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In this work, we computed an average porosity of 0.173% on a 6700 µm3 3domainin sample at a much lower resolution of 6.7 µm.
Specifically, we computed an average μ m and a standard deviation σ m for each modality m∈{g a i t,h e a d,h e i g h t} among the training set, and computed a normalized score (bar {S}_{m}) from a raw score S m as bar{S}_{m}=frac{S_{m}-mu_{m}}{sigma_{m}}.
We then computed an average symmetry score for each experiment, over all pairs of active grid cells in that experiment.
Since we expect considerable variability at each locus with respect to this parameter, we computed an average estimate across coding and putative promoter loci.
To compare each method's ability to select discriminating variables, we ranked the variable importance scores produced by the simulations for each forest and computed an average rank for the 3 equally discriminating variables.
Successful trials were scored as 1 and failures as 0. For each trial, we computed an average
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We then computed an averaged gene expression profile for each strain-sex combination using all probes and computed the correlation between groups.
We assume a QPSK modulation and compute an average uncoded BER.
The other two methods compute an average distance that is very high compared to our method.
As a result, a stack of the five best interferograms can be used to compute an average deformation rate for a fixed time interval.
This method computes an average false diffusivity from a given numerical solution and it is not limited to any particular numerical scheme.
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