This PDF file contains the front matter associated with SPIE Proceedings Volume 6802, including the Title Page, Copyright information, Table of Contents, Introduction, and the Conference Committee listing.

In the present work we demonstrate the application of different physical methods to high-frequency or tick-bytick financial time series data. In particular, we calculate the Hurst exponent and inverse statistics for the price time series taken from a range of futures indices. Additionally, we show that in a limit order book the relaxation times of an imbalanced book state with more demand or supply can be described by stretched exponential laws analogous to those seen in many physical systems.

The concept of (auto)catalytic systems has become a cornerstone in understanding evolutionary processes in various fields. The common ground is the observation that for the production of new species/goods/ideas/elements etc. the pre-existence of specific other elements is a necessary condition. In previous work some of us showed that the dynamics of the catalytic network equation can be understood in terms of topological recurrence relations paving a path towards the analytic tractability of notoriously high dimensional evolution equations. We apply this philosophy to studies in socio-physics, bio-diversity and massive events of creation and destruction in technological and biological networks. Cascading events, triggered by small exogenous fluctuations, lead to dynamics strongly resembling the qualitative picture of Schumpeterian economic evolution. Further we show that this new methodology allows to mathematically treat a variant of the threshold voter-model of opinion formation on networks. For fixed topology we find distinct phases of mixed opinions and consensus.

The quantum Minority game provides a means of studying the effect of multi-partite entanglement in a game theoretic setting. We study symmetric Nash equilibria and symmetric Pareto optimal strategies arising in a four-player quantum Minority game that uses an initial state that is a superposition of a GHZ state and products of EPR pairs. We find that the payoff curve for the symmetric Pareto optimal strategy is the same as that for the maximal violation of the Mermin-Ardehali-Belinskii-Klyshko inequality for the initial state, indicating a correspondence between quantum game theory and Bell inequalities. We also show that no advantage over the classical Minority game can be obtained when the initial state has only two party entanglement.

We use the standard three-party Einstein-Podolsky-Rosen (EPR) setting in order to play general three-player non-cooperative symmetric games. We analyze how the peculiar non-factorizable joint probabilities that may emerge in the EPR setting can change outcome of the game. Our setup requires that the quantum game attains classical interpretation for factor- izable joint probabilities. We analyze the generalized three-player game of Prisoner's Dilemma (PD) and show that the players can indeed escape from the classical outcome of the game because of non-factorizable joint probabilities. This result for three-player PD contrasts strikingly with our earlier result for two-player PD for which even non-factorizable joint prob- abilities are not found to be helpful to escape from the classical outcome of the game.

The persistence phenomenon is studied in the Japanese financial market by using a novel mapping of the time evolution of the values of shares in a portfolio onto Ising spins. The method is applied to historical end of day data from the Japanese stock market over an arbitrarily chosen period. By studying the time dependence of the spins, we find clear evidence for a power law decay of the proportion of shares that remain either above or below their "starting" values. The results are compared with those resulting from data from the London market, where there is evidence of a distinctive double power law. Preliminary results from the Japanese market indicate similar behavior. We estimate a long time persistence exponent for the underlying financial markets to be 0.5.

We extend to the multi-asset case the framework of a discrete time model of a single asset financial market developed in Ghoulmié et al.¹ In particular, we focus on adaptive agents with threshold behavior allocating their resources among two assets. We explore numerically the effect of this diversification as an additional source of complexity in the financial market and we discuss its destabilizing role. We also point out the relevance of these studies for financial decision making.

By pouring equal balls into a container one obtains disordered packings with fascinating properties which might shed light on several elusive properties of complex materials such as amorphous metals or colloids. In any real experiment with equal-sized spheres one cannot reach packing fractions (fraction of volume occupied by the spheres respect to the total volume, ρ) below the Random Loose Packing limit (RLP, ρ ~ 0.555) or above the Random Close Packing limit (RCP, ρ ~ 0.645) unless order is externally induced. What is happening at these two limits is an open unanswered question. In this paper we address this question by combining statistical geometry and statistical mechanics methods. Evidences of phase transitions occurring at the RLP and RCP limits are reported.

