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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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71142212283 · Jun 202019922001200920172026
48 results for metric perturbation

Stability of submanifold cut loci under metric perturbations proved.

problem Stability of submanifold cut loci under metric perturbations.
method Continuity of injectivity radius and Whitney C2C^2 perturbation of submanifolds.
result Hausdorff stability of submanifold cut loci under C2C^2 metric perturbations.

The paper proves conditions for Kähler-Einstein metrics to remain Kähler-Einstein under cscK perturbations.

problem Conditions for Kähler-Einstein metrics to remain Kähler-Einstein under cscK perturbations.
method Study of constant scalar curvature Kähler (cscK) metrics on complete non-compact Kähler--Einstein manifolds.
result Sufficient conditions for a cscK perturbation of a Kähler--Einstein metric to remain Kähler--Einstein.

Ricci flow simulations show unstable Fubini-Study metrics develop singularities.

problem Understanding the behavior of unstable perturbations in Ricci flow.
method Numerical simulations of Ricci flow starting from unstable Fubini-Study metrics.
result Ricci flow solutions from unstable Fubini-Study metrics develop local singularities.

New metrics improve scRNA-seq perturbation modeling by reducing mode collapse.

problem Outperformed by simple mean prediction in scRNA-seq perturbation modeling.
method Introduce DEG-aware metrics (WMSE, Rw2(Δ)R^{2}_{w}(Δ)) and negative/positive baselines.
result WMSE loss function reduces mode collapse and improves model performance.

Stability of cut locus under metric perturbations in compact Riemannian manifolds.

problem Stability of cut locus under C2C^2-perturbations of the metric.
method Proving stability with respect to the Hausdorff metric of the cut locus under C2C^2 perturbation of the metric.
result The Hausdorff distance between cut loci converges to zero as the metrics converge.

Prove that collapsing CSC metrics can be perturbed to invariant collapsing CSC metrics.

problem Prove that collapsing constant scalar curvature metrics can be perturbed to invariant collapsing constant scalar curvature metrics.
method Prove that a sequence of constant scalar curvature metrics which is collapsing with bounded curvature to a manifold can be perturbed to a sequence of invariant collapsing constant scalar curvature metrics.
result Prove that a sequence of constant scalar curvature metrics which is collapsing with bounded curvature to a manifold can be perturbed to a sequence of invariant collapsing constant scalar curvature metrics.

Study magnetic perturbations in Riemannian and Lorentzian Calderón problems.

problem Determining metrics from boundary measurements under magnetic perturbations.
method Runge approximation for Riemannian case, microlocal analysis for Lorentzian case.
result Metrics can be uniquely determined in both Riemannian and Lorentzian cases under specific perturbations.

This paper introduces metrics to evaluate robustness of neural networks to natural adversarial examples.

problem Measuring robustness of neural networks to natural adversarial examples.
method Proposes latent space performance metrics based on generative models.
result Latent adversarial perturbations are often perceptually small and associated with classifier accuracy.

Estimates for harmonic forms on a 3-Torus, proving their existence.

problem Existence of nowhere vanishing harmonic 1-forms on a 3-Torus.
method Explicit computation of injectivity estimates using the Laplace operator on the 3-Torus and its perturbations.
result Existence of a nowhere vanishing harmonic 1-form on a perturbed metric on the 3-Torus.

Study reveals class-dependent effects in perturbation-based feature attribution metrics for time series classification.

problem Varying effectiveness of perturbation-based metrics across different classes in time series models.
method Systematic empirical analysis across multiple datasets, model architectures, and perturbation strategies.
result Perturbation-based metrics show varying effectiveness across classes, with some metrics performing better for certain classes.

We construct continuous families of scattering manifolds with the same scattering phase. The manifolds are compactly supported metric perturbations of Euclidean Rn\mathbf{R}^{n} for n8n\geq8. The metric perturbation may have arbitrarily small support.

2002-11-04abs ↗pdf ↗

We study the (massless) Dirac operator on a 3-sphere equipped with Riemannian metric. For the standard metric the spectrum is known. In particular, the eigenvalues closest to zero are the two double eigenvalues +3/2 and -3/2. Our aim is to analyse the behaviour of eigenvalues when the metric is perturbed in an arbitrar…

2016-05-27abs ↗pdf ↗

We prove that the mass endomorphism associated to the Dirac operator on a Riemannian manifold is non-zero for generic Riemannian metrics. The proof involves a study of the mass endomorphism under surgery, its behavior near metrics with harmonic spinors, and analytic perturbation arguments.

