Previous work on program obfuscation gives strong negative results for general-purpose obfuscators, and positive results for obfuscating simple functions such as equality testing (point functions). In this work, we construct an obfuscator for a more complex algebraic functionality: testing for membership in a hyperplane (of constant dimension). We prove the security of the obfuscator under a ne...

We were fortunate to spend the 2004 fall semester in residence at MSRI, participating in the program on Hyperplane Arrangements and Applications. It was an intense, stimulating, productive, enlightening, eventful and most enjoyable experience. It was especially so for us long-timers in the field because the program truly marked a coming-of-age in the evolution of the subject from relative obscu...

We study spaces obtained from a complete finite volume complex hyperbolic n-manifold M by removing a compact totally geodesic complex (n−1)-submanifold S . The main result is that the fundamental group of M \S is relatively hyperbolic, relative to fundamental groups of the ends of M \S , and M \S admits a complete finite volume A -regular Riemannian metric of negative sectional curvature. It fo...

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative technical efficiency for each member of a set of peer decision making units (DMUs) with multiple inputs and multiple outputs. The original DEA models use positive input and output variables that are measured on a ratio scale, but these models do not apply to the variables in which interval scale data can appe...

Support vector machine (SVM) is a popular classification technique which classifies data using a max-margin separator hyperplane. The normal vector and bias of the mentioned hyperplane is determined by solving a quadratic model implies that SVM training confronts by an optimization problem. Among of the extensions of SVM, cost-sensitive scheme refers to a model with multiple costs which conside...

A novel classifier architecture is introduced which belongs to both hyperplane and hypersphere families. The basic computational unit in the architecture is a perceptron whose input is augmented by its squared length. Traditional methods of training hyperplane classifiers (perceptron training algorithm, backpropagation, etc.) function in the augmented input space, and induce hyperspherical deci...

The aim of this article is to link Schubert varieties in the flag manifold with hyperplane arrangements. For a permutation, we construct a certain graphical hyperplane arrangement. We show that the generating function for regions of this arrangement coincides with the Poincaré polynomial of the corresponding Schubert variety if and only if the Schubert variety is smooth. We give an explicit com...