Complex Systems Studies

🧑🏻‍🏫 #Renormalization w4.p1 Intro to Ising Model «مقدمه‌ای بر بازبهنجارش» هفته چهارم: مدل آیزینگ قسمت اول: مرور جلسات گذشته و معرفی مدل آیزینگ مدل آیزینگ، به عنوان معرف‌ترین مدل در فیزیک آماری، یک مدل ساده برای توصیف گذار فاز در مواد مغناطیسی است. این مدل از متغیرهای گسسته (اسپین) به روی یک گراف مشبکه تشکیل شده است. در این قسمت از مجموعه مقدمه‌ای بر بازبهنجارش، نخست مدل آیزینگ را معرفی می‌کنم و سپس به سراغ درشت‌-دانه‌بندی شبکه‌ اسپینی می‌روم. چالش‌های پیش‌رو را مطرح می‌کنم و سرانجام به پدیدارگی جملات مرتبه‌-بالاتر و نقاط ثابت جریان بازبهنجارش می‌پردازم. 🎞 ویدیو در صفحه اینستاگرام سیتپور 🔗 اطلاعات بیشتر: sitpor.org/2019/10/renorm-week4-ising ~~~~~~ @sitpor ‌instagram.com/sitpor_media ~~~~~~~

🔹 Simons-Emory Workshop on Neural Dynamics - "What could neural dynamics have to say about neural computation, and do we know how to listen?" - Friday December 4th from 11am-2pm EST - Event WebSite - Register - The workshop will assume familiarity with Vyas et al., Ann Rev Neuro 2020. Please review before attending.

🧑🏻‍🏫 #Renormalization w3.p3 Networks of Renormalization «مقدمه‌ای بر بازبهنجارش» هفته سوم: اتوماتای سلولی قسمت سوم: شبکه‌های بازبهنجارش یک اتوماتای سلولی شامل یک شبکه منظم از سلول‌های خاموش و روشن است. تحول این سلول‌ها توسط قواعد ثابتی که فقط وابسته به وضعیت قبلی آن سلول و همسایگانش است مشخص می‌شود. در این جلسه ابتدا اتوماتای سلولی را معرفی می‌کنم و به مفاهیمی چون «کامل بودن تورینگ» و «نمودارهای جابه‌جاشوند» می‌پردازم. سپس سراغ درشت-دانه‌بندی اتوماتای سلولی و مقاله ۲۰۰۴ و ۲۰۰۵ گلدنفلد می‌روم و در نهایت در مورد شبکه‌‌های بازبهنجارش بحث خواهم کرد. 🎞 ویدیو در صفحه اینستاگرام سیتپور 🔗 اطلاعات بیشتر: sitpor.org/2019/09/renorm-week3-ca ~~~~~~ @sitpor ‌instagram.com/sitpor_media ~~~~~~~

"Finland and Norway boast the West’s lowest rates of mortality,..now stand out as the closest Western equivalents to Asian nations that have managed to avoid the worst of the pandemic." https://www.wsj.com/articles/finland-and-norway-avoid-covid-19-lockdowns-but-keep-the-virus-at-bay-11605704407 "Their recipe: a brief, targeted lockdown in March, followed by tight border controls with mandatory testing and quarantine for all travelers. "Elsewhere in Europe, strict lockdowns in the spring helped bring infections down, but as most of the continent reopened borders, summer travelers turned into incubators for a new and bigger wave of infections. https://t.co/XBmrhDqQry

💰 I am currently looking for 2 #PhD Students to work with me on Bayesian workflow topics at the Cluster of Excellence SimTech in Stuttgart, Germany. Deadline ist December 20th and all information can be found here: https://t.co/4FboYYKyJu

💉 The Oxford coronavirus vaccine has been shown to trigger a robust immune response in healthy adults aged 56-69 and 70+. The data suggest that one of the groups most vulnerable to serious illness and death from COVID-19 could build immunity: https://t.co/E5uEcHFcrT #OxfordVaccine

