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Related papers: Redundancy Channels in the Conformal Bootstrap

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Singular Spectrum Analysis and many other subspace-based methods of signal processing are implicitly relying on the assumption of close proximity of unperturbed and perturbed signal subspaces extracted by the Singular Value Decomposition of…

Numerical Analysis · Mathematics 2010-06-16 Vladimir Nekrutkin

We study the convergence of the derivative expansion for flow equations. The convergence strongly depends on the choice for the infrared regularisation. Based on the structure of the flow, we explain why optimised regulators lead to better…

High Energy Physics - Theory · Physics 2009-11-07 Daniel F. Litim

Learning a latent embedding to understand the underlying nature of data distribution is often formulated in Euclidean spaces with zero curvature. However, the success of the geometry constraints, posed in the embedding space, indicates that…

Computer Vision and Pattern Recognition · Computer Science 2022-08-03 Jie Hong , Pengfei Fang , Weihao Li , Junlin Han , Lars Petersson , Mehrtash Harandi

The variation of spectral subspaces for linear self-adjoint operators under an additive bounded semidefinite perturbation is considered. A variant of the Davis-Kahan $ \sin2\Theta $ theorem from [SIAM J. Numer. Anal. 7 (1970), 1--46]…

Spectral Theory · Mathematics 2019-10-24 Albrecht Seelmann

In recent years, the traditional notion of symmetry in quantum theory was expanded to so-called generalised or categorical symmetries, which, unlike ordinary group symmetries, may be non-invertible. This appears to be at odds with Wigner's…

Quantum Physics · Physics 2026-02-18 Thomas Bartsch , Yuhan Gai , Sakura Schafer-Nameki

Optimization in engineering requires appropriate models. In this article, a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity…

A quantum-mechanical system comes naturally equipped with a convex space: each (Hermitian) operator has a (real) expectation value, and the expectation value of the square any Hermitian operator must be non-negative. This space is of…

High Energy Physics - Lattice · Physics 2025-02-05 Scott Lawrence

This paper introduces the multiplicative variant of the recently proposed asynchronous additive coarse-space correction method. Definition of an asynchronous extension of multiplicative correction is not straightforward, however, our…

Numerical Analysis · Mathematics 2023-12-20 Guillaume Gbikpi-Benissan , Frédéric Magoulès

For minimum-error channel discrimination tasks that involve only unitary channels, we show that sequential strategies may outperform the parallel ones. Additionally, we show that general strategies that involve indefinite causal order are…

Quantum Physics · Physics 2022-05-16 Jessica Bavaresco , Mio Murao , Marco Túlio Quintino

We study the conformal bootstrap for systems of correlators involving non-identical operators. The constraints of crossing symmetry and unitarity for such mixed correlators can be phrased in the language of semidefinite programming. We…

High Energy Physics - Theory · Physics 2014-12-05 Filip Kos , David Poland , David Simmons-Duffin

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks over the years. However, this comes at the cost of heavy computation and memory intensive network designs, suggesting potential…

Computer Vision and Pattern Recognition · Computer Science 2020-08-11 Kumara Kahatapitiya , Ranga Rodrigo

In order to gain a better understanding of the state space of programs, with the aim of making their verification more tractable, models based on directed topological spaces have been introduced, allowing to take in account equivalence…

Logic in Computer Science · Computer Science 2021-12-30 Samuel Mimram , Aly-Bora Ulusoy

This manuscript presents a novel framework that integrates higher-order symmetries and category theory into machine learning. We introduce new mathematical constructs, including hyper-symmetry categories and functorial representations, to…

Machine Learning · Computer Science 2024-09-19 Ronald Katende

The S-matrix bootstrap is extended to a 1+1d theory with $O(N)$ symmetry and a boundary in what we call the R-matrix bootstrap since the quantity of interest is the reflection matrix (R-matrix). Given a bulk S-matrix, the space of allowed…

High Energy Physics - Theory · Physics 2021-04-28 Martin Kruczenski , Harish Murali

Parametric model order reduction by matrix interpolation allows for efficient prediction of the behavior of dynamic systems without requiring knowledge about the underlying parametric dependency. Within this approach, reduced models are…

Dynamical Systems · Mathematics 2025-06-03 Sebastian Resch-Schopper , Romain Rumpler , Gerhard Müller

The construction of computer models (mathematical models implemented in computer codes), with respect to observed phenomena, is usually undertaken by building different variants depending on modeller sensibility, and choosing the one…

Methodology · Statistics 2018-03-28 Filippo Monari

Classical spectral theory provides powerful tools for analyzing linear operators, but does not extend naturally to nonlinear or compositional settings. In particular, there is no general way to transport spectral invariants in a functorial…

Category Theory · Mathematics 2026-05-05 Shih-Yu Chang

The essence of the potential algebra concept [3] is that quantum mechanical free motions of scalar particles on curved surfaces of given isometry algebras can be mapped on 1D Schroedinger equations with particular potentials. As long as the…

Nuclear Theory · Physics 2015-06-05 Mariana Kirchbach

In this paper, we are concerned with the reliability assessment of redundant multi-channel systems having multiple controllers with overlapping functionality -- where all controllers are required to respond optimally to the non-faulty…

Dynamical Systems · Mathematics 2015-03-27 Getachew K. Befekadu , Panos J. Antsaklis

Designing neural network architectures is a challenging task and knowing which specific layers of a model must be adapted to improve the performance is almost a mystery. In this paper, we introduce a novel theory and metric to identify…

Machine Learning · Computer Science 2020-11-06 David Peer , Sebastian Stabinger , Antonio Rodriguez-Sanchez
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