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Record W2148137421 · doi:10.1109/pacrim.1991.160694

Hypercycles: a status report

2002· article· en· W2148137421 on OpenAlexaff
N.J. Dimopoulos, R. Sivakumar, Vassilios V. Dimakopoulos, Morshed Chowdhury, D. Radvan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceInterconnectionDegree (music)Routing (electronic design automation)Block (permutation group theory)RouterClass (philosophy)Theoretical computer scienceVariety (cybernetics)Dimension (graph theory)Simple (philosophy)Network topologyPoint (geometry)Topology (electrical circuits)Computer networkMathematicsCombinatoricsArtificial intelligence

Abstract

fetched live from OpenAlex

The authors present the Hypercycles, a class of multidimensional graphs, which are generalizations of the n-cube. These graphs are obtained by allowing each dimension to incorporate more than two elements and a cycle interconnection strategy. Hypercycles offer simple routing and the ability, given a fixed degree, to choose among a number of alternative size graphs. These graphs can be used in the design of interconnection networks for distributed systems tailored specifically to the topology of a particular application. A backtrack-to-the-origin-and-retry routing scheme in which paths that block at intermediate nodes are abandoned and a new attempt is made is presented. Intermediate nodes are chosen at random at each point from among the ones that form the shortest paths from a source to a destination. Simulation results that establish the performance of a variety of configurations are presented. In addition, the initial attempt at constructing a Hypercycle-based router is discussed.>

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0050.008
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0300.016

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 head0.027
GPT teacher head0.221
Teacher spread0.194 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations1
Published2002
Admission routes1
Has abstractyes

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