In some cells pairing of homologous chromosomes happens at particular moments during the cell life cycle. A Statistical Mechanics model based on some experimental data is presented and discussed here for this phenomenon. Under this model, chromosomes pair at special regions whose interaction is mediated by some molecules which diffuse in cells' nuclei and can bind them. Concentration of these molecules acts as a switch for pairing: at low concentrations chromosomes move independently one from another whereas if concentration is above a certain threshold value, chromosomes colocalize. Monte Carlo simulations of this model have been performed to test its eficiency and the effect on pairing levels of chromosomal binding sites deletions.

We investigate complex materials by performing "Virtual Experiments" starting from three-dimensional images of grain packs obtained by X-ray CT imaging [1]. We apply this technique to granular materials by reconstructing a numerical samples of ideal spherical beads with desired (and tunable) properties. The resulting "virtual packing" has a structure that is almost identical to the experimental one. However, from such a digital duplicate we can calculate several static and dynamical properties (e.g. the force network, avalanche precursors, stress paths, stability, fragility, etc.) which are otherwise not directly accessible from experiments. Our simulation code handles three-dimensional spherical grains and it takes into account repulsive elastic normal forces, frictional tangential forces, viscous damping and gravity. The system can be both simulated within a vessel or with periodic boundary conditions.

The Wang-Landau technique is a new Monte Carlo approach which provides an effective means of studying the behaviour of a complex physical system over a wide temperature range. It facilitates the calculation of a system's heat capacity as a function of temperature, enabling transitions between stable phases to be identified. Combined with the calculation of properties such density, cluster size, number of discrete clusters, the evolution of the distribution of different structures across varying temperatures may be determined. We leverage the technique to identify structures representative of the system in each phase. Concrete examples are taken from studies of multiblock copolymers. The phase transitions found include transitions between polyglobular and entwined spiral structures, and the order-disorder transition between ordered striped (lamellar) and disordered random globule phases of the collapsed polymer.

In nature dissipative fluxes of fluid, heat, and/or reacting species couple to each other and may also couple to deformation of a surrounding porous matrix. We use the well-known analogy of Hele-Shaw flow to Darcy flow to make a model porous medium with porosity proportional to local cell height. Time- and space-varying fluid injection from multiple source/sink wells lets us create many different kinds of chaotic flow and chemical concentration patterns. Results of an initial time-dependent potential flow model illustrate that this is a partially open flow, in which parts of the flow remain in the cell forever and parts pass through with residence time and exit time distributions that have self-similar features in the control parameter space of the stirring.

Complex interactions between advection and diffusion give rise to enhanced scalar transport in cases where the advective field generates Lagrangian chaos. As the dispersion rate is a complex function of scalar diffusivity and parameters controlling the flow field, resolution of scalar dispersion over this parameter space is useful for better understanding interactions between advection and diffusion. In this paper we resolve the fine-scale structure asymptotic transport over the flow parameter space for Peclet numbers from 10⁰ to 10⁵ for a physically realizable flow, yielding a 50-fold acceleration of scalar dispersion at Pe = 10⁵. These results generate considerable insight into the global structure of transport and facilitate identification of mechanisms governing scalar dispersion; features include fractal distributions of dispersion rate, solution mode-locking, an order-disorder transition and localisation of transport optima.

