Results 261 to 270 of about 12,817,495 (340)

Gradient boosting-based discrete failure time model for selecting time-varying effects and interactions. [PDF]

Lifetime Data Anal
Luo L, He K, Taylor JMG.
europepmc   +1 more source

Interim analysis in clinical trials: caution against premature conclusions-the SWOG multiple myeloma S0777 case study. [PDF]

Trials
Goren E, Sexton R, Orlowski RZ, Hoering A.   +3 more
europepmc   +1 more source

Reproducible study identification and selection in systematic reviews: barriers and paths forward. [PDF]

BMJ Open
Lin L, Xing X, Wu C, Chen Y, Tong J, McClelland DJ, Chen Z.   +6 more
europepmc   +1 more source

Neural manifolds in spinal networks that orchestrate walking and stopping

Berg R, Komi S, Kaur J, Winther A, Bonfils M, Houser G, Sorensen J, Li G, Sobriel K.   +8 more
europepmc   +1 more source

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Stopping set elimination for LDPC codes

2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2017
This work studies the Stopping-Set Elimination Problem, namely, given a stopping set, how to remove the fewest erasures so that the remaining erasures can be decoded by belief propagation in k iterations (including k =∞). The NP-hardness of the problem is proven. An approximation algorithm is presented for k = 1.
A. Jiang, Pulakesh Upadhyaya, Y. Wang, K. Narayanan, Hong-Chao Zhou, Jin Sima, Jehoshua Bruck   +6 more
semanticscholar   +3 more sources

Stopping set distribution of LDPC code ensembles

IEEE Transactions on Information Theory, 2003
Stopping sets determine the performance of low-density parity-check (LDPC) codes under iterative decoding over erasure channels. We derive several results on the asymptotic behavior of stopping sets in Tanner-graph ensembles, including the following. An expression for the normalized average stopping set distribution, yielding, in particular, a critical
A. Orlitsky, K. Viswanathan, Junan Zhang
semanticscholar   +4 more sources

Pseudocodeword weights and stopping sets

International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings., 2004
We examine the structure of pseudocodewords in Tanner graphs and derive lower bounds of pseudocodeword weights. The weight of a pseudocodeword is related to the size of its support set, which forms a stopping set in the Tanner graph.
Kelley, C, Sridhara, D, Xu, J, Rosenthal, J   +3 more
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Stopping set analysis for Hamming codes

IEEE Information Theory Workshop, 2005., 2005
In the 2004 Shannon Lecture, McEliece presented an expression for the number of stopping sets of size three in a Hamming code. In this paper, we investigate how this number depends on the parity-check matrix used in the decoding process. First, we present basic results on stopping set enumerators for block codes in general.
J. Weber, K. Abdel-Ghaffar
semanticscholar   +3 more sources

Generalized Stopping Sets and Stopping Redundancy

2007 Information Theory and Applications Workshop, 2007
Iterative decoding for linear block codes over erasure channels may be much simpler than optimal decoding but its performance is usually not as good. Here, we present a general iterative decoding technique that gives a more refined trade-off between complexity and performance. In each iteration, a system of equations is solved.
Khaled A.S. Abdel-Ghaffar, Jos H. Weber
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