Forwarded from Complex Networks (SBU)
#سمینارهای_هفتگی
«مقدمهای بر حسابان کسری و کاربردهای آن»
🗣 معین خلیقی - دانشگاه تربیت مدرس
⏰ دوشنبه، ۲۴ تیر - ساعت ۱۶:۰۰
🏛 محل برگزاری: سالن ابنهیثم
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⭕️ به امید دیدار
📞 @herman1
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🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
🕸 @CCNSD 🔗 ccnsd.ir
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«مقدمهای بر حسابان کسری و کاربردهای آن»
🗣 معین خلیقی - دانشگاه تربیت مدرس
⏰ دوشنبه، ۲۴ تیر - ساعت ۱۶:۰۰
🏛 محل برگزاری: سالن ابنهیثم
~~~~~~~~~~~~~~~~
⭕️ به امید دیدار
📞 @herman1
—————————————
🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
🕸 @CCNSD 🔗 ccnsd.ir
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⌨ In July’s issue of Nature Machine Intelligence, find out about machine intelligence in drug discovery, an approach for automatic classification of answers in free-form surveys and whether AI will soon surpass humans in playing Angry Birds!
https://go.nature.com/2S4N24P
https://go.nature.com/2S4N24P
💡 Statistical mechanics of time series.
https://arxiv.org/abs/1907.04925
Countless natural and social multivariate systems are studied through sets of simultaneous and time-spaced measurements of the observables that drive their dynamics, i.e., through sets of time series. Typically, this is done via hypothesis testing: the statistical properties of the empirical time series are tested against those expected under a suitable null hypothesis. This is a very challenging task in complex interacting systems, where statistical stability is often poor due to lack of stationarity and ergodicity. Here, we describe an unsupervised, data-driven framework to perform hypothesis testing in such situations. This consists of a statistical mechanical theory - derived from first principles - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series. We showcase its possible applications on a set of stock market returns from the NYSE.
https://arxiv.org/abs/1907.04925
Countless natural and social multivariate systems are studied through sets of simultaneous and time-spaced measurements of the observables that drive their dynamics, i.e., through sets of time series. Typically, this is done via hypothesis testing: the statistical properties of the empirical time series are tested against those expected under a suitable null hypothesis. This is a very challenging task in complex interacting systems, where statistical stability is often poor due to lack of stationarity and ergodicity. Here, we describe an unsupervised, data-driven framework to perform hypothesis testing in such situations. This consists of a statistical mechanical theory - derived from first principles - for ensembles of time series designed to preserve, on average, some of the statistical properties observed on an empirical set of time series. We showcase its possible applications on a set of stock market returns from the NYSE.
The State of the Art in Multilayer Network Visualization
“literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks, and analytic techniques”
https://t.co/o6sRQfXIJY
“literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks, and analytic techniques”
https://t.co/o6sRQfXIJY
💡 Network science has found many applications in natural language processing (NLP) and text mining. In this recent work, we present a brief introduction to text networks, from their respective construction to applications:
https://t.co/aX34NOhan8
https://t.co/aX34NOhan8
ResearchGate
(PDF) Text Networks (CDT-4)
PDF | On Sep 2, 2018, Henrique Ferraz de Arruda and others published Text Networks (CDT-4)
💡 لیست اساتیدی که در ایران به روی سیستمهای پیچیده کار میکنند:
https://ccnsd.ir/people/icss/
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این لیست در حال کامل شدن است.
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https://ccnsd.ir/people/icss/
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این لیست در حال کامل شدن است.
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Lecture Notes in Social Networks comprises volumes covering the theory, foundations and applications of the new emerging multidisciplinary field of social networks analysis and mining.
