The function of the oculomotor system is to control eye
movements. To accomplish this, oculomotor neural networks must generate
control signals that compensate eyeball dynamics. Early models described
the dynamics of the eye and the oculomotor system using operators
expressing one or two time constants only. More recently, it has been
shown that eyeball dynamics, and the properties of the neurons that control
them, are described better using operators expressing a continuum of time
constants spanning a broad temporal range. Minor, and neurobiologically
plausible, alterations of existing oculomotor neural network models cause
them to switch from expressing one time constant to many time constants,
thereby improving their ability to simulate the properties of real
oculomotor neurons.
The presentation addresses the issues related to theoretical modeling and experimental understanding of multi-instrument measurement systems that deal with multi-dimensional multi-variate data and include sensor networks, wireless communication, data acquisition, fusion, analysis, and modeling and visualization components. Research and development of such systems requires an interdisciplinary approach bridging areas of Sensor Technology, Image and Signal Processing, and Physics- and Statistics-Based Modeling of Sensor Data. We present a range of multi-instrument problems and solutions which fall into the following broad categories: (1) image pre-processing, (2) real-time detection and recognition, (3) multi-spectral 3D scene modeling based on electromagnetic and statistical prediction models, (4) data fusion and (5) hazard aware spaces. Examples from each broad category will demonstrate the modeling and experimental complexity, such as, real-time systems for machine vision and robot control applications, near-real time multi-spectral scene modeling phenomenology, and experimental frameworks for fusing multi-dimensional multi-variate and multi-sensor data using uncertainty analysis.
The effect of a low-pass filtered feedback from a
dynamical variable to the system parameter of a non-
isothermal autocatalator is examined. Parameter values that
lead to chaotic dynamics are found to evolve to nearby
values yielding periodic values. The system thus exhibits
adaptation to the edge of chaos. The use of a low-pass
filter as an alternative to the OGY method of controlling
chaos is discussed.
Many models of Product Development (PD) are concerned with managing the decomposition and integration of tasks, teams and subsystems transforming a conceptual idea into a finished product. Specifically, a PD process is formed of cross-functional teams continuously exchanging information on specified tasks to integrate the product’s final structure. Recently, it has been shown that large PD networks (i.e. tasks, teams, or components) follow Scale Free and Small World structures. That is, each PD network included hubs that control information flow and modules where core information is processed. Nevertheless, there is no literature on the implications of these findings on the management of either hubs or modules. As a consequence, the objective of this research is two-folded. First, we examine a set of mathematical measures such as centrality and brokerage used in Social Networks Analysis to identify critical players in PD networks. Second, we link these findings to insights and recommendations for the management of complex PD organizational networks; specifically, detection and role designation of information leaders based on the given PD network structure.
We study the dynamics of identical spheres in a vertically vibrated
two-layer granular media. This system consists of two horizontal
layers of grains above an oscillating plate.\footnote{G. W. Baxter and
J. S. Olafsen, {\it Nature} {\bf 425} 16 Oct 2003, p680.} The plate
drives the first layer which consists of non-spherical grains, and the
first layer drives the second layer which consists of lighter spherical
grains. We find that the velocity distributions are non-Gaussian in the
layer driven by the plate but Gaussian in the second layer. In addition,
the velocities are correlated in the layer driven by the plate but
uncorrelated in the second layer consistent with the presence of
molecular chaos. These results are robust over a wide range of densities,
accelerations, and frequencies, suggesting that the behavior of the
second layer grains is more gas-like than previously seen in driven
granular media.
* work supported by the Petroleum Research Fund
We study the foraging paths of individual ants to describe their
search mechanism mathematically. Ants have become a popular
species to use in models of artificial life. In this experiment,
we study real ants searching for food on two-dimensional smooth
and textured surfaces in the absence of a food source. The ant's
position is extracted from a time series of images to determine
the trajectory or ant trail. The resulting trails are compared
with various types of random walk. Initial studies suggest that
under these conditions, the foraging strategy is a non-reversing
2d random walk. Moreover, the ant does not use its trail to find
its way home. Instead, it continues its random walk until it
returns to the vicinity of its starting point.
New evidences from epileptic brain activity hint that our brain might be
able to
generate holograms in a high dimension space of associative links - much
like a
recent view of the universe as a hologram projected from hidden dimensions
beyond space and time. The advantage of the 'holographic principle' is that
when you cut hologram into half you will have two smaller whole images from
each half. This "whole in every part" nature of holograms provides the brain
with an entirely new way of coding and decoding of information. For example,
to
produce in parallel multiple copies of the same memory for associative
information processing.
Inspired by the above, we present a new functional holography approach for
analyzing multi-channel recordings such as ECoG (electrocorticograph)
recordings of cortical brain activity and of individual neuron dynamics in
cultured networks. The common approach is to evaluate the matrix of
correlations between the recorded activities (inter-channels correlations).
Ordinarily such matrices are mapped onto a connectivity network between the
channel positions in real space (their locations in the cortex). In our
functional holography, the correlations are normalized by the correlation
distances - Euclidian distances between the matrix columns. Then, we project
the N-dimensional (for N channels) space spanned by the matrix of the
normalized correlations, or correlation affinities, onto a corresponding 4-D
manifold (3-D Cartesian space constructed by the 3 leading principal vectors
of
the principal component algorithm + time ordering of the activity). The
neurons
are located by their principal eigenvalues and linked by their original
(not-normalized) correlations. By looking at these holograms hidden causal
motifs are revealed like the co-existence of functional sub-networks in the
space of affinities that might be connected with initiation of seizure.
Studies of coupled cultured networks hint that the neuro-glia fabric provide
the
'photographic film' for the holograms and that their generation and
retrieval
is sustained by chemical waves generated by the glia cells when act as
excitable media.
We study the placement of charged particles on a two-dimensional conductor.
Particularly, we focus on the placement of N equal discrete charges in the
asymptotic limit as N goes to infinity, for both the case of a smooth
conductor and also the situation in which the conductor contains cusp-like
points.
Earlier work in this area mainly focused on domains bounded by analytic
curves.
In contrast, we are trying to study how non-analyticities of the
conductor boundaries affect the charge placements and expansions of
equilibrium energies in the number of charges.
Furthermore, systems with an intrinsic symmetry of the conductor show a
breaking of this symmetry in the placement of the charges, depending on the
number of charges and the local curvature of the boundary.
These qualitative changes are new and cannot be obtained from a continuum
description of the problem.
Signal transduction systems are complex for combinatorial
reasons: during signaling, a protein may occupy a number of
phosphoforms, and it may interact with multiple binding
partners, such that proteins combine dynamically to form various
heterogeneous complexes. The known interactions and activities
of signaling molecules imply hundreds to thousands of
possible molecular species even for cases that involve
only a few proteins. We have developed an approach and software
for modeling the dynamics of a signal transduction system that
allows one to account comprehensively and precisely for the
possible molecular species implied by specified interactions,
activities, and modifications of the molecules in a system.
Analysis of such detailed models allows one to identify the
molecular species, reactions and pathways that are prevalent
during signaling, and to investigate different assumed signaling
mechanisms. Such models are relevant for rational drug discovery,
analysis of proteomic data, and mechanistic studies of signal
transduction systems.
There has been a long history of research into the structure and evolution of mankind's scientific endeavor. However, recent progress in applying the tools of science to understand science itself has been unprecedented because only recently has there been access to high-volume and high-quality data sets of scientific output (e.g., publications, patents, grants), as well as computers and algorithms capable of handling this enormous stream of data.
This talk will review major work on models that aim to capture and recreate the structure and dynamics of scientific evolution. I will argue that the structure, dynamic evolution, and utilization of scientific networks cannot be studied for isolated networks (e.g., co-author networks) but rather needs to consider network ecologies to truly understand the underlying processes.
Last but not least, I will introduce a general process model that simultaneously grows co-author and paper-citation networks and use the model to demonstrate the influence of topics, aging, and recursive linking on the co-evolution of author and paper networks.
Collaborators on this work are Jeegar Maru and Robert L. Goldstone, Indiana University.
The trajectories followed by animals when they forage
(or look for food) in a complex environment can exhibit
interesting features somehow similar to those known in physics,
such as scale invariance, L\e'vy walks, or power law distributions
for the waiting times. We review a few examples of foraging behaviors
in different species, with a special emphasis on a recent field
study dedicated to social spider monkeys living in a tropical forest
in Yucatan, Mexico. To try to understand this system, we introduce
a simple deterministic walk model in presence of targets of varying
importance. The model reproduces most of the field observations, and
relies on the hypothesis that animals interact with a well-known but
spatially complex environment by using mental maps. A particular
statistical distribution of targets induces trajectories with
singular properties and well defined exponents, analogous to a
critical phenomenon. The field data strongly suggest that the
ecological system under study is in (or close to) that predicted
critical state, where both the mean step length and the mean waiting
time between two moves become very large. The model can be mapped onto
a directed network problem with node deletion and link reconnection,
leading to other nontrivial features such as long range correlations
over time. We discuss some possible implications of this study regarding
the cognitive skills of spider monkeys, and how they can contribute to
the stability of their ecosystem.
New applications in materials, medicine, and computers are being discovered
where the control of events at the molecular and nanoscopic scales is critical
to product quality, although the primary manipulation of these events during
processing occurs at macroscopic length scales. This motivates the creation of
tools for the design and control of multiscale systems that have length scales
ranging from the atomistic to the macroscopic. This paper describes a
systematic approach that consists of stochastic parameter sensitivity
analysis, Bayesian parameter estimation applied to ab initio calculations and
experimental data, model-based experimental design, hypothesis mechanism
selection, and multistep optimization.