Many important concepts used in the study of complex systems have their origin in the lattice statistical mechanics of cooperative phase transitions. The classic order-disorder transitions of Potts/Ising models illustrate emergent phenomena such as spontaneous symmetry breaking, scale-free critical behaviour and power-law singularities. However, a class of lattice statistics models show that rather more complex behaviour can be obtained, under conditions which are suffciently general that they might also occur in less idealised models. A 5-state lattice statistics model is analysed to explore behaviour in and around a so-called "massless phase". The massless phase, characterised by power law decay of correlations, appears as an intermediate regime between high temperature disorder and low-temperature ordering. The critical exponents (and fractal spatial distributions) occur over a finite range of temperatures. The state of the massless phase is characterised by a topological ordering with analogues of spin-waves being the dominant perturbations. The transition from the ordered to massless phases is analysed using new exact series expansions obtained by the finite lattice method. The results are also compared to Monte Carlo simulations. The results are compared to other studies in the small number of cases where such series expansions exist for comparable models, including the 6-state "clock model". One of the properties that makes the massless phase dificult to study is the weak nature of the associated Kosterlitz-Thouless transition with exponentially-weak behaviour rather than power-law behaviour. As a further diffculty, the limiting behaviour is confined to a narrow regime, outside which one sees an apparent "cross-over" to power law behaviour. This suggests that behaviour analogous to the "massless phases" will be diffcult to characterise as one moves beyond idealised lattice systems.

Complex plasma is a rapidly developing area of multidisciplinary world-wide research which is at the frontier of plasma physics where the richness of the complex system approach finds a natural application. Collective phenomena in complex plasma systems, especially related to formation of strongly correlated structures such as dust-plasma crystals attract particular attention since they are often observed in experiments and allow theoretical treatment at the fundamental level of complex system studies. This presentation is focused on physical understanding of the most recent developments in the experiment and theory of self-organized complex structures composed of micrometer-scale dust particles in a plasma. Nowadays, such structures play an important role in complex system science; investigation of their properties gives insights into the basic physics of self-organization in various complex media.

In this paper we study the evolution of cooperation in the one-shot prisoner's dilemma in heterogeneous populations. Heterogeneity is defined by a contact network, which gives interaction partners and possibilities for strategy spreading in the population. We find that the high levels of cooperation previously reported for scalefree networks (SFNWs)^2-4 are unstable, when small probabilities for unmotivated strategy changes (mutations) are included in the evolution dynamics. Investigating the role of the network structure for the persistence and stability of cooperation, we use an optimization technique to generate small networks that allow a large fraction of cooperators to prosper. Analyzing these networks three key characteristics are identified: (i) degree heterogeneity, (ii) a set of hubs characterized by many connections to very low degree nodes, and (iii) a relatively large link density between these hub nodes. These findings motivate the introduction of a class of periphery-core networks, on which complete cooperation can evolve over the entire range of game parameters. On these networks cooperation also overwhelmingly dominates, even when relatively large mutation probabilities are included.

Many constraints on structural and functional cortical network connectivity have been suggested, based on ideas as diverse as minimization of axonal wiring length or volume, minimization of information processing steps, and maximization of complexity. This paper discusses recently suggested roles for static and dynamic network stability in providing further constraints on connectivity. A variety of network types and constraints will be covered, including ones involving purely excitatory, mixed excitatory-inhibitory, and small-world features. Dynamical implications for adaptability of networks and dynamically changing patterns of activity are discussed, with implications for what classes of brain structures are available to be selected by evolution.

A low-dimensional, compact brain model has recently been developed based on physiologically based mean-field continuum formulation of electric activity of the brain. The essential feature of the new compact model is a second order time-delayed differential equation that has physiologically plausible terms, such as rapid corticocortical feedback and delayed feedback via extracortical pathways. Due to its compact form, the model facilitates insight into complex brain dynamics via standard linear and nonlinear techniques. The model successfully reproduces many features of previous models and experiments. For example, experimentally observed typical rhythms of electroencephalogram (EEG) signals are reproduced in a physiologically plausible parameter region. In the nonlinear regime, onsets of seizures, which often develop into limit cycles, are illustrated by modulating model parameters. It is also shown that a hysteresis can occur when the system has multiple attractors. As a further illustration of this approach, power spectra of the model are fitted to those of sleep EEGs of two subjects (one with apnea, the other with narcolepsy). The model parameters obtained from the fittings show good matches with previous literature. Our results suggest that the compact model can provide a theoretical basis for analyzing complex EEG signals.

