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

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

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

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

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