This paper investigates the use of eXplainable Artificial Intelligence (XAI) methods, Integrated Gradients (IG) and SHAP, to interpret fault diagnosis decisions made by a Long Short-Time Memory (LSTM) classifier in a chemical process. The study highlights how XAI can help identify the faulty subsystem, improving the trustworthiness of deep learning models in industrial settings.

This paper introduces a novel quantum optical Bit Commitment (BC) protocol designed to address privacy concerns in AI/ML applications. It provides security proofs in an honest-but-curious setting, leveraging the assumption of secured network transmission lines to overcome known no-go theorems for unconditional quantum BC.

This paper introduces a classification for software integration within Asset Administration Shells (AAS) based on runtime environments and functional components. It systematically analyzes different architectural approaches, evaluating their impact on software quality criteria and relevance for various digital manufacturing use cases.

This paper introduces AIFL, a deterministic LSTM-based model for global daily streamflow forecasting, utilizing a novel two-stage training strategy to bridge the performance gap between historical reanalysis data and operational forecast products. It demonstrates competitive predictive skill and exceptional reliability in extreme-event detection, serving as a robust baseline for the global hydrological community.

This paper introduces Mano, a novel optimizer for training large language models that re-approaches manifold optimization. Mano addresses the limitations of existing optimizers like AdamW and Muon by projecting momentum onto the tangent space of model parameters and constraining it on a rotational Oblique manifold, demonstrating superior performance and efficiency.