We study a system consisting of two coupled phase oscillators in the presence of noise. This system is used as a model for the cardiorespiratory interaction in wakefulness and anaesthesia. We show that longrange correlated noise produces transitions between epochs with different n:m synchronisation ratios, as observed in the cardiovascular system. Also, we see that, the smaller the noise (specially the one acting on the slower oscillator), the bigger the synchronisation time, exactly as happens in anaesthesia compared with wakefulness. The dependence of the synchronisation time on the couplings, in the presence of noise, is studied; such dependence is softened by low-frequency noise. We show that the coupling from the slow oscillator to the fast one (respiration to heart) plays a more important role in synchronisation. Finally, we see that the isolines with same synchronisation time seem to be a linear combination of the two couplings.

In this paper we discuss the nonergodic behavior for a class of long-standing quasi-stationary states in a paradigmatic model of long-range interacting systems, i.e. the HMF model. We show that ensemble averages and time averages for velocities probability density functions (pdfs) do not coincide and in particular the latter exhibit a tendency to converge towards a q-Gaussian attractor instead of the usual Gaussian one predicted by the Central Limit Theorem, when ergodicity applies.

Delays are an important feature in temporal models of genetic regulation due to slow biochemical processes such as transcription and translation. In this paper we show how to model intrinsic noise effects in a delayed setting by either using a delay stochastic simulation algorithm (DSSA) or, for larger and more complex systems, a generalized Binomial tau-leap method (Bt-DSSA). As a particular application we apply these ideas to modeling somite segmentation in zebrafish across a number of cells in which two linked oscillatory genes her1 and her7 are synchronized via Notch signaling between the cells.

A two-lane two-way traffic light controlled X-intersection for ternary mix traffic (cars + buses (equivalent vehicles) + very large trucks/ buses) is developed based on cellular automata model. This model can provide different matrices such as throughput, queue length and delay time. This paper will describe how the model works and how composition of traffic mix effects the throughput (numbers of vehicles navigate through the intersection per unit of time (vph)) and also compare the result with homogeneous counterpart.

In this work we investigate new feature extraction algorithms on the T-ray response of normal human bone cells and human osteosarcoma cells. One of the most promising feature extraction methods is the Discrete Wavelet Transform (DWT). However, the classification accuracy is dependant on the specific wavelet base chosen. Adaptive wavelets circumvent this problem by gradually adapting to the signal to retain optimum discriminatory information, while removing redundant information. Using adaptive wavelets, classification accuracy, using a quadratic Bayesian classifier, of 96.88% is obtained based on 25 features. In addition, the potential of using rational wavelets rather than the standard dyadic wavelets in classification is explored. The advantage it has over dyadic wavelets is that it allows a better adaptation of the scale factor according to the signal. An accuracy of 91.15% is obtained through rational wavelets with 12 coefficients using a Support Vector Machine (SVM) as the classifier. These results highlight adaptive and rational wavelets as an efficient feature extraction method and the enormous potential of T-rays in cancer detection.

We construct a correlation-based biological network from a data set containing temporal expressions of 517 fibroblast tissue genes at transcription level. Four relevant and meaningful connected subgraphs of the network, namely: minimal spanning tree, maximal spanning tree, combined graph of minimal and maximal trees, and planar maximally filtered graph are extracted and the subgraphs' geometrical and topological properties are explored by computing relevant statistical quantities at local and global level. The results show that the subgraphs are extracting relevant information from the data set by retaining high correlation coeffcients. The design principle of the underlying biological functions is reflected in the topology of the graphs.

Presented is the dielectrophoresis of multiwalled carbon nanotubes on piezoelectric substrates patterned with gold inter digitated electrodes. An alternating current oscillating at frequencies of 1 kHz and 150 kHz at a peak-to-peak (p-p) voltage of 1V to 10V was applied to the electrodes, aligning carbon nanotubes suspended in droplets of isopropyl alcohol (IPA). The carbon nanotubes were suspended in a dielectric medium (IPA) at a concentration of approx 0.1 mg/mL and stabilized with sodium citrate (0.02 mg/ml). Sonicated for two hours and spun down in a centrifuge for 30 minutes at 4500 rpm. The carbon nanotubes used in the DEP experimentation were multiwalled carbon nanotubes with aspect ratios of approx 100:1.

