Brand-new online course CS-EJ3311 Deep Learning with Python, dlwithpython.cs.aalto.fi will roll out in Sept.
This course aims at developing intuition and hands-on skills for applying deep learning methods to different datasets. The course material will be in the form of Python notebooks similar in format to those at here.
The mindset behind the course is inspired by the book "Deep Learning with Python" by F. Chollet
You can enroll in this course as non-Aalto student via fitech.io
This course aims at developing intuition and hands-on skills for applying deep learning methods to different datasets. The course material will be in the form of Python notebooks similar in format to those at here.
The mindset behind the course is inspired by the book "Deep Learning with Python" by F. Chollet
You can enroll in this course as non-Aalto student via fitech.io
GitHub
GitHub - fchollet/deep-learning-with-python-notebooks: Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Jupyter notebooks for the code samples of the book "Deep Learning with Python" - fchollet/deep-learning-with-python-notebooks
📺 video
Graphs + Networks Workshop materials that include videos of all the talks from the conference.
Here is a list of papers that our speakers and participants flagged as interesting for wider reading in Graphs, Networks, and meaningful applications.
Graphs + Networks Workshop materials that include videos of all the talks from the conference.
Here is a list of papers that our speakers and participants flagged as interesting for wider reading in Graphs, Networks, and meaningful applications.
YouTube
Graphs and Networks Conference
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ارائه من در معرفی سیستم‌های پیچیده:
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- جمع‌بندی و پرسش و پاسخ
زمستان سال ۹۸ بود که در درس فیزیک معاصر در دانشکده فیزیک دانشگاه صنعتی شریف در مورد پیچیدگی…
- مقدماتی از پیدایش این علم
- سه مثال از سیستم‌های پیچیده
- سه مفهوم در پیچیدگی
- سه متخصص در این حوزه
- جمع‌بندی و پرسش و پاسخ
زمستان سال ۹۸ بود که در درس فیزیک معاصر در دانشکده فیزیک دانشگاه صنعتی شریف در مورد پیچیدگی…
💰 Jobs: Up to 5 #postdoc positions in Computational Social Science (with focus on AI)
Join me at the Max-Planck Center for Humans & Machines in Berlin
https://t.co/ay7o7uzKOM
Join me at the Max-Planck Center for Humans & Machines in Berlin
https://t.co/ay7o7uzKOM
www.mpib-berlin.mpg.de
Karriere
Beschreibung
The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
David I Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, Pierre Vandergheynst
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In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs. The emerging field of signal processing on graphs merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process such signals on graphs. In this tutorial overview, we outline the main challenges of the area, discuss different ways to define graph spectral domains, which are the analogues to the classical frequency domain, and highlight the importance of incorporating the irregular structures of graph data domains when processing signals on graphs. We then review methods to generalize fundamental operations such as filtering, translation, modulation, dilation, and downsampling to the graph setting, and survey the localized, multiscale transforms that have been proposed to efficiently extract information from high-dimensional data on graphs. We conclude with a brief discussion of open issues and possible extensions.
David I Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, Pierre Vandergheynst
Download PDF
In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs. The emerging field of signal processing on graphs merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process such signals on graphs. In this tutorial overview, we outline the main challenges of the area, discuss different ways to define graph spectral domains, which are the analogues to the classical frequency domain, and highlight the importance of incorporating the irregular structures of graph data domains when processing signals on graphs. We then review methods to generalize fundamental operations such as filtering, translation, modulation, dilation, and downsampling to the graph setting, and survey the localized, multiscale transforms that have been proposed to efficiently extract information from high-dimensional data on graphs. We conclude with a brief discussion of open issues and possible extensions.
Unraveling the effects of multiscale network entanglement on disintegration of empirical systems
Arsham Ghavasieh, Massimo Stella, Jacob Biamonte, Manlio De Domenico
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Complex systems are large collections of entities that organize themselves into non-trivial structures that can be represented by networks. A key emergent property of such systems is robustness against random failures or targeted attacks ---i.e. the capacity of a network to maintain its integrity under removal of nodes or links. Here, we introduce network entanglement to study network robustness through a multi-scale lens, encoded by the time required to diffuse information through the system. Our measure's foundation lies upon a recently proposed framework, manifestly inspired by quantum statistical physics, where networks are interpreted as collections of entangled units and can be characterized by Gibbsian-like density matrices. We show that at the smallest temporal scales entanglement reduces to node degree, whereas at the large scale we show its ability to measure the role played by each node in network integrity. At the meso-scale, entanglement incorporates information beyond the structure, such as system's transport properties. As an application, we show that network dismantling of empirical social, biological and transportation systems unveils the existence of a optimal temporal scale driving the network to disintegration. Our results open the door for novel multi-scale analysis of network contraction process and its impact on dynamical processes.
Arsham Ghavasieh, Massimo Stella, Jacob Biamonte, Manlio De Domenico
Download PDF
Complex systems are large collections of entities that organize themselves into non-trivial structures that can be represented by networks. A key emergent property of such systems is robustness against random failures or targeted attacks ---i.e. the capacity of a network to maintain its integrity under removal of nodes or links. Here, we introduce network entanglement to study network robustness through a multi-scale lens, encoded by the time required to diffuse information through the system. Our measure's foundation lies upon a recently proposed framework, manifestly inspired by quantum statistical physics, where networks are interpreted as collections of entangled units and can be characterized by Gibbsian-like density matrices. We show that at the smallest temporal scales entanglement reduces to node degree, whereas at the large scale we show its ability to measure the role played by each node in network integrity. At the meso-scale, entanglement incorporates information beyond the structure, such as system's transport properties. As an application, we show that network dismantling of empirical social, biological and transportation systems unveils the existence of a optimal temporal scale driving the network to disintegration. Our results open the door for novel multi-scale analysis of network contraction process and its impact on dynamical processes.
