PostDoc Position to study "What do the 'people' want?"!
https://t.co/wL7QGKNgW2
https://t.co/wL7QGKNgW2
Balance in signed networks
Alec Kirkley, George T. Cantwell, and M. E. J. Newman
Phys. Rev. E 99, 012320 – Published 22 January 2019
https://arxiv.org/pdf/1809.05140
Alec Kirkley, George T. Cantwell, and M. E. J. Newman
Phys. Rev. E 99, 012320 – Published 22 January 2019
https://arxiv.org/pdf/1809.05140
🎞 Entropy: Gaining Knowledge by Admitting Ignorance
🔗 http://media.podcasts.ox.ac.uk/physics/general/2018-11-17-physics-theoretical-schekochihin-1.mp4
Alexander Schekochihin, Professor of Theoretical Physics, gives a talk on entropy.
When dealing with physical systems that contain many degrees of freedom, a researcher's most consequential realisation is of the enormous amount of detailed information about them that she does not have, and has no hope of obtaining. It turns out that this vast ignorance is not a curse but a blessing: by admitting ignorance and constructing a systematic way of making fair predictions about the system that rely only on the information that one has and on nothing else, one can get surprisingly far in describing the natural world. In an approach anticipated by Boltzmann and Gibbs and given mathematical foundation by Shannon, entropy emerges as a mathematical measure of our uncertainty about large systems and, paradoxically, a way to describe their likely behaviour—and even, some argue, the ultimate fate of the Universe. Alex Schekochihin will admit ignorance and attempt to impart some knowledge.
🔗 http://media.podcasts.ox.ac.uk/physics/general/2018-11-17-physics-theoretical-schekochihin-1.mp4
Alexander Schekochihin, Professor of Theoretical Physics, gives a talk on entropy.
When dealing with physical systems that contain many degrees of freedom, a researcher's most consequential realisation is of the enormous amount of detailed information about them that she does not have, and has no hope of obtaining. It turns out that this vast ignorance is not a curse but a blessing: by admitting ignorance and constructing a systematic way of making fair predictions about the system that rely only on the information that one has and on nothing else, one can get surprisingly far in describing the natural world. In an approach anticipated by Boltzmann and Gibbs and given mathematical foundation by Shannon, entropy emerges as a mathematical measure of our uncertainty about large systems and, paradoxically, a way to describe their likely behaviour—and even, some argue, the ultimate fate of the Universe. Alex Schekochihin will admit ignorance and attempt to impart some knowledge.
🎞 Magnets, superfluids and superconductors
🔗 http://media.podcasts.ox.ac.uk/physics/general/2016-10-29-theoretical-physics-2-720p.mp4
Second lecture "#More_is_different" - how states of matter emerge from quantum theory Saturday morning of Theoretical Physics. With Professor Fabian Essler, introduction by Professor John Wheeler.
Fabian Essler will discuss the hugely successful framework for classifying possible states of quantum matter, pioneered by the great Russian Nobel Laureate, Lev Landau. This framework is conceptually remarkably simple, but is broad enough to describe physics ranging from magnets to superconductors to fundamental physics in the guise of relativistic quantum field theory and the Higgs phenomenon.
More on this mini-series;
The properties of all forms of matter, from the most mundane to the most exotic kinds produced in advanced laboratories, are consequences of the laws of quantum mechanics. Understanding how macroscopic behaviour emerges from microscopic laws in a system of many particles is one of the intellectually most demanding, yet most important, challenges of physics, and is the subject of this series of lectures.
🔗 http://media.podcasts.ox.ac.uk/physics/general/2016-10-29-theoretical-physics-2-720p.mp4
Second lecture "#More_is_different" - how states of matter emerge from quantum theory Saturday morning of Theoretical Physics. With Professor Fabian Essler, introduction by Professor John Wheeler.
Fabian Essler will discuss the hugely successful framework for classifying possible states of quantum matter, pioneered by the great Russian Nobel Laureate, Lev Landau. This framework is conceptually remarkably simple, but is broad enough to describe physics ranging from magnets to superconductors to fundamental physics in the guise of relativistic quantum field theory and the Higgs phenomenon.
