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0f5dbb5a-0012-4e91-b047-5e1d9df50e63 | identifiability-of-discretized-latent | 2306.16334 | null | https://arxiv.org/abs/2306.16334v1 | https://arxiv.org/pdf/2306.16334v1.pdf | Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection | Disentanglement aims to recover meaningful latent ground-truth factors from only the observed distribution. Identifiability provides the theoretical grounding for disentanglement to be well-founded. Unfortunately, unsupervised identifiability of independent latent factors is a theoretically proven impossibility in the i.i.d. setting under a general nonlinear smooth map from factors to observations. In this work, we show that, remarkably, it is possible to recover discretized latent coordinates under a highly generic nonlinear smooth mapping (a diffeomorphism) without any additional inductive bias on the mapping. This is, assuming that latent density has axis-aligned discontinuity landmarks, but without making the unrealistic assumption of statistical independence of the factors. We introduce this novel form of identifiability, termed quantized coordinate identifiability, and provide a comprehensive proof of the recovery of discretized coordinates. | ['Pascal Vincent', 'Simon Lacoste-Julien', 'Kartik Ahuja', 'Vitória Barin-Pacela'] | 2023-06-28 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 5.78406230e-02 4.69780028e-01 -3.37658554e-01 -9.90676656e-02
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49a9a034-32dc-446e-92b8-9d11235587c0 | a-novel-apex-time-network-for-cross-dataset | 1904.03699 | null | https://arxiv.org/abs/1904.03699v7 | https://arxiv.org/pdf/1904.03699v7.pdf | A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition | The automatic recognition of micro-expression has been boosted ever since the successful introduction of deep learning approaches. As researchers working on such topics are moving to learn from the nature of micro-expression, the practice of using deep learning techniques has evolved from processing the entire video clip of micro-expression to the recognition on apex frame. Using the apex frame is able to get rid of redundant video frames, but the relevant temporal evidence of micro-expression would be thereby left out. This paper proposes a novel Apex-Time Network (ATNet) to recognize micro-expression based on spatial information from the apex frame as well as on temporal information from the respective-adjacent frames. Through extensive experiments on three benchmarks, we demonstrate the improvement achieved by learning such temporal information. Specially, the model with such temporal information is more robust in cross-dataset validations. | ['Yu Shi', 'Xiangdong Zhou', 'Min Peng', 'Chongyang Wang', 'Tao Bi', 'Tong Chen'] | 2019-04-07 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.40974700e-01 -3.97072047e-01 -4.45923120e-01 -5.23770928e-01
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3a03dcd4-505c-46e7-a83b-8e388fcf9c8a | deep-dynamic-effective-connectivity | 2202.02393 | null | https://arxiv.org/abs/2202.02393v3 | https://arxiv.org/pdf/2202.02393v3.pdf | Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series | Recently, methods that represent data as a graph, such as graph neural networks (GNNs) have been successfully used to learn data representations and structures to solve classification and link prediction problems. The applications of such methods are vast and diverse, but most of the current work relies on the assumption of a static graph. This assumption does not hold for many highly dynamic systems, where the underlying connectivity structure is non-stationary and is mostly unobserved. Using a static model in these situations may result in sub-optimal performance. In contrast, modeling changes in graph structure with time can provide information about the system whose applications go beyond classification. Most work of this type does not learn effective connectivity and focuses on cross-correlation between nodes to generate undirected graphs. An undirected graph is unable to capture direction of an interaction which is vital in many fields, including neuroscience. To bridge this gap, we developed dynamic effective connectivity estimation via neural network training (DECENNT), a novel model to learn an interpretable directed and dynamic graph induced by the downstream classification/prediction task. DECENNT outperforms state-of-the-art (SOTA) methods on five different tasks and infers interpretable task-specific dynamic graphs. The dynamic graphs inferred from functional neuroimaging data align well with the existing literature and provide additional information. Additionally, the temporal attention module of DECENNT identifies time-intervals crucial for predictive downstream task from multivariate time series data. | ['Sergey Plis', 'Vince Calhoun', 'Zening Fu', 'Usman Mahmood'] | 2022-02-04 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [ 3.21124196e-01 5.93909204e-01 -4.69848216e-01 -2.39847377e-01
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9c4d4dd9-8505-4791-965e-94717ed4df7f | making-attention-mechanisms-more-robust-and | 2104.08763 | null | https://arxiv.org/abs/2104.08763v3 | https://arxiv.org/pdf/2104.08763v3.pdf | Making Attention Mechanisms More Robust and Interpretable with Virtual Adversarial Training | Although attention mechanisms have become fundamental components of deep learning models, they are vulnerable to perturbations, which may degrade the prediction performance and model interpretability. Adversarial training (AT) for attention mechanisms has successfully reduced such drawbacks by considering adversarial perturbations. However, this technique requires label information, and thus, its use is limited to supervised settings. In this study, we explore the concept of incorporating virtual AT (VAT) into the attention mechanisms, by which adversarial perturbations can be computed even from unlabeled data. To realize this approach, we propose two general training techniques, namely VAT for attention mechanisms (Attention VAT) and "interpretable" VAT for attention mechanisms (Attention iVAT), which extend AT for attention mechanisms to a semi-supervised setting. In particular, Attention iVAT focuses on the differences in attention; thus, it can efficiently learn clearer attention and improve model interpretability, even with unlabeled data. Empirical experiments based on six public datasets revealed that our techniques provide better prediction performance than conventional AT-based as well as VAT-based techniques, and stronger agreement with evidence that is provided by humans in detecting important words in sentences. Moreover, our proposal offers these advantages without needing to add the careful selection of unlabeled data. That is, even if the model using our VAT-based technique is trained on unlabeled data from a source other than the target task, both the prediction performance and model interpretability can be improved. | ['Hitoshi Iyatomi', 'Shunsuke Kitada'] | 2021-04-18 | null | null | null | null | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 7.73308948e-02 3.22125167e-01 -1.11756101e-01 -2.61642814e-01
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87f8e780-c8b9-4ebd-8ae2-e59bd42be90f | unified-model-learning-for-various-neural | 2305.02777 | null | https://arxiv.org/abs/2305.02777v2 | https://arxiv.org/pdf/2305.02777v2.pdf | Unified Model Learning for Various Neural Machine Translation | Existing neural machine translation (NMT) studies mainly focus on developing dataset-specific models based on data from different tasks (e.g., document translation and chat translation). Although the dataset-specific models have achieved impressive performance, it is cumbersome as each dataset demands a model to be designed, trained, and stored. In this work, we aim to unify these translation tasks into a more general setting. Specifically, we propose a ``versatile'' model, i.e., the Unified Model Learning for NMT (UMLNMT) that works with data from different tasks, and can translate well in multiple settings simultaneously, and theoretically it can be as many as possible. Through unified learning, UMLNMT is able to jointly train across multiple tasks, implementing intelligent on-demand translation. On seven widely-used translation tasks, including sentence translation, document translation, and chat translation, our UMLNMT results in substantial improvements over dataset-specific models with significantly reduced model deployment costs. Furthermore, UMLNMT can achieve competitive or better performance than state-of-the-art dataset-specific methods. Human evaluation and in-depth analysis also demonstrate the superiority of our approach on generating diverse and high-quality translations. Additionally, we provide a new genre translation dataset about famous aphorisms with 186k Chinese->English sentence pairs. | ['Jie zhou', 'Yufeng Chen', 'Jiaan Wang', 'Jinan Xu', 'Fandong Meng', 'Yunlong Liang'] | 2023-05-04 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 3.65901887e-01 -4.39532220e-01 -5.41404724e-01 -3.88726741e-01
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9670d099-c4f1-4090-b743-57ac079894e1 | wnet-a-data-driven-dual-domain-denoising | 2207.00400 | null | https://arxiv.org/abs/2207.00400v2 | https://arxiv.org/pdf/2207.00400v2.pdf | WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer | Deep learning based solutions are being succesfully implemented for a wide variety of applications. Most notably, clinical use-cases have gained an increased interest and have been the main driver behind some of the cutting-edge data-driven algorithms proposed in the last years. For applications like sparse-view tomographic reconstructions, where the amount of measurement data is small in order to keep acquisition time short and radiation dose low, reduction of the streaking artifacts has prompted the development of data-driven denoising algorithms with the main goal of obtaining diagnostically viable images with only a subset of a full-scan data. We propose WNet, a data-driven dual-domain denoising model which contains a trainable reconstruction layer for sparse-view artifact denoising. Two encoder-decoder networks perform denoising in both sinogram- and reconstruction-domain simultaneously, while a third layer implementing the Filtered Backprojection algorithm is sandwiched between the first two and takes care of the reconstruction operation. We investigate the performance of the network on sparse-view chest CT scans, and we highlight the added benefit of having a trainable reconstruction layer over the more conventional fixed ones. We train and test our network on two clinically relevant datasets and we compare the obtained results with three different types of sparse-view CT denoising and reconstruction algorithms. | ['Tobias Lasser', 'Daniela Pfeiffer', 'Franz Pfeiffer', 'Manuel Schultheiß', 'Felix C. Hofmann', 'Theodor Cheslerean-Boghiu'] | 2022-07-01 | null | null | null | null | ['tomographic-reconstructions'] | ['medical'] | [ 4.64382142e-01 8.86175875e-03 9.95951593e-02 -5.84974885e-01
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c0514c68-b15a-4b95-8242-6420c49512c1 | deep-packet-a-novel-approach-for-encrypted | 1709.02656 | null | http://arxiv.org/abs/1709.02656v3 | http://arxiv.org/pdf/1709.02656v3.pdf | Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning | Internet traffic classification has become more important with rapid growth
of current Internet network and online applications. There have been numerous
studies on this topic which have led to many different approaches. Most of
these approaches use predefined features extracted by an expert in order to
classify network traffic. In contrast, in this study, we propose a \emph{deep
learning} based approach which integrates both feature extraction and
classification phases into one system. Our proposed scheme, called "Deep
Packet," can handle both \emph{traffic characterization} in which the network
traffic is categorized into major classes (\eg, FTP and P2P) and application
identification in which end-user applications (\eg, BitTorrent and Skype)
identification is desired. Contrary to most of the current methods, Deep Packet
can identify encrypted traffic and also distinguishes between VPN and non-VPN
network traffic. After an initial pre-processing phase on data, packets are fed
into Deep Packet framework that embeds stacked autoencoder and convolution
neural network in order to classify network traffic. Deep packet with CNN as
its classification model achieved recall of $0.98$ in application
identification task and $0.94$ in traffic categorization task. To the best of
our knowledge, Deep Packet outperforms all of the proposed classification
methods on UNB ISCX VPN-nonVPN dataset. | ['Ramin Shirali Hossein Zade', 'Mohammad Lotfollahi', 'Mahdi Jafari Siavoshani', 'Mohammdsadegh Saberian'] | 2017-09-08 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 2.15883441e-02 -3.48003060e-01 -3.01508725e-01 -3.12949836e-01
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c936f8ae-6c85-4973-8f97-10af7e78ace4 | enhancing-social-network-hate-detection-using-1 | null | null | https://www.sciencedirect.com/science/article/pii/S1566253523002038?casa_token=bKYei6Rpr7QAAAAA:yxZVs7LXkANAnOA-cpZrHVty_LH0vqvloWhdi89OmHrhuJgl6_O1ph0UDnmcXvPHR6jLgDwBpEs | https://www.sciencedirect.com/science/article/abs/pii/S1566253523002038 | Enhancing Social Network Hate Detection Using Back Translation and GPT-3 Augmentations During Training and Test-Time | Social media platforms have become an essential means of communication, but they also serve as a breeding ground for hateful content. Detecting hate speech accurately is challenging due to factors such as slang and implicit hate speech. In response to these challenges, this paper presents a novel ensemble approach utilizing DeBERTa models, integrating back-translation and GPT-3 augmentation techniques during both training and test time. This method aims to address the complexities associated with detecting hate speech, resulting in more robust and accurate results. Our findings indicate that the proposed approach significantly enhances hate speech detection performance across various metrics and models in both the Parler and GAB datasets. For reproducibility and further exploration, our code is publicly available at https://github.com/OrKatz7/parler-hate-speech. | ['L Rokach', 'S Messica', 'O Arbili', 'O Katz', 'D Presil', 'S Cohen'] | 2023-06-13 | null | null | null | information-fusion-2023-6 | ['hate-speech-normalization', 'hate-speech-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-1.72334164e-02 -2.59967417e-01 -4.68146205e-02 -2.30138265e-02
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419c903f-6e2d-4089-a1b0-0adc2fa72739 | fast-and-order-invariant-inference-in | 2305.16827 | null | https://arxiv.org/abs/2305.16827v1 | https://arxiv.org/pdf/2305.16827v1.pdf | Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks | The shocks which hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non-Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper which uses a Dirichlet process mixture (DPM) to model the shocks. However, we do not follow the obvious strategy of simply modeling the VAR errors with a DPM since this would lead to computationally infeasible Bayesian inference in larger VARs and potentially a sensitivity to the way the variables are ordered in the VAR. Instead we develop a particular additive error structure inspired by Bayesian nonparametric treatments of random effects in panel data models. We show that this leads to a model which allows for computationally fast and order-invariant inference in large VARs with nonparametric shocks. Our empirical results with nonparametric VARs of various dimensions shows that nonparametric treatment of the VAR errors is particularly useful in periods such as the financial crisis and the pandemic. | ['Gary Koop', 'Florian Huber'] | 2023-05-26 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-3.73740643e-01 1.09075628e-01 2.88733453e-01 -1.83662891e-01
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eff53c3b-c122-444e-92be-2caa995ee1a0 | efficient-bayesian-inverse-reinforcement | null | null | https://openreview.net/forum?id=7_a6T7RGNpj | https://openreview.net/pdf?id=7_a6T7RGNpj | Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation | Inverse reinforcement learning (IRL) methods attempt to recover the reward function of an agent by observing its behavior. Given the large amount of uncertainty in the underlying reward function, it is often useful to model this function probabilistically, rather than estimate a single reward function. However, existing Bayesian approaches to IRL use a Q-value function to approximate the likelihood, leading to a computationally intractable and inflexible framework. Here, we introduce kernel density Bayesian IRL (KD-BIRL), a method that uses kernel density estimation to approximate the likelihood, or the probability of the observed states and actions given a reward function. This approximation allows for efficient posterior inference of the reward function given a sequence of agent observations. Empirically, using both linear and nonlinear reward functions in a Gridworld environment, we demonstrate that the KD-BIRL posterior centers around the true reward function. | ['Barbara Engelhardt', 'Andrew Jones', 'Diana Cai', 'Didong Li', 'Aishwarya Mandyam'] | 2021-11-22 | null | null | null | pproximateinference-aabi-symposium-2022-2 | ['birl-cima'] | ['medical'] | [-3.46695989e-01 1.34755179e-01 -3.52039725e-01 -2.43359268e-01
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351521a5-3eba-45f7-9569-e44071df8458 | psdr-room-single-photo-to-scene-using | 2307.03244 | null | https://arxiv.org/abs/2307.03244v1 | https://arxiv.org/pdf/2307.03244v1.pdf | PSDR-Room: Single Photo to Scene using Differentiable Rendering | A 3D digital scene contains many components: lights, materials and geometries, interacting to reach the desired appearance. Staging such a scene is time-consuming and requires both artistic and technical skills. In this work, we propose PSDR-Room, a system allowing to optimize lighting as well as the pose and materials of individual objects to match a target image of a room scene, with minimal user input. To this end, we leverage a recent path-space differentiable rendering approach that provides unbiased gradients of the rendering with respect to geometry, lighting, and procedural materials, allowing us to optimize all of these components using gradient descent to visually match the input photo appearance. We use recent single-image scene understanding methods to initialize the optimization and search for appropriate 3D models and materials. We evaluate our method on real photographs of indoor scenes and demonstrate the editability of the resulting scene components. | ['Shuang Zhao', 'Valentin Deschaintre', 'Thibault Groueix', 'Miloš Hašan', 'Fujun Luan', 'Kai Yan'] | 2023-07-06 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 6.42921269e-01 -7.87422508e-02 8.83327007e-01 -3.63917798e-01
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4f238350-ba28-486a-9f99-cd02dd2cc9d9 | judging-a-book-by-its-cover | 1610.09204 | null | http://arxiv.org/abs/1610.09204v3 | http://arxiv.org/pdf/1610.09204v3.pdf | Judging a Book By its Cover | Book covers communicate information to potential readers, but can that same
information be learned by computers? We propose using a deep Convolutional
Neural Network (CNN) to predict the genre of a book based on the visual clues
provided by its cover. The purpose of this research is to investigate whether
relationships between books and their covers can be learned. However,
