Nature’s reach: narrow work has broad impact
“Nature publishes mostly specialized papers...; these tend to reference a narrower range of disciplines.... Usually, however, Nature papers are cited by a broader range of disciplines than average”
https://t.co/LlyMnDs2ZA
“Nature publishes mostly specialized papers...; these tend to reference a narrower range of disciplines.... Usually, however, Nature papers are cited by a broader range of disciplines than average”
https://t.co/LlyMnDs2ZA
Complex Systems Studies
Nature’s reach: narrow work has broad impact “Nature publishes mostly specialized papers...; these tend to reference a narrower range of disciplines.... Usually, however, Nature papers are cited by a broader range of disciplines than average” https://t.co/LlyMnDs2ZA
YouTube
A network of science: 150 years of Nature papers
Science is a network, each paper linking those that came before with those that followed. In an exclusive analysis, researchers have delved into Nature's part of that network. We explore their results, taking you on a tour of 150 years of interconnected…
💰 ترکیه
We are seeking to hire two #postdocs and two #PhD students in #DynamicalSystems, #NetworkScience, #StatisticalLearning.
More information: https://t.co/E76jWXrEww
We are seeking to hire two #postdocs and two #PhD students in #DynamicalSystems, #NetworkScience, #StatisticalLearning.
More information: https://t.co/E76jWXrEww
Forwarded from Complex Networks (SBU)
#سمینارهای_هفتگی
عنوان:
زمان بحرانی در تشکیل ساختار توازن شبکه کامل
🗣 محمدشرافتی - دانشگاه شهید بهشتی
⏰ دوشنبه، 20 آبان - ساعت ۱۶:۰۰
🏛 محل برگزاری: سالن ابنهیثم
~~~~~~~~~~~~~~~~
🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
تماس با ما: @ccnsd_admin
🕸 @CCNSD 🔗 ccnsd.ir
—————————————
عنوان:
زمان بحرانی در تشکیل ساختار توازن شبکه کامل
🗣 محمدشرافتی - دانشگاه شهید بهشتی
⏰ دوشنبه، 20 آبان - ساعت ۱۶:۰۰
🏛 محل برگزاری: سالن ابنهیثم
~~~~~~~~~~~~~~~~
🕸 مرکز شبکههای پیچیده و علم داده اجتماعی دانشگاه شهید بهشتی
تماس با ما: @ccnsd_admin
🕸 @CCNSD 🔗 ccnsd.ir
—————————————
Forwarded from Sitpor.org سیتپـــــور
خوشحالم که پروژه «#پیچیدگی_برای_همه» هماکنون با این نشانی قابل دسترسه:
💡 complexity.sitpor.org
سعی کردم روایتهای مختلفی از پیچیدگی رو به نمایش بذارم برای مردم. روایتهایی که طی این چند سال تعریفشون کرده بودم. امیدوارم که بپسندید.
#شرح_پیچیدگی #complexityexplained
💡 complexity.sitpor.org
سعی کردم روایتهای مختلفی از پیچیدگی رو به نمایش بذارم برای مردم. روایتهایی که طی این چند سال تعریفشون کرده بودم. امیدوارم که بپسندید.
#شرح_پیچیدگی #complexityexplained
Forwarded from انجمن علمی فیزیک بهشتی (SBU)
#سمینار_عمومی
Structural Analysis of Signed Network with a Focus on Balance Theory
- سهشنبه ۲۱ آبان؛ ساعت ۱۶:۳۰ الی ۱۷:۳۰
- تالار ابن هیثم، دانشکده فیزیک
کانال انجمن علمی فیزیک بهشتی
@sbu_physics
Structural Analysis of Signed Network with a Focus on Balance Theory
- سهشنبه ۲۱ آبان؛ ساعت ۱۶:۳۰ الی ۱۷:۳۰
- تالار ابن هیثم، دانشکده فیزیک
کانال انجمن علمی فیزیک بهشتی
@sbu_physics
The Johannes Kepler University Linz (JKU), Austria, is looking for
doctoral and post-doctoral researchers in machine learning or
knowledge discovery in databases for the newly formed Computational
Data Analytics group of Prof. Johannes Fürnkranz. We are particularly
looking for researchers in one or
more of the following areas
• Interpretability in AI
• Inductive Rule Learning
• Preference Learning
• Multi-Label Classification
• Data Mining and Knowledge Discovery
• Monte-Carlo Tree Search
• Machine Learning and Game Playing
Job description:
• Conduct independent research and collaborate in
machine learning projects
• Publish in renowned international journals and conferences
• Supervise students and support lectures of the group
Requirements:
1. Master’s Degree or Ph.D. in Computer Science or a related area
2. Strong background and track record in machine learning or
knowledge discovery
3. Knowledge and prior work in one or more of the above-mentioned
areas is a plus
4. Willingness and ability to work in a team and to support
students and lectures
About the group:
The Computational Data Analytics group at JKU Linz has started in
October 2019, within the Institute for Application-Oriented
Knowledge Processing (FAW) [1]. Together with the existing groups of
Prof. Sepp Hochreiter (Machine Learning) or Prof. Gerhard Widmer
(Computational Perception), the Computer Science Department of JKU
Linz [2] has become a leading center for machine learning and
artificial intelligence research in Austria and beyond. In Winter Term
2019, JKU has started the first Bachelor’s [3] and Master’s [4] study
programs in Artificial Intelligence in Austria.
