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SYCL compute kernels for ExaHyPE (2024)
Presentation / Conference Contribution
Loi, C. M., Bockhorst, H., & Weinzierl, T. (2024, March). SYCL compute kernels for ExaHyPE. Presented at 2024 SIAM Conference on Parallel Processing for Scientific Computing (PP), Baltimore, MD

We discuss three SYCL realisations of a simple Finite Volume scheme over multiple Cartesian patches. The realisation flavours differ in the way how they map the compute steps onto loops and tasks: We compare an implementation that is exclusively usin... Read More about SYCL compute kernels for ExaHyPE.

Maximizing Matching Cuts (2024)
Book Chapter
Le, V. B., Lucke, F., Paulusma, D., & Ries, B. (2024). Maximizing Matching Cuts. In P. M. Pardalos, & O. A. Prokopyev (Eds.), Encyclopedia of Optimization (1-10). Springer Nature. https://doi.org/10.1007/978-3-030-54621-2_898-1

Graph cut problems belong to a well-studied class of classical graph problems related to network connectivity, which is a central concept within theoretical computer science.

Payment Scheduling in the Interval Debt Model (2024)
Journal Article
Stewart, I., Kutner, D., Friedetzky, T., Trehan, A., & Mertzios, G. (in press). Payment Scheduling in the Interval Debt Model. Theoretical Computer Science,

The network-based study of financial systems has received considerable attention in recent years but has seldom explicitly incorporated the dynamic aspects of such systems. We consider this problem setting from the temporal point of view and introduc... Read More about Payment Scheduling in the Interval Debt Model.

Neural-code PIFu: High-fidelity Single Image 3D Human Reconstruction via Neural Code Integration (2024)
Presentation / Conference Contribution
Liu, R., Remagnino, P., & Shum, H. P. (2024, December). Neural-code PIFu: High-fidelity Single Image 3D Human Reconstruction via Neural Code Integration. Presented at 2024 International Conference on Pattern Recognition, Kolkata, India

We introduce neural-code PIFu, a novel implicit function for 3D human reconstruction, leveraging neural codebooks, our approach learns recurrent patterns in the feature space and reuses them to improve current features. Many existing methods predict... Read More about Neural-code PIFu: High-fidelity Single Image 3D Human Reconstruction via Neural Code Integration.

From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos (2024)
Presentation / Conference Contribution
Qiao, T., Li, R., Li, F. W. B., & Shum, H. P. H. (2024, December). From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos. Presented at ICPR 2024: International Conference on Pattern Recognition, Kolkata, India

Video-based Human-Object Interaction (HOI) recognition explores the intricate dynamics between humans and objects, which are essential for a comprehensive understanding of human behavior and intentions. While previous work has made significant stride... Read More about From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos.

Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions (2024)
Journal Article
Watson, M., Boulitsakis Logothetis, S., Green, D., Holland, M., Chambers, P., & Al Moubayed, N. (2024). Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions. BMJ Health & Care Informatics, 31(1), Article e101088. https://doi.org/10.1136/bmjhci-2024-101088

Objectives Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich fea... Read More about Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.

Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey (2024)
Journal Article
Vijendran, M., Deng, J., Chen, S., Ho, E. S. L., & Shum, H. P. H. (in press). Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey. Artificial Intelligence Review,

Artificial Intelligence significantly enhances the visual art industry by analyzing , identifying and generating digitized artistic images. This review highlights the substantial benefits of integrating geometric data into AI models, addressing chall... Read More about Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey.

Enhanced cross-domain lithology classification in imbalanced datasets using an unsupervised domain Adversarial Network (2024)
Journal Article
Xie, Y., Jin, L., Zhu, C., Luo, W., & Wang, Q. (2024). Enhanced cross-domain lithology classification in imbalanced datasets using an unsupervised domain Adversarial Network. Engineering Applications of Artificial Intelligence, 139(Part B), Article 109668. https://doi.org/10.1016/j.engappai.2024.109668

Recent advancements in Artificial Intelligence (AI), particularly deep learning, have significantly improved lithology identification in reservoir exploration by leveraging micrographic rock imagery. Deep neural networks excel in feature extraction,... Read More about Enhanced cross-domain lithology classification in imbalanced datasets using an unsupervised domain Adversarial Network.

A topic map based learning management system to facilitate meaningful grammar learning: the case of Japanese grammar learning (2024)
Journal Article
Wang, J., Wynn, A., Mendori, T., & Hwang, G.-J. (2024). A topic map based learning management system to facilitate meaningful grammar learning: the case of Japanese grammar learning. Smart Learning Environments, 11(1), Article 53. https://doi.org/10.1186/s40561-024-00338-1

This study investigates the effect of studying with topic maps provided by a self-developed language learning support system on (a) learning perception, (b) learning achievement and (c) variation in learning attitude and motivation, from the perspect... Read More about A topic map based learning management system to facilitate meaningful grammar learning: the case of Japanese grammar learning.

Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities (2024)
Journal Article
Farrell, S., Anderson, K., Noble, P.-J. M., & Al Moubayed, N. (2024). Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities. Scientific Reports, 14(1), Article 28763. https://doi.org/10.1038/s41598-024-77385-8

Monitoring mortality rates offers crucial insights into public health by uncovering the hidden impacts of diseases, identifying emerging trends, optimising resource allocation, and informing effective policy decisions. Here, we present a novel approa... Read More about Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities.