determining the genre of a book is a difficult task because covers can be
ambiguous and genres can be overarching. Despite this, we show that a CNN can
extract features and learn underlying design rules set by the designer to
define a genre. Using machine learning, we can bring the large amount of
resources available to the book cover design process. In addition, we present a
new challenging dataset that can be used for many pattern recognition tasks. | ['Sheraz Ahmed', 'Seiichi Uchida', 'Brian Kenji Iwana', 'Syed Tahseen Raza Rizvi', 'Andreas Dengel'] | 2016-10-28 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 1.53967798e-01 -7.02624470e-02 -5.92559934e-01 -3.67843032e-01
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69f61f2b-13ec-45f4-bd43-50ce08acba78 | unsupervised-video-depth-estimation-based-on | 1909.01028 | null | https://arxiv.org/abs/1909.01028v1 | https://arxiv.org/pdf/1909.01028v1.pdf | Unsupervised Video Depth Estimation Based on Ego-motion and Disparity Consensus | Unsupervised learning based depth estimation methods have received more and more attention as they do not need vast quantities of densely labeled data for training which are touch to acquire. In this paper, we propose a novel unsupervised monocular video depth estimation method in natural scenes by taking advantage of the state-of-the-art method of Zhou et al. which jointly estimates depth and camera motion. Our method advances beyond the baseline method by three aspects: 1) we add an additional signal as supervision to the baseline method by incorporating left-right binocular images reconstruction loss based on the estimated disparities, thus the left frame can be reconstructed by the temporal frames and right frames of stereo vision; 2) the network is trained by jointly using two kinds of view syntheses loss and left-right disparity consistency regularization to estimate depth and pose simultaneously; 3) we use the edge aware smooth L2 regularization to smooth the depth map while preserving the contour of the target. Extensive experiments on the KITTI autonomous driving dataset and Make3D dataset indicate the superiority of our algorithm in training efficiency. We can achieve competitive results with the baseline by only 3/5 times training data. The experimental results also show that our method even outperforms the classical supervised methods that using either ground truth depth or given pose for training. | ['Guizhong Liu', 'Lingtao Zhou', 'Jiaojiao Fang'] | 2019-09-03 | null | null | null | null | ['depth-and-camera-motion', 'l2-regularization'] | ['computer-vision', 'methodology'] | [ 1.03870161e-01 9.71131697e-02 -2.36603171e-01 -5.07449985e-01
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7fe002d9-c667-4ad5-8005-e6bc3b83b110 | measuring-the-impact-of-programming-language | 2302.01973 | null | https://arxiv.org/abs/2302.01973v3 | https://arxiv.org/pdf/2302.01973v3.pdf | Measuring The Impact Of Programming Language Distribution | Current benchmarks for evaluating neural code models focus on only a small subset of programming languages, excluding many popular languages such as Go or Rust. To ameliorate this issue, we present the BabelCode framework for execution-based evaluation of any benchmark in any language. BabelCode enables new investigations into the qualitative performance of models' memory, runtime, and individual test case results. Additionally, we present a new code translation dataset called Translating Python Programming Puzzles (TP3) from the Python Programming Puzzles (Schuster et al. 2021) benchmark that involves translating expert-level python functions to any language. With both BabelCode and the TP3 benchmark, we investigate if balancing the distributions of 14 languages in a training dataset improves a large language model's performance on low-resource languages. Training a model on a balanced corpus results in, on average, 12.34% higher $pass@k$ across all tasks and languages compared to the baseline. We find that this strategy achieves 66.48% better $pass@k$ on low-resource languages at the cost of only a 12.94% decrease to high-resource languages. In our three translation tasks, this strategy yields, on average, 30.77% better low-resource $pass@k$ while having 19.58% worse high-resource $pass@k$. | ['Rishabh Singh', 'Michele Catasta', 'Jacob Austin', 'Jonathan Malmaud', 'Joshua Howland', 'Jeffrey Hui', 'Xavier Garcia', 'Kefan Xiao', 'Gabriel Orlanski'] | 2023-02-03 | null | null | null | null | ['code-translation'] | ['computer-code'] | [-2.99113005e-01 -1.30484596e-01 -2.29409695e-01 -2.33840525e-01
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68ee990d-9a23-4b23-be1e-a70c761af1be | tempel-linking-dynamically-evolving-and-newly | 2302.02500 | null | https://arxiv.org/abs/2302.02500v1 | https://arxiv.org/pdf/2302.02500v1.pdf | TempEL: Linking Dynamically Evolving and Newly Emerging Entities | In our continuously evolving world, entities change over time and new, previously non-existing or unknown, entities appear. We study how this evolutionary scenario impacts the performance on a well established entity linking (EL) task. For that study, we introduce TempEL, an entity linking dataset that consists of time-stratified English Wikipedia snapshots from 2013 to 2022, from which we collect both anchor mentions of entities, and these target entities' descriptions. By capturing such temporal aspects, our newly introduced TempEL resource contrasts with currently existing entity linking datasets, which are composed of fixed mentions linked to a single static version of a target Knowledge Base (e.g., Wikipedia 2010 for CoNLL-AIDA). Indeed, for each of our collected temporal snapshots, TempEL contains links to entities that are continual, i.e., occur in all of the years, as well as completely new entities that appear for the first time at some point. Thus, we enable to quantify the performance of current state-of-the-art EL models for: (i) entities that are subject to changes over time in their Knowledge Base descriptions as well as their mentions' contexts, and (ii) newly created entities that were previously non-existing (e.g., at the time the EL model was trained). Our experimental results show that in terms of temporal performance degradation, (i) continual entities suffer a decrease of up to 3.1% EL accuracy, while (ii) for new entities this accuracy drop is up to 17.9%. This highlights the challenge of the introduced TempEL dataset and opens new research prospects in the area of time-evolving entity disambiguation. | ['Isabelle Augenstein', 'Chris Develder', 'Thomas Demeester', 'Johannes Deleu', 'Lucie-Aimee Kaffee', 'Klim Zaporojets'] | 2023-02-05 | null | null | null | null | ['entity-disambiguation'] | ['natural-language-processing'] | [-5.53665876e-01 3.24859113e-01 -3.17386925e-01 1.53442517e-01
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655b5d4b-e5cd-42d9-af27-d6c6a4468590 | regularized-policy-iteration | null | null | http://papers.nips.cc/paper/3445-regularized-policy-iteration | http://papers.nips.cc/paper/3445-regularized-policy-iteration.pdf | Regularized Policy Iteration | In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation scheme we propose the use of non-parametric methods with regularization, providing a convenient way to control the complexity of the function approximator. We propose two novel regularized policy iteration algorithms by adding L2-regularization to two widely-used policy evaluation methods: Bellman residual minimization (BRM) and least-squares temporal difference learning (LSTD). We derive efficient implementation for our algorithms when the approximate value-functions belong to a reproducing kernel Hilbert space. We also provide finite-sample performance bounds for our algorithms and show that they are able to achieve optimal rates of convergence under the studied conditions. | ['Csaba Szepesvári', 'Shie Mannor', 'Mohammad Ghavamzadeh', 'Amir M. Farahmand'] | 2008-12-01 | null | null | null | neurips-2008-12 | ['l2-regularization'] | ['methodology'] | [-2.09435672e-01 2.26744235e-01 -2.86161900e-01 1.35953659e-02
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161d2a46-9c3f-4363-93cc-37c6294835b6 | morphing-and-sampling-network-for-dense-point | 1912.00280 | null | https://arxiv.org/abs/1912.00280v1 | https://arxiv.org/pdf/1912.00280v1.pdf | Morphing and Sampling Network for Dense Point Cloud Completion | 3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community. For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point cloud in two stages. Specifically, in the first stage, the approach predicts a complete but coarse-grained point cloud with a collection of parametric surface elements. Then, in the second stage, it merges the coarse-grained prediction with the input point cloud by a novel sampling algorithm. Our method utilizes a joint loss function to guide the distribution of the points. Extensive experiments verify the effectiveness of our method and demonstrate that it outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD). | ['Shi-Min Hu', 'Sheng Yang', 'Lu Sheng', 'Jing Shao', 'Minghua Liu'] | 2019-11-30 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 7.23124146e-02 -2.79460456e-02 1.44404694e-01 -2.62797356e-01
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26a30416-0161-4163-87c6-bf32d09c9f20 | the-stem-ecr-dataset-grounding-scientific | 2003.01006 | null | https://arxiv.org/abs/2003.01006v4 | https://arxiv.org/pdf/2003.01006v4.pdf | The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources | We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable. | ['Sören Auer', "Jennifer D'Souza", 'Arthur Brack', 'Anett Hoppe', 'Ralph Ewerth', 'Mohamad Yaser Jaradeh'] | 2020-03-02 | the-stem-ecr-dataset-grounding-scientific-1 | https://aclanthology.org/2020.lrec-1.268 | https://aclanthology.org/2020.lrec-1.268.pdf | lrec-2020-5 | ['entity-extraction'] | ['natural-language-processing'] | [-1.33515045e-01 2.96394914e-01 -2.95064479e-01 2.34995335e-01
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bc167495-0017-4725-a84b-95f43bb2830f | flying-guide-dog-walkable-path-discovery-for | 2108.07007 | null | https://arxiv.org/abs/2108.07007v1 | https://arxiv.org/pdf/2108.07007v1.pdf | Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation | Lacking the ability to sense ambient environments effectively, blind and visually impaired people (BVIP) face difficulty in walking outdoors, especially in urban areas. Therefore, tools for assisting BVIP are of great importance. In this paper, we propose a novel "flying guide dog" prototype for BVIP assistance using drone and street view semantic segmentation. Based on the walkable areas extracted from the segmentation prediction, the drone can adjust its movement automatically and thus lead the user to walk along the walkable path. By recognizing the color of pedestrian traffic lights, our prototype can help the user to cross a street safely. Furthermore, we introduce a new dataset named Pedestrian and Vehicle Traffic Lights (PVTL), which is dedicated to traffic light recognition. The result of our user study in real-world scenarios shows that our prototype is effective and easy to use, providing new insight into BVIP assistance. | ['Rainer Stiefelhagen', 'Kailun Yang', 'Constantin Seibold', 'Jiaming Zhang', 'Xinyu Luo', 'Chang Chen', 'Haobin Tan'] | 2021-08-16 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [-2.54007310e-01 -3.95500898e-01 9.72305983e-02 -2.51487970e-01
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86ee4861-4c98-47c1-bd57-ede7fe104950 | the-atilf-llf-system-for-parseme-shared-task | null | null | https://aclanthology.org/W17-1717 | https://aclanthology.org/W17-1717.pdf | The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger | We describe the ATILF-LLF system built for the MWE 2017 Shared Task on automatic identification of verbal multiword expressions. We participated in the closed track only, for all the 18 available languages. Our system is a robust greedy transition-based system, in which MWE are identified through a MERGE transition. The system was meant to accommodate the variety of linguistic resources provided for each language, in terms of accompanying morphological and syntactic information. Using per-MWE Fscore, the system was ranked first for all but two languages (Hungarian and Romanian). | ['C', 'Marie ito', 'Hazem Al Saied', 'Matthieu Constant'] | 2017-04-01 | null | null | null | ws-2017-4 | ['lexical-analysis'] | ['natural-language-processing'] | [-2.67368913e-01 -1.96899757e-01 -4.80655670e-01 -5.41580975e-01
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85901c72-56f4-4c72-a083-a2c03dc3b484 | probabilistic-spatial-distribution-prior | 2111.09006 | null | https://arxiv.org/abs/2111.09006v2 | https://arxiv.org/pdf/2111.09006v2.pdf | Probabilistic Spatial Distribution Prior Based Attentional Keypoints Matching Network | Keypoints matching is a pivotal component for many image-relevant applications such as image stitching, visual simultaneous localization and mapping (SLAM), and so on. Both handcrafted-based and recently emerged deep learning-based keypoints matching methods merely rely on keypoints and local features, while losing sight of other available sensors such as inertial measurement unit (IMU) in the above applications. In this paper, we demonstrate that the motion estimation from IMU integration can be used to exploit the spatial distribution prior of keypoints between images. To this end, a probabilistic perspective of attention formulation is proposed to integrate the spatial distribution prior into the attentional graph neural network naturally. With the assistance of spatial distribution prior, the effort of the network for modeling the hidden features can be reduced. Furthermore, we present a projection loss for the proposed keypoints matching network, which gives a smooth edge between matching and un-matching keypoints. Image matching experiments on visual SLAM datasets indicate the effectiveness and efficiency of the presented method. | ['Zhengguo Li', 'Fanghong Guo', 'Weihai Chen', 'Xingming Wu', 'Jingmeng Liu', 'Xiaoming Zhao'] | 2021-11-17 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 4.62437840e-03 -1.72167718e-01 -4.17348385e-01 -2.97038078e-01
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19a13e25-b872-4aa8-83ab-ad1695ae1a23 | learning-to-rank-meets-language-boosting | 2306.13856 | null | https://arxiv.org/abs/2306.13856v1 | https://arxiv.org/pdf/2306.13856v1.pdf | Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification | We present a novel language-driven ordering alignment method for ordinal classification. The labels in ordinal classification contain additional ordering relations, making them prone to overfitting when relying solely on training data. Recent developments in pre-trained vision-language models inspire us to leverage the rich ordinal priors in human language by converting the original task into a vision-language alignment task. Consequently, we propose L2RCLIP, which fully utilizes the language priors from two perspectives. First, we introduce a complementary prompt tuning technique called RankFormer, designed to enhance the ordering relation of original rank prompts. It employs token-level attention with residual-style prompt blending in the word embedding space. Second, to further incorporate language priors, we revisit the approximate bound optimization of vanilla cross-entropy loss and restructure it within the cross-modal embedding space. Consequently, we propose a cross-modal ordinal pairwise loss to refine the CLIP feature space, where texts and images maintain both semantic alignment and ordering alignment. Extensive experiments on three ordinal classification tasks, including facial age estimation, historical color image (HCI) classification, and aesthetic assessment demonstrate its promising performance. | ['Zhaofeng He', 'Ran He', 'Chunshui Cao', 'Huaibo Huang', 'Peipei Li', 'Rui Wang'] | 2023-06-24 | null | null | null | null | ['age-estimation', 'learning-to-rank', 'classification-1', 'learning-to-rank', 'age-estimation'] | ['computer-vision', 'graphs', 'methodology', 'miscellaneous', 'miscellaneous'] | [ 3.43721718e-01 -4.44015898e-02 -2.77620733e-01 -7.97410309e-01
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c3829660-f1d8-404c-8b8a-606b1fe0e7da | an-easy-to-use-and-robust-approach-for-the | 2211.01147 | null | https://arxiv.org/abs/2211.01147v1 | https://arxiv.org/pdf/2211.01147v1.pdf | An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documents | Unstructured textual data is at the heart of healthcare systems. For obvious privacy reasons, these documents are not accessible to researchers as long as they contain personally identifiable information. One way to share this data while respecting the legislative framework (notably GDPR or HIPAA) is, within the medical structures, to de-identify it, i.e. to detect the personal information of a person through a Named Entity Recognition (NER) system and then replacing it to make it very difficult to associate the document with the person. The challenge is having reliable NER and substitution tools without compromising confidentiality and consistency in the document. Most of the conducted research focuses on English medical documents with coarse substitutions by not benefiting from advances in privacy. This paper shows how an efficient and differentially private de-identification approach can be achieved by strengthening the less robust de-identification method and by adapting state-of-the-art differentially private mechanisms for substitution purposes. The result is an approach for de-identifying clinical documents in French language, but also generalizable to other languages and whose robustness is mathematically proven. | ['David Laiymani', 'Jean-François Couchot', 'Yakini Tchouka'] | 2022-11-02 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 4.61215675e-01 5.03133714e-01 -5.66807576e-02 -2.02116057e-01
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b3d88514-8aeb-4677-88e8-bf16ec76998a | active-scene-learning | 1903.02832 | null | http://arxiv.org/abs/1903.02832v1 | http://arxiv.org/pdf/1903.02832v1.pdf | Active Scene Learning | Sketch recognition allows natural and efficient interaction in pen-based
interfaces. A key obstacle to building accurate sketch recognizers has been the
difficulty of creating large amounts of annotated training data. Several
authors have attempted to address this issue by creating synthetic data, and by
building tools that support efficient annotation. Two prominent sets of
approaches stand out from the rest of the crowd. They use interim classifiers
trained with a small set of labeled data to aid the labeling of the remainder
of the data. The first set of approaches uses a classifier trained with a
partially labeled dataset to automatically label unlabeled instances. The
others, based on active learning, save annotation effort by giving priority to
labeling informative data instances. The former is sub-optimal since it doesn't
prioritize the order of labeling to favor informative instances, while the
latter makes the strong assumption that unlabeled data comes in an already
segmented form (i.e. the ink in the training data is already assembled into
groups forming isolated object instances). In this paper, we propose an active
learning framework that combines the strengths of these methods, while
addressing their weaknesses. In particular, we propose two methods for deciding
how batches of unsegmented sketch scenes should be labeled. The first method,
scene-wise selection, assesses the informativeness of each drawing (sketch
scene) as a whole, and asks the user to annotate all objects in the drawing.
