68 research outputs found
Psychological Status of High School Students 1 Year After the COVID-19 Emergency
Background: With the control of the epidemic, adolescents\u27 mental outlook might have improved. However, little evidence existed with regard to the psychological status of adolescents in post-COVID-19 era. This present study aimed to explore the psychological status of high school students after the epidemic getting eased. Methods: A web-based cross-sectional survey was used to obtain data from three high schools, including the demographic information, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), the Self-Rating Scale of Sleep (SRSS), and self-designed general recent-status questionnaire. Correlation analysis was performed to explore potential associations between the depression symptoms, anxiety symptoms, and sleep status. The PHQ-9 and GAD-7 differences between nowadays data and the data enrolled 12 months before were also compared. Result: A total of 1,108 qualified questionnaires were obtained. The prevalence of depressive and anxious symptoms was 27.5 and 21.3%, respectively, from mild to severe in all students, while 11.8% of these high students got sleep disturbances. Both the rate and the severity of depression, anxiety and sleep problems of female students were higher than male students. Grade three students suffered higher prevalence and severer mental disturbances than the other two grades. There were significant correlations between the depression symptoms, anxiety symptoms, and sleep status. The psychological status has been improved in nowadays high school students compared with the sample enrolled 12 months before. Conclusion: As a supplement to our former study, this present research provided a perspective on the psychological status of high school students 1 year after the COVID-19 pandemic being well controlled. We should pay attention to the psychological status of high school students, and should also notice the progresses made by this special group after the epidemic
Robot-Assisted Deep Venous Thrombosis Ultrasound Examination using Virtual Fixture
Deep Venous Thrombosis (DVT) is a common vascular disease with blood clots
inside deep veins, which may block blood flow or even cause a life-threatening
pulmonary embolism. A typical exam for DVT using ultrasound (US) imaging is by
pressing the target vein until its lumen is fully compressed. However, the
compression exam is highly operator-dependent. To alleviate intra- and
inter-variations, we present a robotic US system with a novel hybrid force
motion control scheme ensuring position and force tracking accuracy, and soft
landing of the probe onto the target surface. In addition, a path-based virtual
fixture is proposed to realize easy human-robot interaction for repeat
compression operation at the lesion location. To ensure the biometric
measurements obtained in different examinations are comparable, the 6D scanning
path is determined in a coarse-to-fine manner using both an external RGBD
camera and US images. The RGBD camera is first used to extract a rough scanning
path on the object. Then, the segmented vascular lumen from US images are used
to optimize the scanning path to ensure the visibility of the target object. To
generate a continuous scan path for developing virtual fixtures, an arc-length
based path fitting model considering both position and orientation is proposed.
Finally, the whole system is evaluated on a human-like arm phantom with an
uneven surface.Comment: Accepted Paper IEEE T-AS
Prevalence of Depression and Anxiety Symptoms of High School Students in Shandong Province During the COVID-19 Epidemic
© Copyright © 2020 Zhang, Zhai, Yang, Zhang, Zhou, Yang, Duan and Zhou. Background: The coronavirus disease 2019 (covid-19) has brought physical risks as well as psychological challenges to the whole world. High school students are a special group suffering from both the academic pressure and the threat of the epidemic. The present study aims to conduct an online survey to investigate the psychological status of high school students in Shandong Province. Methods: Using a web-based cross-sectional survey, data was collected from 1,018 voluntary high school students assessed with demographic information, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7) and a self-designed online-study effect survey. Correlation analysis was performed to explore the relationships between depression symptoms, anxiety symptoms, and study effect. Result: The prevalence of depressive symptoms, anxiety symptoms, and a combination of depressive and anxiety symptoms was 52.4, 31.4, and 26.8%, respectively, among high school students in Shandong Province during the COVID-19 epidemic. And from moderate to severe severity level, the rates of depressive symptoms and anxious symptoms were 17.6 and 4.6%. Female students exhibited a higher rate and severity of mental symptoms than male, and grade one senior high school students got a higher rate and severity of mental symptoms than the other two grades. Nearly half of the students were not satisfied with their online-study effect. The PHQ-9 score had a strong positive correlation with the GAD-7 score. Both the PHQ-9 score the GAD-7 score had a negative correlation with the study-effect survey score. Conclusion: Quite a number of high school students suffered from depression and anxiety symptoms during the COVID-19 epidemic. Sufficient attentions should be paid, and necessary supports should be provided, to protect the mental health of this special group
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy
Reinforcement Learning from Human Feedback (RLHF) is the prevailing approach
to ensure Large Language Models (LLMs) align with human values. However,
existing RLHF methods require a high computational cost, one main reason being
that RLHF assigns both the generation and alignment tasks to the LLM
simultaneously. In this paper, we introduce Proxy-RLHF, which decouples the
generation and alignment processes of LLMs, achieving alignment with human
values at a much lower computational cost. We start with a novel Markov
Decision Process (MDP) designed for the alignment process and employ
Reinforcement Learning (RL) to train a streamlined proxy model that oversees
the token generation of the LLM, without altering the LLM itself. Experiments
show that our method achieves a comparable level of alignment with only 1\% of
the training parameters of other methods
Ultra-Low-Frequency Radio Astronomy Observations from a Selenocentric Orbit: first results of the Longjiang-2 experiment
This paper introduces the first results of observations with the
Ultra-Long-Wavelength (ULW) -- Low Frequency Interferometer and Spectrometer
(LFIS) on board the selenocentric satellite Longjiang-2. We present a brief
description of the satellite and focus on the LFIS payload. The in-orbit
commissioning confirmed a reliable operational status of the instrumentation.