More on this mini-series;
The properties of all forms of matter, from the most mundane to the most exotic kinds produced in advanced laboratories, are consequences of the laws of quantum mechanics. Understanding how macroscopic behaviour emerges from microscopic laws in a system of many particles is one of the intellectually most demanding, yet most important, challenges of physics, and is the subject of this series of lectures.
🎞 Disordered serendipity: a glassy path to discovery
A workshop in honour of Giorgio Parisi's 70th birthday
23 videos, Sapienza Università di Roma, September 19-22, 2018
https://www.youtube.com/playlist?list=PLWEeoIep_PT-3SKg2TLWuBNpqbG4wcIw6
A workshop in honour of Giorgio Parisi's 70th birthday
23 videos, Sapienza Università di Roma, September 19-22, 2018
https://www.youtube.com/playlist?list=PLWEeoIep_PT-3SKg2TLWuBNpqbG4wcIw6
Complex Systems Studies
🎞 Disordered serendipity: a glassy path to discovery A workshop in honour of Giorgio Parisi's 70th birthday 23 videos, Sapienza Università di Roma, September 19-22, 2018 https://www.youtube.com/playlist?list=PLWEeoIep_PT-3SKg2TLWuBNpqbG4wcIw6
🎞 Marc Mézard - Statistical inference: the impact of statistical physics concepts and methods
https://www.youtube.com/watch?v=hoKphRCtbRQ
In recent years, ideas from statistical physics of disordered systems, notably the cavity method, have helped to develop new algorithms for important inference problems, ranging from community detection to compressed sensing, machine learning (neural networks) and generalized linear regression. The talk will review these developments and explain how they can be used, together with the replica method, to identify phase transitions in benchmark ensembles of inference problems.
https://www.youtube.com/watch?v=hoKphRCtbRQ
In recent years, ideas from statistical physics of disordered systems, notably the cavity method, have helped to develop new algorithms for important inference problems, ranging from community detection to compressed sensing, machine learning (neural networks) and generalized linear regression. The talk will review these developments and explain how they can be used, together with the replica method, to identify phase transitions in benchmark ensembles of inference problems.
YouTube
Marc Mézard - Statistical inference: the impact of statistical physics concepts and methods
In recent years, ideas from statistical physics of disordered systems, notably the cavity method, have helped to develop new algorithms for important inference problems, ranging from community detection to compressed sensing, machine learning (neural networks)…
Complex Systems Studies
🎞 Disordered serendipity: a glassy path to discovery A workshop in honour of Giorgio Parisi's 70th birthday 23 videos, Sapienza Università di Roma, September 19-22, 2018 https://www.youtube.com/playlist?list=PLWEeoIep_PT-3SKg2TLWuBNpqbG4wcIw6
🎞 Florent Krzakala - On statistical physics and inference problems
https://www.youtube.com/watch?v=dFCghDh2aQE
Heuristic tools from statistical physics, in particular the replica method, have been used in the past to locate the phase transitions and compute the optimal learning and generalisation errors in many machine learning tasks. This field is currently witnessing an impressive revival. In this talk, we provide a rigorous justification of these approaches for high-dimensional generalized linear models — used in signal processing, statistical inference, machine learning, communication theory and other fields — and discuss computational to statistical gaps where the learning is possible, but computationally hard.
https://www.youtube.com/watch?v=dFCghDh2aQE
Heuristic tools from statistical physics, in particular the replica method, have been used in the past to locate the phase transitions and compute the optimal learning and generalisation errors in many machine learning tasks. This field is currently witnessing an impressive revival. In this talk, we provide a rigorous justification of these approaches for high-dimensional generalized linear models — used in signal processing, statistical inference, machine learning, communication theory and other fields — and discuss computational to statistical gaps where the learning is possible, but computationally hard.