The latter, segment-wise selection, attempts more precise targeting to locate
informative fragments of drawings for user labeling. We show that both
selection schemes outperform random selection. Furthermore, we demonstrate that
precise targeting yields superior performance. Overall, our approach allows
reaching top accuracy figures with up to 30% savings in annotation cost. | ['Tevfik Metin Sezgin', 'Erelcan Yanik'] | 2019-03-07 | null | null | null | null | ['sketch-recognition'] | ['computer-vision'] | [ 4.64559644e-01 2.95904338e-01 -3.03800911e-01 -3.53916407e-01
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e9ea93c9-3e1b-4d97-b0b5-c1c108325943 | similarity-learning-for-cover-song | null | null | https://ieeexplore.ieee.org/abstract/document/9053257 | https://ieeexplore.ieee.org/abstract/document/9053257 | SIMILARITY LEARNING FOR COVER SONG IDENTIFICATION USING CROSS-SIMILARITY MATRICES OF MULTI-LEVEL DEEP SEQUENCES | In recent years, several deep learning models have been proposed for cover song identification and they have been designed to learn fixed-length feature vectors for music tracks. However, the aspect of temporal progression of music, which is important for measuring the melody similarity between two tracks, is not well represented by fixed-length vectors. In this paper, we propose a new Siamese network architecture for music melody similarity metric learning. The architecture consists of two parts. One part is a network for learn- ing the deep sequence representation of music tracks, and the other is a similarity estimation network which takes as input the cross- similarity matrices calculated from the deep sequences of a pair of tracks. The two networks are jointly trained and optimized to achieve high melody similarity prediction accuracy. Experiments conducted on several public datasets demonstrate the superiority of the proposed architecture. | ['Xiaoou Chen', 'Chaoya Jiang', 'Deshun Yang'] | 2020-05-14 | null | null | null | null | ['cover-song-identification'] | ['music'] | [-5.50812669e-03 -9.23762321e-01 -1.03081256e-01 -2.82247186e-01
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03089506-9290-4a43-9762-76284fde6f18 | crystalbox-future-based-explanations-for-drl | 2302.13483 | null | https://arxiv.org/abs/2302.13483v2 | https://arxiv.org/pdf/2302.13483v2.pdf | CrystalBox: Future-Based Explanations for DRL Network Controllers | Lack of explainability is a key factor limiting the practical adoption of high-performant Deep Reinforcement Learning (DRL) controllers. Explainable RL for networking hitherto used salient input features to interpret a controller's behavior. However, these feature-based solutions do not completely explain the controller's decision-making process. Often, operators are interested in understanding the impact of a controller's actions on performance in the future, which feature-based solutions cannot capture. In this paper, we present CrystalBox, a framework that explains a controller's behavior in terms of the future impact on key network performance metrics. CrystalBox employs a novel learning-based approach to generate succinct and expressive explanations. We use reward components of the DRL network controller, which are key performance metrics meaningful to operators, as the basis for explanations. CrystalBox is generalizable and can work across both discrete and continuous control environments without any changes to the controller or the DRL workflow. Using adaptive bitrate streaming and congestion control, we demonstrate CrytalBox's ability to generate high-fidelity future-based explanations. We additionally present three practical use cases of CrystalBox: cross-state explainability, guided reward design, and network observability. | ['Nina Narodytska', 'Sangeetha Abdu Jyothi', 'Sagar Patel'] | 2023-02-27 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-2.14283690e-01 5.61391890e-01 -4.64070380e-01 -3.39940548e-01
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5b483eb3-ff1c-4832-b34b-5a96076d7c45 | on-evaluating-the-adversarial-robustness-of | 2306.14217 | null | https://arxiv.org/abs/2306.14217v1 | https://arxiv.org/pdf/2306.14217v1.pdf | On Evaluating the Adversarial Robustness of Semantic Segmentation Models | Achieving robustness against adversarial input perturbation is an important and intriguing problem in machine learning. In the area of semantic image segmentation, a number of adversarial training approaches have been proposed as a defense against adversarial perturbation, but the methodology of evaluating the robustness of the models is still lacking, compared to image classification. Here, we demonstrate that, just like in image classification, it is important to evaluate the models over several different and hard attacks. We propose a set of gradient based iterative attacks and show that it is essential to perform a large number of iterations. We include attacks against the internal representations of the models as well. We apply two types of attacks: maximizing the error with a bounded perturbation, and minimizing the perturbation for a given level of error. Using this set of attacks, we show for the first time that a number of models in previous work that are claimed to be robust are in fact not robust at all. We then evaluate simple adversarial training algorithms that produce reasonably robust models even under our set of strong attacks. Our results indicate that a key design decision to achieve any robustness is to use only adversarial examples during training. However, this introduces a trade-off between robustness and accuracy. | ['Mark Jelasity', 'Levente Halmosi'] | 2023-06-25 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [ 6.42536104e-01 3.39771807e-01 2.96647370e-01 -1.56192794e-01
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b0999e23-c45d-4ee8-a48d-19971504235c | understand-waiting-time-in-transaction-fee | 2305.02552 | null | https://arxiv.org/abs/2305.02552v1 | https://arxiv.org/pdf/2305.02552v1.pdf | Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary Perspective | Blockchain enables peer-to-peer transactions in cyberspace without a trusted third party. The rapid growth of Ethereum and smart contract blockchains generally calls for well-designed Transaction Fee Mechanisms (TFMs) to allocate limited storage and computation resources. However, existing research on TFMs must consider the waiting time for transactions, which is essential for computer security and economic efficiency. Integrating data from the Ethereum blockchain and memory pool (mempool), we explore how two types of events affect transaction latency. First, we apply regression discontinuity design (RDD) to study the causal inference of the Merge, the most recent significant upgrade of Ethereum. Our results show that the Merge significantly reduces the long waiting time, network loads, and market congestion. In addition, we verify our results' robustness by inspecting other compounding factors, such as censorship and unobserved delays of transactions via private changes. Second, examining three major protocol changes during the merge, we identify block interval shortening as the most plausible cause for our empirical results. Furthermore, in a mathematical model, we show block interval as a unique mechanism design choice for EIP1559 TFM to achieve better security and efficiency, generally applicable to the market congestion caused by demand surges. Finally, we apply time series analysis to research the interaction of Non-Fungible token (NFT) drops and market congestion using Facebook Prophet, an open-source algorithm for generating time-series models. Our study identified NFT drops as a unique source of market congestion -- holiday effects -- beyond trend and season effects. Finally, we envision three future research directions of TFM. | ['Fan Zhang', 'Luyao Zhang'] | 2023-05-04 | null | null | null | null | ['causal-inference', 'computer-security', 'causal-inference'] | ['knowledge-base', 'miscellaneous', 'miscellaneous'] | [-4.12348270e-01 -1.44692481e-01 -8.18191648e-01 1.31392241e-01
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fe8fd2c7-4ed1-49f8-9782-a550b249c4c6 | a-shared-task-on-multimodal-machine | null | null | https://aclanthology.org/W16-2346 | https://aclanthology.org/W16-2346.pdf | A Shared Task on Multimodal Machine Translation and Crosslingual Image Description | null | ["Khalil Sima{'}an", 'Lucia Specia', 'Stella Frank', 'Desmond Elliott'] | 2016-08-01 | null | null | null | ws-2016-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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80bdc156-c331-4468-a70b-0033074c8b38 | reinforcement-learning-for-instance | null | null | https://openreview.net/forum?id=6EWOVxvJ5FI | https://openreview.net/pdf?id=6EWOVxvJ5FI | Reinforcement learning for instance segmentation with high-level priors | Instance segmentation is an important computer vision problem which remains challenging despite impressive recent advances due to deep learning-based methods. Given sufficient training data, fully supervised methods can yield excellent performance, but annotation of ground-truth data remains a major bottleneck, especially for biomedical applications where it has to be performed by domain experts. The amount of labels required can be drastically reduced by using rules derived from prior knowledge to guide the segmentation. However, these rules are in general not differentiable and thus cannot be used with existing methods. Here, we relax this requirement by using stateless actor critic reinforcement learning, which enables non-differentiable rewards. We formulate the instance segmentation problem as graph partitioning and the actor critic predicts the edge weights driven by the rewards, which are based on the conformity of segmented instances to high-level priors on object shape, position or size. The experiments on toy and real datasets demonstrate that we can achieve excellent performance without any direct supervision based only on a rich set of priors. | ['Anna Kreshuk', 'Constantin Pape', 'Maria Leptin', 'Sourabh Bhide', 'Edgar Kaziakhmedov', 'Paul Hilt'] | 2021-05-21 | null | null | null | neurips-2021-12 | ['graph-partitioning'] | ['graphs'] | [ 3.55634391e-01 5.44822991e-01 -2.82437325e-01 -4.85577822e-01
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8bf8cb24-fb06-448e-9512-ea4bfcf3be9d | channel-estimation-for-underwater-visible | 2303.07248 | null | https://arxiv.org/abs/2303.07248v1 | https://arxiv.org/pdf/2303.07248v1.pdf | Channel Estimation for Underwater Visible Light Communication: A Sparse Learning Perspective | The underwater propagation environment for visible light signals is affected by complex factors such as absorption, shadowing, and reflection, making it very challengeable to achieve effective underwater visible light communication (UVLC) channel estimation. It is difficult for the UVLC channel to be sparse represented in the time and frequency domains, which limits the chance of using sparse signal processing techniques to achieve better performance of channel estimation. To this end, a compressed sensing (CS) based framework is established in this paper by fully exploiting the sparsity of the underwater visible light channel in the distance domain of the propagation links. In order to solve the sparse recovery problem and achieve more accurate UVLC channel estimation, a sparse learning based underwater visible light channel estimation (SL-UVCE) scheme is proposed. Specifically, a deep-unfolding neural network mimicking the classical iterative sparse recovery algorithm of approximate message passing (AMP) is employed, which decomposes the iterations of AMP into a series of layers with different learnable parameters. Compared with the existing non-CS-based and CS-based schemes, the proposed scheme shows better performance of accuracy in channel estimation, especially in severe conditions such as insufficient measurement pilots and large number of multipath components. | ['Sicong Liu', 'Younan Mou'] | 2023-03-13 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 4.91651267e-01 -2.01457679e-01 4.85936373e-01 -1.47398546e-01
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0be136a4-f0ce-427f-8de2-c5f06b1700ae | algorithme-em-regularise | 2307.01955 | null | https://arxiv.org/abs/2307.01955v1 | https://arxiv.org/pdf/2307.01955v1.pdf | Algorithme EM régularisé | Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing maximum likelihood estimate when dealing with Gaussian Mixture Model (GMM). When the sample size is smaller than the data dimension, this could lead to a singular or poorly conditioned covariance matrix and, thus, to performance reduction. This paper presents a regularized version of the EM algorithm that efficiently uses prior knowledge to cope with a small sample size. This method aims to maximize a penalized GMM likelihood where regularized estimation may ensure positive definiteness of covariance matrix updates by shrinking the estimators towards some structured target covariance matrices. Finally, experiments on real data highlight the good performance of the proposed algorithm for clustering purposes | ['Frederic Pascal', 'Matthieu Jonkcheere', 'Pierre Houdouin'] | 2023-07-04 | null | null | null | null | ['clustering'] | ['methodology'] | [ 6.05936125e-02 1.53553262e-01 5.21284193e-02 -1.95269018e-01
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fd63024f-a486-4615-b98a-219eb1d465c5 | artificial-neural-networks-for-finger-vein | 2208.13341 | null | https://arxiv.org/abs/2208.13341v1 | https://arxiv.org/pdf/2208.13341v1.pdf | Artificial Neural Networks for Finger Vein Recognition: A Survey | Finger vein recognition is an emerging biometric recognition technology. Different from the other biometric features on the body surface, the venous vascular tissue of the fingers is buried deep inside the skin. Due to this advantage, finger vein recognition is highly stable and private. They are almost impossible to be stolen and difficult to interfere with by external conditions. Unlike the finger vein recognition methods based on traditional machine learning, the artificial neural network technique, especially deep learning, it without relying on feature engineering and have superior performance. To summarize the development of finger vein recognition based on artificial neural networks, this paper collects 149 related papers. First, we introduce the background of finger vein recognition and the motivation of this survey. Then, the development history of artificial neural networks and the representative networks on finger vein recognition tasks are introduced. The public datasets that are widely used in finger vein recognition are then described. After that, we summarize the related finger vein recognition tasks based on classical neural networks and deep neural networks, respectively. Finally, the challenges and potential development directions in finger vein recognition are discussed. To our best knowledge, this paper is the first comprehensive survey focusing on finger vein recognition based on artificial neural networks. | ['Jinghua Zhang', 'Chen Li', 'Siliang He', 'Wanxia Deng', 'PengFei Liu', 'Renye Zhang', 'Yimin Yin'] | 2022-08-29 | null | null | null | null | ['finger-vein-recognition'] | ['computer-vision'] | [ 2.12781653e-01 -2.46769950e-01 -3.08984965e-01 -1.94301188e-01
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d1dd1fea-f088-459b-b0ac-eaebb878312f | label-free-liver-tumor-segmentation | 2303.14869 | null | https://arxiv.org/abs/2303.14869v1 | https://arxiv.org/pdf/2303.14869v1.pdf | Label-Free Liver Tumor Segmentation | We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors; (II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors -- this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness. | ['Zongwei Zhou', 'Alan Yuille', 'Jieneng Chen', 'Shuwen Sun', 'Junfei Xiao', 'Yixiong Chen', 'Qixin Hu'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hu_Label-Free_Liver_Tumor_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hu_Label-Free_Liver_Tumor_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['tumor-segmentation'] | ['computer-vision'] | [ 1.17475100e-01 7.94856369e-01 1.79534908e-02 -1.67893708e-01
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b7d8e99d-9bdb-4d93-bb4f-462dd5ce514c | exploiting-implicit-rigidity-constraints-via | 2303.02454 | null | https://arxiv.org/abs/2303.02454v2 | https://arxiv.org/pdf/2303.02454v2.pdf | Exploiting Implicit Rigidity Constraints via Weight-Sharing Aggregation for Scene Flow Estimation from Point Clouds | Scene flow estimation, which predicts the 3D motion of scene points from point clouds, is a core task in autonomous driving and many other 3D vision applications. Existing methods either suffer from structure distortion due to ignorance of rigid motion consistency or require explicit pose estimation and 3D object segmentation. Errors of estimated poses and segmented objects would yield inaccurate rigidity constraints and in turn mislead scene flow estimation. In this paper, we propose a novel weight-sharing aggregation (WSA) method for feature and scene flow up-sampling. WSA does not rely on estimated poses and segmented objects, and can implicitly enforce rigidity constraints to avoid structure distortion in scene flow estimation. To further exploit geometric information and preserve local structure, we design a deformation degree module aim to keep the local region invariance. We modify the PointPWC-Net and integrate the proposed WSA and deformation degree module into the enhanced PointPWC-Net to derive an end-to-end scene flow estimation network, called WSAFlowNet. Extensive experimental results on the FlyingThings3D and KITTI datasets demonstrate that our WSAFlowNet achieves the state-of-the-art performance and outperforms previous methods by a large margin. We will release the source code at https://github.com/wangyunlhr/WSAFlowNet.git. | ['Xin Yang', 'Cheng Chi', 'Yun Wang'] | 2023-03-04 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-2.81087756e-01 -2.83981502e-01 -1.52159110e-01 -5.37383378e-01
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7620fc9e-7d04-4081-9e02-ce4063da7709 | pencil-deep-learning-with-noisy-labels | 2202.08436 | null | https://arxiv.org/abs/2202.08436v1 | https://arxiv.org/pdf/2202.08436v1.pdf | PENCIL: Deep Learning with Noisy Labels | Deep learning has achieved excellent performance in various computer vision tasks, but requires a lot of training examples with clean labels. It is easy to collect a dataset with noisy labels, but such noise makes networks overfit seriously and accuracies drop dramatically. To address this problem, we propose an end-to-end framework called PENCIL, which can update both network parameters and label estimations as label distributions. PENCIL is independent of the backbone network structure and does not need an auxiliary clean dataset or prior information about noise, thus it is more general and robust than existing methods and is easy to apply. PENCIL can even be used repeatedly to obtain better performance. PENCIL outperforms previous state-of-the-art methods by large margins on both synthetic and real-world datasets with different noise types and noise rates. And PENCIL is also effective in multi-label classification tasks through adding a simple attention structure on backbone networks. Experiments show that PENCIL is robust on clean datasets, too. | ['Jianxin Wu', 'Guo-Hua Wang', 'Kun Yi'] | 2022-02-17 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 0.17685907 -0.2138053 -0.05941239 -0.7608482 -0.803599 -0.61485445
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0aef6e26-4d48-4a88-8cb9-a9dd9b1cba87 | read-attend-and-comment-a-deep-architecture | 1909.11974 | null | https://arxiv.org/abs/1909.11974v3 | https://arxiv.org/pdf/1909.11974v3.pdf | Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation | Automatic news comment generation is a new testbed for techniques of natural language generation. In this paper, we propose a "read-attend-comment" procedure for news comment generation and formalize the procedure with a reading network and a generation network. The reading network comprehends a news article and distills some important points from it, then the generation network creates a comment by attending to the extracted discrete points and the news title. We optimize the model in an end-to-end manner by maximizing a variational lower bound of the true objective using the back-propagation algorithm. Experimental results on two datasets indicate that our model can significantly outperform existing methods in terms of both automatic evaluation and human judgment. | ['Zhoujun Li', 'Ze Yang', 'Wei Wu', 'Can Xu'] | 2019-09-26 | read-attend-and-comment-a-deep-architecture-2 | https://aclanthology.org/D19-1512 | https://aclanthology.org/D19-1512.pdf | ijcnlp-2019-11 | ['comment-generation'] | ['natural-language-processing'] | [ 2.35044390e-01 9.72614884e-01 -3.16170454e-01 -5.66217661e-01
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decfb259-6239-4faf-9f3f-7cdcf1908540 | enhanced-boundary-learning-for-glass-like | 2103.15734 | null | https://arxiv.org/abs/2103.15734v2 | https://arxiv.org/pdf/2103.15734v2.pdf | Enhanced Boundary Learning for Glass-like Object Segmentation | Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. However, this task is very challenging due to the arbitrary scenes behind glass-like objects. This paper aims to solve the glass-like object segmentation problem via enhanced boundary learning. In particular, we first propose a novel refined differential module that outputs finer boundary cues. We then introduce an edge-aware point-based graph convolution network module to model the global shape along the boundary. We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours. Both modules are lightweight and effective: they can be embedded into various segmentation models. In extensive experiments on three recent glass-like object segmentation datasets, including Trans10k, MSD, and GDD, our approach establishes new state-of-the-art results. We also illustrate the strong generalization properties of our method on three generic segmentation datasets, including Cityscapes, BDD, and COCO Stuff. Code and models is available at \url{https://github.com/hehao13/EBLNet}. | ['Lubin Weng', 'Véronique Prinet', 'Gaofeng Meng', 'Yunhai Tong', 'Jianping Shi', 'Guangliang Cheng', 'Xiangtai Li', 'Hao He'] | 2021-03-29 | null | http://openaccess.thecvf.com//content/ICCV2021/html/He_Enhanced_Boundary_Learning_for_Glass-Like_Object_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/He_Enhanced_Boundary_Learning_for_Glass-Like_Object_Segmentation_ICCV_2021_paper.pdf | iccv-2021-1 | ['thermal-image-segmentation'] | ['computer-vision'] | [-6.09925240e-02 2.33757019e-01 5.08193932e-02 -5.18332720e-01
