Cluster detection and risk estimation for spatio-temporal health data

In epidemiological disease mapping one aims to estimate the spatio-temporal pattern in disease risk and identify high-risk clusters, allowing health interventions to be appropriately targeted. Bayesian spatio-temporal models are used to estimate smoothed risk surfaces, but this is contrary to the aim of identifying groups of areal units that exhibit elevated risks compared with their neighbours. Therefore, in this paper we propose a new Bayesian hierarchical modelling approach for simultaneously estimating disease risk and identifying high-risk clusters in space and time. Inference for this model is based on Markov chain Monte Carlo simulation, using the freely available R package CARBayesST that has been developed in conjunction with this paper. Our methodology is motivated by two case studies, the first of which assesses if there is a relationship between Public health Districts and colon cancer clusters in Georgia, while the second looks at the impact of the smoking ban in public places in England on cardiovascular disease clusters

Co-viewing TV with Twitter: more interesting than the shows?

Social media services, and Twitter in particular, are changing the way in which many people consume traditional broadcast media. Real-time backchannel conversations are now common-place as audiences simultaneously watch TV whilst using Twitter to broadcast their own thoughts, sentiments, opinions and emotions related to what they are watching. This individual behavior, when aggregated, results in a new social experience comprising of mass, real-time, coconsumption of TV services that has, thus far, been neither recognized nor investigated by the HCI community nor the broadcast industry. This paper describes a work-in-progress which aims to understand user behaviour in this burgeoning area and provides some preliminary analysis of viewers’ Twitter activity surrounding the popular UK TV show, The X Factor

A history of the development of the mathematics and statistics support community in the United Kingdom. Part 1: From alpha to sigma

In terms of the history of mathematics higher education, mathematics and statistics support (MSS) is a very recent development, existing as a formal feature for less than 50 years.  However, in this short time, MSS has displayed its own characteristics.  A particularly notable feature of MSS in the United Kingdom (and in other countries) has been the way in which practitioners have collaborated with each other, almost from the outset.  This collaboration has led to the creation of a community (the sigma network) with a written constitution and formal membership.  This two-part article traces the history of the development of the MSS community in the UK from its earliest incarnations to the present day.  The first part of the article reviews the period from the early 1990s to 2005 during which time the key events were the rise and demise of the Mathematics Support Association and the creation of sigma, Centre of Excellence in University-wide Mathematics and Statistics Support.

A Bayesian space–time model for clustering areal units based on their disease trends

Population-level disease risk across a set of non-overlapping areal units varies in space and time, and a large research literature has developed methodology for identifying clusters of areal units exhibiting elevated risks. However, almost no research has extended the clustering paradigm to identify groups of areal units exhibiting similar temporal disease trends. We present a novel Bayesian hierarchical mixture model for achieving this goal, with inference based on a Metropolis-coupled Markov chain Monte Carlo ((MC) 3 ) algorithm. The effectiveness of the (MC) 3 algorithm compared to a standard Markov chain Monte Carlo implementation is demonstrated in a simulation study, and the methodology is motivated by two important case studies in the United Kingdom. The first concerns the impact on measles susceptibility of the discredited paper linking the measles, mumps, and rubella vaccination to an increased risk of Autism and investigates whether all areas in the Scotland were equally affected. The second concerns respiratory hospitalizations and investigates over a 10 year period which parts of Glasgow have shown increased, decreased, and no change in risk

Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN

Over the past few years machine learning has seen a renewed explosion of interest, following a number of studies showing the effectiveness of neural networks in a range of tasks which had previously been considered incredibly hard. Neural networks' effectiveness in the fields of image recognition and natural language processing stems primarily from the vast amounts of data available to companies and researchers, coupled with the huge amounts of compute power available in modern accelerators such as GPUs, FPGAs and ASICs. There are a number of approaches available to developers for utilizing GPGPU technologies such as SYCL, OpenCL and CUDA, however many applications require the same low level mathematical routines. Libraries dedicated to accelerating these common routines allow developers to easily make full use of the available hardware without requiring low level knowledge of the hardware themselves, however such libraries are often provided by hardware manufacturers for specific hardware such as cuDNN for Nvidia hardware or MIOpen for AMD hardware. SYCL-DNN is a new open-source library dedicated to providing accelerated routines for neural network operations which are hardware and vendor agnostic. Built on top of the SYCL open standard and written entirely in standard C++, SYCL-DNN allows a user to easily accelerate neural network code for a wide range of hardware using a modern C++ interface. The library is tested on AMD's OpenCL for GPU, Intel's OpenCL for CPU and GPU, ARM's OpenCL for Mali GPUs as well as ComputeAorta's OpenCL for R-Car CV engine and host CPU. In this talk we will present performance figures for SYCL-DNN on this range of hardware, and discuss how high performance was achieved on such a varied set of accelerators with such different hardware features.Comment: 4 pages, 3 figures. In International Workshop on OpenCL (IWOCL '19), May 13-15, 2019, Bosto

GASP: Guitars with ambisonic spatial performance

‘Guitars with Ambisonic Spatial Performance’ (GASP) is an ongoing project where our expertise in surround sound algorithmic research is combined with off-the-shelf hardware and bespoke software to create a spatial multichannel surround guitar performance system. This poster was funded through the ‘Undergraduate Research Scholarship Scheme’ (URSS) and presented at the University of Derby Buxton Campus 10th Annual Learning & Teaching conference on Wednesday 1st July 2015. The theme being ‘Students as Partners: Linking Teaching, Research and Enterprise’. The poster was also utilised as a contribution to the Creative Technologies Research Group (CTRG) ‘Sounds in Space’ symposium held at the University of Derby in June 2015, at which three pieces of multichannel guitar recordings were demonstrated.‘Undergraduate Research Scholarship Scheme’ (URSS) University of Derb

The Effectiveness of a Primary School Based Badminton Intervention on Children’s Fundamental Movement Skills

This study examined the effects of the Badminton World Federation (BWF) Shuttle Time program on fundamental movement skills (FMS) in English children. A total of 124 children; 66 in key stage 1 (ages 6–7 years) and 58 in key stage 2 (10–11 years) undertook the Shuttle Time program, once weekly for six weeks (n = 63) or acted as controls (n = 61). Pre, post and ten-weeks post, both process and product FMS were determined. Children in the intervention group, aged 6–7 years, had higher total process FMS (via test of gross motor development-2) compared to the control group at post and ten-weeks post intervention (both p = 0.0001, d = 0.6 and 0.7, respectively). There were no significant differences in process FMS scores for children aged 10–11 years. Ten-meter sprint speed decreased pre to post and was maintained at ten-weeks post for the intervention groups aged 6–7 years (p = 0.0001, d = 0.6) and 10–11 years (p = 0.001, d = 0.2) compared to control. Standing long jump distance increased pre to post (p = 0.0001, d = 0.8) and was maintained at ten-weeks post (p = 0.0001, d = 0.5) for the intervention group. Medicine ball throw performance increased pre to post (p = 0.0001, d = 0.3) for the intervention group. The BWF Shuttle Time program is beneficial in developing FMS for key stage 1 children (ages 6–7)