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a2c9dc57-7d42-400c-8813-8bde31ff1ff8	quantification-of-robotic-surgeries-with	2205.03028	null	https://arxiv.org/abs/2205.03028v1	https://arxiv.org/pdf/2205.03028v1.pdf	Quantification of Robotic Surgeries with Vision-Based Deep Learning	Surgery is a high-stakes domain where surgeons must navigate critical anatomical structures and actively avoid potential complications while achieving the main task at hand. Such surgical activity has been shown to affect long-term patient outcomes. To better understand this relationship, whose mechanics remain unknown for the majority of surgical procedures, we hypothesize that the core elements of surgery must first be quantified in a reliable, objective, and scalable manner. We believe this is a prerequisite for the provision of surgical feedback and modulation of surgeon performance in pursuit of improved patient outcomes. To holistically quantify surgeries, we propose a unified deep learning framework, entitled Roboformer, which operates exclusively on videos recorded during surgery to independently achieve multiple tasks: surgical phase recognition (the what of surgery), gesture classification and skills assessment (the how of surgery). We validated our framework on four video-based datasets of two commonly-encountered types of steps (dissection and suturing) within minimally-invasive robotic surgeries. We demonstrated that our framework can generalize well to unseen videos, surgeons, medical centres, and surgical procedures. We also found that our framework, which naturally lends itself to explainable findings, identified relevant information when achieving a particular task. These findings are likely to instill surgeons with more confidence in our framework's behaviour, increasing the likelihood of clinical adoption, and thus paving the way for more targeted surgical feedback.	['Andrew J. Hung', 'Animashree Anandkumar', 'Christian Wagner', 'Jessica Nguyen', 'Taseen F. Haque', 'Runzhuo Ma', 'Dani Kiyasseh']	2022-05-06	null	null	null	null	['skills-assessment', 'surgical-phase-recognition']	['computer-vision', 'computer-vision']	[ 2.85085171e-01 4.29202169e-01 -5.44251323e-01 -7.39065111e-02 -8.58813465e-01 -8.40208769e-01 2.48646393e-01 3.13588619e-01 -6.44629478e-01 3.32377881e-01 8.33366692e-01 -6.49632573e-01 -5.36619782e-01 -2.33575389e-01 -8.01251471e-01 -4.96858329e-01 -2.35709041e-01 -4.26079333e-02 -3.08788896e-01 1.04095796e-02 2.98214108e-01 7.26214886e-01 -1.17880094e+00 4.03922588e-01 3.37506473e-01 8.62424910e-01 3.21122438e-01 6.70740187e-01 5.35717785e-01 1.01563644e+00 -1.26358837e-01 -2.16581792e-01 6.09901309e-01 -1.32212110e-04 -6.76122546e-01 -5.25182858e-02 4.37214255e-01 -6.21847570e-01 -3.91689122e-01 7.26269424e-01 5.60278118e-01 -1.97550794e-03 3.21260631e-01 -4.54198211e-01 -5.89096285e-02 3.22546989e-01 9.03736353e-02 1.21887974e-01 2.23519206e-01 6.45336807e-01 6.53532684e-01 -5.92655241e-01 8.66124213e-01 6.97401583e-01 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35a33392-3327-4156-a0ea-2ceff3125172	relative-density-ratio-estimation-for-robust	null	null	http://papers.nips.cc/paper/4254-relative-density-ratio-estimation-for-robust-distribution-comparison	http://papers.nips.cc/paper/4254-relative-density-ratio-estimation-for-robust-distribution-comparison.pdf	Relative Density-Ratio Estimation for Robust Distribution Comparison	Divergence estimators based on direct approximation of density-ratios without going through separate approximation of numerator and denominator densities have been successfully applied to machine learning tasks that involve distribution comparison such as outlier detection, transfer learning, and two-sample homogeneity test. However, since density-ratio functions often possess high fluctuation, divergence estimation is still a challenging task in practice. In this paper, we propose to use relative divergences for distribution comparison, which involves approximation of relative density-ratios. Since relative density-ratios are always smoother than corresponding ordinary density-ratios, our proposed method is favorable in terms of the non-parametric convergence speed. Furthermore, we show that the proposed divergence estimator has asymptotic variance independent of the model complexity under a parametric setup, implying that the proposed estimator hardly overfits even with complex models. Through experiments, we demonstrate the usefulness of the proposed approach.	['Hirotaka Hachiya', 'Makoto Yamada', 'Taiji Suzuki', 'Masashi Sugiyama', 'Takafumi Kanamori']	2011-12-01	null	null	null	neurips-2011-12	['density-ratio-estimation']	['methodology']	[-1.86212957e-01 -2.39782229e-01 2.76208967e-02 -3.86910141e-01 -9.01868284e-01 -2.49713510e-01 4.93479401e-01 4.53140467e-01 -6.43860102e-01 1.06413102e+00 -5.23366034e-01 -3.17358017e-01 -2.97672629e-01 -6.75176859e-01 -7.05889225e-01 -8.22731972e-01 7.94613585e-02 4.60010618e-01 1.24527283e-01 2.14591935e-01 4.34641868e-01 3.45045388e-01 -1.42080045e+00 -7.65651464e-01 1.47553968e+00 1.17655015e+00 -1.63853720e-01 4.25105482e-01 -7.23729059e-02 2.86952525e-01 -7.46931851e-01 -4.42790240e-01 2.42021099e-01 -6.15890622e-01 -1.40736327e-01 -1.12320758e-01 2.97667325e-01 -2.74497420e-01 3.58779728e-01 1.56970632e+00 2.28156120e-01 3.97503734e-01 1.39779270e+00 -1.36285019e+00 -3.97332907e-01 1.73888266e-01 -9.19152975e-01 2.57467896e-01 1.48424417e-01 -1.83343619e-01 8.42442036e-01 -8.44323933e-01 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db9b89c2-5b40-47ed-a971-692fc159ac10	knowledge-extraction-with-interval-temporal	2305.16864	null	https://arxiv.org/abs/2305.16864v1	https://arxiv.org/pdf/2305.16864v1.pdf	Knowledge Extraction with Interval Temporal Logic Decision Trees	Multivariate temporal, or time, series classification is, in a way, the temporal generalization of (numeric) classification, as every instance is described by multiple time series instead of multiple values. Symbolic classification is the machine learning strategy to extract explicit knowledge from a data set, and the problem of symbolic classification of multivariate temporal series requires the design, implementation, and test of ad-hoc machine learning algorithms, such as, for example, algorithms for the extraction of temporal versions of decision trees. One of the most well-known algorithms for decision tree extraction from categorical data is Quinlan's ID3, which was later extended to deal with numerical attributes, resulting in an algorithm known as C4.5, and implemented in many open-sources data mining libraries, including the so-called Weka, which features an implementation of C4.5 called J48. ID3 was recently generalized to deal with temporal data in form of timelines, which can be seen as discrete (categorical) versions of multivariate time series, and such a generalization, based on the interval temporal logic HS, is known as Temporal ID3. In this paper we introduce Temporal C4.5, that allows the extraction of temporal decision trees from undiscretized multivariate time series, describe its implementation, called Temporal J48, and discuss the outcome of a set of experiments with the latter on a collection of public data sets, comparing the results with those obtained by other, classical, multivariate time series classification methods.	['Stan Ionel Eduard', 'Guido Sciavicco']	2023-05-26	null	null	null	null	['time-series-classification']	['time-series']	[ 6.65021315e-02 -1.97824121e-01 -3.34417582e-01 -3.52895319e-01 -8.63099769e-02 -8.71320128e-01 8.42349291e-01 6.68428242e-01 -2.75612980e-01 7.74850786e-01 -2.72099972e-01 -6.96180165e-01 -7.63333201e-01 -9.96057630e-01 -2.54801154e-01 -5.26283205e-01 -9.70139325e-01 5.15321672e-01 3.72754574e-01 -2.97255665e-01 1.45314187e-01 7.02294052e-01 -1.91020322e+00 3.66295904e-01 4.09819335e-01 1.47410560e+00 -6.61052167e-01 5.68393588e-01 -2.05052882e-01 7.34713316e-01 -5.56815386e-01 -1.53853409e-02 -4.89180200e-02 -3.85419726e-01 -9.20395970e-01 -3.32168311e-01 -6.75451100e-01 2.28366613e-01 1.54034540e-01 4.08136964e-01 -3.03471148e-01 2.52279580e-01 8.35454345e-01 -1.88796699e+00 -6.24964535e-02 7.34885812e-01 -1.96576223e-01 1.09824829e-01 6.90984011e-01 -2.60481149e-01 7.21298397e-01 -1.47571370e-01 6.89153790e-01 1.13008988e+00 7.25500226e-01 -1.03342339e-01 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29af4675-1065-4069-863a-053c52e33293	norppa-novel-ringed-seal-re-identification-by	2206.02498	null	https://arxiv.org/abs/2206.02498v3	https://arxiv.org/pdf/2206.02498v3.pdf	NORPPA: NOvel Ringed seal re-identification by Pelage Pattern Aggregation	We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification. Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal monitoring and conservation and calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. The proposed method NOvel Ringed seal re-identification by Pelage Pattern Aggregation (NORPPA) utilizes the permanent and unique pelage pattern of Saimaa ringed seals and content-based image retrieval techniques. First, the query image is preprocessed, and each seal instance is segmented. Next, the seal's pelage pattern is extracted using a U-net encoder-decoder based method. Then, CNN-based affine invariant features are embedded and aggregated into Fisher Vectors. Finally, the cosine distance between the Fisher Vectors is used to find the best match from a database of known individuals. We perform extensive experiments of various modifications of the method on a new challenging Saimaa ringed seals re-identification dataset. The proposed method is shown to produce the best re-identification accuracy on our dataset in comparisons with alternative approaches.	['Heikki Kälviäinen', 'Tuomas Eerola', 'Ilia Chelak', 'Ekaterina Nepovinnykh']	2022-06-06	null	null	null	null	['content-based-image-retrieval']	['computer-vision']	[ 2.31380276e-02 -1.07670575e-01 1.92230746e-01 -5.86197555e-01 -5.95205307e-01 -6.76497161e-01 5.45600235e-01 5.95568717e-01 -1.30832374e+00 4.31393832e-01 -1.04177810e-01 3.80390137e-01 6.78777089e-03 -6.46922827e-01 -7.16502786e-01 -6.47854388e-01 -5.77106237e-01 4.03002173e-01 3.03709418e-01 -1.91447929e-01 2.94766843e-01 9.47584093e-01 -1.84479833e+00 -3.43036562e-01 1.24917977e-01 7.41346002e-01 3.69980961e-01 5.87909341e-01 3.79683435e-01 4.94296223e-01 -5.59358418e-01 -8.38754237e-01 6.30242884e-01 -2.50609796e-02 -5.31781793e-01 -4.89097089e-01 8.85442615e-01 -9.04008210e-01 -3.07297379e-01 9.80873764e-01 3.95647854e-01 1.57289088e-01 6.51575506e-01 -5.84117293e-01 -2.51379520e-01 5.11599123e-01 -6.37690187e-01 8.66595328e-01 1.47173062e-01 1.03408657e-02 8.26107979e-01 -6.28626704e-01 5.37867486e-01 1.15602565e+00 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-9.37761307e-01 -5.16077220e-01 1.59583718e-01 7.53175378e-01 -8.04253519e-01 -6.06950641e-01 1.34449974e-01]	[8.245160102844238, -1.1261447668075562]
c8b95b87-ed1a-406b-a247-668536708d79	infinite-dimensional-sparse-learning-in	2203.14731	null	https://arxiv.org/abs/2203.14731v2	https://arxiv.org/pdf/2203.14731v2.pdf	Infinite-Dimensional Sparse Learning in Linear System Identification	Regularized methods have been widely applied to system identification problems without known model structures. This paper proposes an infinite-dimensional sparse learning algorithm based on atomic norm regularization. Atomic norm regularization decomposes the transfer function into first-order atomic models and solves a group lasso problem that selects a sparse set of poles and identifies the corresponding coefficients. The difficulty in solving the problem lies in the fact that there are an infinite number of possible atomic models. This work proposes a greedy algorithm that generates new candidate atomic models maximizing the violation of the optimality condition of the existing problem. This algorithm is able to solve the infinite-dimensional group lasso problem with high precision. The algorithm is further extended to reduce the bias and reject false positives in pole location estimation by iteratively reweighted adaptive group lasso and complementary pairs stability selection respectively. Numerical results demonstrate that the proposed algorithm performs better than benchmark parameterized and regularized methods in terms of both impulse response fitting and pole location estimation.	['Roy S. Smith', 'Andrea Iannelli', 'Mehmet Tolga Akan', 'Mingzhou Yin']	2022-03-28	null	null	null	null	['sparse-learning']	['methodology']	[ 2.37949505e-01 1.74683183e-01 -4.15069014e-01 2.61731148e-02 -1.07900703e+00 -3.56880456e-01 -2.82505397e-02 -2.86190480e-01 3.51476610e-01 1.00480485e+00 -6.88032806e-02 4.67822775e-02 -7.21590996e-01 -1.01802379e-01 -7.07875371e-01 -9.31199968e-01 -1.79926932e-01 5.36218286e-01 -3.56426388e-01 -1.69274002e-01 3.29213977e-01 2.92764515e-01 -1.40335834e+00 -1.34845778e-01 1.18719697e+00 8.12224209e-01 -4.59511429e-02 1.11473061e-01 6.17851913e-01 4.38077450e-01 -2.07431093e-01 5.62591791e-01 3.96664292e-01 -5.46568215e-01 -4.75273341e-01 4.30469543e-01 3.85010093e-01 1.40738279e-01 2.34857649e-01 1.15669215e+00 5.58054447e-01 3.30872715e-01 7.19680071e-01 -1.13680220e+00 -1.48593917e-01 5.08930087e-01 -7.17083216e-01 5.44534400e-02 3.40910494e-01 -3.33549738e-01 9.23444986e-01 -1.33967912e+00 1.52577668e-01 1.16135097e+00 1.04998374e+00 8.91841948e-02 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-7.90406704e-01 -8.40208530e-01 2.88893372e-01 4.63904023e-01 -8.70791852e-01 -7.55672932e-01 2.99362212e-01]	[7.135342121124268, 4.314519882202148]
4d19e8ef-25b5-4667-ac70-4615cd70d70c	medical-image-retrieval-using-deep	1703.08472	null	http://arxiv.org/abs/1703.08472v1	http://arxiv.org/pdf/1703.08472v1.pdf	Medical Image Retrieval using Deep Convolutional Neural Network	With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the semantic gap that exists between the low level visual information captured by imaging devices and high level semantic information perceived by human. The efficacy of such systems is more crucial in terms of feature representations that can characterize the high-level information completely. In this paper, we propose a framework of deep learning for CBMIR system by using deep Convolutional Neural Network (CNN) that is trained for classification of medical images. An intermodal dataset that contains twenty four classes and five modalities is used to train the network. The learned features and the classification results are used to retrieve medical images. For retrieval, best results are achieved when class based predictions are used. An average classification accuracy of 99.77% and a mean average precision of 0.69 is achieved for retrieval task. The proposed method is best suited to retrieve multimodal medical images for different body organs.	['Adnan Qayyum', 'Muhammad Awais', 'Syed Muhammad Anwar', 'Muhammad Majid']	2017-03-24	null	null	null	null	['medical-image-retrieval', 'medical-image-retrieval']	['computer-vision', 'medical']	[ 7.16250166e-02 -1.94390699e-01 -2.40253344e-01 -2.97153950e-01 -1.08484304e+00 -2.47157708e-01 5.24676442e-01 6.46755874e-01 -5.22125423e-01 4.11956996e-01 2.98023432e-01 5.71770258e-02 -2.48557597e-01 -8.91559899e-01 -2.36436978e-01 -7.06884086e-01 1.28415868e-01 4.00402874e-01 1.84860095e-01 -6.55268654e-02 3.34417261e-02 5.23620844e-01 -1.65034616e+00 1.01706231e+00 3.88883173e-01 1.58244145e+00 5.11184096e-01 7.44212687e-01 -2.31810972e-01 1.02156365e+00 -5.81506848e-01 -1.68660536e-01 1.15606941e-01 -3.31317902e-01 -9.82305825e-01 2.27365512e-02 2.38402501e-01 -5.08253038e-01 -3.72738600e-01 9.24731672e-01 8.15708041e-01 -8.99545476e-02 8.40369523e-01 -9.22809780e-01 -6.45511568e-01 1.81592628e-01 -1.77401170e-01 3.89416367e-01 2.72105396e-01 -3.12928140e-01 7.00847328e-01 -7.81006753e-01 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c5df04bb-297c-4c5d-be63-c9975fd5edf0	deep-recurrent-spiking-neural-networks	2306.01354	null	https://arxiv.org/abs/2306.01354v1	https://arxiv.org/pdf/2306.01354v1.pdf	Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli	In the real world, visual stimuli received by the biological visual system are predominantly dynamic rather than static. A better understanding of how the visual cortex represents movie stimuli could provide deeper insight into the information processing mechanisms of the visual system. Although some progress has been made in modeling neural responses to natural movies with deep neural networks, the visual representations of static and dynamic information under such time-series visual stimuli remain to be further explored. In this work, considering abundant recurrent connections in the mouse visual system, we design a recurrent module based on the hierarchy of the mouse cortex and add it into Deep Spiking Neural Networks, which have been demonstrated to be a more compelling computational model for the visual cortex. Using Time-Series Representational Similarity Analysis, we measure the representational similarity between networks and mouse cortical regions under natural movie stimuli. Subsequently, we conduct a comparison of the representational similarity across recurrent/feedforward networks and image/video training tasks. Trained on the video action recognition task, recurrent SNN achieves the highest representational similarity and significantly outperforms feedforward SNN trained on the same task by 15% and the recurrent SNN trained on the image classification task by 8%. We investigate how static and dynamic representations of SNNs influence the similarity, as a way to explain the importance of these two forms of representations in biological neural coding. Taken together, our work is the first to apply deep recurrent SNNs to model the mouse visual cortex under movie stimuli and we establish that these networks are competent to capture both static and dynamic representations and make contributions to understanding the movie information processing mechanisms of the visual cortex.	['Yonghong Tian', 'Huihui Zhou', 'Zhengyu Ma', 'Liwei Huang']	2023-06-02	null	null	null	null	['action-recognition-in-videos', 'action-recognition']	['computer-vision', 'computer-vision']	[ 5.21485746e-01 -4.41333890e-01 7.58105591e-02 -1.88125432e-01 4.15601730e-01 -5.15644670e-01 7.12546170e-01 -2.45950833e-01 -4.35545921e-01 4.34502326e-02 1.17757812e-01 8.07183050e-03 2.46670712e-02 -5.42473018e-01 -7.39596188e-01 -9.21403110e-01 -5.40584065e-02 -4.20606107e-01 5.66181958e-01 -2.58266658e-01 3.33092004e-01 5.87381065e-01 -1.90305603e+00 9.27040696e-01 2.50901785e-02 1.24095595e+00 6.60333455e-01 8.61303568e-01 5.55962436e-02 1.28497505e+00 -5.01549184e-01 1.33586973e-01 1.03007503e-01 -7.22147703e-01 -6.37196958e-01 -6.06738403e-02 3.62499982e-01 3.40512954e-02 -7.57135689e-01 9.17519331e-01 4.18966234e-01 1.37051925e-01 6.11404777e-01 -8.46709430e-01 -8.33964109e-01 5.77155232e-01 -1.88559562e-01 9.83441532e-01 2.53272802e-01 4.41097051e-01 8.66445839e-01 -7.01196611e-01 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35ba26a3-20da-477b-9844-665214fc06d5	iso-timeml-event-extraction-in-persian-text	null	null	https://aclanthology.org/C12-1179	https://aclanthology.org/C12-1179.pdf	ISO-TimeML Event Extraction in Persian Text	null	['Gholamreza Ghassem-Sani', 'Mirrosh', 'Yadollah Yaghoobzadeh', 'Seyed Abolghasem el', 'Mahbaneh Eshaghzadeh']	2012-12-01	iso-timeml-event-extraction-in-persian-text-1	https://aclanthology.org/C12-1179	https://aclanthology.org/C12-1179.pdf	coling-2012-12	['temporal-information-extraction']	['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 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45aa66df-ada2-43e3-80ec-3b2ea9e9920b	towards-speech-only-opinion-level-sentiment	null	null	https://aclanthology.org/2022.lrec-1.215	https://aclanthology.org/2022.lrec-1.215.pdf	Towards Speech-only Opinion-level Sentiment Analysis	The growing popularity of various forms of Spoken Dialogue Systems (SDS) raises the demand for their capability of implicitly assessing the speaker’s sentiment from speech only. Mapping the latter on user preferences enables to adapt to the user and individualize the requested information while increasing user satisfaction. In this paper, we explore the integration of rank consistent ordinal regression into a speech-only sentiment prediction task performed by ResNet-like systems. Furthermore, we use speaker verification extractors trained on larger datasets as low-level feature extractors. An improvement of performance is shown by fusing sentiment and pre-extracted speaker embeddings reducing the speaker bias of sentiment predictions. Numerous experiments on Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) databases show that we beat the baselines of state-of-the-art unimodal approaches. Using speech as the only modality combined with optimizing an order-sensitive objective function gets significantly closer to the sentiment analysis results of state-of-the-art multimodal systems.	['Wolfgang Minker', 'Yuri Matveev', 'Aleksei Gusev', 'Alisa Gazizullina', 'Annalena Aicher']	null	null	null	null	lrec-2022-6	['spoken-dialogue-systems']	['speech']	[ 9.12193805e-02 4.27529097e-01 -2.31374115e-01 -1.11073375e+00 -9.51641679e-01 -4.71687019e-01 8.15499485e-01 3.06000799e-01 -7.91879654e-01 5.13428628e-01 9.23768163e-01 3.69115919e-02 2.44290158e-01 -1.44018084e-01 -1.05264589e-01 -5.10470450e-01 4.35689315e-02 6.76517069e-01 -2.61946470e-01 -8.11732590e-01 1.79747924e-01 2.02762187e-01 -1.90401685e+00 8.79231811e-01 5.40614009e-01 1.53252947e+00 -2.23864436e-01 9.15609598e-01 -5.01483500e-01 7.90996373e-01 -6.34332955e-01 -4.13654357e-01 -1.16081774e-01 -1.98201835e-01 -8.59598756e-01 1.04565509e-01 5.46501517e-01 -2.70137675e-02 5.14014550e-02 7.73157477e-01 8.14426303e-01 3.24175835e-01 7.87965238e-01 -1.09174514e+00 -5.26955366e-01 7.35954642e-01 -1.50465086e-01 1.09649962e-02 7.72403181e-01 -3.82478684e-01 1.27939987e+00 -1.18759644e+00 3.25556308e-01 1.42524338e+00 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-1.11589503e+00 -8.11085463e-01 -1.33236814e+00 -4.20614183e-01 2.58436710e-01 7.73379862e-01 -1.25514358e-01 -7.13391185e-01 2.20592916e-02]	[13.362642288208008, 5.5572638511657715]
38baf180-881d-4e1e-bd77-f7929c5cfc6c	twistbytes-hierarchical-classification-at	1908.06493	null	https://arxiv.org/abs/1908.06493v1	https://arxiv.org/pdf/1908.06493v1.pdf	TwistBytes -- Hierarchical Classification at GermEval 2019: walking the fine line (of recall and precision)	We present here our approach to the GermEval 2019 Task 1 - Shared Task on hierarchical classification of German blurbs. We achieved first place in the hierarchical subtask B and second place on the root node, flat classification subtask A. In subtask A, we applied a simple multi-feature TF-IDF extraction method using different n-gram range and stopword removal, on each feature extraction module. The classifier on top was a standard linear SVM. For the hierarchical classification, we used a local approach, which was more light-weighted but was similar to the one used in subtask A. The key point of our approach was the application of a post-processing to cope with the multi-label aspect of the task, increasing the recall but not surpassing the precision measure score.	['Fernando Benites']	2019-08-18	null	null	null	null	['hierarchical-text-classification-of-blurbs']	['natural-language-processing']	[ 4.37516004e-01 2.22465649e-01 1.78830624e-01 -3.30975950e-01 -7.96991229e-01 -6.38848066e-01 8.18328857e-01 6.73183322e-01 -8.76319706e-01 9.13496137e-01 3.23180050e-01 -2.88927168e-01 -3.91946614e-01 -5.49035311e-01 -1.91693544e-01 -7.69737780e-01 1.10075407e-01 3.64886999e-01 7.12406278e-01 -2.79911429e-01 3.70244384e-01 3.56444418e-01 -1.72442794e+00 1.07321560e+00 5.13741672e-01 1.04680824e+00 1.72343493e-01 8.04350853e-01 -1.69511586e-01 7.96949804e-01 -5.87901413e-01 -5.71252465e-01 1.30210042e-01 -1.86722681e-01 -1.34936607e+00 -2.92730480e-02 3.31551313e-01 3.69584680e-01 1.46594256e-01 7.39364922e-01 2.22135842e-01 3.09453476e-02 1.12977326e+00 -7.62092710e-01 1.71404809e-01 6.93873227e-01 -1.27613679e-01 -4.88671521e-03 6.23214543e-01 -2.36617118e-01 1.32606173e+00 -1.02151978e+00 5.19734502e-01 1.06785142e+00 8.26045334e-01 2.68460125e-01 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04e1002b-b44e-4d6c-bd99-2ffac277f447	event-representation-learning-enhanced-with	1909.0519	null	https://arxiv.org/abs/1909.05190v2	https://arxiv.org/pdf/1909.05190v2.pdf	Event Representation Learning Enhanced with External Commonsense Knowledge	Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction. On the other hand, events extracted from raw texts lacks of commonsense knowledge, such as the intents and emotions of the event participants, which are useful for distinguishing event pairs when there are only subtle differences in their surface realizations. To address this issue, this paper proposes to leverage external commonsense knowledge about the intent and sentiment of the event. Experiments on three event-related tasks, i.e., event similarity, script event prediction and stock market prediction, show that our model obtains much better event embeddings for the tasks, achieving 78% improvements on hard similarity task, yielding more precise inferences on subsequent events under given contexts, and better accuracies in predicting the volatilities of the stock market.	['Zhongyang Li', 'Kuo Liao', 'Ting Liu', 'Junwen Duan', 'Xiao Ding']	2019-09-09	event-representation-learning-enhanced-with-1	https://aclanthology.org/D19-1495	https://aclanthology.org/D19-1495.pdf	ijcnlp-2019-11	['stock-market-prediction']	['time-series']	[-1.17729604e-01 -1.58283144e-01 -3.13790858e-01 -7.16832280e-01 -4.09143150e-01 -7.96447754e-01 1.00834024e+00 7.64276385e-01 -3.94015163e-01 6.53914869e-01 9.46709752e-01 -2.90024102e-01 1.34012386e-01 -1.02253735e+00 -4.96924609e-01 -3.19774151e-01 -1.92601413e-01 1.34988859e-01 1.99467093e-01 -2.97910780e-01 4.52253103e-01 3.56830984e-01 -1.28543854e+00 4.18581635e-01 3.85725439e-01 1.43147719e+00 -2.25196838e-01 3.27328920e-01 -3.16103458e-01 1.56508839e+00 -4.77913111e-01 -6.88755035e-01 1.31762832e-01 -5.23513019e-01 -6.66208327e-01 -4.79720205e-01 -1.58121660e-01 -4.70471531e-01 -6.53173625e-01 9.32166636e-01 1.99473962e-01 3.60992134e-01 9.36074674e-01 -1.14744520e+00 -7.07483232e-01 1.12231374e+00 -2.67304152e-01 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3ff30e7c-3993-45db-9f40-8cd5ba039017	rotational-crossed-slit-light-field	null	null	http://openaccess.thecvf.com/content_cvpr_2016/html/Li_Rotational_Crossed-Slit_Light_CVPR_2016_paper.html	http://openaccess.thecvf.com/content_cvpr_2016/papers/Li_Rotational_Crossed-Slit_Light_CVPR_2016_paper.pdf	Rotational Crossed-Slit Light Field	Light fields (LFs) are image-based representation that records the radiance along all rays along every direction through every point in space. Traditionally LFs are acquired by using a 2D grid of evenly spaced pinhole cameras or by translating a pinhole camera along the 2D grid using a robot arm. In this paper, we present a novel LF sampling scheme by exploiting a special non-centric camera called the crossed-slit or XSlit camera. An XSlit camera acquires rays that simultaneously pass through two oblique slits. We show that, instead of translating the camera as in the pinhole case, we can effectively sample the LF by rotating individual or both slits while keeping the camera fixed. This leads a "fixed-location" LF acquisition scheme. We further show through theoretical analysis and experiments that the resulting XSlit LFs provide several advantages: they provide more dense spatial-angular sampling, are amenable multi-view stereo matching and volumetric reconstruction, and can synthesize unique refocusing effects.	['Jingyi Yu', 'Haiting Lin', 'Nianyi Li', 'Mingyuan Zhou', 'Bilin Sun']	2016-06-01	null	null	null	cvpr-2016-6	['stereo-matching']	['computer-vision']	[ 5.09666681e-01 -3.49058270e-01 -2.71165036e-02 -8.46835151e-02 -2.20205501e-01 -6.74940884e-01 7.07953334e-01 -4.64562535e-01 -5.36534667e-01 7.21659303e-01 -4.12067473e-02 -2.62719244e-01 1.65659431e-02 -7.92828381e-01 -8.36271584e-01 -6.80342913e-01 7.72069693e-01 4.91534203e-01 3.53907228e-01 1.30062312e-01 6.06853902e-01 7.56528974e-01 -1.49899721e+00 -1.90856561e-01 2.88677067e-01 7.36015975e-01 7.64637589e-01 7.47775435e-01 3.70483920e-02 6.94247723e-01 -1.84833407e-02 -2.23026294e-02 4.90017802e-01 -5.06015897e-01 -5.95849395e-01 3.25276554e-01 5.69788039e-01 -6.43982768e-01 -4.28449303e-01 9.27717566e-01 2.78089643e-02 6.80652037e-02 3.91259283e-01 -8.11605930e-01 -2.02633366e-01 -9.02718455e-02 -1.27388525e+00 -8.16215351e-02 1.00488186e+00 -1.36047319e-01 5.78679740e-01 -8.32634985e-01 8.92410934e-01 8.15554142e-01 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66b9bf30-8f91-46ef-8c43-1103c3d82f64	a-fully-convolutional-network-for-mr	1911.09846	null	http://arxiv.org/abs/1911.09846v1	http://arxiv.org/pdf/1911.09846v1.pdf	A Fully Convolutional Network for MR Fingerprinting	Magnetic Resonance Fingerprinting (MRF) methods typically rely on dictionary matching to map the temporal MRF signals to quantitative tissue parameters. These methods suffer from heavy storage and computation requirements as the dictionary size grows. To address these issues, we proposed an end to end fully convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly employ linear dimensionality reduction and then use neural network to project the data into the tissue parameters manifold space. Experiments on the MAGIC data demonstrate the effectiveness of the method.	[]	2019-11-22	null	null	null	null	['magnetic-resonance-fingerprinting']	['medical']	[ 5.35833389e-02 -2.44924173e-01 -1.60779580e-01 -5.85982025e-01 -6.35316849e-01 -8.81613344e-02 2.00378180e-01 -4.42545444e-01 -3.89061421e-01 5.51947951e-01 3.24141055e-01 -1.66324794e-01 -1.65736467e-01 -5.47320843e-01 -5.73206961e-01 -6.50547326e-01 -3.69674236e-01 2.77688563e-01 1.45392558e-02 1.46933377e-01 1.41149461e-01 5.91677427e-01 -9.19753671e-01 1.23016253e-01 5.02685010e-01 1.17730856e+00 3.84410053e-01 3.06488544e-01 1.21383153e-01 7.51284361e-01 -9.77412388e-02 1.81220934e-01 4.14064258e-01 3.28992046e-02 -8.43616903e-01 -4.25216228e-01 5.32746255e-01 -7.78047144e-01 -1.26609361e+00 1.18000555e+00 8.81958365e-01 2.39152655e-01 3.43464732e-01 -7.94435084e-01 -7.28974700e-01 6.61270082e-01 -3.81767571e-01 7.32240915e-01 -1.29151359e-01 -2.48914018e-01 3.49773079e-01 -1.23612010e+00 7.95612872e-01 8.62959027e-01 1.06550646e+00 6.92548633e-01 -9.60677803e-01 -7.80962884e-01 -3.72931987e-01 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6675d136-765a-4875-a207-26110303996f	bringing-alive-blurred-moments	1804.02913	null	http://arxiv.org/abs/1804.02913v2	http://arxiv.org/pdf/1804.02913v2.pdf	Bringing Alive Blurred Moments	We present a solution for the goal of extracting a video from a single motion blurred image to sequentially reconstruct the clear views of a scene as beheld by the camera during the time of exposure. We first learn motion representation from sharp videos in an unsupervised manner through training of a convolutional recurrent video autoencoder network that performs a surrogate task of video reconstruction. Once trained, it is employed for guided training of a motion encoder for blurred images. This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder. As an intermediate step, we also design an efficient architecture that enables real-time single image deblurring and outperforms competing methods across all factors: accuracy, speed, and compactness. Experiments on real scenes and standard datasets demonstrate the superiority of our framework over the state-of-the-art and its ability to generate a plausible sequence of temporally consistent sharp frames.	