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773f38b8-2b70-4551-8343-d71e2adfc815 | isolation-kernel-and-its-effect-on-svm | null | null | https://dl.acm.org/doi/abs/10.1145/3219819.3219990 | https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/kdd18.pdf | Isolation Kernel and Its Effect on SVM | This paper investigates data dependent kernels that are derived directly from data. This has been an outstanding issue for about two decades which hampered the development of kernel-based methods. We introduce Isolation Kernel which is solely dependent on data distribution, requiring neither class information nor explicit learning to be a classifier. In contrast, existing data dependent kernels rely heavily on class information and explicit learning to produce a classifier. We show that Isolation Kernel approximates well to a data independent kernel function called Laplacian kernel under uniform density distribution. With this revelation, Isolation Kernel can be viewed as a data dependent kernel that adapts a data independent kernel to the structure of a dataset. We also provide a reason why the proposed new data dependent kernel enables SVM (which employs a kernel through other means) to improve its predictive accuracy. The key differences between Random Forest kernel and Isolation Kernel are discussed to examine the reasons why the latter is a more successful tree-based kernel. | ['Zhi-Hua Zhou', 'Yue Zhu', 'Kai Ming Ting'] | 2018-07-19 | null | null | null | proceedings-of-the-24th-acm-sigkdd-1 | ['classification'] | ['methodology'] | [ 7.37902075e-02 -1.76774412e-01 -5.65447628e-01 -6.20325208e-01
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bff659a6-b3f8-4205-ae2c-4b7867dfcab5 | a-two-stage-real-image-deraining-method-for | 2305.07979 | null | https://arxiv.org/abs/2305.07979v1 | https://arxiv.org/pdf/2305.07979v1.pdf | A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG$^{\textbf{2}}$+ Track 3 | In this technical report, we briefly introduce the solution of our team HUST\li VIE for GT-Rain Challenge in CVPR 2023 UG$^{2}$+ Track 3. In this task, we propose an efficient two-stage framework to reconstruct a clear image from rainy frames. Firstly, a low-rank based video deraining method is utilized to generate pseudo GT, which fully takes the advantage of multi and aligned rainy frames. Secondly, a transformer-based single image deraining network Uformer is implemented to pre-train on large real rain dataset and then fine-tuned on pseudo GT to further improve image restoration. Moreover, in terms of visual pleasing effect, a comprehensive image processor module is utilized at the end of pipeline. Our overall framework is elaborately designed and able to handle both heavy rainy and foggy sequences provided in the final testing phase. Finally, we rank 1st on the average structural similarity (SSIM) and rank 2nd on the average peak signal-to-noise ratio (PSNR). Our code is available at https://github.com/yunguo224/UG2_Deraining. | ['Luxin Yan', 'Yi Chang', 'Yi Li', 'Xiaoxiong Wang', 'Xueyao Xiao', 'Yun Guo'] | 2023-05-13 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 2.49965996e-01 -3.31817955e-01 6.18819833e-01 -5.53470194e-01
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b71a4c00-d5e7-4394-bc52-49cfcbde04d4 | practical-first-order-bayesian-optimization | 2306.10815 | null | https://arxiv.org/abs/2306.10815v1 | https://arxiv.org/pdf/2306.10815v1.pdf | Practical First-Order Bayesian Optimization Algorithms | First Order Bayesian Optimization (FOBO) is a sample efficient sequential approach to find the global maxima of an expensive-to-evaluate black-box objective function by suitably querying for the function and its gradient evaluations. Such methods assume Gaussian process (GP) models for both, the function and its gradient, and use them to construct an acquisition function that identifies the next query point. In this paper, we propose a class of practical FOBO algorithms that efficiently utilizes the information from the gradient GP to identify potential query points with zero gradients. We construct a multi-level acquisition function where in the first step, we optimize a lower level acquisition function with multiple restarts to identify potential query points with zero gradient value. We then use the upper level acquisition function to rank these query points based on their function values to potentially identify the global maxima. As a final step, the potential point of maxima is chosen as the actual query point. We validate the performance of our proposed algorithms on several test functions and show that our algorithms outperform state-of-the-art FOBO algorithms. We also illustrate the application of our algorithms in finding optimal set of hyper-parameters in machine learning and in learning the optimal policy in reinforcement learning tasks. | ['Tejas Bodas', 'Prabuchandran K. J.', 'Kushagra Khatwani', 'Aryan Chollera', 'Utkarsh Prakash'] | 2023-06-19 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-9.21505094e-02 -2.47084379e-01 -2.42902920e-01 -1.85923576e-01
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c56dcfc0-f005-4d44-90ab-4001eedefb59 | midmed-towards-mixed-type-dialogues-for | 2306.02923 | null | https://arxiv.org/abs/2306.02923v2 | https://arxiv.org/pdf/2306.02923v2.pdf | MidMed: Towards Mixed-Type Dialogues for Medical Consultation | Most medical dialogue systems assume that patients have clear goals (medicine querying, surgical operation querying, etc.) before medical consultation. However, in many real scenarios, due to the lack of medical knowledge, it is usually difficult for patients to determine clear goals with all necessary slots. In this paper, we identify this challenge as how to construct medical consultation dialogue systems to help patients clarify their goals. To mitigate this challenge, we propose a novel task and create a human-to-human mixed-type medical consultation dialogue corpus, termed MidMed, covering five dialogue types: task-oriented dialogue for diagnosis, recommendation, knowledge-grounded dialogue, QA, and chitchat. MidMed covers four departments (otorhinolaryngology, ophthalmology, skin, and digestive system), with 8,175 dialogues. Furthermore, we build baselines on MidMed and propose an instruction-guiding medical dialogue generation framework, termed InsMed, to address this task. Experimental results show the effectiveness of InsMed. | ['Shaoting Zhang', 'Xiaofan Zhang', 'Kui Xue', 'Haitao Leng', 'Chuan Wang', 'Zeming Liu', 'Xiaoming Shi'] | 2023-06-05 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 5.63664399e-02 1.22502875e+00 4.81390543e-02 -4.22484368e-01
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0c1f9470-8aee-4574-97eb-be266f435551 | a-large-scale-dataset-for-biomedical | 2211.12124 | null | https://arxiv.org/abs/2211.12124v1 | https://arxiv.org/pdf/2211.12124v1.pdf | A Large-Scale Dataset for Biomedical Keyphrase Generation | Keyphrase generation is the task consisting in generating a set of words or phrases that highlight the main topics of a document. There are few datasets for keyphrase generation in the biomedical domain and they do not meet the expectations in terms of size for training generative models. In this paper, we introduce kp-biomed, the first large-scale biomedical keyphrase generation dataset with more than 5M documents collected from PubMed abstracts. We train and release several generative models and conduct a series of experiments showing that using large scale datasets improves significantly the performances for present and absent keyphrase generation. The dataset is available under CC-BY-NC v4.0 license at https://huggingface.co/ datasets/taln-ls2n/kpbiomed. | ['Beatrice Daille', 'Florian Boudin', 'Mael Houbre'] | 2022-11-22 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 3.14607590e-01 4.49782103e-01 -1.99495286e-01 9.80750006e-03
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f3ec7ff5-43ae-4769-8c43-f124a8ed904c | water-from-two-rocks-maximizing-the-mutual | 1802.08887 | null | http://arxiv.org/abs/1802.08887v3 | http://arxiv.org/pdf/1802.08887v3.pdf | Water from Two Rocks: Maximizing the Mutual Information | We build a natural connection between the learning problem, co-training, and
forecast elicitation without verification (related to peer-prediction) and
address them simultaneously using the same information theoretic approach.
In co-training/multiview learning, the goal is to aggregate two views of data
into a prediction for a latent label. We show how to optimally combine two
views of data by reducing the problem to an optimization problem. Our work
gives a unified and rigorous approach to the general setting.
In forecast elicitation without verification we seek to design a mechanism
that elicits high quality forecasts from agents in the setting where the
mechanism does not have access to the ground truth. By assuming the agents'
information is independent conditioning on the outcome, we propose mechanisms
where truth-telling is a strict equilibrium for both the single-task and
multi-task settings. Our multi-task mechanism additionally has the property
that the truth-telling equilibrium pays better than any other strategy profile
and strictly better than any other "non-permutation" strategy profile when the
prior satisfies some mild conditions. | ['Grant Schoenebeck', 'Yuqing Kong'] | 2018-02-24 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [ 3.31719726e-01 7.01482594e-01 -5.22044539e-01 -7.58493006e-01
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0f798dd7-3868-4dd8-bf75-d22a1f80e42f | evaluation-datasets-for-cross-lingual | null | null | https://aclanthology.org/2021.ranlp-main.59 | https://aclanthology.org/2021.ranlp-main.59.pdf | Evaluation Datasets for Cross-lingual Semantic Textual Similarity | Semantic textual similarity (STS) systems estimate the degree of the meaning similarity between two sentences. Cross-lingual STS systems estimate the degree of the meaning similarity between two sentences, each in a different language. State-of-the-art algorithms usually employ a strongly supervised, resource-rich approach difficult to use for poorly-resourced languages. However, any approach needs to have evaluation data to confirm the results. In order to simplify the evaluation process for poorly-resourced languages (in terms of STS evaluation datasets), we present new datasets for cross-lingual and monolingual STS for languages without this evaluation data. We also present the results of several state-of-the-art methods on these data which can be used as a baseline for further research. We believe that this article will not only extend the current STS research to other languages, but will also encourage competition on this new evaluation data. | ['Pavel Kral', 'Tomáš Hercig'] | null | null | https://aclanthology.org/2021.ranlp-1.59 | https://aclanthology.org/2021.ranlp-1.59.pdf | ranlp-2021-9 | ['cross-lingual-semantic-textual-similarity'] | ['natural-language-processing'] | [-5.07358946e-02 -9.91831124e-02 -2.11089388e-01 -8.24547648e-01
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2fb5c8e3-4d9c-4dcc-bc33-0195d5f4edf4 | proposal-learning-for-semi-supervised-object | 2001.05086 | null | https://arxiv.org/abs/2001.05086v2 | https://arxiv.org/pdf/2001.05086v2.pdf | Proposal Learning for Semi-Supervised Object Detection | In this paper, we focus on semi-supervised object detection to boost performance of proposal-based object detectors (a.k.a. two-stage object detectors) by training on both labeled and unlabeled data. However, it is non-trivial to train object detectors on unlabeled data due to the unavailability of ground truth labels. To address this problem, we present a proposal learning approach to learn proposal features and predictions from both labeled and unlabeled data. The approach consists of a self-supervised proposal learning module and a consistency-based proposal learning module. In the self-supervised proposal learning module, we present a proposal location loss and a contrastive loss to learn context-aware and noise-robust proposal features respectively. In the consistency-based proposal learning module, we apply consistency losses to both bounding box classification and regression predictions of proposals to learn noise-robust proposal features and predictions. Our approach enjoys the following benefits: 1) encouraging more context information to delivered in the proposals learning procedure; 2) noisy proposal features and enforcing consistency to allow noise-robust object detection; 3) building a general and high-performance semi-supervised object detection framework, which can be easily adapted to proposal-based object detectors with different backbone architectures. Experiments are conducted on the COCO dataset with all available labeled and unlabeled data. Results demonstrate that our approach consistently improves the performance of fully-supervised baselines. In particular, after combining with data distillation, our approach improves AP by about 2.0% and 0.9% on average compared to fully-supervised baselines and data distillation baselines respectively. | ['ran Xu', 'Peng Tang', 'Chetan Ramaiah', 'Caiming Xiong', 'Yan Wang'] | 2020-01-15 | null | null | null | null | ['robust-object-detection', 'semi-supervised-object-detection'] | ['computer-vision', 'computer-vision'] | [-5.98241808e-03 1.99398011e-01 -2.71292508e-01 -6.21911287e-01
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496a6edd-17fd-4cd8-b9af-3343e95128ca | on-the-proper-treatment-of-quantifiers-in | null | null | https://aclanthology.org/W15-0119 | https://aclanthology.org/W15-0119.pdf | On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics | null | ['Katrin Erk', 'Islam Beltagy'] | 2015-04-01 | null | null | null | ws-2015-4 | ['fine-grained-opinion-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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d7ccc8ec-0dd6-4ec2-aa86-5645e31e2303 | videnn-deep-blind-video-denoising | 1904.10898 | null | http://arxiv.org/abs/1904.10898v1 | http://arxiv.org/pdf/1904.10898v1.pdf | ViDeNN: Deep Blind Video Denoising | We propose ViDeNN: a CNN for Video Denoising without prior knowledge on the
noise distribution (blind denoising). The CNN architecture uses a combination
of spatial and temporal filtering, learning to spatially denoise the frames
first and at the same time how to combine their temporal information, handling
objects motion, brightness changes, low-light conditions and temporal
inconsistencies. We demonstrate the importance of the data used for CNNs
training, creating for this purpose a specific dataset for low-light
conditions. We test ViDeNN on common benchmarks and on self-collected data,
achieving good results comparable with the state-of-the-art. | ['Jan van Gemert', 'Michele Claus'] | 2019-04-24 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 9.93694216e-02 -4.95868713e-01 3.94219726e-01 -4.01362002e-01
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e75718d0-952e-4176-8894-9906aa5d580d | gpt-nas-neural-architecture-search-with-the | 2305.05351 | null | https://arxiv.org/abs/2305.05351v2 | https://arxiv.org/pdf/2305.05351v2.pdf | GPT-NAS: Evolutionary Neural Architecture Search with the Generative Pre-Trained Model | Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically. Although neural architectures have achieved human-level performances in several tasks, few of them are obtained from the NAS method. The main reason is the huge search space of neural architectures, making NAS algorithms inefficient. This work presents a novel architecture search algorithm, called GPT-NAS, that optimizes neural architectures by Generative Pre-Trained (GPT) model with an evolutionary algorithm (EA) as the search strategy. In GPT-NAS, we assume that a generative model pre-trained on a large-scale corpus could learn the fundamental law of building neural architectures. Therefore, GPT-NAS leverages the GPT model to propose reasonable architecture components given the basic one and then utilizes EAs to search for the optimal solution. Such an approach can largely reduce the search space by introducing prior knowledge in the search process. Extensive experimental results show that our GPT-NAS method significantly outperforms seven manually designed neural architectures and thirteen architectures provided by competing NAS methods. In addition, our experiments also indicate that the proposed algorithm improves the performance of finely tuned neural architectures by up to about 12% compared to those without GPT, further demonstrating its effectiveness in searching neural architectures. | ['Chenwei Tang', 'Wentao Feng', 'Jiancheng Lv', 'Xianggen Liu', 'Caiyang Yu'] | 2023-05-09 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 1.10531628e-01 2.32098833e-01 3.21058109e-02 -2.57183075e-01
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9005d1aa-e72a-4c50-ba51-746d111593de | drg-net-interactive-joint-learning-of-multi | 2212.14615 | null | https://arxiv.org/abs/2212.14615v1 | https://arxiv.org/pdf/2212.14615v1.pdf | DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading | Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner. | ['Daniel Sonntag', 'Pengtao Xie', 'Ngan Le', 'Ngoc T. T. Than', 'Hans-Juergen Profitlich', 'Michael Barz', 'Binh T. Nguyen', 'Triet A. Nguyen', 'Mai T. N. Truong', 'Duy M. H. Nguyen', 'Hasan Md Tusfiqur'] | 2022-12-30 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 6.83065131e-02 4.75582272e-01 -8.57707039e-02 -6.12768352e-01
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40ac134a-a914-46a6-a901-e1543b39838f | investigating-the-effectiveness-of-chatgpt-in | 2306.06331 | null | https://arxiv.org/abs/2306.06331v1 | https://arxiv.org/pdf/2306.06331v1.pdf | Investigating the Effectiveness of ChatGPT in Mathematical Reasoning and Problem Solving: Evidence from the Vietnamese National High School Graduation Examination | This study offers a complete analysis of ChatGPT's mathematics abilities in responding to multiple-choice questions for the Vietnamese National High School Graduation Examination (VNHSGE) on a range of subjects and difficulty levels. The dataset included 250 questions divided into four levels: knowledge (K), comprehension (C), application (A), and high application (H), and it included ten themes that covered diverse mathematical concepts. The outcomes demonstrate that ChatGPT's performance varies depending on the difficulty level and subject. It performed best on questions at Level (K), with an accuracy rate of $83\%$; but, as the difficulty level rose, it scored poorly, with an accuracy rate of $10\%$. The study has also shown that ChatGPT significantly succeeds in providing responses to questions on subjects including exponential and logarithmic functions, geometric progression, and arithmetic progression. The study found that ChatGPT had difficulty correctly answering questions on topics including derivatives and applications, spatial geometry, and Oxyz spatial calculus. Additionally, this study contrasted ChatGPT outcomes with Vietnamese students in VNHSGE and in other math competitions. ChatGPT dominated in the SAT Math competition with a success rate of $70\%$, followed by VNHSGE mathematics ($58.8\%)$. However, its success rates were lower on other exams, such as AP Statistics, the GRE Quantitative, AMC 10, AMC 12, and AP Calculus BC. These results suggest that ChatGPT has the potential to be an effective teaching tool for mathematics, but more work is needed to enhance its handling of graphical data and address the challenges presented by questions that are getting more challenging. | ['Ngoc-Bich Le', 'Xuan-Quy Dao'] | 2023-06-10 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-7.94470549e-01 -5.74372150e-02 6.38442561e-02 -1.60736546e-01
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5b8c33cc-7a60-4021-908c-388a8b723b6f | diverse-human-motion-prediction-via-gumbel | 2207.07351 | null | https://arxiv.org/abs/2207.07351v1 | https://arxiv.org/pdf/2207.07351v1.pdf | Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space | Diverse human motion prediction aims at predicting multiple possible future pose sequences from a sequence of observed poses. Previous approaches usually employ deep generative networks to model the conditional distribution of data, and then randomly sample outcomes from the distribution. While different results can be obtained, they are usually the most likely ones which are not diverse enough. Recent work explicitly learns multiple modes of the conditional distribution via a deterministic network, which however can only cover a fixed number of modes within a limited range. In this paper, we propose a novel sampling strategy for sampling very diverse results from an imbalanced multimodal distribution learned by a deep generative model. Our method works by generating an auxiliary space and smartly making randomly sampling from the auxiliary space equivalent to the diverse sampling from the target distribution. We propose a simple yet effective network architecture that implements this novel sampling strategy, which incorporates a Gumbel-Softmax coefficient matrix sampling method and an aggressive diversity promoting hinge loss function. Extensive experiments demonstrate that our method significantly improves both the diversity and accuracy of the samplings compared with previous state-of-the-art sampling approaches. Code and pre-trained models are available at https://github.com/Droliven/diverse_sampling. | ['Guiqing Li', 'Qing Zhang', 'Chengjiang Long', 'Yongwei Nie', 'Lingwei Dang'] | 2022-07-15 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [ 1.11958936e-01 -9.59184989e-02 -4.49034721e-01 -5.09808004e-01
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f5d33768-844b-463b-b86f-7a7c60f82b2b | panoocc-unified-occupancy-representation-for | 2306.10013 | null | https://arxiv.org/abs/2306.10013v1 | https://arxiv.org/pdf/2306.10013v1.pdf | PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation | Comprehensive modeling of the surrounding 3D world is key to the success of autonomous driving. However, existing perception tasks like object detection, road structure segmentation, depth & elevation estimation, and open-set object localization each only focus on a small facet of the holistic 3D scene understanding task. This divide-and-conquer strategy simplifies the algorithm development procedure at the cost of losing an end-to-end unified solution to the problem. In this work, we address this limitation by studying camera-based 3D panoptic segmentation, aiming to achieve a unified occupancy representation for camera-only 3D scene understanding. To achieve this, we introduce a novel method called PanoOcc, which utilizes voxel queries to aggregate spatiotemporal information from multi-frame and multi-view images in a coarse-to-fine scheme, integrating feature learning and scene representation into a unified occupancy representation. We have conducted extensive ablation studies to verify the effectiveness and efficiency of the proposed method. Our approach achieves new state-of-the-art results for camera-based semantic segmentation and panoptic segmentation on the nuScenes dataset. Furthermore, our method can be easily extended to dense occupancy prediction and has shown promising performance on the Occ3D benchmark. The code will be released at https://github.com/Robertwyq/PanoOcc. | ['Zhaoxiang Zhang', 'Lue Fan', 'Xingyu Liao', 'Yuntao Chen', 'Yuqi Wang'] | 2023-06-16 | null | null | null | null | ['panoptic-segmentation', 'object-localization', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.22204968e-02 -3.11330527e-01 -1.24785542e-01 -3.84481817e-01
