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Results: In this work, we show that in the standard approach of iteration through all triangles of SymBets, the memory scales with at least the number of these triangles, O g) (where g = number of genomes), and construction time scales with the iteration through each pair, i.e. O g).
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In our approach, the number of iterations for producing the 500 units remains the same since in each case (independently of the parameters) we always need to sample the PDF 500 times.
The approach relies on the principle of iteration, and recommends revising the storylines after simulation, adapting the input-data sets to the refined storylines and repeating the simulation.
An incremental load approach with a Newton Raphson type of iteration is used in the analyses of the frames.
It seems that the more idealist the approach, the greater the extent of iteration.
Especially in recent decades, with the development of the computer, many people have coped with numerous applications by utilizing a variety of iteration methods to approach the fixed point and hence they made a breakthrough and brought this subject gradually to perfection.
By using the successive approximation approach, two iteration sequences of vector differential equations are constructed.
Under this approach, each iteration produces a subset of features out of the current feature set (see Line 5 in Algorithm 2 of Additional file 8), then removes these features from the set so that they can't be selected again (see Line 15 in Algorithm 2 of Additional file 8).
For the iterative approach, the total number of iterations is 50 with a mixing parameters (alpha) of 2; the original data were binned by 4. As N increases, the coefficient of variation (c_v) is first lowered (this corresponds to a decrease in the contrast), a trend only triggered by the artifact attenuation.
This dynamic approach of taking decision for every iteration during runtime makes Adaptive-Miner very fast and efficient.
Our approach of determining the frequency (number of iterations) and magnitude of excursions above the NCRI was another way of examining risk, and again the younger age groups were at higher risk (see section on Risk Characterization).
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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