Clauset_2017_SwRI_WhenIsALeadSafe.pdf
6.1 MB
When is a lead safe (or not)?
Insights from complex systems
Insights from complex systems
Forwarded from Deleted Account [SCAM]
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🔸 Using the physics of surface tension to model linguistic evolution and language boundary.
https://physics.aps.org/articles/v10/80
https://physics.aps.org/articles/v10/80
Physics
Viewpoint: Language Boundaries Driven by Surface Tension
A new model of language evolution assumes that changes in the spatial boundaries between dialects are controlled by a surface tension effect.
🗞 Maximum Entropy Flow Networks
Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham
🔗 https://arxiv.org/pdf/1701.03504
📌 ABSTRACT
Maximum entropy modeling is a flexible and popular framework for formulating statistical models given partial knowledge. In this paper, rather than the traditional method of optimizing over the continuous density directly, we learn a smooth and invertible transformation that maps a simple distribution to the desired maximum entropy distribution. Doing so is nontrivial in that the objective being maximized (entropy) is a function of the density itself. By exploiting recent developments in normalizing flow networks, we cast the maximum entropy problem into a finite-dimensional constrained optimization, and solve the problem by combining stochastic optimization with the augmented Lagrangian method. Simulation results demonstrate the effectiveness of our method, and applications to finance and computer vision show the flexibility and accuracy of using maximum entropy flow networks.
Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham
🔗 https://arxiv.org/pdf/1701.03504
📌 ABSTRACT
Maximum entropy modeling is a flexible and popular framework for formulating statistical models given partial knowledge. In this paper, rather than the traditional method of optimizing over the continuous density directly, we learn a smooth and invertible transformation that maps a simple distribution to the desired maximum entropy distribution. Doing so is nontrivial in that the objective being maximized (entropy) is a function of the density itself. By exploiting recent developments in normalizing flow networks, we cast the maximum entropy problem into a finite-dimensional constrained optimization, and solve the problem by combining stochastic optimization with the augmented Lagrangian method. Simulation results demonstrate the effectiveness of our method, and applications to finance and computer vision show the flexibility and accuracy of using maximum entropy flow networks.
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Motion on the Rössler Attractor
🌀 Stochastic Dynamics out of Equilibrium
CEB Trimester, Institut Henri Poincaré, 2017
🎞 24 videos:
https://www.youtube.com/playlist?list=PL9kd4mpdvWcDWCAqqvRe1pOxYtNUqEjZW
🎯 It is common practice in statistical mechanics to use models of large interacting assemblies governed by #stochastic dynamics. In this context “equilibrium” is understood as stochastically (time) reversible dynamics with respect to a prescribed #Gibbs_measure. The IHP trimester “#Stochastic_Dynamics #Out_of_Equilibrium” will focus on various aspects of #nonequilibrium dynamics. #Non-reversible dynamics have features which cannot occur at #equilibrium and for which novel methods have to be developed. In the recent years there have been important advances in the three domains relevant to this trimester
- Transport in non-equilibrium statistical mechanics;
- Towards more efficient simulation methods;
- Life sciences.
and this has led to challenging open questions. This trimester aims at bringing together an audience coming from all the involved domains, to explore these new directions: physicists, mathematicians from various domains, computer scientists, as well as researchers working at the interface between biology, physics and mathematics.
http://www.ihp.fr/ceb/t2-2017
https://indico.math.cnrs.fr/e/stoneq17
CEB Trimester, Institut Henri Poincaré, 2017
🎞 24 videos:
https://www.youtube.com/playlist?list=PL9kd4mpdvWcDWCAqqvRe1pOxYtNUqEjZW
🎯 It is common practice in statistical mechanics to use models of large interacting assemblies governed by #stochastic dynamics. In this context “equilibrium” is understood as stochastically (time) reversible dynamics with respect to a prescribed #Gibbs_measure. The IHP trimester “#Stochastic_Dynamics #Out_of_Equilibrium” will focus on various aspects of #nonequilibrium dynamics. #Non-reversible dynamics have features which cannot occur at #equilibrium and for which novel methods have to be developed. In the recent years there have been important advances in the three domains relevant to this trimester
- Transport in non-equilibrium statistical mechanics;
- Towards more efficient simulation methods;
- Life sciences.
and this has led to challenging open questions. This trimester aims at bringing together an audience coming from all the involved domains, to explore these new directions: physicists, mathematicians from various domains, computer scientists, as well as researchers working at the interface between biology, physics and mathematics.
http://www.ihp.fr/ceb/t2-2017
https://indico.math.cnrs.fr/e/stoneq17
YouTube
CEB 2017 T2 - Stochastic Dynamics out of Equilibrium
It is common practice in statistical mechanics to use models of large interacting assemblies governed by stochastic dynamics. In this context “equilibrium” i...
