Our choices often require appropriate actions to obtain a preferred outcome, but the neural underpinnings that link decision making and action selection remain largely undetermined. Recent theories propose that action selection occurs simultaneously, i.e., parallel in time, with the decision process. Specifically, it is thought that action selection in motor regions originates from a competitiv...

Using a generalized spherical mean operator, we obtain the generalizationof Titchmarsh's theorem for the Dunkl transform for functions satisfyingthe Lipschitz condition in L2(Rd;wk), where wk is a weight function invariantunder the action of an associated reection groups.

Background and Objectives: Action recognition, as the processes of labeling an unknown action of a query video, is a challenging problem, due to the event complexity, variations in imaging conditions, and intra- and inter-individual action-variability. A number of solutions proposed to solve action recognition problem. Many of these frameworks suppose that each video sequence includes only one ...

Gradient based policy optimization algorithms suffer from high gradient variance, this is usually the result of using Monte Carlo estimates of the Qvalue function in the gradient calculation. By replacing this estimate with a function approximator on state-action space, the gradient variance can be reduced significantly. In this paper we present a method for the training of a Gaussian Process t...