We describe a probabilistic method for identifying characters in TV series or movies. We aim at labeling every character appearance, and not only those where a face can be detected. Consequently, our basic unit of appearance is a person track (as opposed to a face track). We model each TV series episode as a Markov Random Field, integrating face recognition, clothing appearance, speaker recogni...

Given the multiple differences between mice and men, it was once thought that mice could not be used to model atherosclerosis, principally a human disease. Apolipoprotein E-deficient (apoEKO) mice have convincingly changed this view, and the ability to model human-like plaques in these mice has provided scientists a platform to study multiple facets of atherogenesis and to explore potential the...

This paper describes the application of a hierarchical modular neural network for an advanced approach solving the knock detection task for combustion engines. The knock detection is realized on a two-level feature extraction approach. It is not only based on the popular cycle-by-cycle classification but a tendency index for the knock condition is determined. The experimental environment consis...

Given a metabolic network in terms of its metabolites and reactions, our goal is to efficiently compute the minimal knock-out sets of reactions required to block a given behavior. We describe an algorithm that improves the computation of these knock-out sets when the elementary modes (minimal functional subsystems) of the network are given. We also describe an algorithm that computes both the k...