(26 resources)

Dynamics of DNA Replication
Moura, Alessandro - University of Aberdeen
2012-05-16 8:30 AM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:35:00

DNA replication is an essential process in biology and its timing must be robust so that cells can divide properly. Random fluctuations in the formation of replication starting points, called origins, and the subsequent activation of proteins lead to variations in the replication time. We analyse these stochastic properties of the dynamics of DNA replication, using very simple models, and derive the positions of origins corresponding to the minimum replication time. We show that under some conditions the minimization of replication time leads to the grouping of origins, and we propose that the minimization of replication time has played an important role in evolution. Finally, we relate our theoretical findings to available experimental data in a number of species showing grouping of replication origins.

Alessandro Moura is a lecturer at the University of Aberdeen, in Scotland, since 2006. He has a background in dynamical systems and statistical physics, and his research interests in this area includes transient chaos and chaotic scattering; and chaotic advection in fluid flows. Since moving to Aberdeen, he has also been active in applying the methods of dynamical systems and statistical physics to understand biological systems. He has worked on mathematical models of DNA replication, DNA damage and repair, and stress response in microorganisms.
Emergence and Decline of Scientific Paradigms and Epidemics
Jensen, Mogens - University of Copenhagen
2012-05-16 10:21 AM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:39:26

Scientific paradigms have a tendency to rise fast and decline slowly. This asymmetry reflects the difficulty in developing a truly original idea. We describe a model for the emergence and spread of ideas where the likelihood of spread is proportional to the ‘size’ of the idea but where it is forbidden to return to an already abandoned concept. We obtain a fairly regular pattern of global paradigm shifts, where older paradigms are eroded and subsequently replaced by new ones. The model allows for frozen events in terms of the coexistence of multiple metastable states. We discussion a similar model where the ability to transmit a particular epidemic disease depends on competing infections as well as on the status of host immunity. The model exhibits an unlimited number of unrelated pathogens whose interaction is simplified to simple mutual exclusion. The model incorporates an immunity (like for paradigms) to past diseases, while leaving the system open for emergence of new diseases. We find a rich dynamical behavior with interacting infection waves, leaving broad trails of immunization in the host population. If time allows we briefly discuss how coherence effects lead to a gain-loss asymmetry in the stock markets.

Mogens H. Jensen received his PhD from University of Copenhagen in 1984 under supervision of Prof. Per Bak. The thesis was about chaos and fractal theory in time and space. He then moved to the University of Chicago working with Prof. Leo Kadanoff for three years. During this period he with collaborators developed the multifractal formalism and applied it to onset of turbulence in Rayleigh-Benard convection. For six years he was employed at Nordita turning his attention to turbulence theory. With a group at Rome University he introduced studies of intermittency in shell models. Later as a professor at the Niels Bohr Institute he turned his attention towards biological and social systems.
Incentives, Dynamics and Collective Wisdom
Page, Scott - University of Michigan
2012-05-16 11:30 AM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:34:31

The ability of a collection of individuals to make accurate forecasts depends on individual competence and collective diversity. In this talk, I discuss the ability of various incentive systems produce those individual and collective characteristics. The model that I present assumes that individuals rely on models to make forecasts. With this model, I study the population dynamics of these models. I show that incentive systems that produce highly accurate individuals often produce too little diversity reducing the collective accuracy of the crowd. I also show how different network assumptions maintain different levels of model diversity. The analysis relies on both mathematical and computational methods.

Scott E Page is the Leonid Hurwicz Collegiate Professor of Complex Systems, Political Science, and Economics at the University of Michigan where he also directs the Center for the Study of Complex Systems. Scott is also a senior research scientist at the Institute for Social Research and an external faculty member of the Santa Fe Institute. Scott’s research focuses on the role of diversity in social systems. In 2011, he was elected to the American Academy of Arts and Sciences.
Synchronization in Nonlinear Optical Networks: Chaos, Communication and Chimeras in the Laboratory
Roy, Rajarshi - University of Maryland
2012-05-16 2:15 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:34:31

We explore synchronization in networks of coupled nonlinear optoelectronic oscillators in our laboratory. Numerical models are developed and simulations are compared with experimental observations to understand how real networks synchronize and desynchronize as coupling coefficients are varied. Convergence to synchrony is found to depend on the network topology. We investigate the dynamics of these coupled nonlinear time-delayed systems in different configurations and periodic as well as chaotic regimes. Next, we report results of experiments on large arrays of coupled-map lattices with liquid crystal spatial light modulators and the observation of chimera-like states when nonlocal coupling is realized. The relevance of such states to neuronal networks will be discussed.

Rajarshi Roy was a student of Leonard Mandel at the University of Rochester, who taught him to design small scale table-top experiments and explore the nature of light and its interaction with atoms and molecules. Understanding order and randomness in light and matter has been a passion ever since. After receiving his Ph.D in 1981, he went to Boulder, Colorado, as a postdoctoral research associate at the Joint Institute for Laboratory Astrophysics (JILA) and then moved to the School of Physics, Georgia Tech, in 1982. He moved again in 1999 to the University of Maryland to join the nonlinear dynamics group there. He is currently director of the Institute for Physical Science and Technology. Over the years he has worked on stochastic, chaotic and synchronization phenomena in lasers and nonlinear optics, and recently in networks and neuroscience. He has guided, individually or jointly with other faculty, the research of thirty Ph. D students, including a dozen from the University of Maryland, and worked with many postdoctoral fellows and visiting faculty. Participation in the first experimental chaos conference (1991) and several subsequent ones has been particularly influential in developing new interests and research directions.
Turbulence Driving Wind Energy
Peinke, Joachim - University of Oldenburg
2012-05-16 4:02 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:36:48

The main focus of the talk is on the impact of turbulent wind conditions on the conversion of wind energy. It is shown that the multifractal features of turbulence improve the actual wind classification. Based on extreme value statistics and intermittency, new quantities to characterize short term wind gusts is proposed. The impact of such turbulent gusts on loads and on fluctuating power production is shown. Finally, based on modern stochastic time series analysis, a method to estimate a new power curve of a wind turbine is explained.

Besides these scientific aspects, an outlook on the promising developments of wind energy and challenges of offshore wind energy to become one major source for our electricity demand is given.

