Signals of New Gauge Bosons in Gauged Two Higgs Doublet Model

Recently a gauged two Higgs doublet model, in which the two Higgs doublets are embedded into the fundamental representation of an extra local $SU(2)_H$ group, is constructed. Both the new gauge bosons $Z^\prime$ and $W^{\prime (p,m)}$ are electrically neutral. While $Z^\prime$ can be singly produced at colliders, $W^{\prime (p,m)}$, which is heavier, must be pair produced. We explore the constraints of $Z^\prime$ using the current Drell-Yan type data from the Large Hadron Collider. Anticipating optimistically that $Z^\prime$ can be discovered via the clean Drell-Yan type signals at high luminosity upgrade of the collider, we explore the detectability of extra heavy fermions in the model via the two leptons/jets plus missing transverse energy signals from the exotic decay modes of $Z^\prime$. For the $W^{\prime (p,m)}$ pair production in a future 100 TeV proton-proton collider, we demonstrate certain kinematical distributions for the two/four leptons plus missing energy signals have distinguishable features from the Standard Model background. In addition, comparisons of these kinematical distributions between the gauged two Higgs doublet model and the littlest Higgs model with T-parity, the latter of which can give rise to the same signals with competitive if not larger cross sections, are also presented.Comment: 39 pages, 23 figures, 7 tables and two new appendixes, to appear in EPJ

DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization

Recent algorithms designed for reinforcement learning tasks focus on finding a single optimal solution. However, in many practical applications, it is important to develop reasonable agents with diverse strategies. In this paper, we propose Diversity-Guided Policy Optimization (DGPO), an on-policy framework for discovering multiple strategies for the same task. Our algorithm uses diversity objectives to guide a latent code conditioned policy to learn a set of diverse strategies in a single training procedure. Specifically, we formalize our algorithm as the combination of a diversity-constrained optimization problem and an extrinsic-reward constrained optimization problem. And we solve the constrained optimization as a probabilistic inference task and use policy iteration to maximize the derived lower bound. Experimental results show that our method efficiently finds diverse strategies in a wide variety of reinforcement learning tasks. We further show that DGPO achieves a higher diversity score and has similar sample complexity and performance compared to other baselines

Impact of Heavy Metals in Ambient Air in Insulin Resistance of Shipyard Welders in Northern Taiwan

Exposure to metals poses potential health risks, including insulin resistance (IR), to those exposed to them in excess. Limited studies have examined such risks in occupational workers, including welders, and these have yielded inconsistent results. Thus, we examined the associations between exposure to welding metals and IR in welders. We recruited 78 welders and 75 administrative staff from a shipyard located in northern Taiwan. Personal exposure to heavy metals, including chromium (Cr), manganese (Mn), iron (Fe), nickel (Ni), copper (Cu), zinc (Zn), and cadmium (Cd), was monitored through particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) and urine analysis by inductively coupled plasma mass spectrometry (ICP–MS). After each participant fasted overnight, blood samples were collected and analyzed for IR assessment through updated homeostasis model assessment (HOMA2) modeling. Air sampling in the personal breathing zone was performed during a Monday shift prior to the blood and urine sample collection the following morning. The welders’ median personal Cr, Mn, Fe, Ni, Cu, and Zn airborne PM2.5 levels and urinary Cd levels were significantly higher than those of the administrative staff. After adjustment for covariates, logarithmic PM2.5-Mn, PM2.5-Fe, PM2.5-Cu, and PM2.5-Zn levels were positively correlated with logarithmic fasting plasma glucose (P-FGAC) levels (PM2.5-Mn: β = 0.0105, 95% C.I.: 0.0027–0.0183; PM2.5-Fe: β = 0.0127, 95% C.I.: 0.0027–0.0227; PM2.5-Cu: β = 0.0193, 95% C.I.: 0.0032–0.0355; PM2.5-Zn: β = 0.0132, 95% C.I.: 0.0005–0.0260). Logarithmic urinary Zn was positively correlated with logarithmic serum insulin and HOMA2-IR levels and negatively correlated with logarithmic HOMA2-insulin sensitivity (%S; βinsulin = 0.2171, 95% C.I.: 0.0025–0.4318; βIR = 0.2179, 95% C.I.: 0.0027–0.4330; β%S = −0.2180, 95% C.I.: −0.4334 to −0.0026). We observed that glucose homeostasis was disrupted by Mn, Fe, Cu, and Zn exposure through increasing P-FGAC and IR levels in shipyard welders

Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS

Zephyr : Stitching Heterogeneous Training Data with Normalizing Flows for Photometric Redshift Inference

We present zephyr, a novel method that integrates cutting-edge normalizing flow techniques into a mixture density estimation framework, enabling the effective use of heterogeneous training data for photometric redshift inference. Compared to previous methods, zephyr demonstrates enhanced robustness for both point estimation and distribution reconstruction by leveraging normalizing flows for density estimation and incorporating careful uncertainty quantification. Moreover, zephyr offers unique interpretability by explicitly disentangling contributions from multi-source training data, which can facilitate future weak lensing analysis by providing an additional quality assessment. As probabilistic generative deep learning techniques gain increasing prominence in astronomy, zephyr should become an inspiration for handling heterogeneous training data while remaining interpretable and robustly accounting for observational uncertainties.Comment: 10 pages, 5 figures, accepted to NeurIPS 2023 workshop on Machine Learning and the Physical Science