🔖 Cluster Monte Carlo algorithms
Werner Krauth

In recent years, a better understanding of the Monte Carlo method has provided us with many new techniques in different areas of statistical physics. Of particular interest are so called cluster methods, which exploit the considerable algorithmic freedom given by the detailed balance condition. Cluster algorithms appear, among other systems, in classical spin models, such as the Ising model, in lattice quantum models (bosons, quantum spins and related systems) and in hard spheres and other `entropic' systems for which the configurational energy is either zero or infinite. In this chapter, we discuss the basic idea of cluster algorithms with special emphasis on the pivot cluster method for hard spheres and related systems, for which several recent applications are presented.We provide less technical detail but more context than in the original papers.

🔗 https://arxiv.org/pdf/cond-mat/0311623v1

〽️ ICTP-ICTS Winter School on Quantitative Systems Biology 2019

🌐 http://indico.ictp.it/event/8736/
🆔 @ComplexSys

💵 PhD Position "Swiss Parliamentary Networks" (100%)

We are currently looking for a PhD candidate working in the project “Analyzing Co-Sponsorship Networks from 127 Years of the Swiss Federal Assembly”, starting as soon as possible or by agreement. The project aims at gaining a deeper understanding of legislative collaboration networks using a data-driven approach. The applicant will work on a newly compiled data set, which comprises of speeches given by members of parliament and information on collaboration networks from 1891 until today. Successful applicants should have a strong academic background in data sciences with an interest in political science. Since the project entails working with German texts, only PhD candidates with excellent German skills are considered.

The successful candidate has graduated from a university and has received a MSc degree (or equivalent) in physics, computer science, or related field, with proven interest in social sciences and interdisciplinary research. Alternatively, he/she has received a MA degree in political science (or sociology) with proven technical skills in data management and quantitative methods. Furthermore, good programming skills in scientific computing languages like Python or R are necessary, knowledge of natural language processing (NLP) tools is a plus.
The candidate should be interested in becoming acquainted with the workings of the Swiss parliament and should have strong abilities to communicate in an interdisciplinary scientific environment. Fluency in German and English is a must, French or Italian language skills are of advantage but are not mandatory.

https://apply.refline.ch/845721/7003/pub/13/index.html

🔹سمینار ۲روزه آمار (با رویکرد علوم‌داده|DataScience)

🗓دوشنبه و سه‌شنبه مورخ ۱۰ و ۱۱ تیرماه ۹۸
⏰از ساعت ۱۳:۳۰ الی ۱۵:۳۰ و ۱۶ الی ۱۸
🏛دانشگاه صنعتی امیرکبیر، سالن آمفی‌تئاتر مرکزی بهمن

✳️لینک ثبت‌نام:
evnd.co/9jiaH

❓در صورت هرگونه مشکل یا سوال به آی‌دی زیر پیام دهید
@amirali_kbl

🚨 What is Complexity Science?
This new site just went online:

🔗 https://complexityexplained.github.io/

Great source for everyone interest in #complexity.

#ComplexityExplained

#سمینارهای_هفتگی

«حالت‌های شبه پایدار در فیزیک و کاربرد آن در اقتصاد»

🗣 محمد بهرامی - دانشگاه شهید بهشتی
⏰ دوشنبه، ۱۰ تیر - ساعت ۱۶:۰۰
🏛 محل برگزاری: سالن ابن‌هیثم

‍~~~~~~~~~~~~~~~~
⭕️ مشتاق دیدار همه اقشار جامعه در مرکز هستیم. برای هماهنگی‌ با مسئول جلسه‌ می‌توانید با آقای محمد شرافتی ‌تماس بگیرید:‍‍‍‍
📞 @herman1
—————————————
🕸 مرکز شبکه‌های پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی

