🌀 You may have once seen a giant face in the clouds. Perhaps it took you aback, amused you, or maybe it prompted an “uncanny valley” kind of sensation—realness, but with a lingering unease.
🔗 http://nautil.us/blog/why-we-hear-voices-in-random-noise?utm_source=RSS_Feed&utm_medium=RSS&utm_campaign=RSS_Syndication
📌 Philip Jaekl is a freelance writer interested in cognitive neuroscience. He’s held postdoctoral research positions, investigating auditory-visual sensory integration, at Pompeu Fabra University in Barcelona and at the University of Rochester in New York State, where he currently resides.
🔗 http://nautil.us/blog/why-we-hear-voices-in-random-noise?utm_source=RSS_Feed&utm_medium=RSS&utm_campaign=RSS_Syndication
📌 Philip Jaekl is a freelance writer interested in cognitive neuroscience. He’s held postdoctoral research positions, investigating auditory-visual sensory integration, at Pompeu Fabra University in Barcelona and at the University of Rochester in New York State, where he currently resides.
Nautilus
Why We Hear Voices in Random Noise
You may have once seen a giant face in the clouds. Perhaps it took you aback, amused you, or maybe it prompted an “uncanny valley”…
🌀Non-equilibrium quantum systems
(Potentially useful lecture-note material)
Giuseppe E. Santoro,
SISSA, Trieste
🔗 http://indico.ictp.it/event/7644/material/2/2.pdf
Here is a collection of notes which contain material relevant to the Course on “Non-equilibrium quantum systems” held within the Spring College on “Physics of Complex Systems”. The material has no pretense of being coherently organized in any way. Being a “collage” of different lecture notes, please be aware that even the LaTex is not perfectly
consistent: there might be undefined references, or multiply defined labels. Sorry, this is not a book.
(Potentially useful lecture-note material)
Giuseppe E. Santoro,
SISSA, Trieste
🔗 http://indico.ictp.it/event/7644/material/2/2.pdf
Here is a collection of notes which contain material relevant to the Course on “Non-equilibrium quantum systems” held within the Spring College on “Physics of Complex Systems”. The material has no pretense of being coherently organized in any way. Being a “collage” of different lecture notes, please be aware that even the LaTex is not perfectly
consistent: there might be undefined references, or multiply defined labels. Sorry, this is not a book.
🗞 Who With Whom And How?: Extracting Large Social Networks Using Search Engines
Stefan Siersdorfer, Philipp Kemkes, Hanno Ackermann, Sergej Zerr
🔗 https://arxiv.org/pdf/1701.08285v1
📌ABSTRACT
Social network analysis is leveraged in a variety of applications such as identifying influential entities, detecting communities with special interests, and determining the flow of information and innovations. However, existing approaches for extracting social networks from unstructured Web content do not scale well and are only feasible for small graphs. In this paper, we introduce novel methodologies for query-based search engine mining, enabling efficient extraction of social networks from large amounts of Web data. To this end, we use patterns in phrase queries for retrieving entity connections, and employ a bootstrapping approach for iteratively expanding the pattern set. Our experimental evaluation in different domains demonstrates that our algorithms provide high quality results and allow for scalable and efficient construction of social graphs.
Stefan Siersdorfer, Philipp Kemkes, Hanno Ackermann, Sergej Zerr
🔗 https://arxiv.org/pdf/1701.08285v1
📌ABSTRACT
Social network analysis is leveraged in a variety of applications such as identifying influential entities, detecting communities with special interests, and determining the flow of information and innovations. However, existing approaches for extracting social networks from unstructured Web content do not scale well and are only feasible for small graphs. In this paper, we introduce novel methodologies for query-based search engine mining, enabling efficient extraction of social networks from large amounts of Web data. To this end, we use patterns in phrase queries for retrieving entity connections, and employ a bootstrapping approach for iteratively expanding the pattern set. Our experimental evaluation in different domains demonstrates that our algorithms provide high quality results and allow for scalable and efficient construction of social graphs.
⭕️ AI just won a poker tournament against professional players:
🔗 https://www.newscientist.com/article/2119815-ai-just-won-a-poker-tournament-against-professional-players/
🔗 https://www.newscientist.com/article/2119815-ai-just-won-a-poker-tournament-against-professional-players/
New Scientist
AI just won a poker tournament against professional players
A poker-playing artificial intelligence has claimed victory against humans, winning with a lead of $1.7 million by constantly tweaking its strategy
📄 Trade-offs between driving nodes and
time-to-control in complex networks
Sergio Pequito, Victor M. Preciado, #Barabasi , George J. Pappas
🔗 https://pdfs.semanticscholar.org/9217/cb81f364d6a5bad0f70d3c905ba49e6f4e5a.pdf
📌ABSTRACT
We first review some concepts from control theory , graph theory, and structural systems theory. We also include some notions of computational complexity needed in our analysis.
time-to-control in complex networks
Sergio Pequito, Victor M. Preciado, #Barabasi , George J. Pappas
🔗 https://pdfs.semanticscholar.org/9217/cb81f364d6a5bad0f70d3c905ba49e6f4e5a.pdf
📌ABSTRACT
We first review some concepts from control theory , graph theory, and structural systems theory. We also include some notions of computational complexity needed in our analysis.