Prof. Joachim Peinke graduated from the University of Tübingen with a diploma in Experimental Physics in and received his PhD with distinction in 1988 on “Nichtlinearitäten und Chaos beim Elektronentransport in Halbleitern” (Nonlinearities and Chaos in the Transport of Electrons in Semiconductors) at the University of Tübingen. He became a researcher first at the University of Tübingen and then from 1990 to 1994 at the Centre de Recherches sur les Très Basses Températures of the Centre National de la Recherche Sientifique in Grenoble. In 1992 he habilitated at the University of Tübingen, and in 1994 he went to the University of Bayreuth (Physics department) where he received the venia legendi. From 1993 until 1998 he was Heisenberg fellow of the Deutschen Forschungsgemeinschaft. In 1998 he became professor in Experimental Physics at the Department of Physics at the University of Oldenburg. His research topics are dynamical systems, turbulence, and stochastic systems and currently his research focuses mainly on wind energy. He is a wind energy expert in several commissions including the European technology platform TPWind, and is the vice president of the European Academy of Wind Energy (EAWE). He has published more than 300 papers.
Laboratory Experiments on Porous Media Mass Transport: Implications for Carbon Sequestration
Ecke, Robert - Los Alamos National Laboratory
2012-05-16 5:00 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:35:24

Carbon dioxide in the atmosphere has continued to increase throughout the 20th century and continues today. The consensus of the scientific community is that this CO2 increase has contributed substantively to the observed increase in global mean temperature over the last 80 years. Further emissions in CO2 from the burning of fossil fuels will accelerate climate change, affecting regional climate in as yet unpredictable ways. To combat the ever-increasing emissions of CO2, a mitigating strategy is the long-term storage of CO2 in geologic reservoirs. Such reservoirs consist of porous media of varying permeability (average pore size) and porosity (fluid fraction), and an accurate estimate of the rate of absorption of CO2 into such reservoirs is necessary to quantitatively evaluate the efficacy of this sequestration strategy. Roughly speaking, the approach consists of pumping high-pressure (above its critical pressure, i.e., supercritical) CO2 into the porous media at depth. The CO2 is less dense than the surrounding salt water and most of it rises until confined by impermeable cap rock. As more fluid is pumped into the reservoir it spreads laterally under the cap rock and slowly diffuses into the water (the saturation concentration of CO2 into water is about 3%). If diffusion were the only transport mechanism, the dissolution of most of the injected CO2 would take thousands of years. Fortuitously, CO2-saturated brine is heavier than water and can transport mass by the process of convection as well as by diffusion. To determine the storage potential for carbon sequestration strategies involving porous media, accurate determination of mass transport efficiency is required. We have made accurate measurements of mass transport in geometries similar to those relevant for sequestration, namely a gravitationally stable two-layer system where the diffusion interface between the two phases is unstable. The two fluids are water and propylene glycol (PPG) with water on top. In one case the porous media is modeled using the Hele-Shaw geometry with an adjustable gap width to vary the permeability (the porosity in this 2D case is 1). The enhanced mass transport efficiency Nu as a function of dimensionless forcing Rayleigh number Ra is determined with enhancements of up to 250 for Ra = 80,000. Geologic reservoirs are, however, three dimensional and typical Rain nature are less than about 5000. Therefore, we also measured mass transport in a fully 3D cylindrical geometry for 150 < Ra < 5000 using the same fluids. We discovered a transition from a high mass-transport state to a low mass transport state that typically occurs between 4 and 6 convective times. The implications of these mass transport measurements for carbon sequestration are evaluated.

Robert Ecke is Director of the Center for Nonlinear Studies at Los Alamos National Laboratory where he guides research on interdisciplinary science including quantitative biology, information science and technology, quantum information science, and non-equilibrium statistical physics. He is a Laboratory Fellow, a position he has held since 1997. He received his Ph.D. in Physics from the University of Washington in 1982, followed by a Postdoctoral Fellowship at Los Alamos National Laboratory working on cryogenic thermal convection involving hydrodynamic stability, dynamical systems, and chaos. As a Technical Staff Member, he continued research on rotating convection and pattern formation, material dissolution and compositional convection, turbulent boundary layers and heat-transport scaling, spatio-temporal dynamics in pattern forming systems, 2D turbulence, turbulence in stratified flows, and statics/dynamics of granular materials. Ecke served in numerous professional society positions including Chair of the APS Group on Statistical and Nonlinear Physics, is an Editor for Chaos, and is Divisional Associate Editor of PRE. Ecke won the Los Alamos Fellows Prize in 1991 and is a Fellow of both the APS and the AAAS. His current research interests include turbulence in atmospheres and oceans, fundamental studies of turbulence, properties of granular materials, and mass transport in porous media.
Extreme Events in Nature, Rogue Waves in Optics: New Light on the Science of Nonlinear Waves
Dudley, John - University of Franche-Comte
2012-05-17 8:02 AM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:35:04

A central challenge in understanding extreme events in science is to develop rigorous models linking the complex (often nonlinear) generation dynamics and the associated statistical behavior. Quantitative studies of extreme phenomena, however, can be frequently hampered in two ways: (i) the scarcity of the events under study and (ii) the fact that such events often appear in environments where measurements are difficult. A case of interest concerns the infamous oceanic rogue waves associated with many catastrophic maritime disasters. Studying rogue waves under controlled conditions is problematic, and the phenomenon remains a subject of intensive research. On the other hand, there are many qualitative and quantitative links between wave propagation in optics and in hydrodynamics, and it is thus natural to consider how insights from studying instability phenomena in optics can be applied to other systems. The field of optical rogue wave physics began in 2007 and has since become a major international research effort involving many international groups and consortia. This talk reviews the current state of the art in this field and present recent results of both theory and experiments. The potential practical impact and extension of these ideas from optics to other fields is also be reviewed. Appropriate introduction to the wider area of nonlinear dynamics and ocean rogue waves is provided.

