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The improvements obtained with the pitch-normalization can be further understood by observing the distribution of the pitch of the adults training set "TR1" and the children test set "CH1" before and after pitch-normalization as shown in Figures 2, 3, and 12, respectively.

The difference in the average pitch values of the children's and the adults' speech data used in this work can be understood by observing the distribution of the average pitch of the signals of the adults training set "TR1" and the children test set "CH1" as shown in Figures 2 and 3, respectively.

The difference in the average speaking rate of the children's and the adults' speech data used in this work can be understood by observing the distribution of the average speaking rate of the adults training set "TR1" and the children test set "CH1" as shown in Figures 5 and 6, respectively.

The improvements obtained with VTLN can be further understood by observing the distribution of the ML-based warping factors chosen for VTLN of the original children's speech signals with respect to the adults' speech trained models as shown in Figure 19.

The difference in the average value of the three glottal parameters of the children's and the adults' speech data used in this work can be understood by observing the distribution of the average values of the OQ, RQ, and SQ for both the adults training set "TR1" and the children test set "CH1" as shown in Figures 8, 9, and 10, respectively.

If such a SNP passes quality control (QC), we may detect the error by observing the distribution of Mendelian errors.

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These results are confirmed by observing the distributions of IRI, a wz and DLI values calculated for real and artificial profiles and depicted, respectively, in Figs. 7a, 8a and 9a.

We set the support threshold ω in a data-driven way by observing the support distribution among the sub-sequences.

By observing the spatial distribution of burned areas, we found that the Landsat based method misclassified cropland and cloud shadows as burned areas due to the similar spectral response, and MODIS burned area product omitted small and fragmented burned areas.

The CDF predictor [ 16, 49] compiles the predictions of a per-residue predictor to a single binary predictor per protein, by observing the cumulative distribution function (CDF) of per-residue predictions, and comparing it to a set of 7 boundary CDF points obtained from a training set [ 16].

By observing the black-hatched distribution, we can clearly see that no positive enhancement is observed in the signal window.

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