A set-membership affine projection algorithm with adaptive error bound
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A new set-membership affine projection (SM-AP) algorithm for adaptive filtering applications is proposed. The new SMAP algorithm eliminates the error-bound estimation problem of the conventional SM-AP algorithm. The poor tracking performance in nonstationary environments of the conventional SM-AP algorithm is also considered. A solution to this problem is proposed by incorporating a switching mechanism in the proposed SM-AP algorithm. The new SM-AP algorithm has better convergence efficiency and yields lower misadjustment than the conventional AP algorithm. On the other hand, with the switching mechanism it has better convergence efficiency and yields lower misadjustment than the conventional SM-AP algorithm in nonstationary environments.
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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