On minimum distance of locally repairable codes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Distributed and cloud storage systems are used to reliably store large-scale data. To enhance data reliability, erasure codes have been recently proposed and used in real-world distributed and cloud storage systems. Conventional erasure codes are not suitable for distributed storage systems, as they cause significant repair bandwidth and disk I/O. As a solution, a class of erasure codes called locally repairable codes (LRCs) have been proposed, where repairing failed nodes requires access to a small number of available nodes, hence reducing the repair bandwidth and disk I/O. Because of their practical importance, LRCs and in particular their achievable minimum distance have been the topic of many recent studies. In this paper, we find an achievable bound on the minimum distance of a class of LRCs. Furthermore, we show how to construct codes that achieve our proposed bound and compare our results with the existing bounds in the literature.
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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 | 0.001 |
| 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 | 0.006 | 0.005 |
| 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