in piece by piece manner. During such a protocol, one party or another may have a slight advantage; a “fair” protocol keeps this advantage within acceptable limits. We present a new protocol that is fair in the sense that, at any stage in its execution, the conditional probability that one party cannot

) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appecvs to have a slight advantage over PROFILESEARCH in terms of lower rates of false

A new and conceptually simple data structure, called a suffix array, for on-line string searches is intro-duced in this paper. Constructing and querying suffix arrays is reduced to a sort and search paradigm that employs novel algorithms. The main advantage of suffix arrays over suffix trees

This paper discusses the prevalence of Silicon Valley–style localiza-tions of individual manufacturing industries in the United States. A model in which localized industry-specific spillovers, natural ad-vantages, and pure random chance all contribute to geographic concentration is used to develop

TBox reasoning is independent of the ABox, and the part of the process requiring access to the ABox can be carried out by an SQL engine, thus taking advantage of the query optimization strategies provided by current Data Base Management Systems. Since it can be shown that even slight extensions

algorithm is only required to obtain a slight advantage in prediction over random guessing. The techniques used demonstrate an interesting duality between learning and cryptography. We also apply our results to obtain strong intractability results for approximating a generalization of graph coloring.

This paper introduces the concept of Geometry Compression, allowing 3D triangle data to be represented with a factor of 6 to 10 times fewer bits than conventional techniques, with only slight losses in object quality. The technique is amenable to rapid decompression in both software and hardware

. Nondeterministic Turing acceptors can be viewed as probabilistic acceptors with errors that are one-sided but not significantly bounded. In his seminal work on resource-bounded probabilistic Turing machines [Gi77], Gill showed how to transform such a machine to one that does have a good error bound, at only modest cost in space usage. We describe a simpler transformation that incurs absolutely no cost in space usage. If we change each probabilistic transition to a nondeterministic one, then we get a space-preserving transformation in the opposite direction as well, assuming the definitions are right. Thus the complexity classes are exactly the same, and we have the same space-bound hierarchy for bounded-one-sided-error probabilistic computation that we have for nondeterministic computation. Similarly, we have the same numberof -heads complexity classes and hierarchy for bounded-one-sided-error probabilistic multihead finite automata as for nondeterministic ones. 1. Introduction Wh...

are presented indicating that for a simple linear quadratic regulator (LQR) problem with no noise and large time steps, advantage updating learns slightly faster than Q- learning. When there is noise or small time steps, advantage updating learns more quickly than Q-learning by a factor of more than 100

for excluding voxels from reconstruction. Besides improved accuracy at object edges, the fitting approach has the advantage of not being restricted to regions yielding immediate sensitivity information. It permits extrapolation over a limited range necessary for dealing with slightly varying tissue