['Anshul Shah', 'Kuldeep Purohit', 'A. N. Rajagopalan']	2018-04-09	bringing-alive-blurred-moments-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Purohit_Bringing_Alive_Blurred_Moments_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Purohit_Bringing_Alive_Blurred_Moments_CVPR_2019_paper.pdf	cvpr-2019-6	['video-reconstruction']	['computer-vision']	[ 4.95139152e-01 -2.67249765e-03 1.55776799e-01 -1.67395800e-01 -6.41577840e-01 -5.50927758e-01 6.03739738e-01 -1.03770983e+00 -1.91944003e-01 6.71303511e-01 6.61640823e-01 -3.13636959e-02 1.51889250e-01 -2.41320580e-01 -1.19722617e+00 -6.71494246e-01 1.73439950e-01 -1.71307176e-01 4.20043245e-02 1.92908555e-01 2.30298296e-01 3.76639992e-01 -1.37755239e+00 5.10382652e-01 4.67327505e-01 8.03294420e-01 7.56231785e-01 1.01549304e+00 4.83105183e-01 1.53267288e+00 -3.61439586e-01 -3.93389240e-02 2.93889463e-01 -5.18540502e-01 -9.32428896e-01 6.09597325e-01 6.02292717e-01 -1.21305203e+00 -1.12010360e+00 8.46236110e-01 3.88387591e-02 2.54508227e-01 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4881a1eb-e6b4-424d-9f56-fa6e625920ed	multi-scanner-canine-cutaneous-squamous-cell	2301.04423	null	https://arxiv.org/abs/2301.04423v2	https://arxiv.org/pdf/2301.04423v2.pdf	Multi-Scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset	In histopathology, scanner-induced domain shifts are known to impede the performance of trained neural networks when tested on unseen data. Multi-domain pre-training or dedicated domain-generalization techniques can help to develop domain-agnostic algorithms. For this, multi-scanner datasets with a high variety of slide scanning systems are highly desirable. We present a publicly available multi-scanner dataset of canine cutaneous squamous cell carcinoma histopathology images, composed of 44 samples digitized with five slide scanners. This dataset provides local correspondences between images and thereby isolates the scanner-induced domain shift from other inherent, e.g. morphology-induced domain shifts. To highlight scanner differences, we present a detailed evaluation of color distributions, sharpness, and contrast of the individual scanner subsets. Additionally, to quantify the inherent scanner-induced domain shift, we train a tumor segmentation network on each scanner subset and evaluate the performance both in- and cross-domain. We achieve a class-averaged in-domain intersection over union coefficient of up to 0.86 and observe a cross-domain performance decrease of up to 0.38, which confirms the inherent domain shift of the presented dataset and its negative impact on the performance of deep neural networks.	['Marc Aubreville', 'Katharina Breininger', 'Andreas Maier', 'Robert Klopfleisch', 'Jingna Qiu', 'Mathias Öttl', 'Nikolas Stathonikos', 'Christof A. Bertram', 'Marco Fragoso', 'Frauke Wilm']	2023-01-11	null	null	null	null	['tumor-segmentation']	['computer-vision']	[ 6.30251348e-01 -1.25394166e-01 -5.06896451e-02 -3.38798463e-01 -1.28616893e+00 -9.41834927e-01 4.24435705e-01 4.73538935e-01 -5.92824221e-01 5.87581515e-01 -4.93927568e-01 -2.96354115e-01 -1.09756850e-01 -5.37550509e-01 -6.88037276e-01 -1.23569286e+00 9.34382826e-02 4.90928560e-01 5.04684329e-01 -1.38156787e-01 7.60670602e-02 9.37461793e-01 -1.11610246e+00 3.81575853e-01 8.73378277e-01 7.93030441e-01 1.10804342e-01 5.38895011e-01 -2.46790245e-01 -5.02123162e-02 -6.76491082e-01 -2.21229985e-01 1.97785124e-01 -2.84128308e-01 -6.19024634e-01 5.35613634e-02 5.30449390e-01 -1.14080362e-01 8.70517343e-02 1.21482873e+00 4.38746601e-01 -3.87515903e-01 1.07235563e+00 -9.26172435e-01 -3.35021406e-01 2.53872573e-01 -8.44457805e-01 4.39882010e-01 -1.80448115e-01 4.16455179e-01 6.01064563e-01 -3.72760087e-01 1.09849823e+00 6.00310028e-01 7.23512769e-01 5.51046789e-01 -1.60011351e+00 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fa7a043b-4ed3-42ee-a13d-006f7dd09e38	an-landcover-fuzzy-logic-classification-by	1407.4739	null	http://arxiv.org/abs/1407.4739v1	http://arxiv.org/pdf/1407.4739v1.pdf	An landcover fuzzy logic classification by maximumlikelihood	In present days remote sensing is most used application in many sectors. This remote sensing uses different images like multispectral, hyper spectral or ultra spectral. The remote sensing image classification is one of the significant method to classify image. In this state we classify the maximum likelihood classification with fuzzy logic. In this we experimenting fuzzy logic like spatial, spectral texture methods in that different sub methods to be used for image classification.	['G. Nagalakshmi', 'T. Sarath']	2014-07-17	null	null	null	null	['remote-sensing-image-classification']	['miscellaneous']	[ 3.71483952e-01 -4.93382305e-01 -5.32027073e-02 -6.66576385e-01 -1.40931547e-01 -8.09578121e-01 3.79216164e-01 8.86216015e-02 -3.92375886e-01 9.55818534e-01 -4.01743978e-01 -4.96954679e-01 -5.39344668e-01 -1.26461601e+00 3.64548713e-02 -3.29387248e-01 2.21351102e-01 4.50723827e-01 2.67660409e-01 -5.00003755e-01 6.88921094e-01 9.50256348e-01 -1.68458009e+00 5.11618137e-01 6.99812412e-01 9.82373536e-01 3.58108580e-01 7.55755961e-01 -1.44903152e-03 2.34202743e-01 -4.42185141e-02 6.93081379e-01 6.17990017e-01 -3.31033260e-01 -9.41362679e-01 5.88689186e-02 1.12968639e-01 -1.60918102e-01 6.47491157e-01 1.51928186e+00 2.64060080e-01 4.55443829e-01 1.40066552e+00 -6.39391363e-01 -6.99101329e-01 7.14707553e-01 -8.83092165e-01 6.99069142e-01 1.47087365e-01 -2.08056405e-01 3.44616652e-01 -7.40622044e-01 1.01669773e-01 1.23282111e+00 4.40116614e-01 -1.77097861e-02 -8.37823391e-01 -3.38894576e-01 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46d36aea-d901-4cf8-bd06-167783800e55	towards-universal-representation-for-unseen	1803.0846	null	http://arxiv.org/abs/1803.08460v1	http://arxiv.org/pdf/1803.08460v1.pdf	Towards Universal Representation for Unseen Action Recognition	Unseen Action Recognition (UAR) aims to recognise novel action categories without training examples. While previous methods focus on inner-dataset seen/unseen splits, this paper proposes a pipeline using a large-scale training source to achieve a Universal Representation (UR) that can generalise to a more realistic Cross-Dataset UAR (CD-UAR) scenario. We first address UAR as a Generalised Multiple-Instance Learning (GMIL) problem and discover 'building-blocks' from the large-scale ActivityNet dataset using distribution kernels. Essential visual and semantic components are preserved in a shared space to achieve the UR that can efficiently generalise to new datasets. Predicted UR exemplars can be improved by a simple semantic adaptation, and then an unseen action can be directly recognised using UR during the test. Without further training, extensive experiments manifest significant improvements over the UCF101 and HMDB51 benchmarks.	['Ling Shao', 'Yang Long', 'Yu Guan', 'Shawn Newsam', 'Yi Zhu']	2018-03-22	towards-universal-representation-for-unseen-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Zhu_Towards_Universal_Representation_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhu_Towards_Universal_Representation_CVPR_2018_paper.pdf	cvpr-2018-6	['zero-shot-action-recognition']	['computer-vision']	[ 9.49283957e-01 8.46547037e-02 -2.72302508e-01 -4.29018676e-01 -1.07331777e+00 -4.35531944e-01 7.13522196e-01 -3.96781355e-01 -2.81971723e-01 9.01879847e-01 3.09725314e-01 1.88940048e-01 -1.22804001e-01 -5.04248500e-01 -8.79584491e-01 -7.77151048e-01 7.37567693e-02 5.77147663e-01 5.90743899e-01 -1.02755673e-01 -1.66613404e-02 5.63270211e-01 -1.81597567e+00 9.44820583e-01 4.25630867e-01 9.29705739e-01 5.97692467e-02 8.95364106e-01 2.87197083e-01 1.12352216e+00 -9.33828950e-01 -2.25182012e-01 5.34278452e-01 -7.39883184e-01 -1.14478695e+00 2.19554991e-01 5.54277420e-01 -2.01459557e-01 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20bfbcd2-d12b-468b-8818-dbc0f17354de	presenting-an-approach-based-on-weighted	2306.17068	null	https://arxiv.org/abs/2306.17068v2	https://arxiv.org/pdf/2306.17068v2.pdf	weighted CapsuleNet networks for Persian multi-domain sentiment analysis	Sentiment classification is a fundamental task in natural language processing, assigning one of the three classes, positive, negative, or neutral, to free texts. However, sentiment classification models are highly domain dependent; the classifier may perform classification with reasonable accuracy in one domain but not in another due to the Semantic multiplicity of words getting poor accuracy. This article presents a new Persian/Arabic multi-domain sentiment analysis method using the cumulative weighted capsule networks approach. Weighted capsule ensemble consists of training separate capsule networks for each domain and a weighting measure called domain belonging degree (DBD). This criterion consists of TF and IDF, which calculates the dependency of each document for each domain separately; this value is multiplied by the possible output that each capsule creates. In the end, the sum of these multiplications is the title of the final output, and is used to determine the polarity. And the most dependent domain is considered the final output for each domain. The proposed method was evaluated using the Digikala dataset and obtained acceptable accuracy compared to the existing approaches. It achieved an accuracy of 0.89 on detecting the domain of belonging and 0.99 on detecting the polarity. Also, for the problem of dealing with unbalanced classes, a cost-sensitive function was used. This function was able to achieve 0.0162 improvements in accuracy for sentiment classification. This approach on Amazon Arabic data can achieve 0.9695 accuracies in domain classification.	['Ramin Mousa', 'Benyamin Pourhosseini', 'Nima Karimi', 'Mahboobeh Sadat Kobari']	2023-06-12	null	null	null	null	['classification-1', 'sentiment-analysis']	['methodology', 'natural-language-processing']	[ 1.13897324e-01 -1.42551437e-01 -2.32350349e-01 -3.64361107e-01 -2.30719447e-01 -7.75246441e-01 4.77250636e-01 7.02253997e-01 -5.42003810e-01 8.71671021e-01 1.14558032e-02 -1.66090056e-02 -3.25105578e-01 -9.61102664e-01 -7.22797364e-02 -8.28763962e-01 1.34567454e-01 3.79223764e-01 5.69613278e-02 -5.54504693e-01 5.88941693e-01 3.78240526e-01 -1.70899606e+00 4.57374841e-01 9.59516406e-01 1.23128831e+00 3.58302705e-02 4.10862684e-01 -3.18190038e-01 8.59127760e-01 -9.61925149e-01 -4.12234277e-01 -2.06891000e-01 -2.30946407e-01 -5.64518988e-01 -8.67959559e-02 -2.14313537e-01 2.40675226e-01 7.03155756e-01 9.90967035e-01 3.34758520e-01 -2.70415749e-02 1.08238780e+00 -1.18443501e+00 -4.88935977e-01 4.93921250e-01 -5.30896246e-01 2.49178056e-02 4.67201173e-01 -7.06798494e-01 8.22302818e-01 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77f890bb-edff-48bf-8c63-917ab0249beb	a-novel-pipeline-for-improving-optical	2307.04245	null	https://arxiv.org/abs/2307.04245v1	https://arxiv.org/pdf/2307.04245v1.pdf	A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing	Optical Character Recognition (OCR) technology finds applications in digitizing books and unstructured documents, along with applications in other domains such as mobility statistics, law enforcement, traffic, security systems, etc. The state-of-the-art methods work well with the OCR with printed text on license plates, shop names, etc. However, applications such as printed textbooks and handwritten texts have limited accuracy with existing techniques. The reason may be attributed to similar-looking characters and variations in handwritten characters. Since these issues are challenging to address with OCR technologies exclusively, we propose a post-processing approach using Natural Language Processing (NLP) tools. This work presents an end-to-end pipeline that first performs OCR on the handwritten or printed text and then improves its accuracy using NLP.	['Anirban Dasgupta', 'Samyak Mehta', 'Aishik Rakshit']	2023-07-09	null	null	null	null	['optical-character-recognition']	['computer-vision']	[ 3.52555603e-01 -6.00782931e-01 -1.86547637e-01 -1.59326017e-01 -4.77581590e-01 -8.95245612e-01 7.11849451e-01 1.69659153e-01 -5.68414330e-01 6.19377196e-01 -1.55348048e-01 -4.95805234e-01 -1.31434396e-01 -5.76021850e-01 -3.15216064e-01 -3.04548681e-01 3.04801494e-01 5.45688748e-01 5.64085066e-01 -5.38262390e-02 1.08204091e+00 1.14418411e+00 -1.17746580e+00 4.57239658e-01 7.96309590e-01 5.59461176e-01 1.49236713e-02 1.23076713e+00 -7.72405863e-01 9.43035305e-01 -8.41860950e-01 -6.64339483e-01 2.22278520e-01 -2.05793947e-01 -5.84431469e-01 4.19230551e-01 5.32799661e-01 -4.26658034e-01 -5.75489521e-01 1.23258710e+00 1.78182781e-01 -2.30505858e-02 7.89527476e-01 -7.23902702e-01 -1.15672803e+00 1.41475439e-01 -8.72821212e-01 1.13898523e-01 6.42626226e-01 1.90038588e-02 6.23825073e-01 -8.68826747e-01 7.81178772e-01 1.31046700e+00 5.42373061e-01 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dd50ba40-a13a-4b17-b6ac-06c5c159b362	clip-driven-fine-grained-text-image-person-re	2210.10276	null	https://arxiv.org/abs/2210.10276v1	https://arxiv.org/pdf/2210.10276v1.pdf	CLIP-Driven Fine-grained Text-Image Person Re-identification	TIReID aims to retrieve the image corresponding to the given text query from a pool of candidate images. Existing methods employ prior knowledge from single-modality pre-training to facilitate learning, but lack multi-modal correspondences. Besides, due to the substantial gap between modalities, existing methods embed the original modal features into the same latent space for cross-modal alignment. However, feature embedding may lead to intra-modal information distortion. Recently, CLIP has attracted extensive attention from researchers due to its powerful semantic concept learning capacity and rich multi-modal knowledge, which can help us solve the above problems. Accordingly, in the paper, we propose a CLIP-driven Fine-grained information excavation framework (CFine) to fully utilize the powerful knowledge of CLIP for TIReID. To transfer the multi-modal knowledge effectively, we perform fine-grained information excavation to mine intra-modal discriminative clues and inter-modal correspondences. Specifically, we first design a multi-grained global feature learning module to fully mine intra-modal discriminative local information, which can emphasize identity-related discriminative clues by enhancing the interactions between global image (text) and informative local patches (words). Secondly, cross-grained feature refinement (CFR) and fine-grained correspondence discovery (FCD) modules are proposed to establish the cross-grained and fine-grained interactions between modalities, which can filter out non-modality-shared image patches/words and mine cross-modal correspondences from coarse to fine. CFR and FCD are removed during inference to save computational costs. Note that the above process is performed in the original modality space without further feature embedding. Extensive experiments on multiple benchmarks demonstrate the superior performance of our method on TIReID.	['Jinhui Tang', 'Liyan Zhang', 'Neng Dong', 'Shuanglin Yan']	2022-10-19	null	null	null	null	['nlp-based-person-retrival']	['computer-vision']	[ 2.12139547e-01 -3.91046256e-01 -3.84793788e-01 -2.69463748e-01 -1.13167369e+00 -5.06624937e-01 5.23376703e-01 -1.93101257e-01 -2.12903872e-01 3.54821771e-01 5.98763704e-01 2.62738496e-01 -5.06102443e-01 -6.51540816e-01 -5.22794962e-01 -8.79628897e-01 3.50881100e-01 1.06821209e-01 2.29452714e-01 -7.03518093e-02 2.07015604e-01 3.40337396e-01 -1.73930562e+00 6.80954516e-01 8.72696280e-01 1.05500424e+00 5.08164167e-01 7.45327920e-02 -2.21942291e-01 3.63469511e-01 -2.12791979e-01 -9.28929523e-02 1.94948241e-01 -2.55035788e-01 -7.19357073e-01 4.94960785e-01 4.66420263e-01 -2.00563297e-01 -3.67111862e-01 1.11580777e+00 4.34735477e-01 1.51371300e-01 5.93834221e-01 -1.12787998e+00 -7.49069273e-01 1.82041228e-01 -9.99527514e-01 -1.47777824e-02 3.09999734e-01 1.42971322e-01 1.35846245e+00 -1.56034803e+00 6.69900894e-01 1.41851234e+00 3.92480075e-01 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6155f332-d090-4978-8a41-868cf7911e33	generalized-spectral-clustering-for-directed	2203.03221	null	https://arxiv.org/abs/2203.03221v2	https://arxiv.org/pdf/2203.03221v2.pdf	Generalized Spectral Clustering for Directed and Undirected Graphs	Spectral clustering is a popular approach for clustering undirected graphs, but its extension to directed graphs (digraphs) is much more challenging. A typical workaround is to naively symmetrize the adjacency matrix of the directed graph, which can however lead to discarding valuable information carried by edge directionality. In this paper, we present a generalized spectral clustering framework that can address both directed and undirected graphs. Our approach is based on the spectral relaxation of a new functional that we introduce as the generalized Dirichlet energy of a graph function, with respect to an arbitrary positive regularizing measure on the graph edges. We also propose a practical parametrization of the regularizing measure constructed from the iterated powers of the natural random walk on the graph. We present theoretical arguments to explain the efficiency of our framework in the challenging setting of unbalanced classes. Experiments using directed K-NN graphs constructed from real datasets show that our graph partitioning method performs consistently well in all cases, while outperforming existing approaches in most of them.	['Matthieu Jonckheere', 'Argyris Kalogeratos', 'Harry Sevi']	2022-03-07	null	null	null	null	['graph-partitioning']	['graphs']	[ 3.15377325e-01 4.06812757e-01 -6.93248361e-02 -2.76203364e-01 -3.01604956e-01 -8.61229718e-01 4.15230215e-01 2.50145555e-01 -8.01557302e-02 3.97156417e-01 1.04488850e-01 -4.14454788e-01 -5.31445086e-01 -9.20182407e-01 -3.63866806e-01 -1.09579241e+00 -2.44981334e-01 8.06220055e-01 3.45460802e-01 -4.39060517e-02 1.36850178e-01 5.12105763e-01 -1.28608990e+00 -1.23053320e-01 7.43508995e-01 2.30331272e-01 -1.75852805e-01 6.33316278e-01 -1.84683874e-01 4.23812419e-01 -3.38859409e-01 -3.46694618e-01 2.28755578e-01 -5.80598354e-01 -1.27203000e+00 7.53900409e-01 -2.57616471e-02 5.52376270e-01 -1.47230729e-01 1.23453212e+00 3.20494890e-01 1.96645096e-01 9.69889641e-01 -1.45477593e+00 -3.53347331e-01 8.32727075e-01 -8.88031185e-01 -5.30746169e-02 2.38691032e-01 -4.52717334e-01 1.34673405e+00 -4.66376960e-01 6.91222966e-01 1.17925632e+00 6.93655849e-01 1.93064749e-01 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f5ef241d-7261-4d11-b967-ec6922918400	dominance-based-rough-set-approach-basic	2210.03233	null	https://arxiv.org/abs/2210.03233v1	https://arxiv.org/pdf/2210.03233v1.pdf	Dominance-based Rough Set Approach, basic ideas and main trends	Dominance-based Rough Approach (DRSA) has been proposed as a machine learning and knowledge discovery methodology to handle Multiple Criteria Decision Aiding (MCDA). Due to its capacity of asking the decision maker (DM) for simple preference information and supplying easily understandable and explainable recommendations, DRSA gained much interest during the years and it is now one of the most appreciated MCDA approaches. In fact, it has been applied also beyond MCDA domain, as a general knowledge discovery and data mining methodology for the analysis of monotonic (and also non-monotonic) data. In this contribution, we recall the basic principles and the main concepts of DRSA, with a general overview of its developments and software. We present also a historical reconstruction of the genesis of the methodology, with a specific focus on the contribution of Roman S{\l}owi\'nski.	['Marcin Szeląg', 'Benedetto Matarazzo', 'Salvatore Greco', 'Jerzy Błaszczyński']	2022-10-06	null	null	null	null	['general-knowledge']	['miscellaneous']	[-1.10614389e-01 3.04086745e-01 -3.12432408e-01 -8.48135471e-01 -1.61024034e-01 -3.84947270e-01 3.86608094e-01 7.07525373e-01 -2.10893631e-01 1.02354908e+00 1.09696187e-01 -6.51625872e-01 -1.27956617e+00 -8.39382410e-01 6.55673519e-02 -3.72507632e-01 -2.35864446e-01 1.12874174e+00 9.54837576e-02 -5.56648731e-01 4.63607371e-01 6.32414103e-01 -1.97755206e+00 5.65273941e-01 1.28128946e+00 1.18837476e+00 1.49560943e-01 3.30893919e-02 -1.85178429e-01 1.07746994e+00 -3.03115487e-01 -5.83237052e-01 2.13640463e-02 -1.25740379e-01 -1.28944290e+00 -1.07447974e-01 -6.23979568e-01 -2.67855763e-01 3.15185368e-01 5.21471500e-01 1.51255101e-01 4.08937693e-01 9.18786108e-01 -1.16744280e+00 -5.15001476e-01 7.58718669e-01 -4.61383015e-01 -1.20211095e-01 7.77202249e-01 -5.85845768e-01 1.07381773e+00 -7.58851826e-01 6.00631773e-01 1.26494908e+00 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627462fa-0f97-4e40-bf7d-620c71521526	weighted-concordance-index-loss-based	2206.11458	null	https://arxiv.org/abs/2206.11458v1	https://arxiv.org/pdf/2206.11458v1.pdf	Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy	Radiation encephalopathy (REP) is the most common complication for nasopharyngeal carcinoma (NPC) radiotherapy. It is highly desirable to assist clinicians in optimizing the NPC radiotherapy regimen to reduce radiotherapy-induced temporal lobe injury (RTLI) according to the probability of REP onset. To the best of our knowledge, it is the first exploration of predicting radiotherapy-induced REP by jointly exploiting image and non-image data in NPC radiotherapy regimen. We cast REP prediction as a survival analysis task and evaluate the predictive accuracy in terms of the concordance index (CI). We design a deep multimodal survival network (MSN) with two feature extractors to learn discriminative features from multimodal data. One feature extractor imposes feature selection on non-image data, and the other learns visual features from images. Because the priorly balanced CI (BCI) loss function directly maximizing the CI is sensitive to uneven sampling per batch. Hence, we propose a novel weighted CI (WCI) loss function to leverage all REP samples effectively by assigning their different weights with a dual average operation. We further introduce a temperature hyper-parameter for our WCI to sharpen the risk difference of sample pairs to help model convergence. We extensively evaluate our WCI on a private dataset to demonstrate its favourability against its counterparts. The experimental results also show multimodal data of NPC radiotherapy can bring more gains for REP risk prediction.	['Jiang Liu', 'Fang-Yun Xie', 'Hongbo Liu', 'Jingwen Wang', 'Jiajian Li', 'Pu-Yun OuYang', 'Anwei Li', 'Jiansheng Fang']	2022-06-23	null	null	null	null	['survival-analysis']	['miscellaneous']	[ 1.96899265e-01 -3.44979674e-01 -5.31995356e-01 -2.95884132e-01 -1.31808925e+00 -4.20075059e-01 6.64604306e-01 -1.50487527e-01 -7.45833278e-01 6.50520325e-01 7.84884155e-01 -5.43684065e-01 -4.66247231e-01 -6.07821822e-01 -5.67531526e-01 -1.39718866e+00 9.33283865e-02 2.47012660e-01 -8.94233584e-02 3.35435346e-02 1.27085209e-01 8.16176891e-01 -8.16385448e-01 4.06036496e-01 9.14488256e-01 1.07319629e+00 5.53436816e-01 5.25688469e-01 2.74701566e-01 6.53570473e-01 -3.82803679e-01 -1.68572575e-01 3.87449056e-01 -1.79834962e-01 -6.64289534e-01 -3.10622156e-01 -1.24251463e-01 -2.81410307e-01 -9.05102432e-01 7.79821038e-01 1.01555645e+00 1.35028496e-01 1.18088198e+00 -1.26325905e+00 -5.09265721e-01 2.00751230e-01 -6.78472757e-01 5.13986707e-01 -5.26926666e-02 4.48414832e-01 7.15028524e-01 -7.82426417e-01 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275de59e-c2be-4f06-8045-177f761260c4	siamese-infrared-and-visible-light-fusion	2103.07302	null	https://arxiv.org/abs/2103.07302v1	https://arxiv.org/pdf/2103.07302v1.pdf	Siamese Infrared and Visible Light Fusion Network for RGB-T Tracking	Due to the different photosensitive properties of infrared and visible light, the registered RGB-T image pairs shot in the same scene exhibit quite different characteristics. This paper proposes a siamese infrared and visible light fusion Network (SiamIVFN) for RBG-T image-based tracking. SiamIVFN contains two main subnetworks: a complementary-feature-fusion network (CFFN) and a contribution-aggregation network (CAN). CFFN utilizes a two-stream multilayer convolutional structure whose filters for each layer are partially coupled to fuse the features extracted from infrared images and visible light images. CFFN is a feature-level fusion network, which can cope with the misalignment of the RGB-T image pairs. Through adaptively calculating the contributions of infrared and visible light features obtained from CFFN, CAN makes the tracker robust under various light conditions. Experiments on two RGB-T tracking benchmark datasets demonstrate that the proposed SiamIVFN has achieved state-of-the-art performance. The tracking speed of SiamIVFN is 147.6FPS, the current fastest RGB-T fusion tracker.	['Wang Bofan', 'Zhuang Yi', 'Hu Zhengwei', 'Zhao Haitao', 'Peng Jingchao']	2021-03-12	null	null	null	null	['rgb-t-tracking']	['computer-vision']	[-1.21387601e-01 -6.09261215e-01 -5.51089644e-02 -1.64654240e-01 -3.66975963e-01 -4.91283506e-01 5.69113553e-01 -6.04449928e-01 -5.05886793e-01 1.09656654e-01 -1.62452504e-01 -7.63213411e-02 3.44800770e-01 -4.52350050e-01 -6.76879823e-01 -9.49666619e-01 4.81728494e-01 -3.15781951e-01 3.38547438e-01 -2.48644054e-01 -2.40226462e-01 5.29559851e-01 -1.83895338e+00 1.77865997e-01 6.26410723e-01 1.48338449e+00 1.37046605e-01 8.49546671e-01 -1.38172194e-01 6.61069930e-01 -5.90200067e-01 -2.46721581e-01 8.69446397e-01 -6.89692572e-02 3.99296544e-02 -1.48190990e-01 1.06796896e+00 -2.40440831e-01 -7.16562748e-01 1.03647017e+00 6.38795197e-01 5.63943572e-02 1.83897659e-01 -1.49780893e+00 -8.44193518e-01 -2.59001777e-02 -8.09487879e-01 5.55444479e-01 4.17051852e-01 6.56614542e-01 3.13896090e-01 -9.52850699e-01 2.58941650e-01 1.24340332e+00 7.66396821e-01 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1e3021d1-ae8f-41c3-adb5-b1d437639e4c	face-generation-and-editing-with-stylegan-a	2212.09102	null	https://arxiv.org/abs/2212.09102v2	https://arxiv.org/pdf/2212.09102v2.pdf	Face Generation and Editing with StyleGAN: A Survey	Our goal with this survey is to provide an overview of the state of the art deep learning technologies for face generation and editing. We will cover popular latest architectures and discuss key ideas that make them work, such as inversion, latent representation, loss functions, training procedures, editing methods, and cross domain style transfer. We particularly focus on GAN-based architectures that have culminated in the StyleGAN approaches, which allow generation of high-quality face images and offer rich interfaces for controllable semantics editing and preserving photo quality. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview.	['Helge Ritter', 'Gustav Reichert', 'Tarek Renusch', 'Dennis Holzmann', 'Eren Akbulut', 'Dzianis Pirshtuk', 'Dzianis Makarovets', 'Maksim Miasayedzenkau', 'Andrew Melnik']	2022-12-18	null	null	null	null	['face-generation']	['computer-vision']	[ 4.22879338e-01 5.58983445e-01 -8.50463100e-03 -5.74559569e-01 -4.68779445e-01 -3.60168099e-01 6.08103156e-01 -8.80105615e-01 -3.16869356e-02 8.91090453e-01 4.05977845e-01 2.59866863e-01 1.84528455e-01 -9.05647337e-01 -6.27848744e-01 -5.80047607e-01 6.12848848e-02 3.06626767e-01 -6.97996795e-01 -3.05729389e-01 -4.88991849e-02 7.16210902e-01 -1.60413003e+00 4.53336686e-01 6.03978276e-01 8.49532604e-01 -2.56098509e-01 6.12517834e-01 -2.62478679e-01 6.45999074e-01 -9.68030512e-01 -1.07069802e+00 1.33159712e-01 -9.18144643e-01 -8.09166074e-01 1.51426971e-01 8.50958943e-01 -7.19717622e-01 -3.89904231e-01 6.62907422e-01 8.60768080e-01 4.65599541e-03 9.15934563e-01 -1.27466738e+00 -1.09953511e+00 3.31492811e-01 -2.40990907e-01 -1.60972700e-01 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ca3f5f75-0c7b-46dc-90f7-9286bcf68478	squant-on-the-fly-data-free-quantization-via-1	2202.07471	null	https://arxiv.org/abs/2202.07471v1	https://arxiv.org/pdf/2202.07471v1.pdf	SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation	Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the original datasets under privacy-sensitive and confidential scenarios. However, current DFQ solutions degrade accuracy, need synthetic data to calibrate networks, and are time-consuming and costly. This paper proposes an on-the-fly DFQ framework with sub-second quantization time, called SQuant, which can quantize networks on inference-only devices with low computation and memory requirements. With the theoretical analysis of the second-order information of DNN task loss, we decompose and approximate the Hessian-based optimization objective into three diagonal sub-items, which have different areas corresponding to three dimensions of weight tensor: element-wise, kernel-wise, and output channel-wise. Then, we progressively compose sub-items and propose a novel data-free optimization objective in the discrete domain, minimizing Constrained Absolute Sum of Error (or CASE in short), which surprisingly does not need any dataset and is even not aware of network architecture. We also design an efficient algorithm without back-propagation to further reduce the computation complexity of the objective solver. Finally, without fine-tuning and synthetic datasets, SQuant accelerates the data-free quantization process to a sub-second level with >30% accuracy improvement over the existing data-free post-training quantization works, with the evaluated models under 4-bit quantization. We have open-sourced the SQuant framework at https://github.com/clevercool/SQuant.	['Minyi Guo', 'Yuhao Zhu', 'Fan Yang', 'Yunxin Liu', 'Chen Zhang', 'Xiaotian Gao', 'Jingwen Leng', 'Yuxian Qiu', 'Cong Guo']	2022-02-14	squant-on-the-fly-data-free-quantization-via	https://openreview.net/forum?id=JXhROKNZzOc	https://openreview.net/pdf?id=JXhROKNZzOc	iclr-2022-4	['data-free-quantization', 'data-free-quantization']	['computer-vision', 'methodology']	[ 3.03539215e-03 5.93691505e-03 -1.32985324e-01 -7.35568106e-01 -7.88150966e-01 -4.65938270e-01 1.79816008e-01 1.84189066e-01 -8.81595910e-01 8.22701275e-01 -1.25599578e-01 -5.62511921e-01 -1.35703743e-01 -9.44988489e-01 -9.73281920e-01 -6.21940136e-01 2.04562768e-02 2.88205057e-01 -6.74374402e-02 1.30371138e-01 -2.09796019e-02 5.03787637e-01 -1.40106821e+00 1.02711901e-01 8.85475814e-01 1.70743895e+00 -1.54265791e-01 4.23752338e-01 1.29563779e-01 6.60531819e-01 -5.64669609e-01 -1.11840153e+00 6.97044015e-01 1.40432715e-02 -6.12113178e-01 -3.28000933e-01 8.25859785e-01 -9.62607265e-01 -7.55607367e-01 1.61064017e+00 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9793c22c-4778-4410-b915-7e46853e42d4	towards-training-billion-parameter-graph-1	2203.09697	null	https://arxiv.org/abs/2203.09697v1	https://arxiv.org/pdf/2203.09697v1.pdf	Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations	Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat climate change. However, the GNNs that have proven most effective for this task are memory intensive as they model higher-order interactions in the graphs such as those between triplets or quadruplets of atoms, making it challenging to scale these models. In this paper, we introduce Graph Parallelism, a method to distribute input graphs across multiple GPUs, enabling us to train very large GNNs with hundreds of millions or billions of parameters. We empirically evaluate our method by scaling up the number of parameters of the recently proposed DimeNet++ and GemNet models by over an order of magnitude. On the large-scale Open Catalyst 2020 (OC20) dataset, these graph-parallelized models lead to relative improvements of 1) 15% on the force MAE metric for the S2EF task and 2) 21% on the AFbT metric for the IS2RS task, establishing new state-of-the-art results.	['C. Lawrence Zitnick', 'Siddharth Goyal', 'Brandon M. Wood', 'Abhishek Das', 'Anuroop Sriram']	2022-03-18	towards-training-billion-parameter-graph	https://openreview.net/forum?id=0jP2n0YFmKG	https://openreview.net/pdf?id=0jP2n0YFmKG	iclr-2022-4	['initial-structure-to-relaxed-energy-is2re']	['graphs']	[ 9.51527730e-02 5.39981760e-02 -1.48659155e-01 2.66988128e-01 -3.76729757e-01 -5.50679743e-01 9.24008131e-01 5.40176630e-01 -5.15118062e-01 7.68415093e-01 -1.09861344e-01 -1.10766923e+00 1.89452976e-01 -1.21061587e+00 -9.66666818e-01 -6.99191213e-01 -2.50279278e-01 8.01965892e-01 4.88445520e-01 -5.07795930e-01 1.47650540e-01 6.55193865e-01 -1.04756653e+00 1.69104159e-01 7.84225285e-01 7.62336731e-01 4.10351120e-02 7.43904293e-01 1.27000362e-01 5.73622465e-01 -1.23398177e-01 -2.05002397e-01 9.40609276e-02 -3.12174886e-01 -8.80189717e-01 -9.04911101e-01 4.35486168e-01 1.88952416e-01 -6.93153441e-01 9.65755820e-01 6.66253328e-01 2.14305058e-01 5.40114522e-01 -7.65069127e-01 -1.77645996e-01 6.44683897e-01 -5.45582831e-01 2.11468473e-01 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3.94002795e-01 8.10419381e-01 7.23078191e-01 8.63623857e-01 8.95229578e-01 5.76873459e-02 -1.14562476e+00 -4.73518729e-01 -8.58703732e-01 -1.28979608e-01 2.19530627e-01 -2.87021130e-01 -4.10710931e-01 2.65075639e-02 -6.47926807e-01]	[5.41118860244751, 5.6596574783325195]
