Bibliographic record
Abstract
We consider the problem of decontaminating a network infected by a mobile virus. The goal is to perform the task using as small a team of antiviral agents, avoiding any recontamination of disinfected areas, and minimizing the amount of agents' movements across the network. In all the existing literature, it is assumed that the immunity level of a disinfected site is nil. In this paper we consider the network decontamination problem under a new model of immunity to recontamination: we consider the case when a disinfected vertex, after the cleaning agent has gone, will become recontaminated only if a weak majority of its neighbours are infected. We study the effects of this level of immunity on the number of antiviral agents necessary to decontaminate the entire network. We focus on tori and on trees, and establish lower-bounds on the team size; we also establish lower bounds on the number of moves performed by an optimal-size time of cleaners. We design and present strategies for disinfecting tori and trees; we prove that these strategies are optimal in terms of both team size and number of moves. In particular, the upper and lower bounds are tight for tree networks and for synchronous tori; the bounds are within a constant factor of each other in the case of asynchronous tori.
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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 | 0.000 |
| 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) | 0.001 | 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".