Explicit Lie-Poisson integration and the Euler equations

We give a wide class of Lie-Poisson systems for which explicit, Lie-Poisson integrators, preserving all Casimirs, can be constructed. The integrators are extremely simple. Examples are the rigid body, a moment truncation, and a new, fast algorithm for the sine-bracket truncation of the 2D Euler equations.Comment: 7 pages, compile with AMSTEX; 2 figures available from autho

Native Speaker Perceptions of Accented Speech: The English Pronunciation of Macedonian EFL Learners

The paper reports on the results of a study that aimed to describe the vocalic and consonantal features of the English pronunciation of Macedonian EFL learners as perceived by native speakers of English and to find out whether native speakers who speak different standard variants of English perceive the same segments as non-native. A specially designed computer web application was employed to gather two types of data: a) quantitative (frequency of segment variables and global foreign accent ratings on a 5-point scale), and b) qualitative (open-ended questions). The result analysis points out to three most frequent markers of foreign accent in the English speech of Macedonian EFL learners: final obstruent devoicing, vowel shortening and substitution of English dental fricatives with Macedonian dental plosives. It also reflects additional phonetic aspects poorly explained in the available reference literature such as allophonic distributional differences between the two languages and intonational mismatch

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page

Qualitative Robustness of Bootstrap Approximations for Kernel Based Methods

status: publishe

Genetic determinants of risk in pulmonary arterial hypertension: international genome-wide association studies and meta-analysis

Background Rare genetic variants cause pulmonary arterial hypertension, but the contribution of common genetic variation to disease risk and natural history is poorly characterised. We tested for genome-wide association for pulmonary arterial hypertension in large international cohorts and assessed the contribution of associated regions to outcomes. Methods We did two separate genome-wide association studies (GWAS) and a meta-analysis of pulmonary arterial hypertension. These GWAS used data from four international case-control studies across 11744 individuals with European ancestry (including 2085 patients). One GWAS used genotypes from 5895 whole-genome sequences and the other GWAS used genotyping array data from an additional 5849 individuals. Cross-validation of loci reaching genome-wide significance was sought by meta-analysis. Conditional analysis corrected for the most significant variants at each locus was used to resolve signals for multiple associations. We functionally annotated associated variants and tested associations with duration of survival. All-cause mortality was the primary endpoint in survival analyses. Findings A locus near SOX17 (rs10103692, odds ratio 1·80 [95% CI 1·55–2·08], p=5·13×10– ¹⁵) and a second locus in HLA-DPA1 and HLA-DPB1 (collectively referred to as HLA-DPA1/DPB1 here; rs2856830, 1·56 [1·42–1·71], p=7·65×10– ²⁰) within the class II MHC region were associated with pulmonary arterial hypertension. The SOX17 locus had two independent signals associated with pulmonary arterial hypertension (rs13266183, 1·36 [1·25–1·48], p=1·69×10– ¹²; and rs10103692). Functional and epigenomic data indicate that the risk variants near SOX17 alter gene regulation via an enhancer active in endothelial cells. Pulmonary arterial hypertension risk variants determined haplotype-specific enhancer activity, and CRISPR-mediated inhibition of the enhancer reduced SOX17 expression. The HLA-DPA1/DPB1 rs2856830 genotype was strongly associated with survival. Median survival from diagnosis in patients with pulmonary arterial hypertension with the C/C homozygous genotype was double (13·50 years [95% CI 12·07 to >13·50]) that of those with the T/T genotype (6·97 years [6·02–8·05]), despite similar baseline disease severity. Interpretation This is the first study to report that common genetic variation at loci in an enhancer near SOX17 and in HLA-DPA1/DPB1 is associated with pulmonary arterial hypertension. Impairment of SOX17 function might be more common in pulmonary arterial hypertension than suggested by rare mutations in SOX17. Further studies are needed to confirm the association between HLA typing or rs2856830 genotyping and survival, and to determine whether HLA typing or rs2856830 genotyping improves risk stratification in clinical practice or trials. Funding UK NIHR, BHF, UK MRC, Dinosaur Trust, NIH/NHLBI, ERS, EMBO, Wellcome Trust, EU, AHA, ACClinPharm, Netherlands CVRI, Dutch Heart Foundation, Dutch Federation of UMC, Netherlands OHRD and RNAS, German DFG, German BMBF, APH Paris, INSERM, Université Paris-Sud, and French ANR

Uncorrelated versus independent elliptically-contoured distributions for anomalous change detection in hyperspectral imagery

The detection of actual changes in a pair of images is confounded by the inadvertent but pervasive differences that inevitably arise whenever two pictures are taken of the same scene, but at different times and under different conditions. These differences include effects due to illumination, calibration, misregistration, etc. If the actual changes are assumed to be rare, then one can 'learn' what the pervasive differences are, and can identify the deviations from this pattern as the anomalous changes. A recently proposed framework for anomalous change detection recasts the problem as one of binary classification between pixel pairs in the data and pixel pairs that are independently chosen from the two images. When an elliptically-contoured (EC) distribution is assumed for the data, then analytical expressions can be derived for the measure of anomalousness of change. However, these expression are only available for a limited class of EC distributions. By replacing independent pixel pairs with uncorrelated pixel pairs, an approximate solution can be found for a much broader class of EC distributions. The performance of this approximation is investigated analytically and empirically, and includes experiments comparing the detection of real changes in real data