YouTube
Florent Krzakala - On statistical physics and inference problems
Heuristic tools from statistical physics, in particular the replica method, have been used in the past to locate the phase transitions and compute the optimal learning and generalisation errors in many machine learning tasks. This field is currently witnessing…
🎞 The Partition Function, Sampling and Equilibration in Physics
Florent Krzakala (ENS Paris) and Lenka Zdeborova (CEA-SACLAY)
Monte Carlo sampling was initiated in the 40s by the likes of Ulam and Metropolis in Los Alamos, to study (atomic) physics problems. Since then, it has become a fantastic tool at the roots of statistical physics. A large part of the activity in this area is to develop better heuristics and to understand their properties. We will present a review of partial results and open problems regarding sampling and estimation of the partition function from a physicist's point of view, with a focus on a very difficult problem called spin glasses. We will attempt to highlight the algorithms and methods used in practice, the problems for which better algorithms are needed, and the open problems for theoretical analysis.
https://simons.berkeley.edu/talks/florent-krzakala-and-lenka-zdeborava-2016-01-26
Florent Krzakala (ENS Paris) and Lenka Zdeborova (CEA-SACLAY)
Monte Carlo sampling was initiated in the 40s by the likes of Ulam and Metropolis in Los Alamos, to study (atomic) physics problems. Since then, it has become a fantastic tool at the roots of statistical physics. A large part of the activity in this area is to develop better heuristics and to understand their properties. We will present a review of partial results and open problems regarding sampling and estimation of the partition function from a physicist's point of view, with a focus on a very difficult problem called spin glasses. We will attempt to highlight the algorithms and methods used in practice, the problems for which better algorithms are needed, and the open problems for theoretical analysis.
https://simons.berkeley.edu/talks/florent-krzakala-and-lenka-zdeborava-2016-01-26
simons.berkeley.edu
The Partition Function, Sampling and Equilibration in Physics | Simons Institute for the Theory of Computing
Monte Carlo sampling was initiated in the 40s by the likes of Ulam and Metropolis in Los Alamos, to study (atomic) physics problems. Since then, it has become a fantastic tool at the roots of statistical physics. A large part of the activity in this area…
Registration is open for the "Physics Challenges for #Machine_Learning and #Network_Science Workshop", 3-4 September 2019, Queen Mary University of London. Deadline for abstract submission is July 20 2019.
https://t.co/lGh5iNRSNN
https://t.co/lGh5iNRSNN
👓 What do we see when we look at networks
Tommaso Venturini, Mathieu Jacomy, Pablo Jensen
🔗 https://arxiv.org/pdf/1905.02202.pdf
It is an increasingly common practice in several natural and social sciences to rely on network visualisations both as heuristic tools to get a first overview of relational datasets and as a way to offer an illustration of network analysis findings. Such practice has been around long enough to prove that scholars find it useful to project networks on a space and to observe their visual appearance as a proxy for their topological features. Yet this practice remains largely based on intuition and no investigation has been carried out on to render explicit the foundations and limits of this type of exploration. This paper provides such analysis, by conceptually and mathematically deconstructing the functioning of force-directed layouts and by providing a step-by-step guidance on how to make networks readable and interpret their visual features.
Tommaso Venturini, Mathieu Jacomy, Pablo Jensen
🔗 https://arxiv.org/pdf/1905.02202.pdf
It is an increasingly common practice in several natural and social sciences to rely on network visualisations both as heuristic tools to get a first overview of relational datasets and as a way to offer an illustration of network analysis findings. Such practice has been around long enough to prove that scholars find it useful to project networks on a space and to observe their visual appearance as a proxy for their topological features. Yet this practice remains largely based on intuition and no investigation has been carried out on to render explicit the foundations and limits of this type of exploration. This paper provides such analysis, by conceptually and mathematically deconstructing the functioning of force-directed layouts and by providing a step-by-step guidance on how to make networks readable and interpret their visual features.
