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Record W2019195080 · doi:10.5555/1400549.1400706

Modeling quantum dot devices in Cell-DEVS environment

2008· article· en· W2019195080 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicQuantum-Dot Cellular Automata
Canadian institutionsCarleton University
Fundersnot available
KeywordsDEVSCellular automatonComputer scienceQuantum dot cellular automatonQuantum dotQuantumQuantum cellular automatonAutomatonTheoretical computer scienceModeling and simulationPhysicsAlgorithmSimulationQuantum mechanics

Abstract

fetched live from OpenAlex

dot, quantum automata, cellular automata, XOR gates, majority vote gates, quantum wire Simulation of selected elements of Brain Machine, specifically those based on quantum dot technology and defined by quantum cellular automata, is demonstrated in Cell-DEVS environment employing CD++ toolkit. Models of quantum dot cellular devices are implemented within Cell-DEVS formal definitions. For Cell-DEVS modeling the following quantum dot based devices have been selected: quantum XOR, quantum wire and majority vote gates. Operation of models of these devices is visualized by CD Modeler animation as well as draw-log tools within Cell-DEVS environment. Test rules for functionality verification of the models are presented and verified to validate the models. 1.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score1.000

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)0.0000.001

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 head0.028
GPT teacher head0.215
Teacher spread0.187 · 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

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

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