Real-time streaming of Gauss-Markov sources over sliding window burst-erasure channels
Bibliographic record
Abstract
We study sequential streaming of Gauss-Markov sources over a burst-erasure channel. In any sliding window of length L, the channel introduces a single erasure burst of maximum length B. The encoder observes a sequence of vector Gaussian sources, where the vectors are i.i.d. across the spatial dimension and correlated across the temporal dimension. The encoder output can depend on all source vectors observed up to that time but not on any future source vectors. The decoder is required to reconstruct the source vectors instantaneously and within a quadratic distortion constraint of D, except those source vectors that either appear during the erasure periods or a recovery period of W following each erasure burst. We focus on time-invariant encoders and establish upper and lower bounds on the minimum compression rate R(L, B, W, D). Our lower bound is obtained by making connection to a Gaussian multi-terminal source coding problem. The upper bound is based on distributed source coding, but requires a careful analysis of the achievable rate. Numerical comparisons indicate that the proposed technique provides significant gains over other baseline schemes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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".