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This paper describes gft (general fine-tuning), a little language for deep nets, introduced at an ACL-2022 tutorial. gft makes deep nets accessible to a broad audience including non-programmers. It is standard practice in many fields to use statistics packages such as R. One should not need to know how to program in order to fit a regression or classification model and to use the model to make predictions for novel inputs. With gft, fine-tuning and inference are similar to fit and predict in regression and classification. gft demystifies deep nets; no one would suggest that regression-like methods are “intelligent.”
Subthreshold depression (sD) negatively impacts well-being and psychosocial function and is more prevalent compared with major depressive disorder (MDD). However, as adults with sD are less likely to seek face-to-face intervention, internet-based cognitive-behavioral therapy (ICBT) may overcome barriers of accessibility to psychotherapy. Although several trials explored the efficacy of ICBT for sD, the results remain inconsistent. This study evaluated whether ICBT is effective in reducing depressive symptoms among Chinese adults with sD.
A randomized controlled trial was performed. The participants were randomly assigned to 5 weeks of ICBT, group-based face-to-face cognitive-behavioral therapy (CBT), or a waiting list (WL). Assessments were conducted at baseline, post-intervention and at a 6-month follow-up. The primary outcome measured depressive symptoms using the Center for Epidemiological Studies Depression Scale (CES-D). Outcomes were analyzed using a mixed-effects model to assess the effects of ICBT.
ICBT participants reported greater reductions on all the outcomes compared to the WL group at post-intervention. The ICBT group showed larger improvement on the Patient Health Questionnaire-9 (PHQ-9) at post-intervention (d = 0.12) and at follow-up (d = 0.10), and with CES-D at post-intervention (d = 0.06), compared to the CBT group.
ICBT is effective in reducing depressive symptoms among Chinese adults with sD, and improvements in outcomes were sustained at a 6-month follow-up. Considering the low rates of face-to-face psychotherapy, our findings highlight the considerable potential and implications for the Chinese government to promote the use of ICBT for sD in China.
The outbreak of COVID-19 generated severe emotional reactions, and restricted mobility was a crucial measure to reduce the spread of the virus. This study describes the changes in public emotional reactions and mobility patterns in the Chinese population during the COVID-19 outbreak.
We collected data on public emotional reactions in response to the outbreak through Weibo, the Chinese Twitter, between 1st January and 31st March 2020. Using anonymized location-tracking information, we analyzed the daily mobility patterns of approximately 90% of Sichuan residents.
There were three distinct phases of the emotional and behavioral reactions to the COVID-19 outbreak. The alarm phase (19th–26th January) was a restriction-free period, characterized by few new daily cases, but a large amount public negative emotions [the number of negative comments per Weibo post increased by 246.9 per day, 95% confidence interval (CI) 122.5–371.3], and a substantial increase in self-limiting mobility (from 45.6% to 54.5%, changing by 1.5% per day, 95% CI 0.7%–2.3%). The epidemic phase (27th January–15th February) exhibited rapidly increasing numbers of new daily cases, decreasing expression of negative emotions (a decrease of 27.3 negative comments per post per day, 95% CI −40.4 to −14.2), and a stabilized level of self-limiting mobility. The relief phase (16th February–31st March) had a steady decline in new daily cases and decreasing levels of negative emotion and self-limiting mobility.
During the COVID-19 outbreak in China, the public's emotional reaction was strongest before the actual peak of the outbreak and declined thereafter. The change in human mobility patterns occurred before the implementation of restriction orders, suggesting a possible link between emotion and behavior.
The optical emission spectra of the plasma generated by a
1.06-μm Nd:YAG laser irradiation of Al target in air was
recorded and analyzed in a spatially resolved manner. Electron
temperatures and densities in the plasma were obtained using
the relative emission intensities and the Stark-broadened
linewidths of Al(I) emission lines, respectively. The dependence
of the electron density and temperature on the distance from
the target surface and on the laser irradiance were manifested.
We also discussed how the air takes part in the plasma evolution
process and confirmed that the ignition of the air plasma was
by the collisions between the energetic electrons and the nitrogen
atoms through a cascade avalanche process.
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