🔸 Insightful thread by Hiroki Sayama on where #networkscience is headed to, after listening to many young network scientists at #complenet19!

https://threadreaderapp.com/thread/1108822448559001601.html

همه دروغ می‌گویند!

https://bit.ly/2RxlWFm

کتاب EVERYBODY LIES یکی از کتاب‌های جذاب برای آشنایی با قابلیت‌های حوزه Big Data می‌باشد که توسط ست استفن‌دیویدویتز یکی از دانشمندان داده شرکت گوگل در جهت معرفی قابلیت‌های تحلیل‌داده منتشر گردیده است.

این محقق چهار سال را صرف تحلیل داده‌های ناشناس گوگل کرده است. تحقیقات او درباره موضوعاتی همچون بیماریهای روانی، سقط جنین، مذهب و پزشکی بوده است. او معتقد است که جستجوهای گوگل مهمترین پایگاه داده‌ای است که تاکنون در مورد روح و روان انسان وجود دارد.

در این کتاب مقایسه‌های مختلفی از جستجوهای کاربران موتور جستجو گوگل با نظرسنجی‌ها عمومی صورت گرفته است. نتایج این تحقیق نشان از وجود اختلاف میان این دو حوزه و ارزش تحلیل‌ اطلاعات در عصر جدید است.

در ادامه بخشی از توضیحات این کتاب آورده شده است:
همه دروغ می‌گویند. مردم در مورد اینکه چند بار به باشگاه می‌روند، قیمت کفش آنها چقدر است و کتاب‌هایی که می‌خوانند، دروغ می‌گویند. آنها سر کار نمی‌روند چون بیمار هستند، اما در واقع دروغ می‌گویند. آنها می‌گویند که با شما تماس می‌گیرند، اما نمی‌گیرند.

محور اصلی این کتاب جمله زیر می‌باشد:
آیا مردم در سرچ‌های خود در موتور جستجو گوگل نیز دروغ خواهد گفت؟!

پی‌نوشت:
1- اگر علاقه‌مند به شنیدن کتاب‌های صوتی و پادکست هستید می‌توانید خلاصه این کتاب را در اپیزود شماره 3 پادکست Bplus گوش دهید. در پست بعدی این پادکست قرار داده خواهد شد.

2-مطالعه این کتاب برای متخصصین جامعه‌شناسی، روان‌شناسی، مدیران و... بسیار مفید خواهد بود.

ارادتمند
محمدرضا محتاط

© @DataAnalysis
🎲 @ComplexSys

3:Everybody Lies اپیزود سوم پادکست بی‌پلاس

❗️❗️❗️hiring: postdoc @CUBoulder in Science of Science❗️❗️❗️

Dear friends,

Dan Larremore and I are looking to hire a postdoc to work on an exciting and interdisciplinary project at the intersection of Computational Social Science, the Science of Science, Statistical Inference, Dynamical Systems, and Theoretical Ecology. We are particularly interested in young scholars who are broad thinkers with high scientific standards, and who have strong mathematical, data, and/or computing backgrounds. The project is joint with Jen Dunne and Mirta Galesic at the Santa Fe Institute.

Below is a text version of our ad. Initial deadline is April 15, and the application URL is below.

Would you mind helping us circulate this ad widely? And, if you know if specific people who would be great, please let us know!

Sincerely,
Aaron Clauset

Associate Professor
Department of Computer Science, and
BioFrontiers Institute, at
University of Colorado Boulder, and
External Faculty at Santa Fe Institute

----------------------------

The research groups of Profs. Daniel Larremore and Aaron Clauset at the University of Colorado Boulder seek exceptional candidates for a postdoctoral research associate, to work on an innovative project at the intersection of Computational Social Science, the Science of Science, Statistical Inference, and Dynamical Systems. The initial term of the position is one year, with the possibility of up to two renewals, and will begin no later than August 2019. The project will include collaborations with Prof. Jen Dunne and Prof. Mirta Galesic at the Santa Fe Institute.

Ideal candidates will have a strong mathematical, statistical, and computing background; a strong track record of innovative research and publications in selective venues; and expertise in computational social science and data science. The project will focus on developing new statistical and mathematical models of the causal forces that shape the structure and dynamics of the scientific workforce, spanning individual researchers and their careers, competition among departmental units, and the evolution of entire fields. Our main tools are probabilistic models, random walks, causal inference, and statistical algorithms, coupled with ideas from statistical physics and ecology. Familiarity with one or more of these techniques is desirable, but is not a requirement.

Qualifications:
-- a Ph.D. (or equivalent) in Applied Mathematics, Computer Science, Statistics, or Physics, or in a quantitative branch of Ecology, Sociology, or Computational Social Science, or in a similar field, conferred no later than August, 2019;
-- education or training in statistics, data analysis, and programming;
-- strong verbal and written communication and presentation skills;
-- a commitment to working in an interdisciplinary and collaborative environment.

Applicants should submit the following:
-- a 1-page cover letter that succinctly describes your background and qualifications, and lists two published papers that illustrate your track record, 
-- a full curriculum vitae (CV),
-- a 2-page statement of research interests and accomplishments, and 
-- contact information for at least 3 references

Applications are submitted via https://jobs.colorado.edu/jobs/JobDetail/?jobId=16885 . Full consideration will be given to complete applications received before April 15, 2019.

