📺 https://www.ted.com/talks/lorna_davis_a_guide_to_collaborative_leadership/

ناگفته‌های دوران دکتری
این نوشته در مورد چیزهاییه که خیلی‌ها نمی‌دونن و ممکنه به خاطر این ناآگاهی‌ها مشکلات زیادی رو تجربه کنند.

اگر قصد ادامه تحصیل در مقطع دکتری رو دارین، حتما این مجموعه از نوشته‌ها رو دنبال کنید:

🔗 sitpor.org/2020/08/phdlife1

#phd #phdlife #phdhack
🧑🏻‍🎓👩🏻‍🎓 @sitpor

💰 #Postdoc position in mathematical/statistical modelling of the COVID-19 pandemic at Institut Pasteur, Paris
https://research.pasteur.fr/en/job/postdoc-in-mathematical-statistical-modelling-of-the-covid-19/

Brand-new online course CS-EJ3311 Deep Learning with Python, dlwithpython.cs.aalto.fi will roll out in Sept.

This course aims at developing intuition and hands-on skills for applying deep learning methods to different datasets. The course material will be in the form of Python notebooks similar in format to those at here.

The mindset behind the course is inspired by the book "Deep Learning with Python" by F. Chollet

You can enroll in this course as non-Aalto student via fitech.io

💡 https://www.countbayesie.com/blog/2020/8/16/why-bayesian-stats-need-monte-carlo-methods

📺 video

Graphs + Networks Workshop materials that include videos of all the talks from the conference.

Here is a list of papers that our speakers and participants flagged as interesting for wider reading in Graphs, Networks, and meaningful applications.

علم سیستم‌های پیچیده
https://www.aparat.com/v/veRud

💰 Jobs: Up to 5 #postdoc positions in Computational Social Science (with focus on AI)

Join me at the Max-Planck Center for Humans & Machines in Berlin

https://t.co/ay7o7uzKOM

The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains

David I Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, Pierre Vandergheynst

Download PDF

In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs. The emerging field of signal processing on graphs merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process such signals on graphs. In this tutorial overview, we outline the main challenges of the area, discuss different ways to define graph spectral domains, which are the analogues to the classical frequency domain, and highlight the importance of incorporating the irregular structures of graph data domains when processing signals on graphs. We then review methods to generalize fundamental operations such as filtering, translation, modulation, dilation, and downsampling to the graph setting, and survey the localized, multiscale transforms that have been proposed to efficiently extract information from high-dimensional data on graphs. We conclude with a brief discussion of open issues and possible extensions.

Unraveling the effects of multiscale network entanglement on disintegration of empirical systems

Arsham Ghavasieh, Massimo Stella, Jacob Biamonte, Manlio De Domenico

Download PDF

Complex systems are large collections of entities that organize themselves into non-trivial structures that can be represented by networks. A key emergent property of such systems is robustness against random failures or targeted attacks ---i.e. the capacity of a network to maintain its integrity under removal of nodes or links. Here, we introduce network entanglement to study network robustness through a multi-scale lens, encoded by the time required to diffuse information through the system. Our measure's foundation lies upon a recently proposed framework, manifestly inspired by quantum statistical physics, where networks are interpreted as collections of entangled units and can be characterized by Gibbsian-like density matrices. We show that at the smallest temporal scales entanglement reduces to node degree, whereas at the large scale we show its ability to measure the role played by each node in network integrity. At the meso-scale, entanglement incorporates information beyond the structure, such as system's transport properties. As an application, we show that network dismantling of empirical social, biological and transportation systems unveils the existence of a optimal temporal scale driving the network to disintegration. Our results open the door for novel multi-scale analysis of network contraction process and its impact on dynamical processes.

🦠 “If schools are reopened in areas with high levels of community transmission, major outbreaks are inevitable and deaths will occur in the community as a result."

#coronavirus #covid19
https://www.nature.com/articles/d41586-020-02403-4

Swiss Government Excellence Scholarships for Foreign Scholars and Artists for the 2021-2022 Academic Year

For Iran, see here

#phd #postdoc

Want to learn more about agent-based modeling applied to social-ecological systems? Apply to our winter school!

https://t.co/nMbKvhplgu

#PhD positions are available at IPM in Cognitive Neuroscience. To apply see the info on:
http://scs.ipm.ac.ir

💡 A simple but powerful idea: Improve the throughput of pooled testing for COVID-19 using a (Reed-Solomon) error-correcting code.
https://t.co/21CTet1b45

💡 "Nonlocal Diffusion Equations with Integrable Kernels" (by Julio D. Rossi): https://t.co/wjvXc68RNV

📺 the first video in a new series on sparsity and compression.

https://t.co/Z4kjonwkgE

چگونه با آمار دروغ بگوییم؟

معرفی، مختصر توضیحی و دعوتی برای مطالعه کتاب «چگونه با آمار دروغ بگوییم؟»

🔗 http://www.sitpor.org/2020/08/how-to-lies-with-statistics/

📈📊📉
@sitpor

♾ https://www.pnas.org/content/104/42/16432.full

🦠 https://time.com/5883081/covid-19-transmitted-aerosols