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Linearizability: a correctness condition for concurrent objects

by Maurice P. Herlihy, Jeannette M. Wing , 1990
"... A concurrent object is a data object shared by concurrent processes. Linearizability is a correctness condition for concurrent objects that exploits the semantics of abstract data types. It permits a high degree of concurrency, yet it permits programmers to specify and reason about concurrent object ..."
Abstract - Cited by 1182 (28 self) - Add to MetaCart
A concurrent object is a data object shared by concurrent processes. Linearizability is a correctness condition for concurrent objects that exploits the semantics of abstract data types. It permits a high degree of concurrency, yet it permits programmers to specify and reason about concurrent

Near Shannon limit error-correcting coding and decoding

by Claude Berrou, Alain Glavieux, Punya Thitimajshima , 1993
"... Abstract- This paper deals with a new class of convolutional codes called Turbo-codes, whose performances in terms of Bit Error Rate (BER) are close to the SHANNON limit. The Turbo-Code encoder is built using a parallel concatenation of two Recursive Systematic Convolutional codes and the associated ..."
Abstract - Cited by 1738 (5 self) - Add to MetaCart
Abstract- This paper deals with a new class of convolutional codes called Turbo-codes, whose performances in terms of Bit Error Rate (BER) are close to the SHANNON limit. The Turbo-Code encoder is built using a parallel concatenation of two Recursive Systematic Convolutional codes and the associated decoder, using a feedback decoding rule, is implemented as P pipelined identical elementary decoders. Consider a binary rate R=1/2 convolutional encoder with constraint length K and memory M=K-1. The input to the encoder at time k is a bit dk and the corresponding codeword

Solving multiclass learning problems via error-correcting output codes

by Thomas G. Dietterich, Ghulum Bakiri - JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH , 1995
"... Multiclass learning problems involve nding a de nition for an unknown function f(x) whose range is a discrete set containing k>2values (i.e., k \classes"). The de nition is acquired by studying collections of training examples of the form hx i;f(x i)i. Existing approaches to multiclass l ..."
Abstract - Cited by 730 (8 self) - Add to MetaCart
output representations. This paper compares these three approaches to a new technique in which error-correcting codes are employed as a distributed output representation. We show that these output representations improve the generalization performance of both C4.5 and backpropagation on a wide range

Good Error-Correcting Codes based on Very Sparse Matrices

by David J.C. MacKay , 1999
"... We study two families of error-correcting codes defined in terms of very sparse matrices. "MN" (MacKay--Neal) codes are recently invented, and "Gallager codes" were first investigated in 1962, but appear to have been largely forgotten, in spite of their excellent properties. The ..."
Abstract - Cited by 741 (23 self) - Add to MetaCart
We study two families of error-correcting codes defined in terms of very sparse matrices. "MN" (MacKay--Neal) codes are recently invented, and "Gallager codes" were first investigated in 1962, but appear to have been largely forgotten, in spite of their excellent properties

Hierarchical correctness proofs for distributed algorithms

by Nancy A. Lynch, Mark R. Tuttle , 1987
"... Abstract: We introduce the input-output automaton, a simple but powerful model of computation in asynchronous distributed networks. With this model we are able to construct modular, hierarchical correctness proofs for distributed algorithms. We de ne this model, and give aninteresting example of how ..."
Abstract - Cited by 439 (66 self) - Add to MetaCart
Abstract: We introduce the input-output automaton, a simple but powerful model of computation in asynchronous distributed networks. With this model we are able to construct modular, hierarchical correctness proofs for distributed algorithms. We de ne this model, and give aninteresting example

Okapi at TREC-3

by S.E. Robertson, S. Walker, S. Jones, M.M. Hancock-Beaulieu, M. Gatford , 1996
"... this document length correction factor is #global": it is added at the end, after the weights for the individual terms have been summed, and is independentofwhich terms match. ..."
Abstract - Cited by 593 (5 self) - Add to MetaCart
this document length correction factor is #global": it is added at the end, after the weights for the individual terms have been summed, and is independentofwhich terms match.

Spurious Regressions in Econometrics

by C. W. J. Granger, P. Newbold - Journal of Econometrics , 1974
"... It is very common to see reported in applied econometric literature time series regression equations with an apparently high degree of fit, as measured by the coefficient of multiple correlation R2 or the corrected coefficient R2, but with an extremely low value for the Durbin-Watson statistic. We f ..."
Abstract - Cited by 739 (6 self) - Add to MetaCart
It is very common to see reported in applied econometric literature time series regression equations with an apparently high degree of fit, as measured by the coefficient of multiple correlation R2 or the corrected coefficient R2, but with an extremely low value for the Durbin-Watson statistic. We

Loopy Belief Propagation for Approximate Inference: An Empirical Study

by Kevin P. Murphy, Yair Weiss, Michael I. Jordan - In Proceedings of Uncertainty in AI , 1999
"... Recently, researchers have demonstrated that "loopy belief propagation" --- the use of Pearl's polytree algorithm in a Bayesian network with loops --- can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon-limit performa ..."
Abstract - Cited by 680 (18 self) - Add to MetaCart
Recently, researchers have demonstrated that "loopy belief propagation" --- the use of Pearl's polytree algorithm in a Bayesian network with loops --- can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon

How much should we trust differences-in-differences estimates? Quarterly Journal of Economics 119:249–75

by Marianne Bertrand, Esther Duflo, Sendhil Mullainathan, Abhijit Banerjee, Victor Chernozhukov, Michael Grossman, Jerry Hausman, Kei Hirano, Bo Honore , 2004
"... Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are incon-sistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on fema ..."
Abstract - Cited by 775 (1 self) - Add to MetaCart
at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the time-series process do not perform well. Bootstrap (taking

Perspectives on Program Analysis

by Flemming Nielson , 1996
"... eing analysed. On the negative side, the semantic correctness of the analysis is seldom established and therefore there is often no formal justification for the program transformations for which the information is used. The semantics based approach [1; 5] is often based on domain theory in the form ..."
Abstract - Cited by 678 (35 self) - Add to MetaCart
eing analysed. On the negative side, the semantic correctness of the analysis is seldom established and therefore there is often no formal justification for the program transformations for which the information is used. The semantics based approach [1; 5] is often based on domain theory
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