IBM and http://IBMblr.Tumblr.com celebrate the life of Benoit B. Mandelbrot, IBM Fellow Emeritus and Fractal Pioneer. In this final interview shot by filmmaker Erol Morris, Mandelbrot shares his love for mathematics and how it led him to his wondrous discovery of fractals. His work lives on today in many innovations in science, design, telecommunications, medicine, renewable energy, film (special effects), gaming (computer graphics) and more.
The fractal dimension (FD) image is generated by considering each pixel in the original CT image as a single fractal dimension estimated from its 7x7 neighbours. The FD generated image remarkably enhances the tissue ...
The PyCX Project aims to develop an online repository of simple, crude, yet easy-to-understand Python sample codes for dynamic complex systems simulations, including iterative maps, cellular automata, dynamical networks and agent-based models.
If the truth be told, few physicists have ever really felt comfortable with quantum theory. Having lived with it now for more than a century, they have managed to forge a good working relationship; physicists now routinely use the mathematics of quantum behaviour to make stunningly accurate calculations about molecular structure, high-energy particle collisions, semiconductor behaviour, spectral emissions and much more.
But the interactions tend to be strictly formal. As soon as researchers try to get behind the mask and ask what the mathematics mean, they run straight into a seemingly impenetrable wall of paradoxes. Can something really be a particle and a wave at the same time? Is Schrödinger's cat really both alive and dead? Is it true that even the gentlest conceivable measurement can somehow have an effect on particles halfway across the Universe?
New York Times A Strange Computer Promises Great Speed New York Times Ray Johnson, Lockheed's chief technical officer, said his company would use the quantum computer to create and test complex radar, space and aircraft systems.
Scientific American (blog) Stephen Hawking's advice for twenty-first century grads: Embrace complexity Scientific American (blog) Hawking replied that in his opinion the twenty-first century would be the “century of complexity”.
Recent advances in fields ranging from cosmology to computer science have hinted at a possible deep connection between intelligence and entropy maximization, but no formal physical relationship between them has yet been established. Here, we explicitly propose a first step toward such a relationship in the form of a causal generalization of entropic forces that we find can cause two defining behaviors of the human “cognitive niche”—tool use and social cooperation—to spontaneously emerge in simple physical systems. Our results suggest a potentially general thermodynamic model of adaptive behavior as a nonequilibrium process in open systems.
RABBIT provides an easy way to explore natural phenomena such as pattern formation, self-organization, emergence, non-linearity. Rabbit helps architects and designers to analyze and integrate these models of organization in their own designs.
Max Planck Institute for the Physics of Complex Systems
Much of the work on non-equilibrium statistical mechanics has relied on the notion of a local order parameter, absent in topological phases. Conversely, studies of topological phenomena have focused on equilibrium and ground-state properties. The intersection of these fields remains largely uncharted territory.
This workshop brings together theorists from both groups, as well as experimentalists studying dynamical phenomena in a variety of systems, covering topics such as topological phenomena in driven systems, hydrodynamic descriptions of phases with emergent gauge fields, ultrafast and inelastic spectral probes of quantum matter, the development of numerical methods, and finally potential experiments in the solid state and ultracold atomic gases.
Shuichi Kinoshita, "Pattern Formations and Oscillatory Phenomena" English | ISBN: 0123970148 | 2013 | 280 pages | PDF | 31 MB Patterns and their formations appear throughout nature, and are studied to.
New Complexity MOOC Started iProgrammer Introduction to Dynamical Systems and Chaos, the second course to be offered through the Santa Fe Institute's Complexity Explorer project started on January 6th and enrollment is still open.
We give exact formulae for a wide family of complexity measures that capture the organization of hidden nonlinear processes. The spectral decomposition of operator-valued functions leads to closed-form expressions involving the full eigenvalue spectrum of the mixed-state presentation of a process's epsilon-machine causal-state dynamic. Measures include correlation functions, power spectra, past-future mutual information, transient and synchronization informations, and many others. As a result, a direct and complete analysis of intrinsic computation is now available for the temporal organization of finitary hidden Markov models and nonlinear dynamical systems with generating partitions and for the spatial organization in one-dimensional systems, including spin systems, cellular automata, and complex materials via chaotic crystallography.
