Bibliographic record
Abstract
Traditionally, Slepian-Wolf (SW) coding theorem relies on source sequences with length tending to infinity. However, in practice, the source sequences are of finite length. Towards closing this gap, we obtain non-asymptotic achievability and non-asymptotic converse for the fixed-rate SW coding by applying the NEP theorem. Furthermore, combining those converse and achievability, we show that the best SW coding rate has a Taylor-type expansion with respect to δn(ϵ) which measures the relative magnitude of the error probability ϵ and block length n. In addition, based on the Taylor-type expansion, we develop some reliable approximations (dubbed SO and NEP) for Rn(ϵ). Numerical comparison with the converse and achievability further shows that while the normal approximation and redundancy approximation reported in the literature can be either below converse or above the achievability in some cases, the SO and NEP approximations are always reliable and can serve as the benchmark for practical SW code design.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".