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For any high-dimensional Weinstein domain and finite collection of primes, we construct a Weinstein subdomain whose wrapped Fukaya category is a localization of the original wrapped Fukaya category away from the given primes. When the…

Symplectic Geometry · Mathematics 2023-05-24 Oleg Lazarev , Zachary Sylvan

In this article we study Weinstein structures endowed with a Lefschetz fibration in terms of the Legendrian front projection. First we provide a systematic recipe for translating from a Weinstein Lefschetz bifibration to a Legendrian…

Symplectic Geometry · Mathematics 2019-03-20 Roger Casals , Emmy Murphy

Let $X$ be a Weinstein manifold with ideal contact boundary $Y$. If $\Lambda\subset Y$ is a link of Legendrian spheres in $Y$ then by attaching Weinstein handles to $X$ along $\Lambda$ we get a Weinstein cobordism $X_{\Lambda}$ with a…

Symplectic Geometry · Mathematics 2019-06-19 Tobias Ekholm

This paper introduces Wasserstein variational inference, a new form of approximate Bayesian inference based on optimal transport theory. Wasserstein variational inference uses a new family of divergences that includes both f-divergences and…

Given a chord-generic horizontally displaceable Legendrian submanifold $\Lambda\subset P\times \mathbb R$ with the property that its characteristic algebra admits a finite-dimensional matrix representation, we prove an Arnold-type lower…

Symplectic Geometry · Mathematics 2016-03-10 Georgios Dimitroglou Rizell , Roman Golovko

We propose a projected Wasserstein gradient descent method (pWGD) for high-dimensional Bayesian inference problems. The underlying density function of a particle system of WGD is approximated by kernel density estimation (KDE), which faces…

Machine Learning · Computer Science 2021-02-16 Yifei Wang , Peng Chen , Wuchen Li

A new purely algebraic algorithm is presented for computation of invariants (generalized Casimir operators) of Lie algebras. It uses the Cartan's method of moving frames and the knowledge of the group of inner automorphisms of each Lie…

Mathematical Physics · Physics 2010-02-23 Vyacheslav Boyko , Jiri Patera , Roman Popovych

Recent proposal of Wasserstein Index Generation model (WIG) has shown a new direction for automatically generating indices. However, it is challenging in practice to fit large datasets for two reasons. First, the Sinkhorn distance is…

Machine Learning · Computer Science 2021-09-21 Fangzhou Xie

The problem of estimating the probability distribution of labels has been widely studied as a label distribution learning (LDL) problem, whose applications include age estimation, emotion analysis, and semantic segmentation. We propose a…

Machine Learning · Computer Science 2021-03-02 Ayato Toyokuni , Sho Yokoi , Hisashi Kashima , Makoto Yamada

Adversarial examples are crafted by adding indistinguishable perturbations to normal examples in order to fool a well-trained deep learning model to misclassify. In the context of computer vision, this notion of indistinguishability is…

Machine Learning · Computer Science 2023-03-23 Wenjie Wang , Li Xiong , Jian Lou

We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves…

Machine Learning · Statistics 2017-12-08 Martin Arjovsky , Soumith Chintala , Léon Bottou

It has been shown beneficial for many types of data which present an underlying hierarchical structure to be embedded in hyperbolic spaces. Consequently, many tools of machine learning were extended to such spaces, but only few…

Machine Learning · Computer Science 2023-06-27 Clément Bonet , Laetitia Chapel , Lucas Drumetz , Nicolas Courty

Helmut Hofer introduced in '93 a novel technique based on holomorphic curves to prove the Weinstein conjecture. Among the cases where these methods apply are all contact 3--manifolds $(M,\xi)$ with $\pi_2(M) \ne 0$. We modify Hofer's…

Dynamical Systems · Mathematics 2012-02-01 Klaus Niederkrüger , Ana Rechtman

The goal of this note is to describe some constructions of Weinstein manifolds with chaotic Reeb dynamics, and to explain how this property can sometimes be detected directly from the skeleton.

Symplectic Geometry · Mathematics 2021-11-23 Laurent Côté

Hierarchies of Lagrangians of degree two, each only partly determined by the choice of leading terms and with some coefficients remaining free, are considered. The free coefficients they contain satisfy the most general differential…

Classical Analysis and ODEs · Mathematics 2022-05-03 Ranses Alfonso-Rodriguez , S. Roy Choudhury

This is an overview paper that describes Eliashberg's Legendrian surgery approach to wrapped Floer cohomology and use it to derive the basic relations between various holomorphic curve theories with additional algebraic constructions. We…

Symplectic Geometry · Mathematics 2024-11-20 Tobias Ekholm

We construct a new surgery type operation by switching between two exact fillings of Legendrians which we call a BSP surgery. In certain cases, this surgery can preserve monotonicity of Lagrangians. We prove a wall-crossing type formula for…

Symplectic Geometry · Mathematics 2026-05-13 Soham Chanda

The Chekanov-Eliashberg dg-algebra is an algebraic invariant of Legendrian submanifolds of contact manifolds, whose definition recently has been extended to singular Legendrians. We describe a way of constructing simpler models of this…

Symplectic Geometry · Mathematics 2023-11-30 Martin Bäcke

A prescription for calculating low-energy one-loop higher-mass dimensional effective Lagrangians for non-Abelian field theories is constructed in the spirit of quasilocal background field method. Basis of Lorentz and gauge-invariant…

High Energy Physics - Theory · Physics 2007-05-23 Joseph Ambrose G. Pagaran

This paper considers the problem of regression over distributions, which is becoming increasingly important in machine learning. Existing approaches often ignore the geometry of the probability space or are computationally expensive. To…

Machine Learning · Computer Science 2025-10-31 Maksim Maslov , Alexander Kugaevskikh , Matthew Ivanov