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While listeners tend to downgrade speakers' accent and comprehensibility when they perceive them to be from a different language community--a process known as reverse linguistic stereotyping (RLS)--research has generally relied solely on quantitative data such as Likert scale ratings. The current study sought to extend the analysis further by investigating the reasons which informed raters' decisions. A sample of 210 Japanese university students (six groups of n = 35) were asked to listen to recorded speeches by native Japanese speakers. In a matched-guise design, the groups were either shown photos of a Japanese, Caucasian, or Southeast Asian male, and asked to provide ratings of accentedness, comprehensibility, and intelligibility. They were then asked to report what factors influenced their comprehensibility ratings. In line with previous research, listeners rated non-Japanese guises as significantly more accented than the Japanese one, though differences in intelligibility were non-significant. A key finding was that while comprehensibility ratings were statistically comparable, the rationales given were qualitatively different. Groups who viewed the Caucasian or Southeast Asian photographs cited pronunciation issues significantly more than the Japanese group which reported grammatical and lexical factors as the reasons behind their downgraded ratings.

Mobile-learning (m-learning), or mobile-assisted language learning (MALL), has been the object of a great deal of research over the last twenty years. However, empirical work in this area has largely failed to produce generalizable conclusions due to variation in methodology, target feature, and task-type (Burston, 2014, 2015). As schools in Japan begin to join the growing number of classrooms worldwide using mobile-based assignments, this study examined how Japanese EFL students' writing task production differed depending on writing medium (i.e., handwritten on paper vs. tapped on a smartphone). Writing samples were collected from N = 1,449 participants, divided into smartphone- or paper-based groups, across a spectrum of English proficiencies. Handwritten submissions were found to be significantly longer than those composed on a smartphone (p < 0.001, d = 0.54), with differences being more pronounced for learners of higher proficiency than lower ones. Significance and effect sizes steadily dropped from p < 0.001, d = 0.66 for advanced learners to p = 0.168, d = 0.38 for beginners. These results indicate that care must be taken in designing m-learning activities, and that students must be given adequate training in smartphone-input skills (i.e., tapping) and time to acclimate before using such tasks for high-stakes assessments.

Research has suggested that the type and frequency of learning strategies employed by successful listeners is greater than their less successful counterparts. Based on evidence that metacognitive strategies (e.g. listening-for-gist; inferring meaning) are more effective than cognitive ones (e.g. word-for-word translation), this study sought to measure the effect that rhymical training had on the listening comprehension acuity of 313 Japanese university students. The theoretical basis was that rhythmical priming assists learners parse the input based on prosody and identify salient words by stress. Small but significant increases were observed among students who were rhythmically trained, compared to a comparison group that received explicit instruction but no rhythm training, and a "true" control group that received no treatment. These results extend the feasibility of input enhancement, rhythmic priming, and perception-based instruction beyond the traditional grammar and pronunciation instruction domains.

This paper seeks to understand a subtle form of relational common sense among Japanese conversational partners. To do so we examine possible explanations of a conversation between two Japanese that at first glance appears atypical. We argue that the most satisfying explanation is grounded in an understanding of the Japanese concept of kenson. Using ethnographic techniques the "common sense" of kenson is detailed in regards to its forms and meanings. Implications of this concept for conversation are discussed as well as comparisons made between kenson, the American notion of modesty and Japanese indirect refusals. Finally, kenson is discussed as an informal ritual grounded in social norms. [ABSTRACT FROM AUTHOR]

• First work to investigate solutions for collaborative medical AI model for COVID-19. • A novel federated semi-supervised learning technique is proposed to train a network. • Method fully utilizes available data from different sources, w/ or w/o annotations. • Multinational dataset of 1704 scans from 3 countries (China, Italy, and Japan). • Studies fully evaluate and understand different configurations of the method. [Display omitted] The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using RT-PCR for testing. As a complimentary tool with diagnostic imaging, chest Computed Tomography (CT) has been shown to be able to reveal visual patterns characteristic for COVID-19, which has definite value at several stages during the disease course. To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis with chest CT scan, which has shown promising results. However, domain shift of data across clinical data centers poses a serious challenge when deploying learning-based models. A common way to alleviate this issue is to fine-tune the model locally with the target domains local data and annotations. Unfortunately, the availability and quality of local annotations usually varies due to heterogeneity in equipment and distribution of medical resources across the globe. This impact may be pronounced in the detection of COVID-19, since the relevant patterns vary in size, shape, and texture. In this work, we attempt to find a solution for this challenge via federated and semi-supervised learning. A multi-national database consisting of 1704 scans from three countries is adopted to study the performance gap, when training a model with one dataset and applying it to another. Expert radiologists manually delineated 945 scans for COVID-19 findings. In handling the variability in both the data and annotations, a novel federated semi-supervised learning technique is proposed to fully utilize all available data (with or without annotations). Federated learning avoids the need for sensitive data-sharing, which makes it favorable for institutions and nations with strict regulatory policy on data privacy. Moreover, semi-supervision potentially reduces the annotation burden under a distributed setting. The proposed framework is shown to be effective compared to fully supervised scenarios with conventional data sharing instead of model weight sharing. [ABSTRACT FROM AUTHOR]

Objectives: The early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2., Methods: Seventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression., Results: Forty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p < 0.01), CRP (p < 0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms., Conclusions: COVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure., Key Points: • Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings. • CT infiltrates pre-dated symptom onset by 3.8 days (range 1-5). • KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms.

As a result of the rushed transition to remote teaching because of the COVID-19 pandemic, teachers have suddenly been forced to design mobile-assisted language learning (MALL; m-learning) activities, mostly for the first time in their careers. However, it is imperative that instructors realize that the challenge is greater than simply converting paper-based assignments into a digital format. There has been very little research done examining the cognitive effects of writing on a smartphone, compared to writing on paper. The current study therefore sought to examine how recollection of content differed depending on the medium used for notetaking. A population of 138 Japanese university students of English-as-a-Foreign-Language (EFL) were asked to transcribe a short text, either by paper or by smartphone, and then quizzed on the content immediately afterwards. Students who wrote the text by hand were found to have significantly greater recollection of the content compared to those who had 'tapped' on smartphones. These results follow precursor research which indicated that Japanese students compose significantly slower on their phones in English (Lee, 2020b), and wrote significantly less in writing tasks (Lee, 2019, 2020a) than handwriting counterparts., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)