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Record W4409843666 · doi:10.1093/imanum/draf006

On the approximation of singular functions by series of noninteger powers

2025· article· en· W4409843666 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIMA Journal of Numerical Analysis · 2025
Typearticle
Languageen
FieldMathematics
TopicMathematical Approximation and Integration
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMathematicsSeries (stratigraphy)Mathematical analysisApplied mathematics

Abstract

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Abstract In this paper, we describe an algorithm for approximating functions of the form $f(x)=\int _{a}^{b} x^{\mu } \sigma (\mu ) \, {\text{d}} \mu $ over $[0,1]$, where $\sigma (\mu )$ is some signed Radon measure, or, more generally, of the form $f(x) = {{\langle \sigma (\mu ), x^\mu \rangle }}$, where $\sigma (\mu )$ is some distribution supported on $[a,b]$, with $0 <a < b< \infty $. One example from this class of functions is $x^{c} (\log{x})^{m}=(-1)^{m} {{\langle \delta ^{(m)}(\mu -c), x^\mu \rangle }}$, where $a\leq c \leq b$ and $m \geq 0$ is an integer. Given the desired accuracy $\varepsilon $ and the values of $a$ and $b$, our method determines a priori a collection of noninteger powers $t_{1}$, $t_{2}$, …, $t_{N}$, so that the functions are approximated by series of the form $f(x)\approx \sum _{j=1}^{N} c_{j} x^{t_{j}}$, and a set of collocation points $x_{1}$, $x_{2}$, …, $x_{N}$, such that the expansion coefficients can be found by collocating the function at these points. We prove that our method has a small uniform approximation error, which is proportional to $\varepsilon $ multiplied by some small constants, and that the number of singular powers and collocation points grows as $N=O(\log{\frac{1}{\varepsilon }})$. We demonstrate the performance of our algorithm with several numerical experiments.

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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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.888
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.289
Teacher spread0.272 · 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