In this paper we illustrate how the capacity to select the most appropriate actions when handling contexts affording multiple conflicting actions can be solved either through a selective attention strategy (in which the stimuli affording alternative actions are filtered out at the perceptual level through top-down regulation) or at later processing stages through an action selection strategy (t...

In this paper, we present a load-balancing strategy (Adaptive Load Balancing strategy) for data parallel applications to balance the work load effectively on a distributed system. We study its impact on computation-hungry matrix multiplication application. The ALB strategy enhances the performance with features such as intelligent node selection, pre-task assignment, adaptive task sizing and bu...

A new adaptive L₁/₂ shooting regularization method for variable selection based on the Cox's proportional hazards mode being proposed. This adaptive L₁/₂ shooting algorithm can be easily obtained by the optimization of a reweighed iterative series of L₁ penalties and a shooting strategy of L₁/₂ penalty. Simulation results based on high dimensional artificial data show that the adaptive L₁/₂ sho...

An adaptive genetic algorithm of service selection in pervasive computing is presented in this paper. By means of matrix encoding, this algorithm carries out selection, crossover and mutation operations of genetic algorithm, with matrix as individual chromosome and matrix array as gene. Based on the elitism selection strategy and adaptive strategy, this algorithm replicates the optimal individu...

this study concerns with a trust-region-based method for solving unconstrained optimization problems. the approach takes the advantages of the compact limited memory bfgs updating formula together with an appropriate adaptive radius strategy. in our approach, the adaptive technique leads us to decrease the number of subproblems solving, while utilizing the structure of limited memory quasi-newt...

This paper proposes two adaptive approaches to inconsistent prioritized belief bases. Both approaches rely on a selection mechanism that is not applied to the premises as they stand, but to the consequence sets of the belief levels. One is based on classical compatibility, the other on the modal logic T of Feys. For both approaches the two main strategies of inconsistency adaptive logics are fo...

This paper proposes two adaptive approaches to inconsistent prioritized belief bases. Both approaches rely on a selection mechanism, that is not applied to the premises as they stand, but to the consequence sets of the belief levels. One is based on classical compatibility, the other on the modal logic T of Feys. For both approaches the two main strategies of inconsistency adaptive logics are f...

Classifying large datasets without any a-priori information poses a problem in numerous tasks. Especially in industrial environments, we often encounter diverse measurement devices and sensors that produce huge amounts of data, but we still rely on a human expert to help give the data a meaningful interpretation. As the amount of data that must be manually classified plays a critical role, we n...