In classification problems, a concept learner usually receives a
set of labeled training instances inferring the underlying boolean-valued
function, or concept, from the instances.
We introduce a class of hierarchically structured classification problems
that call for effective building block identification
and processing. Building blocks refer to lower-level dependency structures
in the underlying concept. In the
introduced hierarchical problems, only the combination of several of those
building blocks allows the generation of an accurate problem solution. We
show that a competent inductive learning system needs to be able to
identify and propagate the building blocks effectively. Experiments with
our inductive genetics-based
machine learning system XCS confirm the theory.
In this work the dynamics of a classical particle under a semi-infinite triangular well driven by a train of delta pulses is studied. An approximated minimum value of the pulse strength for the onset of chaos required by particles to escape from the well is determined. This is done both analytically, by determining the resonances of the system using secular perturbation theory and Chirikov's overlap criterion, and numerically by the formal solution of motion equations.
Intrinsic localized modes (ILMs), also known as "discrete breathers,"
have been appealing theoretical possibilities for more than a decade.
Roughly speaking, they represent the extension of the continuum concept
of "solitons" to spatially extended discrete (lattice) systems.
Importantly, theory suggests that ILMs are far more ubiquitious than
solitons in that they can occur in discrete systems in any number of
spatial dimensions and with a wide range of nonlinear interactions. In
the past several years, ILMs have been observed in physical systems as
distinct as charge-transfer solids, Josephson junction arrays, photonic
structures, and micromechanical oscillator arrays. We review the history
and current status of ILMs and discuss some exciting possible future
directions and applications of these novel nonlinear excitations.
Glassy dynamics is one of the major open problems in Condensed Matter
Physics, and it has a deep impact on many disciplines, from the myriad
engineering applications of amorphous materials, to its role in the
preservation of living organisms in their dormant state. The dramatic
slowdown of relaxation in the glassy state causes materials to fall
out of equilibrium. This has striking consequences, including
``aging'', i.e. the breakdown of time translation invariance (the
system always ``remembers'' how long it has been since it was quenched
below the glass transition temperature); and also the breakdown of the
``Fluctuation-Dissipation Theorem'' connecting spontaneous
fluctuations (noise) with the response to an applied field. Recent
experiments have started to probe glassy materials at distance scales
of the order of tens of nanometers, and uncovered direct evidence for
the presence of heterogeneities in the dynamics, which have not been
understood in the framework of traditional mean field theories. In
this talk I will present a new theoretical framework that goes beyond
mean field theory and is capable of describing scaling properties of
the heterogeneous dynamics in glassy systems. I will discuss some of
the following questions: what is the mechanism for the presence of
heterogeneities in the glassy state? can the fluctuations at
different times be connected in a simple way? is it possible to
obtain a new relationship between noise and response, that applies to
experiments probing nanoscale-size regions?
In this paper, the nonlinear dynamics of a generalized, piecewise linear oscillator with impacts is investigated and the perfect plastic impacts occurring on the displacement discontinuous boundaries is considered. The basic mappings from boundary to boundary are constructed. The sliding motion on the separation boundaries is discussed through the differential inclusion, and the grazing condition is developed. The analytical prediction of periodic solutions of such a system is completed from the corresponding mapping structures. The stability and bifurcation conditions for specified periodic motions are obtained.
Axelrod's cultural dissemination model introduces an agent-based simulation where random agent interactions transmit culture
through an agent population, and the system evolves over time to form multiple stable homogeneous cultural regions. We expand
upon this work by introducing a quantum model. Agents are represented by quantum registers, and agent interactions are
quantum operations performed on those registers. Results indicate that multiple stable hetergeneous cultural regions form,
the number of regions is greater in the quantum model, and there is a greater diversity in the sizes of the cultural regions.
Recent advances in digital technologies invite consideration of organizing as a process that is accomplished by global, flexible, adaptive, and ad hoc networks that can be created, maintained, dissolved, and reconstituted with remarkable alacrity. As developments in information and communication technologies continue to reduce or eliminate the potential logistic barriers to our communication and knowledge network relations, it becomes increasingly important to identify the various social factors that enable or constrain the development of these network linkages. Clearly there is a need for a multi-theoretical multilevel approach to study the emergence of communication and knowledge networks. This paper begins by defining the role of multiple human and non-human agents that constitute the network and the relations that exist among these agents. Next, it describes the Multi-theoretical Multi-level (MTML) framework -- based on network formulations of social theories -- to examine our motivations as individuals to forge network links with other individuals, organizations, as well as non-human agents (such as knowledge repositories). In general terms, we ask the question: What are the social motivations that help us understand why we as individuals seek to forge, sustain, or dissolve our knowledge network ties with other human and non-human agents? We provide empirical results based on applying the MTML framework to understand the factors that explain information retrieval and allocation patterns in organizational knowledge networks. Findings from our research indicate that there are distinct and disparate social motivations that influence an actors tendency to retrieve from, and allocate information to, other individuals as opposed to publishing and retrieving from collective knowledge repositories (such as databases or Intranets).
It has been long recognized that the problem of obtaining
the quantum spectra of classically chaotic systems is
fundamentally more difficulut than obtaining the spectra of
the classically integrable systems. It appears now that the
quantum spectra of the chaotic systems may also differ among
themselves by their complexity. This is indicated by the
hierarchy of the explicit spectral solutions for 1D quantum
networks.
This presentation introduces a new paradigm for system modeling and management where
The goals of this work is reproducing and analyzing results concerning fast global synchronized spatio-temporal AM patterns in phase plateau of EEG. We introduce a computational algorithm for capturing phase transitions and AM modulations to explain the neural mechanism of spontaneous EEG and to estimate the perceptual information in the beta-gamma range. Moreover, we aim at establishing a framework to interprete the experimental observations by modeling cortical dynamics by (1) Freeman's distributed ODE-based K-sets, and (2) neuropercolation approach based on random cellular automata.
The collective behavior of bacterial populations provides an example of
how cell-level decision-making translates into population-level behavior,
and illustrates clearly the difficult multi-scale mathematical problem of
incorporating individual-level behavior into population-level models. In
this talk we focus on the flagellated bacterium E. coli, for which a
great deal is known about signal detection, transduction and cell-level
swimming behavior.
First, we review the biological background on individual and
population-level processes and we present a moment-based approach which
connects these different levels of description by deriving the classical
macroscopic description for chemotaxis from a simplified microscopic
model of the behavior of the individual cells. The analysis is based on
the velocity jump process for describing the motion of individuals such
as bacteria, wherein each individual carries an internal state that
evolves according to a system of ordinary differential equations forced
by an external signal. In particular, we show how aspects of the signal
transduction and random walk behavior enter into the macroscopic
equations.
A rigorous analysis can be done only with simplified models of cellular
behavior which capture the essential features of intracellular processes.
However, in some cases the complexity of the biological system leads to
very detailed microscopic descriptions of intracellular processes. In
such cases we do not have a closed system of macroscopic equations, but
only have an individual-based model and can only do Monte Carlo
simulations. Consequently, in the rest of the talk, we will discuss the
potential of the so called equation-free methods newly developed for
computer assisted analysis of Monte Carlo simulations and for answering
population-level questions in the case where macroscopic equations are
unavailable.
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Research over the last quarter of a century has found communication networks to be significantly associated with relevant organizational phenomena such as formal organizational structures, work-related attitudes and power. This has led to a greater interest in the theoretical mechanisms influencing the emergence -- creation, maintenance, and dissolution -- of organizational communication networks. Complexity theory in general, and self-organizing systems theory in particular, offer a framework to systematically and dynamically study the complex non-linear influences, implied by these theoretical mechanisms, on the emergence of communication networks.
This study identifies seven exogenous (i.e., based on factors other than the communication network itself) and three endogenous (i.e., based on local and global characteristics of the communication network itself) mechanisms which influence the emergence of communication networks in organizations. The seven exogenous mechanisms are supervisor-subordinate relationships, peer communication at higher levels of the hierarchy, spatial proximity, adoption of email, workflow dependencies, friendship, and common activity foci. The three endogenous mechanisms that could affect the emergence of communication networks are the drive towards transitivity in the network, the attraction of group network cohesion, and the management of structural holes in the network.
Each of these ten theoretical mechanisms seeks to explain dynamically the creation, maintenance, and dissolution of networks ties within the organization. These mechanisms are explicated in non-linear dynamic equations. Four computational organizational network models -- baseline, exogenous, endogenous, and combined -- are used to specify the system dynamics associated with these multiple theoretical mechanisms. Simulations of these computational models are used to assess the dynamic implications of the various theoretical mechanisms for network emergence. The emergent network structures resulting from these simulations are validated using longitudinal communication network data collected at 13 points in time over a two year period from employees of a public works organization. Results of the validation suggest that two of the exogenous mechanisms (supervisor-subordinate relationships and spatial proximity) and two of the endogenous mechanisms (transitivity and group cohesion) are important and consistent contributors to the emergence of the communication network. Further, the simulated communication network generated from the combined set of exogenous and endogenous theoretical mechanisms maps more closely to the observed communication network than a simulated communication network generated from the baseline (random) model.
In addition to these substantive insights, the study represents a pioneering effort in the specification and empirical validation of a theoretically deduced computational organization network model, thus demonstrating how these models are well suited to studies of organizations premised on tenets of complexity theory.
We analytically and numerically study the response of discrete time autonomous deterministic dynamical systems subject to various additive aperiodic forcings. Using a discrete variation, we find that the response is maximized by a pattern of forcing that is a transform of the systems unperturbed dynamics. In particular we find that for 1-D linear and nearly-linear systems the dynamics of the optimal forcing is the time-reversed unperturbed dynamics of the system. We find that we can successfully perform spectroscopic system identification on low-dimensional chaotic systems.
