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Domain-Adversarial Based Model with Phonological Knowledge for Cross-Lingual Speech Recognition
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In: http://infoscience.epfl.ch/record/291292 (2022)
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Online Literacy Instruction for Young Korean Dual Language Learners in General Education
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In: J Behav Educ (2022)
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From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network ...
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Temporal trends in incidence and mortality rates of laryngeal cancer at the global, regional and national levels, 1990–2017
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In: BMJ Open (2021)
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Development and Validation of an Unethical Professional Behavior Tendencies Scale for Student Teachers
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In: Front Psychol (2021)
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Pragmatics to Reveal Intent in Social Media Peer Interactions: Mixed Methods Study
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In: J Med Internet Res (2021)
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The second language acquisition of the Chinese aspect marker "le"
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Measurement of single-diffractive dijet production in proton-proton collisions at $\sqrt{s} =$ 8 TeV with the CMS and TOTEM experiments
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In: Eur.Phys.J.C ; https://hal.archives-ouvertes.fr/hal-02507664 ; Eur.Phys.J.C, 2020, 80 (12), pp.1164. ⟨10.1140/epjc/s10052-020-08562-y⟩ (2020)
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Lens on China: Intermediate and Advanced Readings on Film for Learning Chinese
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In: Faculty Books (2020)
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Neural representations of the concepts in simple sentences: Concept activation prediction and context effects ...
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Neural representations of the concepts in simple sentences: Concept activation prediction and context effects ...
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The role of growth mindset, self-efficacy and intrinsic value in self-regulated learning and English language learning achievements ...
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The role of growth mindset, self-efficacy and intrinsic value in self-regulated learning and English language learning achievements ...
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Measurement of the top quark mass with lepton+jets final states using $\mathrm {p}$ $\mathrm {p}$ collisions at $\sqrt{s}=13\,\text {TeV} $
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In: http://infoscience.epfl.ch/record/275278 (2020)
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Conversational topics of social media messages associated with state-level mental distress rates
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In: J Ment Health (2020)
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Abstract:
BACKGROUND: Upstream public health indicators of poor mental health in the United States (U.S.) are currently measured by national telephone-based surveys; however, results are delayed by 1–2 years, limiting real-time assessment of trends. AIM: The aim of this study was to evaluate associations between conversational topics on Twitter from 2018 to 2019 and mental distress rates from 2017 to 2018 for the 50 U.S. states and capital. METHOD: We used a novel lexicon, Empath, to examine conversational topics from aggregate social media messages from Twitter that correlated most strongly with official U.S. state-level rates of mental distress from the Behavioral Risk Factor Surveillance System. RESULTS: The ten lexical categories most positively correlated with rates of frequent mental distress at the state-level included categories about death, illness, or injury. Lexical categories most inversely correlated with mental distress included categories that serve as proxies for economic prosperity and industry. Using the prevalence of the 10 most positively and 10 most negatively correlated lexical categories to predict state-level rates of mental distress via a linear regression model on an independent sample of data yielded estimates that were moderately similar to actual rates (mean difference = 0.52%; Pearson correlation = 0.45, p < 0.001). CONCLUSION: This work informs efforts to use social media to measure population-level trends in mental health.
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Article
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217347/ http://www.ncbi.nlm.nih.gov/pubmed/32223489 https://doi.org/10.1080/09638237.2020.1739251
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