Bibliographic record
Abstract
With recent advances in optical technology, we are closer to building all-optical routers than ever before. A major problem in this area, however, is the lack of all-optical memories similar to what we have in electronics. To overcome this problem, recently, there have been several proposals that show how we can emulate First-In First-Out (FIFO) queues using a combination of fiber delay lines and switches. Unfortunately, FIFO queues cannot be used for implementing many link scheduling policies including weighted fair queuing, weighted round-robin, or strict priority, which are essential components of any modern router today. In this paper, we introduce an architecture based on fiber delay lines and optical switches that can be used for emulating Push-In First-Out (PIFO) queues. In a PIFO queue, an incoming packet can be pushed anywhere in the queue, and therefore it can be used for the implementation of various link scheduling policies. We describe a scheduling algorithm for this architecture and show that with a small speedup, we can build a PIFO queue of size N - 1 using only O(log2N) 3 × 3 optical switches. The resulting system has a minimum reliability of 99.5%, and even for the small portion of departure requests that cannot be fulfilled immediately, the requested packet is ready to depart within approximately five time slots from the request time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".