Colloidal systems may be regarded as model systems that demonstrate
self-assembly. The combined interactions of discrete particles, whose
individual properties are relatively well characterized, can result in
rich phase behavior and structure. We investigate the phase behavior and
3D structure of a model microsphere-nanoparticle system possessing high
charge and size asymmetry in which polystyrene nanoparticles strongly
adsorb to silica microspheres. By varying the nanoparticle:microsphere
ratio, we can tailor the transitions between stable fluid and attractive
gel phases. Using confocal fluorescence scanning microscopy, we precisely
locate the locations of the microspheres in order to analyze their 3D
structure as a function of varying composition. We demonstrate how phase
transitions can be utilized to control the morphology of both colloidal
crystals and gels formed under gravity driven sedimentation.
A basic neural network design for behavioral cost-benefit analysis through integration of sensation, internal state and experience, derived from neural and behavioral studies of the solitary predator sea-slug Pleurobranchaea californica, could solve problems in optimal foraging. I suggest that the basic design can be built onto with sensory feature detection and simple learning ability to achieve a social, altruistic organism capable of solving the Iterated Prisoner's Dilemma through a generous Tit-for-Tat strategy, where pay-offs for cooperation and defection vary with animals' internal states. Acquisition of neural circuitry and function underlying aggression and cooperation can be hypothesized within an evolutionarily plausible context. This and other possible paths in evolution of behavioral complexity, using basic neural network designs similar to those outlined here, are accessible to exploration with computational methods and hardware already available.
An overview will be given of recent work on the solid state of random network media, focusing on the structure and implications of their thermal and architectural fluctuations. Perhaps surprisingly, given the randomness of their architectures, random network solids possess a number of simple, universal, properties, including the quantitative nature of their heterogeneity and rigidity. Near six dimensions -- the upper critical dimension for the random solidification transition -- a renormalization-group treatment of fluctuations reveals the extent to which ideas from percolation theory can and cannot capture the physical properties of the random solid state. By contrast, in and near two dimensions -- the lower critical dimension -- it is low-energy, long-wavelength, Goldstone-type fluctuations that play an essential role. These fluctuations, which amount to shear deformations, furnish an expression for the elastic shear modulus of random solids in terms of the order parameter, as well as giving rise to a constant shift in the distribution of localization lengths, relative to the mean-field distribution. In two dimensions this shift diverges, fluctuations restore the classically-broken translational symmetry, and the constituents of the random solid state are no longer truly localized. However, order-parameter correlations decay algebraically and the shear modulus remains nonzero, so that -- as with crystalline solids -- two-dimensional random solids exhibit quasi-long range, albeit random, order.
In usually encountered incompressible turbulence, a contaminant or dye is dispersed much more rapidly than under the action of thermal diffusion alone. If the dye is initially dispersed uniformly in the fluid, turbulent stirring does nothing. In the experiments and computer simulations to be discussed here, the “dye” takes the form of small particles that float on a turbulent body of water. In this case, turbulence has the opposite effect; the stirring of initially uniform and randomly dispersed floaters, induces their coagulation. As will be discussed, this effect is intimately connected with the (two-dimensional) compressibility of the floaters. In the laboratory this coagulation effect is seen only if the air-water interface, where the floaters reside, is very clean. The surface physics is not yet properly understood.
Can we use computational algorithms to make accurate predictions of
physical phenomena? In this talk, intended for non-experts, I will explain
that physical predictions, whether analytical or computational, are
predicated on the following assumptions: (1) there exists a minimal model
which is a caricature of the phenomenon in question; (2) there exists a
renormalization group fixed point about which universal quantities can be
calculated systematically; (3) the desired predictions concern quantities
that are universal at the appropriate fixed point. I will give examples
where this works and where it fails, and show that in some cases, complex
space-time phenomena can be exquisitely captured with simple computational
algorithms, that not only produce patterns resembling those seen in
experiment, but also make accurate predictions about probes of dynamics and
spatial organisation, such as correlation functions.
Are such predictions amenable to analysis? Using elementary cellular
automata (CA) as an example, I show how to coarse grain CA in all classes
of Wolfram's classification. The resulting coarse-grained CA emulate the
large-scale behavior of the original systems without accounting for
small-scale details. Computationally irreducible physical processes can be
predictable and even computationally reducible at a coarse-grained level of
description. At least one of the CA that can be coarse grained is
irreducible and known to be a universal Turing machine.
Work performed over the years in collaboration with Y. Oono, M. Mondello,
N. Israeli
Work supported by the National Science Foundation and NASA
Computer network infrastructures are prone to complexities
including arbitrary failures of hosts and message deliveries, as well as
rapid and constant change of participant set (often called "churn").
Distributed applications such as peer to peer systems have to mask these
complexities through the use of distributed protocols that are robust and
scalable. We discuss several distributed systems that we have built and that
are derived from natural analogies such as the epidemic spread of diseases,
and hence inherit the robustness and scalability of these latter mechanisms.
The protocols working at the core of these sytems imbibe the basic
philosophy of fighting fire (arbitrary network unreliabilities) with fire
(probabilistic protocols).
This paper presents evidence for the importance of pair-level social network tie strength in explaining group-wide connectivity via communication media. Results from social network studies of communication via multiple media show two unexpected results associated with tie strength: first, that the stronger the tie, the more media a pair uses to communicate, and second, that use within a group conforms to a unidimensional scale. Thus, we find that those who are weakly tied use tend to use only one medium to communicate, and within a group, they all use the same medium. Moreover, since these group members do not know each other well, that one connecting medium is usually established by an authority beyond the pairs, such as managers, administrators, or instructors. This pattern of media use suggests that the presence of certain media creates latent tie connectivity among group members, i.e., that a medium made available to the group as a whole, provides a technical means of connecting with others in the group, laying the groundwork for the activation of those ties into weak ties. Once activated, the tie may grow in strength and branch out to other media. What results is a ‘base’ medium that supports weak ties in a group that is public, reaching the group as a whole, and mandatory, with private, person-to-person, optional media added to this base by those maintaining stronger ties. The differential use of media by weak and strong ties may help explain conflicting results about the advantages and disadvantages of new media such as email and the Internet. It can be expected that where a new medium creates latent ties, adding means and opportunities for previously unconnected others to communicate, positive effects on weak ties and weak tie networks will be found. However, when a new medium replaces a former, common means of communication, negative effects can be expected for weak ties because their dependence on a common medium makes the weak tie networks highly susceptible to dissolution. By contrast, strong tie networks, with their connections via multiple relations and multiple media can be expected to be more robust as media change.
Recently, the Web has been rapidly ``deepened" by myriad
searchable databases online, where data are hidden behind
query interfaces. We propose to build a metaquery system,
MetaQuerier, to integrate these large-scale deep Web sources.
Recently, we have witnessed the rapid growth and prevalence of
databases on the Web. Our survey in December 2002 estimated
that the Web contained, at that time, between 127,000 to
330,000 "deep Web" sources. These myriad online databases
provide dynamic access to the data they contain through
customized query interfaces, rather than static URL links.
Such query interfaces form gateways to the deep Web that must
be integrated automatically by future search engines.
Integrating deep Web sources in the same domain is a
particularly important task on the path to comprehensive
searching. In the typical scenario, a user wants to find the
cheapest vendor of a given book across multiple online
bookstores. The challenge, given many such databases in one
domain, is how best to provide for large-scale integration
such that we can effectively facilitate user queries. This
demonstration highlights the process of automatic interface
integration, step by step.
We study instabilities in the radial segregation patterns that form inside
a slowly rotating, thin cylindrical drum partially filled with a binary
mixture of different-sized beads. For most filling levels, we observe the
well-known radial segregation pattern in which smaller beads segregate
toward the center of rotation, and the larger beads toward an outer ring.
However, for a narrow range of filling levels close to one half, the core
of smaller beads extends into the outer ring of larger beads along a number
of radial stripes. We show that this pattern formation is due to two
spatially disjoint mechanisms: (1) a geometrically-based low-pass filter,
and (2) a positive feedback mechanism within a thin surficial boundary
layer of granular flow. This positive feedback mechanism may be related to
previously reported chaotically-driven segregation patterns whereby
variations in the speed of the boundary layer and possibly subsequent
changes in the granular temperature leads to the formation of complex
segregation patterns
The higher order circuitry of the brain is comprised of a large-scale network of cerebral cortical areas that are individually regulated by loops through subcortical structures, particularly through the basal ganglia and cerebellum. These subcortical loops have powerful computational architectures. Using, as an example, the relatively well-understood processing that occurs in the cortical / basal ganglionic / cerebellar module that generates voluntary motor commands, I postulate that a network of analogous agents is an appropriate framework for exploring the dynamics of the mind.
We presents a novel empirical study for RNA Editing in Biology using our
coevolutionary agent-based model of Genotype Editing. This model is based on
several characteristics that are gleaned from the RNA editing system as
observed in several organisms. The incorporation of editing mechanisms in
an coevolutionary agent-based model provides a means for evolving agents with
heterogeneous post-transcriptional processes. The study of this agent-based
genotype-editing model has shed some light into the evolutionary implications
of RNA editing as well as established an advantageous evolutionary
computation algorithm for complex, real-world problems. We expect that our proposed
model will both facilitate determining the evolutionary role of RNA editing in biology,
and advance the current state of research in computational biology and complex
systems.
We present analytical expressions that describe the phase transition
appearing on the dynamics of Ising-like or XY-like spins on a network.
As the amount of noise is increased, a critical value is reached
where the system looses all its order. We discuss applications of this
result and, in particular, the insight it provides for understanding
an analogous phase transition in a system of self-propelled swarmers.
