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Kitchloo and Morava give a strikingly simple picture of elliptic cohomology at the Tate curve by studying a completed version of $S^1$-equivariant $K$-theory for spaces. Several authors (cf [ABG],[KM],[L]) have suggested that an equivariant…

Algebraic Topology · Mathematics 2022-07-22 Kiran Luecke

An invariant is introduced for negative definite plumbed $3$-manifolds equipped with a spin$^c$-structure. It unifies and extends two theories with rather different origins and structures. One theory is lattice cohomology, motivated by the…

Geometric Topology · Mathematics 2023-03-09 Rostislav Akhmechet , Peter K. Johnson , Vyacheslav Krushkal

While using invariant and equivariant maps, it is possible to apply deep learning to a range of primitive data structures, a formalism for dealing with hierarchy is lacking. This is a significant issue because many practical structures are…

Machine Learning · Computer Science 2020-11-25 Renhao Wang , Marjan Albooyeh , Siamak Ravanbakhsh

We study sheaves on holomorphic spaces of loops and apply this to the study of the complex, defined in \cite{BdSHK}, governing deformations of the \emph{Poisson vertex algebra} structure on the space of holomorphic loops into a Poisson…

Algebraic Geometry · Mathematics 2020-08-20 Emile Bouaziz

We investigate the geometric and topological properties of the group of locally conformally symplectic (LCS) diffeomorphisms, utilizing the LCS flux homomorphism defined by S. Haller. By analyzing the flux map from the universal cover of…

Symplectic Geometry · Mathematics 2026-02-03 S. Tchuiaga , F. Balibuno

Actions of algebraic groups on DG categories provide a convenient, unifying framework in some parts of geometric representation theory, especially the representation theory of reductive Lie algebras. We extend this theory to loop groups and…

Representation Theory · Mathematics 2020-02-05 Sam Raskin

The topological classification of gerbes, as principal bundles with the structure group the projective unitary group of a complex Hilbert space, over a topological space $H$ is given by the third cohomology $\text{H}^3(H, \Bbb Z)$. When $H$…

Mathematical Physics · Physics 2025-12-24 Jouko Mickelsson , Stefan Wagner

To analyze the topological properties of the given discrete data, one needs to consider a continuous transform called filtration. Persistent homology serves as a tool to track changes of homology in the filtration. The outcome of the…

Optimization and Control · Mathematics 2024-10-08 Keunsu Kim , Jae-Hun Jung

Persistent homology (PH) studies the topology of data across multiple scales by building nested collections of topological spaces called filtrations, computing homology and returning an algebraic object that can be vizualised as a…

Algebraic Topology · Mathematics 2024-11-14 David Beers , Heather A Harrington , Jacob Leygonie , Uzu Lim , Louis Theran

In this paper we look at the notion of cohomological triviality of fibrations of homogeneous spaces of affine algebraic groups defined over $\mathbb{C}$ and use topological methods, primarily the theory of covering spaces. This is made…

Algebraic Geometry · Mathematics 2018-12-27 A. J. Parameswaran , Amith Shastri K

We describe polar homology groups for complex manifolds. The polar k-chains are subvarieties of complex dimension k with meromorphic forms on them, while the boundary operator is defined by taking the polar divisor and the Poincare residue…

Algebraic Geometry · Mathematics 2009-11-07 B. Khesin , A. Rosly

A central problem in data-driven scientific inquiry is how to interpret structure in noisy, high-dimensional data. Topological data analysis (TDA) provides a solution via persistent homology, which encodes features of interest as…

Algebraic Topology · Mathematics 2026-02-04 Christian Lentz , Gregory Henselman-Petrusek , Lori Ziegelmeier

We introduce a linear dimensionality reduction technique preserving topological features via persistent homology. The method is designed to find linear projection $L$ which preserves the persistent diagram of a point cloud $\mathbb{X}$ via…

Machine Learning · Statistics 2021-06-15 Byeongsu Yu , Kisung You

In the simplicial theory of hypercoverings, we replace the indexing category $\Delta$ by the \emph{symmetric simplicial category} $\Delta S$ and study (a class of) $\Delta S$-hypercoverings, which we call \emph{spaces admitting symmetric…

Algebraic Geometry · Mathematics 2023-08-14 Oishee Banerjee

We study how the partial group (co)homology of a group $G$ with coefficient in a partial representation $M$ can be described using the usual group (co)homology. To address this, we introduce the concept of the \textit{universal…

Algebraic Topology · Mathematics 2025-10-16 Emmanuel Jerez

In this paper we develop a new homology theory of associative algebras called semiinfinite cohomology in a derived category setting. We show that in the case of small quantum groups the zeroth semiinfinite cohomology of the trivial module…

q-alg · Mathematics 2009-10-30 Sergey Arkhipov

We construct absolute and relative versions of Hamiltonian Floer homology algebras for strongly semi-positive compact symplectic manifolds with convex boundary, where the ring structures are given by the appropriate versions of the…

Symplectic Geometry · Mathematics 2016-05-10 Sergei Lanzat

We study a natural Hodge theoretic generalization of rational (or $\mathbb{Q}$-)homology manifolds through an invariant ${\rm HRH(Z)}$ where $Z$ is a complex algebraic variety. The defining property of this notion encodes the difference…

Algebraic Geometry · Mathematics 2025-01-27 Bradley Dirks , Sebastian Olano , Debaditya Raychaudhury

A generalisation of the equivariant Dixmier-Douady invariant is constructed as a second-degree cohomology class within a new semi-equivariant \v{C}ech cohomology theory. This invariant obstructs liftings of semi-equivariant principal…

Algebraic Topology · Mathematics 2020-03-23 Simon Kitson

Machine learning for point clouds has been attracting much attention, with many applications in various fields, such as shape recognition and material science. For enhancing the accuracy of such machine learning methods, it is often…

Machine Learning · Computer Science 2023-12-29 Naoki Nishikawa , Yuichi Ike , Kenji Yamanishi
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