(Table 1) Heavy fraction minerals in 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.

Sm-Nd, Rb-Sr, and He isotopic composition and hydrocarbon gases in rocks and minerals from the Markov Deep, Mid-Atlantic Ridge rift valley

The paper presents characteristics of the Nd and Sr isotopic systems of ultrabasic rocks, gabbroids, plagiogranites, and their minerals as well as data on helium and hydrocarbons in fluid inclusions of the same samples. Materials presented in this publication were obtained by studying samples dredged from the MAR crest zone at 5°-6°N (U/Pb zircon dating, geochemical and petrological-mineralogical studies). It was demonstrated that variations in the isotopic composition of He entrapped in rocks and minerals were controlled by variable degrees of mixing of juvenile He, which is typical of basaltic glass for MAR (DM source), and atmospheric He. Increase in the atmospheric He fraction in plutonic rocks and, to a lesser degree, in their minerals reflects involvement of seawater or hydrated material of the oceanic crust in magmatic and postmagmatic processes. This conclusion finds further support in positive correlation between the fraction of mantle He (R ratio) and 87Sr/86Sr ratio. High-temperature hydration of ultrabasic rocks (amphibolization) was associated with increase in the fraction of mantle He, while their low-temperature hydration (serpentinization) was accompanied by drastic decrease in this fraction and significant increase in 87Sr/86Sr ratio. Insignificant variations in 143Nd/144Nd (close to 0.5130) and 87Sr/86Sr (0.7035) in most of gabbroids and plagiogranites as well as the fraction of mantle He in these rocks, amphibolites, and their ore minerals indicate that the melts were derived from the depleted mantle. Similar e-Nd values of gabbroids, plagiogranites, and fresh harzburgites (6.77-8.39) suggest that these rocks were genetically related to a single mantle source. e-Nd value of serpentinized lherzolites (2.62) likely reflects relations of these relatively weakly depleted mantle residues to another source. Aforementioned characteristics of the rocks generally reflect various degrees of mixing of depleted mantle components with crustal components (seawater) during metamorphic and hydrothermal processes that accompanied formation of the oceanic crust.

(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.

A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems

We propose a general procedure for solving incomplete data estimation problems. The procedure can be used to find the maximum likelihood estimate or to solve estimating equations in difficult cases such as estimation with the censored or truncated regression model, the nonlinear structural measurement error model, and the random effects model. The procedure is based on the general principle of stochastic approximation and the Markov chain Monte-Carlo method. Applying the theory on adaptive algorithms, we derive conditions under which the proposed procedure converges. Simulation studies also indicate that the proposed procedure consistently converges to the maximum likelihood estimate for the structural measurement error logistic regression model.

The cutoff phenomenon in finite Markov chains.

Natural mixing processes modeled by Markov chains often show a sharp cutoff in their convergence to long-time behavior. This paper presents problems where the cutoff can be proved (card shuffling, the Ehrenfests' urn). It shows that chains with polynomial growth (drunkard's walk) do not show cutoffs. The best general understanding of such cutoffs (high multiplicity of second eigenvalues due to symmetry) is explored. Examples are given where the symmetry is broken but the cutoff phenomenon persists.

Economic crises and tourism competitiveness: A Markov swtiching regression approach

Authors discuss the effects that economic crises generate on the global market shares of tourism destinations, through a series of potential transmission mechanisms based on the main economic competitiveness determinants identified in the previous literature using a non-linear approach. Specifically a Markov Switching Regression approach is used to estimate the effect of two basic transmission mechanisms: reductions of internal and external tourism demands and falling investment.