Pingchuan Ma

Learning representations that separate content and style is crucial for controllable generation and compositional generalization. However, diffusion and flow-based models trained primarily with generative objectives often produce entangled or misaligned factors. To address this gap, we introduce Contrastive Augmented Flow Matching (CAtFM), a framework that integrates contrastive regularization into an invertible flow matching formulation to promote structured content-style representations.

Back to Journals » International Journal of Nanomedicine » Volume 21 Authors Ma P , Gao Y , Wu S , Chen X, Fu X , Cai Y, Men K , Li L , Li C Received 3 December 2025 Accepted for publication 5 March 2026 Published 23 March 2026 Volume 2026:21 Pages 1—27 DOI https://doi.org/10.2147/IJN.S582078 Checked for plagiarism Yes Review by Single anonymous peer review Peer reviewer comments 4 Editor who approved publication: Professor Dong Wang Pingchuan Ma,1,* Yan Gao,2,* Shan Wu,2,* Xiayu...

eLife Assessment This study presents an important computational framework, FLiSimBA (Fluorescence Lifetime Simulation for Biological Applications), for modeling experimental limitations in Fluorescence Lifetime Imaging Microscopy (FLIM). FLiSimBA is readily available in MATLAB and Python, enables users to simulate effects of noise and varying sensor expression levels, and provides practical guidance for both lifetime imaging experiments and biosensor development.