A DCT-domain watermarking scheme, based on nonlinear bistable detectors, is presented. A binary copyright character, i.e. watermark, is firstly reordered into a binary zig-zag sequence, and then mapped into the pulse amplitude modulated waveforms. Certain desyn-chronization time can be arbitrarily placed into one code of the modulated signal, and will be tolerated due to the robust superiority of nonlinear detectors over matched filters. The watermarking signal is then embedded in a selected set of DCT coeficients of an image in medium frequency domain. The selected set of DCT coeffcients is shuffled by Arnold transform and looks more like background noise for the watermark signal. The copyright character can be extracted by the nonlinear bistable detector without resorting to the original image, i.e. blind watermark detection. Interestingly, more higher similarity between the original character and the extracted one can be further achieved by a parallel array of bistable detectors via the mechanism of array stochastic resonance. Efficacy of the proposed watermarking scheme is proved on some common attacks in experiments.

This paper studies the evolutionary prisoner's dilemma game (PDG) in finite dynamic scale-free networks, where the dynamic property is fulfilled by considering an epidemic process in networks. When a person is in infected state, he will not play PDG. Only healthy persons play PDG with their healthy neighbors. Our simulations show that (i) the ratio of healthy persons, R_s, depends not only on the spreading rate λ, but also on the recovery rate δ; (ii) The relationship between cooperation behaviors and the spreading rate λ depends on the value of δ; (iii) Given the same value of R_s and payoff parameter b, the cooperation frequency f changes with δ; (iv) Some curves of f against R_s are monotonic while others are non-monotonic. We have qualitatively explained results (ii)-(iv) through competition mechanism of cooperation enhancement effect and cooperation suppression effect. Our work sheds some lights on the important effect of dynamic topology on evolutionary game.

In this paper, we study the Prisoner's Dilemma Game (PDG) on a scale-free social network where the agents participate the game with a probability proportional to the power of their degree, i.e., P_i ~ k^α_i. In this way, the agents' participation in the game change with time, and our study reveals some properties of PDG in a dynamic social structure. In the generations each active player updates its strategy by following one of the active neighbors' strategy with a probability dependent on the payoff difference. Simulation shows the dynamic attending of agents has an important effect on the evolutionary game. In order to enhance cooperation behavior, we need to constrain participant of low-degree agents and encourage participant of high-degree agents in the game. In most cases, a maximum cooperation frequency is achieved when α is set to be slightly higher than zero. Our study may also shed some light on the policy construction of government.

Air Operations Centres (AOCs) are high stress multitask environments for planning and executing of theatre-wide airpower. Operators have multiple responsibilities to ensure that the orchestration of air assets is coordinated to maximum effect. AOCs utilise a dynamic targeting process to immediately prosecute time-sensitive targets. For this process to work effectively, a timely decision must be made regarding the appropriate course of action before the action is enabled. A targeting solution is typically developed using a number of inter-related processes in the kill chain - the Find, Fix, Track, Target, Engage, and Assess (F2T2EA) model. The success of making a right decision about dynamic targeting is ultimately limited by the cognitive and cooperative skills of the team prosecuting the mission and their associated workload. This paper presents a model of human interaction and tasks within the dynamic targeting sequence. The complex network of tasks executed by the team can be analysed by undertaking simulation of the model to identify possible information-processing bottlenecks and overloads. The model was subjected to various tests to generate typical outcomes, operator utilisation, duration as well as rates of output in the dynamic targeting process. This capability will allow for future "what-if" evaluations of numerous concepts for team formation or task reallocation, complementing live exercises and experiments.

The daily life is developing under permanent changing. Always noticed by scientists, partially explained when studied, these changes influence life and activity no matter our personal opinion and approach. The influence can be a benefice and then we face the wealth. This paper demonstrates how to use the complexity theory to turn the new trends into societal advantages. The purpose is to propose a new way to analyse and model consumption from an economic point of view.

