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We demonstrate that higher-order electric susceptibilities in crystals can be enhanced and understood through nontrivial topological invariants and quantum geometry, using one-dimensional $\pi$-conjugated chains as representative model…

Mesoscale and Nanoscale Physics · Physics 2025-09-18 Wojciech J. Jankowski , Robert-Jan Slager , Michele Pizzochero

Band bending is a central concept in solid-state physics that arises from local variations in charge distribution especially near semiconductor interfaces and surfaces. Its precision measurement is vital in a variety of contexts from the…

A domain decomposition method is proposed based on carefully chosen impedance transmission operators for a hybrid formulation of the eddy current problem. Preliminary analysis and numerical results are provided in the spherical case showing…

Numerical Analysis · Mathematics 2016-02-02 Y. Boubendir , H. Haddar , M. K. Riahi

In 2010, Bezuglyi, Kwiatkowski, Medynets and Solomyak [Ergodic Theory Dynam. Systems 30 (2010), no.4, 973-1007] found a complete description of the set of probability ergodic tail invariant measures on the path space of a standard…

Dynamical Systems · Mathematics 2024-02-28 Sergey Bezuglyi , Olena Karpel , Jan Kwiatkowski

The problem of estimating the eddy diffusivity from Lagrangian observations in the presence of measurement error is studied in this paper. We consider a class of incompressible velocity fields for which is can be rigorously proved that the…

Mathematical Physics · Physics 2009-05-01 C. J. Cotter , G. A. Pavliotis

We study the scaling behavior of the entanglement entropy of two dimensional conformal quantum critical systems, i.e. systems with scale invariant wave functions. They include two-dimensional generalized quantum dimer models on bipartite…

Statistical Mechanics · Physics 2009-03-28 Benjamin Hsu , Michael Mulligan , Eduardo Fradkin , Eun-Ah Kim

We analyse a diffusion process whose invariant measure is the fractional polymer or Edwards measure for fractional Brownian motion in dimension $d\in\mathbb{N}$ with Hurst parameter $H\in(0,1)$ fulfilling $dH < 1$. We make use of a…

Probability · Mathematics 2017-03-31 Wolfgang Bock , Torben Fattler

In Electrical Impedance Tomography (EIT) one wants to image the conductivity distribution of a body from current and voltage measurements carried out on its boundary. In this paper we consider the underlying mathematical model, the inverse…

Numerical Analysis · Mathematics 2017-04-10 Andreas Hauptmann , Matteo Santacesaria , Samuli Siltanen

Hierarchical inference in (generalized) regression problems is powerful for finding significant groups or even single covariates, especially in high-dimensional settings where identifiability of the entire regression parameter vector may be…

Methodology · Statistics 2021-10-22 Claude Renaux , Peter Bühlmann

This paper rigorously solves the challenging problem of recognizing periodic patterns under rigid motion in Euclidean geometry. The 3-dimensional case is practically important for justifying the novelty of solid crystalline materials…

Metric Geometry · Mathematics 2025-10-30 Olga Anosova , Daniel Widdowson , Vitaliy Kurlin

Let $X$ be an algebraic variety equipped with a dominant rational self-map $\phi:X\to X$. A new quantity measuring the interaction of $(X,\phi)$ with trivial dynamical systems is introduced; the stabilised algebraic dimension of $(X,\phi)$…

Algebraic Geometry · Mathematics 2024-03-13 Jason Bell , Rahim Moosa , Matthew Satriano

Classical Density Functional Theory (DFT) is a statistical-mechanical framework to analyze fluids, which accounts for nanoscale fluid inhomogeneities and non-local intermolecular interactions. DFT can be applied to a wide range of…

Computational Engineering, Finance, and Science · Computer Science 2017-02-07 Andreas Nold , Benjamin D. Goddard , Peter Yatsyshin , Nikos Savva , Serafim Kalliadasis

Kendall transformation is a conversion of an ordered feature into a vector of pairwise order relations between individual values. This way, it preserves ranking of observations and represents it in a categorical form. Such transformation…

Machine Learning · Computer Science 2023-08-15 Miron Bartosz Kursa

An artificial intelligence (AI) model can be viewed as a function that maps inputs to outputs in high-dimensional spaces. Once designed and well trained, the AI model is applied for inference. However, even optimized AI models can produce…

Artificial Intelligence · Computer Science 2026-02-27 Sha Hu

X-ray Bragg coherent diffraction imaging has been demonstrated as a powerful three-dimensional (3D) microscopy approach for the investigation of sub-micrometer-scale crystalline particles. It is based on the measurement of a series of…

In [1], we have presented the theoretical background for finding the Elementary Invariants for a 3D system of first order rational differential equations (1ODEs). We have also provided an algorithm to find such Invariants. Here we introduce…

Mathematical Physics · Physics 2017-08-30 L. G. S. Duarte , J. P. C. Eiras , L. A. C. P. da Mota

While powerful methods have been developed for high-dimensional hypothesis testing assuming orthogonal parameters, current approaches struggle to generalize to the more common non-orthogonal case. We propose Stable Distillation (SD), a…

Methodology · Statistics 2025-01-10 Ryan Christ , Ira Hall , David Steinsaltz

The Landauer scattering approach to 4-probe resistance is revisited for the case of a d-dimensional disordered resistor in the presence of decoherence. Our treatment is based on an invariant-embedding equation for the evolution of the…

Mesoscale and Nanoscale Physics · Physics 2009-11-13 Dibyendu Roy , N. Kumar

We present for the first time an experimental investigation of electrohydrodynamic (EHD) flows within a neutrally buoyant drop with initial radius of 2.25 mm. Utilizing particle image velocimetry (PIV) and high-speed shadowgraphy, we…

Fluid Dynamics · Physics 2025-02-19 Joel R. Karp , Bertrand Lecordier , Mostafa S. Shadloo

Many real-world systems modeled using partial differential equations (PDEs) involve unknown parameters that must be estimated from limited, noisy system observations. While typically assumed to be constants, some of these unobserved…

Methodology · Statistics 2025-08-19 Andrea Arnold