Mastering the complexity of programs and systems, particularly distributed systems, should lead to signi cant improvements in program and system understanding. In this paper we pr...
Paulo S. C. Alencar, Donald D. Cowan, Thomas Kunz,...
In this paper, we address the problem of estimating mesoscale dynamics of atmospheric layers from satellite image sequences. Relying on a physically sound vertical decomposition of...
Goal-oriented methods are increasingly popular for elaborating software requirements. They offer systematic support for incrementally building intentional, structural, and operati...
Emmanuel Letier, Jeff Kramer, Jeff Magee, Sebasti&...
We show how chemical background knowledge can be used to improve the prediction performance in structureactivitity relationships (SARs) for non-congeneric compounds. The goal of t...
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...