🗞 Non-Euclidean geometry in nature
Sergei Nechaev
🔗 https://arxiv.org/pdf/1705.08013
📌 ABSTRACT
I describe the manifestation of the non-Euclidean geometry in the behavior of collective observables of some complex physical systems. Specifically, I consider the formation of equilibrium shapes of plants and statistics of sparse random graphs. For these systems I discuss the following interlinked questions: (i) the optimal embedding of plants leaves in the three-dimensional space, (ii) the spectral statistics of sparse random matrix ensembles.
Sergei Nechaev
🔗 https://arxiv.org/pdf/1705.08013
📌 ABSTRACT
I describe the manifestation of the non-Euclidean geometry in the behavior of collective observables of some complex physical systems. Specifically, I consider the formation of equilibrium shapes of plants and statistics of sparse random graphs. For these systems I discuss the following interlinked questions: (i) the optimal embedding of plants leaves in the three-dimensional space, (ii) the spectral statistics of sparse random matrix ensembles.
Forwarded from انجمن علوم اعصاب اصفهان
#Quantum_Chaos emerged as a new field of physics from the efforts to understand the properties of quantum systems which have chaotic deterministic dynamics in the classical limit. Such classical dynamics in a bounded phase space is characterized by a continuous spectrum of motion and exponential instability of trajectories and belongs to the Category Chaos in Dynamical systems. In contrast the corresponding quantum systems have a discrete spectrum and are usually stable in respect to small perturbations. In spite of these differences the correspondence principle of Niels Bohr guaranties that the quantum evolution follows the classical chaotic dynamics during a certain time scale which becomes larger and larger when the dimensionless Planck constant goes to zero (see Figures). Also the Ehrenfest theorem states that a narrow wave packet follows closely even a chaotic trajectory. However, due to the exponential instability of chaotic dynamics a wave packet spreading is exponentially fast and the Ehrenfest time on which the theorem is valid becomes logarithmically short. The problem of semiclassical quantization of such quantum systems had been pointed out by Albert Einstein already in 1917 but it found its solution only at the end of the century. What happens beyond the Ehrenfest time? What are the properties of quantum states in this regime? The answers on these and other questions can be found in this Category.
Quantum Chaos finds applications in number theory, fractal and complex spectra, atomic and molecular physics, clusters and nuclei, quantum transport on small scales, mesoscopic solid-state systems, wave propagation, acoustics, quantum computers and other areas of physics. It has close links with the Random Matrix Theory, invented by Wigner for a description of spectra of complex atoms and nuclei, interacting quantum many-body systems, quantum systems with disorder, quantum complexity of large matrices. A new research area of Quantum Chaos focuses on the relationship between Classical Chaos and Quantum Entanglement.
🔹 http://www.scholarpedia.org/article/Category:Quantum_Chaos
🔗 http://www.sitpor.org/2015/02/quantum-mechanics-and-chaos/
Quantum Chaos finds applications in number theory, fractal and complex spectra, atomic and molecular physics, clusters and nuclei, quantum transport on small scales, mesoscopic solid-state systems, wave propagation, acoustics, quantum computers and other areas of physics. It has close links with the Random Matrix Theory, invented by Wigner for a description of spectra of complex atoms and nuclei, interacting quantum many-body systems, quantum systems with disorder, quantum complexity of large matrices. A new research area of Quantum Chaos focuses on the relationship between Classical Chaos and Quantum Entanglement.
🔹 http://www.scholarpedia.org/article/Category:Quantum_Chaos
🔗 http://www.sitpor.org/2015/02/quantum-mechanics-and-chaos/
🌀 Introduction to Computational Thinking and Data Science
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/
This course is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.
⚡️ Goals
Provide an understanding of the role computation can play in solving problems.
Help students, including those who do not necessarily plan to major in Computer Science and Electrical Engineering, feel confident of their ability to write small programs that allow them to accomplish useful goals.
Position students so that they can compete for research projects and excel in subjects with programming components.
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/
This course is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.
⚡️ Goals
Provide an understanding of the role computation can play in solving problems.
Help students, including those who do not necessarily plan to major in Computer Science and Electrical Engineering, feel confident of their ability to write small programs that allow them to accomplish useful goals.
