❄️ Nice article about extreme difficulty most people have understanding p-values. Even worse: confidence intervals. And most statisticians don't even compute p-values very accurately except for the simplest models.
https://fivethirtyeight.com/features/not-even-scientists-can-easily-explain-p-values/amp/
https://fivethirtyeight.com/features/not-even-scientists-can-easily-explain-p-values/amp/
FiveThirtyEight
Not Even Scientists Can Easily Explain P-values
P-values have taken quite a beating lately. These widely used and commonly misapplied statistics have been blamed for giving a veneer of legitimacy to dodgy stu…
🔖 Tracking Network Dynamics: a review of distances and similarity metrics
Claire Donnat, Susan Holmes
🔗 arxiv.org/pdf/1801.07351.pdf
📌 ABSTRACT
From longitudinal biomedical studies to social networks, graphs have emerged as a powerful framework for describing evolving interactions between agents in complex systems. In such studies, the data typically consists of a set of graphs representing a system's state at different points in time or space. The analysis of the system's dynamics depends on the selection of the appropriate tools. In particular, after specifying properties characterizing similarities between states, a critical step lies in the choice of a distance capable of reflecting such similarities. While the literature offers a number of distances that one could a priori choose from, their properties have been little investigated and no guidelines regarding the choice of such a distance have yet been provided. However, these distances' sensitivity to perturbations in the network's structure and their ability to identify important changes are crucial to the analysis, making the selection of an adequate metric a decisive -- yet delicate -- practical matter.
In the spirit of Goldenberg, Zheng and Fienberg's seminal 2009 review, the purpose of this article is to provide an overview of commonly-used graph distances and an explicit characterization of the structural changes that they are best able to capture. To see how this translates in real-life situations, we use as a guiding thread to our discussion the application of these distances to the analysis a longitudinal microbiome study -- as well as on synthetic examples. Having unveiled some of traditional distances' shortcomings, we also suggest alternative similarity metrics and highlight their relative advantages in specific analysis scenarios. Above all, we provide some guidance for choosing one distance over another in certain types of applications. Finally, we show an application of these different distances to a network created from worldwide recipes.
Claire Donnat, Susan Holmes
🔗 arxiv.org/pdf/1801.07351.pdf
📌 ABSTRACT
From longitudinal biomedical studies to social networks, graphs have emerged as a powerful framework for describing evolving interactions between agents in complex systems. In such studies, the data typically consists of a set of graphs representing a system's state at different points in time or space. The analysis of the system's dynamics depends on the selection of the appropriate tools. In particular, after specifying properties characterizing similarities between states, a critical step lies in the choice of a distance capable of reflecting such similarities. While the literature offers a number of distances that one could a priori choose from, their properties have been little investigated and no guidelines regarding the choice of such a distance have yet been provided. However, these distances' sensitivity to perturbations in the network's structure and their ability to identify important changes are crucial to the analysis, making the selection of an adequate metric a decisive -- yet delicate -- practical matter.
In the spirit of Goldenberg, Zheng and Fienberg's seminal 2009 review, the purpose of this article is to provide an overview of commonly-used graph distances and an explicit characterization of the structural changes that they are best able to capture. To see how this translates in real-life situations, we use as a guiding thread to our discussion the application of these distances to the analysis a longitudinal microbiome study -- as well as on synthetic examples. Having unveiled some of traditional distances' shortcomings, we also suggest alternative similarity metrics and highlight their relative advantages in specific analysis scenarios. Above all, we provide some guidance for choosing one distance over another in certain types of applications. Finally, we show an application of these different distances to a network created from worldwide recipes.
🔖 A General Definition of Network Communities and the Corresponding Detection Algorithm
Haoye Lu, Amiya Nayak
🔗 arxiv.org/pdf/1801.07783.pdf
📌 ABSTRACT
Network structures, consisting of nodes and edges, have applications in almost all subjects. The sets of nodes strongly connected internally are called communities. Industries (including cell phone carriers and online social media companies) need community structures to allocate network resources and provide proper customer services. However, all community detection methods are motivated by solving some concrete problems, while the applicabilities in other fields are open to question. Therefore, confronting a new community problem, researchers need to derive algorithms ad hoc, which is time-consuming and even unnecessary. In this paper, we represent a general procedure to find community structures in concrete problems. We mainly focus on two typical types of networks: transmission networks and similarity networks. We reduce them to a unified graph model, based on which we propose a general method to define and detect communities. Readers can specialize our general algorithm to accommodate their problems. In the end, we also give a demonstration to show how the algorithm works.
