Optimization for Fault Localization in All-Optical Networks
Why this work is in the frame
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Bibliographic record
Abstract
Fault localization is a critical issue in all-optical networks. The limited-perimeter vector matching (LVM) protocol is a novel fault-localization protocol proposed for localizing single-link failures in all-optical networks. In this paper, we study the optimization problems in applying the LVM protocol in static all- optical networks. We consider two optimization problems: one is to optimize the traffic distribution so that the fault-localization probability in terms of the number of localized links is maximized, and the other is to optimize the traffic distribution so that the time for localizing a failed link is minimized. We formulate the two problems into an integer linear programming problem, respectively, and use the CPLEX optimization tool to solve the formulated problems. We show that by optimizing the traffic distribution the fault-localization probability can be maximized and the fault-localization time can be minimized. Moreover, a heuristic algorithm is proposed to evaluate the optimization results through simulation experiments.
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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 | 0.001 | 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