Optimal Solutions for Single Fault Localization in Two Dimensional Lattice Networks
Why this work is in the frame
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Bibliographic record
Abstract
Achieving fast, precise, and scalable fault localization has long been a highly desired feature in all-optical mesh networks. Monitoring tree (m-tree) is an interesting method that has been introduced as the most general monitoring structure for achieving unambiguous failure localization (UFL). Ideally, with J m-trees one can monitor up to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J</sup> -1 links when a single failure has to be located. Such a logarithmic behavior has also been observed in numerous case studies of real life network topologies. It is expected that the m-tree framework will lead to a highly scalable link failure monitoring mechanism for not only all-optical mesh networks, but any possible future information system with mesh topologies, such as all-optical mesh networks, touch panels, quantum computing, and VLSI. It is an important task to investigate the extent such an optimal logarithmic behavior may hold, in particular in practically relevant network topologies. As an endeavor toward this goal, the paper investigates the problem by identifying essentially tight logarithmic bounds for two dimensional lattice networks. Experiments are conducted to show the feasibility and performance of the proposed constructions.
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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 | 0.000 |
| 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