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Abstract: Written language is a complex communication signal capable of conveying information encoded in the form of ordered sequences of words. Beyond the local order ruled by grammar, semantic and thematic structures affect long-range patterns in word usage. Here, we show that a direct application of information theo... |
Title: Fast search for Dirichlet process mixture models |
Abstract: Dirichlet process (DP) mixture models provide a flexible Bayesian framework for density estimation. Unfortunately, their flexibility comes at a cost: inference in DP mixture models is computationally expensive, even when conjugate distributions are used. In the common case when one seeks only a maximum a post... |
Title: Bayesian Query-Focused Summarization |
Abstract: We present BayeSum (for ``Bayesian summarization''), a model for sentence extraction in query-focused summarization. BayeSum leverages the common case in which multiple documents are relevant to a single query. Using these documents as reinforcement for query terms, BayeSum is not afflicted by the paucity of ... |
Title: Frustratingly Easy Domain Adaptation |
Abstract: We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough ``target'' data to do slightly better than just using only ``source'' data. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!) and outperforms state-of-the-art... |
Title: Improvement of random LHD for high dimensions |
Abstract: Designs of experiments for multivariate case are reviewed. Fast algorithm of construction of good Latin hypercube designs is developed. |
Title: Information geometry for testing pseudorandom number generators |
Abstract: The information geometry of the 2-manifold of gamma probability density functions provides a framework in which pseudorandom number generators may be evaluated using a neighbourhood of the curve of exponential density functions. The process is illustrated using the pseudorandom number generator in Mathematica... |
Title: An Evolved Neural Controller for Bipdedal Walking with Dynamic Balance |
Abstract: We successfully evolved a neural network controller that produces dynamic walking in a simulated bipedal robot with compliant actuators, a difficult control problem. The evolutionary evaluation uses a detailed software simulation of a physical robot. We describe: 1) a novel theoretical method to encourage pop... |
Title: Learning Equilibria in Games by Stochastic Distributed Algorithms |
Abstract: We consider a class of fully stochastic and fully distributed algorithms, that we prove to learn equilibria in games. Indeed, we consider a family of stochastic distributed dynamics that we prove to converge weakly (in the sense of weak convergence for probabilistic processes) towards their mean-field limit, ... |
Title: Modeling self-organizing traffic lights with elementary cellular automata |
Abstract: There have been several highway traffic models proposed based on cellular automata. The simplest one is elementary cellular automaton rule 184. We extend this model to city traffic with cellular automata coupled at intersections using only rules 184, 252, and 136. The simplicity of the model offers a clear un... |
Title: Statistical estimation requires unbounded memory |
Abstract: We investigate the existence of bounded-memory consistent estimators of various statistical functionals. This question is resolved in the negative in a rather strong sense. We propose various bounded-memory approximations, using techniques from automata theory and stochastic processes. Some questions of poten... |
Title: Multiresolution Elastic Medical Image Registration in Standard Intensity Scale |
Abstract: Medical image registration is a difficult problem. Not only a registration algorithm needs to capture both large and small scale image deformations, it also has to deal with global and local image intensity variations. In this paper we describe a new multiresolution elastic image registration method that chal... |
Title: An Augmented Lagrangian Approach for Sparse Principal Component Analysis |
Abstract: Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduction with numerous applications in science and engineering. However, the standard PCA suffers from the fact that the principal components (PCs) are usually linear combinations of all the original variables, and ... |
Title: Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing |
Abstract: Portfolio balancing requires estimates of covariance between asset returns. Returns data have histories which greatly vary in length, since assets begin public trading at different times. This can lead to a huge amount of missing data--too much for the conventional imputation-based approach. Fortunately, a we... |
Title: Network-aware Adaptation with Real-Time Channel Statistics for Wireless LAN Multimedia Transmissions in the Digital Home |
Abstract: This paper suggests the use of intelligent network-aware processing agents in wireless local area network drivers to generate metrics for bandwidth estimation based on real-time channel statistics to enable wireless multimedia application adaptation. Various configurations in the wireless digital home are stu... |
Title: Sparsistent Estimation of Time-Varying Discrete Markov Random Fields |
Abstract: Network models have been popular for modeling and representing complex relationships and dependencies between observed variables. When data comes from a dynamic stochastic process, a single static network model cannot adequately capture transient dependencies, such as, gene regulatory dependencies throughout ... |
Title: Pattern Based Term Extraction Using ACABIT System |
Abstract: In this paper, we propose a pattern-based term extraction approach for Japanese, applying ACABIT system originally developed for French. The proposed approach evaluates termhood using morphological patterns of basic terms and term variants. After extracting term candidates, ACABIT system filters out non-terms... |
Title: Why we (usually) don't have to worry about multiple comparisons |
Abstract: Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewe... |
Title: Thoughts on new statistical procedures for age-period-cohort analyses |
