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Outputs (191)

Multi-Task Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising (2023)
Journal Article
Zhou, K., Shum, H. P., Li, F. W., & Liang, X. (2023). Multi-Task Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising. IEEE Transactions on Visualization and Computer Graphics, https://doi.org/10.1109/TVCG.2023.3337868

In many human-computer interaction applications, fast and accurate hand tracking is necessary for an immersive experience. However, raw hand motion data can be flawed due to issues such as joint occlusions and high-frequency noise, hindering the inte... Read More about Multi-Task Spatial-Temporal Graph Auto-Encoder for Hand Motion Denoising.

When in Rome: A Meta-corpus of Functional Harmony (2023)
Journal Article
Gotham, M., Micchi, G., López, N. N., & Sailor, M. (2023). When in Rome: A Meta-corpus of Functional Harmony. Transactions of the International Society for Music Information Retrieval, 6(1), 150-166. https://doi.org/10.5334/tismir.165

‘When in Rome’ brings together all human-made, computer-encoded, functional harmonic analyses of music. This amounts in total to over 2,000 analyses of 1,500 distinct works. The most obvious motivation is scale: gathering these datasets together lead... Read More about When in Rome: A Meta-corpus of Functional Harmony.

MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning (2023)
Journal Article
Yang, F., Li, X., Duan, H., Xu, F., Huang, Y., Zhang, X., …Zheng, Y. (2024). MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning. IEEE Journal of Biomedical and Health Informatics, 28(2), 858-869. https://doi.org/10.1109/jbhi.2023.3336726

Medical image segmentation is a critical task for clinical diagnosis and research. However, dealing with highly imbalanced data remains a significant challenge in this domain, where the region of interest (ROI) may exhibit substantial variations acro... Read More about MRL-Seg: Overcoming Imbalance in Medical Image Segmentation With Multi-Step Reinforcement Learning.

Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI (2023)
Presentation / Conference Contribution
Zhang, X., Zheng, S., Shum, H. P., Zhang, H., Song, N., Song, M., & Jia, H. (2023, November). Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI. Presented at ICONIP 2023: 2023 International Conference on Neural Information Processing, Changsha, China

Resting-state fMRI (rs-fMRI) functional connectivity (FC)
analysis provides valuable insights into the relationships between different brain regions and their potential implications for neurological or psychiatric disorders. However, specific design... Read More about Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI.

A multinational survey of companion animal veterinary clinicians: How can antimicrobial stewardship guidelines be optimised for the target stakeholder? (2023)
Journal Article
Farrell, S., Bagcigil, A. F., Chaintoutis, S. C., Firth, C., Aydin, F. G., Hare, C., …Allerton, F. (2023). A multinational survey of companion animal veterinary clinicians: How can antimicrobial stewardship guidelines be optimised for the target stakeholder?. The Veterinary Journal, Article 106045. https://doi.org/10.1016/j.tvjl.2023.106045

Antimicrobial stewardship initiatives are widely regarded as a cornerstone for ameliorating the global health impact of antimicrobial resistance. Within companion animal health, such efforts have largely focused on development and dissemination of an... Read More about A multinational survey of companion animal veterinary clinicians: How can antimicrobial stewardship guidelines be optimised for the target stakeholder?.

DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring (2023)
Journal Article
Dai, S., Meng, F., Wang, Q., & Chen, X. (2024). DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring. Renewable and Sustainable Energy Reviews, 191, Article 114091. https://doi.org/10.1016/j.rser.2023.114091

Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household level into appliance-level consumption, can help analyze the electricity consumption beha... Read More about DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring.

Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring (2023)
Journal Article
Rajewicz, W., Wu, C., Romano, D., Campo, A., Arvin, F., Casson, A. J., …Thenius, R. (2024). Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring. Bioinspiration & Biomimetics, 19(1), Article 015001. https://doi.org/10.1088/1748-3190/ad0c5d

Rapidly intensifying global warming and water pollution calls for more efficient and continuous environmental monitoring methods. Biohybrid systems connect mechatronic components to living organisms and this approach can be used to extract data from... Read More about Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring.

Distributed Bearing-Only Formation Control for Heterogeneous Nonlinear Multi-Robot Systems (2023)
Presentation / Conference Contribution
Wu, K., Hu, J., Ding, Z., & Arvin, F. (2023, July). Distributed Bearing-Only Formation Control for Heterogeneous Nonlinear Multi-Robot Systems

This paper addresses the bearing-only formation tracking problem for heterogeneous nonlinear multi-robot systems. In contrast to position and distance-based formation algorithms, the robots can only measure the bearing information from their neighbor... Read More about Distributed Bearing-Only Formation Control for Heterogeneous Nonlinear Multi-Robot Systems.