May 6, 2019

2983 words 15 mins read

Paper Group ANR 267

On the Diagnostic of Road Pathway Visibility. Beyond knowing that: a new generation of epistemic logics. Non-Negative Matrix Factorizations for Multiplex Network Analysis. Tensor Ring Decomposition. The “Sprekend Nederland” project and its application to accent location. What is the distribution of the number of unique original items in a bootstrap …

On the Diagnostic of Road Pathway Visibility


Title	On the Diagnostic of Road Pathway Visibility
Authors	Pierre Charbonnier, Jean-Philippe Tarel, Francois Goulette
Abstract	Visibility distance on the road pathway plays a significant role in road safety and in particular, has a clear impact on the choice of speed limits. Visibility distance is thus of importance for road engineers and authorities. While visibility distance criteria are routinely taken into account in road design, only a few systems exist for estimating it on existing road networks. Most existing systems comprise a target vehicle followed at a constant distance by an observer vehicle, which only allows to check if a given, fixed visibility distance is available. We propose two new approaches that allow estimating the maximum available visibility distance, involving only one vehicle and based on different sensor technologies, namely binocular stereovision and 3D range sensing (LIDAR). The first approach is based on the processing of two views taken by digital cameras onboard the diagnostic vehicle. The main stages of the process are: road segmentation, edge registration between the two views, road profile 3D reconstruction and finally, maximal road visibility distance estimation. The second approach involves the use of a Terrestrial LIDAR Mobile Mapping System. The triangulated 3D model of the road and its surroundings provided by the system is used to simulate targets at different distances, which allows estimating the maximum geometric visibility distance along the pathway. These approaches were developed in the context of the SARI-VIZIR PREDIT project. Both approaches are described, evaluated and compared. Their pros and cons with respect to vehicle following systems are also discussed.
Tasks	3D Reconstruction
Published	2016-01-21
URL	http://arxiv.org/abs/1601.05535v1
PDF	http://arxiv.org/pdf/1601.05535v1.pdf
PWC	https://paperswithcode.com/paper/on-the-diagnostic-of-road-pathway-visibility
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Beyond knowing that: a new generation of epistemic logics


Title	Beyond knowing that: a new generation of epistemic logics
Authors	Yanjing Wang
Abstract	Epistemic logic has become a major field of philosophical logic ever since the groundbreaking work by Hintikka (1962). Despite its various successful applications in theoretical computer science, AI, and game theory, the technical development of the field has been mainly focusing on the propositional part, i.e., the propositional modal logics of “knowing that”. However, knowledge is expressed in everyday life by using various other locutions such as “knowing whether”, “knowing what”, “knowing how” and so on (knowing-wh hereafter). Such knowledge expressions are better captured in quantified epistemic logic, as was already discussed by Hintikka (1962) and his sequel works at length. This paper aims to draw the attention back again to such a fascinating but largely neglected topic. We first survey what Hintikka and others did in the literature of quantified epistemic logic, and then advocate a new quantifier-free approach to study the epistemic logics of knowing-wh, which we believe can balance expressivity and complexity, and capture the essential reasoning patterns about knowing-wh. We survey our recent line of work on the epistemic logics of “knowing whether”, “knowing what” and “knowing how” to demonstrate the use of this new approach.
Tasks
Published	2016-05-06
URL	http://arxiv.org/abs/1605.01995v3
PDF	http://arxiv.org/pdf/1605.01995v3.pdf
PWC	https://paperswithcode.com/paper/beyond-knowing-that-a-new-generation-of
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Non-Negative Matrix Factorizations for Multiplex Network Analysis


Title	Non-Negative Matrix Factorizations for Multiplex Network Analysis
Authors	Vladimir Gligorijevic, Yannis Panagakis, Stefanos Zafeiriou
Abstract	Networks have been a general tool for representing, analyzing, and modeling relational data arising in several domains. One of the most important aspect of network analysis is community detection or network clustering. Until recently, the major focus have been on discovering community structure in single (i.e., monoplex) networks. However, with the advent of relational data with multiple modalities, multiplex networks, i.e., networks composed of multiple layers representing different aspects of relations, have emerged. Consequently, community detection in multiplex network, i.e., detecting clusters of nodes shared by all layers, has become a new challenge. In this paper, we propose Network Fusion for Composite Community Extraction (NF-CCE), a new class of algorithms, based on four different non-negative matrix factorization models, capable of extracting composite communities in multiplex networks. Each algorithm works in two steps: first, it finds a non-negative, low-dimensional feature representation of each network layer; then, it fuses the feature representation of layers into a common non-negative, low-dimensional feature representation via collective factorization. The composite clusters are extracted from the common feature representation. We demonstrate the superior performance of our algorithms over the state-of-the-art methods on various types of multiplex networks, including biological, social, economic, citation, phone communication, and brain multiplex networks.
Tasks	Community Detection
Published	2016-12-01
URL	http://arxiv.org/abs/1612.00750v2
PDF	http://arxiv.org/pdf/1612.00750v2.pdf
PWC	https://paperswithcode.com/paper/non-negative-matrix-factorizations-for
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Tensor Ring Decomposition