Forwarded from انجمن علمی ژرفا
۲۵اُمین #گردهمایی ژرفا
🦠 #سیستمهای_پیچیده
[فیزیک، اقتصاد، علوم اعصاب، علوم زیستی، مدلسازی ریاضی]
🧩 سخنرانی + میز گفتگو
با حضور دکتر شاهین روحانی، دکتر سامان مقیمی، دکتر عبدالحسین عباسیان، دکتر سید علی حسینی و دکتر عطا کالیراد
🗓 سهشنبه ۲۴ اردیبهشت
🕜 ساعت ۱۳:۳۰ تا ۱۵:۳۰
🏛 سالن جابرابنحیان دانشگاه شریف
📮اطلاعات بیشتر و ثبتنام میهمانان خارج دانشگاه در zharfa90.ir/گردهمایی-سیستم-پیچیده
❇️ ژرفا، همبند، فیزیک، شناسا
🦠 #سیستمهای_پیچیده
[فیزیک، اقتصاد، علوم اعصاب، علوم زیستی، مدلسازی ریاضی]
🧩 سخنرانی + میز گفتگو
با حضور دکتر شاهین روحانی، دکتر سامان مقیمی، دکتر عبدالحسین عباسیان، دکتر سید علی حسینی و دکتر عطا کالیراد
🗓 سهشنبه ۲۴ اردیبهشت
🕜 ساعت ۱۳:۳۰ تا ۱۵:۳۰
🏛 سالن جابرابنحیان دانشگاه شریف
📮اطلاعات بیشتر و ثبتنام میهمانان خارج دانشگاه در zharfa90.ir/گردهمایی-سیستم-پیچیده
❇️ ژرفا، همبند، فیزیک، شناسا
انجمن علمی ژرفا
۲۵اُمین #گردهمایی ژرفا 🦠 #سیستمهای_پیچیده [فیزیک، اقتصاد، علوم اعصاب، علوم زیستی، مدلسازی ریاضی] 🧩 سخنرانی + میز گفتگو با حضور دکتر شاهین روحانی، دکتر سامان مقیمی، دکتر عبدالحسین عباسیان، دکتر سید علی حسینی و دکتر عطا کالیراد 🗓 سهشنبه ۲۴ اردیبهشت 🕜…
🌀 دربارهی ۲۵اُمین گردهمایی ژرفا با محوریت #سیستمهای_پیچیده
1️⃣ این گردهمایی شامل سه سخنرانی کوتاه از دکتر سامان مقیمی (عضو هیئت علمی دانشکدهی فیزیک دانشگاه صنعتی شریف)، دکتر عبدالحسین عباسیان (عضو هیئت علمی پژوهشکده علوم شناختی پژوهشگاه دانشهای بنیادی)، دکتر عطا کالیراد (محقق پسادکتری پژوهشکده علوم زیستی پژوهشگاه دانشهای بنیادی) است.
2️⃣ بخش دوم گردهمایی به میز گفتگو با اجرای آقای عباس کریمی و با حضور پنج نفر از اساتید متخصص در این حوزه پی گرفته خواهد شد. اساتید حاضر در این قسمت آقایان دکتر شاهین روحانی (عضو هیئت علمی دانشکده ی فیزیک دانشگاه صنعتی شریف)، دکتر سامان مقیمی، دکتر عبدالحسین عباسیان، دکتر سید علی حسینی (عضو هیئت علمی دانشکده فیزیک دانشگاه شهید بهشتی) و دکتر عطا کالیراد است.
این گردهمایی با همکاری انجمن علمی ژرفا، انجمن علمی همبند (علوم ریاضی)، انجمن علمی فیزیک، انجمن علمی شناسا (علوم شناختی) دانشگاه صنعتی شریف و مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی برگزار میگردد.
⭕️ شرکت برای همه آزاد و رایگان است اما نیاز به ثبتنام وجود دارد. اطلاعات بیشتر و ثبتنام:
zharfa90.ir/گردهمایی-سیستم-پیچیده
1️⃣ این گردهمایی شامل سه سخنرانی کوتاه از دکتر سامان مقیمی (عضو هیئت علمی دانشکدهی فیزیک دانشگاه صنعتی شریف)، دکتر عبدالحسین عباسیان (عضو هیئت علمی پژوهشکده علوم شناختی پژوهشگاه دانشهای بنیادی)، دکتر عطا کالیراد (محقق پسادکتری پژوهشکده علوم زیستی پژوهشگاه دانشهای بنیادی) است.
