Distributed Black Virus Decontamination and Rooted Acyclic Orientations
Bibliographic record
Abstract
In a network supporting mobile agents, a particular threat is that posed by the presence of a black virus (BV), a harmful entity capable of destroying any agent arriving at the site where it resides, and of then moving to all the neighbouring sites. A moving BV can only be destroyed if it arrives at a site where an anti-viral agent is located. The objective for a team of mobile anti-viral system agents, called cleaners, is to locate and permanently eliminate the BV, whose initial location is unknown. The goal is to perform this task with the minimum number of network infections and agent casualties. The problem of optimal black virus decontamination (BVD) has been investigated for special classes of highly regular network topologies, a (centralized) solution exists for networks of known arbitrary topology. In this paper, we consider the BVD problem in networks of arbitrary and unknown topology, we prove that it can be solved optimally in a purely decentralized way by asynchronous agents provided with 2-hop visibility. In fact, we prove that our proposed protocols always correctly decontaminate the network with theminimum number of system agents' casualties and network infections. Furthermore, we show thatthe total number of system agents is also optimal. Finally, we prove an interesting correspondence between the BVD problem and the problem of computing a rooted acyclic orientation of a given graph with minimum outdegrees. As a consequence, our protocols provide a distributed optimal solution to this graph optimization problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".