In network science, the activity-driven model is a temporal network model in which each node has a randomly-assigned "activity potential", which governs how it links to other nodes over time. Each node (out of total) has its activity potential drawn from a given distribution . A sequence of timesteps unfolds, and in each timestep each node forms ties to random other nodes at rate (more precisely, it does so with probability per timestep). All links are then deleted after each timestep. Properties of time-aggregated network snapshots are able to be studied in terms of . For example, since each node after timesteps will have on average outgoing links, the degree distribution after timesteps in the time-aggregated network will be related to the activity-potential distribution by Spreading behavior according to the SIS epidemic model was investigated on activity-driven networks, and the following condition was derived for large-scale outbreaks to be possible: where is the per-contact transmission probability, is the per-timestep recovery probability, and are the first and second moments of the random activity-rate . (Wikipedia).

Tony Lelievre (DDMCS@Turing): Coarse-graining stochastic dynamics

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

Greg Pavliotis (DDMCS@Turing): Phase transitions for mean field limits of noisy interacting agents

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

Data-Driven Control: The Goal of Balanced Model Reduction

In this lecture, we discuss the overarching goal of balanced model reduction: Identifying key states that are most jointly controllable and observable, to capture the most inputâ€”output energy. https://www.eigensteve.com/

From playlist Data-Driven Control with Machine Learning

Reactive Systems use a high-performance software architecture. They are resilient under stress, and their reactive design allows them to scale elastically to meet demand. The reactive design approach allows the creation of more complex, more flexible systems and forms the basis for some of

From playlist Software Engineering

Sebastian Reich (DDMCS@Turing): Learning models by making them interact

Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp

From playlist Data driven modelling of complex systems

(ML 13.3) Directed graphical models - formalism (part 1)

Definition of a directed graphical model, or more precisely, what it means for a distribution to respect a directed acyclic graph.

From playlist Machine Learning

(ML 13.4) Directed graphical models - formalism (part 2)

Definition of a directed graphical model, or more precisely, what it means for a distribution to respect a directed acyclic graph.

From playlist Machine Learning

Linear regression is a cornerstone of data-driven modeling; here we show how the SVD can be used for linear regression. Book PDF: http://databookuw.com/databook.pdf Book Website: http://databookuw.com These lectures follow Chapter 1 from: "Data-Driven Science and Engineering: Machine L

From playlist Data-Driven Science and Engineering

Activity Driven Transport in Harmonic Chains by Urna Basu

DISCUSSION MEETING : STATISTICAL PHYSICS OF COMPLEX SYSTEMS ORGANIZERS : Sumedha (NISER, India), Abhishek Dhar (ICTS-TIFR, India), Satya Majumdar (University of Paris-Saclay, France), R Rajesh (IMSc, India), Sanjib Sabhapandit (RRI, India) and Tridib Sadhu (TIFR, India) DATE : 19 December

From playlist Statistical Physics of Complex Systems - 2022

Universal features of current fluctuations of driven and active systems by Udo Seifert

Stochastic Thermodynamics, Active Matter and Driven Systems DATE: 07 August 2017 to 11 August 2017 VENUE: Ramanujan Lecture Hall, ICTS Bangalore. Stochastic Thermodynamics and Active Systems are areas in statistical physics which have recently attracted a lot of attention and many intere

From playlist Stochastic Thermodynamics, Active Matter and Driven Systems - 2017

Distributions and Fluctuations in single file processes by Anupam Kundu

Stochastic Thermodynamics, Active Matter and Driven Systems DATE: 07 August 2017 to 11 August 2017 VENUE: Ramanujan Lecture Hall, ICTS Bangalore. Stochastic Thermodynamics and Active Systems are areas in statistical physics which have recently attracted a lot of attention and many intere

From playlist Stochastic Thermodynamics, Active Matter and Driven Systems - 2017

Entropy production and linear response in active Brownian particles by Debasish Chaudhuri

Stochastic Thermodynamics, Active Matter and Driven Systems DATE: 07 August 2017 to 11 August 2017 VENUE: Ramanujan Lecture Hall, ICTS Bangalore. Stochastic Thermodynamics and Active Systems are areas in statistical physics which have recently attracted a lot of attention and many intere

From playlist Stochastic Thermodynamics, Active Matter and Driven Systems - 2017

Stanford Seminar - Entrepreneurship in India: Its Current and Future Impact on Competitiveness

"Entrepreneurship in India: Its Current and Future Impact on Competitiveness" - Amit Kapoor, India Council on Competitiveness This lecture series presented by the US-Japan Technology Management Center and the US-Asia Technology Management Center explores patterns and challenges of entrepr

From playlist Leadership & Management

Stanford Seminar - Interactive Autonomy: A Human-Centered Approach for Safe Interactions

EE380: Computer Systems Colloquium Seminar Interactive Autonomy: A Human-Centered Approach for Safe Interactions Speaker: Dorsa Sadigh, Electrical Engineering & Computer Science, Stanford University Today's society is rapidly advancing towards robotics systems that interact and collaborat

From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series

Queues and large deviations in stochastic models of gene expression by Rahul Kulkarni

Large deviation theory in statistical physics: Recent advances and future challenges DATE: 14 August 2017 to 13 October 2017 VENUE: Madhava Lecture Hall, ICTS, Bengaluru Large deviation theory made its way into statistical physics as a mathematical framework for studying equilibrium syst

From playlist Large deviation theory in statistical physics: Recent advances and future challenges

Thierry Goudon - A PDE model describing the immune cells-tumor growth interactions

We introduce a mathematical model intended to describe by means of a system of partial differential equations the early stages of the interactions between effector immune cells and tumor cells. The model is structured in size and space: on the one hand the tumor development is governed by

From playlist Workshop "Tissue growth and movement" - 10-14 January 2022

Angular Live - 4 | Angular Forms Tutorial For Beginners | Angular Training | Edureka

ðŸ”¥Edureka Angular 8 Certification Training: https://www.edureka.co/angular-training This Edureka "Angular Forms" video will help you learn about the Reactive and Template-driven forms of Angular. ðŸ”¹Angular 8 Tutorial: https://www.youtube.com/watch?v=pTec1e8oyc8 ðŸ”¹Angular Blog List: https://

From playlist Edureka Live Classes 2020

Second SIAM Activity Group on FME Virtual Talk

This is the second in a series of online talks on topics related to mathematical finance and engineering. The series is organized by the SIAM Activity Group on Financial Mathematics and Engineering. Title: A Data-driven Market Simulator for Small Data Environments Abstract: In this talk w

From playlist SIAM Activity Group on FME Virtual Talk Series

Jonathan Weare (DDMCS@Turing): Stratification for Markov Chain Monte Carlo

From playlist Data driven modelling of complex systems