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382,603
Tiny Families of Functions with Random Properties: A QualitySize Tradeoff for Hashing (Preliminary Version)
, 1994
"... We present three explicit constructions of hash functions, which exhibit a tradeoff between the size of the family (and hence the number of random bits needed to generate a member of the family), and the quality (or error parameter) of the pseudorandom property it achieves. Unlike previous cons ..."
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We present three explicit constructions of hash functions, which exhibit a tradeoff between the size of the family (and hence the number of random bits needed to generate a member of the family), and the quality (or error parameter) of the pseudorandom property it achieves. Unlike previous
Tiny Families of Functions with Random Properties: A QualitySize Tradeoff for Hashing
, 2003
"... We present three explicit constructions of hash functions, which exhibit a tradeo# between the size of the family (and hence the number of random bits needed to generate a member of the family), and the quality (or error parameter) of the pseudorandom property it achieves. Unlike previous const ..."
Abstract

Cited by 51 (10 self)
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We present three explicit constructions of hash functions, which exhibit a tradeo# between the size of the family (and hence the number of random bits needed to generate a member of the family), and the quality (or error parameter) of the pseudorandom property it achieves. Unlike previous
Maté: A Tiny Virtual Machine for Sensor Networks
, 2002
"... Composed of tens of thousands of tiny devices with very limited resources ("motes"), sensor networks are subject to novel systems problems and constraints. The large number of motes in a sensor network means that there will often be some failing nodes; networks must be easy to repopulate. ..."
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Cited by 502 (21 self)
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Composed of tens of thousands of tiny devices with very limited resources ("motes"), sensor networks are subject to novel systems problems and constraints. The large number of motes in a sensor network means that there will often be some failing nodes; networks must be easy to repopu
PseudoRandom Generation from OneWay Functions
 PROC. 20TH STOC
, 1988
"... Pseudorandom generators are fundamental to many theoretical and applied aspects of computing. We show howto construct a pseudorandom generator from any oneway function. Since it is easy to construct a oneway function from a pseudorandom generator, this result shows that there is a pseudorandom gene ..."
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Cited by 887 (22 self)
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Pseudorandom generators are fundamental to many theoretical and applied aspects of computing. We show howto construct a pseudorandom generator from any oneway function. Since it is easy to construct a oneway function from a pseudorandom generator, this result shows that there is a pseudorandom
DART: Directed automated random testing
 In Programming Language Design and Implementation (PLDI
, 2005
"... We present a new tool, named DART, for automatically testing software that combines three main techniques: (1) automated extraction of the interface of a program with its external environment using static sourcecode parsing; (2) automatic generation of a test driver for this interface that performs ..."
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Cited by 823 (41 self)
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We present a new tool, named DART, for automatically testing software that combines three main techniques: (1) automated extraction of the interface of a program with its external environment using static sourcecode parsing; (2) automatic generation of a test driver for this interface
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 800 (26 self)
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The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical
Random Oracles are Practical: A Paradigm for Designing Efficient Protocols
, 1995
"... We argue that the random oracle model  where all parties have access to a public random oracle  provides a bridge between cryptographic theory and cryptographic practice. In the paradigm we suggest, a practical protocol P is produced by first devising and proving correct a protocol P R for the ..."
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Cited by 1643 (75 self)
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We argue that the random oracle model  where all parties have access to a public random oracle  provides a bridge between cryptographic theory and cryptographic practice. In the paradigm we suggest, a practical protocol P is produced by first devising and proving correct a protocol P R
Property Testing and its connection to Learning and Approximation
"... We study the question of determining whether an unknown function has a particular property or is fflfar from any function with that property. A property testing algorithm is given a sample of the value of the function on instances drawn according to some distribution, and possibly may query the fun ..."
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Cited by 498 (68 self)
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We study the question of determining whether an unknown function has a particular property or is fflfar from any function with that property. A property testing algorithm is given a sample of the value of the function on instances drawn according to some distribution, and possibly may query
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
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Cited by 619 (14 self)
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based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown
Results 1  10
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382,603