An optimal station allocation policy for tree Local Area Networks
Bibliographic record
Abstract
This paper reports on the simulation results of a heuristic solution to the station allocation problem in a tree topology Local Area Network (LAN). A local network is a data communication network where communication remains confined within a moderate sized area, such as a plant site, an office building or a university campus. Tree LANs with collision avoidance switches and multiple broadcast facility have, recently, become popular due to their suitability for high speed light wave communications. Given a tree LAN with fanout F and given the total number of stations N to be connected, a combinatorial optimization problem arises regarding how to allocate the stations to the leaf nodes so that the total system availability (a network performance criteria) is maximized. This is known as the optimal station assignment problem. In this paper, it is formulated as a non-linear optimization problem which can be solved by the Lagrangean relaxation and the subgradient optimization techniques. A simple heuristic is developed based on these techniques. The simulation studies show that the proposed heuristic is relatively fast operating only in a subspace of the complete solution space.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".