Born in New Zealand in 1966, John Dudley received his Ph.D. from the University of Auckland in 1992. After two years postdoctoral research in Scotland, he returned to a lecturing position at the University of Auckland in 1994. In 2000, he moved to Europe and was appointed Professor at the University of Franche-Comté, where he heads the Optoelectronics and Photonics research group. He was named a member of the Institut Universitaire de France in 2005 and elected a Fellow of the Optical Society of America in 2007 and Fellow of the IEEE in 2011. He has served as an IEEE Distinguished Lecturer, he has won the Grand prix de l’électronique « Général FERRIE » of the French Société de l’Electricité, de l’Electronique et des Technologies de l’Information et de la Communication, the Prize of the IXCORE Foundation in France and a Service Award of the European Physical Society. He is Deputy Editor of the OSA flagship journal Optics Express and travelling lecturer of SPIE and OSA.
Deterministic Optical Rogue Waves and Chaotic Dynamics
Rios Leite, Jose Roberto - Universidade Federal de Pernambuco
2012-05-17 8:58 AM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:32:25

Rare and irregular extreme events can occur in many different systems in nature. A typical example is rogue waves observed in the oceans, where waves higher than 30 meters are more or less common phenomena. This fact is in contradiction with Gaussian models often used to describe fluctuations of the wave height in the sea [M. S. Longuet-Higgins, Phil. Trans. Roy. Soc. A 249, 321 (1957)]. The understanding of the generation of rare extreme events within optical dynamical systems [D. R. Solli, C. Ropers, P. Koonath, B. Jalali, Nature 450, 1054 (2007)] is interesting for itself and can allow to identify mechanisms to control or suppress the occurrence of such events.

We [C. Bonatto, M. Feyereisen, S. Barland, M. Giudici, C. Masoller, J. R. Rios Leite, and J. R. Tredicce, Phys. Rev. Lett. 107, 053901 (2011)] investigate, both theoretically and experimentally, the appearance of rare giant pulses in a semiconductor laser subject to optical injection. Experimental characterization of the parameter region where the equivalent to rogue waves was done and compared with numerical results from the simplest rate equation model. It is shown that the sporadic large intensity events can be understood as a result of a deterministic non-linear process. Such view is consistent with the fact that Chaos in Dynamical Systems is well known to explain a wide range of phenomena having erratic behaviour without the need to appeal for the presence of stochastic noise [Chaos in Dynamical Systems, E. Ott, Cambridge University Press Second Ed. (2002)].
Multi-Scale and Multi-Compartment Approaches to Understand Host-Pathogen Dynamics: TB as a Case Study
Kirschner, Denise - University of Michigan
2012-05-17 10:15 AM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:35:32

Tuberculosis (TB) is the main cause of death due to infectious disease in the world today. An estimated 2 billion people are carriers of M. tuberculosis, the bacteria that causes TB. The different outcomes from infection with Mtb (primary or active disease, latent infection, disease reactivation) are in large part determined by the formation and function of lung granulomas, structures that dynamically contain the infection in an immune microenvironment but unfortunately also serve as a niche for bacterial survival. Events that play a role in granuloma formation and function occur over a broad range of biological scales – molecular (e.g., cytokines), cellular (e.g., T cells, macrophages, and dendritic cells), tissue (e.g. lung, lymph node) and larger scales (e.g., lymphatics, blood and other organs) – and also time scales (seconds to the lifetime of host). A systems biology approach, combining computational models with data from relevant animal models, offers a path to understanding how these different events influence infection control. A particular challenge for study of Mtb infection is that observing formation of granulomas is not readily accessible within lung tissue.

In this work, multi-scale and multi-compartment computational modeling is coupled with novel experimental data to determine how mechanisms at one biological scale affect dynamics at other scales in order to predict which mechanisms control granuloma formation and function. We develop and apply a methodology to address issues of parameter uncertainty and sensitivity to analyze our computational models. We develop both 2-dimensional (2-D) and 3-D agent-based models to explore these questions, allowing us also to assess the influence of dimensionality and determine what information the simpler model can and cannot provide. Finally, we develop a tool known as tuneable resolution that allows us to control model detail in a mechanistic way. This aids in future studies where performance issues related to efficiency and speed of simulations are important. Finally, our goal of linking immune dynamics occurring in a lymph node to those occurring at the site of infection in the lung has been realized; we present the first hybrid, multi-compartment model exploring the role of dynamics occurring between lymph nodes and lung during infection and determine how mechanisms related to trafficking between these compartments are key to successful infection control.

Dr. Kirschner received her PhD in dynamical systems from Tulane University in 1991, which included training at Los Alamos National Laboratories as part of her studies. She did a postdoctoral Fellow at Vanderbilt University with joint appointments in both Mathematics and Infectious Diseases. She joined the University of Michigan in 1996 where she is now a Full Professor in the department of Microbiology and Immunology. At UM, she is involved with the Center for Complex Systems as well as Biomedical Engineering, Bioinformatics and at the School of Public Health. Her research for the past 20 years has focused on applying mathematical and computational techniques to study questions related to host-pathogen interactions. Her main focus has been to study persistent infections with pathogens that have evolved strategies to evade or circumvent the host-immune responses. Her goal is to understand the complex dynamics involved, together with how perturbations (e.g., treatment) can lead to health. Her work is well funded by the National Institutes of Health and she has published over 100 research papers. In addition, she serves as editor for a number of journals in both immunology and mathematics, including serving as Editor-in-Chief of the Journal of Theoretical Biology.
Learning About Dynamical Systems From an Ensemble Approach
Politi, Antonio - University of Michigan
2012-05-17 1:30 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:32:51

Nonlinear dynamical systems are often studied by following single trajectories (or pairs of them) in phase space to quantify, e.g., Fourier power-spectra, fractal dimensions and Lyapunov exponents. However, especially in the perspective of unveiling and characterizing emergent collective properties, it is more instructive to follow the simultaneous evolution of (large) ensembles of trajectories. This approach implicitly corresponds to studying suitable evolution operators (Frobenius-Perron, Liouville) in some functional space and it is particularly suited to analyse and characterize the emergence of collective properties. As a result, in the context of standard low-dimensional chaos, it is possible to provide a complementary description to the usual "microscopic" one, based on the analysis of single trajectories. In particular, it is possible to determine additional dynamical invariants that are related to the convergence properties of the corresponding functional operator, without the need to integrate it. This approach is potentially useful both in the perspective of characterizing the emergence of collective behaviour (when the different trajectories are coupled with one-another) and while studying the the response of a chaotic system to a given class of external modulations.

I discuss both "simple" chaotic systems (such as the Roessler attractor or the Hindmarsh-Rose neuron) and a set of experimental data that correspond to some chaotic laser dynamics.

