Bibliographic record
Abstract
Transform coding (TC) is one of the best known practical methods for quantizing high-dimensional vectors. In this article, a practical approach to distributed TC of jointly Gaussian vectors is presented. This approach, referred to as source-split distributed transform coding (SP-DTC), can be used to easily implement two terminal transform codes for any given rate-pair. The main idea is to apply source-splitting using orthogonal-transforms, so that only Wyner-Ziv (WZ) quantizers are required for compression of transform coefficients. This approach however requires optimizing the bit allocation among dependent sets of WZ quantizers. In order to solve this problem, a low-complexity tree-search algorithm based on analytical models for transform coefficient quantization is developed. A rate-distortion (RD) analysis of SP-DTCs for jointly Gaussian sources is presented, which indicates that these codes can significantly outperform the practical alternative of independent TC of each source, whenever there is a strong correlation between the sources. For practical implementation of SP-DTCs, the idea of using conditional entropy constrained (CEC) quantizers followed by Slepian-Wolf coding is explored. Experimental results obtained with SP-DTC designs based on both CEC scalar quantizers and CEC trellis-coded quantizers demonstrate that actual implementations of SP-DTCs can achieve RD performance close to the analytically predicted limits.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".