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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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12243547 · May 202619922001200920172026
48 results for Affine hyperspheres

In this paper we study J~\widetilde{J}-tangent affine hyperspheres, where J~\widetilde{J} is the canonical para-complex structure on R2n+2\mathbb{R}^{2n+2}. The main purpose of this paper is to give a classification of J~\widetilde{J}-tangent affine hyperspheres of an arbitrary dimension with an involutive distribution $\…

2018-04-04abs ↗pdf ↗

In this paper, we explicitly construct the Calabi composition of multiple affine hyperspheres possibly including some points viewing as 0-dimensional hypersheres. Then we compute all the basic affine invariants of the composed affine hyperspheres, proving that the composed affine hypersphere is symmetric one if and onl…

2013-10-18abs ↗pdf ↗

In this paper, we study locally strongly convex affine hyperspheres in the unimodular affine space Rn+1\mathbb{R}^{n+1} which, as Riemannian manifolds, are locally isometric to the Riemannian product of two Riemannian manifolds both possessing constant sectional curvatures. As the main result, a complete classification o…

2018-12-19abs ↗pdf ↗

An affine hypersurface M is said to admit a pointwise symmetry, if there exists a subgroup G of Aut(T_p M) for all p in M, which preserves (pointwise) the affine metric h, the difference tensor K and the affine shape operator S. Here, we consider 3-dimensional indefinite affine hyperspheres, i.e. S= H Id (and thus S is…

2005-10-25abs ↗pdf ↗

An affine hypersurface MM is said to admit a pointwise symmetry, if there exists a subgroup GG of Aut(TpM){\rm Aut}(T_p M) for all pMp\in M, which preserves (pointwise) the affine metric hh, the difference tensor KK and the affine shape operator SS. Here, we consider 3-dimensional indefinite affine hyperspheres, i.e. $S…

2009-10-19abs ↗pdf ↗

The study characterizes quadrics among affine hyperspheres based on centroid collinearity of sections.

problem Characterizing quadrics among affine hyperspheres based on section centroid collinearity.
method Extending Meyer and Reisner's theorem to unbounded convex sets and identifying additional assumptions.
result Ellipsoids, paraboloids, and one sheet of a two-sheeted hyperboloid are the only quadrics satisfying the centroid collinearity condition.

Let J~\widetilde{J} be the canonical para-complex structure on R4\mathbb{R}^4. In this paper we study 33-dimensional centro-affine hypersurfaces with a J~\widetilde{J}-tangent centro-affine vector field (sometimes called J~\widetilde{J}-tangent centro-affine hypersurfaces) as well as 33-dimensional J~\widetilde{J}-ta…

2018-04-06abs ↗pdf ↗

We consider non-degenerate centro-affine hypersurface immersions in R^n whose cubic form is parallel with respect to the Levi-Civita connection of the affine metric. There exists a bijective correspondence between homothetic families of proper affine hyperspheres with center in the origin and with parallel cubic form, …

2012-08-06abs ↗pdf ↗

We introduce and study the equiaffine symmetric {\bf hyperspheres}. For the first step we consider the locally strongly convex ones. In fact, by the idea used by Naitoh, we provide in this paper a direct proof of the complete classification for those affine symmetric hyperspheres. Then, via an earlier result of the fir…

2014-08-19abs ↗pdf ↗

We prove that any simply connected special Kaehler manifold admits a canonical immersion as a parabolic affine hypersphere. As an application, we associate a parabolic affine hypersphere to any nondegenerate holomorphic function. Also we show that a classical result of Calabi and Pogorelov on parabolic spheres implies …

1999-11-11abs ↗pdf ↗

In this paper, we study strictly convex affine hypersurfaces centroaffinely congruent to their centre map, in the case when the shape operator has two distinct eigenvalues: one of multiplicity 1, and one nonzero of multiplicity n-1. We show how to construct them from (n-1)-dimensional affine hyperspheres.

2012-06-01abs ↗pdf ↗

We consider non-degenerate graph immersions into affine space An+1\mathbb A^{n+1} whose cubic form is parallel with respect to the Levi-Civita connection of the affine metric. There exists a correspondence between such graph immersions and pairs (J,γ)(J,γ), where JJ is an nn-dimensional real Jordan algebra and γγ is a no…

2013-02-06abs ↗pdf ↗

According to a classical result of E.~Calabi any hyperbolic affine hypersphere endowed with its natural Hessian metric has a non-positive Ricci tensor. The affine hyperspheres can be described as the level sets of solutions to the "hyperbolic" toric Kähler-Einstein equation eΦ=detD2Φe^Φ = \det D^2 Φ on proper convex cones. We…

2016-04-14abs ↗pdf ↗

In this paper, we study locally strongly convex Tchebychev hypersurfaces, namely the {\it centroaffine totally umbilical hypersurfaces}, in the (n+1)(n+1)-dimensional affine space Rn+1\mathbb{R}^{n+1}. We first make an ordinary-looking observation that such hypersurfaces are characterized by having a Riemannian structure ad…

2019-11-13abs ↗pdf ↗

We describe extrinsic hyperspheres and totally geodesic hypersurfaces in manifolds with special holonomy. In particular we prove the nonexistence of extrinsic hyperspheres in quaternion-Kaehler manifolds. We develop a new approach to extrinsic hyperspheres based on the classification of special Killing forms.

