arXiv research

Study spider mechanism configuration spaces using squared distance function.

On a complete, connected, locally compact, non-compact geodesic space $(X,d)$, we assign each compact set a distance-like function. With the help of these functions, we obtain a pseudo-metric on the space of (non-empty) compact subsets of $X$ which is less than the Hausdorff distance. The quotient metric space is close…

Proves limit curve theorem for incomplete metric spaces, applies to null distance in Lorentzian manifolds.

The distance function $\varrho(p,q)$ (or $d(p,q)$) of a distance space (general metric space) is not differentiable in general. We investigate such distance spaces over $\mathbb R^n$, whose distance functions are differentiable like in case of Finsler spaces. These spaces have several good properties, yet they are no F…

The paper examines conditions for compactness in sequences of warped product length spaces.

The paper tackles learning smooth distance functions using query-based methods.

Paper classifies critical points in half-space with new distance function.

We introduce an asymmetric distance function, which we call the `left Hausdorff distance function', on the space of geodesic laminations on a closed hyperbolic surface of genus at least 2. This distance is an asymmetric version of the Hausdorff distance between compact subsets of a metric space. We prove a rigidity res…

The paper proves isoparametric functions on Finsler space forms under specific conditions.

In this paper, we discuss how a Gromov-Hausdorff-like distance function over the space of all isometric classes of compact $C^k$-Riemannian manifolds should be defined in the aspect of the Riemannan submanifold theory, where $k\geq 1$. The most important fact in this discussion is as follows. The Hausdorff distance fun…

The paper explores the shape of filling-systole subspace in surface moduli space and critical points of systole function.

Study distances between special functions on Kähler manifolds.

In this note we define a distance between two pointed locally integral current spaces. We prove that a sequence of pointed locally integral current spaces converges with respect to this distance if and only if it converges in the sense of Lang-Wenger. This enables us to state the compactness theorem by Lang-Wenger for …

Learning a distance function or metric on a given data manifold is of great importance in machine learning and pattern recognition. Many of the previous works first embed the manifold to Euclidean space and then learn the distance function. However, such a scheme might not faithfully preserve the distance function if t…

To optimize a neural network one often thinks of optimizing its parameters, but it is ultimately a matter of optimizing the function that maps inputs to outputs. Since a change in the parameters might serve as a poor proxy for the change in the function, it is of some concern that primacy is given to parameters but tha…

The paper connects geometric and topological concepts to bound distances between metric spaces.

This paper explores infinite-dimensional Teichmüller spaces and their properties.

Unlike the case of surfaces of topologically finite type, there are several different Teichmüller spaces that are associated to a surface of topological infinite type. These Teichmüller spaces first depend (set-theoretically) on whether we work in the hyperbolic category or in the conformal category. They also depend, …

This paper presents a general notion of Mahalanobis distance for functional data that extends the classical multivariate concept to situations where the observed data are points belonging to curves generated by a stochastic process. More precisely, a new semi-distance for functional observations that generalize the usu…

We propose a geometric method for quantifying the difference between parametrized curves in Euclidean space by introducing a distance function on the space of parametrized curves up to rigid transformations (rotations and translations). Given two curves, the distance between them is defined as the infimum of an energy …

Efficiently approximates neural network function space distance.

Note: Causality can be encoded without strict time function choice.

The concept of natural pseudo-distance has proven to be a powerful tool for measuring the dissimilarity between topological spaces endowed with continuous real-valued functions. Roughly speaking, the natural pseudo-distance is defined as the infimum of the change of the functions' values, when moving from one space to …

Study proves finiteness for distance functions on curved surfaces with controlled curvature.

In this paper, we focus on the separability of classes with the cross-entropy loss function for classification problems by theoretically analyzing the intra-class distance and inter-class distance (i.e. the distance between any two points belonging to the same class and different classes, respectively) in the feature s…

A new invariant captures geometric features of circle embeddings.

The paper analyzes distances and volumes in lens spaces using recursion and formulas.

The study characterizes harmonic spaces and their radial eigen-functions and vector fields.

Proves compactness for timed-metric spaces using new distance and maps.

The paper proves inequalities linking Wasserstein distances and eigenfunctions in RCD(K,∞) spaces.

We propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that. Therefore, we extend this method to …

Study shows singular set of distance functions is delta-convex.

Synthetic proof shows globally hyperbolic Lorentzian spaces with specific curvature are warped products.

Diffusion models achieve nearly optimal distribution estimation in various spaces.

This thesis uses Kantorovich-Rubinstein distance for classifying points based on their measures.

Paper calculates distances between strata in Teichmüller space, proving a constant separation.

We produce examples of codimension one foliations of the Euclidean and hyperbolic planes with bounded geometry which are topologically products, but for which leaves are non-recursively distorted. That is, the function which compares intrinsic distances in leaves with extrinsic distances in the ambient space grows fast…

Paper reconstructs compact Riemannian manifolds from travel time data.

We study the level sets of the distance function from a boundary point of a convex set in Euclidean space. We provide a lower bound for the range of connectivity of the level sets, in terms of the critical points of the distance function in the sense of Grove-Shiohama-Gromov-Cheeger.

The Johnson-Lindenstrauss Lemma allows for the projection of $n$ points in $p-$dimensional Euclidean space onto a $k-$dimensional Euclidean space, with $k \ge \frac{24\ln \emph{n}}{3ε^2-2ε^3}$, so that the pairwise distances are preserved within a factor of $1\pmε$. Here, working directly with the distributions of the …

Random walk speed on Teichmüller space is a proper function.

Null distance encodes causal structure in spacetimes.

The paper proves uniform Temple charts and applies them to null distance metrics.

Landmark-based node embeddings approximate shortest path distances in random graphs.

We provide upper bounds of the expected Wasserstein distance between a probability measure and its empirical version, generalizing recent results for finite dimensional Euclidean spaces and bounded functional spaces. Such a generalization can cover Euclidean spaces with large dimensionality, with the optimal dependence…

Novel neural network approach on hyperbolic and SPD spaces.

Proposes a new scoring function for linear classifiers to improve object positioning in feature space.

We generalize the theory of gradient flows of semi-convex functions on CAT(0)-spaces, developed by Mayer and Ambrosio--Gigli--Savaré, to CAT(1)-spaces. The key tool is the so-called "commutativity" representing a Riemannian nature of the space, and all results hold true also for metric spaces satisfying the commutativi…