New Coding Schemes for the Symmetric $K$-Description Problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We propose novel coding schemes for the K-description problem with symmetric rates and symmetric distortion constraints. There are two main new ingredients in these schemes: the first one is akin to the method seen in the well-known butterfly network of network coding literature, and systematic erasure channel codes are applied on certain carefully chosen source coding component; the second approach is built on the quantization splitting technique which was previously proven useful in the Gaussian CEO problem. We first focus on a special case of the three description problem, where any two descriptions are rate-distortion optimal jointly, referred to as the no two description excess rate case. For this special case and the quadratic Gaussian source, we show that the two aforementioned approaches lead to rate-distortion points outside the achievable region based on the source-channel erasure codes, previously proposed by Pradhan, Puri, and Ramchandran. Interestingly, though only the symmetric problem is considered in our work, the proposed schemes in fact benefit from time-sharing several asymmetric rate-distortion points. The insights gained through the no two description excess rate case lead to strategic combination of the new ingredients with the existing coding scheme, yielding new coding schemes for the symmetric K -description problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it