The inability to answer proximity queries efficiently for spaces of dimension d > 2 has led to the study of approximation to proximity problems. Several techniques have been pro...
Sunil Arya, Guilherme Dias da Fonseca, David M. Mo...
A new class of data structures called "bumptrees" is described. These structures are useful for efficiently implementing a number of neural network related operations. A...
Abstract Yossi Matias Je rey Scott Vitter y Neal E. Young z In this paper we introduce the notion of approximate data structures, in which a small amount of error is tolerated in...
We present a method, adapted to polymorphically typed functional languages, to detect and collect more garbage than existing GCs. It can be applied to strict or lazy higher order ...
We consider probabilistic inference in general hybrid networks, which include continuous and discrete variables in an arbitrary topology. We reexamine the question of variable dis...
Given a collection S of n line segments in the plane, the planar point location problem is to construct a data structure that can e ciently determine for a given query point p the...
Michael T. Goodrich, Mark W. Orletsky, Kumar Ramai...
Present databases, whether on centralized or parallel DBMSs, do not deal well with scalability. We present an architecture for Wintel multicomputers termed AMOS-SDDS, coupling a h...
Yakham Ndiaye, Aly Wane Diene, Witold Litwin, Tore...
We develop data structures for dynamic closest pair problems with arbitrary (not necessarily geometric) distance functions, based on a technique previously used by the author for ...
Data races are a common problem in concurrent and multi-threaded programming. They are hard to detect without proper tool support. Despite the successful application of these tools...