Regularized-truncation approach in inverse problem of electrocardiography
Why this work is in the frame
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Bibliographic record
Abstract
In the inverse problem of electrocardiography, an overspecified Cauchy condition for an elliptic operator must be satisfied under realistic conditions for which (i) the geometry of the problem domain has an irregular shape, and (ii) the observation data (volume conductor properties and thoracic potentials) are perturbed by noise. Since numerical treatment is needed, this problem is reduced to one with a finite dimension and the instability of this discretized inverse problem becomes more important. Here, the authors use the generalized singular value decomposition to derive an inverse method based on revising the space of the regularized solution. This method, regularized-truncation, is then applied to simulated data using a 3D finite element model of a human torso, and the inverse solutions are compared with those obtained using Tikhonov regularization.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.001 |
| 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