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Title: Matching Methods for Causal Inference: A Review and a Look Forward |
Abstract: When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and contro... |
Title: Selective Image Super-Resolution |
Abstract: In this paper we propose a vision system that performs image Super Resolution (SR) with selectivity. Conventional SR techniques, either by multi-image fusion or example-based construction, have failed to capitalize on the intrinsic structural and semantic context in the image, and performed "blind" resolution... |
Title: Events! (Reactivity in urbiscript) |
Abstract: Urbi SDK is a software platform for the development of portable robotic applications. It features the Urbi UObject C++ middleware, to manage hardware drivers and/or possibly remote software components, and urbiscript, a domain specific programming language to orchestrate them. Reactivity is a key feature of U... |
Title: A new muscle fatigue and recovery model and its ergonomics application in human simulation |
Abstract: Although automatic techniques have been employed in manufacturing industries to increase productivity and efficiency, there are still lots of manual handling jobs, especially for assembly and maintenance jobs. In these jobs, musculoskeletal disorders (MSDs) are one of the major health problems due to overload... |
Title: Random Graph Generator for Bipartite Networks Modeling |
Abstract: The purpose of this article is to introduce a new iterative algorithm with properties resembling real life bipartite graphs. The algorithm enables us to generate wide range of random bigraphs, which features are determined by a set of parameters.We adapt the advances of last decade in unipartite complex netwo... |
Title: Random Graphs for Performance Evaluation of Recommender Systems |
Abstract: The purpose of this article is to introduce a new analytical framework dedicated to measuring performance of recommender systems. The standard approach is to assess the quality of a system by means of accuracy related statistics. However, the specificity of the environments in which recommender systems are de... |
Title: Estimating False Discovery Proportion Under Arbitrary Covariance Dependence |
Abstract: Multiple hypothesis testing is a fundamental problem in high dimensional inference, with wide applications in many scientific fields. In genome-wide association studies, tens of thousands of tests are performed simultaneously to find if any SNPs are associated with some traits and those tests are correlated. ... |
Title: Network motifs in music sequences |
Abstract: This paper has been withdrawn by the author because it needs a deep methodological revision. |
Title: Maximum Likelihood Estimation of Nonnegative Trigonometric Sum Models Using a Newton-like Algorithm on Manifolds |
Abstract: In Fern\'andez-Dur\'an (2004), a new family of circular distributions based on nonnegative trigonometric sums (NNTS models) is developed. Because the parameter space of this family is the surface of the hypersphere, an efficient Newton-like algorithm on manifolds is generated in order to obtain the maximum li... |
Title: Exact Bayesian Analysis of Mixtures |
Abstract: In this paper, we show how a complete and exact Bayesian analysis of a parametric mixture model is possible in some cases when components of the mixture are taken from exponential families and when conjugate priors are used. This restricted set-up allows us to show the relevance of the Bayesian approach as we... |
Title: On minimum correlation in construction of multivariate distributions |
Abstract: In this paper we present a method for exact generation of multivariate samples with pre-specified marginal distributions and a given correlation matrix, based on a mixture of Fr\'echet-Hoeffding bounds and marginal products. The bivariate algorithm can accommodate any among the theoretically possible correlat... |
Title: Sequential Data-Adaptive Bandwidth Selection by Cross-Validation for Nonparametric Prediction |
Abstract: We consider the problem of bandwidth selection by cross-validation from a sequential point of view in a nonparametric regression model. Having in mind that in applications one often aims at estimation, prediction and change detection simultaneously, we investigate that approach for sequential kernel smoothers... |
Title: The assembly modes of rigid 11-bar linkages |
Abstract: Designing an m-bar linkage with a maximal number of assembly modes is important in robot kinematics, and has further applications in structural biology and computational geometry. A related question concerns the number of assembly modes of rigid mechanisms as a function of their nodes n, which is uniquely def... |
Title: Analysing the behaviour of robot teams through relational sequential pattern mining |
Abstract: This report outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal in a cooperative manner. The aim is to define a systematic method to v... |
Title: Predictive State Temporal Difference Learning |
Abstract: We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications, reinforcement learning (RL) is complicated by the fact that state is either high-dimensional or partially observable. Therefore, R... |
Title: Discussion of "Riemann manifold Langevin and Hamiltonian Monte Carlo methods'' by M. Girolami and B. Calderhead |
Abstract: This technical report is the union of two contributions to the discussion of the Read Paper "Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by B. Calderhead and M. Girolami, presented in front of the Royal Statistical Society on October 13th 2010 and to appear in the Journal of the Royal Stati... |
Title: Fast Color Quantization Using Weighted Sort-Means Clustering |
Abstract: Color quantization is an important operation with numerous applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color... |
Title: Concentration inequalities of the cross-validation estimator for Empirical Risk Minimiser |
