@article {69902, title = {Chromatix: a differentiable, GPU-accelerated wave-optics library}, journal = {bioRxiv}, year = {2025}, month = {2025 May 2}, abstract = {

Modern microscopy methods incorporate computational modeling of optical systems as an integral part of the imaging process, either to solve inverse problems or enable optimization of the optical system design. These methods often depend on differentiable simulations of optical systems, yet no standardized framework exists{\textemdash}forcing computational optics researchers to repeatedly and independently implement simulations that are prone to errors, difficult to reuse in other applications, and often computationally suboptimal. These common problems limit the potential impact of computational optics as a field. We present Chromatix: an open-source, GPU-accelerated differentiable wave optics library. Chromatix builds on JAX to enable fast simulation of diverse optical systems and inverse problem solving, scaling these simulations from single-CPU laptops to multi-GPU servers. The library implements various optical elements (e.g., lenses, polarizers and spatial light modulators) and multiple light propagation models (e.g., Fresnel approximation, angular spectrum and off-axis propagation) that can be flexibly combined to model various computational optics applications such as snapshot microscopy, holography, and phase retrieval of multiple scattering samples. These simulations can be automatically parallelized to scale across multiple GPUs with a single-line change to the modeling code, enabling simulation and optimization of previously impractical optical system designs. We demonstrate Chromatix{\textquoteright}s capacity to substantially accelerate optics simulation and optimization on existing methods in computational optics, speeding up optical simulation and optimization from 2-6{\texttimes} on a single GPU to up to 22{\texttimes} on 8 GPUs (depending on the particular system being modeled) compared to the original implementations. Chromatix establishes a standard for wave optics simulations, democratizing access to and expanding the design space of computational optics.i

}, doi = {10.1101/2025.04.29.651152}, url = {https://www.biorxiv.org/content/early/2025/05/02/2025.04.29.651152}, author = {Deb, Diptodip and Both, Gert-Jan and Bezzam, Eric and Kohli, Amit and Yang, Siqi and Chaware, Amey and Allier, C{\'e}dric and Cai, Changjia and Anderberg, Geneva and Eybposh, M. Hossein and Schneider, Magdalena C. and Heintzmann, Rainer and Rivera-Sanchez, Fabrizio A. and Simmerer, Corey and Meng, Guanghan and Tormes-Vaquerano, Jovan and Han, SeungYun and Shanmugavel, Sibi Chakravarthy and Maruvada, Teja and Yang, Xi and Kim, Yewon and Diederich, Benedict and Joo, Chulmin and Waller, Laura and Durr, Nicholas J. and P{\'e}gard, Nicolas C. and La Rivi{\`e}re, Patrick J. and Horstmeyer, Roarke and Chowdhury, Shwetadwip and Turaga, Srinivas C.} } @article {69567, title = {Decomposing heterogeneous dynamical systems with graph neural networks}, journal = {arXiv}, year = {2024}, month = {2024 Jul 27}, abstract = {

Natural physical, chemical, and biological dynamical systems are often complex, with heterogeneous components interacting in diverse ways. We show that graph neural networks can be designed to jointly learn the interaction rules and the structure of the heterogeneity from data alone. The learned latent structure and dynamics can be used to virtually decompose the complex system which is necessary to parameterize and infer the underlying governing equations. We tested the approach with simulation experiments of moving particles and vector fields that interact with each other. While our current aim is to better understand and validate the approach with simulated data, we anticipate it to become a generally applicable tool to uncover the governing rules underlying complex dynamics observed in nature.

}, doi = {10.48550/arXiv.2407.19160}, url = {https://doi.org/10.48550/arXiv.2407.19160}, author = {Allier, C{\'e}dric and Schneider, Magdalena C and Innerberger, Michael and Heinrich, Larissa and Bogovic, John A and Saalfeld, Stephan} } @article {69317, title = {Detecting abnormal cell behaviors from dry mass time series}, journal = {Scientific Reports}, volume = {14}, year = {2024}, month = {2024 Mar 20}, abstract = {

The prediction of pathological changes on single cell behaviour is a challenging task for deep learning models. Indeed, in self-supervised learning methods, no prior labels are used for the training and all of the information for event predictions are extracted from the data themselves. We present here a novel self-supervised learning model for the detection of anomalies in a given cell population, StArDusTS. Cells are monitored over time, and analysed to extract time-series of dry mass values. We assessed its performances on different cell lines, showing a precision of 96\% in the automatic detection of anomalies. Additionally, anomaly detection was also associated with cell measurement errors inherent to the acquisition or analysis pipelines, leading to an improvement of the upstream methods for feature extraction. Our results pave the way to novel architectures for the continuous monitoring of cell cultures in applied research or bioproduction applications, and for the prediction of pathological cellular changes.

}, doi = {10.1038/s41598-024-57684-w}, url = {https://www.nature.com/articles/s41598-024-57684-whttps://www.nature.com/articles/s41598-024-57684-w.pdfhttps://www.nature.com/articles/s41598-024-57684-w.pdfhttps://www.nature.com/articles/s41598-024-57684-w}, author = {Bailly, Romain and Malfante, Marielle and Allier, C{\'e}dric and Paviolo, Chiara and Ghenim, Lamya and Padmanabhan, Kiran and Bardin, Sabine and Mars, J{\'e}r{\^o}me} }