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The manifold hypothesis posits that high-dimensional data typically resides on low-dimensional sub spaces. In this paper, we assume manifold hypothesis to investigate graph-based semi-supervised learning methods. In particular, we examine…

Machine Learning · Computer Science 2025-11-18 Mary Chriselda Antony Oliver , Michael Roberts , Carola-Bibiane Schönlieb , Matthew Thorpe

In this paper, we present a method for denoising and reconstruction of low-dimensional manifold in high-dimensional space. We suggest a multidimensional extension of the Locally Optimal Projection algorithm which was introduced by Lipman et…

Numerical Analysis · Mathematics 2022-11-17 Shira Faigenbaum-Golovin , David Levin

Recent work shows that the expressive power of Graph Neural Networks (GNNs) in distinguishing non-isomorphic graphs is exactly the same as that of the Weisfeiler-Lehman (WL) graph test. In particular, they show that the WL test can be…

We consider a family of conforming space-time finite element discretizations for the wave equation based on splines of maximal regularity in time. Traditional techniques may require a CFL condition to guarantee stability. Recent works by O.…

Numerical Analysis · Mathematics 2024-10-25 Matteo Ferrari , Sara Fraschini

We propose a novel framework for the regularised inversion of deep neural networks. The framework is based on the authors' recent work on training feed-forward neural networks without the differentiation of activation functions. The…

Numerical Analysis · Mathematics 2023-03-06 Xiaoyu Wang , Martin Benning

We study Feynman integrals in the representation with Schwinger parameters and derive recursive integral formulas for massless 3- and 4-point functions. Properties of analytic (including dimensional) regularization are summarized and we…

Mathematical Physics · Physics 2018-07-09 Erik Panzer

We present an algorithm for determining the minimal order differential equations associated to a given Feynman integral in dimensional or analytic regularisation. The algorithm is an extension of the Griffiths-Dwork pole reduction adapted…

High Energy Physics - Theory · Physics 2024-06-21 Leonardo de la Cruz , Pierre Vanhove

The first self-stabilizing algorithm [Dij73] assumed the existence of a central daemon, that activates one processor at time to change state as a function of its own state and the state of a neighbor. Subsequent research has reconsidered…

Distributed, Parallel, and Cluster Computing · Computer Science 2007-05-23 Shlomi Dolev , Ted Herman

The coherent configuration $\mathsf{WL}(X)$ of a graph $X$ is the smallest coherent configuration on the vertices of $X$ that contains the edge set of $X$ as a relation. The aim of the paper is to study $\mathsf{WL}(X)$ when $X$ is a…

Combinatorics · Mathematics 2024-11-06 Jinzhuan Cai , Jin Guo , Alexander L. Gavrilyuk , Ilia Ponomarenko

The one-leg, two-step time-stepping scheme proposed by Dahlquist, Liniger and Nevanlinna has clear advantages in complex, stiff numerical simulations: unconditional $G$-stability for variable time-steps and second-order accuracy. Yet it has…

Numerical Analysis · Mathematics 2021-08-24 William Layton , Wenlong Pei , Catalin Trenchea

It was observed that hyperlogarithms provide a tool to carry out Feynman integrals. So far, this method has been applied successfully to finite single-scale processes. However, it can be employed in more general situations. We give examples…

High Energy Physics - Theory · Physics 2014-04-01 Erik Panzer

It is confirmed in this work that the graph isomorphism can be tested in polynomial time, which resolves a longstanding problem in the theory of computation. The contributions are in three phases as follows. 1. A description graph…

Computational Complexity · Computer Science 2023-01-25 Rui Xue

Deep neural networks have become invaluable tools for supervised machine learning, e.g., classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in…

Machine Learning · Computer Science 2019-02-19 Eldad Haber , Lars Ruthotto

The subject of deep learning has recently attracted users of machine learning from various disciplines, including: medical diagnosis and bioinformatics, financial market analysis and online advertisement, speech and handwriting recognition,…

Machine Learning · Computer Science 2018-03-12 Charles K. Chui , Shao-Bo Lin , Ding-Xuan Zhou

Topological self-stabilization describes the ability of a distributed system to let the nodes themselves establish a meaningful overlay network. Independent from the initial network topology, the system converges to the desired topology via…

Logic in Computer Science · Computer Science 2016-04-15 Christina Rickmann

We study graph complexes related to configuration spaces and diffeomorphism groups of highly connected manifolds of odd dimension. In particular we compute the cohomology in the "high genus" limit. This paper is a continuation of previous…

Quantum Algebra · Mathematics 2022-08-22 Simon Brun , Thomas Willwacher

We give an overview of recent advances on the graph isomorphism problem. Our main focus will be on Babai's quasi-polynomial time isomorphism test and subsequent developments that led to the design of isomorphism algorithms with a…

Data Structures and Algorithms · Computer Science 2021-01-15 Martin Grohe , Daniel Neuen

This is the second paper in a series studying the nonlinear stability of rarefaction waves in multi-dimensional gas dynamics. We construct initial data near singularities in the rarefaction wave region and, combined with the a priori energy…

Analysis of PDEs · Mathematics 2024-09-20 Tian-Wen Luo , Pin Yu

In 2011, Penttila and Williford constructed an infinite new family of primitive $Q$-polynomial 3-class association schemes, not arising from distance regular graphs, by exploring the geometry of the lines of the unitary polar space…

Combinatorics · Mathematics 2020-09-08 Giusy Monzillo , Alessandro Siciliano

We present and analyze two stabilized finite element methods for solving numerically the Poisson--Nernst--Planck equations. The stabilization we consider is carried out by using a shock detector and a discrete graph Laplacian operator for…

Numerical Analysis · Mathematics 2024-12-24 Jesús Bonilla , Juan Vicente Gutiérrez-Santacreu