Data Driven
Data-driven approaches are revolutionizing scientific research and engineering by leveraging vast datasets to build predictive models and automate complex tasks. Current research focuses on developing and refining algorithms like neural networks (including transformers and graph neural networks), Gaussian processes, and ADMM for diverse applications, ranging from autonomous systems and financial forecasting to scientific discovery and healthcare. This shift towards data-centric methodologies promises to accelerate scientific progress and improve the efficiency and effectiveness of various technological systems, particularly in areas where traditional modeling approaches are limited by complexity or data scarcity.
Papers
Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging
Niloufar Zakariaei, Arman Rahmim, Eldad Haber
Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities
Maria F. Davila R., Sven Groen, Fabian Panse, Wolfram Wingerath
Certified Inventory Control of Critical Resources
Ludvig Hult, Dave Zachariah, Petre Stoica
Large Language Models for Explainable Decisions in Dynamic Digital Twins
Nan Zhang, Christian Vergara-Marcillo, Georgios Diamantopoulos, Jingran Shen, Nikos Tziritas, Rami Bahsoon, Georgios Theodoropoulos
Retrieval-Augmented Mining of Temporal Logic Specifications from Data
Gaia Saveri, Luca Bortolussi
Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review
Md Shahin Ali, Md Manjurul Ahsan, Lamia Tasnim, Sadia Afrin, Koushik Biswas, Md Maruf Hossain, Md Mahfuz Ahmed, Ronok Hashan, Md Khairul Islam, Shivakumar Raman
Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling
Wanghan Xu, Fenghua Ling, Wenlong Zhang, Tao Han, Hao Chen, Wanli Ouyang, Lei Bai