ce45ac54-7943-4a55-b1cd-252ca68ab60a	prediction-of-video-game-development-problems	null	null	https://aclanthology.org/2021.icon-main.56	https://aclanthology.org/2021.icon-main.56.pdf	Prediction of Video Game Development Problems Based on Postmortems using Different Word Embedding Techniques	The interactive entertainment industry is being actively involved with the development, marketing and sale of video games in the past decade. The increasing interest in video games has led to an increase in video game development techniques and methods. It has emerged as an immensely large sector, and now it has grown to be larger than the movie and music industry combined. The postmortem of a game outlines and analyzes the game’s history, team goals, what went right, and what went wrong with the game. Despite its significance, there is little understanding related to the challenges encountered by the programmers. Postmortems are not properly maintained and are informally written, leading to a lack of trustworthiness. In this study, we perform a systematic analysis on different problems faced in the video game development. The need for automation and ML techniques arises because it could help game developers easily identify the exact problem from the description, and hence be able to easily find a solution. This work could also help developers in identifying frequent mistakes that could be avoided, and will provide researchers a beginning point to further consider game development in context of software engineering.	['N L Bhanu Murthy', 'Lov Kumar', 'Anjali Goyal', 'Aman RAJ Singh', 'Anirudh A']	null	null	null	null	icon-2021-12	['marketing']	['miscellaneous']	[-1.10359862e-01 1.48178846e-01 2.41080046e-01 -4.16140594e-02 -1.01122297e-01 -6.12142324e-01 2.31682003e-01 3.39609981e-01 -1.66776642e-01 4.09975350e-01 2.29806211e-02 -4.84793812e-01 -2.58457363e-01 -7.69394755e-01 -2.02745318e-01 -2.73948491e-01 1.28774434e-01 2.52870172e-01 6.57746434e-01 -4.35788631e-01 5.66691041e-01 5.88213503e-02 -1.83144522e+00 1.33538187e-01 3.98533970e-01 5.66767812e-01 4.67991531e-01 4.60784167e-01 -4.22214568e-01 1.41728663e+00 -6.88366473e-01 -7.26255417e-01 1.17469244e-01 -7.96572924e-01 -8.86043489e-01 2.68717706e-01 -2.60257095e-01 -3.19458634e-01 3.94311070e-01 1.15127122e+00 1.23315156e-01 -2.57854372e-01 1.43306375e-01 -1.42120469e+00 -1.18269753e-02 5.59444308e-01 -5.20261824e-01 1.16240062e-01 5.38635671e-01 -8.54978934e-02 7.11233675e-01 -3.80537778e-01 7.82247603e-01 6.10513210e-01 5.24169445e-01 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-6.75452411e-01 -7.88232625e-01 1.30428851e-01 -4.58042353e-01 -5.55665433e-01 -1.94377586e-01 2.09957436e-01]	[9.065300941467285, 6.536074161529541]
e2927cb3-46e6-4baf-a1b8-75c56a960885	mechanical-models-of-pattern-and-form-in	2009.10953	null	https://arxiv.org/abs/2009.10953v6	https://arxiv.org/pdf/2009.10953v6.pdf	Mechanical models of pattern and form in biological tissues: the role of stress-strain constitutive equations	Mechanochemical models of pattern formation in biological tissues have been used to study a variety of biomedical systems and describe the physical interactions between cells and their local surroundings. These models generally consist of a balance equation for the cell density, one for the density of the extracellular matrix (ECM), and a force-balance equation describing the mechanical equilibrium of the cell-ECM system. Assuming this system can be regarded as an isotropic linear viscoelastic material, the force-balance equation is often defined using the Kelvin-Voigt model of linear viscoelasticity to represent the stress-strain relation of the ECM. However, due to the multifaceted bio-physical nature of the ECM constituents, there are rheological aspects that cannot be effectively captured by this model and, therefore, depending on the type of biological tissue considered, other constitutive models of linear viscoelasticity may be better suited. In this work, we systematically assess the pattern formation potential of different stress-strain constitutive equations for the ECM within a mechanical model of pattern formation in biological tissues. The results obtained through linear stability analysis support the idea that constitutive equations capturing viscous flow and permanent set (Maxwell model, Jeffrey model) have a pattern formation potential much higher than the others (Kelvin-Voigt model, standard linear solid model), further confirmed by the results of our numerical simulations. Our findings suggest that further empirical work is required to acquire detailed quantitative information on the mechanical properties of components of the ECM in different biological tissues in order to furnish mechanochemical models of pattern formation with stress-strain constitutive equations for the ECM that provide a more faithful representation of the underlying tissue rheology.	['Tommaso Lorenzi', 'Alf Gerisch', 'Mark A. J. Chaplain', 'Chiara Villa']	2020-09-23	null	null	null	null	['stress-strain-relation']	['miscellaneous']	[ 3.90585475e-02 -7.88459778e-02 -1.01943433e-01 2.86477894e-01 5.65870702e-01 -3.95075411e-01 4.67797905e-01 4.10174340e-01 -2.31666148e-01 8.36780012e-01 -6.34815171e-02 -8.23366493e-02 -5.29162824e-01 -7.71629453e-01 -3.11743140e-01 -1.20276749e+00 -2.67339557e-01 5.78647435e-01 6.41303658e-01 -3.61883432e-01 -1.54131260e-02 9.34486210e-01 -1.11900949e+00 -5.63692823e-02 5.95670819e-01 9.82633889e-01 1.18564218e-01 6.74191236e-01 -6.79052845e-02 7.44551301e-01 -1.95546269e-01 1.73271715e-03 -1.68545276e-01 -3.36086154e-01 -7.67736971e-01 2.50071406e-01 -2.97334164e-01 -1.65597707e-01 -3.75849068e-01 5.19129932e-01 1.65821329e-01 6.58389032e-02 1.21383691e+00 -6.22633994e-01 -3.16418767e-01 2.42915247e-02 -2.09140494e-01 1.57258473e-02 2.56346405e-01 -1.46517828e-01 4.45042074e-01 -6.57481492e-01 1.14488208e+00 7.33717501e-01 4.24744487e-01 5.68421185e-01 -1.33667755e+00 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7dcb5d02-4fe2-4381-8911-c0cd3b4b8073	fault-diagnosis-for-pv-arrays-considering	2304.06493	null	https://arxiv.org/abs/2304.06493v1	https://arxiv.org/pdf/2304.06493v1.pdf	Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules	Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability.	['Zheng Qian', 'Hamidreza Zareipour', 'Qiang Sun', 'Lu Wei', 'Jiaqi Qu']	2023-03-24	null	null	null	null	['blocking']	['natural-language-processing']	[ 3.88020128e-02 -9.21532273e-01 5.62275946e-01 -1.90934300e-01 -8.86487737e-02 -7.08715081e-01 2.56897420e-01 -9.20285359e-02 5.76622605e-01 5.88006139e-01 -3.53622228e-01 -5.20576000e-01 -5.99667847e-01 -9.65514541e-01 -6.87924862e-01 -1.12191391e+00 1.84260234e-02 4.90818545e-02 -2.68878877e-01 2.36273054e-02 3.27009022e-01 1.03008878e+00 -1.72784281e+00 3.06879520e-01 1.61737013e+00 1.33546460e+00 1.20615847e-01 5.70645094e-01 1.38271749e-01 4.20656472e-01 -1.48422348e+00 2.03795984e-01 3.21874656e-02 -2.81267703e-01 -4.07044083e-01 2.47200549e-01 1.14156976e-01 -2.35689640e-01 -4.95452553e-01 1.34023643e+00 6.12432778e-01 -2.14487299e-01 1.06682718e+00 -1.66418934e+00 -1.03234375e+00 9.51400101e-02 -2.04304472e-01 2.33863845e-01 2.88536966e-01 2.18361929e-01 2.60663897e-01 -8.29356611e-01 -2.18932837e-01 1.10887110e+00 7.00726151e-01 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ab4d9ff1-eaf2-4180-8d85-644c95c842c5	a-multi-task-bert-model-for-schema-guided	2207.00828	null	https://arxiv.org/abs/2207.00828v1	https://arxiv.org/pdf/2207.00828v1.pdf	A Multi-Task BERT Model for Schema-Guided Dialogue State Tracking	Task-oriented dialogue systems often employ a Dialogue State Tracker (DST) to successfully complete conversations. Recent state-of-the-art DST implementations rely on schemata of diverse services to improve model robustness and handle zero-shot generalization to new domains [1], however such methods [2, 3] typically require multiple large scale transformer models and long input sequences to perform well. We propose a single multi-task BERT-based model that jointly solves the three DST tasks of intent prediction, requested slot prediction and slot filling. Moreover, we propose an efficient and parsimonious encoding of the dialogue history and service schemata that is shown to further improve performance. Evaluation on the SGD dataset shows that our approach outperforms the baseline SGP-DST by a large margin and performs well compared to the state-of-the-art, while being significantly more computationally efficient. Extensive ablation studies are performed to examine the contributing factors to the success of our model.	['Alexandros Potamianos', 'Efthymios Georgiou', 'Eleftherios Kapelonis']	2022-07-02	null	null	null	null	['dialogue-state-tracking', 'zero-shot-slot-filling', 'slot-filling']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 2.08579481e-01 5.75939834e-01 -2.40521535e-01 -5.70898414e-01 -9.92195249e-01 -6.62579238e-01 1.08917844e+00 -1.42188057e-01 -2.30702743e-01 7.76575863e-01 4.96202528e-01 -4.45819259e-01 1.86851427e-01 -1.65522054e-01 -8.29010978e-02 -9.04952660e-02 7.47841001e-02 1.29966414e+00 6.96217239e-01 -7.55655050e-01 2.86647618e-01 -5.72403744e-02 -1.36030793e+00 4.32194293e-01 8.97582829e-01 6.99252963e-01 2.25319877e-01 7.79956043e-01 -5.19507945e-01 7.87305415e-01 -7.50852168e-01 -3.82221133e-01 1.00844793e-01 -4.77787465e-01 -1.29292285e+00 3.04117382e-01 2.19230149e-02 -5.33974528e-01 -4.39133316e-01 3.68194491e-01 6.18068337e-01 3.23193520e-01 3.26675177e-01 -1.42547810e+00 8.30636173e-02 5.72905660e-01 3.11521497e-02 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e8baf834-df85-4038-b748-aa1453db1002	a-mixture-of-expert-approach-to-rl-based	2206.00059	null	https://arxiv.org/abs/2206.00059v1	https://arxiv.org/pdf/2206.00059v1.pdf	A Mixture-of-Expert Approach to RL-based Dialogue Management	Despite recent advancements in language models (LMs), their application to dialogue management (DM) problems and ability to carry on rich conversations remain a challenge. We use reinforcement learning (RL) to develop a dialogue agent that avoids being short-sighted (outputting generic utterances) and maximizes overall user satisfaction. Most existing RL approaches to DM train the agent at the word-level, and thus, have to deal with a combinatorially complex action space even for a medium-size vocabulary. As a result, they struggle to produce a successful and engaging dialogue even if they are warm-started with a pre-trained LM. To address this issue, we develop a RL-based DM using a novel mixture of expert language model (MoE-LM) that consists of (i) a LM capable of learning diverse semantics for conversation histories, (ii) a number of {\em specialized} LMs (or experts) capable of generating utterances corresponding to a particular attribute or personality, and (iii) a RL-based DM that performs dialogue planning with the utterances generated by the experts. Our MoE approach provides greater flexibility to generate sensible utterances with different intents and allows RL to focus on conversational-level DM. We compare it with SOTA baselines on open-domain dialogues and demonstrate its effectiveness both in terms of the diversity and sensibility of the generated utterances and the overall DM performance.	['Craig Boutilier', 'Mohammad Ghavamzadeh', 'MoonKyung Ryu', 'Ofir Nachum', 'Aza Tulepbergenov', 'Yinlam Chow']	2022-05-31	null	null	null	null	['dialogue-management']	['natural-language-processing']	[ 4.96041588e-02 9.31947827e-01 1.00346357e-01 -4.74901140e-01 -8.45795870e-01 -6.76195323e-01 9.54366326e-01 -2.78975517e-01 -2.85044968e-01 9.45168197e-01 5.50476015e-01 -4.11862105e-01 3.96213531e-01 -7.27173805e-01 -1.52565107e-01 -3.05858672e-01 2.00341940e-01 1.18569994e+00 -1.05181627e-01 -9.44267154e-01 -1.44087840e-02 1.20705917e-01 -1.28910935e+00 4.62362975e-01 9.80601490e-01 5.19864678e-01 4.00827497e-01 1.09408355e+00 -4.86571521e-01 1.19846284e+00 -9.32138801e-01 -4.48322624e-01 -1.81401774e-01 -8.23695123e-01 -1.46872568e+00 4.42163408e-01 -1.39582545e-01 -5.47900140e-01 -1.03149302e-01 3.69276762e-01 7.71341085e-01 4.95440304e-01 6.53103828e-01 -1.09155881e+00 -2.80283451e-01 8.65095079e-01 2.53659308e-01 -2.61875629e-01 9.08433676e-01 6.99145138e-01 1.11545801e+00 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1.65646628e-01 3.75297159e-01 1.06329477e+00 -6.07598066e-01 -1.05520964e+00 -1.39166975e+00 -2.94027388e-01 4.65831757e-01 2.48775035e-01 -2.99183518e-01 -1.79324418e-01 -2.83831149e-01]	[12.917275428771973, 8.066884994506836]
82845cee-a335-4faf-8e5d-dcee45e126d4	rethinking-the-protein-folding-problem-from-a	2210.05004	null	https://arxiv.org/abs/2210.05004v1	https://arxiv.org/pdf/2210.05004v1.pdf	Rethinking the protein folding problem from a new perspective	One of the main concerns of Anfinsen was to reveal the connection between the amino acid sequence and their biologically active conformation. This search gave rise to two crucial questions in structural biology, namely, why the proteins fold and how a sequence encodes its folding. As to the why, he proposes a plausible answer, namely, at a given milieu a protein folds into its functional form -- native state -- because such structure represents the lowest free-energy minimum among all feasible conformations -- the thermodynamic hypothesis or Anfinsen dogma. As to the how, this remains as an unsolved challenge and, hence, this inquiry is examined here from a new perspective of protein folding, namely, as an analytic whole -- a notion proposed by Leibnitz and Kant. This new perspective forces us to discuss in detail why the theoretical force-field-based approaches have failed in both their ability to predict the three-dimensional structure of a protein accurately and in their capacity to answer one of the most critical questions in structural biology: how a sequence encodes its folding.	['Jorge A. Vila']	2022-10-10	null	null	null	null	['protein-folding']	['natural-language-processing']	[ 2.83493102e-01 3.63150477e-01 -2.25967035e-01 -2.64676243e-01 -4.55910824e-02 -6.93361819e-01 2.48568147e-01 3.25956821e-01 -2.49833807e-01 9.12625492e-01 1.26834720e-01 -8.10860872e-01 -1.17641672e-01 -5.11918485e-01 -6.60135508e-01 -9.56269324e-01 1.05533861e-01 3.23478460e-01 1.11066535e-01 -5.22212207e-01 3.98905039e-01 5.98603845e-01 -1.38769269e+00 -9.85429883e-02 1.08986342e+00 6.05211318e-01 3.79715919e-01 4.39827442e-01 -3.56924832e-01 4.05764759e-01 -1.91607609e-01 -6.01678848e-01 -8.40870366e-02 -9.70505357e-01 -1.08495998e+00 -1.79117188e-01 -1.34498449e-02 9.08425823e-02 1.52154118e-01 8.01063597e-01 -9.32892039e-02 -2.96048492e-01 5.77382803e-01 -3.55791748e-01 -7.42708623e-01 -1.23559810e-01 1.28792480e-01 -1.21104889e-01 4.81835902e-01 2.55490035e-01 1.11268175e+00 -6.83098912e-01 8.85457098e-01 9.41192746e-01 4.82635856e-01 6.97423339e-01 -1.49422026e+00 1.95602432e-01 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1.82549059e-01 4.00291502e-01 -2.82902062e-01 -2.05457062e-01 -1.32561866e-02]	[4.781989097595215, 5.2525835037231445]
0aff533d-c7bb-4310-8a38-b4f0935a35cb	sequence-learning-in-a-spiking-neuronal	2211.16592	null	https://arxiv.org/abs/2211.16592v1	https://arxiv.org/pdf/2211.16592v1.pdf	Sequence learning in a spiking neuronal network with memristive synapses	Brain-inspired computing proposes a set of algorithmic principles that hold promise for advancing artificial intelligence. They endow systems with self learning capabilities, efficient energy usage, and high storage capacity. A core concept that lies at the heart of brain computation is sequence learning and prediction. This form of computation is essential for almost all our daily tasks such as movement generation, perception, and language. Understanding how the brain performs such a computation is not only important to advance neuroscience but also to pave the way to new technological brain-inspired applications. A previously developed spiking neural network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. An emerging type of hardware that holds promise for efficiently running this type of algorithm is neuromorphic hardware. It emulates the way the brain processes information and maps neurons and synapses directly into a physical substrate. Memristive devices have been identified as potential synaptic elements in neuromorphic hardware. In particular, redox-induced resistive random access memories (ReRAM) devices stand out at many aspects. They permit scalability, are energy efficient and fast, and can implement biological plasticity rules. In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model. We implement and simulate the model including the ReRAM plasticity using the neural simulator NEST. We investigate the effect of different device properties on the performance characteristics of the sequence learning model, and demonstrate resilience with respect to different on-off ratios, conductance resolutions, device variability, and synaptic failure.	['Dirk J. Wouters', 'Rainer Waser', 'Markus Diesmann', 'Tom Tetzlaff', 'Sebastian Siegel', 'Younes Bouhadjar']	2022-11-29	null	null	null	null	['self-learning']	['natural-language-processing']	[ 5.47559619e-01 -3.75339836e-01 1.43278256e-01 3.78537327e-02 6.25608504e-01 -3.68603468e-01 7.56729126e-01 2.43863434e-01 -6.40423119e-01 8.61005664e-01 -1.34451360e-01 -2.44280234e-01 -7.90721998e-02 -1.06782842e+00 -8.62464070e-01 -1.05145609e+00 -2.70915311e-02 1.28378689e-01 9.68267441e-01 -4.73954618e-01 6.83683753e-01 7.37812042e-01 -2.04056787e+00 5.33398747e-01 5.90818584e-01 8.68198454e-01 5.82408905e-01 4.45362538e-01 -1.28799096e-01 6.94190562e-01 -3.08931559e-01 -5.25702648e-02 2.08047554e-02 -6.06146097e-01 -2.43253767e-01 -8.06692958e-01 -5.02070844e-01 2.36321509e-01 -4.51021224e-01 6.31373048e-01 4.90970016e-01 9.69227329e-02 7.98850358e-01 -6.42046332e-01 -5.64989209e-01 6.01209104e-01 2.01510236e-01 5.17136395e-01 -4.23894152e-02 3.58260572e-01 1.87545538e-01 -7.97722518e-01 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e1930f7b-eaea-4253-acbe-af26a027163c	d-2lv-a-data-driven-and-local-verification	2111.0709	null	https://arxiv.org/abs/2111.07090v2	https://arxiv.org/pdf/2111.07090v2.pdf	D$^2$LV: A Data-Driven and Local-Verification Approach for Image Copy Detection	Image copy detection is of great importance in real-life social media. In this paper, a data-driven and local-verification (D$^2$LV) approach is proposed to compete for Image Similarity Challenge: Matching Track at NeurIPS'21. In D$^2$LV, unsupervised pre-training substitutes the commonly-used supervised one. When training, we design a set of basic and six advanced transformations, and a simple but effective baseline learns robust representation. During testing, a global-local and local-global matching strategy is proposed. The strategy performs local-verification between reference and query images. Experiments demonstrate that the proposed method is effective. The proposed approach ranks first out of 1,103 participants on the Facebook AI Image Similarity Challenge: Matching Track. The code and trained models are available at https://github.com/WangWenhao0716/ISC-Track1-Submission.	['Yi Yang', 'Weipu Zhang', 'Yifan Sun', 'Wenhao Wang']	2021-11-13	null	null	null	null	['unsupervised-pre-training']	['methodology']	[ 3.34190398e-01 -1.79165632e-01 -3.26188177e-01 -6.60744488e-01 -1.07830870e+00 -2.52026826e-01 8.73731077e-01 1.60626024e-01 -7.28512645e-01 2.23367199e-01 1.11811168e-01 -3.18284519e-03 1.05912089e-01 -4.43585753e-01 -8.84097159e-01 -3.44602287e-01 -9.89267379e-02 2.59973526e-01 3.10682863e-01 -2.30999261e-01 3.65648538e-01 2.44715840e-01 -1.75668836e+00 5.12664855e-01 7.45999396e-01 1.14775014e+00 3.73403162e-01 7.01085746e-01 2.59493589e-01 4.82439399e-01 -2.94633865e-01 -7.54853189e-01 7.29411781e-01 -5.90988278e-01 -8.00188184e-01 -1.09794013e-01 9.18168664e-01 -3.27975273e-01 -4.06374246e-01 1.26307356e+00 7.80283749e-01 9.45870057e-02 4.45703894e-01 -1.52471435e+00 -5.12069523e-01 4.07512188e-01 -7.74861813e-01 4.10615146e-01 5.23629010e-01 1.50523394e-01 9.69766617e-01 -1.08999574e+00 7.68785775e-01 1.02422857e+00 6.27255380e-01 5.67158937e-01 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e5f12b88-f56c-41e3-9cca-7729ac79c494	pixel-wise-deep-image-stitching	2112.06171	null	https://arxiv.org/abs/2112.06171v1	https://arxiv.org/pdf/2112.06171v1.pdf	Pixel-wise Deep Image Stitching	Image stitching aims at stitching the images taken from different viewpoints into an image with a wider field of view. Existing methods warp the target image to the reference image using the estimated warp function, and a homography is one of the most commonly used warping functions. However, when images have large parallax due to non-planar scenes and translational motion of a camera, the homography cannot fully describe the mapping between two images. Existing approaches based on global or local homography estimation are not free from this problem and suffer from undesired artifacts due to parallax. In this paper, instead of relying on the homography-based warp, we propose a novel deep image stitching framework exploiting the pixel-wise warp field to handle the large-parallax problem. The proposed deep image stitching framework consists of two modules: Pixel-wise Warping Module (PWM) and Stitched Image Generating Module (SIGMo). PWM employs an optical flow estimation model to obtain pixel-wise warp of the whole image, and relocates the pixels of the target image with the obtained warp field. SIGMo blends the warped target image and the reference image while eliminating unwanted artifacts such as misalignments, seams, and holes that harm the plausibility of the stitched result. For training and evaluating the proposed framework, we build a large-scale dataset that includes image pairs with corresponding pixel-wise ground truth warp and sample stitched result images. We show that the results of the proposed framework are qualitatively superior to those of the conventional methods, especially when the images have large parallax. The code and the proposed dataset will be publicly available soon.	['Kuk-Jin Yoon', 'Wooseong Jeong', 'Youngho Yoon', 'Yoonsu Kang', 'Hyeonseong Kim', 'Hyeokjun Kweon']	2021-12-12	null	null	null	null	['image-stitching', 'homography-estimation']	['computer-vision', 'computer-vision']	[ 4.69589710e-01 -2.71001041e-01 1.50110871e-02 1.04033418e-01 -3.26903611e-01 -5.78096807e-01 5.49881041e-01 -4.83486623e-01 -2.31261030e-01 5.72635710e-01 1.95983112e-01 1.24065645e-01 -1.96196139e-03 -6.67597055e-01 -7.78062582e-01 -9.49368894e-01 4.39108253e-01 1.42078057e-01 3.98392677e-01 -1.05667278e-01 5.03988683e-01 2.80823886e-01 -1.28740525e+00 -9.16386545e-02 8.88947070e-01 7.30317473e-01 1.80425242e-01 4.46592361e-01 1.90829322e-01 2.84179002e-01 -6.02371335e-01 -2.69995600e-01 7.10980237e-01 -5.32654941e-01 -4.22712147e-01 4.60151315e-01 7.81532705e-01 -7.05277681e-01 -5.66397250e-01 1.27861965e+00 2.44318739e-01 1.97589695e-01 2.49180660e-01 -1.48040998e+00 -4.04375941e-01 1.61847457e-01 -1.10755622e+00 7.22051598e-03 1.88347384e-01 3.28362942e-01 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c1c2a842-41dc-4438-bd61-07fc71e3e5a7	a-new-dataset-and-model-for-learning-to	1805.07952	null	http://arxiv.org/abs/1805.07952v1	http://arxiv.org/pdf/1805.07952v1.pdf	A new dataset and model for learning to understand navigational instructions	In this paper, we present a state-of-the-art model and introduce a new dataset for grounded language learning. Our goal is to develop a model that can learn to follow new instructions given prior instruction-perception-action examples. We based our work on the SAIL dataset which consists of navigational instructions and actions in a maze-like environment. The new model we propose achieves the best results to date on the SAIL dataset by using an improved perceptual component that can represent relative positions of objects. We also analyze the problems with the SAIL dataset regarding its size and balance. We argue that performance on a small, fixed-size dataset is no longer a good measure to differentiate state-of-the-art models. We introduce SAILx, a synthetic dataset generator, and perform experiments where the size and balance of the dataset are controlled.	['Ozan Arkan Can', 'Deniz Yuret']	2018-05-21	null	null	null	null	['grounded-language-learning']	['natural-language-processing']	[ 1.92895293e-01 3.20001155e-01 1.34733677e-01 -5.76852798e-01 -8.30204070e-01 -6.68950915e-01 8.08390141e-01 3.86361003e-01 -7.65679717e-01 7.26931691e-01 2.70929128e-01 -5.13239622e-01 -9.33439285e-02 -8.36236298e-01 -1.13452518e+00 -3.30274343e-01 -3.88401419e-01 8.28940213e-01 6.00613594e-01 -6.27222717e-01 5.81961334e-01 4.14357990e-01 -1.70806336e+00 5.64103007e-01 6.82923973e-01 5.48254848e-01 5.27738214e-01 7.90732801e-01 2.31104754e-02 9.60407674e-01 -6.05654895e-01 1.27514526e-01 2.92106122e-01 -7.04693675e-01 -1.10921967e+00 -1.40817970e-01 5.71084082e-01 -1.34000555e-01 -8.05464089e-02 5.57565212e-01 4.19867843e-01 4.82422054e-01 6.27610981e-01 -1.29323828e+00 -6.42079592e-01 8.53038728e-01 1.69040859e-01 1.05412014e-01 6.36912882e-01 1.74870893e-01 8.44909906e-01 -6.08226180e-01 7.36136436e-01 1.33369160e+00 5.72954655e-01 7.65749633e-01 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6b79a8d2-c2d2-4c0a-ac4e-5d43acdf04b8	planning-with-large-language-models-via	2211.09935	null	https://arxiv.org/abs/2211.09935v1	https://arxiv.org/pdf/2211.09935v1.pdf	Planning with Large Language Models via Corrective Re-prompting	Extracting the common sense knowledge present in Large Language Models (LLMs) offers a path to designing intelligent, embodied agents. Related works have queried LLMs with a wide-range of contextual information, such as goals, sensor observations and scene descriptions, to generate high-level action plans for specific tasks; however these approaches often involve human intervention or additional machinery to enable sensor-motor interactions. In this work, we propose a prompting-based strategy for extracting executable plans from an LLM, which leverages a novel and readily-accessible source of information: precondition errors. Our approach assumes that actions are only afforded execution in certain contexts, i.e., implicit preconditions must be met for an action to execute (e.g., a door must be unlocked to open it), and that the embodied agent has the ability to determine if the action is/is not executable in the current context (e.g., detect if a precondition error is present). When an agent is unable to execute an action, our approach re-prompts the LLM with precondition error information to extract an executable corrective action to achieve the intended goal in the current context. We evaluate our approach in the VirtualHome simulation environment on 88 different tasks and 7 scenes. We evaluate different prompt templates and compare to methods that naively re-sample actions from the LLM. Our approach, using precondition errors, improves executability and semantic correctness of plans, while also reducing the number of re-prompts required when querying actions.	['Stefanie Tellex', 'David Paulius', 'Ifrah Idrees', 'Eric Rosen', 'Vanya Cohen', 'Shreyas Sundara Raman']	2022-11-17	null	null	null	null	['common-sense-reasoning']	['reasoning']	[ 8.63897324e-01 4.38469082e-01 2.42146645e-02 -2.59070724e-01 -6.21972263e-01 -8.37687612e-01 7.91601062e-01 3.64243299e-01 -4.23234552e-01 7.22923458e-01 3.83423686e-01 -5.23690522e-01 -1.27315789e-01 -8.60244632e-01 -7.41394758e-01 -1.55000165e-01 8.54583606e-02 2.50396430e-01 4.73899066e-01 -1.76347271e-01 3.94870162e-01 6.04038000e-01 -1.83074164e+00 2.54773527e-01 6.21028543e-01 6.85872555e-01 6.17628932e-01 7.81581581e-01 1.21123251e-02 1.29214680e+00 -6.03667319e-01 4.99730855e-01 2.06640549e-02 -5.22651315e-01 -1.30440915e+00 2.06708804e-01 -7.36977085e-02 -6.49078369e-01 6.12596273e-02 8.77890587e-01 -9.56998300e-03 3.62746745e-01 4.62898344e-01 -1.63980985e+00 1.20413281e-01 4.19038892e-01 2.11170763e-01 -2.33955979e-01 1.18532264e+00 6.29017711e-01 7.89273918e-01 -2.83082306e-01 8.11665118e-01 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5faf8479-767b-4d39-84d7-7a1d3d3834f8	on-the-impact-of-speech-recognition-errors-in	2209.12944	null	https://arxiv.org/abs/2209.12944v1	https://arxiv.org/pdf/2209.12944v1.pdf	On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering	Interacting with a speech interface to query a Question Answering (QA) system is becoming increasingly popular. Typically, QA systems rely on passage retrieval to select candidate contexts and reading comprehension to extract the final answer. While there has been some attention to improving the reading comprehension part of QA systems against errors that automatic speech recognition (ASR) models introduce, the passage retrieval part remains unexplored. However, such errors can affect the performance of passage retrieval, leading to inferior end-to-end performance. To address this gap, we augment two existing large-scale passage ranking and open domain QA datasets with synthetic ASR noise and study the robustness of lexical and dense retrievers against questions with ASR noise. Furthermore, we study the generalizability of data augmentation techniques across different domains; with each domain being a different language dialect or accent. Finally, we create a new dataset with questions voiced by human users and use their transcriptions to show that the retrieval performance can further degrade when dealing with natural ASR noise instead of synthetic ASR noise.	