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7f8afb45-03ef-4f8f-94e2-056cd4530214 | machine-learning-models-in-stock-market | 2202.09359 | null | https://arxiv.org/abs/2202.09359v1 | https://arxiv.org/pdf/2202.09359v1.pdf | Machine Learning Models in Stock Market Prediction | The paper focuses on predicting the Nifty 50 Index by using 8 Supervised Machine Learning Models. The techniques used for empirical study are Adaptive Boost (AdaBoost), k-Nearest Neighbors (kNN), Linear Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM) and Decision Trees (DT). Experiments are based on historical data of Nifty 50 Index of Indian Stock Market from 22nd April, 1996 to 16th April, 2021, which is time series data of around 25 years. During the period there were 6220 trading days excluding all the non trading days. The entire trading dataset was divided into 4 subsets of different size-25% of entire data, 50% of entire data, 75% of entire data and entire data. Each subset was further divided into 2 parts-training data and testing data. After applying 3 tests- Test on Training Data, Test on Testing Data and Cross Validation Test on each subset, the prediction performance of the used models were compared and after comparison, very interesting results were found. The evaluation results indicate that Adaptive Boost, k- Nearest Neighbors, Random Forest and Decision Trees under performed with increase in the size of data set. Linear Regression and Artificial Neural Network shown almost similar prediction results among all the models but Artificial Neural Network took more time in training and validating the model. Thereafter Support Vector Machine performed better among rest of the models but with increase in the size of data set, Stochastic Gradient Descent performed better than Support Vector Machine. | ['Gurjeet Singh'] | 2022-02-06 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-5.17246008e-01 -2.63319880e-01 -2.29380220e-01 -4.04673457e-01
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c9de2156-e857-4825-8503-c3b7534e3a4e | joint-tone-mapping-and-denoising-of-thermal | 2305.00691 | null | https://arxiv.org/abs/2305.00691v1 | https://arxiv.org/pdf/2305.00691v1.pdf | Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning | Cameras digitize real-world scenes as pixel intensity values with a limited value range given by the available bits per pixel (bpp). High Dynamic Range (HDR) cameras capture those luminance values in higher resolution through an increase in the number of bpp. Most displays, however, are limited to 8 bpp. Naive HDR compression methods lead to a loss of the rich information contained in those HDR images. In this paper, tone mapping algorithms for thermal infrared images with 16 bpp are investigated that can preserve this information. An optimized multi-scale Retinex algorithm sets the baseline. This algorithm is then approximated with a deep learning approach based on the popular U-Net architecture. The remaining noise in the images after tone mapping is reduced implicitly by utilizing a self-supervised deep learning approach that can be jointly trained with the tone mapping approach in a multi-task learning scheme. Further discussions are provided on denoising and deflickering for thermal infrared video enhancement in the context of tone mapping. Extensive experiments on the public FLIR ADAS Dataset prove the effectiveness of our proposed method in comparison with the state-of-the-art. | ['Michael Teutsch', 'Gabriel Eilertsen', 'Daniel König', 'Axel Gödrich'] | 2023-05-01 | null | null | null | null | ['video-enhancement', 'tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 9.35676455e-01 -4.16925281e-01 -8.34222957e-02 -3.57061207e-01
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4c9fdfb5-1199-4643-9f22-eb8d9490a845 | class-attention-transfer-based-knowledge | 2304.12777 | null | https://arxiv.org/abs/2304.12777v1 | https://arxiv.org/pdf/2304.12777v1.pdf | Class Attention Transfer Based Knowledge Distillation | Previous knowledge distillation methods have shown their impressive performance on model compression tasks, however, it is hard to explain how the knowledge they transferred helps to improve the performance of the student network. In this work, we focus on proposing a knowledge distillation method that has both high interpretability and competitive performance. We first revisit the structure of mainstream CNN models and reveal that possessing the capacity of identifying class discriminative regions of input is critical for CNN to perform classification. Furthermore, we demonstrate that this capacity can be obtained and enhanced by transferring class activation maps. Based on our findings, we propose class attention transfer based knowledge distillation (CAT-KD). Different from previous KD methods, we explore and present several properties of the knowledge transferred by our method, which not only improve the interpretability of CAT-KD but also contribute to a better understanding of CNN. While having high interpretability, CAT-KD achieves state-of-the-art performance on multiple benchmarks. Code is available at: https://github.com/GzyAftermath/CAT-KD. | ['Xiaodong Lin', 'Hui Li', 'Haonan Yan', 'Ziyao Guo'] | 2023-04-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Guo_Class_Attention_Transfer_Based_Knowledge_Distillation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Guo_Class_Attention_Transfer_Based_Knowledge_Distillation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['model-compression'] | ['methodology'] | [ 3.92239466e-02 4.12285030e-01 -3.43834579e-01 -3.47776204e-01
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caa854fa-2163-4a9b-a952-4e30a584179b | eventgraph-event-extraction-as-semantic-graph | 2210.08646 | null | https://arxiv.org/abs/2210.08646v1 | https://arxiv.org/pdf/2210.08646v1.pdf | EventGraph: Event Extraction as Semantic Graph Parsing | Event extraction involves the detection and extraction of both the event triggers and corresponding event arguments. Existing systems often decompose event extraction into multiple subtasks, without considering their possible interactions. In this paper, we propose EventGraph, a joint framework for event extraction, which encodes events as graphs. We represent event triggers and arguments as nodes in a semantic graph. Event extraction therefore becomes a graph parsing problem, which provides the following advantages: 1) performing event detection and argument extraction jointly; 2) detecting and extracting multiple events from a piece of text; and 3) capturing the complicated interaction between event arguments and triggers. Experimental results on ACE2005 show that our model is competitive to state-of-the-art systems and has substantially improved the results on argument extraction. Additionally, we create two new datasets from ACE2005 where we keep the entire text spans for event arguments, instead of just the head word(s). Our code and models are released as open-source. | ['Lilja Øvrelid', 'Samia Touileb', 'David Samuel', 'Huiling You'] | 2022-10-16 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 3.12099129e-01 4.74846303e-01 -2.29985535e-01 -3.39888930e-01
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eaae584a-6196-46e8-b545-fd4c573a049d | viesum-how-robust-are-transformer-based | 2110.04257 | null | https://arxiv.org/abs/2110.04257v1 | https://arxiv.org/pdf/2110.04257v1.pdf | VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization? | Text summarization is a challenging task within natural language processing that involves text generation from lengthy input sequences. While this task has been widely studied in English, there is very limited research on summarization for Vietnamese text. In this paper, we investigate the robustness of transformer-based encoder-decoder architectures for Vietnamese abstractive summarization. Leveraging transfer learning and self-supervised learning, we validate the performance of the methods on two Vietnamese datasets. | ['Hieu Tran', 'Alec Peltekian', 'James Anibal', 'Long Phan', 'Hieu Nguyen'] | 2021-10-08 | null | null | null | null | ['vietnamese-datasets'] | ['natural-language-processing'] | [ 7.28474677e-01 3.87027174e-01 -2.74712235e-01 -3.46681744e-01
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b7c1f504-3877-4070-afc1-9060128e547f | mrn-multiplexed-routing-network-for | 2305.14758 | null | https://arxiv.org/abs/2305.14758v1 | https://arxiv.org/pdf/2305.14758v1.pdf | MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition | Traditional Multilingual Text Recognition (MLTR) usually targets a fixed set of languages and thus struggles to handle newly added languages or adapt to ever-changing class distributions. In this paper, we introduce the Incremental Multilingual Text Recognition (IMLTR) task in the incremental learning setting, where new language data comes in batches. Compared to generic incremental learning, IMLTR is even more challenging as it suffers from rehearsal-imbalance (uneven distribution of sample characters in the rehearsal set). To address this issue, we propose a Multiplexed Routing Network (MRN), where a series of recognizers is trained for each language. Subsequently, a language predictor is adopted to weigh the recognizers for voting. Since the recognizers are derived from the original model, MRN effectively reduces the reliance on older data and is better suited for rehearsal-imbalance. We extensively evaluate MRN on MLT17 and MLT19 datasets, outperforming existing state-of-the-art methods by a large margin, i.e., accuracy improvement ranging from 10.3% to 27.4% under different settings. | ['Yu-Gang Jiang', 'Wei zhang', 'Bingchen Huang', 'Zhineng Chen', 'Tianlun Zheng'] | 2023-05-24 | null | null | null | null | ['incremental-learning'] | ['methodology'] | [ 3.34674835e-01 -4.97353196e-01 -5.29678404e-01 -5.21195889e-01
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1338cc8e-327d-4f7a-ad7e-309e8d231c97 | on-the-generalizability-of-neural-program | 2008.01566 | null | https://arxiv.org/abs/2008.01566v3 | https://arxiv.org/pdf/2008.01566v3.pdf | On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations | With the prevalence of publicly available source code repositories to train deep neural network models, neural program models can do well in source code analysis tasks such as predicting method names in given programs that cannot be easily done by traditional program analysis techniques. Although such neural program models have been tested on various existing datasets, the extent to which they generalize to unforeseen source code is largely unknown. Since it is very challenging to test neural program models on all unforeseen programs, in this paper, we propose to evaluate the generalizability of neural program models with respect to semantic-preserving transformations: a generalizable neural program model should perform equally well on programs that are of the same semantics but of different lexical appearances and syntactical structures. We compare the results of various neural program models for the method name prediction task on programs before and after automated semantic-preserving transformations. We use three Java datasets of different sizes and three state-of-the-art neural network models for code, namely code2vec, code2seq, and GGNN, to build nine such neural program models for evaluation. Our results show that even with small semantically preserving changes to the programs, these neural program models often fail to generalize their performance. Our results also suggest that neural program models based on data and control dependencies in programs generalize better than neural program models based only on abstract syntax trees. On the positive side, we observe that as the size of the training dataset grows and diversifies the generalizability of correct predictions produced by the neural program models can be improved too. Our results on the generalizability of neural program models provide insights to measure their limitations and provide a stepping stone for their improvement. | ['Ke Wang', 'Md Rafiqul Islam Rabin', 'Lingxiao Jiang', 'Nghi D. Q. Bui', 'Mohammad Amin Alipour', 'Yijun Yu'] | 2020-07-31 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [ 8.42430815e-02 1.16626054e-01 -4.93091822e-01 -5.99548340e-01
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6775e14e-b152-4f89-96f5-5e83c4897366 | myocardial-infarction-detection-from-ecg-a | 2302.13011 | null | https://arxiv.org/abs/2302.13011v1 | https://arxiv.org/pdf/2302.13011v1.pdf | Myocardial Infarction Detection from ECG: A Gramian Angular Field-based 2D-CNN Approach | This paper presents a novel method for myocardial infarction (MI) detection using lead II of electrocardiogram (ECG). Under our proposed method, we first clean the noisy ECG signals using db4 wavelet, followed by an R-peak detection algorithm to segment the ECG signals into beats. We then translate the ECG timeseries dataset to an equivalent dataset of gray-scale images using Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF) operations. Subsequently, the gray-scale images are fed into a custom two-dimensional convolutional neural network (2D-CNN) which efficiently differentiates the ECG beats of the healthy subjects from the ECG beats of the subjects with MI. We train and test the performance of our proposed method on a public dataset, namely, Physikalisch Technische Bundesanstalt (PTB) ECG dataset from Physionet. Our proposed approach achieves an average classification accuracy of 99.68\%, 99.80\%, 99.82\%, and 99.84\% under GASF dataset with noise and baseline wander, GADF dataset with noise and baseline wander, GASF dataset with noise and baseline wander removed, and GADF dataset with noise and baseline wander removed, respectively. Our proposed method is able to cope with additive noise and baseline wander, and does not require handcrafted features by a domain expert. Most importantly, this work opens the floor for innovation in wearable devices (e.g., smart watches, wrist bands etc.) to do accurate, real-time and early MI detection using a single-lead (lead II) ECG. | ['Muhammad Mahboob Ur Rahman', 'Kashif Riaz', 'Muhammad Farooq', 'Saqib Riaz', 'Rehan Hafiz', 'Asim Yousuf'] | 2023-02-25 | null | null | null | null | ['myocardial-infarction-detection'] | ['medical'] | [ 5.01082897e-01 -3.11102986e-01 3.21015358e-01 -1.53617799e-01
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7265804d-547a-4263-a8b3-e1bf5563ebd7 | nsp-ner-a-prompt-based-learner-for-few-shot | null | null | https://openreview.net/forum?id=8CRReJ4AgsG | https://openreview.net/pdf?id=8CRReJ4AgsG | NSP-NER: A Prompt-based Learner for Few-shot NER Driven by Next Sentence Prediction | Recently, prompt-based learning has achieved great success in few-shot learning. This paradigm does better integrate pre-training and downstream tasks and mine the knowledge inherent in the pre-train language model itself. Most research on prompt-learning has been conducted, but the application of prompt-based learning in NER has not been fully explored. The main obstacles of prompt-based learning's application to NER are that, each unit to be predicted is a token or span instead of the whole input text, and the existing prompt-based methods are not sensitive to the boundaries of tokens or spans. To address these issues, we propose a prompt-based learner for few-shot NER driven by Next Sentence Prediction (NSP), reformulating NER as a NSP task with span boundary information enhancement. We conduct experiments on three NER datasets in true few-shot learning setting, which supposes that only a small training set and a small validation set are available. Experimental results show that our method outperforms previous state-of-the-art models and yields promising performance even in such a challenging scenario. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['few-shot-ner'] | ['natural-language-processing'] | [ 1.96401715e-01 8.63851458e-02 -3.11391711e-01 -4.45005298e-01
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5dd96297-d368-49f4-afbc-195e33761f3a | classifying-segmenting-and-tracking-object | 1912.04573 | null | https://arxiv.org/abs/1912.04573v4 | https://arxiv.org/pdf/1912.04573v4.pdf | Classifying, Segmenting, and Tracking Object Instances in Video with Mask Propagation | We introduce a method for simultaneously classifying, segmenting and tracking object instances in a video sequence. Our method, named MaskProp, adapts the popular Mask R-CNN to video by adding a mask propagation branch that propagates frame-level object instance masks from each video frame to all the other frames in a video clip. This allows our system to predict clip-level instance tracks with respect to the object instances segmented in the middle frame of the clip. Clip-level instance tracks generated densely for each frame in the sequence are finally aggregated to produce video-level object instance segmentation and classification. Our experiments demonstrate that our clip-level instance segmentation makes our approach robust to motion blur and object occlusions in video. MaskProp achieves the best reported accuracy on the YouTube-VIS dataset, outperforming the ICCV 2019 video instance segmentation challenge winner despite being much simpler and using orders of magnitude less labeled data (1.3M vs 1B images and 860K vs 14M bounding boxes). | ['Gedas Bertasius', 'Lorenzo Torresani'] | 2019-12-10 | classifying-segmenting-and-tracking-object-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Bertasius_Classifying_Segmenting_and_Tracking_Object_Instances_in_Video_with_Mask_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Bertasius_Classifying_Segmenting_and_Tracking_Object_Instances_in_Video_with_Mask_CVPR_2020_paper.pdf | cvpr-2020-6 | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.98984736e-01 7.68493637e-02 -5.25139093e-01 -3.56119603e-01
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214415ec-fea5-41bf-90e6-7d74d99c4142 | acnet-attention-based-network-to-exploit | 1905.10089 | null | https://arxiv.org/abs/1905.10089v1 | https://arxiv.org/pdf/1905.10089v1.pdf | ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation | Compared to RGB semantic segmentation, RGBD semantic segmentation can achieve better performance by taking depth information into consideration. However, it is still problematic for contemporary segmenters to effectively exploit RGBD information since the feature distributions of RGB and depth (D) images vary significantly in different scenes. In this paper, we propose an Attention Complementary Network (ACNet) that selectively gathers features from RGB and depth branches. The main contributions lie in the Attention Complementary Module (ACM) and the architecture with three parallel branches. More precisely, ACM is a channel attention-based module that extracts weighted features from RGB and depth branches. The architecture preserves the inference of the original RGB and depth branches, and enables the fusion branch at the same time. Based on the above structures, ACNet is capable of exploiting more high-quality features from different channels. We evaluate our model on SUN-RGBD and NYUDv2 datasets, and prove that our model outperforms state-of-the-art methods. In particular, a mIoU score of 48.3\% on NYUDv2 test set is achieved with ResNet50. We will release our source code based on PyTorch and the trained segmentation model at https://github.com/anheidelonghu/ACNet. | ['Kaiwei Wang', 'Lei Fei', 'Kailun Yang', 'Xinxin Hu'] | 2019-05-24 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 6.43664300e-02 2.05396235e-01 -2.01366216e-01 -2.99419403e-01
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815f3375-70e0-4e19-90e7-5082e19ba00c | the-spoken-language-understanding-media | null | null | https://aclanthology.org/2022.lrec-1.171 | https://aclanthology.org/2022.lrec-1.171.pdf | The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools | With the emergence of neural end-to-end approaches for spoken language understanding (SLU), a growing number of studies have been presented during these last three years on this topic. The major part of these works addresses the spoken language understanding domain through a simple task like speech intent detection. In this context, new benchmark datasets have also been produced and shared with the community related to this task. In this paper, we focus on the French MEDIA SLU dataset, distributed since 2005 and used as a benchmark dataset for a large number of research works. This dataset has been shown as being the most challenging one among those accessible to the research community. Distributed by ELRA, this corpus is free for academic research since 2019. Unfortunately, the MEDIA dataset is not really used beyond the French research community. To facilitate its use, a complete recipe, including data preparation, training and evaluation scripts, has been built and integrated to SpeechBrain, an already popular open-source and all-in-one conversational AI toolkit based on PyTorch. This recipe is presented in this paper. In addition, based on the feedback of some researchers who have worked on this dataset for several years, some corrections have been brought to the initial manual annotation: the new version of the data will also be integrated into the ELRA catalogue, as the original one. More, a significant amount of data collected during the construction of the MEDIA corpus in the 2000s was never used until now: we present the first results reached on this subset — also included in the MEDIA SpeechBrain recipe — , that will be used for now as the MEDIA test2. Last, we discuss evaluation issues. | ['Yannick Estève', 'Bassam Jabaian', 'Sahar Ghannay', 'Nathalie Camelin', 'Salima Mdhaffar', 'Antoine Caubrière', 'Valentin Pelloin', 'Gaëlle Laperrière'] | null | null | null | null | lrec-2022-6 | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 3.31001654e-02 4.46060628e-01 2.60023445e-01 -6.85907125e-01
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b5673255-831c-4c99-ab6b-56703bcd76e1 | semi-supervised-vocabulary-informed-learning | 1604.07093 | null | http://arxiv.org/abs/1604.07093v1 | http://arxiv.org/pdf/1604.07093v1.pdf | Semi-supervised Vocabulary-informed Learning | Despite significant progress in object categorization, in recent years, a
number of important challenges remain, mainly, ability to learn from limited
labeled data and ability to recognize object classes within large, potentially
open, set of labels. Zero-shot learning is one way of addressing these
challenges, but it has only been shown to work with limited sized class
vocabularies and typically requires separation between supervised and
unsupervised classes, allowing former to inform the latter but not vice versa.
