Wireless sensors are employed in a wide range of applications. One common feature of most sensor settings is the need to communicate sensed data to some collection point or sink. This communication can be direct (to a mobile collector) or indirect – via other sensors towards a remote sink. In either case, a sensor might not be able to communicate to a sink at will. Instead it collects data and ...

Given the relatively small number of microarrays typically used in gene-expression-based classification, all of the data must be used to train a classifier and therefore the same training data is used for error estimation. The key issue regarding the quality of an error estimator in the context of small samples is its accuracy, and this is most directly analyzed via the deviation distribution o...

A player’s pure strategy is called relevant for an outcome of a game in extensive form with perfect recall if there exists a weakly sequential equilibrium with that outcome for which the strategy is an optimal reply at every information set it does not exclude. The outcome satisfies forward induction if it results from a weakly sequential equilibrium in which players’ beliefs assign positive pr...

Recently, the problem of signal representation in terms of basis vectors from a large, ”overcomplete”, spanning dictionary has been the focus of much research. Achieving a succinct, or ”sparse”, representation is known as the problem of best basis representation. We consider methods which seek to solve this problem by sequentially building up a basis set for the signal. Three distinct algorithm...

The current best conformant probabilistic planners encode the problem as a bounded length CSP or SAT problem. While these approaches can find optimal solutions for given plan lengths, they often do not scale for large problems or plan lengths. As has been shown in classical planning, heuristic search outperforms CSP/SAT techniques (especially when a plan length is not given a priori). The probl...

Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In recent years, many algorithms were proposed that perform very well in many situations. Nevertheless, when outlying values are present in the data, these methods often lead to wrong conclusio...