['Evangelos Kanoulas', 'Svitlana Vakulenko', 'Georgios Sidiropoulos']	2022-09-26	null	null	null	null	['passage-ranking', 'passage-retrieval']	['natural-language-processing', 'natural-language-processing']	[ 4.04147416e-01 1.43782347e-01 3.43882024e-01 -3.71998042e-01 -1.73364365e+00 -8.12135100e-01 5.94527483e-01 2.70898432e-01 -5.18570125e-01 4.32987481e-01 8.04181576e-01 -5.78425586e-01 7.30565609e-03 -4.74778682e-01 -5.54423034e-01 -8.81589800e-02 4.45399553e-01 5.08431137e-01 4.84551787e-01 -7.87925065e-01 1.57847479e-01 -1.56653337e-02 -1.36364746e+00 6.66097581e-01 1.24677002e+00 7.42396712e-01 2.08008781e-01 9.72254217e-01 -1.91118583e-01 9.67270195e-01 -1.09086764e+00 -4.09706116e-01 -2.25125924e-02 -5.71997344e-01 -1.39043534e+00 -2.20750123e-01 7.63997853e-01 -1.52042791e-01 -4.43249106e-01 8.07769120e-01 8.61752808e-01 5.54734647e-01 4.16939497e-01 -5.51066399e-01 -1.02489173e+00 6.23354733e-01 3.02593917e-01 3.73577088e-01 1.12428558e+00 2.29988247e-01 1.10112250e+00 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9b6b1756-b83b-4e71-8337-f957ac4a9dab	shape-illumination-and-reflectance-from	2010.03592	null	https://arxiv.org/abs/2010.03592v1	https://arxiv.org/pdf/2010.03592v1.pdf	Shape, Illumination, and Reflectance from Shading	A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely on multiple observations of the same scene to overconstrain the problem. Recovering these same properties from a single image seems almost impossible in comparison -- there are an infinite number of shapes, paint, and lights that exactly reproduce a single image. However, certain explanations are more likely than others: surfaces tend to be smooth, paint tends to be uniform, and illumination tends to be natural. We therefore pose this problem as one of statistical inference, and define an optimization problem that searches for the most likely explanation of a single image. Our technique can be viewed as a superset of several classic computer vision problems (shape-from-shading, intrinsic images, color constancy, illumination estimation, etc) and outperforms all previous solutions to those constituent problems.	['Jitendra Malik', 'Jonathan T. Barron']	2020-10-07	null	null	null	null	['color-constancy']	['computer-vision']	[ 7.06005156e-01 2.04263619e-04 2.49923348e-01 -5.95421970e-01 -2.73510993e-01 -5.33429384e-01 6.52731717e-01 -2.40890354e-01 -1.45771028e-02 5.83556771e-01 -1.58431739e-01 -2.14680076e-01 3.08615360e-02 -4.19684380e-01 -6.60532713e-01 -7.47181237e-01 3.77166867e-01 5.01517773e-01 1.97852284e-01 -1.14291020e-01 6.31659746e-01 6.55857742e-01 -1.86016035e+00 -6.01621270e-02 5.84991395e-01 7.12809682e-01 5.14268339e-01 9.81359720e-01 -3.72091860e-01 4.85162616e-01 -2.65271604e-01 -2.30147108e-01 1.83430538e-01 -4.70258772e-01 -7.96417296e-01 8.60984623e-01 8.52422953e-01 -1.64464667e-01 7.08586499e-02 1.26656687e+00 -3.02605033e-02 1.80577382e-01 9.27613139e-01 -1.01707435e+00 -8.09798181e-01 -3.82732153e-01 -9.50281203e-01 -2.09037170e-01 6.67245746e-01 -2.43991911e-02 9.70461369e-01 -7.30441928e-01 4.56698090e-01 1.25674236e+00 3.25364411e-01 4.81000513e-01 -1.59286785e+00 1.45799935e-01 8.14590082e-02 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84a1272e-32fc-4692-90ef-71a70b70d0ed	text-visual-prompting-for-efficient-2d	2303.04995	null	https://arxiv.org/abs/2303.04995v2	https://arxiv.org/pdf/2303.04995v2.pdf	Text-Visual Prompting for Efficient 2D Temporal Video Grounding	In this paper, we study the problem of temporal video grounding (TVG), which aims to predict the starting/ending time points of moments described by a text sentence within a long untrimmed video. Benefiting from fine-grained 3D visual features, the TVG techniques have achieved remarkable progress in recent years. However, the high complexity of 3D convolutional neural networks (CNNs) makes extracting dense 3D visual features time-consuming, which calls for intensive memory and computing resources. Towards efficient TVG, we propose a novel text-visual prompting (TVP) framework, which incorporates optimized perturbation patterns (that we call 'prompts') into both visual inputs and textual features of a TVG model. In sharp contrast to 3D CNNs, we show that TVP allows us to effectively co-train vision encoder and language encoder in a 2D TVG model and improves the performance of crossmodal feature fusion using only low-complexity sparse 2D visual features. Further, we propose a Temporal-Distance IoU (TDIoU) loss for efficient learning of TVG. Experiments on two benchmark datasets, Charades-STA and ActivityNet Captions datasets, empirically show that the proposed TVP significantly boosts the performance of 2D TVG (e.g., 9.79% improvement on Charades-STA and 30.77% improvement on ActivityNet Captions) and achieves 5x inference acceleration over TVG using 3D visual features. Codes are available at Open.Intel.	['Ke Ding', 'Sijia Liu', 'Jinghan Jia', 'Xin Chen', 'Yimeng Zhang']	2023-03-09	null	http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Text-Visual_Prompting_for_Efficient_2D_Temporal_Video_Grounding_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Text-Visual_Prompting_for_Efficient_2D_Temporal_Video_Grounding_CVPR_2023_paper.pdf	cvpr-2023-1	['video-grounding', 'visual-prompting']	['computer-vision', 'computer-vision']	[ 1.09195277e-01 -2.61329859e-01 -2.52433836e-01 -2.69087762e-01 -8.25669587e-01 -5.09526610e-01 6.94059491e-01 -6.60959259e-02 -4.35316771e-01 2.40870625e-01 4.07585084e-01 -3.83419752e-01 1.92096859e-01 -4.04889762e-01 -1.22178888e+00 -6.17274523e-01 -2.94765413e-01 -2.67352629e-02 2.48592421e-02 3.56880762e-02 2.17191912e-02 2.54659563e-01 -1.54113770e+00 5.21543860e-01 4.53138828e-01 1.50848472e+00 3.65796059e-01 7.62025654e-01 5.16368123e-03 1.12852585e+00 -3.31415415e-01 -2.06471428e-01 1.85408235e-01 -2.10514277e-01 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6aa38136-2522-4b42-b0ae-39c35dfb0f37	incorporating-background-knowledge-into-video	null	null	https://aclanthology.org/D18-1433	https://aclanthology.org/D18-1433.pdf	Incorporating Background Knowledge into Video Description Generation	Most previous efforts toward video captioning focus on generating generic descriptions, such as, {``}A man is talking.{''} We collect a news video dataset to generate enriched descriptions that include important background knowledge, such as named entities and related events, which allows the user to fully understand the video content. We develop an approach that uses video meta-data to retrieve topically related news documents for a video and extracts the events and named entities from these documents. Then, given the video as well as the extracted events and entities, we generate a description using a Knowledge-aware Video Description network. The model learns to incorporate entities found in the topically related documents into the description via an entity pointer network and the generation procedure is guided by the event and entity types from the topically related documents through a knowledge gate, which is a gating mechanism added to the model{'}s decoder that takes a one-hot vector of these types. We evaluate our approach on the new dataset of news videos we have collected, establishing the first benchmark for this dataset as well as proposing a new metric to evaluate these descriptions.	['Shih-Fu Chang', 'Heng Ji', 'Clare Voss', 'Mohit Bansal', 'Spencer Whitehead']	2018-10-01	null	null	null	emnlp-2018-10	['video-description']	['computer-vision']	[ 3.09696466e-01 2.85241634e-01 -5.38825214e-01 -6.33692682e-01 -1.03536534e+00 -6.31007433e-01 7.69762158e-01 1.96052656e-01 -3.75069141e-01 8.67171288e-01 1.01317942e+00 3.58008415e-01 1.55771285e-01 -5.52073896e-01 -1.18161786e+00 -4.54959810e-01 -3.12037021e-01 3.40941638e-01 4.68733460e-01 -5.19390106e-02 1.72948599e-01 -2.04640076e-01 -1.80251360e+00 8.90581727e-01 2.19456419e-01 9.55059052e-01 2.76139557e-01 7.42335260e-01 -2.03115031e-01 1.21597373e+00 -7.17936277e-01 -4.89424258e-01 -9.56967175e-02 -6.91842198e-01 -1.00762260e+00 3.38843286e-01 4.72998828e-01 -5.65518737e-01 -8.42099369e-01 6.72035038e-01 3.22394192e-01 4.63653952e-01 6.37262702e-01 -1.25627863e+00 -6.27507210e-01 1.24545932e+00 -1.90594569e-01 4.13791388e-01 8.27217460e-01 7.51368850e-02 1.23204947e+00 -7.71834135e-01 1.45218134e+00 8.12633455e-01 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-5.98709524e-01 -1.94739655e-01 9.09918249e-01 3.04564416e-01 -5.28554559e-01 -2.34626591e-01 8.79942298e-01]	[10.515061378479004, 0.6590191721916199]
b546e1c7-528a-4374-9d48-3add488a8ce6	reliability-check-an-analysis-of-gpt-3-s	2306.06199	null	https://arxiv.org/abs/2306.06199v1	https://arxiv.org/pdf/2306.06199v1.pdf	Reliability Check: An Analysis of GPT-3's Response to Sensitive Topics and Prompt Wording	Large language models (LLMs) have become mainstream technology with their versatile use cases and impressive performance. Despite the countless out-of-the-box applications, LLMs are still not reliable. A lot of work is being done to improve the factual accuracy, consistency, and ethical standards of these models through fine-tuning, prompting, and Reinforcement Learning with Human Feedback (RLHF), but no systematic analysis of the responses of these models to different categories of statements, or on their potential vulnerabilities to simple prompting changes is available. In this work, we analyze what confuses GPT-3: how the model responds to certain sensitive topics and what effects the prompt wording has on the model response. We find that GPT-3 correctly disagrees with obvious Conspiracies and Stereotypes but makes mistakes with common Misconceptions and Controversies. The model responses are inconsistent across prompts and settings, highlighting GPT-3's unreliability. Dataset and code of our analysis is available in https://github.com/tanny411/GPT3-Reliability-Check.	['Daniel G. Brown', 'Aisha Khatun']	2023-06-09	null	null	null	null	['misconceptions']	['miscellaneous']	[-3.07799578e-01 3.53950441e-01 -4.62005973e-01 -6.64112985e-01 -8.46241355e-01 -7.18562543e-01 4.65178430e-01 2.89739162e-01 -2.39491627e-01 5.56372702e-01 5.82247913e-01 -8.69151890e-01 3.11033577e-02 -2.76521027e-01 -7.01567411e-01 -1.30626902e-01 5.57852864e-01 3.20267916e-01 8.07637647e-02 -2.63020128e-01 6.65088832e-01 -4.39697020e-02 -8.49351645e-01 4.64701265e-01 9.54083681e-01 3.93014699e-01 -4.13987599e-02 5.46947598e-01 -2.49407113e-01 1.37199235e+00 -7.15406299e-01 -4.84303683e-01 1.07362255e-01 -1.32982537e-01 -9.25798237e-01 -3.00795913e-01 6.14951015e-01 -5.32145023e-01 4.63883579e-02 8.77829611e-01 5.10042012e-01 -2.60843098e-01 2.06708685e-01 -1.24584877e+00 -9.36454535e-01 7.64550030e-01 -5.19718587e-01 2.23049223e-01 6.37440503e-01 4.23834115e-01 7.31570959e-01 -5.17270803e-01 6.28446996e-01 1.56756544e+00 8.95502508e-01 5.44501901e-01 -1.34450376e+00 -1.17623091e+00 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3.66705537e-01 2.81771421e-01 -3.31073880e-01 -3.63533735e-01 -4.60664093e-01]	[9.943741798400879, 7.889563083648682]
b5983da4-1266-4e11-ba0f-5c337ddadc71	learning-to-re-weight-examples-with-optimal	2208.02951	null	https://arxiv.org/abs/2208.02951v1	https://arxiv.org/pdf/2208.02951v1.pdf	Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification	Imbalanced data pose challenges for deep learning based classification models. One of the most widely-used approaches for tackling imbalanced data is re-weighting, where training samples are associated with different weights in the loss function. Most of existing re-weighting approaches treat the example weights as the learnable parameter and optimize the weights on the meta set, entailing expensive bilevel optimization. In this paper, we propose a novel re-weighting method based on optimal transport (OT) from a distributional point of view. Specifically, we view the training set as an imbalanced distribution over its samples, which is transported by OT to a balanced distribution obtained from the meta set. The weights of the training samples are the probability mass of the imbalanced distribution and learned by minimizing the OT distance between the two distributions. Compared with existing methods, our proposed one disengages the dependence of the weight learning on the concerned classifier at each iteration. Experiments on image, text and point cloud datasets demonstrate that our proposed re-weighting method has excellent performance, achieving state-of-the-art results in many cases and providing a promising tool for addressing the imbalanced classification issue.	['Hongyuan Zha', 'Mingyuan Zhou', 'He Zhao', 'Meixi Zheng', 'Zhuo Li', 'Dandan Guo']	2022-08-05	null	null	null	null	['imbalanced-classification']	['miscellaneous']	[-1.00973636e-01 -2.08542094e-01 -3.91592801e-01 -5.98462939e-01 -7.05665350e-01 5.20333694e-03 2.35518292e-01 5.27062535e-01 -6.14287913e-01 5.24781942e-01 -1.61559820e-01 1.40524775e-01 -4.21365768e-01 -9.59860384e-01 -6.39607906e-01 -1.05438483e+00 1.41324759e-01 8.24885786e-01 -1.13669727e-02 -1.57747209e-01 4.47646528e-01 4.43939269e-01 -1.64113498e+00 3.14775735e-01 1.19382882e+00 1.33253026e+00 -1.84728146e-01 2.36245558e-01 -4.26918834e-01 4.05418038e-01 -9.11063790e-01 -5.49350798e-01 2.45889530e-01 -7.27951666e-03 -7.28510916e-01 4.93417773e-03 3.76024604e-01 9.53926295e-02 -1.55297732e-02 1.16349506e+00 5.49715102e-01 5.96111231e-02 9.28086758e-01 -1.56245804e+00 -4.15195405e-01 2.75745273e-01 -1.04201734e+00 2.38525510e-01 -3.06945533e-01 -1.28179848e-01 9.98273015e-01 -7.85803020e-01 2.08411738e-01 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d9a54b1f-e363-4cd3-b6ef-aed6d020decd	audio-text-sentiment-analysis-using-deep	1904.08138	null	https://arxiv.org/abs/1904.08138v5	https://arxiv.org/pdf/1904.08138v5.pdf	Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis	Sentiment analysis, mostly based on text, has been rapidly developing in the last decade and has attracted widespread attention in both academia and industry. However, the information in the real world usually comes from multiple modalities, such as audio and text. Therefore, in this paper, based on audio and text, we consider the task of multimodal sentiment analysis and propose a novel fusion strategy including both multi-feature fusion and multi-modality fusion to improve the accuracy of audio-text sentiment analysis. We call it the DFF-ATMF (Deep Feature Fusion - Audio and Text Modality Fusion) model, which consists of two parallel branches, the audio modality based branch and the text modality based branch. Its core mechanisms are the fusion of multiple feature vectors and multiple modality attention. Experiments on the CMU-MOSI dataset and the recently released CMU-MOSEI dataset, both collected from YouTube for sentiment analysis, show the very competitive results of our DFF-ATMF model. Furthermore, by virtue of attention weight distribution heatmaps, we also demonstrate the deep features learned by using DFF-ATMF are complementary to each other and robust. Surprisingly, DFF-ATMF also achieves new state-of-the-art results on the IEMOCAP dataset, indicating that the proposed fusion strategy also has a good generalization ability for multimodal emotion recognition.	['Ziqian Luo', 'Feiyang Chen', 'Dengfeng Ke', 'Yanyan Xu']	2019-04-17	null	null	null	null	['multimodal-emotion-recognition', 'multimodal-emotion-recognition']	['computer-vision', 'speech']	[-1.29962703e-02 -4.94085550e-01 1.16759382e-01 -5.11688471e-01 -1.09031856e+00 -2.99459904e-01 5.01644671e-01 1.89610898e-01 -3.99956435e-01 4.61691022e-01 5.24020493e-01 1.17894344e-01 2.74764970e-02 -3.33043545e-01 -4.63865519e-01 -7.65453756e-01 3.66313279e-01 -1.35811970e-01 -3.57904620e-02 -5.14840305e-01 -3.91855696e-03 -1.48488209e-01 -1.80391359e+00 6.20299578e-01 7.47327924e-01 1.67773342e+00 -6.63307533e-02 4.66471672e-01 -3.14907640e-01 7.11772919e-01 -4.51728106e-01 -6.76400781e-01 -1.30481780e-01 -2.38011971e-01 -6.48593247e-01 -9.72817838e-02 1.88070178e-01 -8.16005245e-02 -2.68195689e-01 8.58684421e-01 8.61757159e-01 3.86847943e-01 5.56774378e-01 -1.52991462e+00 -4.23538089e-01 5.83253264e-01 -7.40785718e-01 1.53001333e-02 6.70286179e-01 -3.93798292e-01 1.13120174e+00 -1.16743171e+00 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d96ca787-6393-43e3-ac7b-b1925b670e5b	material-classification-using-neural-networks	1710.06854	null	http://arxiv.org/abs/1710.06854v1	http://arxiv.org/pdf/1710.06854v1.pdf	Material Classification using Neural Networks	The recognition and classification of the diversity of materials that exist in the environment around us are a key visual competence that computer vision systems focus on in recent years. Understanding the identification of materials in distinct images involves a deep process that has made usage of the recent progress in neural networks which has brought the potential to train architectures to extract features for this challenging task. This project uses state-of-the-art Convolutional Neural Network (CNN) techniques and Support Vector Machine (SVM) classifiers in order to classify materials and analyze the results. Building on various widely used material databases collected, a selection of CNN architectures is evaluated to understand which is the best approach to extract features in order to achieve outstanding results for the task. The results gathered over four material datasets and nine CNNs outline that the best overall performance of a CNN using a linear SVM can achieve up to ~92.5% mean average precision, while applying a new relevant direction in computer vision, transfer learning. By limiting the amount of information extracted from the layer before the last fully connected layer, transfer learning aims at analyzing the contribution of shading information and reflectance to identify which main characteristics decide the material category the image belongs to. In addition to the main topic of my project, the evaluation of the nine different CNN architectures, it is questioned if, by using the transfer learning instead of extracting the information from the last convolutional layer, the total accuracy of the system created improves. The results of the comparison emphasize the fact that the accuracy and performance of the system improve, especially in the datasets which consist of a large number of images.	['Anca Sticlaru']	2017-10-17	null	null	null	null	['material-classification']	['computer-vision']	[ 3.14196587e-01 -2.63540089e-01 1.51648611e-01 -2.85923660e-01 -9.75955650e-02 -2.97341406e-01 6.17435336e-01 3.57099771e-02 -3.37213188e-01 4.88836974e-01 -2.28081524e-01 -2.01216966e-01 -3.66095543e-01 -1.01055467e+00 -5.74680090e-01 -9.73841250e-01 4.40710783e-02 2.17229530e-01 3.69902849e-01 -4.88229185e-01 6.34207428e-01 9.28363860e-01 -2.04898620e+00 7.60768473e-01 3.89745504e-01 1.43906009e+00 4.88383323e-01 5.98775685e-01 -3.66230667e-01 5.45830905e-01 -5.43012023e-01 -4.07174975e-02 5.25856435e-01 3.75471376e-02 -8.27935636e-01 9.06047001e-02 8.15858781e-01 -6.33911639e-02 -1.25990110e-03 7.72767484e-01 4.85474795e-01 -2.84726501e-01 7.80000389e-01 -8.34812045e-01 -5.91253459e-01 7.98776224e-02 -8.02315176e-02 2.85687238e-01 3.32826793e-01 1.22799098e-01 7.08275795e-01 -8.24729025e-01 3.75559479e-01 7.15954244e-01 8.84224057e-01 2.50914961e-01 -9.88245904e-01 -4.33509439e-01 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d3058ca7-847c-48eb-a4c0-ce578aca06c8	what-food-do-we-tweet-about-on-a-rainy-day	2304.05041	null	https://arxiv.org/abs/2304.05041v1	https://arxiv.org/pdf/2304.05041v1.pdf	What Food Do We Tweet about on a Rainy Day?	Food choice is a complex phenomenon shaped by factors such as taste, ambience, culture or weather. In this paper, we explore food-related tweeting in different weather conditions. We inspect a Latvian food tweet dataset spanning the past decade in conjunction with a weather observation dataset consisting of average temperature, precipitation, and other phenomena. We find which weather conditions lead to specific food information sharing; automatically classify tweet sentiment and discuss how it changes depending on the weather. This research contributes to the growing area of large-scale social network data understanding of food consumers' choices and perceptions.	['Matīss Rikters', 'Maija Kāle']	2023-04-11	null	null	null	null	['culture']	['speech']	[-1.92576408e-01 -3.74427021e-01 -8.90714943e-01 -6.74121857e-01 4.50736731e-01 -7.26450920e-01 3.38155627e-01 1.29757988e+00 -2.93718517e-01 2.04492763e-01 9.68509555e-01 -2.07992330e-01 5.35717420e-02 -1.34453583e+00 -4.91697639e-01 -4.38084990e-01 -3.34227920e-01 -2.04706609e-01 -1.62978649e-01 -7.60568857e-01 2.56296664e-01 -5.13642848e-01 -1.25896060e+00 3.06318775e-02 7.25150526e-01 7.11861432e-01 6.81089535e-02 4.47453171e-01 -2.89237797e-01 4.73843247e-01 -1.93017758e-02 -1.45600423e-01 6.53392896e-02 -7.24389672e-01 -3.99794787e-01 1.54838741e-01 3.09707001e-02 1.57590955e-01 4.27168041e-01 1.20961452e+00 2.38522232e-01 3.56909260e-02 3.93312484e-01 -9.36379135e-01 -1.39072478e+00 1.34408176e+00 -5.23598969e-01 4.04685497e-01 5.91374457e-01 1.67153418e-01 9.68859017e-01 -3.17162164e-02 6.85098946e-01 1.10330749e+00 7.64813602e-01 -1.53696820e-01 -1.15559125e+00 -6.68832004e-01 5.38493574e-01 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287c55ed-8ada-4e6c-9d64-86453c834388	snlp-at-textgraphs-2022-shared-task	null	null	https://aclanthology.org/2022.textgraphs-1.13	https://aclanthology.org/2022.textgraphs-1.13.pdf	SNLP at TextGraphs 2022 Shared Task: Unsupervised Natural Language Premise Selection in Mathematical Texts Using Sentence-MPNet	This paper describes our system for the submission to the TextGraphs 2022 shared task at COLING 2022: Natural Language Premise Selection (NLPS) from mathematical texts. The task of NLPS is about selecting mathematical statements called premises in a knowledge base written in natural language and mathematical formulae that are most likely to be used to prove a particular mathematical proof. We formulated this task as an unsupervised semantic similarity task by first obtaining contextualized embeddings of both the premises and mathematical proofs using sentence transformers. We then obtained the cosine similarity between the embeddings of premises and proofs and then selected premises with the highest cosine scores as the most probable. Our system improves over the baseline system that uses bag of words models based on term frequency inverse document frequency in terms of mean average precision (MAP) by about 23.5% (0.1516 versus 0.1228).	['Ahmed Zahran', 'Evangelos Milios', 'Rosane Minghim', 'Haseeb Younis', 'Provia Kadusabe', 'Paul Trust']	null	null	null	null	coling-textgraphs-2022-10	['mathematical-proofs']	['miscellaneous']	[ 2.43914530e-01 1.91352323e-01 1.56760097e-01 -3.04000467e-01 -9.44250762e-01 -8.86023045e-01 1.16643119e+00 8.72691810e-01 -5.39017797e-01 8.29983830e-01 2.68826038e-01 -8.39305580e-01 -4.84190702e-01 -9.95227754e-01 -1.10788143e+00 -1.65139586e-01 -7.36193806e-02 3.52706492e-01 2.19689265e-01 -2.03634501e-01 9.41819489e-01 7.77093545e-02 -1.42692184e+00 3.74004364e-01 1.22890174e+00 5.52570283e-01 4.50162083e-01 1.01987112e+00 -3.32160592e-01 1.05659902e+00 -5.38865864e-01 -9.66841221e-01 -2.80575722e-01 -3.04866225e-01 -1.13217342e+00 -8.22516441e-01 7.42796957e-01 -1.60320580e-01 -6.65922016e-02 1.25207281e+00 4.00215574e-02 4.27930385e-01 1.05969143e+00 -1.32411277e+00 -1.00561678e+00 9.35841680e-01 1.71023220e-01 4.43073750e-01 9.59675133e-01 -4.34389740e-01 1.34671319e+00 -1.18506444e+00 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00817363-a830-482b-be46-0e394b1083bd	summ-n-a-multi-stage-summarization-framework	2110.1015	null	https://arxiv.org/abs/2110.10150v2	https://arxiv.org/pdf/2110.10150v2.pdf	Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents	Text summarization helps readers capture salient information from documents, news, interviews, and meetings. However, most state-of-the-art pretrained language models (LM) are unable to efficiently process long text for many summarization tasks. In this paper, we propose Summ$^N$, a simple, flexible, and effective multi-stage framework for input texts that are longer than the maximum context length of typical pretrained LMs. Summ$^N$ first splits the data samples and generates a coarse summary in multiple stages and then produces the final fine-grained summary based on it. Our framework can process input text of arbitrary length by adjusting the number of stages while keeping the LM input size fixed. Moreover, it can deal with both single-source documents and dialogues, and it can be used on top of different backbone abstractive summarization models. To the best of our knowledge, Summ$^N$ is the first multi-stage split-then-summarize framework for long input summarization. Our experiments demonstrate that Summ$^N$ outperforms previous state-of-the-art methods by improving ROUGE scores on three long meeting summarization datasets AMI, ICSI, and QMSum, two long TV series datasets from SummScreen, and a long document summarization dataset GovReport. Our data and code are available at https://github.com/psunlpgroup/Summ-N.	['Rui Zhang', 'Dragomir Radev', 'Ahmed H. Awadallah', 'Budhaditya Deb', 'Chenguang Zhu', 'Chen Henry Wu', 'Ziming Mao', 'Ansong Ni', 'Yusen Zhang']	2021-10-16	null	https://aclanthology.org/2022.acl-long.112	https://aclanthology.org/2022.acl-long.112.pdf	acl-2022-5	['meeting-summarization']	['natural-language-processing']	[ 2.8204560e-01 1.3943373e-01 -3.5472575e-01 -2.4079941e-01 -1.5353580e+00 -6.6102844e-01 4.6960136e-01 3.6317137e-01 -4.2390350e-01 9.4018483e-01 8.7537348e-01 -1.9978207e-01 3.6475915e-02 -4.1530064e-01 -5.3248632e-01 -2.0450053e-01 2.5521982e-01 6.2785906e-01 2.1064240e-01 -2.8288296e-01 5.3335166e-01 -2.4675602e-01 -1.0889043e+00 7.4006343e-01 1.3050145e+00 4.6389896e-01 3.7214369e-01 1.0301276e+00 -4.5862097e-01 5.1110303e-01 -1.0108653e+00 -3.6721495e-01 -2.7925518e-01 -7.8623104e-01 -1.0475286e+00 2.6994469e-03 6.0488814e-01 -2.2081213e-01 -2.2763821e-01 6.7843443e-01 9.2639339e-01 2.4913792e-01 5.6603611e-01 -7.3021358e-01 -4.6683386e-01 1.3874993e+00 -7.3587662e-01 5.0008082e-01 6.6454595e-01 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f705cbc6-d6bf-4219-b997-c9a7387010b0	deep-dyna-q-integrating-planning-for-task	1801.06176	null	http://arxiv.org/abs/1801.06176v3	http://arxiv.org/pdf/1801.06176v3.pdf	Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning	Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. One common alternative is to use a user simulator. However, a user simulator usually lacks the language complexity of human interlocutors and the biases in its design may tend to degrade the agent. To address these issues, we present Deep Dyna-Q, which to our knowledge is the first deep RL framework that integrates planning for task-completion dialogue policy learning. We incorporate into the dialogue agent a model of the environment, referred to as the world model, to mimic real user response and generate simulated experience. During dialogue policy learning, the world model is constantly updated with real user experience to approach real user behavior, and in turn, the dialogue agent is optimized using both real experience and simulated experience. The effectiveness of our approach is demonstrated on a movie-ticket booking task in both simulated and human-in-the-loop settings.	['Shang-Yu Su', 'Kam-Fai Wong', 'Jingjing Liu', 'Jianfeng Gao', 'Xiujun Li', 'Baolin Peng']	2018-01-18	deep-dyna-q-integrating-planning-for-task-1	https://aclanthology.org/P18-1203	https://aclanthology.org/P18-1203.pdf	acl-2018-7	['task-completion-dialogue-policy-learning']	['natural-language-processing']	[-2.18229905e-01 6.37192965e-01 7.39067867e-02 -1.73694491e-01 -5.69357812e-01 -8.23587418e-01 7.93236852e-01 -1.37868628e-01 -6.90566838e-01 9.45299387e-01 2.36809134e-01 -1.96521133e-01 3.84923160e-01 -4.84848350e-01 -3.71471763e-01 -2.87153304e-01 8.36539492e-02 9.73138452e-01 4.58774306e-02 -6.94294274e-01 2.42918894e-01 1.44593656e-01 -1.37731874e+00 9.02293921e-02 9.25455809e-01 4.04226959e-01 6.63125157e-01 1.11607420e+00 9.93663967e-02 1.30512607e+00 -8.82713795e-01 -1.23269133e-01 2.02185646e-01 -7.24268258e-01 -9.07418728e-01 2.42371514e-01 -4.34564143e-01 -8.55666697e-01 -2.41848066e-01 7.34522820e-01 7.37690091e-01 5.69025993e-01 1.25702873e-01 -1.00292075e+00 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1aa0bbf3-c744-4846-bc7b-c10a0869b95b	concad-contrastive-learning-based-cross	2105.03037	null	https://arxiv.org/abs/2105.03037v1	https://arxiv.org/pdf/2105.03037v1.pdf	ConCAD: Contrastive Learning-based Cross Attention for Sleep Apnea Detection	With recent advancements in deep learning methods, automatically learning deep features from the original data is becoming an effective and widespread approach. However, the hand-crafted expert knowledge-based features are still insightful. These expert-curated features can increase the model's generalization and remind the model of some data characteristics, such as the time interval between two patterns. It is particularly advantageous in tasks with the clinically-relevant data, where the data are usually limited and complex. To keep both implicit deep features and expert-curated explicit features together, an effective fusion strategy is becoming indispensable. In this work, we focus on a specific clinical application, i.e., sleep apnea detection. In this context, we propose a contrastive learning-based cross attention framework for sleep apnea detection (named ConCAD). The cross attention mechanism can fuse the deep and expert features by automatically assigning attention weights based on their importance. Contrastive learning can learn better representations by keeping the instances of each class closer and pushing away instances from different classes in the embedding space concurrently. Furthermore, a new hybrid loss is designed to simultaneously conduct contrastive learning and classification by integrating a supervised contrastive loss with a cross-entropy loss. Our proposed framework can be easily integrated into standard deep learning models to utilize expert knowledge and contrastive learning to boost performance. As demonstrated on two public ECG dataset with sleep apnea annotation, ConCAD significantly improves the detection performance and outperforms state-of-art benchmark methods.	['Fenglong Ma', 'Guanjie Huang']	2021-05-07	null	null	null	null	['sleep-apnea-detection']	['medical']	[ 6.52392954e-02 -1.35535598e-01 -2.07495540e-01 -6.15018904e-01 -6.62302196e-01 -7.33430237e-02 3.09258848e-02 4.66282070e-01 -7.07092166e-01 6.83274806e-01 1.23663045e-01 -7.80801624e-02 -4.24785644e-01 -6.60721779e-01 -3.46376657e-01 -7.73776412e-01 -1.06807061e-01 1.56189039e-01 1.28649577e-01 -6.18288890e-02 1.26688397e-02 1.94703147e-01 -1.37479913e+00 1.00076124e-01 1.28797698e+00 1.29381227e+00 3.07970881e-01 1.45405680e-01 2.07623709e-02 5.74915767e-01 -5.00040233e-01 -2.54720271e-01 1.00913085e-01 -4.77960736e-01 -5.20375073e-01 -2.60280252e-01 9.75005701e-02 -1.22306988e-01 -2.79561341e-01 9.60025251e-01 8.50111306e-01 1.83711410e-01 4.30602044e-01 -1.11357892e+00 -5.89095116e-01 4.90915209e-01 -5.62278867e-01 8.64001215e-01 -1.13164205e-02 2.31256083e-01 1.22892320e+00 -7.86429584e-01 -1.64069265e-01 7.94329286e-01 7.32097983e-01 5.08248091e-01 -9.77559447e-01 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6258ec40-dc80-4268-98b7-6665abf080ea	a-composite-t60-regression-and-classification	2302.04932	null	https://arxiv.org/abs/2302.04932v1	https://arxiv.org/pdf/2302.04932v1.pdf	A Composite T60 Regression and Classification Approach for Speech Dereverberation	Dereverberation is often performed directly on the reverberant audio signal, without knowledge of the acoustic environment. Reverberation time, T60, however, is an essential acoustic factor that reflects how reverberation may impact a signal. In this work, we propose to perform dereverberation while leveraging key acoustic information from the environment. More specifically, we develop a joint learning approach that uses a composite T60 module and a separate dereverberation module to simultaneously perform reverberation time estimation and dereverberation. The reverberation time module provides key features to the dereverberation module during fine tuning. We evaluate our approach in simulated and real environments, and compare against several approaches. The results show that this composite framework improves performance in environments.	['Donald S. Williamson', 'Yuchen Liu', 'Yuying Li']	2023-02-09	null	null	null	null	['speech-dereverberation']	['speech']	[-4.26875018e-02 -9.00230169e-01 7.48068750e-01 -1.22618586e-01 -1.16917157e+00 -6.69042468e-01 1.28644735e-01 1.39778838e-01 -2.64752626e-01 5.31040549e-01 3.89225990e-01 -2.98593432e-01 -1.62267312e-01 -4.31512207e-01 -5.30742168e-01 -9.15837049e-01 -4.08893377e-01 -4.72955704e-01 -9.25006270e-02 -2.15784922e-01 -6.11441443e-03 3.10921401e-01 -1.51078284e+00 7.26698637e-02 8.01953077e-01 1.11151409e+00 4.47670519e-01 1.51152503e+00 6.29651546e-01 5.17315745e-01 -1.51230228e+00 5.26392758e-01 1.49259105e-01 -4.60788846e-01 9.32483822e-02 -4.95815724e-01 1.98370308e-01 -2.42690239e-02 -1.86224967e-01 7.75368571e-01 1.01077211e+00 4.34050888e-01 3.59823972e-01 -7.22772002e-01 1.93382964e-01 8.35704863e-01 -1.10913344e-01 5.45982599e-01 5.92358172e-01 1.88468352e-01 9.51494038e-01 -7.02661693e-01 -3.08963746e-01 1.02038252e+00 8.70852411e-01 -1.16273291e-01 -1.06017900e+00 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-1.44207388e-01 1.31387973e+00 -2.88684249e-01 -9.48400080e-01 -1.43807307e-02]	[15.076435089111328, 5.964961051940918]
