Abstract:
Dynamic invariants are often estimated from experimental time series with the aim of differentiating between different physical states in the underlying system. The most popular schemes for estimating dynamic invariants are capable of estimating confidence intervals, however, such confidence intervals do not reflect variability in the underlying dynamics. We propose a surrogate based method to estimate the expected distribution of values under the null hypothesis that the underlying deterministic dynamics are stationary. We demonstrate the application of this method by considering four recordings of human pulse waveforms in differing physiological states and show that correlation dimension and entropy are insufficient to differentiate between these states. In contrast, algorithmic complexity can clearly differentiate between all four rhythms.

Abstract:
When building linear or nonlinear models one is faced with the problem of selecting the best set of variable with which to predict the future dynamics. In nonlinear time series analysis the problem is to select the correct time delays in the time delay embedding. We propose a new technique which can quantify the suitability of a particular set of variables and we suggests a computationally efficient scheme to determine the best non-uniform time delay embedding for modeling of time series. Our results are based on the assumption that, in general, the variables which give the best local constant model will also give the best nonlinear model. In a wide variety of experimental and simulated systems we find that this method produces dynamics that are more realistic and predictions that are more accurate than standard uniform embeddings.

Abstract:
We provide an analytic expression for the quantity described in the title. Namely, we perform a preferential attachment growth process to generate a scale-free network. At each stage we add a new node with $m$ new links. Let $k$ denote the degree of a node, and $N$ the number of nodes in the network. The degree distribution is assumed to converge to a power-law (for $k\geq m$) of the form $k^{-\gamma}$ and we obtain an exact implicit relationship for $\gamma$, $m$ and $N$. We verify this with numerical calculations over several orders of magnitude. Although this expression is exact, it provides only an implicit expression for $\gamma(m)$. Nonetheless, we provide a reasonable guess as to the form of this curve and perform curve fitting to estimate the parameters of that curve --- demonstrating excellent agreement between numerical fit, theory, and simulation.

Abstract:
Dynamic invariants are often estimated from experimental time series with the aim of differentiating between different physical states in the underlying system. The most popular schemes for estimating dynamic invariants are capable of estimating confidence intervals, however such confidence intervals do not reflect variability in the underlying dynamics. In this communication we propose a surrogate based method to estimate the expected distribution of values under the null hypothesis that the underlying deterministic dynamics are stationary. We demonstrate the application of this method by considering four recordings of human pulse waveforms in differing physiological states and provide conclusive evidence that correlation dimension is capable of differentiating between three (but not all four) of these states.

Abstract:
Fourier spectral estimates and, to a lesser extent, the autocorrelation function are the primary tools to detect periodicities in experimental data in the physical and biological sciences. We propose a new method which is more reliable than traditional techniques, and is able to make clear identification of periodic behavior when traditional techniques do not. This technique is based on an information theoretic reduction of linear (autoregressive) models so that only the essential features of an autoregressive model are retained. These models we call reduced autoregressive models (RARM). The essential features of reduced autoregressive models include any periodicity present in the data. We provide theoretical and numerical evidence from both experimental and artificial data, to demonstrate that this technique will reliably detect periodicities if and only if they are present in the data. There are strong information theoretic arguments to support the statement that RARM detects periodicities if they are present. Surrogate data techniques are used to ensure the converse. Furthermore, our calculations demonstrate that RARM is more robust, more accurate, and more sensitive, than traditional spectral techniques.

Abstract:
Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both \emph{in vivo} and \emph{in vitro}. In this paper we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.

Abstract:
Using the concept of the geometric measures of redundance and irrelevance tradeoff exponent (RITE)}, we present a new method to determine suitable delay times for continuous systems. After applying the RITE algorithm to both simulation and experimental observations, we find the results obtained are close to those obtained from the criterion of the average mutual information (AMI), while the RITE algorithm has the following advantages: simple implementation, reasonable computational cost and robust performance against observational noise.

Abstract:
In this work, the topologies of networks constructed from time series from an underlying system undergo a period doubling cascade have been explored by means of the prevalence of different motifs using an efficient computational motif detection algorithm. By doing this we adopt a refinement based on the $k$ nearest neighbor recurrence-based network has been proposed. We demonstrate that the refinement of network construction together with the study of prevalence of different motifs allows a full explosion of the evolving period doubling cascade route to chaos in both discrete and continuous dynamical systems. Further, this links the phase space time series topologies to the corresponding network topologies, and thus helps to understand the empirical "superfamily" phenomenon, as shown by Xu.

Abstract:
Computational models of collective behavior in birds has allowed us to infer interaction rules directly from experimental data. Using a generic form of these rules we explore the collective behavior and emergent dynamics of a simulated swarm. For a wide range of flock size and interaction extent (the fixed number of neighbors with which an individual will interact) we find that the computational collective is inherently stable --- individuals are attracted to one another and will position themselves a preferred distance from their fixed neighbors within a rigid lattice. Nonetheless, the irregular overall shape of the flock, coupled with the need for individuals on the boundary to move towards their neighbors creates a torque which leads the flock to rotate and then meander. We argue that this "rolling meander" is a very good proxy for real collective behavior in animal species and yet arises from a simple homogeneous and deterministic rule for interaction. Rather than then introduce leaders --- which has already been shown, quite straightforwardly, to drive collective swarms such as this --- we introduce a small number of "followers". Each follower is bound to consider a random fixed individual to be among their neighbors, irrespective of actual metric distance between them. We find that the introduction of a small number of such followers causes a phase transition that quickly leads to instability in the flock structure (as no stable configuration arises) and the previously rigid crystalline interaction among neighbors now becomes fluid: the distance between neighbors decreases, the flock ceases to rotate and meanders less.

Abstract:
A simplified susceptible-infected-recovered (SIR) epidemic model and a small-world model are applied to analyse the spread and control of Severe Acute Respiratory Syndrome (SARS) for Hong Kong in early 2003. From data available in mid April 2003, we predict that SARS would be controlled by June and nearly 1700 persons would be infected based on the SIR model. This is consistent with the known data. A simple way to evaluate the development and efficacy of control is described and shown to provide a useful measure for the future evolution of an epidemic. This may contribute to improve strategic response from the government. The evaluation process here is universal and therefore applicable to many similar homogeneous epidemic diseases within a fixed population. A novel model consisting of map systems involving the Small-World network principle is also described. We find that this model reproduces qualitative features of the random disease propagation observed in the true data. Unlike traditional deterministic models, scale-free phenomena are observed in the epidemic network. The numerical simulations provide theoretical support for current strategies and achieve more efficient control of some epidemic diseases, including SARS.