Haoye Lu, Amiya Nayak
🔗 arxiv.org/pdf/1801.07783.pdf
📌 ABSTRACT
Network structures, consisting of nodes and edges, have applications in almost all subjects. The sets of nodes strongly connected internally are called communities. Industries (including cell phone carriers and online social media companies) need community structures to allocate network resources and provide proper customer services. However, all community detection methods are motivated by solving some concrete problems, while the applicabilities in other fields are open to question. Therefore, confronting a new community problem, researchers need to derive algorithms ad hoc, which is time-consuming and even unnecessary. In this paper, we represent a general procedure to find community structures in concrete problems. We mainly focus on two typical types of networks: transmission networks and similarity networks. We reduce them to a unified graph model, based on which we propose a general method to define and detect communities. Readers can specialize our general algorithm to accommodate their problems. In the end, we also give a demonstration to show how the algorithm works.
Deep learning for real-time gravitational wave detection and parameter estimation: Results with advanced LIGO data
https://www.sciencedirect.com/science/article/pii/S0370269317310390
https://www.sciencedirect.com/science/article/pii/S0370269317310390
🔖 Correlations and dynamics of consumption patterns in social-economic networks
Yannick Leo, Márton Karsai, Carlos Sarraute, Eric Fleury
🔗 arxiv.org/pdf/1801.08856.pdf
📌 ABSTRACT
We analyse a coupled dataset collecting the mobile phone communications and bank transactions history of a large number of individuals living in a Latin American country. After mapping the social structure and introducing indicators of socioeconomic status, demographic features, and purchasing habits of individuals we show that typical consumption patterns are strongly correlated with identified socioeconomic classes leading to patterns of stratification in the social structure. In addition we measure correlations between merchant categories and introduce a correlation network, which emerges with a meaningful community structure. We detect multivariate relations between merchant categories and show correlations in purchasing habits of individuals. Finally, by analysing individual consumption histories, we detect dynamical patterns in purchase behaviour and their correlations with the socioeconomic status, demographic characters and the egocentric social network of individuals. Our work provides novel and detailed insight into the relations between social and consuming behaviour with potential applications in resource allocation, marketing, and recommendation system design.
https://t.co/h2kl2pQhDV
Yannick Leo, Márton Karsai, Carlos Sarraute, Eric Fleury
🔗 arxiv.org/pdf/1801.08856.pdf
📌 ABSTRACT
We analyse a coupled dataset collecting the mobile phone communications and bank transactions history of a large number of individuals living in a Latin American country. After mapping the social structure and introducing indicators of socioeconomic status, demographic features, and purchasing habits of individuals we show that typical consumption patterns are strongly correlated with identified socioeconomic classes leading to patterns of stratification in the social structure. In addition we measure correlations between merchant categories and introduce a correlation network, which emerges with a meaningful community structure. We detect multivariate relations between merchant categories and show correlations in purchasing habits of individuals. Finally, by analysing individual consumption histories, we detect dynamical patterns in purchase behaviour and their correlations with the socioeconomic status, demographic characters and the egocentric social network of individuals. Our work provides novel and detailed insight into the relations between social and consuming behaviour with potential applications in resource allocation, marketing, and recommendation system design.
https://t.co/h2kl2pQhDV
Twitter
Alessandro Vespignani
Correlations and dynamics of consumption patterns in social-economic networks https://t.co/ybrqLPYGFA
💡 Networks, Crowds, and Markets: Reasoning About a Highly Connected World
By David Easley and Jon Kleinberg
http://www.cs.cornell.edu/home/kleinber/networks-book/
By David Easley and Jon Kleinberg
http://www.cs.cornell.edu/home/kleinber/networks-book/
🔅 The Shallowness of Google Translate
The program uses state-of-the-art AI techniques, but simple tests show that it's a long way from real understanding.
https://www.theatlantic.com/amp/article/551570/
The program uses state-of-the-art AI techniques, but simple tests show that it's a long way from real understanding.
https://www.theatlantic.com/amp/article/551570/
Forwarded from انجمن فیزیک ایران (akram Mirhosseini)
🎞 Trent McConaghy at SFI describing how #blockchains relate to #complex_systems. Catch the livestream on our youtube channel:
https://www.youtube.com/watch?v=gB8oY5RPzxQ
https://www.youtube.com/watch?v=gB8oY5RPzxQ
YouTube
Tokens and Complex Systems
Seminar - January 31, 2018 - Trent McConaghy I will describe how complex systems relate to tokenized ecosystems, aka blockchains. I will describe AI DAOs (de...
⭕️ Fundamentals of Machine Learning
Lead instructor: Brendan Tracey and Artemy Kolchinsky
https://www.complexityexplorer.org/courses/81-fundamentals-of-machine-learning
About the Tutorial:
Machine Learning is a fast growing, rapidly advancing field that touches nearly everyone's lives. There has recently been an explosion of successful machine learning applications - in everything from voice recognition to to text analysis to deeper insights for researchers. While common and frequently talked about, most people have only a vague concept of how machine learning actually works.
