Reliability Optimization of Distributed Access Networks With Constrained Total Cost
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Bibliographic record
Abstract
In this paper, we study the system reliability optimization of distributed access networks subject to a constraint on the total cost. We first formulate the cost-constrained system reliability optimization problem as a searching process in a combinatorial tree, which enumerates all the possible solutions to the problem. Because the calculation of each possible solution for the reliability problem is extremely time-consuming, a novel algorithm, the Shrinking & Searching Algorithm (SSA), is proposed to speed up the searching process. SSA jointly considers the upper bound of the system reliability for each branch in the combinatorial tree, and the cost constraint on the possible solutions. It avoids most of the redundant calculations in the searching process by gradually shrinking the difference between lower & upper bounds of the length of a path in the corresponding combinatorial tree, which represents a feasible solution. Case study & simulation results are presented to demonstrate the performance of the SSA.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it