A spatio-temporal representation for complex optical flow events is developed that generalizes traditional parameterized motion models (e.g. affine). These generative spatio-tempo...
We propose a sensornet programming model based on declarative spatio-temporal constraints on events only, not sensors. Where previous approaches conflate events and sensors becaus...
Using wireless geosensor networks (WGSN), sensor nodes often monitor a phenomenon that is both continuous in time and space. However, sensor nodes take discrete samples, and an ana...
Frequent episode discovery is a popular framework for mining data available as a long sequence of events. An episode is essentially a short ordered sequence of event types and the...
Srivatsan Laxman, P. S. Sastry, K. P. Unnikrishnan
How are space and time represented in the human mind? Here we evaluate two theoretical proposals, one suggesting a symmetric relationship between space and time (ATOM theory) and t...