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The desired weighted unranking algorithm thus generates, for a given size n and a given number i ∈ { 0,..., card (L n ) - 1 }, the ith secondary structure s ∈ L n, where card (L n ) = size (L, n ) is the number of elements in the weighted class L n. First, we have to find a suitable SCFG that generates and models the distribution of the sample data as closely as possible.
This specification (with weighted classes) can be translated directly - into a recursion for the function size of all involved combinatorial (sub classes (where class sizes are weighted) and - into generating algorithms for the specified (weighted) classes, yielding the desired weighted unranking algorithm for generating random elements of L (G 0 ).
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In conclusion, Entropy should be used if a greater number of divergent regions are desired, while Weighted will find use when a small list of only the most significantly-divergent regions is required, and Variability behaves like Entropy when the sequences in the MSA are closely related, but behaves like Weighted in the remainder of cases.
On the wireless link, the FBS receives its desired signal S0 weighted by the channel gain h ff in addition to the signals S1 and S2 which are weighted by the channel coefficients hm 1f and hm 2f, respectively.
Thus, in the presence of asynchronous switching, an H∞ controller is presented to ensure the closed-loop system with a desired non-weighted H∞ performance.
Since there are fore real parameters,, in Theorem 2.6, we can obtain some desired versions of weighted Caccioppoli-type estimates by different choices of them.
By optimizing our criterion we obtain desired responses that produce weighted mean square error optimum filters with extremely good characteristics.
When (0 < alpha< 1), we trade off between the bias towards high weighted degree nodes, as in the RW algorithm, and the desired uniform distribution of weighted degrees, as in the MHRW algorithm.
They were weighted according to the desired stoichiometric molar ratio.
GSA being one of the suitable optimisation algorithms, used to optimise many design parameters of this subsystem to get a number of desired performance parameters using a simple weighted sum type multi-objective function.
One of them depends directly on the weighted channel gain of the desired user, and the other is related to the blind adaptive algorithm whose performance is also influenced by the co‐scheduled weighted channel gains.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com