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Institution

University of Massachusetts Boston

EducationBoston, Massachusetts, United States
About: University of Massachusetts Boston is a education organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Population & Health care. The organization has 6541 authors who have published 12918 publications receiving 411731 citations. The organization is also known as: UMass Boston.


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Journal ArticleDOI
TL;DR: Comparison of obtained Vineland socialization scores to those predicted by CA or MA may be useful in clarifying the diagnosis of autism, suggest findings.
Abstract: Traditional approaches to diagnosing autism emphasize delays in communication and socialization Traditional diagnostic schemes typically list symptoms (eg, lack of eye contact), but provide little guidance on how to incorporate information about developmental level in making a diagnosis Because standardized measures of adaptive behavior can provide information about children's communication, socialization, and other behavior relative to their age, they may be useful tools for diagnosing autism This study investigated the ability of the Vineland Adaptive Behavior Scales to identify children with autism Vineland scores and measures of intellectual functioning were obtained for children with autism, PDDNOS, and other developmental disorders (DD) Discriminant function analyses indicated that the autism and combined nonautism (PDDNOS and DD) groups could be differentiated on the basis of socialization, daily living skills, and serious maladaptive behaviors Socialization alone accounted for 48% of the variance in diagnosis Using regression analyses derived from a large normative sample, adaptive behavior scores were predicted from chronological age (CA) and mental age (MA) Socialization scores in the autism group were substantially below the level predicted from CA or MA An index derived from the ratio of actual to predicted socialization scores correctly classified 86% of both autism and nonautism cases Findings suggest that comparison of obtained Vineland socialization scores to those predicted by CA or MA may be useful in clarifying the diagnosis of autism

130 citations

Journal ArticleDOI
TL;DR: The authors argue that Peters's ideal is inadequate for men as well as women and furthermore, furthermore, its inadequacy for men is intimately connected to the injustice it does women, and explore some of the requirements an adequate ideal must satisfy.
Abstract: R. S. Peters calls it an ideal.’ So do Nash, Kazemias and Perkinson who, in their introduction to a collection of studies in the history of educational thought, say that one cannot go about the business of education without it.‘ Is it the good life? the responsible citizen? personal autonomy? No, it is the educated man. The educated man! In the early 1960s when I was invited to contribute to a book of essays to be entitled The Educated Man, I thought nothing of this phrase. By the early 1970s I felt uncomfortable whenever I came across it, but I told myself it was the thought not the words that counted. It is now the early 1980s. Peters’s use of the phrase “educated man” no longer troubles me for I think it fair to say that he intended it in a gender-neutral way.3 Despite one serious lapse which indicates that on some occasions he was thinking of his educated man as male, I do not doubt that the ideal he set forth was meant for males and females alike.4 Today my concern is not Peters’s language but his conception of the educated man or person, as I will henceforth say. I will begin by outlining Peters’s ideal for you and will then show that it does serious harm to women. From there I will go on to argue that Peters’s ideal is inadequate for men as well as women and, furthermore, that its inadequacy for men is intimately connected to the injustice it does women. In conclusion I will explore some of the requirements an adequate ideal must satisfy. Let me explain at the outset that I have chosen to discuss Peters’s ideal of the educated person here because for many years Peters has been perhaps the dominant figure in philosophy of education. Moreover, although Peters’s ideal is formulated in philosophically sophisticated terms, it is certainly not idiosyncratic. On the contrary, Peters claims to have captured our concept of the educated person, and he may well have done so. Thus, I think it fair to say that the traits Peters claims one must possess to be a truly educated person and the kind of education he assumes one must have in order to acquire those traits would, with minor variations, be cited by any number of people today if they were to describe their own conception of the ideal. I discuss Peters’s ideal, then, because it has significance for the field of philosophy of education as a whole.

130 citations

Journal ArticleDOI
TL;DR: In this paper, the authors assess cumulative survival rates and identify independent predictors of mortality in patients with incident systemic sclerosis (SSc)associated pulmonary arterial hypertension (PAH) who had undergone routine screening for PAH at SSc centers in the US.
Abstract: Objective To assess cumulative survival rates and identify independent predictors of mortality in patients with incident systemic sclerosis (SSc)–associated pulmonary arterial hypertension (PAH) who had undergone routine screening for PAH at SSc centers in the US. Methods The Pulmonary Hypertension Assessment and Recognition of Outcomes in Scleroderma registry is a prospective registry of SSc patients at high risk for PAH or with definite pulmonary hypertension diagnosed by right-sided heart catheterization within 6 months of enrollment. Only patients with World Health Organization group I PAH (mean pulmonary artery pressure ≥25 mm Hg and pulmonary capillary wedge pressure ≤15 mm Hg without significant interstitial lung disease) were included in these analyses. Results In total, 131 SSc patients with incident PAH were followed for a mean ± SD of 2.0 ± 1.4 years. The 1-, 2-, and 3-year cumulative survival rates were 93%, 88%, and 75%, respectively. On multivariate analysis, age >60 years (hazard ratio [HR] 3.0, 95% confidence interval [95% CI] 1.1–8.4), male sex (HR 3.9, 95% CI 1.1–13.9), functional class (FC) IV status (HR 6.5, 95% CI 1.8–22.8), and diffusing capacity for carbon monoxide (DLco) <39% predicted (HR 4.2, 95% CI 1.3–13.8) were significant predictors of mortality. Conclusion This is the largest study describing survival in patients with incident SSc-associated PAH followed up at multiple SSc centers in the US who had undergone routine screening for PAH. The survival rates were better than those reported in other recently described SSc-associated PAH cohorts. Severely reduced DLco and FC IV status at the time of PAH diagnosis portended a poor prognosis in these patients.

130 citations

Journal ArticleDOI
TL;DR: Mental health providers may benefit clients by utilizing interventions that challenge internalized stereotypes about homosexuality, increase social support, and process parental rejection, as well as focusing on how certain crucial experiences of rejection may impact clients' IH and mental health.
Abstract: Sexual minority individuals face unique stressors because of their sexual identity. We explored associations between parental reactions to children's coming out, internalized homophobia (IH), social support, and mental health in a sample of 257 sexual minority adults. Path analyses revealed that higher IH and lower social support mediated the association between past parental rejection and current psychological distress. Mental health providers may benefit clients by utilizing interventions that challenge internalized stereotypes about homosexuality, increase social support, and process parental rejection, as well as focusing on how certain crucial experiences of rejection may impact clients' IH and mental health.