About the location:
The area offers excellent quality of living in the heart of Europe -
close to the alps between Vienna, Salzburg, Prague and Munich. Linz
provides a superb cultural environment, e.g. with the Ars Electronica
Festival and the nearby Salzburg Festival. Moreover, the
world-cultural heritage sites Hallstatt and Český Krumlov are less
than two hours away. The picturesque and versatile landscape provides
countless options for recreation and sports in nature (skiing, hiking,
climbing, cycling, and many more).
How to apply:
Prospective applicants interested in these positions are requested to
electronically send an application via the online portal
http://jku.at/application.
Please specify Job Reference Number 3989 (for Ph.D. student) or 3990
(for Post-Doc) in your application.
Please provide us with
- a CV
- a research statement concerning one or more of the above-mentioned
areas, and
- a sample of your writing in English or German (thesis, publication, …)
The positions are open until filled, next decisions will be made on
November 23th, 2019. If you have questions, please contact Prof.
Johannes Fürnkranz (johannes.fuernkranz@jku.at).
References:
[1]
https://www.jku.at/en/institute-for-application-oriented-knowledge-processing/
[2] http://informatik.jku.at/
[3]
https://www.jku.at/en/degree-programs/types-of-degree-programs/bachelors-and-diploma-degree-programs/ba-artificial-intelligence/
[4]
https://www.jku.at/en/degree-programs/types-of-degree-programs/masters-degree-programs/ma-artificial-intelligence/
--
Johannes Fuernkranz
Computational Data Analytics
JKU Linz, Austria
doctoral and post-doctoral researchers in machine learning or
knowledge discovery in databases for the newly formed Computational
Data Analytics group of Prof. Johannes Fürnkranz. We are particularly
looking for researchers in one or
more of the following areas
• Interpretability in AI
• Inductive Rule Learning
• Preference Learning
• Multi-Label Classification
• Data Mining and Knowledge Discovery
• Monte-Carlo Tree Search
• Machine Learning and Game Playing
Job description:
• Conduct independent research and collaborate in
machine learning projects
• Publish in renowned international journals and conferences
• Supervise students and support lectures of the group
Requirements:
1. Master’s Degree or Ph.D. in Computer Science or a related area
2. Strong background and track record in machine learning or
knowledge discovery
3. Knowledge and prior work in one or more of the above-mentioned
areas is a plus
4. Willingness and ability to work in a team and to support
students and lectures
About the group:
The Computational Data Analytics group at JKU Linz has started in
October 2019, within the Institute for Application-Oriented
Knowledge Processing (FAW) [1]. Together with the existing groups of
Prof. Sepp Hochreiter (Machine Learning) or Prof. Gerhard Widmer
(Computational Perception), the Computer Science Department of JKU
Linz [2] has become a leading center for machine learning and
artificial intelligence research in Austria and beyond. In Winter Term
2019, JKU has started the first Bachelor’s [3] and Master’s [4] study
programs in Artificial Intelligence in Austria.
About the location:
The area offers excellent quality of living in the heart of Europe -
close to the alps between Vienna, Salzburg, Prague and Munich. Linz
provides a superb cultural environment, e.g. with the Ars Electronica
Festival and the nearby Salzburg Festival. Moreover, the
world-cultural heritage sites Hallstatt and Český Krumlov are less
than two hours away. The picturesque and versatile landscape provides
countless options for recreation and sports in nature (skiing, hiking,
climbing, cycling, and many more).
How to apply:
Prospective applicants interested in these positions are requested to
electronically send an application via the online portal
http://jku.at/application.
Please specify Job Reference Number 3989 (for Ph.D. student) or 3990
(for Post-Doc) in your application.
Please provide us with
- a CV
- a research statement concerning one or more of the above-mentioned
areas, and
- a sample of your writing in English or German (thesis, publication, …)
The positions are open until filled, next decisions will be made on
November 23th, 2019. If you have questions, please contact Prof.
Johannes Fürnkranz (johannes.fuernkranz@jku.at).
References:
[1]
https://www.jku.at/en/institute-for-application-oriented-knowledge-processing/
[2] http://informatik.jku.at/
[3]
https://www.jku.at/en/degree-programs/types-of-degree-programs/bachelors-and-diploma-degree-programs/ba-artificial-intelligence/
[4]
https://www.jku.at/en/degree-programs/types-of-degree-programs/masters-degree-programs/ma-artificial-intelligence/
--
Johannes Fuernkranz
Computational Data Analytics
JKU Linz, Austria
JKU - Johannes Kepler Universität Linz
Artificial Intelligence - Bachelor's Degree
Das neue Bachelorstudium Artificial Intelligence. Lerne alles über künstliche Intelligenz. Details zum Studium, Schwerpunkte, Berufsaussichten findest du hier.
