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Outputs (61)

The variable relationship between the National Early Warning Score on admission to hospital, the primary discharge diagnosis and in-hospital mortality Authors information (2025)
Journal Article
Holland, M., Kellett, J., Boulitsakis-Logothetis, S., Watson, M., Al Moubayed, N., & Green, D. (online). The variable relationship between the National Early Warning Score on admission to hospital, the primary discharge diagnosis and in-hospital mortality Authors information. Internal and Emergency Medicine, https://doi.org/10.1007/s11739-024-03828-9

Background: Patients with an elevated admission National Early Warning Score (NEWS) are more likely to die while in hospital. However, it is not known if this increased mortality risk is the same for all diagnoses. The aim of this study was to determ... Read More about The variable relationship between the National Early Warning Score on admission to hospital, the primary discharge diagnosis and in-hospital mortality Authors information.

Algorithmic bias: sexualized violence against women in GPT-3 models (2025)
Journal Article
Wyer, S., & Black, S. (online). Algorithmic bias: sexualized violence against women in GPT-3 models. AI and Ethics, https://doi.org/10.1007/s43681-024-00641-0

This study explores the occurrence and implications of sexualized violence against women in text completion tasks performed by GPT-3 models. The study began as an exploratory investigation into gender inequalities within GPT-3 models to discover what... Read More about Algorithmic bias: sexualized violence against women in GPT-3 models.

Fully-Automated Patient-Agnostic Diabetes Management with Deep Reinforcement Learning (2025)
Presentation / Conference Contribution
Milton, T., & Lieck, R. (2024, December). Fully-Automated Patient-Agnostic Diabetes Management with Deep Reinforcement Learning. Presented at 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal

Type 1 diabetes is a chronic metabolic disease that requires regular insulin injections to regulate blood glucose levels. Recently, traditional manual approaches to diabetes management have been revolutionized by the use of continuous glucose monitor... Read More about Fully-Automated Patient-Agnostic Diabetes Management with Deep Reinforcement Learning.

An on-demand resource allocation algorithm for a quantum network hub and its performance analysis (2025)
Presentation / Conference Contribution
Gauthier, S., Vasantam, T., & Vardoyan, G. (2024, September). An on-demand resource allocation algorithm for a quantum network hub and its performance analysis. Presented at QCE24: IEEE International Conference on Quantum Computing and Engineering, Montréal, Québec, Canada

To support the execution of multiple simultaneously-running quantum network applications, a quantum network must efficiently allocate shared resources. We study traffic models for a type of quantum network hub called an Entanglement Generation Switch... Read More about An on-demand resource allocation algorithm for a quantum network hub and its performance analysis.

Calculating the Capacity Region of a Quantum Switch (2025)
Presentation / Conference Contribution
Tillman, I., Vasantam, T., Towsley, D., & Seshadreesan, K. P. (2024, September). Calculating the Capacity Region of a Quantum Switch. Presented at QCE2024: IEEE International Conference on Quantum Computing and Engineering, Montréal, Québec, Canada

Quantum repeaters are necessary to fully realize the capabilities of the emerging quantum internet, especially applications involving distributing entanglement across long distances. A more general notion of this can be called a quantum switch, which... Read More about Calculating the Capacity Region of a Quantum Switch.

Charge-transfer complexation of coordination cages for enhanced photochromism and photocatalysis (2025)
Journal Article
Li, G., Du, Z., Wu, C., Liu, Y., Xu, Y., Lavendomme, R., Liang, S., Gao, E.-Q., & Zhang, D. (2025). Charge-transfer complexation of coordination cages for enhanced photochromism and photocatalysis. Nature Communications, 16(1), Article 546. https://doi.org/10.1038/s41467-025-55893-z

Intensified host-guest electronic interplay within stable metal-organic cages (MOCs) presents great opportunities for applications in stimuli response and photocatalysis. Zr-MOCs represent a type of robust discrete hosts for such a design, but their... Read More about Charge-transfer complexation of coordination cages for enhanced photochromism and photocatalysis.

Complexity Framework for Forbidden Subgraphs I: The Framework (2025)
Journal Article
Johnson, M., Martin, B., Oostveen, J. J., Pandey, S., Paulusma, D., Smith, S., & van Leeuwen, E. J. (2025). Complexity Framework for Forbidden Subgraphs I: The Framework. Algorithmica, 87(3), 429-464. https://doi.org/10.1007/s00453-024-01289-2

For a set of graphs H, a graph G is H-subgraph-free if G does not contain any graph from H as a subgraph. We propose general and easy-to-state conditions on graph problems that explain a large set of results for H-subgraph-free graphs. Namely, a grap... Read More about Complexity Framework for Forbidden Subgraphs I: The Framework.

CIDER: Cyber‐Security in Industrial IoT Using Deep Learning and Ring Learning with Errors (2025)
Journal Article
Tsoi, S. T., & Jindal, A. (2025). CIDER: Cyber‐Security in Industrial IoT Using Deep Learning and Ring Learning with Errors. IET Cyber-Physical Systems: Theory & Applications, 10(1), Article e70015. https://doi.org/10.1049/cps2.70015

Traditional security measures such as access control and authentication need to be more effective against ever‐evolving threats. Moreover, security concerns increase as more industries shift towards adopting the industrial Internet of things (IIoT).... Read More about CIDER: Cyber‐Security in Industrial IoT Using Deep Learning and Ring Learning with Errors.

Sparse representation for restoring images by exploiting topological structure of graph of patches (2025)
Journal Article
Gao, Y., Cai, Z., Xie, X., Deng, J., Dou, Z., & Ma, X. (2025). Sparse representation for restoring images by exploiting topological structure of graph of patches. IET Image Processing, 19(1), Article e70004. https://doi.org/10.1049/ipr2.70004

Image restoration poses a significant challenge, aiming to accurately recover damaged images by delving into their inherent characteristics. Various models and algorithms have been explored by researchers to address different types of image distortio... Read More about Sparse representation for restoring images by exploiting topological structure of graph of patches.