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We ran the procedures for each of these 5 centrality metrics on each of the real-world networks for 20 iterations and averaged the results.
It has a complexity of O(K S) due to a search for the maximum of K metrics on each of the S resource blocks.
As each of these scheduling rules are based on the proportional fair rule, the calculation of these scheduling metrics on each physical resource block (PRB) allows the exploitation of multi-user time and frequency diversities.
Figure 7 plots the natural logarithm of the average execution time (for the values to be plotted on a comparable scale) incurred for the centrality metrics on each of the real-world networks.
To provide a simple number for comparison between methods, the averages of metrics on each dataset are also listed.
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We elected to demonstrate our new assembly metrics on one of these organisms, Haloferax volcanii strain DS2.
Notice that d is a generalized metric on X if and only if d i are metrics on X for each i ∈ { 1, 2, …, m }.
With it, inevitably, will come the same debates roiling the N.B.A. between advocates of advanced metrics on the one hand and traditionalists on the other.
The metrics obtained on each of the three sets of PET images were compared globally using Friedman tests with a post hoc Dunn test [21] used to compare each pair of reconstructions (PSF vs OSEM, PSF7 vs PSF and PSF7 vs OSEM).
Next, for a cluster C={T P 1,T P 2,…,T P M }, of maximum size M, we define a sub-cluster S as any combination of TPs existing in C. For all sub-clusters of user u, we then calculate the product of metrics (POM) on each resource block k as: Phi_{k} u,S,t) = prod_{r in S} phi_{k} (u,r,t), forall S subset C. (6).
On the other hand, each of these metrics on its own showed no association.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com