SPECTRUM OF SCREENING MASSES FOR SU(2) GAUGE THEORY: UNIVERSALITY CLASS

In this work we study the spectrum of the lowest screening masses for Yang-Mills theories on the lattice. We used the SU(2) gauge group in (3 + 1) dmensions. We adopted the multiple exponential method and the so-called ""variational"" method, in order to detect possible excited states. The calculations were done near the critical temperature of the confinement-deconfinement phase transition. We obtained values for the ratios of the screening masses consistent with predictions from universality arguments. A Monte Carlo evolution of the screening masses in the gauge theory confirms the validity of the predictions.

Augmented Lagrangian methods under the constant positive linear dependence constraint qualification

Two Augmented Lagrangian algorithms for solving KKT systems are introduced. The algorithms differ in the way in which penalty parameters are updated. Possibly infeasible accumulation points are characterized. It is proved that feasible limit points that satisfy the Constant Positive Linear Dependence constraint qualification are KKT solutions. Boundedness of the penalty parameters is proved under suitable assumptions. Numerical experiments are presented.

Bayesian estimation of regression parameters in elliptical measurement error models

The main object of this paper is to discuss the Bayes estimation of the regression coefficients in the elliptically distributed simple regression model with measurement errors. The posterior distribution for the line parameters is obtained in a closed form, considering the following: the ratio of the error variances is known, informative prior distribution for the error variance, and non-informative prior distributions for the regression coefficients and for the incidental parameters. We proved that the posterior distribution of the regression coefficients has at most two real modes. Situations with a single mode are more likely than those with two modes, especially in large samples. The precision of the modal estimators is studied by deriving the Hessian matrix, which although complicated can be computed numerically. The posterior mean is estimated by using the Gibbs sampling algorithm and approximations by normal distributions. The results are applied to a real data set and connections with results in the literature are reported. (C) 2011 Elsevier B.V. All rights reserved.

Tori embedded in R(3) with dense principal lines

In this paper are given examples of tori T(2) embedded in R(3) with all their principal lines dense. These examples are obtained by stereographic projection of deformations of the Clifford torus in S(3). (C) 2008 Elsevier Masson SAS. All rights reserved.

Genericity of Nondegenerate Critical Points and Morse Geodesic Functionals

We consider a family of variational problems on a Hilbert manifold parameterized by an open subset of a Banach manifold, and we discuss the genericity of the nondegeneracy condition for the critical points. Using classical techniques, we prove an abstract genericity result that employs the infinite dimensional Sard-Smale theorem, along the lines of an analogous result of B. White [29]. Applications are given by proving the genericity of metrics without degenerate geodesics between fixed endpoints in general (non compact) semi-Riemannian manifolds, in orthogonally split semi-Riemannian manifolds and in globally hyperbolic Lorentzian manifolds. We discuss the genericity property also in stationary Lorentzian manifolds.

On the semi-Riemannian bumpy metric theorem

We prove the semi-Riemannian bumpy metric theorem using equivariant variational genericity. The theorem states that, on a given compact manifold M, the set of semi-Riemannian metrics that admit only nondegenerate closed geodesics is generic relatively to the C(k)-topology, k=2, ..., infinity, in the set of metrics of a given index on M. A higher-order genericity Riemannian result of Klingenberg and Takens is extended to semi-Riemannian geometry.

A robust Bayesian approach to null intercept measurement error model with application to dental data

Measurement error models often arise in epidemiological and clinical research. Usually, in this set up it is assumed that the latent variable has a normal distribution. However, the normality assumption may not be always correct. Skew-normal/independent distribution is a class of asymmetric thick-tailed distributions which includes the Skew-normal distribution as a special case. In this paper, we explore the use of skew-normal/independent distribution as a robust alternative to null intercept measurement error model under a Bayesian paradigm. We assume that the random errors and the unobserved value of the covariate (latent variable) follows jointly a skew-normal/independent distribution, providing an appealing robust alternative to the routine use of symmetric normal distribution in this type of model. Specific distributions examined include univariate and multivariate versions of the skew-normal distribution, the skew-t distributions, the skew-slash distributions and the skew contaminated normal distributions. The methods developed is illustrated using a real data set from a dental clinical trial. (C) 2008 Elsevier B.V. All rights reserved.