NSF is sponsoring research to explain the scale-dependence of parameters
describing transport properties of various natural media. It is widely
believed that the earth's subsurface supports an increasing hydraulic
conductivity, K, with increasing scale. This assumption appears incompatible
with fundamental results regarding the effects of frozen disorder on the
velocity-velocity autocovariance published in the 1980's in Rev. Mod. Phys.
However, many experiments seem to support the contention. In looking for
theoretical explanations for the disconnect, I found that 1) theoretical
justifications for a scale dependent K in fractal media contain a bias;
measurements of the larger fractures are only made at scales larger than the
fracture, 2) analyses of K for nested heterogeneity presume incorrectly the
relevance of a geometric mean value of a distribution to the effective K at
the next larger scale, whereas the effective conductivity is independent of
scale 3) experiments into anisotropic fracture networks do not isolate the
dimensionality of the conduction, allowing a cross-over from 1D to 3D
conduction to occur with increasing size of the system. Continuum
percolation theory applied to this dimensional cross-over yields the
observed experimental results. The conclusion remains that scale effects on
transport require fundamental non-linearities (such as high Reynolds number
flows in the atmosphere and hydrosphere); in the case of the hydraulic
conductivity of porous media federal agencies are sponsoring research into a
non-issue.
Many scenarios in theoretical biology, physics, and business organizations
can be modeled as systems with several interacting components that can be
in various states. The aim is to maximize a performance measure involving
contributions from each component. This measure may depend on both the
state of each component and on interactions between components. In 1987,
Kauffman and Levin introduced a combinatorial optimization model for such
systems, called the Kauffman $NK$ model, where $N$ is the number of
components of the system and $K$ measures the interaction between the
components. This was proposed to model the evolution of genomes in
theoretical biology but has since been applied in other areas as listed
above.
Previous research on the $NK$ model has emphasized simulations and
analysis of local optima. Here we focus on rigorous results for global
optima. We describe a computational setup using a stochastic network
model, which leads to applicable strategies for computing bounds on global
optima when $K$ is small or is close to $N$. Recent papers used tools
from analysis and probability to obtain bounds on the expected value of
the global optima for fixed $K$ and large $N$. We present bounds when $K$
grows with $N$, using elementary probabilistic combinatorics and order
statistics. We use a `dependency' graph to convert the problem of bounding
order statistics of dependent random variables into that of independent
random variables while incorporating quantitative information about mutual
dependencies among the underlying random variables. If time permits, an
alternate upper bound and the analysis for the cases of underlying uniform
and normal distributions will also be outlined.
Old Faithful Geyser is not only a national treasure, but a
scientific treasure. It's intervals are regularly documented (mostly
for the benefit of the tourists, not the scientists!), and we have
been able to film it, study its seismicity, study the geochemistry of
the erupted water, probed the conduit to measure pressure and
temperature, and visualize the conduit using an ice-cooled miniature
video. The attempts to relate the physical processes to the
statistics of eruption frequency and duration will be discussed.
Contrary to the presently used digital computer memories where information is encoded in the form of a given string of symbols we propose a novel approach, in which encoding is embodied in oscillatory activity patterns of memory nodes. The approach is strongly biologically motivated and based on the observation that humans/ animals can solve difficult identification tasks fast and with high accuracy. Information encoding in such dynamical memories is closely related to percolation phenomena in random media.
For about four decades, percolation theory has been an active area of research at the interface of probability theory, combinatorics, graph theory, and physics. In particular, computer experiments have been conducted, which indicated interesting non-trivial large-scale phenomena. We apply the solid mathematical theory of percolations to lay down the foundations of dynamical memories and related phase transitions. When an input pattern is presented to the model, the large-scale, aperiodic spatio-temporal oscillations undergo phase transitions and the dynamics is constrained to a lower-dimensional subspace. After removing the input stimulus, the system returns to a high-dimensional state. Mathematically such a property has been described in random graphs, where the connectivity density is an order parameter that can induce state transitions. Accordingly, the memory and recognition/identification process can be described as percolation phenomenon though the neurophil medium. The problem is related to 'small-world' phenomena, and recently rigorous proof of the scaling properties of large graphs have been given based on graph-theoretical arguments.
In our work, we extend percolation theory to interpret the behavior of dynamical memories. We introduce a family of infection and recovery functions of bootstrap percolations, and thereby model evolutions and phase transitions in a class of generalized percolations. We aim to construct models that exhibit the complex behavior of biological systems while at the same time they are mathematically tractable.
All of the things that we are aware of in the universe were generated by a sequence of creative processes that began with the formation of the first stars, around 13.5 billion years ago (bya). Through a succession of nucleosynthetic processes the stars, and supernovae, ultimately created all of the basic stable and radioactive elements in the universe, the bases of successive developments.
We discuss the use of genetic programming (GP)---a genetic algorithm that evolves computer programs---for bridging simulation methods across multiple scales of time and/or length. The effectiveness of genetic programming in multiscale simulation is demonstrated using two illustrative, non-trivial case studies in science and engineering. The first case is multi-timescale materials kinetics modeling, where genetic programming is used to symbolically regress a mapping of all diffusion barriers from only a few calculated ones, thereby avoiding explicit calculation of all the barriers. The GP-regressed barrier
function enables use of kinetic Monte Carlo for realistic potentials and simulation of realistic experimental times (seconds). The second case is the
development of constitutive relation between macroscopic variables using measured data, where GP is used to evolve both the function form of the constitutive equation as well as the coefficient values. Specifically, GP regression is used for developing a constitutive relation between flow stress and temperature-compensated strain rate based on microstructural characterization for an aluminum alloy AA7055. We not only reproduce a constitutive relation proposed in literature, but also develop a new constitutive equation that fits both low-strain-rate and high-strain-rate data. We hope these disparate example applications exemplify the power of GP for multiscaling at the price, of course, of not knowing physical details at the
intermediate scales.
Diverse phenomena, including flows in the Earth's outer core
and accretion disks, involve the interplay of rotation,
turbulence, and magnetic fields. There is much
evidence for small magnetic fields destabilizing
differential rotation: a process called the
magnetorotational instability. This phenomena may explain how
rotating clouds proceed to collapse in star formation. The instability
leads to radial outflow of angular momentum, so that matter may fall
inward instead of continuing to orbit. Magnetic fields in differentially
rotating stars and planetary interiors may also be affected.
This talk will describe the first direct observation of this instability.
The experimental device consists of liquid sodium confined between
boundaries defined by a rotating inner sphere and a stationary outer
sphere, with an imposed coaxial magnetic field. We characterize an array
of observed patterns and dynamics in a saturated magnetorotational state
and relate these observations to theoretical expectations.
Cardiovascular dynamics of healthy humans exhibits many similar characteristics found in other natural phenomena. These characteristics have been identified in the fluctuation of the time span of ventricular contraction, known as the RR interval (RRi), and include 1/f-like power spectrum, particular distribution patterns in the increment heart rate, and multifractal scaling. They provide the evidence for scale invariance symmetry in the cardiovascular dynamical system. Since ventricular contraction as measured from the electrocardiogram is a discrete-event process, it is appropriate to consider discrete scale invariance (DSI) in the present application. We will apply the idea of rephrasing developed by Zhou and Sornette and show evidence of multiple DSI groups governing the RRi variability. A dynamic cascade model is proposed to capture these properties.
In the presence of multivalent counterions, polyelectrolytes
self-assemble into larger aggregates. For stiff polyelectrolytes, such
as actin or DNA, these aggregates typically are close-packed
bundles. By contrast, flexible polyelectrolytes undergo a collapse
followed by a reexpansion at higher salt concentrations. The nature
of this 'reentrant condensation' and its relation to `charge
inversion' of the polyelectrolyte are the focus of extensive
theoretical debate. We use Monte Carlo and molecular dynamics
techniques to clarify the situation. In particular, we demonstrate how
various scenarios can arise depending on ionic size and valency. In
addition, results are presented for simulations in the presence of an
external electric field, mimicking electrophoresis experiments.
In this paper, a local theory of non-smooth dynamical systems on connectable
and accessible sub-domains is developed. The properties for specified
separation boundaries based on the characteristics of flows are determined, and
the sliding dynamics on the separation boundary is introduced. The local
singularity and transversality of a flow on the separation boundary from a
domain into its adjacent domains are investigated, and the bouncing and
tangency of the flows to the separation boundary for non-smooth dynamical
systems are discussed as well. The sufficient and necessary conditions for the
local singularity, transversality and bouncing of the flows are developed.
These conditions are applicable for determining complicated dynamical behaviors
of non-smooth dynamical systems.
We present evidence for the presence of entropy driven dynamics in
experiments and simulations of steel spheres which are confined between
two vertically vibrating plates. In the case of monodisperse steel
spheres, a homogeneous nucleation of a crystalline phase with square
symmetry is observed. The resulting crystal coexists with a surrounding
granular fluid and the two-phase coexistence can be understood through
entropy maximization by analogy to equilibrium hard sphere systems. In
the case of of bidisperse mixture, we make measurements of the depletion
force, another entropic force known to exist for equilibrium hard sphere
systems. We find that the pair correlation function for the large spheres
can be accurately described by using the depletion potential, derived for
equilibrium hard spheres, and a Boltzmann factor, which serves to define
an effective temperature for the system. In addition, for both systems,
we find that despite the presence of entropy driven dynamics, strong
nonequilibrium effects, such as nonequipartition of energy, are present.
