph guarantees that the information passed through neighbors is only from the historical payment transactions. It consists of two subgraphs representing historical relationships and real-time links, respectively. The Lambda Neural Network decouples inference into two stages: batch inference of entity embeddings and real-time inference of transaction prediction. Our experiments show that BRIGHT outperforms the baseline models by >2\% in average w.r.t.~precision. Furthermore, BRIGHT is computationally efficient for real-time fraud detection. Regarding end-to-end performance (including neighbor query and inference), BRIGHT can reduce the P99 latency by >75\%. For the inference stage, our speedup is on average 7.8$\times$ compared to the traditional GNN.	['Jiawei Jiang', 'Ce Zhang', 'Ramesh Raghunathan', 'Yinan Shan', 'Yang Zhao', 'Zitao Zhang', 'Susie Xi Rao', 'Zhichao Han', 'Mingxuan Lu']	2022-05-25	null	null	null	null	['entity-embeddings']	['methodology']	[-2.81569749e-01 1.89016517e-02 -4.78596270e-01 -2.55392283e-01 -1.97875306e-01 -2.89425045e-01 2.61373907e-01 5.88506162e-01 -4.65876520e-01 1.31738365e-01 -3.89722496e-01 -1.04181635e+00 4.95207570e-02 -1.49698770e+00 -9.46981728e-01 6.96625561e-02 -7.62628376e-01 6.36971295e-01 4.12833631e-01 -3.40148032e-01 1.29698470e-01 4.79281336e-01 -7.47278929e-01 2.79613197e-01 7.83619106e-01 1.29992890e+00 -4.29102302e-01 7.68104076e-01 -3.69567364e-01 1.10906732e+00 -3.94545615e-01 -1.17332995e+00 4.02646154e-01 -2.30606552e-02 -4.22695339e-01 -5.41252434e-01 1.81336328e-01 -7.62442470e-01 -7.46945977e-01 1.19177175e+00 2.70409048e-01 -3.47272575e-01 1.38829201e-01 -1.60523808e+00 -5.55828214e-01 1.15571499e+00 -6.57168627e-01 2.71323562e-01 2.14221552e-01 2.36950949e-01 1.19831133e+00 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0a6ff4f6-d756-4346-8724-2c6eb1d82593	towards-unbiased-multi-label-zero-shot	2203.03483	null	https://arxiv.org/abs/2203.03483v1	https://arxiv.org/pdf/2203.03483v1.pdf	Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention	Multi-label zero-shot learning extends conventional single-label zero-shot learning to a more realistic scenario that aims at recognizing multiple unseen labels of classes for each input sample. Existing works usually exploit attention mechanism to generate the correlation among different labels. However, most of them are usually biased on several major classes while neglect most of the minor classes with the same importance in input samples, and may thus result in overly diffused attention maps that cannot sufficiently cover minor classes. We argue that disregarding the connection between major and minor classes, i.e., correspond to the global and local information, respectively, is the cause of the problem. In this paper, we propose a novel framework of unbiased multi-label zero-shot learning, by considering various class-specific regions to calibrate the training process of the classifier. Specifically, Pyramid Feature Attention (PFA) is proposed to build the correlation between global and local information of samples to balance the presence of each class. Meanwhile, for the generated semantic representations of input samples, we propose Semantic Attention (SA) to strengthen the element-wise correlation among these vectors, which can encourage the coordinated representation of them. Extensive experiments on the large-scale multi-label zero-shot benchmarks NUS-WIDE and Open-Image demonstrate that the proposed method surpasses other representative methods by significant margins.	['Fushuo Huo', 'Yuanyuan Xu', 'Jingcai Guo', 'Song Guo', 'Ziming Liu']	2022-03-07	null	null	null	null	['multi-label-zero-shot-learning']	['computer-vision']	[ 2.94681519e-01 -4.84021753e-02 -3.30621868e-01 -5.13392508e-01 -7.01700151e-01 -1.65087327e-01 5.61852872e-01 2.10543916e-01 -2.05218256e-01 5.29219329e-01 3.06881636e-01 3.59079957e-01 -1.47254556e-01 -9.10212636e-01 -4.89399940e-01 -1.05678725e+00 7.72817850e-01 1.62995875e-01 3.95083755e-01 -1.51374564e-01 1.05476871e-01 -1.74741112e-02 -1.82074738e+00 3.99951130e-01 6.97955966e-01 1.09938228e+00 2.12783515e-01 -4.12987359e-02 -3.25693578e-01 7.01599836e-01 -6.01907670e-01 -2.21493259e-01 -1.92768816e-02 -6.23694360e-01 -4.33150411e-01 4.44187015e-01 3.38795751e-01 -1.40973642e-01 -9.42085162e-02 1.39360809e+00 5.47010303e-01 3.56586605e-01 9.19370234e-01 -1.39125264e+00 -8.33851933e-01 4.12570596e-01 -9.20247436e-01 2.13675618e-01 -1.41348630e-01 2.02561513e-01 1.20795441e+00 -1.01499248e+00 5.12427747e-01 1.30553436e+00 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ce757fca-30b7-4950-b710-a4e26fe94324	sequential-person-recognition-in-photo-albums	1611.09967	null	http://arxiv.org/abs/1611.09967v1	http://arxiv.org/pdf/1611.09967v1.pdf	Sequential Person Recognition in Photo Albums with a Recurrent Network	Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to non-frontal faces, changes in clothing, location, lighting and similar. Recent studies have shown that rich relational information between people in the same photo can help in recognizing their identities. In this work, we propose to model the relational information between people as a sequence prediction task. At the core of our work is a novel recurrent network architecture, in which relational information between instances' labels and appearance are modeled jointly. In addition to relational cues, scene context is incorporated in our sequence prediction model with no additional cost. In this sense, our approach is a unified framework for modeling both contextual cues and visual appearance of person instances. Our model is trained end-to-end with a sequence of annotated instances in a photo as inputs, and a sequence of corresponding labels as targets. We demonstrate that this simple but elegant formulation achieves state-of-the-art performance on the newly released People In Photo Albums (PIPA) dataset.	['Anton Van Den Hengel', 'Bohan Zhuang', 'Chunhua Shen', 'Yao Li', 'Lingqiao Liu', 'Guosheng Lin']	2016-11-30	sequential-person-recognition-in-photo-albums-1	http://openaccess.thecvf.com/content_cvpr_2017/html/Li_Sequential_Person_Recognition_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Sequential_Person_Recognition_CVPR_2017_paper.pdf	cvpr-2017-7	['person-recognition']	['computer-vision']	[ 4.20928448e-01 -4.70281899e-01 1.25318691e-01 -8.30570519e-01 -2.76680112e-01 -5.24568796e-01 7.93597221e-01 -2.32482895e-01 -2.55490661e-01 4.73140359e-01 3.60266954e-01 3.78720462e-01 2.97209680e-01 -4.25596744e-01 -8.61170650e-01 -4.58478481e-01 1.75856426e-01 2.84253001e-01 1.24923820e-02 -1.68331131e-01 -1.66319218e-02 4.85540599e-01 -1.83781493e+00 6.22636199e-01 2.99022168e-01 8.98268104e-01 5.00648767e-02 6.39996231e-01 1.53351039e-01 1.07824063e+00 -2.16421112e-01 -9.55822885e-01 3.92528087e-01 -4.80963677e-01 -6.83575988e-01 4.87001717e-01 1.17647517e+00 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8e4978a6-2674-46bb-865b-c5e67792c5ea	barcode-annotations-for-medical-image	1505.05212	null	http://arxiv.org/abs/1505.05212v1	http://arxiv.org/pdf/1505.05212v1.pdf	Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation	This paper proposes to generate and to use barcodes to annotate medical images and/or their regions of interest such as organs, tumors and tissue types. A multitude of efficient feature-based image retrieval methods already exist that can assign a query image to a certain image class. Visual annotations may help to increase the retrieval accuracy if combined with existing feature-based classification paradigms. Whereas with annotations we usually mean textual descriptions, in this paper barcode annotations are proposed. In particular, Radon barcodes (RBC) are introduced. As well, local binary patterns (LBP) and local Radon binary patterns (LRBP) are implemented as barcodes. The IRMA x-ray dataset with 12,677 training images and 1,733 test images is used to verify how barcodes could facilitate image retrieval.	['Hamid. R. Tizhoosh']	2015-05-19	null	null	null	null	['medical-image-retrieval', 'medical-image-retrieval']	['computer-vision', 'medical']	[ 3.19391072e-01 -1.07418612e-01 -5.71628988e-01 -4.99420613e-01 -1.15208030e+00 -4.12721157e-01 7.29564309e-01 7.70047843e-01 -4.08166796e-01 7.05298960e-01 8.18362087e-02 -4.15118873e-01 -4.76634949e-01 -9.42012012e-01 -1.73764035e-01 -8.24949503e-01 4.83694375e-02 2.81592399e-01 2.13639215e-01 2.85530508e-01 5.60028374e-01 9.30285156e-01 -1.56538486e+00 6.56203210e-01 4.24145460e-01 1.26531327e+00 2.94335186e-01 6.87614560e-01 -3.19345951e-01 7.88666070e-01 -8.94392192e-01 -1.48414776e-01 1.50304809e-02 -1.62939414e-01 -9.42107081e-01 1.75971955e-01 2.95445353e-01 -1.25928506e-01 -3.34916711e-01 9.42820311e-01 4.68584895e-01 1.01003751e-01 1.16380978e+00 -8.31095457e-01 -1.15866971e+00 1.85479283e-01 -4.93790954e-01 3.38079572e-01 5.48048854e-01 -2.47651652e-01 7.17530370e-01 -9.95325863e-01 7.14554727e-01 8.46349478e-01 6.22570097e-01 3.13159645e-01 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-5.75854540e-01 5.57500362e-01 1.48406774e-01 -7.68094003e-01 -1.22973159e-01 3.81855339e-01]	[14.292566299438477, -1.4684250354766846]
e56279bd-1067-4f38-a98c-9c38ef01c9a6	ldfa-latent-diffusion-face-anonymization-for	2302.08931	null	https://arxiv.org/abs/2302.08931v1	https://arxiv.org/pdf/2302.08931v1.pdf	LDFA: Latent Diffusion Face Anonymization for Self-driving Applications	In order to protect vulnerable road users (VRUs), such as pedestrians or cyclists, it is essential that intelligent transportation systems (ITS) accurately identify them. Therefore, datasets used to train perception models of ITS must contain a significant number of vulnerable road users. However, data protection regulations require that individuals are anonymized in such datasets. In this work, we introduce a novel deep learning-based pipeline for face anonymization in the context of ITS. In contrast to related methods, we do not use generative adversarial networks (GANs) but build upon recent advances in diffusion models. We propose a two-stage method, which contains a face detection model followed by a latent diffusion model to generate realistic face in-paintings. To demonstrate the versatility of anonymized images, we train segmentation methods on anonymized data and evaluate them on non-anonymized data. Our experiment reveal that our pipeline is better suited to anonymize data for segmentation than naive methods and performes comparably with recent GAN-based methods. Moreover, face detectors achieve higher mAP scores for faces anonymized by our method compared to naive or recent GAN-based methods.	