Improved bounds for the online steiner tree problem in graphs of bounded edge-asymmetry
Bibliographic record
Abstract
In this paper we consider the Online Steiner Tree problem in weighted directed \ngraphs of bounded edge-asymmetry α. The edge-asymmetry of a directed graph is \ndefined as the maximum ratio of the cost (weight) of antiparallel edges in the \ngraph. The problem has applications in multicast routing over a network with \nnon-symmetric links. We improve the previously known upper and lower bounds on \nthe competitive ratio of any deterministic algorithm due to Faloutsos et al. In \nparticular, we show that a better analysis of a simple greedy algorithm yields \na competitive ratio of O (min {k, α log k/log log α}), where k denotes the \nnumber of terminals requested. On the negative side, we show a lower bound of Ω\n(min{k1-ε, α log k/log log k}) on the competitive ratio of every deterministic \nalgorithm for the problem, for any arbitrarily small constant ε.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".