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Title: A Modified Cross Correlation Algorithm for Reference-free Image Alignment of Non-Circular Projections in Single-Particle Electron Microscopy |
Abstract: In this paper we propose a modified cross correlation method to align images from the same class in single-particle electron microscopy of highly non-spherical structures. In this new method, First we coarsely align projection images, and then re-align the resulting images using the cross correlation (CC) met... |
Title: Excess entropy in natural language: present state and perspectives |
Abstract: We review recent progress in understanding the meaning of mutual information in natural language. Let us define words in a text as strings that occur sufficiently often. In a few previous papers, we have shown that a power-law distribution for so defined words (a.k.a. Herdan's law) is obeyed if there is a sim... |
Title: Self-organized adaptation of a simple neural circuit enables complex robot behaviour |
Abstract: Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with the environment, this control needs to be fast and adaptive. Current robo... |
Title: On the generic uniform uniqueness of the LASSO estimator |
Abstract: The LASSO is a variable subset selection procedure in statistical linear regression based on $\ell_1$ penalization of the least-squares operator. Uniqueness of the LASSO is an important issue, especially for the study of the LASSO path. The goal of the present paper is to provide a generic sufficient conditio... |
Title: Solving Rubik's Cube Using SAT Solvers |
Abstract: Rubik's Cube is an easily-understood puzzle, which is originally called the "magic cube". It is a well-known planning problem, which has been studied for a long time. Yet many simple properties remain unknown. This paper studies whether modern SAT solvers are applicable to this puzzle. To our best knowledge, ... |
Title: Pivotal estimation via square-root Lasso in nonparametric regression |
Abstract: We propose a self-tuning $$ method that simultaneously resolves three important practical problems in high-dimensional regression analysis, namely it handles the unknown scale, heteroscedasticity and (drastic) non-Gaussianity of the noise. In addition, our analysis allows for badly behaved designs, for exampl... |
Title: EM algorithm and variants: an informal tutorial |
Abstract: The expectation-maximization (EM) algorithm introduced by Dempster et al in 1977 is a very general method to solve maximum likelihood estimation problems. In this informal report, we review the theory behind EM as well as a number of EM variants, suggesting that beyond the current state of the art is an even ... |
Title: Estimating Bernoulli trial probability from a small sample |
Abstract: The standard textbook method for estimating the probability of a biased coin from finite tosses implicitly assumes the sample sizes are large and gives incorrect results for small samples. We describe the exact solution, which is correct for any sample size. |
Title: Approximate inference via variational sampling |
Abstract: A new method called "variational sampling" is proposed to estimate integrals under probability distributions that can be evaluated up to a normalizing constant. The key idea is to fit the target distribution with an exponential family model by minimizing a strongly consistent empirical approximation to the Ku... |
Title: Evaluating the diagnostic powers of variables and their linear combinations when the gold standard is continuous |
Abstract: The receiver operating characteristic (ROC) curve is a very useful tool for analyzing the diagnostic/classification power of instruments/classification schemes as long as a binary-scale gold standard is available. When the gold standard is continuous and there is no confirmative threshold, ROC curve becomes l... |
Title: Estimation of latent variable models for ordinal data via fully exponential Laplace approximation |
Abstract: Latent variable models for ordinal data represent a useful tool in different fields of research in which the constructs of interest are not directly observable. In such models, problems related to the integration of the likelihood function can arise since analytical solutions do not exist. Numerical approxima... |
Title: A Compositional Distributional Semantics, Two Concrete Constructions, and some Experimental Evaluations |
Abstract: We provide an overview of the hybrid compositional distributional model of meaning, developed in Coecke et al. (arXiv:1003.4394v1 [cs.CL]), which is based on the categorical methods also applied to the analysis of information flow in quantum protocols. The mathematical setting stipulates that the meaning of a... |
Title: A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control |
Abstract: Reinforcement Learning (RL) is a method for learning decision-making tasks that could enable robots to learn and adapt to their situation on-line. For an RL algorithm to be practical for robotic control tasks, it must learn in very few actions, while continually taking those actions in real-time. Existing mod... |
Title: Order-preserving factor analysis (OPFA) |
Abstract: We present a novel factor analysis method that can be applied to the discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering property: certain factors are recruited only after some other factors are activated. Precedence-ordering aris... |
Title: Feedback Message Passing for Inference in Gaussian Graphical Models |
Abstract: While loopy belief propagation (LBP) performs reasonably well for inference in some Gaussian graphical models with cycles, its performance is unsatisfactory for many others. In particular for some models LBP does not converge, and in general when it does converge, the computed variances are incorrect (except ... |
Title: Multivariate convex regression with adaptive partitioning |
Abstract: We propose a new, nonparametric method for multivariate regression subject to convexity or concavity constraints on the response function. Convexity constraints are common in economics, statistics, operations research, financial engineering and optimization, but there is currently no multivariate method that ... |
Title: The Hidden Web, XML and Semantic Web: A Scientific Data Management Perspective |
Abstract: The World Wide Web no longer consists just of HTML pages. Our work sheds light on a number of trends on the Internet that go beyond simple Web pages. The hidden Web provides a wealth of data in semi-structured form, accessible through Web forms and Web services. These services, as well as numerous other appli... |
