Time-domain diffuse optical tomography using recursive direct method of calculating Jacobian at selected temporal points
Bibliographic record
Abstract
An algorithm for time-domain diffuse optical tomography based on the resolution of the time-domain diffusion equation using the finite element method has been developed. An efficient direct method including a recursive approach has been used to obtain the light fluence derivatives with respect to tissue optical properties at precise selected points on the temporal profile resulting in a considerable savings in computation time and memory. The algorithm reconstructs the tissue optical properties in a permissible region or a region-of-interest and the input data for reconstruction comprises selections of points on the temporal curve of the measured pulse. The optical properties have been reconstructed by solving an iterative normalized minimization problem. The algorithm has been applied to a three-dimensional simplified model of a new born baby head and to a three-dimensional model of the mouse (MOBY) for a small animal model. The computation speed and memory usage of the algorithm have been compared with that of other techniques based on continuous wave and frequency domain representations. The effects of using different sizes of time steps and number of time steps on the reconstruction accuracy and the computation time have been reported.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".