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Fig. 7 Cumulative sum of squared differences.
The advantage of this difference over others such as the sum of squared differences will be discussed later.
The sum of squared differences allows frames to be grouped based on the similarity of total change.
As similarity metric we used the sum of squared differences.
Second, we used a least-squares approach: we summed the squared differences between observed and simulated individual admixture values for each scenario in each population and defined the best scenario as the one with the smallest sum of squared differences.
Here, the Zero Normalized Sum of Squared Differences (ZNSSD) correlation criterion was adopted.
Within the selected area, use the Sum of Squared Differences (SSD) method to locate the most likely corresponding point pairs in the two input images.
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In order to obtain image feedback, a sum-of-squared-differences tracking algorithm [29] is employed to track the cell.
The sum-of-squared-differences (SSD) statistic was used to test the goodness-of-fit between the observed mismatch distribution and that expected under a sudden expansion model.
Afterwards, we further develop a new constrained zero-normalized sum-of-squared differences (CZNSSD) criterion and construct the corresponding iterative algorithm, based on which the subset size involved can be automatically determined according to the necessary amount of speckle information.
We utilized the standard normalization method in SPM8, which minimizes the sum-of-squared differences between the subject's image and the template (MNI305), while maximizing the prior probability of the transformation.
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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