Decentralized and coordinate-free computation of critical points and surface networks in a discretized scalar field
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
This article provides a decentralized and coordinate-free algorithm, called decentralized gradient field (DGraF), to identify critical points (peaks, pits, and passes) and the topological structure of the surface network connecting those critical points. Algorithms that can operate in the network without centralized control and without coordinates are important in emerging resource-constrained spatial computing environments, in particular geosensor networks. Our approach accounts for the discrepancies between finite granularity sensor data and the underlying continuous field, ignored by previous work. Empirical evaluation shows that our DGraF algorithm can improve the accuracy of critical points identification when compared with the current state-of-the-art decentralized algorithm and matches the accuracy of a centralized algorithm for peaks and pits. The DGraF algorithm is efficient, requiring O(n) overall communication complexity, where n is the number of nodes in the geosensor network. Further, empirical investigations of our algorithm across a range of simulations demonstrate improved load balance of DGraF when compared with an existing decentralized algorithm. Our investigation highlights a number of important issues for future research on the detection of holes and the monitoring of dynamic events in a field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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