2010-09-28abs ↗pdf ↗

Study shows current metrics for audio adversarial examples are unreliable for human perception.

problem The reliability of metrics for evaluating audio adversarial examples.
method Analytical framework and human evaluation experiment.
result Current metrics for audio adversarial examples are not reliable for human perception.

We prove the equivalences of several classical complete metrics on the Teichmüller and the moduli spaces of Riemann surfaces. We use as bridge two new Kähler metrics, the Ricci metric and the perturbed Ricci metric and prove that the perturbed Ricci metric is a complete Kähler metric with bounded negative holomorphic s…

2004-03-03abs ↗pdf ↗

Proposes a text perturbation method using a Mahalanobis metric to balance privacy and utility.

problem Low utility of text analysis when using spherical noise for privacy-preserving text embedding.
method Regularized Mahalanobis metric to add elliptical noise, accounting for embedding space density.
result Improves privacy statistics while maintaining utility, outperforming Laplace mechanism.

Consider the massless Dirac operator on a 3-torus equipped with Euclidean metric and standard spin structure. It is known that the eigenvalues can be calculated explicitly: the spectrum is symmetric about zero and zero itself is a double eigenvalue. The aim of the paper is to develop a perturbation theory for the eigen…

2013-06-24abs ↗pdf ↗

We study the perturbations of two classes of static black ellipsoid solutions of four dimensional vacuum Einstein equations. Such solutions are described by generic off--diagonal metrics which are generated by anholonomic transforms of diagonal metrics. The analysis is performed in the approximation of small eccentrici…

2002-06-05abs ↗pdf ↗

Among all conformal classes of Riemannian metrics on CP2{\Bbb CP}_2, that of the Fubini-Study metric is shown to have the largest Yamabe constant. The proof, which involves perturbations of the Seiberg-Witten equations, also yields new results on the total scalar curvature of almost-Kähler 4-manifolds.

1996-05-29abs ↗pdf ↗

Let MM a compact connected orientable 4-manifold. We study the space ΞΞ of SpincSpin^c-structures of fixed fundamental class, as an infinite dimensional principal bundle on the manifold of riemannian metrics on MM. In order to study perturbations of the metric in Seiberg-Witten equations, we study the transversality of…

2009-02-26abs ↗pdf ↗

We compute the Ricci curvature of a curved noncommutative three torus. The computation is done both for conformal and non-conformal perturbations of the flat metric. To perturb the flat metric, the standard volume form on the noncommutative three torus is perturbed and the corresponding perturbed Laplacian is analyzed.…

2018-08-09abs ↗pdf ↗

Compact solutions persist even with linear perturbations of the mean curvature term.

problem Compactness of solutions to the Yamabe problem on manifolds with boundary.
method Linear perturbation of the mean curvature term, proving compactness of solutions.
result Set of solutions remains compact even with negative perturbations.

Paper introduces metrics for evaluating multi-agent policies using best response dynamics.

problem Evaluation and ranking of multi-agent policies in reinforcement learning.
method Adopting strict best response dynamics (SBRD) to model selfish behaviors, proposing perturbed SBRD for dynamic and non-stationary settings.
result Proposed perturbed SBRD can observe policies with maximum metrics and differ from optimal by any given tolerance.

Paper examines stability of Bayesian posterior measures using integral probability metrics.

problem Stability of Bayesian inference in large-scale inverse problems.
method New families of integral probability metrics for likelihood and prior perturbations.
result Constructs new stability results for Bayesian posterior measures.

New metrics produce discrete zero sets for nondegenerate harmonic forms.

problem Creating metrics to produce discrete zero sets for nondegenerate harmonic forms.
method Metric perturbation to produce new nondegenerate harmonic forms with discrete zero sets.
result Existence of metrics producing discrete zero sets for nondegenerate harmonic forms.

The paper defines and analyzes Kähler metrics near a compact manifold, showing their deviation from Poincaré-type metrics.

problem Understanding the behavior of Kähler metrics near a compact manifold.
method Defining and analyzing Kähler metrics on a trivial holomorphic open disk bundle, showing their deviation from Poincaré-type metrics.
result The Kähler metrics near a compact manifold deviate exponentially from Poincaré-type metrics, and they arise naturally in perturbing cscK metrics.

PerturBench benchmarks ML models for cellular perturbation analysis.

problem Standardizing benchmarking in modeling single cell transcriptomic responses to perturbations.
method Modular platform, diverse datasets, metrics, extensive evaluation, rank metrics.
result Simpler models are competitive and scale well with larger datasets.