Title: Self-configuration from a Machine-Learning Perspective |
Abstract: The goal of machine learning is to provide solutions which are trained by data or by experience coming from the environment. Many training algorithms exist and some brilliant successes were achieved. But even in structured environments for machine learning (e.g. data mining or board games), most applications ... |
Title: Generalized Boosting Algorithms for Convex Optimization |
Abstract: Boosting is a popular way to derive powerful learners from simpler hypothesis classes. Following previous work (Mason et al., 1999; Friedman, 2000) on general boosting frameworks, we analyze gradient-based descent algorithms for boosting with respect to any convex objective and introduce a new measure of weak... |
Title: A Poisson Mixed Model with Nonnormal Random Effect Distribution |
Abstract: We propose in this paper a random intercept Poisson model in which the random effect distribution is assumed to follow a generalized log-gamma (GLG) distribution. We derive the first two moments for the marginal distribution as well as the intraclass correlation. Even though numerical integration methods are ... |
Title: Confidence bands for Horvitz-Thompson estimators using sampled noisy functional data |
Abstract: When collections of functional data are too large to be exhaustively observed, survey sampling techniques provide an effective way to estimate global quantities such as the population mean function. Assuming functional data are collected from a finite population according to a probabilistic sampling scheme, w... |
Title: Matrix Variate Logistic Regression Model with Application to EEG Data |
Abstract: Logistic regression has been widely applied in the field of biomedical research for a long time. In some applications, covariates of interest have a natural structure, such as being a matrix, at the time of collection. The rows and columns of the covariate matrix then have certain physical meanings, and they ... |
Title: A Framework for Optimization under Limited Information |
Abstract: In many real world problems, optimization decisions have to be made with limited information. The decision maker may have no a priori or posteriori data about the often nonconvex objective function except from on a limited number of points that are obtained over time through costly observations. This paper pr... |
Title: A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures |
Abstract: Nuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to obtain profiles of metabolites dissolved in biofluids such as cell supernatants. Methods for estimating metabolite concentrations from these spectra are presently confined to manual peak fitting and to binning procedures for integrati... |
Title: Dual Control with Active Learning using Gaussian Process Regression |
Abstract: In many real world problems, control decisions have to be made with limited information. The controller may have no a priori (or even posteriori) data on the nonlinear system, except from a limited number of points that are obtained over time. This is either due to high cost of observation or the highly non-s... |
Title: Large-sample tests of extreme-value dependence for multivariate copulas |
Abstract: Starting from the characterization of extreme-value copulas based on max-stability, large-sample tests of extreme-value dependence for multivariate copulas are studied. The two key ingredients of the proposed tests are the empirical copula of the data and a multiplier technique for obtaining approximate p-val... |
Title: Symmetries in observer design: review of some recent results and applications to EKF-based SLAM |
Abstract: In this paper, we first review the theory of symmetry-preserving observers and we mention some recent results. Then, we apply the theory to Extended Kalman Filter-based Simultaneous Localization and Mapping (EKF SLAM). It allows to derive a new (symmetry-preserving) Extended Kalman Filter for the non-linear S... |
Title: Exact recording of Metropolis-Hastings-class Monte Carlo simulations using one bit per sample |
Abstract: The Metropolis-Hastings (MH) algorithm is the prototype for a class of Markov chain Monte Carlo methods that propose transitions between states and then accept or reject the proposal. These methods generate a correlated sequence of random samples that convey information about the desired probability distribut... |
Title: Data-Distributed Weighted Majority and Online Mirror Descent |
Abstract: In this paper, we focus on the question of the extent to which online learning can benefit from distributed computing. We focus on the setting in which $N$ agents online-learn cooperatively, where each agent only has access to its own data. We propose a generic data-distributed online learning meta-algorithm.... |
Title: PAC-Bayesian Analysis of Martingales and Multiarmed Bandits |
Abstract: We present two alternative ways to apply PAC-Bayesian analysis to sequences of dependent random variables. The first is based on a new lemma that enables to bound expectations of convex functions of certain dependent random variables by expectations of the same functions of independent Bernoulli random variab... |
Title: Optimal grid exploration by asynchronous oblivious robots |
Abstract: We consider a team of \em autonomous weak robots that are endowed with visibility sensors and motion actuators. Autonomous means that the team cannot rely on any kind of central coordination mechanism or scheduler. By weak we mean that the robots are devoid of (1) any (observable) IDs allowing to differentiat... |
Title: A Multiple Component Matching Framework for Person Re-Identification |
Abstract: Person re-identification consists in recognizing an individual that has already been observed over a network of cameras. It is a novel and challenging research topic in computer vision, for which no reference framework exists yet. Despite this, previous works share similar representations of human body based ... |
Title: Closed-form EM for Sparse Coding and its Application to Source Separation |
Abstract: We define and discuss the first sparse coding algorithm based on closed-form EM updates and continuous latent variables. The underlying generative model consists of a standard `spike-and-slab' prior and a Gaussian noise model. Closed-form solutions for E- and M-step equations are derived by generalizing proba... |
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