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We present the large-scale clustering of 1.6 million quasars between z=0.5 and z=2.5 that have been classified from this imaging, representing the highest density of quasars ever studied for clustering measurements.
Both simulations (red) and galaxy surveys (blue/purple) display the same large-scale clustering patterns.
The running time for large-scale clustering is reduced when there are large amounts of data.
The large-scale clustering data (dots) and the prediction of a Universe with 85% dark matter and 15% normal matter (solid line) match up incredibly well.
Current research on data mining algorithms in data-intensive calculation environments have concentrated on improving traditional large-scale clustering algorithms.
We present the first measurement large-scale clustering of far-infrared galaxies in the AKARI FIS All-Sky Survey.
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He also has conducted statistical evaluations of large-scale cluster-randomized trials of education interventions.
Spiking Neural Network (SNN) simulators have been traditionally simulated on large-scale clusters, super-computers, or on dedicated hardware architectures.
Many recent major search engines on Internet use a large-scale cluster to store a large database and cope with high query arrival rate.
While genomics have significantly advanced modern biological achievements, it requires extensive computational power, traditionally employed on large-scale cluster machines as well as multi-core systems.
High Performance Computing usually leverages messaging libraries such as MPI, GASNet, or OpenSHMEM, among others, in order to exchange data among processes in large-scale clusters.
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