Abstract. Bayesian inference provides a powerful framework to optimally integrate statistically learned prior knowledge into numerous computer vision algorithms. While the Bayesian...
In this paper, we study the problem of scheduling task sets with (m,k) constraints. In our approach, jobs of each task are partitioned into two sets: mandatory and optional. Manda...
Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination problems in domains with uncertainty and time constraints but very difficult to solve...
When vectorizing for SIMD architectures that are commonly employed by today’s multimedia extensions, one of the new challenges that arise is the handling of memory alignment. Pr...
Proving ownership rights on outsourced relational databases is a crucial issue in today internet-based application environments and in many content distribution applications. In th...