Antonio Politi (AP) graduated in Physics in 1978 at the University of Florence (Italy). In 1981 he was hired at the National Institute of Optics (INO)in Florence as a Resercher, where it was later (1992) appointed Chief Researcher and then(2005) Director of Research. From 1994 to 2005 he has been appointed Manager of the Quantum Optics Section of INO. From 2005 to 2011 AP moved to the Institute of Complex Sytems of CNR, where he managed the Florence section. Since 2012, AP is 6th Century Chair Professor in the Physics of Life Sciences at the University of Aberdeen (UK). AP is currently visiting professor at Strathclyde University and has covered similar positions at the University of Nice and at the Ecole Normale Superieure in Lyon (France). In 2004, AP was appointed Fellow of the Instute of Physics and in 2012, Fellow of the American Physical Society. In 2004 AP was awarded the Gutzwiller Prize by the Institute of Complex Systems in Dresden (Germany) and in 2010 he received the Humboldt Prize. AP authored around 160 publications in peer-review journals. AP has co-authored (with R. Badii) a book on Complexity and has written a Chapter on “Complex Systems” within the “New Physics for the 21st Century” volume published by CUP. AP is associate Editor of Physical Review E since 1998 and has managed the Complex Systems section of the Journal of Physics A from 2005 to 2011. His current scientific interests range from non-equilibrium thermodynamics, to neurocomputation, and nonlinear-dynamics in general.
Subwavelength Position Sensing Using Nonlinear Feedback and Wave Chaos
Cohen, Seth - Duke University
2012-05-17 3:35 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:35:00

We demonstrate a position-sensing technique that utilizes the inherent sensitivity of chaos, where we illuminate a subwavelength dielectric object in a cavity with a complex structured radio-frequency field generated using wave chaos and an electronic nonlinear circuit. We operate the system’s dynamics in a two-tone quasi-periodic state and analyze changes in the frequency content of the scalar voltage signal in an electronic feedback loop. These frequencies allow us to extract the object’s position in a cavity with a one-dimensional resolution of λ/10,000 and a two-dimensional resolution of λ/300, where λ is the shortest wavelength of the illuminating source (15 cm). Possible extensions of this work include three-dimensional subwavelength position-sensing, through-wall tracking, and subwavelength optical microscopy.

Seth D. Cohen is a fifth-year Ph.D. candidate in the Physics Department at Duke University in North Carolina. He received his B.S. and B.A. in Physics and Mathematics, respectively, in 2007 at the University of Rochester in New York and his M.A. in Physics at Duke in 2009. He currently works under the advisement of Dr. Daniel Gauthier in the Quantum Electronics Laboratory at Duke, where he investigates time-delayed feedback in electronic oscillators in order to realize inexpensive and high-speed chaotic elements. Mr. Cohen’s work focuses on connecting the sensitivity of complex dynamical systems to high-resolution measurements. His general interests are in the development of simple, robust systems for real-time radar or imaging that can be deployed in military or commercial applications.
Electronic Circuits Verify Importance of Dynamics in Perception and Predict Failure of Prominent Algorithms Used in Bioinformatics
Stoop, Ruedi - Duke University
2012-05-17 4:47 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:30:00

This topic focuses on continued insights triggered by our paper “Real-World Existence and Origins of the Spiral Organization of Shrimp-Shaped Domains,” (PRL 105, 074102 (2010)). The talk first gives a detailed outline of this paper, emphasizing on the relation between mathematical models and their simulation, and between the two and real-world experiments. Then we will answer some questions related to, but not treated in the original work. Finally, we will emphasize and illustrate how nonlinear physics may be of key importance for difficulties encountered in various fields of bioinformatics analysis.

Areas in parameter space of dimensions higher than one on which the solutions of a smooth nonlinear dynamical system are regular, are found to be connected and to have the peculiar characteristic form of a shrimp. We give the first detailed demonstration of the emergence of shrimps in electronic systems, discuss the influence that different approximate hardware descriptions have, and clarify questions that have recently been raised about the nature of the organization of shrimps in parameter space. In particular, we demonstrate explicitly that the phenomenon was theoretically predicted by the (very unfortunately: recently passed away) Russian mathematician L.P. Shilnikov in the sixties. Moreover, we design a simpler circuit that provides the explicit proof of this fact. We then reveal the mathematical key elements that lead to the formation of shrimps.

We finally show that the phenomenon of shrimp emergence is likely to underlie a number of problems found in different fields of bioinformatics. One important example will be clustering as the fundamental process behind the modeling of neuroinformatic perception and, more generally, in most bioinformatics applications. We show how the objects to be clustered emerge from general nonlinear processes, and that they therefore have shrimps-shaped boundaries. One consequence of this is that by means of most traditional clustering approaches, they cannot properly be clustered. We demonstrate exemplarily how an electronics implementation of a simple blueprint borrowed from the mammalian brain outperforms by far the algorithms commonly used for clustering, both in time and quality of the result.

The talk will mediate between theoretical concepts of nonlinear dynamics with an emphasis on chaos, their implementation in electronics and their applications to real-world systems. It will demonstrate the importance of the underlying nonlinear concepts and the fact that we, as the specialists in this field, are now well-equipped for solving key questions in eminent fields of technology such as bioinformatics.

Ruedi Stoop received a masters in mathematics from the University of Zurich, for work in the field of partial differential equations using the Sobolev formulation. A PhD in computer-aided physics followed, focusing on the modeling and time series analysis of an NMR laser system. During this time, he developed one of the first algorithms to extract Lyapunov exponents from time series and (in parallel to work performed in Budapest) he developed the generalized thermodynamic formalism and its application to models and experimental data. After a habilitation at the University of Berne focusing on phase transitions in dynamical systems, he joined the newly founded Neuroinformatics research institute run jointly by the University and ETH of Zürich, where he has become a professor. He works presently in three main research areas: Measurable concepts of natural complexity and computation, with some emphasis on the role of noise; Neural networks concepts and algorithms of perception, their relation to dynamical systems, with practical and industrial applications; Sensory systems, including the first electronic hardware construction of a Hopf cochlea, a brain-like electronic model of clustering, and, more recently, the first physics model of pitch perception explaining all known nonlinear pitch phenomena.
How Bacteria in Colonies Can Survive By Killing Siblings and Reversibly Changing Shape
Swinney, Harry - University of Texas at Austin
2012-05-17 7:40 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 01:28:14

A few bacteria on a surface (e.g., skin or a medical instrument) can grow into a colony consisting of a billion bacteria and spanning several centimeters. What happens when neighboring colonies of bacteria grow and approach one another? Studies of Paenibacillus dendritiformis (a bacterium found commonly in soil) reveal that neighboring bacterial colonies mutually inhibit growth through secretions of a lethal protein. An immediate question is why doesn’t this toxin kill the bacteria secreting it? A mathematical model helps answer this question. Further, sub-lethal concentrations of the toxin are found to induce the rod- shaped bacteria to switch shape to cocci, a spherical shape that is resistant to the toxin and to other antibiotics. But if the cocci encounter persistent favorable growth conditions, they switch back to rods. Thus the bacteria adapt to adverse environmental conditions by a reversible change in form.

