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In den USA stehen sich heute zwei politische Lager - Demokraten und Republikaner, Liberale und Konservative - in existenzieller Feindschaft gegenüber. Doch was erklärt den tiefen ideologischen Graben, der das Land durchzieht? In seinem Buch zeigt Torben Lütjen, wie die USA in ein Land politischer Echokammern zerfielen: virtuelle und soziale Räume, die vor allem von Gleichgesinnten bevölkert werden und sich durch das Fehlen von Widerspruch ideologisch radikalisiert haben. Der Blick geht dabei vor allem nach Wisconsin, in den Mittleren Westen der USA: Hier verkörpert sich in zwei extremen Parteihochburgen von Demokraten und Republikanern paradigmatisch der Konflikt, der die modernen USA prägt. »Das Buch zeichnet sich durch sprachliche Klarheit und Präzision aus. Lütjen analysiert auf hohem wissenschaftlichen Niveau, ohne sich in Fachjargon zu verfangen. Ein gelungenes Buch, welches die politische Entwicklung in den USA nachvollziehbar erklärt und informativ beleuchtet. Ein Buch für ein breites Publikum, welches sich für die Entwicklung der us-amerikanischen Politik interessiert.« Jan Rebuschat, www.freitag.de, 26.10.2016 Besprochen in: Portal für Politikwissenschaft, 08.02.2017, Natalie Wohlleben
United States --- Politics and government. --- Government --- History, Political --- America. --- Amerika. --- Conservatism. --- Democrats. --- Demokraten. --- Echo Chambers. --- Echokammern. --- Ideologie. --- Ideology. --- Konservatismus. --- Liberalism. --- Liberalismus. --- Polarisierung. --- Polarization. --- Political Ideologies. --- Political Parties. --- Political Science. --- Political Sociology. --- Politics. --- Politik. --- Politikwissenschaft. --- Politische Ideologien. --- Politische Parteien. --- Politische Soziologie. --- Radicalization. --- Radikalisierung. --- Republicans. --- Republikaner. --- Wisconsin. --- Politik --- Polarisierung --- POLITICAL SCIENCE / History & Theory. --- Soziale Polarisierung --- Staatspolitik --- Politische Lage --- Politische Entwicklung --- Politische Situation --- USA; Polarisierung; Wisconsin; Echokammern; Ideologie; Demokraten; Republikaner; Liberalismus; Konservatismus; Radikalisierung; Politik; Amerika; Politische Soziologie; Politische Ideologien; Politische Parteien; Politikwissenschaft; Polarization; Echo Chambers; Ideology; Democrats; Republicans; Liberalism; Conservatism; Radicalization; Politics; America; Political Sociology; Political Ideologies; Political Parties; Political Science --- Wisconsin --- Politics and government
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[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.]
melanoma detection --- deep learning --- transfer learning --- ensemble classification --- 3D-CNN --- immunotherapy --- radiomics --- self-attention --- breast imaging --- microwave imaging --- image reconstruction --- segmentation --- unsupervised machine learning --- k-means clustering --- Kolmogorov-Smirnov hypothesis test --- statistical inference --- performance metrics --- contrast source inversion --- brain tumor segmentation --- magnetic resonance imaging --- survey --- brain MRI image --- tumor region --- skull stripping --- region growing --- U-Net --- BRATS dataset --- incoherent imaging --- clutter rejection --- breast cancer detection --- MRgFUS --- proton resonance frequency shift --- temperature variations --- referenceless thermometry --- RBF neural networks --- interferometric optical fibers --- breast cancer --- risk assessment --- machine learning --- texture --- mammography --- medical imaging --- imaging biomarkers --- bone scintigraphy --- prostate cancer --- semisupervised classification --- false positives reduction --- computer-aided detection --- breast mass --- mass detection --- mass segmentation --- Mask R-CNN --- dataset partition --- brain tumor --- classification --- shallow machine learning --- breast cancer diagnosis --- Wisconsin Breast Cancer Dataset --- feature selection --- dimensionality reduction --- principal component analysis --- ensemble method --- n/a
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