Diamond vs Graphite (Donald Sadoway, MIT)

"Network Wanderings in Paris" A summary of some of the great talks at #NetSci2018: https://t.co/UCNQsxncoD.

🌀 https://softbites.org/2018/06/22/a-storm-of-oscillators/amp/?__twitter_impression=true

#اطلاع_رسانی_سمینارهای_دانشکده
#سمينار_فيزیک_آماری #سمینار_ماده_چگال_نرم
امروز آقای متئو مارسیلی از عبدالسلام سخنرانی‌ با موضوع زیر ارائه می‌دهند.(یکشنبه سوم تیر ساعت ۱۵:۰۰ اتاق ۴۱۲ تالار پرتوی)

How simple are simple spin models?

The talk is about spin Hamiltonians with interactions of arbitrary order. An information
theoretic classification of these models into complexity classes will be discussed. The
results show that the information theoretic notion of a ``simple model’’ conforms to the
one assumed in physics. Applications to high dimensional inference will also be
discussed.

@anjoman_elmi_phys_sut

♦️ آزمایشگاه ملی نقشه برداری مغز برگزار میکند:

🔹دومین کارگاه مبانی پردازش سیگنالهای حیاتی با نرم افزار متلب با محوریت علوم اعصاب محاسباتی

🔹 اطلاعات بیشتر و ثبت نام در:
https://goo.gl/Xi13Tk

Claudio Castellano' slides on #spreading_processes on complex networks.
24th Annual #IASBS Meeting on Condensed Matter Physics & School on Complex Systems.

👌 Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.

https://graph-tool.skewed.de/

🌀 "A data scientist is a statistician who lives in San Francisco"

What’s the Difference Between Data Science and Statistics?

https://t.co/7zHlPKNf5u

🌀 به مناسبت تولد ۲۰سالگی مقاله واتس و استروگتز:

«بیست سال علم شبکه»

http://www.sitpor.org/2018/06/twenty-years-of-network-science/

🎞 "The Trouble With Community Detection."

metadata, statistical inference, the mirage of "ground truth", and community detection's No Free Lunch theorem.
https://www.aparat.com/v/QPnsm
https://youtu.be/cWFhxiRmHPo

دومین همایش مرزهای علوم‌زیستی
2 الی 4 مرداد 1397
http://biofrontiers.ipm.ir/

Here's a curious property of Brownian motion: it will fill out the whole space in 1D (a line), 2D (a sheet), but it can't fill a 3D or higher space.

https://t.co/kGDhOOFEJC

Are most systems really organized into scale-free networks? A new paper has cracked open a raging argument in network science.
https://t.co/aJJmWSLETo

Netlab gives an intuitive UI for interactive lessons in complexity.
Dear ImGui: https://t.co/E0fH1VcPFY
Course: https://t.co/L1AUcnqpMH https://t.co/Rxm0ppX6W8

How to deal with missing values in time series with R
https://cran.r-project.org/web/packages/imputeTS/imputeTS.pdf
https://github.com/SteffenMoritz/imputeTS

#R #missing_values #imputeTS

How to fix the problem imputeTS installation along installing:
use this command to install this package:

install.packages("https://cran.r-project.org/src/contrib/Archive/RcppArmadillo/RcppArmadillo_0.6.100.0.0.tar.gz", repos=NULL, type="source")
install.packages("imputeTS")

🌀 Tehran school on Theory and Applications of Complex Networks

3-7 Shahrivar 1397

🔗 More info:
http://facultymembers.sbu.ac.ir/jafari/events/

📄 Registration:
http://psi.ir/tacn2018_3.asp

⚠ این یک مدرسه تخصصیه و به دوستانی که در حوزه علم شبکه و سیستم‌های پیچیده مشغول به پژوهش هستن توصیه می‌شه. به‌طور ويژه دوستان باید پیش‌زمینه‌ای از علم شبکه داشته باشن. در حد کتاب علم‌شبکه باراباشی لینک. همین‌طور مهارت‌های برنامه‌نویسی به شدت نیازه.

🇬🇧 تمام ارائه‌ها و درس‌ها به زبان انگلیسی هست.

💰هزینه شرکت در این مدرسه ۵ روزه، برای دانشجویان ایرانی ۱۸۰ هزار تومن و برای دوستان خارجی ۲۰۰ یورو (به همراه اسکان) هست. ثبت‌نام شامل دو مرحله است: ابتدا شما فرم‌های مشخص شده رو پر می‌کنید و رزومه‌تون رو آپلود می‌کنید. مرحله بعدی، دریافت پذیرش و اقدام برای پرداخت هزینه است.

🔖 Typology of phase transitions in Bayesian inference problems

Federico Ricci-Tersenghi, Guilhem Semerjian, Lenka Zdeborova

🔗 Download

📌 ABSTRACT
Many inference problems, notably the stochastic block model (SBM) that generates a random graph with a hidden community structure, undergo phase transitions as a function of the signal-to-noise ratio, and can exhibit hard phases in which optimal inference is information-theoretically possible but computationally challenging. In this paper we refine this denoscription in two ways. In a qualitative perspective we emphasize the existence of more generic phase diagrams with a hybrid-hard phase in which it is computationally easy to reach a non-trivial inference accuracy, but computationally hard to match the information theoretically optimal one. We support this discussion by quantitative expansions of the functional cavity equations that describe inference problems on sparse graphs. These expansions shed light on the existence of hybrid-hard phases, for a large class of planted constraint satisfaction problems, and on the question of the tightness of the Kesten-Stigum (KS) bound for the associated tree reconstruction problem. Our results show that the instability of the trivial fixed point is not a generic evidence for the Bayes-optimality of the message passing algorithms. We clarify in particular the status of the symmetric SBM with 4 communities and of the tree reconstruction of the associated Potts model: in the assortative (ferromagnetic) case the KS bound is always tight, whereas in the disassortative (antiferromagnetic) case we exhibit an explicit criterion involving the degree distribution that separates a large degree regime where the KS bound is tight and a low degree regime where it is not. We also investigate the SBM with 2 communities of different sizes, a.k.a. the asymmetric Ising model, and describe quantitatively its computational gap as a function of its asymmetry. We complement this study with numerical simulations of the Belief Propagation iterative algorithm.