Bibliographic record
Abstract
The high-throughput delayed LMS (DLMS) adaptive algorithm suffers from a slower convergence rate compared to the LMS algorithm. Different versions of the DLMS adaptive algorithm using a conversion scheme have been proposed to improve the convergence rate. This improved convergence was achieved at the expense of an increased computational complexity and a lower throughput rate than the original DLMS algorithm. We propose a new modified DLMS adaptive algorithm that, compared to the existing conversion-based DLMS algorithm, provides a higher throughput rate for a similar convergence rate. Alternatively, the proposed algorithm provides a faster convergence for the same throughput rate compared to the conversion-based DLMS algorithm. In both the cases, the computational complexity of the proposed algorithm is smaller than that of the conversion-based DLMS algorithm. The proposed algorithm uses the error signal from each stage of the adaptive FIR filter independently to update the value of the corresponding coefficient. Simulations illustrate the convergence performance of the new algorithm. The performance of its architecture is evaluated in terms of computational complexity, throughput, and latency. The proposed algorithm provides a better throughput rate and a computational complexity lower than that of the conversion-based DLMS algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".