We propose the notion of semi-supervised vocabulary-informed learning to
alleviate the above mentioned challenges and address problems of supervised,
zero-shot and open set recognition using a unified framework. Specifically, we
propose a maximum margin framework for semantic manifold-based recognition that
incorporates distance constraints from (both supervised and unsupervised)
vocabulary atoms, ensuring that labeled samples are projected closest to their
correct prototypes, in the embedding space, than to others. We show that
resulting model shows improvements in supervised, zero-shot, and large open set
recognition, with up to 310K class vocabulary on AwA and ImageNet datasets. | ['Yanwei Fu', 'Leonid Sigal'] | 2016-04-24 | semi-supervised-vocabulary-informed-learning-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Fu_Semi-Supervised_Vocabulary-Informed_Learning_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Fu_Semi-Supervised_Vocabulary-Informed_Learning_CVPR_2016_paper.pdf | cvpr-2016-6 | ['object-categorization'] | ['computer-vision'] | [ 3.68966222e-01 2.19328552e-01 -5.71036696e-01 -7.18410015e-01
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da1840f5-5f87-46b0-a0d1-4016987ae10d | adaptive-bayesian-beamforming-for-imaging-by | 2212.03824 | null | https://arxiv.org/abs/2212.03824v2 | https://arxiv.org/pdf/2212.03824v2.pdf | Adaptive Bayesian Beamforming for Imaging by Marginalizing the Speed of Sound | Imaging methods based on array signal processing often require a fixed propagation speed of the medium, or speed of sound (SoS) for methods based on acoustic signals. The resolution of the images formed using these methods is strongly affected by the assumed SoS, which, due to multipath, nonlinear propagation, and non-uniform mediums, is challenging at best to select. In this letter, we propose a Bayesian approach to marginalize the influence of the SoS on beamformers for imaging. We adapt Bayesian direction-of-arrival estimation to an imaging setting and integrate a popular minimum variance beamformer over the posterior of the SoS. To solve the Bayesian integral efficiently, we use numerical Gauss quadrature. We apply our beamforming approach to shallow water sonar imaging where multipath and nonlinear propagation is abundant. We compare against the minimum variance distortionless response (MVDR) beamformer and demonstrate that its Bayesian counterpart achieves improved range and azimuthal resolution while effectively suppressing multipath artifacts. | ['Jason F. Ralph', 'Simon Maskell', 'Kyurae Kim'] | 2022-12-07 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 5.60509384e-01 -5.25202096e-01 1.06126177e+00 -1.90390095e-01
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1addf3b5-9153-4f21-b67e-aa3dc6bf24ae | an-audio-video-deep-and-transfer-learning | 2010.03692 | null | https://arxiv.org/abs/2010.03692v3 | https://arxiv.org/pdf/2010.03692v3.pdf | An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild | In this paper, we present our contribution to ABAW facial expression challenge. We report the proposed system and the official challenge results adhering to the challenge protocol. Using end-to-end deep learning and benefiting from transfer learning approaches, we reached a test set challenge performance measure of 42.10%. | ['Wolfgang Minker', 'Alexey Karpov', 'Maxim Markitantov', 'Heysem Kaya', 'Elena Ryumina', 'Denis Dresvyanskiy'] | 2020-10-07 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-6.11823872e-02 1.86056614e-01 1.03916049e-01 -9.70317304e-01
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0511e436-243d-427b-b979-06a0a1a09e3e | latincy-synthetic-trained-pipelines-for-latin | 2305.04365 | null | https://arxiv.org/abs/2305.04365v1 | https://arxiv.org/pdf/2305.04365v1.pdf | LatinCy: Synthetic Trained Pipelines for Latin NLP | This paper introduces LatinCy, a set of trained general purpose Latin-language "core" pipelines for use with the spaCy natural language processing framework. The models are trained on a large amount of available Latin data, including all five of the Latin Universal Dependency treebanks, which have been preprocessed to be compatible with each other. The result is a set of general models for Latin with good performance on a number of natural language processing tasks (e.g. the top-performing model yields POS tagging, 97.41% accuracy; lemmatization, 94.66% accuracy; morphological tagging 92.76% accuracy). The paper describes the model training, including its training data and parameterization, and presents the advantages to Latin-language researchers of having a spaCy model available for NLP work. | ['Patrick J. Burns'] | 2023-05-07 | null | null | null | null | ['lemmatization', 'morphological-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-3.67769241e-01 1.79938465e-01 -4.91575867e-01 -4.86722559e-01
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f574cee1-598f-47d1-81e7-11b1a37f90f5 | signed-latent-factors-for-spamming-activity | 2209.13814 | null | https://arxiv.org/abs/2209.13814v1 | https://arxiv.org/pdf/2209.13814v1.pdf | Signed Latent Factors for Spamming Activity Detection | Due to the increasing trend of performing spamming activities (e.g., Web spam, deceptive reviews, fake followers, etc.) on various online platforms to gain undeserved benefits, spam detection has emerged as a hot research issue. Previous attempts to combat spam mainly employ features related to metadata, user behaviors, or relational ties. These works have made considerable progress in understanding and filtering spamming campaigns. However, this problem remains far from fully solved. Almost all the proposed features focus on a limited number of observed attributes or explainable phenomena, making it difficult for existing methods to achieve further improvement. To broaden the vision about solving the spam problem and address long-standing challenges (class imbalance and graph incompleteness) in the spam detection area, we propose a new attempt of utilizing signed latent factors to filter fraudulent activities. The spam-contaminated relational datasets of multiple online applications in this scenario are interpreted by the unified signed network. Two competitive and highly dissimilar algorithms of latent factors mining (LFM) models are designed based on multi-relational likelihoods estimation (LFM-MRLE) and signed pairwise ranking (LFM-SPR), respectively. We then explore how to apply the mined latent factors to spam detection tasks. Experiments on real-world datasets of different kinds of Web applications (social media and Web forum) indicate that LFM models outperform state-of-the-art baselines in detecting spamming activities. By specifically manipulating experimental data, the effectiveness of our methods in dealing with incomplete and imbalanced challenges is valida | ['Yuli Liu'] | 2022-09-28 | null | null | null | null | ['activity-detection', 'spam-detection'] | ['computer-vision', 'natural-language-processing'] | [ 1.57237679e-01 -1.73945412e-01 -4.88976359e-01 -2.92551428e-01
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7aeb302f-b23f-4380-9c50-ea626e45a9ff | upper-middle-and-lower-region-learning-for | 2002.04023 | null | https://arxiv.org/abs/2002.04023v2 | https://arxiv.org/pdf/2002.04023v2.pdf | Upper, Middle and Lower Region Learning for Facial Action Unit Detection | Facial action units (AUs) detection is fundamental to facial expression analysis. As AU occurs only in a small area of the face, region-based learning has been widely recognized useful for AU detection. Most region-based studies focus on a small region where the AU occurs. Focusing on a specific region helps eliminate the influence of identity, but bringing a risk for losing information. It is challenging to find balance. In this study, I propose a simple strategy. I divide the face into three broad regions, upper, middle, and lower region, and group AUs based on where it occurs. I propose a new end-to-end deep learning framework named three regions based attention network (TRA-Net). After extracting the global feature, TRA-Net uses a hard attention module to extract three feature maps, each of which contains only a specific region. Each region-specific feature map is fed to an independent branch. For each branch, three continuous soft attention modules are used to extract higher-level features for final AU detection. In the DISFA dataset, this model achieves the highest F1 scores for the detection of AU1, AU2, and AU4, and produces the highest accuracy in comparison with the state-of-the-art methods. | ['Yao Xia'] | 2020-02-10 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection', 'hard-attention'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.27240241e-01 1.61874015e-02 -2.60553837e-01 -2.10505888e-01
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0924f860-4775-45e5-b43f-9eaab84308df | an-open-source-tool-for-negation-detection-a | null | null | https://aclanthology.org/W17-1810 | https://aclanthology.org/W17-1810.pdf | An open-source tool for negation detection: a maximum-margin approach | This paper presents an open-source toolkit for negation detection. It identifies negation cues and their corresponding scope in either raw or parsed text using maximum-margin classification. The system design draws on best practice from the existing literature on negation detection, aiming for a simple and portable system that still achieves competitive performance. Pre-trained models and experimental results are provided for English. | ['Lilja {\\O}vrelid', 'Martine Enger', 'Erik Velldal'] | 2017-04-01 | null | null | null | ws-2017-4 | ['negation-detection'] | ['natural-language-processing'] | [ 3.19517791e-01 1.64935350e-01 -6.65521085e-01 -6.68188810e-01
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5fc4cd18-0a7a-4bd5-a95a-e9da0bc11d99 | technical-report-for-ego4d-long-term-action | 2307.01467 | null | https://arxiv.org/abs/2307.01467v1 | https://arxiv.org/pdf/2307.01467v1.pdf | Technical Report for Ego4D Long Term Action Anticipation Challenge 2023 | In this report, we describe the technical details of our approach for the Ego4D Long-Term Action Anticipation Challenge 2023. The aim of this task is to predict a sequence of future actions that will take place at an arbitrary time or later, given an input video. To accomplish this task, we introduce three improvements to the baseline model, which consists of an encoder that generates clip-level features from the video, an aggregator that integrates multiple clip-level features, and a decoder that outputs Z future actions. 1) Model ensemble of SlowFast and SlowFast-CLIP; 2) Label smoothing to relax order constraints for future actions; 3) Constraining the prediction of the action class (verb, noun) based on word co-occurrence. Our method outperformed the baseline performance and recorded as second place solution on the public leaderboard. | ['Yuji Sato', 'Noriyuki Kugo', 'Kosuke Ono', 'Tatsuya Ishibashi'] | 2023-07-04 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 4.16071922e-01 1.69197142e-01 -3.62988859e-01 -7.07368910e-01
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cd89dc5a-5a20-46af-80ca-aa0d4193df1a | improving-commonsense-causal-reasoning-by | 2101.04966 | null | https://arxiv.org/abs/2101.04966v1 | https://arxiv.org/pdf/2101.04966v1.pdf | Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation | Determining the plausibility of causal relations between clauses is a commonsense reasoning task that requires complex inference ability. The general approach to this task is to train a large pretrained language model on a specific dataset. However, the available training data for the task is often scarce, which leads to instability of model training or reliance on the shallow features of the dataset. This paper presents a number of techniques for making models more robust in the domain of causal reasoning. Firstly, we perform adversarial training by generating perturbed inputs through synonym substitution. Secondly, based on a linguistic theory of discourse connectives, we perform data augmentation using a discourse parser for detecting causally linked clauses in large text, and a generative language model for generating distractors. Both methods boost model performance on the Choice of Plausible Alternatives (COPA) dataset, as well as on a Balanced COPA dataset, which is a modified version of the original data that has been developed to avoid superficial cues, leading to a more challenging benchmark. We show a statistically significant improvement in performance and robustness on both datasets, even with only a small number of additionally generated data points. | ['Ignacio Iacobacci', 'Philip John Gorinski', 'Ieva Staliūnaitė'] | 2021-01-13 | null | null | null | null | ['commonsense-causal-reasoning'] | ['natural-language-processing'] | [ 5.58793902e-01 7.43330717e-01 -1.15896866e-01 -4.09296125e-01
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826b6957-4377-4a95-985d-e4dadb71b9da | structured-sentiment-analysis-as-transition | 2305.05311 | null | https://arxiv.org/abs/2305.05311v1 | https://arxiv.org/pdf/2305.05311v1.pdf | Structured Sentiment Analysis as Transition-based Dependency Parsing | Structured sentiment analysis (SSA) aims to automatically extract people's opinions from a text in natural language and adequately represent that information in a graph structure. One of the most accurate methods for performing SSA was recently proposed and consists of approaching it as a dependency parsing task. Although we can find in the literature how transition-based algorithms excel in dependency parsing in terms of accuracy and efficiency, all proposed attempts to tackle SSA following that approach were based on graph-based models. In this article, we present the first transition-based method to address SSA as dependency parsing. Specifically, we design a transition system that processes the input text in a left-to-right pass, incrementally generating the graph structure containing all identified opinions. To effectively implement our final transition-based model, we resort to a Pointer Network architecture as a backbone. From an extensive evaluation, we demonstrate that our model offers the best performance to date in practically all cases among prior dependency-based methods, and surpass recent task-specific techniques on the most challenging datasets. We additionally include an in-depth analysis and empirically prove that the overall time-complexity cost of our approach is quadratic in the sentence length, being more efficient than top-performing graph-based parsers. | ['Daniel Fernández-González'] | 2023-05-09 | null | null | null | null | ['dependency-parsing', 'transition-based-dependency-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.27666485e-01 5.06249726e-01 -6.80484846e-02 -5.60709417e-01
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c6667757-a576-4b83-bc32-cf67ffbded9d | online-attentive-kernel-based-temporal | 2201.09065 | null | https://arxiv.org/abs/2201.09065v1 | https://arxiv.org/pdf/2201.09065v1.pdf | Online Attentive Kernel-Based Temporal Difference Learning | With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficiency. However, online RL often suffers from complex Value Function Approximation (VFA) and catastrophic interference, creating difficulty for the deep neural network to be applied to an online RL algorithm in a fully online setting. Therefore, a simpler and more adaptive approach is introduced to evaluate value function with the kernel-based model. Sparse representations are superior at handling interference, indicating that competitive sparse representations should be learnable, non-prior, non-truncated and explicit when compared with current sparse representation methods. Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA. In this paper, we propose an Online Attentive Kernel-Based Temporal Difference (OAKTD) algorithm using two-timescale optimization and provide convergence analysis of our proposed algorithm. Experimental evaluations showed that OAKTD outperformed several Online Kernel-based Temporal Difference (OKTD) learning algorithms in addition to the Temporal Difference (TD) learning algorithm with Tile Coding on public Mountain Car, Acrobot, CartPole and Puddle World tasks. | ['Yang Gao', 'Shaokang Dong', 'Huihui Wang', 'Shangdong Yang', 'Xingguo Chen', 'Guang Yang'] | 2022-01-22 | null | null | null | null | ['acrobot'] | ['playing-games'] | [-2.37467259e-01 -2.09322110e-01 -1.58427179e-01 -1.32598624e-01
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8164b4d0-b149-4a3f-a4bd-c576d21d708a | deep-double-incomplete-multi-view-multi-label | null | null | https://ieeexplore.ieee.org/abstract/document/10086538 | https://ieeexplore.ieee.org/abstract/document/10086538 | Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing Views | View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery. In the past years, many efforts have been made to address the incomplete multi-view learning or incomplete multi-label learning problem. However, few works can simultaneously handle the challenging case with both two incomplete issues. In this paper, we propose a new incomplete multi-view multi-label learning network to address this challenging issue. The proposed method is composed of four major parts: view-specific deep feature extraction network, weighted representation fusion module, classification module, and view-specific deep decoder network. By respectively integrating the view missing information and label missing information into the weighted fusion module and classification module, the proposed method can effectively reduce the negative influence caused by such two incomplete issues and sufficiently explore the available data and label information to obtain the most discriminative feature extractor and classifier. Furthermore, our method can be trained in both supervised and semi-supervised manners, which has important implications for flexible deployment. Experimental results on five benchmarks in supervised and semi-supervised cases demonstrate that the proposed method can greatly enhance the classification performance on the difficult incomplete multi-view multi-label classification tasks with missing labels and missing views. | ['Yong Xu', 'Ke Yan', 'Lunke Fei', 'Yicheng Liu', 'Shijie Deng', 'Chengliang Liu', 'Jie Wen'] | 2023-03-23 | null | null | null | ieee-transactions-on-neural-networks-and-14 | ['multi-view-learning', 'multi-label-learning'] | ['computer-vision', 'methodology'] | [ 4.35144007e-01 -3.42691898e-01 -4.31080312e-01 -7.25465357e-01
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dcdbc98f-a39f-4ef1-be9a-091230482cde | licamgait-gait-recognition-in-the-wild-by | 2211.12371 | null | https://arxiv.org/abs/2211.12371v1 | https://arxiv.org/pdf/2211.12371v1.pdf | LiCamGait: Gait Recognition in the Wild by Using LiDAR and Camera Multi-modal Visual Sensors | LiDAR can capture accurate depth information in large-scale scenarios without the effect of light conditions, and the captured point cloud contains gait-related 3D geometric properties and dynamic motion characteristics. We make the first attempt to leverage LiDAR to remedy the limitation of view-dependent and light-sensitive camera for more robust and accurate gait recognition. In this paper, we propose a LiDAR-camera-based gait recognition method with an effective multi-modal feature fusion strategy, which fully exploits advantages of both point clouds and images. In particular, we propose a new in-the-wild gait dataset, LiCamGait, involving multi-modal visual data and diverse 2D/3D representations. Our method achieves state-of-the-art performance on the new dataset. Code and dataset will be released when this paper is published. | ['Yuexin Ma', 'Jingyi Yu', 'Jingya Wang', 'Lan Xu', 'Peishan Cong', 'Xiao Han'] | 2022-11-22 | null | null | null | null | ['gait-recognition-in-the-wild', 'gait-recognition'] | ['computer-vision', 'computer-vision'] | [-1.57372192e-01 -1.03000033e+00 -1.33941174e-01 -2.57624477e-01
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9127fbc6-3114-41e3-ad20-e8872cb70d74 | an-analysis-of-parallelized-motion-masking | 1702.05156 | null | http://arxiv.org/abs/1702.05156v1 | http://arxiv.org/pdf/1702.05156v1.pdf | An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models | Motion detection in video is important for a number of applications and
fields. In video surveillance, motion detection is an essential accompaniment
to activity recognition for early warning systems. Robotics also has much to
gain from motion detection and segmentation, particularly in high speed motion
tracking for tactile systems. There are a myriad of techniques for detecting
and masking motion in an image. Successful systems have used Gaussian Models to
discern background from foreground in an image (motion from static imagery).
