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Experimentally observed networks of interacting dynamical systems are inferred from recorded multivariate time series by evaluating a statistical measure of dependence, usually the cross-correlation coefficient, or mutual information. These…

Data Analysis, Statistics and Probability · Physics 2017-07-03 Milan Palus

A method based on optical interferences for measuring optical nonlinearities is presented. In a proof-of-principle experiment, the technique is applied to the experimental determination of the intensity dependence of the photoionization…

Optics · Physics 2013-08-06 F. Billard , P. Béjot , E. Hertz , B. Lavorel , O. Faucher

Financial correlation matrices measure the unsystematic correlations between stocks. Such information is important for risk management. The correlation matrices are known to be ``noise dressed''. We develop a new and alternative method to…

Statistical Mechanics · Physics 2009-11-07 Thomas Guhr , Bernd Kaelber

The problem of measuring an unbounded system attribute near a singularity has been discussed. Lenses have been introduced as formal objects to study increasingly precise measurements around the singularity and a specific family of lenses…

General Mathematics · Mathematics 2020-07-13 Swagatam Sen

Mathematical analysis of the analytic hierarchy process (AHP) led to the development of a mathematical function, usually called the inconsistency index, which has the center role in measuring the inconsistency of the judgements in AHP.…

Logic in Computer Science · Computer Science 2024-08-27 Sangeeta Pant , Anuj Kumar , Jiří Mazurek

The weak lensing power spectrum carries cosmological information via its dependence on the growth of structure and on geometric factors. Since much of the cosmological information comes from scales affected by nonlinear clustering,…

Astrophysics · Physics 2010-11-02 Masahiro Takada , Bhuvnesh Jain

The decoherence of quantum fluctuations into classical perturbations during inflation is discussed. A simple quantum mechanical argument, using a spatial particle wavefunction rather than a field description, shows that observable…

Astrophysics · Physics 2007-05-23 Craig J. Hogan

Clustering is the technique to partition data according to their characteristics. Data that are similar in nature belong to the same cluster [1]. There are two types of evaluation methods to evaluate clustering quality. One is an external…

Machine Learning · Computer Science 2024-09-05 Anupriya Vysala , Joseph Gomes

The complete characterization of spatial coherence is difficult because the mutual coherence function is a complex-valued function of four independent variables. This difficulty limits the ability of controlling and optimizing spatial…

The microstructure of coagulated colloidal particles, for which the inter-particle potential is described by the DLVO theory, is strongly influenced by the particles' surface potential. Depending on its value, the resulting microstructures…

Soft Condensed Matter · Physics 2009-11-09 Iwan Schenker , Frank T. Filser , Tomaso Aste , Hans J. Herrmann , Ludwig J. Gauckler

The evolution of the electron density during electron cloud formation can be reproduced using a bunch-to-bunch iterative map formalism. The reliability of this formalism has been proved for RHIC [1] and LHC [2]. The linear coefficient has a…

Accelerator Physics · Physics 2014-11-21 T. Demma , S. Petracca , A. Stabile

Inequality measures are quantitative measures that take values in the unit interval, with a zero value characterizing perfect equality. Although originally proposed to measure economic inequalities, they can be applied to several other…

Physics and Society · Physics 2024-01-26 Paolo Giudici , Emanuela Raffinetti , Giuseppe Toscani

Aperiodic variability is a characteristic feature of young stars, massive stars, and active galactic nuclei. With the recent proliferation of time domain surveys, it is increasingly essential to develop methods to quantify and analyze…

Instrumentation and Methods for Astrophysics · Physics 2026-04-01 Krzysztof Findeisen , Ann Marie Cody , Lynne Hillenbrand

We discuss fluctuations in the measurement process and how these fluctuations are related to the dissipational parameter characterising quantum damping or decoherence. On the example of the measuring current of the variable-barrier or QPC…

Quantum Physics · Physics 2008-11-26 L. Stodolsky

To quantify single mode nonclassicality, we start from an operational approach. A positive semi-definite observable is introduced to describe a measurement setup. The quantification is based on the negativity of the normally ordered version…

Quantum Physics · Physics 2012-11-29 C. Gehrke , J. Sperling , W. Vogel

When employing non-linear methods to characterise complex systems, it is important to determine to what extent they are capturing genuine non-linear phenomena that could not be assessed by simpler spectral methods. Specifically, we are…

Methodology · Statistics 2021-09-22 Pedro A. M. Mediano , Fernando E. Rosas , Adam B. Barrett , Daniel Bor

How to estimate heterogeneity, e.g. the effect of some variable differing across observations, is a key question in political science. Methods for doing so make simplifying assumptions about the underlying nature of the heterogeneity to…

Methodology · Statistics 2021-03-31 Max Goplerud

This paper aims to stress the role of the Cahill-Glauber quasi-probability densities in defining, detecting, and quantifying the non-classicality of field states in quantum optics. The distance between a given pure state and the set of all…

Quantum Physics · Physics 2020-04-22 Paulina Marian , Tudor A. Marian

The analysis of continuously spatially varying processes usually considers two sources of variation, namely, the large-scale variation collected by the trend of the process, and the small-scale variation. Parametric trend models on latitude…

Uncertainty quantification for image data is dominated by complex deep learning methods, yet the field lacks an interpretable, mathematically grounded baseline. We propose Bayesian scattering to fill this gap, serving as a first-step…

Machine Learning · Computer Science 2026-03-24 Bernardo Fichera , Zarko Ivkovic , Kjell Jorner , Philipp Hennig , Viacheslav Borovitskiy