Incorporating Spatial Priors in Microwave Imaging via Multiplicative Regularization
Why this work is in the frame
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Bibliographic record
Abstract
This article presents a microwave imaging (MWI) algorithm that can incorporate prior structural information, also known as spatial priors (SP), about the object being imaged to enhance the achievable image quantitative accuracy. This algorithm: 1) is fully automated and 2) can work with both complete and partially available structural information. The core idea of this imaging algorithm is to use a multiplicative regularization term to incorporate SP, and a second regularization term to handle the lack of structural information in a given part of the imaging domain. This algorithm, which has been implemented for the 2-D transverse magnetic case, is evaluated against single-frequency and multiple-frequency synthetic and experimental MWI data sets.
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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) | 0.000 | 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 it