George D. Montanez

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Ownership and use of multiple devices such as desktop computers, smartphones, and tablets is increasing rapidly. Search is popular and people often perform search tasks that span device boundaries. Understanding how these devices are used and how people transition between them during information seeking is essential in developing search support for a(More)
Faced with the problem of characterizing systematic changes in multivariate time series in an unsupervised manner, we derive and test two methods of regularizing hidden Markov models for this task. Regularization on state transitions provides smooth transitioning among states, such that the sequences are split into broad, contiguous segments. Our methods(More)
Recent advances in genome-wide identification of protein-protein interactions (PPIs) have produced an abundance of interaction data which give an insight into functional associations among proteins. However, it is known that the PPI datasets determined by high-throughput experiments or inferred by computational methods include an extremely large number of(More)
—Spatio-temporal data is intrinsically high dimensional , so unsupervised modeling is only feasible if we can exploit structure in the process. When the dynamics are local in both space and time, this structure can be exploited by splitting the global field into many lower-dimensional " light cones ". We review light cone decompositions for predictive state(More)
Spatio-temporal data is intrinsically high dimensional, so unsupervised modeling is only feasible if we can exploit structure in the process. When the dynamics are local in both space and time, this structure can be exploited by splitting the global field into many lower-dimensional “light cones”. We review light cone decompositions for(More)
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