🎞 F. Guerra “Equilibrium and off equilibrium properties of ferromagnetic and disordered statistical mechanics systems”
http://www.aparat.com/v/9tp6c
http://www.aparat.com/v/9tp6c
آپارات - سرویس اشتراک ویدیو
Courses - F. Guerra “Equilibrium and off equilibrium properties of ferroma
F. Guerra “Equilibrium and off equilibrium properties of ferromagnetic and disordered statistical mechanics systems”
A self-contained review will be given about the equilibrium and off equilibrium properties of statistical mechanics systems, both in the…
A self-contained review will be given about the equilibrium and off equilibrium properties of statistical mechanics systems, both in the…
🎞 E. PRESUTTI "Phase transitions in systems with spatially non homogeneous interactions
http://www.aparat.com/v/fJIE4
http://www.aparat.com/v/fJIE4
آپارات - سرویس اشتراک ویدیو
Courses - E. PRESUTTI "Phase transitions in systems with spatially non homo
I will describe recent results and works in progress on the absence/presence of phase transitions in systems with spatially non homogeneous interactions. In a first part I will consider the d ≥ 2 nearest neighbor ferromagnetic Ising model under the action…
🎞 http://www.aparat.com/v/eyqA4
🌀The Kardar–Parisi–Zhang (#KPZ) equation (named after its creators Mehran Kardar, Giorgio Parisi, and Yi-Cheng Zhang) is a non-linear stochastic partial differential equation.
🔗https://en.wikipedia.org/wiki/Kardar%E2%80%93Parisi%E2%80%93Zhang_equation
🌀The Kardar–Parisi–Zhang (#KPZ) equation (named after its creators Mehran Kardar, Giorgio Parisi, and Yi-Cheng Zhang) is a non-linear stochastic partial differential equation.
🔗https://en.wikipedia.org/wiki/Kardar%E2%80%93Parisi%E2%80%93Zhang_equation
آپارات - سرویس اشتراک ویدیو
Introduction to KPZ - Jeremy Quastel
Jeremy Quastel
University of Toronto; Member, School of Mathematics
October 2, 2013
For more videos, visit http://video.ias.edu
University of Toronto; Member, School of Mathematics
October 2, 2013
For more videos, visit http://video.ias.edu
🌀 ماتریسهای تصادفی
🎞📄 http://videos.math.sharif.ir/courses.php?course=random_matrix-spring92
مشخصات درس
- ترم ارائه : بهار 1392
- مقطع : کارشناسی ارشد
- استاد درس: کسری علیشاهی
🎞📄 http://videos.math.sharif.ir/courses.php?course=random_matrix-spring92
مشخصات درس
- ترم ارائه : بهار 1392
- مقطع : کارشناسی ارشد
- استاد درس: کسری علیشاهی
🌀 Your life’s memories could, in principle, be stored in the universe’s structure.
http://nautil.us/issue/50/emergence/the-strange-similarity-of-neuron-and-galaxy-networks
http://nautil.us/issue/50/emergence/the-strange-similarity-of-neuron-and-galaxy-networks
Nautilus
The Strange Similarity of Neuron and Galaxy Networks
Christof Koch, a leading researcher on consciousness and the human brain, has famously called the brain “the most complex object…
🔸 Multilayer Networks!
Schematic illustration of multilayer architecture composed of two networks. Though the social network (namely, Network A) and infection contact network (namely, Network B) possess the same nodes marked by numbers, they support different dynamic processes, which are separately studied in most previous literature. Now, if both networks are encapsulated into a multilayer framework (namely, Network C), the interaction between them may create completely different outcomes that go beyond what isolated networks can capture.
read more:
http://www.sciencedirect.com/science/article/pii/S1571064515001372
Schematic illustration of multilayer architecture composed of two networks. Though the social network (namely, Network A) and infection contact network (namely, Network B) possess the same nodes marked by numbers, they support different dynamic processes, which are separately studied in most previous literature. Now, if both networks are encapsulated into a multilayer framework (namely, Network C), the interaction between them may create completely different outcomes that go beyond what isolated networks can capture.
read more:
http://www.sciencedirect.com/science/article/pii/S1571064515001372