Skip to main content

Research Repository

Advanced Search

All Outputs (5750)

Quant Hub seminar: Fifteen years of Pupil Premium policy in England. What have we learned from Pupil Parent Matched Data (PPMD)? (2025)
Presentation / Conference Contribution
Siddiqui, N. (2025, March). Quant Hub seminar: Fifteen years of Pupil Premium policy in England. What have we learned from Pupil Parent Matched Data (PPMD)?. Presented at Quant Hub seminar, Oxford ,15 Norham Gardens

The introduction and nationwide implementation of the Pupil Premium policy in 2011 was a major policy initiative by the then Coalition Government to address socio-economic segregation between schools in England, and reduce the persistent attainment g... Read More about Quant Hub seminar: Fifteen years of Pupil Premium policy in England. What have we learned from Pupil Parent Matched Data (PPMD)?.

An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning (2025)
Presentation / Conference Contribution
Liu, J., Kazemtabrizi, B., Du, H., Matthews, P., & Sun, H. (2024, November). An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning. Presented at 50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, USA

With the increasing integration of renewable energy sources into the power grid, accurate and reliable ultra-short-term forecasting of wind power is critical for optimizing grid stability and energy efficiency, especially for a highly dynamic and var... Read More about An Integrated Stacked Sparse Autoencoder and CNN-BLSTM Model for Ultra-Short-Term Wind Power Forecasting with Advanced Feature Learning.

Integrated Satellite-Terrestrial Network for Smart Grid Communications in 6G Era (2025)
Presentation / Conference Contribution
Bisu, A. A., Sun, H., & Gallant, A. (2025, January). Integrated Satellite-Terrestrial Network for Smart Grid Communications in 6G Era. Presented at 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC), University of Nevada, Las Vegas, USA

In this work, we developed and proposed a real testbed with Integrated Satellite-Terrestrial Network (ISTN) scenario. This topology was used to measure the actual parameters that were used as the Smart Grid (SG) Quality of Service (QoS) metrics. Perf... Read More about Integrated Satellite-Terrestrial Network for Smart Grid Communications in 6G Era.

Lo stato solido e la nuova mappa della fisica (2025)
Presentation / Conference Contribution
Martin, J. D. (2024, September). Lo stato solido e la nuova mappa della fisica. Presented at XLIV Congresso Nazionale Sisfa, Firenze

Neither solid state nor condensed matter physics existed at the end of World War II. Physical problems related to the properties of materials, of course, have a much longer history, but the physics community was not yet subdivided in a way that recog... Read More about Lo stato solido e la nuova mappa della fisica.

Coordination Mechanisms in AI Development: Practitioner Experiences on Integrating UX Activities (2025)
Presentation / Conference Contribution
Bruun, A., Van Berkel, N., Raptis, D., & Law, E. L.-C. (2025, April). Coordination Mechanisms in AI Development: Practitioner Experiences on Integrating UX Activities. Presented at CHI 2025 (Conference on Human Factors in Computing Systems), Yokohama, Japan

Software development relies on collaboration and alignment between a variety of roles, including software developers and user experience designers. The increasing focus on artificial intelligence in today's development projects has given rise to new... Read More about Coordination Mechanisms in AI Development: Practitioner Experiences on Integrating UX Activities.

Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient (2025)
Presentation / Conference Contribution
Dinh, V., Ho, L., & Nguyen, C. (2024, December). Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient. Presented at The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada

Weather Impact on DER Long-term Performance: A Formal Verification Approach (2025)
Presentation / Conference Contribution
Santana, M. A., Stefanakos, I., Fang, X., Garg, A., Sun, H., & Osman, A. (2024, November). Weather Impact on DER Long-term Performance: A Formal Verification Approach. Presented at 2024 IEEE PES Innovative Smart Grid Technologies - Asia (ISGT Asia), Bangalore, India

Distributed energy resources (DERs), such as solar photovoltaic (PV) panels, are essential to modern energy systems, providing resilience and producing clean, local energy. However, their long-term performance is vulnerable to environmental factors,... Read More about Weather Impact on DER Long-term Performance: A Formal Verification Approach.

SKDU at De-Factify 4.0: Natural language features for AI-Generated Text-Detection (2025)
Presentation / Conference Contribution
Maviya, S., Arnau-González, P., Arevalillo-Herráez, M., & Katsigiannis, S. (2025, February). SKDU at De-Factify 4.0: Natural language features for AI-Generated Text-Detection. Presented at De-factify 4.0 Workshop at 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA

A robust Bayesian model to quantify and adjust for study quality and conflict of interest in meta-analyses (2025)
Presentation / Conference Contribution
Troffaes, M. C. M., Casini, L., Landes, J., & Sahlin, U. (2025, July). A robust Bayesian model to quantify and adjust for study quality and conflict of interest in meta-analyses. Presented at 14th International Symposium on Imprecise Probabilities: Theories and Applications, Bielefeld, Germany

Meta-analyses are vital for synthesizing evidence in medical research, but conflicts of interest can introduce research bias, undermining the reliability of the synthesized findings. This paper proposes a new robust Bayesian meta-analysis model. The... Read More about A robust Bayesian model to quantify and adjust for study quality and conflict of interest in meta-analyses.

