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Through extensive simulations the available modes can be evaluated for a large range of network context parameters of influence [9, 26].
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The proposed methodologies are generally applicable over a large range of networks.
As for the computations, we used the Graphical Lasso algorithm which provided a large range of candidate network topologies.
In this paper, we are interested in maximizing simultaneously IP, the KS-entropy, and the average 〈IP〉, for a large range of the coupling strength, characteristics of a quasi-optimal network.
The same trend, in which mixed-CLR coupled with Inferelator 1.0 outperforms the other evaluated method combinations for a large range of tested filtration cutoffs, holds for DREAM3 50-gene networks (for which our method ranked 4th out of 27) and 10-gene networks (for which our method ranked 5th out of 29) (data not shown).
Self-excitable networks have communication channels that transmit information in a higher rate for a large range of the coupling strength.
For food you have a large range of choice.
A set of experimental data, covering a large range of parameters, for the training and testing of neural networks is used.
Non-self-excitable networks have communication channels that transmit information in a higher rate for a small range of the coupling strength, however, they have channels that transmit reliable information in a moderate rate for large range of coupling strengths.
Statistically speaking, the results of simple neural network are not proper, for permeability data have a large range and are multi-populations.
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