['Martin Lauer', 'Jannik Quehl', 'Royden Wagner', 'Kevin Rösch', 'Marvin Klemp']	2023-02-17	null	null	null	null	['face-detection', 'face-anonymization']	['computer-vision', 'computer-vision']	[ 2.41733700e-01 4.35036302e-01 2.46141702e-01 -5.61371744e-01 -7.40446746e-01 -8.28350663e-01 7.19017446e-01 -4.74665821e-01 -4.80528742e-01 7.33101904e-01 1.28801197e-01 -3.32542866e-01 3.83099884e-01 -1.22672081e+00 -8.73920977e-01 -3.52741390e-01 2.75042236e-01 6.35971010e-01 -6.78361133e-02 -6.41215146e-02 -2.60327637e-01 1.01425576e+00 -1.34092867e+00 2.55412340e-01 9.77745473e-01 7.51533329e-01 -8.21645856e-01 4.56131905e-01 1.21102436e-02 3.44532847e-01 -6.54943883e-01 -1.18135011e+00 8.15729320e-01 -4.56027925e-01 -7.78494000e-01 -1.15970150e-01 9.24697936e-01 -7.57742584e-01 -5.40862560e-01 8.94470870e-01 5.50958872e-01 -3.22499461e-02 7.70277441e-01 -1.63536692e+00 -9.36764956e-01 3.51269960e-01 -4.06270176e-01 -1.50727764e-01 2.48624116e-01 5.31205416e-01 3.29892844e-01 -4.81766373e-01 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0a1664d3-24ce-4541-a8dc-c3410ef67035	self-supervised-and-weakly-supervised	2212.03125	null	https://arxiv.org/abs/2212.03125v4	https://arxiv.org/pdf/2212.03125v4.pdf	Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations	Previous work on action representation learning focused on global representations for short video clips. In contrast, many practical applications, such as video alignment, strongly demand learning the intensive representation of long videos. In this paper, we introduce a new framework of contrastive action representation learning (CARL) to learn frame-wise action representation in a self-supervised or weakly-supervised manner, especially for long videos. Specifically, we introduce a simple but effective video encoder that considers both spatial and temporal context by combining convolution and transformer. Inspired by the recent massive progress in self-supervised learning, we propose a new sequence contrast loss (SCL) applied to two related views obtained by expanding a series of spatio-temporal data in two versions. One is the self-supervised version that optimizes embedding space by minimizing KL-divergence between sequence similarity of two augmented views and prior Gaussian distribution of timestamp distance. The other is the weakly-supervised version that builds more sample pairs among videos using video-level labels by dynamic time wrapping (DTW). Experiments on FineGym, PennAction, and Pouring datasets show that our method outperforms previous state-of-the-art by a large margin for downstream fine-grained action classification and even faster inference. Surprisingly, although without training on paired videos like in previous works, our self-supervised version also shows outstanding performance in video alignment and fine-grained frame retrieval tasks.	['Deng Cai', 'Boxi Wu', 'Yuqi Lin', 'Chenxi Huang', 'Renbo Tu', 'Minghao Chen']	2022-12-06	null	null	null	null	['action-classification', 'video-alignment']	['computer-vision', 'computer-vision']	[ 4.40395623e-01 -4.23721462e-01 -6.17062449e-01 -4.49756861e-01 -9.87451911e-01 -4.14833188e-01 8.10158610e-01 -6.47277981e-02 -4.31861997e-01 6.90833747e-01 7.22700298e-01 1.97797984e-01 -1.03012137e-01 -4.66069996e-01 -9.43629086e-01 -8.44328821e-01 -3.61894518e-01 1.63151413e-01 3.55196953e-01 -3.50387767e-02 3.17763746e-01 3.23442608e-01 -1.67322290e+00 7.11660087e-01 3.49870026e-01 1.19572747e+00 2.27185205e-01 7.16142416e-01 4.85456437e-02 1.57383895e+00 -2.43541047e-01 -1.93386585e-01 4.72655296e-01 -7.57213235e-01 -9.70223010e-01 3.01800966e-01 7.39539981e-01 -4.95875090e-01 -6.33393943e-01 7.79807448e-01 3.83240342e-01 5.71278989e-01 3.71793181e-01 -1.41860175e+00 -5.83657920e-01 3.76348346e-01 -6.50690138e-01 5.24002075e-01 5.82954288e-01 1.69338793e-01 1.11962080e+00 -7.10338295e-01 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-1.21195257e+00 -5.91781735e-01 -8.30643117e-01 -7.12649703e-01 8.92449498e-01 4.51876044e-01 -8.15178573e-01 -2.94946581e-01 4.15081501e-01]	[8.641005516052246, 0.7576315999031067]
f7e058ea-6237-40ab-8b37-0f295551d6b0	parameter-selection-why-we-should-pay-more	2107.05393	null	https://arxiv.org/abs/2107.05393v1	https://arxiv.org/pdf/2107.05393v1.pdf	Parameter Selection: Why We Should Pay More Attention to It	The importance of parameter selection in supervised learning is well known. However, due to the many parameter combinations, an incomplete or an insufficient procedure is often applied. This situation may cause misleading or confusing conclusions. In this opinion paper, through an intriguing example we point out that the seriousness goes beyond what is generally recognized. In the topic of multi-label classification for medical code prediction, one influential paper conducted a proper parameter selection on a set, but when moving to a subset of frequently occurring labels, the authors used the same parameters without a separate tuning. The set of frequent labels became a popular benchmark in subsequent studies, which kept pushing the state of the art. However, we discovered that most of the results in these studies cannot surpass the approach in the original paper if a parameter tuning had been conducted at the time. Thus it is unclear how much progress the subsequent developments have actually brought. The lesson clearly indicates that without enough attention on parameter selection, the research progress in our field can be uncertain or even illusive.	['Chih-Jen Lin', 'Si-An Chen', 'Tsung-Han Yang', 'Jie-Jyun Liu']	2021-07-08	null	https://aclanthology.org/2021.acl-short.104	https://aclanthology.org/2021.acl-short.104.pdf	acl-2021-5	['medical-code-prediction']	['medical']	[ 3.40226829e-01 8.12015980e-02 -3.54912996e-01 -4.32569951e-01 -5.76047003e-01 -4.86184299e-01 3.55063200e-01 6.48034573e-01 -5.35216749e-01 8.70742440e-01 3.06023676e-02 -4.25642401e-01 -5.46070278e-01 -4.71364528e-01 -3.96785498e-01 -1.01626456e+00 1.43903896e-01 5.02610445e-01 2.44187132e-01 -3.29626016e-02 5.85569918e-01 2.14955539e-01 -1.74119163e+00 1.34507373e-01 6.05762362e-01 5.38973331e-01 6.79883808e-02 1.99250653e-01 -2.44018659e-01 7.19251871e-01 -6.69427872e-01 -5.41468441e-01 5.44808768e-02 -4.41484094e-01 -8.73998940e-01 5.91031350e-02 1.14357434e-01 1.49666384e-01 4.70404446e-01 9.57116306e-01 4.73832518e-01 -1.71050400e-01 6.22795463e-01 -9.27876651e-01 -2.35473529e-01 7.56570101e-01 -5.40401816e-01 1.18304946e-01 1.23690821e-01 -9.80445370e-02 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deed8e78-baa2-43cf-9957-929b8bfaf68a	transition-based-dependency-parsing-with-3	null	null	https://aclanthology.org/D16-1254	https://aclanthology.org/D16-1254.pdf	Transition-Based Dependency Parsing with Heuristic Backtracking	null	['Miguel Ballesteros', 'Chris Dyer', 'Jacob Buckman']	2016-11-01	null	null	null	emnlp-2016-11	['transition-based-dependency-parsing']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 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64283fb1-6c28-4c3a-87a6-e1cfe9275b06	cross-modal-attention-congruence	2212.10549	null	https://arxiv.org/abs/2212.10549v2	https://arxiv.org/pdf/2212.10549v2.pdf	Cross-modal Attention Congruence Regularization for Vision-Language Relation Alignment	Despite recent progress towards scaling up multimodal vision-language models, these models are still known to struggle on compositional generalization benchmarks such as Winoground. We find that a critical component lacking from current vision-language models is relation-level alignment: the ability to match directional semantic relations in text (e.g., "mug in grass") with spatial relationships in the image (e.g., the position of the mug relative to the grass). To tackle this problem, we show that relation alignment can be enforced by encouraging the directed language attention from 'mug' to 'grass' (capturing the semantic relation 'in') to match the directed visual attention from the mug to the grass. Tokens and their corresponding objects are softly identified using the cross-modal attention. We prove that this notion of soft relation alignment is equivalent to enforcing congruence between vision and language attention matrices under a 'change of basis' provided by the cross-modal attention matrix. Intuitively, our approach projects visual attention into the language attention space to calculate its divergence from the actual language attention, and vice versa. We apply our Cross-modal Attention Congruence Regularization (CACR) loss to UNITER and improve on the state-of-the-art approach to Winoground.	['Louis-Philippe Morency', 'Ruslan Salakhutdinov', 'Paul Pu Liang', 'Rulin Shao', 'Rohan Pandey']	2022-12-20	null	null	null	null	['visual-reasoning', 'visual-reasoning']	['computer-vision', 'reasoning']	[ 3.01649958e-01 6.56735152e-02 -1.48113519e-01 -2.40040585e-01 -4.26933169e-01 -6.80594146e-01 9.60448742e-01 1.47312716e-01 -4.27383661e-01 5.65136336e-02 4.69908178e-01 -2.78117776e-01 -7.76864141e-02 -5.75215816e-01 -1.10055709e+00 -5.03726423e-01 2.94419557e-01 4.72168028e-01 1.58568099e-01 -5.50714135e-01 2.04946488e-01 2.58893162e-01 -1.61920130e+00 7.35917449e-01 5.44729590e-01 7.67980576e-01 5.89326859e-01 7.38367260e-01 -2.59936988e-01 9.34747815e-01 -2.37394080e-01 -4.33598906e-01 1.89410031e-01 -4.40370619e-01 -1.23589754e+00 2.19623819e-01 1.16580462e+00 6.47349982e-03 -3.57256949e-01 1.45004463e+00 1.21815249e-01 1.39642373e-01 6.70751035e-01 -1.39330387e+00 -1.47986555e+00 6.62165523e-01 -9.43967402e-01 1.80913195e-01 4.11229283e-01 8.29999223e-02 1.73217237e+00 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75e312ff-0f8a-4d23-b3ba-6f35777142ed	inductive-relation-prediction-from-relational	2304.00215	null	https://arxiv.org/abs/2304.00215v3	https://arxiv.org/pdf/2304.00215v3.pdf	Inductive Relation Prediction from Relational Paths and Context with Hierarchical Transformers	Relation prediction on knowledge graphs (KGs) is a key research topic. Dominant embedding-based methods mainly focus on the transductive setting and lack the inductive ability to generalize to new entities for inference. Existing methods for inductive reasoning mostly mine the connections between entities, i.e., relational paths, without considering the nature of head and tail entities contained in the relational context. This paper proposes a novel method that captures both connections between entities and the intrinsic nature of entities, by simultaneously aggregating RElational Paths and cOntext with a unified hieRarchical Transformer framework, namely REPORT. REPORT relies solely on relation semantics and can naturally generalize to the fully-inductive setting, where KGs for training and inference have no common entities. In the experiments, REPORT performs consistently better than all baselines on almost all the eight version subsets of two fully-inductive datasets. Moreover. REPORT is interpretable by providing each element's contribution to the prediction results.	['Zhendong Mao', 'Quan Wang', 'Jiaang Li']	2023-04-01	null	null	null	null	['inductive-relation-prediction']	['graphs']	[-8.78400952e-02 8.57980192e-01 -8.07507515e-01 -4.30307478e-01 -8.14540163e-02 -6.62557423e-01 7.29220569e-01 6.24291897e-01 1.47019401e-02 8.10377479e-01 6.24632955e-01 -4.65364426e-01 -4.46864247e-01 -1.52659464e+00 -9.01602030e-01 -2.07832336e-01 -3.20772529e-01 8.00679445e-01 3.59730601e-01 -2.96310216e-01 -4.58969563e-01 6.90659210e-02 -1.17538702e+00 2.90830940e-01 7.13055849e-01 7.85768449e-01 -4.40714300e-01 2.16876552e-01 -2.57311702e-01 1.35905552e+00 -1.49015686e-03 -1.21311271e+00 -1.34238750e-01 -7.24557638e-02 -1.28419089e+00 -4.82141167e-01 1.82857782e-01 -1.69134676e-01 -6.08772039e-01 6.58089638e-01 5.93678802e-02 7.14632124e-02 7.61528313e-01 -1.37114871e+00 -8.95582080e-01 1.49233246e+00 -1.76818684e-01 2.09722713e-01 5.84060013e-01 -5.51418066e-01 1.95356035e+00 -1.05982816e+00 9.80547667e-01 1.21526659e+00 9.40903962e-01 1.30292401e-01 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613b151b-4fcd-4e3e-82e3-b8e2c445850f	policy-based-self-competition-for-planning	2306.04403	null	https://arxiv.org/abs/2306.04403v1	https://arxiv.org/pdf/2306.04403v1.pdf	Policy-Based Self-Competition for Planning Problems	AlphaZero-type algorithms may stop improving on single-player tasks in case the value network guiding the tree search is unable to approximate the outcome of an episode sufficiently well. One technique to address this problem is transforming the single-player task through self-competition. The main idea is to compute a scalar baseline from the agent's historical performances and to reshape an episode's reward into a binary output, indicating whether the baseline has been exceeded or not. However, this baseline only carries limited information for the agent about strategies how to improve. We leverage the idea of self-competition and directly incorporate a historical policy into the planning process instead of its scalar performance. Based on the recently introduced Gumbel AlphaZero (GAZ), we propose our algorithm GAZ 'Play-to-Plan' (GAZ PTP), in which the agent learns to find strong trajectories by planning against possible strategies of its past self. We show the effectiveness of our approach in two well-known combinatorial optimization problems, the Traveling Salesman Problem and the Job-Shop Scheduling Problem. With only half of the simulation budget for search, GAZ PTP consistently outperforms all selected single-player variants of GAZ.	