Diffusion tensor imaging has accelerated the study of brain connectivity, but single-tensor diffusion models are too simplistic to model fiber crossing and mixing. Hybrid diffusio...
Liang Zhan, Alex D. Leow, Iman Aganj, Christophe L...
We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. denite clause programs containing probabilistic facts with a ...
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Cache coherence protocols of current shared-memory multiprocessors are difficult to verify. Our previous work proposed an extension of Lamport's logical clocks for showing th...
Anne Condon, Mark D. Hill, Manoj Plakal, Daniel J....
We present a method for using real world mobility traces to identify tractable theoretical models for the study of distributed algorithms in mobile networks. We validate the metho...