🌀 What Are Inverse Problems?
http://www.siltanen-research.net/IPexamples/inverse_problems

⚡️ Inverse Problems course, spring 2017

🔗 http://wiki.helsinki.fi/display/mathstatKurssit/Inverse+problems%2C+spring+2017
🎞 https://www.youtube.com/playlist?list=PLyIjfdC_fHWYSVIcrNtV9Hr7zAGE3GF-6

Teacher: Samuli Siltanen
Topics: Inverse problems are about measuring something indirectly and trying to recover that something from the data. For example, a doctor may take several X-ray images of a patient from different directions and wish to understand the three-dimensional structure of the patient's inner organs. But each of the 2D images only shows a projection of the inner organs; one has to actually calculate the 3D structure using a reconstruction algorithm. This course teaches how to

🔹 model a (linear) measurement process as a matrix equation m = Ax + noise,
🔹 detect if the matrix A leads to an ill-posed inverse problem,
🔹 design and implement a regularized reconstruction method for recovering x from m. We study truncated singular value decomposition, Tikhonov regularization, total variation regularization and wavelet-based sparsity,
🔹 measure tomographic data in X-ray laboratory,
🔹 report your findings in the form of a scientific poster.

Prerequisites: Linear algebra, basic Matlab programming skills, interest in practical applications, and a curious mind. The course is suitable (and very useful) for students of mathematics, statistics, physics or computer science.

⚡️ 3 lectures: #Nonequilibrium_Statistical_Mechanics
Chris Jarzynski, University of Maryland

1️⃣ Fundamental Problems and Applications Nonequilibrium work relations
🎞 http://www.aparat.com/v/ehcsf

2️⃣ Microscopic systems driven away from equilibrium
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3️⃣ Dissipation and the Arrow of Time
🎞 http://www.aparat.com/v/0BJAY

☑️ Round table on open problems in #Nonequilibrium_Statistical_Mechanics

🎞 Michael Aizenmann, Princeton University
🔗 http://www.aparat.com/v/a4FqV

🎞 Juerg Froehlich Institute for Advanced Study
🔗 http://www.aparat.com/v/V3dzE

Introduction to the Modeling and Analysis of
Complex Systems
Hiroki Sayama

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike
3.0 Unported License.

🌀 https://medium.com/complex-systems-channel/1-4-universality-1e51ec144390#.qhgfoy49g

🔗 http://www.necsi.edu/research/multiscale/universalitytext.pdf

🗞 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.012307

🎞 http://www.aparat.com/v/H2azX

🎞 http://www.aparat.com/v/yT7X4

#مدرسه سه روزه #علم_داده (Data science)

🗓 مرداد و شهريور 96
📍دانشگاه شهید بهشتی
درصورت نیاز،خوابگاه به شرکت کنندگان تعلق میگیرد!

منتظر اطلاعیه های بعدی باشید...
انجمن علمی فیزیک بهشتی
@sbu_physics

🗞 Community Discovery in Dynamic Networks: a Survey

Giulio Rossetti, Rémy Cazabet

🔗 https://arxiv.org/pdf/1707.03186

📌 ABSTRACT
Networks built to model real world phenomena are characeterised by some properties that have attracted the attention of the scientific community: (i) they are organised according to community structure and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and challenging problem started capturing researcher interest recently: the identification of evolving communities. To model the evolution of a system, dynamic networks can be used: nodes and edges are mutable and their presence, or absence, deeply impacts the community structure that composes them. The aim of this survey is to present the distinctive features and challenges of dynamic community discovery, and propose a classification of published approaches. As a "user manual", this work organizes state of art methodologies into a taxonomy, based on their rationale, and their specific instanciation. Given a desired definition of network dynamics, community characteristics and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers to choose in which direction should future research be oriented

🔸 https://www.nature.com/news/ant-colonies-flow-like-fluid-to-build-tall-towers-1.22290
🎞👇

Ant architecture: The simple rules of ant construction

🔸 http://news.harvard.edu/gazette/story/2013/10/as-complex-as-a-toy/

Summer School on Complex Socio-Technical Systems, 4-8/09
#IFISC10years #SocioComplex17

https://sociocomplex2017.ifisc.uib-csic.es/

🎞 http://www.aparat.com/v/LphAK

Dr. Paul Hooper challenges Dr. Eleanor Power to discuss how religion and suffering bring people together while eating mouth scalding New Mexico green chile!

🔅 http://www.cidid.org/publications-1/2017/7/14/monte-carlo-profile-confidence-intervals-for-dynamic-systems

🔗 https://static1.squarespace.com/static/53d8f599e4b0ba978ec978d1/t/5969291bc534a5cc075a1e3f/1500064029155/20170126.full.pdf

Thomas House explains the maths behind memes