🦠 “If schools are reopened in areas with high levels of community transmission, major outbreaks are inevitable and deaths will occur in the community as a result."
#coronavirus #covid19
https://www.nature.com/articles/d41586-020-02403-4
#coronavirus #covid19
https://www.nature.com/articles/d41586-020-02403-4
Nature
How schools can reopen safely during the pandemic
Masks, class sizes and hygiene are important, but low community spread is key.
Want to learn more about agent-based modeling applied to social-ecological systems? Apply to our winter school!
https://t.co/nMbKvhplgu
https://t.co/nMbKvhplgu
#PhD positions are available at IPM in Cognitive Neuroscience. To apply see the info on:
https://scs.ipm.ac.ir
https://scs.ipm.ac.ir
💡 A simple but powerful idea: Improve the throughput of pooled testing for COVID-19 using a (Reed-Solomon) error-correcting code.
https://t.co/21CTet1b45
https://t.co/21CTet1b45
Science
Efficient high-throughput SARS-CoV-2 testing to detect asymptomatic carriers
Recent reports suggest that 10-30% of SARS-CoV-2 infected patients are asymptomatic and that significant viral shedding may occur prior to symptom onset. Therefore, there is an urgent need to increase diagnostic testing capabilities to prevent disease spread.…
💡 "Nonlocal Diffusion Equations with Integrable Kernels" (by Julio D. Rossi): https://t.co/wjvXc68RNV
Forwarded from Sitpor.org سیتپـــــور
چگونه با آمار دروغ بگوییم؟
معرفی، مختصر توضیحی و دعوتی برای مطالعه کتاب «چگونه با آمار دروغ بگوییم؟»
🔗 https://www.sitpor.org/2020/08/how-to-lies-with-statistics/
📈📊📉
@sitpor
معرفی، مختصر توضیحی و دعوتی برای مطالعه کتاب «چگونه با آمار دروغ بگوییم؟»
🔗 https://www.sitpor.org/2020/08/how-to-lies-with-statistics/
📈📊📉
@sitpor
💡 "Introducing students to research codes: A short course on solving partial differential equations in Python"
(by Pavan Inguva, Vijesh J. Bhute, Thomas N.H. Cheng, Pierre J. Walker)
Download PDF
Recent releases of open-source research codes and solvers for numerically solving partial differential equations in Python present a great opportunity for educators to integrate these codes into the classroom in a variety of ways. The ease with which a problem can be implemented and solved using these codes reduce the barrier to entry for users. We demonstrate how one of these codes,FiPy, can be introduced to students through a short course using progression as the guiding philosophy. Four exercises of increasing complexity were developed. Basic concepts from more advanced numerical methods courses are also introduced at appropriate points. To further engage students, we demonstrate how an open research problem can be readily implemented and also incorporate the use of ParaView to post-process their results. Student engagement and learning outcomes were evaluated through a pre and post-course survey and a focus group discussion. Students broadly found the course to be engaging and useful with the ability to easily visualise the solution to PDEs being greatly valued. Due to the introductory nature of the course, due care in terms of set-up and the design of learning activities during the course is essential. This course, if integrated with appropriate level of support, can encourage students to use the provided codes and improve their understanding of concepts used in numerical analysis and PDEs.
(by Pavan Inguva, Vijesh J. Bhute, Thomas N.H. Cheng, Pierre J. Walker)
Download PDF
Recent releases of open-source research codes and solvers for numerically solving partial differential equations in Python present a great opportunity for educators to integrate these codes into the classroom in a variety of ways. The ease with which a problem can be implemented and solved using these codes reduce the barrier to entry for users. We demonstrate how one of these codes,FiPy, can be introduced to students through a short course using progression as the guiding philosophy. Four exercises of increasing complexity were developed. Basic concepts from more advanced numerical methods courses are also introduced at appropriate points. To further engage students, we demonstrate how an open research problem can be readily implemented and also incorporate the use of ParaView to post-process their results. Student engagement and learning outcomes were evaluated through a pre and post-course survey and a focus group discussion. Students broadly found the course to be engaging and useful with the ability to easily visualise the solution to PDEs being greatly valued. Due to the introductory nature of the course, due care in terms of set-up and the design of learning activities during the course is essential. This course, if integrated with appropriate level of support, can encourage students to use the provided codes and improve their understanding of concepts used in numerical analysis and PDEs.
Why, even during lockdown, do #coronavirus infection curves continue to grow linearly? The answer lies in networks.
"For any given #transmission rate there exists a critical degree of contact #networks below which linear #infection curves must occur and above which the classical S-shaped curves appear that are known from epidemiological models."
https://t.co/j70KwyijkW
"For any given #transmission rate there exists a critical degree of contact #networks below which linear #infection curves must occur and above which the classical S-shaped curves appear that are known from epidemiological models."
https://t.co/j70KwyijkW