Lossless Source Coding Using Nested Error Correcting Codes
Bibliographic record
Abstract
We propose a tree-structured variable-length random binning scheme for lossless source coding. The existing source coding schemes based on turbo codes, low-density parity check codes, and repeat accumulate codes can be regarded as practical implementations of this random binning scheme. For sufficiently large data blocks, we show that the proposed scheme asymptotically achieves the entropy limit. We also derive the distribution of the compression rate achieved by the tree-structured random binning scheme. Comparing this distribution with the distribution obtained using a library of random binning schemes, we show that a nested code can achieve rates close to a library of codes but with much lower encoding/decoding complexity. With lossless turbo source coding being one of the most powerful source compression techniques, we investigate its performance relative to the proposed tree-structured random binning scheme. Our numerical results show that the compression rate achieved by lossless turbo source coding is far from the tree-structured random binning bound. In that, we suggest improvements to enable short-block-length turbo source codes to achieve compression rates close to the tree-structured random binning bound
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".