Harish-Chandra modules over the \Q Heisenberg-Virasoro Algebra

In this paper, it is proved that all irreducible Harish-Chandra modules over the \Q Heisenberg-Virasoro algebra are of intermediate series (all weight spaces are 1-dimensional)

Axion-assisted Resonance Oscillation Rescues the Dodelson-Widrow Mechanism

The $\rm{keV}$ scale sterile neutrino was a qualified candidate for dark matter particles in the Dodelson-Widrow mechanism. But the mixing angle, needed to provide enough amount of dark matter, is in contradiction with the astrophysical observations. To alleviate such tension, we introduce an effective interaction, i.e. $g_a (\phi/\Lambda)\partial_{\mu}a \overline{\nu_\alpha}\gamma^{\mu} \gamma_5 \nu_\alpha$, among Standard Model neutrino $\nu_\alpha$, axion $a$, and singlet $\phi$. The axial-vector interaction form is determined by the axion shift symmetry, and the singlet $\phi$ with dynamically varied vacuum expectation value is introduced to reinforce the axial-vector coupling strength and evade the stringent neutrino oscillation constraints. The effective potential generated by the new interaction {could cancel} the SM counterpart, resulting in an {enhanced converting} probability between SM neutrino and sterile neutrino. Hence, the production rate of sterile neutrinos can be substantially enlarged with smaller mixing compared to the DW mechanism.Comment: 5 pages, 2 figure

Correlating Gravitational Waves with WW-boson Mass, FIMP Dark Matter, and Majorana Seesaw Mechanism

We study a minimal extension of the Standard Model by introducing three right-handed neutrinos and a new scotogenic scalar doublet, in which the mass splittings between neutral and charged components are responsible for the $W$-boson mass newly measured by the CDF collaboration. This model can not only generate non-vanishing Majorana neutrino masses via the interaction of right-handed neutrinos and scotogenic scalars, but also explain the Universe's missing matter in the form of FIMP dark matter. We also study the influence of the mass splitting on the first order electroweak phase transition, and find that it can further enhance the transition strength and thus induce gravitational waves during the phase transition, which may be detected in the forthcoming detectors such as U-DECIGO.Comment: References updated, accepted for publication in Science Bulleti

Adaptive Pattern Extraction Multi-Task Learning for Multi-Step Conversion Estimations

Multi-task learning (MTL) has been successfully used in many real-world applications, which aims to simultaneously solve multiple tasks with a single model. The general idea of multi-task learning is designing kinds of global parameter sharing mechanism and task-specific feature extractor to improve the performance of all tasks. However, challenge still remains in balancing the trade-off of various tasks since model performance is sensitive to the relationships between them. Less correlated or even conflict tasks will deteriorate the performance by introducing unhelpful or negative information. Therefore, it is important to efficiently exploit and learn fine-grained feature representation corresponding to each task. In this paper, we propose an Adaptive Pattern Extraction Multi-task (APEM) framework, which is adaptive and flexible for large-scale industrial application. APEM is able to fully utilize the feature information by learning the interactions between the input feature fields and extracted corresponding tasks-specific information. We first introduce a DeepAuto Group Transformer module to automatically and efficiently enhance the feature expressivity with a modified set attention mechanism and a Squeeze-and-Excitation operation. Second, explicit Pattern Selector is introduced to further enable selectively feature representation learning by adaptive task-indicator vectors. Empirical evaluations show that APEM outperforms the state-of-the-art MTL methods on public and real-world financial services datasets. More importantly, we explore the online performance of APEM in a real industrial-level recommendation scenario.Comment: 18 pages, 9 figure

Farnesoid X Receptor (FXR) Aggravates Amyloid-β-Triggered Apoptosis by Modulating the cAMP-Response Element-Binding Protein (CREB)/Brain-Derived Neurotrophic Factor (BDNF) Pathway In Vitro

BACKGROUND: Alzheimer’s disease (AD), which results in cognitive deficits, usually occurs in older people and is mainly caused by amyloid beta (Aß) deposits and neurofibrillary tangles. The bile acid receptor, farnesoid X receptor (FXR), has been extensively studied in cardiovascular diseases and digestive diseases. However, the role of FXR in AD is not yet understood. The purpose of the present study was to investigate the mechanism of FXR function in AD. MATERIAL AND METHODS: Lentivirus infection, flow cytometry, real-time PCR, and western blotting were used to detect the gain or loss of FXR in cell apoptosis induced by Aß. Co-immunoprecipitation was used to analyze the molecular partners involved in Aß-induced apoptosis. RESULTS: We found that the mRNA and protein expression of FXR was enhanced in Ab-triggered neuronal apoptosis in differentiated SH-SY5Y cells and in mouse hippocampal neurons. Overexpression of FXR aggravated Aß-triggered neuronal apoptosis in differentiated SH-SY5Y cells, and this effect was further increased by treatment with the FXR agonist 6ECDCA. Molecular mechanism analysis by co-immunoprecipitation and immunoblotting revealed that FXR interacted with the cAMP-response element-binding protein (CREB), leading to decreased CREB and brain-derived neurotrophic factor (BDNF) protein levels. Low expression of FXR mostly reversed the Aß-triggered neuronal apoptosis effect and prevented the reduction in CREB and BDNF. CONCLUSIONS: These data suggest that FXR regulates Aß-induced neuronal apoptosis, which may be dependent on the CREB/BDNF signaling pathway in vitro