Efficient path selection and fast restoration algorithms for shared restorable optical networks
Why this work is in the frame
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Bibliographic record
Abstract
Efficient path selection combined with fast restoration algorithms is a key requirement for designing shared restorable mesh networks. In this paper we first discuss a distributed path selection algorithm for efficient routing of restorable connections in optical networks. This approach relies on the knowledge of global information, maintained at each node, to determine link sharability and compute optimal shared paths; we compare its performance to another protocol [C. Assi et al., 2002] that only requires the knowledge of local resource usage. Second, we study the network's ability to recover from single element failures in a shared mesh network and we propose a new restoration algorithm for rapid recovery upon a failure. The significant contribution of this algorithm is that the network restoration time is independent of the protection path length (i.e., the effect of propagation delay is eliminated) as well as the accumulation of the switch configuration times. We evaluate the performance of these protocols 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 | 0.000 | 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