Abstract: Age-period-cohort analysis is mathematically intractable because of fundamental nonidentifiability of linear trends. However, some understanding can be gained in the context of individual problems. |
Title: On Cyclic and Nearly Cyclic Multiagent Interactions in the Plane |
Abstract: We discuss certain types of cyclic and nearly cyclic interactions among N "point"-agents in the plane, leading to formations of interesting limiting geometric configurations. Cyclic pursuit and local averaging interactions have been analyzed in the context of multi-agent gathering. In this paper, we consider ... |
Title: Bayesian methods to overcome the winner's curse in genetic studies |
Abstract: Parameter estimates for associated genetic variants, report ed in the initial discovery samples, are often grossly inflated compared to the values observed in the follow-up replication samples. This type of bias is a consequence of the sequential procedure in which the estimated effect of an associated geneti... |
Title: Modelling Concurrent Behaviors in the Process Specification Language |
Abstract: In this paper, we propose a first-order ontology for generalized stratified order structure. We then classify the models of the theory using model-theoretic techniques. An ontology mapping from this ontology to the core theory of Process Specification Language is also discussed. |
Title: Distribution Fitting 1. Parameters Estimation under Assumption of Agreement between Observation and Model |
Abstract: The methods for parameter estimation under assumption of agreement between observation and model are reviewed. The distribution parameters are obtained for one set of experimental data by using different estimation methods under assumption of Gauss-Laplace theoretical distribution. The results are presented a... |
Title: Distribution Fitting 2. Pearson-Fisher, Kolmogorov-Smirnov, Anderson-Darling, Wilks-Shapiro, Cramer-von-Misses and Jarque-Bera statistics |
Abstract: The methods measuring the departure between observation and the model were reviewed. The following statistics were applied on two experimental data sets: Chi-Squared, Kolmogorov-Smirnov, Anderson-Darling, Wilks-Shapiro, and Jarque-Bera. Both investigated sets proved not to be normal distributed. The Grubbs te... |
Title: The Single Machine Total Weighted Tardiness Problem - Is it (for Metaheuristics) a Solved Problem ? |
Abstract: The article presents a study of rather simple local search heuristics for the single machine total weighted tardiness problem (SMTWTP), namely hillclimbing and Variable Neighborhood Search. In particular, we revisit these approaches for the SMTWTP as there appears to be a lack of appropriate/challenging bench... |
Title: Improvements for multi-objective flow shop scheduling by Pareto Iterated Local Search |
Abstract: The article describes the proposition and application of a local search metaheuristic for multi-objective optimization problems. It is based on two main principles of heuristic search, intensification through variable neighborhoods, and diversification through perturbations and successive iterations in favora... |
Title: Graph Theory and Optimization Problems for Very Large Networks |
Abstract: Graph theory provides a primary tool for analyzing and designing computer communication networks. In the past few decades, Graph theory has been used to study various types of networks, including the Internet, wide Area Networks, Local Area Networks, and networking protocols such as border Gateway Protocol, O... |
Title: Checking election outcome accuracy Post-election audit sampling |
Abstract: This article * provides an overview of post-election audit sampling research and compares various approaches to calculating post-election audit sample sizes, focusing on risklimiting audits, * discusses fundamental concepts common to all risk-limiting post-election audits, presenting new margin error bounds, ... |
Title: Registration of Standardized Histological Images in Feature Space |
Abstract: In this paper, we propose three novel and important methods for the registration of histological images for 3D reconstruction. First, possible intensity variations and nonstandardness in images are corrected by an intensity standardization process which maps the image scale into a standard scale where the sim... |
Title: Fully Automatic 3D Reconstruction of Histological Images |
Abstract: In this paper, we propose a computational framework for 3D volume reconstruction from 2D histological slices using registration algorithms in feature space. To improve the quality of reconstructed 3D volume, first, intensity variations in images are corrected by an intensity standardization process which maps... |
Title: Parallel AdaBoost Algorithm for Gabor Wavelet Selection in Face Recognition |
Abstract: In this paper, the problem of automatic Gabor wavelet selection for face recognition is tackled by introducing an automatic algorithm based on Parallel AdaBoosting method. Incorporating mutual information into the algorithm leads to the selection procedure not only based on classification accuracy but also on... |
Title: Inter Genre Similarity Modelling For Automatic Music Genre Classification |
Abstract: Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre... |
Title: Neural Modeling and Control of Diesel Engine with Pollution Constraints |
Abstract: The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnecte... |
Title: A network-based approach for surveillance of occupational health exposures |
Abstract: In the context of surveillance of health problems, the research carried out by the French national occupational disease surveillance and prevention network (R\'eseau National de Vigilance et de Pr\'evention des Pathologies Professionnelles, RNV3P) aims to develop, among other approaches, methods of surveillan... |
Title: Occupational Health Problem Network : the Exposome |
Abstract: We present a thinking on the concept of relational networks applied to the french national occupational disease surveillance and prevention network (R\'eseau National de Vigilance et de Pr\'evention des Pathologies Professionnelles, RNV3P). This approach consists in searching common exposures to occupational ... |
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