Random number generation as an index of controlled processing

Random number generation (RNG) is a functionally complex process that is highly controlled and therefore dependent on Baddeley's central executive. This study addresses this issue by investigating whether key predictions from this framework are compatible with empirical data. In Experiment 1, the effect of increasing task demands by increasing the rate of the paced generation was comprehensively examined. As expected, faster rates affected performance negatively because central resources were increasingly depleted. Next, the effects of participants' exposure were manipulated in Experiment 2 by providing increasing amounts of practice on the task. There was no improvement over 10 practice trials, suggesting that the high level of strategic control required by the task was constant and not amenable to any automatization gain with repeated exposure. Together, the results demonstrate that RNG performance is a highly controlled and demanding process sensitive to additional demands on central resources (Experiment 1) and is unaffected by repeated performance or practice (Experiment 2). These features render the easily administered RNG task an ideal and robust index of executive function that is highly suitable for repeated clinical use.

Simulating global properties of electroencephalograms with minimal random neural networks

The human electroencephalogram (EEG) is globally characterized by a 1/f power spectrum superimposed with certain peaks, whereby the "alpha peak" in a frequency range of 8-14 Hz is the most prominent one for relaxed states of wakefulness. We present simulations of a minimal dynamical network model of leaky integrator neurons attached to the nodes of an evolving directed and weighted random graph (an Erdos-Renyi graph). We derive a model of the dendritic field potential (DFP) for the neurons leading to a simulated EEG that describes the global activity of the network. Depending on the network size, we find an oscillatory transition of the simulated EEG when the network reaches a critical connectivity. This transition, indicated by a suitably defined order parameter, is reflected by a sudden change of the network's topology when super-cycles are formed from merging isolated loops. After the oscillatory transition, the power spectra of simulated EEG time series exhibit a 1/f continuum superimposed with certain peaks. (c) 2007 Elsevier B.V. All rights reserved.

Examining random number generators used in stochastic iteration algorithms

This paper investigates random number generators in stochastic iteration algorithms that require infinite uniform sequences. We take a simple model of the general transport equation and solve it with the application of a linear congruential generator, the Mersenne twister, the mother-of-all generators, and a true random number generator based on quantum effects. With this simple model we show that for reasonably contractive operators the theoretically not infinite-uniform sequences perform also well. Finally, we demonstrate the power of stochastic iteration for the solution of the light transport problem.

Enhancing change detection in low-quality surveillance footage using markov random fields

Urban surveillance footage can be of poor quality, partly due to the low quality of the camera and partly due to harsh lighting and heavily reflective scenes. For some computer surveillance tasks very simple change detection is adequate, but sometimes a more detailed change detection mask is desirable, eg, for accurately tracking identity when faced with multiple interacting individuals and in pose-based behaviour recognition. We present a novel technique for enhancing a low-quality change detection into a better segmentation using an image combing estimator in an MRF based model.

Dynamics of shear-induced orientation transitions in block copolymers

This review discusses liquid crystal phase formation by biopolymers in solution. Lyotropic mesophases have been observed for several classes of biopolymer including DNA, peptides, polymer/peptide conjugates, glycopolymers and proteoglycans. Nematic or chiral nematic (cholesteric) phases are the most commonly observed mesophases, in which the rod-like fibrils have only orientational order. Hexagonal columnar phases are observed for several systems (DNA, PBLG, polymer/peptide hybrids) at higher concentration. Lamellar (smectic) phases are reported less often, although there are examples such as the layer arrangement of amylopectin side chains in starch. Possible explanations for the observed structures are discussed. The biological role of liquid crystal phases for several of these systems is outlined. Commonly, they may serve as a template to align fibrils for defined structural roles when the biopolymer is extruded and dried, for instance in the production of silk by spiders or silkworms, or of chitin in arthropod shells. In other cases, liquid crystal phase formation may occur in vivo simply as a consequence of high concentration, for instance the high packing density of DNA within cell nuclei.

Nonspherical assemblies generated from polystyrene-b-poly(L-lysine) polyelectrolyte block copolymers

This report describes the aqueous solution self-assembly of a series of polystyrene(m)-b-poly(L-lysine)n block copolymers (m = 8-10; n = 10-70). The polymers are prepared by ring-opening polymerization of epsilon-benzyloxycarbonyl-L-lysine N-carboxyanhydride using amine terminated polystyrene macroinitiators, followed by removal of the benzyloxycarbonyl side chain protecting groups. The critical micelle concentration of the block copolymers determined using the pyrene probe technique shows a parabolic dependence on peptide block length exhibiting a maximum at n = approximately 20 (m = 8) or n = approximately 60 (m = 10). The shape and size of the aggregates has been studied by dynamic and static light scattering, small-angle neutron scattering (SANS), and analytical ultracentrifugation (AUC). Surprisingly, Holtzer and Kratky analysis of the static light scattering results indicates the presence of nonspherical, presumably cylindrical objects independent of the poly(L-lysine)n block length. This is supported by SANS data, which can be fitted well by assuming cylindrical scattering objects. AUC analysis allows the molecular weight of the aggregates to be estimated as several million g/mol, corresponding to aggregation numbers of several 10s to 100s. These aggregation numbers agree with those that can be estimated from the length and diameter of the cylinders obtained from the scattering results.