2011-07-08abs ↗pdf ↗

Study infinite Euclidean distance discriminants of algebraic varieties.

problem Understanding the structure of data points with infinitely many critical points in Euclidean distance correspondence.
method Developed computer code to compute discriminants and proved properties of fibers.
result Infinite Euclidean distance discriminants contain all data points with infinitely many critical points for the nearest-point problem.

Constructs hyperspheres with prescribed mean curvature in Euclidean space.

problem Creating hyperspheres with a specific curvature in Euclidean space.
method Constructs families of smooth functions to fill Euclidean space with hyperspheres of prescribed mean curvature.
result Euclidean space can be filled with hyperspheres of prescribed mean curvature.

Study on hyperspheres in 4-spaces as special Riemannian manifolds.

problem Characterizing hyperspheres in Euclidean and Minkowski 4-spaces as specific Riemannian manifolds.
method Constructing and studying hyperspheres in 4-dimensional spaces (Euclidean and pseudo-Euclidean) as almost paracontact almost paracomplex Riemannian manifolds.
result Characterization and geometric properties of these manifolds.

This paper introduces hyperspherical prototype networks, which unify classification and regression with prototypes on hyperspherical output spaces. For classification, a common approach is to define prototypes as the mean output vector over training examples per class. Here, we propose to use hyperspheres as output spa…

2019-01-29abs ↗pdf ↗

The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often leading to competitive results, we show that this parameterization fails …

2018-04-03abs ↗pdf ↗

Convolution as inner product has been the founding basis of convolutional neural networks (CNNs) and the key to end-to-end visual representation learning. Benefiting from deeper architectures, recent CNNs have demonstrated increasingly strong representation abilities. Despite such improvement, the increased depth and l…

2017-11-08abs ↗pdf ↗

Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to other competing similarity measures. Specifically, the idea of usi…

2017-02-05abs ↗pdf ↗

Paper optimizes hyperspherical prototypes for better class separation.

problem Previous HPL approaches either lack principled optimisation or are limited to one latent dimension.
method Develops a principled optimisation procedure and uses linear block codes to create well-separated prototypes in various dimensions.
result Optimal prototype placement is characterized with achievable and converse bounds, showing near-optimality.

This work optimizes alignment and uniformity of features on a hypersphere for better downstream performance.

problem Improving the performance of contrastive representation learning.
method Identifying and optimizing alignment and uniformity of features on a hypersphere.
result Directly optimizing alignment and uniformity leads to comparable or better performance than contrastive learning.

Researchers prove constant mean curvature graphs in hyperbolic 3-space for specific domains.

problem Existence of hyperbolic Killing graphs with constant mean curvature in exterior domains.
method Existence proof using CMC graphs and Killing vector fields.
result Existence of hyperbolic Killing graphs of constant mean curvature H in exterior domains.

Learning suitable latent representations for observed, high-dimensional data is an important research topic underlying many recent advances in machine learning. While traditionally the Gaussian normal distribution has been the go-to latent parameterization, recently a variety of works have successfully proposed the use…

2019-10-07abs ↗pdf ↗

An AH (affine hypersurface) structure is a pair comprising a projective equivalence class of torsion-free connections and a conformal structure satisfying a compatibility condition which is automatic in two dimensions. They generalize Weyl structures, and a pair of AH structures is induced on a co-oriented non-degenera…

2010-11-26abs ↗pdf ↗

This work studies the chord length distribution, in the case where both ends lie on a NN-dimensional hypersphere (N2N \geq 2). Actually, after connecting this distribution to the recently estimated surface of a hyperspherical cap \cite{SLi11}, closed-form expressions of both the probability density function and the cu…

2014-11-20abs ↗pdf ↗

Proposes a new latent variable model for hyperspherical latent spaces.

problem Efficiently modeling heavy-tailed distributions in hyperspherical latent spaces.
method Introduces spherical Cauchy (spCauchy) latent variables and applies Möbius transformations.
result Shows spCauchy recovers vMF geometry in high-concentration limits and avoids complex evaluations.

In this paper, we give a necessarly and sufficient condition for orbits of linear isotropy representations of Riemannian symmetric spaces are biharmonic submanifolds in hyperspheres in Euclidean spaces. In particular, we obtain examples of biharmonic submanifolds in hyperspheres whose co-dimension is greater than one.

2017-04-25abs ↗pdf ↗

New method uses hyperspherical geometry to improve community detection.

problem Improving community detection methods in network analysis.
method Mapping networks to points on a hypersphere, then projecting to clustering vectors.
result Modularity maximization is equivalent to minimizing angular distance on the hypersphere.

This paper proves the manifold hypothesis for lower embedding dimensions using osculating hyperspheres.

problem The dataset lies on a low-dimensional submanifold in high-dimensional space.
method Constructing osculating hyperspheres and applying surgery theory to embed the hypersurface.
result The manifold hypothesis holds for embedding dimensionalities up to d1d-1.

A new method uses hyperspherical latent spaces to disentangle data with periodic structures.

problem Disentangling data with periodic or cyclic underlying factors in Euclidean space.
method Diffusion Variational Autoencoder with a modified Evidence Lower Bound.
result The method can recover periodic true factors effectively.

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.