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 …

In the past 35 years, parallel computing has drawn increasing interest from the academic
community, especially in solving complex optimization problems that require large amounts …

As multi-core computing is now standard, it seems irresponsible for constraints researchers
to ignore the implications of it. Researchers need to address a number of issues to exploit …

Finding a maximum clique in a given graph is one of the fundamental NP-hard problems.
We compare two multicore thread-parallel adaptations of a state-of-the-art branch-and …

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 …

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 thesis improves the state of the art in exact, practical algorithms for finding subgraphs.
We study maximum clique, subgraph isomorphism, and maximum common subgraph …

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 …