collaborators

12 papers

cs.AI2026

CATO: Charted Attention for Neural PDE Operators

Chun-Wun Cheng, Sifan Wang, Carola-Bibiane Schönlieb +1

Neural operators have emerged as powerful data-driven solvers for PDEs, offering substantial acceleration over classical numerical methods. However, existing transformer-based oper…

cs.LG2026

Bridging Input Feature Spaces Towards Graph Foundation Models

Moshe Eliasof, Krishna Sri Ipsit Mantri, Beatrice Bevilacqua +2

Unlike vision and language domains, graph learning lacks a shared input space, as input features differ across graph datasets not only in semantics, but also in value ranges and di…

cs.CV2026

No-reference based automatic parameter optimization for iterative reconstruction using a novel search space aware crow search algorithm

Poorya MohammadiNasab, Ander Biguri, Philipp Steininger +9

Iterative reconstruction technique's ability to reduce radiation exposure by using fewer projections has attracted significant attention. However, these methods typically require a…

q-bio.NC2026

Hierarchical Multiscale Structure-Function Coupling for Brain Connectome Integration

Jianwei Chen, Zhengyang Miao, Wenjie Cai +10

Integrating structural and functional connectomes remains challenging because their relationship is non-linear and organized over nested modular hierarchies. We propose a hierarchi…

cs.CR2026

Building Privacy-and-Security-Focused Federated Learning Infrastructure for Global Multi-Centre Healthcare Research

Fan Zhang, Daniel Kreuter, Javier Fernandez-Marques +10

Collaborative healthcare research across multiple institutions increasingly requires diverse clinical datasets, but cross-border data sharing is strictly constrained by privacy reg…

cs.CV2026

ProSMA-UNet: Decoder Conditioning for Proximal-Sparse Skip Feature Selection

Chun-Wun Cheng, Yanqi Cheng, Peiyuan Jing +4

Medical image segmentation commonly relies on U-shaped encoder-decoder architectures such as U-Net, where skip connections preserve fine spatial detail by injecting high-resolution…