SCOPE OF THE BUSINESS: THE BORROWED SERVANT PROBLEM

If your client wants to erect an office building he may be advised of the cost within narrow limits. The necessary expenditure will be X dollars plus Y lives or limbs. If his talents take the turn of bridge construction similar computations may be made. To carry forward to completion either of these projects he must use materials of various kinds, and he must use men. The expenditure of the human, animate, material is as inevitable as the expenditure of the inanimate. With increased care and skill the curve of expenditure of the human material will approach the asymptote of zero, but as long as men and materials are susceptible of failure, the Utopian condition of no loss whatever will never be reached. Loss, expenditure, there will always be. The business machine requires fuel. Perpetual motion has not yet arrived. So much is conceded. It is the last concession we shall make, for while someone must pay, there is no unanimity of opinion as to who that someone shall be

Parallelized Inference for Gravitational-Wave Astronomy

Bayesian inference is the workhorse of gravitational-wave astronomy, for example, determining the mass and spins of merging black holes, revealing the neutron star equation of state, and unveiling the population properties of compact binaries. The science enabled by these inferences comes with a computational cost that can limit the questions we are able to answer. This cost is expected to grow. As detectors improve, the detection rate will go up, allowing less time to analyze each event. Improvement in low-frequency sensitivity will yield longer signals, increasing the number of computations per event. The growing number of entries in the transient catalog will drive up the cost of population studies. While Bayesian inference calculations are not entirely parallelizable, key components are embarrassingly parallel: calculating the gravitational waveform and evaluating the likelihood function. Graphical processor units (GPUs) are adept at such parallel calculations. We report on progress porting gravitational-wave inference calculations to GPUs. Using a single code - which takes advantage of GPU architecture if it is available - we compare computation times using modern GPUs (NVIDIA P100) and CPUs (Intel Gold 6140). We demonstrate speed-ups of $\sim 50 \times$ for compact binary coalescence gravitational waveform generation and likelihood evaluation and more than $100\times$ for population inference within the lifetime of current detectors. Further improvement is likely with continued development. Our python-based code is publicly available and can be used without familiarity with the parallel computing platform, CUDA.Comment: 5 pages, 4 figures, submitted to PRD, code can be found at https://github.com/ColmTalbot/gwpopulation https://github.com/ColmTalbot/GPUCBC https://github.com/ADACS-Australia/ADACS-SS18A-RSmith Add demonstration of improvement in BNS spi

Paediatric neuropsychological assessment: an analysis of parents' perspectives

Purpose: Modern healthcare services are commonly based on shared models of care, in which a strong emphasis is placed upon the views of those in receipt of services. The purpose of this paper is to examine the parents' experiences of their child's neuropsychological assessment. Design/methodology/approach: This was a mixed-methodology study employing both quantitative and qualitative measures. Findings: The questionnaire measure indicated a high overall level of satisfaction. Qualitative analysis of parental interviews provided a richer insight into the parental experience and indicated four major themes. Practical implications: Implications covered three major areas. Firstly, whilst a high value was placed upon the assessment, the need for further comprehensive neurorehabilitation and intervention was highlighted. Secondly, this study highlights the significant adversity experienced by such families and subsequent unmet psychological needs which also require consideration. Finally, findings from the current study could assist in improving future measures of satisfaction in similar services. Originality/value: This is the first published study of parental experiences of and satisfaction with paediatric neuropsychological assessment in the UK. © Emerald Group Publishing Limited

Male figural rating scales: A critical review of the literature

Figural rating scales are tools used to measure male body dissatisfaction. The present review aimed to examine the design and psychometric properties of male figural rating scales and make recommendations based on findings. Relevant databases were systematically searched for studies that had developed and validated male figural rating scales. Twenty studies were included in this review. Figural rating scales differed in terms of the number of images represented and type of stimuli used (hand-drawn silhouettes, hand-drawn figures, computer-rendered figures, and photograph figures). Reliability and validity evidence varied greatly in strength across all scales. Four of the 20 scales included a correlational analysis between figural rating scale scores and eating disorder symptoms. Results showed the moderate to high positive correlations between eating disorder symptoms and figural rating scale perceived and index scores, suggesting that figural rating scales are sensitive to detecting eating disorder symptoms. Ideally, male figural rating scales should show strong validity and reliability, include variations in both body fat and muscularity, utilise realistic body stimuli, and be interval scales. No existing male figural rating scale meets these criteria. However, this review identifies five figural rating scales that meet the majority of the recommended criteria

Massively parallel Bayesian inference for transient gravitational-wave astronomy

Understanding the properties of transient gravitational waves and their sources is of broad interest in physics and astronomy. Bayesian inference is the standard framework for astro-physical measurement in transient gravitational-wave astronomy. Usually, stochastic sampling algorithms are used to estimate posterior probability distributions over the parameter spaces of models describing experimental data. The most physically accurate models typically come with a large computational overhead which can render data analysis extremely time consuming, or possibly even prohibitive. In some cases highly specialized optimizations can mitigate these issues, though they can be difficult to implement, as well as to generalize to arbitrary models of the data. Here, we propose an accurate, flexible and scalable method for astro-physical inference: parallelized nested sampling. The reduction in the wall-time of inference scales almost linearly with the number of parallel processes running on a high-performance computing cluster. By utilizing a pool of several hundreds or thousands of CPUs in a high-performance cluster, the large wall times of many astrophysical inferences can be alleviated while simultaneously ensuring that any gravitational-wave signal model can be used "out of the box", i.e., without additional optimization or approximation. Our method will be useful to both the LIGO-Virgo-KAGRA collaborations and the wider scientific community performing astrophysical analyses on gravitational waves. An implementation is available in the open source gravitational-wave inference library $\texttt{pBilby}$ (parallel $\texttt{bilby}$).Comment: 9 pages, 2 figures, 1 tabl

Measuring the Primordial Gravitational-Wave Background in the Presence of Astrophysical Foregrounds

Primordial gravitational waves are expected to create a stochastic background encoding information about the early Universe that may not be accessible by other means. However, the primordial background is obscured by an astrophysical foreground consisting of gravitational waves from compact binaries. We demonstrate a Bayesian method for estimating the primordial background in the presence of an astrophysical foreground. Since the background and foreground signal parameters are estimated simultaneously, there is no subtraction step, and therefore we avoid astrophysical contamination of the primordial measurement, sometimes referred to as “residuals.” Additionally, since we include the non-Gaussianity of the astrophysical foreground in our model, this method represents the statistically optimal approach to the simultaneous detection of a multicomponent stochastic background