['Dominik Gerhard Grimm', 'Jakob Burger', 'Quirin Göttl', 'Jonathan Pirnay']	2023-06-07	null	null	null	null	['combinatorial-optimization']	['methodology']	[ 9.69118178e-02 5.24286211e-01 -3.62569213e-01 -2.80586239e-02 -8.08431804e-01 -6.15090251e-01 5.37855685e-01 2.97095329e-01 -8.27324450e-01 1.11808014e+00 -1.01387925e-01 -3.88202459e-01 -5.67714751e-01 -7.45387197e-01 -7.05229402e-01 -8.56367946e-01 -3.41925621e-01 1.10751796e+00 5.26666045e-01 -3.86357635e-01 3.57428551e-01 1.55107185e-01 -1.19600904e+00 -4.46928106e-03 8.86532664e-01 7.58930981e-01 4.42791432e-01 8.77116501e-01 1.01594046e-01 7.92174339e-01 -7.29609907e-01 -1.09515630e-01 4.71175700e-01 -5.01136720e-01 -1.04983854e+00 2.16006085e-01 -6.69756949e-01 -1.89058617e-01 -2.28352159e-01 1.08260012e+00 1.62078038e-01 4.80832070e-01 3.64299268e-01 -1.59308088e+00 1.72068059e-01 1.14073110e+00 -6.56793654e-01 4.70740527e-01 1.64111018e-01 5.18884301e-01 9.19898331e-01 2.90007800e-01 8.63106072e-01 1.19138980e+00 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20ba1f95-bbaf-4979-9411-f66729a0e31e	koala-an-index-for-quantifying-overlaps-with	2303.14770	null	https://arxiv.org/abs/2303.14770v1	https://arxiv.org/pdf/2303.14770v1.pdf	Koala: An Index for Quantifying Overlaps with Pre-training Corpora	In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour. Despite the importance, there is no public tool that supports such analysis of pre-training corpora at large scale. To help research in this space, we launch Koala, a searchable index over large pre-training corpora using compressed suffix arrays with highly efficient compression rate and search support. In its first release we index the public proportion of OPT 175B pre-training data. Koala provides a framework to do forensic analysis on the current and future benchmarks as well as to assess the degree of memorization in the output from the LLMs. Koala is available for public use at https://koala-index.erc.monash.edu/.	['Ehsan Shareghi', 'Gholamreza Haffari', 'Xuanli He', 'Thuy-Trang Vu']	2023-03-26	null	null	null	null	['memorization']	['natural-language-processing']	[-7.46012926e-02 -1.16014160e-01 -2.04572871e-01 -1.55146653e-02 -1.21915042e+00 -8.42463255e-01 4.60286230e-01 4.71450895e-01 -7.30593860e-01 6.04479671e-01 3.44426960e-01 -1.02504230e+00 -1.35698706e-01 -5.90524912e-01 -7.87930965e-01 -4.87323284e-01 -4.86579956e-03 5.20271897e-01 1.06763005e-01 2.14205552e-02 4.80764270e-01 4.23960745e-01 -1.01631892e+00 4.78420109e-01 3.70618463e-01 2.44675100e-01 3.43126237e-01 7.91587293e-01 -6.58953711e-02 3.66662264e-01 -6.06506228e-01 -1.07375026e+00 2.87905484e-01 -1.85337380e-01 -1.05085099e+00 -5.20310044e-01 2.97401756e-01 -4.30527553e-02 -6.52250886e-01 9.33962762e-01 8.17623556e-01 -1.57632843e-01 2.38959849e-01 -9.45714414e-01 -2.77155012e-01 1.40429831e+00 -2.13412821e-01 1.01601529e+00 2.60036767e-01 4.49149340e-01 1.15602231e+00 -7.23102331e-01 1.02986860e+00 1.12732744e+00 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09c88e1b-41c3-4f67-869f-78c2faa4fe9f	video-instance-segmentation-by-instance-flow	2110.10599	null	https://arxiv.org/abs/2110.10599v1	https://arxiv.org/pdf/2110.10599v1.pdf	Video Instance Segmentation by Instance Flow Assembly	Instance segmentation is a challenging task aiming at classifying and segmenting all object instances of specific classes. While two-stage box-based methods achieve top performances in the image domain, they cannot easily extend their superiority into the video domain. This is because they usually deal with features or images cropped from the detected bounding boxes without alignment, failing to capture pixel-level temporal consistency. We embrace the observation that bottom-up methods dealing with box-free features could offer accurate spacial correlations across frames, which can be fully utilized for object and pixel level tracking. We first propose our bottom-up framework equipped with a temporal context fusion module to better encode inter-frame correlations. Intra-frame cues for semantic segmentation and object localization are simultaneously extracted and reconstructed by corresponding decoders after a shared backbone. For efficient and robust tracking among instances, we introduce an instance-level correspondence across adjacent frames, which is represented by a center-to-center flow, termed as instance flow, to assemble messy dense temporal correspondences. Experiments demonstrate that the proposed method outperforms the state-of-the-art online methods (taking image-level input) on the challenging Youtube-VIS dataset.	['Yan Lu', 'Xiao Li', 'Jinglu Wang', 'Xiang Li']	2021-10-20	null	null	null	null	['video-instance-segmentation']	['computer-vision']	[ 4.10627037e-01 -2.44675040e-01 -4.17342871e-01 -3.73504192e-01 -8.31823051e-01 -6.69102132e-01 4.82526630e-01 2.17518240e-01 -4.34347123e-01 3.80781025e-01 -2.26521462e-01 1.27331108e-01 -7.51372278e-02 -5.86368620e-01 -9.49550688e-01 -6.22643173e-01 -1.62761316e-01 2.31979370e-01 8.60897005e-01 -2.21546106e-02 2.44870707e-01 4.85169679e-01 -1.74227703e+00 5.02318919e-01 6.50586545e-01 1.26082957e+00 4.00932044e-01 5.25113761e-01 -3.69325966e-01 6.50583625e-01 -2.85495818e-01 -3.50025952e-01 4.33655292e-01 -2.81693399e-01 -8.78046751e-01 6.62091434e-01 8.64193201e-01 -3.55991125e-01 -2.37999842e-01 1.14318228e+00 1.62445903e-01 1.37599021e-01 2.32968912e-01 -1.19482648e+00 -6.45887330e-02 3.45838249e-01 -6.68635011e-01 3.69113058e-01 4.46994692e-01 2.40095973e-01 1.02267170e+00 -8.98332477e-01 1.04588056e+00 1.20101058e+00 5.43152630e-01 3.54160815e-01 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6.18501186e-01 5.32318592e-01 -7.06499279e-01 -1.71116203e-01 5.05360126e-01]	[9.131818771362305, -0.08862917870283127]
bb0b9647-8988-4f15-a663-a1b8982a9cae	3d-dense-face-alignment-with-fused-features	2203.04643	null	https://arxiv.org/abs/2203.04643v1	https://arxiv.org/pdf/2203.04643v1.pdf	3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs	In this paper, we propose a novel multi-level aggregation network to regress the coordinates of the vertices of a 3D face from a single 2D image in an end-to-end manner. This is achieved by seamlessly combining standard convolutional neural networks (CNNs) with Graph Convolution Networks (GCNs). By iteratively and hierarchically fusing the features across different layers and stages of the CNNs and GCNs, our approach can provide a dense face alignment and 3D face reconstruction simultaneously for the benefit of direct feature learning of 3D face mesh. Experiments on several challenging datasets demonstrate that our method outperforms state-of-the-art approaches on both 2D and 3D face alignment tasks.	['Yalin Zheng', 'Xiaowei Huang', 'Yihong Qiao', 'Xiaoyun Yang', 'Yitian Zhao', 'Dongxu Gao', 'Xu Chen', 'Yanda Meng']	2022-03-09	null	null	null	null	['3d-face-reconstruction', 'face-alignment', 'face-reconstruction']	['computer-vision', 'computer-vision', 'computer-vision']	[-1.23681992e-01 2.97602236e-01 8.20718110e-02 -7.31147647e-01 -2.32105657e-01 -2.15996087e-01 5.31548262e-01 -1.96062565e-01 -5.76369949e-02 -6.18345402e-02 -4.60375508e-04 -1.02002509e-02 1.35267526e-01 -8.12677503e-01 -8.72045994e-01 -1.80463418e-01 -8.27947408e-02 6.16235495e-01 -2.74258912e-01 -7.94899985e-02 -5.55180721e-02 1.36518645e+00 -1.50277436e+00 1.33093759e-01 3.51343244e-01 1.31725228e+00 -5.55901349e-01 5.02367556e-01 -2.16127768e-01 3.25423658e-01 -6.68982938e-02 -6.07299507e-01 5.63683271e-01 -1.14990659e-01 -6.59026146e-01 3.18054765e-01 1.36299682e+00 -4.84976232e-01 -4.61051524e-01 8.36902797e-01 5.26504457e-01 -2.65858233e-01 5.22592545e-01 -1.29255593e+00 -6.46403193e-01 -4.39277887e-02 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1cfc19b6-3822-4014-99da-c8c7c4037684	sat-size-aware-transformer-for-3d-point-cloud	2301.06869	null	https://arxiv.org/abs/2301.06869v1	https://arxiv.org/pdf/2301.06869v1.pdf	SAT: Size-Aware Transformer for 3D Point Cloud Semantic Segmentation	Transformer models have achieved promising performances in point cloud segmentation. However, most existing attention schemes provide the same feature learning paradigm for all points equally and overlook the enormous difference in size among scene objects. In this paper, we propose the Size-Aware Transformer (SAT) that can tailor effective receptive fields for objects of different sizes. Our SAT achieves size-aware learning via two steps: introduce multi-scale features to each attention layer and allow each point to choose its attentive fields adaptively. It contains two key designs: the Multi-Granularity Attention (MGA) scheme and the Re-Attention module. The MGA addresses two challenges: efficiently aggregating tokens from distant areas and preserving multi-scale features within one attention layer. Specifically, point-voxel cross attention is proposed to address the first challenge, and the shunted strategy based on the standard multi-head self attention is applied to solve the second. The Re-Attention module dynamically adjusts the attention scores to the fine- and coarse-grained features output by MGA for each point. Extensive experimental results demonstrate that SAT achieves state-of-the-art performances on S3DIS and ScanNetV2 datasets. Our SAT also achieves the most balanced performance on categories among all referred methods, which illustrates the superiority of modelling categories of different sizes. Our code and model will be released after the acceptance of this paper.	['Xiangyang Gong', 'Fangyu Liu', 'Chinwai Chiu', 'Yongping Xiong', 'Junjie Zhou']	2023-01-17	null	null	null	null	['point-cloud-segmentation']	['computer-vision']	[-2.66701542e-02 -1.83765128e-01 -6.69736192e-02 -3.55372548e-01 -8.46449971e-01 -2.87489653e-01 2.52958447e-01 7.83213675e-02 -3.64590168e-01 3.12529981e-01 -3.60381813e-03 -5.74421026e-02 -3.33272636e-01 -9.31391478e-01 -8.81984055e-01 -8.53499830e-01 -1.84033047e-02 6.67694449e-01 9.09849644e-01 -1.49218351e-01 5.33478260e-01 8.29694331e-01 -1.74242830e+00 3.32326740e-01 9.83599365e-01 1.34893978e+00 6.85873628e-01 4.77798253e-01 -4.76512462e-01 4.95996714e-01 -4.67939973e-01 -8.92119780e-02 3.59433949e-01 1.99464634e-01 -9.39266503e-01 6.91883117e-02 6.44717634e-01 -2.57432669e-01 -1.25688598e-01 8.74399006e-01 5.27119160e-01 2.47347102e-01 5.11766136e-01 -1.26716948e+00 -8.26626301e-01 6.08200669e-01 -9.65990305e-01 7.35470951e-01 -2.53430694e-01 2.17313305e-01 1.02074075e+00 -8.97191525e-01 2.34256759e-02 1.32229400e+00 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-2.94257611e-01 -8.49575341e-01 -5.70780396e-01 2.77825892e-01 4.64793980e-01 -8.22058320e-01 -1.97480798e-01 -2.59984732e-01]	[7.924476146697998, -3.3699522018432617]
