Abstract: The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like ...
Abstract. Contemporary inferences about evolution occasionally involve analyzing infinitely large feature spaces, requiring specific algorithmic techniques. We consider parsimony a...
We study the problem of performing sensor fusion and distributed consensus in networks, where the objective is to calculate some linear function of the initial sensor values at so...
Value-at-Risk (VaR) is one of the most widely accepted risk measures in the financial and insurance industries, yet efficient optimization of VaR remains a very difficult problem....
This paper describes the behavior observed in a class of cellular automata that we have defined as "dissipative", i.e., cellular automata for which the external environm...