Outputs (116)

Beyond Trees: Calculating Graph-Based Compilers (Functional Pearl) (2024)
Journal Article
Bahr, P., & Hutton, G. (2024). Beyond Trees: Calculating Graph-Based Compilers (Functional Pearl). Proceedings of the ACM on Programming Languages, 8(ICFP), 370-394. https://doi.org/10.1145/3674638

Bahr and Hutton recently developed an approach to compiler calculation that allows a wide range of compilers to be derived from specifications of their correctness. However, a limitation of the approach is that it results in compilers that produce tr... Read More about Beyond Trees: Calculating Graph-Based Compilers (Functional Pearl).

Gase: graph attention sampling with edges fusion for solving vehicle routing problems (2024)
Journal Article
Wang, Z., Bai, R., Khan, F., Özcan, E., & Zhang, T. (2024). Gase: graph attention sampling with edges fusion for solving vehicle routing problems. Memetic Computing, 16(3), 337–353. https://doi.org/10.1007/s12293-024-00428-0

Learning-based methods have become increasingly popular for solving vehicle routing problems (VRP) due to their near-optimal performance and fast inference speed. Among them, the combination of deep reinforcement learning and graph representation all... Read More about Gase: graph attention sampling with edges fusion for solving vehicle routing problems.

Insights from explainable AI in oesophageal cancer team decisions (2024)
Journal Article
Thavanesan, N., Farahi, A., Parfitt, C., Belkhatir, Z., Azim, T., Vallejos, E. P., Walters, Z., Ramchurn, S., Underwood, T. J., & Vigneswaran, G. (2024). Insights from explainable AI in oesophageal cancer team decisions. Computers in Biology and Medicine, 180, Article 108978. https://doi.org/10.1016/j.compbiomed.2024.108978

Background
Clinician-led quality control into oncological decision-making is crucial for optimising patient care. Explainable artificial intelligence (XAI) techniques provide data-driven approaches to unravel how clinical variables influence this de... Read More about Insights from explainable AI in oesophageal cancer team decisions.

Decoding AI in Contemporary Art: A Five-Trope Classification for Understanding and Categorisation (2024)
Journal Article
Salimbeni, G., Benford, S., Reeves, S., & Martindale, S. (2024). Decoding AI in Contemporary Art: A Five-Trope Classification for Understanding and Categorisation. Leonardo, 57(4), 415–421. https://doi.org/10.1162/leon_a_02546

The article presents a historical overview of the classification of contemporary artworks that either have utilized artificial intelligence as a tool in their creation or focus on AI as their central theme or subject matter. The authors analyze artwo... Read More about Decoding AI in Contemporary Art: A Five-Trope Classification for Understanding and Categorisation.

AI and the iterable epistopics of risk (2024)
Journal Article
Crabtree, A., McGarry, G., & Urquhart, L. (2024). AI and the iterable epistopics of risk. AI & Society, https://doi.org/10.1007/s00146-024-02021-y

The risks AI presents to society are broadly understood to be manageable through ‘general calculus’, i.e., general frameworks designed to enable those involved in the development of AI to apprehend and manage risk, such as AI impact assessments, ethi... Read More about AI and the iterable epistopics of risk.

Understanding user needs of personalisation-based automated systems with development and application of novel ideation cards (2024)
Presentation / Conference Contribution
Duvnjak, J., Kucukyilmaz, A., & Houghton, R. (2024, July). Understanding user needs of personalisation-based automated systems with development and application of novel ideation cards. Presented at 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024), Nice, France

Personalisation is a commonly utilised technology in socially focused online platforms. It has gathered widespread usage through its ability to match a system to the needs of users through their data. This allows systems to be more user-friendly or e... Read More about Understanding user needs of personalisation-based automated systems with development and application of novel ideation cards.

Machine Learning for Evolutionary Computation - the Vehicle Routing Problems Competition (2024)
Presentation / Conference Contribution
Meng, W., Qu, R., & Pillay, N. (2023, July). Machine Learning for Evolutionary Computation - the Vehicle Routing Problems Competition. Presented at Proceedings of the Genetic and Evolutionary Computation Conference Companion, Lisbon

The Competition of Machine Learning for Evolutionary Computation for Solving Vehicle Routing Problems (ML4VRP) seeks to bring together machine learning and evolutionary computation communities to propose innovative techniques for vehicle routing prob... Read More about Machine Learning for Evolutionary Computation - the Vehicle Routing Problems Competition.

Implementing responsible innovation: the role of the meso-level(s) between project and organisation (2024)
Journal Article
Stahl, B. C., Portillo, V., Wagner, H., Craigon, P. J., Darzentas, D., De Ossorno Garcia, S., Dowthwaite, L., Greenhalgh, C., Middleton, S. E., Nichele, E., Wagner, C., & Webb, H. (2024). Implementing responsible innovation: the role of the meso-level(s) between project and organisation. Journal of Responsible Innovation, 11(1), Article 2370934. https://doi.org/10.1080/23299460.2024.2370934

Much of academic discussion of responsible innovation (RI) has focused on RI integration into research projects. In addition, significant attention has also been paid to RI structures and policies at the research policy and institutional level. This... Read More about Implementing responsible innovation: the role of the meso-level(s) between project and organisation.

Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra (2024)
Journal Article
Kok, Y. E., Crisford, A., Parkes, A., Venkateswaran, S., Oreffo, R., Mahajan, S., & Pound, M. (2024). Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra. Scientific Reports, 14(1), Article 15902. https://doi.org/10.1038/s41598-024-66857-6

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional... Read More about Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra.

On symmetries of spheres in univalent foundations (2024)
Presentation / Conference Contribution
Cagne, P., Buchholtz, U. T., Kraus, N., & Bezem, M. (2024, July). On symmetries of spheres in univalent foundations. Presented at LICS '24: 39th Annual ACM/IEEE Symposium on Logic in Computer Science, Tallinn

Working in univalent foundations, we investigate the symmetries of spheres, i.e., the types of the form Sn = Sn. The case of the circle has a slick answer: the symmetries of the circle form two copies of the circle. For higher-dimensional spheres, th... Read More about On symmetries of spheres in univalent foundations.