A Comparison between Tnime-slot Scheduling Approaches for All-Photonic Networks
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Bibliographic record
Abstract
The internal switches in all-photonic networks do not perform data conversion into the electronic domain. Although this removal of O-E-O conversion eliminates a potential capacity bottleneck, it also introduces scheduling challenges; photonic switches cannot perform queuing operations, so traffic arrivals at these switches must be carefully scheduled. The (overlaid) star topology is an excellent match for an all-photonic network because it simplifies the scheduling problem. In such a network architecture, optical time division multiplexing (OTDM) approaches for scheduling the state of the central switch in the star are attractive. In this paper, we describe two OTDM algorithms that we have recently developed, one that performs scheduling on a slot-by-slot basis and another that schedules frames of multiple slots. We report and analyze the results of OPNET simulations that compare the performance of these scheduling algorithms
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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