130 citations

Journal ArticleDOI
Kim Albertsson1, Piero Altoè2, Dustin Anderson3, Michael Benjamin Andrews4, Juan Pedro Araque Espinosa, Adam Aurisano5, Laurent Basara, Adrian John Bevan6, Wahid Bhimji7, Daniele Bonacorsi8, Paolo Calafiura7, Mario Campanelli6, Louis Capps2, Federico Carminati9, Stefano Carrazza9, Taylor Childers10, Elias Coniavitis11, Kyle Cranmer12, Claire David, Douglas Davis13, Javier Duarte14, Martin Erdmann15, Jonas Nathanael Eschle16, Amir Farbin17, Matthew Feickert18, Nuno Filipe Castro, Conor Fitzpatrick19, Michele Floris9, Alessandra Forti20, Jordi Garra-Tico21, J. Gemmler22, Maria Girone9, Paul Glaysher, Sergei Gleyzer23, Vladimir Gligorov24, Tobias Golling25, Jonas Graw2, Lindsey Gray14, Dick Greenwood26, Thomas J. Hacker27, John T Harvey9, Benedikt Hegner9, Lukas Heinrich12, Benjamin Henry Hooberman28, Johannes Josef Junggeburth29, Michael Kagan14, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński9, Zahari Kassabov30, Gaurav Kaul31, Dorian Kcira3, T. Keck22, Alexei Klimentov32, Jim Kowalkowski14, L. Kreczko33, A. B. Kurepin34, Rob Kutschke14, Valentin Kuznetsov35, Nicolas Maximilian Köhler29, Igor Lakomov9, Kevin Lannon36, Mario Lassnig9, Antonio Limosani37, Gilles Louppe12, Aashrita Mangu38, Pere Mato9, H. Meinhard9, Dario Menasce39, Lorenzo Moneta9, Seth Moortgat40, Meenakshi Narain41, Mark Neubauer42, Harvey B Newman3, Hans Pabst31, Michela Paganini43, Manfred Paulini4, Gabriel Perdue14, Uzziel Perez44, Attilio Picazio45, Jim Pivarski46, Harrison Prosper47, Fernanda Psihas48, A. Radovic49, Ryan Reece50, A. Rinkevicius35, Eduardo Rodrigues5, Jamal Rorie51, David Rousseau52, Aaron G. Sauers14, Steven Schramm25, Ariel Schwartzman14, Horst Severini53, Paul Seyfert9, Filip Siroky54, Konstantin Skazytkin34, M. D. Sokoloff5, Graeme Stewart55, Bob Stienen56, Ian Stockdale57, Giles Strong, Savannah Jennifer Thais43, Karen Tomko58, Eli Upfal41, Emanuele Usai41, Andrey Ustyuzhanin59, Martin Vala60, Sofia Vallecorsa61, J. Vasel48, Mauro Verzetti62, Xavier Vilasis-Cardona63, Jean Roch Vlimant3, Ilija Vukotic64, Sean Jiun Wang23, Gordon Watts65, Michael Williams66, Wenjing Wu67, Stefan Wunsch22, Omar Zapata68 
Luleå University of Technology1, Nvidia2, California Institute of Technology3, Carnegie Mellon University4, University of Cincinnati5, University of London6, Lawrence Berkeley National Laboratory7, Istituto Nazionale di Fisica Nucleare8, CERN9, Argonne National Laboratory10, University of Freiburg11, New York University12, Duke University13, Fermilab14, RWTH Aachen University15, University of Zurich16, University of Texas at Arlington17, Southern Methodist University18, École Polytechnique Fédérale de Lausanne19, University of Manchester20, University of Cambridge21, Karlsruhe Institute of Technology22, University of Florida23, Centre national de la recherche scientifique24, University of Geneva25, Louisiana Tech University26, Purdue University27, University of Illinois at Urbana–Champaign28, Max Planck Society29, University of Milan30, Intel31, Brookhaven National Laboratory32, University of Bristol33, Russian Academy of Sciences34, Cornell University35, University of Notre Dame36, University of Melbourne37, University of California, Berkeley38, University of Milano-Bicocca39, Vrije Universiteit Brussel40, Brown University41, National Center for Supercomputing Applications42, Yale University43, University of Alabama44, University of Massachusetts Boston45, Princeton University46, Florida State University47, Indiana University48, College of William & Mary49, University of California, Santa Cruz50, Rice University51, University of Paris52, University of Oklahoma53, Masaryk University54, University of Glasgow55, Radboud University Nijmegen56, Altair Engineering57, Ohio Supercomputer Center58, Yandex59, Technical University of Košice60, Gangneung–Wonju National University61, University of Rochester62, University of Barcelona63, University of Chicago64, University of Washington65, Massachusetts Institute of Technology66, Chinese Academy of Sciences67, University of Antioquia68
08 Jul 2018
TL;DR: Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applicatio ...
Abstract: Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

130 citations


Authors

Showing all 6667 results

NameH-indexPapersCitations
Derek R. Lovley16858295315
Wei Li1581855124748
Susan E. Hankinson15178988297
Roger J. Davis147498103478
Thomas P. Russell141101280055
George Alverson1401653105074
Robert H. Brown136117479247
C. Dallapiccola1361717101947
Paul T. Costa13340688454
Robert R. McCrae13231390960
David Julian McClements131113771123
Mauro Giavalisco12841269967
Benjamin Brau12897172704
Douglas T. Golenbock12331761267
Zhifeng Ren12269571212
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202367
2022131
2021833
2020851
2019823
2018776