Modern database applications show a growing demand for efficient and dynamic management of intervals, particularly for temporal and spatial data or for constraint handling. Common...
We investigate temporal resolution of documents, such as determining the date of publication of a story based on its text. We describe and evaluate a model that build histograms e...
Learning temporal causal structures between time series is one of the key tools for analyzing time series data. In many real-world applications, we are confronted with Irregular T...
Polynomial chaos theory (PCT) has been proven to be an efficient and effective way to represent and propagate uncertainty through system models and algorithms in general. In partic...
We address how listeners perceive temporal regularity in music performances, which are rich in temporal irregularities. A computational model is described in which a small system ...