Harry Swinney’s research concerns the dynamics of macroscopic systems driven far from thermodynamic equilibrium by imposed gradients. In 1975 at City College of New York, Swinney and Jerry P. Gollub (from Haverford College) examined flow between concentric rotating cylinders, hoping to validate Landau’s picture of the transition to turbulence as an infinite sequence of instabilities, but instead the experiments revealed an abrupt transition from doubly-periodic to nonperiodic (“chaotic”) behavior. In 1978 Swinney moved to the University of Texas where his group has conducted studies to characterize chaotic behavior and pattern formation in different systems. Experiments and numerical studies of models have revealed instabilities and chaos in homogeneous chemical reactions; mechanisms that stabilize Jupiter’s Great Red Spot; chemical “Turing” patterns; spatial patterns and shock waves in granular materials; wrinkling in garbage bags and tree leaves; dynamics of gravity waves internal to the ocean; and deadly competition between bacterial colonies. Swinney co-founded and co-directs annual two-week long schools (2008-) for early career scientists in developing countries (cf. handsonresearch.org). He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences.
Drops and Elastic Sheets
Roman, Benoit - ESPCI
2012-05-18 8:00 AM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:34:18

Usually, man-made structures are designed to withstand the effect of their weight or aerodynamic forces. But as we scale down structures, surface forces become dominant. For example, slender micro-structures (like the accelerometer in cell phones) may be destroyed by capillary forces. But in some cases, surface tension may also have a positive role and deform thin objects into a new shape or pattern. I present some problems that we have studied recently: hierarchical sticking of wet elastic lamellae, capillary origami. The selection of the final shape is a strongly non-linear problem, with a rich phase diagram. Although these situations are particularly relevant at small scale, the approach that we follow is to study them through macroscopic model experiments.

Benoit Roman is CNRS research fellow working in Paris at ESPCI. He also teaches mechanics at Ecole Polytechnique. His research interests are related with the non-linear mechanics of thin plates and rods. Because of large deformations, universal non-linearities arise from geometry, which plays a predominant role in the mechanics of thin sheets: crumpling singularities, instabilities, pattern formation. He worked on the effect of capillary forces on elastic plates, and on the propagation of fracture in thin sheets (tearing).
Continuous Measurement of the Position of a Single Cold Atom: Towards the Quantum-Classical Transition
Steck, Daniel - University of Oregon
2012-05-18 8:55 AM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:33:49

Quantum mechanics is fundamentally a theory of measurement, and recently a paradigm in quantum optics has arisen for describing the continuous measurement of quantum systems. Interesting phenomena can happen in continuously observed systems, due to the interplay of the dynamical evolution and the measurement process. In particular, the evolution of a quantum system under a continuous measurement process is both nonlinear and stochastic. I describe our interests in continuous measurements of atomic motion, especially in applying them in understanding the quantum–classical transition for classically chaotic systems. I also describe our experimental progress towards studying these systems, as well as the theoretical formalism for incorporating measurement information into the evolving quantum state.

Daniel Steck received his Bachelor’s degree in Physics and Mathematics from the University of Dayton in 1995. The following fall he joined the research group of Prof. Mark Raizen in the Department of Physics at The University of Texas at Austin to perform his experimental dissertation research on quantum nonlinear dynamics of atoms in optical lattices. He was then a Postdoctoral Fellow from 2001-2004 at Los Alamos National Laboratory, where he performed theoretical research on quantum and cold-atom systems, particularly in the area of continuous quantum measurement and quantum feedback control. He is now an Associate Professor at the University of Oregon and Oregon Center for Optics, where he studies quantum measurement and control with ultracold atoms.
The Critical Point For Pipe Flow
Barkley, Dwight - University of Warwick
2012-05-18 10:20 AM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:31:32

More than 125 years ago Osborne Reynolds launched the quantitative study of turbulent transition as he sought to understand the conditions under which fluid flowing through a pipe would be laminar or turbulent. Since laminar and turbulent flow have vastly different drag laws, this question is as important now as it was in Reynolds’ day. Reynolds understood how one should define “the real critical value’’ for the fluid velocity beyond which turbulence can persist indefinitely. He also appreciated the difficulty in obtaining this value. For years this critical Reynolds number, as we now call it, has been the subject of study, controversy, and uncertainty. Now, more than a century after Reynolds pioneering work, we know that the onset of turbulence in shear flows is properly understood as a statistical phase transition. How turbulence first develops in these flows is more closely related to the onset of an infectious disease than to, for example, the onset of oscillation in the flow past a body or the onset of motion in a fluid layer heated from below. Through the statistical analysis of large samples of individual decay and proliferation events, we at last have an accurate estimate of the real critical Reynolds number for the onset of turbulence in pipe flow, and with it, an understanding of the nature of transitional turbulence.

This work is joint with: K. Avila, D. Moxey, M. Avila, A. de Lozar, and B. Hof.

Dwight Barkley received his B.S. with honors in Physics from the Catholic University of America in 1980. He then went to graduate school at the University of Texas to study general relativity, but after a few years decided instead to study nonlinear phenomena. He was a member of the Prigogine Center for Studies in Statistical Mechanics and of the Center for Nonlinear Dynamics, and received a PhD in Physics in 1988. While at Texas he met and began a long-standing collaboration with Laurette Tuckerman on numerical methods for computing bifurcations and patterns. After his PhD he went to Caltech to work with Philip Saffman and then to Princeton where he worked with Yannis Kevrekidis and Steve Orszag. In 1992 he received both NSF and NATO postdoctoral fellowships which he used at the University of Texas and the Ecole Normale Superieure de Lyon. In 1994 he took a position on the faculty of the University of Warwick where he is now Professor of Mathematics. He has been recognized by a number of awards including the SIAM J.D. Crawford Prize (2005); Senior Fellowship, Ville de Paris, (2006-2007); Fellow of the Institute of Mathematics and Its Applications (2008); Fellow of the American Physical Society (2008); and the Royal Society-Leverhulme Trust Senior Research Fellow (2009-2010).
Human Epileptic Brain Networks
Lehnertz, Klaus - University of Bonn, Germany
2012-05-18 12:48 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:23:29

The human brain is a complex network of interacting subsystems, and it is now commonly accepted that synchronization plays an important role in brain functioning and dysfunctioning. A prominent example for pathophysiologic neuronal synchronization is epilepsy along with its cardinal symptom, the epileptic seizure. Epilepsy affects approximately 1% of the world’s population, and in about 25% of individuals with epilepsy seizures cannot be controlled by any available therapy. Knowledge about mechanisms underlying generation, spread, and termination of the extreme event seizure in humans is still fragmentary. There is now growing evidence that an improved understanding of the epileptic process can be achieved through the analysis of interactions in large scale epileptic brain networks. I present synchronization phenomena related to seizures in patients suffering from focal drug- resistant epilepsies and discuss their impact for the development of new therapeutic possibilities based on seizure prediction.