Title	Tensor Ring Decomposition
Authors	Qibin Zhao, Guoxu Zhou, Shengli Xie, Liqing Zhang, Andrzej Cichocki
Abstract	Tensor networks have in recent years emerged as the powerful tools for solving the large-scale optimization problems. One of the most popular tensor network is tensor train (TT) decomposition that acts as the building blocks for the complicated tensor networks. However, the TT decomposition highly depends on permutations of tensor dimensions, due to its strictly sequential multilinear products over latent cores, which leads to difficulties in finding the optimal TT representation. In this paper, we introduce a fundamental tensor decomposition model to represent a large dimensional tensor by a circular multilinear products over a sequence of low dimensional cores, which can be graphically interpreted as a cyclic interconnection of 3rd-order tensors, and thus termed as tensor ring (TR) decomposition. The key advantage of TR model is the circular dimensional permutation invariance which is gained by employing the trace operation and treating the latent cores equivalently. TR model can be viewed as a linear combination of TT decompositions, thus obtaining the powerful and generalized representation abilities. For optimization of latent cores, we present four different algorithms based on the sequential SVDs, ALS scheme, and block-wise ALS techniques. Furthermore, the mathematical properties of TR model are investigated, which shows that the basic multilinear algebra can be performed efficiently by using TR representaions and the classical tensor decompositions can be conveniently transformed into the TR representation. Finally, the experiments on both synthetic signals and real-world datasets were conducted to evaluate the performance of different algorithms.
Tasks	Tensor Networks
Published	2016-06-17
URL	http://arxiv.org/abs/1606.05535v1
PDF	http://arxiv.org/pdf/1606.05535v1.pdf
PWC	https://paperswithcode.com/paper/tensor-ring-decomposition
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The “Sprekend Nederland” project and its application to accent location


Title	The “Sprekend Nederland” project and its application to accent location
Authors	David A. van Leeuwen, Rosemary Orr
Abstract	This paper describes the data collection effort that is part of the project Sprekend Nederland (The Netherlands Talking), and discusses its potential use in Automatic Accent Location. We define Automatic Accent Location as the task to describe the accent of a speaker in terms of the location of the speaker and its history. We discuss possible ways of describing accent location, the consequence these have for the task of automatic accent location, and potential evaluation metrics.
Tasks
Published	2016-02-08
URL	http://arxiv.org/abs/1602.02499v2
PDF	http://arxiv.org/pdf/1602.02499v2.pdf
PWC	https://paperswithcode.com/paper/the-sprekend-nederland-project-and-its
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What is the distribution of the number of unique original items in a bootstrap sample?


Title	What is the distribution of the number of unique original items in a bootstrap sample?
Authors	Alex F. Mendelson, Maria A. Zuluaga, Brian F. Hutton, Sébastien Ourselin
Abstract	Sampling with replacement occurs in many settings in machine learning, notably in the bagging ensemble technique and the .632+ validation scheme. The number of unique original items in a bootstrap sample can have an important role in the behaviour of prediction models learned on it. Indeed, there are uncontrived examples where duplicate items have no effect. The purpose of this report is to present the distribution of the number of unique original items in a bootstrap sample clearly and concisely, with a view to enabling other machine learning researchers to understand and control this quantity in existing and future resampling techniques. We describe the key characteristics of this distribution along with the generalisation for the case where items come from distinct categories, as in classification. In both cases we discuss the normal limit, and conduct an empirical investigation to derive a heuristic for when a normal approximation is permissible.
Tasks
Published	2016-02-18
URL	http://arxiv.org/abs/1602.05822v1
PDF	http://arxiv.org/pdf/1602.05822v1.pdf
PWC	https://paperswithcode.com/paper/what-is-the-distribution-of-the-number-of
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DecomposeMe: Simplifying ConvNets for End-to-End Learning


Title	DecomposeMe: Simplifying ConvNets for End-to-End Learning
Authors	Jose Alvarez, Lars Petersson
Abstract	Deep learning and convolutional neural networks (ConvNets) have been successfully applied to most relevant tasks in the computer vision community. However, these networks are computationally demanding and not suitable for embedded devices where memory and time consumption are relevant. In this paper, we propose DecomposeMe, a simple but effective technique to learn features using 1D convolutions. The proposed architecture enables both simplicity and filter sharing leading to increased learning capacity. A comprehensive set of large-scale experiments on ImageNet and Places2 demonstrates the ability of our method to improve performance while significantly reducing the number of parameters required. Notably, on Places2, we obtain an improvement in relative top-1 classification accuracy of 7.7% with an architecture that requires 92% fewer parameters compared to VGG-B. The proposed network is also demonstrated to generalize to other tasks by converting existing networks.
Tasks
Published	2016-06-17
URL	http://arxiv.org/abs/1606.05426v1
PDF	http://arxiv.org/pdf/1606.05426v1.pdf
PWC	https://paperswithcode.com/paper/decomposeme-simplifying-convnets-for-end-to
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Modelling Office Energy Consumption: An Agent Based Approach