A theoretical study on the XeF(2) molecule

A relativistic four-component study was performed for the XeF(2) molecule by using the Dirac-Coulomb (DC) Hamiltonian and the relativistic adapted Gaussian basis sets (RAGBSs). The comparison of bond lengths obtained showed that relativistic effects on this property are small (increase of only 0.01 angstrom) while the contribution of electron correlation, obtained at CCSD(T) or CCSD-T levels, is more important (increase of 0.05 angstrom). Electron correlation is also dominant over relativistic effects for dissociation energies. Moreover, the correlation-relativity interaction is shown to be negligible for these properties. The electron affinity, the first ionization potential and the double ionization potential are obtained by means of the Fock-space coupled cluster (FSCC) method, resulting in DC-CCSD-T values of 0.3 eV, 12.5 eV and 32.3 eV, respectively. Vibrational frequencies and some anharmonicity constants were also calculated under the four-component formalism by means of standard perturbation equations. All these molecular properties are, in general, ill satisfactory agreement with available experimental results. Finally, a partition in terms of charge-charge flux-dipole flux (CCFDF) contributions derived by means of the quantum theory of atoms in molecules (QTAIM) in non-relativistic QCISD(FC)/3-21G* calculations was carried out for XeF(2) and KrF(2). This analysis showed that the most remarkable difference between both molecules lies on the charge flux contribution to the asymmetric stretching mode, which is negligible in KrF(2) but important in XeF(2). (c) 2008 Elsevier B.V. All rights reserved.

Simultaneous prediction of symptom severity and cause in data from a test battery for Parkinson patients, using machine learning methods

The main purpose of this thesis project is to prediction of symptom severity and cause in data from test battery of the Parkinson’s disease patient, which is based on data mining. The collection of the data is from test battery on a hand in computer. We use the Chi-Square method and check which variables are important and which are not important. Then we apply different data mining techniques on our normalize data and check which technique or method gives good results.The implementation of this thesis is in WEKA. We normalize our data and then apply different methods on this data. The methods which we used are Naïve Bayes, CART and KNN. We draw the Bland Altman and Spearman’s Correlation for checking the final results and prediction of data. The Bland Altman tells how the percentage of our confident level in this data is correct and Spearman’s Correlation tells us our relationship is strong. On the basis of results and analysis we see all three methods give nearly same results. But if we see our CART (J48 Decision Tree) it gives good result of under predicted and over predicted values that’s lies between -2 to +2. The correlation between the Actual and Predicted values is 0,794in CART. Cause gives the better percentage classification result then disability because it can use two classes.

Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease

The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.

Um assistente de feedback para o serviço de filtragem do software direto

Este trabalho descreve a especificação e implementação do protótipo Assistente de Feedback que ajuda os usuários a ajustarem os parâmetros do serviço de filtragem de mensagens vindas do correio eletrônico de sistemas como o Direto. O Assistente de Feedback é instalado no computador do usuário do Direto para monitorar suas preferências representadas pelas ações aplicadas nas mensagens do correio eletrônico. O trabalho apresenta, ainda, uma revisão bibliográfica sobre os conceitos gerais de probabilidades, redes Bayesianas e classificadores. Procura-se descrever as características gerais dos classificadores, em especial o Naive Bayes, sua lógica e seu desempenho comparado a outros classificadores. São abordados, também, conceitos relacionados ao modelo de perfil de usuário e o ambiente Direto. O Naive Bayes torna-se atraente para ser utilizado no Assistente de Feedback por apresentar bom desempenho sobre os demais classificadores e por ser eficiente na predição, quando os atributos são independentes entre si. O Assistente de Feedback utiliza um classificador Naive Bayes para predizer as preferências por intermédio das ações do usuário. Utiliza, também, pesos que representarão a satisfação do usuário para os termos extraídos do corpo da mensagem. Esses pesos são associados às ações do usuário para estimar os termos mais interessantes e menos interessantes, pelo valor de suas médias finais. Quando o usuário desejar alterar os filtros de mensagens do Direto, ele solicita ao Assistente de Feedback sugestões para possíveis exclusões dos termos menos interessantes e as possíveis inclusões dos termos mais interessantes. O protótipo é testado utilizando dois métodos de avaliação para medir o grau de precisão e o desempenho do Assistente de Feedback. Os resultados obtidos na avaliação de precisão apresentam valores satisfatórios, considerando o uso de cinco classes pelo classificador do Assistente de Feedback. Os resultados dos testes de desempenho permitem observar que, se forem utilizadas máquinas com configurações mais atualizadas, os usuários conseguirão receber sugestões com tempo de respostas mais toleráveis.