🕸 @CCNSD 🔗 ccnsd.ir
—————————————

Deep Learning from the Foundations course is here!!! 15 hours of videos, Jupyter notebooks, all free. Covers foundations of deep learning, state of the art research, & software engineering best practices. All new material! https://t.co/Nar3Uspqii

🎞 Statistical Mechanics (PSI 13/14, Core, PHYS 602) - Anton Burkov (University of Waterloo)

https://www.youtube.com/playlist?list=PLFMKfDJ8QzbNzBPtYEQiOdjZjvA29_Orm

Lecture 1 Introduction to phase transitions, the Ising model, Mean Field Theory (MFT)

Lecture 2 Critical exponents α, β, γ, δ out of MFT, Hubbard-Stratonovich Transformation

Lecture 3 Spin-spin correlation function; calculation in the functional integral formalism and in MFT

Lecture 4 Calculation of the correlation function in the MFT through Fourier transform; Fluctuations

Lecture 5 Corrections in Cv from fluctuations, Ginzburg criterion, Landau-Ginzburg theory

Lecture 6 Wilsonian RG: fast and slow modes

Lecture 7 Calculation of the first cumulant; the Gaussian fixed point; Feynman diagrams

Lecture 8 Calculation of the 2nd cumulant; Wilson-Fisher fixed point; linearized flow around the Gaussian fixed point

Lecture 9 Linearized flow around fixed points; calculation of critical exponents from RG

Lecture 10 Mermin-Wagner theorem, lower critical dimension

Lecture 11 Results of 2+ε expansion; Topological order in d=2

Lecture 12 Electrostatic analogy, Duality transformation of the XY model

Lecture 13 & 14 Renormalization Group (RG) for the sine-Gordon model

این آقای بورکُف(اگر درست خوانده باشم) چند کار مهم در زمینه‌ی فرمیون‌های وایل
(Weyl fermions)
کرده است.

این مقاله یک مرور خیلی کلی روی استفاده از شبکه‌های عصبی به عنوان یک تابع موج برای حالت پایه و‌ حتی برخی حالات برانگیخته‌‌ی یک سیستم کوانتومی می‌کنه. نویسنده‌ی اول، راجر مِلکو است که خودش جزو اولین‌ها در استفاده از این روش است.

توضیحات مقاله خیلی کلی است، اما به نظرم لیست مراجع ارزشمندی داره.

توجه کنید که به دو روش(تا جایی که من می‌دونم) میشه از هوش مصنوعی استفاده کرد(اینجا به هر دو اشاره میشه):

راه اول این است که شما شبکه را با تصاویری از فازها در حالات حدی تغذیه می‌کنید و بعد سعی می‌کنید گذار فاز رو با استفاده از شبکه پیدا کنید. مثلن به نحوی از تابع همبستگی یک تصویر می‌سازید، و این را به شبکه می‌دهید.

راه دوم استفاده، روش وردشی و استفاده از شبکه برای نمایش تابع موج سیستم بس‌ذره‌ای است. این می‌شود شبیه به کاری که با
Matrix Product States
می‌کنیم. به ارتباط بین این دو‌نمایش هم تو این مقاله اشاره شده.

نگاهی به کتاب «فرمول: قوانین جهان‌شمول موفقیت» باراباشی

http://www.sitpor.org/2019/07/the-formula/

آلبرت لازلو باراباشی، یک دانشمند شبکه معروفه که اخیرا پروژه‌ای به اسم «علم موفقیت» در دپارتمان «علم شبکه» دانشگاه نورث‌ایسترن شروع کرده. منظور از علم موفقیت، بررسی افراد، شرکت‌ها، کسب‌وکارها و … به صورت کمی برای رسیدن به تحلیل‌های داده‌محور از موفقیت اون‌هاست. خلاصه که کارشون استفاده از روش علمی برای مطالعه میزان موفقیت افراد یا شرکت‌ها در موضوعات مختلفه. باراباشی تجربیات پژوهشی پروژه علم موفقیت رو در کتاب عامه‌پسندی به اسم «The Formula: The Universal Laws of Success» منتشر کرده. این نوشته کوتاه، نظر من در مورد این کتابه.

