A physiologically-based framework for the simulation of skin tanning dynamics
Bibliographic record
Abstract
A comprehensive understanding about the dynamics of time-dependent, photoinduced physiological processes affecting the spectral attributes and, consequently, the appearance of human tissues is essential for new advances in biology, medicine, biomedical photonics and computer graphics, just to name a few fields that can benefit from it. Skin is arguably the most investigated of these complex biological systems. Its interactions with light have been the object of extensive studies aimed at a wide range of applications, from the detection and treatment of diseases to the synthesis of realistic images for educational and entertainment purposes. However, the dynamics of photoinduced physiological processes leading to skin appearance changes over time remains an open research topic. In this paper, we address the effects of tanning, one of the most prominent and persistent photobiological phenomena leading to such appearance changes. More specifically, we present a novel physiologically-based framework for the simulation of skin tanning dynamics, and describe how it can be employed in the visualization of the tanning-induced variations on skin’s spectral attributes. Its first-principles algorithms explicitly account for the connections between spectrally-dependent light stimuli and time-dependent physiological reactions occurring within the cutaneous tissues. This enables the effective simulation of these tissues’ main mechanisms of adaptation to ultraviolet radiation. As a result, nonlinear skin appearance changes elicited by distinct light exposure regimes can be correctly reproduced. We demonstrate the predictive capabilities of the proposed framework through quantitative and qualitative comparisons of its outcomes with measurements and experimental observations reported in the literature. We believe that it provides a high-fidelity testbed for interdisciplinary research involving time-dependent skin responses to light exposure.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".