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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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69138207276 · Jun 202019922001200920172026
48 results for equivariant theory

Unified classification of equivariant principal bundles using higher homotopy theory.

problem Unified classification of equivariant principal bundles.
method Smooth Oka principle, singular-cohesive homotopy theory, internally describing principal bundles.
result Unified classification results for equivariant principal bundles.

Following Hopkins and Singer, we give a definition for the differential equivariant K-theory of a smooth manifold acted upon by a finite group. The ring structure for differential equivariant K-theory is developed explicitly. We also construct a pushforward map which parallels the topological pushforward in equivariant…

2009-05-04abs ↗pdf ↗

We construct for an equivariant cohomology theory for proper equivariant CW-complexes an equivariant Chern character, provided that certain conditions about the coefficients are satisfied. These conditions are fulfilled if the coefficients of the equivariant cohomology theory possess a Mackey structure. Such a structur…

2004-01-06abs ↗pdf ↗

We compute the equivariant complex K-theory ring of a cohomogeneity-one action of a compact Lie group at the level of generators and relations and derive a characterization of K-theoretic equivariant formality for these actions. Less explicit expressions survive for a range of equivariant cohomology theories including …

2018-05-01abs ↗pdf ↗

Lin and Sjamaar have used symplectic Hodge theory to obtain canonical equivariant extensions for Hamiltonian actions on closed symplectic manifolds that have the strong Lefschetz property. Here we obtain canonical equivariant extensions much more generally by means of classical Hodge theory.

2004-06-24abs ↗pdf ↗

Equivariant neural networks improve performance and generalization in complex scalar field theory tasks.

problem Improving performance and generalization in neural networks for complex scalar field theory tasks.
method Incorporating translational equivariance into neural network architectures.
result Equivariant neural networks significantly outperform non-equivariant networks in various tasks, including those beyond the training set and across different lattice sizes.

In this paper we introduce exotic twisted T\mathbb T-equivariant K-theory of loop space LZLZ depending on the (typically non-flat) holonomy line bundle LB{\mathcal L}^B on LZLZ induced from a gerbe with connection BB on ZZ. We also define exotic twisted T\mathbb T-equivariant Chern character that maps the exotic tw…

2017-12-18abs ↗pdf ↗

Equivalence proven between equivariant K-theory and K-homology for certain matrix group actions.

problem Establishing Poincaré duality for proper cocompact matrix group actions.
method Using equivariant K-theory and K-homology, with geometric models of Baum and Douglas.
result Poincaré duality holds between equivariant K-theory and K-homology for GG-spinc^c manifolds with compact quotient.

In this paper, for a compact Lie group action,we prove the anomaly formula and the functoriality of the equivariant Bismut-Cheeger eta forms with perturbation operators when the equivariant family index vanishes. In order to prove them, we extend the Melrose-Piazza spectral section and its main properties to the equiva…

2016-10-07abs ↗pdf ↗

Equivariant neural networks use symmetry to interpret complex data.

problem Interpreting and understanding the behavior of equivariant neural networks.
method Decompose layers into simple representations and analyze nonlinear activation functions.
result Equivariant neural networks can be interpreted using a filtration generalizing Fourier series.

Constructs 2-vector bundles and 2K-theory for Lie groupoids and 2-equivariant settings.

problem Developing a theory of 2-vector bundles and 2K-theory for Lie groupoids and their equivariant versions.
method Defines 2-vector bundles over Lie groupoids, constructs 2K-theory as Grothendieck completion, and proves classification theorems.
result Establishes an equivalence between homotopy categories of 2-vector bundles and simplicial maps, and computes 2-equivariant 2K-theories for specific Lie groups.

The paper develops methods for calculating equivariant homology from Morse functions.

problem Calculating equivariant homology from equivariant Morse functions.
method Alter equivariant Morse functions to stable ones, use generic equivariant metrics, and analyze the Morse spectral sequence.
result Equivariant Morse functions induce a filtration that computes equivariant homology.

Equivariant neural networks improve performance and generalization in lattice field theory tasks.

problem Improving neural network performance and generalization in lattice field theory.
method Investigation of translationally equivariant neural networks in a two-dimensional scalar field model.
result Equivariant neural networks significantly outperform non-equivariant ones in various tasks, including physical parameters and lattice sizes.

Non-Abelian actions are resolved using equivariant K-theory and delocalized cohomology.

problem Resolving non-Abelian actions on manifolds.
method Using equivariant K-theory and delocalized cohomology, the structure of the quotient space is described.
result A new model for non-Abelian equivariant K-theory and cohomology is developed.

L-CNNs maintain gauge symmetry on non-Abelian lattice theories.

problem Applying convolutional neural networks to non-Abelian lattice gauge theories while preserving gauge symmetry.
method Developed a geometric formulation of L-CNNs that are equivariant under global symmetries and gauge transformations.
result Convolutional operations in L-CNNs are a specific case of gauge-equivariant neural networks on SU(NN) principal bundles.

