arXiv research

Universal spaces for finite topological spaces simplify shape descriptions.

Study shows some complex shapes don't fit a certain property.

Paper characterizes star-shaped risk measures and their properties.

Direct proof of Alexander polynomial scaling for L-shaped representations.

Optimizes shapes on non-standard manifolds.

In shape analysis, the concept of shape spaces has always been vague, requiring a case-by-case approach for every new type of shape. In this paper, we give a general definition for an abstract space of shapes in a manifold. This notion encompasses every shape space studied so far in the literature, and offers a rigorou…

Kernel-based tests for shape constraints in finance.

Paper controls shape stability in infinite Riemannian manifolds.

A registration-free framework monitors shape and color in 4D point clouds.

The paper explores metrics and models for analyzing biological shapes.

METASET selects diverse unit cells for efficient data-driven metamaterial design.

This paper explores how environmental properties can simplify reinforcement learning in non-episodic settings.

We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures. The guide clarifies the relationship between various properties (input shape, kernel shape, zero padding, strides and output shape) of convolutional, pooling and transposed convolutional layers…

Let $M$ be a most singular orbit of the isotropy representation of a simple symmetric space. Let $(ν_i, Φ_i)$ be an irreducible factor of the normal holonomy representation $(ν_pM, Φ(p))$. We prove that there exists a basis of a section $Σ_i\subset ν_i$ of $Φ_i$ such that the corresponding shape operators have rational…

We present and study a family of metrics on the space of compact subsets of $R^N$ (that we call ``shapes''). These metrics are ``geometric'', that is, they are independent of rotation and translation; and these metrics enjoy many interesting properties, as, for example, the existence of minimal geodesics. We view our s…

In this paper, we define a new metric structure on the shape space of a high genus surface. We introduce a rigorous definition of a shape of a surface and construct a metric based on two energies measuring the area distortion and the angle distortion of a quasiconformal homeomorphism. We show that the energy minimizer …

Modeling functional data, this study uncovers the size-and-shape of functions under noisy observations.

This article provides an overview of various notions of shape spaces, including the space of parametrized and unparametrized curves, the space of immersions, the diffeomorphism group and the space of Riemannian metrics. We discuss the Riemannian metrics that can be defined thereon, and what is known about the propertie…

Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks. So far, such representations were computed using either: (i) pre-computed implicit shape representations; or (ii) loss functions explicitly defined over the neural level sets…

The paper proposes a deep learning approach to efficiently approximate diffeomorphisms for shape alignment.

In this paper, we obtain some properties of biconservative Lorentz hypersurface $M_{1}^{n}$ in $E_{1}^{n+1}$ having shape operator with complex eigen values. We prove that every biconservative Lorentz hypersurface $M_{1}^{n}$ in $E_{1}^{n+1}$ whose shape operator has complex eigen values with at most five distinct prin…

Curvature estimate for stable free boundary minimal hypersurfaces in wedge-shaped manifolds.

The classification of shapes is of great interest in diverse areas ranging from medical imaging to computer vision and beyond. While many statistical frameworks have been developed for the classification problem, most are strongly tied to early formulations of the problem - with an object to be classified described as …

We consider the problem of nonparametric regression under shape constraints. The main examples include isotonic regression (with respect to any partial order), unimodal/convex regression, additive shape-restricted regression, and constrained single index model. We review some of the theoretical properties of the least …

This work establishes properties on diffeological structures for set-valued maps and measures.

A framework for generating 3D shapes by sequentially assembling primitives.

This paper provides further investigation of the concept of shape m$_{\rm simpl}$-fibrators (previously introduced by the author). The main results identify shape m$_{\rm simpl}$-fibrators among direct products of Hopfian manifolds. First it is established that every closed orientable manifold homotopically determined …

In the elastic shape analysis approach to shape matching and object classification, plane curves are represented as points in an infinite-dimensional Riemannian manifold, wherein shape dissimilarity is measured by geodesic distance. A remarkable result of Younes, Michor, Shah and Mumford says that the space of closed p…

We study completeness properties of Sobolev metrics on the space of immersed curves and on the shape space of unparametrized curves. We show that Sobolev metrics of order $n\geq 2$ are metrically complete on the space $\mathcal I^n(S^1,\mathbb R^d)$ of Sobolev immersions of the same regularity and that any two curves i…

By introducing a shape manifold as a solution set to solve inverse obstacle scattering problems we allow the reconstruction of general, not necessarily star-shaped curves. The bending energy is used as a stabilizing term in Tikhonov regularization to gain independence of the parametrization. Moreover, we discuss how se…

This paper introduces a new reward shaping method for average-reward reinforcement learning.

This paper connects monetary and star-shaped risk measures by showing their equivalence under certain conditions.

Neural networks predict shapes of first passage percolation sets.

New method uses Riemannian geometry to describe molecular shapes.

We describe all families of star-shaped n-polygons in the Euclidean plane with prescribed perimeter and area ; they are leaves of a foliation F on the space of star-shaped n-polygons. By the way, we study some geometric properties of convex polygons, for instance their inscriptibility in a circle and their regularity i…

ML predicts alloy properties considering chemistry, processing, and data transformations.

New method groups similar functional covariates for better modeling.

Statistical shape analysis can be done in a Riemannian framework by endowing the set of shapes with a Riemannian metric. Sobolev metrics of order two and higher on shape spaces of parametrized or unparametrized curves have several desirable properties not present in lower order metrics, but their discretization is stil…

Homotopy theory of differentiable sheaves connects manifold properties to underlying homotopy types.

The paper presents a method for analyzing shape graphs using specific features.

New metrics for surface shapes incorporating curve properties.

A new method monitors unstructured 3D shapes without registration.

Study properties of hyperbolic Yamabe solitons in submanifolds.

New metrics on curve spaces improve shape analysis.

Proposes a new metric for comparing shapes in different spaces.

The moving sofa problem, posed by L. Moser in 1966, asks for the planar shape of maximal area that can move around a right-angled corner in a hallway of unit width, and is conjectured to have as its solution a complicated shape derived by Gerver in 1992. We extend Gerver's techniques by deriving a family of six differe…

A novel 3D shape registration method using spectral graph embedding and probabilistic matching.

The study examines hypersurfaces in pseudo-Euclidean space with specific curvature properties.