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TIT
2002
129views more  TIT 2002»
13 years 7 months ago
Arbitrary source models and Bayesian codebooks in rate-distortion theory
-- We characterize the best achievable performance of lossy compression algorithms operating on arbitrary random sources, and with respect to general distortion measures. Direct an...
Ioannis Kontoyiannis, Junshan Zhang
SAC
2005
ACM
14 years 1 months ago
Time-varying, multivariate volume data reduction
Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by largescale si...
Nathaniel Fout, Kwan-Liu Ma, James P. Ahrens
SYSTOR
2009
ACM
14 years 1 months ago
Energy and performance evaluation of lossless file data compression on server systems
Data compression has been claimed to be an attractive solution to save energy consumption in high-end servers and data centers. However, there has not been a study to explore this...
Rachita Kothiyal, Vasily Tarasov, Priya Sehgal, Er...
ECAI
2006
Springer
13 years 11 months ago
Background Default Knowledge and Causality Ascriptions
A model is defined that predicts an agent's ascriptions of causality (and related notions of facilitation and justification) between two events in a chain, based on background...
Jean-François Bonnefon, Rui Da Silva Neves,...
IJAR
2008
92views more  IJAR 2008»
13 years 7 months ago
Predicting causality ascriptions from background knowledge: model and experimental validation
A model is defined that predicts an agent's ascriptions of causality (and related notions of facilitation and justification) between two events in a chain, based on backgroun...
Jean-François Bonnefon, Rui Da Silva Neves,...