d48338a5-0a2b-4e66-9adc-ddca46335359	attention-u-net-learning-where-to-look-for	1804.03999	null	http://arxiv.org/abs/1804.03999v3	http://arxiv.org/pdf/1804.03999v3.pdf	Attention U-Net: Learning Where to Look for the Pancreas	We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN architectures such as the U-Net model with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed Attention U-Net architecture is evaluated on two large CT abdominal datasets for multi-class image segmentation. Experimental results show that AGs consistently improve the prediction performance of U-Net across different datasets and training sizes while preserving computational efficiency. The code for the proposed architecture is publicly available.	['Nils Y. Hammerla', 'Kensaku Mori', 'Loic Le Folgoc', 'Steven McDonagh', 'Mattias Heinrich', 'Jo Schlemper', 'Daniel Rueckert', 'Ozan Oktay', 'Matthew Lee', 'Kazunari Misawa', 'Bernhard Kainz', 'Ben Glocker']	2018-04-11	null	null	null	null	['pancreas-segmentation']	['medical']	[ 5.08709669e-01 5.90903819e-01 -2.45846063e-01 -3.27940464e-01 -7.01913893e-01 -1.81749880e-01 1.47038713e-01 4.76829678e-01 -5.33071816e-01 4.26709324e-01 7.47003853e-02 -3.66156667e-01 7.89828449e-02 -6.86759830e-01 -8.06214094e-01 -8.16549063e-01 -1.49426207e-01 1.19020171e-01 4.34692472e-01 2.24136170e-02 -8.50855559e-02 5.65993726e-01 -9.01005149e-01 4.39602554e-01 7.54816771e-01 1.16989625e+00 5.72887719e-01 6.41722023e-01 1.88955903e-01 1.13425469e+00 -3.34604323e-01 -1.73433512e-01 1.94596291e-01 -3.12695146e-01 -9.01783884e-01 -1.88406810e-01 3.53075117e-01 -2.51417816e-01 -3.99285257e-01 1.19323754e+00 6.28446519e-01 2.12618083e-01 5.44235885e-01 -6.28763974e-01 -7.52850115e-01 6.12234056e-01 -4.63031560e-01 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3e3e3e76-2aa7-40c3-85ff-ce3d549dc83c	extended-pipeline-for-content-based-feature	1805.05324	null	http://arxiv.org/abs/1805.05324v1	http://arxiv.org/pdf/1805.05324v1.pdf	Extended pipeline for content-based feature engineering in music genre recognition	We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification. The key idea is to extend the traditional two-step process of extraction and classification with additive stand-alone phases which are no longer organized in a waterfall scheme. The whole system is realized by traversing backtrack arrows and cycles between various stages. In order to give a compact and effective representation of the features, the standard early temporal integration is combined with other selection and extraction phases: on the one hand, the selection of the most meaningful characteristics based on information gain, and on the other hand, the inclusion of the nonlinear correlation between this subset of features, determined by an autoencoder. The results of the experiments conducted on GTZAN dataset reveal a noticeable contribution of this methodology towards the model's performance in classification task.	['Tina Raissi', 'Alessandro Tibo', 'Paolo Bientinesi']	2018-05-12	null	null	null	null	['music-genre-recognition']	['music']	[ 3.12550992e-01 -8.57297704e-02 4.44909632e-01 -1.82585746e-01 -3.32651615e-01 -5.93341589e-01 5.37680149e-01 4.64668244e-01 -4.18894380e-01 1.27914160e-01 1.53867468e-01 1.32743735e-02 -8.05891037e-01 -5.00544667e-01 2.91174725e-02 -6.95088983e-01 -3.85617971e-01 3.35508078e-01 1.69807911e-01 -4.70543683e-01 3.88289720e-01 5.23667812e-01 -1.83408463e+00 3.55119556e-01 2.87192822e-01 1.13748085e+00 -5.59725538e-02 6.27380192e-01 -1.27194235e-02 8.28997672e-01 -4.21330750e-01 -1.75460115e-01 1.13717817e-01 -5.14629245e-01 -7.47787654e-01 2.03009322e-01 -2.10818768e-01 -6.02706671e-02 1.35109171e-01 5.13780415e-01 3.93759876e-01 3.33190829e-01 4.62395370e-01 -6.88418269e-01 2.03868613e-01 8.29095840e-01 -1.39525771e-04 -1.29873291e-01 5.39157867e-01 -3.52673866e-02 1.24641633e+00 -8.22143376e-01 6.42833769e-01 7.85168529e-01 8.16295326e-01 -2.61764787e-02 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-1.24082303e+00 -4.48874950e-01 4.98305976e-01 8.53154436e-02 -4.78731811e-01 -1.45222187e-01 2.84407973e-01]	[15.817154884338379, 5.334970474243164]
fbc0554d-c8ae-4641-9623-12200c2054db	wider-face-and-pedestrian-challenge-2018	1902.06854	null	http://arxiv.org/abs/1902.06854v1	http://arxiv.org/pdf/1902.06854v1.pdf	WIDER Face and Pedestrian Challenge 2018: Methods and Results	This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurate association of identities. It comprises of three tracks: (i) WIDER Face which aims at soliciting new approaches to advance the state-of-the-art in face detection, (ii) WIDER Pedestrian which aims to find effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments, and (iii) WIDER Person Search which presents an exciting challenge of searching persons across 192 movies. In total, 73 teams made valid submissions to the challenge tracks. We summarize the winning solutions for all three tracks. and present discussions on open problems and potential research directions in these topics.	['Jian-Feng Wang', 'Yiheng Liu', 'Shiying Luo', 'Nikolay Sergievskiy', 'Boyan Zhou', 'Bingpeng Ma', 'Cheng Chi', 'Shifeng Zhang', 'Zuoxin Li', 'Ping Luo', 'Yuanjun Xiong', 'Ye Yuan', 'Xilin Chen', 'Wengang Zhou', 'Shuai Shao', 'Quanquan Li', 'Qiaokang Xie', 'Peng Cheng', 'Mingjun Sun', 'Lin Chen', 'Dongzhan Zhou', 'Dong Chen', 'Artem Vasenin', 'Zhen Lei', 'Yongjun Li', 'Yirong Mao', 'Xiang Ming', 'Stan Z. Li', 'Ruiping Wang', 'Hua Yang', 'Hongkai Zhang', 'Artem Kukharenko', 'Wu Liu', 'Wanli Ouyang', 'Shiguang Shan', 'Qiling Xu', 'Huaxiong Li', 'Hong Chang', 'Gang Yu', 'Chen Change Loy', 'Boxun Li', 'Ziyang Wu', 'Yuan Hong', 'Wei Xia', 'Tao Mei', 'Shuo Yang', 'Qingqiu Huang', 'Liangqi Li', 'Lei Lu', 'Junjie Yan', 'Fangyun Wei', 'Dahua Lin']	2019-02-19	null	null	null	null	['person-search']	['computer-vision']	[-1.18387774e-01 -2.05834076e-01 1.42957807e-01 -3.91533345e-01 -7.35981584e-01 -5.69642544e-01 6.30864620e-01 -4.66376603e-01 -4.24565315e-01 5.68631709e-01 2.64101118e-01 2.00383380e-01 2.70784676e-01 -3.66255701e-01 -5.10428011e-01 -3.63311261e-01 -1.65929988e-01 4.84489143e-01 -3.25051211e-02 -5.14700934e-02 9.61548164e-02 5.41261971e-01 -1.99537098e+00 5.31530380e-01 8.82224441e-02 1.09865832e+00 -2.78875649e-01 7.97838092e-01 3.04325074e-01 3.62848014e-01 -5.73580921e-01 -1.13709867e+00 4.15449500e-01 -1.13720894e-01 -9.33606625e-01 9.60558131e-02 1.47621703e+00 -5.18625081e-01 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b1ae7d17-657c-4c03-9f91-3cf97a1b09b0	deep-leaning-based-ultra-fast-stair-detection	2201.05275	null	https://arxiv.org/abs/2201.05275v2	https://arxiv.org/pdf/2201.05275v2.pdf	Deep Leaning-Based Ultra-Fast Stair Detection	Staircases are some of the most common building structures in urban environments. Stair detection is an important task for various applications, including the environmental perception of exoskeleton robots, humanoid robots, and rescue robots and the navigation of visually impaired people. Most existing stair detection algorithms have difficulty dealing with the diversity of stair structure materials, extreme light and serious occlusion. Inspired by human perception, we propose an end-to-end method based on deep learning. Specifically, we treat the process of stair line detection as a multitask involving coarse-grained semantic segmentation and object detection. The input images are divided into cells, and a simple neural network is used to judge whether each cell contains stair lines. For cells containing stair lines, the locations of the stair lines relative to each cell are regressed. Extensive experiments on our dataset show that our method can achieve high performance in terms of both speed and accuracy. A lightweight version can even achieve 300+ frames per second with the same resolution. Our code and dataset will be soon available at GitHub.	['Zhiyong Tang', 'Shuang Qiu', 'Zhongcai Pei', 'Chen Wang']	2022-01-14	null	null	null	null	['line-detection']	['computer-vision']	[ 3.12887169e-02 -4.38405573e-01 -7.58853108e-02 -1.21657401e-01 -2.11600915e-01 -4.68565166e-01 1.22525118e-01 -4.93379235e-02 -5.48370421e-01 5.64545453e-01 -2.73667760e-02 -2.28519976e-01 -2.72979643e-02 -8.23280632e-01 -6.39711261e-01 -5.69294691e-01 3.20717752e-01 4.35726136e-01 9.71342504e-01 -4.41380709e-01 2.15671703e-01 4.57360268e-01 -1.58486700e+00 2.22840384e-01 7.56039858e-01 1.22407758e+00 5.40342152e-01 2.46026009e-01 1.74175709e-01 5.17793357e-01 -2.84589380e-01 3.28537405e-01 2.81882286e-01 -8.92941933e-03 -4.17380005e-01 2.68428802e-01 6.89815953e-02 -4.05020773e-01 -3.28121364e-01 8.09340119e-01 5.41363835e-01 7.66869783e-02 6.91446543e-01 -1.19614685e+00 -1.10148214e-01 1.65735215e-01 -9.24604833e-01 3.10796648e-01 2.49462247e-01 -4.40226048e-02 6.37055278e-01 -8.01611781e-01 4.01724279e-01 1.27821076e+00 6.96850479e-01 3.49449754e-01 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e99b2966-fda7-4bf1-b0d7-b4ecb7c2f5d9	variable-selection-for-nonparametric-learning	1806.00569	null	http://arxiv.org/abs/1806.00569v2	http://arxiv.org/pdf/1806.00569v2.pdf	Variable Selection for Nonparametric Learning with Power Series Kernels	In this paper, we propose a variable selection method for general nonparametric kernel-based estimation. The proposed method consists of two-stage estimation: (1) construct a consistent estimator of the target function, (2) approximate the estimator using a few variables by l1-type penalized estimation. We see that the proposed method can be applied to various kernel nonparametric estimation such as kernel ridge regression, kernel-based density and density-ratio estimation. We prove that the proposed method has the property of the variable selection consistency when the power series kernel is used. This result is regarded as an extension of the variable selection consistency for the non-negative garrote to the kernel-based estimators. Several experiments including simulation studies and real data applications show the effectiveness of the proposed method.	['Mitsuaki Nishikimi', 'Kenta Kanamori', 'Wataru Kumagai', 'Kota Matsui', 'Takafumi Kanamori']	2018-06-02	null	null	null	null	['density-ratio-estimation']	['methodology']	[-8.33911523e-02 -3.65551084e-01 -6.21316254e-01 -5.50293088e-01 -7.62086272e-01 -3.90871800e-02 1.92653075e-01 -1.05083488e-01 -2.57305622e-01 1.28642762e+00 -4.05946344e-01 -3.15659881e-01 -2.15205163e-01 -7.84705698e-01 -5.32860696e-01 -8.33044589e-01 -1.85342103e-01 3.03593367e-01 1.71613619e-01 1.96173653e-01 4.35219288e-01 6.37030065e-01 -1.14647853e+00 -7.25205004e-01 1.20964503e+00 1.17828310e+00 -1.27774745e-01 6.48925126e-01 1.36264294e-01 4.67904121e-01 -3.77081335e-01 -1.34683862e-01 1.48622869e-02 -5.03004611e-01 -2.57364333e-01 6.29759654e-02 1.01323254e-01 -2.74928629e-01 2.64115661e-01 1.27014828e+00 3.37390929e-01 5.63403904e-01 1.30910587e+00 -1.35101068e+00 -7.28453100e-01 1.59191236e-01 -1.24123383e+00 1.59593046e-01 -7.61685371e-02 -6.17149770e-01 5.45152485e-01 -1.23166502e+00 1.15866713e-01 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3d68f6d4-d51c-42eb-841f-56673c3f1fc2	syntax-driven-approach-for-semantic-role	null	null	https://aclanthology.org/2022.lrec-1.772	https://aclanthology.org/2022.lrec-1.772.pdf	Syntax-driven Approach for Semantic Role Labeling	As an important task to analyze the semantic structure of a sentence, semantic role labeling (SRL) aims to locate the semantic role (e.g., agent) of noun phrases with respect to a given predicate and thus plays an important role in downstream tasks such as dialogue systems. To achieve a better performance in SRL, a model is always required to have a good understanding of the context information. Although one can use advanced text encoder (e.g., BERT) to capture the context information, extra resources are also required to further improve the model performance. Considering that there are correlations between the syntactic structure and the semantic structure of the sentence, many previous studies leverage auto-generated syntactic knowledge, especially the dependencies, to enhance the modeling of context information through graph-based architectures, where limited attention is paid to other types of auto-generated knowledge. In this paper, we propose map memories to enhance SRL by encoding different types of auto-generated syntactic knowledge (i.e., POS tags, syntactic constituencies, and word dependencies) obtained from off-the-shelf toolkits. Experimental results on two English benchmark datasets for span-style SRL (i.e., CoNLL-2005 and CoNLL-2012) demonstrate the effectiveness of our approach, which outperforms strong baselines and achieves state-of-the-art results on CoNLL-2005.	