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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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73146219292 · Jun 202019922001200920172026
48 results for uniform domains

Uniform estimates for elliptic problems near polygonal domains.

problem Proving uniform solvability estimates for elliptic problems near polygonal domains.
method Suitable conformal modification of the metric to make the union of domains a manifold with boundary and relative bounded geometry.
result Rounding off the corners of the limit polygonal domain.

The paper studies invariant weighted Bergman metrics on domains.

problem Investigating invariant weighted Bergman metrics under biholomorphisms.
method Introducing invariant weight assignments, using Bergman's minimum integral method and domain version of Tian-Yau-Zelditch expansion.
result Uniform convergence of weighted Bergman kernels and metrics on uniform squeezing domains.

Equivalence proven between uniformizing varieties and tensors, generalizing uniformization results.

problem Characterizing complex-projective varieties with klt singularities and ample canonical divisors.
method Constructing a uniformizing variation of Hodge structure from slope zero tensors and vice versa.
result Generalization of uniformization results to singular settings, including quotients of tube domains.

We show here that the Nielsen core of the bumping set of the domain of discontinuity of a Kleinian group ΓΓ is the boundary of the characteristic submanifold of the associated 3-manifold with boundary. Some examples of interesting characteristic submanifolds are given. We also give a construction of the characteristic…

2012-08-08abs ↗pdf ↗

The paper proposes a uniformity regularization scheme to improve deep neural network transferability.

problem Improving deep neural network transferability and adaptation to new tasks.
method Introduces a uniformity regularization scheme to encourage high uniformity in embedding space.
result Uniformity regularization consistently offers benefits over baseline methods and achieves state-of-the-art performance in Deep Metric Learning and Meta-Learning.

Quantitative Sobolev extensions lead to Neumann heat kernel bounds.

problem Bounding Neumann heat kernels for domains with integral Ricci curvature.
method Quantitative Sobolev extension operators and Neumann heat kernel estimates.
result Uniform bounds on Neumann heat kernels and eigenvalues.

Defines W-volume for planar domains with circular boundaries, relating to Laplacian determinant and Schottky uniformization.

problem Determining the volume of conformal metrics on planar domains with circular boundaries.
method Extending Epstein maps to conformal metrics, defining W-volume, using Schottky uniformization and Loewner energy.
result Shows a bound on the renormalized volume of Schottky uniformization and provides a realization of Loewner energy.

The paper studies complex Finsler metrics and their equivalence to the Kobayashi metric.

problem Investigating properties and equivalence of complex Finsler metrics.
method Using curvature properties of Bergman metrics and Schwarz lemma, the paper analyzes complex Finsler metrics and their equivalence to the Kobayashi metric.
result Uniform equivalences of the Kobayashi metric and Carathéodory metric on bounded strongly convex domains with smooth boundaries are proven.

We review some recent results in the generic rigidity theory of planar frameworks with forced symmetry, giving a uniform treatment to the topic. We also give new combinatorial characterizations of minimally rigid periodic frameworks with fixed-area fundamental domain and fixed-angle fundamental domain.

2012-03-04abs ↗pdf ↗

New approach to adversarial robustness with non-uniform perturbations.

problem Real-world adversaries craft adversarial examples with non-uniform perturbations.
method Proposes non-uniform perturbations based on feature dependencies and data distribution.
result Shows improved robustness to real-world attacks compared to uniform perturbations.

A geometric framework for metrics of maximal acceleration which is applicable to large proper accelerations is discussed, including a theory of connections associated with the geometry of maximal acceleration. In such a framework it is shown that the uniform bound on the proper maximal acceleration implies an uniform b…

2019-06-28abs ↗pdf ↗

The current article stems from our study on the asymptotic behavior of holomorphic isometric embeddings of the Poincaré disk into bounded symmetric domains. As a first result we prove that any holomorphic curve exiting the boundary of a bounded symmetric domain ΩΩ must necessarily be asymptotically totally geodesic. A…

2018-07-19abs ↗pdf ↗

New subsets without interior support Poincaré inequalities, expanding previous results.

problem Finding subsets without interior that satisfy Poincaré inequalities.
method Employing uniform domains and measure density, focusing on boundary regularity and separation.
result Existence of subsets supporting Poincaré inequalities without interior, applicable to various spaces.