The ex-Ante non-optimality of the Dempster-Schafer updating rule for ambiguous beliefs

The most widely used updating rule for non-additive probalities is the Dempster-Schafer rule. Schmeidles and Gilboa have developed a model of decision making under uncertainty based on non-additive probabilities, and in their paper “Updating Ambiguos Beliefs” they justify the Dempster-Schafer rule based on a maximum likelihood procedure. This note shows in the context of Schmeidler-Gilboa preferences under uncertainty, that the Dempster-Schafer rule is in general not ex-ante optimal. This contrasts with Brown’s result that Bayes’ rule is ex-ante optimal for standard Savage preferences with additive probabilities.

A comment on "Rational learning lead to nash equilibrium" by professors Ehud Kalai and Ehud Lehrer

Kalai and Lebrer (93a, b) have recently show that for the case of infinitely repeated games, a coordination assumption on beliefs and optimal strategies ensures convergence to Nash equilibrium. In this paper, we show that for the case of repeated games with long (but finite) horizon, their condition does not imply approximate Nash equilibrium play. Recently Kalai and Lehrer (93a, b) proved that a coordination assumption on beliefs and optimal strategies, ensures that pIayers of an infinitely repeated game eventually pIay 'E-close" to an E-Nash equilibrium. Their coordination assumption requires that if players believes that certain set of outcomes have positive probability then it must be the case that this set of outcomes have, in fact, positive probability. This coordination assumption is called absolute continuity. For the case of finitely repeated games, the absolute continuity assumption is a quite innocuous assumption that just ensures that pIayers' can revise their priors by Bayes' Law. However, for the case of infinitely repeated games, the absolute continuity assumption is a stronger requirement because it also refers to events that can never be observed in finite time.

Estudo de caso-controle para desenvolvimento e validação de um questionário de avaliação de abuso sexual em crianças

Objetivos: Desenvolver e validar instrumento que auxilie o pediatra a determinar a probabilidade de ocorrência do abuso sexual em crianças. Métodos: Estudo de caso-controle com 201 crianças que consultaram em ambulatórios de pediatria e locais de referência para vítimas de abuso sexual, entre março e novembro de 2004: grupo caso (com suspeita ou revelação de abuso sexual) e grupo controle (sem suspeita de abuso sexual). Aplicou-se, junto aos responsáveis, um questionário com 18 itens e cinco opções de respostas segundo a escala Likert, abordando comportamento, sintomas físicos e emocionais apresentados pelas crianças. Excluíram-se nove crianças sem controle esfincteriano e um item respondido por poucas pessoas. A validade e consistência interna dos itens foram avaliadas com obtenção de coeficientes de correlação (Pearson, Spearman e Goodman-Kruskal), coeficiente α de Cronbach e cálculo da área da curva ROC. Calculou-se, após, a razão de verossimilhança (RV) e os valores preditivo positivos (VPP) para os cinco itens do questionário que apresentaram os melhores desempenhos. Resultados: Obteve-se um questionário composto pelos cinco itens que melhor discriminaram crianças com e sem abuso sexual em dois contextos. Cada criança recebeu um escore resultante da soma das respostas com pesos de 0 a 4 (amplitude de 0 a 20), o qual, através do teorema de Bayes (RV), indicou sua probabilidade pós-teste (VPP) de abuso sexual. Conclusões: O instrumento proposto é útil por ser de fácil aplicação, auxiliando o pediatra na identificação de crianças vítimas de abuso sexual. Ele fornecerá, conforme o escore obtido, a probabilidade (VPP) de abuso sexual, orientando na conduta de cuidado à criança.

Aplicação de redes bayesianas na previsão de crescimento de fluxos de caixa

Redes Bayesianas podem ser ferramentas poderosas para construção de modelos econômico-financeiros utilizados para auxílio à tomada de decisão em situações que envolvam grau elevado de incerteza. Relações não-lineares entre variáveis não são capturadas em modelos econométricos lineares. Especialmente em momentos de crise ou de ruptura, relações lineares, em geral, não mais representam boa aproximação da realidade, contribuindo para aumentar a distância entre os modelos teóricos de previsão e dados reais. Neste trabalho, é apresentada uma metodologia para levantamento de dados e aplicação de Redes Bayesianas na obtenção de modelos de crescimento de fluxos de caixa de empresas brasileiras. Os resultados são comparados a modelos econométricos de regressão múltipla e finalmente comparados aos dados reais observados no período. O trabalho é concluído avaliando-se as vantagens de desvantagens da utilização das Redes de Bayes para esta aplicação.