On The Double-Vacua Duality of Multi-Scalar Higgs and NGB-Dual Higgses in Scherk-Schwarz Breaking of 5-dimensional SU(6) Symmetry

A special condition of Scherk-Schwarz and S^1/Z2 orbifold breaking brings about both a weakly-coupled SU(6) baby Higgs and a strongly-coupled will-be simplest little Higgs scalar in the near-brane of SU(3) x SU(3)x U(1). The latter produces SU(3) VEVs and simplest little-like Higgs after triplet-triplet splitting and, under quadratic-based and non-quadratic-based Coleman-Weinberg potential, the simplest little-like Higgs yields exotic Higgses, scalar-pair and 3-scalar Higgses in the so-called one-by-one and collective breakings. A generalized non-quadratic-based Coleman-Weinberg potential utilizing a NGB-like scalar produces NGB-dual Higgses with a squared mass relevant to the components of a 3-scalar Higgs that further create a duality of 3-scalar Higgs and NGB-dual Higgses. This is due to a double-vacua property such that each vacuum responds equally to the shifts happening at either non-zero or zero-VEV vacuum

Neutrino Mixing and Flavour Changing Processes

We study the implications of a large nu_mu - nu_tau mixing angle on flavour changing transitions of quarks and leptons in supersymmetric extensions of the standard model. Two patterns of supersymmetry breaking are considered, models with modular invariance and the standard scenario of universal soft breaking terms at the GUT scale. The analysis is performed for two symmetry groups G x U(1)_F, with G=SU(5) and G=SU(3)^3, where U(1)_F is a family symmetry. Models with modular invariance are in agreement with observations only for restricted scalar quark and gaugino masses, (M_squark^2)/(m_gluino^2) \simeq 7/9 and m_bino > 350 GeV. A characteristic feature of models with large tan beta and radiatively induced flavour mixing is a large branching ratio for mu -> e gamma. For both symmetry groups and for the considered range of supersymmetry breaking mass parameters we find BR(mu -> e gamma) > 10^(-14).Comment: 25 pages, 6 figure

Individual Expert Selection and Ranking of Scientific Articles Using Document Length

Individual expert selection and ranking is a challenging research topic that has received a lot attention in recent years because of its importance related to referencing experts in particular domains and research fund allocation and management. In this work, scientific articles were used as the most common source for ranking expertise in particular domains. Previous studies only considered title and abstract content using language modeling. This study used the whole content of scientific documents obtained from Aminer citation data. The modified weighted language model (MWLM) is proposed that combines document length and number of citations as prior document probability to improve precision. Also, the author's dominance in a single document is computed using the Learning-to-Rank (L2R) method. The evaluation results using p@n, MAP, MRR, r-prec, and bpref showed a precision enhancement. MWLM improved the weighted language model (WLM) by p@n (4%), MAP (22.5%), and bpref (1.7%). MWLM also improved the precision of a model that used author dominance by MAP (4.3%), r-prec (8.2%), and bpref (2.1%)