The Search for Neutrino-Antineutrino Mixing from Lorentz Invariance Violation using Neutrino Interactions in MINOS

We searched for a sidereal modulation in the rate of neutrinos observed by the MINOS far detector. The detection of these signals could be a signature of neutrino-antineutrino mixing due to Lorentz and CPT violation as described by the Standard-Model Extension framework. We found no evidence for these sidereal signals and we placed limits on the coefficients in this theory describing the effect.Comment: Presented at the Sixth Meeting on CPT and Lorentz Symmetry, Bloomington, Indiana, June 17-21, 201

Methodological Issues in a Real-Time Study of Glaswegian Vowels: Automation and Comparability

No abstract available

The Continuing Cost of Privatization: Extra Payments to Medicare Advantage Plans Jump to $11.4 Billion in 2009

Outlines the impact of the 2003 increase in payments to private Medicare Advantage plans, efforts to phase out extra payments in order to finance improvements for low-income beneficiaries, and alternative approaches to providing equitable coverage

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

The new SAAO infrared photometric standards based on the E regions

Since 1974, when the Glass standards were published, four other infrared groups have been set up at the other main southern observatories, i.e. the two Australian observatories, Mount Stromlo and Siding Spring Observatory CMSO) and Anglo-Australian Observatory (AAO) and the two observatories in Chile, European Southern Observatory (ESO) and the Cerro Tololo Inter-American Observatory (which uses the California Institute of Technology standard system) (CIT). Unfortunately there has been a serious lack of conformity. Each group has used a different set of filters and has different zero points. No standards are common to all the groups and only a few are in common between any of the systems. Several transformat1ons between the various systems have recently been published (Elias et al 1983 and Glass 1983) but the large errors in the transformation equations show the need for more accurate standard stars. Therefore, on the surface, the introduction by us of a new set of standards would appear in danger of adding confusion to the issue, but this is not, in fact, the case. Firstly, the new set is basically the same system as tha Glass standards but with greatly increased accuracy. Secondly, the work involved has shown up several anomalies and inaccuracies in other systems. Thirdly, this work has shown why such large errors are apparent when comparing the various sets of standards and where the causes probably lie. The observations for these new standards were started in August 1979. The standards were introduced at the SAAO at the beginning of March 1984. The set is likely to evolve with time, increase in number of stars and improve in accuracy