🔵 From Navigation on the High Seas to Magnets at High Temperatures
How Conformal Maps Enter Our Lives
https://sinews.siam.org/DetailsPage/TabId/900/ArtMID/2243/ArticleID/1738/From-Navigation-on-the-High-Seas-to-Magnets-at-High-Temperatures.aspx
#CFT
How Conformal Maps Enter Our Lives
https://sinews.siam.org/DetailsPage/TabId/900/ArtMID/2243/ArticleID/1738/From-Navigation-on-the-High-Seas-to-Magnets-at-High-Temperatures.aspx
#CFT
SIAM News
From Navigation on the High Seas to Magnets at High Temperatures
Just imagine that you have attended the SIAM Conference on Industrial and Applied Geometry (GD29) in San Francisco and you are aboard a plane that is taking you back home to your research institute in Berlin. You see on the monitor in front of you that the…
📝 The physics of multilayer networks
Manlio De Domenico, Clara Granell, Mason A. Porter, Alex Arenas
https://arxiv.org/pdf/1604.02021v1
📌 ABSTRACT
The study of networks plays a crucial role in investigating the structure, dynamics, and function of a wide variety of complex systems in myriad disciplines. Despite the success of traditional network analysis, standard networks provide a limited representation of these systems, which often includes different types of relationships (i.e., "multiplexity") among their constituent components and/or multiple interacting subsystems. Such structural complexity has a significant effect on both dynamics and function. Throwing away or aggregating available structural information can generate misleading results and provide a major obstacle towards attempts to understand the system under analysis. The recent "multilayer' approach for modeling networked systems explicitly allows the incorporation of multiplexity and other features of realistic networked systems. On one hand, it allows one to couple different structural relationships by encoding them in a convenient mathematical object. On the other hand, it also allows one to couple different dynamical processes on top of such interconnected structures. The resulting framework plays a crucial role in helping to achieve a thorough, accurate understanding of complex systems. The study of multilayer networks has also revealed new physical phenomena that remained hidden when using the traditional network representation of graphs. Here we survey progress towards a deeper understanding of dynamical processes on multilayer networks, and we highlight some of the physical phenomena that emerge from multilayer structure and dynamics.
Manlio De Domenico, Clara Granell, Mason A. Porter, Alex Arenas
https://arxiv.org/pdf/1604.02021v1
📌 ABSTRACT
The study of networks plays a crucial role in investigating the structure, dynamics, and function of a wide variety of complex systems in myriad disciplines. Despite the success of traditional network analysis, standard networks provide a limited representation of these systems, which often includes different types of relationships (i.e., "multiplexity") among their constituent components and/or multiple interacting subsystems. Such structural complexity has a significant effect on both dynamics and function. Throwing away or aggregating available structural information can generate misleading results and provide a major obstacle towards attempts to understand the system under analysis. The recent "multilayer' approach for modeling networked systems explicitly allows the incorporation of multiplexity and other features of realistic networked systems. On one hand, it allows one to couple different structural relationships by encoding them in a convenient mathematical object. On the other hand, it also allows one to couple different dynamical processes on top of such interconnected structures. The resulting framework plays a crucial role in helping to achieve a thorough, accurate understanding of complex systems. The study of multilayer networks has also revealed new physical phenomena that remained hidden when using the traditional network representation of graphs. Here we survey progress towards a deeper understanding of dynamical processes on multilayer networks, and we highlight some of the physical phenomena that emerge from multilayer structure and dynamics.
🔴 The Paper Shredder
Great, curated collection of recent papers about complex systems and networks
http://www.uvm.edu/~cmplxsys/teaching-learning/papershredder/
Great, curated collection of recent papers about complex systems and networks
http://www.uvm.edu/~cmplxsys/teaching-learning/papershredder/
Vermont Complex Systems Center
The Paper Shredder
Every two weeks, and in the grand tradition of courageous academics everywhere, we review the painstaking, meticulous work of others not present to defend themselves. Pure of motivation, we endeavo…
🚦 Dear Friends
In case you want to participate in gathering the proper content for this channel, you can simply mention your motivation with one of the admins.
E.g,
@e_amirzadeh @onmjnl @NoOneFromNeverland @carimi
Thank you for your interest in "Complex Systems Studies" Channel
In case you want to participate in gathering the proper content for this channel, you can simply mention your motivation with one of the admins.
E.g,
@e_amirzadeh @onmjnl @NoOneFromNeverland @carimi
Thank you for your interest in "Complex Systems Studies" Channel
This media is not supported in your browser
VIEW IN TELEGRAM
A New Spin on the Quantum Brain
A new theory explains how fragile quantum states may be able to exist for hours or even days in our warm, wet brain. Experiments should soon test the idea.
