From spatio-temporal data to a weighted and lagged network between functional domains: Applications in climate and neuroscience
Spatio-temporal data have become increasingly prevalent and important for both science and enterprises. Such data are typically embedded in a grid with a resolution larger than the true dimensionality of the underlying system. One major task is to identify the distinct semi-autonomous functional co...
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| Format: | Others |
| Language: | en_US |
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Georgia Institute of Technology
2016
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| Online Access: | http://hdl.handle.net/1853/55008 |