A Model-Based Approach to Predicting Graduate-Level Performance Using Indicators of Undergraduate-Level Performance👇👇👇
#سلسله_سمینارهای_هفتگی گروه سیستم های پیچیده شهید بهشتی
علاقه مندان می توانند برای ارائه موضوعات خود به ادمین پیام داده یا به صورت حضوری در جلسه مطرح نمایند.
@onmjnl
علاقه مندان می توانند برای ارائه موضوعات خود به ادمین پیام داده یا به صورت حضوری در جلسه مطرح نمایند.
@onmjnl
🔵 Traffic generation model - Wikipedia
https://en.wikipedia.org/wiki/Traffic_generation_model
https://en.wikipedia.org/wiki/Traffic_generation_model
Wikipedia
Traffic generation model
simulated flow of data in a communications network
🎯 Chaos, Complexity, and Entropy
A physics talk for non-physicists
http://www.necsi.edu/projects/baranger/cce.pdf
🌀 The twenty-first century is starting with a huge bang. For the person in the street, the bang is about a technical revolution that may eventually dwarf the industrial revolution of the 18th and 19th centuries, having already produced a drastic change in the rules of economics. For the scientifically minded, one aspect of this bang is the complexity revolution, which is changing the focus of research in all scientific disciplines, for instance human biology and medicine. What role does physics, the oldest and simplest science, have to play in this? Being a theoretical physicist to the core, I want to focus on theoretical physics. Is it going to change also?
🌀 Twentieth-century theoretical physics came out of the relativistic revolution and the quantum mechanical revolution. It was all about simplicity and continuity (in spite of quantum jumps). Its principal tool was calculus. Its final expression was field theory.
🌀 Twenty-first-century theoretical physics is coming out of the chaos revolution. It will be about complexity and its principal tool will be the computer. Its final expression remains to be found. Thermodynamics, as a vital part of theoretical physics, will partake in the transformation.
A physics talk for non-physicists
http://www.necsi.edu/projects/baranger/cce.pdf
🌀 The twenty-first century is starting with a huge bang. For the person in the street, the bang is about a technical revolution that may eventually dwarf the industrial revolution of the 18th and 19th centuries, having already produced a drastic change in the rules of economics. For the scientifically minded, one aspect of this bang is the complexity revolution, which is changing the focus of research in all scientific disciplines, for instance human biology and medicine. What role does physics, the oldest and simplest science, have to play in this? Being a theoretical physicist to the core, I want to focus on theoretical physics. Is it going to change also?
🌀 Twentieth-century theoretical physics came out of the relativistic revolution and the quantum mechanical revolution. It was all about simplicity and continuity (in spite of quantum jumps). Its principal tool was calculus. Its final expression was field theory.
🌀 Twenty-first-century theoretical physics is coming out of the chaos revolution. It will be about complexity and its principal tool will be the computer. Its final expression remains to be found. Thermodynamics, as a vital part of theoretical physics, will partake in the transformation.
🌀 Neural Networks for Machine Learning
About this course: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform. Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python).
https://www.coursera.org/learn/neural-networks?recoOrder=16&utm_medium=email&utm_source=recommendations&utm_campaign=recommendationsEmail%7Erecs_email_2016_11_20_17%3A58
About this course: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well. This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform. Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python).
https://www.coursera.org/learn/neural-networks?recoOrder=16&utm_medium=email&utm_source=recommendations&utm_campaign=recommendationsEmail%7Erecs_email_2016_11_20_17%3A58
🌀 Introduction to Data Science in Python
About this course: This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course is number 1 in the Applied Data Science with Python specialization and should be taken before any other courses in the specialization.
https://www.coursera.org/learn/python-data-analysis?recoOrder=4&utm_medium=email&utm_source=recommendations&utm_campaign=recommendationsEmail~recs_email_2016_11_20_17%3A58
About this course: This course will introduce the learner to the basics of the python programming environment, including how to download and install python, expected fundamental python programming techniques, and how to find help with python programming questions. The course will also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the DataFrame as the central data structure for data analysis. The course will end with a statistics primer, showing how various statistical measures can be applied to pandas DataFrames. By the end of the course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course is number 1 in the Applied Data Science with Python specialization and should be taken before any other courses in the specialization.
https://www.coursera.org/learn/python-data-analysis?recoOrder=4&utm_medium=email&utm_source=recommendations&utm_campaign=recommendationsEmail~recs_email_2016_11_20_17%3A58
Coursera
Introduction to Data Science in Python | Coursera
Learn Introduction to Data Science in Python from ...
