A Generalized Grouping Scheme in Coded Caching
Why this work is in the frame
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Bibliographic record
Abstract
Coded caching, which could significantly reduce the maximum amount of transmission rate during the peak traffic times in wireless network, has been widely studied recently. Apart from the transmission rate, sub-packetization F reflecting the implementation complexity, is also concerned in coded caching. The grouping method proposed by Shanmugam et al. is wellknown and widely used to reduce the sub-packetization level of the coded caching problem. In this paper, we propose a concatenating construction method for coded caching schemes, which generalizes the grouping method. Moreover, we demonstrate the advantage of our method in reducing the transmission rate over the grouping method. In particular, some new explicit schemes are obtained from previously known schemes. From one of these schemes, we can derive all the results by Tang and Ramamoorthy as special cases. Furthermore, the analysis and comparison of these new schemes are also performed.
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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 | 0.000 |
| Open science | 0.002 | 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