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Figure 12 Density function of access score in an arbitrary week of the Wiki dataset.
The process of access score calculation for every access associated with s3 is depicted in Figure 3.
A range of easy-to-calculate surrogate variables was developed and tested as possible candidates to improve the explanatory power of the IMD2000 access score.
The ρ value is 0.866 in the EHR dataset, and 1 in the Wiki dataset, which suggests that the real access score distributions are highly correlated with Laplace.
The relation between the real and Laplace distributions can be observed in Figure 13 and Figure 14. Figure 11 Density function of access score in an arbitrary week of the EHR dataset.
Finally, and similar to SNAD, the spectral model computes an access score for each user by measuring the change of distance in the access network after suppressing the user.
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Supporters of the third-generation networks talk of high-speed mobile Internet access, scores of mobile commerce applications, and even the ability to receive streams of video over a cellular phone.
The lower plot corresponds to the distribution of the access scores.
Figure 13 Cumulative distribution function of access scores in an arbitrary week of the EHR.
In this figure, the x-axis corresponds to the access scores.
This figure reports the frequency of access scores, as a cumulative distribution function for one week of the Wiki dataset.
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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