Xin Wang

Learn More
Negative-feedback (inhibitory) and positive-feedforward (stimulatory) processes regulate physiological systems. Whether such processes are themselves rhythmic is not known. Here, we apply cross-approximate entropy (cross-ApEn), a noninvasive measurement of joint (pairwise) signal synchrony, to inferentially assess hypothesized circadian and ultradian(More)
Graph pattern matching is commonly used in a variety of emerging applications such as social network analysis. These applications highlight the need for studying the following two issues. First, graph pattern matching is traditionally defined in terms of subgraph isomorphism or graph simulation. These notions, however, often impose too strong a topological(More)
We develop and investigate probabilistic approaches of state clustering in higher-order Markov chains. A direct extension of the Aggregate Markov model to higher orders turns out to be problematic due to the large number of parameters required. However, in many cases, the events in the finite memory are not equally salient in terms of their pre-dictive(More)
Predictive modelling of online dynamic user-interaction recordings and community identification from such data becomes more and more important with the widespread use of online communication technologies. Despite of the time-dependent nature of the problem, existing approaches of community identification are based on static or fully observed network(More)
We study the value and quality of deterministic solutions to scheduled stochastic capacitated multi-commodity service network design problems. We study both the fixed and variable (integer and continuous) capacity cases, and investigate models with and without asset-balance constraints. For the deterministic cases, we replace the random variables in the(More)
This paper demonstrates the use of kernel methods in a challenging problem of localization in sensor networks. We show that the coarse-grained localization problems for ad hoc sensor networks can be posed and solved as a pattern recognition problem using kernel methods from statistical learning theory. We provide an evaluation of our algorithm on simulated(More)