Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been app...
Sylvie C. W. Ong, Shao Wei Png, David Hsu, Wee Sun...
In this article, we show several results obtained by combining the use of stable distributions with pseudorandom generators for bounded space. In particular: —We show that, for a...
Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function e...
In this paper we present a novel approach to estimate and analyze 3D uid structure and motion of clouds from multi-spectrum 2D cloud image sequences. Accurate cloud-top structure ...
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...