Novel Efficient Coding Scheme for Data-Rate Limited Journey-Aware Graph-Data Transmission
Bibliographic record
Abstract
How to efficiently transmit graph-data to mobile devices is quite appealing nowadays, especially for autonomous vehicles and positioning systems. When a mobile receiver is set out for a journey, the objective is often to reconstruct topologically-complete subgraphs according to the arrival data in real time. Hence, we dedicate a novel coding scheme to segmenting and arranging the data efficiently from a huge mesh-grid graph. When the data-rate is restricted, through our proposed partitioning and scheduling schemes, mobile receivers can greatly relax the requirement for the number of transmission times to reconstruct topologically-complete subgraphs. To evaluate our proposed techniques, we derive the expected number of transmission times required to reconstruct a subgraph theoretically. Moreover, we define the k-hop completeness to measure the probability of reconstructing a topologically-complete subgraph by any scheme. Our proposed new method greatly outperforms the conventional scheme theoretically and by simulation. Our proposed method can enable the future journey-aware dynamic mapping system on vehicles without any need of pre-stored huge map database.
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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 | 0.003 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".