Probabilistic mixture models are used for a broad range of data analysis tasks such as clustering, classification, predictive modeling, etc. Due to their inherent probabilistic na...
Object-orientation and distributed systems are quickly becoming norms for new system development, generating renewed interest in distribution schemes traditionally directed at rela...
Permutation of class labels is a common approach to build null distributions for significance analyis of microarray data. It is assumed to produce random score distributions, which...
—Opportunistic scheduling in random beamforming maximizes the sum-rate by allocating resources to the users with the best channel condition, thus leveraging on multiuser diversit...
This paper studies a new generalization of the regular permutation flowshop scheduling problem (PFSP) referred to as the distributed permutation flowshop scheduling problem or DPF...