Sentence examples for and for the generalization from inspiring English sources

Theorem  1.3 in [7] is a special case of Corollary 2.16 for the same reasons in Remark 2.9 II) and for the generalization of the conditions (1.6) and (1.7) in [7, Theorem  1.3] to the condition (iii) in Corollary 2.16.

Further, the application of priming as a means of exposure to assist in transitions or to new situations (Schreibman, Whalen, & Stahmer, 2000), and programming for the generalization of skills to new situations (Kleeberger & Mirenda, 2010), is viewed as an evidence-based strategy in applied behavior analysis for children with ASD.

The results of our feasibility study [ 80] allowed us to derive training group effect sizes; these ranged from 0.45-1.1 on the SocialVille measures, from 0.53-0.77 for the social cognitive measures and from 0.4-0.67 for the generalization and functional measures.

Upon completion of synthetic dye decolorization investigation, wastewater was collected from ten local textile units and tested for decolorization and cytotoxicity reduction by MnP-I-PVAA and F-MnP for the generalization of results.

This was true both for hits and for correct rejections, and the generalization also held for both word lists (Experiment 1) and sets of faces (Experiment 2).

First, the study has a large sample size of office-based workers with varying educational levels and job grades allowing for the generalization of findings.

The former is a natural extension of the conventional generative model (9) and has often been used for the generalization of Laplace IVA or time-varying Gaussian IVA as GGD-IVA.

Specifically, as we have provided backing for the Generalization, Extrapolation, and Utilization inferences, we should in the future examine other areas related to the Domain representation, Evaluation, Explanation, and Extrapolation inferences, as well as testing the consequences in the Utilization inference (see Xi, 2010, for specific questions to be examined).

Despite these, the 95% confidence interval of AUC was the same for both models ([0.722 0.831] for BL versus [0.721 0.830] for LR) and the generalization power was similar.

The AIC and CV techniques were used to calculate the value of AIC and the generalization error for the elastic and Newtonian viscous matrix models for each data set.

Traditional feature extraction algorithms usually utilize shallow architectures, which result in the limited capability to characterize HRRP data and restrict the generalization performance for RATR.