A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time by at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration

the Boolean answer deterministically with only a polynomial increase in communication with respect to the information lower bound given by the nondeterministic communication complexity of the function.

After [10, 15, 12, 2, 4] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time

Abstract. We present a polynomial time approximation scheme for Euclidean TSP in fixed dimensions. For every fixed c Ͼ 1 and given any n nodes in 2 , a randomized version of the scheme finds a (1 ϩ 1/c)-approximation to the optimum traveling salesman tour in O(n(log n) O(c) ) time. When the nodes

approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence

strictly increases the set of functions which are known to be polynomially learnable, in the sense of Valiant (1984). Our proof is constructive: we present an algorithm which can efficiently construct an element of k-DL consistent with a given set of examples, if one exists.

Role-based access control (RBAC) models are receiving increasing attention as a generalized approach to access control. Roles may be available to users at certain time periods, and unavailable at others. Moreover, there can be temporal dependencies among roles. To tackle such dynamic aspects, we

graphics hardware, illumination, image processing, image-based rendering, reflectance & shading models, texture mapping In this paper we present a new form of texture mapping that produces increased photorealism. Coefficients of a biquadratic polynomial are stored per texel, and used

We consider discrete orthogonal polynomial ensembles which are discrete analogues of the orthogonal polynomial ensembles in random matrix theory. These ensembles occur in certain problems in combinatorial probability and can be thought of as probability measures on partitions. The Meixner ensemble

/2, and m ≥ ɛn 2, XL and relinearization are expected to run in polynomial time of approximately n O(1/ √ ɛ). Moreover, we provide strong evidence that relinearization and XL can solve randomly generated systems of polynomial equations in subexponential time when m exceeds n by a number that increases