Paper Group ANR 419
Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks. Convergence analysis of belief propagation for pairwise linear Gaussian models. Asynchronous parallel primal-dual block coordinate update methods for affinely constrained convex programs. Multi-Label Segmentation via Residual-Driven Adaptive Regularization. Te …
Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks
| Title | Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks |
| Authors | Eric L. Ferguson, Stefan B. Williams, Craig T. Jin |
| Abstract | The propagation of sound in a shallow water environment is characterized by boundary reflections from the sea surface and sea floor. These reflections result in multiple (indirect) sound propagation paths, which can degrade the performance of passive sound source localization methods. This paper proposes the use of convolutional neural networks (CNNs) for the localization of sources of broadband acoustic radiated noise (such as motor vessels) in shallow water multipath environments. It is shown that CNNs operating on cepstrogram and generalized cross-correlogram inputs are able to more reliably estimate the instantaneous range and bearing of transiting motor vessels when the source localization performance of conventional passive ranging methods is degraded. The ensuing improvement in source localization performance is demonstrated using real data collected during an at-sea experiment. |
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| Published | 2017-10-27 |
| URL | http://arxiv.org/abs/1710.10948v1 |
| http://arxiv.org/pdf/1710.10948v1.pdf | |
| PWC | https://paperswithcode.com/paper/sound-source-localization-in-a-multipath |
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Convergence analysis of belief propagation for pairwise linear Gaussian models
| Title | Convergence analysis of belief propagation for pairwise linear Gaussian models |
| Authors | Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, José M. F. Moura |
| Abstract | Gaussian belief propagation (BP) has been widely used for distributed inference in large-scale networks such as the smart grid, sensor networks, and social networks, where local measurements/observations are scattered over a wide geographical area. One particular case is when two neighboring agents share a common observation. For example, to estimate voltage in the direct current (DC) power flow model, the current measurement over a power line is proportional to the voltage difference between two neighboring buses. When applying the Gaussian BP algorithm to this type of problem, the convergence condition remains an open issue. In this paper, we analyze the convergence properties of Gaussian BP for this pairwise linear Gaussian model. We show analytically that the updating information matrix converges at a geometric rate to a unique positive definite matrix with arbitrary positive semidefinite initial value and further provide the necessary and sufficient convergence condition for the belief mean vector to the optimal estimate. |
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| Published | 2017-06-12 |
| URL | http://arxiv.org/abs/1706.04074v4 |
| http://arxiv.org/pdf/1706.04074v4.pdf | |
| PWC | https://paperswithcode.com/paper/convergence-analysis-of-belief-propagation |
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Asynchronous parallel primal-dual block coordinate update methods for affinely constrained convex programs
| Title | Asynchronous parallel primal-dual block coordinate update methods for affinely constrained convex programs |
| Authors | Yangyang Xu |
| Abstract | Recent several years have witnessed the surge of asynchronous (async-) parallel computing methods due to the extremely big data involved in many modern applications and also the advancement of multi-core machines and computer clusters. In optimization, most works about async-parallel methods are on unconstrained problems or those with block separable constraints. In this paper, we propose an async-parallel method based on block coordinate update (BCU) for solving convex problems with nonseparable linear constraint. Running on a single node, the method becomes a novel randomized primal-dual BCU with adaptive stepsize for multi-block affinely constrained problems. For these problems, Gauss-Seidel cyclic primal-dual BCU needs strong convexity to have convergence. On the contrary, merely assuming convexity, we show that the objective value sequence generated by the proposed algorithm converges in probability to the optimal value and also the constraint residual to zero. In addition, we establish an ergodic $O(1/k)$ convergence result, where $k$ is the number of iterations. Numerical experiments are performed to demonstrate the efficiency of the proposed method and significantly better speed-up performance than its sync-parallel counterpart. |
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| Published | 2017-05-18 |
| URL | https://arxiv.org/abs/1705.06391v2 |
