DROW: Real-Time Deep Learning based Wheelchair Detection in 2D Range Data

We introduce the DROW detector, a deep learning based detector for 2D range data. Laser scanners are lighting invariant, provide accurate range data, and typically cover a large field of view, making them interesting sensors for robotics applications. So far, research on detection in laser range data has been dominated by hand-crafted features and boosted classifiers, potentially losing performance due to suboptimal design choices. We propose a Convolutional Neural Network (CNN) based detector for this task. We show how to effectively apply CNNs for detection in 2D range data, and propose a depth preprocessing step and voting scheme that significantly improve CNN performance. We demonstrate our approach on wheelchairs and walkers, obtaining state of the art detection results. Apart from the training data, none of our design choices limits the detector to these two classes, though. We provide a ROS node for our detector and release our dataset containing 464k laser scans, out of which 24k were annotated.Comment: Lucas Beyer and Alexander Hermans contributed equall

Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal

Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approaches and machine learning (ML) algorithms have been shown to help improve house price prediction and mass appraisal assessment. Nonetheless, the adoption a of ML within mass appraisal has been protracted and subject to scrutiny by assessment jurisdictions due to their failure to account for spatial autocorrelation and limited practicality in terms of value significant estimates needed for tribunal defense and explainability. Existing research comparing traditional regression approaches has tended to examine unsupervised ML methods such as Random Forest (RF) models which remain more esoteric and less transparent in producing value significant estimates necessary for mass appraisal explainability and defense. Therefore, the purpose of this study is to apply the supervised Regularized regression technique which offers a more transparent alternative, and integrate this with a more nuanced geo-statistical technique, the Eigenvector Spatial Filter (ESF) approach, to more accurately account for spatial autocorrelation and enhance prediction accuracy whilst improving explainability needed for mass appraisal exercises. By undertaking such an approach, the research demonstrates the application of this method can be easily adopted for property tax jurisdictions in a framework which is more interpretable, transparent and useable within mass appraisal given its simple and appealing approach. The findings reveal that the integration of the ESFs improves model explainability, prediction accuracy and spatial residual error compared to baseline classical regression and Elastic-net regularized regression architectures, whilst offering the necessary ‘front-facing’ and flexible structure for in-sample and out-of-sample assessment needed by the assessment community for valuing the unsold housing stock. In terms of policy and practice, the study demonstrates some important considerations for mass appraisal tax assessment and for the improvement of taxation assessment and the alleviation of horizontal and vertical inequity

Automated Electrical Quantification of Vitamin B1 in a Bodily Fluid using an Engineered Nanopore-Sensor

The ability to measure the concentration of metabolites in biological samples is important, both in the clinic and for home diagnostics. Here we present a nanopore‐based biosensor and automated data analysis for quantification of thiamine in urine in less than a minute, without the need for recalibration. For this we use the Cytolysin A nanopore and equip it with an engineered periplasmic thiamine binding protein (TbpA). To allow fast measurements we tuned the affinity of TbpA for thiamine by redesigning the π‐π stacking interactions between the thiazole group of thiamine and TbpA. This substitution resulted furthermore in a marked difference between unbound and bound state, allowing the reliable discrimination of thiamine from its two phosphorylated forms by residual current only. Using an array of nanopores, this will allow the quantification within seconds, paving the way for next‐generation single‐molecule metabolite detection systems

Molecular and Serological Intraocular Fluid Analysis of Coxiella burnetii-seropositive Patients with Concurrent Idiopathic Uveitis

Purpose: Previous studies have suggested a link between Q fever and uveitis. We determined whether Coxiella burnetii causes intraocular infection in C. burnetii-seropositive patients with idiopathic uveitis. Methods: From a retrospective observational case series, paired aqueous humor and serum samples from 10 C. burnetii-seropositive patients with idiopathic uveitis were examined for intraocular antibody production by using the Goldmann-Witmer coefficient and by polymerase chain reaction (PCR). Results: Although intraocular IgG against C. burnetii was detected, no intraocular antibody production was observed (low Goldmann Wittmer coefficients). All PCR results were negative. Conclusions: Uveitis due to an intraocular infection with C. burnetii is unlikely

