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Unlike other channels in which the gap between mutual information with uniform and nonuniform source distributions is small, this figure demonstrates that for optical wireless systems the use of nonuniform distributions provides a relevant improvement in performance.
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For (D<0), KNN-DTW generates more conservative scores, and the separation between the organic and promoted class distributions is smaller.
It can still be observed indirectly as the integrated area below those two normalized distributions is smaller than for the other two distributions resulting from the zero and the synchronized export scheme.
In all cases we find that the differences between the various distributions are small (JSD is defined in ((0, log2))).
The cumulative probability distribution is small for both LRS/HRS as well as set/reset voltage.
In the case of a regular topology (Fig. 2 a), the influence of the distribution is small and relatively weak in terms of convergence time but stronger in terms of the final accuracy achieved.
We observed small values of D (mean 0.24), indicating that the deviation of each subsample from the uniform distribution is small, and moderate values of p (mean 0.2, with 32% of the p values<0.05), suggesting that this deviation from uniformity is at best only marginally significant (Figure 4A).
Similarly, results reported in Table 1 for a different SNP ascertainment scheme applied on the COA simulations which consisted in keeping only those SNPs segregating (MAF>0.01) in at least two populations suggested that the influence of ascertainment bias on the PPP-value distribution is small.
The -7 nm extension state is rare, as seen from the fact that the corresponding peak in the distribution is small.
For all methods the difference in accuracy between datasets generated assuming uniform and geometric distribution is small, with the latter one typically having a slightly worse accuracy.
However, it is recognized that (D_{hbox{max} } sqrt {N_{text{b}} }) values for the exponential distributions are smaller than those for the power law for all EXs except Suwanosejima.
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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