Online Tuning of Retinal Imaging Adaptive Optics Systems
Why this work is in the frame
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Bibliographic record
Abstract
This brief addresses the problem of adaptive regulation in retinal imaging adaptive optics systems against the unknown and time-varying aberrations present in the eye. The proposed controller design approach relies on two steps. The first step is to construct a -parameterized set of stabilizing controllers for the multi-input multi-output system under consideration and to derive conditions on the parameter in the controller expression to achieve regulation. Partial diagonal decoupling of the closed-loop system dynamics is performed to facilitate the development of the adaptive regulator. Since the eye's aberrations are unknown and time-varying, the second step is to derive an online tuning algorithm for the parameter in the expression for the parameterized stabilizing controller. The online tuning of the parameter allows the controller to converge to the controller needed to achieve regulation, hence compensating for the lack of information on the eye's aberrations. The partial decoupling introduced in the closed loop system allows the tuning to be performed using decentralized adaptation algorithms. Experimental results are presented to validate the proposed regulation approach.
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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) | 0.001 | 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