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APPROX
2007
Springer
100views Algorithms» more  APPROX 2007»
14 years 4 months ago
Implementing Huge Sparse Random Graphs
Consider a scenario where one desires to simulate the execution of some graph algorithm on random input graphs of huge, perhaps even exponential size. Sampling and storing these h...
Moni Naor, Asaf Nussboim
BMCBI
2008
136views more  BMCBI 2008»
13 years 10 months ago
LOMA: A fast method to generate efficient tagged-random primers despite amplification bias of random PCR on pathogens
Background: Pathogen detection using DNA microarrays has the potential to become a fast and comprehensive diagnostics tool. However, since pathogen detection chips currently utili...
Wah-Heng Lee, Christopher W. Wong, Wan Yee Leong, ...
TC
2008
13 years 10 months ago
The Convergence-Guaranteed Random Walk and Its Applications in Peer-to-Peer Networks
Network structure construction and global state maintenance are expensive in large-scale, dynamic peer-to-peer (p2p) networks. With inherent topology independence and low state mai...
Ming Zhong, Kai Shen, Joel I. Seiferas
FOCS
2000
IEEE
14 years 2 months ago
Extracting Randomness from Samplable Distributions
Randomness extractors convert weak sources of randomness into an almost uniform distribution; the conversion uses a small amount of pure randomness. In algorithmic applications, t...
Luca Trevisan, Salil P. Vadhan
BMCBI
2007
147views more  BMCBI 2007»
13 years 10 months ago
Bias in random forest variable importance measures: Illustrations, sources and a solution
Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and relate...
Carolin Strobl, Anne-Laure Boulesteix, Achim Zeile...