Benquan Wang, Trishita Das, Yuhan Peng, Tatjana Kleine, Shanshan Chang, Jinhui Chen, Nilo Mata-Cervera, Chunyu Li 等 11 位作者
The evolution of modern-day communication networks towards enhanced capacity and robustness is driving interest in topological light wave as a next-generation information carrier. However, harnessing its potential is hindered by the lack of an effective topological reader, particularly when strong disorder renders conventional readout intractable. Here we address this by introducing TOPO$^{2}$, an artificial intelligence framework that leverages on topological learning to recover topological information from light. We demonstrate it on highly distorted optical fields with embedded Skyrmion topologies, showing that we can extract the topological numbers in a single shot with just a conventional camera. We showcase its capability in a TOPO$^{2}$-based communication scheme, sending and recovering information encoded in a topological alphabet, confirming that it outperforms conventional approaches. Our connection of topological photonics with topological AI as a powerful paradigm for robust high-capacity information encoding and processing opens a new path towards topological information optics.
RGB-X tracking complements RGB appearance with thermal, depth, or event observations, but centralized training requires collecting multimodal videos at a single location. Federated training keeps raw videos local, but sensor heterogeneity can create a mismatch between parameter-sharing domains and the clients contributing to them. This mismatch is particularly relevant when RGB-only clients coexist with sensor-equipped clients: their RGB observations remain useful, but they provide no auxiliary measurements for learning sensor-dependent interactions. We propose Fed-MAPTrack, a modality-aware federated prompt tuning framework for RGB-X tracking. The pretrained tracking backbone and prediction head are frozen, and clients optimize and exchange only prompt-related parameters. The server aggregates RGB-shared parameters globally and maintains separate thermal, event, and depth banks for auxiliary-specific, fusion, and normalization parameters. Selective participation retains RGB-only clients' contributions to the global RGB bank while excluding their fusion and normalization updates from modality-specific aggregation. Across three matched training repetitions under a fixed client partition, Fed-MAPTrack improves the mean FedAvg scores by 2.35 AUC points on LasHeR, 3.45 SR points on VisEvent, and 2.11 F-score points on DepthTrack. Among the nine evaluated methods, it achieves the highest mean thermal AUC and depth F-score, while its mean event SR is 0.07 points below that of Independent FL. Controlled ablations show that extending the bucketing scope without a compatible participation rule can reduce performance. These results highlight the need to jointly define the sharing domain and eligible contributors for each parameter group.