Deep Reinforcement Learning for Overtaking Decision-Making and Planning of Autonomous Vehicles (2025)
Presentation / Conference Contribution
Aihaiti, A., Arvin, F., & Hu, J. (2025, March). Deep Reinforcement Learning for Overtaking Decision-Making and Planning of Autonomous Vehicles. Presented at 2025 IEEE International Conference on Industrial Technology, Wuhan, China

The safe overtaking of autonomous vehicles has become an important focus in recent robotics and AI research. Considering the scenario of overtaking with oncoming vehicles, this paper proposes a hierarchical framework based on deep reinforcement learn... Read More about Deep Reinforcement Learning for Overtaking Decision-Making and Planning of Autonomous Vehicles.

On the Locality of the Lovász Local Lemma (2025)
Presentation / Conference Contribution
Davies-Peck, P. (2025, June). On the Locality of the Lovász Local Lemma. Presented at 57th Annual ACM Symposium on Theory of Computing (STOC '25), Prague

The Lovász Local Lemma is a versatile result in probability theory, characterizing circumstances in which a collection of n ‘bad events’, each occurring with probability at most p and dependent on a set
of underlying random variables, can be avoided... Read More about On the Locality of the Lovász Local Lemma.

A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments (2025)
Presentation / Conference Contribution
Zhang, K., Zahmatkesh, M., Stefanec, M., Arvin, F., & Hu, J. (2025, March). A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments. Presented at 2025 IEEE International Conference on Industrial Technology, China

This paper proposes a real-time multi-robot navigation method that integrates the Rapidly-exploring Random Tree (RRT) algorithm with the improved Artificial Potential Field (APF) approach. Since traditional path planning methods often face problems s... Read More about A Real-Time RRT-APF Approach for Efficient Multi-Robot Navigation in Complex Environments.

NP-completeness of the combinatorial distance matrix realisation problem (2025)
Presentation / Conference Contribution
Fairbairn, D., Mertzios, G., & Peyerimhoff, N. (2025, December). NP-completeness of the combinatorial distance matrix realisation problem. Presented at 14th International Symposium on Algorithms and Complexity (CIAC 2025), Rome, Italy

The k-CombDMR problem is that of determining whether an n×n distance matrix can be realised by n vertices in some undirected graph with n+k vertices. This problem has a simple solution in the case k=0. In this paper we show that this problem is polyn... Read More about NP-completeness of the combinatorial distance matrix realisation problem.

Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving (2025)
Presentation / Conference Contribution
E, W., Yuan, C., Sun, Y., Gaus, Y., Atapour-Abarghouei, A., & Breckon, T. (2025, May). Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving. Presented at IEEE International Conference on Robotics and Automation (ICRA), Atlanta, USA

We present Dur360BEV, a novel spherical camera autonomous driving dataset equipped with a high-resolution 128-channel 3D LiDAR and a RTK-refined GNSS/INS system, along with a benchmark architecture designed to generate Bird-Eye-View (BEV) maps using... Read More about Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving.

Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots (2025)
Presentation / Conference Contribution
Chen, S., He, Y., Lennox, B., Arvin, F., & Atapour-Abarghouei, A. (2025, May). Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots. Presented at IEEE International Conference on Robotics & Automation, Atlanta, USA

Long-term monitoring and exploration of extreme environments, such as underwater storage facilities, is costly, labor-intensive, and hazardous. Automating this process with low-cost, collaborative robots can greatly improve efficiency. These robots c... Read More about Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots.

Introducing Code Quality at CS1 Level: Examples and Activities (2025)
Presentation / Conference Contribution
Izu, C., Mirolo, C., Börstler, J., Connamacher, H., Crosby, R., Glassey, R., Haldeman, G., Kiljunen, O., Kumar, A. N., Liu, D., Luxton-Reilly, A., Matsumoto, S., Carneiro De Oliveira, E., Russell, S., Shah, A., Izu, C., Mirolo, C., Börstler, J., Connamacher, H., Crosby, R., …Shah, A. (2024, July). Introducing Code Quality at CS1 Level: Examples and Activities. Presented at ITiCSE 2024: Innovation and Technology in Computer Science Education, Milan

Characterising code quality is a challenge that was addressed by a previous ITiCSE Working Group (Börstler et al., 2017). As emerged from that study, educators, developers, and students have different perceptions of the aspects involved. The percepti... Read More about Introducing Code Quality at CS1 Level: Examples and Activities.

The Complexity of Diameter on H-free Graphs (2025)
Presentation / Conference Contribution
Oostveen, J. J., Paulusma, D., & van Leeuwen, E. J. (2024, June). The Complexity of Diameter on H-free Graphs. Presented at WG 2024, Gozd Martuljek, Slovenia

Sparse Autoencoders Do Not Find Canonical Units of Analysis (2025)
Presentation / Conference Contribution
Leask, P., Bussmann, B., Pearce, M. T., Isaac Bloom, J., Tigges, C., Al Moubayed, N., Sharkey, L., & Nanda, N. (2025, April). Sparse Autoencoders Do Not Find Canonical Units of Analysis. Presented at The Thirteenth International Conference on Learning Representations, Singapore

A common goal of mechanistic interpretability is to decompose the activations of neural networks into features: interpretable properties of the input computed by the model. Sparse autoencoders (SAEs) are a popular method for finding these features in... Read More about Sparse Autoencoders Do Not Find Canonical Units of Analysis.