Partially Penalized Immersed Finite Element Methods for Elliptic Interface Problems

This article presents new immersed finite element (IFE) methods for solving the popular second order elliptic interface problems on structured Cartesian meshes even if the involved interfaces have nontrivial geometries. These IFE methods contain extra stabilization terms introduced only at interface edges for penalizing the discontinuity in IFE functions. With the enhanced stability due to the added penalty, not only these IFE methods can be proven to have the optimal convergence rate in the H1-norm provided that the exact solution has sufficient regularity, but also numerical results indicate that their convergence rates in both the H1-norm and the L2-norm do not deteriorate when the mesh becomes finer which is a shortcoming of the classic IFE methods in some situations. Trace inequalities are established for both linear and bilinear IFE functions that are not only critical for the error analysis of these new IFE methods, but also are of a great potential to be useful in error analysis for other IFE methods

Mono-Higgs Detection of Dark Matter at the LHC

Motivated by the recent discovery of the Higgs boson, we investigate the possibility that a missing energy plus Higgs final state is the dominant signal channel for dark matter at the LHC. We consider examples of higher-dimension operators where a Higgs and dark matter pair are produced through an off-shell Z or photon, finding potential sensitivity at the LHC to cutoff scales of around a few hundred GeV. We generalize this production mechanism to a simplified model by introducing a Z' as well as a second Higgs doublet, where the pseudoscalar couples to dark matter. Resonant production of the Z' which decays to a Higgs plus invisible particles gives rise to a potential mono-Higgs signal. This may be observable at the 14 TeV LHC at low tan beta and when the Z' mass is roughly in the range 600 GeV to 1.3 TeV.Comment: 11 page

Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights into the data. In this paper, we explore the structure of LSTM recurrent neural networks to learn variable-wise hidden states, with the aim to capture different dynamics in multi-variable time series and distinguish the contribution of variables to the prediction. With these variable-wise hidden states, a mixture attention mechanism is proposed to model the generative process of the target. Then we develop associated training methods to jointly learn network parameters, variable and temporal importance w.r.t the prediction of the target variable. Extensive experiments on real datasets demonstrate enhanced prediction performance by capturing the dynamics of different variables. Meanwhile, we evaluate the interpretation results both qualitatively and quantitatively. It exhibits the prospect as an end-to-end framework for both forecasting and knowledge extraction over multi-variable data.Comment: Accepted to International Conference on Machine Learning (ICML), 201

Generalized Clifford Algebras as Algebras in Suitable Symmetric Linear Gr-Categories

By viewing Clifford algebras as algebras in some suitable symmetric Gr-categories, Albuquerque and Majid were able to give a new derivation of some well known results about Clifford algebras and to generalize them. Along the same line, Bulacu observed that Clifford algebras are weak Hopf algebras in the aforementioned categories and obtained other interesting properties. The aim of this paper is to study generalized Clifford algebras in a similar manner and extend the results of Albuquerque, Majid and Bulacu to the generalized setting. In particular, by taking full advantage of the gauge transformations in symmetric linear Gr-categories, we derive the decomposition theorem and provide categorical weak Hopf structures for generalized Clifford algebras in a conceptual and simpler manner

Probing the fermionic Higgs portal at lepton colliders

We study the sensitivity of future electron-positron colliders to UV completions of the fermionic Higgs portal operator $H^\dagger H \bar \chi \chi$. Measurements of precision electroweak $S$ and $T$ parameters and the $e^+e^- \to Zh$ cross section at the CEPC, FCC-ee, and ILC are considered. The scalar completion of the fermionic Higgs portal is closely related to the scalar Higgs portal, and we summarize existing results. We devote the bulk of our analysis to a singlet-doublet fermion completion. Assuming the doublet is sufficiently heavy, we construct the effective field theory (EFT) at dimension-6 in order to compute contributions to the observables. We also provide full one-loop results for $S$ and $T$ in the general mass parameter space. In both completions, future precision measurements can probe the new states at the (multi-)TeV scale, beyond the direct reach of the LHC.Comment: 33 pages, 5 figures. Published versio