2️⃣ بخش دوم گردهمایی به میز گفتگو با اجرای آقای عباس کریمی و با حضور پنج نفر از اساتید متخصص در این حوزه پی گرفته خواهد شد. اساتید حاضر در این قسمت آقایان دکتر شاهین روحانی (عضو هیئت علمی دانشکده ی فیزیک دانشگاه صنعتی شریف)، دکتر سامان مقیمی، دکتر عبدالحسین عباسیان، دکتر سید علی حسینی (عضو هیئت علمی دانشکده فیزیک دانشگاه شهید بهشتی) و دکتر عطا کالیراد است.
این گردهمایی با همکاری انجمن علمی ژرفا، انجمن علمی همبند (علوم ریاضی)، انجمن علمی فیزیک، انجمن علمی شناسا (علوم شناختی) دانشگاه صنعتی شریف و مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی برگزار میگردد.
⭕️ شرکت برای همه آزاد و رایگان است اما نیاز به ثبتنام وجود دارد. اطلاعات بیشتر و ثبتنام:
zharfa90.ir/گردهمایی-سیستم-پیچیده
انجمن علمی ژرفا
۲۵اُمین #گردهمایی ژرفا 🦠 #سیستمهای_پیچیده [فیزیک، اقتصاد، علوم اعصاب، علوم زیستی، مدلسازی ریاضی] 🧩 سخنرانی + میز گفتگو با حضور دکتر شاهین روحانی، دکتر سامان مقیمی، دکتر عبدالحسین عباسیان، دکتر سید علی حسینی و دکتر عطا کالیراد 🗓 سهشنبه ۲۴ اردیبهشت 🕜…
🌀 دربارهی ۲۵اُمین گردهمایی ژرفا با محوریت #سیستمهای_پیچیده
⭕️ شرکت برای همه آزاد و رایگان است اما نیاز به ثبتنام وجود دارد. اطلاعات بیشتر و ثبتنام:
zharfa90.ir/گردهمایی-سیستم-پیچیده
⭕️ شرکت برای همه آزاد و رایگان است اما نیاز به ثبتنام وجود دارد. اطلاعات بیشتر و ثبتنام:
zharfa90.ir/گردهمایی-سیستم-پیچیده
Forwarded from Complex Networks (SBU)
#سمینارهای_هفتگی
«تفاوت همبستگی و همبستگی جزئی و سازوکار لاسو گرافیکی»
🗣 مهسا باقری - دانشگاه شهیدبهشتی
⏰ دوشنبه، ۲۳ اردیبهشت ساعت ۱۵:۰۰
🏛 محل برگزاری: سالن ابنهیثم
🔴 به تغییر زمان برگزاری برنامه توجه کنید!
—————————————
🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
🕸 @CCNSD 🔗 ccnsd.ir
—————————————
«تفاوت همبستگی و همبستگی جزئی و سازوکار لاسو گرافیکی»
🗣 مهسا باقری - دانشگاه شهیدبهشتی
⏰ دوشنبه، ۲۳ اردیبهشت ساعت ۱۵:۰۰
🏛 محل برگزاری: سالن ابنهیثم
🔴 به تغییر زمان برگزاری برنامه توجه کنید!
—————————————
🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
🕸 @CCNSD 🔗 ccnsd.ir
—————————————
Machine learning, practically speaking
“To apply machine learning, labs needn’t have years of computational expertise, but they do need a cautious mind-set”
https://t.co/ZZtDbWk4Ky
“To apply machine learning, labs needn’t have years of computational expertise, but they do need a cautious mind-set”
https://t.co/ZZtDbWk4Ky
Intervention Threshold for Epidemic Control in Susceptible-Infected-Recovered Metapopulation Models
“two types of patches including high-risk and low-risk ones, in order to evaluate intervention strategies for epidemic control”
https://t.co/Z1VMrm35dk
“two types of patches including high-risk and low-risk ones, in order to evaluate intervention strategies for epidemic control”
https://t.co/Z1VMrm35dk
Our work provides a first step towards answering the question whether the traditional ways by which physicists model nature naturally arise from the experimental data without any mathematical and physical pre-knowledge, or if there are alternative elegant formalisms, which may solve some of the fundamental conceptual problems in modern physics, such as the measurement problem in quantum mechanics.
https://arxiv.org/abs/1807.10300
https://arxiv.org/abs/1807.10300