Is it Social Influence on Beliefs Under Ambiguity? A Possible Explanation for Volatility Clustering

Influencing and being influenced by others is the very essence of human behaviour. We put forward an exploratory asset-pricing model allowing for social influence on investor judgments under ambiguity. The time series of returns generated by our model displays volatility clustering, a puzzling stylised fact observed in financial markets. This suggests that social influence on investor judgments may be playing a role in generating volatility clustering.Social Influence, Knightian Uncertainty, Ambiguity, Volatility Clustering

Higgs boson decays into {\gamma}{\gamma} and Z{\gamma} in the MSSM and BLSSM

We calculate Higgs decay rates into {\gamma}{\gamma} and Z{\gamma} in the Minimal Supersymmetric Standard Model (MSSM) and (B-L) Supersymmetric Standard Model (BLSSM) by allowing for contributions from light staus and charginos. We show that sizable departures are possible from the SM predictions for the 125 GeV state and that they are testable during run 2 at the Large Hadron Collider. Furthermore, we illustrate how a second light scalar Higgs signal in either or both these decay modes can be accessed at the CERN machine rather promptly within the BLSSM, a possibility instead precluded to the MSSM owing to the much larger mass of its heavy scalar state.Comment: Plots slightly modified, no significant chang

Prospects for Heavy Scalar Searches at the LHeC

In this article we study the prospects of the proposed Large Hadron electron Collider (LHeC) in the search for heavy neutral scalar particles. We consider a minimal model with one additional complex scalar singlet that interacts with the Standard Model (SM) via mixing with the Higgs doublet, giving rise to a SM-like Higgs boson $h_1$ and a heavy scalar particle $h_2$. Both scalar particles are produced via vector boson fusion and can be tested via their decays into pairs of SM particles, analogously to the SM Higgs boson. Using multivariate techniques we show that the LHeC is sensitive to $h_2$ with masses between 200 and 800 GeV down to scalar mixing of $\sin^2 \alpha \sim 10^{-3}$

Low scale type II seesaw: Present constraints and prospects for displaced vertex searches

The type II seesaw mechanism is an attractive way to generate the observed light neutrino masses. It postulates a SU(2)$_\mathrm{L}$-triplet scalar field, which develops an induced vacuum expectation value after electroweak symmetry breaking, giving masses to the neutrinos via its couplings to the lepton SU(2)$_\mathrm{L}$-doublets. When the components of the triplet field have masses around the electroweak scale, the model features a rich phenomenology. We discuss the current allowed parameter space of the minimal low scale type II seesaw model, taking into account all relevant constraints, including charged lepton flavour violation as well as collider searches. We point out that the symmetry protected low scale type II seesaw scenario, where an approximate "lepton number"-like symmetry suppresses the Yukawa couplings of the triplet to the lepton doublets, is still largely untested by the current LHC results. In part of this parameter space the triplet components can be long-lived, potentially leading to a characteristic displaced vertex signature where the doubly-charged component decays into same-sign charged leptons. By performing a detailed analysis at the reconstructed level we find that already at the current run of the LHC a discovery would be possible for the considered parameter point, via dedicated searches for displaced vertex signatures. The discovery prospects are further improved at the HL-LHC and the FCC-hh/SppC.Comment: 21 pages, 10 figures, 1 tabl

Comparative analysis of spatial and transform domain methods for meningioma subtype classification

Pattern recognition in histopathological image analysis requires new techniques and methods. Various techniques have been presented and some state of the art techniques have been applied to complex textural data in histological images. In this paper, we compare the novel Adaptive Discriminant Wavelet Packet Transform (ADWPT) with a few prominent techniques in texture analysis namely Local Binary Patterns (LBP), Grey Level Co-occurrence Matrices (GLCMs) and Gabor Transforms. We show that ADWPT is a better technique for Meningioma subtype classification and produces classification accuracies of as high as 90%