Complex Systems Studies – Telegram
Complex Systems Studies
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What's up in Complexity Science?!
Check out here:

@ComplexSys

#complexity #complex_systems #networks #network_science

📨 Contact us: @carimi
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💰🚨 JOB ALERT! We are looking for a Data Scientist to provide technical expertise to our investees in #DataScience + #artificalintelligence, to support building efficiency-increasing prototypes, and to explore AI-based functionality for #digitalpublicgoods: https://t.co/I04HoaupvR
🦠 ‏شایعه پخش بیشتر ‎#کرونای_انگلیسی (گونه جهش‌یافته B.1.1.7) در بین کودکان صحت نداره؛ داده‌ها نشون می‌دن که این گونه قدرت پخش بیشتری داره اما در همه سنین! همین‌طور در مورد بقیه‌ جهش‌های اخیر (مثل افریقای جنوبی 501Y.V2) ثابت نشده که کودکان آسیب‌پذیرترن!

https://t.co/tA1ae3KpQj
Please join us on Thursday 28th January at 2pm GMT for our next exciting seminar talk led by @suneman on Modeling temporal communication networks and dynamical processes: https://t.co/Sbl6cNSdCO
#JobOffer | Our #interdisciplinary #PhD call 2021 is online!
We recruit up to 10 students in #Marseille!

📝 Apply to up to 3 projects or submit your own...

🗓 Deadline: February 26

More info https://t.co/fzU3HO365s
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🦠 چرا تست‌های با حساسیت کمتر می‌تونن گزینه‌های بهتری باشن؟

این ویدئو در مورد اینه که چرا «تست‌های سریع آنتی‌ژن» با این‌که سریعتر، ارزون‌تر و با حساسیت «کمتری» نسبت به تست‌های پی‌سی‌آر هستند اما برای غربالگری (و شاید مراقبت) ابزار بهتری به حساب میان! مزایا و معایب تست پی‌سی‌آر و تست سریع آنتی‌ژن چیه؟!


🔗 sitpor.org/2021/01/less-sensitive-rapid-antigen-tests

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@sitpor
‌instagram.com/sitpor_media
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Fractional electric charges have been observed at crystal defects in artificial structures - casting light on what might happen in exotic materials called topological crystalline insulators http://go.nature.com/3o3eDl2
شواهدی روبه‌روشدی نشون میدن که بدن افراد آلوده به کرونا می‌تونه پادتنی بسازه که به بافت‌های بدن آسیب بزنه؛

There's growing evidence that SARS-CoV-2 might cause the body to generate antibodies that attack its own tissues, perhaps explaining why some get so sick or for so long. #LongCovid
https://t.co/I8w6DQeTQF
ولفرم و پروژه فیزیک
محسن مهرانی

در قسمت پیشین گوشه‌ای از خلاقیت‌های ولفرم را باهم مرور کردیم. قصد دارم در این قسمت و قسمت‌های پیش رو کمی در مورد تلاش‌های او در فیزیک بنیادی برایتان بنویسم.

🔗 sitpor.org/2021/01/wolfram2/

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@sitpor
‌instagram.com/sitpor_media
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An estimate that there have been 105 million Americans (not the confirmed 25 million) with covid infections and why, beyond vaccination, masks and distancing will be essential to end the pandemic
https://t.co/PNcBki2Iex
Data on COVID-19 vaccination is updated: https://t.co/03pQ8rRViP

Total doses administered per 100 people:
🇮🇱 Israel 41.8 (+1.9 daily)
🇦🇪 UAE 25.1 (+0.9)
🇬🇧 UK 10.1 (+0.5)
🇧🇭 Bahrain 8.5 (+0.4)
🇺🇸 US 6.2 (+0.3)
🇲🇹 Malta 4.3 (+0.3)
🇩🇰 Denmark 3.5 (+0.1)
🇸🇮 Slovenia 2.7 (+0.1) https://t.co/0yCsV5ViHk
💰 Interested in the brain, and you live outside Norway? Apply to one of 16 new #PhD positions opening in computational neuroscience, bioinformatics and AI at University of Oslo! Open until March 1st!

https://www.mn.uio.no/compsci/english/
💰 Fantastic position with Lauren Hadley to study prediction in conversation - apply here https://t.co/Kg1S6tKLKf but based in Glasgow (deadline 4th Feb)
"Machine-Learning Mathematical Structures" (by Yang-Hui He): https://arxiv.org/abs/2101.06317

"We review, for a general audience, a variety of recent experiments on extracting structure from machine-learning mathematical data that have been compiled over the years."
What is economic complexity? And how it is helping us understand the economy? More than a decade ago, two papers helped ignite the field.

The first comprehensive review of Economic Complexity in Nature Review Physics

https://t.co/hQTDpn9IMs
The Network Pages
The math and algorithms that keep us connected

https://www.networkpages.nl/

we will publish interactive demonstrations
🎞 The Structure of Complex Networks: Scale-Free and Small-World Random Graphs - Remco van der Hofstad

https://www.aparat.com/v/gryA3

Abstract:
Many phenomena in the real world can be phrased in terms of networks. Examples include the World-Wide Web, social interactions and Internet, but also the interaction patterns between proteins, food webs and citation networks.

Many large-scale networks have, despite their diversity in backgrounds, surprisingly much in common. Many of these networks are small worlds, in the sense that one requires few links to hop between pairs of vertices. Also the variability of the number of connections between elements tends to be enormous, which is related to the scale-free phenomenon.

In this lecture for a broad audience, we describe a few real-world networks and some of their empirical properties. We also describe the effectiveness of abstract network modeling in terms of graphs and how real-world networks can be modeled, as well as how these models help us to give sense to the empirical findings. We continue by discussing some random graph models for real-world networks and their properties, as well as their merits and flaws as network models. We conclude by discussing the implications of some of the empirical findings on information diffusion and competition on such networks.

We assume no prior knowledge in graph theory, probability or otherwise.