A (4<i>n</i> − 9)/3 diagnosis algorithm for generalised cube networks
Why this work is in the frame
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Bibliographic record
Abstract
The goal of the t/k-diagnosis is to isolate all faulty processors (nodes) in a multiprocessor system to within a set of nodes in which at most k nodes are correct, provided the number of faulty nodes does not exceed t. As compared to the classical precise diagnosis strategy, the t/k-diagnosis strategy can significantly improve the self-diagnosing capability of multiprocessor system. The generalised cube network (GCN), or equivalently the BC graphs, is a regular topology, which provides a unified view of the hypercube and some of its variants. This paper addressed the t/k-diagnosis of GCNs. By exploring the relationship between the size of a largest connected component of the 0-test subgraph of a faulty GCN and the distribution of the faulty nodes over the network, an time (4n − 9)/3 diagnosis algorithm on an n-dimensional GCN is presented, where N = 2 n is the total number of the nodes of the network being diagnosed. To our knowledge, this is the first time to give a t/k-diagnosis algorithm for GCNs and .
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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