Large file distribution using efficient generation-based network coding
Bibliographic record
Abstract
To distribute a large amount of data through a network, much coordination information, such as packet scheduling and ARQ feedback, is needed between nodes. In this paper, we propose a network coding based scheme to ratelessly distribute data without any coordination between network nodes. To overcome the cubic decoding complexity of network coding, we follow a generation-based strategy, which groups the source packets into subsets called generations and only permits coding among packets belonging to the same generation. A maximum local potential innovativeness strategy is proposed to schedule transmissions of generations at intermediate nodes, where only local buffer information is needed. The transmission overhead at sink nodes, which is defined as the number of extra packets required for successful decoding caused by the non-optimal scheduling, is reduced by using overlapping generations where one source packet may be present in multiple generations. The and-or tree analysis technique is applied to analyze the performance of the overlapping generation-based network coding scheme and used to determine the optimal amount of overlap needed when constructing generations. The overall design is shown to achieve promising throughput rate while maintaining low decoding complexity.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 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".