Given semantic descriptions of object classes, zero-shot learning aims to accurately
recognize objects of the unseen classes, from which no examples are available at the …

We present the parallel implementation of a constraint-based Local Search algorithm and
investigate its performance on several hardware platforms with several hundreds or …

In this paper we study the performance of constraint-based local search solvers on a GPU.
The massively parallel architecture of the GPU makes it possible to explore parallelism at …

Classifier combination methods have proved to be an effective tool for increasing the
performance in pattern recognition applications. The rationale of this approach follows from …

Parallelizing software is a popular way of achieving high energy efficiency since parallel
applications can be mapped on many cores and the clock frequency can be lowered. Perfect …

In this paper we address the problem of parallelizing local search. We propose a general
framework where different local search engines cooperate (through communication) in the …

This paper presents a detailed analysis of the scalability and parallelization of Local Search
algorithms for constraint-based and SAT (Boolean satisfiability) solvers. We propose a …

The growing scientific demands of climate predication and climate projection have promoted
to manage the computational resources of climate model rationally. The Community Earth …

Local search metaheuristics are a recognized means of solving hard combinatorial
problems. Over the last couple of decades, significant advances have been made in terms of …

This paper presents a detailed analysis of the scalability and parallelization of local search
algorithms for the Satisfiability problem. We propose a framework to estimate the parallel …