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 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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".