DECONTAMINATING CHORDAL RINGS AND TORI USING MOBILE AGENTS
Bibliographic record
Abstract
In this paper we consider a network where an intruder is moving "contaminating" the nodes it passes by, and we focus on the problem of decontaminating such a network by a team of mobile agents. The contamination/decontamination process has the following asynchronous dynamics: when the team is deployed all nodes are assumed to be contaminated, when an agent transits on a node, it will clean the node, when the node is left with no agent, the node will be recontaminated as soon as at least one of its neighbours is contaminated. We study the problem in asynchronous chordal ring networks and in tori. We consider two variations of the model: one where agents have only local knowledge, the other in which they have "visibility", i.e., they can "see" the state of their neighbouring nodes. We first derive lower bounds on the minimum number of agents necessary for the decontamination. In the case of chordal rings we show that the number of agents necessary to perform the cleaning does not depend on the size of the network; in fact it is linear in the length of the longest chord (provided that it is not too long). In the case of a torus, the minimum number of agents is equal to 2 · h, where h is the smallest dimension. We then propose optimal strategies for decontamination and we analyse the number of moves and the time complexity of the decontamination algorithms, showing that the visibility assumption allows us to decrease substantially both complexity measures. Another advantage of the "visibility 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".