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We conduct inversion using the first n basis vectors obtained by the CG method.
As an efficient alternative to using the first N j -sorted elements, a relative threshold T S is suggested.
In the initialization step, the MMSE-based noise estimator is initialized by using the first N frames and the pitch filter banks are designed (see [13] for details).
By using the first n, no less than l, linear undamped modal functions as a functional basis and applying the Galerkin procedure, a 2n-degree-of-freedom discrete model with quadratic and cubic terms owing to geometric effect is derived.
The first array of dimension (N × N × 3) is built by choosing the first three matrix slices from the fourth-order cumulant array, while the second array of dimension (N × N× N) is built using the first N matrix slices.
The zeros can be removed by using the first N g columns of the N-point DFT matrix F that is (mathbf {F}_{N_{g}}) begin{array}{*{20}l} {tilde{mathbf{y}}} &=mathbf{C} epsilon) mathbf{X} mathbf{F}_{N_{g}} left lbrack begin{array}{c} h[!0] vdots h[!N_{g}-1] end{array} right rbrack + mathbf{z} &={tilde{mathbf{A}}} mathbf{h} + mathbf{z}, end{array} (10).
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Description: Per protein topology prediction accuracies of the consensus algorithm are shown using the first n-best methods (red line) and the first n-worst methods (black line).
Thus, we just use the first N moments to get the results, where N is much less than n (the length of genome).
Then, we used the first n principal components (PCs) that explained 95% of the variance to find good representatives of the variation in the dataset, while n was constrained to be smaller than six.
For circular single-strand genomes, we use the first N components (M1, M2, …, M N ) of this normalized moment vector to represent a genome as a point in an N-dimensional space.
In order to use whole genome information to make comparative analysis among genomes, we can use the first N components (M1, M2, …, M N ) of the moment vector of a genome sequence graph to represent a genome as a point in N-dimensional space.
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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