Forwarded from کافه فیزیک بهشتی (sbuPhysics)
#کافه_فیزیک #جلسه ششم
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🕰ساعت ١٢:٤٠
📍دانشكده فيزيك دانشگاه شهيد بهشتى، طبقه اول!
عنوان:
"مدل تپه شنى و مغز"
با كافه فيزيك همراه باشيد! ☕️😊
@farzin23i
@sbu_physicscafe
🗓سه شنبه ٢ خرداد ٩٦
🕰ساعت ١٢:٤٠
📍دانشكده فيزيك دانشگاه شهيد بهشتى، طبقه اول!
عنوان:
"مدل تپه شنى و مغز"
با كافه فيزيك همراه باشيد! ☕️😊
@farzin23i
@sbu_physicscafe
🎞 http://www.cornell.edu/video/william-bialek-are-biological-networks-poised-at-criticality
Using examples from families of proteins, networks of neurons and flocks of birds, William Bialek of Princeton University describes successes constructing statistical mechanics models of biological systems directly from real data, March 16, 2015, as part of the Department of Physics Bethe Lecture Series.
Using examples from families of proteins, networks of neurons and flocks of birds, William Bialek of Princeton University describes successes constructing statistical mechanics models of biological systems directly from real data, March 16, 2015, as part of the Department of Physics Bethe Lecture Series.
CornellCast
Are Biological Networks Poised at Criticality? - CornellCast
Using examples from families of proteins, networks of neurons and flocks of birds, William Bialek of Princeton University describes successes constructing statistical mechanics models of biological systems directly from real data, March 16, 2015, as part…
Forwarded from Deleted Account [SCAM]
Audio
055 - Unintended Consequences with Complexity Scientist Yaneer Bar-Yam
😳 Analysis on 39,110 Twitter users show that an individual on average is less happy than their "friends" on the social network.
https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0100-1
https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0100-1
SpringerOpen
The happiness paradox: your friends are happier than you - EPJ Data Science
Most individuals in social networks experience a so-called Friendship Paradox: they are less popular than their friends on average. This effect may explain recent findings that widespread social network media use leads to reduced happiness. However the relation…
🔥 An emerging branch of complex systems mathematics suggests in cold, analytical terms that Donald Trump is not smart enough to be president.
https://motherboard.vice.com/en_us/article/the-math-that-suggests-donald-trump-is-too-dumb-to-be-president
https://motherboard.vice.com/en_us/article/the-math-that-suggests-donald-trump-is-too-dumb-to-be-president
Vice
The World Is Too Complicated for Donald Trump to Be President, Theoretical Physics Suggests
An emerging branch of complex systems mathematics suggests in cold, analytical terms that Donald Trump is not smart enough to be president.
🔺PhD Student Positions Available in Systems Science / Complex Systems
http://coco.binghamton.edu/GRAs.html
http://coco.binghamton.edu/GRAs.html
🗞 Asymmetry-Induced Synchronization in Oscillator Networks
Yuanzhao Zhang, Takashi Nishikawa, Adilson E. Motter
🔗 https://arxiv.org/pdf/1705.07907
📌 ABSTRACT
A scenario has recently been reported in which in order to stabilize complete synchronization of an oscillator network---a symmetric state---the symmetry of the system itself has to be broken by making the oscillators nonidentical. But how often does such behavior---which we term asymmetry-induced synchronization (AISync)---occur in oscillator networks? Here we present the first general scheme for constructing AISync systems and demonstrate that this behavior is the norm rather than the exception in a wide class of physical systems that can be seen as multilayer networks. Since a symmetric network in complete synchrony is the basic building block of cluster synchronization in more general networks, AISync should be common also in facilitating cluster synchronization by breaking the symmetry of the cluster subnetworks.
Yuanzhao Zhang, Takashi Nishikawa, Adilson E. Motter
🔗 https://arxiv.org/pdf/1705.07907
📌 ABSTRACT
A scenario has recently been reported in which in order to stabilize complete synchronization of an oscillator network---a symmetric state---the symmetry of the system itself has to be broken by making the oscillators nonidentical. But how often does such behavior---which we term asymmetry-induced synchronization (AISync)---occur in oscillator networks? Here we present the first general scheme for constructing AISync systems and demonstrate that this behavior is the norm rather than the exception in a wide class of physical systems that can be seen as multilayer networks. Since a symmetric network in complete synchrony is the basic building block of cluster synchronization in more general networks, AISync should be common also in facilitating cluster synchronization by breaking the symmetry of the cluster subnetworks.
🗞 Predicting stock market movements using network science: An information theoretic approach
Minjun Kim, Hiroki Sayama
🔗 https://arxiv.org/pdf/1705.07980
📌 ABSTRACT
A stock market is considered as one of the highly complex systems, which consists of many components whose prices move up and down without having a clear pattern. The complex nature of a stock market challenges us on making a reliable prediction of its future movements. In this paper, we aim at building a new method to forecast the future movements of Standard & Poor's 500 Index (S&P 500) by constructing time-series complex networks of S&P 500 underlying companies by connecting them with links whose weights are given by the mutual information of 60-minute price movements of the pairs of the companies with the consecutive 5,340 minutes price records. We showed that the changes in the strength distributions of the networks provide an important information on the network's future movements. We built several metrics using the strength distributions and network measurements such as centrality, and we combined the best two predictors by performing a linear combination. We found that the combined predictor and the changes in S&P 500 show a quadratic relationship, and it allows us to predict the amplitude of the one step future change in S&P 500. The result showed significant fluctuations in S&P 500 Index when the combined predictor was high. In terms of making the actual index predictions, we built ARIMA models. We found that adding the network measurements into the ARIMA models improves the model accuracy. These findings are useful for financial market policy makers as an indicator based on which they can interfere with the markets before the markets make a drastic change, and for quantitative investors to improve their forecasting models.
