The use of constraint propagation is the main feature of any constraint solver. It is thus of prime importance to manage the propagation in an efficient and effective fashion. There are two classes of propagation algorithms for general constraints: fine-grained algorithms where the removal of a value for a variable will be propagated to the corresponding values for other variables, and coarse-g...

We describe our formulation of transformation enhanced data modeling used to develop a multi-grained data analysis approach to text independent speaker recognition. The broad goal is to address difficulties caused by sparse training and test data. First, our development of maximum likelihood transformation based recognition with diagonally constrained Gaussian mixture models is detailed. We giv...

The understanding of the relation between the amino acid sequence and the spatial structure of the protein is an open task since many decades. Some tools for the prediction of protein structures from known ones were developed, but they let unanswered fundamental questions about properties of folded proteins and the folding process itself. Recently, coarse-grained models were developed, which ar...