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Record W2619218210 · doi:10.1145/3039870

Time vs. Information Tradeoffs for Leader Election in Anonymous Trees

2017· article· en· W2619218210 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM Transactions on Algorithms · 2017
Typearticle
Languageen
FieldComputer Science
TopicComplexity and Algorithms in Graphs
Canadian institutionsUniversité du Québec en Outaouais
FundersAgence Nationale de la Recherche
KeywordsLeader electionNode (physics)LogarithmUpper and lower boundsComputer scienceString (physics)Tree (set theory)Binary logarithmComputer networkAdvice (programming)Time complexitySimple (philosophy)Multiplicative functionMathematicsCombinatoricsDiscrete mathematicsTheoretical computer scienceAlgorithm

Abstract

fetched live from OpenAlex

Leader election is one of the fundamental problems in distributed computing. It calls for all nodes of a network to agree on a single node, called the leader . If the nodes of the network have distinct labels, then agreeing on a single node means that all nodes have to output the label of the elected leader. If the nodes of the network are anonymous, the task of leader election is formulated as follows: every node v of the network must output a simple path, which is coded as a sequence of port numbers, such that all these paths end at a common node, the leader. In this article, we study deterministic leader election in anonymous trees. Our aim is to establish tradeoffs between the allocated time τ and the amount of information that has to be given a priori to the nodes to enable leader election in time τ in all trees for which leader election in this time is at all possible. Following the framework of algorithms with advice , this information (a single binary string) is provided to all nodes at the start by an oracle knowing the entire tree. The length of this string is called the size of advice . For a given time τ allocated to leader election, we give upper and lower bounds on the minimum size of advice sufficient to perform leader election in time τ. For most values of τ, our upper and lower bounds are either tight up to multiplicative constants, or they differ only by a logarithmic factor. Let T be an n -node tree of diameter diam ⩽ D . While leader election in time diam can be performed without any advice, for time diam − 1 we give tight upper and lower bounds of Θ(log D ). For time diam − 2 we give tight upper and lower bounds of Θ(log D ) for even values of diam , and tight upper and lower bounds of Θ(log n ) for odd values of diam . Moving to shorter time, in the interval [β · diam , diam − 3] for constant β > 1/2, we prove an upper bound of O ( n log n / D ) and a lower bound of Ω( n / D ), the latter being valid whenever diam is odd or when the time is at most diam − 4. Hence, with the exception of the special case when diam is even and time is exactly diam − 3, our bounds leave only a logarithmic gap in this time interval. Finally, for time α · diam for any constant α < 1/2 (except for the case of very small diameters), we again give tight upper and lower bounds, this time Θ( n ).

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Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.275
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it