Outputs (2236)

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.

Primitive Recursive Dependent Type Theory (2024)
Presentation / Conference Contribution
Buchholtz, U. T., & Schipp von Branitz, J. (2024, July). Primitive Recursive Dependent Type Theory. Presented at LICS '24: 39th Annual ACM/IEEE Symposium on Logic in Computer Science, Tallinn, Estonia

We show that restricting the elimination principle of the natural numbers type in Martin-Löf Type Theory (MLTT) to a universe of types not containing ####II-types ensures that all definable functions are primitive recursive. This extends the concept... Read More about Primitive Recursive Dependent Type Theory.

Generating Locally Relevant Explanations Using Causal Rule Discovery (2024)
Presentation / Conference Contribution
Zhang, T., & Wagner, C. (2024, June). Generating Locally Relevant Explanations Using Causal Rule Discovery. Presented at 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Yokohama, Japan

In the real-world an effect often arises via multiple causal mechanisms. Conversely, the behaviour of AI systems is commonly driven by correlations which may-or may not-be themselves linked to causal mechanisms in the associated real-world system the... Read More about Generating Locally Relevant Explanations Using Causal Rule Discovery.

Interval Agreement Weighted Average - Sensitivity to Data Set Features (2024)
Presentation / Conference Contribution
Zhao, Y., Wagner, C., Ryan, B., Pekaslan, D., & Navarro, J. (2024, June). Interval Agreement Weighted Average - Sensitivity to Data Set Features. Presented at 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Yokohama, Japan

The growing use of intervals in fields like survey analysis necessitates effective aggregation methods that can summarize and represent such uncertain data representations. The Interval Agreement Approach (IAA) addresses this by aggregating interval... Read More about Interval Agreement Weighted Average - Sensitivity to Data Set Features.

An Interval Creation Approach to Construct Interval Type-2 Fuzzy Sets (2024)
Presentation / Conference Contribution
Amartya, P. S., Kabir, S., Babu, S. C. K., & Jahan, M. (2024, June). An Interval Creation Approach to Construct Interval Type-2 Fuzzy Sets. Presented at IEEE International Conference on Fuzzy Systems, Yokohama, Japan

Interval type-2 fuzzy sets (IT2 FSs) are more popular over type-1 fuzzy sets (T1 FSs) as they capture uncertainty in a better way in many real-world problems. Modelling uncertainty with an IT2 FS is relatively complex and solely depends on how to def... Read More about An Interval Creation Approach to Construct Interval Type-2 Fuzzy Sets.

A Hierarchical Cooperative Genetic Programming for Complex Piecewise Symbolic Regression (2024)
Presentation / Conference Contribution
Chen, X., Yi, W., Bai, R., Qu, R., & Jin, Y. (2024, June). A Hierarchical Cooperative Genetic Programming for Complex Piecewise Symbolic Regression. Presented at 2024 IEEE Congress on Evolutionary Computation (CEC 2024), Yokohama, Japan

In regression analysis, methodologies range from black-box approaches like artificial neural networks to white-box techniques like symbolic regression. Renowned for its trans-parency and interpretability, symbolic regression has become increasingly p... Read More about A Hierarchical Cooperative Genetic Programming for Complex Piecewise Symbolic Regression.

ARU2-Net: A Deep Learning Approach for Global-Scale Oceanic Eddy Detection (2024)
Journal Article
Geng, J., Gao, H., Huang, B., Radenkovic, M., & Chen, G. (2024). ARU2-Net: A Deep Learning Approach for Global-Scale Oceanic Eddy Detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 11997-12007. https://doi.org/10.1109/jstars.2024.3419175

Ocean eddies have a significant impact on marine ecosystems and the climate because they transport essential substances in the ocean. Detection of ocean eddies has become one of the most active topics in physical ocean research. In recent years, rese... Read More about ARU2-Net: A Deep Learning Approach for Global-Scale Oceanic Eddy Detection.

Telepresence Robots for Remote Participation in Higher Education (2024)
Presentation / Conference Contribution
Hu, J., Reyes Cruz, G., Fischer, J., & Maior, H. A. (2024, June). Telepresence Robots for Remote Participation in Higher Education. Presented at CHIWORK 2024, Newcastle upon Tyne, UK

Telepresence robotics enable people to synchronously communicate and interact at a distance. The Covid-19 pandemic caused in-person teaching and research activities to migrate online in almost all society sectors (including higher education). In hybr... Read More about Telepresence Robots for Remote Participation in Higher Education.

Towards sentience: A path through jazz, datasets and digital scores (2024)
Presentation / Conference Contribution
Poltronieri, F., & Vear, C. (2024, June). Towards sentience: A path through jazz, datasets and digital scores. Paper presented at International Symposium of Electronic Art 2024, Brisbane, Australia

This short paper is a provocation in which we lean into the notion of sentience in Creative-AI music. The purpose of this is to highlight that a critical component when using bucket terms such as ”creativity”, ”intelligence” or ”sentience” in the des... Read More about Towards sentience: A path through jazz, datasets and digital scores.