arXiv subjects

Jakub Ślęzak

Publications and source records attributed to Jakub Ślęzak.

7 recordsLinked to original sources

arXiv2026

Improved mean squared displacement analysis for anomalous single particle trajectories

The mean squared displacement (MSD) is a cornerstone in the analysis of diffusion processes in complex media. When the system is heterogeneous and, in particular, when single-particle trajectories are short, it is essential to extract maximal information from each measured trajectory. This is typically done by time-averaging squared increments and examining the scaling of the time-averaged MSD in log-log space. However, classical regression methods perform poorly in this setting because time-averaging introduces correlations aggravated by those inherent to anomalous diffusion. We tackle these limitations by applying a generalized least squares framework, which substantially reduces variance and bias in diffusion parameter estimates, especially for short (around 100 points) and ultra-short (around 10 points) trajectories. The method is fully automated and requires no supervision. Furthermore, it enables prediction of estimation error probability density, which is asymptotically Gaussian, for both classical and enhanced approaches. Leveraging this prediction, we introduce a specialized deconvolution algorithm that reconstructs the underlying particle ensemble structure from experimental data.

cond-mat.stat-mech↗

arXiv2025

Codifference as a measure of dispersion and dependence for mixture models

Codifference is a commonly used measure of dependence for stable vectors and processes for which covariance is infinite. However, we argue that it can also be used for other heavy-tail distributions and it provides useful information for other non-Gaussian distributions as well, no matter the tails. Motivated by this, we analyse codifference using as little assumptions as possible about the studied model. It leads us to propose its natural domain and three natural variants of it. Using the wide class of variable scale mixture distributions we argue that the codifference can be interpreted as the measure of bulk properties which ignores the tails much more than the covariance. It can also detect forms of non-linear memory which covariance cannot. Finally, we show the asymptotic distribution of its estimator.

math.ST↗

arXiv2020

From diffusion in compartmentalized media to non-Gaussian random walks

In this work we establish a link between two different phenomena that were studied in a large and growing number of biological, composite and soft media: the diffusion in compartmentalized environment and the Brownian yet non-Gaussian diffusion that exhibits linear growth of the mean square displacement joined by the exponential shape of the positional probability density. We explore a microscopic model that gives rise to transient confinement, similar to the one observed for hop-diffusion on top of a cellular membrane. The compartmentalization of the media is achieved by introducing randomly placed, identical barriers. Using this model of a heterogeneous medium we derive a general class of random walks with simple jump rules that are dictated by the geometry of the compartments. Exponential decay of positional probability density is observed and we also quantify the significant decrease of the long time diffusion constant. Our results suggest that the observed exponential decay is a general feature of the transient regime in compartmentalized media.

cond-mat.stat-mech↗

arXiv2019

Random coefficient autoregressive processes describe Brownian yet non-Gaussian diffusion in heterogeneous systems

Many studies on biological and soft matter systems report the joint presence of a linear mean-squared displacement and a non-Gaussian probability density exhibiting, for instance, exponential or stretched-Gaussian tails. This phenomenon is ascribed to the heterogeneity of the medium and is captured by random parameter models such as "superstatistics" or "diffusing diffusivity". Independently, scientists working in the area of time series analysis and statistics have studied a class of discrete-time processes with similar properties, namely, random coefficient autoregressive models. In this work we try to reconcile these two approaches and thus provide a bridge between physical stochastic processes and autoregressive models. We start from the basic Langevin equation of motion with time-varying damping or diffusion coefficients and establish the link to random coefficient autoregressive processes. By exploring that link we gain access to efficient statistical methods which can help to identify data exhibiting Brownian yet non-Gaussian diffusion.

cond-mat.stat-mech↗

arXiv2018

Langevin equation and fractional dynamics

Recent rapid advances in single particle tracking and supercomputing techniques resulted in an unprecedented abundance of diffusion data exhibiting complex behaviours, such the presence of power law tails of the msd and memory functions, commonly referred to as "fractional dynamics". Motivated by these developments, we study the stationary solutions of the classical and generalized Langevin equation as models of the contemporarily observed phenomena. In the first chapter we sketch the historical background of the generalized Langevin equation. The second chapter is devoted to the brief overview of the theory of the Gaussian variables and processes. In the third chapter we derive the generalized Langevin equation from Hamilton's equations of motion. In the fourth chapter the series of propositions and theorems shows that a large class of Langevin equations has a solutions that, sampled in discrete time, are the moving average autoregressive processes, which can be analysed using large number of available statistical methods. The fifth chapter starts with a short introduction into the basic notions of ergodic theory. Then we prove a generalized version of the classical Maruyama's theorem, which describes the time averages of all stationary Gaussian processes with a finite number of spectral atoms. In the last, sixth chapter we continue the study of the non-ergodic solutions of the Langevin equation, this time introducing the ergodicity breaking through the random parametrisation of the equation itself. This leads to a specific form of non-Gaussianity which affects only many-dimensional distributions of the process. The main result of this chapter is the series of propositions which describes the second order structure of solutions of the superstatistical Langevin equation.

cond-mat.stat-mech↗

arXiv2018

Superstatistical generalised Langevin equation: non-Gaussian viscoelastic anomalous diffusion

Recent advances in single particle tracking and supercomputing techniques demonstrate the emergence of normal or anomalous, viscoelastic diffusion in conjunction with non-Gaussian distributions in soft, biological, and active matter systems. We here formulate a stochastic model based on a generalised Langevin equation in which non-Gaussian shapes of the probability density function and normal or anomalous diffusion have a common origin, namely a random parametrisation of the stochastic force. We perform a detailed analytical analysis demonstrating how various types of parameter distributions for the memory kernel result in the exponential, power law, or power-log law tails of the memory functions. The studied system is also shown to exhibit a further unusual property: the velocity has a Gaussian one point probability density but non-Gaussian joint distributions. This behaviour is reflected in relaxation from Gaussian to non-Gaussian distribution observed for the position variable. We show that our theoretical results are in excellent agreement with Monte Carlo simulations.

cond-mat.stat-mech↗

arXiv2017

From physical linear systems to discrete-time series. A guide for analysis of the sampled experimental data

Modelling physical data with linear discrete time series, namely Fractionally Integrated Autoregressive Moving Average (ARFIMA), is a technique which achieved attention in recent years. However, these models are used mainly as a statistical tool only, with weak emphasis on physical background of the model. The main reason for this lack of attention is that ARFIMA model describes discrete-time measurements, whereas physical models are formulated using continuous-time parameter. In order to remove this discrepancy we show that time series of this type can be regarded as sampled trajectories of the coordinates governed by system of linear stochastic differential equations with constant coefficients. The observed correspondence provides formulas linking ARFIMA parameters and the coefficients of the underlying physical stochastic system, thus providing a bridge between continuous-time linear dynamical systems and ARFIMA models.

physics.data-an↗