no code implementations • 6 Jun 2022 • Jack D. Saunders, Alex, A. Freitas
Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances.
no code implementations • 24 Oct 2013 • M. Baak, A. Blondel, A. Bodek, R. Caputo, T. Corbett, C. Degrande, O. Eboli, J. Erler, B. Feigl, A. Freitas, J. Gonzalez Fraile, M. C. Gonzalez-Garcia, J. Haller, J. Han, S. Heinemeyer, A. Hoecker, J. L. Holzbauer, S. -C. Hsu, B. Jaeger, P. Janot, W. Kilian, R. Kogler, A. Kotwal, P. Langacker, S. Li, L. Linssen, M. Marx, O. Mattelaer, J. Metcalfe, K. Monig, G. Moortgat-Pick, M. -A. Pleier, C. Pollard, M. Ramsey-Musolf, M. Rauch, J. Reuter, J. Rojo, M. Rominsky, W. Sakumoto, M. Schott, C. Schwinn, M. Sekulla, J. Stelzer, E. Torrence, A. Vicini, D. Wackeroth, G. Weiglein, G. Wilson, L. Zeune
In this report, we investigate two themes in the arena of precision electroweak measurements: the electroweak precision observables (EWPOs) that test the particle content and couplings in the SM and the minimal supersymmetric SM, and the measurements involving multiple gauge bosons in the final state which provide unique probes of the basic tenets of electroweak symmetry breaking.
High Energy Physics - Phenomenology
