Prospicient Real-Time Coding of Markov Sources over Burst Erasure Channels: Lossless Case
Bibliographic record
Abstract
We introduce a framework to study fundamental limits of sequential coding of Markov sources under an error propagation constraint. An encoder sequentially compresses a sequence of vector-sources that are spatially i.i.d. but temporally correlated according to a Markov process. The channel erases up to B packets in a single burst, but reveals all other packets to the destination. The destination is required to reproduce all the source-vectors instantaneously and in a loss less manner, except those sequences that occur in a window of length B+W following the start of the erasure burst. We define a rate-recovery function R(B, W), the minimum compression rate that can be achieved in this framework, and develop upper and lower bounds for first-order Markov sources. For the special class of linear diagonally correlated deterministic sources, we propose a new coding technique -- prospicient coding -- that achieves the rate-recovery function. Finally, a lossy extension to the rate-recovery function is also studied for a class of Gaussian sources where the source is temporally and spatially i.i.d. and the decoder aims to recover a collection of past K sources with a quadratic distortion measure. The optimal rate-recovery function is compared with the sub-optimal techniques including forward error correction coding (FEC) and Wyner-Ziv coding, and performance gains are quantified.
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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 | 0.002 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".