Position students so that they can compete for research projects and excel in subjects with programming components.
MIT OpenCourseWare
Introduction to Computational Thinking and Data Science | Electrical Engineering and Computer Science | MIT OpenCourseWare
6.0002 is the continuation of _[6.0001 Introduction to Computer Science and Programming in Python](/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/)_ and is intended for students with little or no programming experience.…
Forwarded from NJess Chch
https://www.goodreads.com/ شبکه ی اجتماعی «گودردز» که در سال ۲۰۰۷ راه اندازی شده، یه شبکه ی اجتماعی کتابه، توی این شبکه شما میتونین کتابهایی که خوندین رو به قفسه ی مجازیتون اضافه کنین و با رتبه دادن و نقد نوشتن به دوستانتون در انتخاب کتاب کمک کنین. این شبکه که فقط تا ۲۰۱۳ بیست میلیون کاربر داشته، اکثر کتابهای فارسی زبان رو هم توی لیستهاش داره. یه قابلیت خیلی جالب این سایت اینه که میتونین هر سال برای مطالعه شخصیتون یه هدف تعیین کنین، مثلا بگین تا پایان سال ۱۰ کتاب میخونین، گودردز پیشرفت شما در مطالعه رو نشون میده و تشویقتون میکنه که چالشتون رو به پایان برسونین. همین امروز توی گودردز عضو شیم و دوستامونم دعوت کنیم! #گامی_در_راستای_افزایش_مطالعه #کتاب #مطالعه #بخوانیم
Goodreads
Discover and share books you love on Goodreads, the world's largest site for readers and book recommendations!
🌀 Reaction-Diffusion Model as a Framework for Understanding Biological Pattern Formation
Science, 2010
🔗 http://science.sciencemag.org/content/329/5999/1616?sid=9d6f83eb-8fff-498a-ac84-5010a39f4502
📌 Abstract
The Turing, or reaction-diffusion (RD), model is one of the best-known theoretical models used to explain self-regulated pattern formation in the developing animal embryo. Although its real-world relevance was long debated, a number of compelling examples have gradually alleviated much of the skepticism surrounding the model. The RD model can generate a wide variety of spatial patterns, and mathematical studies have revealed the kinds of interactions required for each, giving this model the potential for application as an experimental working hypothesis in a wide variety of morphological phenomena. In this review, we describe the essence of this theory for experimental biologists unfamiliar with the model, using examples from experimental studies in which the RD model is effectively incorporated.
Science, 2010
🔗 http://science.sciencemag.org/content/329/5999/1616?sid=9d6f83eb-8fff-498a-ac84-5010a39f4502
📌 Abstract
The Turing, or reaction-diffusion (RD), model is one of the best-known theoretical models used to explain self-regulated pattern formation in the developing animal embryo. Although its real-world relevance was long debated, a number of compelling examples have gradually alleviated much of the skepticism surrounding the model. The RD model can generate a wide variety of spatial patterns, and mathematical studies have revealed the kinds of interactions required for each, giving this model the potential for application as an experimental working hypothesis in a wide variety of morphological phenomena. In this review, we describe the essence of this theory for experimental biologists unfamiliar with the model, using examples from experimental studies in which the RD model is effectively incorporated.
Science
Reaction-Diffusion Model as a Framework for Understanding Biological Pattern Formation
The reaction-diffusion (Turing) model is a theoretical model used to explain self-regulated pattern formation in biology. Although many biologists have heard of this model, a better understanding of the concept would aid its ...
🌀 A mathematical theory proposed by Alan Turing in 1952 can explain the formation of fingers
https://phys.org/news/2014-07-mathematical-theory-alan-turing-formation.html
https://phys.org/news/2014-07-mathematical-theory-alan-turing-formation.html
🎯 «فیزیکطوری™ 🔭» یک گروه دانشگاهی با اعضای حرفهای، باحال، با حوصله و کنجکاوه که علم رو به صورت «#حرفهای» دنبال میکنند.
🚩 هدف این گروه خلاقبودن در #تولید محتوای علمی هست، نه #کپی کردن پیدرپی از گروهها یا کانالهای دیگه! هر چیز مرتبط با فیزیک که #منبع موثقی داشته باشه میتونه به این گروه فرستاده بشه، به شرطی که تحت عنوان «#شبهعلم» طبقهبندی نشه!