For additional information, please contact Dan Larremore at daniel.larremore@colorado.edu or Aaron Clauset at aaron.clauset@colorado.edu with subject line "Science of Science Postdoc". Applications submitted via email will not be considered.

For more information about our groups, applicants should visit http://danlarremore.com and http://santafe.edu/~aaronc/.

🎞 'Tips and Tricks for Machine Learning' a live presentation from #KaggleDays Paris by Kaggle Grandmaster Stanislav Semenov.

https://t.co/yOoVKPL0Z0 //

Join us for a Workshop on "Higher-Order Interaction Networks" at @OxUniMaths on Sept 9-11, 2019!

Visit https://t.co/dXnXUtylwO for more information and to register your interest.

🔸 Workshop: Oscillations, Transients and Fluctuations in Complex Networks (OTFCN)

July 1–3, 2019
Copenhagen, Denmark

For more information on the workshop, see: https://t.co/LX8M9tG0Jx

Deadlines:
- abstract submission (talks/posters): April 14, 2019
- registration: May 1, 2019

🎞 https://youtu.be/wV1mWE8Zyi0

#فیلم سخنرانی‌های بیست‌و‌چهارمین مدرسه‌ی فیزیک

دکتر علی‌رضا ولی‌زاده

#فیلم سخنرانی‌های بیست‌و‌چهارمین مدرسه‌ی فیزیک

دکتر غلامرضا جعفری ۱

#فیلم سخنرانی‌های بیست‌و‌چهارمین مدرسه‌ی فیزیک

دکتر غلامرضا جعفری ۲

This algorithm browses Wikipedia to auto-generate textbooks https://t.co/bExALBY6u0

#DeepLearning #MachineLearning #AI #DataScience

“Rich data are revealing that complex dependencies between the nodes of a network may not be captured by models based on pairwise interactions. Higher-order network models go beyond these limitations”

https://t.co/vYBRPpUIJ7

🔥 Machine learning and the physical sciences

Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, Lenka Zdeborová

🔗 https://arxiv.org/pdf/1903.10563.pdf

📌 ABSTRACT
Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences.This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges.

کارسوق علم‌داده - IPM

دوره‌ی کارسوق‌های علم داده تمام ابزار مورد نیاز علم داده در علوم علی‌الخصوص فیزیک را پوشش می‌دهد. این دوره با مباحث پایه آغاز شده و شرکت‌کنندگان در پایان اطلاعات کافی و توانایی حل مسئله خواهند داشت. با توجه به اهمیت این ابزار، فرصت شغلی وسیعتری در انتظار شرکت‌کنندگان خواهد بود. شرکت‌کنندگان حضوری ملزم به انجام تمرینات خواهند بود و در پایان دوره گواهینامه‌ی شرکت دریافت خواهند کرد.
ویدئوی کلاس‌ها ضبط و در شبکه‌های عمومی منتشر خواهد شد و افرادی که به طور غیر حضوری در انجام تمرینات شرکت کنند نیز بنا به درخواست گواهی دریافت خواهند کرد.

برای هماهنگی شرکت حضوری به آقای علیرضا وفایی‌صدر ایمیل (vafaei.sadr@gmail.com) بزنید.

🔗 اطلاعات بیشتر در:
http://physics.ipm.ac.ir/~vafaei/

علیرضا وفایی‌صدر
محقق پسادکتری پژوهشکده‌ی فیزیک، پژوهشگاه دانش‌های بنیادی

🔸 اگر در تهران نيستيد می‌توانيد در كلاس‌های غيرحضوری شركت كنيد!

🎞 We all intuitively understand what is alive and what is not. Equally intuitively we know that time flows forward and the past is distinct from the future. Yet casting these ideas into a predictive mathematical framework and applying it to understand the unique science of living things has been extraordinarily challenging. We now know that the appropriate language for exploring these questions is the mathematics of chance. In other contexts, such as investment strategies which account for fluctuating stock values, we have had success in using the rules of chance to make profitable predictions. Can we do the same to better understand the laws of life and time?

About the Speaker
Using rapid, iterative feedback between theory and experiments, SFI External Professor Srividya Iyer-Biswas (Purdue University) works to discover the basic physical laws that govern the probabilistic behavior of single cells, and that transcend details of specific biological systems. Her research uses a top-down physics approach, rather than more traditional approaches that focus on the cartography of genetic networks and on molecular details.

Iyer-Biswas and her team have reported predicative scaling laws governing the stochastic growth and division of cells, and have developed a theory that reveals the emergence of a scalable, cellular unit of time. Her current work involves extending these results to thermodynamics of organismal computation, time-dependent phenomena involving cellular decision-making, and laws that dictate complex biological and social phenomena.

https://www.aparat.com/v/k4h7e

🎞 "Introduction to Empirical Dynamic Modeling": a very nice video from the Sugihara Lab

"This movie demonstrates the relationship between time series and dynamic attractors"

https://t.co/XsrzlSc8BW

🎬 https://youtu.be/hR3llUTdefk

😺 https://physics.aps.org/articles/v12/35

🔥 Can the laws of physics untangle traffic jams, stock markets, and other #complexsystems?

https://t.co/UNdXXBvuVI