Streaming Data Transmission in the Moderate Deviations and Central Limit\n Regimes
Bibliographic record
Abstract
We consider streaming data transmission over a discrete memoryless channel. A\nnew message is given to the encoder at the beginning of each block and the\ndecoder decodes each message sequentially, after a delay of $T$ blocks. In this\nstreaming setup, we study the fundamental interplay between the rate and error\nprobability in the central limit and moderate deviations regimes and show that\ni) in the moderate deviations regime, the moderate deviations constant improves\nover the block coding or non-streaming setup by a factor of $T$ and ii) in the\ncentral limit regime, the second-order coding rate improves by a factor of\napproximately $\\sqrt{T}$ for a wide range of channel parameters. For both\nregimes, we propose coding techniques that incorporate a joint encoding of\nfresh and previous messages. In particular, for the central limit regime, we\npropose a coding technique with truncated memory to ensure that a summation of\nconstants, which arises as a result of applications of the central limit\ntheorem, does not diverge in the error analysis.\n Furthermore, we explore interesting variants of the basic streaming setup in\nthe moderate deviations regime. We first consider a scenario with an erasure\noption at the decoder and show that both the exponents of the total error and\nthe undetected error probabilities improve by factors of $T$. Next, by\nutilizing the erasure option, we show that the exponent of the total error\nprobability can be improved to that of the undetected error probability (in the\norder sense) at the expense of a variable decoding delay. Finally, we also\nextend our results to the case where the message rate is not fixed but\nalternates between two values.\n
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".