Klaus Lehnertz is head of the Neurophysics research group at the Department of Epileptology and co-director of the Interdisciplinary Centre for Complex System at the University Bonn, Germany. Research in Klaus’ group is centered on the understanding, modeling, and prediction of the spatial-temporal evolution of complex dynamical systems. A particular field of interest is the dynamics of the human epileptic brain. Current research projects include the characterization of the complex dynamics of the epileptic process and the predictability of epileptic seizures with concepts of dynamical systems, statistical physics, and network theory.
Predicting and Evaluating Extreme Weather Events
Gilliland, Erk - National Center for Atmospheric Research
2012-05-18 2:00 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:32:23

High impact weather events that have large societal impacts often are extreme or rare. Examples of such events include hurricane landfall events which can lead to evacuation of cities and extensive damage from rain, wind, and severe weather; rainfall events that lead to severe flooding; and wind events that lead to tree and roof damage. Because of these impacts, it is highly desirable to be able to forecast the occurrence of such weather events with a large degree of accuracy and reliability to allow decision makers to take appropriate actions in anticipation of the forecasted event. Unfortunately, the extreme nature of these weather events typically also makes their prediction difficult. High temporal and spatial variability as well as limited frequencies of occurrence and poor or limited observations contribute to these predictive limitations. Moreover, evaluation of forecasts of these events – which can help lead to improvements in the forecasts – is also difficult due to many of the same factors that make the forecasting process difficult. Atmospheric scientists have striven to overcome these limitations using several strategies. These strategies have included (i) the evolution of numerical weather prediction (NWP) models (which provide gridded predictions of the translation and development of weather systems) toward higher temporal and spatial resolution, (ii) the inclusion of more realistic parameterizations of subgrid scale processes such as cloud microphysics, (iii) the integration of observations with NWP forecasts to provide more realistic representations of weather in subsequent short time periods, (iv) the use of statistical post-processing methods to create more localized predictions, and (v) the use of techniques to assess the uncertainty associated with the predicted events. The evolution of these methods of forecasting, the methods used for their evaluation, and their implications for extreme weather events will be considered in this talk.

Barbara G. Brown is Director of the Joint Numerical Testbed (JNT) program in the Research Applications Laboratory of the National Center for Atmospheric Research. The JNT provides support for community codes, forecast verification tools, and testing and evaluation of numerical weather prediction models and forecasts to facilitate the transition of research capabilities into operational forecasting. Her research interests include weather and climate applications of statistics, including forecast evaluation and development of advanced user-focused verification approaches; measuring user needs and forecast value; probability forecasts and forecast uncertainty; statistical forecasting methods; and weather and climate extremes. She is past chair, and member, of the World Meteorological Organization’s Joint Working Group on Forecast Verification Research and is a recognized international expert on the development and application of methods to evaluate forecasts and models. Barbara has served on several advisory committees, including an NRC committee on uncertainty in weather and climate forecasts. She is a Fellow of the American Meteorological Society (AMS) and is Chair of the AMS Committee on Probability and Statistics. Barbara has a B.S. degree in statistics from Colorado State University and M.S. degrees in environmental sciences and statistics from the University of Virginia and Oregon State University, respectively.
Extreme Events in Coupled Social Networks: Media Events as External Shocks
Jurgens, Pascal - Johannes Gutenberg-Universitat Mainz
2012-05-18 3:00 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:27:00

There is a long-established tradition of theories about the way in which media information, people’s minds and behavior interact. Perhaps one of the best known is the theory of agenda setting, which postulates several links through which the media are thought to define what people deem important and vice versa. The common characteristic of such models is that they consider long-term relations and work on an aggregate level.

With the broad-scale adoption of social communication channels on the internet, researchers for the first time gain insight into popular communication—unaggregated and with high temporal fidelity. Whereas we used to quantify beliefs and attitudes in the limited scope of surveys, we can now quantify behavior through non-reactive measurements.

The new opportunities for empirical research carry with them a shift in focus. Previous reliance on directed, deep and meaningful questions gives way to a superficial interpretation of traces of behavior. This shift can be seen as part of a larger trend away from the model of rational choice and deep beliefs towards a picture of individuals easily swayed by mental heuristics and external cues, as nobel prize winners Kahneman and Tversky have prominently painted it.

Armed with such data from social networks and other communication networks, scholars have started to look closer at the empirical reality of networked communication, uncovering new and unexpected aspects of human behavior. Of peculiar interest are extreme events—situations in which the behavior of a network or a significant subset changes drastically and often seemingly arbitrarily.

As far as some extreme events (such as political protests) go, mass communication research appears to offer a unique complement to network theory. Its theoretical models of event dynamics offer a compelling addition to the temporal analysis of one-mode networks. In this respect, media events can be regarded as external shocks which diffuse into an observable network, inducing and interacting with extreme events.

This talk presents recent research in the field of social communication networks in order to shed light on the interaction between media and social communication systems. It will attempt to bring theoretical concepts of audience behavior and media choice into a network perspective on communication, offering explanations for the behavior of some observable extreme events in those networks.

