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We investigate recurrent neural network architectures for event-sequence processing. Event sequences, characterized by discrete observations stamped with continuous-valued times of occurrence, are challenging due to the potentially wide…

Neural and Evolutionary Computing · Computer Science 2017-10-12 Michael C. Mozer , Denis Kazakov , Robert V. Lindsey

Linear theory of stationary response in thermal systems subjected to external perturbations requires to find equilibrium correlation function of the responding system variable in the absence of external perturbations. Studies of the…

Statistical Mechanics · Physics 2014-09-24 Igor Goychuk

Continuum robots, characterized by their high flexibility and infinite degrees of freedom (DoFs), have gained prominence in applications such as minimally invasive surgery and hazardous environment exploration. However, the intrinsic…

Robotics · Computer Science 2023-09-26 Peiyu Luo , Shilong Yao , Yiyao Yue , Jiankun Wang , Hong Yan , Max Q. -H. Meng

We study a one-dimensional random walk with memory. The behavior of the walker is modified with respect to the simple symmetric random walk (SSRW) only when he is at the maximum distance ever reached from his starting point (home). In this…

Data Analysis, Statistics and Probability · Physics 2013-09-27 Maurizio Serva

This thesis explores a central question: how does memory affect the way random walkers explore space? By analyzing various non-Markovian models, where past behavior directly influences future dynamics, we uncover new mechanisms and…

Statistical Mechanics · Physics 2025-07-30 Julien Brémont

It is a well known fact that subdiffusion equations in terms of fractional derivatives can be obtained from Continuous Time Random Walk (CTRW) models with long-tailed waiting time distributions. Over the last years various authors have…

Biological Physics · Physics 2010-06-15 S. B. Yuste , E. Abad , K. Lindenberg

We consider Activated Random Walk (ARW), a particle system with mass conservation, on the cycle $\mathbb{Z}/n\mathbb{Z}$. One starts with a mass density $\mu>0$ of initially active particles, each of which performs a simple symmetric random…

Probability · Mathematics 2018-04-09 Riddhipratim Basu , Shirshendu Ganguly , Christopher Hoffman , Jacob Richey

The persistent walk is a classical model in kinetic theory, which has also been studied as a toy model for MCMC questions. Its continuous limit, the telegraph process, has recently been extended to various velocity jump processes (Bouncy…

Probability · Mathematics 2020-02-19 Pierre Monmarché

Motivated by novel results in the theory of complex adaptive systems, we analyze the dynamics of random walks in which the jumping probabilities are {\it time-dependent}. We determine the survival probability in the presence of an absorbing…

Condensed Matter · Physics 2016-08-31 Shahar Hod

We show that for a weakly dense subset of the domain of attraction of a positive stable random variable of index $0<\alpha<1$($DOA\left(\alpha\right))$ the functional stable convergence is a time-changed renewal convergence of distribution…

Probability · Mathematics 2017-09-12 Ofer Busani

In a recent work we introduced a semi-Markovian discrete-time generalization of the telegraph process. We referred this random walk to as squirrel random walk (SRW). The SRW is a discrete-time random walk on the one-dimensional infinite…

Probability · Mathematics 2023-01-18 Thomas M. Michelitsch , Federico Polito , Alejandro P. Riascos

Levy flights were introduced through the mathematical research of the algebra or random variables with infinite moments. Mandelbrot recognized that the Levy flight prescription had a deep connection to scale-invariant fractal random walk…

Soft Condensed Matter · Physics 2009-11-07 A. Posadas , J. Morales , F. Vidal , O. Sotolongo-Costa , J. C. Antoranz

In this paper we study continuous time random walks (CTRWs) such that the holding time in each state has a distribution depending on the state itself. For such processes, we provide integro-differential (backward and forward) equations of…

Probability · Mathematics 2017-10-11 Costantino Ricciuti , Bruno Toaldo

Coarse-grained (CG) models facilitate an efficient exploration of complex systems by reducing the unnecessary degrees of freedom of the fine-grained (FG) system while recapitulating major structural correlations. Unlike structural…

Chemical Physics · Physics 2023-01-18 Jaehyeok Jin , Kenneth S. Schweizer , Gregory A. Voth

Let $\{\boldsymbol{X}_n\}$ be a discrete-time $d$-dimensional process on $\mathbb{Z}_+^d$ with a supplemental (background) process $\{J_n\}$ on a finite set and assume the joint process $\{\boldsymbol{Y}_n\}=\{(\boldsymbol{X}_n,J_n)\}$ to…

Probability · Mathematics 2015-02-17 Toshihisa Ozawa

The damped random walk (DRW) model is increasingly used to model the variability in quasar optical light curves, but it is still uncertain whether the DRW model provides an adequate description of quasar optical variability across all time…

Cosmology and Nongalactic Astrophysics · Physics 2013-02-25 Ying Zu , C. S. Kochanek , Szymon Kozłowski , Andrzej Udalski

A random walk is a basic stochastic process on graphs and a key primitive in the design of distributed algorithms. One of the most important features of random walks is that, under mild conditions, they converge to a stationary distribution…

Probability · Mathematics 2020-06-19 Leran Cai , Thomas Sauerwald , Luca Zanetti

We consider one-dimensional discrete-time random walks (RWs) in the presence of finite size traps of length $\ell$ over which the RWs can jump. We study the survival probability of such RWs when the traps are periodically distributed and…

Statistical Mechanics · Physics 2022-01-05 Gaia Pozzoli , Benjamin De Bruyne

We investigate how a weak constant force becomes detectable through fluctuations in anomalous transport in strongly heterogeneous media. Rather than focusing on the mean drift, we show that the key signature of the force appears in the…

Statistical Mechanics · Physics 2026-03-17 Masahiro Shirataki , Takuma Akimoto

Representation learning on dynamic graphs requires capturing complex dependencies that evolve across both time and structure. Existing approaches typically adopt fixed temporal decay schemes or predetermined structural propagation depths,…

Machine Learning · Computer Science 2026-05-29 Qian Chang , Ciprian Doru Giurcaneanu , Runsong Jia , Xia Li , Guoping Hu , Xiufeng Cheng , Jinqing Yang , Mengjia Wu , Yi Zhang