In the recent years, a new wave of interest spurred the involvement of complexity in finance which might provide a guideline to understand the mechanism of financial markets, and researchers with different backgrounds have made increasing contributions introducing new techniques and methodologies. In this paper, Markov-switching multifractal models (MSM) are briefly reviewed and the multi-scaling properties of different financial data are analyzed by computing the scaling exponents by means of the generalized Hurst exponent H(q). In particular we have considered H(q) for price data, absolute returns and squared returns of different empirical financial time series. We have computed H(q) for the simulated data based on the MSM models with Binomial and Lognormal distributions of the volatility components. The results demonstrate the capacity of the multifractal (MF) models to capture the stylized facts in finance, and the ability of the generalized Hurst exponents approach to detect the scaling feature of financial time series.

By using a reduced model for dissipative optical soliton beams, we show that there are two disjoint sets of fixed points. These correspond to stationary solitons of the radial complex cubic-quintic Ginzburg - Landau equation with concave and convex phase profiles, respectively. We confirm these results by numerical simulations which reveal soliton solutions of two different types: continuously self-focussing and continuously self-defocusing.

The region of transition between solitons and fronts in dissipative systems governed by the complex Ginzburg- Landau equation is rich with bifurcations. We found that the number of transitions between various types of localized structures is enormous. For the first time, we have found a sequence of period-doubling bifurcations of creeping solitons and also a symmetry-breaking instability of creeping solitons. Creeping solitons may involve many frequencies in their dynamics resulting, in particular, in a variety of zig-zag motions.

One of the main goals in the field of complex systems is the selection and extraction of relevant and meaningful information about the properties of the underlying system from large datasets. In the last years different methods have been proposed for filtering financial data by extracting a structure of interactions from cross-correlation matrices where only few entries are selected by means of criteria borrowed from network theory. We discuss and compare the stability and robustness of two methods: the Minimum Spanning Tree and the Planar Maximally Filtered Graph. We construct such graphs dynamically by considering running windows of the whole dataset. We study their stability and their edges's persistence and we come to the conclusion that the Planar Maximally Filtered Graph offers a richer and more signi.cant structure with respect to the Minimum Spanning Tree, showing also a stronger stability in the long run.

A persistent feature of complex systems in engineering and science is the emergence of macroscopic, coarse grained, coherent behaviour from microscale interactions. In current modeling, ranging from ecology to materials science, the underlying microscopic mechanisms are known, but the closures to translate microscale knowledge to a large scale macroscopic description are rarely available in closed form. Kevrekidis proposes new 'equation free' computational methodologies to circumvent this stumbling block in multiscale modelling. Nonlinear coordinate transforms underpin analytic techniques that support these computational methodologies. But to do so we must cross multiple space and time scales, in both deterministic and stochastic systems, and where the microstructure is either smooth or detailed. Using examples, I describe progress in using nonlinear coordinate transforms to illuminate such multiscale modelling issues.

We study the seasonality pattern of stock returns within the year. We found that while overall the size premium is small after adjusting for delisting bias, the January returns are still larger than returns from remaining months and it is more significant for small sized companies. This obvious conflict is caused by the consistent negative returns around September, October and November. And the absolute value for small sized companies during these months are larger than large size companies. Extending to different time period and using different delisting returns do not change this pattern. We argue that demand pressure cause the results.

A recent development based on optical flow applied onto Fast Imaging in Steady State Free Precession (TrueFISP) magnetic resonance imaging is able to deliver good estimation of the flow profile in the human heart chamber. The examination of cardiac flow based on tracking of MR signals emitted by moving blood is able to give medical doctors insight into the flow patterns within the human heart using standard MRI procedure without specifically subjecting the patient to longer scan times using more dedicated scan protocols such as phase contrast MRI. Although MR fluid motion estimation has its limitations in terms of accurate flow mapping, the use of a comparatively quick scan procedure and computational post-processing gives satisfactory flow quantification and can assist in management of cardiac patients. In this study, we present flow in the left atria of five human subjects using MR fluid motion tracking. The measured flow shows that vortices exist within the atrium of heart. Although the scan is two-dimensional, we have produced multiple slices of flow maps in a spatial direction to show that the vortex exist in a three-dimensional space.