CNS_Papers

AI Paper

• Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." nature 518.7540 (2015): 529-533. [Paper]

AI4Science

• Li Z, Han W, Zhang Y, et al. Learning spatiotemporal dynamics with a pretrained generative model[J]. Nature Machine Intelligence, 2024, 6(12): 1566-1579. [Paper] [Nature]

• Du P, Parikh M H, Fan X, et al. Conditional neural field latent diffusion model for generating spatiotemporal turbulence[J]. Nature Communications, 2024, 15(1): 10416. [Paper]

Medical

• DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice, Zijie Meng, Jin Hao, Xiwei Dai, Yang Feng, Jiaxiang Liu, Bin Feng, Huikai Wu, Xiaotang Gai, Hengchuan Zhu, Tianxiang Hu, Yangyang Wu, Hongxia Xu, Jin Li, Jun Xiao, Xiaoqiang Liu, Joey Tianyi Zhou, Fudong Zhu, Zhihe Zhao, Lunguo Xia, Bing Fang, Jimeng Sun, Jian Wu, Zuozhu Liu [NC]

• AI-based diagnosis of acute aortic syndrome from noncontrast CT. Hu, Y., Xiang, Y., Zhou, Y. J., He, Y., Lang, D., Yang, S., ... & Zhang, H. (2025). Nature Medicine, 1-13. [Paper]

• "A generative model uses healthy and diseased image pairs for pixel-level chest X-ray pathology localization." Dong, Kaiming, Yuxiao Cheng, Kunlun He, and Jinli Suo. Nature Biomedical Engineering (2025): 1-13. [Paper]

• Exploring scalable medical image encoders beyond text supervision, Fernando Pérez-García et al. [Paper] [Code]

Event/Spike Data

• Gehrig, D., & Scaramuzza, D. (2024). Low-latency automotive vision with event cameras. Nature, 629(8014), 1034-1040. [Paper] [Code]

Fusion

• Tokuzawa, Tokihiko, et al. "Cross-scale nonlinear interaction and bifurcation in multi-scale turbulence of high-temperature plasmas." Communications Physics 8.1 (2025): 394. [Paper] [微信公众号]

• Wang, Allen M., et al. "Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV." Nature Communications 16.1 (2025): 8877. [Paper]

• Degrave J, Felici F, Buchli J, et al. Magnetic control of tokamak plasmas through deep reinforcement learning[J]. Nature, 2022, 602(7897): 414-419. [Paper]

• Seo J, Kim S K, Jalalvand A, et al. Avoiding fusion plasma tearing instability with deep reinforcement learning[J]. Nature, 2024, 626(8000): 746-751. [Paper]

• Kates-Harbeck, Julian, Alexey Svyatkovskiy, and William Tang. "Predicting disruptive instabilities in controlled fusion plasmas through deep learning." Nature 568.7753 (2019): 526-531. [Paper]

• Ding S, Garofalo A M, Wang H Q, et al. A high-density and high-confinement tokamak plasma regime for fusion energy[J]. Nature, 2024, 629(8012): 555-560. [Paper]

• Kim S K, Shousha R, Yang S M, et al. Highest fusion performance without harmful edge energy bursts in tokamak[J]. Nature communications, 2024, 15(1): 3990. [Paper]

• Murari A, Rossi R, Craciunescu T, et al. A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors[J]. Nature Communications, 2024, 15(1): 2424. [Paper]

• Yang S M, Park J K, Jeon Y M, et al. Tailoring tokamak error fields to control plasma instabilities and transport[J]. Nature Communications, 2024, 15(1): 1275. [Paper]

• Dominguez-Palacios J, Futatani S, Garcia-Munoz M, et al. Effect of energetic ions on edge-localized modes in tokamak plasmas[J]. Nature Physics, 2025, 21(1): 43-51. [Paper]

• Jenko, Frank. "Accelerating fusion research via supercomputing." Nature Reviews Physics (2025): 1-13. [Paper]

• Garcia J, Kazakov Y, Coelho R, et al. Stable Deuterium-Tritium plasmas with improved confinement in the presence of energetic-ion instabilities[J]. Nature Communications, 2024, 15(1): 7846. [Paper]

• Gibney E. ITER delay: what it means for nuclear fusion[J]. Nature, 2024, 631(8021): 488-489. [Paper]

• Conroy G. CHINA’S RACE FOR FUSION ENERGY[J]. Nature, 2024, 632(8027): 968-970. [Paper]

• Ding S, Garofalo A M, Wang H Q, et al. Author Correction: A high-density and high-confinement tokamak plasma regime for fusion energy[J]. Nature, 2024, 630(8016): E4. [Paper]

• Willensdorfer, Matthias, et al. "Observation of magnetic islands in tokamak plasmas during the suppression of edge-localized modes." Nature Physics 20.12 (2024): 1980-1988. [Paper]

• Chouchene, Sarah, et al. "Application of machine learning for detecting and tracking turbulent structures in plasma fusion devices using ultra fast imaging." Scientific Reports 14.1 (2024): 27965. [Paper]

• Li, Wenyang, et al. "Excited ion-scale turbulence by a magnetic island in fusion plasmas." Scientific Reports 14.1 (2024): 25362. [Paper]

• Höfler, Klara, et al. "Milestone in predicting core plasma turbulence: successful multi-channel validation of the gyrokinetic code GENE." Nature communications 16.1 (2025): 2558. [Paper]

• Geng J S, Li P, Li Y D, et al. Spontaneous evolution of density peaking factor in TEM turbulence-dominated H-mode plasma on the EAST Tokamak[J]. Scientific Reports, 2025, 15(1): 7738. [Paper]

• Yang C, Li K, Li G, et al. Prediction of the kinetic profiles in H-mode plasma discharges on EAST using core-pedestal coupling[J]. Scientific Reports, 2025, 15(1): 9207. [Paper]