Cooperative node localization using nonlinear data projection
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
Cooperative node localization schemes that employ nonlinear data reduction often deliver higher network node position accuracy compared to many other approaches. Other advantages of such algorithms are that they require only a minimum number of anchor nodes (if we require absolute locations) and that they can be applied under both range-based and range-free conditions. This article presents a novel cooperative node localization scheme, applying an efficient neural network nonlinear projection method called Curvilinear Component Analysis (CCA). A thorough comparative performance study of the proposed scheme in different mission-critical operational network scenarios is conducted. Compared with another leading cooperative node localization algorithm, MDS-MAP, which employs Multi-Dimensional Scaling (MDS), the proposed CCA-MAP approach significantly improves position estimate accuracy in many of the scenarios. We also propose a new local edge model for range-free distance matrix approximation that considerably enhances the performance for both MDS-MAP and CCA-MAP in certain irregular network configurations which are very challenging for node positioning.
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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 | 0.000 | 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 | 0.000 |
| 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