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We study numerically the coarsening kinetics of a two-dimensional ferromagnetic system with aleatory bond dilution. We show that interfaces between domains of opposite magnetisation are fractal on every lengthscale, but with different…

Statistical Mechanics · Physics 2019-05-30 Federico Corberi , Leticia F. Cugliandolo , Ferdinando Insalata , Marco Picco

We introduce a generalization of the Stirling numbers via symmetric functions involving two weight functions. The resulting extension unifies previously known Stirling-type sequences with known symmetric function forms, as well as other…

In recent years several attempts have been made to extend tail modelling towards the modal part of the data. Frigessi et al. (2002) introduced dynamic mixtures of two components with a weight function {\pi} = {\pi}(x) smoothly connecting…

Methodology · Statistics 2018-10-03 Jan Beirlant , Gaonyalelwe Maribe , Philippe Naveau , Andrehette Verster

A new class of planar tessellations, named T-tessellations, was introduced in ([10]). A model was proposed to be considered as a completely random T-tessellation model (CRTT) and its Gibbsian variants were discussed. A general simulation…

Applications · Statistics 2020-08-03 Katarzyna Adamczyk-Chauvat , Mouna Kassa , Kiên Kiêu , Julien Papaïx , Radu S. Stoica

In previous work we established a multilinear duality and factorisation theory for norm inequalities for pointwise weighted geometric means of positive linear operators defined on normed lattices. In this paper we extend the reach of the…

Functional Analysis · Mathematics 2023-05-10 Anthony Carbery , Timo S. Hänninen , Stefán Ingi Valdimarsson

In quantum mechanics, each conserved quantity (e.g., energy, position, linear momentum and angular momentum) is associated with a Hermitian operator. Its expected value can then be determined by performing a measurement on the wavefunction.…

In this paper, we present the results of Monte Carlo simulations for two popular techniques of long-range correlations detection - classical and modified rescaled range analyses. A focus is put on an effect of different distributional…

Statistical Finance · Quantitative Finance 2012-05-24 Ladislav Kristoufek

Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding electronic structure calculations. The accuracy and…

Materials Science · Physics 2018-01-24 Andrea Grisafi , David M. Wilkins , Gábor Csányi , Michele Ceriotti

A recent article on generalised linear mixed model asymptotics, Jiang et al. (2022), derived the rates of convergence for the asymptotic variances of maximum likelihood estimators. If $m$ denotes the number of groups and $n$ is the average…

Statistics Theory · Mathematics 2023-04-03 Luca Maestrini , Aishwarya Bhaskaran , Matt P. Wand

A new and rather broad class of stationary (i.e. stochastically translation invariant) random tessellations of the $d$-dimensional Euclidean space is introduced, which are called shape-driven nested Markov tessellations. Locally, these…

Probability · Mathematics 2013-09-16 Tomasz Schreiber , Christoph Thaele

We show that by using intuitive and accessible molecular features it is possible to predict the temperature-dependent second virial coefficient of organic and inorganic compounds using Gaussian process regression. In particular, we built a…

Chemical Physics · Physics 2021-02-24 Miruna T. Cretu , Jesús Pérez-Ríos

A discrete Gelfand-Tsetlin pattern is a configuration of particles in Z^2. The particles are arranged in a finite number of consecutive rows, numbered from the bottom. There is one particle on the first row, two particles on the second row,…

Probability · Mathematics 2015-07-24 Erik Duse , Anthony Metcalfe

The existence of primordial magnetic fields can induce matter perturbations with additional power at small scales as compared to the usual $\Lambda$CDM model. We study its implication within the context of two-point shear correlation…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-03 Kanhaiya L. Pandey , Shiv K. Sethi

Tensor decomposition methods are popular tools for learning latent variables given only lower-order moments of the data. However, the standard assumption is that we have sufficient data to estimate these moments to high accuracy. In this…

Machine Learning · Statistics 2019-03-13 Omer Gottesman , Weiwei Pan , Finale Doshi-Velez

A scheme suggested in the literature to determine the symmetry-imposed shape of linear response tensors is revised and extended to allow for the treatment of more complex situations. The extended scheme is applied to discuss the shape of…

Materials Science · Physics 2015-10-28 Marten Seemann , Diemo Ködderitzsch , Sebastian Wimmer , Hubert Ebert

In this paper, the primary goal is to offer additional insights into the value iteration through the lens of switching system models in the control community. These models establish a connection between value iteration and switching system…

Optimization and Control · Mathematics 2025-04-04 Donghwan Lee

This paper continues earlier work and is concerned with the inverse problem of parameter identification in variational inequalities of the second kind that does not only treat the parameter linked to a bilinear form, but importantly also…

Optimization and Control · Mathematics 2021-01-01 Joachim Gwinner

Gravitational self-force theory is the primary way of modelling extreme-mass-ratio inspirals (EMRIs). One difficulty that appears in second-order self-force calculations is the strong divergence at the worldline of the small object, which…

General Relativity and Quantum Cosmology · Physics 2024-02-15 Samuel D. Upton

These notes survey the theory of (twisted) conformal blocks from an algebro-geometric perspective and have two main goals. The first one is to summarize the construction of conformal blocks from vertex operator algebras, and to describe…

Algebraic Geometry · Mathematics 2026-04-02 Chiara Damiolini

We define a multi-scale metric $d_\rho$ on strings by aggregating angle distances between all $n$-gram count vectors with exponential weights $\rho^n$. We benchmark $d_\rho$ in DBSCAN clustering against edit and $n$-gram baselines, give a…

Metric Geometry · Mathematics 2026-04-23 Grant Molnar
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