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Item Authoring Visualisation of Routinely Collected Data Using LLMs(The Eurographics Association, 2024) Hosseini, Amir; Wood, Jo; Elshehaly, Mai; Hunter, David; Slingsby, AidanThe integration of routinely collected healthcare data into decision-making processes has the potential to revolutionise patient care and health outcomes. However, the complexity and heterogeneity of these datasets pose significant challenges for effective querying and analysis. Visualisation supports socio-technical processes where data analytics are augmented with human expertise to overcome data complexity. However, the authorship of effective visualisation is a challenging task, especially for users without a technical background, such as commissioners, clinicians and population health experts. This complexity calls for more efforts to develop natural language interfaces (NLIs) to democratise access to and understanding of routine data through visualisation. This short paper presents an innovative approach utilising Large Language Models (LLMs) to facilitate the querying and visualisation of routinely collected healthcare data. We present a preliminary framework for combining natural language queries with visualisation recommendation systems to retrieve and visualise relevant information from electronic health records (EHRs). We propose a human-in-the-loop approach for establishing accurate and efficient LLM-enabled information retrieval. Our preliminary findings suggest that LLMs can significantly streamline the visualisation authoring process, enabling stakeholders and healthcare professionals to access critical information rapidly and accurately. This work underscores the potential of LLM-driven solutions in advancing healthcare data utilisation and paves the way for future research in this promising intersection of artificial intelligence and medical informatics.Item EBBVH: A Novel Method for Constructing Bounding Volume Hierarchies(The Eurographics Association, 2024) Houghton, Matthew; Spoerer, Kristian; Hunter, David; Slingsby, AidanWe present an attempt to improve upon the construction of the most prevalent acceleration structure that is used in ray traced rendering techniques, the Bounding Volume Hierarchy. Our improvement is a novel technique for BVH construction called 'Edge-Based Bounding Volume Hierarchy'. This algorithm uses a hybrid top-down & bottom-up approach to improve performance for raytracing in large scenes, by up to 10x in some scenes.Item Extended Reality (XR) Immersive Visualisation: Identifying AI project member 'needs' in order to design for a more effective Low-Code Machine Learning Model Development experience(The Eurographics Association, 2022) Wheeler, Richard; Carroll, Fiona; Peter Vangorp; Martin J. TurnerWith organisations making machine learning projects more of a priority, issues have been found regarding the presentation of these types of projects and in particular, in explaining how the models that are produced work, not only internally but also to the final user. The following paper discusses the design and development of a novel Extended Reality (XR) solution that enables rapid development, experimentation and clear presentation of complex machine learning models using eXplainable AI (XAI) principles. The paper documents the findings from a short initial feasibility questionnaire study which probed participant's opinions around their current use of XR environments, low-code development platforms, and their experience of working on machine learning model development projects. The findings of that study showed that the proposed solution could be deemed novel especially regarding its use of extended reality, as none of the participants had used this technology for machine learning development productivity or collaboration. The aim of the paper is to highlight the development of a system that uses a low-code development platform for the development of machine learning models and then uses an extended reality environment to not only enable collaboration within development teams but also as a system for presenting a model's output. This paper documents the early phases of the research process (i.e. identifying the need) whilst also sharing ideas on how the issue can be solved.Item Immersive WebXR Data Visualisation Tool(The Eurographics Association, 2023) Ogbonda, Ebube Glory; Vangorp, Peter; Hunter, DavidThis paper presents a study of a WebXR data visualisation tool designed for the immersive exploration of complex datasets in a 3D environment. The application developed using AFrame, D3.js, and JavaScript enables an interactive, device-agnostic platform compatible with various devices and systems. A user study is proposed to assess the tool's usability, user experience, and mental workload using the NASA Task Load Index (NASA TLX). The evaluation is planned to employ questionnaires, task completion times, and open-ended questions to gather feedback and insights. The anticipated results aim to provide insights into the effectiveness of the application in supporting users in understanding and extracting insights from complex data while delivering an engaging and intuitive experience. Future work will refine and expand the tool's capabilities by exploring interaction guidance, visualisation layout optimisation, and long-term user experience assessment. This research contributes to the growing field of immersive data visualisation and informs future tool design.Item Towards Developing a Digital application for the Five Design-Sheets Methodology(The Eurographics Association, 2022) Owen, Aron E.; Roberts, Jonathan C.; Peter Vangorp; Martin J. TurnerThe Five Design-Sheet Methodology is a sketching methodology that helps people ideate different designs; it has been used to develop computer interfaces, games and data visualisations. Traditionally, it is a paper-based process that structures the developer to think about their design solution over five sheets with five sections. However, with the rise of mobile phones and tablets, there is an emerging opportunity to achieve the sketched design ideation process in a digital form. This work investigates the transition of the Five Design-Sheets from a paper-based methodology into a digital sketching application. The paper introduces how we considered the challenge, and have started to develop an application. Currently our application implements the first sheet of the FdS process. We describe the application and present a brief evaluation of the work with designers and developers.Item Interweaving Data and Stories: A Case Study on Unveiling the Human Dimension of U.S. Refugee Movements through Narrative Visualisation(The Eurographics Association, 2023) Ogbonda, Ebube Glory; Roberts, Jonathan C.; Butcher, Peter W. S.; Vangorp, Peter; Hunter, DavidIn response to the escalating global refugee crisis, we present a case-study of developing an advanced tool for interpreting high-dimensional refugee data. Developed using Mapbox and D3.js, our interactive