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» Algorithmic randomness of continuous functions
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IGARSS
2010
13 years 8 months ago
SMOS L1 algorithms
The Level 1 Processing of SMOS transforms the data acquired by MIRAS (Microwave Imaging Radiometer with Aperture Synthesis) into geolocated TOA Brightness Temperatures, providing ...
Antonio Gutierrez, Jose Barbosa, Nuno Catarino, Ri...
CEC
2010
IEEE
13 years 11 months ago
An analysis of massively distributed evolutionary algorithms
Computational science is placing new demands on optimization algorithms as the size of data sets and the computational complexity of scientific models continue to increase. As thes...
Travis J. Desell, David P. Anderson, Malik Magdon-...
STOC
2004
ACM
153views Algorithms» more  STOC 2004»
14 years 10 months ago
Quantum and classical query complexities of local search are polynomially related
Let f be an integer valued function on a finite set V . We call an undirected graph G(V, E) a neighborhood structure for f. The problem of finding a local minimum for f can be phr...
Miklos Santha, Mario Szegedy
ASPDAC
1995
ACM
108views Hardware» more  ASPDAC 1995»
14 years 1 months ago
Synthesis-for-testability using transformations
- We address the problem of transforming a behavioral specification so that synthesis of a testable implementation from the new specification requires significantly less area and ...
Miodrag Potkonjak, Sujit Dey, Rabindra K. Roy
ECCC
2006
96views more  ECCC 2006»
13 years 10 months ago
When Does Greedy Learning of Relevant Features Succeed? --- A Fourier-based Characterization ---
Detecting the relevant attributes of an unknown target concept is an important and well studied problem in algorithmic learning. Simple greedy strategies have been proposed that s...
Jan Arpe, Rüdiger Reischuk