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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.

168,657 papers · 148 categories

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23477093 · Jun 202019922001200920172026
48 results for Spherical Polynomials

The paper studies surfaces with spherical curvature lines and their generation by constrained elastic curves.

problem Understanding surfaces with spherical curvature lines and their generation mechanisms.
method The approach involves Lie sphere transformations, Legendre curves, and polynomial conserved quantities of connections.
result Lie applicable surfaces with exactly one family of spherical curvature lines are generated by the lift of constrained elastic curves.

The study finds polynomial upper bounds for singularities in Einstein-scalar field system.

problem Understanding the strength of singularities in gravitational collapse.
method Analyzing geometric quantities, focusing on the Kretschmann scalar.
result Polynomial blow-up upper bounds O(1/rN)O(1/r^N) for the Kretschmann scalar, improving previous bounds.

Observational data hints at a finite universe, with spherical manifolds such as the Poincare dodecahedral space tentatively providing the best fit. Simulating the physics of a model universe requires knowing the eigenmodes of the Laplace operator on the space. The present article provides explicit polynomial eigenmodes…

2005-02-27abs ↗pdf ↗

Two-layer NN with channel attention learns low-degree spherical polynomials efficiently.

problem Learning low-degree spherical polynomials with over-parameterized neural networks.
method Two-layer neural network with channel attention, vanilla gradient descent, learnable channel selection.
result Minimally improved sample complexity of $n \asymp Θ(d^{\ell_0}/\eps)$ for learning low-degree polynomials.

Polynomial networks converge to Gaussian processes at a rate of O(n^(-1/2)).

problem Understanding the convergence rate of polynomial networks to Gaussian processes.
method Examined one-hidden-layer neural networks with random weights, focusing on polynomial activations and their convergence rate in the 2-Wasserstein metric.
result The rate of convergence for polynomial networks to Gaussian processes is $O(n^{- rac{1}{2}})$.

New method uses spherical harmonics to simplify learning single-index models.

problem Learning single-index models with unknown one-dimensional projections.
method Proposes using spherical harmonics instead of Hermite polynomials to capture rotational symmetry.
result Characterizes the complexity of learning single-index models under arbitrary spherically symmetric input distributions.

We give a method of decomposing bundle-valued polynomials compatible with the action of the Lie group Spin(n)Spin(n), where important tools are Spin(n)Spin(n)-equivariant operators and their spectral decompositions. In particular, the top irreducible component is realized as an intersection of kernels of these operators.

2000-10-30abs ↗pdf ↗

Study on complexity of random polynomials with deterministic spikes, identifying phase transitions.

problem Complexity of random Gaussian polynomials with deterministic spikes on a sphere.
method Variational formulas, Kac-Rice formula, determinant asymptotics of finite-rank perturbation of Gaussian Wigner matrices.
result Identification of a topological phase transition in the complexity function.

Study of Milnor invariants and ropelength of spherical links.

problem Understanding the relationship between the thickness of spherical links and their Milnor invariants.
method Generalized Massey products and Milnor invariants to spherical links, finding optimal asymptotic bounds.
result Optimal asymptotic bounds on Milnor invariants in terms of thickness, revealing a polynomial vs exponential regime.

We study the uniqueness of a vortex equation involving an entire function on the complex plane. As geometric applications, we show that there is a unique harmonic map u:CH2u:\mathbb{C}\rightarrow \mathbb{H}^2 satisfying u0\partial u\neq 0 with prescribed polynomial Hopf differential; there is a unique affine spherical imm…

2017-10-30abs ↗pdf ↗

Gradient Descent with Projection learns low-degree polynomials efficiently.

problem Learning low-degree spherical polynomials with neural networks.
method Over-parameterized two-layer neural network with Gradient Descent with Projection.
result Achieves nearly minimax optimal sample complexity and risk bound.

New results on algebraic knots with Brieskorn polynomials.

problem Understanding cobordisms of algebraic knots defined by Brieskorn polynomials.
method Analyzing Fox--Milnor type relations, decomposing algebraic cobordism classes, and studying cyclic suspensions.
result Spherical algebraic knots associated with Brieskorn polynomials have infinite order in the knot cobordism group.

New algorithms learn multi-index models via harmonic analysis, achieving statistical and computational trade-offs.

problem Learning multi-index models with unknown projections of input data.
method Exploiting the equivariance of the problem under the orthogonal group, we derive lower bounds and construct spectral algorithms based on harmonic tensor unfolding.
result Achieve statistical and computational trade-offs between sample and runtime complexity.

Study of spacelike singularities in spherical spacetimes with scalar matter.

problem Characterize spacelike singularities in spherically symmetric spacetimes with scalar matter.
method Analyzes the properties of spacelike singularities in spherically symmetric spacetimes with scalar matter, proving inverse polynomial blow-up rates and providing a BKL-type expansion.
result Provides a rigorous description of Kasner-like singularities in spherically symmetric gravitational collapse.

A new method using spherical harmonics approximates the Sliced-Wasserstein distance.

problem Approximating the Sliced-Wasserstein distance between probability measures.
method Spherical Harmonics Control Variates (SHCV) method for Monte Carlo approximation of the SW distance.
result SHCV method provides an improved rate of convergence compared to Monte Carlo for general measures.