The κ-statistics approach to epidemiology Giorgio Kaniadakis, Mauro M. Baldi, Thomas S. Deisboeck, Giulia Grisolia, Dionissios T. Hristopulos, Antonio M. Scarfone, Amelia Sparavigna, Tatsuaki Wada & Umberto Lucia Scientific Reports volume 10, Article number: 19949 (2020) Abstract A great variety of complex physical, natural and artificial systems are governed by statistical distributions, which often follow a standard exponential function in the bulk, while their tail obeys the Pareto power law. The recently introduced 𝜅 -statistics framework predicts distribution functions with this feature. A growing number of applications in different fields of investigation are beginning to prove the relevance and effectiveness of 𝜅-statistics in fitting empirical data. In this paper, we use 𝜅-statistics to formulate a statistical approach for epidemiological analysis. We validate the theoretical results by fitting the derived 𝜅-Weibull distributions with data from the plague pandemic of 1417 in Florence as well as data from the COVID-19 pandemic in China over the entire cycle that concludes in April 16, 2020. As further validation of the proposed approach we present a more systematic analysis of COVID-19 data from countries such as Germany, Italy, Spain and United Kingdom, obtaining very good agreement between theoretical predictions and empirical observations. For these countries we also study the entire first cycle of the pandemic which extends until the end of July 2020. The fact that both the data of the Florence plague and those of the Covid-19 pandemic are successfully described by the same theoretical model, even though the two events are caused by different diseases and they are separated by more than 600 years, is evidence that the 𝜅-Weibull model has universal features.

The growth equation of cities Vincent Verbavatz & Marc Barthelemy Nature volume 587, pages397–401(2020) Abstract The science of cities seeks to understand and explain regularities observed in the world’s major urban systems. Modelling the population evolution of cities is at the core of this science and of all urban studies. Quantitatively, the most fundamental problem is to understand the hierarchical organization of city population and the statistical occurrence of megacities. This was first thought to be described by a universal principle known as Zipf’s law1,2; however, the validity of this model has been challenged by recent empirical studies3,4. A theoretical model must also be able to explain the relatively frequent rises and falls of cities and civilizations5, but despite many attempts6,7,8,9,10 these fundamental questions have not yet been satisfactorily answered. Here we introduce a stochastic equation for modelling population growth in cities, constructed from an empirical analysis of recent datasets (for Canada, France, the UK and the USA). This model reveals how rare, but large, interurban migratory shocks dominate city growth. This equation predicts a complex shape for the distribution of city populations and shows that, owing to finite-time effects, Zipf’s law does not hold in general, implying a more complex organization of cities. It also predicts the existence of multiple temporal variations in the city hierarchy, in agreement with observations5. Our result underlines the importance of rare events in the evolution of complex systems11 and, at a more practical level, in urban planning.

The scales of human mobility Laura Alessandretti, Ulf Aslak & Sune Lehmann Nature volume 587, pages402–407(2020) Abstract There is a contradiction at the heart of our current understanding of individual and collective mobility patterns. On the one hand, a highly influential body of literature on human mobility driven by analyses of massive empirical datasets finds that human movements show no evidence of characteristic spatial scales. There, human mobility is described as scale free1,2,3. On the other hand, geographically, the concept of scale—referring to meaningful levels of description from individual buildings to neighbourhoods, cities, regions and countries—is central for the description of various aspects of human behaviour, such as socioeconomic interactions, or political and cultural dynamics4,5. Here we resolve this apparent paradox by showing that day-to-day human mobility does indeed contain meaningful scales, corresponding to spatial ‘containers’ that restrict mobility behaviour. The scale-free results arise from aggregating displacements across containers. We present a simple model—which given a person’s trajectory—infers their neighbourhood, city and so on, as well as the sizes of these geographical containers. We find that the containers—characterizing the trajectories of more than 700,000 individuals—do indeed have typical sizes. We show that our model is also able to generate highly realistic trajectories and provides a way to understand the differences in mobility behaviour across countries, gender groups and urban–rural areas.

