Advice to young scholars, Aaron Clauset

Panel 1. The Academic Job Market
Panel 2. Life / Work Balance
Panel 3. Interdisciplinary Research
Panel 4. Grants and Fundraising

The why, how, and when of representations for complex systems

Leo Torres, Ann S. Blevins, Danielle S. Bassett, Tina Eliassi-Rad

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Complex systems thinking is applied to a wide variety of domains, from neuroscience to computer science and economics. The wide variety of implementations has resulted in two key challenges: the progenation of many domain-specific strategies that are seldom revisited or questioned, and the siloing of ideas within a domain due to inconsistency of complex systems language. In this work we offer basic, domain-agnostic language in order to advance towards a more cohesive vocabulary. We use this language to evaluate each step of the complex systems analysis pipeline, beginning with the system and data collected, then moving through different mathematical formalisms for encoding the observed data (i.e. graphs, simplicial complexes, and hypergraphs), and relevant computational methods for each formalism. At each step we consider different types of \emph{dependencies}; these are properties of the system that describe how the existence of one relation among the parts of a system may influence the existence of another relation. We discuss how dependencies may arise and how they may alter interpretation of results or the entirety of the analysis pipeline. We close with two real-world examples using coauthorship data and email communications data that illustrate how the system under study, the dependencies therein, the research question, and choice of mathematical representation influence the results. We hope this work can serve as an opportunity of reflection for experienced complexity scientists, as well as an introductory resource for new researchers.

به طور خلاصه مقاله ‌مروری بالا در مورد اینه که در دهه پیش رو بنیادهای علم شبکه چه شکلی خواهد بود!

"Few-body and many-body chaos" with Vladimir Rosenhaus:

June 4 lecture: https://youtu.be/onTWbF5fVyM
June 5 lecture: https://youtu.be/-EE24Bi77OQ


See here for slides and other details:
https://itsatcuny.org/summerschool/rosenhaus-lecture

💰 #PhD Student in Hybrid Algorithms: Combining Deep Learning and Physical Models

https://jobs.ethz.ch/job/view/JOPG_ethz_ajGObJpwqsyDc2qXsS

Three seminars on thermodynamics and machine learning.
APS Data Science

https://t.co/bO8ryaN75f

Want to analyse multilayer network data and develop network models in my group at CS Aalto in Finland? I have a #postdoc position open in an interdisciplinary project on climate change communication, polarisation and more.

Full information here: https://www.mkivela.com/postdoc/

🖥 وبینار: سیستم‌های دینامیکی در نظریه‌ی کنترل غیرخطی
👤 نسرین صدری
📋 پنجشنبه، ۲۲ خردادماه؛ ساعت ۱۱ تا ۱۲
📍‌لینک وبینار:
vmeeting.ipm.ir/b/isf-q2n-prq

#IPM
© @IPMMath
🆔 @Zharfa90

💰 Looking for a #PhD in Deep Learning Models for Human Behaviours? Come and join our group FBK

https://t.co/Nn0eMyfz1R

💰 Do you love temporal networks? How about dynamic embeddings? Are you a fan of deep learning sequence modeling? Then come work in Copenhagen on a super cool and nerdy #PhD Project: https://t.co/hNGohb4Ham.

Index of Complex Networks (ICON) was launched @netsci2016 and now lists 689 data sets and 5403 networks, spanning all domains of science and all types of networks, in a fully searchable index. https://t.co/QNmdXmWlfn

The Markov Chain Monte Carlo Revolution https://t.co/JgoF9qoh1x by Persi Diaconis

AI course, Machine Learning, based on the renowned Stanford CS course taught by Andrew Y Ng. The online course starts July 13th and runs for 10 weeks.

https://online.stanford.edu/courses/xcs229i-machine-learning

COVID-19 is breaking records for its speed of global domination – what are the changes in global connectivity behind this and how can we measure them?
Nice perspective by @AndyTatem
https://t.co/j35Vemv3Qd https://t.co/Tx3mmI2ifb

💰 Two #PhD positions in design and optimization of robust deep neural networks in safety-critical applications

Ref.nr: 2020/1317

At Mälardalen University people meet who want to develop themselves and the future. Our 16 000 students read courses and study programmes in Business, Health, Engineering and Education. We conduct research within all areas of education and have internationally outstanding research in future energy and embedded systems. Our close cooperation with the private and public sectors enables us at MDH to help people feel better and the earth to last longer. Mälardalen University is located on both sides of Lake Mälaren with campuses in Eskilstuna and Västerås.

https://web103.reachmee.com/ext/I018/1151/job?site=6&lang=SE&validator=b794921ef43b510ae6e5f2dee2761c1b&job_id=886

💡 "The concept of velocity in the history of Brownian motion — From physics to mathematics and vice versa" (by Arthur Genthon):

https://arxiv.org/pdf/2006.05399

Brownian motion is a complex object shared by different communities: first observed by the botanist Robert Brown in 1827, then theorised by physicists in the 1900s, and eventually modelled by mathematicians from the 1920s. Consequently, it is now ambiguously referring to the natural phenomenon but also to the theories accounting for it. There is no published work telling its entire history from its discovery until today, but rather partial histories either from 1827 to Perrin's experiments in the late 1900s, from a physicist's point of view; or from the 1920s from a mathematician's point of view. In this article, we tackle a period straddling the two `half-histories' just mentioned, in order to highlight its continuity, to question the relationship between physics and mathematics, and to remove the ambiguities mentioned above. We study the works of Einstein, Smoluchowski, Langevin, Wiener, Ornstein and Uhlenbeck from 1905 to 1934 as well as experimental results, through the concept of Brownian velocity. We show how Brownian motion became a research topic for the mathematician Wiener in the 1920s, why his model was an idealization of physical reality, what Ornstein and Uhlenbeck added to Einstein's results and how Wiener, Ornstein and Uhlenbeck developed in parallel contradictory theories concerning Brownian velocity.

امروز ساعت ۶/۵ به وقت تهران‌

11 June - Manlio de Domenico, CoMuNe Lab, Fondazione Bruno Kessler

"Tackling complexity: foundations and appplications."

Link del webinar: https://eu.bbcollab.com/guest/995d03a3a751431fbc3f4999aa88e8b7

The concept of velocity in the history of Brownian motion
From physics to mathematics and vice versa