Algorithm discovers physical laws. Are you a scientist with interesting yet unexplained data that you don't have the time to analyse? You might want to get in touch with two physicist in the US who have created an algorithm that can deduce physical laws from raw experimental data with little help from humans. Without any knowledge of physics or geometry, the algorithm discovered exact energy and momentum relations governing the dynamics of mass-spring systems as well as single and double pendulums.
The researchers envisage such algorithms speeding up the scientific process by reducing the time needed to identify potentially interesting models of particular systems. Since the 1960s scientists have been using artificial intelligence to design and run experiments, developing ever more powerful programs to generate, collect and store data. However, they have had less success in automatically distilling these data into new scientific laws. Now, two new papers in the journal Science confront this problem.
Chaos in simple systems. Research and Markets: Stochastic Transport in Complex Systems. From Molecules to Vehicles. DUBLIN--(BUSINESS WIRE)--Research and Markets ( has announced the addition of Elsevier Science and Technology's new report "Stochastic Transport in Complex Systems. From Molecules to Vehicles" to their offering. “Stochastic Transport in Complex Systems. From Molecules to Vehicles” The first part of the book provides a pedagogical introduction to the physics of complex systems driven far from equilibrium. In this part we discuss the basic concepts and theoretical techniques which are commonly used to study classical stochastic transport in systems of interacting driven particles.
The analytical techniques include mean-field theories, matrix product ansatz, renormalization group, etc. and the numerical methods are mostly based on computer simulations. Key Topics Covered: I. 1. 2. 3. 4. II. 5. 6. 7. 8. 9. 10. 11. Authors: Schadschneider, Andreas. A Case Study of Complex Adaptive Systems Theory. The capacity of biological and ecological systems for self-adaptation or self-organization has been a significant theme in the current life-science academic literature. The article is a case study of complex adaptive systems theory, focusing upon the global political system as a part of a biophysical aggregate system in which we are embedded. This paper explains why self-adaptation does not explain the global political system at this time and postulate what conditions must be met if it did.
Self-adaptation, if it were achieved and maintained in some proximate form, would constitute a phase transition. Our species’ cumulative actions on the environment (including those generating global warming, environmental pollution, ozone depletion, and biodiversity loss) are the dominant source of the increasing density of causal connectedness between human and natural systems.
Economic globalization has contributed to the dense, causal interconnectedness both within and between nations. The Emergence of Complexity Theory in Consulting - Tom Klein - Blog - Sustainability Strategy. Www.actaphys.uj.edu.pl/vol38/pdf/v38p2201.pdf. Truthy. This is a good example of a "truthy" meme. The account @PeaceKaren_25 does not disclose information about the identity of its owner. It has generated a very large number of tweets (over 10,000 in four months). Almost all of these tweets support several candidates, retweeting or mentioning them. @PeaceKaren_25's tweets also frequently include links to various websites supporting the same candidates. There is another account, @HopeMarie_25, that also does not disclose its identity, and has generated a similar number of tweets. @HopeMarie_25 has a similar behavior to @PeaceKaren_25 in retweeting the accounts of the same candidates and boosting the same websites, but it does not produce any original tweets, and in addition it retweets all of @PeaceKaren_25's tweets, promoting the @PeaceKaren_25 account.
Www.cs.kent.edu/~mhardas/publications/ICICT08DL-IncorporatingpersonalizationIntoFederation-KBH.pdf. Filippo Menczer | Center for Complex Networks and Systems Research. Research (NaN) | Group | Papers | Talks | Teaching | Service | Bio | Blog | WebCam | Personal Fil Menczer I am a Professor of Informatics and Computer Science and the Director of the Center for Complex Networks and Systems Research at the Indiana University School of Informatics and Computing. I also have courtesy appointments in Cognitive Science and Physics, and am affiliated with the Center for Data and Search Informatics and the Biocomplexity Institute. Finally I am a Fellow at the ISI Foundation in Torino, Italy. Research in my group, NaN, focuses on Web science, social media, social networks, social computing, Web search and data mining, distributed and intelligent Web applications, and modeling of complex information networks. Prospective students interested in joining my group, NaN, should look at this and this and this and this and this and this and this and these before contacting me.
Latest News from the Blog DESPIC team presents Bot Or Not demo and six posters at DARPA meeting. Fall 2011 Talk Series on Networks and Complex Systems. Labs - cxnets. The CX-Nets collaboratory gathers people from three different institutions in Europe and the US. The Indiana University School of Informatics is at the forefront in the United States for excellence and leadership in Informatics programs, including undergraduate and graduate education, research, placement and outreach. Its goal is to provide an innovative and successful new curriculum for information technology and its applications, encouraging interdisciplinary research focused on distributed systems technology, information theory and information management, human factors and human-computer interaction, and study of the social impacts of information technology. The Complex Systems group at the School of Informatics focuses its research activity on complex adaptive systems and complex network models and their computational applications to both natural science and technology problems.
