Real-time coding of Gauss-Markov sources over burst erasure channels
Bibliographic record
Abstract
We study real-time coding of Gauss-Markov sources over burst-erasure channels. A time-invariant encoder sequentially compresses a sequence of vector Gaussian sources, which are spatially i.i.d. and temporally correlated according to a Gauss-Markov model. The channel is a burst erasure channel that erases up to B packets in a single burst. The decoder is interested in instantaneously reconstructing all the source sequences within a quadratic (mean square error) distortion D, except for packets lost in the erasure burst window. We study the minimum achievable rate for the encoder under these constraints and define it as the lossy rate-recovery function R(B, D). We develop lower and upper bounds for the function and observe that the bounds coincide in the high resolution limit. Numerical comparisons indicate that the proposed joint source-channel coding scheme provides significant gains over a separation based scheme.
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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) | 0.001 | 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 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".