We consider the problem of minimizing the total weighted completion time on identical parallel machines when jobs have stochastic processing times and may arrive over time. We give...
One of the most fundamental problems in large-scale network analysis is to determine the importance of a particular node in a network. Betweenness centrality is the most widely us...
In an investigation into the transformation of mossy ber input to Purkinje cell output in the cerebellar cortex, we have developed a network model including a sophisticated compar...
F. W. Howell, Jonas Dyhrfjeld-Johnsen, Reinoud Mae...
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
We introduce and study a randomized quasi-Monte Carlo method for estimating the state distribution at each step of a Markov chain. The number of steps in the chain can be random an...