Asynchronous distributed particle filter via decentralized evaluation of Gaussian products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a distributed particle filtering algorithm for target tracking in sensor networks. Several existing algorithms rely on the establishment and maintenance of a spanning path or tree. This is challenging in networks with dynamic topologies induced by mobile nodes and changing wireless conditions; the algorithms are vulnerable to link or node failure. More recent algorithms employ consensus algorithms to improve robustness but they adopt suboptimal fusion rules leading to a significant deterioration in performance. In our algorithm, nodes run local particle filters and then approximate their local posteriors using Gaussian approximations. A global posterior approximation is then computed using a novel gossiping approach that implements the optimal fusion rule. The resultant protocol is simple, robust and efficient. We present simulation results demonstrating a significant performance improvement over the best-performing existing algorithm with similar communication and computation requirements.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it