Interactive encoding and decoding based on syndrome accumulation over binary LDPC ensembles: Universality and rate-adaptivity
Bibliographic record
Abstract
In this paper we investigate the performance of linear interactive encoding and decoding based on syndrome accumulation(SA-IED) over binary LDPC ensembles. Assume that the source alphabet is GF(2), and the side information alphabet is finite. It is shown that we can construct universal SA-IED schemes, which are asymptotically optimal for any stationary ergodic source-side information pair. Our analysis further shows that the word error probability will approach 0 sub-exponentially with respect to the block length, while at the same time, the rate approaches H(X|Y) as the average variable node degree of the LDPC ensemble approaches ¿. Further, if the source and side information are correlated through a binary symmetrical memoryless channel, but the cross-over probability of the channel is not known to either the encoder or the decoder, our result on the performance of SA-IED can be further improved for LDPC ensembles with finite average variable node degree. Simulation results on binary source-side information pairs confirm the theoretical analysis above, and further show that SA-IED schemes using LDPC codes coupled with linear time belief propagation decoding consistently outperform Slepian-Wolf coding schemes based on LDPC codes.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".