| Research Thread | Goal | Potential Impact | |-----------------|------|-------------------| | Adaptive Token Routing | Dynamically select next hop based on information gain or link quality. | Further reduce latency and improve robustness. | | Privacy‑Preserving Fusion | Homomorphic encryption of particle weights. | Enable cooperative surveillance across organizations with data‑sensitivity constraints. | | Cross‑Domain Transfer Learning | Leverage pre‑trained deep models for likelihood estimation, combined with particle‑filter belief. | Boost detection accuracy in novel environments without retraining on‑board. | | Multi‑Token Parallelism | Deploy several tokens simultaneously in disjoint sub‑graphs. | Scale to thousands of agents while preserving near‑optimal fusion. |
| Product | Format | Size | Access | |---------|--------|------|--------| | Catalog (positions, magnitudes) | FITS/CSV | 350 MB | https://doi.org/xx.xxxx/fsdss548 | | Image cutouts (JPEG/PNG) | 10 GB | https://doi.org/xx.xxxx/fsdss548_images | | Spectra (1‑D) | FITS | 2 TB | https://doi.org/xx.xxxx/fsdss548_spec | FSDSS-548
Given B‑connectivity of the communication graph and a token hop budget ( H \geq N \cdot B ), the token belief ( \beta_H ) converges almost surely to the exact posterior ( p(\mathbfxt \mid Z1:N) ), where ( Z_1:N ) denotes the union of all measurements up to time ( t ). | Research Thread | Goal | Potential Impact
Proof Sketch: