📽 Nonequilibrium Statistical Mechanics
Chris Jarzynski
University of Maryland
1⃣ Fundamental Problems and Applications Nonequilibrium work relations
https://www.youtube.com/watch?v=LXcQx6Bu3OQ
2⃣ Microscopic systems driven away from equilibrium
https://www.youtube.com/watch?v=Epud4i_Y5KM
3⃣ Dissipation and the Arrow of Time
https://www.youtube.com/watch?v=anG9DlF-EgY
Chris Jarzynski
University of Maryland
1⃣ Fundamental Problems and Applications Nonequilibrium work relations
https://www.youtube.com/watch?v=LXcQx6Bu3OQ
2⃣ Microscopic systems driven away from equilibrium
https://www.youtube.com/watch?v=Epud4i_Y5KM
3⃣ Dissipation and the Arrow of Time
https://www.youtube.com/watch?v=anG9DlF-EgY
YouTube
Nonequilibrium Statistical Mechanics I - Chris Jarzynski
Lecture 1 of 3 in Series
Fundamental Problems and Applications Nonequilibrium work relations - Chris Jarzynski, University of Maryland
Hits from scivee.tv prior to youtube upload : 2113
Fundamental Problems and Applications Nonequilibrium work relations - Chris Jarzynski, University of Maryland
Hits from scivee.tv prior to youtube upload : 2113
📽 Emergent Order in Biology
https://www.youtube.com/playlist?list=PLdV51-1AzT3OVG3n9TG5s-hGkXMhcgLji
https://www.youtube.com/playlist?list=PLdV51-1AzT3OVG3n9TG5s-hGkXMhcgLji
📌 Equalities and Inequalities:
Irreversibility and the Second Law of Thermodynamics at the Nanoscale
http://www.chem.umd.edu/wp-content/uploads/2012/12/Jarzynski_AnnuRevCondMattPhys_2_329_20111.pdf
Christopher Jarzynski
Irreversibility and the Second Law of Thermodynamics at the Nanoscale
http://www.chem.umd.edu/wp-content/uploads/2012/12/Jarzynski_AnnuRevCondMattPhys_2_329_20111.pdf
Christopher Jarzynski
🌀 Are we living in the #matrix ? Great interview with network scientist Dmitri Krioukov
http://www.northeastern.edu/news/2016/11/3qs-are-we-living-in-a-matrix-style-simulation/
http://www.northeastern.edu/news/2016/11/3qs-are-we-living-in-a-matrix-style-simulation/
🎯 Interested in #complexity and cellular automata?
Then some of Stephen Wolfram´s collected papers might be
of interest to you!
https://www.complexityexplorer.org/explore/resources/461-cellular-automata-and-complexity-collected-papers
Then some of Stephen Wolfram´s collected papers might be
of interest to you!
https://www.complexityexplorer.org/explore/resources/461-cellular-automata-and-complexity-collected-papers
📄 A biology journal that can teach physicists a lesson in peer review
Raymond E. Goldstein
https://arxiv.org/pdf/1612.00241v1
🔻This is a Commentary in Physics Today on the novel review process developed by the biology journal eLife, with the suggestion that it be adopted by physics journals.
Raymond E. Goldstein
https://arxiv.org/pdf/1612.00241v1
🔻This is a Commentary in Physics Today on the novel review process developed by the biology journal eLife, with the suggestion that it be adopted by physics journals.
📖 Phenomenological theory of collective decision-making
Anna Zafeiris, Zsombor Koman, Enys Mones, Tamás Vicsek
https://arxiv.org/pdf/1612.00071v1
🔗 ABSTRACT
An essential task of groups is to provide efficient solutions for the complex problems they face. Indeed, considerable efforts have been devoted to the question of collective decision-making related to problems involving a single dominant feature. Here we introduce a quantitative formalism for finding the optimal distribution of the group members' competences in the more typical case when the underlying problem is complex, i.e., multidimensional. Thus, we consider teams that are aiming at obtaining the best possible answer to a problem having a number of independent sub-problems. Our approach is based on a generic scheme for the process of evaluating the proposed solutions (i.e., negotiation). We demonstrate that the best performing groups have at least one specialist for each sub-problem -- but a far less intuitive result is that finding the optimal solution by the interacting group members requires that the specialists also have some insight into the sub-problems beyond their unique field(s). We present empirical results obtained by using a large-scale database of citations being in good agreement with the above theory. The framework we have developed can easily be adapted to a variety of realistic situations since taking into account the weights of the sub-problems, the opinions or the relations of the group is straightforward. Consequently, our method can be used in several contexts, especially when the optimal composition of a group of decision-makers is designed.
Subjects: #Physics and #Society (physics.soc-ph); Social and #Information #Networks
Anna Zafeiris, Zsombor Koman, Enys Mones, Tamás Vicsek
https://arxiv.org/pdf/1612.00071v1
🔗 ABSTRACT
An essential task of groups is to provide efficient solutions for the complex problems they face. Indeed, considerable efforts have been devoted to the question of collective decision-making related to problems involving a single dominant feature. Here we introduce a quantitative formalism for finding the optimal distribution of the group members' competences in the more typical case when the underlying problem is complex, i.e., multidimensional. Thus, we consider teams that are aiming at obtaining the best possible answer to a problem having a number of independent sub-problems. Our approach is based on a generic scheme for the process of evaluating the proposed solutions (i.e., negotiation). We demonstrate that the best performing groups have at least one specialist for each sub-problem -- but a far less intuitive result is that finding the optimal solution by the interacting group members requires that the specialists also have some insight into the sub-problems beyond their unique field(s). We present empirical results obtained by using a large-scale database of citations being in good agreement with the above theory. The framework we have developed can easily be adapted to a variety of realistic situations since taking into account the weights of the sub-problems, the opinions or the relations of the group is straightforward. Consequently, our method can be used in several contexts, especially when the optimal composition of a group of decision-makers is designed.
Subjects: #Physics and #Society (physics.soc-ph); Social and #Information #Networks
📽 Algorithms for Big Data (COMPSCI 229r)
Harvard University
https://www.youtube.com/playlist?list=PL2SOU6wwxB0v1kQTpqpuu5kEJo2i-iUyf
Harvard University
https://www.youtube.com/playlist?list=PL2SOU6wwxB0v1kQTpqpuu5kEJo2i-iUyf
🎯 Eye-catching visualization of chaotic flow on the Lorenz attractor, using the power of #GPU.
http://rickyreusser.com/demos/lorenz/
http://rickyreusser.com/demos/lorenz/
Rickyreusser
lorenz
Computing the lorenz attractor on the GPU
🎯 Samuel Arbesman on #Complex_Adaptive_Systems and the Difference between #Biological and #Physics Based Thinking
https://www.farnamstreetblog.com/2016/11/samuel-arbesman-biological-physics-thinking/?utm_source=twitter.com&utm_medium=social&utm_campaign=buffer&utm_content=bufferb2052
https://www.farnamstreetblog.com/2016/11/samuel-arbesman-biological-physics-thinking/?utm_source=twitter.com&utm_medium=social&utm_campaign=buffer&utm_content=bufferb2052
Farnam Street
Samuel Arbesman on Complex Adaptive Systems and the Difference between Biological and Physics Based Thinking
Knowledge Project and Shane Parrish. Samuel Arbesman (@arbesman) is a complexity scientist focusing on the nature of scientific and technological change.