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In addition, under different geographic region division ratio s g /r f conditions, the trend of error probability p err is depicted in Fig. 11.
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The difference between the two estimators in Figs. 3 and 4 is that the developing trend of errors in Fig. 3 is smoother than that in Fig. 4.
The observed trends of error rate corresponded to the conclusions drawn from the phoneme experiments.
However, all of them show the same trend of low error (below 6%) in the middle span and high error at near both fixed and free ends.
Thus, a non-linear trend of the error is identified when such data are analyzed geographically.
Figures 3 and 4 display the change trend of the error for u and λ.
In Figure 2, it is showed that the trend of reconstruction error ε versus the number of CS observations.
Figure 2 The chart of varying trend of reconstruction error and differential entropy with p = 150, q = 20.
To test the effective of the algorithm of the two phases, we simulate the trend of reconstruction error ϵ versus the number of training signals.
But, in real-time measuring, the time consume should be considered to select favorable p and q. Figure 3 The chart of varying trend of reconstruction error and evaluation index with different p, q. (A) p = 50, q = 20.
The varying trend of reconstruction error R err = x − x ' 2 x 2 and the differential entropy h(x) were showed in Figure 2 when p = 150 ; q = 20, where x' is the recovery signal.
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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