Title	Modelling Office Energy Consumption: An Agent Based Approach
Authors	Tao Zhang, Peer-Olaf Siebers, Uwe Aickelin
Abstract	In this paper, we develop an agent-based model which integrates four important elements, i.e. organisational energy management policies/regulations, energy management technologies, electric appliances and equipment, and human behaviour, based on a case study, to simulate the energy consumption in office buildings. With the model, we test the effectiveness of different energy management strategies, and solve practical office energy consumption problems. This paper theoretically contributes to an integration of four elements involved in the complex organisational issue of office energy consumption, and practically contributes to an application of agent-based approach for office building energy consumption study.
Tasks
Published	2016-07-20
URL	http://arxiv.org/abs/1607.06332v1
PDF	http://arxiv.org/pdf/1607.06332v1.pdf
PWC	https://paperswithcode.com/paper/modelling-office-energy-consumption-an-agent
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Parallelizing Spectral Algorithms for Kernel Learning


Title	Parallelizing Spectral Algorithms for Kernel Learning
Authors	Gilles Blanchard, Nicole Mücke
Abstract	We consider a distributed learning approach in supervised learning for a large class of spectral regularization methods in an RKHS framework. The data set of size n is partitioned into $m=O(n^\alpha)$ disjoint subsets. On each subset, some spectral regularization method (belonging to a large class, including in particular Kernel Ridge Regression, $L^2$-boosting and spectral cut-off) is applied. The regression function $f$ is then estimated via simple averaging, leading to a substantial reduction in computation time. We show that minimax optimal rates of convergence are preserved if m grows sufficiently slowly (corresponding to an upper bound for $\alpha$) as $n \to \infty$, depending on the smoothness assumptions on $f$ and the intrinsic dimensionality. In spirit, our approach is classical.
Tasks
Published	2016-10-24
URL	http://arxiv.org/abs/1610.07487v4
PDF	http://arxiv.org/pdf/1610.07487v4.pdf
PWC	https://paperswithcode.com/paper/parallelizing-spectral-algorithms-for-kernel
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Evolutionary stability implies asymptotic stability under multiplicative weights


Title	Evolutionary stability implies asymptotic stability under multiplicative weights
Authors	Ioannis Avramopoulos
Abstract	We show that evolutionarily stable states in general (nonlinear) population games (which can be viewed as continuous vector fields constrained on a polytope) are asymptotically stable under a multiplicative weights dynamic (under appropriate choices of a parameter called the learning rate or step size, which we demonstrate to be crucial to achieve convergence, as otherwise even chaotic behavior is possible to manifest). Our result implies that evolutionary theories based on multiplicative weights are compatible (in principle, more general) with those based on the notion of evolutionary stability. However, our result further establishes multiplicative weights as a nonlinear programming primitive (on par with standard nonlinear programming methods) since various nonlinear optimization problems, such as finding Nash/Wardrop equilibria in nonatomic congestion games, which are well-known to be equipped with a convex potential function, and finding strict local maxima of quadratic programming problems, are special cases of the problem of computing evolutionarily stable states in nonlinear population games.
Tasks
Published	2016-01-27
URL	http://arxiv.org/abs/1601.07267v2
PDF	http://arxiv.org/pdf/1601.07267v2.pdf
PWC	https://paperswithcode.com/paper/evolutionary-stability-implies-asymptotic
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Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis


