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
This study presents an adaptive data‐transition decision feedback equaliser (DT‐DFE) with a sign 3 least‐mean‐square (LMS) tap adaptation. Commonly used data‐state (DS) DFE suffers from reduced vertical eye‐opening when consecutive 1's or 0's are encountered. The proposed DT‐DFE performs DFE only when a data transition is detected. It boosts the eye‐opening of the high‐frequency components of data without attenuating the low‐frequency components of data whereas DS‐DFE boosts the eye‐opening of the high‐frequency components of data at the expense of the attenuated low‐frequency components of data. The reference voltages of DS‐DFE is tap‐dependent whereas those of DT‐DFE are tap‐independent and are obtained by conveying consecutive 1's and 0's to the channel in a training phase. The proposed DT‐DFE utilises loop unrolling to detect the occurrence of data transition. The performance of the proposed DT‐DFE is compared with that of DS‐DFE using two 5 Gbps backplane serial links designed in a TSMC 65 nm CMOS technology. Simulation results demonstrate that the eye‐opening of the link with DT‐DFE is 1.54 times that with DS‐DFE. The vertical eye‐opening of the link with DT‐DFE activating tap adaptation on two consecutive state transitions of opposite polarities is 1.2 times that that activates tap adaptation on single state transition. The proposed DT‐DFE is less sensitive to process uncertainty whereas DS‐DFE is prone to process uncertainty with severely deteriorating performance.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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