A new theory explains how fragile quantum states may be able to exist for hours or even days in our warm, wet brain. Experiments should soon test the idea.
Complex Systems Studies
A New Spin on the Quantum Brain A new theory explains how fragile quantum states may be able to exist for hours or even days in our warm, wet brain. Experiments should soon test the idea.
🎯 https://www.quantamagazine.org/20161102-quantum-neuroscience/
#Neuroscience #Quantum_Mechanics #Biology #Chemistry #Nuclear_Physics
#Neuroscience #Quantum_Mechanics #Biology #Chemistry #Nuclear_Physics
Quanta Magazine
A New Spin on the Quantum Brain
A new theory explains how fragile quantum states may be able to exist for hours or even days in our warm, wet brain. Experiments should soon test the idea.
#سلسله_سمینارهای_هفتگی گروه سیستم های پیچیده شهید بهشتی
علاقه مندان می توانند برای ارائه موضوعات خود به ادمین پیام داده یا به صورت حضوری در جلسه مطرح نمایند.
@onmjnl
علاقه مندان می توانند برای ارائه موضوعات خود به ادمین پیام داده یا به صورت حضوری در جلسه مطرح نمایند.
@onmjnl
اعلام برنامه های سلسله سمینارهای بیوفیزیک پژوهشکده IBB دانشگاه تهران:
🔵 A great review of #networkscience and neurology by @AmarDhand
http://www.nature.com/nrneurol/journal/v12/n10/full/nrneurol.2016.119.html
http://www.nature.com/nrneurol/journal/v12/n10/full/nrneurol.2016.119.html
🔵 🎓 Data Science MOOCs starting this week!
http://us8.campaign-archive1.com/?u=c4e45e47176fffe244e832ba8&id=14066ba5e9
http://us8.campaign-archive1.com/?u=c4e45e47176fffe244e832ba8&id=14066ba5e9
📝 Sensitive dependence of network dynamics on network structure
Takashi Nishikawa, Jie Sun, Adilson E. Motter
https://arxiv.org/pdf/1611.01164v1
📌 ABSTRACT
The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important longstanding problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, we demonstrate that the stability of the dynamical state, as determined by the maximum Lyapunov exponent, can exhibit a cusp-like dependence on the number of nodes and links as well as on the size of perturbations applied to the network structure. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of optimal networks and the prevalence of eigenvector degeneracy in these networks. These findings establish a unified characterization of networks optimized for dynamical stability in diffusively coupled systems, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, and several other examples. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.
#Adaptation and #Self-Organizing Systems (nlin.AO); Physics and Society (physics.soc-ph)
Takashi Nishikawa, Jie Sun, Adilson E. Motter
https://arxiv.org/pdf/1611.01164v1
📌 ABSTRACT
The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important longstanding problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, we demonstrate that the stability of the dynamical state, as determined by the maximum Lyapunov exponent, can exhibit a cusp-like dependence on the number of nodes and links as well as on the size of perturbations applied to the network structure. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of optimal networks and the prevalence of eigenvector degeneracy in these networks. These findings establish a unified characterization of networks optimized for dynamical stability in diffusively coupled systems, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, and several other examples. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.
#Adaptation and #Self-Organizing Systems (nlin.AO); Physics and Society (physics.soc-ph)
📄 Hidden geometric correlations in real multiplex networks
Kaj-Kolja Kleineberg, Marián Boguñá, M. Ángeles Serrano & Fragkiskos Papadopoulos
http://www.nature.com/nphys/journal/v12/n11/full/nphys3812.html
📌 ABSTRACT:
Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the layers. We find that these correlations are significant in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers. They also enable accurate trans-layer link prediction, meaning that connections in one layer can be predicted by observing the hidden geometric space of another layer. And they allow efficient targeted navigation in the multilayer system using only local knowledge, outperforming navigation in the single layers only if the geometric correlations are sufficiently strong.
Subject terms:
#Applied_physics
#Complex_networks
#Statistics
Kaj-Kolja Kleineberg, Marián Boguñá, M. Ángeles Serrano & Fragkiskos Papadopoulos
http://www.nature.com/nphys/journal/v12/n11/full/nphys3812.html
📌 ABSTRACT:
Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the layers. We find that these correlations are significant in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers. They also enable accurate trans-layer link prediction, meaning that connections in one layer can be predicted by observing the hidden geometric space of another layer. And they allow efficient targeted navigation in the multilayer system using only local knowledge, outperforming navigation in the single layers only if the geometric correlations are sufficiently strong.
Subject terms:
#Applied_physics
#Complex_networks
#Statistics