393b9a15-cdc9-43d6-b570-3f1265591c01	using-convolution-neural-network-with-bert	null	null	https://aclanthology.org/2022.lrec-1.783	https://aclanthology.org/2022.lrec-1.783.pdf	Using Convolution Neural Network with BERT for Stance Detection in Vietnamese	Stance detection is the task of automatically eliciting stance information towards a specific claim made by a primary author. While most studies have been done for high-resource languages, this work is dedicated to a low-resource language, namely Vietnamese. In this paper, we propose an architecture using transformers to detect stances in Vietnamese claims. This architecture exploits BERT to extract contextual word embeddings instead of using traditional word2vec models. Then, these embeddings are fed into CNN networks to extract local features to train the stance detection model. We performed extensive comparison experiments to show the effectiveness of the proposed method on a public dataset1 Experimental results show that this proposed model outperforms the previous methods by a large margin. It yielded an accuracy score of 75.57% averaged on four labels. This sets a new SOTA result for future research on this interesting problem in Vietnamese.	['Bach Xuan Ngo', 'Anh Cong Phung', 'Oanh Tran']	null	null	null	null	lrec-2022-6	['stance-detection']	['natural-language-processing']	[ 1.23400711e-01 7.16655701e-02 -5.54491222e-01 -1.74718738e-01 -1.07993269e+00 -4.88166332e-01 9.54133034e-01 1.39370471e-01 -8.44082952e-01 7.48061419e-01 7.34183490e-01 -5.00822663e-01 4.93966758e-01 -8.29405665e-01 -3.73154342e-01 -6.60984099e-01 3.50546181e-01 4.74622011e-01 2.54776776e-01 -4.54928905e-01 7.71824777e-01 3.59722465e-01 -1.01601171e+00 4.07390118e-01 6.86178982e-01 6.52895749e-01 -1.90410569e-01 1.46405637e-01 -1.09464582e-02 9.72923994e-01 -9.07866120e-01 -8.47132385e-01 1.49740830e-01 -2.19362408e-01 -8.79462600e-01 -4.90855463e-02 1.92981437e-01 -7.95503631e-02 -1.92699850e-01 1.04844391e+00 7.98392355e-01 -2.50529945e-01 4.54127342e-01 -9.36803341e-01 -1.14810026e+00 1.03912067e+00 -5.70297897e-01 6.08460188e-01 7.48532861e-02 -1.53869212e-01 1.40107477e+00 -1.09648907e+00 9.95346606e-01 1.02368939e+00 6.14617944e-01 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dd5651b4-e6b2-4b5b-8b14-1a771e85d852	pastnet-introducing-physical-inductive-biases	2305.11421	null	https://arxiv.org/abs/2305.11421v2	https://arxiv.org/pdf/2305.11421v2.pdf	PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction	In this paper, we investigate the challenge of spatio-temporal video prediction, which involves generating future videos based on historical data streams. Existing approaches typically utilize external information such as semantic maps to enhance video prediction, which often neglect the inherent physical knowledge embedded within videos. Furthermore, their high computational demands could impede their applications for high-resolution videos. To address these constraints, we introduce a novel approach called Physics-assisted Spatio-temporal Network (PastNet) for generating high-quality video predictions. The core of our PastNet lies in incorporating a spectral convolution operator in the Fourier domain, which efficiently introduces inductive biases from the underlying physical laws. Additionally, we employ a memory bank with the estimated intrinsic dimensionality to discretize local features during the processing of complex spatio-temporal signals, thereby reducing computational costs and facilitating efficient high-resolution video prediction. Extensive experiments on various widely-used datasets demonstrate the effectiveness and efficiency of the proposed PastNet compared with state-of-the-art methods, particularly in high-resolution scenarios. Our code is available at https://github.com/easylearningscores/PastNet.	['Wei Xiong', 'Haixin Wang', 'Xian-Sheng Hua', 'Chong Chen', 'Xiao Luo', 'Fan Xu', 'Hao Wu']	2023-05-19	null	null	null	null	['video-prediction']	['computer-vision']	[ 9.59923416e-02 -2.41235688e-01 -8.44007134e-02 -1.16280213e-01 -3.34769249e-01 -8.90259724e-03 5.22199214e-01 -3.69046241e-01 -1.93514735e-01 7.50298142e-01 3.11021954e-01 -5.63836321e-02 -1.75279096e-01 -9.53104138e-01 -9.34571803e-01 -6.45736933e-01 -2.78290898e-01 -3.23318183e-01 4.52592641e-01 3.12547758e-02 1.92327321e-01 3.55517566e-01 -1.64883459e+00 3.35073024e-01 9.65803146e-01 1.29257190e+00 5.83784699e-01 5.44715703e-01 2.50735581e-01 1.08490312e+00 6.19209558e-02 -2.24690214e-01 2.43848577e-01 -2.78736174e-01 -4.75961596e-01 -1.13272063e-01 -2.35521700e-02 -6.59156024e-01 -1.11889184e+00 8.61730993e-01 2.66460359e-01 5.13950109e-01 2.78310508e-01 -1.05101407e+00 -5.82036555e-01 1.20337225e-01 -2.66792059e-01 4.06302840e-01 4.48200881e-01 4.11622882e-01 8.00011456e-01 -1.07152486e+00 5.35342038e-01 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5.11800528e-01 -1.24152470e+00 -9.53954011e-02 -6.07348204e-01 -3.40489805e-01 4.08740520e-01 2.92701215e-01 -7.95458794e-01 -1.48678645e-01 3.83746088e-01]	[10.566471099853516, -1.0261040925979614]
124bd220-5b36-4532-8170-27b536190237	data-resources-for-structural-bioinformatics	2307.02171	null	https://arxiv.org/abs/2307.02171v2	https://arxiv.org/pdf/2307.02171v2.pdf	Data Resources for Structural Bioinformatics	While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. Structural bioinformatics involves a variety of computational methods, all of which require input data. Typical inputs include protein structures and sequences, which are usually retrieved from a public or private database. This chapter introduces several key resources that make such data available, as well as a handful of tools that derive additional information from experimentally determined or computationally predicted protein structures and sequences.	['Halima Mouhib', 'K. Anton Feenstra', 'Sanne Abeln', 'Olga Ivanova', 'Bas Stringer', 'Jose Gavaldá-Garciá']	2023-07-05	null	null	null	null	['protein-structure-prediction']	['miscellaneous']	[ 4.33339119e-01 -2.10471183e-01 -2.35107571e-01 -2.45717421e-01 -4.40404803e-01 -6.18286431e-01 2.74709873e-02 5.04735231e-01 -2.38900453e-01 1.34694052e+00 -2.96744734e-01 -7.41364598e-01 7.02822057e-04 -3.92796814e-01 -5.63364863e-01 -1.21921206e+00 -1.00749008e-01 6.65700912e-01 3.13056916e-01 -4.92557824e-01 3.33620578e-01 8.84132266e-01 -1.55596566e+00 9.39498022e-02 8.86009336e-01 4.51014698e-01 7.10039020e-01 8.26636076e-01 -3.21535915e-01 6.28878117e-01 -4.00682926e-01 -5.21249235e-01 -3.18266839e-01 -7.57971346e-01 -9.65040982e-01 -1.20010838e-01 -3.03279996e-01 1.79866776e-01 3.63456905e-01 5.79708874e-01 5.82148612e-01 -2.65574634e-01 6.50437474e-01 -6.29459798e-01 -4.29535151e-01 -9.96222869e-02 -9.50958878e-02 2.95570016e-01 8.04312766e-01 4.13145512e-01 6.33706391e-01 -6.92378879e-01 9.78377938e-01 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b6260780-1607-49de-915e-3308e755cf01	speaker-change-detection-for-transformer	2302.08549	null	https://arxiv.org/abs/2302.08549v1	https://arxiv.org/pdf/2302.08549v1.pdf	Speaker Change Detection for Transformer Transducer ASR	Speaker change detection (SCD) is an important feature that improves the readability of the recognized words from an automatic speech recognition (ASR) system by breaking the word sequence into paragraphs at speaker change points. Existing SCD solutions either require additional ensemble for the time based decisions and recognized word sequences, or implement a tight integration between ASR and SCD, limiting the potential optimum performance for both tasks. To address these issues, we propose a novel framework for the SCD task, where an additional SCD module is built on top of an existing Transformer Transducer ASR (TT-ASR) network. Two variants of the SCD network are explored in this framework that naturally estimate speaker change probability for each word, while allowing the ASR and SCD to have independent optimization scheme for the best performance. Experiments show that our methods can significantly improve the F1 score on LibriCSS and Microsoft call center data sets without ASR degradation, compared with a joint SCD and ASR baseline.	['Jinyu Li', 'Xiong Xiao', 'Min Hu', 'Zhuo Chen', 'Jian Wu']	2023-02-16	null	null	null	null	['change-detection']	['computer-vision']	[ 4.08724666e-01 -2.34521143e-02 -8.68658870e-02 -5.52613556e-01 -1.26899946e+00 -6.34566188e-01 6.11567378e-01 -7.80447274e-02 -3.05842638e-01 1.89483896e-01 4.12929028e-01 -6.34363651e-01 4.46444094e-01 -1.91185489e-01 -4.90224689e-01 -6.18016124e-01 3.10964972e-01 2.44068317e-02 3.92357826e-01 -3.09868634e-01 2.40226790e-01 4.25439149e-01 -1.48195148e+00 1.88925833e-01 1.12618315e+00 8.60738158e-01 3.39932084e-01 1.01729596e+00 -2.45371416e-01 5.97642899e-01 -8.33016932e-01 -8.16216245e-02 1.02717645e-01 -4.80660111e-01 -6.08819187e-01 -5.64777330e-02 4.02808070e-01 -6.81746453e-02 -4.21999663e-01 1.03830600e+00 9.21023428e-01 3.11691344e-01 4.38402385e-01 -7.43622780e-01 -4.88562822e-01 9.44195330e-01 -4.34310585e-01 5.81253767e-01 5.34376740e-01 -1.84714999e-02 9.04088140e-01 -9.72949386e-01 -1.24977082e-01 1.44945002e+00 3.90166909e-01 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-9.77172673e-01 -1.28711212e+00 -6.21813953e-01 -1.28203586e-01 7.22046673e-01 -1.82759359e-01 -5.88975310e-01 2.05226853e-01]	[14.562073707580566, 6.361776828765869]
8d56dc9f-e473-4414-865a-2dd0682deb89	euclidnet-deep-visual-reasoning-for	2301.13007	null	https://arxiv.org/abs/2301.13007v1	https://arxiv.org/pdf/2301.13007v1.pdf	EuclidNet: Deep Visual Reasoning for Constructible Problems in Geometry	In this paper, we present a deep learning-based framework for solving geometric construction problems through visual reasoning, which is useful for automated geometry theorem proving. Constructible problems in geometry often ask for the sequence of straightedge-and-compass constructions to construct a given goal given some initial setup. Our EuclidNet framework leverages the neural network architecture Mask R-CNN to extract the visual features from the initial setup and goal configuration with extra points of intersection, and then generate possible construction steps as intermediary data models that are used as feedback in the training process for further refinement of the construction step sequence. This process is repeated recursively until either a solution is found, in which case we backtrack the path for a step-by-step construction guide, or the problem is identified as unsolvable. Our EuclidNet framework is validated on complex Japanese Sangaku geometry problems, demonstrating its capacity to leverage backtracking for deep visual reasoning of challenging problems.	['Chee Wei Tan', 'Xintong Qi', 'Man Fai Wong']	2022-12-27	null	null	null	null	['visual-reasoning', 'automated-theorem-proving', 'automated-theorem-proving', 'visual-reasoning']	['computer-vision', 'miscellaneous', 'reasoning', 'reasoning']	[-6.22245558e-02 5.22125542e-01 3.18868518e-01 3.54706636e-03 -5.97680449e-01 -1.09137309e+00 3.48745048e-01 3.69296908e-01 1.71397805e-01 4.22213525e-01 -7.45780915e-02 -1.13140249e+00 -2.28496492e-01 -1.18861473e+00 -1.07293963e+00 -3.05103838e-01 -2.67490178e-01 6.52740240e-01 -2.51053832e-02 -4.32148606e-01 4.67185497e-01 7.44595230e-01 -1.20970404e+00 3.91892910e-01 4.73975986e-01 4.59884912e-01 8.29550922e-02 9.23884273e-01 -1.56392068e-01 8.83048296e-01 -3.05450320e-01 -4.32822943e-01 4.43053663e-01 -2.39895970e-01 -1.63241422e+00 3.73065323e-02 5.25501192e-01 -5.30517876e-01 -5.70560843e-02 7.55803227e-01 -1.42006531e-01 -9.92250666e-02 5.13541460e-01 -1.32177043e+00 -5.38074672e-01 8.30963135e-01 -3.86523694e-01 -1.17270246e-01 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abb80976-1028-45bb-ab8f-28cd10fdbafd	some-of-the-variables-some-of-the-parameters	2304.14214	null	https://arxiv.org/abs/2304.14214v1	https://arxiv.org/pdf/2304.14214v1.pdf	Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information	Experimental data is often comprised of variables measured independently, at different sampling rates (non-uniform ${\Delta}$t between successive measurements); and at a specific time point only a subset of all variables may be sampled. Approaches to identifying dynamical systems from such data typically use interpolation, imputation or subsampling to reorganize or modify the training data $\textit{prior}$ to learning. Partial physical knowledge may also be available $\textit{a priori}$ (accurately or approximately), and data-driven techniques can complement this knowledge. Here we exploit neural network architectures based on numerical integration methods and $\textit{a priori}$ physical knowledge to identify the right-hand side of the underlying governing differential equations. Iterates of such neural-network models allow for learning from data sampled at arbitrary time points $\textit{without}$ data modification. Importantly, we integrate the network with available partial physical knowledge in "physics informed gray-boxes"; this enables learning unknown kinetic rates or microbial growth functions while simultaneously estimating experimental parameters.	['Ioannis G. Kevrekidis', 'Michael Betenbaugh', 'Jose L. Avalos', 'Tianqi Cui', 'Tom S. Bertalan', 'Saurabh Malani']	2023-04-27	null	null	null	null	['numerical-integration']	['miscellaneous']	[ 3.29438001e-01 -1.43756688e-01 -7.24014118e-02 -9.80496630e-02 -4.73347932e-01 -4.68938202e-01 3.77360493e-01 4.41352874e-01 -6.50765538e-01 1.50018847e+00 -7.01923013e-01 -5.17445803e-01 -5.36912978e-01 -9.14291799e-01 -1.13519394e+00 -9.75439250e-01 -3.02809328e-01 6.08099341e-01 -3.91326040e-01 1.94056556e-01 3.53191614e-01 8.34094703e-01 -1.56294203e+00 -2.60712147e-01 6.92471445e-01 1.18745553e+00 9.84242931e-02 9.96670067e-01 -1.56320840e-01 7.18426883e-01 -4.34282213e-01 3.81099761e-01 2.65171856e-01 -2.54033983e-01 -4.10639644e-01 -2.88628459e-01 -1.16560027e-01 -2.16481924e-01 -4.37575728e-01 8.88281286e-01 1.99035123e-01 6.13935351e-01 9.83853638e-01 -8.82105708e-01 -6.61195815e-01 3.06550890e-01 -1.07773848e-01 3.45079042e-02 -2.29333967e-01 6.00210607e-01 3.83394748e-01 -5.99377275e-01 2.79083550e-01 6.39077485e-01 1.00498497e+00 2.23867431e-01 -1.76362848e+00 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2.68305978e-03 5.99289834e-01 -6.61722004e-01 -6.06581092e-01 4.27436411e-01]	[6.469693660736084, 3.5164337158203125]
0cecaaaa-7811-427a-8c6b-29b9e6a5411f	190807644	1908.07644	null	https://arxiv.org/abs/1908.07644v3	https://arxiv.org/pdf/1908.07644v3.pdf	Saccader: Improving Accuracy of Hard Attention Models for Vision	Although deep convolutional neural networks achieve state-of-the-art performance across nearly all image classification tasks, their decisions are difficult to interpret. One approach that offers some level of interpretability by design is \textit{hard attention}, which uses only relevant portions of the image. However, training hard attention models with only class label supervision is challenging, and hard attention has proved difficult to scale to complex datasets. Here, we propose a novel hard attention model, which we term Saccader. Key to Saccader is a pretraining step that requires only class labels and provides initial attention locations for policy gradient optimization. Our best models narrow the gap to common ImageNet baselines, achieving $75\%$ top-1 and $91\%$ top-5 while attending to less than one-third of the image.	['Gamaleldin F. Elsayed', 'Simon Kornblith', 'Quoc V. Le']	2019-08-20	saccader-improving-accuracy-of-hard-attention	http://papers.nips.cc/paper/8359-saccader-improving-accuracy-of-hard-attention-models-for-vision	http://papers.nips.cc/paper/8359-saccader-improving-accuracy-of-hard-attention-models-for-vision.pdf	neurips-2019-12	['hard-attention']	['methodology']	[ 1.26299649e-01 5.32632530e-01 -5.47145724e-01 -5.10185242e-01 -6.01248145e-01 -4.39085960e-01 6.00004494e-01 -1.61352716e-02 -8.25009465e-01 5.81239164e-01 2.05026548e-02 -5.97373605e-01 7.10736290e-02 -3.65223557e-01 -9.31310773e-01 -2.29696482e-01 3.61063153e-01 3.86333287e-01 5.26066795e-02 -1.49634749e-01 1.40372768e-01 2.80660957e-01 -1.16335773e+00 4.33353662e-01 6.36768818e-01 1.16858625e+00 2.88044661e-01 6.09663427e-01 -8.72424543e-02 1.00826824e+00 -6.43435538e-01 -4.51387227e-01 1.71598792e-01 -1.29264086e-01 -1.12674081e+00 -9.13437903e-02 7.61691213e-01 -4.83754158e-01 -2.43063807e-01 1.06062901e+00 2.48433903e-01 1.48319632e-01 7.55744815e-01 -1.13126421e+00 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cc879adc-8359-4733-a4b5-9b288368d34f	reconstructing-spectral-functions-via	2111.1476	null	https://arxiv.org/abs/2111.14760v3	https://arxiv.org/pdf/2111.14760v3.pdf	Reconstructing spectral functions via automatic differentiation	Reconstructing spectral functions from Euclidean Green's functions is an important inverse problem in many-body physics. However, the inversion is proved to be ill-posed in the realistic systems with noisy Green's functions. In this Letter, we propose an automatic differentiation(AD) framework as a generic tool for the spectral reconstruction from propagator observable. Exploiting the neural networks' regularization as a non-local smoothness regulator of the spectral function, we represent spectral functions by neural networks and use the propagator's reconstruction error to optimize the network parameters unsupervisedly. In the training process, except for the positive-definite form for the spectral function, there are no other explicit physical priors embedded into the neural networks. The reconstruction performance is assessed through relative entropy and mean square error for two different network representations. Compared to the maximum entropy method, the AD framework achieves better performance in the large-noise situation. It is noted that the freedom of introducing non-local regularization is an inherent advantage of the present framework and may lead to substantial improvements in solving inverse problems.	['Kai Zhou', 'Shuzhe Shi', 'Lingxiao Wang']	2021-11-29	null	null	null	null	['spectral-reconstruction']	['computer-vision']	[ 2.81288683e-01 1.84682012e-01 2.73640543e-01 -2.64857531e-01 -4.84220892e-01 5.54068536e-02 1.33351341e-01 -2.55097300e-01 -6.66941941e-01 1.10984778e+00 2.88512278e-02 2.78758556e-02 -4.75962073e-01 -7.02634573e-01 -5.14820039e-01 -1.32509077e+00 -4.98136654e-02 2.62389392e-01 -1.12188295e-01 -3.01053315e-01 1.55334368e-01 5.12205422e-01 -1.23595309e+00 -2.24016398e-01 1.15701771e+00 1.05561018e+00 -6.21954277e-02 3.49471629e-01 8.89543444e-02 7.24350929e-01 -2.78915197e-01 2.52992846e-02 1.71910733e-01 -6.66139424e-01 -7.57853389e-01 -2.73831397e-01 -4.34980005e-01 1.68880783e-02 -3.85379791e-01 1.44929671e+00 6.41859651e-01 5.61992526e-01 1.02654755e+00 -6.46033585e-01 -5.54315805e-01 2.10134059e-01 -2.77029067e-01 1.56413168e-01 1.17949560e-01 1.10626087e-01 6.03811026e-01 -6.89466774e-01 3.55652869e-01 8.76150310e-01 1.14713001e+00 4.97706294e-01 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4.27986264e-01 3.93038839e-01 -8.39675903e-01 -6.83503211e-01 -1.19156979e-01]	[12.054479598999023, -2.4909660816192627]
73868239-cad9-4c46-8a98-a534026250b3	generalizable-prediction-of-academic	1912.00463	null	https://arxiv.org/abs/1912.00463v1	https://arxiv.org/pdf/1912.00463v1.pdf	Generalizable prediction of academic performance from short texts on social media	It has already been established that digital traces can be used to predict various human attributes. In most cases, however, predictive models rely on features that are specific to a particular source of digital trace data. In contrast, short texts written by users $-$ tweets, posts, or comments $-$ are ubiquitous across multiple platforms. In this paper, we explore the predictive power of short texts with respect to the academic performance of their authors. We use data from a representative panel of Russian students that includes information about their educational outcomes and activity on a popular networking site, VK. We build a model to predict academic performance from users' posts on VK and then apply it to a different context. In particular, we show that the model could reproduce rankings of schools and universities from the posts of their students on social media. We also find that the same model could predict academic performance from tweets as well as from VK posts. The generalizability of a model trained on a relatively small data set could be explained by the use of continuous word representations trained on a much larger corpus of social media posts. This also allows for greater interpretability of model predictions.	['Ivan Smirnov']	2019-12-01	null	null	null	null	['small-data']	['computer-vision']	[-2.57079959e-01 2.15144902e-01 -3.08473468e-01 -3.30920517e-01 -7.97381997e-01 -4.68146682e-01 8.86552036e-01 1.09331703e+00 -5.75461388e-01 5.80860436e-01 7.11505771e-01 -4.90308732e-01 -3.27867329e-01 -1.21668875e+00 -5.95162690e-01 -3.02541554e-02 2.99413055e-01 2.00311244e-01 2.71347433e-01 -3.65174443e-01 4.84015852e-01 3.53507668e-01 -1.59204924e+00 1.69417441e-01 7.59640753e-01 5.18572152e-01 2.24589154e-01 5.55206537e-01 -2.03426525e-01 9.49873149e-01 -6.85224175e-01 -5.82474172e-01 -9.57958251e-02 -1.67440295e-01 -9.30460632e-01 -2.01986834e-01 6.37921810e-01 -1.27242878e-01 -6.17270231e-01 7.94145346e-01 1.42108619e-01 2.88151741e-01 5.63666165e-01 -8.37446809e-01 -7.67049134e-01 7.58367836e-01 -1.18878655e-01 4.34433162e-01 7.95759678e-01 -3.59391756e-02 1.30246556e+00 -3.95512581e-01 8.92401576e-01 6.48530245e-01 8.55880976e-01 -9.31981653e-02 -1.37346721e+00 -6.71056569e-01 9.37630609e-02 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71febb9d-b483-4fa0-9e29-5e9288b96f80	lat-latent-translation-with-cycle-consistency	2207.04858	null	https://arxiv.org/abs/2207.04858v2	https://arxiv.org/pdf/2207.04858v2.pdf	LaT: Latent Translation with Cycle-Consistency for Video-Text Retrieval	Video-text retrieval is a class of cross-modal representation learning problems, where the goal is to select the video which corresponds to the text query between a given text query and a pool of candidate videos. The contrastive paradigm of vision-language pretraining has shown promising success with large-scale datasets and unified transformer architecture, and demonstrated the power of a joint latent space. Despite this, the intrinsic divergence between the visual domain and textual domain is still far from being eliminated, and projecting different modalities into a joint latent space might result in the distorting of the information inside the single modality. To overcome the above issue, we present a novel mechanism for learning the translation relationship from a source modality space $\mathcal{S}$ to a target modality space $\mathcal{T}$ without the need for a joint latent space, which bridges the gap between visual and textual domains. Furthermore, to keep cycle consistency between translations, we adopt a cycle loss involving both forward translations from $\mathcal{S}$ to the predicted target space $\mathcal{T'}$, and backward translations from $\mathcal{T'}$ back to $\mathcal{S}$. Extensive experiments conducted on MSR-VTT, MSVD, and DiDeMo datasets demonstrate the superiority and effectiveness of our LaT approach compared with vanilla state-of-the-art methods.	['Lele Cheng', 'Xiaofeng Guo', 'Mengying Hu', 'Haofan Wang', 'Feiyue Ni', 'Chunhui Liu', 'Jinbin Bai']	2022-07-11	null	null	null	null	['video-text-retrieval']	['computer-vision']	[ 2.27551684e-01 -4.26985711e-01 -2.72626698e-01 -3.33983839e-01 -9.58016753e-01 -5.17692983e-01 6.85645282e-01 -2.96206713e-01 -4.40935552e-01 3.86797518e-01 2.21722826e-01 -1.70841873e-01 -6.93596676e-02 -5.58589697e-01 -9.25715506e-01 -7.09012628e-01 1.89556614e-01 3.51511896e-01 1.80938199e-01 -2.08097789e-02 -2.52725407e-02 -7.55224302e-02 -1.48635924e+00 7.46627152e-01 6.46556616e-01 1.15245640e+00 4.00582790e-01 1.51184350e-01 -2.05071449e-01 6.95507109e-01 -4.43608046e-01 -4.70441967e-01 2.83014268e-01 -5.72928250e-01 -5.15300870e-01 4.39157858e-02 7.67966747e-01 -3.86447996e-01 -9.38813984e-01 1.26577461e+00 2.17820525e-01 2.45976716e-01 7.26171553e-01 -1.27623343e+00 -1.08792245e+00 3.44343722e-01 -8.17833185e-01 6.32064641e-02 3.52077991e-01 1.51126102e-01 1.16301000e+00 -1.16372037e+00 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6caed314-3df7-4c01-9a32-d189ffa1dc5e	deep-learning-serves-traffic-safety-analysis	2203.10939	null	https://arxiv.org/abs/2203.10939v2	https://arxiv.org/pdf/2203.10939v2.pdf	Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review	This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated vehicles. We present a typical processing pipeline, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilization, semantic and incident segmentation, object detection and classification, trajectory extraction, speed estimation, event analysis, modeling and anomaly detection. Our main goal is to guide traffic analysts to develop their own custom-built processing frameworks by selecting the best choices for each step and offering new designs for the lacking modules by providing a comparative analysis of the most successful conventional and DL-based algorithms proposed for each step. We also review existing open-source tools and public datasets that can help train DL models. To be more specific, we review exemplary traffic problems and mentioned requires steps for each problem. Besides, we investigate connections to the closely related research areas of drivers' cognition evaluation, Crowd-sourcing-based monitoring systems, Edge Computing in roadside infrastructures, Automated Driving Systems (ADS)-equipped vehicles, and highlight the missing gaps. Finally, we review commercial implementations of traffic monitoring systems, their future outlook, and open problems and remaining challenges for widespread use of such systems.	['Hongbin Yu', 'Yan Chen', 'Brendan Russo', 'Hao Wang', 'Huayu Li', 'Xiwen Chen', 'Abolfazl Razi']	2022-03-07	null	null	null	null	['video-stabilization', 'video-enhancement']	['computer-vision', 'computer-vision']	[-2.09648892e-01 -2.80391991e-01 -2.61267245e-01 -5.41301250e-01 -3.97742510e-01 -2.44209096e-01 3.88322622e-01 9.43532437e-02 -4.76571202e-01 3.50592077e-01 -2.21185222e-01 -8.43213737e-01 -2.84003559e-02 -7.77803183e-01 -5.18642187e-01 -6.38497770e-01 -9.75063816e-02 3.57448280e-01 7.94388473e-01 -3.94797713e-01 1.48375809e-01 9.58540738e-01 -2.31874418e+00 4.13224995e-01 6.51365042e-01 1.42221677e+00 -1.68662563e-01 9.31579053e-01 -2.47528240e-01 1.02919781e+00 -4.27362531e-01 -6.26876652e-01 3.20694119e-01 2.45340720e-01 -3.01160544e-01 3.00914291e-02 6.02671862e-01 -4.46077496e-01 -8.85405302e-01 6.96158230e-01 3.93423975e-01 2.71088421e-01 5.32958150e-01 -2.04235578e+00 -6.89874440e-02 -8.46881494e-02 -1.48482680e-01 9.29858029e-01 -1.62732471e-02 8.03363860e-01 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cd13c24e-756d-4f16-900a-5e4de2b780c3	real-time-visual-tracking-and-identification	1810.06411	null	http://arxiv.org/abs/1810.06411v2	http://arxiv.org/pdf/1810.06411v2.pdf	Real-Time Visual Tracking and Identification for a Team of Homogeneous Humanoid Robots	The use of a team of humanoid robots to collaborate in completing a task is an increasingly important field of research. One of the challenges in achieving collaboration, is mutual identification and tracking of the robots. This work presents a real-time vision-based approach to the detection and tracking of robots of known appearance, based on the images captured by a stationary robot. A Histogram of Oriented Gradients descriptor is used to detect the robots and the robot headings are estimated by a multiclass classifier. The tracked robots report their own heading estimate from magnetometer readings. For tracking, a cost function based on position and heading is applied to each of the tracklets, and a globally optimal labeling of the detected robots is found using the Hungarian algorithm. The complete identification and tracking system was tested using two igus Humanoid Open Platform robots on a soccer field. We expect that a similar system can be used with other humanoid robots, such as Nao and DARwIn-OP	['Hafez Farazi', 'Sven Behnke']	2018-10-15	null	null	null	null	['real-time-visual-tracking']	['computer-vision']	[-4.46986258e-01 -1.84639338e-02 3.07875544e-01 -1.86175570e-01 4.47973050e-02 -5.06451249e-01 3.49803030e-01 1.33082047e-01 -8.13687861e-01 6.63006127e-01 -7.56527007e-01 3.72920573e-01 -2.91595399e-01 -1.61705196e-01 -5.31432450e-01 -7.15694785e-01 -4.21586722e-01 1.22491872e+00 6.30571425e-01 -2.96408623e-01 3.40940714e-01 6.32824063e-01 -1.75477993e+00 -4.11025524e-01 3.14898610e-01 8.97160947e-01 9.99825656e-01 7.89475918e-01 4.35448200e-01 7.50175178e-01 -6.03216171e-01 2.14408860e-01 6.17889464e-01 -2.12941140e-01 -7.17079699e-01 3.81145954e-01 1.25402316e-01 -1.63425311e-01 5.86171858e-02 1.07279229e+00 2.86777645e-01 2.70338595e-01 6.24793947e-01 -1.77032769e+00 2.21844241e-01 4.61253196e-01 -4.47500020e-01 -2.74342865e-01 5.83330393e-01 -3.31309065e-02 4.77502882e-01 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6.88145578e-01 -1.59437668e+00 -6.15110815e-01 -1.07151353e+00 -1.00947988e+00 2.69841164e-01 -1.30122947e-02 -1.49625731e+00 -3.36032957e-01 -8.55619274e-03]	[7.254027366638184, -2.0572972297668457]
