We present a computational framework to automatically discover high-order temporal social patterns from very noisy and sparse location data. We introduce the concept of social foo...
Over the last years many statistical models have been proposed to restore tomographical images. However, their use in medical environment has been limited due to several factors. ...
We propose Action-Reaction Learning as an approach for analyzing and synthesizing human behaviour. This paradigm uncovers causal mappings between past and future events or between...
We propose statistical data association techniques for visual tracking of enormously large numbers of objects. We do not assume any prior knowledge about the numbers involved, and...
Margrit Betke, Diane E. Hirsh, Angshuman Bagchi, N...
We examine correlations in a time series of electric power system blackout sizes using scaled window variance analysis and R/S statistics. The data shows some evidence of long tim...
Benjamin A. Carreras, David E. Newman, Ian Dobson,...