Program connects birational invariants with G-equivariant ones using Gromov-Witten theory.

problem Establishing a connection between birational invariants and G-equivariant ones.
method Gromov-Witten theory, Chen-Ruan cohomology, and equivariant atoms.
result New interpretations and applications of classical invariants.

We construct a new equivariant cohomology theory for a certain class of differential vertex algebras, which we call the chiral equivariant cohomology. A principal example of a differential vertex algebra in this class is the chiral de Rham complex of Malikov-Schechtman-Vaintrob of a manifold with a group action. The ma…

2005-01-06abs ↗pdf ↗

The study proves a generic multiplicity one theorem for GG-invariant minimal hypersurfaces.

problem Proving a generic multiplicity one theorem for GG-invariant minimal hypersurfaces.
method Equivariant min-max theory and analysis of GG-homology classes.
result Shows a generic multiplicity one theorem for GG-invariant minimal hypersurfaces.

New invariants for 3-manifolds derived from equivariant Cerf theory.

problem Existence of perturbative SU(n)SU(n) Casson invariants on integer homology spheres.
method Equivariant Cerf theory for Morse functions, adapted to infinite-dimensional setting.
result Existence and explicit formula for SU(4)SU(4) Casson invariants.

We investigate an equivariant generalization of Morse theory for a general class of integrable models. In particular, we derive equivariant versions of the classical Poincaré-Hopf and Gauss-Bonnet-Chern theorems and present the corresponding path integral generalizations. Our approach is based on equivariant cohomology…

1994-06-13abs ↗pdf ↗

We compute the equivariant KK-theory KG(G)K_G^*(G) for a simply connected Lie group GG (acting on itself by conjugation). We prove that KG(G)K_G^*(G) is isomorphic to the algebra of Grothendieck differentials on the representation ring. We also study a special example of a non-simply connected Lie group GG, namely PSU(3),…

1997-10-30abs ↗pdf ↗

We present a general theory of Group equivariant Convolutional Neural Networks (G-CNNs) on homogeneous spaces such as Euclidean space and the sphere. Feature maps in these networks represent fields on a homogeneous base space, and layers are equivariant maps between spaces of fields. The theory enables a systematic cla…

2018-11-05abs ↗pdf ↗

Category theory enhances understanding of group-equivariant neural networks.

problem Understanding and working with group-equivariant neural networks.
method Application of category theory to tensor power spaces of Rn\mathbb{R}^{n} for groups SnS_n, O(n)O(n), Sp(n)Sp(n), and SO(n)SO(n).
result New insights and an algorithm for computing equivariant linear layers.

Universal MLPs with a single hidden layer can learn any function.

problem Learning on various data structures like sequences, images, sets, and graphs.
method Using group theory, the paper proves the universality of a broad class of equivariant MLPs with a single hidden layer.
result Having a hidden layer on which the group acts regularly is sufficient for universal equivariance (invariance).

The purpose of this paper is to construct and study equivariant Khovanov homology - a version of Khovanov homology theory for periodic links. Since our construction works regardless of the characteristic of the coefficient ring it generalizes a previous construction by Chbili. We establish invariance under equivariant …

2015-04-01abs ↗pdf ↗

The objective of this paper is to clarify the relationships between the quantum D-module and equivariant Floer theory. Equivariant Floer theory was introduced by Givental in his paper ``Homological Geometry''. He conjectured that the quantum D-module of a symplectic manifold is isomorphic to the equivariant Floer cohom…

2004-10-22abs ↗pdf ↗

Goresky, Kottwitz and MacPherson have recently shown that the computation of the equivariant cohomology ring of a G-manifold can be reduced to a computation in graph theory. This opens up the possibility that many of the fundamental theorems in equivariant de Rham theory may, on closer inspection, turn out simply to be…

1998-08-31abs ↗pdf ↗

There are fundamental open problems in the precise global nature of RR-field tadpole cancellation conditions in string theory. Moreover, the non-perturbative lift as M5/MO5-anomaly cancellation in M-theory had been based on indirect plausibility arguments,lacking a microscopic underpinning in M-brane charge quantizatio…

2019-09-26abs ↗pdf ↗

L-CNNs preserve gauge symmetry in neural networks.

problem Applying machine learning to lattice gauge theory while preserving gauge symmetry.
method L-CNNs use gauge equivariance to construct a gauge equivariant convolutional layer and bilinear layer.
result L-CNNs achieve higher accuracy in non-linear regression tasks compared to non-equivariant CNNs.

Generalizes Molino's theory for Riemannian foliations.

problem Studying Riemannian foliations and their properties.
method Generalization of Molino's theory with discussion of projections and equivariant basic Â-genus characters.
result Equivariant basic cohomological isomorphism for Killing foliation.