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Record W3212374164 · doi:10.1109/lwc.2021.3125625

Age- and Correlation-Aware Information Gathering

2021· article· en· W3212374164 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Wireless Communications Letters · 2021
Typearticle
Languageen
FieldComputer Science
TopicAge of Information Optimization
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsComputer scienceMetric (unit)Set (abstract data type)Ant colony optimization algorithmsCorrelationOptimization problemProcess (computing)Data miningArtificial intelligenceAlgorithmMathematicsEngineering

Abstract

fetched live from OpenAlex

Age-of-information (AoI) is a metric that quantifies the freshness of gathered information. In this letter, we expand the concept of AoI by introducing a metric called correlation-aware AoI (CAAoI) to capture both the freshness and the degree of correlation in gathered information. The CAAoI of an information gathering system is evaluated when an unmanned aerial vehicle gathers information about a set of physical processes from a set of ground devices, such that each physical process is sensed by one or more devices. An optimization problem is formulated to minimize the normalized weighted sum of the time-average CAAoI in the considered information gathering system. An ant colony optimization algorithm is developed to solve the formulated problem. Simulation results illustrate that the proposed CAAoI captures both the freshness and diversity of the gathered information.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score0.527

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 communication0.0000.003
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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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