By determining, statically, where the structure of a program requires sets of variables to share a common tation, we can identify abstract data types, detect ion violations, find ...
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
This paper presents an interactive visualization toolkit for navigating and analyzing the National Science Foundation (NSF) funding information. Our design builds upon the treemap...
Abstract. A new method is introduced for estimating single-trial magnetoor electro-encephalography (M/EEG), based on a non-linear fit of timefrequency atoms. The method can be appl...
In this report, we consider the problem of real-time transmission scheduling over time-varying channels. We first formulate the transmission scheduling problem as a Markov decisio...