We also present results of a transition observation, which offers unique
measurements on several novel aspects. We estimate the RFI suppression required
for such a radio astronomy instrumentation at the Moon distances from Earth to
be of the order of 80 dB. We analyse a method of separating Earth- and
satellite-originated radio frequency interference (RFI). It is found that the
RFI level at frequencies lower than a few MHz is smaller than the receiver
noise floor.Comment: Accepted for publication in Experimental Astronomy; 22 pages, 11
figure
Detecting Neutrinos from Supernova Bursts in PandaX-4T
Neutrinos from core-collapse supernovae are essential for the understanding
of neutrino physics and stellar evolution. The dual-phase xenon dark matter
detectors can provide a way to track explosions of galactic supernovae by
detecting neutrinos through coherent elastic neutrino-nucleus scatterings. In
this study, a variation of progenitor masses as well as explosion models are
assumed to predict the neutrino fluxes and spectra, which result in the number
of expected neutrino events ranging from 6.6 to 13.7 at a distance of 10 kpc
over a 10-second duration with negligible backgrounds at PandaX-4T. Two
specialized triggering alarms for monitoring supernova burst neutrinos are
built. The efficiency of detecting supernova explosions at various distances in
the Milky Way is estimated. These alarms will be implemented in the real-time
supernova monitoring system at PandaX-4T in the near future, providing the
astronomical communities with supernova early warnings.Comment: 9 pages,6 figure
Search for light dark matter from atmosphere in PandaX-4T
We report a search for light dark matter produced through the cascading decay
of mesons, which are created as a result of inelastic collisions between
cosmic rays and Earth's atmosphere. We introduce a new and general framework,
publicly accessible, designed to address boosted dark matter specifically, with
which a full and dedicated simulation including both elastic and quasi-elastic
processes of Earth attenuation effect on the dark matter particles arriving at
the detector is performed. In the PandaX-4T commissioning data of 0.63
tonneyear exposure, no significant excess over background is observed.
The first constraints on the interaction between light dark matter generated in
the atmosphere and nucleus through a light scalar mediator are obtained. The
lowest excluded cross-section is set at for
dark matter mass of MeV and mediator mass of 300 MeV. The
lowest upper limit of to dark matter decay branching ratio is
A Search for Light Fermionic Dark Matter Absorption on Electrons in PandaX-4T
We report a search on a sub-MeV fermionic dark matter absorbed by electrons
with an outgoing active neutrino using the 0.63 tonne-year exposure collected
by PandaX-4T liquid xenon experiment. No significant signals are observed over
the expected background. The data are interpreted into limits to the effective
couplings between such dark matter and electrons. For axial-vector or vector
interactions, our sensitivity is competitive in comparison to existing
astrophysical bounds on the decay of such dark matter into photon final states.
In particular, we present the first direct detection limits for an axial-vector
(vector) interaction which are the strongest in the mass range from 25 to 45
(35 to 50) keV/c
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
The rapid development of open-source large language models (LLMs) has been
truly remarkable. However, the scaling law described in previous literature
presents varying conclusions, which casts a dark cloud over scaling LLMs. We
delve into the study of scaling laws and present our distinctive findings that
facilitate scaling of large scale models in two commonly used open-source
configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek
LLM, a project dedicated to advancing open-source language models with a
long-term perspective. To support the pre-training phase, we have developed a
dataset that currently consists of 2 trillion tokens and is continuously
expanding. We further conduct supervised fine-tuning (SFT) and Direct
Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the
creation of DeepSeek Chat models. Our evaluation results demonstrate that
DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in
the domains of code, mathematics, and reasoning. Furthermore, open-ended
evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance
compared to GPT-3.5
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