Symmetry in port-labelled anonymous networks
Bibliographic record
Abstract
Our work investigates symmetry in a port-labelled anonymous network, a relatively weak but yet useful model of computation in distributed computing. In such a network, nodes have no identifiers, and for each node v, its incident links are labelled bijectively with the integers {1, 2, …, deg(v)} called ‘ports’. Each node has access to its ‘view’, a mathematical object completely representing the information that the node can learn from communicating with its neighbours. The question that arises is to understand how many nodes have their identities uniquely determined by such a view and what parameters for the number of nodes sharing a similar view are possible. This thesis provides a detailed survey in this area and addresses a number of new questions (detailed in Chapter 3). Applications and related problems of port-labelled anonymous networks are explained in Chapter 4. The new results are presented in Chapters 5, 6, and 7. Some open problems are given in Chapter 8 and Appendix B.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".