Optimal Index Assignment for Multiple Description Lattice Vector Quantization
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
Optimal index assignment of multiple description lattice vector quantizer (MDLVQ) can be posed as a large-scale linear assignment problem. But is this expensive algorithmic approach necessary? This paper presents a simple index assignment algorithm for high-resolution MDLVQ of K /spl ges/ 2 balanced descriptions in any dimensions. Despite its simplicity, the new algorithm is optimal for a large family of lattices encountered in theory and practice, in terms of minimizing the expected distortion for any side description loss rate and any side entropy rate. This work offers exact combinatoric constructions of optimal index assignments, rather than arguing for the optimality asymptotically. Consequently, the optimality holds for all values of sublattice index N (i.e., over all trade-offs between the central and side distortions), rather than for very large N only. Furthermore, the time complexity of the new algorithm is O(N) as opposed to O(N/sup 6/) for a current linear assignment-based method. New and improved closed form expressions of the expected distortion as the function of N and K are also presented. Thus the optimal values of N and K can be computed.
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 | 0.000 | 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 | 0.000 | 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