Eating my words

Language learning is common preparation for much anthropological fieldwork, but the choices researchers make in this area are distinctly political. Prompted by a chance encounter while studying Hindi, the author reflects on this realisation in view of the numerous languages spoken in India, the interactions involved in hospital-based research, and the place of language in Indian politics more broadly

Mapping Antarctic crevasses and their evolution with deep learning applied to satellite radar imagery

The fracturing of glaciers and ice shelves in Antarctica influences their dynamics and stability. Hence, data on the evolving distribution of crevasses are required to better understand the evolution of the ice sheet, though such data have traditionally been difficult and time-consuming to generate. Here, we present an automated method of mapping crevasses on grounded and floating ice with the application of convolutional neural networks to Sentinel-1 synthetic aperture radar backscatter data. We apply this method across Antarctica to images acquired between 2015 and 2022, producing a 7.5-year record of composite fracture maps at monthly intervals and 50 m spatial resolution and showing the distribution of crevasses around the majority of the ice sheet margin. We develop a method of quantifying changes to the density of ice shelf fractures using a time series of crevasse maps and show increases in crevassing on Thwaites and Pine Island ice shelves over the observational period, with observed changes elsewhere in the Amundsen Sea dominated by the advection of existing crevasses. Using stress fields computed using the BISICLES ice sheet model, we show that much of this structural change has occurred in buttressing regions of these ice shelves, indicating a recent and ongoing link between fracturing and the developing dynamics of the Amundsen Sea sector.</p

Amundsen Sea Embayment ice-sheet mass-loss predictions to 2050 calibrated using observations of velocity and elevation change

Mass loss from the Amundsen Sea Embayment of the West Antarctic Ice Sheet is a major contributor to global sea-level rise (SLR) and has been increasing over recent decades. Predictions of future SLR are increasingly modelled using ensembles of simulations within which model parameters and external forcings are varied within credible ranges. Accurately reporting the uncertainty associated with these predictions is crucial in enabling effective planning for, and construction of defences against, rising sea levels. Calibrating model simulations against current observations of ice-sheet behaviour enables the uncertainty to be reduced. Here we calibrate an ensemble of BISICLES ice-sheet model simulations of ice loss from the Amundsen Sea Embayment using remotely sensed observations of surface elevation and ice speed. Each calibration type is shown to be capable of reducing the 90% credibility bounds of predicted contributions to SLR by 34 and 43% respectively

Mindfulness-based Cognitive Therapy (MBCT) For Severe Health Anxiety

Initial evidence indicates that MBCT, which combines aspects of cognitive therapy with meditation, may be an effective treatment for health anxiety. Freda McManus, Kate Muse and Christina Surawy describe its benefits

A Framework for Supervision for Mindfulness-Based Teachers:a Space for Embodied Mutual Inquiry

Over recent decades, there has been an exponential growth in mindfulness-based interventions (MBIs). To disseminate MBIs with fidelity, care needs to be taken with the training and supervision of MBI teachers. A wealth of literature exists describing the process and practice of supervision in a range of clinical approaches, but, as of yet, little consideration has been given to how this can best be applied to the supervision of MBI teachers. This paper articulates a framework for supervision of MBI teachers. It was informed by the following: the experience of eight experienced mindfulness-based supervisors, the literature and understandings from MBIs, and by the authors’ experience of training and supervision. It sets out the nature and distinctive features of mindfulness-based supervision (MBS), representing this complex, multilayered process through a series of circles that denote its essence, form, content and process. This paper aims to be a basis for further dialogue on MBS, providing a foundation to increase the availability of competent supervision so that MBIs can expand without compromising integrity and efficacy

Mapping Antarctic crevasses and their evolution with deep learning applied to satellite radar imagery

The fracturing of glaciers and ice shelves in Antarctica influences their dynamics and stability. Hence, data on the evolving distribution of crevasses are required to better understand the evolution of the ice sheet, though such data have traditionally been difficult and time-consuming to generate. Here, we present an automated method of mapping crevasses on grounded and floating ice with the application of convolutional neural networks to Sentinel-1 synthetic aperture radar backscatter data. We apply this method across Antarctica to images acquired between 2015 and 2022, producing a 7.5-year record of composite fracture maps at monthly intervals and 50 m spatial resolution and showing the distribution of crevasses around the majority of the ice sheet margin. We develop a method of quantifying changes to the density of ice shelf fractures using a time series of crevasse maps and show increases in crevassing on Thwaites and Pine Island ice shelves over the observational period, with observed changes elsewhere in the Amundsen Sea dominated by the advection of existing crevasses. Using stress fields computed using the BISICLES ice sheet model, we show that much of this structural change has occurred in buttressing regions of these ice shelves, indicating a recent and ongoing link between fracturing and the developing dynamics of the Amundsen Sea sector

Projected Least-Squares Quantum Process Tomography

We propose and investigate a new method of quantum process tomography (QPT) which we call projected least squares (PLS). In short, PLS consists of first computing the least-squares estimator of the Choi matrix of an unknown channel, and subsequently projecting it onto the convex set of Choi matrices. We consider four experimental setups including direct QPT with Pauli eigenvectors as input and Pauli measurements, and ancilla-assisted QPT with mutually unbiased bases (MUB) measurements. In each case, we provide a closed form solution for the least-squares estimator of the Choi matrix. We propose a novel, two-step method for projecting these estimators onto the set of matrices representing physical quantum channels, and a fast numerical implementation in the form of the hyperplane intersection projection algorithm. We provide rigorous, non-asymptotic concentration bounds, sampling complexities and confidence regions for the Frobenius and trace-norm error of the estimators. For the Frobenius error, the bounds are linear in the rank of the Choi matrix, and for low ranks, they improve the error rates of the least squares estimator by a factor d2, where d is the system dimension. We illustrate the method with numerical experiments involving channels on systems with up to 7 qubits, and find that PLS has highly competitive accuracy and computational tractability

Chronic fatigue syndrome: identifying zebras amongst the horses

There are currently no investigative tools or physical signs that can confirm or refute the presence of chronic fatigue syndrome (CFS). As a result, clinicians must decide how long to keep looking for alternative explanations for fatigue before settling on a diagnosis of CFS. Too little investigation risks serious or easily treatable causes of fatigue being overlooked, whilst too many increases the risk of iatrogenic harm and reduces the opportunity for early focused treatment. A paper by Jones et al published this month in BMC Medicine may help clinicians in deciding how to undertake such investigations. Their results suggest that if clinicians look for common psychiatric and medical conditions in those complaining of prolonged fatigue, the rate of detection will be higher than previously estimated. The most common co-morbid condition identified was depression, suggesting a simple mental state examination remains the most productive single investigation in any new person presenting with unexplained fatigue. Currently, most diagnostic criteria advice CFS should not be diagnosed when an active medical or psychiatric condition which may explain the fatigue is identified. We discuss a number of recent prospective studies that have provided valuable insights into the aetiology of chronic fatigue and describe a model for understanding chronic fatigue which may be equally relevant regardless of whether or not an apparent medical cause for fatigue can be identified