A robust constrained set-membership affine-projection adaptive-filtering algorithm
Why this work is in the frame
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Bibliographic record
Abstract
A robust constrained set-membership (SM) affine-projection adaptive-filtering algorithm is proposed that works with two error bounds. One of the error bounds works during transience and the other works during steady state and in this way a faster convergence and significantly reduced steady-state misalignment can be achieved relative to what can be achieved with the constrained normalized least mean-squares (CNLMS), the constrained affine-projection (CAP), and the constrained set-membership affine-projection (CSMAP) algorithms. The proposed algorithm achieves in addition robust performance with respect to impulsive noise and yields good tracking performance compared to the CNLMS, CAP, and CSMAP algorithms. These features of the proposed CSMAP algorithm are demonstrated in system identification, sinusoid-filtering, and interference suppression applications.
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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