Lyapunov exponents for products of complex Gaussian random matrices

The exact value of the Lyapunov exponents for the random matrix product $P_N = A_N A_{N-1}...A_1$ with each $A_i = \Sigma^{1/2} G_i^{\rm c}$, where $\Sigma$ is a fixed $d \times d$ positive definite matrix and $G_i^{\rm c}$ a $d \times d$ complex Gaussian matrix with entries standard complex normals, are calculated. Also obtained is an exact expression for the sum of the Lyapunov exponents in both the complex and real cases, and the Lyapunov exponents for diffusing complex matrices.Comment: 15 page

Neural network for quality control of submunitions produced by injection loading

Injection loading of submunitions for smart weapons is a novel automated processing technique that can benefit from adaptive process control. This paper describes how the quality of submunitions could be controlled by using a neural network code in real time. Future work is planned to demonstrate fewer rejects and pollution reduction during submunition manufacturing

Self-optimization, community stability, and fluctuations in two individual-based models of biological coevolution

We compare and contrast the long-time dynamical properties of two individual-based models of biological coevolution. Selection occurs via multispecies, stochastic population dynamics with reproduction probabilities that depend nonlinearly on the population densities of all species resident in the community. New species are introduced through mutation. Both models are amenable to exact linear stability analysis, and we compare the analytic results with large-scale kinetic Monte Carlo simulations, obtaining the population size as a function of an average interspecies interaction strength. Over time, the models self-optimize through mutation and selection to approximately maximize a community fitness function, subject only to constraints internal to the particular model. If the interspecies interactions are randomly distributed on an interval including positive values, the system evolves toward self-sustaining, mutualistic communities. In contrast, for the predator-prey case the matrix of interactions is antisymmetric, and a nonzero population size must be sustained by an external resource. Time series of the diversity and population size for both models show approximate 1/f noise and power-law distributions for the lifetimes of communities and species. For the mutualistic model, these two lifetime distributions have the same exponent, while their exponents are different for the predator-prey model. The difference is probably due to greater resilience toward mass extinctions in the food-web like communities produced by the predator-prey model.Comment: 26 pages, 12 figures. Discussion of early-time dynamics added. J. Math. Biol., in pres

Quintessence Cosmology and the Cosmic Coincidence

Within present constraints on the observed smooth energy and its equation of state parameter, it is important to find out whether the smooth energy is static (cosmological constant) or dynamic (quintessence). The most dynamical quintessence fields observationally allowed are now still fast-rolling and no longer satisfy the tracker approximation if the equation of state parameter varies moderately with cosmic scale. We are optimistic about distinguishing between a cosmological constant and appreciably dynamic quintessence, by measuring average values for the effective equation of state parameter. However, reconstructing the quintessence potential from observations of any scale dependence appears problematic in the near future. For our flat universe, at present dominated by smooth energy in the form of either a cosmological constant (LCDM) or quintessence (QCDM), we calculate the asymptotic collapsed mass fraction to be maximal at the observed smooth energy/matter ratio. Identifying this collapsed fraction as a conditional probability for habitable galaxies, we infer that the prior distribution is flat. Interpreting this prior as a distribution over theories, rather than as a distribution over unobservable subuniverses, leads us to heuristic predictions about the class of future quantum cosmology theories and the static or quasi-static nature of the smooth energy.Comment: Typos corrected, as presented at Cosmo-01 Workshop, Rovaniemi, Finland and accepted for publication in Physical Review D. 9 pages, 4 figure

Distributed flow optimization and cascading effects in weighted complex networks

We investigate the effect of a specific edge weighting scheme $\sim (k_i k_j)^{\beta}$ on distributed flow efficiency and robustness to cascading failures in scale-free networks. In particular, we analyze a simple, yet fundamental distributed flow model: current flow in random resistor networks. By the tuning of control parameter $\beta$ and by considering two general cases of relative node processing capabilities as well as the effect of bandwidth, we show the dependence of transport efficiency upon the correlations between the topology and weights. By studying the severity of cascades for different control parameter $\beta$, we find that network resilience to cascading overloads and network throughput is optimal for the same value of $\beta$ over the range of node capacities and available bandwidth

