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To confirm that the difference in RIC curves of the 6 ×2048 networks is not merely a result of 110 h of transcribed data being insufficient for that network size, we also included a 6 ×2048 SI input DNN trained with 300-h transcribed data.
Regarding non-meritocratic factors, we show that network size, individual reputation, and gender matters.
They showed that network size can be increased in steps, keeping the same number of keys per node.
This indicates that network size does not influence the average travel distance because each node moves at most R c /2 in RIM, while R c is fixed to be 80.
One conclusion drawn from that work is that network size should be minimized, scale-free topology maintained, and intermediate network connectivity evolved in order to maximize adaptability.
Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers.
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But they also find that social network size of older people is dynamic.
Other factors that influence network size include the proportion of DEGs with fly homologues, and the proportion of these with co-citation information currently available in PubMed.
Also, their memory requirements are huge, so that networks sizes beyond N = 50 could not be dealt with on a unit with 4 GB RAM.
We observe that irrespective of network size, for 97.5% of the trials, the exploration overhead is smaller than 1.
First, for each curve there exists an optimal operating point; that is, a network size for which distortion can be minimized.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com