We develop an optical flow estimation framework that focuses on motion estimation over time formulated in a Dynamic Bayesian Network. It realizes a spatiotemporal integration of ...
Volker Willert, Marc Toussaint, Julian Eggert, Edg...
Requirements from new types of applications call for new database system solutions. Computational science applications performing distributed computations on Grid networks with req...
Abstract. We propose a general methodology based on robust optimization to address the problem of optimally controlling a supply chain subject to stochastic demand in discrete time...
In this paper, air combat simulation data is reconstructed into a dynamic Bayesian network. It gives a compact probabilistic model that describes the progress of air combat and al...
In the very active field of complex networks, research advances have largely been stimulated by the availability of empirical data and the increase in computational power needed ...
Adrien Friggeri, Guillaume Chelius, Eric Fleury, A...