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Outputs (2727)

SYCL compute kernels for ExaHyPE (2024)
Conference Proceeding
Loi, C. M., Bockhorst, H., & Weinzierl, T. (2024). SYCL compute kernels for ExaHyPE. In Proceedings of the 2024 SIAM Conference on Parallel Processing for Scientific Computing (PP) (90-103). https://doi.org/10.1137/1.9781611977967.8

We discuss three SYCL realisations of a simple Finite Volume scheme over multiple Cartesian patches. The realisation flavours differ in the way how they map the compute steps onto loops and tasks: We compare an implementation that is exclusively usin... Read More about SYCL compute kernels for ExaHyPE.

Multi-Feature Fusion Enhanced Monocular Depth Estimation With Boundary Awareness (2024)
Journal Article
Song, C., Chen, Q., Li, F. W. B., Jiang, Z., Zheng, D., Shen, Y., & Yang, B. (2024). Multi-Feature Fusion Enhanced Monocular Depth Estimation With Boundary Awareness. Visual Computer,

Self-supervised monocular depth estimation has opened up exciting possibilities for practical applications, including scene understanding, object detection, and autonomous driving, without the need for expensive depth annotations. However, traditiona... Read More about Multi-Feature Fusion Enhanced Monocular Depth Estimation With Boundary Awareness.

Explainable text-tabular models for predicting mortality risk in companion animals (2024)
Journal Article
Burton, J., Farrell, S., Mäntylä Noble, P.-J., & Al Moubayed, N. (2024). Explainable text-tabular models for predicting mortality risk in companion animals. Scientific Reports, 14(1), Article 14217. https://doi.org/10.1038/s41598-024-64551-1

As interest in using machine learning models to support clinical decision-making increases, explainability is an unequivocal priority for clinicians, researchers and regulators to comprehend and trust their results. With many clinical datasets contai... Read More about Explainable text-tabular models for predicting mortality risk in companion animals.

Dynamic adversarial adaptation network with selective pseudo-labels for enhanced Unsupervised Domain Adaptation in rock microscopic image analysis (2024)
Journal Article
Xie, Y., Jin, L., Zhu, C., Luo, W., & Wang, Q. (2024). Dynamic adversarial adaptation network with selective pseudo-labels for enhanced Unsupervised Domain Adaptation in rock microscopic image analysis. Geoenergy Science and Engineering, 213011. https://doi.org/10.1016/j.geoen.2024.213011

The critical role of lithology classification in reservoir exploration is increasingly germinating interest in intelligent rock image classification applications. Nonetheless, the efficacy of these classification methods predominan... Read More about Dynamic adversarial adaptation network with selective pseudo-labels for enhanced Unsupervised Domain Adaptation in rock microscopic image analysis.

Self-Regulated Sample Diversity in Large Language Models (2024)
Conference Proceeding
Liu, M., Frawley, J., Wyer, S., Shum, H. P. H., Uckelman, S. L., Black, S., & Willcocks, C. G. (2024). Self-Regulated Sample Diversity in Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (1891–1899)

Signal automatic modulation based on AMC neural network fusion (2024)
Journal Article
Yin, H., & Diao, J. (in press). Signal automatic modulation based on AMC neural network fusion. PLoS ONE, 19(6), Article e0304531. https://doi.org/10.1371/journal.pone.0304531

With the rapid development of modern communication technology, it has become a core problem in the field of communication to find new ways to effectively modulate signals and to classify and recognize the results of automatic modulation. To further i... Read More about Signal automatic modulation based on AMC neural network fusion.

Decentralized Multi-Agent Coverage Path Planning with Greedy Entropy Maximization (2024)
Conference Proceeding
Champagnie, K., Arvin, F., & Hu, J. (2024). Decentralized Multi-Agent Coverage Path Planning with Greedy Entropy Maximization. In 2024 IEEE International Conference on Industrial Technology (ICIT). https://doi.org/10.1109/ICIT58233.2024.10540869

In this paper, we present GEM, a novel approach to online coverage path planning in which a swarm of homogeneous agents act to maximize the entropy of pheromone deposited within their environment. We show that entropy maximization (EM) coincides with... Read More about Decentralized Multi-Agent Coverage Path Planning with Greedy Entropy Maximization.