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Record W2547389319 · doi:10.1109/ccece.2016.7726779

Reconstruction-error distortion in LTI system modeling

2016· article· en· W2547389319 on OpenAlex
Soosan Beheshti, A. Sahebalam

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
FieldEngineering
TopicControl Systems and Identification
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDistortion (music)Parametric statisticsDistortion functionAlgorithmMathematicsFunction (biology)LTI system theoryUpper and lower boundsLinear systemMathematical optimizationControl theory (sociology)Computer scienceArtificial intelligenceStatisticsMathematical analysisTelecommunicationsBandwidth (computing)

Abstract

fetched live from OpenAlex

This paper presents a novel approach to estimate the order of a parametric Linear Time-Invariant (LTI) system. To achieve this goal, we define a new objective function. This measure is denoted by reconstruction-error distortion. Tight probabilistic upper and lower bounds are obtained and a gap function for these bounds on the reconstruction-error distortion is derived. We propose to choose the order of the LTI system based on optimality the considered objective function in the form of reconstruction-error distortion. Information rate distortion function for this distortions is calculated. We show that the rate for reconstruction-error distortion is robust with respect to the probabilistic parameters in reconstruction error estimation. In addition, it is shown that the estimated order by reconstruction error distortion not only provides the maximum possible rate distortion, but also minimized the gap function.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.136

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 head0.011
GPT teacher head0.180
Teacher spread0.169 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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