Bibliographic record
Abstract
In several scientific areas, data are sampled irregularly and insufficiently due to practical and economical limitations. The use of such data in applications results in some artifacts and poor spatial resolution. Therefore, before being used, the data are to be interpolated onto a regular grid. One of the methods achieving this objective is based on the Fourier reconstruction, which deals with the under-determined system of equations. The Stagewise Orthogonal Matching Pursuit (StOMP) is a recently proposed greedy algorithm. Compared to the other recent algorithms like l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> - minimization techniques, StOMP admits certain promising features such as faster and simpler implementation even in large scale settings. The present work applies StOMP to the Fourier-based interpolation problem for the signals that have sparse Fourier spectra. The basic objective is to verify empirically the performance of the algorithm if, and how far, the measurement coordinates can be shifted from uniform distribution on the continuous interval. Taking kurtosis as a quantifier for the deviation of distribution from being uniform, we show numerically that the measurement coordinates can be significantly shifted from uniform distribution.
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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".