👩🏻‍💻 اینجا کلی ویدیو خوب برای پایتون هست: https://calmcode.io/

🦠 این‌که نتیجه آزمایش کرونای شما منفی باشه به این معنی نیست که واقعا ایمن هستید؛ ممکنه آزمان خطا داشته باشه (منفی کاذب) یا این‌که بیماری شما در مرحله اولیه غیرقابل تشخیص باشه. برای همین حتی در صورت منفی بودن نتیجه آزمایش کرونا، باز هم مراقبت‌های لازم رو انجام بدین!

📺 New lecture on how the diffusion equation arises from random walks. https://t.co/5tt5Iewgaw

🧑🏻‍🏫 #Renormalization w3.p1 Intro to CA «مقدمه‌ای بر بازبهنجارش» هفته سوم: اتوماتای سلولی قسمت اول: معرفی اتوماتای سلولی یک اتوماتای سلولی شامل یک شبکه منظم از سلول‌های خاموش و روشن است. تحول این سلول‌ها توسط قواعد ثابتی که فقط وابسته به وضعیت قبلی آن سلول و همسایگانش است مشخص می‌شود. در این جلسه ابتدا اتوماتای سلولی را معرفی می‌کنم و به مفاهیمی چون «کامل بودن تورینگ» و «نمودارهای جابه‌جاشوند» می‌پردازم. سپس سراغ درشت-دانه‌بندی اتوماتای سلولی و مقاله ۲۰۰۴ و ۲۰۰۵ گلدنفلد می‌روم و در نهایت در مورد شبکه‌‌های بازبهنجارش بحث خواهم کرد. 🎞 ویدیو در صفحه اینستاگرام سیتپور 🔗 اطلاعات بیشتر: sitpor.org/2019/09/renorm-week3-ca ~~~~~~ @sitpor ‌instagram.com/sitpor_media ~~~~~~~

💉 واکسن شرکت مدرنا با اثربخشی ۹۴/۵٪ دومین واکسن موثر تا به امروز بوده. این واکسن از ایده مشابهی با واکسن BioNTech استفاده می‌کنه که به رِنای پیام‌رسان مربوطه. https://www.sciencemag.org/news/2020/11/just-beautiful-another-covid-19-vaccine-newcomer-moderna-succeeds-large-scale-trial

An introduction to Quantum programming languages, overviewing some of the existing languages and the ecosystem around them Read here: https://t.co/it55qrrbNr

💉 Moderna COVID-19 vaccine: two press releases one for Primary Efficacy Endpoint and one Shelf Life & Refrigerated Temperatures 1⃣ https://t.co/ZM2JghFk4y 2⃣ https://t.co/TZjQpYTbvF

“The Hitchhiker's Guide to #CondensedMatter and #StatisticalPhysics” is a series of virtual events that will provide a roadmap of current research directions in the field. 🔹 Register to the first virtual school on #MachineLearning for CM by November 30: http://indico.ictp.it/event/9471/

Evolutionary Surrogate-assisted Prescription

How does machine learning help in decision-making and optimizing non-pharmaceutical interventions against Covid19

Risto Miikkulainen Professor of Computer Science The University of Texas at Austin and Cognizant Technology Solutions Thursday 19 November at 18:00-19:30 Register! by 16 November at https://webropol.com/ep/HDLS-19112020 Abstract How can we make good decisions in business, engineering design, science, education, and indeed in life in general? Good decisions are often based on experience: recalling what decisions were made in similar situations in the past, how well they worked out, and modifying them to achieve good outcomes in current situation. Evolutionary Surrogate-assisted Prescription (ESP) is a machine learning technology that makes it possible to come up with good decision strategies automatically. The idea is to use historical data to build a predictive surrogate model, and population based search (i.e. evolutionary computation) to discover good decision strategies against it. I’ll review the technology and evaluate it in several examples, including optimizing behavior in sequential decision tasks, and optimizing non-pharmaceutical interventions in the COVID-19 pandemic. The method is found to be sample efficient and creative, forming a foundation for optimizing many decision tasks in the future.

💾 New review article in Nature Reviews Physics: "Topological methods for data modelling" (by Gunnar Carlsson): https://t.co/16PD6hriSo