🎞 Why Stock Markets Crash?
http://www.aparat.com/v/h5HYd
https://www.youtube.com/watch?v=l-ohH1DGqRs
#complex_systems#stock_market#sornette#predict#crash
http://www.aparat.com/v/h5HYd
https://www.youtube.com/watch?v=l-ohH1DGqRs
#complex_systems#stock_market#sornette#predict#crash
آپارات - سرویس اشتراک ویدیو
Didier Sornette, Reflexivity-endogeneity pervades financial markets from high fr
Instabilities in financial markets
Simposio per il 202° anniversario del decreto di fondazione della Scuola Normale Superiore
19 ottobre 2012, Scuola Normale Superiore
Simposio per il 202° anniversario del decreto di fondazione della Scuola Normale Superiore
19 ottobre 2012, Scuola Normale Superiore
A predictor of financial crisis based on statistical methods
http://tasmania.ethz.ch/pubfco/fco.html
#crash#stock_market#sornette#financial_crisis_observatory
http://tasmania.ethz.ch/pubfco/fco.html
#crash#stock_market#sornette#financial_crisis_observatory
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What is Critical Slowing Down?
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Mesmerizing drone and aerial video shows sharks swimming through massive schools of fish
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Visualizing Frustration: Through the Spinning Glass.webm
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Visualizing Frustration: Through the Spinning Glass
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Visualizing Frustration: Through the Spinning Glass.webm
🔹 Visualizing Frustration:
Through the Spinning-Glass
Randy Andrews Mentor: Ruben Andrist
August 15, 2014
📄 http://samoa.santafe.edu/media/cms_page_media/583/randypaper.pdf
Through the Spinning-Glass
Randy Andrews Mentor: Ruben Andrist
August 15, 2014
📄 http://samoa.santafe.edu/media/cms_page_media/583/randypaper.pdf
🔖 An exact method for computing the frustration index in signed networks using binary programming
Samin Aref, Andrew J. Mason, Mark C. Wilson
🔗 https://arxiv.org/pdf/1611.09030
📌 ABSTRACT
Computing the frustration index of a signed graph is a key to solving problems in different fields of research including social networks, physics, material science, and biology. In social networks the frustration index determines network distance from a state of structural balance. Although the definition of frustration index goes back to 1960, an exact algorithmic computation method has not yet been proposed. The main reason seems to be the complexity of computing the frustration index which is closely related to well-known NP-hard problems such as MAXCUT.
New quadratic and linear binary programming models are developed to compute the frustration index exactly. We introduce several speed-up techniques involving prioritised branching, local search heuristics, and valid inequalities inferred from graph structural properties. The computational improvements achieved by implementing the speed-up techniques allow us to calculate the exact values of the frustration index by running the optimisation models in Gurobi solver.
The speed-up techniques make our models capable of processing graphs with thousands of nodes and edges in seconds on inexpensive hardware. The solve time and solution quality comparison against the literature shows the superiority of our models in both random and real signed networks.
Samin Aref, Andrew J. Mason, Mark C. Wilson
🔗 https://arxiv.org/pdf/1611.09030
📌 ABSTRACT
Computing the frustration index of a signed graph is a key to solving problems in different fields of research including social networks, physics, material science, and biology. In social networks the frustration index determines network distance from a state of structural balance. Although the definition of frustration index goes back to 1960, an exact algorithmic computation method has not yet been proposed. The main reason seems to be the complexity of computing the frustration index which is closely related to well-known NP-hard problems such as MAXCUT.
New quadratic and linear binary programming models are developed to compute the frustration index exactly. We introduce several speed-up techniques involving prioritised branching, local search heuristics, and valid inequalities inferred from graph structural properties. The computational improvements achieved by implementing the speed-up techniques allow us to calculate the exact values of the frustration index by running the optimisation models in Gurobi solver.
The speed-up techniques make our models capable of processing graphs with thousands of nodes and edges in seconds on inexpensive hardware. The solve time and solution quality comparison against the literature shows the superiority of our models in both random and real signed networks.
#Review_Article on #granular_matter & #networks
🔖 Network Analysis of Particles and Grains
Lia Papadopoulos, Mason A. Porter, Karen E. Daniels, Danielle S. Bassett
🔗 https://arxiv.org/pdf/1708.08080
📌 ABSTRACT
The arrangements of particles and forces in granular materials and particulate matter have a complex organization on multiple spatial scales that range from local structures to mesoscale and system-wide ones. This multiscale organization can affect how a material responds or reconfigures when exposed to external perturbations or loading. The theoretical study of particle-level, force-chain, domain, and bulk properties requires the development and application of appropriate mathematical, statistical, physical, and computational frameworks. Traditionally, granular materials have been investigated using particulate or continuum models, each of which tends to be implicitly agnostic to multiscale organization. Recently, tools from network science have emerged as powerful approaches for probing and characterizing heterogeneous architectures in complex systems, and a diverse set of methods have yielded fascinating insights into granular materials. In this paper, we review work on network-based approaches to studying granular materials (and particulate matter more generally) and explore the potential of such frameworks to provide a useful description of these materials and to enhance understanding of the underlying physics. We also outline a few open questions and highlight particularly promising future directions in the analysis and design of granular materials and other particulate matter.
🔖 Network Analysis of Particles and Grains
Lia Papadopoulos, Mason A. Porter, Karen E. Daniels, Danielle S. Bassett
🔗 https://arxiv.org/pdf/1708.08080
📌 ABSTRACT
The arrangements of particles and forces in granular materials and particulate matter have a complex organization on multiple spatial scales that range from local structures to mesoscale and system-wide ones. This multiscale organization can affect how a material responds or reconfigures when exposed to external perturbations or loading. The theoretical study of particle-level, force-chain, domain, and bulk properties requires the development and application of appropriate mathematical, statistical, physical, and computational frameworks. Traditionally, granular materials have been investigated using particulate or continuum models, each of which tends to be implicitly agnostic to multiscale organization. Recently, tools from network science have emerged as powerful approaches for probing and characterizing heterogeneous architectures in complex systems, and a diverse set of methods have yielded fascinating insights into granular materials. In this paper, we review work on network-based approaches to studying granular materials (and particulate matter more generally) and explore the potential of such frameworks to provide a useful description of these materials and to enhance understanding of the underlying physics. We also outline a few open questions and highlight particularly promising future directions in the analysis and design of granular materials and other particulate matter.
#Review_Article on #granular_matter & #networks
🔖 Network Analysis of Particles and Grains
🔗 https://arxiv.org/pdf/1708.08080
🔖 Network Analysis of Particles and Grains
🔗 https://arxiv.org/pdf/1708.08080