['Yan Song', 'Fei Xia', 'Han Qin', 'Yuanhe Tian']	null	null	null	null	lrec-2022-6	['semantic-role-labeling']	['natural-language-processing']	[ 1.52863964e-01 2.17977434e-01 -4.04459596e-01 -7.31659412e-01 -3.95597428e-01 -6.75967395e-01 5.15146911e-01 4.11801308e-01 -6.28525317e-01 7.06274152e-01 9.42095101e-01 -2.72932529e-01 2.43406519e-01 -8.56785119e-01 -7.07333565e-01 -3.97618860e-01 1.16642773e-01 4.82846498e-01 3.84390265e-01 -7.06816018e-01 1.67485982e-01 -1.63965985e-01 -1.10133612e+00 5.38120866e-01 9.17649090e-01 7.96345532e-01 8.00682187e-01 1.92729160e-01 -6.21303439e-01 1.28453672e+00 -7.38713264e-01 -3.55589956e-01 -3.61933500e-01 -5.04860044e-01 -1.08104956e+00 -1.30104095e-01 -2.34190419e-01 -3.86528373e-02 -2.30406955e-01 1.02680027e+00 1.48947194e-01 3.38147461e-01 2.04368562e-01 -8.06118309e-01 -6.12172067e-01 1.16874218e+00 -1.88665465e-01 3.37359279e-01 3.96445870e-01 2.45269015e-02 1.65280890e+00 -8.36953640e-01 6.70006990e-01 1.67841065e+00 9.12191346e-02 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ccbedebe-710a-423c-8a7d-87544b49d557	fedala-adaptive-local-aggregation-for	2212.01197	null	https://arxiv.org/abs/2212.01197v2	https://arxiv.org/pdf/2212.01197v2.pdf	FedALA: Adaptive Local Aggregation for Personalized Federated Learning	A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (ALA) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy.	['Haibing Guan', 'Ruhui Ma', 'Zhengui Xue', 'Tao Song', 'Hao Wang', 'Yang Hua', 'Jianqing Zhang']	2022-12-02	null	null	null	null	['personalized-federated-learning']	['methodology']	[-2.91089118e-01 -1.63408667e-01 -4.23754096e-01 -5.80443680e-01 -1.23341513e+00 -4.72024798e-01 6.67117178e-01 -3.33388746e-01 -3.42662334e-02 6.21894658e-01 2.84321487e-01 -1.42183363e-01 4.86102141e-03 -6.47209525e-01 -8.60347807e-01 -8.17839086e-01 -4.33118753e-02 3.76876593e-01 7.05197304e-02 3.43866557e-01 -3.31831753e-01 3.98998499e-01 -1.57627690e+00 8.94844413e-01 1.05158150e+00 1.38657200e+00 1.09453127e-01 5.27545214e-01 -3.20078701e-01 1.17547345e+00 -4.45636839e-01 -5.85256517e-01 3.11460674e-01 -2.78415769e-01 -8.35194588e-01 -6.23888001e-02 8.74220073e-01 -6.23722613e-01 -3.09524626e-01 9.38020647e-01 6.66952312e-01 1.05494961e-01 3.07214677e-01 -1.26316059e+00 -6.67117655e-01 1.02622759e+00 -1.69676960e-01 4.53316383e-02 -3.06871645e-02 5.01647055e-01 9.42732692e-01 -1.06281090e+00 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-5.48676550e-01 -1.29994881e+00 -8.44897807e-01 -3.50047708e-01 5.05142450e-01 2.78091550e-01 -2.29762912e-01 -4.58205342e-01 -5.16012497e-02]	[5.829578876495361, 6.287938594818115]
3a5563e4-542b-49c6-89e3-474f5244c583	3d-object-classification-via-spherical	1712.04426	null	http://arxiv.org/abs/1712.04426v1	http://arxiv.org/pdf/1712.04426v1.pdf	3D Object Classification via Spherical Projections	In this paper, we introduce a new method for classifying 3D objects. Our main idea is to project a 3D object onto a spherical domain centered around its barycenter and develop neural network to classify the spherical projection. We introduce two complementary projections. The first captures depth variations of a 3D object, and the second captures contour-information viewed from different angles. Spherical projections combine key advantages of two main-stream 3D classification methods: image-based and 3D-based. Specifically, spherical projections are locally planar, allowing us to use massive image datasets (e.g, ImageNet) for pre-training. Also spherical projections are similar to voxel-based methods, as they encode complete information of a 3D object in a single neural network capturing dependencies across different views. Our novel network design can fully utilize these advantages. Experimental results on ModelNet40 and ShapeNetCore show that our method is superior to prior methods.	['Qi-Xing Huang', 'Karthik Ramani', 'Zhangjie Cao']	2017-12-12	null	null	null	null	['3d-object-classification', '3d-classification']	['computer-vision', 'computer-vision']	[-1.11096904e-01 9.55641195e-02 -1.03965513e-01 -4.64812011e-01 -1.55222282e-01 -6.84495330e-01 6.55940533e-01 -3.60532820e-01 -4.55940589e-02 -2.16780931e-01 2.72984445e-01 -2.37676516e-01 3.06236297e-01 -8.41668487e-01 -8.23518693e-01 -4.04938161e-01 2.59783238e-01 7.38266408e-01 5.99742651e-01 1.24876358e-01 2.99957335e-01 1.15862215e+00 -1.21300304e+00 2.51743555e-01 3.18685412e-01 1.40133560e+00 2.79359937e-01 2.58686662e-01 -2.16749340e-01 2.52883971e-01 -1.65228158e-01 -3.07877436e-02 5.58303177e-01 1.27598375e-01 -5.83855271e-01 3.81171525e-01 7.02544212e-01 -5.43819845e-01 -4.65424418e-01 7.77016282e-01 3.89484048e-01 -1.78800613e-01 9.91672873e-01 -1.02841830e+00 -8.83733511e-01 1.45410359e-01 -5.95267951e-01 -1.22975856e-01 3.74403298e-01 -1.27596661e-01 7.90218771e-01 -1.29118383e+00 7.67155826e-01 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3f9b6815-5501-4c02-ac56-a072028b73fa	when-a-rf-beats-a-cnn-and-gru-together-a	2206.08004	null	https://arxiv.org/abs/2206.08004v1	https://arxiv.org/pdf/2206.08004v1.pdf	When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification	Internet traffic classification is widely used to facilitate network management. It plays a crucial role in Quality of Services (QoS), Quality of Experience (QoE), network visibility, intrusion detection, and traffic trend analyses. While there is no theoretical guarantee that deep learning (DL)-based solutions perform better than classic machine learning (ML)-based ones, DL-based models have become the common default. This paper compares well-known DL-based and ML-based models and shows that in the case of malicious traffic classification, state-of-the-art DL-based solutions do not necessarily outperform the classical ML-based ones. We exemplify this finding using two well-known datasets for a varied set of tasks, such as: malware detection, malware family classification, detection of zero-day attacks, and classification of an iteratively growing dataset. Note that, it is not feasible to evaluate all possible models to make a concrete statement, thus, the above finding is not a recommendation to avoid DL-based models, but rather empirical proof that in some cases, there are more simplistic solutions, that may perform even better.	['Chen Hajaj', 'Amit Dvir', 'Ran Dubin', 'Ofek Bader', 'Adi Lichy']	2022-06-16	null	null	null	null	['traffic-classification']	['miscellaneous']	[-1.02422491e-01 -3.91734391e-01 -4.95798737e-01 -1.15088850e-01 -1.79108996e-02 -3.11124802e-01 6.99750364e-01 4.08821464e-01 -4.22801465e-01 8.13019037e-01 -5.08870542e-01 -1.09942830e+00 -5.01330853e-01 -8.89115512e-01 -2.80781746e-01 -7.05513716e-01 -4.72088009e-01 6.67236745e-01 5.13465405e-01 -3.60219270e-01 3.03617209e-01 1.03589487e+00 -1.52563953e+00 9.71199870e-02 4.67302233e-01 1.32339251e+00 -3.47991943e-01 5.10910988e-01 -4.19790804e-01 9.34131324e-01 -7.77872026e-01 -6.28305376e-01 2.27610528e-01 -2.15745300e-01 -6.98001504e-01 -7.79567733e-02 1.20301330e-02 -2.05911044e-02 -2.10906878e-01 8.32069218e-01 8.84981081e-02 -1.73768684e-01 6.94777250e-01 -1.72612572e+00 -1.58254862e-01 3.82508934e-01 -4.41147387e-01 7.05984175e-01 -1.89875923e-02 3.29071730e-01 9.98644471e-01 -1.29801989e-01 6.10305429e-01 1.16301429e+00 6.75421834e-01 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7a484d84-ae6c-4d21-b747-ee9de5ad167b	gist-aiter-system-for-the-diarization-task-of	2209.10357	null	https://arxiv.org/abs/2209.10357v4	https://arxiv.org/pdf/2209.10357v4.pdf	GIST-AiTeR System for the Diarization Task of the 2022 VoxCeleb Speaker Recognition Challenge	This report describes the submission system of the GIST-AiTeR team at the 2022 VoxCeleb Speaker Recognition Challenge (VoxSRC) Track 4. Our system mainly includes speech enhancement, voice activity detection , multi-scaled speaker embedding, probabilistic linear discriminant analysis-based speaker clustering, and overlapped speech detection models. We first construct four different diarization systems according to different model combinations with the best experimental efforts. Our final submission is an ensemble system of all the four systems and achieves a diarization error rate of 5.12% on the challenge evaluation set, ranked third at the diarization track of the challenge.	['Hong Kook Kim', 'Ji Won Kim', 'Kyeong Wan Park', 'Yechan Yu', 'Dongkeon Park']	2022-09-21	null	null	null	null	['activity-detection']	['computer-vision']	[-6.86301738e-02 3.29336822e-01 -4.97555435e-02 -4.37905550e-01 -1.21331620e+00 -4.42233503e-01 6.75155520e-01 -3.04742277e-01 -4.05350983e-01 -6.60288483e-02 5.81239343e-01 -1.53885454e-01 2.63375282e-01 2.27556467e-01 1.16579339e-01 -4.82327849e-01 -3.66380870e-01 3.99998963e-01 1.77210927e-01 -1.50853261e-01 -3.04808803e-02 4.45054591e-01 -1.39177060e+00 3.56813669e-01 5.95617652e-01 8.52432728e-01 -3.74377221e-01 1.21628726e+00 7.33839944e-02 3.66352618e-01 -9.25238848e-01 -1.32675901e-01 -4.95741256e-02 -3.22615236e-01 -4.83549297e-01 -1.92679584e-01 6.41330063e-01 -1.70882434e-01 -6.18923604e-01 6.55240357e-01 1.28915334e+00 1.66530341e-01 6.17001891e-01 -1.47261024e+00 -3.62666577e-01 1.04065692e+00 -1.37913153e-01 7.82543480e-01 4.77686405e-01 5.41779958e-03 8.75234008e-01 -1.27139664e+00 3.15978795e-01 1.58862579e+00 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6ad03229-5f1f-4423-bffa-5e33bd9b56db	re-thinking-supertags-in-linear-context-free	null	null	https://openreview.net/forum?id=udaYBjwpU_M	https://openreview.net/pdf?id=udaYBjwpU_M	Re-thinking Supertags in Linear Context-free Rewriting Systems for Constituency Parsing	Recently, a supertagging-based approach for parsing discontinuous constituent trees with linear context-free rewriting systems (LCFRS) was introduced. We reformulate their algorithm for the extraction of supertags from treebanks to be more concise. Moreover, we add some extensions that give us control over the extraction process in terms of supertag granularity and which terminal symbols are associated with supertags. Our additions lead to an increase in parsing quality with LCFRS supertagging in all three compared treebanks. The scores are among the state of the art in discontinuous constituent parsing.