We deal with temporal aspects of distributed systems, introducing and studying a new model called timed distributed -calculus. This model extends distributed -calculus with timers...
: Recently lots of studies aim at modeling and inferring gene networks. Modeling tools propose graphical models having almost nothing about time description of events and regards t...
In a semantic environment data is described by ontologies and ontology mapping has become a crucial aspect in solving the heterogeneity problems of semantically described data. Th...
We present an adaptation technique for statistical machine translation, which applies the well-known Bayesian learning paradigm for adapting the model parameters. Since state-of-t...
We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, an estimate of the transit...