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This paper is focused on solving the problem of the 2D compressible subsonic fluid flow around an obstacle using BEM with higher order boundary elements, with special consideration regarding to the treatment of singulatities. The 2D problem of a compressible fluid flow around an obstacle is equivalent with a singular boundary integral, obtained in terms of primary variables of the problem, the components of the velocity field. This singular boundary integral equation is solved by using quadratic isoparametric boundary elements. The problem is finally reduced to a linear system of equations. Aspects regarding the evaluation of the matrix coefficients are presented and a special attention is given to the treatment of integrals of singular kernels. A method based on the definition of the Cauchy Principal Value of an integral is developed. The method described is implemented into a computer code made in MathCAD and numerical results are obtained for different types of obstacles. We validate the computer through an analytical checking, made by comparing the numerical results with the exact solutions that exist in some particular cases, which are in very good agreement. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Relevance Vector Machines (RVM) were used to predict daily returns for an FX carry basket. Market observable exogenous variables known to have a relationship with the basket along with lags of the basket's return were used as inputs into these methods. Combinations of these networks were used in a committee and simple trading rules based on this amalgamated output were used to predict when carry basket returns would be negative for a day and hence a trader should go short this long-biased asset. The effect of using the networks for regression to predict actual returns was compared to their use as classifiers to predict whether the following day's return would be up or down. Assuming highly conservative estimates of trading costs, over the 10.5 year (2751 trading day) rolling out of sample period investigated, improvements of 120% in MAR ratio, 110% in Sortino and 80% in Sharpe relative to the 'Always In' benchmark were found. Furthermore, the extent of the maximum draw-down was reduced by 19% and the longest draw-down period was 53% shorter. [ABSTRACT FROM AUTHOR]

We report in this paper the evolution of a physically based drop size distribution coupling the Maximum Entropy Formalism and the Monte Carlo method to solve the coagulation equation. Using the discrete or continuous population balance equation, the Mass Flow Algorithm is formulated taking into account interactions between droplets via coalescence. After proposing a kernel for coalescence, we solve the time dependent drop size distribution equation using a Monte Carlo method which is convergent. The evolution of the drop size distribution shows the effect of spray droplets coalescence. [ABSTRACT FROM AUTHOR]

Performances of speaker verification systems are superb in clean noise-free conditions but the reliability of the systems drop severely in noisy environments. Fusion of audio and visual information is one of the solutions to this limitation. However, this approach requires appropriate weighting for each biometric trait when the systems are implemented under inconsistent conditions. In this study, we propose a novel approach by introducing Support Vector Machine (SVM) as indicator system for audio reliability estimation. This approach directly validate the quality of the incoming (claimant) speech signal so as to adaptively change the weighting factor for fusion of both subsystems scores. It is important to priory check the speech signal quality because unreliable speech data give incorrect scores hence affect the accuracy of the total scores of the fusion systems. The effectiveness of this approach has been experimented to a multibiometric verification system that employs lipreading images as visual features. This verification system uses SVM as a classifier for both subsystems. Principle Component Analysis (PCA) technique is executed for visual features extraction while for the audio feature extraction; Linear Predictive Coding (LPC) technique has been utilized. In this study, we found that the SVM indicator system is able to determine the quality of the speech signal up to 99.66%. We then observed that by using the proposed adaptive fusion system, EER percentage in noisy condition (10dB) has been decreased to 0.27% compared to 9.3% for non-adaptive fusion system and 51.13% for audio only system. [ABSTRACT FROM AUTHOR]

Chapter 16 of the book "The Hypoelliptic Laplacian and Ray-Singer Metrics," by Jean-Michel Bismut and Gilles Lebeau is presented. It offers information on the construction of Bargman kernel, by Stepehen Bargman, mathematician that pass from the classical form of the harmonic oscillator. It states that the formalism of the bosonic annihilation and creation operators are presented in the chapter.

Kernel-based Regularized least-squares Regression (RLSR) is a technique originally from Statistical Learning (SL) theory. RLSR can deal with non-linear problem through mapping the samples into a higher dimension space using a kernel function. This paper adopts the RLSR to time series forecasting and the resulted model is termed RLS-TS model getting the idea from applying neural network and support vector regression to time series forecasting. This paper applies the RLS-TS model to GBP/USD currency exchange rates. A grid search is used to choose the optimal parameters. [ABSTRACT FROM AUTHOR]

Describes the Basic Experimental Fault-Tolerant Transputer Architecture (EFTA) Low-Level Environment (BELLE) kernel, a basic low-level environment for supporting fault-tolerant transputer nodes. Basic EFTA node structure; Details of the user process model adopted by the Reconfiguration and Fault Tolerance project; Characteristics of the BELLE kernel.

Presents a strategy for adaptive dynamic load balancing (LB) in large heterogeneous distributed system aimed at minimizing the application response times comprising phases enough to be implemented as a μ Kernel-level service. Importance of the LB algorithms; Model of the heterogeneous distributed system; Discussion on the computation and communication costs of dynamic LD simulations.