162f6acd-e083-4e40-89f6-66b2e8ec40f5	do-we-need-online-nlu-tools	2011.09825	null	https://arxiv.org/abs/2011.09825v1	https://arxiv.org/pdf/2011.09825v1.pdf	Do We Need Online NLU Tools?	The intent recognition is an essential algorithm of any conversational AI application. It is responsible for the classification of an input message into meaningful classes. In many bot development platforms, we can configure the NLU pipeline. Several intent recognition services are currently available as an API, or we choose from many open-source alternatives. However, there is no comparison of intent recognition services and open-source algorithms. Many factors make the selection of the right approach to the intent recognition challenging in practice. In this paper, we suggest criteria to choose the best intent recognition algorithm for an application. We present a dataset for evaluation. Finally, we compare selected public NLU services with selected open-source algorithms for intent recognition.	['Jan Šedivý', 'Jakub Konrád', 'Jan Pichl', 'Petr Marek', 'Petr Lorenc']	2020-11-19	null	null	null	null	['intent-recognition']	['natural-language-processing']	[ 2.25623652e-01 -3.68741572e-01 -2.63523787e-01 -5.15140891e-01 -2.92789012e-01 -8.51064444e-01 9.17805970e-01 -3.56021881e-01 -3.12378794e-01 2.69875109e-01 3.59601736e-01 -4.90056694e-01 3.25545780e-02 -5.07209957e-01 2.97852308e-01 -5.39982736e-01 3.50261740e-02 8.38095486e-01 5.91110528e-01 -2.84765750e-01 7.20053554e-01 4.61255133e-01 -1.64519429e+00 7.30978966e-01 5.73915839e-01 7.79940963e-01 2.83341318e-01 1.02246320e+00 -5.16731858e-01 1.24580324e+00 -1.02655852e+00 -4.00631636e-01 2.70948768e-01 -4.47708398e-01 -1.40133834e+00 -3.67794961e-01 -1.18334942e-01 -5.75038433e-01 -8.44281688e-02 6.80930316e-01 3.52511793e-01 2.33076960e-02 7.37460017e-01 -1.88218665e+00 -3.68072838e-01 7.32322395e-01 2.37641916e-01 5.01824737e-01 1.02697945e+00 5.75611174e-01 1.13139379e+00 -3.16894412e-01 9.09787893e-01 9.85633850e-01 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f257d4ac-db8c-417c-9c48-2f1b5d19d0de	paparazzi-a-deep-dive-into-the-capabilities	2302.10282	null	https://arxiv.org/abs/2302.10282v1	https://arxiv.org/pdf/2302.10282v1.pdf	Paparazzi: A Deep Dive into the Capabilities of Language and Vision Models for Grounding Viewpoint Descriptions	Existing language and vision models achieve impressive performance in image-text understanding. Yet, it is an open question to what extent they can be used for language understanding in 3D environments and whether they implicitly acquire 3D object knowledge, e.g. about different views of an object. In this paper, we investigate whether a state-of-the-art language and vision model, CLIP, is able to ground perspective descriptions of a 3D object and identify canonical views of common objects based on text queries. We present an evaluation framework that uses a circling camera around a 3D object to generate images from different viewpoints and evaluate them in terms of their similarity to natural language descriptions. We find that a pre-trained CLIP model performs poorly on most canonical views and that fine-tuning using hard negative sampling and random contrasting yields good results even under conditions with little available training data.	['Sina Zarrieß', 'Kai Lawonn', 'Monique Meuschke', 'Jan Hombeck', 'Henrik Voigt']	2023-02-13	null	null	null	null	['open-question']	['natural-language-processing']	[ 1.79150492e-01 1.56056702e-01 -7.92094171e-02 -5.01909435e-01 -7.01875865e-01 -9.54593956e-01 1.05423164e+00 -3.18020999e-01 -1.42376274e-01 1.61654830e-01 2.67038971e-01 -2.80361861e-01 3.85360837e-01 -4.94310290e-01 -8.96304011e-01 -2.22096696e-01 5.09728730e-01 1.11246371e+00 2.51482129e-01 -1.24030367e-01 3.86874139e-01 5.17004132e-01 -1.70738506e+00 7.01344430e-01 2.33610719e-01 5.02018213e-01 5.17539740e-01 7.87250042e-01 -2.49255732e-01 7.88105786e-01 -4.69793111e-01 -5.45483112e-01 4.20495152e-01 -2.34791771e-01 -8.70150983e-01 6.19995952e-01 1.12653589e+00 -5.93880177e-01 -1.84527978e-01 1.06343699e+00 2.39447981e-01 -1.47902608e-01 7.64302373e-01 -1.10230231e+00 -9.57041621e-01 1.23086140e-01 -3.36173147e-01 4.65301089e-02 9.79535460e-01 2.00013116e-01 1.06814420e+00 -1.30158103e+00 9.97290730e-01 1.61076188e+00 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a2f463eb-ebdf-458c-9e13-3c3ec5648d10	causal-bandits-for-linear-structural-equation	2208.12764	null	https://arxiv.org/abs/2208.12764v3	https://arxiv.org/pdf/2208.12764v3.pdf	Causal Bandits for Linear Structural Equation Models	This paper studies the problem of designing an optimal sequence of interventions in a causal graphical model to minimize cumulative regret with respect to the best intervention in hindsight. This is, naturally, posed as a causal bandit problem. The focus is on causal bandits for linear structural equation models (SEMs) and soft interventions. It is assumed that the graph's structure is known and has $N$ nodes. Two linear mechanisms, one soft intervention and one observational, are assumed for each node, giving rise to $2^N$ possible interventions. Majority of the existing causal bandit algorithms assume that at least the interventional distributions of the reward node's parents are fully specified. However, there are $2^N$ such distributions (one corresponding to each intervention), acquiring which becomes prohibitive even in moderate-sized graphs. This paper dispenses with the assumption of knowing these distributions or their marginals. Two algorithms are proposed for the frequentist (UCB-based) and Bayesian (Thompson Sampling-based) settings. The key idea of these algorithms is to avoid directly estimating the $2^N$ reward distributions and instead estimate the parameters that fully specify the SEMs (linear in $N$) and use them to compute the rewards. In both algorithms, under boundedness assumptions on noise and the parameter space, the cumulative regrets scale as $\tilde{\cal O} (d^{L+\frac{1}{2}} \sqrt{NT})$, where $d$ is the graph's maximum degree, and $L$ is the length of its longest causal path. Additionally, a minimax lower of $\Omega(d^{\frac{L}{2}-2}\sqrt{T})$ is presented, which suggests that the achievable and lower bounds conform in their scaling behavior with respect to the horizon $T$ and graph parameters $d$ and $L$.	['Ali Tajer', 'Prasanna Sattigeri', 'Karthikeyan Shanmugam', 'Burak Varici']	2022-08-26	null	null	null	null	['thompson-sampling']	['methodology']	[ 3.80137861e-01 6.23724461e-01 -7.58632541e-01 -1.50131807e-01 -5.02747118e-01 -5.50709486e-01 2.08660647e-01 1.47889838e-01 -5.19342005e-01 1.01035690e+00 -1.17205614e-02 -8.17218542e-01 -9.10270870e-01 -1.01349211e+00 -1.00738740e+00 -7.32455373e-01 -5.47111690e-01 4.37077194e-01 -1.40617922e-01 1.88738391e-01 1.45330250e-01 2.40697891e-01 -1.12873781e+00 -4.24171150e-01 9.32889342e-01 8.91978204e-01 1.62813231e-01 6.05143607e-01 8.35343078e-02 6.61648989e-01 -3.42155933e-01 -7.19728887e-01 1.66012332e-01 -6.98909700e-01 -6.48918569e-01 -1.10335216e-01 -2.65641540e-01 -4.61190879e-01 -4.59192157e-01 1.22911119e+00 2.45425537e-01 6.13898300e-02 7.96056569e-01 -1.10051441e+00 -3.23097289e-01 9.87576723e-01 -9.71875668e-01 1.87344983e-01 2.04878926e-01 -7.93093517e-02 1.23358190e+00 1.28373891e-01 3.68115097e-01 1.27003181e+00 3.81005764e-01 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796d11e9-5f90-4958-8a64-704cb54fdb87	composition-aware-image-aesthetics-assessment	1907.10801	null	https://arxiv.org/abs/1907.10801v1	https://arxiv.org/pdf/1907.10801v1.pdf	Composition-Aware Image Aesthetics Assessment	Automatic image aesthetics assessment is important for a wide variety of applications such as on-line photo suggestion, photo album management and image retrieval. Previous methods have focused on mapping the holistic image content to a high or low aesthetics rating. However, the composition information of an image characterizes the harmony of its visual elements according to the principles of art, and provides richer information for learning aesthetics. In this work, we propose to model the image composition information as the mutual dependency of its local regions, and design a novel architecture to leverage such information to boost the performance of aesthetics assessment. To achieve this, we densely partition an image into local regions and compute aesthetics-preserving features over the regions to characterize the aesthetics properties of image content. With the feature representation of local regions, we build a region composition graph in which each node denotes one region and any two nodes are connected by an edge weighted by the similarity of the region features. We perform reasoning on this graph via graph convolution, in which the activation of each node is determined by its highly correlated neighbors. Our method naturally uncovers the mutual dependency of local regions in the network training procedure, and achieves the state-of-the-art performance on the benchmark visual aesthetics datasets.	['Nagendra Kamath', 'Subhabrata Bhattachary', 'Rohit Puri', 'Dong Liu']	2019-07-25	null	null	null	null	['aesthetics-quality-assessment']	['computer-vision']	[-4.36062217e-02 -2.11547688e-02 -2.53809541e-01 -4.97112215e-01 -1.18652798e-01 -4.38707530e-01 3.97637635e-01 3.76773745e-01 5.75750396e-02 -1.37134060e-01 5.47197163e-01 1.05341792e-01 -2.84545243e-01 -1.01978683e+00 -3.72467160e-01 -7.41179287e-01 1.31544501e-01 -1.04903728e-01 -1.49226978e-01 -3.74997348e-01 1.83307752e-01 4.87382174e-01 -1.47635746e+00 4.63410437e-01 8.54958415e-01 1.55210412e+00 -5.74620776e-02 2.46415168e-01 -1.90857083e-01 6.65596128e-01 -3.92388254e-01 -5.00451744e-01 2.67445058e-01 -5.52186489e-01 -7.81287372e-01 2.94673383e-01 1.20472588e-01 4.50184569e-02 -2.54829496e-01 1.26021636e+00 2.95027912e-01 2.28962079e-01 7.75637686e-01 -1.38975048e+00 -1.17223370e+00 4.39907879e-01 -8.73001873e-01 -2.01460674e-01 1.29922047e-01 2.64088720e-01 1.58742464e+00 -6.82137966e-01 5.05405366e-01 1.22174776e+00 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1.11691105e+00 -6.56984568e-01 5.00322413e-03 -5.79372704e-01 -7.29141176e-01 7.85863638e-01 1.02802189e-02 -5.58980823e-01 -1.37971923e-01 -5.29723652e-02]	[11.516581535339355, -1.0237187147140503]
eb3a5118-9281-4047-9b78-a4bb6e8ebd6f	adversarially-trained-actor-critic-for	2202.02446	null	https://arxiv.org/abs/2202.02446v2	https://arxiv.org/pdf/2202.02446v2.pdf	Adversarially Trained Actor Critic for Offline Reinforcement Learning	We propose Adversarially Trained Actor Critic (ATAC), a new model-free algorithm for offline reinforcement learning (RL) under insufficient data coverage, based on the concept of relative pessimism. ATAC is designed as a two-player Stackelberg game: A policy actor competes against an adversarially trained value critic, who finds data-consistent scenarios where the actor is inferior to the data-collection behavior policy. We prove that, when the actor attains no regret in the two-player game, running ATAC produces a policy that provably 1) outperforms the behavior policy over a wide range of hyperparameters that control the degree of pessimism, and 2) competes with the best policy covered by data with appropriately chosen hyperparameters. Compared with existing works, notably our framework offers both theoretical guarantees for general function approximation and a deep RL implementation scalable to complex environments and large datasets. In the D4RL benchmark, ATAC consistently outperforms state-of-the-art offline RL algorithms on a range of continuous control tasks.	['Alekh Agarwal', 'Nan Jiang', 'Tengyang Xie', 'Ching-An Cheng']	2022-02-05	null	null	null	null	['d4rl']	['robots']	[-2.45245054e-01 5.45383990e-01 -6.18817985e-01 9.66433063e-02 -1.28800356e+00 -9.28318083e-01 3.15640539e-01 3.48133408e-02 -8.43577564e-01 1.04887080e+00 4.40602414e-02 -5.63023627e-01 -1.12883531e-01 -5.14474332e-01 -1.17297876e+00 -7.75306284e-01 -3.30026597e-01 8.92931938e-01 -6.61463439e-02 -3.14679384e-01 1.45449657e-02 2.23725699e-02 -1.13855529e+00 -1.27537176e-01 6.43690526e-01 1.27618122e+00 -1.40649393e-01 7.67542660e-01 4.13768470e-01 1.24923873e+00 -7.73084819e-01 -3.56673121e-01 7.71874726e-01 -3.83623987e-01 -6.90697968e-01 -2.15207636e-01 3.17169912e-02 -7.29428768e-01 -1.98732898e-01 1.17568743e+00 4.73035663e-01 1.38546258e-01 8.75873640e-02 -1.41593945e+00 -2.33240902e-01 9.24727380e-01 -4.26664203e-01 -1.48833051e-01 1.41746953e-01 8.37231755e-01 1.11057067e+00 4.49324213e-02 5.76055944e-01 1.54839611e+00 4.08234090e-01 1.07887983e+00 -1.34336364e+00 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5dc5c406-e8f3-43af-b53f-10a8227407dd	time-efficient-and-high-quality-graph	2101.07026	null	https://arxiv.org/abs/2101.07026v1	https://arxiv.org/pdf/2101.07026v1.pdf	Time-Efficient and High-Quality Graph Partitioning for Graph Dynamic Scaling	The dynamic scaling of distributed computations plays an important role in the utilization of elastic computational resources, such as the cloud. It enables the provisioning and de-provisioning of resources to match dynamic resource availability and demands. In the case of distributed graph processing, changing the number of the graph partitions while maintaining high partitioning quality imposes serious computational overheads as typically a time-consuming graph partitioning algorithm needs to execute each time repartitioning is required. In this paper, we propose a dynamic scaling method that can efficiently change the number of graph partitions while keeping its quality high. Our idea is based on two techniques: preprocessing and very fast edge partitioning, called graph edge ordering and chunk-based edge partitioning, respectively. The former converts the graph data into an ordered edge list in such a way that edges with high locality are closer to each other. The latter immediately divides the ordered edge list into an arbitrary number of high-quality partitions. The evaluation with the real-world billion-scale graphs demonstrates that our proposed approach significantly reduces the repartitioning time, while the partitioning quality it achieves is on par with that of the best existing static method.	['Georgios Theodoropoulos', 'Wentong Cai', 'Toyotaro Suzumura', 'Nikos Tziritas', 'Masatoshi Hanai']	2021-01-18	null	null	null	null	['graph-partitioning']	['graphs']	[-3.86466771e-01 -1.34659737e-01 1.14299543e-03 -7.96549767e-03 -5.86088449e-02 -6.90799057e-01 -5.23127913e-02 7.21074283e-01 -3.09431642e-01 5.43893516e-01 -1.29917175e-01 -4.91639018e-01 -4.19752568e-01 -1.47531080e+00 -2.42524117e-01 -7.73085773e-01 -4.00245339e-01 1.03056419e+00 8.96119416e-01 -1.68339093e-03 2.51083702e-01 7.85700500e-01 -1.63819671e+00 1.03341006e-01 9.18048501e-01 9.47878897e-01 2.33094901e-01 7.03751802e-01 -3.81461769e-01 4.13863748e-01 -4.33762789e-01 -5.77635765e-02 6.31874263e-01 -3.31938237e-01 -7.78272212e-01 2.18545124e-01 -1.00521281e-01 -1.99394554e-01 -5.01298793e-02 9.62380767e-01 4.97536302e-01 3.94852549e-01 1.70401543e-01 -1.42598104e+00 -2.03700766e-01 5.93212485e-01 -9.46563303e-01 5.03511131e-01 2.25388542e-01 -1.70070156e-01 7.99104810e-01 -4.56128538e-01 8.22919071e-01 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cf2a7326-e9c1-46df-9d38-66e364dee45a	nearly-optimal-hierarchical-clustering-for	2306.0995	null	https://arxiv.org/abs/2306.09950v1	https://arxiv.org/pdf/2306.09950v1.pdf	Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs	This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta's cost function. For any input graph $G$ with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of $G$, and return an $O(1)$-approximate HC tree with respect to Dasgupta's cost function. We compare the performance of our algorithm against the previous state-of-the-art on synthetic and real-world datasets and show that our designed algorithm produces comparable or better HC trees with much lower running time.	['He Sun', 'Bogdan-Adrian Manghiuc', 'Steinar Laenen']	2023-06-16	null	null	null	null	['clustering']	['methodology']	[-5.72314821e-02 2.77121514e-01 8.59858692e-02 -2.69943535e-01 -8.88940334e-01 -5.04569769e-01 7.44835958e-02 5.91386974e-01 -3.69120747e-01 3.78751516e-01 -2.54023671e-01 -5.65243602e-01 -4.58782256e-01 -1.25548947e+00 -6.28989577e-01 -7.27903664e-01 -8.27000916e-01 8.26182783e-01 7.17736959e-01 2.14062944e-01 4.39281046e-01 1.72865555e-01 -1.43755412e+00 -1.44419104e-01 7.79290378e-01 9.13283050e-01 -1.62557457e-02 9.67938066e-01 1.64677396e-01 4.26765829e-01 -4.80597109e-01 -4.88191664e-01 6.22718811e-01 -6.37878120e-01 -1.36045718e+00 4.31187302e-01 6.58961833e-02 5.21780074e-01 -5.20416737e-01 1.06395841e+00 2.82076657e-01 -2.21925914e-01 5.68704307e-01 -1.13880670e+00 -2.25527883e-01 1.13290775e+00 -1.11904836e+00 -7.29185417e-02 1.40233293e-01 -2.94877030e-02 9.73230302e-01 -4.80545700e-01 7.55007982e-01 1.30810714e+00 8.71876776e-01 -2.43018940e-02 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8e1070e0-dd8d-473c-9214-cb8aab2c8cfe	human-pose-transfer-with-disentangled-feature	2107.10984	null	https://arxiv.org/abs/2107.10984v3	https://arxiv.org/pdf/2107.10984v3.pdf	Human Pose Transfer with Augmented Disentangled Feature Consistency	Deep generative models have made great progress in synthesizing images with arbitrary human poses and transferring poses of one person to others. Though many different methods have been proposed to generate images with high visual fidelity, the main challenge remains and comes from two fundamental issues: pose ambiguity and appearance inconsistency. To alleviate the current limitations and improve the quality of the synthesized images, we propose a pose transfer network with augmented Disentangled Feature Consistency (DFC-Net) to facilitate human pose transfer. Given a pair of images containing the source and target person, DFC-Net extracts pose and static information from the source and target respectively, then synthesizes an image of the target person with the desired pose from the source. Moreover, DFC-Net leverages disentangled feature consistency losses in the adversarial training to strengthen the transfer coherence and integrates a keypoint amplifier to enhance the pose feature extraction. With the help of the disentangled feature consistency losses, we further propose a novel data augmentation scheme that introduces unpaired support data with the augmented consistency constraints to improve the generality and robustness of DFC-Net. Extensive experimental results on Mixamo-Pose and EDN-10k have demonstrated DFC-Net achieves state-of-the-art performance on pose transfer.	['Gangyi Ding', 'Zheng Guan', 'Jian Tang', 'Bo Jiang', 'Zhengping Che', 'Chengxiang Yin', 'Kun Wu']	2021-07-23	null	null	null	null	['pose-transfer']	['computer-vision']	[ 1.79177955e-01 1.79858327e-01 1.66573063e-01 -2.05732629e-01 -5.92680395e-01 -4.93076235e-01 5.52481890e-01 -8.85426581e-01 -9.12446156e-02 7.14973271e-01 3.12168002e-01 3.85454774e-01 6.94658048e-03 -5.72543442e-01 -8.62568557e-01 -7.64932573e-01 2.75603622e-01 3.34445357e-01 -1.71095729e-01 -3.79766792e-01 -3.39917183e-01 2.28519619e-01 -1.15352094e+00 3.38804610e-02 8.50050986e-01 9.77143168e-01 -8.72299075e-02 4.81664896e-01 6.88531578e-01 4.74897921e-01 -7.69059300e-01 -8.79580259e-01 8.38457108e-01 -5.96903384e-01 -3.22960675e-01 3.60661656e-01 8.83553684e-01 -7.56222963e-01 -1.00428069e+00 8.82955730e-01 8.46976161e-01 -2.96729943e-03 4.57390487e-01 -1.69386983e+00 -9.06708062e-01 1.68437198e-01 -7.52984226e-01 -2.47606963e-01 5.27924001e-01 5.53179860e-01 4.65058416e-01 -8.94546807e-01 6.04874849e-01 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3d4cccc6-0849-4f38-a554-1ca4165a08b1	dapr-a-benchmark-on-document-aware-passage	2305.13915	null	https://arxiv.org/abs/2305.13915v1	https://arxiv.org/pdf/2305.13915v1.pdf	DAPR: A Benchmark on Document-Aware Passage Retrieval	Recent neural retrieval mainly focuses on ranking short texts and is challenged with long documents. Existing work mainly evaluates either ranking passages or whole documents. However, there are many cases where the users want to find a relevant passage within a long document from a huge corpus, e.g. legal cases, research papers, etc. In this scenario, the passage often provides little document context and thus challenges the current approaches to finding the correct document and returning accurate results. To fill this gap, we propose and name this task Document-Aware Passage Retrieval (DAPR) and build a benchmark including multiple datasets from various domains, covering both DAPR and whole-document retrieval. In experiments, we extend the state-of-the-art neural passage retrievers with document-level context via different approaches including prepending document summary, pooling over passage representations, and hybrid retrieval with BM25. The hybrid-retrieval systems, the overall best, can only improve on the DAPR tasks marginally while significantly improving on the document-retrieval tasks. This motivates further research in developing better retrieval systems for the new task. The code and the data are available at https://github.com/kwang2049/dapr	['Iryna Gurevych', 'Nils Reimers', 'Kexin Wang']	2023-05-23	null	null	null	null	['passage-retrieval']	['natural-language-processing']	[-4.83216345e-02 -7.99344242e-01 -4.05700237e-01 -2.34103948e-02 -1.81326056e+00 -6.81518137e-01 9.09718275e-01 6.48898780e-01 -6.55382156e-01 7.63774633e-01 6.64597213e-01 -1.76523194e-01 -5.51602483e-01 -7.18317151e-01 -3.91265243e-01 -4.37211186e-01 2.20628425e-01 5.65659404e-01 5.21036804e-01 -5.32024384e-01 9.93004739e-01 3.32588971e-01 -1.27724564e+00 6.81785226e-01 7.47014940e-01 5.88478208e-01 2.69671381e-01 8.26299429e-01 -4.11581665e-01 6.72977746e-01 -1.01270509e+00 -4.21131372e-01 -1.17696919e-01 -3.25767189e-01 -1.13204098e+00 -7.39173830e-01 4.42848474e-01 -6.07734263e-01 -5.21409273e-01 7.95856953e-01 9.52780843e-01 6.03722036e-01 8.45663130e-01 -5.32071352e-01 -1.35682726e+00 8.56566310e-01 -5.88144481e-01 6.64441586e-01 6.50563002e-01 -5.02980173e-01 1.43978322e+00 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5e4ea4fa-80b8-44c2-bb33-1824ac7e1269	is-ai-the-better-programming-partner-human	2306.05153	null	https://arxiv.org/abs/2306.05153v2	https://arxiv.org/pdf/2306.05153v2.pdf	Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming	The emergence of large-language models (LLMs) that excel at code generation and commercial products such as GitHub's Copilot has sparked interest in human-AI pair programming (referred to as "pAIr programming") where an AI system collaborates with a human programmer. While traditional pair programming between humans has been extensively studied, it remains uncertain whether its findings can be applied to human-AI pair programming. We compare human-human and human-AI pair programming, exploring their similarities and differences in interaction, measures, benefits, and challenges. We find that the effectiveness of both approaches is mixed in the literature (though the measures used for pAIr programming are not as comprehensive). We summarize moderating factors on the success of human-human pair programming, which provides opportunities for pAIr programming research. For example, mismatched expertise makes pair programming less productive, therefore well-designed AI programming assistants may adapt to differences in expertise levels.	['Tongshuang Wu', 'Qianou Ma', 'Kenneth Koedinger']	2023-06-08	null	null	null	null	['code-generation']	['computer-code']	[-3.88789624e-01 5.97694218e-01 -2.11184565e-02 -3.91037792e-01 -3.56251925e-01 -8.11834335e-01 6.62446976e-01 6.24519348e-01 -2.15795591e-01 -1.71978891e-01 9.82136726e-02 -6.29048765e-01 -1.25637472e-01 -5.17726421e-01 -6.84510648e-01 -9.95576382e-02 2.40936384e-01 5.60532749e-01 -2.17667997e-01 -4.41061109e-01 5.80742121e-01 -8.12652558e-02 -1.49291766e+00 2.15237632e-01 1.24774444e+00 -2.37946227e-01 2.93172836e-01 8.43011916e-01 -2.37415716e-01 1.24977434e+00 -4.99974132e-01 -4.79745537e-01 5.18034637e-01 -4.61838812e-01 -6.85408056e-01 -5.42827189e-01 6.13209248e-01 -1.91372231e-01 -1.19042508e-02 8.66203249e-01 3.86365265e-01 -3.35911602e-01 4.26548511e-01 -1.84386837e+00 -1.20241594e+00 1.07432926e+00 -4.18610662e-01 -2.01639429e-01 6.01076841e-01 4.80186135e-01 8.54189336e-01 -7.39941657e-01 5.82778752e-01 1.29533780e+00 9.87093449e-01 5.35942972e-01 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67773d5b-a433-4c0a-93af-5ed2869e02db	face-deblurring-based-on-separable	2112.09833	null	https://arxiv.org/abs/2112.09833v1	https://arxiv.org/pdf/2112.09833v1.pdf	Face Deblurring Based on Separable Normalization and Adaptive Denormalization	Face deblurring aims to restore a clear face image from a blurred input image with more explicit structure and facial details. However, most conventional image and face deblurring methods focus on the whole generated image resolution without consideration of special face part texture and generally produce unsufficient details. Considering that faces and backgrounds have different distribution information, in this study, we designed an effective face deblurring network based on separable normalization and adaptive denormalization (SNADNet). First, We fine-tuned the face parsing network to obtain an accurate face structure. Then, we divided the face parsing feature into face foreground and background. Moreover, we constructed a new feature adaptive denormalization to regularize fafcial structures as a condition of the auxiliary to generate more harmonious and undistorted face structure. In addition, we proposed a texture extractor and multi-patch discriminator to enhance the generated facial texture information. Experimental results on both CelebA and CelebA-HQ datasets demonstrate that the proposed face deblurring network restores face structure with more facial details and performs favorably against state-of-the-art methods in terms of structured similarity indexing method (SSIM), peak signal-to-noise ratio (PSNR), Frechet inception distance (FID) and L1, and qualitative comparisons.	['Xiaojie Li', 'Jiancheng Lv', 'Hao Zhang', 'Xian Zhang']	2021-12-18	null	null	null	null	['face-parsing']	['computer-vision']	[ 3.36303532e-01 -2.42889687e-01 2.09168360e-01 -4.15292114e-01 -3.90444875e-01 -3.44376981e-01 4.00292575e-01 -1.05348587e+00 9.50417891e-02 7.03099132e-01 6.51374936e-01 3.62021506e-01 -2.35465780e-01 -7.14563012e-01 -4.43779856e-01 -1.14461887e+00 3.81396681e-01 -4.80008684e-02 -2.38110855e-01 -2.24814042e-01 1.29841015e-01 8.60694766e-01 -1.95007992e+00 1.98272750e-01 9.77539957e-01 1.08831429e+00 -2.76235361e-02 3.70888323e-01 -6.08159415e-02 4.97812122e-01 -6.07589960e-01 -3.59746635e-01 2.52342790e-01 -6.01599097e-01 -6.42825723e-01 3.11075121e-01 1.02808988e+00 -6.25466168e-01 -4.06559587e-01 1.43803251e+00 7.47172177e-01 -2.85637770e-02 7.77192593e-01 -8.14949393e-01 -1.21164191e+00 1.09442016e-02 -1.00537276e+00 5.16836524e-01 2.08888754e-01 1.21159345e-01 1.47005498e-01 -1.09682572e+00 6.76230371e-01 1.69075847e+00 6.16678536e-01 8.35504115e-01 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-6.72806740e-01 2.92260349e-01 5.44429004e-01 -6.36946499e-01 -5.77318311e-01 5.47900379e-01]	[12.839801788330078, 0.05429908633232117]
