3,912 research outputs found
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
group, is constructed. Both the new gauge bosons and are electrically neutral. While can be singly produced at
colliders, , which is heavier, must be pair produced. We
explore the constraints of using the current Drell-Yan type data
from the Large Hadron Collider. Anticipating optimistically that 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 . For the 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
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