Delayed-dictionary compression for packet networks
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers compression in packet networks. Since data packets may be dropped or arrive reordered, streaming compression algorithms result in a considerable decoding latency. On the other hand, standard stateless packet compression algorithms that compress each packet independently, give a relatively poor compression ratio. We introduce a novel compression algorithm for packet networks: delayed-dictionary compression. By allowing delay in the dictionary construction, the algorithm handles effectively the problems of packet drops and packet reordering, while resulting with a compression quality which is often substantially better than standard stateless packet compression and has a smaller decoding latency than that of streaming compression. We conducted extensive experiments to establish the potential improvement for packet compression techniques, using many data files including the Calgary corpus and the Canterbury corpus. Experimental results of the new delayed-dictionary compression show that its main advantage is in low to medium speed links.
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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 | 0.000 | 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 | 0.001 | 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 it