94f2057c-93a0-4ff9-8ce4-1c30ccb5c6e1	deep-learning-driven-natural-languages-text	2208.04415	null	https://arxiv.org/abs/2208.04415v1	https://arxiv.org/pdf/2208.04415v1.pdf	Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Survey	With the future striving toward data-centric decision-making, seamless access to databases is of utmost importance. There is extensive research on creating an efficient text-to-sql (TEXT2SQL) model to access data from the database. Using a Natural language is one of the best interfaces that can bridge the gap between the data and results by accessing the database efficiently, especially for non-technical users. It will open the doors and create tremendous interest among users who are well versed in technical skills or not very skilled in query languages. Even if numerous deep learning-based algorithms are proposed or studied, there still is very challenging to have a generic model to solve the data query issues using natural language in a real-work scenario. The reason is the use of different datasets in different studies, which comes with its limitations and assumptions. At the same time, we do lack a thorough understanding of these proposed models and their limitations with the specific dataset it is trained on. In this paper, we try to present a holistic overview of 24 recent neural network models studied in the last couple of years, including their architectures involving convolutional neural networks, recurrent neural networks, pointer networks, reinforcement learning, generative models, etc. We also give an overview of the 11 datasets that are widely used to train the models for TEXT2SQL technologies. We also discuss the future application possibilities of TEXT2SQL technologies for seamless data queries.	['Sanjeev Vijayakumar', 'Prabhav Nalhe', 'Parth Nagarkar', 'Ayush Kumar']	2022-08-08	null	null	null	null	['text-to-sql']	['computer-code']	[-4.65898246e-01 -8.19693599e-03 -3.69014218e-02 -5.65550506e-01 -3.82904530e-01 -2.50004768e-01 5.19102573e-01 3.58613908e-01 -4.31985676e-01 6.69225335e-01 1.30462684e-02 -4.12784249e-01 -3.43799412e-01 -1.24426961e+00 -6.14719331e-01 -2.33201444e-01 2.71188498e-01 9.73839283e-01 1.86142489e-01 -7.19585240e-01 3.36140454e-01 7.68321276e-01 -1.96369982e+00 3.62718314e-01 7.11789966e-01 1.27207971e+00 4.09669608e-01 5.56706071e-01 -1.03906763e+00 1.20003700e+00 -6.83916628e-01 -3.54070902e-01 -1.07903562e-01 1.22263983e-01 -1.01704335e+00 -4.57967192e-01 2.42862597e-01 -2.88140506e-01 -1.82830274e-01 6.66054726e-01 6.69404745e-01 -5.77779226e-02 1.73122063e-01 -1.18066704e+00 -6.99387968e-01 4.42075729e-01 7.55420849e-02 -3.63763608e-02 4.82292742e-01 3.06248441e-02 5.70836961e-01 -9.50537086e-01 6.21059895e-01 1.07598293e+00 5.27506292e-01 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61a7df5d-8fb8-45dc-b6eb-3423197cb19f	comparative-analysis-of-methods-for-cloud	2012.0693	null	https://arxiv.org/abs/2012.06930v3	https://arxiv.org/pdf/2012.06930v3.pdf	Comparative Analysis of Methods for Cloud Segmentation in Ground-Based Infrared Images	The increasing penetration of photovoltaic systems in the power grid makes it vulnerable to cloud shadow projection. Real-time cloud segmentation in ground-based infrared images is important to reduce the noise in intra-hour global solar irradiance forecasting. We present a comparison between discriminative and generative models for cloud segmentation. The performances of supervised and unsupervised learning methods in cloud segmentation are evaluated. The discriminative models are solved in the primal formulation to make them feasible in real-time applications. The performances are compared using the j-statistic. Infrared image preprocessing to remove stationary artifacts increases the overall performance in the analyzed methods. The inclusion of features from neighboring pixels in the feature vectors leads to a performance improvement in some of the cases. Markov Random Fields achieve the best performance in both unsupervised and supervised generative models. Discriminative models solved in the primal yield a dramatically lower computing time along with high performance in the segmentation. Generative and discriminative models are comparable when preprocessing is applied to the infrared images.	['Manel Martínez-Ramón', 'Guillermo Terrén-Serrano']	2020-12-13	null	null	null	null	['solar-irradiance-forecasting']	['time-series']	[ 2.96788424e-01 -7.38728881e-01 1.10515833e-01 -3.17121327e-01 -6.94244087e-01 -8.34053814e-01 5.66984296e-01 -2.78732955e-01 -1.61178365e-01 8.24204028e-01 -2.47997403e-01 -2.36539826e-01 -1.99890181e-01 -8.91598105e-01 -1.70016423e-01 -1.57048404e+00 2.70725846e-01 3.07022095e-01 -2.13662282e-01 4.58004028e-01 2.78352320e-01 6.82129085e-01 -1.62076819e+00 8.94759521e-02 1.48071289e+00 7.48052478e-01 7.01450884e-01 7.74902642e-01 -2.20070630e-01 3.96001756e-01 -6.07294679e-01 4.82050329e-01 4.74728167e-01 -5.49632430e-01 -3.71013314e-01 5.57159603e-01 3.93598944e-01 -2.96649020e-02 3.22519362e-01 1.09166586e+00 5.78304052e-01 7.10174162e-03 9.33531523e-01 -1.15254104e+00 -5.53809516e-02 -9.77347344e-02 -7.73462296e-01 2.78645515e-01 2.22715959e-01 7.88675994e-02 5.27293384e-01 -7.30194747e-01 2.74944425e-01 6.18277788e-01 6.00963533e-01 -1.79201946e-01 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-8.87669921e-01 -3.22056592e-01 1.97371915e-01 -2.39850417e-01 -4.77901340e-01 5.05447805e-01]	[9.723876953125, -1.7698118686676025]
9936aaba-6722-42de-b215-4975fa4ce3a6	univariate-long-term-municipal-water-demand	2105.08486	null	https://arxiv.org/abs/2105.08486v1	https://arxiv.org/pdf/2105.08486v1.pdf	Univariate Long-Term Municipal Water Demand Forecasting	This study describes an investigation into the modelling of citywide water consumption in London, Canada. Multiple modelling techniques were evaluated for the task of univariate time series forecasting with water consumption, including linear regression, Facebook's Prophet method, recurrent neural networks, and convolutional neural networks. Prophet was identified as the model of choice, having achieved a mean absolute percentage error of 2.51%, averaged across a 5-fold cross validation. Prophet was also found to have other advantages deemed valuable to water demand management stakeholders, including inherent interpretability and graceful handling of missing data. The implementation for the methods described in this paper has been open sourced, as they may be adaptable by other municipalities.	['Daniel Hsia', 'Matthew A. S. Ross', 'Blake VanBerlo']	2021-05-18	null	null	null	null	['univariate-time-series-forecasting']	['time-series']	[-3.46207112e-01 3.34879637e-01 -2.24706963e-01 -1.68404296e-01 -5.65852642e-01 -2.14461342e-01 6.66486621e-01 4.11097229e-01 -2.82234490e-01 6.28206551e-01 8.99154365e-01 -8.35274458e-01 -2.68967450e-01 -1.03114998e+00 1.23708688e-01 -1.00008512e+00 -5.28830230e-01 -4.05054763e-02 -2.70306766e-01 -4.46861982e-01 1.53896496e-01 4.27180797e-01 -1.39367306e+00 9.66545418e-02 7.54886746e-01 7.50699580e-01 -7.00626373e-02 6.08112872e-01 3.63539793e-02 1.39492989e+00 -3.98597181e-01 -2.63979528e-02 1.30634412e-01 -4.01773527e-02 -8.06426048e-01 -4.18184668e-01 -2.29932427e-01 -4.42908585e-01 -2.09387615e-01 2.13021338e-01 8.04188132e-01 9.58516598e-02 5.17543018e-01 -1.17513788e+00 -3.77698332e-01 6.72276556e-01 1.30077107e-02 3.91053230e-01 3.98954481e-01 3.77075166e-01 6.94518268e-01 -5.88278294e-01 1.45213991e-01 9.46570754e-01 1.29007411e+00 -8.63216892e-02 -1.06391025e+00 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-1.69948921e-01 6.76153600e-01 2.72893608e-02 -3.92080724e-01 -9.84853059e-02]	[6.414000988006592, 2.998826503753662]
8bccfeff-317f-409d-a998-81d4ed811dd7	contrast-and-clustering-learning-neighborhood	2301.13428	null	https://arxiv.org/abs/2301.13428v3	https://arxiv.org/pdf/2301.13428v3.pdf	Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain Adaptation	Unsupervised domain adaptation uses source data from different distributions to solve the problem of classifying data from unlabeled target domains. However, conventional methods require access to source data, which often raise concerns about data privacy. In this paper, we consider a more practical but challenging setting where the source domain data is unavailable and the target domain data is unlabeled. Specifically, we address the domain discrepancy problem from the perspective of contrastive learning. The key idea of our work is to learn a domain-invariant feature by 1) performing clustering directly in the original feature space with nearest neighbors; 2) constructing truly hard negative pairs by extended neighbors without introducing additional computational complexity; and 3) combining noise-contrastive estimation theory to gain computational advantage. We conduct careful ablation studies and extensive experiments on three common benchmarks: VisDA, Office-Home, and Office-31. The results demonstrate the superiority of our methods compared with other state-of-the-art works.	['Haojie Fang', 'Yingjian Li', 'Yonggang Li', 'Xiangbin Zhu', 'Yuqi Chen']	2023-01-31	null	null	null	null	['source-free-domain-adaptation']	['computer-vision']	[ 2.21128508e-01 -1.68562576e-01 -4.75956708e-01 -7.10075974e-01 -1.08891261e+00 -8.38889003e-01 4.77520764e-01 7.02916086e-02 -5.30670702e-01 1.09168196e+00 -3.83629315e-02 -1.69488952e-01 -1.01581298e-01 -5.82053959e-01 -5.44451416e-01 -9.19645250e-01 2.38902807e-01 5.01967490e-01 3.57201286e-02 7.33323246e-02 6.90943236e-03 1.39953449e-01 -1.25283742e+00 -9.30222124e-02 1.14596772e+00 1.12390447e+00 -3.25806141e-01 -7.20727220e-02 1.62081942e-02 5.01007557e-01 -4.77504343e-01 -3.78726542e-01 8.55377376e-01 -4.40850288e-01 -7.19573677e-01 3.78062427e-01 3.61466169e-01 -3.49822640e-01 -1.60127774e-01 1.42258775e+00 4.39201951e-01 2.79680610e-01 8.12259078e-01 -1.71510172e+00 -9.86758411e-01 2.22041294e-01 -9.35671628e-01 9.45255458e-02 6.57883063e-02 -1.45638615e-01 7.55645514e-01 -7.24506855e-01 6.80343866e-01 1.01317346e+00 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82e6b57f-72e4-4cca-b3b2-5dc98d04d803	interpretable-sparsification-of-brain-graphs	2306.14375	null	https://arxiv.org/abs/2306.14375v1	https://arxiv.org/pdf/2306.14375v1.pdf	Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks	Brain graphs, which model the structural and functional relationships between brain regions, are crucial in neuroscientific and clinical applications involving graph classification. However, dense brain graphs pose computational challenges including high runtime and memory usage and limited interpretability. In this paper, we investigate effective designs in Graph Neural Networks (GNNs) to sparsify brain graphs by eliminating noisy edges. While prior works remove noisy edges based on explainability or task-irrelevant properties, their effectiveness in enhancing performance with sparsified graphs is not guaranteed. Moreover, existing approaches often overlook collective edge removal across multiple graphs. To address these issues, we introduce an iterative framework to analyze different sparsification models. Our findings are as follows: (i) methods prioritizing interpretability may not be suitable for graph sparsification as they can degrade GNNs' performance in graph classification tasks; (ii) simultaneously learning edge selection with GNN training is more beneficial than post-training; (iii) a shared edge selection across graphs outperforms separate selection for each graph; and (iv) task-relevant gradient information aids in edge selection. Based on these insights, we propose a new model, Interpretable Graph Sparsification (IGS), which enhances graph classification performance by up to 5.1% with 55.0% fewer edges. The retained edges identified by IGS provide neuroscientific interpretations and are supported by well-established literature.	['Yujun Yan', 'Danai Koutra', 'Xiang Zhang', 'Marlena Duda', 'Gaotang Li']	2023-06-26	null	null	null	null	['graph-classification']	['graphs']	[ 5.07858336e-01 5.27128756e-01 -1.98539436e-01 -3.68301839e-01 1.75909668e-01 -3.36140543e-01 2.37173513e-01 3.60913992e-01 -3.49896938e-01 7.85407722e-01 2.82266676e-01 -4.07578230e-01 -6.83199763e-01 -7.20766127e-01 -5.91850936e-01 -3.39205176e-01 -5.14502227e-01 4.34004754e-01 8.82110521e-02 -2.42657229e-01 -8.97010937e-02 6.29095435e-01 -1.19477963e+00 -2.73943320e-02 1.23043072e+00 6.58024669e-01 3.11789572e-01 2.60909677e-01 1.65674575e-02 4.03363317e-01 -3.19034100e-01 -4.35275793e-01 3.31960917e-01 -4.37954098e-01 -6.10458016e-01 -3.68337072e-02 4.19361353e-01 -1.35545120e-01 -6.25976026e-01 1.26950800e+00 3.90856951e-01 1.32264018e-01 4.55998749e-01 -1.35797846e+00 -7.71070659e-01 1.23545909e+00 -5.14635384e-01 5.49188614e-01 -1.66000500e-01 1.63064823e-01 1.25046051e+00 -5.72294235e-01 7.84578383e-01 9.25668418e-01 8.42323124e-01 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619aa8b5-108b-4c1b-afd1-9d3c858f8c9f	automated-whole-slide-imaging-for-label-free	2304.13736	null	https://arxiv.org/abs/2304.13736v2	https://arxiv.org/pdf/2304.13736v2.pdf	Automated Whole Slide Imaging for Label-Free Histology using Photon Absorption Remote Sensing Microscopy	The field of histology relies heavily on antiquated tissue processing and staining techniques that limit the efficiency of pathologic diagnoses of cancer and other diseases. Current staining and advanced labeling methods are often destructive and mutually incompatible, requiring new tissue sections for each stain. This prolongs the diagnostic process and depletes valuable biopsy samples. In this study, we present an alternative label-free histology platform using the first transmission-mode Photon Absorption Remote Sensing microscope. Optimized for automated whole slide scanning of unstained tissue samples, the system provides slide images at magnifications up to 40x that are fully compatible with existing digital pathology tools. The scans capture high quality and high-resolution images with subcellular diagnostic detail. After imaging, samples remain suitable for histochemical, immunohistochemical, and other staining techniques. Scattering and absorption (radiative and non-radiative) contrasts are shown in whole slide images of malignant human breast and skin tissues samples. Clinically relevant features are highlighted, and close correspondence and analogous contrast is demonstrated with one-to-one gold standard H&E stained images. Our previously reported pix2pix virtual staining model is applied to an entire whole slide image, showcasing the potential of this approach in whole slide label-free H&E emulation. This work is a critical advance for integrating label-free optical methods into standard histopathology workflows, both enhancing diagnostic efficiency, and broadening the number of stains that can be applied while preserving valuable tissue samples.	['Parsin Haji Reza', 'John R. Mackey', 'Deepak Dinakaran', 'Marian Boktor', 'Benjamin R. Ecclestone', 'James E. D. Tweel']	2023-04-26	null	null	null	null	['whole-slide-images']	['computer-vision']	[ 5.16348958e-01 -1.24873705e-01 5.72099239e-02 1.11317346e-02 -9.68690038e-01 -8.26715171e-01 -5.89405261e-02 5.02086461e-01 -6.38322115e-01 7.14196205e-01 -5.83329380e-01 -3.90918523e-01 3.88207793e-01 -6.83277667e-01 1.46130070e-01 -1.41334510e+00 2.17334971e-01 7.38828421e-01 5.57552457e-01 4.29110751e-02 -1.49209067e-01 1.08509874e+00 -1.05381525e+00 1.14653118e-01 5.23598082e-02 5.53848088e-01 4.13595527e-01 8.99738908e-01 -3.35642695e-01 5.72576404e-01 -1.92892745e-01 -1.28962189e-01 1.50204003e-01 -2.81982064e-01 -6.88380003e-01 -2.21068814e-01 -5.99108487e-02 -2.69138455e-01 2.12131515e-01 8.28005314e-01 5.87651312e-01 -4.62808371e-01 5.15710473e-01 -6.60406232e-01 -2.57418245e-01 -7.83566572e-03 -8.73209298e-01 4.13658768e-01 2.70754099e-01 3.12106609e-01 4.39056367e-01 -6.60152316e-01 1.05727780e+00 6.56751692e-01 7.39532232e-01 8.74377131e-01 -1.66376686e+00 -5.20619929e-01 -8.13839197e-01 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b263b3eb-3143-48c4-b599-b8957073ebd0	anorand-a-semi-supervised-deep-learning	2305.18389	null	https://arxiv.org/abs/2305.18389v1	https://arxiv.org/pdf/2305.18389v1.pdf	AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling	Anomaly detection or more generally outliers detection is one of the most popular and challenging subject in theoretical and applied machine learning. The main challenge is that in general we have access to very few labeled data or no labels at all. In this paper, we present a new semi-supervised anomaly detection method called \textbf{AnoRand} by combining a deep learning architecture with random synthetic label generation. The proposed architecture has two building blocks: (1) a noise detection (ND) block composed of feed forward ferceptron and (2) an autoencoder (AE) block. The main idea of this new architecture is to learn one class (e.g. the majority class in case of anomaly detection) as well as possible by taking advantage of the ability of auto encoders to represent data in a latent space and the ability of Feed Forward Perceptron (FFP) to learn one class when the data is highly imbalanced. First, we create synthetic anomalies by randomly disturbing (add noise) few samples (e.g. 2\%) from the training set. Second, we use the normal and the synthetic samples as input to our model. We compared the performance of the proposed method to 17 state-of-the-art unsupervised anomaly detection method on synthetic datasets and 57 real-world datasets. Our results show that this new method generally outperforms most of the state-of-the-art methods and has the best performance (AUC ROC and AUC PR) on the vast majority of reference datasets. We also tested our method in a supervised way by using the actual labels to train the model. The results show that it has very good performance compared to most of state-of-the-art supervised algorithms.	['Michel Riveill', 'Mansour Zoubeirou A Mayaki']	2023-05-28	null	null	null	null	['supervised-anomaly-detection', 'semi-supervised-anomaly-detection']	['computer-vision', 'computer-vision']	[ 1.30508333e-01 6.13227002e-02 4.89051312e-01 -4.19861287e-01 -3.27679545e-01 -6.44132197e-02 6.03460789e-01 4.55601245e-01 -4.49232727e-01 6.99404895e-01 -2.26646319e-01 -2.22659528e-01 -9.31032654e-03 -9.05656099e-01 -8.49636674e-01 -9.06570137e-01 -1.48245275e-01 7.76772022e-01 2.83008248e-01 -1.27031520e-01 1.49813697e-01 5.72667599e-01 -1.90750813e+00 1.85475484e-01 9.99047995e-01 1.16560006e+00 -4.15299565e-01 4.40207064e-01 -2.43753120e-01 9.31756556e-01 -7.23614693e-01 -5.82469031e-02 2.66706884e-01 -4.35426205e-01 -5.20297706e-01 1.24045804e-01 3.79216462e-01 -2.70623446e-01 5.24865165e-02 1.00651360e+00 5.08786678e-01 3.02180946e-01 8.18386376e-01 -1.37668240e+00 -2.05671027e-01 3.87486666e-01 -5.00707567e-01 4.01681095e-01 1.84928998e-01 4.99869287e-02 5.88911712e-01 -8.13812673e-01 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ae7eb239-f4ba-4eb0-927a-2f364af15826	a-joint-framework-for-coreference-resolution	null	null	https://aclanthology.org/K15-1002	https://aclanthology.org/K15-1002.pdf	A Joint Framework for Coreference Resolution and Mention Head Detection	null	['Kai-Wei Chang', 'Dan Roth', 'Haoruo Peng']	2015-07-01	null	null	null	conll-2015-7	['head-detection']	['computer-vision']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 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5c5e1c80-8504-445b-83aa-fc0257708329	towards-harnessing-feature-embedding-for	2206.13025	null	https://arxiv.org/abs/2206.13025v1	https://arxiv.org/pdf/2206.13025v1.pdf	Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels	The memorization effect of deep neural networks (DNNs) plays a pivotal role in recent label noise learning methods. To exploit this effect, the model prediction-based methods have been widely adopted, which aim to exploit the outputs of DNNs in the early stage of learning to correct noisy labels. However, we observe that the model will make mistakes during label prediction, resulting in unsatisfactory performance. By contrast, the produced features in the early stage of learning show better robustness. Inspired by this observation, in this paper, we propose a novel feature embedding-based method for deep learning with label noise, termed LabEl NoiseDilution (LEND). To be specific, we first compute a similarity matrix based on current embedded features to capture the local structure of training data. Then, the noisy supervision signals carried by mislabeled data are overwhelmed by nearby correctly labeled ones (\textit{i.e.}, label noise dilution), of which the effectiveness is guaranteed by the inherent robustness of feature embedding. Finally, the training data with diluted labels are further used to train a robust classifier. Empirically, we conduct extensive experiments on both synthetic and real-world noisy datasets by comparing our LEND with several representative robust learning approaches. The results verify the effectiveness of our LEND.	['Chen Gong', 'Jian Yang', 'Li Shen', 'Chuang Zhang']	2022-06-27	null	null	null	null	['learning-with-noisy-labels', 'learning-with-noisy-labels']	['computer-vision', 'natural-language-processing']	[ 1.26452953e-01 -7.05911778e-03 2.09257126e-01 -3.50843668e-01 -4.59677994e-01 -2.57478833e-01 4.51394737e-01 1.85706109e-01 -4.23628688e-01 8.44515681e-01 1.24429807e-01 2.02085599e-01 -2.09398821e-01 -7.54595578e-01 -6.93015873e-01 -1.19833040e+00 4.01108503e-01 -1.42717600e-01 7.06986785e-02 -7.61415213e-02 1.13224640e-01 1.89725131e-01 -1.58066797e+00 5.50211407e-02 7.86001205e-01 1.17549038e+00 2.02932090e-01 -3.32409292e-02 -2.00984567e-01 1.08631086e+00 -7.47860312e-01 -1.17191799e-01 9.71345454e-02 -5.55730343e-01 -3.49115759e-01 4.33325619e-02 -6.53860271e-02 -1.59073025e-01 -5.67888856e-01 1.48079729e+00 7.30542898e-01 2.89514184e-01 4.12386239e-01 -9.58645225e-01 -5.17040968e-01 7.65003622e-01 -2.87226796e-01 1.72610730e-01 -1.57791376e-01 1.72820538e-01 7.46320069e-01 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8d57d49a-d4e7-48a8-bd13-85313d79b679	integrating-uncertainty-awareness-into	2306.08693	null	https://arxiv.org/abs/2306.08693v1	https://arxiv.org/pdf/2306.08693v1.pdf	Integrating Uncertainty Awareness into Conformalized Quantile Regression	Conformalized Quantile Regression (CQR) is a recently proposed method for constructing prediction intervals for a response $Y$ given covariates $X$, without making distributional assumptions. However, as we demonstrate empirically, existing constructions of CQR can be ineffective for problems where the quantile regressors perform better in certain parts of the feature space than others. The reason is that the prediction intervals of CQR do not distinguish between two forms of uncertainty: first, the variability of the conditional distribution of $Y$ given $X$ (i.e., aleatoric uncertainty), and second, our uncertainty in estimating this conditional distribution (i.e., epistemic uncertainty). This can lead to uneven coverage, with intervals that are overly wide (or overly narrow) in regions where epistemic uncertainty is low (or high). To address this, we propose a new variant of the CQR methodology, Uncertainty-Aware CQR (UACQR), that explicitly separates these two sources of uncertainty to adjust quantile regressors differentially across the feature space. Compared to CQR, our methods enjoy the same distribution-free theoretical guarantees for coverage properties, while demonstrating in our experiments stronger conditional coverage in simulated settings and tighter intervals on a range of real-world data sets.	['Rebecca Willett', 'Rina Foygel Barber', 'Raphael Rossellini']	2023-06-14	null	null	null	null	['prediction-intervals']	['miscellaneous']	[-7.15692565e-02 2.36059487e-01 -4.44279373e-01 -5.33663273e-01 -1.33725357e+00 -6.87089622e-01 4.08855468e-01 5.02143443e-01 -1.46639705e-01 1.14050019e+00 2.31815964e-01 -4.04834896e-01 -5.69084108e-01 -1.19590199e+00 -9.50316191e-01 -6.89236045e-01 -9.78665575e-02 2.90795535e-01 -7.86556583e-03 3.25506441e-02 3.75021219e-01 2.52981424e-01 -1.46082628e+00 -1.70285523e-01 1.13117123e+00 1.06558847e+00 -6.77134216e-01 1.20819345e-01 5.13756983e-02 4.21814203e-01 -8.36156547e-01 -5.94738603e-01 7.39669427e-02 -2.16905802e-01 -2.97728568e-01 -5.42148113e-01 -1.64223760e-01 -2.97648413e-03 2.13851035e-01 1.10571599e+00 2.42693380e-01 4.42019403e-02 1.01807153e+00 -1.39080715e+00 -6.91837609e-01 1.29753673e+00 -9.09060061e-01 -9.83756185e-02 3.76541555e-01 -1.58855885e-01 9.52399075e-01 -6.56291544e-01 7.31699914e-02 1.33358741e+00 7.59065449e-01 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2dee8092-10e9-47cd-b9d2-0bd313d710a3	invertible-low-dimensional-modelling-of-x-ray	2307.04484	null	https://arxiv.org/abs/2307.04484v1	https://arxiv.org/pdf/2307.04484v1.pdf	Invertible Low-Dimensional Modelling of X-ray Absorption Spectra for Potential Applications in Spectral X-ray Imaging	X-ray interaction with matter is an energy-dependent process that is contingent on the atomic structure of the constituent material elements. The most advanced models to capture this relationship currently rely on Monte Carlo (MC) simulations. Whilst these very accurate models, in many problems in spectral X-ray imaging, such as data compression, noise removal, spectral estimation, and the quantitative measurement of material compositions, these models are of limited use, as these applications typically require the efficient inversion of the model, that is, they require the estimation of the best model parameters for a given spectral measurement. Current models that can be easily inverted however typically only work when modelling spectra in regions away from their K-edges, so they have limited utility when modelling a wider range of materials. In this paper, we thus propose a novel, non-linear model that combines a deep neural network autoencoder with an optimal linear model based on the Singular Value Decomposition (SVD). We compare our new method to other alternative linear and non-linear approaches, a sparse model and an alternative deep learning model. We demonstrate the advantages of our method over traditional models, especially when modelling X-ray absorption spectra that contain K-edges in the energy range of interest.	['Thomas Blumensath', 'Raziye Kubra Kumrular']	2023-07-10	null	null	null	null	['data-compression']	['time-series']	[ 3.73758286e-01 -5.00482678e-01 1.26831517e-01 -2.85226941e-01 -6.20712936e-01 -1.48200681e-02 5.57568610e-01 2.70055681e-01 -6.95365489e-01 6.81275308e-01 4.84260917e-02 -1.05412222e-01 -5.15309989e-01 -1.04223275e+00 -7.56749690e-01 -1.12746060e+00 2.11341023e-01 8.73512089e-01 2.48996407e-01 -1.12037070e-01 1.80368900e-01 6.52118742e-01 -1.48975754e+00 3.41539830e-01 5.41539669e-01 1.11900270e+00 4.06481475e-01 5.22593200e-01 -4.16117981e-02 5.80703437e-01 -1.72732532e-01 -1.09637389e-02 1.17926605e-01 -4.99801457e-01 -5.90755224e-01 -1.23954535e-01 -5.97801059e-02 -3.52474123e-01 -4.53429997e-01 1.15832615e+00 6.63594604e-01 4.31262612e-01 1.03302360e+00 -4.53918695e-01 -4.63566601e-01 5.50684869e-01 -5.29839754e-01 9.43248793e-02 2.78235406e-01 7.20242783e-02 7.72777855e-01 -4.61003035e-01 4.79624957e-01 7.16970742e-01 9.47378099e-01 4.75878209e-01 -1.29043841e+00 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60493e9e-6b07-463b-9e33-b5c0761417a8	web-api-based-chatbot-generation-with	null	null	https://aclanthology.org/2022.rocling-1.31	https://aclanthology.org/2022.rocling-1.31.pdf	Web-API-Based Chatbot Generation with Analysis and Expansion for Training Sentences	With Web API technology becoming increasingly mature, how to integrate Web API and Chatbot technology has become an issue of great interest. This study plans to build a semi-automatic method and tool, BOTEN. This method allows application developers to build Chatbot interfaces with specified Web APIs quickly. To ensure that the Chatbot has sufficient natural language understanding (NLU) capability, this research evaluates the training sentences written by the developer through TF-IDF, WordNet, and SpaCy techniques, and suggests the developer modify the training sentences with poor quality. This technique can also be used to automatically increase the number of training sentences to improve the capability of Intent recognition.	['Shang-Pin Ma', 'Wan-Lin You', 'Sheng-Kai Wang']	null	null	null	null	rocling-2022-11	['intent-recognition']	['natural-language-processing']	[-2.09876776e-01 -3.33730280e-02 -1.06833100e-01 -4.39494908e-01 -2.21245438e-01 -7.20124543e-01 2.98211217e-01 -4.30394560e-01 -1.33881122e-01 6.21056259e-01 3.46321553e-01 -5.09305716e-01 3.26035954e-02 -7.51905382e-01 1.42329887e-01 -1.26756448e-02 5.65411270e-01 1.09695457e-01 5.19334137e-01 -5.16878486e-01 8.07019353e-01 -8.03166330e-02 -1.33728445e+00 8.08118880e-01 1.07966971e+00 3.32192421e-01 6.81433260e-01 5.30201674e-01 -1.17186379e+00 1.36754394e+00 -8.91473949e-01 -5.66249967e-01 1.48280933e-01 -5.74694455e-01 -1.39362049e+00 -2.14994997e-01 -1.49437502e-01 -3.80975693e-01 1.90071449e-01 1.08074355e+00 2.12677240e-01 -3.80334593e-02 1.68073788e-01 -1.24204969e+00 -5.88425338e-01 1.08552849e+00 1.45278767e-01 2.38109738e-01 8.83173585e-01 1.26516402e-01 8.50955307e-01 -5.03168166e-01 6.85643435e-01 1.09670496e+00 5.79935074e-01 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29310ebe-68a5-4fbf-9339-4bfe8d4a951e	kepler-a-unified-model-for-knowledge	1911.06136	null	https://arxiv.org/abs/1911.06136v3	https://arxiv.org/pdf/1911.06136v3.pdf	KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation	Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) with informative entity embeddings, but conventional KE models cannot take full advantage of the abundant textual information. In this paper, we propose a unified model for Knowledge Embedding and Pre-trained LanguagE Representation (KEPLER), which can not only better integrate factual knowledge into PLMs but also produce effective text-enhanced KE with the strong PLMs. In KEPLER, we encode textual entity descriptions with a PLM as their embeddings, and then jointly optimize the KE and language modeling objectives. Experimental results show that KEPLER achieves state-of-the-art performances on various NLP tasks, and also works remarkably well as an inductive KE model on KG link prediction. Furthermore, for pre-training and evaluating KEPLER, we construct Wikidata5M, a large-scale KG dataset with aligned entity descriptions, and benchmark state-of-the-art KE methods on it. It shall serve as a new KE benchmark and facilitate the research on large KG, inductive KE, and KG with text. The source code can be obtained from https://github.com/THU-KEG/KEPLER.	['Zhengyan Zhang', 'Zhaocheng Zhu', 'Jian Tang', 'Xiaozhi Wang', 'Tianyu Gao', 'Zhiyuan Liu', 'Juanzi Li']	2019-11-13	null	null	null	null	['inductive-knowledge-graph-completion']	['knowledge-base']	[-7.89184451e-01 4.75434512e-01 -8.32643628e-01 -1.69716880e-01 -3.23196560e-01 -3.04482788e-01 6.34845614e-01 2.82752782e-01 -3.31926525e-01 6.32872283e-01 6.56589210e-01 -1.86602056e-01 -4.53509808e-01 -1.22598398e+00 -8.20731342e-01 -3.97307009e-01 -1.38927191e-01 6.81605160e-01 1.77691415e-01 -2.54849970e-01 -2.56986946e-01 4.25709188e-02 -1.09992909e+00 1.02136815e-02 1.05988550e+00 6.21130049e-01 1.50632605e-01 3.26431721e-01 -4.78896141e-01 1.12317586e+00 -1.24967650e-01 -1.00770390e+00 -2.99333215e-01 4.09533754e-02 -9.44899440e-01 -6.54974699e-01 1.57288224e-01 -8.88800621e-02 -1.24398661e+00 7.90130138e-01 3.50298226e-01 7.18345493e-02 7.88080871e-01 -1.43312478e+00 -1.69016922e+00 1.39981902e+00 -3.47849637e-01 5.39959036e-02 1.66479647e-01 -2.66363084e-01 1.45063186e+00 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bf160070-24e3-4d9b-a911-c829dc08bede	adapterem-pre-trained-language-model	2305.18725	null	https://arxiv.org/abs/2305.18725v1	https://arxiv.org/pdf/2305.18725v1.pdf	AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuning	Entity Matching (EM) involves identifying different data representations referring to the same entity from multiple data sources and is typically formulated as a binary classification problem. It is a challenging problem in data integration due to the heterogeneity of data representations. State-of-the-art solutions have adopted NLP techniques based on pre-trained language models (PrLMs) via the fine-tuning paradigm, however, sequential fine-tuning of overparameterized PrLMs can lead to catastrophic forgetting, especially in low-resource scenarios. In this study, we propose a parameter-efficient paradigm for fine-tuning PrLMs based on adapters, small neural networks encapsulated between layers of a PrLM, by optimizing only the adapter and classifier weights while the PrLMs parameters are frozen. Adapter-based methods have been successfully applied to multilingual speech problems achieving promising results, however, the effectiveness of these methods when applied to EM is not yet well understood, particularly for generalized EM with heterogeneous data. Furthermore, we explore using (i) pre-trained adapters and (ii) invertible adapters to capture token-level language representations and demonstrate their benefits for transfer learning on the generalized EM benchmark. Our results show that our solution achieves comparable or superior performance to full-scale PrLM fine-tuning and prompt-tuning baselines while utilizing a significantly smaller computational footprint $\approx 13\%$ of the PrLM parameters.	['Akiyoshi Matono', 'Toshiyuki Amagasa', 'Steven Lynden', 'John Bosco Mugeni']	2023-05-30	null	null	null	null	['data-integration']	['knowledge-base']	[ 1.52657837e-01 1.47650376e-01 -1.94360569e-01 -3.22165608e-01 -1.13403690e+00 -3.77246022e-01 5.60328841e-01 2.36055523e-01 -7.55104303e-01 6.39434397e-01 1.61815763e-01 -3.91658157e-01 -4.84865382e-02 -6.41371369e-01 -1.02919877e+00 -3.09343815e-01 1.82486162e-01 8.53430092e-01 4.41043340e-02 -3.40747058e-01 -1.36889279e-01 2.62695372e-01 -1.56018126e+00 4.98878330e-01 1.14631474e+00 7.49331236e-01 4.52342272e-01 2.47268781e-01 -5.72052121e-01 4.60125685e-01 -5.58008552e-01 -6.04490459e-01 1.11168653e-01 7.21196607e-02 -9.70859706e-01 -4.03950989e-01 4.91871029e-01 6.92997426e-02 -1.15885168e-01 9.18263614e-01 6.03602827e-01 3.26881737e-01 6.28349781e-01 -1.18903744e+00 -7.34741747e-01 9.82969224e-01 -2.53329366e-01 -1.89827625e-02 1.33843377e-01 -8.05539936e-02 9.64452028e-01 -1.04494989e+00 6.01045966e-01 1.35040236e+00 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2.43040010e-01 -6.72525823e-01 -8.50807607e-01 -1.22057641e+00 -1.72528490e-01 6.08253658e-01 2.05377415e-01 -8.58698726e-01 -1.85695603e-01 -1.58928052e-01]	[9.837754249572754, 8.62270450592041]
