Topological Design of Two-Level Telecommunication Networks with Modular Switches
Why this work is in the frame
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Bibliographic record
Abstract
In this article we propose a mixed 0-1 linear programming model for the topological network design problem with modular switches such as the ones that will be used in asynchronous transfer mode (ATM) frame relay and other broadband networks. The model includes the location of switches, their configuration with respect to ports and multiplexers, the design of an access network with a star topology, and a backbone network with a fixed topology (ring or tree). To obtain a solution, we propose a greedy heuristic that provides a good starting solution, and a tabu search heuristic to improve the solution. Finally, we present an example of the application of the heuristics and results for a set of randomly generated problems with up to 500 users and 30 potential switch sites. For the hundreds of problems generated, the tabu algorithm produced solutions that were, on average, within 1.5% of the optimal solution, and in the worst case within 4.95% of the optimal solution.
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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 | 0.000 | 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 it