Joint Source-Channel Decoding of Multiple Description Quantized Markov Sequences
Bibliographic record
Abstract
This paper proposes a framework for joint source-channel decoding of Markov sequences that are coded by a fixed-rate multiple description quantizer (MDQ), and transmitted via a lossy network. This framework is suited for lossy networks of primitive energy-deprived source encoders. Our technical approach is one of maximum a posteriori probability (MAP) sequence estimation that exploits both the source memory and the correlation between different MDQ descriptions. We solve the MAP estimation problem by computing the longest path in a weighted directed acyclic graph, at a complexity of O(L/sup 2/NK), where N is the number of source symbols in the input sequence, K is the number of MDQ descriptions, and L is the number of codewords of the central quantizer. If the source sequence is Gaussian Markovian, the decoder complexity can be reduced to O(LNK). For MDQ-compressed Markov sequences impaired by both bit errors and erasure errors, the performance of joint source-channel MAP decoder can be 6 dB higher than the conventional hard-decision decoder. Furthermore, the new MDQ decoding technique unifies the treatments of different subsets of the K descriptions available at the decoder, circumventing the thorny issue of requiring up to 2/sup K/ - 1 MDQ side decoders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".