This paper addresses the issue of how to meet the strict timing constraints of (soft) real-time virtualized applications while the Virtual Machine (VM) hosting them is undergoing a...
We have been developing a stochastic model for figure-ground separation[9][3][12]. The model selects/constructs theforeground with preferenceforfigures with "moreconvex"...
A variety of (dis)similarity measures for one-dimensional point processes (e.g., spike trains) are investigated, including the Victor-Purpura distance metric, the van Rossum distan...
The Partially Observable Markov Decision Process (POMDP) model is explored for high level decision making for Unmanned Air Vehicles (UAVs). The type of UAV modeled is a flying mun...
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 ...