However, particularly in the case of a moving camera or frame of reference, it
is necessary to compensate for the motion of the camera when attempting to
discern objects moving in the foreground. For example, it is possible to
estimate motion of the camera through optical flow methods or temporal
differencing and then compensate for this motion in a background subtraction
model. We selection a method by Yi et al. using Dual-Mode Single Gaussian
Models which does just this. We implement the technique in Intel's Thread
Building Blocks (TBB) and NVIDIA's CUDA libraries. We then compare
parallelization improvements with a theoretical analysis of speedups based on
the characteristics of our selected model and attributes of both TBB and CUDA.
We make our implementation available to the public. | ['Peter Henderson', 'Matthew Vertescher'] | 2017-02-16 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 4.00739908e-01 -5.91925025e-01 -1.16396900e-02 1.14224963e-01
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164fb04f-13fd-4235-ae6c-59da3fa37b53 | sparql-as-a-foreign-language | 1708.07624 | null | https://arxiv.org/abs/1708.07624v2 | https://arxiv.org/pdf/1708.07624v2.pdf | SPARQL as a Foreign Language | In the last years, the Linked Data Cloud has achieved a size of more than 100 billion facts pertaining to a multitude of domains. However, accessing this information has been significantly challenging for lay users. Approaches to problems such as Question Answering on Linked Data and Link Discovery have notably played a role in increasing information access. These approaches are often based on handcrafted and/or statistical models derived from data observation. Recently, Deep Learning architectures based on Neural Networks called seq2seq have shown to achieve state-of-the-art results at translating sequences into sequences. In this direction, we propose Neural SPARQL Machines, end-to-end deep architectures to translate any natural language expression into sentences encoding SPARQL queries. Our preliminary results, restricted on selected DBpedia classes, show that Neural SPARQL Machines are a promising approach for Question Answering on Linked Data, as they can deal with known problems such as vocabulary mismatch and perform graph pattern composition. | ['André Valdestilhas', 'Gustavo Publio', 'Diego Esteves', 'Edgard Marx', 'Ciro Baron Neto', 'Tommaso Soru', 'Diego Moussallem'] | 2017-08-25 | null | null | null | null | ['graph-question-answering', 'knowledge-base-question-answering'] | ['graphs', 'natural-language-processing'] | [ 1.69239506e-01 6.17455661e-01 -2.68466473e-01 -5.79455435e-01
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76ca6e47-265e-4e0c-a90e-8e5c2cf606f3 | morphganformer-transformer-based-face | 2302.09404 | null | https://arxiv.org/abs/2302.09404v1 | https://arxiv.org/pdf/2302.09404v1.pdf | MorphGANFormer: Transformer-based Face Morphing and De-Morphing | Semantic face image manipulation has received increasing attention in recent years. StyleGAN-based approaches to face morphing are among the leading techniques; however, they often suffer from noticeable blurring and artifacts as a result of the uniform attention in the latent feature space. In this paper, we propose to develop a transformer-based alternative to face morphing and demonstrate its superiority to StyleGAN-based methods. Our contributions are threefold. First, inspired by GANformer, we introduce a bipartite structure to exploit long-range interactions in face images for iterative propagation of information from latent variables to salient facial features. Special loss functions are designed to support the optimization of face morphing. Second, we extend the study of transformer-based face morphing to demorphing by presenting an effective defense strategy with access to a reference image using the same generator of MorphGANFormer. Such demorphing is conceptually similar to unmixing of hyperspectral images but operates in the latent (instead of pixel) space. Third, for the first time, we address a fundamental issue of vulnerability-detectability trade-off for face morphing studies. It is argued that neither doppelganger norrandom pair selection is optimal, and a Lagrangian multiplier-based approach should be used to achieve an improved trade-off between recognition vulnerability and attack detectability. | ['Guo-Jun Qi', 'Xin Li', 'Xudong Liu', 'Na Zhang'] | 2023-02-18 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 8.45018208e-01 2.54653573e-01 9.02109221e-02 -1.14649773e-01
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675f5ab5-55d2-4f75-8a7d-5b6522044978 | adaptive-graph-representation-learning-and | 2101.07034 | null | https://arxiv.org/abs/2101.07034v3 | https://arxiv.org/pdf/2101.07034v3.pdf | AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing | Face parsing infers a pixel-wise label to each facial component, which has drawn much attention recently. Previous methods have shown their success in face parsing, which however overlook the correlation among facial components. As a matter of fact, the component-wise relationship is a critical clue in discriminating ambiguous pixels in facial area. To address this issue, we propose adaptive graph representation learning and reasoning over facial components, aiming to learn representative vertices that describe each component, exploit the component-wise relationship and thereby produce accurate parsing results against ambiguity. In particular, we devise an adaptive and differentiable graph abstraction method to represent the components on a graph via pixel-to-vertex projection under the initial condition of a predicted parsing map, where pixel features within a certain facial region are aggregated onto a vertex. Further, we explicitly incorporate the image edge as a prior in the model, which helps to discriminate edge and non-edge pixels during the projection, thus leading to refined parsing results along the edges. Then, our model learns and reasons over the relations among components by propagating information across vertices on the graph. Finally, the refined vertex features are projected back to pixel grids for the prediction of the final parsing map. To train our model, we propose a discriminative loss to penalize small distances between vertices in the feature space, which leads to distinct vertices with strong semantics. Experimental results show the superior performance of the proposed model on multiple face parsing datasets, along with the validation on the human parsing task to demonstrate the generalizability of our model. | ['Tao Mei', 'Hailin Shi', 'Yinglu Liu', 'Wei Hu', 'Gusi Te'] | 2021-01-18 | null | null | null | null | ['face-parsing', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 4.31964248e-01 4.87461269e-01 -4.93290275e-02 -8.20492744e-01
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aab80ef6-cb03-45f7-82ec-da8e1654a5da | speaker-turn-modeling-for-dialogue-act | 2109.05056 | null | https://arxiv.org/abs/2109.05056v1 | https://arxiv.org/pdf/2109.05056v1.pdf | Speaker Turn Modeling for Dialogue Act Classification | Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue. Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout the dialogue, therefore treating it no different than non-interactive written text. In this paper, we propose to integrate the turn changes in conversations among speakers when modeling DAs. Specifically, we learn conversation-invariant speaker turn embeddings to represent the speaker turns in a conversation; the learned speaker turn embeddings are then merged with the utterance embeddings for the downstream task of DA classification. With this simple yet effective mechanism, our model is able to capture the semantics from the dialogue content while accounting for different speaker turns in a conversation. Validation on three benchmark public datasets demonstrates superior performance of our model. | ['Mohammad Soleymani', 'Kristina Lerman', 'Leili Tavabi', 'Zihao He'] | 2021-09-10 | null | https://aclanthology.org/2021.findings-emnlp.185 | https://aclanthology.org/2021.findings-emnlp.185.pdf | findings-emnlp-2021-11 | ['dialogue-act-classification'] | ['natural-language-processing'] | [-6.67399839e-02 5.02369761e-01 -1.42477661e-01 -7.88554311e-01
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7e41475a-2f94-44e2-9a8a-954292721a7e | think-rationally-about-what-you-see | 2305.03503 | null | https://arxiv.org/abs/2305.03503v1 | https://arxiv.org/pdf/2305.03503v1.pdf | Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction | Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Previous works either focus on how to leverage the entity information (e.g., entity types, entity verbalization) to inference relations, but ignore context-focused content, or use counterfactual thinking to remove the model's bias of potential relations in entities, but the relation reasoning process will still be hindered by irrelevant content. Therefore, how to preserve relevant content and remove noisy segments from sentences is a crucial task. In addition, retained content needs to be fluent enough to maintain semantic coherence and interpretability. In this work, we propose a novel rationale extraction framework named RE2, which leverages two continuity and sparsity factors to obtain relevant and coherent rationales from sentences. To solve the problem that the gold rationales are not labeled, RE2 applies an optimizable binary mask to each token in the sentence, and adjust the rationales that need to be selected according to the relation label. Experiments on four datasets show that RE2 surpasses baselines. | ['Philip S. Yu', 'Irwin King', 'Chenwei Zhang', 'Zhaochen Hong', 'Xuming Hu'] | 2023-05-02 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 4.42446947e-01 5.72703958e-01 -5.28905571e-01 -5.77735722e-01
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c4c8ee44-6f59-493c-ac3e-759e7d5c8885 | learning-by-example-fast-reliability-aware | 2104.06255 | null | https://arxiv.org/abs/2104.06255v1 | https://arxiv.org/pdf/2104.06255v1.pdf | Learning by example: fast reliability-aware seismic imaging with normalizing flows | Uncertainty quantification provides quantitative measures on the reliability of candidate solutions of ill-posed inverse problems. Due to their sequential nature, Monte Carlo sampling methods require large numbers of sampling steps for accurate Bayesian inference and are often computationally infeasible for large-scale inverse problems, such as seismic imaging. Our main contribution is a data-driven variational inference approach where we train a normalizing flow (NF), a type of invertible neural net, capable of cheaply sampling the posterior distribution given previously unseen seismic data from neighboring surveys. To arrive at this result, we train the NF on pairs of low- and high-fidelity migrated images. In our numerical example, we obtain high-fidelity images from the Parihaka dataset and low-fidelity images are derived from these images through the process of demigration, followed by adding noise and migration. During inference, given shot records from a new neighboring seismic survey, we first compute the reverse-time migration image. Next, by feeding this low-fidelity migrated image to the NF we gain access to samples from the posterior distribution virtually for free. We use these samples to compute a high-fidelity image including a first assessment of the image's reliability. To our knowledge, this is the first attempt to train a conditional network on what we know from neighboring images to improve the current image and assess its reliability. | ['Felix J. Herrmann', 'Ali Siahkoohi'] | 2021-04-13 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 4.57711548e-01 2.66122054e-02 4.78499055e-01 -4.52889651e-01
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568b13c4-ebd9-4a25-af82-f949b94d5eb1 | pregan-answer-oriented-passage-ranking-with | 2207.01762 | null | https://arxiv.org/abs/2207.01762v1 | https://arxiv.org/pdf/2207.01762v1.pdf | PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN | Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability). While a few recent studies have incorporated some reading capability into a ranker to account for answerability, the ranker is still hindered by the noisy nature of the training data typically available in this area, which considers any passage containing an answer entity as a positive sample. However, the answer entity in a passage is not necessarily mentioned in relation with the given question. To address the problem, we propose an approach called \ttt{PReGAN} for Passage Reranking based on Generative Adversarial Neural networks, which incorporates a discriminator on answerability, in addition to a discriminator on topical relevance. The goal is to force the generator to rank higher a passage that is topically relevant and contains an answer. Experiments on five public datasets show that \ttt{PReGAN} can better rank appropriate passages, which in turn, boosts the effectiveness of QA systems, and outperforms the existing approaches without using external data. | ['Xiaohui Yan', 'Lixin Zou', 'Hao Jiang', 'Yutao Zhu', 'Jian-Yun Nie', 'Pan Du'] | 2022-07-05 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 1.92795649e-01 3.59445602e-01 5.08510582e-02 -2.60426730e-01
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8b01e23a-2b28-4229-9427-502992629541 | multilingual-neural-machine-translation-2 | 2306.12693 | null | https://arxiv.org/abs/2306.12693v1 | https://arxiv.org/pdf/2306.12693v1.pdf | Multilingual Neural Machine Translation System for Indic to Indic Languages | This paper gives an Indic-to-Indic (IL-IL) MNMT baseline model for 11 ILs implemented on the Samanantar corpus and analyzed on the Flores-200 corpus. All the models are evaluated using the BLEU score. In addition, the languages are classified under three groups namely East Indo- Aryan (EI), Dravidian (DR), and West Indo-Aryan (WI). The effect of language relatedness on MNMT model efficiency is studied. Owing to the presence of large corpora from English (EN) to ILs, MNMT IL-IL models using EN as a pivot are also built and examined. To achieve this, English- Indic (EN-IL) models are also developed, with and without the usage of related languages. Results reveal that using related languages is beneficial for the WI group only, while it is detrimental for the EI group and shows an inconclusive effect on the DR group, but it is useful for EN-IL models. Thus, related language groups are used to develop pivot MNMT models. Furthermore, the IL corpora are transliterated from the corresponding scripts to a modified ITRANS script, and the best MNMT models from the previous approaches are built on the transliterated corpus. It is observed that the usage of pivot models greatly improves MNMT baselines with AS-TA achieving the minimum BLEU score and PA-HI achieving the maximum score. Among languages, AS, ML, and TA achieve the lowest BLEU score, whereas HI, PA, and GU perform the best. Transliteration also helps the models with few exceptions. The best increment of scores is observed in ML, TA, and BN and the worst average increment is observed in KN, HI, and PA, across all languages. The best model obtained is the PA-HI language pair trained on PAWI transliterated corpus which gives 24.29 BLEU. | ['Asif Ekbal', 'Bidyut Kr. Patra', 'Tapas Kumar Mishra', 'Divyajyoti Panda', 'Sudhansu Bala Das'] | 2023-06-22 | null | null | null | null | ['machine-translation', 'transliteration'] | ['natural-language-processing', 'natural-language-processing'] | [-1.98059663e-01 -1.60818726e-01 -3.41458797e-01 -2.22708732e-02
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d4ea5276-f29c-46af-8246-ea5a234bd5a7 | comparing-ptb-and-ud-information-for-pdtb | null | null | https://aclanthology.org/2020.jeptalnrecital-recital.10 | https://aclanthology.org/2020.jeptalnrecital-recital.10.pdf | Comparing PTB and UD information for PDTB discourseconnective identification | Our work on the automatic detection of English discourse connectives in the Penn Discourse Treebank (PDTB) shows that syntactic information from the Universal Dependencies (UD) framework is a viable alternative to that from the Penn Treebank (PTB) framework. In fact, we found minor increases when comparing between the use of gold standard PTB part-of-speech (POS) tag information and automatically parsed UD information. The former has traditionally been used for the task but there are now much more UD corpora and in many more languages than that available in the PTB framework. As such, this finding is promising for areas in discourse parsing such as in multilingual as well as under production settings, where gold standard PTB information may be scarce. | ['Kelvin Han', 'Srilakshmi Balard', 'Phyllicia Leavitt'] | 2020-06-01 | null | null | null | jeptalnrecital-2020-6 | ['discourse-parsing'] | ['natural-language-processing'] | [-3.41598429e-02 6.83526158e-01 -4.46344048e-01 -4.20853376e-01
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54399e8f-cb92-4cf4-9c79-d5eaf72d11b4 | cgua-context-guided-and-unpaired-assisted | 2203.14307 | null | https://arxiv.org/abs/2203.14307v1 | https://arxiv.org/pdf/2203.14307v1.pdf | CGUA: Context-Guided and Unpaired-Assisted Weakly Supervised Person Search | Recently, weakly supervised person search is proposed to discard human-annotated identities and train the model with only bounding box annotations. A natural way to solve this problem is to separate it into detection and unsupervised re-identification (Re-ID) steps. However, in this way, two important clues in unconstrained scene images are ignored. On the one hand, existing unsupervised Re-ID models only leverage cropped images from scene images but ignore its rich context information. On the other hand, there are numerous unpaired persons in real-world scene images. Directly dealing with them as independent identities leads to the long-tail effect, while completely discarding them can result in serious information loss. In light of these challenges, we introduce a Context-Guided and Unpaired-Assisted (CGUA) weakly supervised person search framework. Specifically, we propose a novel Context-Guided Cluster (CGC) algorithm to leverage context information in the clustering process and an Unpaired-Assisted Memory (UAM) unit to distinguish unpaired and paired persons by pushing them away. Extensive experiments demonstrate that the proposed approach can surpass the state-of-the-art weakly supervised methods by a large margin (more than 5% mAP on CUHK-SYSU). Moreover, our method achieves comparable or better performance to the state-of-the-art supervised methods by leveraging more diverse unlabeled data. Codes and models will be released soon. | ['Qinghua Zheng', 'Xiaojun Chang', 'Caixia Yan', 'Minnan Luo', 'Chengyou Jia'] | 2022-03-27 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 1.63437337e-01 -2.26384357e-01 -3.27905230e-02 -5.46316326e-01
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e8dd5a2a-6ec4-4461-b9c6-cf1dab7b38b2 | structure-representation-learning-by-jointly | null | null | https://openreview.net/forum?id=u7APnx5qszb | https://openreview.net/pdf?id=u7APnx5qszb | Structure Representation Learning by Jointly Learning to Pool and Represent | Structure representation learning is a task to provide an overall representation for a given structure (e.g., sequential text, non-sequential graph). This representation characterizes the property of that structure. Previous methods decompose the task into an element representation learning phase and a pooling phase to aggregate element representations. Their pooling phase only considers the final representation of each element without considering the relationship between these elements that are used only to construct representations of elements. In this paper, we conjecture that classification performance suffers from the lack of relation exploitation while pooling and propose the Self-Attention Pooling to dynamically provide centrality scores for pooling based on the self-attention scores from the element representation learning. Simply applying Self-Attention Pooling improves model performance on $3$ sentence classification tasks ({$\boldsymbol{\uparrow 2.9}$}) and $5$ graph classification tasks ({$\boldsymbol{\uparrow 2.1}$}) on average.
| ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['sentence-classification'] | ['natural-language-processing'] | [ 5.36812067e-01 5.69835126e-01 6.72512576e-02 -3.23292941e-01
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0decc1ea-5487-46a2-8dd6-b81ba22f5528 | searching-for-the-fakes-efficient-neural | 2306.08830 | null | https://arxiv.org/abs/2306.08830v2 | https://arxiv.org/pdf/2306.08830v2.pdf | Searching for the Fakes: Efficient Neural Architecture Search for General Face Forgery Detection | As the saying goes, "seeing is believing". However, with the development of digital face editing tools, we can no longer trust what we can see. Although face forgery detection has made promising progress, most current methods are designed manually by human experts, which is labor-consuming. In this paper, we develop an end-to-end framework based on neural architecture search (NAS) for deepfake detection, which can automatically design network architectures without human intervention. First, a forgery-oriented search space is created to choose appropriate operations for this task. Second, we propose a novel performance estimation metric, which guides the search process to select more general models. The cross-dataset search is also considered to develop more general architectures. Eventually, we connect the cells in a cascaded pyramid way for final forgery classification. Compared with state-of-the-art networks artificially designed, our method achieves competitive performance in both in-dataset and cross-dataset scenarios. | ['Jing Xu', 'Xin-Yue Mu', 'Xiao Jin'] | 2023-06-15 | null | null | null | null | ['deepfake-detection', 'face-swapping', 'architecture-search'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.45974219e-01 -2.82275289e-01 2.28382185e-01 -5.37450075e-01
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f5ee23f0-a5be-4860-928d-068fa092fdbc | optimal-operation-of-a-hydrogen-based | 2109.10754 | null | https://arxiv.org/abs/2109.10754v1 | https://arxiv.org/pdf/2109.10754v1.pdf | Optimal Operation of a Hydrogen-based Building Multi-Energy System Based on Deep Reinforcement Learning | Since hydrogen has many advantages (e.g., free pollution, extensive sources, convenient storage and transportation), hydrogen-based multi-energy systems (HMESs) have received wide attention. However, existing works on the optimal operation of HMESs neglect building thermal dynamics, which means that the flexibility of building thermal loads can not be utilized for reducing system operation cost. In this paper, we investigate an optimal operation problem of an HMES with the consideration of building thermal dynamics. Specifically, we first formulate an expected operational cost minimization problem related to an HMES. Due to the existence of uncertain parameters, inexplicit building thermal dynamics models, temporally coupled operational constraints related to three kinds of energy storage systems and indoor temperatures, as well as the coupling between electric energy subsystems and thermal energy subsystems, it is challenging to solve the formulated problem. To overcome the challenge, we reformulate the problem as a Markov game and propose an energy management algorithm to solve it based on multi-agent discrete actor-critic with rules (MADACR). Note that the proposed algorithm does not require any prior knowledge of uncertain parameters, parameter prediction, and explicit building thermal dynamics model. Simulation results based on real-world traces show the effectiveness of the proposed algorithm. | ['Dong Yue', 'Chao Shen', 'Xiaohong Guan', 'Zhanbo Xu', 'Shuqi Qin', 'Liang Yu'] | 2021-09-22 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [-3.90979171e-01 7.33344536e-03 3.83234993e-02 2.60468811e-01
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303d1664-a3c7-4304-96df-a9527efb19b3 | mw-gan-multi-warping-gan-for-caricature | 2001.01870 | null | https://arxiv.org/abs/2001.01870v2 | https://arxiv.org/pdf/2001.01870v2.pdf | MW-GAN: Multi-Warping GAN for Caricature Generation with Multi-Style Geometric Exaggeration | Given an input face photo, the goal of caricature generation is to produce stylized, exaggerated caricatures that share the same identity as the photo. It requires simultaneous style transfer and shape exaggeration with rich diversity, and meanwhile preserving the identity of the input. To address this challenging problem, we propose a novel framework called Multi-Warping GAN (MW-GAN), including a style network and a geometric network that are designed to conduct style transfer and geometric exaggeration respectively. We bridge the gap between the style and landmarks of an image with corresponding latent code spaces by a dual way design, so as to generate caricatures with arbitrary styles and geometric exaggeration, which can be specified either through random sampling of latent code or from a given caricature sample. Besides, we apply identity preserving loss to both image space and landmark space, leading to a great improvement in quality of generated caricatures. Experiments show that caricatures generated by MW-GAN have better quality than existing methods. | ['Yu-Kun Lai', 'Jing Huo', 'Yang Gao', 'Haodi Hou', 'Jing Wu'] | 2020-01-07 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 3.30263495e-01 2.48703748e-01 2.26299390e-01 -2.76298255e-01
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fc235212-044f-48b8-bce9-6e19ce48cf64 | overview-of-the-2022-validity-and-novelty | null | null | https://aclanthology.org/2022.argmining-1.7 | https://aclanthology.org/2022.argmining-1.7.pdf | Overview of the 2022 Validity and Novelty Prediction Shared Task | This paper provides an overview of the Argument Validity and Novelty Prediction Shared Task that was organized as part of the 9th Workshop on Argument Mining (ArgMining 2022). The task focused on the prediction of the validity and novelty of a conclusion given a textual premise. Validity is defined as the degree to which the conclusion is justified with respect to the given premise. Novelty defines the degree to which the conclusion contains content that is new in relation to the premise. Six groups participated in the task, submitting overall 13 system runs for the subtask of binary classification and 2 system runs for the subtask of relative classification. The results reveal that the task is challenging, with best results obtained for Validity prediction in the range of 75% F1 score, for Novelty prediction of 70% F1 score and for correctly predicting both Validity and Novelty of 45% F1 score. In this paper we summarize the task definition and dataset. We give an overview of the results obtained by the participating systems, as well as insights to be gained from the diverse contributions. | ['Philipp Cimiano', 'Moritz Plenz', 'Juri Opitz', 'Anette Frank', 'Philipp Heinisch'] | null | null | null | null | argmining-acl-2022-10 | ['valnov', 'argument-mining'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.24553376e-01 7.44804978e-01 -4.80473846e-01 -4.27608490e-01
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3079cc3b-78a3-49d9-a6bb-b1e6748ea652 | stability-and-generalization-of-stochastic-2 | 2307.03357 | null | https://arxiv.org/abs/2307.03357v1 | https://arxiv.org/pdf/2307.03357v1.pdf | Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms | Many machine learning tasks can be formulated as a stochastic compositional optimization (SCO) problem such as reinforcement learning, AUC maximization, and meta-learning, where the objective function involves a nested composition associated with an expectation. While a significant amount of studies has been devoted to studying the convergence behavior of SCO algorithms, there is little work on understanding their generalization, i.e., how these learning algorithms built from training examples would behave on future test examples. In this paper, we provide the stability and generalization analysis of stochastic compositional gradient descent algorithms through the lens of algorithmic stability in the framework of statistical learning theory. Firstly, we introduce a stability concept called compositional uniform stability and establish its quantitative relation with generalization for SCO problems. Then, we establish the compositional uniform stability results for two popular stochastic compositional gradient descent algorithms, namely SCGD and SCSC. Finally, we derive dimension-independent excess risk bounds for SCGD and SCSC by trade-offing their stability results and optimization errors. To the best of our knowledge, these are the first-ever-known results on stability and generalization analysis of stochastic compositional gradient descent algorithms. | ['Yiming Ying', 'Tianbao Yang', 'Xiyuan Wei', 'Ming Yang'] | 2023-07-07 | null | null | null | null | ['meta-learning', 'learning-theory'] | ['methodology', 'miscellaneous'] | [-3.45239788e-02 -8.61994922e-02 -1.73613161e-01 -4.30508703e-01
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0a2e83b2-2087-411c-90f0-c9cf27ecb933 | global-and-local-epidemiology-of-group-a | 2111.06498 | null | https://arxiv.org/abs/2111.06498v1 | https://arxiv.org/pdf/2111.06498v1.pdf | Global and local epidemiology of Group A Streptococcus indicates that naturally-acquired immunity is enduring and strain-specific | The bacterium Group A Streptococcus (Streptococcus pyogenes, GAS) is a human-specific pathogen and a major cause of global morbidity and mortality. Despite decades of research our knowledge of GAS infection and immunity is incomplete, hampering vaccine design and other efforts to reduce disease prevalence. Epidemiological studies indicate positive associations between the prevalence of GAS-related disease, the diversity of circulating strains and the degree of poverty in host populations. However, the infection and immune mechanisms underlying these associations are not clear. In this work, we use an agent-based model to demonstrate that observed diversity and prevalence are best accounted for by the hypothesis that GAS infection confers enduring strain-specific immunity, with reduced or absent cross-protection against infection by other strains. Our results suggest that the success of GAS vaccines will depend on their ability to elicit long-lasting cross-protective immunity over multiple strain types. | ['Nicholas Geard', 'Jodie McVernon', 'Steven Y. C. Tong', 'Mark R. Davies', 'Jukka Corander', 'Malcolm I. McDonald', 'Patricia T. Campbell', 'Jan Kokko', 'Jake A. Lacey', 'Rebecca H. Chisholm'] | 2021-11-11 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 6.14068747e-01 -5.54875433e-01 -1.33360624e-01 7.06747249e-02
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730368ed-5da3-4703-bd06-697742d00202 | spatio-temporal-graph-few-shot-learning-with | 2205.13947 | null | https://arxiv.org/abs/2205.13947v2 | https://arxiv.org/pdf/2205.13947v2.pdf | Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer | Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some developing cities have few available data, which makes it infeasible to train a well-performed model. To address this challenge, cross-city knowledge transfer has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. However, the spatio-temporal graphs among different cities show irregular structures and varied features, which limits the feasibility of existing Few-Shot Learning (\emph{FSL}) methods. Therefore, we propose a model-agnostic few-shot learning framework for spatio-temporal graph called ST-GFSL. Specifically, to enhance feature extraction by transfering cross-city knowledge, ST-GFSL proposes to generate non-shared parameters based on node-level meta knowledge. The nodes in target city transfer the knowledge via parameter matching, retrieving from similar spatio-temporal characteristics. Furthermore, we propose to reconstruct the graph structure during meta-learning. The graph reconstruction loss is defined to guide structure-aware learning, avoiding structure deviation among different datasets. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods. | ['Xinbing Wang', 'Luoyi Fu', 'Huaxiu Yao', 'Weinan Zhang', 'Xiaoying Gan', 'Bin Lu'] | 2022-05-27 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [-1.26739413e-01 -2.07086414e-01 -4.88534033e-01 -1.08997360e-01
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b5e4f8fe-8d71-46b2-8985-2ba7a204c811 | end-to-end-integration-of-speech-separation | 2303.12002 | null | https://arxiv.org/abs/2303.12002v1 | https://arxiv.org/pdf/2303.12002v1.pdf | End-to-End Integration of Speech Separation and Voice Activity Detection for Low-Latency Diarization of Telephone Conversations | Recent works show that speech separation guided diarization (SSGD) is an increasingly promising direction, mainly thanks to the recent progress in speech separation. It performs diarization by first separating the speakers and then applying voice activity detection (VAD) on each separated stream. In this work we conduct an in-depth study of SSGD in the conversational telephone speech (CTS) domain, focusing mainly on low-latency streaming diarization applications. We consider three state-of-the-art speech separation (SSep) algorithms and study their performance both in online and offline scenarios, considering non-causal and causal implementations as well as continuous SSep (CSS) windowed inference. We compare different SSGD algorithms on two widely used CTS datasets: CALLHOME and Fisher Corpus (Part 1 and 2) and evaluate both separation and diarization performance. To improve performance, a novel, causal and computationally efficient leakage removal algorithm is proposed, which significantly decreases false alarms. We also explore, for the first time, fully end-to-end SSGD integration between SSep and VAD modules. Crucially, this enables fine-tuning on real-world data for which oracle speakers sources are not available. In particular, our best model achieves 8.8% DER on CALLHOME, which outperforms the current state-of-the-art end-to-end neural diarization model, despite being trained on an order of magnitude less data and having significantly lower latency, i.e., 0.1 vs. 1 seconds. Finally, we also show that the separated signals can be readily used also for automatic speech recognition, reaching performance close to using oracle sources in some configurations. | ['Stefano Squartini', 'Alessio Brutti', 'Enrico Zovato', 'Luca Serafini', 'Samuele Cornell', 'Giovanni Morrone'] | 2023-03-21 | null | null | null | null | ['activity-detection', 'speech-separation'] | ['computer-vision', 'speech'] | [ 1.06033780e-01 1.32310838e-01 5.93944639e-02 -2.07640529e-01
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89d19f37-1ba0-4a53-a5f7-8a15b8756bc3 | cola-contextualized-commonsense-causal | 2305.05191 | null | https://arxiv.org/abs/2305.05191v1 | https://arxiv.org/pdf/2305.05191v1.pdf | COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective | Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task. Given that events are complicated, an event may have different causes under various contexts. Thus, exploiting context plays an essential role in detecting causal relations. Meanwhile, previous works about commonsense causation only consider two events and ignore their context, simplifying the task formulation. This paper proposes a new task to detect commonsense causation between two events in an event sequence (i.e., context), called contextualized commonsense causal reasoning. We also design a zero-shot framework: COLA (Contextualized Commonsense Causality Reasoner) to solve the task from the causal inference perspective. This framework obtains rich incidental supervision from temporality and balances covariates from multiple timestamps to remove confounding effects. Our extensive experiments show that COLA can detect commonsense causality more accurately than baselines. | ['Simon See', 'Ginny Y. Wong', 'Yangqiu Song', 'Tianqing Fang', 'Weiqi Wang', 'Jiayao Zhang', 'Hongming Zhang', 'Quyet V. Do', 'Zhaowei Wang'] | 2023-05-09 | null | null | null | null | ['causal-inference', 'causal-inference', 'commonsense-causal-reasoning'] | ['knowledge-base', 'miscellaneous', 'natural-language-processing'] | [ 5.21835744e-01 -2.10390821e-01 -4.95038509e-01 -6.08008027e-01
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ede54547-89ba-4eca-b427-a7b374f4dded | seven-open-problems-in-applied-combinatorics | 2303.11464 | null | https://arxiv.org/abs/2303.11464v1 | https://arxiv.org/pdf/2303.11464v1.pdf | Seven open problems in applied combinatorics | We present and discuss seven different open problems in applied combinatorics. The application areas relevant to this compilation include quantum computing, algorithmic differentiation, topological data analysis, iterative methods, hypergraph cut algorithms, and power systems. | ['Stephen J. Young', 'Nate Veldt', 'Ignacio Segovia-Dominguez', 'Sandip Roy', 'Anthony V. Petyuk', 'Carlos Ortiz Marrero', 'Uwe Naumann', 'Bill Kay', 'Yulia R. Gel', 'José Frías', 'Yuzhou Chen', 'Ryan Bennink', 'Sinan G. Aksoy'] | 2023-03-20 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 2.38809049e-01 1.92018121e-01 -3.74198079e-01 5.01745105e-01
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4.10530642e-02 -9.26245093e-01 -3.30892026e-01 -7.35810518e-01] | [5.618183135986328, 4.945427417755127] |
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