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Explainable artificial intelligence and advanced feature selection methods for predicting gas concentration in longwall mining (2025)
Journal Article
Chang, H., Wang, X., Cristea, A. I., Meng, X., Hu, Z., & Pan, Z. (2025). Explainable artificial intelligence and advanced feature selection methods for predicting gas concentration in longwall mining. Information Fusion, 118, Article 102976. https://doi.org/10.1016/j.inffus.2025.102976

Accurate prediction of gas concentrations at longwall mining faces is critical for safety production, yet current methods still face challenges in interpretability and reliability. This study aims to enhance prediction accuracy and model interpretabi... Read More about Explainable artificial intelligence and advanced feature selection methods for predicting gas concentration in longwall mining.

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.

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.

Laser: Efficient Language-Guided Segmentation in Neural Radiance Fields (2025)
Journal Article
Miao, X., Duan, H., Bai, Y., Shah, T., Song, J., Long, Y., Ranjan, R., & Shao, L. (online). Laser: Efficient Language-Guided Segmentation in Neural Radiance Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, https://doi.org/10.1109/TPAMI.2025.3535916

In this work, we propose a method that leverages CLIP feature distillation, achieving efficient 3D segmentation through language guidance. Unlike previous methods that rely on multi-scale CLIP features and are limited by processing speed and storage... Read More about Laser: Efficient Language-Guided Segmentation in Neural Radiance Fields.

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.

The Progress and Prospects of Data Capital for Zero-Shot Deep Brain–Computer Interfaces (2025)
Journal Article
Ma, W., Ma, T., Organisciak, D., Waide, J. E. T., Meng, X., & Long, Y. (2025). The Progress and Prospects of Data Capital for Zero-Shot Deep Brain–Computer Interfaces. Electronics, 14(3), Article 508. https://doi.org/10.3390/electronics14030508

The vigorous development of deep learning (DL) has been propelled by big data and high-performance computing. For brain–computer interfaces (BCIs) to benefit from DL in a reliable and scalable manner, the scale and quality of data are crucial. Specia... Read More about The Progress and Prospects of Data Capital for Zero-Shot Deep Brain–Computer Interfaces.