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Title: Algorithms for Dynamic Spectrum Access with Learning for Cognitive Radio |
Abstract: We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperatively tries to exploit vacancies in primary (licensed) channels whose occupancies follow a Markovian evolution. We first consider t... |
Title: ABC likelihood-freee methods for model choice in Gibbs random fields |
Abstract: Gibbs random fields (GRF) are polymorphous statistical models that can be used to analyse different types of dependence, in particular for spatially correlated data. However, when those models are faced with the challenge of selecting a dependence structure from many, the use of standard model choice methods ... |
Title: Visual Grouping by Neural Oscillators |
Abstract: Distributed synchronization is known to occur at several scales in the brain, and has been suggested as playing a key functional role in perceptual grouping. State-of-the-art visual grouping algorithms, however, seem to give comparatively little attention to neural synchronization analogies. Based on the fram... |
Title: On Probability Distributions for Trees: Representations, Inference and Learning |
Abstract: We study probability distributions over free algebras of trees. Probability distributions can be seen as particular (formal power) tree series [Berstel et al 82, Esik et al 03], i.e. mappings from trees to a semiring K . A widely studied class of tree series is the class of rational (or recognizable) tree ser... |
Title: A note on state space representations of locally stationary wavelet time series |
Abstract: In this note we show that the locally stationary wavelet process can be decomposed into a sum of signals, each of which following a moving average process with time-varying parameters. We then show that such moving average processes are equivalent to state space models with stochastic design components. Using... |
Title: Principle of detailed balance and convergence assessment of Markov Chain Monte Carlo methods and simulated annealing |
Abstract: Markov Chain Monte Carlo (MCMC) methods are employed to sample from a given distribution of interest, whenever either the distribution does not exist in closed form, or, if it does, no efficient method to simulate an independent sample from it is available. Although a wealth of diagnostic tools for convergenc... |
Title: The NAO humanoid: a combination of performance and affordability |
Abstract: This article presents the design of the autonomous humanoid robot called NAO that is built by the French company Aldebaran-Robotics. With its height of 0.57 m and its weight about 4.5 kg, this innovative robot is lightweight and compact. It distinguishes itself from its existing Japanese, American, and other ... |
Title: Exploiting Bird Locomotion Kinematics Data for Robotics Modeling |
Abstract: We present here the results of an analysis carried out by biologists and roboticists with the aim of modeling bird locomotion kinematics for robotics purposes. The aim was to develop a bio-inspired kinematic model of the bird leg from biological data. We first acquired and processed kinematic data for sagitta... |
Title: Constructing a Knowledge Base for Gene Regulatory Dynamics by Formal Concept Analysis Methods |
Abstract: Our aim is to build a set of rules, such that reasoning over temporal dependencies within gene regulatory networks is possible. The underlying transitions may be obtained by discretizing observed time series, or they are generated based on existing knowledge, e.g. by Boolean networks or their nondeterministic... |
Title: Universal Denoising of Discrete-time Continuous-Amplitude Signals |
Abstract: We consider the problem of reconstructing a discrete-time signal (sequence) with continuous-valued components corrupted by a known memoryless channel. When performance is measured using a per-symbol loss function satisfying mild regularity conditions, we develop a sequence of denoisers that, although independ... |
Title: Elastic-Net Regularization in Learning Theory |
Abstract: Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie for the selection of groups of correlated variables. To investigate on the statistical properties of this scheme and in particular on its consistency properties, ... |
Title: Inference with Discriminative Posterior |
Abstract: We study Bayesian discriminative inference given a model family $p(c,\x, \theta)$ that is assumed to contain all our prior information but still known to be incorrect. This falls in between "standard" Bayesian generative modeling and Bayesian regression, where the margin $p(\x,\theta)$ is known to be uninform... |
Title: Implementing general belief function framework with a practical codification for low complexity |
Abstract: In this chapter, we propose a new practical codification of the elements of the Venn diagram in order to easily manipulate the focal elements. In order to reduce the complexity, the eventual constraints must be integrated in the codification at the beginning. Hence, we only consider a reduced hyper power set ... |
Title: TuLiPA: Towards a Multi-Formalism Parsing Environment for Grammar Engineering |
Abstract: In this paper, we present an open-source parsing environment (Tuebingen Linguistic Parsing Architecture, TuLiPA) which uses Range Concatenation Grammar (RCG) as a pivot formalism, thus opening the way to the parsing of several mildly context-sensitive formalisms. This environment currently supports tree-based... |
Title: A new probabilistic transformation of belief mass assignment |
Abstract: In this paper, we propose in Dezert-Smarandache Theory (DSmT) framework, a new probabilistic transformation, called DSmP, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the DSmP transf... |
Title: Data spectroscopy: Eigenspaces of convolution operators and clustering |
Abstract: This paper focuses on obtaining clustering information about a distribution from its i.i.d. samples. We develop theoretical results to understand and use clustering information contained in the eigenvectors of data adjacency matrices based on a radial kernel function with a sufficiently fast tail decay. In pa... |
Title: A path following algorithm for Sparse Pseudo-Likelihood Inverse Covariance Estimation (SPLICE) |
Abstract: Given n observations of a p-dimensional random vector, the covariance matrix and its inverse (precision matrix) are needed in a wide range of applications. Sample covariance (e.g. its eigenstructure) can misbehave when p is comparable to the sample size n. Regularization is often used to mitigate the problem.... |
