Bibliographic record
Abstract
Abstract We consider the wavelength assignment problem in WDM optical networks with multiple parallel fibers: Given a setPof paths, assign a color to each path such that the number of paths with the same color containing any link is at most the number of fibers in the link. Assuming the number of fibers in each link is fixed, we study two optimization problems. One is to minimize the number of colors for coloringP. The other is to color as many paths ofPas possible with a given number of colors. We show that both the minimization and the maximization problems are NP‐hard in star networks with a uniform odd number of fibers. We give polynomial time optimal algorithms for the minimization and maximization problems in star networks with an even number of fibers and in the generalized star networks with a uniform even number of fibers. We also give a 1.58‐approximation algorithm for the maximization problem in the generalized star networks with an arbitrary number of fibers. The algorithms for the maximization problem in the generalized stars are based on our newly developed algorithm, which optimally solves the call control problem in the generalized star networks. The call control algorithm is of independent interest. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".