Constellation design for color-shift keying using interior point methods
Why this work is in the frame
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Bibliographic record
Abstract
Color-shift keying (CSK) is a visible light modulation scheme that transmits data imperceptibly through the variation of output light color. In this work, a rigorous design framework for CSK constellations is presented based on interior point methods. A differentiable approximation to the CSK constellation design problem is presented which is compatible with commercial interior point optimization algorithms and is used to find locally optimal symbol sets. The benefits of the approach are that it can operate with a variety of peak and color-cross talk constraints (i.e., convex constraint regions), permits any desired operating color and is able to produce results for any size constellation. Design guidelines on tradeoffs between minimum distance and operating color as well a heuristic for handling larger constellations are also provided.
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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 | 0.001 | 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