Title: Formal semantics of language and the Richard-Berry paradox |
Abstract: The classical logical antinomy known as Richard-Berry paradox is combined with plausible assumptions about the size i.e. the descriptional complexity of Turing machines formalizing certain sentences, to show that formalization of language leads to contradiction. |
Title: A Distributed Process Infrastructure for a Distributed Data Structure |
Abstract: The Resource Description Framework (RDF) is continuing to grow outside the bounds of its initial function as a metadata framework and into the domain of general-purpose data modeling. This expansion has been facilitated by the continued increase in the capacity and speed of RDF database repositories known as ... |
Title: Prediction of multivariate responses with a select number of principal components |
Abstract: This paper proposes a new method and algorithm for predicting multivariate responses in a regression setting. Research into classification of High Dimension Low Sample Size (HDLSS) data, in particular microarray data, has made considerable advances, but regression prediction for high-dimensional data with con... |
Title: Estimating a difference between Kullback-Leibler risks by a normalized difference of AIC |
Abstract: AIC is commonly used for model selection but the precise value of AIC has no direct interpretation. We are interested in quantifying a difference of risks between two models. This may be useful for both an explanatory point of view or for prediction, where a simpler model may be preferred if it does nearly as... |
Title: Positive factor networks: A graphical framework for modeling non-negative sequential data |
Abstract: We present a novel graphical framework for modeling non-negative sequential data with hierarchical structure. Our model corresponds to a network of coupled non-negative matrix factorization (NMF) modules, which we refer to as a positive factor network (PFN). The data model is linear, subject to non-negativity... |
Title: QR-Adjustment for Clustering Tests Based on Nearest Neighbor Contingency Tables |
Abstract: The spatial interaction between two or more classes of points may cause spatial clustering patterns such as segregation or association, which can be tested using a nearest neighbor contingency table (NNCT). A NNCT is constructed using the frequencies of class types of points in nearest neighbor (NN) pairs. Fo... |
Title: On the Use of Nearest Neighbor Contingency Tables for Testing Spatial Segregation |
Abstract: For two or more classes (or types) of points, nearest neighbor contingency tables (NNCTs) are constructed using nearest neighbor (NN) frequencies and are used in testing spatial segregation of the classes. Pielou's test of independence, Dixon's cell-specific, class-specific, and overall tests are the tests ba... |
Title: Avoider robot design to dim the fire with dt basic mini system |
Abstract: Avoider robot is mean robot who is designed to avoid the block in around. Except that, this robot is also added by an addition application to dim the fire. This robot is made with ultrasonic sensor PING. This sensor is set on the front, right and left from robot. This sensor is used robot to look for the righ... |
Title: On Introspection, Metacognitive Control and Augmented Data Mining Live Cycles |
Abstract: We discuss metacognitive modelling as an enhancement to cognitive modelling and computing. Metacognitive control mechanisms should enable AI systems to self-reflect, reason about their actions, and to adapt to new situations. In this respect, we propose implementation details of a knowledge taxonomy and an au... |
Title: An Image-Based Sensor System for Autonomous Rendez-Vous with Uncooperative Satellites |
Abstract: In this paper are described the image processing algorithms developed by SENER, Ingenieria y Sistemas to cope with the problem of image-based, autonomous rendez-vous (RV) with an orbiting satellite. The methods developed have a direct application in the OLEV (Orbital Life Extension Extension Vehicle) mission.... |
Title: AceWiki: A Natural and Expressive Semantic Wiki |
Abstract: We present AceWiki, a prototype of a new kind of semantic wiki using the controlled natural language Attempto Controlled English (ACE) for representing its content. ACE is a subset of English with a restricted grammar and a formal semantics. The use of ACE has two important advantages over existing semantic w... |
Title: AceWiki: Collaborative Ontology Management in Controlled Natural Language |
Abstract: AceWiki is a prototype that shows how a semantic wiki using controlled natural language - Attempto Controlled English (ACE) in our case - can make ontology management easy for everybody. Sentences in ACE can automatically be translated into first-order logic, OWL, or SWRL. AceWiki integrates the OWL reasoner ... |
Title: Hacia una teoria de unificacion para los comportamientos cognitivos |
Abstract: Each cognitive science tries to understand a set of cognitive behaviors. The structuring of knowledge of this nature's aspect is far from what it can be expected about a science. Until now universal standard consistently describing the set of cognitive behaviors has not been found, and there are many question... |
Title: Covariance fields |
Abstract: We introduce and study covariance fields of distributions on a Riemannian manifold. At each point on the manifold, covariance is defined to be a symmetric and positive definite (2,0)-tensor. Its product with the metric tensor specifies a linear operator on the respected tangent space. Collectively, these oper... |
Title: An image processing analysis of skin textures |
Abstract: Colour and coarseness of skin are visually different. When image processing is involved in the skin analysis, it is important to quantitatively evaluate such differences using texture features. In this paper, we discuss a texture analysis and measurements based on a statistical approach to the pattern recogni... |
Title: On an Auxiliary Function for Log-Density Estimation |
Abstract: In this note we provide explicit expressions and expansions for a special function which appears in nonparametric estimation of log-densities. This function returns the integral of a log-linear function on a simplex of arbitrary dimension. In particular it is used in the R-package "LogCondDEAD" by Cule et al.... |
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