Quantifying the Simulation-Reality Gap for Deep Learning-Based Drone Detection

The detection of drones or unmanned aerial vehicles is a crucial component in protecting safety-critical infrastructures and maintaining privacy for individuals and organizations. The widespread use of optical sensors for perimeter surveillance has made optical sensors a popular choice for data collection in the context of drone detection. However, efficiently processing the obtained sensor data poses a significant challenge. Even though deep learning-based object detection models have shown promising results, their effectiveness depends on large amounts of annotated training data, which is time consuming and resource intensive to acquire. Therefore, this work investigates the applicability of synthetically generated data obtained through physically realistic simulations based on three-dimensional environments for deep learning-based drone detection. Specifically, we introduce a novel three-dimensional simulation approach built on Unreal Engine and Microsoft AirSim for generating synthetic drone data. Furthermore, we quantify the respective simulation-reality gap and evaluate established techniques for mitigating this gap by systematically exploring different compositions of real and synthetic data. Additionally, we analyze the adaptation of the simulation setup as part of a feedback loop-based training strategy and highlight the benefits of a simulation-based training setup for image-based drone detection, compared to a training strategy relying exclusively on real-world data

Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving

Excessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially in critical moments such as driving. This requires real-time information on a person's blood alcohol concentration (BAC). Here, we develop an in-vehicle machine learning system to predict critical BAC levels. Our system leverages driver monitoring cameras mandated in numerous countries worldwide. We evaluate our system with n=30 participants in an interventional simulator study. Our system reliably detects driving under any alcohol influence (area under the receiver operating characteristic curve [AUROC] 0.88) and driving above the WHO recommended limit of 0.05g/dL BAC (AUROC 0.79). Model inspection reveals reliance on pathophysiological effects associated with alcohol consumption. To our knowledge, we are the first to rigorously evaluate the use of driver monitoring cameras for detecting drunk driving. Our results highlight the potential of driver monitoring cameras and enable next-generation drunk driver interaction preventing alcohol-related harm

Generating Synthetic Data for Deep Learning-Based Drone Detection

Drone detection is an important yet challenging task in the context of object detection. The development of robust and reliable drone detection systems requires large amounts of labeled data, especially when using deep learning (DL) models. Unfortunately, acquiring real data is expensive, time-consuming, and often limited by external factors. This makes synthetic data a promising approach to addressing data deficiencies. In this paper, we present a data generation pipeline based on Unreal Engine 4.25 and Microsoft AirSim, designed to create synthetic labeled data for drone detection using three-dimensional environments. As part of an ablation study, we investigate the potential use of synthetic data in drone detection by analyzing different training strategies, influencing factors, and data generation parameters, specifically related to the visual appearance of a drone

Validation of the 2019 EULAR/ACR classification criteria for systemic lupus erythematosus in an academic tertiary care centre

Objectives To assess the sensitivity and specificity of the 2019 EULAR/American College of Rheumatology (ACR) classification criteria for systemic lupus erythematosus (SLE) in outpatients at an academic tertiary care centre and to compare them to the 1997 ACR and the 2012 Systemic Lupus International Collaborating Clinics criteria.Methods Prospective and retrospective observational cohort study.Results 3377 patients were included: 606 with SLE, 1015 with non-SLE autoimmune-mediated rheumatic diseases (ARD) and 1756 with non-ARD diseases (hepatocellular carcinoma, primary biliary cirrhosis, autoimmune hepatitis). The 2019 criteria were more sensitive than the 1997 criteria (87.0% vs 81.8%), but less specific (98.1% vs 99.5% in the entire cohort and 96.5% vs 98.8% in patients with non-SLE ARD), resulting in Youden Indexes for patients with SLE/non-SLE ARD of 0.835 and 0.806, respectively. The most sensitive items were history of antinuclear antibody (ANA) positivity and detection of anti-double-stranded deoxyribonucleic acid (dsDNA) antibodies. These were also the least specific items. The most specific items were class III/IV lupus nephritis and the combination of low C3 and low C4 complement levels, followed by class II/V lupus nephritis, either low C3 or low C4 complement levels, delirium and psychosis, when these were not attributable to non-SLE causes.Conclusions In this cohort from an independent academic medical centre, the sensitivity and specificity of the 2019 lupus classification criteria were confirmed. Overall agreement of the 1997 and the 2019 criteria was very good

Clinical Effectiveness of FlexiTeam (Home Treatment and Intensive Outpatient Treatment) - Comparison of a Model Project According to 64b in Berlin with Inpatient Treatment-as-Usual