| https://arxiv.org/pdf/1705.06391v2.pdf | |
| PWC | https://paperswithcode.com/paper/asynchronous-parallel-primal-dual-block |
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Multi-Label Segmentation via Residual-Driven Adaptive Regularization
| Title | Multi-Label Segmentation via Residual-Driven Adaptive Regularization |
| Authors | Byung-Woo Hong, Ja-Keoung Koo, Stefano Soatto |
| Abstract | We present a variational multi-label segmentation algorithm based on a robust Huber loss for both the data and the regularizer, minimized within a convex optimization framework. We introduce a novel constraint on the common areas, to bias the solution towards mutually exclusive regions. We also propose a regularization scheme that is adapted to the spatial statistics of the residual at each iteration, resulting in a varying degree of regularization being applied as the algorithm proceeds: the effect of the regularizer is strongest at initialization, and wanes as the solution increasingly fits the data. This minimizes the bias induced by the regularizer at convergence. We design an efficient convex optimization algorithm based on the alternating direction method of multipliers using the equivalent relation between the Huber function and the proximal operator of the one-norm. We empirically validate our proposed algorithm on synthetic and real images and offer an information-theoretic derivation of the cost-function that highlights the modeling choices made. |
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| Published | 2017-02-27 |
| URL | http://arxiv.org/abs/1702.08336v1 |
| http://arxiv.org/pdf/1702.08336v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-label-segmentation-via-residual-driven |
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Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods
| Title | Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods |
| Authors | Alexander Terenin, Eric P. Xing |
| Abstract | Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are finding widespread use in applied statistics and machine learning. These often lead to difficult computational problems, which are increasingly being solved on parallel and distributed systems such as compute clusters. Recent work has proposed running iterative algorithms such as gradient descent and MCMC in parallel asynchronously for increased performance, with good empirical results in certain problems. Unfortunately, for MCMC this parallelization technique requires new convergence theory, as it has been explicitly demonstrated to lead to divergence on some examples. Recent theory on Asynchronous Gibbs sampling describes why these algorithms can fail, and provides a way to alter them to make them converge. In this article, we describe how to apply this theory in a generic setting, to understand the asynchronous behavior of any MCMC algorithm, including those implemented using parameter servers, and those not based on Gibbs sampling. |
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| Published | 2017-11-17 |
| URL | http://arxiv.org/abs/1711.06719v5 |
| http://arxiv.org/pdf/1711.06719v5.pdf | |
| PWC | https://paperswithcode.com/paper/techniques-for-proving-asynchronous |
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Semi-supervised Text Categorization Using Recursive K-means Clustering
| Title | Semi-supervised Text Categorization Using Recursive K-means Clustering |
| Authors | Harsha S. Gowda, Mahamad Suhil, D. S. Guru, Lavanya Narayana Raju |
| Abstract | In this paper, we present a semi-supervised learning algorithm for classification of text documents. A method of labeling unlabeled text documents is presented. The presented method is based on the principle of divide and conquer strategy. It uses recursive K-means algorithm for partitioning both labeled and unlabeled data collection. The K-means algorithm is applied recursively on each partition till a desired level partition is achieved such that each partition contains labeled documents of a single class. Once the desired clusters are obtained, the respective cluster centroids are considered as representatives of the clusters and the nearest neighbor rule is used for classifying an unknown text document. Series of experiments have been conducted to bring out the superiority of the proposed model over other recent state of the art models on 20Newsgroups dataset. |
| Tasks | Text Categorization |
| Published | 2017-06-24 |
| URL | http://arxiv.org/abs/1706.07913v1 |
| http://arxiv.org/pdf/1706.07913v1.pdf | |
| PWC | https://paperswithcode.com/paper/semi-supervised-text-categorization-using |
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Printed Arabic Text Recognition using Linear and Nonlinear Regression
| Title | Printed Arabic Text Recognition using Linear and Nonlinear Regression |
| Authors | Ashraf A. Shahin |
