How to apply:
Online application:
http://indico.ictp.it/event/8706/
Female students and scientists are
encouraged to apply.
DEADLINE: 18/04/2019
Online application:
http://indico.ictp.it/event/8706/
Female students and scientists are
encouraged to apply.
DEADLINE: 18/04/2019
Forwarded from Complex Systems Studies
Deadline for registration and paper submission
April 12, 2019
23 Farvardin, 1398
https://iasbs.ac.ir/seminars/condmat-meeting/m25/
is extended to:
April 12, 2019
23 Farvardin, 1398
https://iasbs.ac.ir/seminars/condmat-meeting/m25/
👍1
We're hiring a Postdoctoral Fellow in computational neuroscience @AarhusUni and UCL @MPC_CompPsych - deadline May 1st! Please apply, RT, and share this fantastic chance to join us @visceral_mind! More details in thread below.
https://t.co/XXLytuRShS
https://t.co/XXLytuRShS
🎞 Stanford CS234: Reinforcement Learning | Winter 2019 - with Emma Brunskill
https://t.co/tmP8ISn7M9
https://t.co/tmP8ISn7M9
YouTube
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 1 - Introduction - Emma Brunskill
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai
Professor Emma Brunskill, Stanford University
https://stanford.io/3eJW8yT
Professor Emma Brunskill
Assistant Professor, Computer…
Professor Emma Brunskill, Stanford University
https://stanford.io/3eJW8yT
Professor Emma Brunskill
Assistant Professor, Computer…
🔸 Symmetry-enhanced discontinuous phase transition in a two-dimensional quantum magnet https://t.co/MtCsAvdJVd
Nature Physics
Symmetry-enhanced discontinuous phase transition in a two-dimensional quantum magnet
A phase transition often implies symmetry breaking in the system. However, an unconventional first-order phase transition is predicted, where higher-order symmetry than that of the underlying Hamiltonian emerges exactly at the phase boundary.
Forwarded from رادیو پیچیدگی
🎚 Mindscape Episode 41: Steven Strogatz on Synchronization, Networks, and the Emergence of Complex Behavior.
#MindscapePodcast
https://t.co/Z9Lx1YTINa
#MindscapePodcast
https://t.co/Z9Lx1YTINa
🌡 Temperature in and out of equilibrium: a review of concepts, tools and attempts
https://arxiv.org/pdf/1711.03770
A. Puglisi, A. Sarracino, A. Vulpiani
(Submitted on 10 Nov 2017)
Abstract:
We review the general aspects of the concept of temperature in equilibrium and non-equilibrium statistical mechanics. Although temperature is an old and well-established notion, it still presents controversial facets. After a short historical survey of the key role of temperature in thermodynamics and statistical mechanics, we tackle a series of issues which have been recently reconsidered. In particular, we discuss different definitions and their relevance for energy fluctuations. The interest in such a topic has been triggered by the recent observation of negative temperatures in condensed matter experiments. Moreover, the ability to manipulate systems at the micro and nano-scale urges to understand and clarify some aspects related to the statistical properties of small systems (as the issue of temperature's "fluctuations"). We also discuss the notion of temperature in a dynamical context, within the theory of linear response for Hamiltonian systems at equilibrium and stochastic models with detailed balance, and the generalised fluctuation-response relations, which provide a hint for an extension of the definition of temperature in far-from-equilibrium systems. To conclude we consider non-Hamiltonian systems, such as granular materials, turbulence and active matter, where a general theoretical framework is still lacking.
https://arxiv.org/pdf/1711.03770
A. Puglisi, A. Sarracino, A. Vulpiani
(Submitted on 10 Nov 2017)
Abstract:
We review the general aspects of the concept of temperature in equilibrium and non-equilibrium statistical mechanics. Although temperature is an old and well-established notion, it still presents controversial facets. After a short historical survey of the key role of temperature in thermodynamics and statistical mechanics, we tackle a series of issues which have been recently reconsidered. In particular, we discuss different definitions and their relevance for energy fluctuations. The interest in such a topic has been triggered by the recent observation of negative temperatures in condensed matter experiments. Moreover, the ability to manipulate systems at the micro and nano-scale urges to understand and clarify some aspects related to the statistical properties of small systems (as the issue of temperature's "fluctuations"). We also discuss the notion of temperature in a dynamical context, within the theory of linear response for Hamiltonian systems at equilibrium and stochastic models with detailed balance, and the generalised fluctuation-response relations, which provide a hint for an extension of the definition of temperature in far-from-equilibrium systems. To conclude we consider non-Hamiltonian systems, such as granular materials, turbulence and active matter, where a general theoretical framework is still lacking.
