565 research outputs found
Profile Likelihood Biclustering
Biclustering, the process of simultaneously clustering the rows and columns
of a data matrix, is a popular and effective tool for finding structure in a
high-dimensional dataset. Many biclustering procedures appear to work well in
practice, but most do not have associated consistency guarantees. To address
this shortcoming, we propose a new biclustering procedure based on profile
likelihood. The procedure applies to a broad range of data modalities,
including binary, count, and continuous observations. We prove that the
procedure recovers the true row and column classes when the dimensions of the
data matrix tend to infinity, even if the functional form of the data
distribution is misspecified. The procedure requires computing a combinatorial
search, which can be expensive in practice. Rather than performing this search
directly, we propose a new heuristic optimization procedure based on the
Kernighan-Lin heuristic, which has nice computational properties and performs
well in simulations. We demonstrate our procedure with applications to
congressional voting records, and microarray analysis.Comment: 40 pages, 11 figures; R package in development at
https://github.com/patperry/biclustp
Writing Their Faith into the Law of the Land: Jehovah\u27s Witnesses, the Supreme Court and the Battle for the Meaning of the Free Exercise Clause, 1939-1945
The article traces the development of free exercise jurisprudence through the battles of Jehovah\u27s Witnesses before the Court, and the battles on the Court between Justices Black, Douglas and Frankfurter to establish their constitutional faiths as the law of the land during a brief period in the early 1940\u27s when these issues came before the Court in a flurry of decisions, then disappeared
Science Panel Discussion presentation: You Want to Do What? Managing and Distributing Identifying Data without Running Afoul of Your Research Sponsor, Your IRB, or Your Office of Counsel
Patrick Flynn, PhD, is Professor, Department of Computer Science and Engineering, University of Notre Dame. He discussed the data management for his university\u27s biometric data sampling projects, including human subjects issues, data distribution, and infrastructure
Self-Affirmation and Perspective Taking in Organizations: An Integrated Framework for Examining Process-Oriented Phenomena as Trajectories of Change
Individuals’ perceptions of their fit within in an organization unfold as a process over time that is subject to influence and change. This dissertation is a program of research that takes a process-oriented approach to understanding change from patterns of outcome trajectories and trajectory changes. Appendix A presents a study that introduces a conceptual framework for a temporal approach to change. Appendix A showed that strong events serve to change the trajectory of individuals’ affective commitment. Appendix B presents a first intervention study with surprising results where instead of self-affirmation, perspective taking appeared to facilitate positive trajectory changes in individuals’ identification with, commitment to, and intent to remain in their organization. The present study aimed to replicate and extend the surprising results. I integrated self-affirmation theory and motivated information processing to my conceptual change framework to design a new set of intervention procedures that were hypothesized to facilitate growth in individuals’ organizational attachment and pro-organizational interpersonal behaviors. The results show a lack of significant support for the majority of the theoretical predicts. Implications and future directions are discussed
Myeloid-derived suppressor cell, arginase-1, IL-17 and cl-CD95L: an explosive cocktail in lupus?
International audienceComment on: Wu H, Zhen Y, Ma Z, et al. Arginase-1-dependent promotion of TH17 differentiation and disease progression by MDSCs in systemic lupus erythematosus. Sci Transl Med 2016;8:331ra40
Movement demands and perceived wellness associated with preseason training camp in NCAA Division I college football players
The aims of this study were to examine the movement demands of preseason practice in National Collegiate Athletic Association Division I college football players using portable global positioning system (GPS) technology and to assess perceived wellness associated with preseason practice to determine whether GPS-derived variables from the preceding day influence perceived wellness the following day. Twenty-nine players were monitored using GPS receivers (Catapult Innovations, Melbourne, Australia) during 20 preseason practices. Individual observations (n = 550) were divided into offensive and defensive position groups. Movement variables including low-, medium-, high-intensity, and sprint distance, player load, and acceleration and deceleration distance were assessed. Perceived wellness ratings (n = 469) were examined using a questionnaire which assessed fatigue, soreness, sleep quality, sleep quantity, stress, and mood. A 1-way analysis of variance for positional movement demands and multilevel regressions for wellness measures were used, followed by post hoc testing to evaluate the relational significance between categorical outcomes of perceived wellness scores and movement variables. Results demonstrated significantly (p ≤ 0.05) greater total, high-intensity, and sprint distance, along with greater acceleration and deceleration distances for the defensive back and wide receiver position groups compared with their respective offensive and defensive counterparts. Significant (p ≤ 0.05) differences in movement variables were demonstrated for individuals who responded more or less favorably on each of the 6 factors of perceived wellness. Data from this study provide novel quantification of the position-specific physical demands and perceived wellness associated with college football preseason practice. Results support the use of position-specific training and individual monitoring of college football players
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