Constrained min-max optimization of multi-rate digital filters with simultaneous magnitude and group-delay specifications
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Bibliographic record
Abstract
This paper is concerned with the development of a new constrained min-max optimization technique for multi-rate digital filter transfer functions. The salient feature of this technique is that it can handle the simultaneous optimization of the magnitude/frequency and group-delay/frequency response characteristics for both finite and infinite precision transfer function coefficients. Moreover, it can handle not only inequality constraints (as in the case of existing approaches) but also equality constraints both on the magnitude/frequency and group-delay/frequency response characteristics. In addition, it is guaranteed to converge to a solution, of course, if a solution exists. The proposed optimization technique has been incorporated in a software package called Papillon I. The effectiveness and practical application of Papillon I is demonstrated by applying it to the optimization of a digital CODEC filter transfer function.
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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 | 0.001 |
| 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