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Exact(10)
We propose an algorithm to this computation.
We refer to this computation as feedforward control.
Here we refer to this computation as motor planning.
Similarly to this computation we can also derive results for higher degree Lagrange interpolation.
Due to this computation, one can adjust the window size based on the similarity of the PFMs.
It seems as though the techniques we have developed here are not directly applicable to this computation.
Similar(50)
The basic principle of the one-to-one DHT is to generalise this computation to all pixels through a voting process.
Note that the calculation for each of the K demes is independent, so it would be easy to parallelize this computation and compute the recursion step for each deme on a separate core.
To accomplish this computation, each learning resource needs to contribute a score (0 to 1) whenever a resource is recruited and involves the KC.
To circumvent this computation issue, we have developed a step-wise algorithm to search for the minimal p-value over the set, the time complexity of which is O(L2).
We used the mle function to perform this computation in R (R Development Core Team, 2013), and the confint function to estimate the 95% confidence intervals from the log-likelihood profiles.
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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