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Finally, an algorithm is proposed to select the kernel term and its associated terms as term clusters to represent the hot topics with multi-facets expression.
The constant of the kernel term, which varies with the volume, is not included for clarity.
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Specifically, utilizing a local reformulation of the electrostatic and kernel terms, we develop a generalized framework for performing OF-DFT simulations with different variants of the electronic kinetic energy.
An empirical correlation is developed for the calculation of the agglomeration kernel in terms of the particle surface temperature and polymer softening point based on reported experimental measurements.
Here, we introduce a process of orthogonal norm expansion along a subvariety of (complex) codimension 1, which also leads to a series expansion of the reproducing kernel in terms of reproducing kernels defined on the subvariety.
Finally, in Sect. 10 we discuss a link between the lower bound for the Bergman kernel in terms of the pluricomplex Green function and possible symmetrization results for the complex Monge Ampère equation and complex isoperimetric inequalities.
This is an interesting lower bound for the Bergman kernel in terms of a solution to the complex Monge Ampère equation and is in fact a quantitative version of the following result of Ohsawa [96]: Bounded hyperconvex domains are Bergman exhaustive.
We categorize each kernel in terms of the primary mechanism by which it affects behavior, although clearly many kernels involve more than one process.
Next, we ranked the features of the gappy triplet kernel in terms of their weights in MI-1 SVM learning in order to find motifs that are associated with CaM binding.
This procedure does not require the set of covariants to be orthogonal, it is completely general, also with respect to the kernel, the driving term, and possible terms multiplying the amplitude on the left-hand side of the BSE.
In addition, diverse sets of parameters are obtained to create multi-kernels in terms of the constructed series of filters.
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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