✅«همایش فیزیک و سامانه های اجتماعی»

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سخنرانان:
دکتر سامانی
دانشگاه صنعتی اصفهان
دکتر شهبازی
دانشگاه صنعتی اصفهان
دکتر حسینی
دانشگاه شهیدبهشتی تهران
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⚠️زمان:پنج شنبه 8 خرداد ساعت 17⚠️

علاقه‌مندان می‌توانند با
نام کاربری: physics_ss
و
گذرواژه: pss1399
از طریق لینک زیر در همایش شرکت نمایند:
https://www.skyroom.online/ch/iut_farhangi/physics-ss

هزینه ی ثبت نام:رایگان

منتظر مشارکت شما عزیزان هستیم
@physicsiut

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📲@iut_news

The Many Faces of Balance: Multilevel Structural Evaluation of Signed Directed Social Networks

Samin Aref, Ly Dinh, Rezvaneh Rezapour, Jana Diesner

Download PDF

Balance theory explains the forces behind the structure of social systems commonly modeled as static undirected signed networks. We expand the modeling to incorporate directionality of the edges and consider three levels of analysis: triads, subgroups, and the whole network. For triad-level balance, we utilize semicycles that satisfy the condition of transitivity. For subgroup-level balance, we derive measures of cohesiveness (internal solidarity) and divisiveness (external antagonism) to capture balance in subgroups using the most fitting partition of nodes into two groups. For network-level balance, we use the normalized line index which relies on the proportion of edges whose position suits balance. Through extensive computational analysis, we document frequently repeated patterns of social structure in triads, subgroups, and the whole network across a range of social settings from college students and Wikipedia users to philosophers and Bitcoin traders. We then apply our multilevel framework to examine balance in temporal and multilayer networks which demonstrates the generalizability of our approach and leads to new observations on balance with respect to time and layer dimensions. Our complementary findings on a variety of social networks highlight the need to evaluate balance at different levels for which we propose a comprehensive yet parsimonious approach.

Neurolinguistics

This course introduces the key principles and goals of modern neurolinguistics. Neurolinguistics is a science that incorporates methods and paradigms of linguistics and neuroscience.

This course discusses the main units and organizational principles of the human nervous system that underlie our language capacity. You will learn about the neurophysiological aspects of first and second language learning, discover clinical research in speech, reading and writing disorders, and also find out about speech disorders accompanying various psychiatric conditions. The course includes information on the history of neurolinguistics, modern techniques and methods of neurolinguistic research, and also provides detailed examples of several recent studies in the field.

🎞 Missing Semester:

computing ecosystem literacy

https://www.youtube.com/watch?v=Z56Jmr9Z34Q&list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J

As computer scientists, we know that computers are great at aiding in repetitive tasks. However, far too often, we forget that this applies just as much to our use of the computer as it does to the computations we want our programs to perform. We have a vast range of tools available at our fingertips that enable us to be more productive and solve more complex problems when working on any computer-related problem. Yet many of us utilize only a small fraction of those tools; we only know enough magical incantations by rote to get by, and blindly copy-paste commands from the internet when we get stuck.

This class is an attempt to address this.

We want to teach you how to make the most of the tools you know, show you new tools to add to your toolbox, and hopefully instill in you some excitement for exploring (and perhaps building) more tools on your own. This is what we believe to be the missing semester from most Computer Science curriculum

در آپارات:
https://www.aparat.com/playlist/406966

Enhanced Large Scale Colloquial Persian Language Understanding
https://iasbs.ac.ir/~ansari/lscp/index.html

LSCP is hierarchically organized in a semantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem.
The proposed corpus consists of 120M Persian (Farsi) sentences resulted from 27M tweets annotated with parsing tree, part-of-speech tags, sentiment polarity and translations in English, German, Czech, Italian and Hindi spoken languages.

