Sentence examples for unified random from inspiring English sources
For automated imaging, the unified random sampling module was utilized, 63 randomized images of each sample were recorded, and at least 500 single cells from 3 independent experiments from 3 different parabolas were analyzed.
The remaining 373 children were drawn from a unified random sample distribution generated using the RANUNI command in SAS V.9.0 (SAS Institute, Cary, North Carolina, USA).
In this article, we propose iRafNet a new algorithm in which different data types are integrated under a unified random forest framework.
To overcome this problem, as future work, we consider to design a new model where the contribution of each data source is estimated and appropriately weighted within the unified random forest framework.
Mr. Wasik, who said that he initially relied on e-mail chains to organize a mob, believes that while the purpose has changed somewhat, the use of technology, like smartphones and social media, to pull together large groups of people to perform a random, unifying act holds true to his original mob vision.
It is not so reasonable to use a single unified rather than adaptable perception threshold for a random natural stereoscopic image as the texture complexity typically varies in different blocks of image.
Unsurprisingly, the overall feeling you get from flicking through an edition is not a cohesive, editorially unified whole, but an algorithmically generated bunch of mostly random stories with (at best) a few loose, overlapping themes.
The threshold crossing procedure of the receiver for the noise-only case and the case when a pulse plus noise is received has been unified to a common first-crossing problem in a continuous random process.
In this paper we review these four random-motion models–henceforth termed "fractional motions" –via a unified physical setting that is based on Langevin's equation, the Einstein Smoluchowski paradigm, and stochastic scaling limits.
In the second level, non-dominant solutions obtained from sub-population bi-objective random key genetic algorithm (SBG) in the first level will be unified as one big population.
As shown in the feature of Tables 1 and 2, the normal random imputation dataset (nr.i) resulted in a considerably more stable selection of features compared to the mean imputation unified dataset (m.i).
