Few scientists are trained in how to write a review — here are some tips:

https://www.nature.com/articles/d41586-020-03422-x

💉 UK hospitals start vaccinating tomorrow

Doses of the Pfizer—BioNTech vaccine have begun to arrive in UK hospitals after it received emergency authorization last week. The first shots will be given to people over age 80, starting tomorrow. Care-home residents had been designated as a top priority to receive the jab, but health authorities are still exploring how to distribute the vaccine outside hospitals because it comes in deep-frozen packs containing 975 doses that must be stored at –70 ℃ .

Degree difference: a simple measure to characterize structural heterogeneity in complex networks

Amirhossein Farzam, Areejit Samal & Jürgen Jost

https://www.nature.com/articles/s41598-020-78336-9

Abstract
Despite the growing interest in characterizing the local geometry leading to the global topology of networks, our understanding of the local structure of complex networks, especially real-world networks, is still incomplete. Here, we analyze a simple, elegant yet underexplored measure, ‘degree difference’ (DD) between vertices of an edge, to understand the local network geometry. We describe the connection between DD and global assortativity of the network from both formal and conceptual perspective, and show that DD can reveal structural properties that are not obtained from other such measures in network science. Typically, edges with different DD play different structural roles and the DD distribution is an important network signature. Notably, DD is the basic unit of assortativity. We provide an explanation as to why DD can characterize structural heterogeneity in mixing patterns unlike global assortativity and local node assortativity. By analyzing synthetic and real networks, we show that DD distribution can be used to distinguish between different types of networks including those networks that cannot be easily distinguished using degree sequence and global assortativity. Moreover, we show DD to be an indicator for topological robustness of scale-free networks. Overall, DD is a local measure that is simple to define, easy to evaluate, and that reveals structural properties of networks not readily seen from other measures.

💉 https://theguardian.com/commentisfree/2020/dec/03/the-covid-vaccine-trust-safe-works-political

🧶 تمدید مهلت ارسال مقاله به یازدهمین کنفرانس فیزیک آماری، ماده چگال نرم و سیستم‌های پیچیده ۱۳۹۹

یازدهمین کنفرانس فیزیک آماری، ماده چگال نرم و سیستم‌های پیچیده که قرار بود فروردین ماه ۱۳۹۹ برگزار شود و به منظور پیشگیری از انتشار ویروس کرونا به تعویق افتاد. ۱ و ۲ بهمن ماه ۱۳۹۹ با همکاری دانشگاه شهیدبهشتی و به صورت برخط (Online) برگزار خواهد شد.

بنابراین مهلت ارسال مقاله تا ۱۰ دی ماه تمدید شد.

اطلاعات بیشتر در نشانی زیر:
www.psi.ir/f/smc99

@ccnsd

Network effect: visualizing AI connections in the natural sciences
https://www.nature.com/articles/d41586-020-03410-1

MuxViz is a framework for the multilayer analysis and visualization of networks. It allows an interactive visualization and exploration of multilayer networks, i.e., graphs where nodes exhibit multiple relationships simultaneously. It is suitable for the analysis of social networks exhibiting relationships of different type (e.g., family, work, etc) or interactions on different platforms (Twitter, Facebook, etc), biological networks characterized by different type of interactions (e.g., electric, chemical, etc, or allelic, non-allelic, etc), transportation networks consisting of different means of transport (e.g., trains, bus, etc), to cite just some of the possible applications.

http://muxviz.net

How to Build Trust in Covid-19 Vaccines

Why people distrust vaccines and how they can be convinced otherwise.

http://nautil.us/issue/93/forerunners/how-to-build-trust-in-covid_19-vaccines

Who needs polymer physics when you can get worms drunk instead?

https://softbites.org/2020/12/07/study-polymer-physics-with-drunk-worms/

Original paper: Rheology of Entangled Active Polymer-Like T. Tubifex Worms (arXiv here)

Our latest article for teens and pre-teens is now available as a preprint: https://t.co/a7X7J7zyjb

"How do our brains support our real-life friendships?"

در این روزها که در خانه نشسته‌ایم، خوب است که دستی بر ویکی‌پدیای فارسی بکشیم:

http://facultymembers.sbu.ac.ir/jafari/farsi/wikipedia/

💰 Join us to do research in dynamical systems at VU Amsterdam: We have a 3-year #postdoc position open without many strings (or any existing projects) attached, so excellent candidates with their own ideas are wanted. Apply at https://t.co/u5Xr91qObU by Feb 1. Please share!