این دوره جزو چیزایی هست که:

۱) هیچ‌جایی به آدم درس نمیدن
۲) همه حرفه‌ای‌ها بلدش هستن
۳) همه چون خودشون یاد گرفتن فکر نکردن که این چیزا بدیهی نیست
۴) بدون اینها شاید زندگیتون بگذره، ولی با دونستن این نکته‌های ریز، نگاهتون به کامپیوتر و توانایی‌هاتون خیلی ساده چند برابر میشه!

https://missing.csail.mit.edu/2020/course-shell/

ویدیوها:
https://www.aparat.com/playlist/406966

💰 Great #PhD opportunity in sunny #brisbane - @ProfMJSimpson with the School of #maths at @QUTSciEng is looking for 2 PhD students for his project in mathematical biology: Mathematical and statistical modelling of cell migration in 4D tumour spheroids. Info: https://t.co/sbvV6JFQCI

Waiting-Time Paradox in 1922

Naoki Masuda, University at Buffalo
Takayuki Hiraoka, Aalto University

Abstract
We present an English translation and discussion of an essay that a Japanese physicist, Torahiko Terada, wrote in 1922. In the essay, he described the waiting-time paradox, also called the bus paradox, which is a known mathematical phenomenon in queuing theory, stochastic processes, and modern temporal network analysis. He also observed and analyzed data on Tokyo City trams to verify the relevance of the waiting-time paradox to busy passengers in Tokyo at the time. This essay seems to be one of the earliest documentations of the waiting-time paradox in a sufficiently scientific manner.

💡 Interested in a rather peculiar type of a phase transition in computational problems, an infinite-order one? Want to learn about a bunch of conjectures to prove rigorously? Here is a paper just for you https://t.co/SLPkEWxiCw

Recovery thresholds in the sparse planted matching problem

Guilhem Semerjian, Gabriele Sicuro, Lenka Zdeborová

We consider the statistical inference problem of recovering an unknown perfect matching, hidden in a weighted random graph, by exploiting the information arising from the use of two different distributions for the weights on the edges inside and outside the planted matching. A recent work has demonstrated the existence of a phase transition, in the large size limit, between a full and a partial recovery phase for a specific form of the weights distribution on fully connected graphs. We generalize and extend this result in two directions: we obtain a criterion for the location of the phase transition for generic weights distributions and possibly sparse graphs, exploiting a technical connection with branching random walk processes, as well as a quantitatively more precise description of the critical regime around the phase transition.

Artificial Intelligence
Instructor:‌ Prof. Patrick Henry Winston
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/index.htm

Course Description
This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

Network Science

Winter - Spring 2020.
Instructors: Prof. Leonid Zhukov, Ilya Makarov
https://www.leonidzhukov.net/hse/2020/networks/

Structural Analysis and Visualization of Networks.
Leonid Zhukov

20 Video lectures
https://www.youtube.com/playlist?list=PLriUvS7IljvkBLqU4nPOZtAkp7rgpxjg1

💰 Postdoctoral position in mathematical modelling of #COVID__19 epidemics:

we are seeking a #postdoc to work in an international collaboration between Bern University Hospital and ICTP. 2 years position funded by SNF grant:
https://t.co/7xK8AQszys

💡 Understanding deep learning is also a job for physicists

Lenka Zdeborová, Nature Physics (2020)

Automated learning from data by means of deep neural networks is finding use in an ever-increasing number of applications, yet key theoretical questions about how it works remain unanswered. A physics-based approach may help to bridge this gap.

https://www.nature.com/articles/s41567-020-0929-2

It's now online:

https://www.facebook.com/CSHVienna/videos/350256895942877

🥁 Undergraduate interested in biological physics? Come explore the Physics of Life in an ONLINE summer school! Free and open to all undergraduates. Mondays, June 15-August 3. More information and registration info here: https://t.co/FSQhNrfFGh

How to cut #SARSCoV2 spread?
Like this:
https://t.co/ijWQzq75eD

#MasksforAll

How to translate a verbal theory into a formal model.

https://t.co/Iqr1lMCAaD
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