Enhancing dominant modes in nonstationary time series by means of the symbolic resonance analysis

We present the symbolic resonance analysis (SRA) as a viable method for addressing the problem of enhancing a weakly dominant mode in a mixture of impulse responses obtained from a nonlinear dynamical system. We demonstrate this using results from a numerical simulation with Duffing oscillators in different domains of their parameter space, and by analyzing event-related brain potentials (ERPs) from a language processing experiment in German as a representative application. In this paradigm, the averaged ERPs exhibit an N400 followed by a sentence final negativity. Contemporary sentence processing models predict a late positivity (P600) as well. We show that the SRA is able to unveil the P600 evoked by the critical stimuli as a weakly dominant mode from the covering sentence final negativity. (c) 2007 American Institute of Physics. (c) 2007 American Institute of Physics.

Parallel Hybrid Monte Carlo Algorithms for Matrix Computations

In this paper we consider hybrid (fast stochastic approximation and deterministic refinement) algorithms for Matrix Inversion (MI) and Solving Systems of Linear Equations (SLAE). Monte Carlo methods are used for the stochastic approximation, since it is known that they are very efficient in finding a quick rough approximation of the element or a row of the inverse matrix or finding a component of the solution vector. We show how the stochastic approximation of the MI can be combined with a deterministic refinement procedure to obtain MI with the required precision and further solve the SLAE using MI. We employ a splitting A = D – C of a given non-singular matrix A, where D is a diagonal dominant matrix and matrix C is a diagonal matrix. In our algorithm for solving SLAE and MI different choices of D can be considered in order to control the norm of matrix T = D –1C, of the resulting SLAE and to minimize the number of the Markov Chains required to reach given precision. Further we run the algorithms on a mini-Grid and investigate their efficiency depending on the granularity. Corresponding experimental results are presented.

Monte Carlo methods for matrix computations on the grid

Many scientific and engineering applications involve inverting large matrices or solving systems of linear algebraic equations. Solving these problems with proven algorithms for direct methods can take very long to compute, as they depend on the size of the matrix. The computational complexity of the stochastic Monte Carlo methods depends only on the number of chains and the length of those chains. The computing power needed by inherently parallel Monte Carlo methods can be satisfied very efficiently by distributed computing technologies such as Grid computing. In this paper we show how a load balanced Monte Carlo method for computing the inverse of a dense matrix can be constructed, show how the method can be implemented on the Grid, and demonstrate how efficiently the method scales on multiple processors. (C) 2007 Elsevier B.V. All rights reserved.

A sparse parallel hybrid Monte Carlo algorithm for matrix computations

In this paper we introduce a new algorithm, based on the successful work of Fathi and Alexandrov, on hybrid Monte Carlo algorithms for matrix inversion and solving systems of linear algebraic equations. This algorithm consists of two parts, approximate inversion by Monte Carlo and iterative refinement using a deterministic method. Here we present a parallel hybrid Monte Carlo algorithm, which uses Monte Carlo to generate an approximate inverse and that improves the accuracy of the inverse with an iterative refinement. The new algorithm is applied efficiently to sparse non-singular matrices. When we are solving a system of linear algebraic equations, Bx = b, the inverse matrix is used to compute the solution vector x = B(-1)b. We present results that show the efficiency of the parallel hybrid Monte Carlo algorithm in the case of sparse matrices.

Antecedents of attitude and intention towards counterfeit symbolic and experiential products

This paper investigates the impact of price consciousness, perceived risk, and ethical obligation on attitude and intention towards counterfeit products. Data were collected from a sample of 200 respondents via an online questionnaire. A conceptual model was derived and tested via structural equation modelling in the contexts of symbolic and experiential counterfeit products. Findings show differences in the factors (and weight thereof) impacting attitude and purchase intention in the two product contexts. Specifically, ethical obligation and perceived risk are found to be significant predictors of attitude towards both symbolic and counterfeit products, while price consciousness is found to predict only attitude towards experiential products, but not purchase intention in either counterfeit product context.

Rights, symbolic violence and the micro-politics of the rural: the case of the Parish Paths Partnership scheme

In this paper microlevel politics and conflict associated with social and economic change in the countryside and linked changes in rural governance are explored with a focus upon research carried out on a recent rural policy initiative aimed at local 'empowerment'. This acts as a touchstone for a wider theoretical discussion. The paper is theorised within a conceptual framework derived and extended from the work of Pierre Bourdieu and others in order to explore case studies of the English Countryside Commission's Parish Paths Partnership scheme. The micropolitics involved with this scheme are examined and used to highlight more general issues raised by increased 'parish empowerment' in the 'postrural'.

The unsteady flow of a weakly compressible fluid in a thin porous layer III: three-dimensional computations

We describe a novel method for determining the pressure and velocity fields for a weakly compressible fluid flowing in a thin three-dimensional layer composed of an inhomogeneous, anisotropic porous medium, with vertical side walls and variable upper and lower boundaries, in the presence of vertical wells injecting and/or extracting fluid. Our approach uses the method of matched asymptotic expansions to derive expressions for all significant process quantities, the computation of which requires only the solution of linear, elliptic, two-dimensional boundary value and eigenvalue problems. In this article, we provide full implementation details and present numerical results demonstrating the efficiency and accuracy of our scheme.

Statistical computations with AstroGrid and the Grid

We outline our first steps towards marrying two new and emerging technologies; the Virtual Observatory (e.g, Astro- Grid) and the computational grid. We discuss the construction of VOTechBroker, which is a modular software tool designed to abstract the tasks of submission and management of a large number of computational jobs to a distributed computer system. The broker will also interact with the AstroGrid workflow and MySpace environments. We present our planned usage of the VOTechBroker in computing a huge number of n–point correlation functions from the SDSS, as well as fitting over a million CMBfast models to the WMAP data.

Self-construals, Symbolic and Hedonic Preferences, and Actual Purchase Behavior

The study explores the influence of the independent and interdependent self-construals on actual purchase behavior and the mediating role of consumer preferences for symbolic and hedonic meanings. Data were collected through a survey of about 1,000 respondents. Results indicate that independent consumers draw on the self/hedonic- and status-symbolic resources of clothing in the construction and expression of their identities. Regarding the interdependent consumers, they show no interest in clothing affiliation and status symbolism. The degree of preference for status-symbolic meaning mediates all effects of the independent and interdependent self-construals on actual purchase behavior; self-expressive/hedonic preferences mediate two of the three effects of the independent self on actual purchase behavior when accounting for suppression effects, whereas the expected mediation of preference for affiliation meaning is not supported.