Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
—This paper is concerned with the representation and recognition of the observed dynamics (i.e., excluding purely spatial appearance cues) of spacetime texture based on a spatiot...
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
Abstract Up to now biometric methods have been used in cryptography for authentication purposes. In this paper we propose to use biological data for generating sequences of random ...
Abstract—The error patterns of a wireless digital communication channel can be described by looking at consecutively correct or erroneous bits (runs and bursts) and at the distri...