We develop a generic framework for the analysis of programs with recursive procedures and dynamic process creation. To this end we combine the approach of weighted pushdown systems...
We present a neural-network-based statistical parser, trained and tested on the Penn Treebank. The neural network is used to estimate the parameters of a generative model of left-...
We present SQUIRREL, a stream-oriented programming framework for storage-centric sensor networks. The storagecentric paradigm—where storage operations prevail over communication...
We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network's model structure is a function of the predicted output stru...
Network programming is notoriously hard to understand: one has to deal with a variety of protocols (IP, ICMP, UDP, TCP etc), concurrency, packet loss, host failure, timeouts, the c...