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Title: Serializing the Parallelism in Parallel Communicating Pushdown Automata Systems |
Abstract: We consider parallel communicating pushdown automata systems (PCPA) and define a property called known communication for it. We use this property to prove that the power of a variant of PCPA, called returning centralized parallel communicating pushdown automata (RCPCPA), is equivalent to that of multi-head pu... |
Title: Computational methods for Bayesian model choice |
Abstract: In this note, we shortly survey some recent approaches on the approximation of the Bayes factor used in Bayesian hypothesis testing and in Bayesian model choice. In particular, we reassess importance sampling, harmonic mean sampling, and nested sampling from a unified perspective. |
Title: On Classification from Outlier View |
Abstract: Classification is the basis of cognition. Unlike other solutions, this study approaches it from the view of outliers. We present an expanding algorithm to detect outliers in univariate datasets, together with the underlying foundation. The expanding algorithm runs in a holistic way, making it a rather robust ... |
Title: Hilbert space embeddings and metrics on probability measures |
Abstract: A Hilbert space embedding for probability measures has recently been proposed, with applications including dimensionality reduction, homogeneity testing, and independence testing. This embedding represents any probability measure as a mean element in a reproducing kernel Hilbert space (RKHS). A pseudometric o... |
Title: Multiple pattern classification by sparse subspace decomposition |
Abstract: A robust classification method is developed on the basis of sparse subspace decomposition. This method tries to decompose a mixture of subspaces of unlabeled data (queries) into class subspaces as few as possible. Each query is classified into the class whose subspace significantly contributes to the decompos... |
Title: Monotonicity properties of the asymptotic relative efficiency between common correlation statistics in the bivariate normal model |
Abstract: Pearson's is the most common correlation statistic, used mainly in parametric settings. Most common among nonparametric correlation statistics are Spearman's and Kendall's. We show that for bivariate normal i.i.d. samples the pairwise asymptotic relative efficiency between these three statistics depends monot... |
Title: How the initialization affects the stability of the k-means algorithm |
Abstract: We investigate the role of the initialization for the stability of the k-means clustering algorithm. As opposed to other papers, we consider the actual k-means algorithm and do not ignore its property of getting stuck in local optima. We are interested in the actual clustering, not only in the costs of the so... |
Title: Convergence of Expected Utility for Universal AI |
Abstract: We consider a sequence of repeated interactions between an agent and an environment. Uncertainty about the environment is captured by a probability distribution over a space of hypotheses, which includes all computable functions. Given a utility function, we can evaluate the expected utility of any computatio... |
Title: Online Learning for Matrix Factorization and Sparse Coding |
Abstract: Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the large-scale matrix factorization problem that consists of learning the basis set, adapting it to specif... |
Title: Making statistical methods in management research more useful: some suggestions from a case study |
Abstract: I present a critique of the methods used in a typical paper. This leads to three broad conclusions about the conventional use of statistical methods. First, results are often reported in an unnecessarily obscure manner. Second, the null hypothesis testing paradigm is deeply flawed: estimating the size of effe... |
Title: Knowledge Discovery of Hydrocyclone s Circuit Based on SONFIS and SORST |
Abstract: This study describes application of some approximate reasoning methods to analysis of hydrocyclone performance. In this manner, using a combining of Self Organizing Map (SOM), Neuro-Fuzzy Inference System (NFIS)-SONFIS- and Rough Set Theory (RST)-SORST-crisp and fuzzy granules are obtained. Balancing of crisp... |
Title: A Class of DSm Conditional Rules |
Abstract: In this paper we introduce two new DSm fusion conditioning rules with example, and as a generalization of them a class of DSm fusion conditioning rules, and then extend them to a class of DSm conditioning rules. |
Title: Empirical assessment of the impact of highway design exceptions on the frequency and severity of vehicle accidents |
Abstract: Compliance to standardized highway design criteria is considered essential to ensure the roadway safety. However, for a variety of reasons, situations arise where exceptions to standard-design criteria are requested and accepted after review. This research explores the impact that design exceptions have on th... |
Title: FPGA-based Controller for a Mobile Robot |
Abstract: With application in the robotics and automation, more and more it becomes necessary the development of applications based on methodologies that facilitate future modifications, updates and enhancements in the original projected system. This project presents a conception of mobile robots using rapid prototypin... |
Title: Geometry of diagonal-effect models for contingency tables |
Abstract: In this work we study several types of diagonal-effect models for two-way contingency tables in the framework of Algebraic Statistics. We use both toric models and mixture models to encode the different behavior of the diagonal cells. We compute the invariants of these models and we explore their geometrical ... |
Title: Regret Bounds for Opportunistic Channel Access |
Abstract: We consider the task of opportunistic channel access in a primary system composed of independent Gilbert-Elliot channels where the secondary (or opportunistic) user does not dispose of a priori information regarding the statistical characteristics of the system. It is shown that this problem may be cast into ... |
Title: A Reflection on the Structure and Process of the Web of Data |