GANzzle + + : Generative approaches for jigsaw puzzle solving as local to global assignment in latent spatial representations (2024)
Journal Article
Talon, D., Del Bue, A., & James, S. (2025). GANzzle + + : Generative approaches for jigsaw puzzle solving as local to global assignment in latent spatial representations. Pattern Recognition Letters, 187, 35-41. https://doi.org/10.1016/j.patrec.2024.11.010

Jigsaw puzzles are a popular and enjoyable pastime that humans can easily solve, even with many pieces. However, solving a jigsaw is a combinatorial problem, and the space of possible solutions is exponential in the number of pieces, intractable for... Read More about GANzzle + + : Generative approaches for jigsaw puzzle solving as local to global assignment in latent spatial representations.

ExaGRyPE: Numerical general relativity solvers based upon the hyperbolic PDEs solver engine ExaHyPE (2024)
Journal Article
Zhang, H., Li, B., Weinzierl, T., & Barrera-Hinojosa, C. (2025). ExaGRyPE: Numerical general relativity solvers based upon the hyperbolic PDEs solver engine ExaHyPE. Computer Physics Communications, 307, Article 109435. https://doi.org/10.1016/j.cpc.2024.109435

ExaGRyPE describes a suite of solvers and solver ingredients for numerical relativity that are based upon ExaHyPE 2, the second generation of our Exascale Hyperbolic PDE Engine. Numerical relativity simulations are crucial in resolv... Read More about ExaGRyPE: Numerical general relativity solvers based upon the hyperbolic PDEs solver engine ExaHyPE.

Evidence Retrieval for Fact Verification using Multi-stage Reranking (2024)
Presentation / Conference Contribution
Malviya, S., & Katsigiannis, S. (2024, November). Evidence Retrieval for Fact Verification using Multi-stage Reranking. Presented at 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), Miami, FL, USA

Democratizing Uncertainty Quantification (2024)
Journal Article
Seelinger, L., Reinarz, A., Lykkegaard, M. B., Akers, R., Alghamdi, A. M., Aristoff, D., Bangerth, W., Bénézech, J., Diez, M., Frey, K., Jakeman, J. D., Jørgensen, J. S., Kim, K.-T., Kent, B. M., Martinelli, M., Parno, M., Pellegrini, R., Petra, N., Riis, N. A., Rosenfeld, K., …Scheichl, R. (2025). Democratizing Uncertainty Quantification. Journal of Computational Physics, 521(1), Article 113542. https://doi.org/10.1016/j.jcp.2024.113542

Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling... Read More about Democratizing Uncertainty Quantification.

Toward a framework for Responsible AI in storytelling for nonprofit fundraising (2024)
Book Chapter
Herrero, M., & Concannon, S. (2024). Toward a framework for Responsible AI in storytelling for nonprofit fundraising. In G. Ugazio, & M. Maricic (Eds.), . Routledge. https://doi.org/10.4324/9781003468615-11

AI techniques offer novel and compelling possibilities for nonprofit fundraising (e.g., data science applications can provide a deeper understanding of audiences and donors, and generative methods can create more personalized and persuasive communica... Read More about Toward a framework for Responsible AI in storytelling for nonprofit fundraising.

Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images (2024)
Presentation / Conference Contribution
Toso, M., Fiorini, S., James, S., & Del Bue, A. (2025, March). Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images. Presented at International Conference on 3D Vision (3DV), Singapore

World-wide detailed 2D maps require enormous collective efforts. OpenStreetMap is the result of 11 million registered users manually annotating the GPS location of over 1.75 billion entries, including distinctive landmarks and common urban objects. A... Read More about Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images.

From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers (2024)
Journal Article
Watson, M., Chambers, P., Steventon, L., Harmsworth King, J., Ercia, A., Shaw, H., & Al Moubayed, N. (2024). From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers. BMJ Oncology, 3(1), Article e000430. https://doi.org/10.1136/bmjonc-2024-000430

Objectives: Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clearance of subsequent treatment is not hindered; however, frequency and timing are not optimal. Model... Read More about From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.

Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation (2024)
Journal Article
Zhu, C., Zhang, L., Luo, W., Jiang, G., & Wang, Q. (2025). Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation. Neural Networks, 181, Article 106859. https://doi.org/10.1016/j.neunet.2024.106859

Unsupervised Domain Adaptation (UDA) is a machine learning technique that facilitates knowledge transfer from a labeled source domain to an unlabeled target domain, addressing distributional discrepancies between these domains. Existing UDA methods o... Read More about Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation.

MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment (2024)
Presentation / Conference Contribution
Zhou, K., Wang, L., Zhang, X., Shum, H. P. H., Li, F. W. B., Li, J., & Liang, X. (2024, September). MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment. Presented at ECCV 2024: The 18th European Conference on Computer Vision, Milan, Italy

Action Quality Assessment (AQA) evaluates diverse skills but models struggle with non-stationary data. We propose Continual AQA (CAQA) to refine models using sparse new data. Feature replay preserves memory without storing raw inputs. However, the mi... Read More about MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment.