🌀 Ising model: exact results
The Ising model is a simple classical model of a ferromagnet which has the remarkable property that in two dimensions its physical properties may be exactly calculated. These exact calculations have given microscopic insight into the many body collective phenomena of phase transitions and have developed new areas of mathematics.
🔗 http://www.scholarpedia.org/article/Ising_model:_exact_results
The Ising model is a simple classical model of a ferromagnet which has the remarkable property that in two dimensions its physical properties may be exactly calculated. These exact calculations have given microscopic insight into the many body collective phenomena of phase transitions and have developed new areas of mathematics.
🔗 http://www.scholarpedia.org/article/Ising_model:_exact_results
🔹 PhD fellowship in the field of Shareable Dynamic Media in Hybrid Meetings (5+3)
The Graduate School at Arts, Faculty of Arts, Aarhus University, in collaboration with Microsoft Research, Cambrige and Participatory Information Technology (PIT), invites applications for a fully-funded PhD fellowship in Shareable Dynamic Media in Hybrid Meetings provided the necessary funding is available. This PhD fellowship is available as of 1 September 2017 for a period of up to three years (5+3). The candidate who is awarded the fellowship must commence his/her PhD degree programme on 1 September 2017.
The PhD fellowship will be financed by the 3 parties.
http://talent.au.dk/phd/arts/open-calls/phd-call-7/
The Graduate School at Arts, Faculty of Arts, Aarhus University, in collaboration with Microsoft Research, Cambrige and Participatory Information Technology (PIT), invites applications for a fully-funded PhD fellowship in Shareable Dynamic Media in Hybrid Meetings provided the necessary funding is available. This PhD fellowship is available as of 1 September 2017 for a period of up to three years (5+3). The candidate who is awarded the fellowship must commence his/her PhD degree programme on 1 September 2017.
The PhD fellowship will be financed by the 3 parties.
http://talent.au.dk/phd/arts/open-calls/phd-call-7/
🗞 Geometric explanation of the rich-club phenomenon in complex networks
Máté Csigi, Attila Kőrösi, József Bíró, Zalán Heszberger, Yury Malkov, András Gulyás
🔗 https://arxiv.org/pdf/1702.02399
📌 ABSTRACT
The rich club organization (the presence of highly connected hub core in a network) influences many structural and functional characteristics of networks including topology, the efficiency of paths and distribution of load. Despite its major role, the literature contains only a very limited set of models capable of generating networks with realistic rich club structure. One possible reason is that the rich club organization is a divisive property among complex networks which exhibit great diversity, in contrast to other metrics (e.g. diameter, clustering or degree distribution) which seem to behave very similarly across many networks. Here we propose a simple yet powerful geometry-based growing model which can generate realistic complex networks with high rich club diversity by controlling a single geometric parameter. The growing model is validated against the Internet, protein-protein interaction, airport and power grid networks.
Máté Csigi, Attila Kőrösi, József Bíró, Zalán Heszberger, Yury Malkov, András Gulyás
🔗 https://arxiv.org/pdf/1702.02399
📌 ABSTRACT
The rich club organization (the presence of highly connected hub core in a network) influences many structural and functional characteristics of networks including topology, the efficiency of paths and distribution of load. Despite its major role, the literature contains only a very limited set of models capable of generating networks with realistic rich club structure. One possible reason is that the rich club organization is a divisive property among complex networks which exhibit great diversity, in contrast to other metrics (e.g. diameter, clustering or degree distribution) which seem to behave very similarly across many networks. Here we propose a simple yet powerful geometry-based growing model which can generate realistic complex networks with high rich club diversity by controlling a single geometric parameter. The growing model is validated against the Internet, protein-protein interaction, airport and power grid networks.
🌀 Aging and Complex Systems: Motivation
http://necsi.edu/events/cxintensive/cxintensiveam.html
As the baby-boom generation rapidly approaches age 65 in the next decade, the mechanisms and clinical consequences of the aging process are becoming a central focus of scientific investigation. The study of aging is particularly appropriate for complex systems analysis. While medicine is generally focused on failure of individual organs or specific diseases, the aging process is a systemic one resulting from changes in multiple subsystems affecting overall system structure, dynamic response, adaptation and function. The physical degradation of non-equilibrium biological structures result in reduced fine scale complexity of structure and changes in dynamic response. Moreover, primary system failures result from prior changes in interrelated repair, regulatory, homeostatic and adaptive mechanisms.
For example, in research on aging, complex systems concepts can be applied to the analysis of temporal behavior of heart rate dynamics and other physiologic time series reflecting aging induced changes in dynamic response, as well as structural changes in the fine scale structure of neural systems and bone tissue. Additional areas of application include experimental and theoretical studies that can elucidate the relationship between the structural and dynamic changes, and how these changes lead to disease and disability.
In addition, the role of environmental and social factors in aging can be examined through various methods and concepts which have been developed in the general study of complex systems. Particularly relevant is the interaction of behavior, and physical and social environment in system maintenance and repair where research indicates that active individuals in stimulating (appropriately complex) environments can maintain high levels of function. Research on human aging will demonstrate how complex system approaches can be effectively applied both to understand and to prevent or alleviate processes of system deterioration, which is an important area of study for all complex systems.
http://necsi.edu/events/cxintensive/cxintensiveam.html
As the baby-boom generation rapidly approaches age 65 in the next decade, the mechanisms and clinical consequences of the aging process are becoming a central focus of scientific investigation. The study of aging is particularly appropriate for complex systems analysis. While medicine is generally focused on failure of individual organs or specific diseases, the aging process is a systemic one resulting from changes in multiple subsystems affecting overall system structure, dynamic response, adaptation and function. The physical degradation of non-equilibrium biological structures result in reduced fine scale complexity of structure and changes in dynamic response. Moreover, primary system failures result from prior changes in interrelated repair, regulatory, homeostatic and adaptive mechanisms.
For example, in research on aging, complex systems concepts can be applied to the analysis of temporal behavior of heart rate dynamics and other physiologic time series reflecting aging induced changes in dynamic response, as well as structural changes in the fine scale structure of neural systems and bone tissue. Additional areas of application include experimental and theoretical studies that can elucidate the relationship between the structural and dynamic changes, and how these changes lead to disease and disability.
In addition, the role of environmental and social factors in aging can be examined through various methods and concepts which have been developed in the general study of complex systems. Particularly relevant is the interaction of behavior, and physical and social environment in system maintenance and repair where research indicates that active individuals in stimulating (appropriately complex) environments can maintain high levels of function. Research on human aging will demonstrate how complex system approaches can be effectively applied both to understand and to prevent or alleviate processes of system deterioration, which is an important area of study for all complex systems.
necsi.edu
Intensive Course on the Fundamentals of Complex Systems
The New England Complex Systems Institute (NECSI) is an independent educational and research institute established as a joint effort of faculty from New England area institutions for the advancement of interdisciplinary communication and collaboration, building…
#سمینارهای_هفتگی گروه سیستمهای پیچیده و علم شبکه دانشگاه شهید بهشتی
🔹شنبه، ۳۰ بهمنماه، ساعت ۴/۵ - کلاس ۴ دانشکده فیزیک دانشگاه شهید بهشتی
@carimi
🔹شنبه، ۳۰ بهمنماه، ساعت ۴/۵ - کلاس ۴ دانشکده فیزیک دانشگاه شهید بهشتی
@carimi
Complex Systems Studies
#سمینارهای_هفتگی گروه سیستمهای پیچیده و علم شبکه دانشگاه شهید بهشتی 🔹شنبه، ۳۰ بهمنماه، ساعت ۴/۵ - کلاس ۴ دانشکده فیزیک دانشگاه شهید بهشتی @carimi
⚡️ Evolution of Communities in the Medical Sciences: Evidence from the Medical Words Network
Amir Hossein Shirazi
PHD Student, Department of Physics, Shahid Beheshti University
Classification of medical sciences into its sub-branches is crucial for optimum administration of healthcare and specialty training. Due to the rapid and continuous evolution of medical sciences, development of unbiased tools for monitoring the evolution of medical disciplines is required.
Network analysis was used to explore how the medical sciences have evolved between 1980 and 2015 based on the shared words contained in more than 9 million PubMed abstracts. The k-clique percolation method was used to extract local research communities within the network. Analysis of the shared vocabulary in research papers reflects the trends of collaboration and splintering among different disciplines in medicine. Our model identifies distinct communities within each discipline that preferentially collaborate with other communities within other domains of specialty, and overturns some common perceptions.
Our analysis provides a tool to assess growth, merging, splitting and contraction of research communities and can thereby serve as a guide to inform policymakers about funding and training in healthcare.
🔹Feel free to contact us: @carimi
Amir Hossein Shirazi
PHD Student, Department of Physics, Shahid Beheshti University
Classification of medical sciences into its sub-branches is crucial for optimum administration of healthcare and specialty training. Due to the rapid and continuous evolution of medical sciences, development of unbiased tools for monitoring the evolution of medical disciplines is required.
Network analysis was used to explore how the medical sciences have evolved between 1980 and 2015 based on the shared words contained in more than 9 million PubMed abstracts. The k-clique percolation method was used to extract local research communities within the network. Analysis of the shared vocabulary in research papers reflects the trends of collaboration and splintering among different disciplines in medicine. Our model identifies distinct communities within each discipline that preferentially collaborate with other communities within other domains of specialty, and overturns some common perceptions.
Our analysis provides a tool to assess growth, merging, splitting and contraction of research communities and can thereby serve as a guide to inform policymakers about funding and training in healthcare.
🔹Feel free to contact us: @carimi
با سلام
🚩 به آگاهی میرساند نشست یکصدوچهلوپنجم باشگاه فیزیک تهران، ساعت ۱۷ روز دوشنبه 2 اسفندماه 1395، در سالن آمفیتئاتر دانشکده فیزیک دانشگاه تهران (انتهای خیابان کارگرشمالی، روبهروی کوچه نوزدهم) برگزار خواهد شد.
در باشگاه فیزیک این ماه، آقای دکتر غلامرضا #جعفری از دانشکده فیزیک دانشگاه شهید بهشتی، درباره «#دادههای_بزرگ و #ظهور #رفتار_جمعی» خواهند گفت. ساعت 18:20 آقای علی فرنودی پرسش ماه را مطرح و سپس اخبار فیزیک در ماه گذشته را به آگاهی حاضران خواهند رساند.
یادآوری میشود که مخاطبان باشگاه، علاقهمندان به فیزیک هستند و از شما درخواست میشود با فرستادن این نامه به دوستان خود یا چاپ و نصب پوستر باشگاه در محل کار خود، دیگر علاقهمندان فیزیک را آگاه کنید.
با احترام
انجمن فیزیک ایران
🚩 به آگاهی میرساند نشست یکصدوچهلوپنجم باشگاه فیزیک تهران، ساعت ۱۷ روز دوشنبه 2 اسفندماه 1395، در سالن آمفیتئاتر دانشکده فیزیک دانشگاه تهران (انتهای خیابان کارگرشمالی، روبهروی کوچه نوزدهم) برگزار خواهد شد.
در باشگاه فیزیک این ماه، آقای دکتر غلامرضا #جعفری از دانشکده فیزیک دانشگاه شهید بهشتی، درباره «#دادههای_بزرگ و #ظهور #رفتار_جمعی» خواهند گفت. ساعت 18:20 آقای علی فرنودی پرسش ماه را مطرح و سپس اخبار فیزیک در ماه گذشته را به آگاهی حاضران خواهند رساند.
یادآوری میشود که مخاطبان باشگاه، علاقهمندان به فیزیک هستند و از شما درخواست میشود با فرستادن این نامه به دوستان خود یا چاپ و نصب پوستر باشگاه در محل کار خود، دیگر علاقهمندان فیزیک را آگاه کنید.
با احترام
انجمن فیزیک ایران
💲 Fully funded PhD studentship in data science at Warwick business school
http://www.jobs.ac.uk/job/AWI911/phd-studentship-in-data-sciencemeasuring-and-predicting-human-behaviour-with-online-data/
http://www.jobs.ac.uk/job/AWI911/phd-studentship-in-data-sciencemeasuring-and-predicting-human-behaviour-with-online-data/
Jobs.ac.uk
PhD Studentship in Data Science: Measuring and Predicting Human Behaviour with Online Data at University of Warwick
View details for this PhD Studentship in Data Science: Measuring and Predicting Human Behaviour with Online Data job vacancy at University of...
🗞 Why We Read Wikipedia
Philipp Singer, Florian Lemmerich, Robert West, Leila Zia, Ellery Wulczyn, Markus Strohmaier, Jure Leskovec
🔗 https://arxiv.org/pdf/1702.05379
📌 ABSTRACT
Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit Wikipedia. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, we build a taxonomy of Wikipedia use cases along several dimensions, capturing users' motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, we quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Our analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, we match survey responses to the respondents' digital traces in Wikipedia's server logs, enabling the discovery of behavioral patterns associated with specific use cases. For instance, we observe long and fast-paced page sequences across topics for users who are bored or exploring randomly, whereas those using Wikipedia for work or school spend more time on individual articles focused on topics such as science. Our findings advance our understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia's user experience, editors striving to cater to their readers' needs, third-party services (such as search engines) providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.
Philipp Singer, Florian Lemmerich, Robert West, Leila Zia, Ellery Wulczyn, Markus Strohmaier, Jure Leskovec
🔗 https://arxiv.org/pdf/1702.05379
📌 ABSTRACT
Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit Wikipedia. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, we build a taxonomy of Wikipedia use cases along several dimensions, capturing users' motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, we quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Our analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, we match survey responses to the respondents' digital traces in Wikipedia's server logs, enabling the discovery of behavioral patterns associated with specific use cases. For instance, we observe long and fast-paced page sequences across topics for users who are bored or exploring randomly, whereas those using Wikipedia for work or school spend more time on individual articles focused on topics such as science. Our findings advance our understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia's user experience, editors striving to cater to their readers' needs, third-party services (such as search engines) providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.