In this paper, we present an efficient method to solve the obstacle-avoiding rectilinear Steiner minimum tree (OARSMT) problem optimally. Our work is a major improvement over the w...
How to adaptively choose optimal neighborhoods is very important to pixel-domain image denoising algorithms since too many neighborhoods may cause over-smooth artifacts and too fe...
Abstract-- Scheduling problems are already difficult on traditional parallel machines, and they become extremely challenging on heterogeneous clusters. In this paper we deal with t...
Anne Benoit, Loris Marchal, Jean-Francois Pineau, ...
The main theme of this paper is to develop a novel eigenvalue optimization framework for learning a Mahalanobis metric. Within this context, we introduce a novel metric learning a...
Being able to efficiently answer arbitrary OLAP queries that aggregate along any combination of dimensions over numerical and categorical attributes has been a continued, major co...