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Coset methods are used to construct the action describing the dynamics associated with the spontaneous breaking of the local supersymmetries. The resulting action is an invariant form of the Einstein-Hilbert action, which in addition to the…

High Energy Physics - Theory · Physics 2008-11-26 T. E. Clark , S. T. Love , Muneto Nitta , T. ter Veldhuis

This contribution features an accelerated computation of the Sinkhorn's algorithm, which approximates the Wasserstein transportation distance, by employing nonequispaced fast Fourier transforms (NFFT). The algorithm proposed allows…

Optimization and Control · Mathematics 2023-03-07 Rajmadan Lakshmanan , Alois Pichler , Daniel Potts

We study the role of latent space dimensionality in Wasserstein auto-encoders (WAEs). Through experimentation on synthetic and real datasets, we argue that random encoders should be preferred over deterministic encoders. We highlight the…

Machine Learning · Statistics 2018-02-13 Paul K. Rubenstein , Bernhard Schoelkopf , Ilya Tolstikhin

An extended two-hadron operator is developed to extract the spectra of irreducible representations (irreps) in the finite volume. The irreps of the group for the finite volume system are projected using a coordinate-space operator. The…

High Energy Physics - Lattice · Physics 2022-05-04 Jia-Jun Wu , Waseem Kamleh , Derek B. Leinweber , Yan Li , Gerrit Schierholz , Ross D. Young , James M. Zanotti

We derive some new presentations for the Yangian associated to the Lie algebra gl_n(C) that are adapted to parabolic subalgebras. At one extreme, the presentation is just the usual RTT presentation, whilst at the other extreme it is a…

Quantum Algebra · Mathematics 2009-11-10 Jonathan Brundan , Alexander Kleshchev

The Wasserstein distance has become increasingly important in machine learning and deep learning. Despite its popularity, the Wasserstein distance is hard to approximate because of the curse of dimensionality. A recently proposed approach…

Machine Learning · Computer Science 2021-09-29 Minhui Huang , Shiqian Ma , Lifeng Lai

Domain generalization aims at learning a universal model that performs well on unseen target domains, incorporating knowledge from multiple source domains. In this research, we consider the scenario where different domain shifts occur among…

Machine Learning · Computer Science 2024-03-26 Jingge Wang , Liyan Xie , Yao Xie , Shao-Lun Huang , Yang Li

Data unfolding -- the removal of noise or artifacts from measurements -- is a fundamental task across the experimental sciences. Of particular interest are applications in physics, where the dominant approach is Richardson-Lucy (RL)…

Optimization and Control · Mathematics 2026-04-24 Katy Craig , Benjamin Faktor , Benjamin Nachman

We propose a balanced coarsening scheme for multilevel hypergraph partitioning. In addition, an initial partitioning algorithm is designed to improve the quality of k-way hypergraph partitioning. By assigning vertex weights through the LPT…

Machine Learning · Computer Science 2023-07-14 Zhicheng Guo , Jiaxuan Zhao , Licheng Jiao , Xu Liu

This paper makes two main contributions. First, we present a pedagogical review of the derivation of the three-term recurrence relation for Legendre polynomials, without relying on the classical Legendre differential equation, Rodrigues'…

Computational Engineering, Finance, and Science · Computer Science 2025-07-15 Michal Béreš

As a mathematical model of high-speed flow and shock wave propagation in a complex multimaterial setting, Lagrangian hydrodynamics is characterized by moving meshes, advection-dominated solutions, and moving shock fronts with sharp…

Numerical Analysis · Mathematics 2021-11-24 Dylan Matthew Copeland , Siu Wun Cheung , Kevin Huynh , Youngsoo Choi

This work presents a reformulation of the recently proposed Wasserstein autoencoder framework on a non-Euclidean manifold, the Poincar\'e ball model of the hyperbolic space. By assuming the latent space to be hyperbolic, we can use its…

Machine Learning · Computer Science 2020-03-18 Ivan Ovinnikov

We develop a new method for bounding the relative entropy of a random vector in terms of its Stein factors. Our approach is based on a novel representation for the score function of smoothly perturbed random variables, as well as on the de…

Probability · Mathematics 2013-08-20 Ivan Nourdin , Giovanni Peccati , Yvik Swan

We propose an explanation via string theory of the correspondence between the Coulomb branch of certain three-dimensional supersymmetric gauge theories and certain moduli spaces of magnetic monopoles. The same construction also gives an…

High Energy Physics - Theory · Physics 2010-04-07 Amihay Hanany , Edward Witten

We consider an inverse problem of reconstructing the conductivity function in a hyperbolic equation using single space-time domain noisy observations of the solution on the backscattering boundary of the computational domain. We formulate…

Numerical Analysis · Mathematics 2017-12-21 Larisa Beilina , Kati Niinimäki

A new local world volume supersymmetric Lagrangian for the bosonic membrane is presented. The starting Lagrangian is the one constructed by Dolan and Tchrakian with vanishing cosmological constant, with quadratic and quartic derivative…

High Energy Physics - Theory · Physics 2007-05-23 Carlos Castro

The Legendre transform expresses dynamics of a classical system through first-order Hamiltonian equations. We consider coherent state transforms with a similar effect in quantum mechanics: they reduce certain quantum Hamiltonians to…

Mathematical Physics · Physics 2021-06-04 Fadhel Almalki , Vladimir V. Kisil

This article provides a quick guide to the implementation of supersymmetry with Cadabra, a symbolic computer algebra system. Details are provided on the implementation of Grassmann variables, fermionic fields, supercharges, and…

High Energy Physics - Theory · Physics 2021-12-20 Behzad Mansouri

We introduce and study strongly invertible Legendrian links in the standard contact three-dimensional space. We establish the equivariant analogs of basic results separately well-known for strongly invertible and Legendrian links, i.e. the…

Geometric Topology · Mathematics 2023-11-15 Carlo Collari , Paolo Lisca

This study aims to better understand the functional geometry of the motor cortex, starting from different sources of experimental evidence. Recent studies have proved that cells of the primary motor cortex (M1) are sensitive to short hand…

Neurons and Cognition · Quantitative Biology 2026-03-24 Jawad Ali , Giovanna Citti , Alessandro Sarti