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The results obtained show a significant acceleration compared with the direct implementation of the algorithms on the GPU.
A careful tuning of the diarylboronic acid structure led to the discovery that introducing a trifluoromethyl group at the para position brought about a significant rate acceleration compared with the unsubstituted molecule.
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Furthermore, the 4-thread mode achieved about a 3.5X acceleration compared with single-thread mode, while the 8-thread mode achieved an almost 6X acceleration, indicating that our multi-threading implementation is efficient.
These partial r are plotted in Figure 6 A. The majority of FEF neurons antidromically activated following rNRTP stimulation have the largest contributions from eye acceleration compared with eye position, velocity, or retinal error motion during step-ramp tracking (Fig. 6 A, red circles).
Although the set of polarized DA blocks is not enriched for acceleration compared with all other MAF blocks (FET: P = 0.29, OR = 1.34 [95% CI, 0.67 2.39, ]; see row two in table 3), we see a clear pattern among polarized DA blocks themselves.
DA blocks near daughters are significantly enriched for acceleration compared with DA blocks near parents (FET: P = 0.001, OR = 7.82 [95% CI, 1.98 36.43]; see row three in table 3).
Comparing rates of acceleration in both sets, we found that nonchained DA blocks are significantly enriched for acceleration compared with chained ones (FET: P < 1 × 10−15, OR = 4.8 [95% CI, 3.48 6.67]; see row four in table 3).
The resulting parameters are applied to finite element analysis and the calculated time histories of accelerations are compared with the experimental results to examine the validity of these identified parameters.
This algorithm achieved an impressive acceleration in convergence compared with the use of a fixed set of selected training samples.
These classifications are based on the fetal heart rate, its variability and the presence of accelerations or decelerations, compared with the occurrence of uterine contractions.
These results are compatible with a Bayesian interpretation of the estimate of gravitational acceleration, if one assumes that the variance in the prior of 1 g acceleration is very small compared with the variance in the sensory likelihood.
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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