❌ لطفا از ارسال هر گونه مطلبی که به عنوان تبلیغ بهش نگاه میشه کرد خودداری کنید. همینطور از کانالهای دیگه بیشتر از ۳ مطلب متوالی ارسال نکنید. هدف ما جمعآوری مطالب از بقیه گروهها یا کانالها نیست، بلکه تولید محتوای جدید و بدون تکراره.
1️⃣ لینک گروه:
"Eat, Sleep, Physics"
https://xn--r1a.website/joinchat/AAAAAD0S6fe7Qyt57FYi1Q
لطفا این لینک رو برای هر کس که میفرستید، قبلش از شرایط و سیاستهای گروه آگاهش کنید. ما دوستداریم کسایی که حضور و فعالیتشون به گروه کمک میکنه در گروه حضور پیدا کنند.
2️⃣ راستی، ترجیح ما اینه که بحثی در گروه نداشته باشیم. در صورت نیاز و علاقه، مباحث بحثبرانگیز رو میتونید به گروه «بحث فیزیکطوری 🎓» منتقل کنید:
https://telegram.me/joinchat/A0ATzD5lub60apbedf_1vA
🚩 هدف این گروه خلاقبودن در #تولید محتوای علمی هست، نه #کپی کردن پیدرپی از گروهها یا کانالهای دیگه! هر چیز مرتبط با فیزیک که #منبع موثقی داشته باشه میتونه به این گروه فرستاده بشه، به شرطی که تحت عنوان «#شبهعلم» طبقهبندی نشه!
❌ لطفا از ارسال هر گونه مطلبی که به عنوان تبلیغ بهش نگاه میشه کرد خودداری کنید. همینطور از کانالهای دیگه بیشتر از ۳ مطلب متوالی ارسال نکنید. هدف ما جمعآوری مطالب از بقیه گروهها یا کانالها نیست، بلکه تولید محتوای جدید و بدون تکراره.
1️⃣ لینک گروه:
"Eat, Sleep, Physics"
https://xn--r1a.website/joinchat/AAAAAD0S6fe7Qyt57FYi1Q
لطفا این لینک رو برای هر کس که میفرستید، قبلش از شرایط و سیاستهای گروه آگاهش کنید. ما دوستداریم کسایی که حضور و فعالیتشون به گروه کمک میکنه در گروه حضور پیدا کنند.
2️⃣ راستی، ترجیح ما اینه که بحثی در گروه نداشته باشیم. در صورت نیاز و علاقه، مباحث بحثبرانگیز رو میتونید به گروه «بحث فیزیکطوری 🎓» منتقل کنید:
https://telegram.me/joinchat/A0ATzD5lub60apbedf_1vA
🌀 This Nautilus reprint explores #fractal #patterns and computations found in nature "that bump up against the mathematical limits of efficiency"
http://nautil.us/issue/48/chaos/nature-the-it-wizard-rp
http://nautil.us/issue/48/chaos/nature-the-it-wizard-rp
Nautilus
Nautilus | Science Connected
Nautilus is a different kind of science magazine. Our stories take you into the depths of science and spotlight its ripples in our lives and cultures.
🌀 https://www.quantamagazine.org/a-theory-of-reality-as-more-than-the-sum-of-its-parts-20170601/
#Emergence #Causality
#Emergence #Causality
Quanta Magazine
A Theory of Reality as More Than the Sum of Its Parts
New math shows how, contrary to conventional scientific wisdom, conscious beings and other macroscopic entities might have greater influence over the future than do the sum of their microscopic…
🗞 Forecasting in the light of Big Data
Hykel Hosni, Angelo Vulpiani
🔗 https://arxiv.org/pdf/1705.11186
📌 ABSTRACT
Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a #conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on the first principles, and the naive inductivist one, based only on data. This latter view has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann
Hykel Hosni, Angelo Vulpiani
🔗 https://arxiv.org/pdf/1705.11186
📌 ABSTRACT
Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a #conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on the first principles, and the naive inductivist one, based only on data. This latter view has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann