Double-Layer Potentials, Configuration Constants, and Applications to Numerical Ranges
Bibliographic record
Abstract
Abstract Given a compact convex planar domain $\Omega $ with non-empty interior, the classical Neumann’s configuration constant $c_{\mathbb{R}}(\Omega )$ is the norm of the Neumann–Poincaré operator $K_\Omega $ acting on the space of continuous real-valued functions on the boundary $\partial \Omega $, modulo constants. We investigate the related operator norm $c_{\mathbb{C}}(\Omega )$ of $K_\Omega $ on the corresponding space of complex-valued functions, and the norm $a(\Omega )$ on the subspace of analytic functions. This change requires introduction of techniques much different from the ones used in the classical setting. We prove the equality $c_{\mathbb{R}}(\Omega ) = c_{\mathbb{C}}(\Omega )$, the analytic Neumann-type inequality $a(\Omega ) < 1$, and provide various estimates for these quantities expressed in terms of the geometry of $\Omega $. We apply our results to estimates for the holomorphic functional calculus of operators on Hilbert space of the type $\|p(T)\| \leq K \sup _{z \in \Omega } |p(z)|$, where $p$ is a polynomial and $\Omega $ is a domain containing the numerical range of the operator $T$. Among other results, we show that the well-known Crouzeix–Palencia bound $K \leq 1 + \sqrt{2}$ can be improved to $K \leq 1 + \sqrt{1 + a(\Omega )}$. In the case that $\Omega $ is an ellipse, this leads to an estimate of $K$ in terms of the eccentricity of the ellipse.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".