Complex Systems Studies

"2020 SIAM Conference on Mathematics of Data Science: A Meeting Goes Virtual" (by Gitta Kutyniok, Ali Pinar, and Joel A. Tropp; in ): https://t.co/K8QZHZJiHq

The Biggest Ideas in the Universe | 23. Criticality and Complexity https://www.aparat.com/v/X5nkF

💡 https://petterhol.me/2020/09/01/common-mistakes-in-network-code/

Graph Theory and Additive Combinatorics This course examines classical and modern developments in graph theory and additive combinatorics, with a focus on topics and themes that connect the two subjects. The course also introduces students to current research topics and open problems. https://ocw.mit.edu/courses/mathematics/18-217-graph-theory-and-additive-combinatorics-fall-2019

Advice on giving Zoom talks from David Tong @Cambridge_Uni https://t.co/xclqEfsYR8

NeurIPS2020 workshop on 'Topological Data Analysis & Beyond' is open for submissions now: https://t.co/4KhsCW0QXb This is your chance to showcase work that combines #MachineLearning and #Topology.

Graph algorithms in the language of linear algebra https://t.co/yjtE70cMVs

فیزیک حیرت‌آور ماسک‌های n95 ماسک‌های n95 یکی از موثرترین ماسک‌ها برای جلوگیری از عبور قطرات حامل ویروس #کرونا هستند. این ماسک‌ها با طراحی هوشمندانه‌ای سبب می‌شوند که ذرات با اندازه‌های مختلف از آن‌ها عبور نکنند؛ طراحی که پر است از نکته‌های فیزیکی! این شما و این ویدیو «فیزیک حیرت‌آور ماسک‌های n95» که توسط one minute physics ساخته شده. ویدیو زیرنویس فارسی دارد. برای دیدن نسخه‌های مختلف به لینک زیر سر بزنید: 🔗 sitpor.org/2020/08/n95masks/ 🎞 @sitpor

Computational social science (CSS) has exploded in prominence over the past decade, with thousands of papers published using observational data, experimental designs, and large-scale simulations that were once unfeasible or unavailable to researchers. https://t.co/tguwTFSChP

خب سریع ایشان به فارسی ترجمه کرد :) جدول ریسک انتقال بیماری - براساس این مقاله

👍 A gallery of interesting Jupyter Notebooks This page is a curated collection of Jupyter/IPython notebooks that are notable. Feel free to add new content here, but please try to only include links to notebooks that include interesting visual or technical content; this should not simply be a dump of a Google search on every ipynb file out there. https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks

Computational Methods for Complex Systems Instructor: Chris Myers Computational science and engineering involves the synthesis of data structures, algorithms, numerical analysis, programming methodologies, simulation, visualization, data analysis, performance optimization, and use of emerging technologies, all applied to the study of complex problems in science and engineering. Physics 7682 is a graduate computational science laboratory course, emphasizing hands-on programming to address a number of interesting problems arising in physics, biology, engineering, applied mathematics, and computer science. The course is largely self-paced, allowing students to choose from among a variety of topics, and explore new problems of particular interest. Unlike other courses focused more specifically on algorithms, data structures or numerical analysis, this course emphasizes the integration of those and other topics to understand a variety of scientific phenomena and computational methods. Computer Exercises Topics The course is organized around computational modules, which are drawn from a number of different fields. The course originally was a core element in the curriculum of Cornell's IGERT Program in Nonlinear Systems, which was organized broadly around the themes of complex networks, biolocomotion and manipulation, gene regulation, and pattern formation. Topics have evolved organically from that starting point. Modules are designed to expose students to techniques and methods from a variety of disciplines, not normally encompassed in a single course. Computational methods include solution of ordinary and partial differential equations, root finding, graph traversal, stochastic simulation via Monte Carlo, and various techniques in data analysis. Scientific topics include: Complex networks, small worlds, and percolation Human locomotion and models of walking Dynamical systems, chaos, and iterated maps Pattern formation and spiral waves in cardiac tissue Chemical kinetics and gene regulatory networks Random matrix theory Random walks, extremal statistics, and stock fluctuations Lattice Monte Carlo and the Ising model Satisfiability and phase transitions in NP-complete problems Molecular dynamics and the emergence of thermodynamics http://pages.physics.cornell.edu/~myers/teaching/ComputationalMethods/index.html

💡 Mongolia’s pandemic response has been so effective that nobody has died of covid-19 there, despite the long border with China. An epidemiologist there explains how the country has kept the virus at bay without a great public health system. https://t.co/mMDFz05Psa

Why, even during lockdown, do #coronavirus infection curves continue to grow linearly? The answer lies in networks. "For any given #transmission rate there exists a critical degree of contact #networks below which linear #infection curves must occur and above which the classical S-shaped curves appear that are known from epidemiological models." https://t.co/j70KwyijkW

💡 "Introducing students to research codes: A short course on solving partial differential equations in Python" (by Pavan Inguva, Vijesh J. Bhute, Thomas N.H. Cheng, Pierre J. Walker) Download PDF Recent releases of open-source research codes and solvers for numerically solving partial differential equations in Python present a great opportunity for educators to integrate these codes into the classroom in a variety of ways. The ease with which a problem can be implemented and solved using these codes reduce the barrier to entry for users. We demonstrate how one of these codes,FiPy, can be introduced to students through a short course using progression as the guiding philosophy. Four exercises of increasing complexity were developed. Basic concepts from more advanced numerical methods courses are also introduced at appropriate points. To further engage students, we demonstrate how an open research problem can be readily implemented and also incorporate the use of ParaView to post-process their results. Student engagement and learning outcomes were evaluated through a pre and post-course survey and a focus group discussion. Students broadly found the course to be engaging and useful with the ability to easily visualise the solution to PDEs being greatly valued. Due to the introductory nature of the course, due care in terms of set-up and the design of learning activities during the course is essential. This course, if integrated with appropriate level of support, can encourage students to use the provided codes and improve their understanding of concepts used in numerical analysis and PDEs.

🦠 https://time.com/5883081/covid-19-transmitted-aerosols

♾ https://www.pnas.org/content/104/42/16432.full

چگونه با آمار دروغ بگوییم؟ معرفی، مختصر توضیحی و دعوتی برای مطالعه کتاب «چگونه با آمار دروغ بگوییم؟» 🔗 http://www.sitpor.org/2020/08/how-to-lies-with-statistics/ 📈📊📉 @sitpor

📺 the first video in a new series on sparsity and compression. https://t.co/Z4kjonwkgE