Sentence examples for kernel terms from inspiring English sources
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.
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.
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.
As for the difference between the diffusion and the Gaussian kernels in terms of predictive ability, the diffusion kernel had the highest predictive correlation and the lowest MSE with θ=11 in the Holstein data, but the difference with the Gaussian kernel was negligible.
Finally, the performance of different TFMFs is evaluated for different t f kernels in terms of classification accuracy using real newborn EEG signals.