Objective To investigate the clinical effectiveness of Home Treatment (HT) together with intensive outpatient treatment (IAB) in comparison to the usual psychiatric inpatient treatment. Methods In a retrospective controlled pre-post-study 83 patients receiving HT plus IAB were matched with 83 patients receiving inpatient treatment as usual. Routine data were compared with regard to length of stay and hospital readmission rate in a follow-up period of 6 and 12 months respectively. Results There was no significant reduction of the length of stay of the first hospital admission. However, there was a significant, notable reduction with regard to length of stay and hospital readmission rate in the intervention group in a follow-up period of 6 and 12 months respectively. Conclusion HT plus intensive outpatient treatment is an effective complement to the usual psychiatric inpatient treatment. It can reduce the risk of hospital readmission and the length of stay for eligible patients

Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving

Excessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially in critical moments such as driving. This requires real-time information on a person's blood alcohol concentration (BAC). Here, we develop an in-vehicle machine learning system to predict critical BAC levels. Our system leverages driver monitoring cameras mandated in numerous countries worldwide. We evaluate our system with n = 30 participants in an interventional simulator study. Our system reliably detects driving under any alcohol influence (area under the receiver operating characteristic curve [AUROC] 0.88) and driving above the WHO recommended limit of 0.05 g/dL BAC (AUROC 0.79). Model inspection reveals reliance on pathophysiological effects associated with alcohol consumption. To our knowledge, we are the first to rigorously evaluate the use of driver monitoring cameras for detecting drunk driving. Our results highlight the potential of driver monitoring cameras and enable next-generation drunk driver interaction preventing alcohol-related harm

Treating major depression with yoga: A prospective, randomized, controlled pilot trial

<div><p>Background</p><p>Conventional pharmacotherapies and psychotherapies for major depression are associated with limited adherence to care and relatively low remission rates. Yoga may offer an alternative treatment option, but rigorous studies are few. This randomized controlled trial with blinded outcome assessors examined an 8-week hatha yoga intervention as mono-therapy for mild-to-moderate major depression.</p><p>Methods</p><p>Investigators recruited 38 adults in San Francisco meeting criteria for major depression of mild-to-moderate severity, per structured psychiatric interview and scores of 14–28 on Beck Depression Inventory-II (BDI). At screening, individuals engaged in psychotherapy, antidepressant pharmacotherapy, herbal or nutraceutical mood therapies, or mind-body practices were excluded. Participants were 68% female, with mean age 43.4 years (SD = 14.8, range = 22–72), and mean BDI score 22.4 (SD = 4.5). Twenty participants were randomized to 90-minute hatha yoga practice groups twice weekly for 8 weeks. Eighteen participants were randomized to 90-minute attention control education groups twice weekly for 8 weeks. Certified yoga instructors delivered both interventions at a university clinic. Primary outcome was depression severity, measured by BDI scores every 2 weeks from baseline to 8 weeks. Secondary outcomes were self-efficacy and self-esteem, measured by scores on the General Self-Efficacy Scale (GSES) and Rosenberg Self-Esteem Scale (RSES) at baseline and at 8 weeks.</p><p>Results</p><p>In intent-to-treat analysis, yoga participants exhibited significantly greater 8-week decline in BDI scores than controls (p-value = 0.034). In sub-analyses of participants completing final 8-week measures, yoga participants were more likely to achieve remission, defined per final BDI score ≤ 9 (p-value = 0.018). Effect size of yoga in reducing BDI scores was large, per Cohen’s d = -0.96 [95%CI, -1.81 to -0.12]. Intervention groups did not differ significantly in 8-week change scores for either the GSES or RSES.</p><p>Conclusion</p><p>In adults with mild-to-moderate major depression, an 8-week hatha yoga intervention resulted in statistically and clinically significant reductions in depression severity.</p><p>Trial registration</p><p>ClinicalTrials.gov <a href="https://clinicaltrials.gov/ct2/show/NCT01210651" target="_blank">NCT01210651</a></p></div

Effect sizes for primary and secondary outcomes in study completers.

<p>Effect sizes for primary and secondary outcomes in study completers.</p

Intent-to-treat analysis: Adjusted mean BDI scores plotted by time.

<p>Intent-to-treat analysis: Adjusted mean BDI scores plotted by time.</p

Analyses of GSES scores and RSES scores in study completers.

<p>Analyses of GSES scores and RSES scores in study completers.</p