🔹 Reka Albert gave a wonderful talk at CEU about boolean models in gene regulatory networks. Watch it here :
🎞 http://www.aparat.com/v/YLItE
🎞 http://www.aparat.com/v/YLItE
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Reka Albert - Network Structure and Logic Determines the Dynamic Repertoire of B
Reka Albert’s group at Penn State is collaborating with wet-bench biologists to develop and validate predictive models of various biological systems, from the molecular to the ecological level. They recently developed an efficient method to determine the…
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Network Mathematics and Rival Factions | Infinite Series
🗞 Hamiltonian analysis of subcritical stochastic epidemic dynamics
Lee Worden, Ira B. Schwartz, Simone Bianco, Sarah F. Ackley, Thomas M. Lietman, Travis C. Porco
🔗 https://arxiv.org/pdf/1707.00021
📌 ABSTRACT
We extend a technique of approximation of the long-term behavior of a supercritical stochastic epidemic model, using the WKB approximation and a Hamiltonian phase space, to the subcritical case. The limiting behavior of the model and approximation are qualitatively different in the subcritical case, requiring a novel analysis of the limiting behavior of the Hamiltonian system away from its deterministic subsystem. This yields a novel, general technique of approximation of the quasistationary distribution of stochastic epidemic and birth-death models, and may lead to techniques for analysis of these models beyond the quasistationary distribution. For a classic SIS model, the approximation found for the quasistationary distribution is very similar to published approximations but not identical. For a birth-death process without depletion of susceptibles, the approximation is exact. Dynamics on the phase plane similar to those predicted by the Hamiltonian analysis are demonstrated in cross-sectional data from trachoma treatment trials in Ethiopia, in which declining prevalences are consistent with subcritical epidemic dynamics.
Lee Worden, Ira B. Schwartz, Simone Bianco, Sarah F. Ackley, Thomas M. Lietman, Travis C. Porco
🔗 https://arxiv.org/pdf/1707.00021
📌 ABSTRACT
We extend a technique of approximation of the long-term behavior of a supercritical stochastic epidemic model, using the WKB approximation and a Hamiltonian phase space, to the subcritical case. The limiting behavior of the model and approximation are qualitatively different in the subcritical case, requiring a novel analysis of the limiting behavior of the Hamiltonian system away from its deterministic subsystem. This yields a novel, general technique of approximation of the quasistationary distribution of stochastic epidemic and birth-death models, and may lead to techniques for analysis of these models beyond the quasistationary distribution. For a classic SIS model, the approximation found for the quasistationary distribution is very similar to published approximations but not identical. For a birth-death process without depletion of susceptibles, the approximation is exact. Dynamics on the phase plane similar to those predicted by the Hamiltonian analysis are demonstrated in cross-sectional data from trachoma treatment trials in Ethiopia, in which declining prevalences are consistent with subcritical epidemic dynamics.
💎 Topological analysis of data: success and challenges of a new branch of data science
https://www.isi.it/en/news-events/topological-analysis-of-data-success-and-challenges-of-a-new-branch-of-data-science
https://www.isi.it/en/news-events/topological-analysis-of-data-success-and-challenges-of-a-new-branch-of-data-science
🗞 The Fall of the Empire: The Americanization of English
Bruno Gonçalves, Lucía Loureiro-Porto, José J. Ramasco, David Sánchez
🔗 https://arxiv.org/pdf/1707.00781
📌 ABSTRACT
As global political preeminence gradually shifted from the United Kingdom to the United States, so did the capacity to culturally influence the rest of the world. In this work, we analyze how the world-wide varieties of written English are evolving. We study both the spatial and temporal variations of vocabulary and spelling of English using a large corpus of geolocated tweets and the Google Books datasets corresponding to books published in the US and the UK. The advantage of our approach is that we can address both standard written language (Google Books) and the more colloquial forms of microblogging messages (Twitter). We find that American English is the dominant form of English outside the UK and that its influence is felt even within the UK borders. Finally, we analyze how this trend has evolved over time and the impact that some cultural events have had in shaping it.
Bruno Gonçalves, Lucía Loureiro-Porto, José J. Ramasco, David Sánchez
🔗 https://arxiv.org/pdf/1707.00781
📌 ABSTRACT
As global political preeminence gradually shifted from the United Kingdom to the United States, so did the capacity to culturally influence the rest of the world. In this work, we analyze how the world-wide varieties of written English are evolving. We study both the spatial and temporal variations of vocabulary and spelling of English using a large corpus of geolocated tweets and the Google Books datasets corresponding to books published in the US and the UK. The advantage of our approach is that we can address both standard written language (Google Books) and the more colloquial forms of microblogging messages (Twitter). We find that American English is the dominant form of English outside the UK and that its influence is felt even within the UK borders. Finally, we analyze how this trend has evolved over time and the impact that some cultural events have had in shaping it.
Complex Systems Studies
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Complexity Economics:.pdf
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Complexity Economics:
A Different Framework for Economic Thought
W. Brian Arthur
A Different Framework for Economic Thought
W. Brian Arthur
#Review_article , 45 pages
🗞 Inverse statistical problems:
from the inverse Ising problem to data science
H. Chau Nguyen, Riccardo Zecchina, Johannes Berg
🔗 https://arxiv.org/pdf/1702.01522
📌 ABSTRACT
Inverse problems in statistical physics are motivated by the challenges of `big data' in different fields, in particular high-throughput experiments in biology. In inverse problems, the usual procedure of statistical physics needs to be reversed: Instead of calculating observables on the basis of model parameters, we seek to infer parameters of a model based on observations. In this review, we focus on the inverse Ising problem and closely related problems, namely how to infer the coupling strengths between spins given observed spin correlations, magnetisations, or other data. We review applications of the inverse Ising problem, including the reconstruction of neural connections, protein structure determination, and the inference of gene regulatory networks. For the inverse Ising problem in equilibrium, a number of controlled and uncontrolled approximate solutions have been developed in the statistical mechanics community. A particularly strong method, pseudolikelihood, stems from statistics. We also review the inverse Ising problem in the non-equilibrium case, where the model parameters must be reconstructed based on non-equilibrium statistics.
🗞 Inverse statistical problems:
from the inverse Ising problem to data science
H. Chau Nguyen, Riccardo Zecchina, Johannes Berg
🔗 https://arxiv.org/pdf/1702.01522
📌 ABSTRACT
Inverse problems in statistical physics are motivated by the challenges of `big data' in different fields, in particular high-throughput experiments in biology. In inverse problems, the usual procedure of statistical physics needs to be reversed: Instead of calculating observables on the basis of model parameters, we seek to infer parameters of a model based on observations. In this review, we focus on the inverse Ising problem and closely related problems, namely how to infer the coupling strengths between spins given observed spin correlations, magnetisations, or other data. We review applications of the inverse Ising problem, including the reconstruction of neural connections, protein structure determination, and the inference of gene regulatory networks. For the inverse Ising problem in equilibrium, a number of controlled and uncontrolled approximate solutions have been developed in the statistical mechanics community. A particularly strong method, pseudolikelihood, stems from statistics. We also review the inverse Ising problem in the non-equilibrium case, where the model parameters must be reconstructed based on non-equilibrium statistics.