Kernel Spectral Clustering and applications

In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective. Just as in the classifier case, the binary clustering model is expressed by a hyperplane in a high dimensional space induced by a kernel. In addition, the multi-way clustering can be obtained by combining a set of binary decision functions via an Error Correcting Output Codes (ECOC) encoding scheme. Because of its model-based nature, the KSC method encompasses three main steps: training, validation, testing. In the validation stage model selection is performed to obtain tuning parameters, like the number of clusters present in the data. This is a major advantage compared to classical spectral clustering where the determination of the clustering parameters is unclear and relies on heuristics. Once a KSC model is trained on a small subset of the entire data, it is able to generalize well to unseen test points. Beyond the basic formulation, sparse KSC algorithms based on the Incomplete Cholesky Decomposition (ICD) and $L_0$, $L_1, L_0 + L_1$, Group Lasso regularization are reviewed. In that respect, we show how it is possible to handle large scale data. Also, two possible ways to perform hierarchical clustering and a soft clustering method are presented. Finally, real-world applications such as image segmentation, power load time-series clustering, document clustering and big data learning are considered.Comment: chapter contribution to the book "Unsupervised Learning Algorithms

Tunable local polariton modes in semiconductors

We study the local states within the polariton bandgap that arise due to deep defect centers with strong electron-phonon coupling. Electron transitions involving deep levels may result in alteration of local elastic constants. In this case, substantial reversible transformations of the impurity polariton density of states occur, which include the appearance/disappearance of the polariton impurity band, its shift and/or the modification of its shape. These changes can be induced by thermo- and photo-excitation of the localized electron states or by trapping of injected charge carriers. We develop a simple model, which is applied to the $O_P$ center in $GaP$. Further possible experimental realizations of the effect are discussed.Comment: 7 pages, 3 figure

Hierarchical Spherical Model from a Geometric Point of View

A continuous version of the hierarchical spherical model at dimension d=4 is investigated. Two limit distribution of the block spin variable X^{\gamma}, normalized with exponents \gamma =d+2 and \gamma =d at and above the critical temperature, are established. These results are proven by solving certain evolution equations corresponding to the renormalization group (RG) transformation of the O(N) hierarchical spin model of block size L^{d} in the limit L to 1 and N to \infty . Starting far away from the stationary Gaussian fixed point the trajectories of these dynamical system pass through two different regimes with distinguishable crossover behavior. An interpretation of this trajectories is given by the geometric theory of functions which describe precisely the motion of the Lee--Yang zeroes. The large--$N$ limit of RG transformation with L^{d} fixed equal to 2, at the criticality, has recently been investigated in both weak and strong (coupling) regimes by Watanabe \cite{W}. Although our analysis deals only with N=\infty case, it complements various aspects of that work.Comment: 27 pages, 6 figures, submitted to Journ. Stat. Phy

Quintessence and Gravitational Waves

We investigate some aspects of quintessence models with a non-minimally coupled scalar field and in particular we show that it can behave as a component of matter with $-3 \lesssim P/\rho \lesssim 0$. We study the properties of gravitational waves in this class of models and discuss their energy spectrum and the cosmic microwave background anisotropies they induce. We also show that gravitational waves are damped by the anisotropic stress of the radiation and that their energy spectrum may help to distinguish between inverse power law potential and supergravity motivated potential. We finish by a discussion on the constraints arising from their density parameter \Omega_\GW.Comment: 21 pages, 18 figures, fianl version, accepted for publication in PR

Nonlinear wave transmission and pressure on the fixed truncated breakwater using NURBS numerical wave tank

Fully nonlinear wave interaction with a fixed breakwater is investigated in a numerical wave tank (NWT). The potential theory and high-order boundary element method are used to solve the boundary value problem. Time domain simulation by a mixed Eulerian-Lagrangian (MEL) formulation and high-order boundary integral method based on non uniform rational B-spline (NURBS) formulation is employed to solve the equations. At each time step, Laplace equation is solved in Eulerian frame and fully non-linear free-surface conditions are updated in Lagrangian manner through material node approach and fourth order Runge-Kutta time integration scheme. Incident wave is fed by specifying the normal flux of appropriate wave potential on the fixed inflow boundary. To ensure the open water condition and to reduce the reflected wave energy into the computational domain, two damping zones are provided on both ends of the numerical wave tank. The convergence and stability of the presented numerical procedure are examined and compared with the analytical solutions. Wave reflection and transmission of nonlinear waves with different steepness are investigated. Also, the calculation of wave load on the breakwater is evaluated by first and second order time derivatives of the potential