An Efficient Binary Locally Repairable Code for Hadoop Distributed File System
Why this work is in the frame
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Bibliographic record
Abstract
In the Hadoop distributed file systems (HDFSs), to lower costly communication traffic for data recovery, the concept of locally repairable codes (LRCs) has been recently proposed. With regard to the immense size of modern energy-hungry HDFS, computational complexity reduction can be attractive. In this letter, to avoid finite field multiplications, which are the major source of complexity, we put forward the idea of designing binary locally repairable codes (BLRCs). More specifically, we design a BLRC with a length of 15, rate of 2/3, and minimum distance of 4, which has the minimum possible locality among its type. We show that our code has lower complexity than most recent non-binary LRC in the literature while meeting other desirable requirements in HDFS such as storage overhead and reliability.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.001 |
| 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