We study gravitational instability in a one dimensional
model of a matter-dominated universe. Careful scaling in
both space and time results in an autonomous set of coupled
Poisson-Vlasov equations for the gravitational field and
phase space density, as well as the N-body problem. We obtain general
properties of the evolution from the Vlasov-Poisson
equations and use particle simulation to study the evolution
of a wide class of initial conditions, finding direct
evidence of hierarchical clustering. A multi-fractal
analysis of both the projected phase space and density simulations
reveals a bifractal geometry similar to that observed in the
distribution of galaxies. We show that the model yields an
estimate of the time of galaxy formation of the correct
order.
To design a network, one can specify rules for its structural features and criteria for evaluating its performance. To implement the rules one can use a language a set of building blocks and operations for constructing networks. A search through alternative networks generated with the language can show which ones obey the rules and meet the performance criteria. If these networks include real networks of interest, the rules and criteria are reasonable. Intracellular signaling networks are likely to be the smallest that meet the criteria and to have default inhibition of the initiation of major processes. To implement these rules one can represent model proteins as combinations of domains, assemble networks of proteins, and evaluate the performance of the networks using Boolean functions for protein activity. Small networks that implement the logical functions AND and OR use the same logical operations as networks that regulate the initiation of translation.
Key-Words: - module, Boolean network, network design, optimization, robustness, intracellular signaling, translation
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Neurophysiological recordings of extracellular action potentials (neuronal spikes) in the basal ganglia and cortex of non-human primates demonstrate findings strikingly similar to results of mathematical modeling of non-linear oscillators embedded in loosely-coupled networks. Specifically, that nearly all neurons have sustained simultaneous multiple and high frequency periodic activity. This was directly demonstrated using a new method for determining periodic activity in the time rather than frequency domain. These findings also were supported by demonstration of resonance effects to paired-pulse stimulation.
Frequency analysis demonstrated that neurons display sets of simultaneous frequencies that persist in the range of seconds and then make transitions to other sets of simultaneous frequencies. This has the appearance of state bifurcations between different limit cycles.
Resonance effects were demonstrated using paired-pulse stimulation in the vicinity of the subthalamic nucleus and recording neuronal responses in other nuclei of the basal ganglia-thalamic-cortex system. The principle is that if a second or test pulse is given at the precise time that activity initiated by the conditioning or first pulse returns to the site of stimulation effect, there should be an increase in the probability of the second or test pulse eliciting a neuronal discharge. Such resonance effects were found and displayed short latencies (<4 ms), long latencies (4 – 10 ms) and very long latencies (>10 ms). Some short latency resonance effects may be related to neuronal membrane properties but others were not suggesting a network mechanism. Long latency responses may reflect the architecture of the individual reentrant oscillators. Very long latency responses suggest a gain >1 with continued iterations of the oscillators.
These and other observations have lead to a radical re-conceptualization of the functional architecture of the basal ganglia-thalamic-cortical systems. Also importantly, these results demonstrate the relevance of computational and mathematical modeling, particularly of complex systems, to interpreting and understanding neurophysiological observations. Further, these observations provide confidence that underlying mathematical principles can be translated in to neurophysiological mechanisms and thus, create testable hypotheses for future neurophysiological research.
Rep helicase is a E. coli enzyme that belongs to the helicase super family 1. Its crystal structures indicated its existence in two conformations, referred to as “open” and “closed” forms which differ in the orientation of the 2B sub domain by 130o relative to the rest of the protein. A monomer of the Rep helicase can not unwind DNA but can move along single stranded DNA powered by ATP hydrolysis. Using ensemble and single molecule fluorescence measurements of Cys-light Rep helicases doubly labeled with donor and acceptor, we have measured the 2B domain swiveling under a variety of conditions. Our data suggest that, under solution conditions for typical in vitro unwinding studies, the Rep protein is mostly in the closed form, but exists in the open form when bound to a ssDNA. Salt concentration dependence suggests that the protein is in dynamic equilibrium between the open and closed forms and single molecule measurements show that the initial binding to ssDNA occurs through the rare open form. Once binding occurs, the molecule can fluctuate between the two forms the equilibrium of which depends on the length of the DNA. We also performed measurements as a function of ATP analogues that represent the reaction intermediates. From our results, it is evident that the 2B domain movement is highly correlated with DNA binding and ATPase cycle and such a coordinated motion may be important for the biological function of this class of enzymes.
The basic design of the vertebrate nervous system (this means
you) evolved in the lower Cambrian, simultaneously with the emergence of
metazoan v. metazoan predation. Metazoans evolved a variety of attack and
defense mechanisms. Our ancestors, the vertebrates, chose agility: Out-run
and out-think the competition. A small number of symmetry-breaking
processes, driven by the huge selection pressure in that ancient ocean, may
have co-opted biophysical mechanisms already present in protist ancestors,
and laid the ground-plan for vertebrate bodies and brains.
An important lesson learned from recent disasters involving critical physical infrastructure is the need to increase the collaboration among organizations supporting the first response and recovery processes. The limited collaboration among such organizations in disaster relief environments is emphasized by the fact that each organization has its own plans, policies, protocols and culture and mainly uses incomplete information to make decisions. As a consequence of the limited collaboration, individual and local decisions eventually generate unexpected adverse consequences on other participants of the first response system. This paper presents an approach to deal with the collaboration problems occurring in these inhospitable and chaotic scenarios. This approach is based on algorithms, principles and heuristics drawn from entomology due to: (a) the envisioned modeling similarity among insect societies and the socio-technical communities formed by the organizations participating in first response systems; (b) evidence supporting the fact that insect societies successfully behave as a whole entity in complex and dynamic contexts, where the dynamics of the community is determined by interactions among individuals following a reduced set of rules, e.g., foraging process of honey bees (Apis mellifera); and (c) the demonstrated applicability of borrowing models from insect societies dynamics to solve or reduce problems in other domains like telecommunications, scheduling, and robotics. In disaster relief operations, the models borrowed and modified from social insects may be useful in understanding the basic principles and best practices to be considered when developing strategies that will coordinate knowledge sharing and decision making in chaotic social settings where a small set of rules applied to local information, drives decision-making. In particular, the collective decision making process carried out by honey bees when foraging has been identified as a key analogy to support collaboration and decision-making in chaotic scenarios. Currently, this and other analogies brought from entomology are being incorporated into a collaboration framework and implemented in an IT platform to support collaboration on first response and recovery processes during a disaster involving critical physical infrastructures.
Spatiotemporal patterns are found in numerous physical, chemical and biological settings. Several chemical reactions and biological signals have been found to oscillate in time and to display propagating chemical waves. These chemical processes appear in biological systems such as lymphocytes, neutrophils, tumor cells and intact retina. These chemical instabilities are affected by a variety of biochemical and physiological parameters. The relevance of these findings may have far-reaching effects in understanding disease mechanisms and drug development based on biophysical dynamics.
The quantum mechanical properties of classically chaotic systems (quantum chaotic systems), become richer and more complex with the appearance of Anderson localization when disorder is added to the system. We study the statistics of the eigenfunctions and eigenvalues of chaotic and disordered billiards via microwave experiments. We compare our experimental results with numerical calculations of the Anderson disordered tight binding model (TBM) Hamiltonian. The eigenfunction statistics are studied in terms of the moments of their spatial distributions, such as the inverse participation ratio (IPR) which measures the amount of localization in an eigenstate and density-density auto-correlation functions. The path from chaos to disorder is described in terms of the increasing IPR. The statistics of the experimental microwave eigenvalues and their associated TBM numerical results are analyzed in terms of energy level spacing, level number variance, two-level correlation, and spectral rigidity. The analyzed systems range from chaotic to strongly disordered billiards. Our TBM calculations and microwave results are in good agreement, and can be projected upon one another with proper parametrization. In the chaotic, ballistic limit, our results correspond well with universal results from random matrix theory. Deviations from universal distributions of the eigenfunction and eigenvalue statistics are observed due to disorder-induced localization. For the weakly disordered case, the statistics are well-described by including finite conductance and mean free path contributions in the framework of supersymmetric nonlinear sigma models.
Work supported by the National Science Foundation PHY-0098801
In any driven threshold model complex avalanche behavior is brought on
by a local dynamical instability. Fully dynamical simulations show the
behavior of these models is highly sensitive to the nature of this
instability. For example, in earthquake models, this represents a slip
and velocity-dependent frictional weakening law. Coupled-lattice map
models are capable of simulating large systems, but do not include
physically motivated weakening effects. I present a method for
simulating the effects of an arbitrary weakening law in a discrete time
environment. This leads to a rigorous implementation-independent
analysis for mean field models, and clues for an approach to locally
correlated systems.
Previous studies indicated that 2-dimensional cellular automata with random
update rule can successfully describe the dynamics of neural tissue, called
the neuropil. Scalling properties near the critical point indicate Ising or
weak Ising class for local interactions. The present work introduces the
interaction of two such lattices, which have excitatory and inhibitory
connections, respectively. We show the role of several control parameters,
including the noise level and the strenght of connectivity accross layers.
We demonstrate our findings using computer simulations.
The Problem of Information Formats (for networks)" is a paper dedicated to
understanding how an organization approaches a network with information.
'New media' networks differ from the press, TV and radio, where the formats
are more established. To old media, you send a press release, a video can,
a scripted event, a sound bite. But what do you send to a network? Does one
send information in the 'old media' formats? What does a network do with a
press release?
Generally, we are interested in learning how networks deal with information
formats. Are certain formats routinely filed away or deleted, whilst others
tend to circulate in networks? The paper - an analysis of the formats
circulated by the Association for Progressive Communications (APC) - treats
formats broadly, and also makes distinctions between various kinds of
networks. The purpose is to understand how different formats operate in
various types of networks. What does a network do with a gender audit
methodology, an open source tool, an invitation to attend a summit, and
other typical formats put into circulation by NGOs?
