Conformal light delivery using tailored cylindrical diffusers
Bibliographic record
Abstract
Tailored light diffusers offer the flexibility of shaping the delivered light dose (fluence rate) distribution, potentially leading to conformal light delivery. Because of scattering and absorption, tissue acts as a spatial low pass filter of the diffuser's emission profile, and therefore some dose distributions with high spatial frequencies cannot be delivered. We characterize the set of attainable light dose distributions in terms of the spatial frequency of the emission profile and identify regimes where such distributions are less sensitive to changes in optical properties. Furthermore, we contrast two different algorithms to solve the inverse problem: Simulated Annealing (SA) and Non-negative Least Squares (NNLS). SA is plagued by superimposed high frequency components that do not contribute significantly to the cost. We present an iterative low pass filter that smooths the emission profile without considerably increasing the cost. A non-negative least square (NNLS) algorithm is also tested. We conclude that non-negative least squares (NNLS) is superior to simulated annealing (SA) in terms of time performance and cost minimization.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".