Pascal Jürgens is a doctoral candidate at the Institute for Communication at U of Mainz, Germany. Prior to obtaining an MA in the classic social scientific field of mass communication, he worked as a radio journalist. His research interests include political communication online, models of opinion dynamics, network analysis of social behavior, computational methods for content analysis and time series analysis in networked communication. Previous research involved (among other subjects) the German electronic petition system, models of opinion dynamics with back-channels, the use of twitter during the German general election, event mapping through linguistic time series analyses and topic cycles in social media. Aside from the PhD-thesis, which investigates interrelations between conventional mass media and social communication online on a large scale, he is involved in a project on media convergence and pursues methodical advances in mass communication through the incorporation of computational techniques.
Strain Competition Dynamics in an Evolutionary Context: H3N2 Influenza as a Case Study
Pascual, Mercedes - University of Michigan
2012-05-18 4:00 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:36:54

Mathematical models for the population dynamics of pathogens that take into consideration their antigenic variation (the variation seen by the immune system) in a finite space, can exhibit an array of nonlinear behavior including periodic or chaotic cycles in the successive replacement of strains. These are essentially competitive systems in which strains compete for hosts at the population level. These models have been proposed as one possible explanation for the patterns of genetic and antigenic diversity observed in H3N2 influenza, and in particular, for the limited standing diversity at any given point in time (Recker et al., PNAS 2007). We extend these deterministic models to incorporate explicit evolution in the form of mutations in antigenic space, with an individual-based and stochastic implementation that also tracks the genealogy of the virus. Parameter space is explored between the two extremes of dynamics dominated respectively by the opening of niches (frequency-dependent competition) and by the arrival of new types (innovation). The resulting patterns of pathogen diversity and population dynamics are discussed in the context of H3N2 influenza, as well as of competitive systems in general.

Mercedes Pascual is a Professor in the Department of Ecology and Evolutionary Biology, and is affiliated with the Center for the Study of Complex Systems, at the University of Michigan. She is also an external faculty of the Santa Fe Institute. Dr. Pascual is a theoretical ecologist interested in the nonlinear population dynamics of infectious diseases, and their response to climate change and climate variability. She is also working on the structure and dynamics of food webs, the complex networks formed by species and their interactions in ecosystems. She received her Ph.D. degree in 1995 from the joint program of the Woods Hole Oceanographic Institution and the Massachusetts Institute of Technology. She was awarded a U.S. Department of Energy Alexander Hollaender Distinguished Postdoctoral Fellowship for studies at Princeton University. As a starting assistant professor, she received one of two Centennial Fellowships in the area of Global and Complex Systems awarded by the James S. McDonnell Foundation internationally. She is also a fellow of the American Association for the Advancement of Science and has been recently appointed an investigator of the Howards Hughes Medical Institute.
Fracturing Ranked Surfaces
Herrmann, Hans J. - ETH Zurich
2012-05-19 8:26 AM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:37:04

A “ranked surface” is a lattice on which every site has a rank. Examples are discretized landscapes or sequential percolation. If one cuts the most important connecting bonds a crack appears which has in two dimensions a fractal dimension of 1.217. A classical example is the watershed that separates hydrological basins. In percolation, “bridges” are those sites or bonds which, if occupied, would create the spanning cluster. Suppressing systematically the occupation of these bridges delays the percolation threshold and produces at the end a connected line of bridges which corresponds to the watershed of a random landscape. Also optimal path cracks, the shortest path on loop-less percolation, minimal spanning trees, specific min-max paths and multiple invasion percolation clusters belong to the same universality class. At the percolation threshold bridge percolation exhibits a different exponent, namely 3⁄4, and one finds theta point scaling with a novel crossover exponent. For all dimensions below the upper critical dimension d_c = 6 these exponents are calculated. In dimensions larger than two another universality class appears corresponding to the cutting bonds in percolation, i.e., those bonds which, if removed, would disconnect the spanning cluster.

Hans J. Herrmann received his diploma in physics in 1978 at Cologne University where he also received his PhD in 1981. From 1981 to 1982 he was post-doc fellow at Athens University and from 1982 to 1990 he worked at the nuclear research centres C.T.E and C.N.R.S. at Saclay. From 1990 to 1994 he was director of Many Particle Group at HLRZ Jülich. From 1994 to 2000 he had a chaire de la Matière Diviseé at E.S.PC.I. After that he was Professor at Stuttgart University from 1995 to 2006 as director of the Institute for Computational Physics. Since 2006 he is Professor for Computational Physics for Engineering Materials and since 2007 director of the Institute for Building Materials at ETH Zurich. He is also member of the Departamento de Fisica at Universidade Federal do Cerea in Fortaleza, Brazil. Presently he is working mainly on dunes and Apollonian packings. He is also investigating density waves, fragmentation, stratification, segregation, compactification, sedimentation, dissipative gases, the shape of a sand pile, the dip under the heap, non-linear elasticity of packings and shear bands which he also studies micromechanically.
Complex Network Analysis of Recurrences in Phase Space
Marwan, Norbert - Potsdam Institute for Climate Impact Research
2012-05-19 9:04 AM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:24:35

During the last years, increasing efforts have been spent on applying network-based concepts also for the analysis of dynamically relevant statistical properties of time series. We present a novel approach for analysing time series using complex network theory. Starting from the concept of recurrences in phase space, we identify the recurrence matrix (calculated from time series) with the adjacency matrix of a complex network, thus linking different points in time if the considered states are closely neighboured in phase space.

We demonstrate that fundamental relationships between topological properties of recurrence networks and different nontrivial statistical properties of the phase space density of the underlying dynamical system exist. This novel interpretation of the recurrence matrix provides new quantitative characteristics (such as average path length, clustering coefficient, or centrality measures of the recurrence network) and, thus, complementary information about structural features of dynamical systems that substantially enrich the knowledge gathered from other existing (linear as well as nonlinear) methods.

An application from palaeo-climate illustrates the potential of the new approach.

Norbert Marwan has studied Physics at the University of Technology Dresden and the University of Potsdam. He received his PhD in Theoretical Physics/ Nonlinear Dynamics at the University of Potsdam in 2003 in the group of Prof. Jürgen Kurths. During the PhD he further developed the recurrence plot approach, e.g., by extending it to multivariate systems. He continued at the University of Potsdam during his post-doc in a project about 3D image analysis of trabecular bone (ESA project “2D and 3D Quantification of Bone Structure and its Changes in Microgravity Condition by Measures of Complexity”). Since 2008, Norbert Marwan is a senior researcher in the research domain Transdisciplinary Concepts and Methods of the Potsdam Institute for Climate Impact Research. He is working in nonlinear data analysis, with special interests in recurrence plots and complex networks and their applications in Earth, climate, and life science. He is principal investigator of several projects, e.g., in the “Potsdam Research Cluster for Georisk Analysis, Environmental Change and Sustainability”, in the WGL project “Evolving Complex Networks”, and the DFG projects “Shaping the Earth’s Surface in a Variable Environment“, and „Interactions and complex structures in the dynamics of changing climate: impact of tipping elements in presence and past“. He is author of about 50 peer reviewed journal articles (approx. 790 citations, H-index 15) and the main organizer of the biennial International Recurrence Plot Symposium.
Modulational Stability of Ocean Swell
Henderson, Diane - Pennsylvania State University
2012-05-19 10:22 AM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:35:36

Narrow-banded plane waves at the air-water interface of an inviscid fluid were shown to be unstable to modulational perturbations by several investigators in the late 60s, including Benjamin & Feir. More recently, Segur et al showed, with theory and laboratory experiments, that dissipation stabilizes this Benjamin-Feir instability. Here we discuss this stability result in the context of ocean swell on deep water and its subsequent propagation onto water of finite depth with slowly-varying bathymetry.