In particular, we are interested in which formats organize networks. The
analysis is empirical, and also centers on terminological, calendrical and
other formatting work intended to organize classic issue networks, as
opposed to social networks or smart mobs.
"The Problem of the Information Format (for Networks)" is at
http://www.issuenetwork.org/reports/apc/gco_format.pdf
We present novel experiments in the evolution of Cellular Automata (CA) to
solve nontrivial tasks. Using a genetic algorithm, we evolved CA rules that
can solve non-trivial logical tasks related to the density task (or majority
classification problem) commonly used in the literature. We present the
particle catalogs of the new rules following the computational mechanics
framework. We know from Crutchfield et al (2002) that particle computation
in CA is a process of information processing and integration. Here, we
discuss the type of memory that emerges from the evolving CA experiments for
storing and manipulating information. In particular, we contrast this type
of evolved memory with the type of memory we are familiar with in Computer
Science, and also with the type of biological memory instantiated by DNA. A
novel CA rule obtained from our own experiments is used to elucidate the
type of memory that one-dimensional CA can attain.
Spatial models for spread of an epidemic may be mapped onto bond
percolation. It is suspected that many contact networks on which
infections spread are 'scale-free' i.e., there is a power-law
distribution of contacts with a given site. In many cases, the
percolation threshold is zero if the fluctuations in the number of
contacts diverges. The power laws observed usually have this
property. This corresponds to an infection that is cannot be controlled by
random vaccination.
However, we give an example, a scale-free network with a well defined
geography, where the threshold is not zero. We discuss whether real
infection networks are likely to have this property.
Sufficiently complicated enzyme/substrate networks can have arbitrarily complex dynamics. There are literature examples (e.g. from Lars Olsen at Odense) where a 5% change in rate constant for an enzyme can change network dynamics from periodic oscillations to chaos. Understanding and (potentially) controlling such networks requires precise measurement of mass transfer and kinetics behavior of all species and their binary (and in some cases ternary) interactions. Compounding the difficulty of such measurements is the limited availability of some of the enzymes and transient existence of some of the intermediates involved.
My group thus is focused on new microsensor and microfluidic strategies for studying enzyme networks. This talk will briefly discuss upcoming approaches using levitated drops as microliter continuously-stirred tank reactors and microfabricated amperometric sensors as detectors.
,
Time series from complex phenomena are characterized by complicated memory
and/or distribution patterns. We discuss models of different statistics and
explore the relationship between the statistical properties of time series
and observed patterns in various phenomena. In particular, we discuss the
difference between L\'evy-walk intermittent noise and fractal Gaussian
intermittent noise. We show that two complementary scaling analysis
techniques (Diffusion Standard Deviation Analysis and Diffusion Entropy
Analysis), when used together, can distinguish between the two kinds of
intermittent statistics. Finally, we apply these methodologies to
geophysical phenomena to model: (a) a coupling of solar flare intermittency
with the total solar irradiance and global temperature anomalies; (b) the
earthquake occurrences.
In this poster we present our approach for studying the self-organization of neural networks using agent-based modeling. We take inspiration from the self organization of biological neural networks. We are interested in determining factors in local behavior that generate favorable changes in global network topology. We focus on the effects of pruning on network topology. In our simulations, the network topology uses a Hebbian learning guided pruning algorithm. Rather than focus on network development solely from a contructive perspective, we are also interested in the effects of removing elements from the network. Recent advances in complex network research are used to guide our simulations. An overview of our simulation and results are presented.
A motivational question: The physical structure of a cell is largely
determined by the expression level of each of its genes. These levels
are governed by complicated transcriptional and translational
processes that form proteins, whose presence can then alter those
processes and hence influence the expression levels of the very genes
that produced them. What are the organizational principles that
govern such a complex web of interactions?
At its deepest level, the genetic regulatory network embodies a set of
rules that guide the cell through a progression of states in an
abstract space of gene expression patterns. An important piece of the
puzzle then is to understand the behavior of idealized versions of
such networks. We study random Boolean networks (RBNs), which show a
surprisingly high degree of dynamical structure. I will review
the basic phenomenology of RBNs, describe some of the important concepts
needed to characterize their behavior, and present results on the way
the dynamical properties scale with increasing system size.
We analyze the properties of model food webs and of fifteen community
food webs from a variety of environments---including freshwater,
marine-freshwater interfaces, and terrestrial environments.
We find that two recent food web models---the niche model and
nested-hierarchy model---produce the exact same analytical expressions
for the distributions of species' number of prey, predators, and number
of links, and that these distributions follow the same universal
functional forms as empirical data. From our analytical treatment of the
models we surmise that these models' ability to reproduce empirical data
is determined principally by two mechanisms: (i) a totally ordered set
of niche values and (ii) a species-specific, exponentially decaying
probability p(x) of preying on a fraction x of the species with
lower niche values. To test this hypothesis, we propose a generalized
cascade model---representing the simplest model incorporating these
two mechanisms---and find that it is also able to reproduce the basic
properties of empirical food webs, validating our claim.
We then use our analytical predictions as a guide to the analysis of
fifteen of the most complete empirical food webs available. We
demonstrate that the quantitative unifying patterns which describe the
properties of these three food web models also describe the majority
of the trophic webs considered. Our results strongly support the
hypotheses that: (1) any model which incorporates our two mechanisms
will accurately reproduce the statistical properties of empirical food
webs and (2) the empirical distributions of number of prey and number
of predators follow universal functional forms that, without free
parameters, match our analytical predictions.
We present a theoretical study of thermal effect in Quantum-dot Cellular Automata (QCA). A Hubbard type model for the Hamiltonian of the QCA arrays, and canonical distribution were used, to obtain the thermal average of polarization for the QCA cells. Full quantum calculations have been used for the response function of a 2-cell array, while different approximations, such Intercellular Hartree Approximation and a 2-state model, were used complicated systems (an inverter, a majority gate and planar arrays of different sizes).
PACS: 61.46.+w, 68.65.Hb, 81.07.Ta
Conventionally, lawyers explain patterns in legal systems' content by
reference to exogenous ordering (e.g. the directives of legislatures or
appellate courts). U.S. bankruptcy law lacks meaningful exogenous
ordering, and yet patterns emerge among local practice communities. I will
present some empirical evidence of bankruptcy's self-organization. I will
also explore the possibility of modeling the adaptive behavior of
bankruptcy legal systems, predicated on the notion that the
forms comprising a legal system's content (e.g. particular expressions of
public doctrine or of private ordering) compete against each other for
finite institutional resources. More generally, I wish to explore whether
the concepts of chaos, complexity, and self-organizing criticality help
explain the behavior of data generated by legal systems.
We study a relatively simple yet nonlinear class of cellular automata (CA), those whose local update rules are symmetric Boolean threshold functions. First, we show that such threshold CA cannot have any temporal cycles if the nodes update sequentially, that is, one at a time. In contrast, temporal 2-cycles exist in most such CA when the nodes update concurrently, i.e., in perfect synchrony. Hence, the fine-grain parallelism of the classical CA turns out not to be fine enough, at least insofar as capturing concurrency via "interleaving semantics" of the corresponding sequential computations is concerned.
Second, we completely characterize the configuration spaces, and therefore possible dynamics or computations, of 1-D simple threshold CA. In particular, we compare and contrast the MAJORITY CA with some less interesting Boolean threshold rules. We also indicate which properties do, and which do not, carry over to cellular spaces of dimensionality higher than one. We conclude by motivating the study of genuinely asynchronous CA as a useful abstraction for various distributed computational and communication infrastructures.
The physiology of a cell is largely determined by complex networks of interacting proteins. For example, eukaryotic cell division is regulated by an underlying cell cycle engine that is known in great detail. The basic molecular mechanisms controlling DNA replication, mitosis and cell division are highly conserved among eukaryotes, with homologous proteins functioning in both yeast and humans. To understand the dynamics of such a complicated control system requires sophisticated theoretical and computational tools. Our approach is to decompose the cell cycle engine into "modules" that are responsible for the characteristic transitions of the cell cycle (G1/S, G2/M and meta/anaphase) and to analyze these modules by standard tools of dynamical system theory (phase plane techniques, stability analysis, bifurcation theory etc.). We will show that some of the modules (G1/S and G2/M) are based on antagonistic relationships between cell cycle regulators. As a consequence of this antagonism, these modules operate as switches with different turning-on and turning-off points, a phenomenon called hysteresis. In contrast, the mitotic module, which is based on a negative feedback loop, operates as an oscillator. We will also describe how to assemble G1/S-, G2/M- and meta/anaphase-modules into a comprehensive model of the eukaryotic cell cycle, using fission yeast as an example. With this comprehensive model, we will also discuss the mechanisms by which cell cycle checkpoint pathways stabilize cell cycle states and inhibit the transitions that drive cell cycle progression.
This paper compares the adaptability of centralized and decentralized coordination of production. I present an agent-based model in which agents produce a single product and search for opportunities to cooperate by specializing in parts of the production process. In the model, decentralized agents exploit local opportunities for cooperation more fully than centralized agents and reach higher average utility. The two coordination systems also lead to very different patterns of trade: the decentralized system leads to broadly connected trading networks, in which almost any two agents are indirectly linked, while centralized coordination leads to small connected subgroups that are isolated from one another. The broadly connected trading topology of the decentralized system makes it less adaptive when changes disrupt the trading network or require mutual adjustment in production. When agent turnover is introduced in the model, the decentralized system achieves lower average welfare because it exhibits greater disruption when agents exit the trading network. The model illustrates an adaptive advantage of central coordination in dynamic settings. Centralized systems minimize the interdependence of agents, which reduces the information processing and mutual adjustment that must be made to adapt production to a changing environment. The model suggests that to overcome the brittleness inherent in deeply interdependent trading networks, decentralized systems may require slack resources to allow them to adapt in dynamic settings. This runs counter to the conventional wisdom that decentralized markets are lean while centralized organizations operate with a high level of slack resources.