| Abstract | Arabic language is one of the most popular languages in the world. Hundreds of millions of people in many countries around the world speak Arabic as their native speaking. However, due to complexity of Arabic language, recognition of printed and handwritten Arabic text remained untouched for a very long time compared with English and Chinese. Although, in the last few years, significant number of researches has been done in recognizing printed and handwritten Arabic text, it stills an open research field due to cursive nature of Arabic script. This paper proposes automatic printed Arabic text recognition technique based on linear and ellipse regression techniques. After collecting all possible forms of each character, unique code is generated to represent each character form. Each code contains a sequence of lines and ellipses. To recognize fonts, a unique list of codes is identified to be used as a fingerprint of font. The proposed technique has been evaluated using over 14000 different Arabic words with different fonts and experimental results show that average recognition rate of the proposed technique is 86%. |
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| Published | 2017-02-05 |
| URL | http://arxiv.org/abs/1702.01444v1 |
| http://arxiv.org/pdf/1702.01444v1.pdf | |
| PWC | https://paperswithcode.com/paper/printed-arabic-text-recognition-using-linear |
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DeepIso: A Deep Learning Model for Peptide Feature Detection
| Title | DeepIso: A Deep Learning Model for Peptide Feature Detection |
| Authors | Fatema Tuz Zohora, Ngoc Hieu Tran, Xianglilan Zhang, Lei Xin, Baozhen Shan, Ming Li |
| Abstract | Liquid chromatography with tandem mass spectrometry (LC-MS/MS) based proteomics is a well-established research field with major applications such as identification of disease biomarkers, drug discovery, drug design and development. In proteomics, protein identification and quantification is a fundamental task, which is done by first enzymatically digesting it into peptides, and then analyzing peptides by LC-MS/MS instruments. The peptide feature detection and quantification from an LC-MS map is the first step in typical analysis workflows. In this paper we propose a novel deep learning based model, DeepIso, that uses Convolutional Neural Networks (CNNs) to scan an LC-MS map to detect peptide features and estimate their abundance. Existing tools are often designed with limited engineered features based on domain knowledge, and depend on pretrained parameters which are hardly updated despite huge amount of new coming proteomic data. Our proposed model, on the other hand, is capable of learning multiple levels of representation of high dimensional data through its many layers of neurons and continuously evolving with newly acquired data. To evaluate our proposed model, we use an antibody dataset including a heavy and a light chain, each digested by Asp-N, Chymotrypsin, Trypsin, thus giving six LC-MS maps for the experiment. Our model achieves 93.21% sensitivity with specificity of 99.44% on this dataset. Our results demonstrate that novel deep learning tools are desirable to advance the state-of-the-art in protein identification and quantification. |
| Tasks | Drug Discovery |
| Published | 2017-12-09 |
| URL | http://arxiv.org/abs/1801.01539v1 |
| http://arxiv.org/pdf/1801.01539v1.pdf | |
| PWC | https://paperswithcode.com/paper/deepiso-a-deep-learning-model-for-peptide |
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Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets
| Title | Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets |
| Authors | Mirko Lai, Delia Irazú Hernández Farías, Viviana Patti, Paolo Rosso |
| Abstract | Stance detection, the task of identifying the speaker’s opinion towards a particular target, has attracted the attention of researchers. This paper describes a novel approach for detecting stance in Twitter. We define a set of features in order to consider the context surrounding a target of interest with the final aim of training a model for predicting the stance towards the mentioned targets. In particular, we are interested in investigating political debates in social media. For this reason we evaluated our approach focusing on two targets of the SemEval-2016 Task6 on Detecting stance in tweets, which are related to the political campaign for the 2016 U.S. presidential elections: Hillary Clinton vs. Donald Trump. For the sake of comparison with the state of the art, we evaluated our model against the dataset released in the SemEval-2016 Task 6 shared task competition. Our results outperform the best ones obtained by participating teams, and show that information about enemies and friends of politicians help in detecting stance towards them. |
| Tasks | Stance Detection |
| Published | 2017-02-26 |
| URL | http://arxiv.org/abs/1702.08021v1 |
| http://arxiv.org/pdf/1702.08021v1.pdf | |
| PWC | https://paperswithcode.com/paper/friends-and-enemies-of-clinton-and-trump |
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The Advantage of Evidential Attributes in Social Networks