http://www.sitpor.org/2019/07/the-formula/

#سمینارهای_هفتگی_محتوا

فرآیند تولید نوسانات مغزی و نقش احتمالی آنها در کارکردهای سیستم عصبی

🗣 دکتر علیرضا ولی‌زاده
دانشگاه تحصیلات تکمیلی علوم پایه زنجان

🎞 https://www.aparat.com/v/TwD2c
‌~~~~~~~~~~~~~~~~~
🔗 سخنرانی‌های بیشتر در:
https://ccnsd.ir/events-news/weekly-seminars/

🕸 مرکز شبکه‌های پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی

🕸 @CCNSD 🔗 ccnsd.ir
~~~~~~~~~~~~~~~~~

#سمینارهای_هفتگی_محتوا

«حالت‌های شبه پایدار در فیزیک و کاربرد آن در اقتصاد»
🗣 محمد بهرامی - دانشگاه شهید بهشتی

🎞 https://www.aparat.com/v/q9Fyo
‌~~~~~~~~~~~~~~~~~
🔗 سخنرانی‌های بیشتر در:
https://ccnsd.ir/events-news/weekly-seminars/

🕸 مرکز شبکه‌های پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی

🕸 @CCNSD 🔗 ccnsd.ir
~~~~~~~~~~~~~~~~~

📢 The 41st Conference on #StochasticProcesses and their Applications 2019 (SPA 2019)
⏰ 8–12 July 2019
📍 Chicago, IL, USA
https://t.co/HGgz2Y8g0c

More related conferences: https://t.co/QsT67x9JQh

🎬 Universal Biology, the Genetic Code and the First Billion Years of Life on Earth

Dr. Nigel Goldenfeld
University of Illinois at Urbana-Champaign

https://www.youtube.com/watch?v=ACdJ4uS2ULQ

This colloquium concerns two ideas. First, that there are universal laws of life, which can be deduced by abstracting what we know about life on Earth. Second, universal dynamical signatures of early life, preceding even the last universal common ancestor of all life on Earth, are present in the structure of the modern day canonical genetic code --- the map between DNA sequence and amino acids that form proteins. The code is not random, as often assumed, but instead is now known to have certain error minimisation properties. How could such a code evolve, when it would seem that mutations to the code itself would cause the wrong proteins to be translated, thus killing the organism? Using digital life simulations, I show how a unique and optimal genetic code can emerge over evolutionary time, but only if early life was dominated by collective effects, very different from the present era where individuals and species are well-defined concepts. I will also discuss a second universal signature of life: the complete breaking of chiral symmetry in biological amino acids and sugars, and explain how such transitions can arise in principle as a result of the non-equilibrium dynamics of early-life autocatalytic replicators.

🎬 Beyond Chaos: The Continuing Enigma of Turbulence - KITP Public Lecture by Nigel Goldenfeld

https://www.youtube.com/watch?v=LW1C-HVJN-o

Turbulence is the last great unsolved problem of classical physics. This seemingly random, unpredictable motion of fluids is pervasive and completely familiar to us all. Turbulence governs the speed at which rivers flow and the air drag as you drive your car; it is the bane of air travelers. Turbulence can kill, by causing arteries and aneurisms to burst. Turbulence makes stars twinkle. Its random but structured patterns have inspired artists and scientists alike. And yet, despite a century of scientific investigation, our understanding is primarily based upon a mere handful of early seminal insights. In this talk, I'll try to explain why this problem is so difficult — much harder than chaos — and what it would mean to solve it. Finally, I'll discuss recent dramatic advances in both experiment and theory that account for the way in which fluids start to become turbulent as their flow speed is increased, making precise mathematical contact with transitional behavior in other fields such as ecology and even neuroscience.