Sentence examples for a robust discrimination between from inspiring English sources
Gouyon et al. assume a given segmentation of the song on the beat level and then focus on a robust discrimination between duple and triple meter [11] on the measure level.
The main contribution of this work lies in the proposal of a methodology for determining the Regions of Interest (ROI's) and feature extraction, which result in a robust discrimination between people with or without accessories and objects (either static or dynamic), even when people and objects are close together.
In addition, a robust discrimination between 22q11-ASD and KS-ASD and idiopathic ASD phenotypes was feasible on the basis of a reduced number of autistic scales and symptoms.
The miRDeep, however, involves comparison of (posterior probabilities of) MFE of real and background hairpins, enabling a more robust discrimination between them.
Using the model overlap (expected likelihood) as a robust discrimination criterion to measure dissimilarities between expected model response PDFs, we benchmark the proposed nonlinear design approach against linearization with respect to prediction accuracy and design quality for two nonlinear biological reaction networks.
Here, we present the model overlap as a robust discrimination criterion, measuring dissimilarities of model response PDFs used to rate the discriminative power of a design during optimization.
For example, BWASW, SHRiMP2, SMALT, SSAHA2 and TMAP, might perform particularly well for sequencing focused on rare variant discovery because they show a robust discrimination of variations.
The authors in [22] incorporate the DOA information in the a priori DSPP q s, to provide more robust discrimination between desired and undesired speakers.
The resulting assay allowed robust discrimination between the perfect match and a three-base mismatch sequence.
This highlighted that the newly adopted set-ups enable robust discrimination between true binders and nonbinders, which is a critical requisite in biophysical fragment screening.
Compared with physical and mental component summary scores, the patient-generated symptom severity classification scheme showed robust discrimination between mild and moderate severity (p <0.0001 and p = 0.0009) and between moderate and severe groups (p = 0.0001 and p = 0.012).