Final 192-week efficacy and safety results of the ADVANCE trial, comparing 3 first-line antiretroviral regimens

BACKGROUND: ADVANCE compared 3 World Health Organization-recommended first-line regimens in participants with HIV who were antiretroviral naive. METHODS: This randomized, open-label, noninferiority trial enrolled participants living with HIV with no antiretroviral exposure in the previous 6 months to 1 of the following arms: tenofovir alafenamide (TAF) / emtricitabine (FTC) + dolutegravir (DTG) (2 tablets), tenofovir disoproxil fumarate (TDF) / FTC + DTG (2 tablets), or a fixed-dose combination of TDF / FTC / efavirenz (EFV) (1 tablet). We report the final safety and efficacy data up to 192 weeks. RESULTS: Repeat consent from the original 351 participants randomized to each arm was obtained from 230 participants (66%) in the TAF/FTC + DTG arm, 209 (60%) in the TDF/FTC + DTG arm, and 183 (52%) in the TDF/FTC/EFV arm. At 192 weeks, 213 (61%) of the original 351 participants in the TAF/FTC + DTG arm, 195 (56%) in the TDF/FTC + DTG arm, and 172 (49%) in the TDF/FTC/EFV arm had confirmed RNA <50 copies/mL, with low virologic failure in all groups and no significant integrase inhibitor mutations in any arm. Mean weight gain was 8.9 kg (SD, 7.1) in the TAF/FTC + DTG arm, 5.9 kg (SD, 7.1) in the TDF/FTC + DTG arm, and 3.2 kg (SD, 8.1) in the TDF/FTC/EFV arm at 192 weeks from baseline and was greatest among women, those taking TAF, and those with lower baseline CD4 counts. The weight trajectory slowed after week 96. There were few clinical events and minor laboratory changes and differences among arms after 96 weeks. There were no significant differences in treatment-emergent hypertension or pregnancy outcomes by arm. CONCLUSIONS: High viral suppression was seen across arms, with no resistance to DTG. Weight gain continued but slowed after 96 weeks, with few clinical events or laboratory changes

The STRANDS project: long-term autonomy in everyday environments

Thanks to the efforts of the robotics and autonomous systems community, the myriad applications and capacities of robots are ever increasing. There is increasing demand from end users for autonomous service robots that can operate in real environments for extended periods. In the Spatiotemporal Representations and Activities for Cognitive Control in Long-Term Scenarios (STRANDS) project (http://strandsproject.eu), we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots and deploying these systems for long-term installations in security and care environments. Our robots have been operational for a combined duration of 104 days over four deployments, autonomously performing end-user-defined tasks and traversing 116 km in the process. In this article, we describe the approach we used to enable long-term autonomous operation in everyday environments and how our robots are able to use their long run times to improve their own performance

Clinical impact of pretreatment human immunodeficiency virus drug resistance in people initiating nonnucleoside reverse transcriptase inhibitor-containing antiretroviral therapy: a systematic review and meta-analysis

Background: Increased access to antiretroviral therapy (ART) has resulted in rising levels of pretreatment human immunodeficiency virus drug resistance (PDR). This is the first systematic review and meta-analysis to assess the impact of PDR on treatment outcomes among people initiating nonnucleoside reverse transcriptase inhibitor (NNRTI)-based ART, including the combination of efavirenz (EFV), tenofovir (TDF), and lamivudine or emtricitabine (XTC). Methods: We systematically reviewed studies and conference proceedings comparing treatment outcomes in populations initiating NNRTI-based ART with and without PDR. We conducted subgroup analyses by regimen: (1) NNRTIs + 2 nucleoside reverse transcriptase inhibitors (NRTIs), (2) EFV + 2 NRTIs, or (3) EFV/TDF/XTC; by population (children vs adults); and by definition of resistance (PDR vs NNRTI PDR). Results: Among 6197 studies screened, 32 were analyzed (31 441 patients). We found that individuals with PDR initiating NNRTIs across all the subgroups had increased risk of virological failure compared to those without PDR. Risk of acquisition of new resistance mutations and ART switch was also higher in people with PDR. Conclusions: This review shows poorer treatment outcomes in the presence of PDR, supporting the World Health Organization's recommendation to avoid using NNRTIs in countries where levels of PDR are high