Exact Complexity: The Spectral Decomposition of Intrinsic Computation James P. Crutchfield, Christopher J. Ellison, Paul M. Riechers
ECAL 2013, the twelfth European Conference on Artificial Life, presents the current state of the art of a mature and autonomous discipline collocated at the intersection of a theoretical perspective (the scientific explanations of different levels of life organizations, e.g., molecules, compartments, cells, tissues, organs, organisms, societies, collective and social phenomena) and advanced technological applications (bio-inspired algorithms and techniques to building-up concrete solutions such as in robotics, data analysis, search engines, gaming).
Advances in Artificial Life, ECAL 2013
Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems
Edited by Pietro Liò, Orazio Miglino, Giuseppe Nicosia, Stefano Nolfi and Mario Pavone
The hallmark of deterministic chaos is that it creates information---the rate being given by the Kolmogorov-Sinai metric entropy. Since its introduction half a century ago, the metric entropy has been used as a unitary quantity to measure a system's intrinsic unpredictability. Here, we show that it naturally decomposes into two structurally meaningful components: A portion of the created information---the ephemeral information---is forgotten and a portion---the bound information---is remembered. The bound information is a new kind of intrinsic computation that differs fundamentally from information creation: it measures the rate of active information storage. We show that it can be directly and accurately calculated via symbolic dynamics, revealing a hitherto unknown richness in how dynamical systems compute.
Ever since Darwin a great deal of the conceptual history of biology may be read as a struggle between two philosophical positions: reductionism and holism. On the one hand, we have the reductionist claim that evolution has to be understood in terms of changes at the fundamental causal level of the gene. As Richard Dawkins famously put it, organisms are just ‘lumbering robots’ in the service of their genetic masters. On the other hand, there is a long holistic tradition that focuses on the complexity of developmental systems, on the non-linearity of gene– environment interactions, and on multi-level selective processes to argue that the full story of biology is a bit more complicated than that. Reductionism can marshal on its behalf the spectacular successes of genetics and molecular biology throughout the 20th and 21st centuries. Holism has built on the development of entirely new disciplines and conceptual frameworks over the past few decades, including evo-devo and phenotypic plasticity. Yet, a number of biologists are still actively looking for a way out of the reductionism–holism counterposition, often mentioning the word ‘emergence’ as a way to deal with the conundrum. This paper briefly examines the philosophical history of the concept of emergence, distinguishes between epistemic and ontological accounts of it, and comments on conceptions of emergence that can actually be useful for practising evolutionary biologists.
Between holism and reductionism: a philosophical primer on emergence Massimo Pigliucci Biological Journal of the Linnean Society (2013)
A few years ago, Hawking was asked what he thought of the common opinion that the twentieth century was that of biology and the twenty-first century would be that of physics. Hawking replied that in his opinion the twenty-first century would be the “century of complexity”. That remark probably holds more useful advice for contemporary students than they realize since it points to at least two skills which are going to be essential for new college grads in the age of complexity: statistics and data visualization.
Chaos theory deals with the description of motion (in a general sense) which cannot be predicted in the long term although produced by deterministic system, as well exemplified by meteorological phenomena. It directly comes from the Lunar theory -- a three-body problem -- and the difficulty encountered by astronomers to accurately predict the long-term evolution of the Moon using "Newtonian" mechanics. Henri Poincare's deep intuitions were at the origin of chaos theory. They also led the meteorologist Edward Lorenz to draw the first chaotic attractor ever published. But the main idea consists of plotting a curve representative of the system evolution rather than finding an analytical solution as commonly done in classical mechanics. Such a novel approach allows the description of population interactions and the solar activity as well. Using the original sources, the book draws on the history of the concepts underlying chaos theory from the 17th century to the last decade, and by various examples, show how general is this theory in a wide range of applications: meteorology, chemistry, populations, astrophysics, biomedicine, etc.
Complex systems are pervasive in many fields of science and we encounter them everyday and everywhere in our life. Their examples include financial markets, highway transportation networks, telecommunication networks, human economies, social networks, immunological systems, living organisms, ant colonies, ect. The key feature of a complex system is that it is composed of large number of interconnected and interacting entities exhibiting much richer dynamical properties on global scale than they could be inferred from the properties and behaviors of its individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. An important part of these interdisciplinary studies forms discrete modeling. These models can be seen as the simplest laboratories to study phenomena exhibited by complex systems like self-organization processes, pattern formation, cooperation, adaptation, competition, attractors, or multi-scaling phenomena. The objective of this conference is to bring together researchers working on discrete modeling of complex systems and to provide a forum for exchange of ideas and presentation of results of their research.