Sentence examples for a stronger approximation from inspiring English sources

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To limit the computational time, we also considered a stronger approximation, including only the 374 potential species supported by at least 10 sequencing reads.

Similar(59)

His nephew, Jake Clemons, took over many of his riffs and solos, delivering a strong approximation of his tone, though only a portion of his presence.

Although this is a strong approximation, it gives first insights into the possible dynamics of homogeneous neural populations.

In the present work we make efforts in this direction and propose a new efficient scheme, which enjoys cheap computational costs in a strong approximation of stiff SDEs.

This formula is exact only for symmetric sections, but it is a strong approximation when the section closely approaches perfect symmetry, which all of the measured pterosaur elements do at their midshaft (the measured location for each bone).

However, a k value close to n is undesirable, because one obtains a strong approximation to the original matrix D, which is useless to uncover relationships.

The model gave a strong approximation of the data for three harvests, although the correlation coefficient for the October Alamo harvest was fairly low (65%).

Unfortunately, information on educational level is collected using the highest degree obtained and great diversity in the education systems in the maternal countries of origin could determine a non homogeneous classification [ 39]; to have a single classification we tried to translate diplomas/ degrees into schooling years but the translation introduced a strong approximation.

It exhibits advantage of stronger approximation property, faster convergence rate, greater storage capacity and higher fault tolerance than low-order neural networks.

High-order neural networks have been the object of intensive analysis by numerous authors since high-order neural networks have stronger approximation property, faster convergence rate, greater storage capacity, and higher fault tolerance than lower-order neural networks [4 8].

To perform this analysis for the LIF model, we make a fairly strong approximation.

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