—In this paper, we investigate the reliable data transport problem in underwater sensor networks. Underwater sensor networks are significantly different from terrestrial sensor ...
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
This paper considers the problem of Bayesian inference in dynamical models with time-varying dimension. These models have been studied in the context of multiple target tracking pr...
Computational models of grounded language learning have been based on the premise that words and concepts are learned simultaneously. Given the mounting cognitive evidence for conc...
Traditionally, instruction-set simulators (ISS’s) are sequential programs running on individual processors. Besides the advances of simulation techniques, ISS’s have been main...