Bibliographic record
Abstract
Motivated by applications to distributed storage and computing, the multi-version coding problem was formulated by Wang and Cadambe in [4]. In this problem, a client sequently over time stores v independent versions of a message in a storage system with n server nodes. It is assumed that, a message version may not reach some servers, and that each server is unaware of what has been stored in other servers. The problem requires that any c servers must be able to reconstruct their latest common version. An extended multi-version problem introduced in [5] relaxes the above requirement by requiring any c servers to be able to reconstruct their latest common version or any version later than that. The objective in both the original and extended multi-version problem is to minimize the worst case storage cost. In this work, we propose codes for both the multi-version problem and its extension. For the original multi-version coding problem, we show that the storage cost of our proposed codes are near-optimal. For the extended multi-version coding problem, we show that the storage cost of our first algorithm is optimal when v|c - 1. Our second proposed extended multi-version code shows that storage cost of strictly less than one is achievable even when v is 50% larger than c. This is interesting, as the storage cost of existing codes becomes one as soon as v becomes larger than c.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".