Effect of current corrugations on the stability of the tearing mode

The generation of zonal magnetic fields in laboratory fusion plasmas is predicted by theoretical and numerical models and was recently observed experimentally. It is shown that the modification of the current density gradient associated with such corrugations can significantly affect the stability of the tearing mode. A simple scaling law is derived that predicts the impact of small stationary current corrugations on the stability parameter $\Delta'$. The described destabilization mechanism can provide an explanation for the trigger of the Neoclassical Tearing Mode (NTM) in plasmas without significant MHD activity.Comment: Accepted to Physics of Plasma

Classification using distance nearest neighbours

This paper proposes a new probabilistic classification algorithm using a Markov random field approach. The joint distribution of class labels is explicitly modelled using the distances between feature vectors. Intuitively, a class label should depend more on class labels which are closer in the feature space, than those which are further away. Our approach builds on previous work by Holmes and Adams (2002, 2003) and Cucala et al. (2008). Our work shares many of the advantages of these approaches in providing a probabilistic basis for the statistical inference. In comparison to previous work, we present a more efficient computational algorithm to overcome the intractability of the Markov random field model. The results of our algorithm are encouraging in comparison to the k-nearest neighbour algorithm.Comment: 12 pages, 2 figures. To appear in Statistics and Computin

Extensive telomere repeat arrays in mouse are hypervariable

In this study we have analysed mouse telomeres by Pulsed Field Gel Electrophoresis (PFGE). A number of specific restriction fragments hybridising to a (TTA-GGG)4 probe in the size range 50-150kb can be detected. These fragments are devoid of sites for most restriction enzymes suggesting that they comprise simple repeats; we argue that most of these are likely to be (TTAGGG)n. Each discrete fragment corresponds to the telomere of an individual chromosome and segregates as a Mendelian character. However, new size variants are being generated in the germ line at very high rates such that inbred mice are heterozygous at all telomeres analysable. In addition we show that specific small (approximately 4-12kb) fragments can be cleaved within some terminal arrays by the restriction enzyme MnII which recognises 5'(N7)GAGG3'. Like the complete telomere-repeat arrays (TRA's) these fragments form new variants at high rates and possibly by the same process. We speculate on the mechanisms that may be involved

Fast calibrated additive quantile regression

We propose a novel framework for fitting additive quantile regression models, which provides well calibrated inference about the conditional quantiles and fast automatic estimation of the smoothing parameters, for model structures as diverse as those usable with distributional GAMs, while maintaining equivalent numerical efficiency and stability. The proposed methods are at once statistically rigorous and computationally efficient, because they are based on the general belief updating framework of Bissiri et al. (2016) to loss based inference, but compute by adapting the stable fitting methods of Wood et al. (2016). We show how the pinball loss is statistically suboptimal relative to a novel smooth generalisation, which also gives access to fast estimation methods. Further, we provide a novel calibration method for efficiently selecting the 'learning rate' balancing the loss with the smoothing priors during inference, thereby obtaining reliable quantile uncertainty estimates. Our work was motivated by a probabilistic electricity load forecasting application, used here to demonstrate the proposed approach. The methods described here are implemented by the qgam R package, available on the Comprehensive R Archive Network (CRAN)

Additive Nonparametric Reconstruction of Dynamical Systems from Time Series

We present a nonparametric way to retrieve a system of differential equations in embedding space from a single time series. These equations can be treated with dynamical systems theory and allow for long term predictions. We demonstrate the potential of our approach for a modified chaotic Chua oscillator.Comment: accepted for Phys. Rev. E, Rapid Com

Geo-additive models of Childhood Undernutrition in three Sub-Saharan African Countries

We investigate the geographical and socioeconomic determinants of childhood undernutrition in Malawi, Tanzania and Zambia, three neighboring countries in Southern Africa using the 1992 Demographic and Health Surveys. We estimate models of undernutrition jointly for the three countries to explore regional patterns of undernutrition that transcend boundaries, while allowing for country-specific interactions. We use semiparametric models to flexibly model the effects of selected so-cioeconomic covariates and spatial effects. Our spatial analysis is based on a flexible geo-additive model using the district as the geographic unit of anal-ysis, which allows to separate smooth structured spatial effects from random effect. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques. While the socioeconomic determinants generally confirm what is known in the literature, we find distinct residual spatial patterns that are not explained by the socioeconomic determinants. In particular, there appears to be a belt run-ning from Southern Tanzania to Northeastern Zambia which exhibits much worse undernutrition, even after controlling for socioeconomic effects. These effects do transcend borders between the countries, but to a varying degree. These findings have important implications for targeting policy as well as the search for left-out variables that might account for these residual spatial patterns

Time-varying Learning and Content Analytics via Sparse Factor Analysis

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc, or the forgetting effect), and (iii) estimates the content organization and intrinsic difficulty of the assessment questions. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and a summary of the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instance. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, as well as analyzing the quality and content organization of learning resources, the question-concept associations, and the question intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses than existing collaborative filtering and knowledge tracing approaches for personalized education

A comparison of block and semi-parametric bootstrap methods for variance estimation in spatial statistics

Efron (1979) introduced the bootstrap method for independent data but it cannot be easily applied to spatial data because of their dependency. For spatial data that are correlated in terms of their locations in the underlying space the moving block bootstrap method is usually used to estimate the precision measures of the estimators. The precision of the moving block bootstrap estimators is related to the block size which is difficult to select. In the moving block bootstrap method also the variance estimator is underestimated. In this paper, first the semi-parametric bootstrap is used to estimate the precision measures of estimators in spatial data analysis. In the semi-parametric bootstrap method, we use the estimation of the spatial correlation structure. Then, we compare the semi-parametric bootstrap with a moving block bootstrap for variance estimation of estimators in a simulation study. Finally, we use the semi-parametric bootstrap to analyze the coal-ash data

Twelve (not so) angry men: jurors work better in small groups. Lorraine Hope and Bridget Waller propose a simple modification to jury deliberations

Twelve-person juries are often regarded as one of the cornerstones of democracy. In the UK, the right to a trial by jury is considered an important feature of the criminal justice system. Indeed, it has been rated as more important than a number of other rights, including the right to protest against the government, the right not to be detained for an extended period without charge and the right to free speech in public (Roberts and Hough, 2009). The public also trusts juries comprising randomly selected ordinary people and relies on the contribution of 12 individuals to eliminate bias and prejudice from the decision making process