Open AccessProceedings Article
Psuedorandom Generators for Space-Bounded Computation
Noam Nisan
- pp 204-212
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This article is published in Symposium on the Theory of Computing.The article was published on 1990-01-01 and is currently open access. It has received 54 citations till now. The article focuses on the topics: Bounded function & Space (mathematics).read more
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Global hosting system
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Identifying and requesting data in network using identifiers which are based on contents of data
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Self-testing/correcting for polynomials and for approximate functions
TL;DR: It is shown that one can self-test the whole class of polynomial functions over Zp for prime p and that the class of linear functions, the log function and floating point exponentiation can be self-tested, and a notion which is essentially equivalent to self-correcting is introduced.
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Method and apparatus for distributing requests among a plurality of resources
David R. Karger,Eric Lehman,F. Thomson Leighton,Matthew S. Levine,Daniel M. Lewin,Rina Panagrahy +5 more
TL;DR: In this paper, a distribution of a request to one of a plurality of resources is proposed, where each of the resources is assigned to a respective location or locations in the mathematical mapping space.
References
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Proceedings ArticleDOI
Theory and application of trapdoor functions
TL;DR: A new information theory is introduced and the concept of trapdoor functions is studied and applications of such functions in cryptography, pseudorandom number generation, and abstract complexity theory are examined.
Proceedings ArticleDOI
Random walks, universal traversal sequences, and the complexity of maze problems
TL;DR: Results are derived suggesting that the undirected reachability problem is structurally different from, and easier than, the directed version of NSPACE(logn), an affirmative answer to a question of S. Cook.
Proceedings ArticleDOI
How to recycle random bits
TL;DR: It is shown that modified versions of the linear congruential generator and the shift register generator are provably good for amplifying the correctness of a probabilistic algorithm.