Improving Compression via Substring Enumeration by Explicit Phase Awareness
Bibliographic record
Abstract
Compression by Substring Enumeration (CSE) is a recent and promising lossless compression scheme. The first experiments on CSE showed that it yields compression ratios that favorably compare to other lossless compression techniques. However, the experiments also showed that it tends to incur a performance loss on non-textual, byte-oriented sources and it was conjectured that CSE's phase unawareness was responsible for this loss of performance. Subsequent work confirmed the conjecture by obtaining improved compression ratios when synchronization codes get inserted in the data source, indirectly solving the phase-unawareness problem. This indirect solution does not give an absolute measure of the loss incurred by the phase unawareness problem. This paper presents a modified CSE algorithm that is made explicitly phase aware. It compares the synchronization-code approach to the explicitly phase-aware approach and shows that, in the end, the approach based on synchronization codes is almost as good as the phase-aware approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".