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Asymptotical rotating consensus algorithm with processing delay

2019· article· en· W2980328257 on OpenAlex
Fen Nie, Xiaojun Duan, Yicheng Liu

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

VenueJournal of Physics Conference Series · 2019
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLaplacian matrixRotation (mathematics)Eigenvalues and eigenvectorsAlgorithmLaplace operatorMathematicsMatrix (chemical analysis)Topology (electrical circuits)LogarithmGeometryMathematical analysisPhysicsCombinatorics

Abstract

fetched live from OpenAlex

Abstract In this paper, we studied some consensus algorithms for the collective rotating motions of a team of agents, which has been widely studied in different disciplines ranging from physics, networks and engineering. Both discrete and continues consensus algorithm with processing delays are investigated. There are three motion patterns determined by the information exchange topology of systems and rotation angle of rotation matrices. The asymptotic consensus appears when 0 is an simple eigenvalue of Laplacian matrix and the rotation angle is less than the critical value, and the rotating consensus achieves when the rotation angle is equal to the critical value. At this point, all agents move on circular orbits and the relative radii of orbits are equal to the relative magnitudes of the components of a right eigenvector associated with 0 eigenvalue of the non-symmetric Laplacian matrix. Finally, all agents move along logarithmic spiral curves with a fixed center when the rotation angle is larger than the critical value.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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 score0.882
Threshold uncertainty score0.616

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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