Browsing by Author "Arana-Catania, Miguel"
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Item Open Access Autonomous robotic arm manipulation for planetary missions using causal machine learning(European Space Agency (ESA), 2023-10-20) McDonnell, Cian; Arana-Catania, Miguel; Upadhyay, SaurabhAutonomous robotic arm manipulators have the potential to make planetary exploration and in-situ resource utilization missions more time efficient and productive, as the manipulator can handle the objects itself and perform goal-specific actions. We train a manipulator to autonomously study objects of which it has no prior knowledge, such as planetary rocks. This is achieved using causal machine learning in a simulated planetary environment. Here, the manipulator interacts with objects, and classifies them based on differing causal factors. These are parameters, such as mass or friction coefficient, that causally determine the outcomes of its interactions. Through reinforcement learning, the manipulator learns to interact in ways that reveal the underlying causal factors. We show that this method works even without any prior knowledge of the objects, or any previously collected training data. We carry out the training in planetary exploration conditions, with realistic manipulator models.Item Open Access Causal discovery to understand hot corrosion(Wiley, 2024-02-12) Varghese, Akhil; Arana-Catania, Miguel; Mori, Stefano; Encinas-Oropesa, Adriana; Sumner, JoyGas turbine superalloys experience hot corrosion, driven by factors including corrosive deposit flux, temperature, gas composition, and component material. The full mechanism still needs clarification and research often focuses on laboratory work. As such, there is interest in causal discovery to confirm the significance of factors and identify potential missing causal relationships or codependencies between these factors. The causal discovery algorithm fast causal inference (FCI) has been trialled on a small set of laboratory data, with the outputs evaluated for their significance to corrosion propagation, and compared to existing mechanistic understanding. FCI identified salt deposition flux as the most influential corrosion variable for this limited data set. However, HCl was the second most influential for pitting regions, compared to temperature for more uniformly corroding regions. Thus, FCI generated causal links aligned with literature from a randomised corrosion data set, while also identifying the presence of two different degradation modes in operation.Item Open Access PANACEA: an automated misinformation detection system on COVID-19(Association for Computational Linguistics, 2023-05-04) Zhao, Runcong; Arana-Catania, Miguel; Zhu, Lixing; Kochkina, Elena; Gui, Lin; Zubiaga, Arkaitz; Procter, Rob; Liakata, Maria; He, YulanIn this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-ofthe-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available.