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We propose a general framework to combine multiple sequence classifiers working on different sequence representations of a given input. This framework, based on Multi-Stream Hidden Markov Models (MS-HMMs), allows the combination of multiple HMMs operating on partially asynchronous information streams. This combination may operate at different levels of modeling: from the feature level to the post-processing level. This framework is applied to on-line handwriting word recognition by combining temporal and spatial representation of the signal. Different combination schemes are compared experimentally on isolated character recognition and word recognition tasks, using the UNIPEN international database.

We explore the properties of the quantum kicked rotator, its classical equivalent being the standard map. Its behavior, as found by computer studies, depends very much on the strength of the external forcing. At low strength it is seemingly recurrent in the sense of Hogg and Huberman. However, its energy increases with time at large forcings. For quantum systems, a unitary map defines the evolution over one period of time. The spectrum of this map in an infinite space does not seem to change continuously when one approaches the ratio of the frequencies of the external and of the unperturbed system by rational approximations of the golden mean.