Title	Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis
Authors	Xiao Fu, Kejun Huang, Mingyi Hong, Nicholas D. Sidiropoulos, Anthony Man-Cho So
Abstract	Generalized canonical correlation analysis (GCCA) aims at finding latent low-dimensional common structure from multiple views (feature vectors in different domains) of the same entities. Unlike principal component analysis (PCA) that handles a single view, (G)CCA is able to integrate information from different feature spaces. Here we focus on MAX-VAR GCCA, a popular formulation which has recently gained renewed interest in multilingual processing and speech modeling. The classic MAX-VAR GCCA problem can be solved optimally via eigen-decomposition of a matrix that compounds the (whitened) correlation matrices of the views; but this solution has serious scalability issues, and is not directly amenable to incorporating pertinent structural constraints such as non-negativity and sparsity on the canonical components. We posit regularized MAX-VAR GCCA as a non-convex optimization problem and propose an alternating optimization (AO)-based algorithm to handle it. Our algorithm alternates between {\em inexact} solutions of a regularized least squares subproblem and a manifold-constrained non-convex subproblem, thereby achieving substantial memory and computational savings. An important benefit of our design is that it can easily handle structure-promoting regularization. We show that the algorithm globally converges to a critical point at a sublinear rate, and approaches a global optimal solution at a linear rate when no regularization is considered. Judiciously designed simulations and large-scale word embedding tasks are employed to showcase the effectiveness of the proposed algorithm.
Tasks
Published	2016-05-31
URL	http://arxiv.org/abs/1605.09459v4
PDF	http://arxiv.org/pdf/1605.09459v4.pdf
PWC	https://paperswithcode.com/paper/scalable-and-flexible-multiview-max-var
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The Utility of Hedged Assertions in the Emergence of Shared Categorical Labels


Title	The Utility of Hedged Assertions in the Emergence of Shared Categorical Labels
Authors	Martha Lewis, Jonathan Lawry
Abstract	We investigate the emergence of shared concepts in a community of language users using a multi-agent simulation. We extend results showing that negated assertions are of use in developing shared categories, to include assertions modified by linguistic hedges. Results show that using hedged assertions positively affects the emergence of shared categories in two distinct ways. Firstly, using contraction hedges like `very' gives better convergence over time. Secondly, using expansion hedges such as` quite’ reduces concept overlap. However, both these improvements come at a cost of slower speed of development.
Tasks
Published	2016-01-25
URL	http://arxiv.org/abs/1601.06755v1
PDF	http://arxiv.org/pdf/1601.06755v1.pdf
PWC	https://paperswithcode.com/paper/the-utility-of-hedged-assertions-in-the
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Automatic learning of gait signatures for people identification


Title	Automatic learning of gait signatures for people identification
Authors	F. M. Castro, M. J. Marin-Jimenez, N. Guil, N. Perez de la Blanca
Abstract	This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task with an image resolution eight times lower than the previously reported results (i.e. 80x60 pixels).
Tasks	Optical Flow Estimation
Published	2016-03-03
URL	http://arxiv.org/abs/1603.01006v2
PDF	http://arxiv.org/pdf/1603.01006v2.pdf
PWC	https://paperswithcode.com/paper/automatic-learning-of-gait-signatures-for
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Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks


Title	Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks
Authors	Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
Abstract	This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially, we train a variant of the SegNet architecture on remote sensing data over an urban area and study different strategies for performing accurate semantic segmentation. Our contributions are the following: 1) we transfer efficiently a DFCNN from generic everyday images to remote sensing images; 2) we introduce a multi-kernel convolutional layer for fast aggregation of predictions at multiple scales; 3) we perform data fusion from heterogeneous sensors (optical and laser) using residual correction. Our framework improves state-of-the-art accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset.
Tasks	Scene Labeling, Semantic Segmentation
Published	2016-09-22
URL	http://arxiv.org/abs/1609.06846v1
PDF	http://arxiv.org/pdf/1609.06846v1.pdf
PWC	https://paperswithcode.com/paper/semantic-segmentation-of-earth-observation
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Framework

Is a Picture Worth Ten Thousand Words in a Review Dataset?


Title	Is a Picture Worth Ten Thousand Words in a Review Dataset?
Authors	Roberto Camacho Barranco, Laura M. Rodriguez, Rebecca Urbina, M. Shahriar Hossain
Abstract	While textual reviews have become prominent in many recommendation-based systems, automated frameworks to provide relevant visual cues against text reviews where pictures are not available is a new form of task confronted by data mining and machine learning researchers. Suggestions of pictures that are relevant to the content of a review could significantly benefit the users by increasing the effectiveness of a review. We propose a deep learning-based framework to automatically: (1) tag the images available in a review dataset, (2) generate a caption for each image that does not have one, and (3) enhance each review by recommending relevant images that might not be uploaded by the corresponding reviewer. We evaluate the proposed framework using the Yelp Challenge Dataset. While a subset of the images in this particular dataset are correctly captioned, the majority of the pictures do not have any associated text. Moreover, there is no mapping between reviews and images. Each image has a corresponding business-tag where the picture was taken, though. The overall data setting and unavailability of crucial pieces required for a mapping make the problem of recommending images for reviews a major challenge. Qualitative and quantitative evaluations indicate that our proposed framework provides high quality enhancements through automatic captioning, tagging, and recommendation for mapping reviews and images.
Tasks
Published	2016-06-23
URL	http://arxiv.org/abs/1606.07496v1
PDF	http://arxiv.org/pdf/1606.07496v1.pdf
PWC	https://paperswithcode.com/paper/is-a-picture-worth-ten-thousand-words-in-a
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