Reconfigurable optical differential phase-shift-keying pattern recognition based on incoherent photonic processing
Why this work is in the frame
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Bibliographic record
Abstract
We propose and experimentally demonstrate asynchronous optical differential phase-shift-keying (DPSK) pattern recognition using a fully reconfigurable technique. The proposed method uses optical phase-to-bipolar intensity conversion through all-optical differentiation in conjunction with an incoherent time-spectrum convolution system where the pattern to be recognized is implemented directly in the spectral domain through optical amplitude-only linear filtering. Full reconfigurability in terms of bit rate, pattern sequence, and pattern length is achieved using electronically programmable optical filters. We demonstrate dynamically switching recognition of different 64 bit patterns in a continuous 12 Gb/s DPSK pseudorandom optical bit stream with contrast ratio up to 3.8 dB.
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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 | 0.001 |
| 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