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An Advanced Data Type with Irrational Numbers to Implement Time in DEVS Simulator

2016· article· en· W4246059378 on OpenAlexaff
Damián Vicino, Olivier Dalle, Gabriel Wainer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsRotation formalisms in three dimensionsDEVSIrrational numberComputer scienceRepresentation (politics)Data typeTheoretical computer scienceDiscrete event simulationType (biology)Point (geometry)AlgorithmModeling and simulationMathematicsSimulationProgramming language

Abstract

fetched live from OpenAlex

In the Discrete-Event System Specification (DEVS) time variables are Real numbers. This is common to other simulation formalisms for Discrete-Event Simulation (DES). Current simulators for these formalisms approximate time variables using floating-point or rational representations. Neither of them is capable to adequately represent irrational numbers. The representation of these numbers is important, especially for studying systems with geometrical properties. The use of approximations, as floating-point, may silently introduce errors to the causality chain. These errors may produce incorrect simulation trajectories without informing about them. Here, we propose a new data type combining rational data types with computable calculus concepts. This new data type extension provides representation, and operation, with subsets of irrational numbers. The proposed data type only provides four operations (+, -, <, =), those are sufficient for implementing simulators for DEVS and other DES formalisms. Usage of this data type has no significant complexity penalty for simulation not using irrational numbers.

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 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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.861
Threshold uncertainty score0.292

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 communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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 teacher head, not a consensus.

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

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

Citations0
Published2016
Admission routes1
Has abstractyes

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