Abstract
The efficient execution of irregular data parallel applications, on dynamically shared computing clusters, requires novel approaches to manage the runtime load distribution. Stich environments have an unpredictable dynamic behaviour both due to the application requirements and to the available system's resources. This uncertainty was the main motivation to propose and evaluate an application level scheduler, where decisions are efficiently taken with improved accurate predictions on the environment's current and near future state, based on available incomplete and aged measured data. Bayesian decision networks are used as the scheduler's decision making mechanism: its effectiveness to manage the load distribution of a parallel ray tracer is assessed and compared with alternative strategies. The evaluation results, with complex scenes on a 7 shared nodes cluster with dynamically variable workloads, show considerable performance improvements over blind strategies, and stress the benefits over a sensor based deterministic approach of identical complexity.

Abstract

Abstract
There is a growing interest in understanding consumer ethical actions in relation to their dealings with firms. This paper examines whether there are differences between Northern and Southern European Union (EU) consumers' perceptions of ethical consumer behaviour using Muncy and Vitell's (1992) Consumer Ethics Scale (CES). The study samples 962 university students across four Northern EU countries (Germany, Denmark, Scotland, The Netherlands) and four Southern EU countries (Portugal, Spain, Italy, Greece). Some differences are identified between the two samples, which might question the ability of organisations to consider the EU as one homogeneous market.

Abstract
The design of algorithms that explore multiple representation languages and explore different search spaces has an intuitive appeal. In the context of classification problems, algorithms that generate multivariate trees are able to explore multiple representation languages by using decision tests based on a combination of attributes. The same applies to model trees algorithms, in regression domains, but using linear models at leaf nodes. In this paper we study where to use combinations of attributes in regression and classification tree learning. We present an algorithm for multivariate tree learning that combines a univariate decision tree with a linear function by means of constructive induction. This algorithm is able to use decision nodes with multivariate tests, and leaf nodes that make predictions using linear functions. Multivariate decision nodes are built when growing the tree, while functional leaves are built when pruning the tree. The algorithm has been implemented both for classification problems and regression problems. The experimental evaluation shows that our algorithm has clear advantages with respect to the generalization ability when compared against its components, two simplified versions, and competes well against the state-of-the-art in multivariate regression and classification trees. © Springer-Verlag Berlin Heidelberg 2001.

Abstract
In this paper, we examine the possibility that the spatiotemporal receptive field properties of visual cortical neurons can be understood in terms of a statistically efficient strategy for encoding natural time-varying images. It is believed that the sense of object motion and velocity are also related to these fields, as objects in natural scenes are represented by a sparse set of statistically independent components, such as edges. Currently, computational models of receptive fields consider only spatial components, and thus cannot account for time-varying sensory stimuli In this paper, a model based on independent components analysis and cellular neural networks is proposed. We describe an artificial neural network that attempts to accurately reconstruct its spatiotemporal input data while simultaneously reducing the statistical dependencies between its outputs, as advocated by the redundancy reduction principle. This approach extends existing models to incorporate temporal aspects of sequences of images of natural scenes.

Abstract
Resource virtualization on FPGA devices, achievable due to its dynamic reconfiguration capabilities, provides an attractive solution to save silicon area. Architectural synthesis for dynamically reconfigurable FPGA-based digital systems needs to consider the case of reducing the number of temporal partitions (reconfigurations), by enabling sharing of some functional units in the same temporal partition. This paper proposes a novel algorithm for automated datapath design, from behavioral input descriptions (represented by a dataflow graph), which simultaneously performs temporal partitioning and sharing of functional units. The proposed algorithm attempts to minimize both the number of temporal partitions and the execution latency of the generated solution. Temporal partitioning, resource sharing, scheduling, and a simple form of allocation and binding are all integrated in a single task. The algorithm is based on heuristics and on a new concept of construction by gradually enlarging timing slots. Results show the efficiency and effectiveness of the algorithm when compared to existent approaches. © 2001 Non IEEE.