A Practical Algorithm for Embedding Graphs on Torus
Why this work is in the frame
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Bibliographic record
Abstract
Embedding graphs on the torus is a problem with both theoretical and practical importance. It is required to embed a graph on the torus for solving many application problems such as VLSI design, graph drawing and so on. Polynomial time and exponential time algorithms for embedding graphs on the torus are known. However, the polynomial time algorithms are very complex and their implementation has been a challenge for a long time. On the other hand, the implementations of some exponential time algorithms are known but they are not efficient for large graphs in practice. To develop an efficient practical tool for embedding graphs on the torus, we propose a new exponential time algorithm for embedding graphs on the torus. Compared with a well used previous exponential time algorithm, our algorithm has a better practical running time.
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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