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Record W2739312141 · doi:10.15803/ijnc.7.2_336

On the Cost of Waking Up

2017· article· en· W2739312141 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

VenueInternational Journal of Networking and Computing · 2017
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaVedecká Grantová Agentúra MŠVVaŠ SR a SAV
KeywordsComputer scienceAsynchronous communicationHypercubeBipartite graphBroadcasting (networking)Enhanced Data Rates for GSM EvolutionTask (project management)InterconnectionFlooding (psychology)Computer networkTheoretical computer scienceDistributed computingParallel computingGraphTelecommunications

Abstract

fetched live from OpenAlex

Often, in a distributed system, a task must be performed in which all entities must be involved; however only some of them are active, while the others are inactive, unaware of the new computation that has to take place. In these situations, all entities must become active, a task known as Wake-Up. It is not difficult to see that Broadcast is just the special case of the Wake-Up problem, when there is only one initially active entity. Both problems can be solved with the same trivial but expensive solution: Flooding. More efficient broadcast protocols exist for some classes of dense interconnection networks. The research question we examine is whether also wake-up can be performed significantly better in three classes of regular interconnection networks: hypercubes, complete networks, and regular complete bipartite graphs.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.904
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.034
GPT teacher head0.305
Teacher spread0.270 · 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