Regular graph-based logical topology design in multi-hop optical networks.
Bibliographic record
Abstract
Existing approaches for logical topology design and routing for multi-hop optical networks become intractable for large networks. One approach which has been used is to treat the logical topology design problem separately from the routing problem which can be solved as a LP problem. The straightforward formulation of the LP problem has been reported but this is also feasible only for relatively smaller networks since the basis size for the simplex method is O(n3) where n is the number of nodes in the network. In this paper, by exploiting the special structure of the routing problem, we present an efficient column generation scheme embedded into the revised simplex method. This approach makes it feasible to handle networks with relatively large number of nodes. To study the approach experimentally we have used a number of traffic based heuristics for generating the logical topologies. These include a variation of the well known HLDA heuristic and two simple traffic based heuristics to generate logical topologies based on regular graphs. Many researchers feel that regular graphs are not well suited for wide area optical networks. The interesting result is that logical topologies based on regular graphs perform quite well compared to others. This suggests that it is useful to consider regular graphs as possible topologies for wide area networks and should be included as potential candidates for large wide area networks. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .H36. Source: Masters Abstracts International, Volume: 40-03, page: 0722. Advisers: Arunita Jaekel; Yash P. Aneja. Thesis (M.Sc.)--University of Windsor (Canada), 2001.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".