Wenwen Cai, Xuanxuan Wang, Mingqian Zhu, Dengfeng Kuang
Supplementary Materials
Wenwen Cai, Xuanxuan Wang, Mingqian Zhu, Dengfeng Kuang
Vortex beams carrying orbital angular momentum enable high-capacity optical communication and imaging, yet multiple scattering in dynamic medias such as biological tissues disrupts their wavefront. Brownian motion decorrelates the scattered field and invalidates conventional meth…
Kangjian Di, Fuhao Yu, Silin Chen, Jiashu Li, Andy Liu, Sen Shao, et al.
Supplemental Document
Accurate, simultaneous determination of particle positions and physical characteristics is a fundamental requirement in digital holography for three-dimensional (3D) particle-field measurements. Recent approaches based on deep neural networks (DNNs) using two-dimensional (2D) U-N…
Élodie Laine, Valentin Lombard, Sergei Grudinin (429172), Julien Nguyen Van
This archive contains the data associated with PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networks. Lombard <i>et al.</i> 2026.For questions, please contact elodie.laine@sorbonne-universite.fr.
Kangjian Di, Fuhao Yu, Silin Chen, Jiashu Li, Andy Liu, Sen Shao, et al.
Optical neural networks (ONNs) promise ultra-fast and energy-efficient computing but are hampered by the critical challenge of on-chip training. Here, we propose an on-chip training distillation-guided optical neural network (DGONN) and introduce a forward distilled algorithm to…
Verification of neural networks against relational specifications, such as global robustness, is crucial for safety-critical applications of cyber-physical systems (CPS), given their increasing adoption of AI components. Compared to simple properties (e.g., local robustness), ver…