Moment computation is essential to the analysis of stochastic kinetic models of biochemical reaction networks. It is often the case that the moment evolution, usually the first and...
We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under...
Realistic acoustic modeling is essential for spatializing sound in distributed virtual environments where multiple networked users move around and interact visually and aurally in...
We propose a novel efficient algorithm for robust tracking of a fixed number of targets in real-time with low failure rate. The method is an instance of Sequential Importance Resa...
Many real processes are composed of a n-fold repetition of some simpler process. If the whole process can be modelled with a neural network, we present a method to derive a model ...