This work establishes uniform convergence of subdifferentials in stochastic optimization.

problem Understanding how empirical stationary points approximate population ones in nonsmooth, nonconvex stochastic optimization.
method Reduction principle for weakly convex stochastic objectives, focusing on subgradient convergence.
result Sharp uniform convergence rates for subdifferential mappings in stochastic convex-composite optimization.

Uniformizes Hodge structures, proving Lyapunov exponents and log-Anosov monodromy.

problem Analyzing weight 3 variations of Hodge structures and their Lyapunov exponents.
method Developed uniformizations and used analytic properties to prove conjectures and properties of monodromy representations.
result Proved log-Anosov property and established strong Torelli theorem for the VHS.

There has been significant study on the sample complexity of testing properties of distributions over large domains. For many properties, it is known that the sample complexity can be substantially smaller than the domain size. For example, over a domain of size nn, distinguishing the uniform distribution from distrib…

2019-07-06abs ↗pdf ↗

This study uses neural networks to approximate Bayesian filtering problems.

problem Estimating latent time-series signal statistics from observation sequences.
method Formulated a generic recurrent neural network framework to learn recursive mappings directly.
result Approximation error bounds for filtering in non-compact domains and strong time-uniform bounds.

Unified method to calculate Gromov norm for Kähler classes of bounded symmetric domains.

problem Calculating the Gromov norm of Kähler classes for all bounded symmetric domains.
method Combination of ideas from Domin-Toledo and Toledo, aided by the Polydisc Theorem.
result Unified and simplified calculation of Gromov norm for all bounded symmetric domains.

Minimum width for ReLU networks on compact domain is exactly max{d_x, d_y, 2}

problem Characterizing the minimum width for ReLU networks to approximate functions on compact domains
method Analyzing the minimum width for LpL^p approximation of LpL^p functions from [0,1]d[0,1]^d to Rdy\mathbb R^{d_y} using ReLU-like activation functions
result The minimum width for LpL^p approximation on a compact domain is exactly max{d_x, d_y, 2} for ReLU-like activation functions

We prove that if DCnD\subset C^n is a bounded domain with real analytic boundary and D is pseudoconvex then the compact open topology in the group of holomorphic automorphisms of D is the topology of uniform convergence on D.

1999-11-10abs ↗pdf ↗

One fundamental goal in any learning algorithm is to mitigate its risk for overfitting. Mathematically, this requires that the learning algorithm enjoys a small generalization risk, which is defined either in expectation or in probability. Both types of generalization are commonly used in the literature. For instance, …

2016-08-22abs ↗pdf ↗

In this paper we study the automorphism group of smoothly bounded convex domains. We show that such a domain is biholomorphic to a "polynomial ellipsoid" (that is, a domain defined by a weighted homogeneous balanced polynomial) if and only if the limit set of the automorphism group intersects at least two closed comple…

2015-06-25abs ↗pdf ↗

Let (Mn,g)(M^n, g) be a complete non-compact Kähler manifold with non-negative and bounded holomorphic bisectional curvature. We prove that MM is holomorphically covered by a pseudoconvex domain in $\C^n$ which is homeomorphic to R2n\R^{2n}, provided (Mn,g)(M^n, g) has uniform linear average quadratic curvature decay.

2006-10-18abs ↗pdf ↗

Uniform convergence of metrics on surfaces with bounded curvature measures proved.

problem Proving uniform convergence of metrics on Alexandrov surfaces with bounded integral curvature.
method Weak convergence of measures and analytic approximation of metrics.
result Uniform convergence of metrics on Alexandrov surfaces proved.

Given a monotone convex function on the space of essentially bounded random variables with the Lebesgue property (order continuity), we consider its extension preserving the Lebesgue property to as big solid vector space of random variables as possible. We show that there exists a maximum such extension, with explicit …

2013-04-30abs ↗pdf ↗

The paper develops quantitative estimates for holomorphic sections over bounded domains.

problem Establishing precise inequalities for holomorphic sections over bounded domains.
method Develops Sobolev-type inequalities and applies them to holomorphic sections of Hermitian vector bundles.
result Quantitative Carleman-type estimates for holomorphic sections are derived, improving on previous non-quantitative results.

Paper tackles estimating initial conditions of spatio-temporal processes from sparse data.

problem Estimating initial conditions of spatio-temporal advection-diffusion processes from sparse data.
method Regularized convex optimization problem with Alternating Direction Method of Multipliers.
result Efficient solutions for non-uniform and shifted uniform sampling schemes.