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We showed that compared to state-of-the-art algorithms, our approach outperforms them in terms of runtime performance by several orders of magnitude, providing solutions whose quality was on average 98.1% of the optimum in tested cases (with standard deviation of 1.4%).
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The national average differences between predicted and observed annual averages are < 1% in all cases, with standard deviations ranging from 10 to 12%.
We just display two group results (Figures 10 and 11) for the face part of "Lena" image of various algorithms in the cases of Gaussian noise with standard deviation, respectively.
We display two group results (Figures 12, 13, 14, and 15) for the face part of "Barbara" image of various algorithms in the cases of Gaussian noise with standard deviation and, respectively.
Descriptive statistics are represented as numbers of cases, percentages, and means with standard deviation (SD).
Descriptive statistics are presented as number of cases, percentage and mean with standard deviation (SD).
For analysis of characteristics of cases and controls, we calculated means with standard deviation for continuous variables stratified by cohort.
Continuous variables were summarized as means with standard deviation in case of normal distribution or medians with inter-quartile range (IQR) otherwise.
Results were expressed as mean with standard deviation in case of normal distribution or as median with interquartile range for non-normal data.
Three different cases of list-length-heterogeneity are considered in Fig. 5: No heterogeneity (standard deviation 0), heterogeneity with standard deviation 0.283 and heterogeneity with standard deviation 0.401 in the occurrence probabilities.
Gaussian with standard deviation 10−4.
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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