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Singularity of optimal prefix codes for pairs of geometricallydistributed random variables
"... Abstract—Optimal prefix codes are studied for pairs of independent, integervalued symbols emitted by a source with a geometric probability distribution of parameter q ∈ (0, 1). By encoding pairs of symbols, it is possible to reduce the redundancy penalty of symbolbysymbol encoding, while preserv ..."
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Abstract—Optimal prefix codes are studied for pairs of independent, integervalued symbols emitted by a source with a geometric probability distribution of parameter q ∈ (0, 1). By encoding pairs of symbols, it is possible to reduce the redundancy penalty of symbolbysymbol encoding, while
Optimal prefix codes for pairs of geometricallydistributed random variables
"... Optimal prefix codes are studied for pairs of independent, integervalued symbols emitted by a source with a geometric probability distribution of parameter q, 0<q<1. By encoding pairs of symbols, it is possible to reduce the redundancy penalty of symbolbysymbol encoding, while preserving th ..."
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Optimal prefix codes are studied for pairs of independent, integervalued symbols emitted by a source with a geometric probability distribution of parameter q, 0<q<1. By encoding pairs of symbols, it is possible to reduce the redundancy penalty of symbolbysymbol encoding, while preserving
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
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Cited by 1513 (20 self)
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law), then it is possible to reconstruct f to within very high accuracy from a small number of random measurements. typical result is as follows: we rearrange the entries of f (or its coefficients in a fixed basis) in decreasing order of magnitude f  (1) ≥ f  (2) ≥... ≥ f  (N), and define the weakℓp ball
An introduction to variable and feature selection
 Journal of Machine Learning Research
, 2003
"... Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. ..."
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Cited by 1283 (16 self)
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Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available.
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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techniques constitute Directed Automated Random Testing,or DART for short. The main strength of DART is thus that testing can be performed completely automatically on any program that compiles – there is no need to write any test driver or harness code. During testing, DART detects standard errors
Iterative decoding of binary block and convolutional codes
 IEEE Trans. Inform. Theory
, 1996
"... Abstract Iterative decoding of twodimensional systematic convolutional codes has been termed “turbo ” (de)coding. Using loglikelihood algebra, we show that any decoder can he used which accepts soft inputsincluding a priori valuesand delivers soft outputs that can he split into three terms: the ..."
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Cited by 600 (43 self)
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is controlled by a stop criterion derived from cross entropy, which results in a minimal number of iterations. Optimal and suboptimal decoders with reduced complexity are presented. Simulation results show that very simple component codes are sufficient, block codes are appropriate for high rates
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