An Adaptive Rate Allocation Scheme for Time-Varying Graph Signal Quantization
Bibliographic record
Abstract
To address the communication resource limitations the uplink data in some distributed networks suffers from, quantization enables these graph signals to realize compression. However, the compression process is accompanied by quantization errors, which pose threat to the communication quality. In addition, in real-world scenarios, graph signals tend to evolve with time, where the information loss would be larger without adaption to the evolvement. To tackle the above problems, we first propose an adaptive rate allocation scheme, which allocates rate to each quantizer under a total rate constraint, for time-varying graph signals. Along with the smoothness of graph signals at the same time instant and the rate-distortion features of scalar quantization, the smoothly evolving characteristics of time-varying graph signals are leveraged to adaptively adjust the allocated rate with time to reduce quantization distortion of uplink data incurred by the time-varying feature. Simulation results demonstrate the superiority of distortion performance of the proposed rate allocation scheme on both synthetic and real-world graphs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".