Analog filter adaptation using a dithered linear search algorithm
Why this work is in the frame
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Bibliographic record
Abstract
A variation of the differential steepest descent algorithm, here called the dithered linear search (DLS), is examined and applied to analog filter adaptation. The DLS algorithm is a gradient descent optimizer with a straightforward and robust hardware implementation. Gradient estimates are obtained by applying independent additive dither to all of the filter's parameters simultaneously and correlating the resulting changes in the output squared error to the dither signals. Unlike the popular LMS algorithm, the DLS algorithm does not require access to the filter's internal states. No additional analog hardware is required making it ideal for adaptive analog filters in mixed-signal systems. A theoretical analysis shows no gradient misalignment. The algorithm is verified on an integrated analog filter. The effects of dc offsets are also examined.
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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