The modeling of high level semantic events from low level sensor signals is important in order to understand distributed phenomena. For such content-modeling purposes, transformat...
We study the concept of choice for true concurrency models such as prime event structures and safe Petri nets. We propose a dynamic variation of the notion of cluster previously in...
This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...
We analyze various stochastic processes for generating permutations almost uniformly at random in distributed and parallel systems. All our protocols are simple, elegant and are b...
To have a robust and informative image content representation for image categorization, we often need to extract as many as possible visual features at various locations, scales a...