Bibliographic record
Abstract
We study models of mobile robots with limited capabilities that are deployed either on a cycle or an infinite line or on a segment.Robots start moving at the same time and when two robots collide their speeds and movement directions are instantaneously updated.Each of them possesses a collision detector and a clock to measure the times of its collisions.They do not have any knowledge on the total number of robots and do not have a common sense of direction.Besides, they neither have visibility nor control over their movements.We investigate the feasibility of the localization task in the cycle and the segment by bouncing robots: every robot should figure out the starting position and initial velocity of all the other robots.We consider two different scenarios when robots have common masses and speeds and robots of arbitrary masses and speeds.We give complete characterizations of all feasible configurations for the cycle in both scenarios.We study the survivability of bouncing robots.We say a robot survives if it never returns to its starting position.Non-surviving robots disappear from the environment.We provide sufficient and necessary conditions to have surviving robots in the cycle and in the segment.Finally we investigate communication protocols for bouncing robots that only communicate at the time of their collisions.We establish necessary and sufficient conditions for bouncing robots to perform gossiping, broadcasting and convergecast.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".