Optimal regenerator placement in survivable translucent networks
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
In optical networks, the optical reach is defined as the distance an optical signal can travel, before its quality degrades to a level that requires 3R-regeneration. In a translucent optical network, if an optical signal has to be communicated over a distance that exceeds the optical reach, the signal is regenerated at selected nodes of the network, so that the signal quality never degrades to an unacceptable level. Given a value of the optical reach, the goal of the Regenerator Placement Problem (RPP) in dynamic Physical Impairment aware Route and Wavelength Assignment (PI-RWA), for survivable translucent networks, is to identify the minimum number of nodes capable of 3R regeneration, so that every pair of nodes (u, v) can establish a lightpath (either transparent or translucent) from u to v. In a survivable network, even if any fault occurs, it must be guaranteed that every pair of surviving nodes (u, v) can still establish a lightpath (either transparent or translucent) from u to v, avoiding all faulty nodes/edges. In this paper we have presented a Integer Linear Program (ILP) formulation that can optimally solve the survivable RPP problem for practical-sized networks within a reasonable amount of time. We have used a branch-and-cut approach to implement our algorithm, where we have intercepted the optimization process with control callbacks from the CPLEX callable library to introduce new constraints, as needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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