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Recent advancements in diffusion models have demonstrated significant success in unsupervised anomaly segmentation. For anomaly segmentation, these models are first trained on normal data; then, an anomalous image is noised to an…

Computer Vision and Pattern Recognition · Computer Science 2025-08-08 Mehrdad Moradi , Kamran Paynabar

The behaviors and skills of models in many geosciences (e.g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration. A well-calibrated model can reasonably propagate information from…

Machine Learning · Computer Science 2022-03-24 Wen-Ping Tsai , Dapeng Feng , Ming Pan , Hylke Beck , Kathryn Lawson , Yuan Yang , Jiangtao Liu , Chaopeng Shen

The main purpose of this paper is to introduce a new class of regression models for bounded continuous data, commonly encountered in applied research. The models, named the power logit regression models, assume that the response variable…

Methodology · Statistics 2026-05-15 Francisco Felipe Queiroz , Silvia Lopes Paula Ferrari

Random sequential adsorption (RSA) models have been studied due to their relevance to deposition processes on surfaces. The depositing particles are represented by hard-core extended objects; they are not allowed to overlap. Numerical Monte…

Condensed Matter · Physics 2016-07-12 P. Nielaba , V. Privman , J. -S. Wang

Based on discrete element method simulations, we propose a new form of the constitution equation for granular flows independent of packing fraction. Rescaling the stress ratio $\mu$ by a power of dimensionless temperature $\Theta$ makes the…

Soft Condensed Matter · Physics 2020-08-26 Seongmin Kim , Ken Kamrin

We introduce a deterministic model for scale-free networks, whose degree distribution follows a power-law with the exponent $\gamma$. At each time step, each vertex generates its offsprings, whose number is proportional to the degree of…

Statistical Mechanics · Physics 2009-11-07 S. Jung , S. Kim , B. Kahng

We consider the reduction of parametric families of linear dynamical systems having an affine parameter dependence that differ from one another by a low-rank variation in the state matrix. Usual approaches for parametric model reduction…

Numerical Analysis · Mathematics 2019-12-25 Christopher Beattie , Serkan Gugercin , Zoran Tomljanovic

In the 1960's, four famous scaling relations were developed which relate the six standard critical exponents describing continuous phase transitions in the thermodynamic limit of statistical physics models. They are well understood at a…

Statistical Mechanics · Physics 2024-04-16 Ralph Kenna , Bertrand Berche

The notions of self-organised criticality (SOC) and turbulence are traditionally considered to be applicable to disjoint classes of phenomena. Nevertheless, scale-free burst statistics is a feature shared by turbulent as well as…

Predicting the rheology of dense suspensions under inhomogeneous flow is crucial in many industrial and geophysical applications, yet the conventional `$\mu(J)$' framework is limited to homogeneous conditions in which the shear rate and…

Soft Condensed Matter · Physics 2023-08-17 Bhanu Prasad Bhowmik , Christopher Ness

Scale free dynamics are observed in a variety of physical and biological systems. These include neural activity in which evidence for scale freeness has been reported using a range of imaging modalities. Here, we derive the ways in which…

In this work, we derive particle schemes, based on micro-macro decomposition, for linear kinetic equations in the diffusion limit. Due to the particle approximation of the micro part, a splitting between the transport and the collision part…

Numerical Analysis · Mathematics 2017-01-19 Anaïs Crestetto , Nicolas Crouseilles , Mohammed Lemou

We investigate the ability of state-of-the-art redshift-space distortions models for the galaxy anisotropic two-point correlation function \xi(r_p, \pi), to recover precise and unbiased estimates of the linear growth rate of structure f,…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-04 Sylvain de la Torre , Luigi Guzzo

A multiscale mathematical model is presented to describe the de novo granulation and the evolution of multispecies granular biofilms within a continuous reactor. The granule is modelled as a spherical free boundary domain with radial…

Populations and Evolution · Quantitative Biology 2022-02-02 A. Tenore , F. Russo , M. R. Mattei , B. D'Acunto , G. Collins , L. Frunzo

Diffusion models generate samples by incrementally reversing a process that turns data into noise. We show that when the step size goes to zero, the reversed process is invariant to the distribution of these increments. This reveals a…

Machine Learning · Computer Science 2024-12-12 Henry Li

The diffusion process of N hard rods in a 1D interval of length L (--> inf) is studied using scaling arguments and an asymptotic analysis of the exact N-particle probability density function (PDF). In the class of such systems, the…

Soft Condensed Matter · Physics 2010-08-16 O. Flomenbom , A. Taloni

We investigate the effect of incomplete information on the growth process of scale-free networks - a situation that occurs frequently e.g. in real existing citation networks. Two models are proposed and solved analytically for the scaling…

Other Condensed Matter · Physics 2007-05-23 K. Hamacher

To study the kinetics of phase separation in active matter systems, we consider models that impose a Vicsek-type self-propulsion rule on otherwise passive particles interacting via the Lennard-Jones potential. Two types of kinetics are of…

Statistical Mechanics · Physics 2023-10-31 Tanay Paul , Nalina Vadakkayil , Subir K. Das

Recently we have shown that a one-parameter scaling, the Coherence Temperature, describes the physical behavior of several heavy fermions in a region of their phase diagram. In this paper we fully characterize this region, obtaining the…

Strongly Correlated Electrons · Physics 2009-10-31 Mucio A. Continentino

We develop a general mathematical framework to analyze scaling regimes and derive explicit analytic solutions for gradient flow (GF) in large learning problems. Our key innovation is a formal power series expansion of the loss evolution,…

Machine Learning · Computer Science 2026-02-05 Dmitry Yarotsky , Eugene Golikov , Yaroslav Gusev