"Bridging data science and dynamical systems theory" (by Tyrus Berry, Dimitrios Giannakis, John Harlim): https://arxiv.org/abs/2002.07928

"This short review describes mathematical techniques for statistical analysis and prediction in dynamical systems."

Bayesian stochastic blockmodeling
Tiago P. Peixoto
last revised 6 Feb 2020 (this version, v8)

https://arxiv.org/pdf/1705.10225

This chapter provides a self-contained introduction to the use of Bayesian inference to extract large-scale modular structures from network data, based on the stochastic blockmodel (SBM), as well as its degree-corrected and overlapping generalizations. We focus on nonparametric formulations that allow their inference in a manner that prevents overfitting, and enables model selection. We discuss aspects of the choice of priors, in particular how to avoid underfitting via increased Bayesian hierarchies, and we contrast the task of sampling network partitions from the posterior distribution with finding the single point estimate that maximizes it, while describing efficient algorithms to perform either one. We also show how inferring the SBM can be used to predict missing and spurious links, and shed light on the fundamental limitations of the detectability of modular structures in networks.

یازدهمین کنفرانس فیزیک آماری، ماده چگال نرم و سیستم‌های پیچیده
۲۷ و ۲۸ فروردین ۱۳۹۹

⚡ مهلت ارسال مقاله تا ۵ اسفند ماه تمدید شد. ⚡

http://www.psi.ir/farsi.asp?page=smc99

‏قبل از اینکه سر رأی دادن و ندادن با هم بجنگید، این را یک دور بازی کنید:
https://t.co/JejBjCAECa‎

@hamed_allaei

On looking below the shoulders of giants is now out in PRR.

Learn how to deal with the full body of literature behind direct citations to articles.

https://t.co/72gIRPlnyD

💰 Fantastic opportunity to join a #PhD training programme with strong industry links, a large cohort of students, and of course brilliant academic supervision https://t.co/gcz6eFsorc

به سرعت انتشار ویروس #کرونا توجه کنند.

با سلام
با کلیک بر روی لینک زیر👇 بصورت لحظه ای آخرین آمار مرگ ومیر ، ابتلا و جزئیات کروناویروس در ایران و جهان را می توانید دریافت نمائید .

https://www.worldometers.info/coronavirus/

💡 Lessons learnt from 288 #COVID19 international cases:
importations over time, effect of interventions,
underdetection of imported cases

https://t.co/xY2wfUSJeO

🎞 https://www.ted.com/talks/nicholas_christakis_how_social_networks_predict_epidemics?language=fa

#کرونا | این فصل از کتاب شبکه باراباشی مدل‌های اپیدمی رو توضیح داده. شاید خوب باشه که لاقل ما اهالی علم این بخش رو درست بخونیم و درک بهتری داشته باشیم از این مسئله. اگه جایی خواستیم نظر بدیم، کمی دقیق‌تر حرف بزنیم.

Just advertising three new #PhD positions focused on networks dynamics, biodiversity and social data. More soon.

💰 https://t.co/AKdpLLXPds
💰 https://t.co/Bk8o6YlZcL
💰 https://t.co/YrDLvDw851

🦠 https://pourianazemi.com/2020/02/25/fgsw-cov/

Interacting contagions
Sune Lehmann

Nature Physics (2020)

Complex contagions — for example when ideas spread across a network — are thought to be different from the simple contagions observed for infections. Simple contagions are now shown to exhibit a key macroscopic characteristic of complex behaviour when they interact.

https://www.nature.com/articles/s41567-020-0817-9

🧬 Lecture on the Coronavirus outbreak
Feb 26th 10:15

https://memento.epfl.ch/event/lecture-on-the-coronavirus-outbreak-on-feb-26th-10/

The lecture will focus on the epidemiology of the ongoing coronavirus (COVID-19) outbreak, and will exceptionally be streamed on Zoom.

📺 Live stream from Youtube:

https://www.youtube.com/channel/UCyUR1k228kWH_coPfR7ffqw

🧬 #Coronavirius #COVID19 #کرونا
https://www.sciencemag.org/news/2020/02/coronavirus-seems-unstoppable-what-should-world-do-now

🔅PhD position:
Candidates are sought for a funded PhD position in Data-Driven (Bayesian) Optimization for Problems with Dynamic Resource Constraints in the Decision and Cognitive Sciences Research Centre, at the Alliance Manchester Business School, The University of Manchester. The position is funded for a period of 4 years by EPSRC and IBM.


The #PhD position is associated with an iCASE studentship with IBM and will focus on the development of algorithms and decision support tools for closed-loop problems with dynamic resource constraints. This includes developing data-driven/Bayesian optimization algorithms capable of dealing with closed-loop problems subject to constraints that model the temporal availability of resources needed to evaluate a candidate solution. 


The PhD student will be encouraged to collaborate with peers in the research centre and develop a wide range of skills including project management, mentoring of interns, and presentation skills. They will also be expected to present their work at major international conferences and participate in events linked to the Institute for Data Science and Artificial Intelligence at Manchester. Both the Institute and the Decision and Cognitive Sciences Research Centre are affiliated with the Alan Turing Institute providing access to the resources and network of the Institute through its fellows programme. This includes access to data study groups and interest groups at the Turing.


The student will be supervised by Dr Richard Allmendinger, Dr Manuel Lopez-Ibanez, Dr Jonathan Shapiro, and Prof Joshua Knowles. Dr Matt Benatan, Algorithms Subgroup Lead in Machine Learning and AI at IBM Research UK will act as external supervisor and lead the industrial input into this research. The ICASE studentship also offers the opportunity to be partly based at IBM Research UK.


The team at IBM Research is focused on cutting-edge research in advanced Bayesian modelling, including Bayesian Optimization, and are thus invested in how Bayesian Optimization can be extended to challenging optimization tasks. The project will therefore contribute directly to IBM's ongoing research with the opportunity for influencing future IBM products. 


For instructions on how to apply, please refer to: https://www.jobs.ac.uk/job/BYV319/epsrc-ibm-industrial-case-phd-studentship-in-data-driven-optimization-tuning-bayesian-optimization-for-problems-with-dynamic-resource-constraints