We present a framework for automatically summarizing social group activity over time. The problem is important in understanding large scale online social networks, which have dive...
—Millions of people are using the World Wide Web and are publishing content online. This user generated content contains many information relevant not only to marketing but to co...
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Abstract. We present fast distributed algorithms for coloring and (connected) dominating set construction in wireless ad hoc networks. We present our algorithms in the context of U...
For more than thirty years, the parallel programming community has used the dependence graph as the main abstraction for reasoning about and exploiting parallelism in “regularâ€...
Keshav Pingali, Donald Nguyen, Milind Kulkarni, Ma...