See more…
https://t.co/I95yzngObH
See more…
https://t.co/I95yzngObH
Micro, Meso, Macro: the effect of triangles on communities in networks
“communities can emerge spontaneously from simple processes of motiff generation happening at a micro-level”
https://t.co/plew3LOnrg
“communities can emerge spontaneously from simple processes of motiff generation happening at a micro-level”
https://t.co/plew3LOnrg
Markovian approach to tackle the interaction of simultaneous diseases
“transition point between the full-dominance phase, in which only one pathogen propagates, and the coexistence regime”
https://t.co/7xnh1cG81p
“transition point between the full-dominance phase, in which only one pathogen propagates, and the coexistence regime”
https://t.co/7xnh1cG81p
💡 "Introduction to a renormalisation group method" (by Roland Bauerschmidt, David C. Brydges, Gordon Slade): https://t.co/kYLg2P6bIn
"This book provides an introduction to a renormalisation group method in the spirit of that of Wilson."
(Note: This book looks really useful!)
"This book provides an introduction to a renormalisation group method in the spirit of that of Wilson."
(Note: This book looks really useful!)
arXiv.org
Introduction to a renormalisation group method
This book provides an introduction to a renormalisation group method in the
spirit of that of Wilson. It starts with a concise overview of the theory of
critical phenomena and the introduction of...
spirit of that of Wilson. It starts with a concise overview of the theory of
critical phenomena and the introduction of...
تبریک به مهدی یوسفزاده به خاطر کسب بهترین رتبه در رقابت CheXpert دانشگاه استنفورد در زمینه شناسایی ناهنجاریهای ریه بر اساس تصاویر پرتو اکس با استفاده از یادگیری ماشین (CNN).
https://stanfordmlgroup.github.io/competitions/chexpert/
با آرزوی موفقیتهای بیشتر برای مهدی :)
دانشکده فیزیک، دانشگاه شهید بهشتی
https://stanfordmlgroup.github.io/competitions/chexpert/
با آرزوی موفقیتهای بیشتر برای مهدی :)
دانشکده فیزیک، دانشگاه شهید بهشتی
Hubs and authorities of the scientific migration network
“presence of a set of countries acting both as hubs and authorities, occupying a privileged position in the scientific migration network, and having similar local characteristics”
https://t.co/rjCxcG7vKR
“presence of a set of countries acting both as hubs and authorities, occupying a privileged position in the scientific migration network, and having similar local characteristics”
https://t.co/rjCxcG7vKR
🚼 Who Is the Most Important Character in Frozen? This article is a fantastic way for #kids to learn about #networks. Big ideas, yet readily understandable by young people. All they need is arithmetic and curiosity.
https://t.co/Qbi4pvnqj6
https://t.co/Qbi4pvnqj6
🗞 Phase transition in a network model of social balance with Glauber dynamics
Rana Shojaei, Pouya Manshour, Afshin Montakhab
🔗 https://arxiv.org/pdf/1907.07389
We study the evolution of a social network with friendly/enmity connections into a balanced state by introducing a dynamical model with an intrinsic randomness, similar to Glauber dynamics in statistical mechanics. We include the possibility of the tension promotion as well as the tension reduction in our model. Such a more realistic situation enables the system to escape from local minima in its energy landscape and thus to exit out of frozen imbalanced states, which are unwanted outcomes observed in previous models. On the other hand, in finite networks the dynamics takes the system into a balanced phase, if the randomness is lower than a critical value. For large networks, we also find a sharp phase transition at the initial positive link density of ρ∗0=1/2, where the system transitions from a bipolar state into a paradise. This modifies the gradual phase transition at a nontrivial value of ρ∗0=0.65, observed in recent studies.
Rana Shojaei, Pouya Manshour, Afshin Montakhab
🔗 https://arxiv.org/pdf/1907.07389
We study the evolution of a social network with friendly/enmity connections into a balanced state by introducing a dynamical model with an intrinsic randomness, similar to Glauber dynamics in statistical mechanics. We include the possibility of the tension promotion as well as the tension reduction in our model. Such a more realistic situation enables the system to escape from local minima in its energy landscape and thus to exit out of frozen imbalanced states, which are unwanted outcomes observed in previous models. On the other hand, in finite networks the dynamics takes the system into a balanced phase, if the randomness is lower than a critical value. For large networks, we also find a sharp phase transition at the initial positive link density of ρ∗0=1/2, where the system transitions from a bipolar state into a paradise. This modifies the gradual phase transition at a nontrivial value of ρ∗0=0.65, observed in recent studies.