Immigration's Impact on Republican Political Prospects, 1980 to 2012

This Backgrounder examines the partisan political implications of large-scale immigration. A comparison of voting patterns in presidential elections across counties over the last three decades shows that mass immigration has caused a steady drop in presidential Republican vote shares, particularly in the nation's largest counties. Each one percentage-point increase in the immigrant share of a large county's population reduces the Republican share of the two-party vote by nearly 0.6 percentage points on average

"Super" Cocktails for Heavy Ion Testing

The 4.5 MeV/nucleon heavy ion cocktail at the 88-Inch Cyclotron has been expanded by incorporating beams from solid material to fill in the linear energy transfer curve. This supercocktail is available by special request and is useful when only normal incidence between the beam and the device under test is possible or desirable

Understanding U.S. regional linguistic variation with Twitter data analysis

We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S

Erwartungsbildung über den Wahlausgang und ihr Einfluss auf die Wahlentscheidung

Erwartungen über den Wahlausgang haben einen festen Platz sowohl in Rational-Choice-Theorien des Wählerverhaltens als auch in stärker sozialpsychologisch orientierten Ansätzen. Die Bildung von Erwartungen und ihr Einfluss auf die Wahlentscheidung ist dabei jedoch ein noch relativ unerforschtes Gebiet. In diesem Beitrag werden anhand von Wahlstudien für Belgien, Österreich und Deutschland verschiedene Fragen der Erwartungsbildung und ihrer Auswirkungen untersucht. Zunächst wird die Qualität der Gesamterwartungen analysiert und verschiedene Faktoren identifiziert, die einen systematischen Einfluss auf die Erwartungsbildung haben. Im zweiten Schritt wenden wir uns den Einzelerwartungen über verschiedene Parteien und Koalitionen zu und finden eine moderate Verzerrung zugunsten der präferierten Parteien und Koalitionen. Dabei kann gezeigt werden, dass der Effekt des Wunschdenkens mit dem politischen Wissen und dem Bildungsgrad abnimmt. Schließlich werden in einem letzten Schritt zwei unterschiedliche Logiken für die Auswirkungen von Erwartungen getestet, das rationale Kalkül des koalitionsstrategischen Wählens zur Vermeidung der Stimmenvergeudung sowie der sozialpsychologisch begründete Bandwagon-Effekt. Das Ausmaß an politischem Wissen scheint dabei eine zentrale vermittelnde Variable zwischen den beiden Logiken zu sein

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

A Systematic Review of Social Presence: Definition, Antecedents, and Implications

Social presence, or the feeling of being there with a “real” person, is a crucial component of interactions that take place in virtual reality. This paper reviews the concept, antecedents, and implications of social presence, with a focus on the literature regarding the predictors of social presence. The article begins by exploring the concept of social presence, distinguishing it from two other dimensions of presence—telepresence and self-presence. After establishing the definition of social presence, the article offers a systematic review of 233 separate findings identified from 152 studies that investigate the factors (i.e., immersive qualities, contextual differences, and individual psychological traits) that predict social presence. Finally, the paper discusses the implications of heightened social presence and when it does and does not enhance one's experience in a virtual environment

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Patchwork nation: sectionalism and political change in American politics/ Gimpel

xv, 428 hal.: ill, tab.; 23 cm