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Record W2118670142 · doi:10.1109/icc.1995.524274

A connectionist implementation of the VCQ ATM switch

2002· article· en· W2118670142 on OpenAlexaff
M. Vishnu, J.W. Mark

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceConnectionismAsynchronous Transfer ModeQueueQuality of serviceScheduling (production processes)Queueing theoryDistributed computingComputer networkArtificial neural networkArtificial intelligence

Abstract

fetched live from OpenAlex

The virtual connection queue (VCQ) ATM switch is based on the definition of the versatile traffic descriptor (VTD) [Vishnu and Mark 1994]. Its per-VC architecture enables a per-VC flow control to provide per-VS QoS guarantees. A connectionist implementation of a system is comprised of a large number of simple processing elements interconnected to form a cooperative parallelism, and is suited for very high, speed systems. The authors propose a connectionist implementation of the VCQ ATM switch which avoids the use of any central controller such as the virtual connection unit (VGU) [Vishnu and Mark 1995]. That is, the proposed implementation performs all the functions of the VCQ ATM switch, namely, cell buffering, service scheduling, VTD reaction program execution and VC statistics collection, in, a distributed fashion. Moreover, it offers a substantial increase in the hardware utilization.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2002
Admission routes1
Has abstractyes

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