We propose a framework for general multiple target tracking, where the input is a set of candidate regions in each frame, as obtained from a state of the art background learning, ...
Learning useful and predictable features from past workloads and exploiting them well is a major source of improvement in many operating system problems. We review known parallel ...
Slice sampling provides an easily implemented method for constructing a Markov chain Monte Carlo (MCMC) algorithm. However, slice sampling has two major drawbacks: (i) it requires...
Knowledge about the workload is an important aspect for scheduling of resources as parallel computers or Grid components. As the scheduling quality highly depends on the character...
Published results show that various models may be obtained by combining parallel composition with probability and with or without non-determinism. In this paper we treat this probl...