Nonlinearity + Networks: A 2020 Vision
“topics include temporal networks, stochastic and deterministic dynamical processes on networks, adaptive networks, and network structure and dynamics”
https://t.co/5wtIolAQt9
“topics include temporal networks, stochastic and deterministic dynamical processes on networks, adaptive networks, and network structure and dynamics”
https://t.co/5wtIolAQt9
Network Inference in Systems Biology: Recent Developments, Challenges, and Applications.
https://t.co/ngN5YUVth
One of the most interesting, difficult, and potentially useful topics in computational biology is the inference of gene regulatory networks (GRNs) from expression data. Although researchers have been working on this topic for more than a decade and much progress has been made, it remains an unsolved problem and even the most sophisticated inference algorithms are far from perfect. In this paper, we review the latest developments in network inference, including state-of-the-art algorithms like PIDC, Phixer, and more. We also discuss unsolved computational challenges, including the optimal combination of algorithms, integration of multiple data sources, and pseudo-temporal ordering of static expression data. Lastly, we discuss some exciting applications of network inference in cancer research, and provide a list of useful software tools for researchers hoping to conduct their own network inference analyses.
https://t.co/ngN5YUVth
One of the most interesting, difficult, and potentially useful topics in computational biology is the inference of gene regulatory networks (GRNs) from expression data. Although researchers have been working on this topic for more than a decade and much progress has been made, it remains an unsolved problem and even the most sophisticated inference algorithms are far from perfect. In this paper, we review the latest developments in network inference, including state-of-the-art algorithms like PIDC, Phixer, and more. We also discuss unsolved computational challenges, including the optimal combination of algorithms, integration of multiple data sources, and pseudo-temporal ordering of static expression data. Lastly, we discuss some exciting applications of network inference in cancer research, and provide a list of useful software tools for researchers hoping to conduct their own network inference analyses.
A detailed characterization of complex networks using Information Theory
“two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyze networks”
https://t.co/xc8ZxboAuc
“two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyze networks”
https://t.co/xc8ZxboAuc
Machine learning dynamical phase transitions in complex networks
Qi Ni, Ming Tang, Ying Liu, Ying-Cheng Lai
https://arxiv.org/abs/1911.04633
In recent years, machine learning has been adopted to complex networks, but most existing works concern about the structural properties. To use machine learning to detect phase transitions and accurately identify the critical transition point associated with dynamical processes on complex networks thus stands out as an open and significant problem. Here we develop a framework combining supervised and unsupervised learning, incorporating proper sampling of training data set. In particular, using epidemic spreading dynamics on complex networks as a paradigmatic setting, we start from supervised learning alone and identify situations that degrade the performance. To overcome the difficulties leads to the idea of exploiting confusion scheme, effectively a combination of supervised and unsupervised learning. We demonstrate that the scheme performs well for identifying phase transitions associated with spreading dynamics on homogeneous networks, but the performance deteriorates for heterogeneous networks. To strive to meet this challenge leads to the realization that sampling the training data set is necessary for heterogeneous networks, and we test two sampling methods: one based on the hub nodes together with their neighbors and another based on k-core of the network. The end result is a general machine learning framework for detecting phase transition and accurately identifying the critical transition point, which is robust, computationally efficient, and universally applicable to complex networks of arbitrary size and topology. Extensive tests using synthetic and empirical networks verify the virtues of the articulated framework, opening the door to exploiting machine learning for understanding, detection, prediction, and control of complex dynamical systems in general.
https://arxiv.org/abs/1911.04633
Qi Ni, Ming Tang, Ying Liu, Ying-Cheng Lai
https://arxiv.org/abs/1911.04633
In recent years, machine learning has been adopted to complex networks, but most existing works concern about the structural properties. To use machine learning to detect phase transitions and accurately identify the critical transition point associated with dynamical processes on complex networks thus stands out as an open and significant problem. Here we develop a framework combining supervised and unsupervised learning, incorporating proper sampling of training data set. In particular, using epidemic spreading dynamics on complex networks as a paradigmatic setting, we start from supervised learning alone and identify situations that degrade the performance. To overcome the difficulties leads to the idea of exploiting confusion scheme, effectively a combination of supervised and unsupervised learning. We demonstrate that the scheme performs well for identifying phase transitions associated with spreading dynamics on homogeneous networks, but the performance deteriorates for heterogeneous networks. To strive to meet this challenge leads to the realization that sampling the training data set is necessary for heterogeneous networks, and we test two sampling methods: one based on the hub nodes together with their neighbors and another based on k-core of the network. The end result is a general machine learning framework for detecting phase transition and accurately identifying the critical transition point, which is robust, computationally efficient, and universally applicable to complex networks of arbitrary size and topology. Extensive tests using synthetic and empirical networks verify the virtues of the articulated framework, opening the door to exploiting machine learning for understanding, detection, prediction, and control of complex dynamical systems in general.
https://arxiv.org/abs/1911.04633