On the approximation of singular functions by series of noninteger powers
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Bibliographic record
Abstract
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it