(Table 2) Composition of native metals and intermetallides from bottom sediments of the Markov Deep, Mid-Atlantic Ridge

An additional ore field in the central part of the MARhas been discovered. Together with previously discovered Logachev (14°45'N) and Ashadze (12°58'N) ore fields, the new ore field constitutes a cluster with preliminarily estimated total ore reserve of >10 Mt, which is comparable with large continental massive sulfide deposits.

(Table 3) Compositions of minerals of native elements from bottom sediments of the Markov Deep, Mid-Atlantic Ridge

An additional ore field in the central part of the MARhas been discovered. Together with previously discovered Logachev (14°45'N) and Ashadze (12°58'N) ore fields, the new ore field constitutes a cluster with preliminarily estimated total ore reserve of >10 Mt, which is comparable with large continental massive sulfide deposits.

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson’s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson’s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.

Cooperative off-policy prediction of markov decision processes in adaptive networks

We apply diffusion strategies to propose a cooperative reinforcement learning algorithm, in which agents in a network communicate with their neighbors to improve predictions about their environment. The algorithm is suitable to learn off-policy even in large state spaces. We provide a mean-square-error performance analysis under constant step-sizes. The gain of cooperation in the form of more stability and less bias and variance in the prediction error, is illustrated in the context of a classical model. We show that the improvement in performance is especially significant when the behavior policy of the agents is different from the target policy under evaluation.

On-road visual vehicle tracking using Markov chain Monte Carlo with metropolis sampling

In this study, a method for vehicle tracking through video analysis based on Markov chain Monte Carlo (MCMC) particle filtering with metropolis sampling is proposed. The method handles multiple targets with low computational requirements and is, therefore, ideally suited for advanced-driver assistance systems that involve real-time operation. The method exploits the removed perspective domain given by inverse perspective mapping (IPM) to define a fast and efficient likelihood model. Additionally, the method encompasses an interaction model using Markov Random Fields (MRF) that allows treatment of dependencies between the motions of targets. The proposed method is tested in highway sequences and compared to state-of-the-art methods for vehicle tracking, i.e., independent target tracking with Kalman filtering (KF) and joint tracking with particle filtering. The results showed fewer tracking failures using the proposed method.

COMUNICAÇAO E TECNOLOGIA NA CADEIA DA SOJA EM MT

Esta tese trata do sistema de informação tecnologica (SIT) da cadeia produtiva da soja em Mato Grosso. O objetivo foi identificar, modelar, descrever e analisar o SIT, apresentando fontes, canais, fluxos, mediadores e usuários de informação sobre tecnologias agropecuarias, limitações, oprotunidades e estrategias para aumentar o desempenho do sistema. Os procedimentos metodologicos na pesquisa de campo incluem exames de documentos e outros dados secundarios, realização de grupo focal e, particularmente, entrevista com 44 conhecedores da cadeia. O estudo mostra que, a partir das transformações na agricultura, o modelo tradicional de transferencia de tecnologia baseada na articulação pesquisa/extensao/agricultor foi substituido por sistemas com multiplas conexoes e atores. Identifica-se que a informação é insumo fundamental e esta baseada na perspectiva de negocios; a principal maneira de transmissao de informações sao os meios informais; o agricultor possui fontes de informação em excesso com experiencias, pressupostos e interesses diferentes. Demonstra-se que a configuração atual do SIT da cadeia produtiva de soja em MT se ressente da ausencia do setor publico e apresenta -se estrategias para que o fluxo de informacao tecnologica aumente a competitividade da cadeia.(AU)