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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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90180269359 · Jun 202019922001200920172026
48 results for nonlinear metric perturbations

Study shows local rigidity for hyperbolic cusped manifolds under certain metric perturbations.

problem Local rigidity of manifolds with hyperbolic cusps under nonlinear metric perturbations.
method Combines linear and nonlinear analysis, using the linear theory from [arXiv:1907.01809] and the generalized X-ray transform operator Π2Π_2.
result Manifolds with hyperbolic cusps are locally rigid for nonlinear perturbations that slightly decrease at infinity.

Study enhances robustness of In-CVaR based regression models under perturbation and contamination.

problem Enhancing robustness of nonlinear regression models under perturbation and contamination.
method Introduces interval conditional value-at-risk (In-CVaR) and rigorously analyzes its robustness properties under both perturbation and contamination.
result The In-CVaR based estimator is qualitatively robust in terms of the Prokhorov metric if and only if the largest portion of losses is trimmed.

Study on future stability of FLRW spacetime solutions with decelerated expansion.

problem Stability of solutions to Einstein equations coupled with a nonlinear scalar field.
method Decomposition of metric and scalar field perturbations into spatial averages and oscillatory remainders.
result Future-stability of FLRW spacetime solutions for 1/3<p<11/3 < p < 1.

S. Donaldson introduced a metric on the space of volume forms, with fixed total volume on any compact Riemmanian manifold. With this metric, the space of volume forms formally has non-positive curvature. The geodesic equation is a fully nonlinear degenerate elliptic equation. We solve the geodesic equation and its pert…

2008-10-21abs ↗pdf ↗

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 measure EC assesses node contributions in nonlinear, time-varying systems.

problem Existing node contribution measures assume linear, time-invariant dynamics, failing for complex, real-world systems.
method Defined 'emergent contribution (EC)' as a dynamical leverage measure from Jacobians of differentiable models.
result EC diverges from average controllability under persistent regime switching and sign reversal, identifying limits of local linearization.

We prove the linear stability of slowly rotating Kerr black holes as solutions of the Einstein vacuum equation: linearized perturbations of a Kerr metric decay at an inverse polynomial rate to a linearized Kerr metric plus a pure gauge term. We work in a natural wave map/DeTurck gauge and show that the pure gauge term …

2019-06-03abs ↗pdf ↗

Recent work has developed methods for learning deep network classifiers that are provably robust to norm-bounded adversarial perturbation; however, these methods are currently only possible for relatively small feedforward networks. In this paper, in an effort to scale these approaches to substantially larger models, w…

2018-05-31abs ↗pdf ↗

Stability of Schwarzschild singularity in near-Schwarzschild black holes under perturbations.

problem Stability of the Schwarzschild singularity in near-Schwarzschild black holes.
method Energy methods and new approach to Einstein vacuum equations in axial symmetry.
result The solution displays asymptocially-velocity-term-dominated dynamics and approaches a different Kasner solution at each point of the singularity.

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.

For a system of second order differential equations we determine a nonlinear connection that is compatible with a given generalized Lagrange metric. Using this nonlinear connection, we can find the whole family of metric nonlinear connections that can be associated with a system of SODE and a generalized Lagrange struc…

2004-12-06abs ↗pdf ↗

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.

This paper studies non-compactness in spinorial Yamabe-type problems on manifolds.

problem Non-compactness in spinorial Yamabe-type problems on manifolds.
method Analysis of two specific models on the manifold \(S^m\).
result The solution set is not compact for certain perturbations of the background metric.

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.

New geometric interpretation explains over-parameterized models and adversarial perturbations.

problem Geometric understanding of over-parameterized regression and adversarial perturbations.
method Alternative geometric interpretation of regression in feature space.
result Adversarial perturbations are a natural feature of biased models due to underlying geometry.

Study on signal-plus-noise decomposition in nonlinear spiked random matrices.

problem Nonlinear spiked random matrix models with rank-one signal and noise.
method Signal-plus-noise decomposition and phase transition analysis.
result Identified precise phase transitions in signal components at critical thresholds.

Gursky-Streets introduced a formal Riemannian metric on the space of conformal metrics in a fixed conformal class of a compact Riemannian four-manifold in the context of the σ2σ_2-Yamabe problem. The geodesic equation of Gursky-Streets' metric is a fully nonlinear degenerate elliptic equation and Gursky-Streets have pr…

2017-07-15abs ↗pdf ↗

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.

The paper introduces a method to make neural networks more robust to adversarial attacks.

problem Vulnerability of deep neural networks to small, adversarially designed perturbations.
method A bottom-up strategy using a nonlinear front end that polarizes and quantizes data.
result The approach can completely eliminate adversarial perturbations on MNIST and Fashion MNIST datasets.

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.

CG-EnKF and NS-EnKF outperform deep learning-based SF in data assimilation.

problem Data assimilation with non-linear perturbations.
method Two non-linear extensions of EnKF: CG-EnKF and NS-EnKF.
result CG-EnKF and NS-EnKF outperform SF in high-dimensional multiscale data assimilation.

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.