Flexible Symmetric Multiple Description Lattice Vector Quantizer With <inline-formula> <tex-math notation="TeX">$L\geq 3$</tex-math></inline-formula> Descriptions
Bibliographic record
Abstract
In the previous work on multiple description lattice vector quantizers (MDLVQs) with L ≥ 3 descriptions, once the central and side lattice codebooks are fixed, the decoding quality is determined for all numbers k of received descriptions. Therefore, it is not possible to achieve tradeoffs between the quality of reconstruction for different values of k, 1 k ≤ L - 1. This paper proposes a flexible MDLVQ capable of overcoming the above drawback. For this, a different reconstruction method is employed and a heuristic index assignment (IA) algorithm, which uses L - 2 parameters to control the distortions for 2 k ≤ L - 1, is developed. Experimental results show that the proposed MDLVQ, in addition to achieving the desired tradeoffs, significantly outperforms the classic MD scheme based on unequal erasure protection. The second contribution of this paper is a structured IA for the case of L = 3 and the derivation of the corresponding expressions of the distortions at high resolution. The proposed IA has a simple mechanism for controlling the tradeoff between the reconstruction quality for k = 1, 2. The IA is able to achieve a wide range of distortion values, while keeping the product of the distortions for k = 1, 2 the same as in the prior work.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".