Abstract: In this article, we derive concentration inequalities for the cross-validation estimate of the generalization error for empirical risk minimizers. In the general setting, we prove sanity-check bounds in the spirit of \textquotedblleft\textitbounds showing that the worst-case error of this estimate is not much... |
Title: Sparse Inverse Covariance Selection via Alternating Linearization Methods |
Abstract: Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse covariance matrix of the Gaussian distribution, one can learn the structure of the graph by estimating a sparse inverse covariance m... |
Title: Qualitative Reasoning about Relative Direction on Adjustable Levels of Granularity |
Abstract: An important issue in Qualitative Spatial Reasoning is the representation of relative direction. In this paper we present simple geometric rules that enable reasoning about relative direction between oriented points. This framework, the Oriented Point Algebra OPRA_m, has a scalable granularity m. We develop a... |
Title: A Comparison of Methods for Computing Autocorrelation Time |
Abstract: This paper describes four methods for estimating autocorrelation time and evaluates these methods with a test set of seven series. Fitting an autoregressive process appears to be the most accurate method of the four. An R package is provided for extending the comparison to more methods and test series. |
Title: A Distributed AI Aided 3D Domino Game |
Abstract: In the article a turn-based game played on four computers connected via network is investigated. There are three computers with natural intelligence and one with artificial intelligence. Game table is seen by each player's own view point in all players' monitors. Domino pieces are three dimensional. For distr... |
Title: Prunnig Algorithm of Generation a Minimal Set of Rule Reducts Based on Rough Set Theory |
Abstract: In this paper it is considered rule reduct generation problem, based on Rough Set Theory. Rule Reduct Generation (RG) and Modified Rule Generation (MRG) algorithms are well-known. Alternative to these algorithms Pruning Algorithm of Generation A Minimal Set of Rule Reducts, or briefly Pruning Rule Generation ... |
Title: Reasoning about Cardinal Directions between Extended Objects: The Hardness Result |
Abstract: The cardinal direction calculus (CDC) proposed by Goyal and Egenhofer is a very expressive qualitative calculus for directional information of extended objects. Early work has shown that consistency checking of complete networks of basic CDC constraints is tractable while reasoning with the CDC in general is ... |
Title: Imitation learning of motor primitives and language bootstrapping in robots |
Abstract: Imitation learning in robots, also called programing by demonstration, has made important advances in recent years, allowing humans to teach context dependant motor skills/tasks to robots. We propose to extend the usual contexts investigated to also include acoustic linguistic expressions that might denote a ... |
Title: Developing courses with HoloRena, a framework for scenario- and game based e-learning environments |
Abstract: However utilizing rich, interactive solutions can make learning more effective and attractive, scenario- and game-based educational resources on the web are not widely used. Creating these applications is a complex, expensive and challenging process. Development frameworks and authoring tools hardly support r... |
Title: Optimization of artificial flockings by means of anisotropy measurements |
Abstract: An effective procedure to determine the optimal parameters appearing in artificial flockings is proposed in terms of optimization problems. We numerically examine genetic algorithms (GAs) to determine the optimal set of parameters such as the weights for three essential interactions in BOIDS by Reynolds (1987... |
Title: CUR from a Sparse Optimization Viewpoint |
Abstract: The CUR decomposition provides an approximation of a matrix $X$ that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span of only a few columns of $X$. In this regard, it appears to be similar to many sparse PCA methods. However, CUR takes a randomized... |
Title: Learning Networks of Stochastic Differential Equations |
Abstract: We consider linear models for stochastic dynamics. To any such model can be associated a network (namely a directed graph) describing which degrees of freedom interact under the dynamics. We tackle the problem of learning such a network from observation of the system trajectory over a time interval $T$. We an... |
Title: From Sparse Signals to Sparse Residuals for Robust Sensing |
Abstract: One of the key challenges in sensor networks is the extraction of information by fusing data from a multitude of distinct, but possibly unreliable sensors. Recovering information from the maximum number of dependable sensors while specifying the unreliable ones is critical for robust sensing. This sensing tas... |
Title: A Note on an R^2 Measure for Fixed Effects in the Generalized Linear Mixed Model |
Abstract: Using the LRT statistic, a model R^2 is proposed for the generalized linear mixed model for assessing the association between the correlated outcomes and fixed effects. The R^2 compares the full model to a null model with all fixed effects deleted. |
Title: Regularized Risk Minimization by Nesterov's Accelerated Gradient Methods: Algorithmic Extensions and Empirical Studies |
Abstract: Nesterov's accelerated gradient methods (AGM) have been successfully applied in many machine learning areas. However, their empirical performance on training max-margin models has been inferior to existing specialized solvers. In this paper, we first extend AGM to strongly convex and composite objective funct... |
Title: A Very Fast Algorithm for Matrix Factorization |
Abstract: We present a very fast algorithm for general matrix factorization of a data matrix for use in the statistical analysis of high-dimensional data via latent factors. Such data are prevalent across many application areas and generate an ever-increasing demand for methods of dimension reduction in order to undert... |
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