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.
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…
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…
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…
This article presents a machine learning-based predictive framework for assessing organizational Decision-Making Quality (DMQ) using Big Data Analytics Capabilities (BDAC) in healthcare organizations. The proposed framework integrates five BDAC dimensions—organizational, technica…
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…