Automated Classification of Written Proficiency Levels on the CEFR-Scale through Complexity Contours and RNNs

Automatically predicting the level of second language (L2) learner proficiency is an emerging topic of interest and research based on machine learning approaches to language learning and development. The key to the present paper is the combined use of what we refer to as ‘complexity contours’, a series of measurements of indices of L2 proficiency obtained by a computational tool that implements a sliding window technique, and recurrent neural network (RNN) classifiers that adequately capture the sequential information in those contours. We used the EF-Cambridge Open Language Database (Geertzen et al. 2013) with its labelled Common European Framework of Reference (CEFR) levels (Council of Europe 2018) to predict six classes of L2 proficiency levels (A1, A2, B1, B2, C1, C2) in the assessment of writing skills. Our experiments demonstrate that an RNN classifier trained on complexity contours achieves higher classification accuracy than one trained on text-average complexity scores. In a secondary experiment, we determined the relative importance of features from four distinct categories through a sensitivity-based pruning technique. Our approach makes an important contribution to the field of automated identification of language proficiency levels, more specifically, to the increasing efforts towards the empirical validation of CEFR levels

RADAR-base: A Novel Open Source m-Health Platform

Smartphones with embedded and connected sensors are playing vital role in healthcare through various apps and mHealth platforms. RADAR-base is a modern mHealth data collection platform built around Confluent and Apache Kafka. RADAR-base enables study design and set up, active and passive remote data collection. It provides secure data transmission, and scalable solutions for data storage, management and access. The application is used presently in RADAR-CNS study to collect data from patients suffering from Multiples Sclerosis, Depression and Epilepsy. Beyond RADAR-CNS, RADAR-base is being deployed across a number of other funded research programmes

Hamstring stretch reflex:could it be a reproducible objective measure of functional knee stability?"

Background: The anterior cruciate ligament (ACL) plays an important role in anterior knee stability by preventing anterior translation of the tibia on the femur. Rapid translation of the tibia with respect to the femur produces an ACL-hamstring stretch reflex which may provide an object measure of neuromuscular function following ACL injury or reconstruction. The aim of this study was to determine if the ACL-hamstring stretch reflex could be reliably and consistently obtained using the KT-2000 arthrometer.  Methods: A KT-2000 arthrometer was used to translate the tibia on the femur while recording the electromyography over the biceps femoris muscle in 20 participants, all with intact ACLs. In addition, a sub-group comprising 4 patients undergoing a knee arthroscopy for meniscal pathology, were tested before and after anaesthetic and with direct traction on the ACL during arthroscopy. The remaining 16 participants underwent testing to elicit the reflex using the KT-2000 only.  Results: A total number of 182 trials were performed from which 70 trials elicited stretch reflex (38.5 %). The mean onset latency of the hamstring stretch reflexes was 58.9 ± 17.9 ms. The average pull force was 195 ± 47 N, stretch velocity 48 ± 35 mm/s and rate of force 19.7 ± 6.4 N/s. Conclusions Based on these results, we concluded that the response rate of the anterior cruciate ligament-hamstring reflex is too low for it to be reliably used in a clinical setting, and thus would have limited value in assessing the return of neuromuscular function following ACL injuries

Capturing Rest-Activity Profiles in Schizophrenia Using Wearable and Mobile Technologies: Development, Implementation, Feasibility, and Acceptability of a Remote Monitoring Platform

Background: There is growing interest in the potential for wearable and mobile devices to deliver clinically relevant information in real-world contexts. However, there is limited information on their acceptability and barriers to long-term use in people living with psychosis. Objective: This study aimed to describe the development, implementation, feasibility, acceptability, and user experiences of the Sleepsight platform, which harnesses consumer wearable devices and smartphones for the passive and unobtrusive capture of sleep and rest-activity profiles in people with schizophrenia living in their homes. Methods: A total of 15 outpatients with a diagnosis of schizophrenia used a consumer wrist-worn device and smartphone to continuously and remotely gather rest-activity profiles over 2 months. Once-daily sleep and self-rated symptom diaries were also collected via a smartphone app. Adherence with the devices and smartphone app, end-of-study user experiences, and agreement between subjective and objective sleep measures were analyzed. Thresholds for acceptability were set at a wear time or diary response rate of 70% or greater. Results: Overall, 14 out of 15 participants completed the study. In individuals with a mild to moderate symptom severity at baseline (mean total Positive and Negative Syndrome Scale score 58.4 [SD 14.4]), we demonstrated high rates of engagement with the wearable device (all participants meeting acceptability criteria), sleep diary, and symptom diary (93% and 86% meeting criteria, respectively), with negative symptoms being associated with lower diary completion rate. The end-of-study usability and acceptability questionnaire and qualitative analysis identified facilitators and barriers to long-term use, and paranoia with study devices was not a significant barrier to engagement. Comparison between sleep diary and wearable estimated sleep times showed good correspondence (ρ=0.50, P<.001). Conclusions: Extended use of wearable and mobile technologies are acceptable to people with schizophrenia living in a community setting. In the future, these technologies may allow predictive, objective markers of clinical status, including early markers of impending relapse

Cohomological Hasse principle and motivic cohomology for arithmetic schemes

In 1985 Kazuya Kato formulated a fascinating framework of conjectures which generalizes the Hasse principle for the Brauer group of a global field to the so-called cohomological Hasse principle for an arithmetic scheme. In this paper we prove the prime-to-characteristic part of the cohomological Hasse principle. We also explain its implications on finiteness of motivic cohomology and special values of zeta functions.Comment: 47 pages, final versio

Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): a multi-centre prospective cohort study protocol

BACKGROUND: There is a growing body of literature highlighting the role that wearable and mobile remote measurement technology (RMT) can play in measuring symptoms of major depressive disorder (MDD). Outcomes assessment typically relies on self-report, which can be biased by dysfunctional perceptions and current symptom severity. Predictors of depressive relapse include disrupted sleep, reduced sociability, physical activity, changes in mood, prosody and cognitive function, which are all amenable to measurement via RMT. This study aims to: 1) determine the usability, feasibility and acceptability of RMT; 2) improve and refine clinical outcome measurement using RMT to identify current clinical state; 3) determine whether RMT can provide information predictive of depressive relapse and other critical outcomes. METHODS: RADAR-MDD is a multi-site prospective cohort study, aiming to recruit 600 participants with a history of depressive disorder across three sites: London, Amsterdam and Barcelona. Participants will be asked to wear a wrist-worn activity tracker and download several apps onto their smartphones. These apps will be used to either collect data passively from existing smartphone sensors, or to deliver questionnaires, cognitive tasks, and speech assessments. The wearable device, smartphone sensors and questionnaires will collect data for up to 2-years about participants' sleep, physical activity, stress, mood, sociability, speech patterns, and cognitive function. The primary outcome of interest is MDD relapse, defined via the Inventory of Depressive Symptomatology- Self-Report questionnaire (IDS-SR) and the World Health Organisation's self-reported Composite International Diagnostic Interview (CIDI-SF). DISCUSSION: This study aims to provide insight into the early predictors of major depressive relapse, measured unobtrusively via RMT. If found to be acceptable to patients and other key stakeholders and able to provide clinically useful information predictive of future deterioration, RMT has potential to change the way in which depression and other long-term conditions are measured and managed. KEYWORDS: M-health; Major depressive disorder; Observational cohort; Outcome measurement; Passive sensing; Prospective study; Remote measurement technolog