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Verifying design with proof scores

by Kokichi Futatsugi, Joseph A. Goguen, Kazuhiro Ogata - Proceedings, Verified Software: Theories, Tools, Experiments , 2005
"... Abstract: Verifying design instead of code can be an effective and practical approach to obtaining verified software. This paper argues that proof scores are an attractive method for verifying design, in that they achieve a balance in which the respective capabilities of humans and machines are util ..."
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are utilized optimally. 1 Verifying Code or Design Although creation of a verifying compiler is a difficult challenge, recent developments suggest that there are ways to make it easier. Systems that generate lexical analyzers and parsers already have a long history (e.g. Lex and Yacc), and recent work of Sorin

The linux pseudorandom number generator revisited

by Patrick Lacharme, Andrea Röck, Vincent Strubel, Marion Videau , 2012
"... The Linux pseudorandom number generator (PRNG) is a PRNG with entropy inputs which is widely used in many security related applications and protocols. This PRNG is written as an open source code which is subject to regular changes. It was last analyzed in the work of Gutterman et al. in 2006 [GPR06] ..."
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The Linux pseudorandom number generator (PRNG) is a PRNG with entropy inputs which is widely used in many security related applications and protocols. This PRNG is written as an open source code which is subject to regular changes. It was last analyzed in the work of Gutterman et al. in 2006 [GPR06

Entrainment and Communication with Dissipative Pseudorandom Dynamics

by N. Gershenfeld, G. Grinstein - Physical Review Letters , 1995
"... We introduce a new class of dynamical systems, analog generalizations of linear feedback shift registers, that can be designed with any number of degrees of freedom, generate optimal pseudo-random noise, and exhibit nonlinear dissipative entrainment which can be used to decode signals in communicati ..."
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We introduce a new class of dynamical systems, analog generalizations of linear feedback shift registers, that can be designed with any number of degrees of freedom, generate optimal pseudo-random noise, and exhibit nonlinear dissipative entrainment which can be used to decode signals

Pseudorandom Generators against CFL/n

by Tomoyuki Yamakami , 902
"... Abstract. Pseudorandomness has played a central role in modern cryptography, finding theoretical and practical applications to various fields of computer science. A function that generates such pseudorandom strings from shorter but truly random seeds is known as a pseudorandom generator. Our generat ..."
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Abstract. Pseudorandomness has played a central role in modern cryptography, finding theoretical and practical applications to various fields of computer science. A function that generates such pseudorandom strings from shorter but truly random seeds is known as a pseudorandom generator. Our

Application of Pseudorandom m-Sequences for Seismic Acquisition

by Joe Wong
"... Maximal-length sequences (or m-sequences) are well-defined mathematical constructs with impulse-like autocorrelations that make them attractive for use in seismic acquisition. They are easily produced by logic statements in software, or they can be generated electronically by simple circuits with sh ..."
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Maximal-length sequences (or m-sequences) are well-defined mathematical constructs with impulse-like autocorrelations that make them attractive for use in seismic acquisition. They are easily produced by logic statements in software, or they can be generated electronically by simple circuits

On Pseudorandom Generators withLinear Stretch in

by unknown authors
"... Abstract. We consider the question of constructing cryptographic pseudoran-dom generators (PRGs) in NC0, namely ones in which each bit of the outputdepends on just a constant number of input bits. Previous constructions of such PRGs were limited to stretching a seed of n bits to n + o(n) bits. This ..."
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Abstract. We consider the question of constructing cryptographic pseudoran-dom generators (PRGs) in NC0, namely ones in which each bit of the outputdepends on just a constant number of input bits. Previous constructions of such PRGs were limited to stretching a seed of n bits to n + o(n) bits

Techniques for Testing the Quality of Parallel Pseudorandom Number Generators

by Steven Cuccaro, Michael Mascagni, Daniel V. Pryor - in Proc. of the 7th SIAM Conf. on Parallel Processing for Scientific Computing, SIAM , 1995
"... Ensuring that pseudorandom number generators have good randomness properties is more complicated in a multiprocessor implementationthan in the uniprocessor case. We discuss simple extensions of uniprocessor testing for SIMD parallel streams, and develop in detail a repeatability test for the SPMD pa ..."
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to the development of pseudorandom number generators (PRNGs). In the course of this development, testing procedures were designed to ensure that the necessarily deterministic sequence of numbers produced by these PRNGs had analytical and statistical properties which compared well with those of a true random stream

Some Methods Of Parallel Pseudorandom Number Generation

by Michael Mascagni - in Proceedings of the IMA Workshop on Algorithms for Parallel Processing , 1997
"... . We detail several methods used in the production of pseudorandom numbers for scalable systems. We will focus on methods based on parameterization, meaning that we will not consider splitting methods. We describe parameterized versions of the following pseudorandom number generation: 1. linear cong ..."
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of their quality in parallel applications. Several of these methods are currently part of scalable library for pseudorandom number generation, called the SPRNG package available at the URL: www.ncsa.uiuc.edu/Apps/CMP/RNG. Key words. pseudorandom number generation, parallel computing, linear congruential, lagged

Pseudorandom Generators for Group Products (version with the geometric proof — 2nd draft)

by Prajakta Nimbhorkar, Pavel Pudlák , 2011
"... We prove that the pseudorandom generator introduced by Impagliazzo et al. in [INW94] with proper choice of parameters fools group products of a given finite group G. The seed length is O(log n(|G | O(1) +log 1 δ)), where n is the length of the word and δ is the allowed error. The result implies that ..."
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that the pseudorandom generator with seed length O(log n(2O(w log w) + log 1 δ)) fools read-once permutation branching programs of width w. As an application of the pseudorandom generator one obtains small-bias spaces for products over all finite groups [MZ09].

On the Fast Generation of Long-period Pseudorandom Number Sequences

by Graduate Student Member, IEEE Ishaan L Dalal , Student Member, IEEE Jared Harwayne-Gidansky , Student Member, IEEE Deian Stefan
"... Abstract-Monte Carlo simulations and other scientific applications that depend on random numbers are increasingly implemented in parallel configurations in programmable hardware. High-quality pseudo-random number generators (PRNGs), such as the Mersenne Twister, are based on binary linear recurrenc ..."
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Abstract-Monte Carlo simulations and other scientific applications that depend on random numbers are increasingly implemented in parallel configurations in programmable hardware. High-quality pseudo-random number generators (PRNGs), such as the Mersenne Twister, are based on binary linear
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