arXiv:2606. 07569v1 Announce Type: new Abstract: Accurate carbon emission monitoring is critical for climate policy and emerging regulatory mechanisms such as the EU Carbon Border Adjustment Mechanism, yet city-level high-frequency monitoring data remain extremely scarce, severely limiting data-hungry deep learning models.
By Zesen Wang, Lijuan Lan, Yonggang Li, Chunhua Yang
arXiv:2510.24046v2 Announce Type: replace-cross
Abstract: Existing tabular data generation methods primarily focus on matching statistical distributions between real and synthetic data, often overloo...
By Tu Anh Hoang Nguyen, Dang Nguyen, Tri-Nhan Vo, Thuc Duy Le, Trung Le, Sunil Gupta
arXiv:2606. 29324v1 Announce Type: new Abstract: Low-Earth orbit (LEO) satellite Internet has become an indispensable infrastructure that provide growing coverage for global users.
By Xiang Shi, Yifei Zhang, Peng Hu
arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.
By Mingxuan Jiang, Keyang Chen, Yongxin Wang, Yongsheng Zhao, Ziyue Dai, Yicun Liu, Zeping Li, Qiuyang Zhang, Hongyi Nie, Hongbin Zhu, Sen Liu, Guangnan Ye, Hongfeng Chai
arXiv:2605.19892v2 Announce Type: replace-cross
Abstract: Dramatic cost reductions driven by private sector innovations have led to a rapid increase in the number of satellites in orbit and a corresp...
By Jonas Weiss, Patricia Sagmeister, Gabriel Maiolini Capez, Dinesh Verma, Roberto Garello, Alberto Perotti, Dawid Lazaj, Alicja Musial, Jakub Nalepa, Thomas Morf, Martin Schmatz, Marek Krawczyk, Mateusz Przeliorz, Kevin Roche, Sagar Tayal, Mahalakshmi Lakshminarayanan, Nicolas Long\'ep\'e, Pierre-Philippe Mathieu, Agata Wijata
This study introduces the first controlled benchmark of generative models for weather data assimilation using real station observations from 11,849 NOAA MADIS stations across the U.S. It evaluates key design choices—diffusion vs. flow matching, pixel vs. latent-space formulations, and inference-time conditioning strategies—against a classical 3D-Var baseline. The benchmark finds that learned generative priors and full-gradient guidance improve RMSE over ERA5, while other design variations offer minimal benefit, especially under sparse observation conditions.
By Ruizhe Huang, Qidong Yang, Jonathan Giezendanner, Sherrie Wang