Cache-Aided Networks with Shared Caches and Correlated Content under Non-Uniform Demands
Bibliographic record
Abstract
In this paper, we propose a cache-aided delivery network with multiple shared caches and correlated content under non-uniform popularity demand. From an information theoretic perspective, we formulate the caching problem as a distributed source coding with side information at the decoder. To address the placement challenge, we propose an automatic clustering scheme considering the popularity and similarity of library content to extract the most efficient side information for caching. Next, we use a hybrid placement strategy, in which the popular side information is fully stored in all caches, while the clusters' side information is partially placed in different caches according to the coded caching strategy (CC). In the delivery phase, the server transmits coded multicast messages and encoded messages (refinement segments) so that users can reconstruct the cluster representatives and clustered files. Our simulation result demonstrates a significant improvement in the peak delivery rate of the system, which resulted from our perspective on problem formulation and the careful extraction of side information during the placement phase.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".