| Title | The Advantage of Evidential Attributes in Social Networks |
| Authors | Salma Ben Dhaou, Kuang Zhou, Mouloud Kharoune, Arnaud Martin, Boutheina Ben Yaghlane |
| Abstract | Nowadays, there are many approaches designed for the task of detecting communities in social networks. Among them, some methods only consider the topological graph structure, while others take use of both the graph structure and the node attributes. In real-world networks, there are many uncertain and noisy attributes in the graph. In this paper, we will present how we detect communities in graphs with uncertain attributes in the first step. The numerical, probabilistic as well as evidential attributes are generated according to the graph structure. In the second step, some noise will be added to the attributes. We perform experiments on graphs with different types of attributes and compare the detection results in terms of the Normalized Mutual Information (NMI) values. The experimental results show that the clustering with evidential attributes gives better results comparing to those with probabilistic and numerical attributes. This illustrates the advantages of evidential attributes. |
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| Published | 2017-07-26 |
| URL | http://arxiv.org/abs/1707.08418v2 |
| http://arxiv.org/pdf/1707.08418v2.pdf | |
| PWC | https://paperswithcode.com/paper/the-advantage-of-evidential-attributes-in |
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The Risk of Machine Learning
| Title | The Risk of Machine Learning |
| Authors | Alberto Abadie, Maximilian Kasy |
| Abstract | Many applied settings in empirical economics involve simultaneous estimation of a large number of parameters. In particular, applied economists are often interested in estimating the effects of many-valued treatments (like teacher effects or location effects), treatment effects for many groups, and prediction models with many regressors. In these settings, machine learning methods that combine regularized estimation and data-driven choices of regularization parameters are useful to avoid over-fitting. In this article, we analyze the performance of a class of machine learning estimators that includes ridge, lasso and pretest in contexts that require simultaneous estimation of many parameters. Our analysis aims to provide guidance to applied researchers on (i) the choice between regularized estimators in practice and (ii) data-driven selection of regularization parameters. To address (i), we characterize the risk (mean squared error) of regularized estimators and derive their relative performance as a function of simple features of the data generating process. To address (ii), we show that data-driven choices of regularization parameters, based on Stein’s unbiased risk estimate or on cross-validation, yield estimators with risk uniformly close to the risk attained under the optimal (unfeasible) choice of regularization parameters. We use data from recent examples in the empirical economics literature to illustrate the practical applicability of our results. |
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| Published | 2017-03-31 |
| URL | http://arxiv.org/abs/1703.10935v1 |
| http://arxiv.org/pdf/1703.10935v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-risk-of-machine-learning |
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On the Sampling Problem for Kernel Quadrature
| Title | On the Sampling Problem for Kernel Quadrature |
| Authors | Francois-Xavier Briol, Chris J. Oates, Jon Cockayne, Wilson Ye Chen, Mark Girolami |
| Abstract | The standard Kernel Quadrature method for numerical integration with random point sets (also called Bayesian Monte Carlo) is known to converge in root mean square error at a rate determined by the ratio $s/d$, where $s$ and $d$ encode the smoothness and dimension of the integrand. However, an empirical investigation reveals that the rate constant $C$ is highly sensitive to the distribution of the random points. In contrast to standard Monte Carlo integration, for which optimal importance sampling is well-understood, the sampling distribution that minimises $C$ for Kernel Quadrature does not admit a closed form. This paper argues that the practical choice of sampling distribution is an important open problem. One solution is considered; a novel automatic approach based on adaptive tempering and sequential Monte Carlo. Empirical results demonstrate a dramatic reduction in integration error of up to 4 orders of magnitude can be achieved with the proposed method. |
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| Published | 2017-06-11 |
| URL | http://arxiv.org/abs/1706.03369v1 |
| http://arxiv.org/pdf/1706.03369v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-sampling-problem-for-kernel-quadrature |
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Deception Detection in Videos
| Title | Deception Detection in Videos |
