Sentence examples for samples were generated from from inspiring English sources
Speech samples were generated from two sentence groups (A and B), each comprising four sentences.
The samples were generated from breast tissue biopsy slides, stained with hematoxylin and eosin.
Positive examples come from the labeled upright faces in the wild database [17], and negative samples were generated from 1,310 images containing no face.
The positive examples again come from the labeled upright faces in the wild database, and negative samples were generated from 2,500 images containing no face.
Four-week-old wild-type (WT), OsWRKY80-overexpression (line 5-2) and RNAi (line 3-1) plants were inoculated with R. solani race GD118, and RNA samples were generated from the fourth leaves of each genotype.
RNA samples were generated from three independent biological replicates.
In the experiment, a test set of 2000 samples is generated from uniform priors for and with each noise variance ( and ).
After training the first RBM, a sufficiently large number of samples are generated from the joint distribution p u (mid) h)as the input data for the next RBM in hierarchy (step 3 of Algorithm 2).
The second set of samples was generated from unstimulated whole blood culture as described above.
Non-parametric bootstrap samples are generated from the original data set by drawing with replacement.
The samples are generated from genomes of two species (Eubacterium eligens and Lactobacillus amylovorus), but they have different abundance ratios.