2Department of Bioengineering and Neurology
Epileptogenic human hippocampus generates spontaneous energy fluctuations with a wide range of amplitude and temporal variation, which are often assumed to be entirely random. However, the temporal dynamics of these fluctuations are poorly understood. We use detrended fluctuation analysis (DFA) to show that the energy fluctuations in human hippocampus show long-range temporal correlations with power-law scaling, and that these correlations differ between epileptogenic and non-epileptogenic hippocampus. The DFA derived scaling exponents demonstrate that there are LRTC of energy fluctuations in human hippocampus, and that the temporal persistence of energy fluctuations is characterized by a bias for large (small) energy fluctuations to be followed by large (small) energy fluctuations.
We study a simple multiple flow particle deposition system where a
brownian conducting particle is exposed to both a heat flow and a
constant electrical current through the system. We calculate the
steady-state probability density for the position of the particle as a
function of the relative strengths of the two flows. We find a phase
transition between smooth and ramified patterns where critical heat
flow scales almost linearly as a function of the current. We simulate our
model numerically and find it to be in agreement with our theory.
We study the diffraction patterns of a one-dimensional
Fibonacci
chain from quasiperiodic pulse trains. We find a single
prominent
peak when the dynamics of the incident wave matches the
arrangement of the scatterers, that is, when the pulse train
and
the scatterers are in resonance. The maximum diffraction
angle and
the resonant pulse train determine the positions of the
scatterers.
Inside eukaryotic cells there is a transportation system with
molecular motors that pull cargos along filaments that can be
thought of as roads. While there have been careful studies
of single-motor properties as well as of the structure of
the filamentary networks, there are deficiencies in our understanding of
how these components work together to provide efficient,
reliable transport. Using single particle tracking of pigment granules
in frog skin cells (Xenopus Melanophores) together with theoretical
modeling and numerical simulations, we show that cells can change
the way they transport cargo by changing the way the cargos
utilize the network, rather than by changing individual motor
properties, or by relying on changes to the structure of the
filamentary networks.
We show that a glass transition, signaled by a peak in
the specific heat vs. temperature, can occur because a glassy
system that shows no signs of aging progresses so slowly through the
energy landscape that the time needed to obtain an accurate estimate of the
thermodynamic averages exceeds the observation time.
We find that below the glass transition temperature of a
three dimensional binary mixture of soft spheres,
the specific heat increases with measurement time spans orders
of magnitude longer than previously recognized equilibration times
such as the alpha relaxation time and the aging time. The specific heat
displays frequency dependence down to correspondingly low frequencies.
Dependency structure matrices (DSMs) are commonly used to represent
product, organization, or other system architectures. Identifying
product, organizational, or system modularity or substructure can be
achieved by a variety of DSM clustering methods. This presentation shows
(1) how genetic algorithms (GAs) can be used as an effective means of DSM
clustering, and (2) how DSM clustering can be used to design a competent
GA called the dependency structure matric genetic algorithm or DSMGA.
The talk starts by showing that the GA is capable of clustering
complex DSMs, a task difficult for human experts. This result is then
used to create a mechanism that identifies building blocks in difficult
search and optimization problems, which is in turn used to direct a
building-block-wise crossover mechanism iteratively in a DSM-based genetic
algorithm. The resulting algorithm solves hard problems quickly,
reliably, and accurately. Moreover, the building block information
obtained by DSM clustering has the nice property that it is explicit and
comprehensible to human users. This latter property suggests a number of
continuations of the work, and the talk concludes with a broad discussion
of those and other directions for future work.
We used evolutionary computation methods to simulate the evolution of a
particular class of intracellular signaling networks. This class of
signaling networks mimics the cellular state transition (or mode switch)
in
a living cell in response to a specific number of prerequisites. Two
different signaling regulations, namely absence receptor regulation and
presence receptor regulation, are represented in our network model. An
evolutionary argument based on a minimum evolution hypothesis accounts for
the empirical observation that an absence receptor-regulated network is
more likely to regulate a mode switch than a presence receptor-regulated
network. We simulated the evolution of networks regulated by absence
and/or
presence receptors. The only simulation that produced networks of maximum
fitness had only absence receptors. We developed a model to calculate the
probability of evolving a maximum-fitness, minimum-evolution network. The
calculation gives a qualitative view of the complexity of signaling
network evolution.
Multiple time scale control of eye movement by neural networks.
Thomas J. Anastasio, Beckman Institute, UIUC
Nanoparticle Diffusion on Desoperbing Solids: The Role of Elementary Excitations in Buffer-Layer-assisted Growth
Vasil Antonov, Physics, UIUC,
Physiological Adaptions of the Black Bear to a Changing External Environment
Janice Bahr, Reproductive Biology, UIUC
Understanding Multi-Instrument Measurement Systems
Peter Bajcsy, NCSA, UIUC
Adaptation to the Edge of Chaos in a Non-Isothermal
Autocatalator
Alex Barr, and Alfred Hubler, Physics, UIUC,
Gradient Flow Networks
Kevin E. Bassler, Physics, University of Houston
Information
Leaders in Product Development Organizational Networks:
Social Network Analysis of the Design Structure Matrix
Diego Batallas, Industrial Engineering, UIUC
Experimental Evidence of Molecular Chaos in a Granular Gas*
G. William Baxter@ and J. S. Olafsen
Physics Department, Penn State Erie, The Behrend College
Department of Physics and Astronomy, University of Kansas
@ supported by a summer research fellowship from the Petroleum Research
Fund
Foraging Dynamics of a Single Ant
G. William Baxter, D. J. Slomski, and S. M. Covert, Physics, Penn State University
Let the complex be simple - functional holography of recorded brain activity
Eshel Ben-Jacob, Physics, Tel Aviv University, Israel
Discrete charges on two-dimensional conductors
Marko Kleine Berkenbusch
Modeling and analysis of combinatorial complexity in signal transduction
M.L. Blinov, J.R. Faeder, B. Goldstein, W.S Hlavacek
Mapping and Modeling Scientific Network Ecologies
Katy Borner, Information Science, Indiana University
Complex patterns in animal foraging:
the example of a knowledgeable hungry primate
Denis Boyer, National Autonomous University of Mexico
Design and Control of Multiscale Systems
Richard D. Braatz, Chemical Engineering, UIUC
Machine Learning in Hierarchical Classification Problems Demands Effective Building Block Identification and Processing
Martin Butz, General Engineering, UIUC
The role of the resonancies in a Driven Triangular Well: Numerical and analytical results
Gabriel Villalobos Camargo, Physics, University of Bogotá, Colombia
Intrinsic Localized Modes: Localizing Energy through Nonlinearity and Discreteness Bilayers
David Campbell, College of Engineering
Boston University
Random Polymer Networks and the crumpling-induced roughening of lipid
bilayers
Sahraoui Chaieb, Theoretical and Applied Mechanics, UIUC
Fluctuations in Glassy Dynamics
Horacio Castillo, Physics and Astronomy, Ohio University
PAC-Learnability of Temporal Relations between
Partially-Observable Chaotic Events within Geographic
Networks
John Benjamin Cassel, Computer Science, UIUC
Periodic motions in a periodically forced, piecewise linear, impacting oscillator
Lidi Chen and A.C.J. Luo
Department of Mechanical and Industrial Engineering
Southern Illinois University Edwardsville
Edwardsville, IL 62026-1805
Dissemination of Culture using a Quantum Model
Scott Christley, Computer Science, Notre Dame
Coevolution of knowledge networks in 21st century organizational forms
Noshir Contractor, UIUC
Complexity of Quantum Spectra
Yuri Dabaghian, Neuroscience, University of California, San Francisco
Bridging the Temporal Threshold between Reality and Virtuality
Wayne J. Davis
Department of General Engineering
University of Illinois at Urbana-Champaign
In particular, the proposed paradigm integrates the dynamic response over three temporal regimes:
The discussed concepts will be applied to the design of a distributed vehicular traffic management system.
Identification of cortical dynamics based on Hilbert analysis of EEG data
Murat R. Demirer, Walter J. Freeman, Robert Kozma
University of Memphis and UC Berkeley
Optimizing teraherz emission from quantum double wells
Ke Dong, Physics, UIUC
From Signal Transduction to Spatial Pattern Formation in E. coli -
A paradigm for multi-scale modeling in biology
Radek Erban, Mathematics, University of Minnesota
Complexity theory and the evolution of communication networks.
Fabio Fonti, Organization Studies Dept., Boston College
Nonlinear Resonant Forcing of Dynamical Systems
Glenn Foster and Alfred Hubler
Center for Complex Systems Research
Departement of Physics, UIUC
Self-assembly of strongly adsorbing microsphere-nanoparticle mixtures
James F. Gilchrist, Materials Sciene and Engineering, UIUC
Evolving biological neural networks for social cooperation
Rhanor Gillette, Molecular & Integrative Physiology, UIUC
Fluctuating Random Network Media and their Universal Properties
Paul Goldbart, Physics, UIUC
Turbulence at an air-water interface
Walter Goldburg, Physics, University of Pittsburgh
Physics, universality, computational algorithms and complexity
Nigel Goldenfeld, Physics, UIUC
Fighting Fire with fire: using probabilistic techniques to build stress-resistant networked computer systems
Indranil Gupta, Computer Science, UIUC
Strong, Weak and Latent Ties: Evidence from Computer-Mediated Networks
Caroline Haythornthwaite, Library and Information Science, UIUC
Holistic Integration across Large Scale Web Sources
Bin He, Computer Science, UIUC
Unlike traditional integration scenarios which mainly focus
on relatively small-scaled, pre-configured systems,
MetaQuerier faces a new challenge of on-the-fly integration
for large scale autonomous data sources, where no pre-
configured source and query knowledge can be assumed. We
observe that the semantic information of sources are often
revealed through hidden syntactic or statistic regularities
across many sources. Motivated by this observation, we
propose holistic integration-- to discover the semantics by
exploiting the hidden regularities. In particular, we study
two concrete problems in MetaQuerier: query interface
understanding based on hidden syntax and matching query
interfaces based on hidden generative models. Our overall
experience indicates high promise for such holistic
techniques.