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['constituency-parsing']	['natural-language-processing']	[ 1.51482671e-01 6.69202268e-01 1.03718936e-02 -5.36327541e-01 -1.24123967e+00 -9.76414979e-01 4.02761936e-01 2.82412589e-01 -2.76136428e-01 8.22592735e-01 3.62172335e-01 -7.05439806e-01 3.30813885e-01 -8.70481312e-01 -3.47564638e-01 -4.27533686e-01 -3.26783538e-01 2.90899009e-01 8.93751681e-01 -7.68029988e-01 -9.99845415e-02 2.21181825e-01 -9.33913231e-01 5.24567902e-01 8.08872640e-01 4.84327614e-01 4.30016518e-01 9.12009239e-01 -5.90161145e-01 5.42522490e-01 -7.17477620e-01 -7.83235669e-01 1.89876333e-01 -3.10645938e-01 -1.13303745e+00 -4.26617205e-01 2.22433835e-01 1.59566075e-01 7.36379027e-02 9.02216434e-01 6.24291524e-02 -2.74765700e-01 5.19171953e-02 -5.15084386e-01 -5.56311846e-01 1.30639160e+00 -2.35139012e-01 3.93947810e-01 4.36236739e-01 -5.26192367e-01 1.63806081e+00 -5.62791824e-01 9.95672822e-01 1.52214873e+00 5.44413090e-01 6.71903193e-01 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33d6a725-e74d-48c1-bd84-bab754d3aadd	flow-based-anomaly-detection	2010.03002	null	https://arxiv.org/abs/2010.03002v3	https://arxiv.org/pdf/2010.03002v3.pdf	OneFlow: One-class flow for anomaly detection based on a minimal volume region	We propose OneFlow - a flow-based one-class classifier for anomaly (outlier) detection that finds a minimal volume bounding region. Contrary to density-based methods, OneFlow is constructed in such a way that its result typically does not depend on the structure of outliers. This is caused by the fact that during training the gradient of the cost function is propagated only over the points located near to the decision boundary (behavior similar to the support vectors in SVM). The combination of flow models and a Bernstein quantile estimator allows OneFlow to find a parametric form of bounding region, which can be useful in various applications including describing shapes from 3D point clouds. Experiments show that the proposed model outperforms related methods on real-world anomaly detection problems.	['Przemysław Spurek', 'Jacek Tabor', 'Łukasz Struski', 'Marcin Sendera', 'Marek Śmieja', 'Łukasz Maziarka']	2020-10-06	null	null	null	null	['one-class-classifier']	['methodology']	[-7.07659960e-01 -1.60883889e-01 -2.15451717e-01 -3.65637958e-01 -1.98167950e-01 -4.35492814e-01 3.21716040e-01 8.34371567e-01 -2.52923351e-02 5.16065955e-01 -3.36795390e-01 -2.37975597e-01 -1.87508643e-01 -7.70590127e-01 -6.04454458e-01 -6.67070985e-01 -5.42020977e-01 5.03554702e-01 6.07676625e-01 9.70195420e-03 7.17769384e-01 1.08247864e+00 -1.48968363e+00 -6.16500974e-02 1.26010823e+00 1.27664959e+00 -7.35250533e-01 4.00499314e-01 -5.81323743e-01 3.34682196e-01 -7.07444370e-01 -5.58880232e-02 5.18074214e-01 -3.05962652e-01 -3.80454928e-01 -7.60571286e-02 7.28474021e-01 -3.58479381e-01 -5.98914959e-02 1.28130960e+00 -2.62496192e-02 2.92303890e-01 1.24937212e+00 -1.69933903e+00 -1.52502537e-01 -1.58164248e-01 -6.76913142e-01 5.47563255e-01 3.35361034e-01 -1.87422022e-01 7.52875447e-01 -9.68457460e-01 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9086bd35-91fd-44a0-92d0-a4ce7c16cf02	multi-granulariy-time-based-transformer-for	2304.05257	null	https://arxiv.org/abs/2304.05257v1	https://arxiv.org/pdf/2304.05257v1.pdf	Multi-granulariy Time-based Transformer for Knowledge Tracing	In this paper, we present a transformer architecture for predicting student performance on standardized tests. Specifically, we leverage students historical data, including their past test scores, study habits, and other relevant information, to create a personalized model for each student. We then use these models to predict their future performance on a given test. Applying this model to the RIIID dataset, we demonstrate that using multiple granularities for temporal features as the decoder input significantly improve model performance. Our results also show the effectiveness of our approach, with substantial improvements over the LightGBM method. Our work contributes to the growing field of AI in education, providing a scalable and accurate tool for predicting student outcomes.	['Tong Zhou']	2023-04-11	null	null	null	null	['knowledge-tracing']	['miscellaneous']	[ 1.07154131e-01 -2.12533712e-01 -7.67832279e-01 -6.93440020e-01 -7.43577421e-01 -6.25247359e-01 2.55586773e-01 6.02110147e-01 -2.18886837e-01 6.81094170e-01 3.60187471e-01 -6.91432595e-01 -5.67392826e-01 -9.85468507e-01 -6.46656692e-01 -2.37193517e-03 1.53807804e-01 2.83218533e-01 3.24235797e-01 -1.85161620e-01 4.84919429e-01 4.44825619e-01 -1.75117493e+00 3.03417355e-01 1.26771295e+00 5.85147798e-01 -5.35352603e-02 8.59159470e-01 -3.01926751e-02 1.15905988e+00 -6.73774958e-01 -5.78091085e-01 -1.32277414e-01 -4.97581095e-01 -1.04494321e+00 -2.15425611e-01 7.15201199e-01 -3.16573918e-01 -5.76710403e-01 5.05916417e-01 1.92460269e-01 6.06305182e-01 4.26032126e-01 -8.03206861e-01 -8.46297801e-01 9.01855469e-01 -2.94355810e-01 4.58290607e-01 6.59575045e-01 -2.52085980e-02 1.08209491e+00 -3.30501795e-01 4.91227865e-01 7.52279818e-01 4.56253171e-01 4.95693624e-01 -1.22237289e+00 -6.41959667e-01 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684e562e-8979-4ef0-97f8-ac22d2161684	deviant-depth-equivariant-network-for	2207.10758	null	https://arxiv.org/abs/2207.10758v1	https://arxiv.org/pdf/2207.10758v1.pdf	DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection	Modern neural networks use building blocks such as convolutions that are equivariant to arbitrary 2D translations. However, these vanilla blocks are not equivariant to arbitrary 3D translations in the projective manifold. Even then, all monocular 3D detectors use vanilla blocks to obtain the 3D coordinates, a task for which the vanilla blocks are not designed for. This paper takes the first step towards convolutions equivariant to arbitrary 3D translations in the projective manifold. Since the depth is the hardest to estimate for monocular detection, this paper proposes Depth EquiVarIAnt NeTwork (DEVIANT) built with existing scale equivariant steerable blocks. As a result, DEVIANT is equivariant to the depth translations in the projective manifold whereas vanilla networks are not. The additional depth equivariance forces the DEVIANT to learn consistent depth estimates, and therefore, DEVIANT achieves state-of-the-art monocular 3D detection results on KITTI and Waymo datasets in the image-only category and performs competitively to methods using extra information. Moreover, DEVIANT works better than vanilla networks in cross-dataset evaluation. Code and models at https://github.com/abhi1kumar/DEVIANT	['Xiaoming Liu', 'Armin Parchami', 'Enrique Corona', 'Garrick Brazil', 'Abhinav Kumar']	2022-07-21	null	null	null	null	['3d-object-detection-from-monocular-images']	['computer-vision']	[-4.12544221e-01 -1.32286251e-01 -1.34164527e-01 -1.74513727e-01 -3.15767884e-01 -8.11518431e-01 6.45349205e-01 -8.12996507e-01 -6.27606034e-01 3.17360163e-01 7.77972117e-02 -3.07939321e-01 5.77178538e-01 -7.40161419e-01 -1.01565146e+00 -5.93534172e-01 1.05051585e-01 2.15197757e-01 4.64151651e-01 -2.27127850e-01 1.32945001e-01 7.47673452e-01 -1.16644752e+00 6.86598569e-02 1.70913056e-01 6.72456801e-01 3.53515483e-02 9.81173933e-01 1.75089121e-01 4.24969643e-01 1.83611531e-02 -2.37234756e-01 1.00384510e+00 -1.28635198e-01 -3.72180104e-01 -8.07347149e-02 1.17708731e+00 -8.95146966e-01 -1.10986125e+00 1.32781959e+00 4.39007401e-01 -3.45625728e-01 7.65087306e-01 -1.11553788e+00 -8.05213869e-01 4.18766022e-01 -6.00515246e-01 2.61453480e-01 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80f631ce-8e46-4fad-9283-f2b3bcc72579	generalized-category-discovery	2201.02609	null	https://arxiv.org/abs/2201.02609v2	https://arxiv.org/pdf/2201.02609v2.pdf	Generalized Category Discovery	In this paper, we consider a highly general image recognition setting wherein, given a labelled and unlabelled set of images, the task is to categorize all images in the unlabelled set. Here, the unlabelled images may come from labelled classes or from novel ones. Existing recognition methods are not able to deal with this setting, because they make several restrictive assumptions, such as the unlabelled instances only coming from known - or unknown - classes, and the number of unknown classes being known a-priori. We address the more unconstrained setting, naming it 'Generalized Category Discovery', and challenge all these assumptions. We first establish strong baselines by taking state-of-the-art algorithms from novel category discovery and adapting them for this task. Next, we propose the use of vision transformers with contrastive representation learning for this open-world setting. We then introduce a simple yet effective semi-supervised $k$-means method to cluster the unlabelled data into seen and unseen classes automatically, substantially outperforming the baselines. Finally, we also propose a new approach to estimate the number of classes in the unlabelled data. We thoroughly evaluate our approach on public datasets for generic object classification and on fine-grained datasets, leveraging the recent Semantic Shift Benchmark suite. Project page at https://www.robots.ox.ac.uk/~vgg/research/gcd	['Andrew Zisserman', 'Andrea Vedaldi', 'Kai Han', 'Sagar Vaze']	2022-01-07	null	http://openaccess.thecvf.com//content/CVPR2022/html/Vaze_Generalized_Category_Discovery_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Vaze_Generalized_Category_Discovery_CVPR_2022_paper.pdf	cvpr-2022-1	['fine-grained-visual-recognition', 'open-world-semi-supervised-learning']	['computer-vision', 'computer-vision']	[ 7.28125334e-01 5.79937622e-02 -2.49675751e-01 -6.90714180e-01 -7.44030595e-01 -7.29014218e-01 8.19312453e-01 -1.39220968e-01 -6.67587817e-01 6.06535792e-01 7.12567046e-02 -3.59439105e-02 -1.15927458e-01 -5.62035799e-01 -9.87980604e-01 -9.12140548e-01 1.93325654e-01 8.18502843e-01 2.18338862e-01 -9.57516804e-02 3.16900432e-01 3.14280152e-01 -2.05098820e+00 2.72593766e-01 3.55879724e-01 1.05040264e+00 1.80540800e-01 4.86538112e-01 9.43343267e-02 5.18251479e-01 -4.03277248e-01 -2.37001464e-01 4.90674496e-01 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387c367c-0109-46f1-b6d2-f01b38e19507	multi-domain-conversation-quality-evaluation	1911.08567	null	https://arxiv.org/abs/1911.08567v1	https://arxiv.org/pdf/1911.08567v1.pdf	Multi-domain Conversation Quality Evaluation via User Satisfaction Estimation	An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and employ annotation schemes with limited generalizability to conversations spanning multiple domains. To address these gaps, we created a new Response Quality annotation scheme, introduced five new domain-independent feature sets and experimented with six machine learning models to estimate User Satisfaction at both turn and dialogue level. Response Quality ratings achieved significantly high correlation (0.76) with explicit turn-level user ratings. Using the new feature sets we introduced, Gradient Boosting Regression model achieved best (rating [1-5]) prediction performance on 26 seen (linear correlation ~0.79) and one new multi-turn domain (linear correlation 0.67). We observed a 16% relative improvement (68% -> 79%) in binary ("satisfactory/dissatisfactory") class prediction accuracy of a domain-independent dialogue-level satisfaction estimation model after including predicted turn-level satisfaction ratings as features.	