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Group equivariant non-expansive operators have been recently proposed as basic components in topological data analysis and deep learning. In this paper we study some geometric properties of the spaces of group equivariant operators and show…

Differential Geometry · Mathematics 2024-01-02 Pasquale Cascarano , Patrizio Frosini , Nicola Quercioli , Amir Saki

Finite tight frames for polynomial subspaces are constructed using monic Hahn polynomials and Krawtchouk polynomials of several variables. Based on these polynomial frames, two methods for constructing tight frames for the Euclidean spaces…

Classical Analysis and ODEs · Mathematics 2014-03-04 Yuan Xu

I introduce the problem of finding maximal sets of equiangular lines, in both its real and complex versions, attempting to write the treatment that I would have wanted when I first encountered the subject. Equiangular lines intersect in the…

Quantum Physics · Physics 2020-09-01 Blake C. Stacey

A perfect matching in an undirected graph $G=(V,E)$ is a set of vertex disjoint edges from $E$ that include all vertices in $V$. The perfect matching problem is to decide if $G$ has such a matching. Recently Rothvo{\ss} proved the striking…

Discrete Mathematics · Computer Science 2018-04-26 David Avis , David Bremner , Hans Raj Tiwary , Osamu Watanabe

We present a precise definition of extended homotopy quantum field theories and develop an orbifold construction for these theories when the target space is the classifying space of a finite group $G$, i.e. for $G$-equivariant topological…

Quantum Algebra · Mathematics 2019-08-16 Christoph Schweigert , Lukas Woike

Fix a finite group G and an n-dimensional orthogonal G-representation V. We define the equivariant factorization homology of a V-framed smooth G-manifold with coefficients in an $E_V$-algebra using a two-sided bar construction, generalizing…

Algebraic Topology · Mathematics 2022-10-11 Foling Zou

A generalized Gaussian process model (GGPM) is a unifying framework that encompasses many existing Gaussian process (GP) models, such as GP regression, classification, and counting. In the GGPM framework, the observation likelihood of the…

Machine Learning · Statistics 2013-11-28 Lifeng Shang , Antoni B. Chan

We study the problem of discrete geometric packing. Here, given weighted regions (say in the plane) and points (with capacities), one has to pick a maximum weight subset of the regions such that no point is covered more than its capacity.…

Computational Geometry · Computer Science 2011-12-01 Alina Ene , Sariel Har-Peled , Benjamin Raichel

A line packing is optimal if its coherence is as small as possible. Most interesting examples of optimal line packings are achieving equality in some of the known lower bounds for coherence. In this paper two infinite families of real and…

Functional Analysis · Mathematics 2022-07-19 Ganzhinov Mikhail

Short-range entangled topological phases of matter are closely connected to Topological Quantum Field Theory. We use this connection to classify bosonic Symmetry Protected Topological Phases in low dimensions, including the case when the…

Strongly Correlated Electrons · Physics 2015-04-09 Anton Kapustin , Alex Turzillo

This thesis establishes a generalised setting with which to unify the study of finite local complexity (FLC) patterns. The abstract notion of a "pattern" is introduced, which may be seen as an analogue of the space group of isometries…

General Topology · Mathematics 2014-05-26 James J. Walton

We study the tensor-triangular geometry of the category of equivariant $G$-spectra for $G$ a profinite group, $\mathsf{Sp}_G$. Our starting point is the construction of a ``continuous'' model for this category, which we show agrees with all…

Algebraic Topology · Mathematics 2024-01-04 Scott Balchin , David Barnes , Tobias Barthel

Genuinely entangled subspaces (GESs) are the class of completely entangled subspaces that contain only genuinely multiparty entangled states. They constitute a particularly useful notion in the theory of entanglement but also have found an…

Quantum Physics · Physics 2020-06-11 Maciej Demianowicz , Remigiusz Augusiak

We prove rigidity results describing contextually-constrained maps defined on Grassmannians and manifolds of ordered independent line tuples in finite-dimensional vector or Hilbert spaces. One statement in the spirit of the Fundamental…

Functional Analysis · Mathematics 2026-01-21 Alexandru Chirvasitu

An equivariant topological field theory is defined on a cobordism category of manifolds with principal fiber bundles for a fixed (finite) structure group. We provide a geometric construction which for any given morphism $G \to H$ of finite…

Quantum Algebra · Mathematics 2018-10-22 Christoph Schweigert , Lukas Woike

We study equivariant embeddings with small boundary of a given homogeneous space $G/H$, where $G$ is a connected, linear algebraic group with trivial Picard group and only trivial characters, and $H \subset G$ is an extension of a connected…

Algebraic Geometry · Mathematics 2007-05-23 Ivan V. Arzhantsev , Juergen Hausen

Mixture modelling using elliptical distributions promises enhanced robustness, flexibility and stability over the widely employed Gaussian mixture model (GMM). However, existing studies based on the elliptical mixture model (EMM) are…

Machine Learning · Computer Science 2020-09-30 Shengxi Li , Zeyang Yu , Danilo Mandic

In a previous paper we outlined how discrete torsion can be understood geometrically as an analogue of orbifold U(1) Wilson lines. In this paper we shall prove the remaining details. More precisely, in this paper we describe gerbes in terms…

High Energy Physics - Theory · Physics 2007-05-23 Eric R. Sharpe

Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches either resort to large scale transformer-based models that…

Quantitative Methods · Quantitative Biology 2024-10-31 Majdi Hassan , Nikhil Shenoy , Jungyoon Lee , Hannes Stark , Stephan Thaler , Dominique Beaini

In this paper, we consider the problem of manifold approximation with affine subspaces. Our objective is to discover a set of low dimensional affine subspaces that represents manifold data accurately while preserving the manifold's…

Machine Learning · Computer Science 2015-09-08 Sofia Karygianni , Pascal Frossard
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