3 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Derived data, run outputs and figure source data supporting the article "Joint optimisation of amino acid and coding sequence for de novo designed proteins". Includes the inverse-folding marginals for all 862 backbones and the full 230,992-row double-mutant additivity table.
Derived data, run outputs and figure source data supporting the article "Joint optimisation of amino acid and coding sequence for de novo designed proteins". Includes the inverse-folding marginals for all 862 backbones and the full 230,992-row double-mutant additivity table.
Introduction While spatial Vision Transformers (ViTs) achieve high precision in urban scene parsing, their frame-by-frame application in autonomous driving suffers from severe temporal flickering and prohibitive retraining costs across decentralized vehicle fleets. Methods To overcome these dual bottlenecks, this paper introduces a unified Spatiotemporal Hierarchical Mask-Refinement (ST-HMR) framework integrated with a Byzantine-Robust Federated Learning (BR-FL) protocol. The ST-HMR module caches fine-grained prompt tokens via an asymmetrical Temporal Cross- Attention buffer to enforce inter-frame geometric continuity. Concurrently, the BR-FL pipeline employs Multi-Krum geometric distance filtration to aggregate 3 localized prompt gradients from decentralized fleets securely, updating only a 1.4% active parameter subset. Results Evaluated on the Cityscapes Video dataset, the ST-HMR framework improves the video segmentation mean Intersection over Union (mIoU) to 83.5%, elevates the Temporal Consistency (TC) score to 88.5, and reduces depth Absolute Relative Error (Abs Rel) to 0.085, all while maintaining real-time edge processing at 38 FPS. Under severe adversarial network conditions (up to 30% Byzantine/malicious sensor nodes), the BR-FL protocol achieves a 98.4% Byzantine detection rate and maintains a global mIoU of 81.9%, while reducing Over-The-Air (OTA) transmission payloads by over 99% (3.8 MB vs. 1.2 GB per round). Discussion These findings demonstrate that parameter-efficient prompt caching eliminates temporal boundary jitter without heavy 3D transformer overhead, while geometric gradient filtering provides robust defense against decentralized poisoning, establishing a scalable, secure, and temporally coherent perception paradigm for next-generation edge robotics.