📄 Experimental econophysics: Complexity, selforganization, and emergent properties
J.P.Huang
Department of Physics and State Key Laboratory of Surface Physics, Fudan University, Shanghai 200433, China
🔗 http://polymer.bu.edu/hes/rp-huang15econ.pdf
📌 A B S T R A C T
Experimental econophysics is concerned with statistical physics of humans in the laboratory, and it is based on controlled human experiments developed by physicists to study some problems related toe conomics or finance. It relies on controlled human experiments in the laboratory together with agent-based modeling (for computer simulations and/or analytical theory), with an attempt to reveal the general cause-effect relationship between specific conditions and emergent properties of real economic/financial markets (a kind of complex adaptive systems). Here I #review the latest progress in the field, namely, stylized facts, herd behavior, contrarian behavior, spontaneous cooperation, partial information, and risk management. Also, I highlight the connections between such progress and other topics of traditional statistical physics. The main theme of the review is to show diverse emergent properties of the laboratory markets, originating from self-organization due to the nonlinear interactions among heterogeneous humans or agents (complexity).
J.P.Huang
Department of Physics and State Key Laboratory of Surface Physics, Fudan University, Shanghai 200433, China
🔗 http://polymer.bu.edu/hes/rp-huang15econ.pdf
📌 A B S T R A C T
Experimental econophysics is concerned with statistical physics of humans in the laboratory, and it is based on controlled human experiments developed by physicists to study some problems related toe conomics or finance. It relies on controlled human experiments in the laboratory together with agent-based modeling (for computer simulations and/or analytical theory), with an attempt to reveal the general cause-effect relationship between specific conditions and emergent properties of real economic/financial markets (a kind of complex adaptive systems). Here I #review the latest progress in the field, namely, stylized facts, herd behavior, contrarian behavior, spontaneous cooperation, partial information, and risk management. Also, I highlight the connections between such progress and other topics of traditional statistical physics. The main theme of the review is to show diverse emergent properties of the laboratory markets, originating from self-organization due to the nonlinear interactions among heterogeneous humans or agents (complexity).
http://www.biophysics.org/2017taiwan/Home/tabid/6881/Default.aspx
Single-Cell Biophysics: Measurement, Modulation, and Modeling
Taipei, Taiwan| June 17-20, 2017
Single-Cell Biophysics: Measurement, Modulation, and Modeling
Taipei, Taiwan| June 17-20, 2017
🗞 The Computer Science and Physics of Community Detection: Landscapes, Phase Transitions, and Hardness
Cristopher Moore
🔗 https://arxiv.org/pdf/1702.00467v1
📌 A B S T R A C T
Community detection in graphs is the problem of finding groups of vertices which are more densely connected than they are to the rest of the graph. This problem has a long history, but it is currently motivated by social and biological networks. While there are many ways to formalize it, one of the most popular is as an inference problem, where there is a planted "ground truth" community structure around which the graph is generated probabilistically. Our task is then to recover the ground truth knowing only the graph.
Recently it was discovered, first heuristically in physics and then rigorously in probability and computer science, that this problem has a phase transition at which it suddenly becomes impossible. Namely, if the graph is too sparse, or the probabilistic process that generates it is too noisy, then no algorithm can find a partition that is correlated with the planted one---or even tell if there are communities, i.e., distinguish the graph from a purely random one with high probability. Above this information-theoretic threshold, there is a second threshold beyond which polynomial-time algorithms are known to succeed; in between, there is a regime in which community detection is possible, but conjectured to be exponentially hard.
For computer scientists, this field offers a wealth of new ideas and open questions, with connections to probability and combinatorics, message-passing algorithms, and random matrix theory. Perhaps more importantly, it provides a window into the cultures of statistical physics and statistical inference, and how those cultures think about distributions of instances, landscapes of solutions, and hardness.
Cristopher Moore
🔗 https://arxiv.org/pdf/1702.00467v1
📌 A B S T R A C T
Community detection in graphs is the problem of finding groups of vertices which are more densely connected than they are to the rest of the graph. This problem has a long history, but it is currently motivated by social and biological networks. While there are many ways to formalize it, one of the most popular is as an inference problem, where there is a planted "ground truth" community structure around which the graph is generated probabilistically. Our task is then to recover the ground truth knowing only the graph.
Recently it was discovered, first heuristically in physics and then rigorously in probability and computer science, that this problem has a phase transition at which it suddenly becomes impossible. Namely, if the graph is too sparse, or the probabilistic process that generates it is too noisy, then no algorithm can find a partition that is correlated with the planted one---or even tell if there are communities, i.e., distinguish the graph from a purely random one with high probability. Above this information-theoretic threshold, there is a second threshold beyond which polynomial-time algorithms are known to succeed; in between, there is a regime in which community detection is possible, but conjectured to be exponentially hard.
For computer scientists, this field offers a wealth of new ideas and open questions, with connections to probability and combinatorics, message-passing algorithms, and random matrix theory. Perhaps more importantly, it provides a window into the cultures of statistical physics and statistical inference, and how those cultures think about distributions of instances, landscapes of solutions, and hardness.