Abstract: The Web community has introduced a set of standards and technologies for representing, querying, and manipulating a globally distributed data structure known as the Web of Data. The proponents of the Web of Data envision much of the world's data being interrelated and openly accessible to the general public. ... |
Title: Byzantine Convergence in Robots Networks: The Price of Asynchrony |
Abstract: We study the convergence problem in fully asynchronous, uni-dimensional robot networks that are prone to Byzantine (i.e. malicious) failures. In these settings, oblivious anonymous robots with arbitrary initial positions are required to eventually converge to an a apriori unknown position despite a subset of ... |
Title: Nonlinear Principal Components and Long-run Implications of Multivariate Diffusions |
Abstract: We investigate a method for extracting nonlinear principal components (NPCs). These NPCs maximize variation subject to smoothness and orthogonality constraints; but we allow for a general class of constraints and multivariate probability densities, including densities without compact support and even densitie... |
Title: The Infinite Hierarchical Factor Regression Model |
Abstract: We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coal... |
Title: Streamed Learning: One-Pass SVMs |
Abstract: We present a streaming model for large-scale classification (in the context of $\ell_2$-SVM) by leveraging connections between learning and computational geometry. The streaming model imposes the constraint that only a single pass over the data is allowed. The $\ell_2$-SVM is known to have an equivalent formu... |
Title: Functional Partial Linear Model |
Abstract: When predicting scalar responses in the situation where the explanatory variables are functions, it is sometimes the case that some functional variables are related to responses linearly while other variables have more complicated relationships with the responses. In this paper, we propose a new semi-parametr... |
Title: Online Learning of Assignments that Maximize Submodular Functions |
Abstract: Which ads should we display in sponsored search in order to maximize our revenue? How should we dynamically rank information sources to maximize value of information? These applications exhibit strong diminishing returns: Selection of redundant ads and information sources decreases their marginal utility. We ... |
Title: Clustering for Improved Learning in Maze Traversal Problem |
Abstract: The maze traversal problem (finding the shortest distance to the goal from any position in a maze) has been an interesting challenge in computational intelligence. Recent work has shown that the cellular simultaneous recurrent neural network (CSRN) can solve this problem for simple mazes. This thesis focuses ... |
Title: An Application of Bayesian classification to Interval Encoded Temporal mining with prioritized items |
Abstract: In real life, media information has time attributes either implicitly or explicitly known as temporal data. This paper investigates the usefulness of applying Bayesian classification to an interval encoded temporal database with prioritized items. The proposed method performs temporal mining by encoding the d... |
Title: Side-channel attack on labeling CAPTCHAs |
Abstract: We propose a new scheme of attack on the Microsoft's ASIRRA CAPTCHA which represents a significant shortcut to the intended attacking path, as it is not based in any advance in the state of the art on the field of image recognition. After studying the ASIRRA Public Corpus, we conclude that the security margin... |
Title: Discrete Temporal Models of Social Networks |
Abstract: We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readily adapted for these models, including maximum likelihood estimation algorithms. We discuss models of this type and... |
Title: Segmentation for radar images based on active contour |
Abstract: We exam various geometric active contour methods for radar image segmentation. Due to special properties of radar images, we propose our new model based on modified Chan-Vese functional. Our method is efficient in separating non-meteorological noises from meteorological images. |
Title: Study of the Nonequilibrium Critical Quenching and Annealing Dynamics for the Long-Range Ising Model |
Abstract: Extensive Monte Carlo simulations are employed in order to study the dynamic critical behavior of the one-dimensional Ising magnet, with algebraically decaying long-range interactions of the form $r^d+\sigma$, with $\sigma=0.75$. The critical temperature, as well as the critical exponents, is evaluated from t... |
Title: Approximating the Permanent with Belief Propagation |
Abstract: This work describes a method of approximating matrix permanents efficiently using belief propagation. We formulate a probability distribution whose partition function is exactly the permanent, then use Bethe free energy to approximate this partition function. After deriving some speedups to standard belief pr... |
Title: A dyadic solution of relative pose problems |
Abstract: A hierarchical interval subdivision is shown to lead to a $p$-adic encoding of image data. This allows in the case of the relative pose problem in computer vision and photogrammetry to derive equations having 2-adic numbers as coefficients, and to use Hensel's lifting method to their solution. This method is ... |
Title: Simultaneous confidence bands for nonparametric regression with functional data |
Abstract: We consider nonparametric regression in the context of functional data, that is, when a random sample of functions is observed on a fine grid. We obtain a functional asymptotic normality result allowing to build simultaneous confidence bands (SCB) for various estimation and inference tasks. Two applications t... |
Title: View-based Propagator Derivation |
Abstract: When implementing a propagator for a constraint, one must decide about variants: When implementing min, should one also implement max? Should one implement linear constraints both with unit and non-unit coefficients? Constraint variants are ubiquitous: implementing them requires considerable (if not prohibiti... |
Title: Statistical inference for stochastic epidemic models with three levels of mixing |
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