In this tutorial, Dr. Artemy Kolchinsky and Dr. Brendan Tracey outline exactly what it is that makes machine learning so special in an accessible way. The principles of training and generalization in machine learning are explained with ample metaphors and visual intuitions, an extended analysis of machine learning in games provides a thorough example, and a closer look at the deep neural nets that are the core of successful machine learning. Finally it addresses when it's appropriate to use (and not use) machine learning in problem solving, as well as an example of scientific research incorporating machine learning principles.
Students of all levels should be able to follow this reasonably-paced introduction to one of the most important engineering breakthroughs of our time.
High quality videos:
🎞 https://www.aparat.com/video/video/listuser/username/carimi/usercat/110284
Lead instructor: Brendan Tracey and Artemy Kolchinsky
https://www.complexityexplorer.org/courses/81-fundamentals-of-machine-learning
About the Tutorial:
Machine Learning is a fast growing, rapidly advancing field that touches nearly everyone's lives. There has recently been an explosion of successful machine learning applications - in everything from voice recognition to to text analysis to deeper insights for researchers. While common and frequently talked about, most people have only a vague concept of how machine learning actually works.
In this tutorial, Dr. Artemy Kolchinsky and Dr. Brendan Tracey outline exactly what it is that makes machine learning so special in an accessible way. The principles of training and generalization in machine learning are explained with ample metaphors and visual intuitions, an extended analysis of machine learning in games provides a thorough example, and a closer look at the deep neural nets that are the core of successful machine learning. Finally it addresses when it's appropriate to use (and not use) machine learning in problem solving, as well as an example of scientific research incorporating machine learning principles.
Students of all levels should be able to follow this reasonably-paced introduction to one of the most important engineering breakthroughs of our time.
High quality videos:
🎞 https://www.aparat.com/video/video/listuser/username/carimi/usercat/110284
⭕️ Introduction to Computation Theory
Lead instructor: Josh Grochow
https://www.complexityexplorer.org/courses/58-introduction-to-computation-theory
About the Tutorial:
Introduction to Computation Theory is an overview of some basic principles of computation and computational complexity, with an eye towards things that might actually be useful without becoming a researcher. Students will examine the formal mathematics for foundational computation proofs, as well as gain tools to analyze hard computational problems themselves.
Students who take this course should have basic knowledge of the principles of graphs. Some tutorial material references linear algebra, but familiarity is not necessary. This tutorial uses proofs, and requires understandings of formal math notations.
High quality videos:
🎞 https://www.aparat.com/video/profile/one/usercat/110290/username/carimi
Lead instructor: Josh Grochow
https://www.complexityexplorer.org/courses/58-introduction-to-computation-theory
About the Tutorial:
Introduction to Computation Theory is an overview of some basic principles of computation and computational complexity, with an eye towards things that might actually be useful without becoming a researcher. Students will examine the formal mathematics for foundational computation proofs, as well as gain tools to analyze hard computational problems themselves.
Students who take this course should have basic knowledge of the principles of graphs. Some tutorial material references linear algebra, but familiarity is not necessary. This tutorial uses proofs, and requires understandings of formal math notations.
High quality videos:
🎞 https://www.aparat.com/video/profile/one/usercat/110290/username/carimi
🔹 The central nervous system of an octopus, and the central government of a nation: parallel examples of the emergence of collective behavior.
http://crookedtimber.org/2018/02/01/the-birth-of-intermediacy/
http://crookedtimber.org/2018/02/01/the-birth-of-intermediacy/
Crooked Timber
The Birth of Intermediacy?
I’m taking a break from reading stuff about political theory and liberalism and reading, instead, Other Minds: The Octopus, the Sea, and the Deep Origins of Consciousness [amazon]. It turns o…
🔰 Critical response | In this month's Thesis, Mark Buchanan surveys recent developments in our understanding of criticality in biological systems:
#Nature
https://t.co/i690D0FILT
#Nature
https://t.co/i690D0FILT
Forwarded from انجمن علمی شناسا
📈آیا با ریاضیات میتوان رفتارهای اجتماعی را پیشبینی کرد؟
🗣دکتر میثم علیزاده
🗣دکتر عبدالحسین کلانتری
⏱دوشنبه ۱۶بهمن ساعت ۱۶ تا ۲۰
🏛دانشکده فیزیک شریف تالار دکتر جناب
@Shenasa_SUT
@Hamband_Math_CS
🗣دکتر میثم علیزاده
🗣دکتر عبدالحسین کلانتری
⏱دوشنبه ۱۶بهمن ساعت ۱۶ تا ۲۰
🏛دانشکده فیزیک شریف تالار دکتر جناب
@Shenasa_SUT
@Hamband_Math_CS