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.

Analytic model of a cache-only memory architecture

An approximate analytic model of a shared memory multiprocessor with a Cache Only Memory Architecture (COMA), the busbased Data Difussion Machine (DDM), is presented and validated. It describes the timing and interference in the system as a function of the hardware, the protocols, the topology and the workload. Model results have been compared to results from an independent simulator. The comparison shows good model accuracy specially for non-saturated systems, where the errors in response times and device utilizations are independent of the number of processors and remain below 10% in 90% of the simulations. Therefore, the model can be used as an average performance prediction tool that avoids expensive simulations in the design of systems with many processors.

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.

Reducing Cache Hierarchy Energy Consumption by Predicting Forwarding and Disabling Associative Sets

The first level data cache un modern processors has become a major consumer of energy due to its increasing size and high frequency access rate. In order to reduce this high energy con sumption, we propose in this paper a straightforward filtering technique based on a highly accurate forwarding predictor. Specifically, a simple structure predicts whether a load instruction will obtain its corresponding data via forwarding from the load-store structure -thus avoiding the data cache access - or if it will be provided by the data cache. This mechanism manages to reduce the data cache energy consumption by an average of 21.5% with a negligible performance penalty of less than 0.1%. Furthermore, in this paper we focus on the cache static energy consumption too by disabling a portin of sets of the L2 associative cache. Overall, when merging both proposals, the combined L1 and L2 total energy consumption is reduced by an average of 29.2% with a performance penalty of just 0.25%. Keywords: Energy consumption; filtering; forwarding predictor; cache hierarchy

A multi-resource load balancing algorithm for cloud cache systems

With the advent of cloud computing model, distributed caches have become the cornerstone for building scalable applications. Popular systems like Facebook [1] or Twitter use Memcached [5], a highly scalable distributed object cache, to speed up applications by avoiding database accesses. Distributed object caches assign objects to cache instances based on a hashing function, and objects are not moved from a cache instance to another unless more instances are added to the cache and objects are redistributed. This may lead to situations where some cache instances are overloaded when some of the objects they store are frequently accessed, while other cache instances are less frequently used. In this paper we propose a multi-resource load balancing algorithm for distributed cache systems. The algorithm aims at balancing both CPU and Memory resources among cache instances by redistributing stored data. Considering the possible conflict of balancing multiple resources at the same time, we give CPU and Memory resources weighted priorities based on the runtime load distributions. A scarcer resource is given a higher weight than a less scarce resource when load balancing. The system imbalance degree is evaluated based on monitoring information, and the utility load of a node, a unit for resource consumption. Besides, since continuous rebalance of the system may affect the QoS of applications utilizing the cache system, our data selection policy ensures that each data migration minimizes the system imbalance degree and hence, the total reconfiguration cost can be minimized. An extensive simulation is conducted to compare our policy with other policies. Our policy shows a significant improvement in time efficiency and decrease in reconfiguration cost.

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.

Study and evaluation of several cache replacement policies on a commercial MIPS Processor

Actualmente, el rendimiento de los computadores es un tema candente. Existen importantes limitaciones físicas y tecnológicas en los semiconductores de hoy en día, por lo que se realiza un gran esfuerzo desde las universidades y la industria para garantizar la continuidad de la ley de Moore. Este proyecto está centrado en el estudio de la cache y la jerarquía de memoria, uno de los grandes temas en la materia. Para ello, hemos escogido MIPSfpga, una plataforma hardware abierta de Imagination Technologies, lo que nos ha permitido implementar y testear diferentes políticas de reemplazamiento como prueba de concepto, demostrando, además, las bondades de la plataforma.

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.

Rôle de l'isoforme p35 de la chaîne invariante humaine dans la formation et le transport de complexes de CMH II

La présentation antigénique par les molécules de classe II du complexe majeur d’histocompatibilité (CMH II) est un mécanisme essentiel au contrôle des pathogènes par le système immunitaire. Le CMH II humain existe en trois isotypes, HLA-DP, DQ et DR, tous des hétérodimères composés d’une chaîne α et d’une chaîne β. Le CMH II est entre autres exprimé à la surface des cellules présentatrices d’antigènes (APCs) et des cellules épithéliales activées et a pour fonction de présenter des peptides d’origine exogène aux lymphocytes T CD4+. L’oligomérisation et le trafic intracellulaire du CMH II sont largement facilités par une chaperone, la chaîne invariante (Ii). Il s’agit d’une protéine non-polymorphique de type II. Après sa biosynthèse dans le réticulum endoplasmique (ER), Ii hétéro- ou homotrimérise, puis interagit via sa région CLIP avec le CMH II pour former un complexe αβIi. Le complexe sort du ER pour entamer son chemin vers différents compartiments et la surface cellulaire. Chez l’homme, quatre isoformes d’Ii sont répertoriées : p33, p35, p41 et p43. Les deux isoformes exprimées de manière prédominante, Iip33 et p35, diffèrent par une extension N-terminale de 16 acides aminés portée par Iip35. Cette extension présente un motif de rétention au réticulum endoplasmique (ERM) composé des résidus RXR. Ce motif doit être masqué par la chaîne β du CMH II pour permettre au complexe de quitter le ER. Notre groupe s’est intéressé au mécanisme du masquage et au mode de sortie du ER des complexes αβIi. Nous montrons ici que l’interaction directe, ou en cis, entre la chaîne β du CMH II et Iip35 dans une structure αβIi est essentielle pour sa sortie du ER, promouvant la formation de structures de haut niveau de complexité. Par ailleurs, nous démontrons que NleA, un facteur de virulence bactérien, permet d’altérer le trafic de complexes αβIi comportant Iip35. Ce phénotype est médié par l’interaction entre p35 et les sous-unités de COPII. Bref, Iip35 joue un rôle central dans la formation des complexes αβIi et leur transport hors du ER. Ceci fait d’Iip35 un régulateur clef de la présentation antigénique par le CMH II.