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Record W1926575136 · doi:10.1190/tle34101256.1

Introduction to compressed sensing

2015· article· en· W1926575136 on OpenAlexaff
Ben Bougher

Bibliographic record

VenueThe Leading Edge · 2015
Typearticle
Languageen
FieldEngineering
TopicSparse and Compressive Sensing Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsField (mathematics)Computer scienceCompressed sensingFoundation (evidence)PhotographySimple (philosophy)Digital imagingData scienceComputer graphics (images)Digital imageArtificial intelligenceImage processingArchaeologyVisual artsArtEpistemologyMathematicsImage (mathematics)HistoryPhilosophy

Abstract

fetched live from OpenAlex

Compressed sensing (CS) is an emerging field of mathematics and engineering that challenges the conventional paradigms of digital data acquisition. Since the seminal publication of Candès et al. (2006), the field has developed a substantial academic literature and has provided the foundation for major innovations in medical imaging, astronomy, and digital photography. Although CS is very mathematical, it can be conceptually intuitive. This tutorial provides a brief introduction to CS concepts through simple examples and interactive figures in an accompanying Jupyter notebook at github.com/seg/tutorials.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.457

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 head0.037
GPT teacher head0.254
Teacher spread0.217 · 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 designNot applicable
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

Citations10
Published2015
Admission routes1
Has abstractyes

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