Deterministic M2M Multicast in Radio Networks (Extended Abstract)
Bibliographic record
Abstract
Leszek G asieniec 1? , Evangelos Kranakis 2?? , Andrzej Pelc 3? ? ? , and Qin Xin Department of Computer Science, University of Liverpool, Liverpool L69 7ZF, UK, {leszek,qinxin}@csc.liv.ac.uk School of Computer Science, Carleton University, Ottawa, Ontario, K1S 5B6, Canada, kranakis@scs.carleton.ca Dp. d'informatique, Universit du Qubec en Outaouais, Hull, Qubec, J8X 3X7, Canada, andrzej.pelc@uqo.ca Abstract. We study the problem of exchanging messages within a fixed group of k nodes, in an n-node multi-hop radio network, also known as the problem of Multipoint-to-Multipoint (M2M) multicasting. While the radio network topology is known to all nodes, we assume that the participating nodes are not aware of each other's positions. We give a new fully distributed deterministic algorithm for the M2M multicasting problem, and analyze its complexity. We show that if the maximum distance between any two out of k participants is d then this local information n). Hence our algorithm is linear in the size of the subnetwork induced by the participating nodes and only polylogarithmic in the size of the entire radio network.
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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) | 0.003 | 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 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".