The magnetic nature of disk accretion onto black holes

Although disk accretion onto compact objects - white dwarfs, neutron stars, and black holes - is central to much of high energy astrophysics, the mechanisms which enable this process have remained observationally elusive. Accretion disks must transfer angular momentum for matter to travel radially inward onto the compact object. Internal viscosity from magnetic processes and disk winds can in principle both transfer angular momentum, but hitherto we lacked evidence that either occurs. Here we report that an X-ray-absorbing wind discovered in an observation of the stellar-mass black hole binary GRO J1655-40 must be powered by a magnetic process that can also drive accretion through the disk. Detailed spectral analysis and modeling of the wind shows that it can only be powered by pressure generated by magnetic viscosity internal to the disk or magnetocentrifugal forces. This result demonstrates that disk accretion onto black holes is a fundamentally magnetic process.Comment: 15 pages, 2 color figures, accepted for publication in Nature. Supplemental materials may be obtained by clicking http://www.astro.lsa.umich.edu/~jonmm/nature1655.p

A model for the waveform behavior of accreting millisecond pulsars: Nearly aligned magnetic fields and moving emission regions

We investigate further a model of the accreting millisecond X-ray pulsars we proposed earlier. In this model, the X-ray-emitting regions of these pulsars are near their spin axes but move. This is to be expected if the magnetic poles of these stars are close to their spin axes, so that accreting gas is channeled there. As the accretion rate and the structure of the inner disk vary, gas is channeled along different field lines to different locations on the stellar surface, causing the X-ray-emitting areas to move. We show that this "nearly aligned moving spot model" can explain many properties of the accreting millisecond X-ray pulsars, including their generally low oscillation amplitudes and nearly sinusoidal waveforms; the variability of their pulse amplitudes, shapes, and phases; the correlations in this variability; and the similarity of the accretion- and nuclear-powered pulse shapes and phases in some. It may also explain why accretion-powered millisecond pulsars are difficult to detect, why some are intermittent, and why all detected so far are transients. This model can be tested by comparing with observations the waveform changes it predicts, including the changes with accretion rate.Comment: 21 pages, 6 figures; includes 3 new sections, 14 additional pages, 4 additional figures with 11 new plots, and additional references; accepted for publication in Ap

Depression after low-energy fracture in older women predicts future falls: a prospective observational study

<p>Abstract</p> <p>Background</p> <p>Falls are one of the main causes of fractures in elderly people and after a recent fracture, the risk of another fall is increased, resulting in subsequent fracture. Therefore, risk factors for future falls should be determined. We prospectively investigated the relationship between depression and the incidence of falls in post-menopausal women after a low-energy fracture.</p> <p>Methods</p> <p>At baseline, 181 women aged 60 years and older who presented with a recent low-energy fracture were evaluated at the fracture and osteoporosis outpatient clinics of two hospitals. As well as clinical evaluation and bone mineral density tests, the presence of depression (measured using the Edinburgh Depression Scale, EDS, depression cut-off > 11) and risk factors for falling were assessed. During two years of follow-up, the incidence of falls was registered annually by means of detailed questionnaires and interviews.</p> <p>Results</p> <p>Seventy-nine (44%) of the women sustained at least one fall during follow-up. Of these, 28% (<it>n </it>= 22) suffered from depression at baseline compared to 10% (<it>n </it>= 10) of the 102 women who did not sustain a fall during follow-up (<it>Χ</it>²= 8.76, df = 1, <it>p </it>= .003). Multiple logistic regression showed that the presence of depression and co-morbidity at baseline were independently related to falls (OR = 4.13, 95% CI = 1.58-10.80; OR = 2.25, 95% CI = 1.11-4.56, respectively) during follow-up.</p> <p>Conclusions</p> <p>The presence of depression in women aged 60 years and older with recent low-energy fractures is an important risk factor for future falls. We propose that clinicians treating patients with recent low-energy fractures should anticipate not only on skeletal-related risk factors for fractures, but also on fall-related risk factors including depression.</p

Black hole spin: theory and observation

In the standard paradigm, astrophysical black holes can be described solely by their mass and angular momentum - commonly referred to as `spin' - resulting from the process of their birth and subsequent growth via accretion. Whilst the mass has a standard Newtonian interpretation, the spin does not, with the effect of non-zero spin leaving an indelible imprint on the space-time closest to the black hole. As a consequence of relativistic frame-dragging, particle orbits are affected both in terms of stability and precession, which impacts on the emission characteristics of accreting black holes both stellar mass in black hole binaries (BHBs) and supermassive in active galactic nuclei (AGN). Over the last 30 years, techniques have been developed that take into account these changes to estimate the spin which can then be used to understand the birth and growth of black holes and potentially the powering of powerful jets. In this chapter we provide a broad overview of both the theoretical effects of spin, the means by which it can be estimated and the results of ongoing campaigns.Comment: 55 pages, 5 figures. Published in: "Astrophysics of Black Holes - From fundamental aspects to latest developments", Ed. Cosimo Bambi, Springer: Astrophysics and Space Science Library. Additional corrections mad

Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks

Abstract Driver drowsiness increases crash risk, leading to substantial road trauma each year. Drowsiness detection methods have received considerable attention, but few studies have investigated the implementation of a detection approach on a mobile phone. Phone applications reduce the need for specialised hardware and hence, enable a cost-effective roll-out of the technology across the driving population. While it has been shown that three-dimensional (3D) operations are more suitable for spatiotemporal feature learning, current methods for drowsiness detection commonly use frame-based, multi-step approaches. However, computationally expensive techniques that achieve superior results on action recognition benchmarks (e.g. 3D convolutions, optical flow extraction) create bottlenecks for real-time, safety-critical applications on mobile devices. Here, we show how depthwise separable 3D convolutions, combined with an early fusion of spatial and temporal information, can achieve a balance between high prediction accuracy and real-time inference requirements. In particular, increased accuracy is achieved when assessment requires motion information, for example, when sunglasses conceal the eyes. Further, a custom TensorFlow-based smartphone application shows the true impact of various approaches on inference times and demonstrates the effectiveness of real-time monitoring based on out-of-sample data to alert a drowsy driver. Our model is pre-trained on ImageNet and Kinetics and fine-tuned on a publicly available Driver Drowsiness Detection dataset. Fine-tuning on large naturalistic driving datasets could further improve accuracy to obtain robust in-vehicle performance. Overall, our research is a step towards practical deep learning applications, potentially preventing micro-sleeps and reducing road trauma

The potential impact of digital biomarkers in multiple sclerosis in the netherlands: An early health technology assessment of ms sherpa

(1) Background: Monitoring of Multiple Sclerosis (MS) with eHealth interventions or digital biomarkers provides added value to the current care path. Evidence in the literature is currently scarce. MS sherpa is an eHealth intervention with digital biomarkers, aimed at monitoring symptom progression and disease activity. To show the added value of digital biomarker–based eHealth interventions to the MS care path, an early Health Technology Assessment (eHTA) was performed, with MS sherpa as an example, to assess the potential impact on treatment switches. (2) Methods: The eHTA was performed according to the Dutch guidelines for health economic evaluations. A decision analytic MS model was used to estimate the costs and benefits of MS standard care with and without use of MS sherpa, expressed in incremental cost-effectiveness ratios (ICERs) from both societal and health care perspectives. The efficacy of MS sherpa on early detection of active disease and the initiation of a treatment switch were modeled for a range of assumed efficacy (5%, 10%, 15%, 20%). (3) Results: From a societal perspective, for the efficacy of 15% or 20%, MS sherpa became dominant, which means cost-saving compared to the standard of care. MS sherpa is cost-effective in the 5% and 10% scenarios (ICERs EUR 14,535 and EUR 4069, respectively). From the health care perspective, all scenarios were cost-effective. Sensitivity analysis showed that increasing the efficacy of MS sherpa in detecting active disease early leading to treatment switches be the most impactful factor in the MS model. (4) Conclusions: The results indicate the potential of eHealth interventions to be cost-effective or even cost-saving in the MS care path. As such, digital biomarker–based eHealth interventions, like MS sherpa, are promising cost-effective solutions in optimizing MS disease management for people with MS, by detecting active disease early and helping neurologists in decisions on treatment switch

Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning

Computer vision techniques enable automated detection of sky pixels in outdoor imagery. In urban climate, sky detection is an important first step in gathering information about urban morphology and sky view factors. However, obtaining accurate results remains challenging and becomes even more complex using imagery captured under a variety of lighting and weather conditions. To address this problem, we present a new sky pixel detection system demonstrated to produce accurate results using a wide range of outdoor imagery types. Images are processed using a selection of mean-shift segmentation, K-means clustering, and Sobel filters to mark sky pixels in the scene. The algorithm for a specific image is chosen by a convolutional neural network, trained with 25,000 images from the Skyfinder data set, reaching 82% accuracy for the top three classes. This selection step allows the sky marking to follow an adaptive process and to use different techniques and parameters to best suit a particular image. An evaluation of fourteen different techniques and parameter sets shows that no single technique can perform with high accuracy across varied Skyfinder and Google Street View data sets. However, by using our adaptive process, large increases in accuracy are observed. The resulting system is shown to perform better than other published techniques