52,687 research outputs found

    Investigating microstructural variation in the human hippocampus using non-negative matrix factorization

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    In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses

    Measuring access: how accurate are patient-reported waiting times?

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    Introduction: A national audit of waiting times in England’s genitourinary medicine clinics measures patient access. Data are collected by patient questionnaires, which rely upon patients’ recollection of first contact with health services, often several days previously. The aim of this study was to assess the accuracy of patient-reported waiting times. Methods: Data on true waiting times were collected at the time of patient booking over a three-week period and compared with patient-reported data collected upon clinic attendance. Factors contributing to patient inaccuracy were explored. Results: Of 341 patients providing initial data, 255 attended; 207 as appointments and 48 ‘walk-in’. The accuracy of patient-reported waiting times overall was 52% (133/255). 85% of patients (216/255) correctly identified themselves as seen within or outside of 48 hours. 17% of patients (17/103) seen within 48 hours reported a longer waiting period, whereas 20% of patients (22/108) reporting waits under 48 hours were seen outside that period. Men were more likely to overestimate their waiting time (10.4% versus 3.1% p<0.02). The sensitivity of patient-completed questionnaires as a tool for assessing waiting times of less than 48 hours was 83.5%. The specificity and positive predictive value were 85.5% and 79.6%, respectively. Conclusion: The overall accuracy of patient reported waiting times was poor. Although nearly one in six patients misclassified themselves as being seen within or outside of 48 hours, given the under and overreporting rates observed, the overall impact on Health Protection Agency waiting time data is likely to be limited

    Measuring brand image: Shopping centre case studies

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    'Branding' is well known for consumer products but power has shifted from manufacturers' brands towards retailers'. The term 'image' is more common than 'brand' in the context of shopping centres, but 'branding' may become more important. In this study, the authors first investigated qualitatively, asking shoppers to describe centres in 'personality' terms and eliciting clear descriptive differences between centres. For example, one in-town centre was 'dull, boring and old-fashioned . . . not exciting, just OK'; a larger regional centre was 'trendy, prestigious . . . strong, vibrant, big and colourful'. Second, the authors evaluated six UK shopping centres quantitatively using a questionnaire survey (n = 287). The 'strong and vibrant' centre scored significantly higher than the 'dull and boring' one. Despite 'branding' being little used by shopping centres, those with the better 'brand images' tended to have larger catchment areas, sales and rental incomes. The authors contend that brand management could pay rewards in terms of customer numbers, sales turnover and rental income

    Real-to-Virtual Domain Unification for End-to-End Autonomous Driving

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    In the spectrum of vision-based autonomous driving, vanilla end-to-end models are not interpretable and suboptimal in performance, while mediated perception models require additional intermediate representations such as segmentation masks or detection bounding boxes, whose annotation can be prohibitively expensive as we move to a larger scale. More critically, all prior works fail to deal with the notorious domain shift if we were to merge data collected from different sources, which greatly hinders the model generalization ability. In this work, we address the above limitations by taking advantage of virtual data collected from driving simulators, and present DU-drive, an unsupervised real-to-virtual domain unification framework for end-to-end autonomous driving. It first transforms real driving data to its less complex counterpart in the virtual domain and then predicts vehicle control commands from the generated virtual image. Our framework has three unique advantages: 1) it maps driving data collected from a variety of source distributions into a unified domain, effectively eliminating domain shift; 2) the learned virtual representation is simpler than the input real image and closer in form to the "minimum sufficient statistic" for the prediction task, which relieves the burden of the compression phase while optimizing the information bottleneck tradeoff and leads to superior prediction performance; 3) it takes advantage of annotated virtual data which is unlimited and free to obtain. Extensive experiments on two public driving datasets and two driving simulators demonstrate the performance superiority and interpretive capability of DU-drive

    The H1 Forward Track Detector at HERA II

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    In order to maintain efficient tracking in the forward region of H1 after the luminosity upgrade of the HERA machine, the H1 Forward Track Detector was also upgraded. While much of the original software and techniques used for the HERA I phase could be reused, the software for pattern recognition was completely rewritten. This, along with several other improvements in hit finding and high-level track reconstruction, are described in detail together with a summary of the performance of the detector.Comment: Minor revision requested by journal (JINST) edito
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