Index assignment capable of detecting one bit errors for multiple description scalar quantizers
Bibliographic record
Abstract
This work is concerned with increasing the error resilience of two description scalar quantizers by applying a permutation to the index of each description, for an m-diagonal initial index assignment. First we address the existence of permutations achieving a minimum Hamming distance of at least 2. Such permutations allow the central decoder to detect any 1 bit error pattern. We establish the connection with the hypercube antibandwidth problem, connection which allows us to determine the highest value of m for which such a permutation exists and to show its construction. Further, we highlight the relation between the error robustness at the side decoders and the bandwidth of the hypercube labeling associated to the permutation. To ensure error resilience at both the central and side decoders we are interested in labelings with the lowest bandwidth given that the antibandwidth is larger or equal to m. We make some progress toward the solution of this problem by constructing a class of hypercube labelings trading the increase in antibandwidth for the decrease in bandwidth.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".