Eliminating Redundant Training Data Using Unsupervised Clustering Techniques

Training data for supervised learning neural networks can be clustered such that the input/output pairs in each cluster are redundant. Redundant training data can adversely affect training time. In this paper we apply two clustering algorithms, ART2 -A and the Generalized Equality Classifier, to identify training data clusters and thus reduce the training data and training time. The approach is demonstrated for a high dimensional nonlinear continuous time mapping. The demonstration shows six-fold decrease in training time at little or no loss of accuracy in the handling of evaluation data

QCD and strongly coupled gauge theories : challenges and perspectives

We highlight the progress, current status, and open challenges of QCD-driven physics, in theory and in experiment. We discuss how the strong interaction is intimately connected to a broad sweep of physical problems, in settings ranging from astrophysics and cosmology to strongly coupled, complex systems in particle and condensed-matter physics, as well as to searches for physics beyond the Standard Model. We also discuss how success in describing the strong interaction impacts other fields, and, in turn, how such subjects can impact studies of the strong interaction. In the course of the work we offer a perspective on the many research streams which flow into and out of QCD, as well as a vision for future developments.Peer reviewe

Clinical Sequencing Exploratory Research Consortium: Accelerating Evidence-Based Practice of Genomic Medicine

Despite rapid technical progress and demonstrable effectiveness for some types of diagnosis and therapy, much remains to be learned about clinical genome and exome sequencing (CGES) and its role within the practice of medicine. The Clinical Sequencing Exploratory Research (CSER) consortium includes 18 extramural research projects, one National Human Genome Research Institute (NHGRI) intramural project, and a coordinating center funded by the NHGRI and National Cancer Institute. The consortium is exploring analytic and clinical validity and utility, as well as the ethical, legal, and social implications of sequencing via multidisciplinary approaches; it has thus far recruited 5,577 participants across a spectrum of symptomatic and healthy children and adults by utilizing both germline and cancer sequencing. The CSER consortium is analyzing data and creating publically available procedures and tools related to participant preferences and consent, variant classification, disclosure and management of primary and secondary findings, health outcomes, and integration with electronic health records. Future research directions will refine measures of clinical utility of CGES in both germline and somatic testing, evaluate the use of CGES for screening in healthy individuals, explore the penetrance of pathogenic variants through extensive phenotyping, reduce discordances in public databases of genes and variants, examine social and ethnic disparities in the provision of genomics services, explore regulatory issues, and estimate the value and downstream costs of sequencing. The CSER consortium has established a shared community of research sites by using diverse approaches to pursue the evidence-based development of best practices in genomic medicine

Characterization of cancer survivors clustered by subjective and objective cognitive function scores

Abstract Background Cancer‐related cognitive impairment (CRCI) is a prevalent condition that significantly impacts the quality of life of individuals who receive cancer treatment. Clinical management of CRCI presents challenges due to the absence of a standardized assessment. This study identified clinically relevant phenotypic clusters of CRCI based on subjective and objective cognitive function scores. Methods In this cross‐sectional study, participants were clustered using the VARCLUS™ based on subjective cognitive impairment assessed through the PROMIS® version 1.0 short‐form subscales of cognitive abilities and cognitive concerns and the CANTAB Cambridge Cognition® scores, which included measures of visuospatial working memory capacity, visual episodic memory, new learning, working memory, executive function, and sustained attention. Each cluster's characteristics were described using demographics, physical and psychosocial factors (physical function, affect, optimism, and social support), and psychoneurological symptoms (anxiety, depression, fatigue, neuropathic pain, and sleep disturbance). Results We obtained five clusters from a total of 414 participants, where 99% were female, and 93% were self‐reported white. Clusters 4 and 5 showed the highest PROMIS® cognitive abilities and the lowest measures of cognitive concern, while Clusters 1 and 2 showed the lowest cognitive abilities and the highest cognitive concerns. Clusters 4 and 5 had higher education, income, employment, and higher scores in physical function, positive affect, optimism, and social support. Additionally, individuals in these clusters were less prone to experience severe cancer‐related psychoneurological symptoms. Conclusion Our clustering approach, combining subjective and objective cognitive function information, shows promise in identifying phenotypes that hold clinical relevance for categorizing patient presentation of CRCI and facilitating individualized management strategies