Distributed tree comparison with nodes of limited memory
Bibliographic record
Abstract
Abstract We consider the task of comparing two rooted trees with port labels. Roots of the trees are joined by an edge and the comparison has to be performed distributedly, by exchanging messages among nodes. If the two trees are isomorphic, all nodes must finish in a state YES; otherwise they have to finish in a state NO and break symmetry, nodes of one tree getting label 0 and nodes of the other getting label 1. Nodes are modeled as identical automata, and our goal is to establish trade‐offs between the memory size of such an automaton and the efficiency of distributed tree comparison, measured either by the time or by the number of messages used for communication between nodes. We consider both the synchronous and the asynchronous communication and establish exact trade‐offs in both scenarios. For the synchronous scenario, we are concerned with memory versus time trade‐offs. We show that if the automaton has x bits of memory, where x ≥ c log n , for a small constant c , then the optimal time to accomplish the comparison task in the class of trees of size at most n and of height at most h > 1 is Θ( h + n / x ). For the asynchronous scenario, we study memory versus number of messages trade‐offs. We show that if the automaton has x bits of memory, where n ≥ x ≥ c log n , then the optimal number of messages to accomplish the comparison task in the class of trees of size at most n is Θ( n 2 / x ). © 2012 Wiley Periodicals, Inc. NETWORKS, Vol. 2012
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".