📄 Big data need physical ideas and methods
https://arxiv.org/pdf/1412.6848v1
📌 If a person looks at WHITE paper through BLUE glasses, the paper will become BLUE in the eye of the person. Likewise, in the current study of big data which play the same role as the white paper being looked at, various statistical methods just serve as the blue glasses. That is, results obtained from big data often depend on the statistical methods in use, which may often defy reality. Here I suggest using physical ideas and methods to overcome this problem to the greatest extent. This suggestion is helpful to development and application of big data.
#Data_Analysis , #Statistics and #Probability (physics.data-an)
https://arxiv.org/pdf/1412.6848v1
📌 If a person looks at WHITE paper through BLUE glasses, the paper will become BLUE in the eye of the person. Likewise, in the current study of big data which play the same role as the white paper being looked at, various statistical methods just serve as the blue glasses. That is, results obtained from big data often depend on the statistical methods in use, which may often defy reality. Here I suggest using physical ideas and methods to overcome this problem to the greatest extent. This suggestion is helpful to development and application of big data.
#Data_Analysis , #Statistics and #Probability (physics.data-an)
🎯 A new quantum approach to big data
System for handling massive digital datasets could make impossibly complex problems solvable.
http://news.mit.edu/2016/quantum-approach-big-data-0125
System for handling massive digital datasets could make impossibly complex problems solvable.
http://news.mit.edu/2016/quantum-approach-big-data-0125
MIT News
A new quantum approach to big data
A new quantum computing system for handling massive digital datasets could make impossibly complex problems solvable.
📄 Thermodynamics of information
Juan M. R. Parrondo, Jordan M. Horowitz & Takahiro Sagawa
http://www.nature.com/nphys/journal/v11/n2/abs/nphys3230.html
📌 Abstract
By its very nature, the second law of thermodynamics is probabilistic, in that its formulation requires a probabilistic description of the state of a system. This raises questions about the objectivity of the second law: does it depend, for example, on what we know about the system? For over a century, much effort has been devoted to incorporating information into thermodynamics and assessing the entropic and energetic costs of manipulating information. More recently, this historically theoretical pursuit has become relevant in practical situations where information is manipulated at small scales, such as in molecular and cell biology, artificial nano-devices or quantum computation. Here we give an introduction to a novel theoretical framework for the thermodynamics of information based on stochastic thermodynamics and fluctuation theorems, review some recent experimental results, and present an overview of the state of the art in the field.
Juan M. R. Parrondo, Jordan M. Horowitz & Takahiro Sagawa
http://www.nature.com/nphys/journal/v11/n2/abs/nphys3230.html
📌 Abstract
By its very nature, the second law of thermodynamics is probabilistic, in that its formulation requires a probabilistic description of the state of a system. This raises questions about the objectivity of the second law: does it depend, for example, on what we know about the system? For over a century, much effort has been devoted to incorporating information into thermodynamics and assessing the entropic and energetic costs of manipulating information. More recently, this historically theoretical pursuit has become relevant in practical situations where information is manipulated at small scales, such as in molecular and cell biology, artificial nano-devices or quantum computation. Here we give an introduction to a novel theoretical framework for the thermodynamics of information based on stochastic thermodynamics and fluctuation theorems, review some recent experimental results, and present an overview of the state of the art in the field.
Nature
Thermodynamics of information : Nature Physics : Nature Research
The task of integrating information into the framework of thermodynamics dates back to Maxwell and his infamous demon. Recent advances have made these ideas rigorous[mdash]and brought them into the laboratory.
🔵 New Python library to download &analyze street networks from #OpenStreetMap
#Python
http://urbandemographics.blogspot.co.uk/2016/11/python-links.html?m=1
#Python
http://urbandemographics.blogspot.co.uk/2016/11/python-links.html?m=1
urbandemographics.blogspot.co.uk
Python Links
An overview of data science in Python PySAL: a Python library for spatial analysis , great collaborative by Serge Rey and Dani Arribas...