Complex Systems Studies

🌀 https://medium.com/activewizards-machine-learning-company/top-15-python-libraries-for-data-science-in-in-2017-ab61b4f9b4a7

🎞 https://youtu.be/RZGdnAM9Kis

🔗 https://arxiv.org/pdf/1806.05977

🌀 Tehran school on Theory and Applications of Complex Networks 3-7 Shahrivar 1397 🔗 More info: http://facultymembers.sbu.ac.ir/jafari/events/ 📄 Registration: http://psi.ir/tacn2018_3.asp

A tweak to the infamous “critical brain” hypothesis accounts for the brain’s stability and adaptivity. https://t.co/Hs8pYKxpt0 https://t.co/ehWuun9kep

🔖 طرح پیشنهادی دروس گرایش «فیزیک آماری و سامانه‌های پیچیده» مقطع کارشناسی‌ارشد در دانشگاه شهید بهشتی http://facultymembers.sbu.ac.ir/jafari/fa/sbu-complex-systems/

🌀 “How to build your own Neural Network from scratch in Python” by James Loy https://towardsdatascience.com/how-to-build-your-own-neural-network-from-scratch-in-python-68998a08e4f6

An interdisciplinary forum for complexity research : PLOS Complexity Channel https://t.co/Z6X8qNEuN2 Meet the editor here https://t.co/slUfig9dUC https://t.co/0eiYrC40O9

NEW M.Sc. Program in Natural Language Processing (NLP) and Data Science Université de Lorraine, Nancy (France) The Institute of Digital science, Management and Cognition  is opening a a new Masters Program in NLP - ​​ Computer Sciences, Speech, Language and Knowledge Representation **********         http://institut-sciences-digitales.fr/idmc-master-degree-in-natural-language-processing/ ********** So you want to be a specialist in Neural Networks, Logic, Speech  Processing, Information Retrieval, Knowledge Representation ... all  for Natural Language? Well, now's your chance! Natural Language Processing (NLP) lies at the crossroads of  linguistics, computer science and artificial intelligence. This Masters Program offers a modern curriculum which combines the different  approaches, and  covers both theoretical and applied perspectives. In each semester, the program includes a hands-on project.  It ends with a 6-month paid internship in a company or a research  lab. You can find the course description below. ---------------------------------------------------------------------------------------- You can apply to directly enter at either the first year or second year level. ---------------------------------------------------------------------------------------- Language ------------- All courses are taught in English (except the "French for non-native Speakers" class). Fees ------- The University of Lorraine is publicly funded and thus offers  tuition-free education for all students including students from  both inside and outside EU/EEA/EFTA countries. The only student  expenditures are a nominal semester fee of about 600 euros which  includes health insurance. Nancy’s high quality of life goes  hand-in-hand with a low cost of living.

🌋http://www.quantamagazine.org/the-physics-of-glass-opens-a-window-into-biology-20180611/ In glassy systems, we think that many of these interesting properties occur because there’s what’s called a complex potential energy landscape. If you consider the total energy of the entire system as a function of where the atoms are, then in a glass, which is disordered, that landscape is incredibly complex. It turns out that the neural networks used for deep learning and optimization share a surprisingly large number of properties with glasses. You can think of the nodes of the network as particles, and the connections between them as the bonds between particles. If you do, the neural networks and the glasses have complex potential energy landscapes with nearly identical properties. For example, questions about the energy barriers between states in a neural network are related to questions about how likely it is for a glassy material to flow. So the hope is that understanding some of the properties of glasses can help you understand optimization in these neural networks, too.

💫 http://blogs.nature.com/naturejobs/2017/07/21/from-doctorate-to-data-science-a-very-short-guide/

"The importance of the whole: topological data analysis for the network neuroscientist" https://arxiv.org/pdf/1806.05167

🔖 The importance of the whole: topological data analysis for the network neuroscientist Ann E. Sizemore, Jennifer Phillips-Cremins, Robert Ghrist, Danielle S. Bassett 🔗 https://arxiv.org/pdf/1806.05167 📌 ABSTRACT The application of network techniques to the analysis of neural data has greatly improved our ability to quantify and describe these rich interacting systems. Among many important contributions, networks have proven useful in identifying sets of node pairs that are densely connected and that collectively support brain function. Yet the restriction to pairwise interactions prevents us from realizing intrinsic topological features such as cavities within the interconnection structure that may be just as crucial for proper function. To detect and quantify these topological features we must turn to methods from algebraic topology that encode data as a simplicial complex built of sets of interacting nodes called simplices. On this substrate, we can then use the relations between simplices and higher-order connectivity to expose cavities within the complex, thereby summarizing its topological nature. Here we provide an introduction to persistent homology, a fundamental method from applied topology that builds a global descriptor of system structure by chronicling the evolution of cavities as we move through a combinatorial object such as a weighted network. We detail the underlying mathematics and perform demonstrative calculations on the mouse structural connectome, electrical and chemical synapses in \textit{C. elegans}, and genomic interaction data. Finally we suggest avenues for future work and highlight new advances in mathematics that appear ready for use in revealing the architecture and function of neural systems.

⚽ https://projects.fivethirtyeight.com/2018-world-cup-predictions/

🌀 https://www.scoop.it/t/ecolophysics

2018 Fellows of the Network Science Society announced at #netsci2018.

🤹‍♂ https://www.nature.com/articles/ncomms10196