Sentence examples for this model generates from inspiring English sources

Instead of having a parameter p f to terminate the generation of a thread, as in Kumar et al. [83], this model generates threads of a given size, drawn from the empirical distribution of a dataset of conversation threads.

This model generates a polygon feature class, containing rectangular features with a unique shape that represents the clean-up parcels.

For each departure date, this model generates a full tree of possible transfers from the departure to the destination planet.

Optimization in this model generates sensitivity messages which are exchanged among sector controllers and which are used to adjust power allocations.

This model generates a simulation of what a person with a given contrast sensitivity would see when looking at an image.

A single class I disparity in this model generates chronic rejection associated with potent CD4+ T-cell responses induced by the indirect recognition pathway.

Simulations using this model generate a large body of informative results, but they have shortcomings.

This model generated four important estimates: 1.

This model generated nine rules with 71.29% ± 11.52% and 79.31% ± 13.25% for accuracy and precision, respectively.

However, numerically this model generated many problems, which suggested empirical under-identification.

This model generated 15 rules, with an accuracy of 61.33% ± 14.26% and a precision of 72.98% ± 14.64%, respectively.