Minjun Kim, Hiroki Sayama
🔗 https://arxiv.org/pdf/1705.07980
📌 ABSTRACT
A stock market is considered as one of the highly complex systems, which consists of many components whose prices move up and down without having a clear pattern. The complex nature of a stock market challenges us on making a reliable prediction of its future movements. In this paper, we aim at building a new method to forecast the future movements of Standard & Poor's 500 Index (S&P 500) by constructing time-series complex networks of S&P 500 underlying companies by connecting them with links whose weights are given by the mutual information of 60-minute price movements of the pairs of the companies with the consecutive 5,340 minutes price records. We showed that the changes in the strength distributions of the networks provide an important information on the network's future movements. We built several metrics using the strength distributions and network measurements such as centrality, and we combined the best two predictors by performing a linear combination. We found that the combined predictor and the changes in S&P 500 show a quadratic relationship, and it allows us to predict the amplitude of the one step future change in S&P 500. The result showed significant fluctuations in S&P 500 Index when the combined predictor was high. In terms of making the actual index predictions, we built ARIMA models. We found that adding the network measurements into the ARIMA models improves the model accuracy. These findings are useful for financial market policy makers as an indicator based on which they can interfere with the markets before the markets make a drastic change, and for quantitative investors to improve their forecasting models.
🗞 Statistical physics of human cooperation
Matjaz Perc, Jillian J. Jordan, David G. Rand, Zhen Wang, Stefano Boccaletti, Attila Szolnoki
🔗 https://arxiv.org/pdf/1705.07161
📌 ABSTRACT
Extensive cooperation among unrelated individuals is unique to humans, who often sacrifice personal benefits for the common good and work together to achieve what they are unable to execute alone. The evolutionary success of our species is indeed due, to a large degree, to our unparalleled other-regarding abilities. Yet, a comprehensive understanding of human cooperation remains a formidable challenge. Recent research in social science indicates that it is important to focus on the collective behavior that emerges as the result of the interactions among individuals, groups, and even societies. Non-equilibrium statistical physics, in particular Monte Carlo methods and the theory of collective behavior of interacting particles near phase transition points, has proven to be very valuable for understanding counterintuitive evolutionary outcomes. By studying models of human cooperation as classical spin models, a physicist can draw on familiar settings from statistical physics. However, unlike pairwise interactions among particles that typically govern solid-state physics systems, interactions among humans often involve group interactions, and they also involve a larger number of possible states even for the most simplified description of reality. The complexity of solutions therefore often surpasses that observed in physical systems. Here we review experimental and theoretical research that advances our understanding of human cooperation, focusing on spatial pattern formation, on the spatiotemporal dynamics of observed solutions, and on self-organization that may either promote or hinder socially favorable states.
Matjaz Perc, Jillian J. Jordan, David G. Rand, Zhen Wang, Stefano Boccaletti, Attila Szolnoki
🔗 https://arxiv.org/pdf/1705.07161
📌 ABSTRACT
Extensive cooperation among unrelated individuals is unique to humans, who often sacrifice personal benefits for the common good and work together to achieve what they are unable to execute alone. The evolutionary success of our species is indeed due, to a large degree, to our unparalleled other-regarding abilities. Yet, a comprehensive understanding of human cooperation remains a formidable challenge. Recent research in social science indicates that it is important to focus on the collective behavior that emerges as the result of the interactions among individuals, groups, and even societies. Non-equilibrium statistical physics, in particular Monte Carlo methods and the theory of collective behavior of interacting particles near phase transition points, has proven to be very valuable for understanding counterintuitive evolutionary outcomes. By studying models of human cooperation as classical spin models, a physicist can draw on familiar settings from statistical physics. However, unlike pairwise interactions among particles that typically govern solid-state physics systems, interactions among humans often involve group interactions, and they also involve a larger number of possible states even for the most simplified description of reality. The complexity of solutions therefore often surpasses that observed in physical systems. Here we review experimental and theoretical research that advances our understanding of human cooperation, focusing on spatial pattern formation, on the spatiotemporal dynamics of observed solutions, and on self-organization that may either promote or hinder socially favorable states.
⚡️ Six-Year Report on the Arab Spring
(February 24, 2017).
🔗 http://www.necsi.edu/research/social/arabspring.html
📄 http://www.necsi.edu/research/social/arabspring.pdf
(February 24, 2017).
🔗 http://www.necsi.edu/research/social/arabspring.html
📄 http://www.necsi.edu/research/social/arabspring.pdf
🗞 Non-Euclidean geometry in nature
Sergei Nechaev
🔗 https://arxiv.org/pdf/1705.08013
📌 ABSTRACT
I describe the manifestation of the non-Euclidean geometry in the behavior of collective observables of some complex physical systems. Specifically, I consider the formation of equilibrium shapes of plants and statistics of sparse random graphs. For these systems I discuss the following interlinked questions: (i) the optimal embedding of plants leaves in the three-dimensional space, (ii) the spectral statistics of sparse random matrix ensembles.
Sergei Nechaev
🔗 https://arxiv.org/pdf/1705.08013
📌 ABSTRACT
I describe the manifestation of the non-Euclidean geometry in the behavior of collective observables of some complex physical systems. Specifically, I consider the formation of equilibrium shapes of plants and statistics of sparse random graphs. For these systems I discuss the following interlinked questions: (i) the optimal embedding of plants leaves in the three-dimensional space, (ii) the spectral statistics of sparse random matrix ensembles.