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For the first iteration, the same methodology was applied to both sites.
For the second iteration, the same procedure is repeated, conditioning on the sampled values from the first iteration.
Taking this new (y_{{2(C_{1} )}}^{(1)}) as the starting point for the next iteration, the same procedure like before can be followed to estimate the value of (y_{{2(C_{1} )}}) at the end of the second iteration (i.e. (y_{{2(C_{1} )}}^{(2)} )).
For each iteration, the same label shuffling was applied to all subjects.
Once solved, in the next iteration, the same search will identify a best variable, because the other one will have been already handled in previous iterations.
The XM scheme improves the conditioning of the tap-input covariance matrix via exclusive updates of the two adaptive filters, i.e., in each iteration the same filter tap index is never selected in both channels.
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After several iterations, the same label tends to become associated with all the members of a community; Step 3 Vertices labeled alike are added to one community.
Alternatively, instead of using the conventional differential decoding in the first iteration, a channel estimation followed by the decoding of the inner code can be used, which makes the first iteration exactly the same as subsequent iterations.
If the new point is inserted to the right of, then would now (iteration ) serve the same role that it played in iteration.
The iteration ends when the rank detection result of the current iteration is the same as the result of the previous one.
In this way, two more Λ values are tested in the next iteration, and the same procedure is repeated until the predefined number of iterations is accomplished.
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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