West Virginia University

About: West Virginia University is a education organization based out in Morgantown, West Virginia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 25632 authors who have published 48308 publications receiving 1343934 citations. The organization is also known as: WVU & West Virginia University, WVU.

A new measure of cognitive flexibility

Abstract: This paper contains a description of a scale to measure cognitive flexibility. In Study 1, 247 participants completed the Cognitive Flexibility Scale, the Communication Flexibility Scale, and the R...

Treatment of Wilms' tumor. Results of the Third National Wilms' Tumor Study.

Abstract: The Third National Wilms' Tumor Study sought to reduce treatment for low-risk patients and find better chemotherapy for those at high risk for relapse. Eligible patients (1439) were randomized according to stage (I-IV) and histology (favorable [FH] or unfavorable [UH]), and contributed data to survival and relapse-free survival (RFS) analyses. Four-year (postnephrectomy) survival percentages and randomized treatment regimens for low-risk patients were 96.5% for 607 Stage I/FH patients who received dactinomycin (Actinomycin D [AMD], Merck Sharp & Dohme, West Point, PA) and vincristine (VCR) for 10 weeks versus 6 months; 92.2% for 278 Stage II/FH patients; and 86.9% for 275 Stage III/FH patients who received AMD + VCR +/- Adriamycin (ADR, Adria Laboratories, Columbus, OH) for 15 months. Stage II/FH patients also had either zero or 2000 cGy irradiation (RT) postoperatively and Stage III/FH patients either 1000 or 2000 cGy. Four-year survival was 73.0% for 279 high-risk patients (any Stage IV, all UH) who received postoperative radiation therapy (RT) and AMD + VCR + ADR +/- cyclophosphamide (CPM). Statistical analysis of survival and RFS experience shows that the less intensive therapy does not worsen results for low-risk patients and CPM does not benefit those at high risk.

Unified Deep Supervised Domain Adaptation and Generalization

Abstract: This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models. The main idea is to exploit the Siamese architecture to learn an embedding subspace that is discriminative, and where mapped visual domains are semantically aligned and yet maximally separated. The supervised setting becomes attractive especially when only few target data samples need to be labeled. In this scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by reverting to point-wise surrogates of distribution distances and similarities provides an effective solution. In addition, the approach has a high “speed” of adaptation, which requires an extremely low number of labeled target training samples, even one per category can be effective. The approach is extended to domain generalization. For both applications the experiments show very promising results.