['Praveen Kumar Bodigutla', 'Lazaros Polymenakos', 'Spyros Matsoukas']	2019-11-18	null	null	null	null	['dialogue-management']	['natural-language-processing']	[-2.12045357e-01 5.82249582e-01 -3.42064142e-01 -1.15150654e+00 -1.23096585e+00 -4.11135226e-01 4.79840577e-01 3.20804268e-01 -5.05984783e-01 1.08047152e+00 7.84453034e-01 -1.75940424e-01 -7.35338731e-03 -3.35928500e-01 4.89103198e-01 -1.60759911e-02 1.97045058e-01 7.31728554e-01 -1.26577109e-01 -9.82317984e-01 6.43693686e-01 2.62453239e-02 -1.02084744e+00 7.10895777e-01 9.91277099e-01 9.42869842e-01 -1.66751817e-01 1.18814945e+00 -4.07039493e-01 8.50883961e-01 -1.11645162e+00 -5.40643156e-01 -2.15362445e-01 -7.59489238e-01 -1.51472867e+00 2.24688858e-01 -4.30663116e-03 -4.34241623e-01 -9.77632180e-02 3.00107330e-01 5.66243529e-01 3.98903072e-01 5.17168283e-01 -1.18895805e+00 -2.96143919e-01 2.44630605e-01 -2.30478123e-02 1.47845209e-01 1.31241441e+00 1.99219376e-01 1.27725542e+00 -6.35351181e-01 4.00061309e-01 1.17867362e+00 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e2b9a15a-85e3-441e-9208-624d8cecf37d	an-automatic-cardiac-segmentation-framework	1909.05488	null	https://arxiv.org/abs/1909.05488v1	https://arxiv.org/pdf/1909.05488v1.pdf	An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image	LGE CMR is an efficient technology for detecting infarcted myocardium. An efficient and objective ventricle segmentation method in LGE can benefit the location of the infarcted myocardium. In this paper, we proposed an automatic framework for LGE image segmentation. There are just 5 labeled LGE volumes with about 15 slices of each volume. We adopted histogram match, an invariant of rotation registration method, on the other labeled modalities to achieve effective augmentation of the training data. A CNN segmentation model was trained based on the augmented training data by leave-one-out strategy. The predicted result of the model followed a connected component analysis for each class to remain the largest connected component as the final segmentation result. Our model was evaluated by the 2019 Multi-sequence Cardiac MR Segmentation Challenge. The mean testing result of 40 testing volumes on Dice score, Jaccard score, Surface distance, and Hausdorff distance is 0.8087, 0.6976, 2.8727mm, and 15.6387mm, respectively. The experiment result shows a satisfying performance of the proposed framework. Code is available at https://github.com/Suiiyu/MS-CMR2019.	['Wei Wang', 'Kuanquan Wang', 'Yashu Liu', 'Gongning Luo', 'Chengqin Ye']	2019-09-12	null	null	null	null	['cardiac-segmentation']	['medical']	[-3.95551994e-02 5.10133728e-02 4.58846577e-02 -3.13477367e-01 -7.81225801e-01 -5.59754193e-01 -2.08136857e-01 -2.93114223e-02 -4.97346729e-01 7.66002536e-01 9.27259102e-02 -2.19258815e-01 1.98567554e-01 -5.32812119e-01 -3.16314191e-01 -6.44898176e-01 -4.07342374e-01 4.77901369e-01 4.43983257e-01 3.43768507e-01 3.18347931e-01 4.78398830e-01 -4.30314243e-01 2.08630219e-01 8.08218241e-01 9.46732342e-01 3.61690044e-01 8.84726226e-01 2.24201202e-01 6.25665963e-01 -3.32171768e-01 8.35536141e-03 5.18414557e-01 -8.23145807e-01 -9.30160582e-01 1.76397830e-01 7.42960647e-02 -4.72112268e-01 -2.19764844e-01 9.44928825e-01 7.60623038e-01 -1.80961773e-01 7.06277847e-01 -8.17695916e-01 -1.46772608e-01 5.81348598e-01 -6.42344773e-01 7.10641325e-01 -4.49594378e-01 1.14457689e-01 3.82934958e-01 -8.76977563e-01 9.82490659e-01 3.36343199e-01 7.98693419e-01 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e08cfb8c-ba41-4875-99aa-c36076a756cc	auditing-predictive-models-for-intersectional	2306.13064	null	https://arxiv.org/abs/2306.13064v1	https://arxiv.org/pdf/2306.13064v1.pdf	Auditing Predictive Models for Intersectional Biases	Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we propose Conditional Bias Scan (CBS), a flexible auditing framework for detecting intersectional biases in classification models. CBS identifies the subgroup for which there is the most significant bias against the protected class, as compared to the equivalent subgroup in the non-protected class, and can incorporate multiple commonly used fairness definitions for both probabilistic and binarized predictions. We show that this methodology can detect previously unidentified intersectional and contextual biases in the COMPAS pre-trial risk assessment tool and has higher bias detection power compared to similar methods that audit for subgroup fairness.	['Daniel B. Neill', 'Edward McFowland III', 'Kate S. Boxer']	2023-06-22	null	null	null	null	['fairness', 'fairness', 'bias-detection']	['computer-vision', 'miscellaneous', 'natural-language-processing']	[ 4.10401374e-01 3.35635871e-01 -9.73189890e-01 -7.67382622e-01 -8.00415754e-01 -6.04743838e-01 5.74241757e-01 7.98071742e-01 -6.33434415e-01 9.68643963e-01 5.62499046e-01 -1.10117805e+00 -4.69364464e-01 -8.79202902e-01 -2.36869052e-01 -4.20021743e-01 7.35058039e-02 4.66623753e-01 -2.29720827e-02 3.20232153e-01 4.55265045e-01 5.35423994e-01 -9.91047502e-01 6.19578540e-01 1.12733865e+00 4.99194413e-01 -1.04969394e+00 2.45296925e-01 4.76540238e-01 6.83592081e-01 -4.42374498e-01 -8.91502500e-01 2.12294266e-01 -4.33285534e-01 -6.80909991e-01 -7.65938818e-01 1.07354486e+00 -7.73126185e-01 1.43574759e-01 9.92405355e-01 6.24389410e-01 -5.31617180e-03 9.59239662e-01 -1.41470456e+00 -4.25495148e-01 1.03301108e+00 -3.62452626e-01 2.23104522e-01 2.01227456e-01 2.41187826e-01 1.13649797e+00 -3.18277031e-01 2.47220829e-01 1.52811921e+00 9.50173378e-01 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fe728266-c8b1-4af1-bad4-1a34695fb876	robust-face-recognition-via-multimodal-deep	1509.00244	null	http://arxiv.org/abs/1509.00244v1	http://arxiv.org/pdf/1509.00244v1.pdf	Robust Face Recognition via Multimodal Deep Face Representation	Face images appeared in multimedia applications, e.g., social networks and digital entertainment, usually exhibit dramatic pose, illumination, and expression variations, resulting in considerable performance degradation for traditional face recognition algorithms. This paper proposes a comprehensive deep learning framework to jointly learn face representation using multimodal information. The proposed deep learning structure is composed of a set of elaborately designed convolutional neural networks (CNNs) and a three-layer stacked auto-encoder (SAE). The set of CNNs extracts complementary facial features from multimodal data. Then, the extracted features are concatenated to form a high-dimensional feature vector, whose dimension is compressed by SAE. All the CNNs are trained using a subset of 9,000 subjects from the publicly available CASIA-WebFace database, which ensures the reproducibility of this work. Using the proposed single CNN architecture and limited training data, 98.43% verification rate is achieved on the LFW database. Benefited from the complementary information contained in multimodal data, our small ensemble system achieves higher than 99.0% recognition rate on LFW using publicly available training set.	['DaCheng Tao', 'Changxing Ding']	2015-09-01	null	null	null	null	['robust-face-recognition']	['computer-vision']	[ 2.11868420e-01 -1.71072349e-01 -7.94809163e-02 -9.38695014e-01 -6.01790130e-01 -1.54820323e-01 4.49942350e-01 -5.96994281e-01 -3.42881858e-01 5.43682635e-01 6.03579618e-02 1.55264974e-01 8.73980895e-02 -5.15068948e-01 -6.93645298e-01 -8.83046508e-01 -2.32362047e-01 -1.76350579e-01 -6.21611238e-01 -1.53296858e-01 -1.20494343e-01 6.69224143e-01 -1.94244540e+00 4.45283234e-01 3.97915155e-01 1.66374576e+00 -1.90557450e-01 3.52835506e-01 1.42406896e-01 6.10788524e-01 -4.58915919e-01 -7.84436285e-01 1.27598062e-01 -1.60104305e-01 -2.26435676e-01 2.67328709e-01 6.97996855e-01 -7.15105534e-01 -8.12119067e-01 7.38617897e-01 7.68971503e-01 -3.70914824e-02 5.00779629e-01 -1.35450995e+00 -7.20807791e-01 2.48321071e-01 -4.50638503e-01 -2.82310516e-01 1.23343781e-01 1.22803636e-01 7.30412841e-01 -1.41946912e+00 3.79461467e-01 1.32869613e+00 6.25874758e-01 8.26141894e-01 -1.03096712e+00 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5.24020970e-01 6.36650026e-01 -8.41737032e-01 -2.24857360e-01 5.85351251e-02]	[13.306188583374023, 0.940438985824585]
9c14cb71-e410-4a45-aea2-811d81713a65	sir-nerd-a-chinese-named-entity-recognition	null	null	https://aclanthology.org/W12-6322	https://aclanthology.org/W12-6322.pdf	SIR-NERD: A Chinese Named Entity Recognition and Disambiguation System using a Two-Stage Method	null	['Zehuan Peng', 'Xianpei Han', 'Le Sun']	2012-12-01	sir-nerd-a-chinese-named-entity-recognition-1	https://aclanthology.org/W12-6322	https://aclanthology.org/W12-6322.pdf	ws-2012-12	['chinese-named-entity-recognition']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 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bb7470ee-1239-4902-994e-e8605842fad3	dual-information-enhanced-multi-view	2211.14987	null	https://arxiv.org/abs/2211.14987v1	https://arxiv.org/pdf/2211.14987v1.pdf	Dual Information Enhanced Multi-view Attributed Graph Clustering	Multi-view attributed graph clustering is an important approach to partition multi-view data based on the attribute feature and adjacent matrices from different views. Some attempts have been made in utilizing Graph Neural Network (GNN), which have achieved promising clustering performance. Despite this, few of them pay attention to the inherent specific information embedded in multiple views. Meanwhile, they are incapable of recovering the latent high-level representation from the low-level ones, greatly limiting the downstream clustering performance. To fill these gaps, a novel Dual Information enhanced multi-view Attributed Graph Clustering (DIAGC) method is proposed in this paper. Specifically, the proposed method introduces the Specific Information Reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables GCN to capture the more essential low-level representations. Besides, the Mutual Information Maximization (MIM) module maximizes the agreement between the latent high-level representation and low-level ones, and enables the high-level representation to satisfy the desired clustering structure with the help of the Self-supervised Clustering (SC) module. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed DIAGC method compared with the state-of-the-art baselines.	['Haizhang Zhang', 'Chang-Dong Wang', 'Xi-Ran Zhu', 'Man-Sheng Chen', 'Jia-Qi Lin']	2022-11-28	null	null	null	null	['graph-clustering']	['graphs']	[-6.89233616e-02 1.56543236e-02 -3.34332436e-01 -7.85255730e-02 -5.41549385e-01 -2.65253156e-01 4.29482311e-01 1.12503260e-01 2.30707020e-01 1.00611664e-01 4.16510701e-01 1.79337114e-01 -5.53637564e-01 -6.83679044e-01 -1.26647964e-01 -1.20080090e+00 -1.52508169e-01 4.17686105e-01 -1.09549858e-01 1.30333513e-01 9.02104750e-02 -5.66377155e-02 -1.44464755e+00 4.78277743e-01 9.07160521e-01 6.47225559e-01 3.41810822e-01 -1.30015500e-02 -8.07505623e-02 7.02126801e-01 -1.00448035e-01 -5.68514131e-02 -2.29491554e-02 -5.46464086e-01 -4.42132980e-01 5.97221196e-01 -7.59531185e-02 2.01922879e-01 -3.00679713e-01 1.36975801e+00 3.40947360e-01 6.46478981e-02 7.60548055e-01 -1.44093370e+00 -6.40704393e-01 6.47744715e-01 -1.05983758e+00 -2.16154024e-01 1.08531713e-01 -2.25281611e-01 1.18628502e+00 -9.40820932e-01 6.22252584e-01 1.20622981e+00 1.84422240e-01 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-9.32454169e-01 6.45035625e-01 -6.36851639e-02 -6.39882386e-01 -4.50205147e-01 -3.72830941e-03]	[8.207466125488281, 4.75744104385376]