The Centre de Physique Théorique is situated on the campus of Luminy, close to Marseille, France. The Adaptive Web and Bio-inspired designs for Recommendation System | Center for Complex Networks and Systems Research. Back to Research Recommender Systems: MyLibrary@LANL: a working collaborative and recommender system for digital libraries.TalkMine: an adaptive, distributed, highly personalized recommender.Non-metric network topologies: exploring indirect associations in data for recommendation, and the prediction of trends and social relationships in time Effects of transitive closures: which closures preserve scale free behavior and lead to highest performance in information retrieval.
Network of 1300 journal titles accessed by the users of the MyLibrary Web Service at the Los Alamos National Laboratory. The links shown denote a strong measure of co-occurrence (proximity) of two journals (the nodes) in user personalities. From this network, two main clusters were identified via Singular Value Decomposition: one pertaining to journals in chemistry, materials science, physics and the other to computer science and applied mathematics. Funding Project partially funded by Project Members Johan Bollen. Network Analysis of Weighted and Fuzzy Graphs | Center for Complex Networks and Systems Research. Back to CASCI Research Weighted and Proximity Graphs An associative network of people names extracted from co-occurrence in documents in a database The prime example of a Document Network (DN) is the World Wide Web (WWW).
But many other types of such networks exist: bibliographic databases containing scientific publications, social networking services, as well as databases of datasets used in scientific endeavors. Each of these databases possesses several distinct relationships among documents and between documents and semantic tags or indices that classify documents appropriately.
For instance, documents in the WWW are related via a hyperlink network, while documents in bibliographic databases are related by citation and collaboration networks. Subnetwork of word co-occurrence proximity (with 34 words) for a specific document from the first BioCreative competition. Cumulative degree distribution for network sizes as predicted by stochastic model of (Simas and Rocha, 2008) Funding T. CASCI | Center for Complex Networks and Systems Research. Research | People | Academics | News and Meetings | Publications-online | Media Mentions | Relevant Conferences Complex Adaptive Systems and Computational Intelligence We are a research group at Indiana University and the Instituto Gulbenkian de Ciencia working on complex systems. We are particularly interested in the informational properties of natural and artificial systems which enable them to adapt and evolve.
This means both understanding how information is fundamental for the evolutionary capabilities of natural systems, as well as abstracting principles from natural systems to produce adaptive information technology. Our research projects (see below) are on computational and systems biology, complex networks, text and literature mining, evolutionary systems, adaptive search and recommendation, cognitive science, artificial life, and biosemiotics. For information on joining our group see our Academics page. You are welcome to join our mailing list CASCI-L by either: CASCI projects. Www.ickn.org/documents/COINS2009_Doshi_Krauss_Nann_Gloor.pdf. MIT Media Lab: Human Dynamics Group. Trust and Networks. Trust makes networks work. When trust is high among members of a network, there’s a wonderful cohesiveness and capacity to get work done. When trust is low and relationships are plagued by suspicion, networks collapse into brittle organizational structures that rarely offset their operational costs in real world outcomes.
Trust builds living networks that are highly resilient, flexible and efficient. People who trust each other more easily forgive each other for the bumps that inevitably arise from working together. That’s network resilience. Defining a network: If you want to understand why trust is so important to the vibrancy of a network, it helps to have a working definition of networks. “Networks are voluntary connections between autonomous peers.”
Let’s break this definition down; first, autonomy. What does this have to do with networks? Networks are an alternative organizational structure to hierarchies. Relationship is the Net in Network In Trust We Trust R-e-s-p-e-c-t Like this: Actor Network Theory. Definition Actor Network Theory was first proposed by sociologist Bruno Latour, Actor Network Theory, or ANT, has proven to be one of the most valiant theories of understanding how these different elements work together to produce techno-cultural phenomena. Actor-Network theory is a way to understand the phenomena as distributed networks with interrelated nodes. It draws from emergence theory, computing, and other disciplines to understand both the nodes of the system and the lines of communication that allow for information flow between different nodes.
Latour situates actors/subjects as actor nodes that function within larger distributed networks of mutual interaction and feedback loops. This means that relations need to be repeatedly “performed” or the network will dissolve. Social relations, in other words, are only ever in process, and must be performed continuously.
Questions of subjectivities, agencies, actors, and structures have been of perennial interest in Anthropology. Formation of Economic and Social Networks (Tesfatsion)