visualisation harmonises geographical and temporal dimensions of U.S. refugee data from the State Department's Refugee Processing Center. Our modular approach and robust data preprocessing enable seamless interactions among diverse visual components. The result is a narrative-driven visualisation that reveals broad immigration trends and individual refugee movements, fostering a nuanced and empathetic understanding of refugee dynamics. This work highlights the power of narrative visualisations in shaping policy decisions and promoting global discourse on the refugee crisis, marking a significant leap in data visualisation for refugee and immigration challenges.Item Map Augmentation and Sketching for Cycling Experience Elicitation(The Eurographics Association, 2024) Reljan-Delaney, Mirela; Wood, Jo D.; Taylor, Alex S.; Hunter, David; Slingsby, AidanThis work examines the use of maps for knowledge elicitation in the sphere of urban cycling. The study involved running 14 distinct workshops, each serving as a unique data collection session for a particular individual. In each workshop, the participant was provided with 12 different renditions of the geographical areas as well as drawing materials. The geographical area renditions contained regions specified by the participant as cycling locations during the preparatory correspondence. The outputs were analysed for patterns in map augmentations and thematic content in the sketches. We have found that participants engaged deeply with the map augmentation process expressing their preferences and giving new insights. Themes such as connectivity, scenic beauty, and temporality emerged prominently from the analysed data, shedding light on the subjective experiences and preferences of urban cyclists.Item Inpainting Normal Maps for Lightstage data(The Eurographics Association, 2023) Zuo, Hancheng; Tiddeman, Bernard; Vangorp, Peter; Hunter, DavidThis paper presents a new method for inpainting of normal maps using a generative adversarial network (GAN) model. Normal maps can be acquired from a lightstage, and when used for performance capture, there is a risk of areas of the face being obscured by the movement (e.g. by arms, hair or props). Inpainting aims to fill missing areas of an image with plausible data. This work builds on previous work for general image inpainting, using a bow tie-like generator network and a discriminator network, and alternating training of the generator and discriminator. The generator tries to sythesise images that match the ground truth, and that can also fool the discriminator that is classifying real vs processed images. The discriminator is occasionally retrained to improve its performance at identifying the processed images. In addition, our method takes into account the nature of the normal map data, and so requires modification to the loss function. We replace a mean squared error loss with a cosine loss when training the generator. Due to the small amount of available training data available, even when using synthetic datasets, we require significant augmentation, which also needs to take account of the particular nature of the input data. Image flipping and in-plane rotations need to properly flip and rotate the normal vectors. During training, we monitored key performance metrics including average loss, Structural Similarity Index Measure (SSIM), and Peak Signal-to-Noise Ratio (PSNR) of the generator, alongside average loss and accuracy of the discriminator. Our analysis reveals that the proposed model generates high-quality, realistic inpainted normal maps, demonstrating the potential for application to performance capture. The results of this investigation provide a baseline on which future researchers could build with more advanced networks and comparison with inpainting of the source images used to generate the normal maps.Item Skipping Spheres: SDF Scaling & Early Ray Termination for Fast Sphere Tracing(The Eurographics Association, 2024) Polychronakis, Andreas; Koulieris, George Alex; Mania, Katerina; Hunter, David; Slingsby, AidanThis paper presents a rapid rendering pipeline for sphere tracing Signed Distance Functions (SDFs), showcasing a notable boost in performance compared to the current state-of-the-art. Existing methods endeavor to reduce the ray step count by adjusting step size using heuristics or by rendering multiple intermediate lower-resolution buffers to pre-calculate non-salient pixels at reduced quality. However, the accelerated performance with low-resolution buffers often introduces artifacts compared to fully sphere-traced scenes, especially for smaller features, which might go unnoticed altogether. Our approach significantly reduces steps compared to prior work while minimising artifacts. We accomplish this based on two key observations and by employing a single low-resolution buffer: Firstly, we perform SDF scaling in the low-resolution buffer, effectively enlarging the footprint of the implicit surfaces when rendered in low resolution, ensuring visibility of all SDFs. Secondly, leveraging the low-resolution buffer rendering, we detect when a ray converges to high-cost surface edges and can terminate sphere tracing earlier than usual, further reducing step count. Our method achieves a substantial performance improvement (exceeding 3× in certain scenes) compared to previous approaches, while minimizing artifacts, as demonstrated in our visual fidelity evaluation.Item Meshing of Spiny Neuronal Morphologies using Union Operators(The Eurographics Association, 2022) Abdellah, Marwan; Cantero, Juan José García; Foni, Alessandro; Guerrero, Nadir Román; Boci, Elvis; Schürmann, Felix; Peter Vangorp; Martin J. TurnerNeurons are characterized by thin and long interleaving arborizations in which creating accurate mesh models of their cellular membranes is challenging. While union operators are central for CAD/CAM modeling and computer graphics applications, their applicability to neuronal mesh generation has not been explored. In this work, we present the results of exploring the effectiveness of using union operators to generate high fidelity surface meshes of spiny neurons from their morphological traces. To improve the visual realism of the resulting models, a plausible shape of the cell body is also realized with implicit surfaces (metaballs). The algorithm is implemented in Blender based on its Python API and is integrated into NeuroMorphoVis, a neuroscience-specific framework for visualization and analysis of neuronal morphologies. Our method is applied to a dataset consisting of more than 600 neurons representing 60 morphological types reconstructed from the neocortex of a juvenile rat. The performance of our implementation is quantitatively analyzed, and the results are qualitatively compared to previous implementation. The resulting meshes are applicable in multiple contexts including visualization and analysis of full compartmental simulations and generation of high quality multimedia content for scientific visualization and visual computing (Figure 1).