The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean...
One of the most efficient analysis technique is to reduce an original model into a simpler one such that the reduced model has the same properties than the original one. G. Berthel...
For a variety of signal processing applications polynomials are implemented in circuits. Recent work on polynomial datapath optimization achieved significant reductions of hardware...
Given the numerous knowledge representation models (KR-schemes) that have been proposed, it would be desirable to have a formal, unifying model for the description of a KR-scheme,...
Background: Paralog reduction, the loss of duplicate genes after whole genome duplication (WGD) is a pervasive process. Whether this loss proceeds gene by gene or through deletion...