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

💰 I'm hiring 2 graduate students for my ERC project, focused on predicting depression onset in 2,000 students. Looking for a #PhD position? Come work with me @UniLeiden!

Core topics: #depression, #complexity, #timeseries, #networks, #EMA, & #MachineLearning.

You can find the 2 positions in the link below.
https://t.co/jGQIPX2DuG

Here is a blog in which I describe the project in some more detail, including a short video. /
https://t.co/pdZx1k7xXW

💰 Come do a #PhD with me in #cognitive #data #science and #complex #networks at @UniofExeter!

In an EPSRC scholarship by @exetercompsci , we'll investigate how to give structure to #knowledge and its influence in socio-cognitive systems.

Deadline 25/01/21:
https://t.co/BBokMFV3za

📯We are hiring!📯
4-year fully funded #PhD position in Sample-Efficient Probabilistic Machine Learning @UnivHelsinkiCS with links to @FCAI_fi

Please see blurb below, and full ad here: https://t.co/w7Y3Nhxn3w

Application deadline: Jan 10, 2021. Please RT! https://t.co/Nr5plMuJ2C

💰 The Helsinki Doctoral Education Network in Information and Communications Technology (HICT) has 40 open positions for Doctoral Students!

The participating units of HICT have currently funding available for exceptionally qualified doctoral students. We offer the possibility to join world-class research groups, with multiple research projects to choose from. The activities of HICT and the open positions are structured along five research area specific tracks:

🔵 Algorithms and machine learning
🔵 Life science informatics
🔵 Networks, networked systems and services
🔵 Software and service engineering and systems
🔵 User centered and creative technologies

If you wish to be considered as a potential new doctoral student in HICT you can apply to one or a number of doctoral student positions. We welcome applicants with diverse backgrounds, and qualified female candidates are explicitly encouraged to apply.

#phd Deadline 02.02.2021

http://www.hict.fi/spring2021

‘A divisive disease” .
https://t.co/4Z7x9sRaaL

#Networks2021 session on advances in #multilayer #network analysis. Abstract submission open until Jan 24

https://networks2021.net/program