Decontamination of chordal rings and tori
Bibliographic record
Abstract
In this paper we consider the problem of decontami-nating a network, i.e., protecting it from unwanted and dangerous intrusions. Initially all nodes are contam-inated and a team of agents is deployed to clean the entire network. When an agent transits on a node, it can clean it, when the node is left unguarded, however, it will be recontaminated as soon as at least one of its neighbour is contaminated. We study the problem in asynchronous chordal ring networks with n nodes and chord lengths d1 = 1, d2,..., dk, and in tori. We consider two variations of the model: one where an agent has only local knowledge, the other in which it has “visibility”, i.e., it can “see ” the state of its neigh-bouring nodes. We first show that, when the largest chord dk is not too large (dk ≤ √n), the number of agents necessary to perform the task in chordal rings does not depend on the size of the network but only on the length of the longest chord. We also show a lower bound on the number of agents for the torus topology. We then pro-pose tight strategies for decontamination. We analyse the number of moves and the time complexity of the decontamination algorithms showing that the visibil-ity assumption allows us to decrease substantially both complexity measures. Another advantage of the “vis-ibility model ” is that agents move independently and autonomously without requiring any coordination.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".