Decomposing hypercubes into cycles: An approach to the oberwolfach problem
Bibliographic record
Abstract
Cartesian-product networks combine well-studied graphs to create new structures with inherited properties, making them valuable for interconnection networks and parallel algorithms. Cycle decompositions of these networks are crucial for fault tolerance and adaptive routing. In this paper, we address the hypercube version of the Oberwolfach problem, focusing on decompositions of \(Q_n\) into cycles of equal or unequal lengths. We present an algorithm that enumerates all possible cycle types in \(Q_n\) and determine which decompositions are feasible or infeasible for \(Q_4\). Using an inductive approach, we extend these results to \(Q_n\) by leveraging distinct perfect matchings of \(Q_4\), yielding a variety of cycle decompositions. Additionally, we present results on factorizations of \(Q_n\) when \(n\) is a power of \(2\). These findings enhance the understanding of cycle structures in hypercubes and their applications to interconnection networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 source (direct Gemma or distilled Codex), 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".