446f67f1-0a4c-40ce-a918-6f94a615111d	spatiotemporal-feature-learning-for-event	1903.06923	null	http://arxiv.org/abs/1903.06923v1	http://arxiv.org/pdf/1903.06923v1.pdf	Spatiotemporal Feature Learning for Event-Based Vision	Unlike conventional frame-based sensors, event-based visual sensors output information through spikes at a high temporal resolution. By only encoding changes in pixel intensity, they showcase a low-power consuming, low-latency approach to visual information sensing. To use this information for higher sensory tasks like object recognition and tracking, an essential simplification step is the extraction and learning of features. An ideal feature descriptor must be robust to changes involving (i) local transformations and (ii) re-appearances of a local event pattern. To that end, we propose a novel spatiotemporal feature representation learning algorithm based on slow feature analysis (SFA). Using SFA, smoothly changing linear projections are learnt which are robust to local visual transformations. In order to determine if the features can learn to be invariant to various visual transformations, feature point tracking tasks are used for evaluation. Extensive experiments across two datasets demonstrate the adaptability of the spatiotemporal feature learner to translation, scaling and rotational transformations of the feature points. More importantly, we find that the obtained feature representations are able to exploit the high temporal resolution of such event-based cameras in generating better feature tracks.	['Anupam Gupta', 'Alcimar Soares', 'Rohan Ghosh', 'Siyi Tang', 'Nitish Thakor']	2019-03-16	null	null	null	null	['event-based-vision']	['computer-vision']	[ 5.13196588e-01 -8.42105985e-01 -4.42766361e-02 -4.61081862e-01 -5.05388677e-01 -7.50920057e-01 9.27647889e-01 3.00878435e-01 -5.36598861e-01 7.08331287e-01 -1.60167292e-01 3.54214549e-01 -2.53002673e-01 -5.91772676e-01 -1.08850396e+00 -8.23540330e-01 -2.72264808e-01 -2.77804047e-01 8.07346225e-01 1.09445408e-01 4.18080777e-01 1.12218058e+00 -2.19446421e+00 4.53372955e-01 1.62984207e-01 1.29424489e+00 2.20086068e-01 6.46699071e-01 1.72662616e-01 4.94726390e-01 -3.48865032e-01 5.02626657e-01 2.85192817e-01 -2.83290863e-01 -1.79940075e-01 3.76080684e-02 3.97412419e-01 2.71974485e-02 -2.53720045e-01 7.98423171e-01 2.24712566e-01 2.19062030e-01 6.37774527e-01 -1.27152216e+00 -2.57469267e-01 -7.29235783e-02 -2.32604250e-01 4.63357955e-01 8.44422877e-01 4.03397202e-01 6.02064550e-01 -1.09283805e+00 9.29623723e-01 9.37064469e-01 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2c95d5bc-5c13-41f2-9e2c-463a4aa929f3	e-panns-sound-recognition-using-efficient-pre	2305.18665	null	https://arxiv.org/abs/2305.18665v1	https://arxiv.org/pdf/2305.18665v1.pdf	E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networks	Sounds carry an abundance of information about activities and events in our everyday environment, such as traffic noise, road works, music, or people talking. Recent machine learning methods, such as convolutional neural networks (CNNs), have been shown to be able to automatically recognize sound activities, a task known as audio tagging. One such method, pre-trained audio neural networks (PANNs), provides a neural network which has been pre-trained on over 500 sound classes from the publicly available AudioSet dataset, and can be used as a baseline or starting point for other tasks. However, the existing PANNs model has a high computational complexity and large storage requirement. This could limit the potential for deploying PANNs on resource-constrained devices, such as on-the-edge sound sensors, and could lead to high energy consumption if many such devices were deployed. In this paper, we reduce the computational complexity and memory requirement of the PANNs model by taking a pruning approach to eliminate redundant parameters from the PANNs model. The resulting Efficient PANNs (E-PANNs) model, which requires 36\% less computations and 70\% less memory, also slightly improves the sound recognition (audio tagging) performance. The code for the E-PANNs model has been released under an open source license.	['Mark D. Plumbley', 'Haohe Liu', 'Arshdeep Singh']	2023-05-30	null	null	null	null	['audio-tagging']	['audio']	[ 6.39576972e-01 -1.61990523e-02 -1.64723024e-02 -7.80946761e-02 -5.72658956e-01 -2.55856782e-01 1.33789271e-01 5.19357920e-02 -5.19456089e-01 3.93667370e-01 2.40459934e-01 -1.40425876e-01 -8.45388845e-02 -1.00830400e+00 -5.86818576e-01 -4.92613703e-01 -1.40340775e-01 -1.34939268e-01 5.88007450e-01 2.90323436e-01 -8.39862078e-02 4.00950789e-01 -1.97844720e+00 2.67651707e-01 1.40108839e-01 1.51725459e+00 2.24740177e-01 8.39487672e-01 -3.22687551e-02 6.61996186e-01 -8.97510707e-01 -1.83644399e-01 6.20112345e-02 -2.36358792e-01 -7.08309770e-01 -4.80998367e-01 3.80249709e-01 -1.62247747e-01 -4.47636247e-01 1.03669369e+00 7.76934624e-01 4.03491646e-01 -1.79223821e-01 -1.31903708e+00 9.53396317e-03 1.01678252e+00 -8.36558547e-03 2.83518076e-01 7.55089000e-02 -2.27018341e-01 9.72528160e-01 -5.78392088e-01 1.24437183e-01 9.84735429e-01 1.03051686e+00 5.39187253e-01 -6.79597020e-01 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5ad0035b-e2a3-4ea5-b5da-caee9d86e7f2	communities-in-c-elegans-connectome-through	2207.00767	null	https://arxiv.org/abs/2207.00767v3	https://arxiv.org/pdf/2207.00767v3.pdf	Perspectives and constraints on neural network models of neurobiological processes	Artificial and natural neural network models are a new toolkit which could be potentially have been used for clarifying of complex brain functions. To attend this goal, such models need to be neurobiologically realistic. However, although neural networks have advanced keenly in recent decades their strict similarity in aspects of brain anatomy and physiology is imperfect. In this work we discuss different types of neural models, including localist, attractor and deep network models, and also identify aspects under which their biological credibility can be improved. These conditions range from the choice of neuron models and of mechanisms of synaptic plasticity and learning to implementation of inhibition and control, along with network architectures (modularity, connectivity). We highlight recent advances in biologically inspired neural network models and their constraints.	['Arsenii Onuchin']	2022-07-02	null	null	null	null	['stochastic-block-model']	['graphs']	[ 9.41850170e-02 1.00498810e-01 2.30899140e-01 3.41499001e-02 1.01277709e+00 -5.73374987e-01 8.29306901e-01 -1.69958428e-01 -4.15003806e-01 9.31832552e-01 3.44289728e-02 -2.78776228e-01 -6.89798474e-01 -6.89936578e-01 -4.77942050e-01 -7.60207534e-01 -4.94235367e-01 2.02259526e-01 3.06784004e-01 -5.51981449e-01 5.15519440e-01 8.96522582e-01 -1.55623865e+00 4.81101945e-02 7.49622464e-01 4.80509520e-01 3.71118873e-01 6.80425465e-01 1.02127194e-02 6.53446078e-01 -4.52693850e-01 -3.17376517e-02 1.62431188e-02 -5.73105812e-01 -8.31382871e-01 -3.91254723e-01 -3.36154670e-01 2.64207989e-01 -3.22058827e-01 8.93338323e-01 5.83838522e-01 1.09931052e-01 8.75881553e-01 -1.05820012e+00 -7.75826693e-01 6.69453025e-01 1.86964065e-01 5.34109712e-01 -1.40145034e-01 4.63431865e-01 4.04438555e-01 -7.83329546e-01 6.65782511e-01 1.12973559e+00 5.28853834e-01 9.90005374e-01 -1.43130124e+00 -6.26477003e-01 -6.33320808e-02 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8be87062-55e5-4e81-913c-eb4b0505d591	coarse3d-class-prototypes-for-contrastive	2210.01784	null	https://arxiv.org/abs/2210.01784v2	https://arxiv.org/pdf/2210.01784v2.pdf	COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation	Annotation of large-scale 3D data is notoriously cumbersome and costly. As an alternative, weakly-supervised learning alleviates such a need by reducing the annotation by several order of magnitudes. We propose COARSE3D, a novel architecture-agnostic contrastive learning strategy for 3D segmentation. Since contrastive learning requires rich and diverse examples as keys and anchors, we leverage a prototype memory bank capturing class-wise global dataset information efficiently into a small number of prototypes acting as keys. An entropy-driven sampling technique then allows us to select good pixels from predictions as anchors. Experiments on three projection-based backbones show we outperform baselines on three challenging real-world outdoor datasets, working with as low as 0.001% annotations.	['Raoul de Charette', 'Anh-Quan Cao', 'Rong Li']	2022-10-04	null	null	null	null	['lidar-semantic-segmentation', 'point-cloud-segmentation', 'weakly-supervised-3d-point-cloud-segmentation']	['computer-vision', 'computer-vision', 'computer-vision']	[ 1.66401505e-01 2.95114785e-01 -5.65541804e-01 -4.73898470e-01 -1.23482668e+00 -9.75850224e-01 4.08351988e-01 2.48208076e-01 -5.43104708e-01 6.32276118e-01 7.12756291e-02 -2.79230297e-01 2.81837195e-01 -4.76133674e-01 -8.43331575e-01 -4.78611380e-01 -4.19651344e-02 8.94487441e-01 6.75500572e-01 2.13624567e-01 2.46522218e-01 5.52324176e-01 -1.39835525e+00 2.21287757e-01 8.64619136e-01 1.35378563e+00 3.44355315e-01 4.71446395e-01 -1.43324733e-01 1.43955588e-01 -4.81662542e-01 -2.15147510e-01 7.62898445e-01 4.14192677e-04 -9.18060601e-01 2.10902706e-01 1.10216224e+00 -3.43116015e-01 9.99032632e-02 7.35845029e-01 6.40841782e-01 9.37802196e-02 5.18197000e-01 -8.90974879e-01 -2.63668150e-01 3.29452455e-01 -5.39691687e-01 7.20921308e-02 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4fbb3a0e-0fc7-4f18-a622-45e72c54693f	multi-task-self-supervised-pre-training-for	2102.03229	null	https://arxiv.org/abs/2102.03229v1	https://arxiv.org/pdf/2102.03229v1.pdf	Multi-Task Self-Supervised Pre-Training for Music Classification	Deep learning is very data hungry, and supervised learning especially requires massive labeled data to work well. Machine listening research often suffers from limited labeled data problem, as human annotations are costly to acquire, and annotations for audio are time consuming and less intuitive. Besides, models learned from labeled dataset often embed biases specific to that particular dataset. Therefore, unsupervised learning techniques become popular approaches in solving machine listening problems. Particularly, a self-supervised learning technique utilizing reconstructions of multiple hand-crafted audio features has shown promising results when it is applied to speech domain such as emotion recognition and automatic speech recognition (ASR). In this paper, we apply self-supervised and multi-task learning methods for pre-training music encoders, and explore various design choices including encoder architectures, weighting mechanisms to combine losses from multiple tasks, and worker selections of pretext tasks. We investigate how these design choices interact with various downstream music classification tasks. We find that using various music specific workers altogether with weighting mechanisms to balance the losses during pre-training helps improve and generalize to the downstream tasks.	['Chao Wang', 'Juan Pablo Bello', 'Brian McFee', 'Ming Sun', 'Qingming Tang', 'Chieh-Chi Kao', 'Ho-Hsiang Wu']	2021-02-05	null	null	null	null	['music-classification']	['music']	[ 4.38706756e-01 -2.38821898e-02 -2.54965425e-01 -7.00024843e-01 -1.01450622e+00 -4.66623664e-01 7.35413581e-02 5.66930287e-02 -6.97115064e-01 4.90345538e-01 3.53111267e-01 9.10736620e-02 -2.83204317e-01 -4.10880655e-01 -5.00452936e-01 -6.38376534e-01 2.10151553e-01 5.17203629e-01 -8.64103436e-02 -1.51827380e-01 4.05048132e-02 -4.92910817e-02 -1.62691963e+00 6.09386683e-01 4.56944972e-01 1.26556540e+00 2.68711895e-01 7.12098598e-01 -1.63317427e-01 1.10022175e+00 -7.93951809e-01 -5.79844236e-01 1.31862313e-01 -3.60061437e-01 -8.19660544e-01 1.78902112e-02 1.49089843e-01 1.91343874e-01 4.20191810e-02 9.00934577e-01 1.15770459e+00 2.73316443e-01 5.82382560e-01 -1.24158430e+00 -4.73043412e-01 1.20220423e+00 -5.25477648e-01 1.51118726e-01 -6.07986636e-02 8.36560503e-02 1.32701957e+00 -6.84380710e-01 1.95549130e-01 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83ae2554-8e26-4927-b759-d25b4f6f1395	symmetric-exploration-in-combinatorial	2306.01276	null	https://arxiv.org/abs/2306.01276v1	https://arxiv.org/pdf/2306.01276v1.pdf	Symmetric Exploration in Combinatorial Optimization is Free!	Recently, deep reinforcement learning (DRL) has shown promise in solving combinatorial optimization (CO) problems. However, they often require a large number of evaluations on the objective function, which can be time-consuming in real-world scenarios. To address this issue, we propose a "free" technique to enhance the performance of any deep reinforcement learning (DRL) solver by exploiting symmetry without requiring additional objective function evaluations. Our key idea is to augment the training of DRL-based combinatorial optimization solvers by reward-preserving transformations. The proposed algorithm is likely to be impactful since it is simple, easy to integrate with existing solvers, and applicable to a wide range of combinatorial optimization tasks. Extensive empirical evaluations on NP-hard routing optimization, scheduling optimization, and de novo molecular optimization confirm that our method effortlessly improves the sample efficiency of state-of-the-art DRL algorithms. Our source code is available at https://github.com/kaist-silab/sym-rd.	['Jinkyoo Park', 'Sungsoo Ahn', 'Minsu Kim', 'Hyeonah Kim']	2023-06-02	null	null	null	null	['combinatorial-optimization']	['methodology']	[-1.21234670e-01 -1.06148906e-01 -4.54534203e-01 -2.70723812e-02 -8.70096684e-01 -7.70689249e-01 1.62341505e-01 2.63935894e-01 -3.87034923e-01 1.25003004e+00 -1.39678344e-01 -5.65341949e-01 -1.77237540e-01 -9.78447616e-01 -9.61345434e-01 -7.10812688e-01 1.42798265e-02 6.88507378e-01 -2.54963934e-01 -3.86987299e-01 2.19957143e-01 6.04200482e-01 -1.04116893e+00 -4.05197144e-02 9.42251384e-01 8.29051971e-01 1.84756994e-01 3.70954275e-01 -4.46309857e-02 5.82317352e-01 -4.01221305e-01 -2.03646749e-01 3.90765190e-01 -3.59542876e-01 -9.91022468e-01 -1.33072317e-01 -2.95931567e-02 -5.88854700e-02 -2.33294666e-01 9.29541528e-01 6.29890084e-01 1.89340889e-01 1.23654552e-01 -1.28714871e+00 -3.66062343e-01 5.79129219e-01 -5.67927241e-01 1.34622557e-02 1.64585352e-01 4.33588505e-01 1.33374286e+00 -5.15100837e-01 6.18100762e-01 1.07007599e+00 4.09657180e-01 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3f271431-61c7-4176-af44-c3c7cca2d2dd	vudenc-vulnerability-detection-with-deep	2201.08441	null	https://arxiv.org/abs/2201.08441v1	https://arxiv.org/pdf/2201.08441v1.pdf	VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python	Context: Identifying potential vulnerable code is important to improve the security of our software systems. However, the manual detection of software vulnerabilities requires expert knowledge and is time-consuming, and must be supported by automated techniques. Objective: Such automated vulnerability detection techniques should achieve a high accuracy, point developers directly to the vulnerable code fragments, scale to real-world software, generalize across the boundaries of a specific software project, and require no or only moderate setup or configuration effort. Method: In this article, we present VUDENC (Vulnerability Detection with Deep Learning on a Natural Codebase), a deep learning-based vulnerability detection tool that automatically learns features of vulnerable code from a large and real-world Python codebase. VUDENC applies a word2vec model to identify semantically similar code tokens and to provide a vector representation. A network of long-short-term memory cells (LSTM) is then used to classify vulnerable code token sequences at a fine-grained level, highlight the specific areas in the source code that are likely to contain vulnerabilities, and provide confidence levels for its predictions. Results: To evaluate VUDENC, we used 1,009 vulnerability-fixing commits from different GitHub repositories that contain seven different types of vulnerabilities (SQL injection, XSS, Command injection, XSRF, Remote code execution, Path disclosure, Open redirect) for training. In the experimental evaluation, VUDENC achieves a recall of 78%-87%, a precision of 82%-96%, and an F1 score of 80%-90%. VUDENC's code, the datasets for the vulnerabilities, and the Python corpus for the word2vec model are available for reproduction. Conclusions: Our experimental results suggest...	['Lars Grunske', 'Timo Kehrer', 'Thomas Vogel', 'Yannic Noller', 'Laura Wartschinski']	2022-01-20	null	null	null	null	['vulnerability-detection']	['miscellaneous']	[-3.29747468e-01 -3.28294665e-01 -6.57493696e-02 -2.26096869e-01 -8.62807393e-01 -9.89292383e-01 2.20803879e-02 6.80377305e-01 1.50274783e-02 4.96546645e-03 2.38329276e-01 -9.13098633e-01 1.99169487e-01 -9.53008235e-01 -6.32236421e-01 -2.55196020e-02 -4.28929418e-01 -4.73999381e-01 3.32505941e-01 -1.81493759e-01 7.25308776e-01 1.05797932e-01 -1.40046322e+00 6.63725495e-01 5.27883768e-01 5.70418000e-01 7.07268938e-02 7.61746526e-01 -6.36509597e-01 7.97143877e-01 -9.21594918e-01 -5.90867579e-01 1.43240720e-01 2.20325202e-01 -1.05581188e+00 -1.06074917e+00 3.61128330e-01 -1.59652218e-01 4.19521742e-02 1.42152572e+00 3.92403543e-01 -3.68481845e-01 3.13333213e-01 -1.04220009e+00 -9.27178502e-01 1.00751925e+00 -6.30023777e-01 3.82610559e-01 6.23675942e-01 2.15652525e-01 9.96448457e-01 -7.33026564e-01 6.93554401e-01 9.74523783e-01 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a3123bff-e1d6-4254-8bef-522fb2563cc8	evaluation-of-gpt-3-5-and-gpt-4-for	2304.13714	null	https://arxiv.org/abs/2304.13714v3	https://arxiv.org/pdf/2304.13714v3.pdf	Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery	Despite growing interest in using large language models (LLMs) in healthcare, current explorations do not assess the real-world utility and safety of LLMs in clinical settings. Our objective was to determine whether two LLMs can serve information needs submitted by physicians as questions to an informatics consultation service in a safe and concordant manner. Sixty six questions from an informatics consult service were submitted to GPT-3.5 and GPT-4 via simple prompts. 12 physicians assessed the LLM responses' possibility of patient harm and concordance with existing reports from an informatics consultation service. Physician assessments were summarized based on majority vote. For no questions did a majority of physicians deem either LLM response as harmful. For GPT-3.5, responses to 8 questions were concordant with the informatics consult report, 20 discordant, and 9 were unable to be assessed. There were 29 responses with no majority on "Agree", "Disagree", and "Unable to assess". For GPT-4, responses to 13 questions were concordant, 15 discordant, and 3 were unable to be assessed. There were 35 responses with no majority. Responses from both LLMs were largely devoid of overt harm, but less than 20% of the responses agreed with an answer from an informatics consultation service, responses contained hallucinated references, and physicians were divided on what constitutes harm. These results suggest that while general purpose LLMs are able to provide safe and credible responses, they often do not meet the specific information need of a given question. A definitive evaluation of the usefulness of LLMs in healthcare settings will likely require additional research on prompt engineering, calibration, and custom-tailoring of general purpose models.	['Nigam H. Shah', 'Eric Horvitz', 'Matthew P Lungren', 'Honor Magon', 'Garret Kenn Morris', 'Angel Arnaout', 'Ethan Goh', 'Rachel Pedreira', 'Lance Downing', 'Saurabh Gombar', 'Jonathan H. Chen', 'Nikesh Kotecha', 'Mehr Kashyap', 'Morgan Cheatham', 'Akshay Swaminathan', 'Juan M. Banda', 'Rahul Thapa', 'Debadutta Dash']	2023-04-26	null	null	null	null	['prompt-engineering']	['natural-language-processing']	[ 5.75967208e-02 5.02275050e-01 -1.03652023e-01 -6.46079421e-01 -1.25547457e+00 -9.18955624e-01 1.56798363e-01 8.70616138e-01 -5.80138326e-01 6.05898142e-01 7.87799895e-01 -1.16532528e+00 -5.49452126e-01 -2.88441956e-01 -3.16462994e-01 -2.81385873e-02 6.99520469e-01 7.12235272e-01 -2.28192225e-01 4.86876294e-02 3.81787151e-01 2.73343891e-01 -3.02257419e-01 6.66670322e-01 6.39058232e-01 5.48498094e-01 -3.09660494e-01 5.19814432e-01 -1.33349001e-01 1.23780942e+00 -7.78817832e-01 -3.47305089e-01 1.14795327e-01 -5.32077730e-01 -8.13709497e-01 -1.02185905e-01 2.92361289e-01 -7.75748670e-01 1.66056871e-01 6.14495218e-01 7.73600042e-01 -3.15258324e-01 2.70648211e-01 -7.92279422e-01 -5.81024766e-01 4.27135468e-01 2.34352797e-01 2.85660774e-02 1.11018002e+00 6.80581212e-01 3.33212078e-01 -2.47595787e-01 7.51814306e-01 1.10116315e+00 9.03748631e-01 5.04116714e-01 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aa5e1108-80d7-4583-9b64-15764851db4a	narrative-xl-a-large-scale-dataset-for-long	2305.13877	null	https://arxiv.org/abs/2305.13877v1	https://arxiv.org/pdf/2305.13877v1.pdf	Narrative XL: A Large-scale Dataset For Long-Term Memory Models	Despite their tremendous successes, most large language models do not have any long-term memory mechanisms, which restricts their applications. Overcoming this limitation would not only require changes to the typical transformer architectures or training procedures, but also a dataset on which these new models could be trained and evaluated. We argue that existing resources lack a few key properties, and that at present, there are no naturalistic datasets of sufficient scale to train (and not only evaluate) long-term memory language models. We then present our solution that capitalizes on the advances in short-term memory language models to create such a dataset. Using GPT 3.5, we summarized each scene in 1500 hand-curated books from Project Gutenberg, which resulted in approximately 150 scene-level summaries per book. We then created a number of reading comprehension questions based on these summaries, including three types of multiple-choice scene recognition questions, as well as free-form narrative reconstruction questions. Each book is thus associated with more than 500 reading comprehension questions. Crucially, most questions have a known ``retention demand'', indicating how long-term of a memory is needed to answer it, which should aid long-term memory performance evaluation. We validate our data in three small-scale experiments: one with human labelers, and two with existing language models. We show that our questions 1) adequately represent the source material 2) can be used to diagnose the model's memory capacity 3) are not trivial for modern language models even when the memory demand does not exceed those models' context lengths. Lastly, we provide our code which can be used to further expand the dataset in an automated manner.	['Ky-Vinh Mai', 'Arseny Moskvichev']	2023-05-23	null	null	null	null	['scene-recognition', 'reading-comprehension']	['computer-vision', 'natural-language-processing']	[ 1.66568533e-01 1.01609722e-01 -1.77702844e-01 -2.26078525e-01 -1.15435588e+00 -9.90663052e-01 8.54890287e-01 3.13453823e-01 -4.52141911e-01 5.63349545e-01 5.90403497e-01 -7.60068238e-01 -2.49927372e-01 -9.79365587e-01 -7.88595200e-01 -1.21117011e-02 2.64584482e-01 7.06893682e-01 4.38189983e-01 -3.43871444e-01 7.24843800e-01 2.53879279e-01 -1.52878904e+00 6.68867886e-01 7.60156810e-01 6.11495554e-01 8.70488763e-01 8.68509352e-01 -4.79302168e-01 1.39047456e+00 -6.78371608e-01 -1.95792675e-01 -3.52817744e-01 -3.26126814e-01 -1.54949069e+00 -1.33307204e-01 5.64635754e-01 -4.74759251e-01 -4.40986395e-01 3.55479389e-01 3.77958328e-01 4.54034954e-01 5.10684431e-01 -7.06939816e-01 -9.27612662e-01 6.57326460e-01 5.05623966e-02 4.55837637e-01 7.56356418e-01 1.81571051e-01 1.09089744e+00 -1.16633511e+00 7.03579426e-01 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-1.47486582e-01 -6.23002589e-01 -2.22717687e-01 -9.60556328e-01 -2.47610435e-01 1.66147217e-01 -5.12023941e-02 -4.88516241e-01 -3.25906634e-01 1.43678948e-01]	[11.069449424743652, 7.968624591827393]
363f7b35-06e8-4138-b8eb-88b0786755cb	discriminative-cross-domain-feature-learning	2008.1136	null	https://arxiv.org/abs/2008.11360v1	https://arxiv.org/pdf/2008.11360v1.pdf	Discriminative Cross-Domain Feature Learning for Partial Domain Adaptation	Partial domain adaptation aims to adapt knowledge from a larger and more diverse source domain to a smaller target domain with less number of classes, which has attracted appealing attention. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better fight off the cross-domain distribution divergences. However, it is essential to align target data with only a small set of source data. In this paper, we develop a novel Discriminative Cross-Domain Feature Learning (DCDF) framework to iteratively optimize target labels with a cross-domain graph in a weighted scheme. Specifically, a weighted cross-domain center loss and weighted cross-domain graph propagation are proposed to couple unlabeled target data to related source samples for discriminative cross-domain feature learning, where irrelevant source centers will be ignored, to alleviate the marginal and conditional disparities simultaneously. Experimental evaluations on several popular benchmarks demonstrate the effectiveness of our proposed approach on facilitating the recognition for the unlabeled target domain, through comparing it to the state-of-the-art partial domain adaptation approaches.	['Ming Shao', 'Zhengming Ding', 'Taotao Jing']	2020-08-26	null	null	null	null	['partial-domain-adaptation']	['methodology']	[ 2.90326506e-01 -2.33127698e-01 -5.16775727e-01 -7.37059236e-01 -9.82618213e-01 -7.03021049e-01 4.94168758e-01 2.45902874e-03 -2.15105116e-01 9.17423666e-01 7.34969750e-02 3.27604711e-01 -2.22772300e-01 -5.67086875e-01 -5.36580205e-01 -8.72825384e-01 3.35234880e-01 5.04619658e-01 4.63484257e-01 1.54805062e-02 7.80023411e-02 3.27181667e-01 -1.09020281e+00 -4.26383242e-02 1.19668031e+00 9.45684671e-01 2.74280250e-01 -6.19100928e-02 -3.66667092e-01 3.41032892e-01 -5.30750036e-01 -2.42626324e-01 3.25800478e-01 -5.52560508e-01 -3.90136272e-01 2.74949968e-01 5.52551270e-01 -2.49972064e-02 -1.04015790e-01 1.20458198e+00 4.93578017e-01 2.83580750e-01 1.10176742e+00 -1.20852649e+00 -9.91125643e-01 2.38086343e-01 -9.55809176e-01 2.10580602e-01 2.92546097e-02 -1.68912169e-02 8.22895169e-01 -1.05098772e+00 5.74229598e-01 1.26074266e+00 4.40204322e-01 6.68492794e-01 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7.26555645e-01 3.39287311e-01 -9.36590433e-01 -3.50359082e-01 2.67126441e-01]	[10.378421783447266, 3.0718345642089844]
3b0c2db7-9403-4b04-aadd-4163dae01c0e	automated-radiology-report-generation-using	null	null	https://doi.org/10.1016/j.imu.2021.100557	https://doi.org/10.1016/j.imu.2021.100557	Automated radiology report generation using conditioned transformers	Radiology report writing in hospitals is a time-consuming task that also requires experience from the involved radiologists. This paper proposes a deep learning model to automatically generate radiology reports given a chest x-ray image from the public IU-Xray dataset. Our work consists of three stages: (1) Fine-tune a pre-trained Chexnet to predict specific tags from the image. (2) Calculate weighted semantic features from the predicted tag's pre-trained embeddings. (3) Condition a pre-trained GPT2 model on the visual and semantic features to generate the full medical reports. We analyze the generated reports using word-overlap metrics while also adding new meaningful semantic-based similarity metrics. The proposed model, which we call CDGPT2, surpassed most non-hierarchical recurrent models and transformer-based models in quantitative metrics while being considerably faster to train. Moreover, the model does not require a specific vocabulary and can be trained on different datasets without changing the architecture. Furthermore, we include a qualitative analysis from a radiologist from Egypt's national institute of cancer which showed that 61.6% of the generated reports on the test set were expertly written, and only 10% contained false information. We represent the first work to condition a pre-trained transformer on visual and semantic features to generate medical reports and to include semantic similarity metrics in the quantitative analysis of the generated reports.	['Aly Fahmy', 'Maha Helal', 'Abeer Elkorany', 'Rana Khaled', 'Omar Alfarghaly']	2021-03-26	null	null	null	null	['medical-report-generation']	['medical']	[ 4.75172281e-01 5.54400682e-01 2.64205784e-01 -6.01503074e-01 -1.34273434e+00 -3.01148862e-01 4.85787183e-01 4.83004063e-01 -2.89437830e-01 7.27794826e-01 5.63436687e-01 -5.28436899e-01 -3.30293119e-01 -8.28354657e-01 -5.19185007e-01 -5.93488872e-01 8.20489824e-02 7.02896833e-01 2.17059255e-01 1.07180215e-01 3.59177709e-01 2.93384820e-01 -1.14869547e+00 7.84488261e-01 3.78577918e-01 8.66651654e-01 5.06414711e-01 9.59754169e-01 -4.06200066e-02 1.15019917e+00 -7.19291091e-01 -4.09880489e-01 -9.08418298e-02 -8.74076545e-01 -9.10976112e-01 2.34421656e-01 5.75888017e-03 -4.81713451e-02 -1.99613988e-01 7.04752743e-01 7.30514467e-01 -3.42281982e-02 9.23178434e-01 -5.28274715e-01 -8.59099686e-01 7.46473432e-01 -3.17153245e-01 4.32822615e-01 3.22829455e-01 -1.19066909e-01 7.68047154e-01 -1.01829290e+00 9.95107293e-01 7.95075536e-01 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