❌ WARNING:
⚠️We strongly recommend you to update all of your devices, including desktops, laptops, and mobile devices:
https://en.wikipedia.org/wiki/Spectre_(security_vulnerability)
https://en.wikipedia.org/wiki/Meltdown_(security_vulnerability)
⚠️We strongly recommend you to update all of your devices, including desktops, laptops, and mobile devices:
https://en.wikipedia.org/wiki/Spectre_(security_vulnerability)
https://en.wikipedia.org/wiki/Meltdown_(security_vulnerability)
🛑 Microsoft released an emergency update to Windows 10, 8.1, and 7 SP1 to address the vulnerability on January 3, 2018, as well as Windows Server. These patches are known to cause conflicts with specific third-party antivirus software that use unsupported kernel calls;
https://en.wikipedia.org/wiki/Meltdown_(security_vulnerability)
https://en.wikipedia.org/wiki/Meltdown_(security_vulnerability)
Complex Systems Studies pinned «❌ WARNING: ⚠️We strongly recommend you to update all of your devices, including desktops, laptops, and mobile devices: https://en.wikipedia.org/wiki/Spectre_(security_vulnerability) https://en.wikipedia.org/wiki/Meltdown_(security_vulnerability)»
IBM Research - Zurich, Computational systems biology
https://www.zurich.ibm.com/compsysbio/
https://www.zurich.ibm.com/compsysbio/
Complexity Matters: A Re-Introduction to Complexity | Denise Easton | LinkedIn
https://www.linkedin.com/pulse/complexity-matters-re-introduction-denise-easton
https://www.linkedin.com/pulse/complexity-matters-re-introduction-denise-easton
Linkedin
Complexity Matters: A Re-Introduction to Complexity
"Scientific knowledge, originally seen to make possible the prediction and manipulation of nature, appears now to be pointing us toward a new relationship with the natural world based on sensitive observation and participation, rather than control." Brian…
📖 Phase Transitions and Collective Phenomena Lecture Notes
Prof Ben Simons
http://www.tcm.phy.cam.ac.uk/~bds10/phase.html
Prof Ben Simons
http://www.tcm.phy.cam.ac.uk/~bds10/phase.html
Arthur "Tree" Cayley (RB, 2018). 1024 vertices. 1023 edges. Connected and acylic. https://t.co/p2MUGW5Wdz
🔸 دکتر رامین گلستانیان به عنوان مدیر علمی به انستیتو ماکس پلانک پیوست
http://www.psi.ir/news2_fa.asp?id=2381
آقای دکتر رامین گلستانیان، فیزیکدان و دانشآموخته دانشگاههای صنعتی شریف و تحصیلات تکمیلی زنجان به عنوان دایراکتور (مدیر علمی) به پژوهشگاه دینامیک و خودساماندهی ماکس پلانک در شهر گوتینگن آلمان پیوست. دکتر گلستانیان کارشناسی فیزیک خود را از دانشگاه صنعتی شریف و کارشناسی ارشد و دکتری خود را از دانشگاه تحصیلات تکمیلی زنجان گرفت. او که در کارنامه خود ریاست دانشکده فیزیک دانشگاه تحصیلات تکمیلی زنجان و استادی دانشگاه آکسفورد را دارد، از ابتدای سال جاری مسیحی، به پژوهشگاه ماکس پلانک در شهر گوتینگن پیوست تا بخشی را با عنوان فیزیک مواد زنده، در آن پژوهشگاه راهاندازی و هدایت کند.
goo.gl/xW9MvU
http://www.psi.ir/news2_fa.asp?id=2381
آقای دکتر رامین گلستانیان، فیزیکدان و دانشآموخته دانشگاههای صنعتی شریف و تحصیلات تکمیلی زنجان به عنوان دایراکتور (مدیر علمی) به پژوهشگاه دینامیک و خودساماندهی ماکس پلانک در شهر گوتینگن آلمان پیوست. دکتر گلستانیان کارشناسی فیزیک خود را از دانشگاه صنعتی شریف و کارشناسی ارشد و دکتری خود را از دانشگاه تحصیلات تکمیلی زنجان گرفت. او که در کارنامه خود ریاست دانشکده فیزیک دانشگاه تحصیلات تکمیلی زنجان و استادی دانشگاه آکسفورد را دارد، از ابتدای سال جاری مسیحی، به پژوهشگاه ماکس پلانک در شهر گوتینگن پیوست تا بخشی را با عنوان فیزیک مواد زنده، در آن پژوهشگاه راهاندازی و هدایت کند.
goo.gl/xW9MvU
4_532580664873119184.mp4
15.8 MB
Lorenz Attractor Simulation
Tetris-like falling sticky disks
https://mathoverflow.net/questions/101309/tetris-like-falling-sticky-disks
https://mathoverflow.net/questions/101309/tetris-like-falling-sticky-disks
🔖 Scale-free networks are rare
Anna D. Broido, Aaron Clauset
🔗 arxiv.org/pdf/1801.03400.pdf
📌 ABSTRACT
A central claim in modern network science is that real-world networks are typically "scale free," meaning that the fraction of nodes with degree k follows a power law, decaying like k−α, often with 2<α<3. However, empirical evidence for this belief derives from a relatively small number of real-world networks. We test the universality of scale-free structure by applying state-of-the-art statistical tools to a large corpus of nearly 1000 network data sets drawn from social, biological, technological, and informational sources. We fit the power-law model to each degree distribution, test its statistical plausibility, and compare it via a likelihood ratio test to alternative, non-scale-free models, e.g., the log-normal. Across domains, we find that scale-free networks are rare, with only 4% exhibiting the strongest-possible evidence of scale-free structure and 52% exhibiting the weakest-possible evidence. Furthermore, evidence of scale-free structure is not uniformly distributed across sources: social networks are at best weakly scale free, while a handful of technological and biological networks can be called strongly scale free. These results undermine the universality of scale-free networks and reveal that real-world networks exhibit a rich structural diversity that will likely require new ideas and mechanisms to explain.
Anna D. Broido, Aaron Clauset
🔗 arxiv.org/pdf/1801.03400.pdf
📌 ABSTRACT
A central claim in modern network science is that real-world networks are typically "scale free," meaning that the fraction of nodes with degree k follows a power law, decaying like k−α, often with 2<α<3. However, empirical evidence for this belief derives from a relatively small number of real-world networks. We test the universality of scale-free structure by applying state-of-the-art statistical tools to a large corpus of nearly 1000 network data sets drawn from social, biological, technological, and informational sources. We fit the power-law model to each degree distribution, test its statistical plausibility, and compare it via a likelihood ratio test to alternative, non-scale-free models, e.g., the log-normal. Across domains, we find that scale-free networks are rare, with only 4% exhibiting the strongest-possible evidence of scale-free structure and 52% exhibiting the weakest-possible evidence. Furthermore, evidence of scale-free structure is not uniformly distributed across sources: social networks are at best weakly scale free, while a handful of technological and biological networks can be called strongly scale free. These results undermine the universality of scale-free networks and reveal that real-world networks exhibit a rich structural diversity that will likely require new ideas and mechanisms to explain.