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Record W2022401652 · doi:10.1109/lsp.2012.2205142

Tractable Bound for Spherical Section Property in the Presence of Side-Information

2012· article· en· W2022401652 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

VenueIEEE Signal Processing Letters · 2012
Typearticle
Languageen
FieldEngineering
TopicSparse and Compressive Sensing Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSemidefinite programmingRelaxation (psychology)Upper and lower boundsCompressed sensingSemidefinite embeddingLinear programmingConstraint (computer-aided design)Dual (grammatical number)Property (philosophy)MathematicsComputer scienceAlgorithmMathematical optimizationComputational complexity theoryCombinatoricsQuadratically constrained quadratic programQuadratic programmingMathematical analysisGeometry

Abstract

fetched live from OpenAlex

This letter provides a tractable bound for a perfect recovery condition in compressed sensing matrices using the spherical section property in the presence of side information. In particular, when the signal of interest is provided with side-information, we derive an equivalent semidefinite relaxation bound by introducing the related prior knowledge as an additional constraint to the semidefinite programming (SDP) problem. We recast a linear program (LP) cone to this problem and found the dual-SDP to be less complex to handle. Numerical evaluations on the proposed dual-SDP, validates the existence of sparse solutions with high- cardinalities.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.226

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 science0.0000.000
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 head0.022
GPT teacher head0.237
Teacher spread0.215 · 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