سومین گردهمایی پیوند
📅 سهشنبه ۱۶ مرداد
⏰ ۹ تا ۱۸
🏛 تالار ابن هیثم، دانشکده فیزیک دانشگاه شهیدبهشتی
📌 ثبتنام:
peyvand.me
@peyvandgathering
📅 سهشنبه ۱۶ مرداد
⏰ ۹ تا ۱۸
🏛 تالار ابن هیثم، دانشکده فیزیک دانشگاه شهیدبهشتی
📌 ثبتنام:
peyvand.me
@peyvandgathering
🧓 "Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares"
Book by Prof. Stephen P. Boyd (Stanford) and Prof. Lieven Vandenberghe (UCLA)
Available online
https://t.co/843XLv8xWD
Book by Prof. Stephen P. Boyd (Stanford) and Prof. Lieven Vandenberghe (UCLA)
Available online
https://t.co/843XLv8xWD
🔅 "Reexamining the renormalization group: Period doubling onset of chaos" (new paper by Archishman Raju, James P Sethna): https://t.co/DdB1BAHDFX
🔖 A different approach to introducing statistical mechanics
Thomas A. Moore, Daniel V. Schroeder
🔗 https://arxiv.org/pdf/1502.07051.pdf
📌 ABSTRACT
The basic notions of statistical mechanics (microstates, multiplicities) are quite simple, but understanding how the second law arises from these ideas requires working with cumbersomely large numbers. To avoid getting bogged down in mathematics, one can compute multiplicities numerically for a simple model system such as an Einstein solid -- a collection of identical quantum harmonic oscillators. A computer spreadsheet program or comparable software can compute the required combinatoric functions for systems containing a few hundred oscillators and units of energy. When two such systems can exchange energy, one immediately sees that some configurations are overwhelmingly more probable than others. Graphs of entropy vs. energy for the two systems can be used to motivate the theoretical definition of temperature, T=(∂S/∂U)−1, thus bridging the gap between the classical and statistical approaches to entropy. Further spreadsheet exercises can be used to compute the heat capacity of an Einstein solid, study the Boltzmann distribution, and explore the properties of a two-state paramagnetic system.
Thomas A. Moore, Daniel V. Schroeder
🔗 https://arxiv.org/pdf/1502.07051.pdf
📌 ABSTRACT
The basic notions of statistical mechanics (microstates, multiplicities) are quite simple, but understanding how the second law arises from these ideas requires working with cumbersomely large numbers. To avoid getting bogged down in mathematics, one can compute multiplicities numerically for a simple model system such as an Einstein solid -- a collection of identical quantum harmonic oscillators. A computer spreadsheet program or comparable software can compute the required combinatoric functions for systems containing a few hundred oscillators and units of energy. When two such systems can exchange energy, one immediately sees that some configurations are overwhelmingly more probable than others. Graphs of entropy vs. energy for the two systems can be used to motivate the theoretical definition of temperature, T=(∂S/∂U)−1, thus bridging the gap between the classical and statistical approaches to entropy. Further spreadsheet exercises can be used to compute the heat capacity of an Einstein solid, study the Boltzmann distribution, and explore the properties of a two-state paramagnetic system.
Here is the paper presented by Barabasi at #ICCS2018:
Controllability of complex networks
https://t.co/3YklaJm78N
Controllability of complex networks
https://t.co/3YklaJm78N
Social Networks and the Intention to Migrate
CID Research Fellow & Graduate Student Working Paper No. 90
Miriam Manchin and Sultan Orazbayev
March 2018
Abstract:
Using a large individual-level survey spanning several years and more than 150 countries, we examine the importance of social networks in influencing individuals' intention to migrate internationally and locally. We distinguish close social networks (composed of friends and family) abroad and at the current location, and broad social networks (composed of same-country residents with intention to migrate, either internationally or locally). We find that social networks abroad are the most important driving forces of international migration intentions, with close and broad networks jointly explaining about 37% of variation in the probability intentions. Social networks are found to be more important factors driving migration intentions than work-related aspects or wealth (wealth accounts for less than 3% of the variation). In addition, we nd that having stronger close social networks at home has the opposite effect by reducing the likelihood of migration intentions, both internationally and locally. https://www.hks.harvard.edu/centers/cid/publications/fellow-graduate-student-working-papers/social-networks-migration
CID Research Fellow & Graduate Student Working Paper No. 90
Miriam Manchin and Sultan Orazbayev
March 2018
Abstract:
Using a large individual-level survey spanning several years and more than 150 countries, we examine the importance of social networks in influencing individuals' intention to migrate internationally and locally. We distinguish close social networks (composed of friends and family) abroad and at the current location, and broad social networks (composed of same-country residents with intention to migrate, either internationally or locally). We find that social networks abroad are the most important driving forces of international migration intentions, with close and broad networks jointly explaining about 37% of variation in the probability intentions. Social networks are found to be more important factors driving migration intentions than work-related aspects or wealth (wealth accounts for less than 3% of the variation). In addition, we nd that having stronger close social networks at home has the opposite effect by reducing the likelihood of migration intentions, both internationally and locally. https://www.hks.harvard.edu/centers/cid/publications/fellow-graduate-student-working-papers/social-networks-migration
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A billiard ball in a rectangle with two circular ends: slight changes in initial conditions make such a big difference that eventually it could be anywhere.
