Investigating the Regularization Properties of the Virtual Veselago Lens for Inverse Problems
Bibliographic record
Abstract
The concept of including a virtual ideal Veselago lens into the inverse problem associated with a free-space microwave imaging scenario has recently been introduced. The numerical procedure requires the creation of “add-on” data produced by the integration of the free-space total field existing on the two infinite planar boundaries of the virtual Veselago lens. The sum of the add-on field and the originally collected field is subsequently inverted using the ideal Veselago lens inverse operator to obtain contrast sources. Here we show that this process of adding new data can be interpreted to be similar to a typical regularization technique one might use for iteratively inverting a poorly conditioned matrix equation. For the imaging problem, if the new data can be computed accurately then the first step of the iteration produces the required solution to the matrix equation. A technique for using the field measured around the object of interest and expanded in terms of cylindrical wave-functions is introduced for obtaining the new data. This procedure is investigated numerically for a 2D imaging scenario.
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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 | 0.001 | 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 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".