MetaQuerier: Exploring and Integrating the Deep Web
Bin He, Computer Science, UIUC
Transcriptional Profiles of GnRH Neurons: Implications of Circadian Input into the HPG
Jason Ralph Hickok, Cell and Structural Biology, UIUC
Instabilities in the radial segregation patterns of granular mixtures
Kimberley Hill, Theoretical and Applied Mechanics, UIUC
Agents of the Mind
Jim Houk, Neurophysiology, Northwestern University Medical School
A Coevolutionary Agent Based Model of Genotype Editing
Chien-Feng Huang and Luis M. Rocha, Computer and Computational Sciences, LANL
Spin Dynamics on a Network: Phase Transition and Applications
Cristian Huepe, ESAM, Northwestern University
No scale-depedence of the hydraulic conductivity in porous media
Allen Hunt, Physics, Wright State University
Global Optima Results for the Kauffman NK Model
Hemanhsu Kaul, S.H. Jacobson, Mathematics, UIUC,
Monte Carlo Bohmian Dynamics from Trajectory Stability Properties
Jian Liu, Chemistry, UIUC
Physics and Statistics of Old Faithful Geyser
Susan Kieffer, Geology, UIUC
Neuropercolations: Dynamical Percolation Models of Phase Transitions in Physical and Biological Systems
Robert Kozma (University of Memphis),Paul Balister, Bela Bollobas (UoM and University of Cambridge, UK), Marko Puljic (U of Memphis), and Walter Freeman (UC Berkeley)
The Successions of Creative Networks in the Universe
E. Atlee Jackson, Professor Physics Emeritus, UIUC
Biology, thanks to Darwin, was the first field of science that was based on the historical vision of natural developments. It is noteworthy that Darwin never used the word ‘evolution’ in his masterpiece, The Origin of Species, because of its association with religious creationism. However, today “evolution” is used quite indiscriminately, to describe everything from the life cycles of stars, to biological history, and all human cultural and conceptual developments, including in Technology. Biologists like Ernst Mayr condemned this indiscriminate use, while Lynn Margulas defined evolution thus: “The study of evolution is vast enough to include the cosmos and its stars as well as life, including human life, and our bodies and our technologies. Evolution is simply all of history.” Even the well-known physicist, John Ziman, edited a collection in 2002, Technological Innovation as an Evolutionary Process, which the reviewer found, correctly, to be basically meaningless.
It is clear that such an indiscriminate characterization of ‘the Universe’s history’ excludes the possibility of trying to understand at least some of the dynamic processes that have led the wondrous structural and dynamic forms that are presently known to exist in nature.
To initiate such a search, it is useful to describe this history in terms of successive special cases of dynamic ‘creative processes’, given a sufficiently general definition. For more specific definitions, this requires the introduction of at least six broad dynamic categories of such creative processes, for a starter. From this preliminary study a number of general scientific lessons and new dynamic concepts require clarification, as will be briefly described in this talk.
Genetic-Programming for Multiscale Modeling
Duane Johnson, Kumara Sastry, David Goldberg, Pascal Bellon, Material Science and Engineering, UIUC
Nonlinear instabilities and turbulence in 15, 110 or 15000 kg of liquid sodium
Daniel P. Lathrop,
Department of Physics,
Institute for Research in Electronics and Applied Physics,
Institute for Physical Sciences and Technology,
University of Maryland
Discrete Scale Invariance in Long-Term Heart Rate Variability of Healthy Humans
D.C., Bill, Lin, Mechanical Engineering, Ryerson University, Toronto, Ontario, Canada, M5B 2K3
Collapsing polyelectrolytes: Self-assembly driven by counterions
Erik Luijten, Materials Science and Engineering, UIUC
A Theory for Non-smooth Dynamic Systems on the Connectable Domains
Albert C. J. Luo
Department of Mechanical and Industrial Engineering
Southern Illinois University Edwardsville
Edwardsville, IL 6026-1805
Entropy driven dynamics in vibrated granular media
Paul Melby, Physics, Georgetown University
Influence of Expansion on Hierarchical Structure Formation
Bruce Miller, Texas Christian University
Principles of Design for Molecular Modules
with Application to Intracellular Signaling
Jay E. Mittenthal, Cell and Structural Biology, UIUC
The basal ganglia-thalamic-cortical system as reentrant non-linear oscillators embedded in loosely-coupled networks
Erwin B. Montgomery Jr., Neurology, Wisconson University
Silicon nanoparticles in quasi networks of various dimensionalities
Sua Myong, Ivan Rasnik and Taekjip Ha
Department of Physics, University of Illinois, Urbana-Champaign,Urbana, IL61801, USA
E-mail: smyong@uiuc.edu
Co-evolution of Morphology and Agility in Early Vertebrates
Mike Paulin, Department of Zoology and Centre for Neuroscience,
University of Otago, New Zealand
Collaborative First Response to Disasters Involving Critical Physical Infrastructure: Drawing from Robust Natural Systems
Feniosky Peña-Mora, Roberto Aldunate1, and Sergio F. Ochoa2, Civil and Environmental Engineering, UIUC
1Department of Civil and Environmental Engineering, University of Illinois, Newmark Civil Engineering Laboratory, Room 3106, MC-250, 205 North Mathews Avenue, University of Illinois, Urbana-Champaign, IL 61801, USA. E-mail: {feniosky, aldunate}@uiuc.edu
2Computer Sciences Department, University of Chile, Av. Blanco Encalada 2120, 3er. Piso, Santiago, Chile. E-mail: sochoa@dcc.uchile.cl
Chemical Oscillations and Propagating Chemical Waves in Cells and Tissues
Howard Petty, Ophthalmology and Visual Sciences, University of Michigan
Statistics of the Eigenvalues and the Eigenfunctions of Chaotic and
Disordered Quantum Systems: Tight-Binding Model Calculations and Microwave Experiments
Prabhakar Pradhan (Department of Electrical & Computer Engineering, Northwestern University, Evanston, IL), Wentao Lu and S. Sridhar (Department of Physics & Electronic Materials Research Institute, Northeastern University, Boston, MA)
Simulated Weakening in
Driven Threshold Models
Eric Preston, Indiana State University
Nontrivial Limit Cycle Oscillations in Random Cellular Automata Models of
Excitatory and Inhibitory Neural Populations
Marko Puljic, University of Memphis, Robert Kozma, Paul Balister, and Walter Freeman (University of Memphis,
and UC Berkley).
Do formats organize networks?
Richard Rogers, University of Amsterdam
,
Evolving Memory: Logical Tasks for Cellular Automata
Luis Rocha, LANL
Scale-Free Networks with Geography
Leonard Sander, Physics, University of Michigan
Chemical Sensing and Biological Networks
Alex Scheeline, Chemistry, UIUC
Detecting Levy and Fractal Gaussian Intermittencies in Geophysical Phenomena
Nicola Scafetta, Department of Physics, Duke University
Agent based Exploration of Self-Organizing Neural Networks
Tim Schoenharl, Computer Science, Notre Dame
Dynamics of random Boolean networks
Joshua E. S. Socolar, Physics, Duke University
Two mechanisms to explain trophic food web structure
Daniel B. Stouffer, Chemical Engineering,
Northwestern University
Quantum calculation of the Thermal Effect in Quantum-dot Cellular Automata
Ioan Sturzu, M. Khatun and Melissa Hendrichsen
Center for Computational Nanoscience, Department of Physics and Astronomy, Ball State University, Muncie, IN 47306
Self-organization in bankruptcy legal systems
Bernie Trujillo, Law School, University of Wisconsin-Madison
Characterizing Configuration Space Properties of Symmetric Threshold Cellular Automata
Predrag Tosic, Computer Science, UIUC
Kinetic analysis of cell cycle regulation
John J. Tyson, Biology, Virginia Tech
Managing Interdependence: An Agent-Based Model of Adaptation in Centralized and Decentralized Systems
Charles Williams, UIUC
Long-range Temporal Correlations in Epileptogenic Human Hippocampus
G. A. Worrell1, S. M. Stead1, and B. Litt2
1Department of Neurology
Mayo Clinic
Rochester, Minnesota, 55905
University of Pennsylvania
Philadelphia, Pennsylvania, 19104
Smooth-Rough Phase Transitions in Deposition Processes with a Heat Flow and an Electrical Current
Tim Wotherspoon and Alfred Hubler, Physics, UIUC
Enhanced diffraction pattern from a Fibonacci chain
Jian Xu and Alfred Hubler, Physics, UIUC
How does Intracellular Molecular Motor Driven Transport Work?
J. Snider, F. Lin, N. Zahedi, V. Rodionov, C. C. Yu, and S. P. Gross, Physics, University of California, Irvine
Equilibration and Frequency Dependence of the
Specific Heat of Glass Forming Liquids
Clare Yu and Herve M. Carruzzo, Physics, University of California, Irvine
Dependency structure matrices, organizational clustering and genetic algorithms
Tian-Li Yu, General Engineering, UIUC
An Evolutionary Computation Model of Intracellular Signaling Networks
Lihua Zou, Biology, UIUC
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