#وبینار_4

انجمن علمی دانشجویی فیزیک دانشگاه بوعلی سینا برگزار میکند :

چهارمین وبینار از وبینارهای همایش فیزیک دانشگاه بوعلی سینا
👨‍🏫سخنران : دکتر افشین منتخب
📝موضوع : بحرانیت و سیستم های پیچیده
🗓تاریخ : سه شنبه 25 آذر ماه 99
🕐ساعت : 14_16
🌐مکان برگزاری :
webinar.mlpapers.ml

برای اطلاع بیشتر از اخبار همایش با BSPS همراه باشید .
🆔@Basu_physics
🆔https://news.1rj.ru/str/buali_physics_week99

————————————————-

Greetings from snowy Santa Fe, New Mexico. We are wishing you all a safe and joyous holiday season this December. For the end of the year, we have a few projects and upcoming courses that we are excited to share with you.

Here is the tentative schedule for Complexity Explorer courses that will run next year:

Non-Linear Dynamics is now open for enrollment

https://www.complexityexplorer.org/

THURSDAY #ComplexSystemsAndCovid webinar: “The economic impact of the COVID-19 pandemic: A non-equilibrium network model" by Maria del Rio @RMaria_drc, INET and MI, Oxford.
Link to the webinar:
https://t.co/MYdIM8NUGx

📍 ارائه‌ی اول ویژه‌برنامه‌ی «چند خط از داستان جهان»

🦠 برخی پدیده‌های جالب در فیزیک سیستم‌های پیچیده
👤 دکتر افشین منتخب (عضو هیئت علمی دانشکده‌ی فیزیک دانشگاه شیراز)
⏰ چهارشنبه، ۲۶ آذرماه؛ ساعت ۱۸
🌐 vc.sharif.edu/ch/zharfa

➕ مخاطب اصلی این برنامه، دانش‌آموزان و همه‌ی علاقه‌مندان به فیزیک و مشتاقان آشنایی با حوزه‌ی سیستم‌های پیچیده‌اند!
________________
#روز_فیزیک
#چند_خط_از_داستان_جهان
🆔 @Zharfa90
🆔 @RastaihaClub

"In Praise of Small Data" (by Nancy Reid, in Notices of the @amermathsoc): https://t.co/hkzNUs98X1

"This paper is based on the Gibbs Lecture presented at the 2020 Joint Mathematical Meetings in Denver, Colorado."

Inference and Prediction Part 1: Machine Learning

https://www.countbayesie.com/blog/2020/12/15/inference-and-prediction-part-1-machine-learning

#phd in Machine learning, inverse problems and signal processing

💰 PhD position “When computational physics meets observations: using machine learning to bridge the gap”

https://academicpositions.fr/ad/labex-lio/2020/phd-position-when-computational-physics-meets-observations-using-machine-learning-to-bridge-the-gap/151934


Objectives
The ultimate goal of the proposed thesis is to build a fast interpolation method on a grid of computational physics simulated images (in a broad sense as it can also be 3D volumes or spectra). With such a method, we will quickly have an approximation of a simulated image from any possible set of parameters, without having to run the expensive simulation. It then will be possible to use any method (optimization, Bayesian inference) to derive the so sought-after distribution of parameters.

The main idea is to use a deep learning framework to build the interpolator. Indeed, all possible modeled images are concentrated on a lower-dimensional subspace or manifold. Deep neural networks such as Generative Adversarial Networks (GAN) appear to be very efficient to model manifolds and could be much more efficient interpolators than classical polynomial interpolators. Trained on a grid on simulated images, these deep neural networks will produce continuous approximations of the images. As a toy example, in a properly defined manifold, the images of a single circle vary continuously with the circle radius. Interpolation between two images of circles with different radius must follow this manifold whereas any polynomial interpolation will produce an image with two circles rather than an image of a single circle with intermediate radius.

Grids of models are quite ubiquitous in physics, and hence such a project can have important impact. To ensure that it will be both robust and useful in practice, the deep learning based interpolator will be developed for two different applications: (i) planet forming disk characterization using VLTI in collaboration with J. Kluska (KU Leuven) and (ii) reconstruction of mantle structure based on geophysical surface observations


Application deadline
May the 1st, 2021