Segment p-cycle design with full node protection in WDM mesh 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
Segment p-cycles offer an interesting compromise between the classical (link) p-cycles and the path p-cycles (also known as FIPP p-cycles), inheriting most advantages of both p-cycle schemes. In their original form, segment p-cycles do not offer 100% node protection, i.e., do not guarantee any protection against node failure for the endpoints of the segments. Indeed, if we allow some p-cycle overlapping, it is possible to ensure 100% node protection: this is the focus of the present study. We propose a new efficient design approach for segment p-cycles, called segment Np-cycles, which ensure 100% protection against any single failure, either link or node (endpoints of requests are excluded). In order to evaluate the performances of segment Np-cycles, we develop a new optimization model based on column generation (CG) techniques. The use of such techniques eliminates the need to explicitly enumerate all segment Np-cycle configurations, but instead leads to a process where only improving segment Np-cycle configurations are generated. Numerical results demonstrate that segment Np-cycles are comparable, sometimes even more efficient, than path p-cycles with respect to their capacity requirement. In addition, in order to ensure 100% node protection, they only require a marginal extra spare capacity than the regular segment p-cycles.
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