Statistical and machine-learning algorithms are frequently applied to high-dimensional data. In many of these applications data is scarce, and often much more costly than computation time. We provide the first sample-efficient polynomial-time estimator for high-dimensional spherical Gaussian mixtures. For mixtures of a…

2014-02-19abs ↗pdf ↗

We study the Gibbs sampling algorithm for continuous determinantal point processes. We show that, given a warm start, the Gibbs sampler generates a random sample from a continuous kk-DPP defined on a dd-dimensional domain by only taking poly(k)\text{poly}(k) number of steps. As an application, we design an algorithm to ge…

2018-10-20abs ↗pdf ↗

Study on curvature blow-up rates in black hole interiors from gravitational collapse.

problem Understanding curvature blow-up rates in black hole interiors during gravitational collapse.
method Investigation of spherically symmetric Einstein-scalar field spacetimes, focusing on blow-up rates of curvature and mass.
result Kretschmann scalar blows up faster than in Schwarzschild setting, indicating a new blow-up phenomenon.

Sharp bounds for eigenfunctions on product spaces restricted to submanifolds.

problem Establishing bounds for eigenfunctions on product spaces.
method Combining asymptotics of Jacobi polynomials and positivity of Fourier coefficients of spherical functions.
result Sharp LpL^p bounds for eigenfunctions on products of rank-one symmetric spaces.

A Levi nondegenerate real analytic hypersurface M of C^2 represented in local coordinates (z, w) in C^2 by a complex defining equation of the form w = Theta (z, \bar z, \bar w) which satisfies an appropriate reality condition, is spherical if and only if its complex graphing function Theta satisfies an explicitly writt…

2009-10-09abs ↗pdf ↗

Bayesian framework for sphere regression using Gaussian fields.

problem Nonparametric regression on the sphere with Gaussian priors.
method Isotropic Gaussian field priors, harmonic structure, exact posterior distributions, optimal spectral truncation, posterior contraction rates.
result Sharp posterior contraction rates for Gaussian priors with polynomially decaying angular power spectra.

The paper projects unknown manifolds onto hyperspheres for efficient function approximation.

problem Function approximation from data on unknown manifolds with added errors.
method Projects unknown manifold onto hypersphere and uses localized spherical polynomial kernels.
result Optimal rates of approximation for rough functions are given.

We prove boundedness and polynomial decay statements for solutions to the spin ±1\pm1 Teukolsky-type equation projected to the =1\ell=1 spherical harmonic on Reissner-Nordström spacetime. The equation is verified by a gauge-invariant quantity which we identify and which involves the electromagnetic and curvature tensor…

2018-12-06abs ↗pdf ↗

New geometric approach for analyzing compositional data like gut microbiomes.

problem Analyzing non-negative compositional data with relative values only.
method Reinterpret compositional data as quotient topology of a sphere, using spherical harmonics and reflection group actions.
result Construction of Reproducing Kernel Hilbert Space (RKHS) for compositional data.

We develop a solution theory for a generalized electro-magneto static Maxwell system in an exterior domain with anisotropic coefficients converging at infinity with a certain rate towards the identity. Our main goal is to treat right hand side data from some polynomially weighted Sobolev spaces and obtain solutions whi…

2011-05-20abs ↗pdf ↗

The coefficient of the logarithmic term in the entropy on even spheres is re-computed by the local technique of integrating the finite temperature energy density up to the horizon on static d--dimensional de Sitter space and thence finding the entropy by thermodynamics. Numeric evaluation yields the known answer i.e. (…

2010-09-20abs ↗pdf ↗

We study the rational permutation braids, that is the elements of an Artin-Tits group of spherical type which can be written x1yx^{-1} y where xx and yy are prefixes of the Garside element of the braid monoid. We give a geometric characterization of these braids in type AnA_n and BnB_n and then show that in spherical …

2015-08-27abs ↗pdf ↗

New estimates quantify blow-up rates of spacelike singularities in gravitational collapse.

problem Quantifying the blow-up rates of spacelike singularities in gravitational collapse.
method Deriving new quantitative estimates using spherical symmetry and double-null coordinates.
result Polynomial blow-up rates O(1/rN)O(1/r^N) for various quantities, with improved estimates for rurr\partial_u r and rvrr\partial_v r.

The abstract proves spherical surface decompositions with conical singularities.

problem Decomposing surfaces with spherical metrics and conical singularities.
method Geometric triangulations and irreducible components of standard shapes.
result Spherical polygons, including half-spherical concave polygons, can be arbitrarily complicated.

Study spherical curves with curvature dependent on distance to a great circle.

problem Understanding spherical curves with curvature dependent on distance to a great circle.
method Introducing spherical angular momentum, characterizing known curves, finding new families, and obtaining arc length parametrizations.
result New families of spherical curves with intrinsic equations in elementary or Jacobi elliptic functions.

Study calculates eigenvalues and eigenfunctions for spherical triangles and finds fundamental gap behavior.

problem Understanding eigenvalues and gaps in spherical triangles.
method Explicit computation of Dirichlet eigenvalues and eigenfunctions for spherical lunes and triangles.
result Fundamental gap of spherical triangles increases as the angle of the lune decreases.

Algorithm distinguishes Gaussian mixtures from pure Gaussians in quasi-polynomial time.

problem Distinguishing mixtures of Gaussian components from pure Gaussians, especially when components are well-separated.
method Sum-of-Squares method, quasi-polynomial time algorithm, bipartitioning sample to separate components.
result Algorithm can reliably distinguish between mixtures and pure Gaussians in quasi-polynomial time.