Diane Henderson grew up loving ocean waves and being amazed at how powerful they are, as they shoved her face into the sand for unreasonably long periods of time. She did a BS and MS in Engineering Sciences at the University of Florida and went on to Scripps Institution of Oceanography at the University of California, San Diego for her PhD in Physical Oceanography; her thesis concerned chaos in parametrically excited surface waves. Her thesis defense was on December 15, and her first child was born December 30. She worked as an Affiliated Scholar at the University of Florida for a year and then began as an Assistant Professor of Mathematics at Penn State University in 1991, where she has been ever since. There she has the somewhat unusual position of conducting laboratory experiments in the Mathematics department, which houses the William G. Pritchard Fluid Mechanics Laboratory. She works primarily on the evolution of nonlinear surface waves that propagate in 1 or 2 horizontal dimensions with a particular interest in patterns of surface waves and their stability. She is trying to apply her and her colleagues’ theoretical, numerical and experimental work to ocean field data.
Adaptive Design of Multi-Functional Network in a Primitive Organism
Nakagaki, Toshiyuki - Pennsylvania State University
2012-05-19 1:32 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:32:59

Transport networks are a ubiquitous feature of both social and biological systems. In each context, there is a complex trade-off between the cost of making and maintaining the network, its transport efficiency and its fault tolerance. Traditionally, the emphasis in man-made infrastructure networks has been to balance high efficiency with low cost. More recently, the consequences of accidental network failure or increased risk of deliberate attack, has focused attention on development of more robust network architectures. However, finding the most appropriate solution to such a combinatorial optimization problem is not straightforward. Here we show that the true slime mold {¥it Physarum polycephalum} can be used as a model experimental system to gain insight into the rules governing de-centralized, self-organized, adaptive network development. This simple biological system can establish networks with comparable efficiency, fault-tolerance and cost to real-world infrastructure networks, in this case judged in comparison to the Tokyo rail system. We argue that the solutions reached by such biological systems have been honed by many cycles of evolutionary selection pressure and are likely to yield a reasonable balance between cost, efficiency and resilience. Furthermore such systems develop without centralized control and may represent a readily scalable solution for growing networks in general. We have therefore developed a biologically-inspired mathematical model that captures adaptive network formation in {¥it Physarum} with a minimal set of equations. We anticipate that this model encapsulates the core mechanisms needed for adaptive network development, and should have wide applicability to guide network construction in other domains.

Toshiyuki Nakagaki received the Bachelor and Master of Pharmaceutical Sciences from Hokkaido University in 1987 and 1989, respectively, and Ph.D. degree in Biophysics from Nagoya University in 1997. His research interests include information processing at cell level, evolutionary development of intelligence, material basis of biological smartness and organic computing by biochemical nonlinear dynamics.
Communities, Modules, and Large-Scale Structure in Networks
Newman, Mark - University of Michigan
2012-05-18 2:25 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:40:52

Many networks, including social and technological networks, are found to divide into communities or modules, groups of network nodes with dense connections within groups and sparser connections between groups. The ability to detect and identify communities plays an important role in network visualization, data analysis, and other areas. In this talk I discuss the theory and practice of community detection and demonstrate applications to networks from a range of fields. I also discuss some recent results showing that in certain cases it is impossible to detect communities in networks, even though they are present, implying that there are fundamental limits to our ability to pry understanding from network data. Finally I discuss other forms of large-scale structure in networks, such as hierarchical structure and overlapping communities, and methods for their detection. These studies are in their infancy but may lead us to new understanding of the connection between the structure and function of networked systems.

Mark Newman received a Ph.D. in theoretical physics from the University of Oxford in 1991 and conducted postdoctoral research at Cornell University before joining the staff of the Santa Fe Institute, a think-tank in New Mexico devoted to the study of complex systems. In 2002 he left Santa Fe for the University of Michigan, where he is currently the Paul Dirac Collegiate Professor of Physics and a professor in the university’s Center for the Study of Complex Systems. Professor Newman is a Fellow of the American Physical Society and the author of over a hundred scientific publications and six books, including “Networks: An Introduction”, a textbook on network theory, and “The Atlas of the Real World,” a popular book on cartography. Professor Newman’s research focuses on networked systems, such as social and information networks, and particularly on questions of community structure, network resilience, mixing patterns, and statistical inference for networks.
Reverse Engineering of Complex Networks: System and Dynamics Prediction via Compressive Sensing
Yang, Rui - Arizona State University
2012-05-19 3:29 PM
Ann Arbor, MI - University of Michigan - Rackham Graduate School Building 4th Floor Amphitheater
Duration: 00:34:36

A strong confirmation of a scientific theory or method is its power to predict and reveal hidden objects from which no direct observational or experimental information is available. We propose a method, based on the recently developed idea of compressive sensing in the singal-processing community, for accurately predicting both detailed node dynamics and interactions. We show how the general problems of time-series based prediction of nonlinear dynamical systems and complex networks can be cast naturally in the framework of compressive sensing. Advantages of the method include (1) extremely low data requirement, (2) robustness, (3) scalability, and (4) applicability to general networked systems with heterogeneous node dynamics and couplings. We also show some results about the potential application of our method on catastrophe and future-state prediction in nonlinear dynamical systems.

Rui Yang received his B.S. in Applied Physics at University of Science and Technology of China (USTC) in 2007, and his Ph.D. in Electrical Engineering at Arizona State University in 2012 under the advisement of Dr. Ying-Cheng Lai. His research interests are broad and mainly in interdisciplinary science and technology, such as nonlinear dynamics and Chaos, complex-network dynamics, system/network prediction, time- series analysis, data discovery and information visualization, signal processing (compressive sensing/sparse sampling), solid-state electron transport simulation (Graphene), etc. Until now, he has published more than 20 peer-reviewed journal papers in Physical Review Letters, Applied Physics Letters, Physical Review E, Europhysics Letters, etc. At the same time, he also served as reviewers for couple of journals and assistant editor for the open access book – ASE Physics in Versita. In 2012, he received the Chinese Government Award for Outstanding Students Abroad.