| Authors | Zhe Wu, Bharat Singh, Larry S. Davis, V. S. Subrahmanian |
| Abstract | We present a system for covert automated deception detection in real-life courtroom trial videos. We study the importance of different modalities like vision, audio and text for this task. On the vision side, our system uses classifiers trained on low level video features which predict human micro-expressions. We show that predictions of high-level micro-expressions can be used as features for deception prediction. Surprisingly, IDT (Improved Dense Trajectory) features which have been widely used for action recognition, are also very good at predicting deception in videos. We fuse the score of classifiers trained on IDT features and high-level micro-expressions to improve performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio domain also provide a significant boost in performance, while information from transcripts is not very beneficial for our system. Using various classifiers, our automated system obtains an AUC of 0.877 (10-fold cross-validation) when evaluated on subjects which were not part of the training set. Even though state-of-the-art methods use human annotations of micro-expressions for deception detection, our fully automated approach outperforms them by 5%. When combined with human annotations of micro-expressions, our AUC improves to 0.922. We also present results of a user-study to analyze how well do average humans perform on this task, what modalities they use for deception detection and how they perform if only one modality is accessible. Our project page can be found at \url{https://doubaibai.github.io/DARE/}. |
| Tasks | Deception Detection, Deception Detection In Videos, Temporal Action Localization |
| Published | 2017-12-12 |
| URL | http://arxiv.org/abs/1712.04415v1 |
| http://arxiv.org/pdf/1712.04415v1.pdf | |
| PWC | https://paperswithcode.com/paper/deception-detection-in-videos |
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Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System
| Title | Anonymous Hedonic Game for Task Allocation in a Large-Scale Multiple Agent System |
| Authors | Inmo Jang, Hyo-Sang Shin, Antonios Tsourdos |
| Abstract | This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents. We consider cooperation of self-interested agents, and show that our proposed decentralized algorithm guarantees convergence of agents with social inhibition to a Nash stable partition (i.e., social agreement) within polynomial time. The algorithm is simple and executable based on local interactions with neighbor agents under a strongly-connected communication network and even in asynchronous environments. We analytically present a mathematical formulation for computing the lower bound of suboptimality of the solution, and additionally show that 50% of suboptimality can be at least guaranteed if social utilities are non-decreasing functions with respect to the number of co-working agents. The results of numerical experiments confirm that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation. |
| Tasks | Decision Making |
| Published | 2017-11-18 |
| URL | http://arxiv.org/abs/1711.06871v2 |
| http://arxiv.org/pdf/1711.06871v2.pdf | |
| PWC | https://paperswithcode.com/paper/anonymous-hedonic-game-for-task-allocation-in |
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Fast Back-Projection for Non-Line of Sight Reconstruction
| Title | Fast Back-Projection for Non-Line of Sight Reconstruction |
| Authors | Victor Arellano, Diego Gutierrez, Adrian Jarabo |
| Abstract | Recent works have demonstrated non-line of sight (NLOS) reconstruction by using the time-resolved signal frommultiply scattered light. These works combine ultrafast imaging systems with computation, which back-projects the recorded space-time signal to build a probabilistic map of the hidden geometry. Unfortunately, this computation is slow, becoming a bottleneck as the imaging technology improves. In this work, we propose a new back-projection technique for NLOS reconstruction, which is up to a thousand times faster than previous work, with almost no quality loss. We base on the observation that the hidden geometry probability map can be built as the intersection of the three-bounce space-time manifolds defined by the light illuminating the hidden geometry and the visible point receiving the scattered light from such hidden geometry. This allows us to pose the reconstruction of the hidden geometry as the voxelization of these space-time manifolds, which has lower theoretic complexity and is easily implementable in the GPU. We demonstrate the efficiency and quality of our technique compared against previous methods in both captured and synthetic data |
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| Published | 2017-03-06 |
| URL | http://arxiv.org/abs/1703.02016v2 |
| http://arxiv.org/pdf/1703.02016v2.pdf | |
| PWC | https://paperswithcode.com/paper/fast-back-projection-for-non-line-of-sight |
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