2138a529-57da-4fd2-83b1-4a418951ad74	clip2point-transfer-clip-to-point-cloud	2210.01055	null	https://arxiv.org/abs/2210.01055v2	https://arxiv.org/pdf/2210.01055v2.pdf	CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training	Pre-training across 3D vision and language remains under development because of limited training data. Recent works attempt to transfer vision-language pre-training models to 3D vision. PointCLIP converts point cloud data to multi-view depth maps, adopting CLIP for shape classification. However, its performance is restricted by the domain gap between rendered depth maps and images, as well as the diversity of depth distributions. To address this issue, we propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain, and adapt it to point cloud classification. We introduce a new depth rendering setting that forms a better visual effect, and then render 52,460 pairs of images and depth maps from ShapeNet for pre-training. The pre-training scheme of CLIP2Point combines cross-modality learning to enforce the depth features for capturing expressive visual and textual features and intra-modality learning to enhance the invariance of depth aggregation. Additionally, we propose a novel Dual-Path Adapter (DPA) module, i.e., a dual-path structure with simplified adapters for few-shot learning. The dual-path structure allows the joint use of CLIP and CLIP2Point, and the simplified adapter can well fit few-shot tasks without post-search. Experimental results show that CLIP2Point is effective in transferring CLIP knowledge to 3D vision. Our CLIP2Point outperforms PointCLIP and other self-supervised 3D networks, achieving state-of-the-art results on zero-shot and few-shot classification.	['WangMeng Zuo', 'Wanli Ouyang', 'Rynson W. H. Lau', 'Xiaoshui Huang', 'Yunhan Yang', 'Bowen Dong', 'Tianyu Huang']	2022-10-03	null	null	null	null	['training-free-3d-point-cloud-classification', '3d-point-cloud-classification', 'zero-shot-transfer-3d-point-cloud', 'point-cloud-classification']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 1.14666019e-02 -2.73541093e-01 -2.40949765e-01 -2.74305731e-01 -8.58004808e-01 -5.14562666e-01 8.01682353e-01 -2.63091296e-01 -1.65203944e-01 -1.62762105e-02 1.38408870e-01 -1.12893954e-01 2.78821081e-01 -9.95316625e-01 -8.89089048e-01 -4.90848839e-01 3.80882412e-01 3.71783406e-01 6.35966182e-01 -1.67723745e-01 1.94367751e-01 5.69812298e-01 -2.00104427e+00 6.17612898e-01 7.56171286e-01 1.28353214e+00 5.49469531e-01 5.23132443e-01 -7.43460596e-01 6.06292307e-01 -2.49771699e-01 -2.16400534e-01 4.75854784e-01 -1.19740039e-01 -4.68032598e-01 2.84126997e-01 9.48828280e-01 -6.57597005e-01 -3.62748921e-01 8.24242771e-01 8.68965030e-01 2.46667579e-01 6.61299586e-01 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ce5c8afd-6249-480d-be93-666d4ccdaa1f	deep-bayesian-inference-for-seismic-imaging	2110.04825	null	https://arxiv.org/abs/2110.04825v3	https://arxiv.org/pdf/2110.04825v3.pdf	Deep Bayesian inference for seismic imaging with tasks	We propose to use techniques from Bayesian inference and deep neural networks to translate uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as horizon tracking. Seismic imaging is an ill-posed inverse problem because of bandwidth and aperture limitations, which is hampered by the presence of noise and linearization errors. Many regularization methods, such as transform-domain sparsity promotion, have been designed to deal with the adverse effects of these errors, however, these methods run the risk of biasing the solution and do not provide information on uncertainty in the image space and how this uncertainty impacts certain tasks on the image. A systematic approach is proposed to translate uncertainty due to noise in the data to confidence intervals of automatically tracked horizons in the image. The uncertainty is characterized by a convolutional neural network (CNN) and to assess these uncertainties, samples are drawn from the posterior distribution of the CNN weights, used to parameterize the image. Compared to traditional priors, it is argued in the literature that these CNNs introduce a flexible inductive bias that is a surprisingly good fit for a diverse set of problems. The method of stochastic gradient Langevin dynamics is employed to sample from the posterior distribution. This method is designed to handle large scale Bayesian inference problems with computationally expensive forward operators as in seismic imaging. Aside from offering a robust alternative to maximum a posteriori estimate that is prone to overfitting, access to these samples allow us to translate uncertainty in the image, due to noise in the data, to uncertainty on the tracked horizons. For instance, it admits estimates for the pointwise standard deviation on the image and for confidence intervals on its automatically tracked horizons.	['Felix J. Herrmann', 'Gabrio Rizzuti', 'Ali Siahkoohi']	2021-10-10	null	null	null	null	['seismic-imaging']	['miscellaneous']	[ 2.81425327e-01 2.33995110e-01 3.91473711e-01 -1.58432797e-01 -1.03755510e+00 -3.83759707e-01 5.05918324e-01 -2.90532380e-01 -5.79654992e-01 8.24503243e-01 3.62124175e-01 -1.34684592e-01 -6.39850438e-01 -8.05121720e-01 -8.62754464e-01 -1.03545547e+00 -1.01739734e-01 4.92369711e-01 2.65511692e-01 1.49287790e-01 4.29311901e-01 4.82911766e-01 -1.24134338e+00 -1.69694886e-01 4.98209506e-01 1.22793043e+00 2.06831664e-01 3.26266080e-01 1.48563059e-02 5.98323345e-01 -4.77446795e-01 -3.72899771e-02 8.12298432e-02 -4.22899574e-02 -5.68210661e-01 5.05669452e-02 1.36478677e-01 -7.77509749e-01 -1.75533384e-01 1.25505054e+00 5.74536860e-01 2.35017225e-01 1.03878868e+00 -5.02151072e-01 -2.57393390e-01 6.35597229e-01 -5.08962810e-01 1.80964366e-01 3.56839821e-02 2.01307580e-01 5.68505943e-01 -1.08658028e+00 5.58387518e-01 1.15809667e+00 1.12769353e+00 1.38653994e-01 -1.29176641e+00 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1.54825658e-01 4.28090900e-01 -7.28444517e-01 -7.96412945e-01 6.56963214e-02]	[6.842742919921875, 3.4971163272857666]
3e90a1ce-778e-48b6-881b-b65bd4724758	lr-csnet-low-rank-deep-unfolding-network-for	2212.09088	null	https://arxiv.org/abs/2212.09088v1	https://arxiv.org/pdf/2212.09088v1.pdf	LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing	Deep unfolding networks (DUNs) have proven to be a viable approach to compressive sensing (CS). In this work, we propose a DUN called low-rank CS network (LR-CSNet) for natural image CS. Real-world image patches are often well-represented by low-rank approximations. LR-CSNet exploits this property by adding a low-rank prior to the CS optimization task. We derive a corresponding iterative optimization procedure using variable splitting, which is then translated to a new DUN architecture. The architecture uses low-rank generation modules (LRGMs), which learn low-rank matrix factorizations, as well as gradient descent and proximal mappings (GDPMs), which are proposed to extract high-frequency features to refine image details. In addition, the deep features generated at each reconstruction stage in the DUN are transferred between stages to boost the performance. Our extensive experiments on three widely considered datasets demonstrate the promising performance of LR-CSNet compared to state-of-the-art methods in natural image CS.	['Zhenming Peng', 'Fabian Gieseke', 'Stefan Oehmcke', 'Christian Igel', 'Lei LI', 'Tianfang Zhang']	2022-12-18	null	null	null	null	['compressive-sensing']	['computer-vision']	[ 4.68685120e-01 -2.91357003e-02 9.42499191e-02 -1.74838871e-01 -8.34698200e-01 -8.74116644e-02 5.03071547e-01 -3.85134220e-01 -1.93194866e-01 5.60115457e-01 3.78656149e-01 4.02751528e-02 -6.82186931e-02 -6.53197587e-01 -9.47359741e-01 -8.10494363e-01 -1.70879588e-01 -8.88352022e-02 -1.09218545e-01 -2.69081116e-01 1.07042447e-01 3.52901399e-01 -1.22849929e+00 5.26855528e-01 9.87362206e-01 1.13168025e+00 5.40990949e-01 4.27941471e-01 8.82586539e-02 1.14842010e+00 -1.77126024e-02 -4.01965566e-02 4.84076649e-01 -6.25027478e-01 -5.06259024e-01 -1.67707633e-02 4.42472398e-01 -4.47340041e-01 -8.92505467e-01 1.16811955e+00 3.97372365e-01 2.09344372e-01 3.00924748e-01 -8.11968267e-01 -8.82788241e-01 8.72307062e-01 -6.37263536e-01 7.34893829e-02 4.41883296e-01 -1.36147276e-01 1.05910015e+00 -1.43762088e+00 6.41911805e-01 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c2304116-f92c-4b93-ba50-209d4aa750f4	dereverberation-using-joint-estimation-of-dry	2007.12581	null	https://arxiv.org/abs/2007.12581v1	https://arxiv.org/pdf/2007.12581v1.pdf	Dereverberation using joint estimation of dry speech signal and acoustic system	The purpose of speech dereverberation is to remove quality-degrading effects of a time-invariant impulse response filter from the signal. In this report, we describe an approach to speech dereverberation that involves joint estimation of the dry speech signal and of the room impulse response. We explore deep learning models that apply to each task separately, and how these can be combined in a joint model with shared parameters.	['Keunwoo Choi', 'Simon Durand', 'Sanna Wager']	2020-07-24	null	null	null	null	['room-impulse-response', 'speech-dereverberation']	['audio', 'speech']	[ 1.32576466e-01 -2.67325729e-01 7.21275747e-01 -3.62516373e-01 -1.29721928e+00 -5.37376881e-01 5.80730960e-02 -5.99430978e-01 -1.40260577e-01 5.63207209e-01 7.47303545e-01 -4.69876289e-01 -5.99635132e-02 -1.28994684e-03 -5.86813152e-01 -9.28336799e-01 -2.73506786e-03 -4.06984836e-01 -2.88807720e-01 -1.84987590e-01 -3.19100231e-01 7.39655733e-01 -1.32949734e+00 3.23552817e-01 8.57511878e-01 7.41977215e-01 2.89845318e-01 1.50365686e+00 6.97632551e-01 6.03545785e-01 -1.44293320e+00 3.18500519e-01 8.08909014e-02 -4.59518611e-01 -2.93593824e-01 9.46245044e-02 6.94304705e-01 -6.56760275e-01 -8.24624479e-01 7.16285944e-01 1.22533298e+00 6.37156308e-01 6.00538194e-01 -5.87163568e-01 -7.55264819e-01 5.40917635e-01 1.62882552e-01 5.09044766e-01 3.53310525e-01 2.36855537e-01 7.68950701e-01 -8.88586044e-01 2.77679809e-03 1.33908188e+00 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-1.09310174e+00 -8.32716703e-01 -1.09552824e+00 -8.41904879e-01 -2.19064187e-02 1.45668232e+00 -1.87365524e-02 -1.07965887e+00 -8.41495395e-02]	[15.099871635437012, 5.946348190307617]
0a810469-aa60-4530-b75d-a32387186eb5	learning-to-identify-physical-parameters-from	2009.08292	null	https://arxiv.org/abs/2009.08292v1	https://arxiv.org/pdf/2009.08292v1.pdf	Learning to Identify Physical Parameters from Video Using Differentiable Physics	Video representation learning has recently attracted attention in computer vision due to its applications for activity and scene forecasting or vision-based planning and control. Video prediction models often learn a latent representation of video which is encoded from input frames and decoded back into images. Even when conditioned on actions, purely deep learning based architectures typically lack a physically interpretable latent space. In this study, we use a differentiable physics engine within an action-conditional video representation network to learn a physical latent representation. We propose supervised and self-supervised learning methods to train our network and identify physical properties. The latter uses spatial transformers to decode physical states back into images. The simulation scenarios in our experiments comprise pushing, sliding and colliding objects, for which we also analyze the observability of the physical properties. In experiments we demonstrate that our network can learn to encode images and identify physical properties like mass and friction from videos and action sequences in the simulated scenarios. We evaluate the accuracy of our supervised and self-supervised methods and compare it with a system identification baseline which directly learns from state trajectories. We also demonstrate the ability of our method to predict future video frames from input images and actions.	['Jörg Stückler', 'Michael Möller', 'Rama Krishna Kandukuri', 'Jan Achterhold']	2020-09-17	null	null	null	null	['predict-future-video-frames']	['computer-vision']	[ 4.38683659e-01 7.02489465e-02 -3.48467350e-01 -2.62057960e-01 -2.00232342e-01 -3.38588089e-01 9.88803685e-01 -3.65323365e-01 -8.36884305e-02 6.32761300e-01 2.80299067e-01 -1.17705077e-01 -1.95957616e-01 -6.24951541e-01 -1.24674475e+00 -9.26642001e-01 -3.86897564e-01 1.38558194e-01 1.86044484e-01 3.19913983e-01 9.26489010e-02 5.14891982e-01 -1.60767794e+00 3.93948495e-01 3.57144982e-01 9.56454694e-01 4.50721681e-01 1.22617888e+00 4.22426283e-01 1.66865444e+00 -2.99608968e-02 4.35958743e-01 4.18646336e-01 -5.66920757e-01 -7.59804666e-01 4.69744503e-01 4.14024264e-01 -7.35164642e-01 -1.13872421e+00 8.13190222e-01 -2.62144536e-01 4.11795557e-01 8.69991481e-01 -1.33151221e+00 -6.60778642e-01 1.93374872e-01 2.48188954e-02 1.22201212e-01 5.22737920e-01 5.42998791e-01 7.20198572e-01 -3.52183670e-01 7.11733639e-01 1.28123188e+00 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