To be precise, we say its motion is ergodic
To be precise, we say its motion is ergodic
Scholarpedia article by Lyonia Bunimovich on 'dynamical billiards': https://t.co/TK6wnPgHgd
🌀 یه مدل ساده شده از دینامیک عقاید مختلف توی جامعه:
http://www.complexity-explorables.org/explorables/loyale-with-cheese/
تو مرورگرتون میتونید پارامترها رو تغییر بدید و نتایج رو ببینید.
http://www.complexity-explorables.org/explorables/loyale-with-cheese/
تو مرورگرتون میتونید پارامترها رو تغییر بدید و نتایج رو ببینید.
www.complexity-explorables.org
Echo Chambers
A dynamic network that explains the emergence of groups of uniform opinion
Slides for #JSM2018 talk on networks and complex data available at https://t.co/PDbaFit5Ej
🔅Machine Learning Explained: Dimensionality Reduction:
🔗 https://t.co/tk4IPArpCZ
🔅Data Science with Python & R: Dimensionality Reduction and Clustering:
🔗 https://www.codementor.io/jadianes/data-science-python-pandas-r-dimensionality-reduction-du1081aka
🔗 https://t.co/tk4IPArpCZ
🔅Data Science with Python & R: Dimensionality Reduction and Clustering:
🔗 https://www.codementor.io/jadianes/data-science-python-pandas-r-dimensionality-reduction-du1081aka
R-bloggers
Machine Learning Explained: Dimensionality Reduction
Dealing with a lot of dimensions can be painful for machine learning algorithms. High dimensionality will increase the computational complexity, increase the risk of overfitting (as your algorithm has more degrees of freedom) and the sparsity of the data…
Complex Systems Studies
Photo
Awesome set of #DataScience #MachineLearning graphics from #100DaysOfMLCode:
🔗 https://t.co/BQpyn75uQ6
#BigData #DataScientists #Algorithms #Coding #Python
🔗 https://t.co/BQpyn75uQ6
#BigData #DataScientists #Algorithms #Coding #Python
GitHub
Avik-Jain/100-Days-Of-ML-Code
100 Days of ML Coding. Contribute to Avik-Jain/100-Days-Of-ML-Code development by creating an account on GitHub.
🔖 DeepLink: A Novel Link Prediction Framework based on Deep Learning
Mohammad Mehdi Keikha, Maseud Rahgozar, Masoud Asadpour
🔗 https://arxiv.org/pdf/1807.10494.pdf
📌 ABSTRACT
Recently, link prediction has attracted more attentions from various disciplines such as computer science, bioinformatics and economics. In this problem, unknown links between nodes are discovered based on numerous information such as network topology, profile information and user generated contents. Most of the previous researchers have focused on the structural features of the networks. While the recent researches indicate that contextual information can change the network topology. Although, there are number of valuable researches which combine structural and content information, but they face with the scalability issue due to feature engineering. Because, majority of the extracted features are obtained by a supervised or semi supervised algorithm. Moreover, the existing features are not general enough to indicate good performance on different networks with heterogeneous structures. Besides, most of the previous researches are presented for undirected and unweighted networks. In this paper, a novel link prediction framework called "DeepLink" is presented based on deep learning techniques. In contrast to the previous researches which fail to automatically extract best features for the link prediction, deep learning reduces the manual feature engineering. In this framework, both the structural and content information of the nodes are employed. The framework can use different structural feature vectors, which are prepared by various link prediction methods. It considers all proximity orders that are presented in a network during the structural feature learning. We have evaluated the performance of DeepLink on two real social network datasets including Telegram and irBlogs. On both datasets, the proposed framework outperforms several structural and hybrid approaches for link prediction problem.
Mohammad Mehdi Keikha, Maseud Rahgozar, Masoud Asadpour
🔗 https://arxiv.org/pdf/1807.10494.pdf
📌 ABSTRACT
Recently, link prediction has attracted more attentions from various disciplines such as computer science, bioinformatics and economics. In this problem, unknown links between nodes are discovered based on numerous information such as network topology, profile information and user generated contents. Most of the previous researchers have focused on the structural features of the networks. While the recent researches indicate that contextual information can change the network topology. Although, there are number of valuable researches which combine structural and content information, but they face with the scalability issue due to feature engineering. Because, majority of the extracted features are obtained by a supervised or semi supervised algorithm. Moreover, the existing features are not general enough to indicate good performance on different networks with heterogeneous structures. Besides, most of the previous researches are presented for undirected and unweighted networks. In this paper, a novel link prediction framework called "DeepLink" is presented based on deep learning techniques. In contrast to the previous researches which fail to automatically extract best features for the link prediction, deep learning reduces the manual feature engineering. In this framework, both the structural and content information of the nodes are employed. The framework can use different structural feature vectors, which are prepared by various link prediction methods. It considers all proximity orders that are presented in a network during the structural feature learning. We have evaluated the performance of DeepLink on two real social network datasets including Telegram and irBlogs. On both datasets, the proposed framework outperforms several structural and hybrid approaches for link prediction problem.