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    Real-Time Rendering of Molecular Dynamics Simulation Data: A Tutorial
    (The Eurographics Association, 2017) Alharbi, Naif; Chavent, Matthieu; Laramee, Robert S.; Tao Ruan Wan and Franck Vidal
    Achieving real-time molecular dynamics rendering is a challenge, especially when the rendering requires intensive computation involving a large simulation data-set. The task becomes even more challenging when the size of the data is too large to fit into random access memory (RAM) and the final imagery depends on the input and output (I/O) performance. The large data size and the complex computation processing per frame pose a number of challenges. i.e. the I/O performance bottleneck, the computational processing performance costs, and the fast rendering challenge. Handling these challenges separately consumes a significant portion of the total processing time which may result in low frame rates. We address these challenges by proposing an approach utilizing advanced memory management and bridging the Open Computing Language (OpenCL) and Open Graphics Library (OpenGL) drivers to optimize the final rendering frame rate. We illustrate the concept of the memory mapping technique and the hybrid OpenCL and OpenGL combination through a real molecular dynamics simulation example. The simulation data-set specifies the evolution of 336,260 particles over 1981 time steps occupying 8 Gigabyte of memory. The dynamics of the system including the lipid-protein interactions can be rendered at up to 40 FPS.
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    Time-oriented Cartographic Treemaps for Visualization of Public Healthcare Data
    (The Eurographics Association, 2017) Tong, Chao; McNabb, Liam; Laramee, Robert S.; Lyons, Jane; Walters, Angharad; Berridge, Damon; Thayer, Daniel; Tao Ruan Wan and Franck Vidal
    Cartographic treemaps offer a way to explore and present hierarchical multi-variate data that combines the space-efficient advantages of treemaps for the display of hierarchical data together with relative geo-spatial location from maps in the form of a modified cartogram. They offer users a space-efficient overview of the complex, multi-variate data coupled with the relative geo-spatial location to enable and facilitate exploration, analysis, and comparison. In this paper, we introduce time as an additional variate, in order to develop time-oriented cartographic treemaps. We design, implement and compare a range of visual layout options highlighting advantages and disadvantage of each. We apply the method to the study of UK-centric electronic health records data as a case study. We use the results to explore the trends of a range of health diagnoses in each UK healthcare region over multiple years exploiting both static and animated visual designs. We provide several examples and user options to evaluate the performance in exploration, analysis, and comparison. We also report the reaction of domain experts from health science.
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    Cartographic Treemaps for Visualization of Public Healthcare Data
    (The Eurographics Association, 2017) Tong, Chao; Roberts, Richard; Laramee, Robert S.; Berridge, Damon; Thayer, Daniel; Tao Ruan Wan and Franck Vidal
    The National healthcare Service (NHS) in the UK collects a massive amount of high-dimensional, region-centric data concerning individual healthcare units throughout Great Britain. It is challenging to visually couple the large number of multivariate attributes about each region unit together with the geo-spatial location of the clinical practices for visual exploration, analysis, and comparison. We present a novel multivariate visualization we call a cartographic treemap that attempts to combine the space-filling advantages of treemaps for the display of hierarchical, multivariate data together with the relative geo-spatial location of NHS practices in the form of a modified cartogram. It offers both space filling and geospatial error metrics that provide the user with interactive control over the space-filling versus geographic error trade-off. The result is a visualization that offers users a more space efficient overview of the complex, multivariate healthcare data coupled with the relative geo-spatial location of each practice to enable and facilitate exploration, analysis, and comparison. We evaluate the two metrics and demonstrate the use of our approach on real, large high-dimensional NHS data and derive a number of multivariate observations based on healthcare in the UK as a result. We report the reaction of our software from two domain experts in health science.