Quick Birkhoff-von Neumann Decomposition Algorithm for Agile All-Photonic Network Cores
Bibliographic record
Abstract
This paper presents a simple and efficient algorithm for timeslot allocation in agile all-photonic network (AAPN) cores working under a time division multiplexing (TDM) mode, called the Quick Birkhoff-von Neumann Decomposition Algorithm (QBvN). The time complexity of QBvN can reach O(Nn) for a N×N switch with a TDM frame size of n. Another version of QBvN, called QBvN-cover, is also proposed to provide guaranteed scheduling with configuration overhead. For QBvN-cover, the bound of the number of generated switch configurations is provided and hence the necessary speedup for AAPN cores. Under stream-type, continuous bit rate traffic, QBvN-cover shows superior delay performance compared with other heuristics in the literature. Although QBvN-cover is unlike other BvN algorithms that use a service matrix as input, we show that service matrix construction from traffic demand is necessary for QBvN-cover to perform well.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".