We introduce two abstract models for multithreaded programs based on dynamic networks of pushdown systems. We address the problem of symbolic reachability analysis for these models...
Point Distribution Models are useful tools for modelling the variability of particular classes of shapes. A common approach is to apply a Principle Component Analysis to the data,...
James Orwell, Darrel Greenhill, Jonathan D. Rymel,...
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
— In this paper we look at TCP data which was passively collected from an edge ISP, and analyze it to obtain some new results and deeper understanding of TCP loss process. The fo...
Background: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validatio...
Tim Van den Bulcke, Koen Van Leemput, Bart Naudts,...