The effects of velocities and lensing on moments of the Hubble diagram

We consider the dispersion on the supernova distance-redshift relation due to peculiar velocities and gravitational lensing, and the sensitivity of these effects to the amplitude of the matter power spectrum. We use the MeMo lensing likelihood developed by Quartin, Marra & Amendola (2014), which accounts for the characteristic non-Gaussian distribution caused by lensing magnification with measurements of the first four central moments of the distribution of magnitudes. We build on the MeMo likelihood by including the effects of peculiar velocities directly into the model for the moments. In order to measure the moments from sparse numbers of supernovae, we take a new approach using Kernel Density Estimation to estimate the underlying probability density function of the magnitude residuals. We also describe a bootstrap re-sampling approach to estimate the data covariance matrix. We then apply the method to the Joint Light-curve Analysis (JLA) supernova catalogue. When we impose only that the intrinsic dispersion in magnitudes is independent of redshift, we find $\sigma_8=0.44^{+0.63}_{-0.44}$ at the one standard deviation level, although we note that in tests on simulations, this model tends to overestimate the magnitude of the intrinsic dispersion, and underestimate $\sigma_8$. We note that the degeneracy between intrinsic dispersion and the effects of $\sigma_8$ is more pronounced when lensing and velocity effects are considered simultaneously, due to a cancellation of redshift dependence when both effects are included. Keeping the model of the intrinsic dispersion fixed as a Gaussian distribution of width 0.14 mag, we find $\sigma_8 = 1.07^{+0.50}_{-0.76}$.Comment: 16 pages, updated to match version accepted in MNRA

Performance Evaluation of Judgmental Directional Exchange Rate Predictions

Cataloged from PDF version of article.A procedure is proposed for examining different aspects of performance for judgemental directional probability predictions of exchange rate movements. In particular, a range of new predictive performance measures is identified to highlight specific expressions of strengths and weaknesses in judgemental directional forecasts. Proposed performance qualifiers extend the existing accuracy measures, enabling detailed comparisons of probability forecasts with ex-post empirical probabilities that are derived from changes in the logarithms of the series. This provides a multi-faceted evaluation that is straightforward for practitioners to implement, while affording the flexibility of being used in situations where the time intervals between the predictions have variable lengths. The proposed procedure is illustrated via an application to a set of directional probability exchange rate forecasts for the US Dollar/Swiss Franc from 23/7/96 to 7/12/99 and the findings are discussed. D 2005 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved

Evaluating predictive performance of judgemental extrapolations from simulated currency series

Cataloged from PDF version of article.Judgemental forecasting of exchange rates is critical for ®nancial decision-making. Detailed investigations of the potential e ects of time-series characteristics on judgemental currency forecasts demand the use of simulated series where the form of the signal and probability distribution of noise are known. The accuracy measures Mean Absolute Error (MAE) and Mean Squared Error (MSE) are frequently applied quantities in assessing judgemental predictive performance on actual exchange rate data. This paper illustrates that, in applying these measures to simulated series with Normally distributed noise, it may be desirable to use their expected values after standardising the noise variance. A method of calculating the expected values for the MAE and MSE is set out, and an application to ®nancial experts' judgemental currency forecasts is presented. Ó 1999 Elsevier Science B.V. All rights reserved

Assessing the genetic diversity of rice originating from Bangladesh, Assam and West Bengal

Acknowledgements This work was funded by BBSRC research project BB/J00336/1. FS and a part of the proportion of the cost of the Illumina genotyping was funded by a Beachell-Borlag International Fellowship. The authors would like to acknowledge the help of Dr MK Sarmah in collecting seed samples of the landraces and improved cultivars from Assam used in this study and Dr. Ma. Elizabeth B. Naredo and Ms. Sheila Mae Q. Mercado for handling of IRGC accessions and preparation of DNAs for genotyping. All rice seeds used here were obtained with MTA agreements and seed and dry leaves imported into the UK under import licence IMP⁄SOIL⁄18⁄2009 issued by Science and Advice for Scottish Agriculture.Peer reviewedPublisher PD

Cyber-risks in the Industrial Internet of Things (IIoT): towards a method for continuous assessment.

Continuous risk monitoring is considered in the context of cybersecurity management for the Industrial Internet-of-Thing. Cyber risk management best practice is for security controls to be deployed and configured in order to bring down risk exposure to an acceptable level. However, threats and known vulnerabilities are subject to change, and estimates of risk are subject to many uncertainties, so it is important to review risk assessments and update controls when required. Risks are typically reviewed periodically (e.g. once per month), but the accelerating pace of change means that this approach is not sustainable, and there is a requirement for continuous monitoring of cybersecurity risks. The method described in this paper aims to alert security staff of significant changes or trends in estimated risk exposure to facilitate rational and timely decisions. Additionally, it helps predict the success and impact of a nascent security breach allowing better prioritisation of threats and selection of appropriate responses. The method is illustrated using a scenario based on environmental control in a data centre