سومین کارگاه یادگیری ماشینی در فیزیک: کاربردها در نجوم و کیهانشناسی
۱۱ و ۱۲ اردیبهشت ۱۳۹۸
دانشکده فیزیک، دانشگاه شهید بهشتی
http://www.psi.ir/farsi.asp?page=wml98
۱۱ و ۱۲ اردیبهشت ۱۳۹۸
دانشکده فیزیک، دانشگاه شهید بهشتی
http://www.psi.ir/farsi.asp?page=wml98
🔹 University of Chicago statistics professor, Stephen Stigler, writes a nice article, The Epic Story of Maximum Likelihood.
https://t.co/fXKDyVFLht
https://t.co/fXKDyVFLht
Media is too big
VIEW IN TELEGRAM
طراحی و ساخت میکروسکوپ نوری با قابلیت تصویر برداری از نورونهای مغز در گروه دکتر برادران قاسمی در دانشگاه شهید بهشتی
Detecting Phase Transitions with Artificial Neural Networks
https://www.thphys.uni-heidelberg.de/~cqc/Slides/2017-05-02_Sebastian_Wetzel_CQC2017v2.pdf
https://www.thphys.uni-heidelberg.de/~cqc/Slides/2017-05-02_Sebastian_Wetzel_CQC2017v2.pdf
💰 Are you starting on the path to a job in #datascience? Check out this talk by Chang Lee, Lowe's Data Scientist, where he discusses his experience of going into industry and provides guidelines and tips that save time and help avoid potential frustrations. https://t.co/XqO9Gb99cY
www.ima.umn.edu
So, How Do You Get a Job? | Institute for Mathematics and its Applications
I was late. I only decided to find a job in industry in the 4th year of my graduate study even though I never had a job outside of academia. The one question that I kept asking was: how do you get a job? It was scary because I did't know what to do. But I…
🔸What does it mean to be central in a complex network? Nobody knows, b/c there are many distinct ways of being central. Our recent work, led by our great @GiuliaTtt, attacks the problem from a #stats perspective: the most central node is the median of the network.
What's that? https://t.co/emcX2P5dRr
What's that? https://t.co/emcX2P5dRr
Twitter
Manlio De Domenico
What does it mean to be central in a complex network? Nobody knows, b/c there are many distinct ways of being central. Our recent work, led by our great @GiuliaTtt, attacks the problem from a #stats perspective: the most central node is the median of the…
⚠ A mathematical model from 103 years ago predicted something that was seen for the first time today: a #black_hole.
#MachineLearning could never do that: it needs observations to model anything. This is a major weak-point of ML. Let's fix it.
A stark contrast between Machine Learning vs other forms of mathematical modeling is that ML models often don't model extreme corner cases very well, because #data in those areas is rare. Gathering data in important areas is as important a skill as building fancy neural networks.
Sadly, too often, using extreme inputs to a model is more useful: e.g. by modeling physics of levers on light objects with short levers, we then built very long levers to lift extremely heavy things. Instead, ML is better suited at modeling everyday phenomena with complex models.
https://twitter.com/Reza_Zadeh/status/1053771110410375168?s=19
#MachineLearning could never do that: it needs observations to model anything. This is a major weak-point of ML. Let's fix it.
A stark contrast between Machine Learning vs other forms of mathematical modeling is that ML models often don't model extreme corner cases very well, because #data in those areas is rare. Gathering data in important areas is as important a skill as building fancy neural networks.
Sadly, too often, using extreme inputs to a model is more useful: e.g. by modeling physics of levers on light objects with short levers, we then built very long levers to lift extremely heavy things. Instead, ML is better suited at modeling everyday phenomena with complex models.
https://twitter.com/Reza_Zadeh/status/1053771110410375168?s=19
Twitter
Reza Zadeh
A stark contrast between Machine Learning vs other forms of mathematical modeling is that ML models often don't model extreme corner cases very well, because data in those areas is rare. Gathering data in important areas is as important a skill as building…