Surface structure of thin asymmetric PS-b-PMMA diblock copolymers investigated by atomic force microscopy

Asymmetric poly(styrene-b-methyl methacrylate) (PS-b-PMMA) diblock copolymers of molecular weight M-n = 29,700g mol(-1) (M-PS = 9300 g mol(-1) M-PMMA = 20,100 g mol(-1), PD = 1.15, chi(PS) = 0.323, chi(PMMA) = 0.677) and M-n = 63,900 g mol(-1) (M-PS = 50,500 g mol(-1), M-PMMA = 13,400 g mol(-1), PD = 1.18, chi(PS) = 0.790, chi(PMMA) = 0.210) were prepared via reversible addition-fragmentation chain transfer (RAFT) polymerization. Atomic force microscopy (AFM) was used to investigate the surface structure of thin films, prepared by spin-coating the diblock copolymers on a silicon substrate. We show that the nanostructure of the diblock copolymer depends on the molecular weight and volume fraction of the diblock copolymers. We observed a perpendicular lamellar structure for the high molar mass sample and a hexagonal-packed cylindrical patterning for the lower molar mass one. Small-angle X-ray scattering investigation of these samples without annealing did not reveal any ordered structure. Annealing of PS-b-PMMA samples at 160 degrees C for 24 h led to a change in surface structure.

Neural networks for identification of nonlinear systems under random piecewise polynomial disturbances

The problem of identification of a nonlinear dynamic system is considered. A two-layer neural network is used for the solution of the problem. Systems disturbed with unmeasurable noise are considered, although it is known that the disturbance is a random piecewise polynomial process. Absorption polynomials and nonquadratic loss functions are used to reduce the effect of this disturbance on the estimates of the optimal memory of the neural-network model.

Induced random fields in the LiHoxY1-xF4 quantum ising magnet in a transverse magnetic field

The LiHoxY1-xF4 magnetic material in a transverse magnetic field Bxx̂ perpendicular to the Ising spin direction has long been used to study tunable quantum phase transitions in a random disordered system. We show that the Bx-induced magnetization along the x̂ direction, combined with the local random dilution-induced destruction of crystalline symmetries, generates, via the predominant dipolar interactions between Ho3+ ions, random fields along the Ising ẑ direction. This identifies LiHoxY1-xF4 in Bx as a new random field Ising system. The random fields explain the rapid decrease of the critical temperature in the diluted ferromagnetic regime and the smearing of the nonlinear susceptibility at the spin-glass transition with increasing Bx and render the Bx-induced quantum criticality in LiHoxY1-xF4 likely inaccessible.

Random orthogonal matrix simulation

This paper introduces a method for simulating multivariate samples that have exact means, covariances, skewness and kurtosis. We introduce a new class of rectangular orthogonal matrix which is fundamental to the methodology and we call these matrices L matrices. They may be deterministic, parametric or data specific in nature. The target moments determine the L matrix then infinitely many random samples with the same exact moments may be generated by multiplying the L matrix by arbitrary random orthogonal matrices. This methodology is thus termed “ROM simulation”. Considering certain elementary types of random orthogonal matrices we demonstrate that they generate samples with different characteristics. ROM simulation has applications to many problems that are resolved using standard Monte Carlo methods. But no parametric assumptions are required (unless parametric L matrices are used) so there is no sampling error caused by the discrete approximation of a continuous distribution, which is a major source of error in standard Monte Carlo simulations. For illustration, we apply ROM simulation to determine the value-at-risk of a stock portfolio.

Phase separation in thin films of end-grafted block copolymers

An atomic force microscopy investigation was carried out on various thick (30–120 nm) polymethyl methacrylate-bpolystyrene and poly(2-(dimethyl amino)ethyl methacrylate)-b-polystyrene films prepared via a grafting-from method. The structure of the films was examined with both topographic and phase imaging. Several different morphologies were observed including a perforated lamellar phase with irregular perforations. In addition, complementary small-angle X-ray scattering and reflectometry results measurements on a non-grafted polymer are presented.

Self-consistent field theory for diblock copolymers grafted to a sphere

An efficient numerical self-consistent field theory (SCFT) algorithm is developed for treating structured polymers on spherical surfaces. The method solves the diffusion equations of SCFT with a pseudospectral approach that combines a spherical-harmonics expansion for the angular coordinates with a modified real-space Crank–Nicolson method for the radial direction. The self-consistent field equations are solved with Anderson-mixing iterations using dynamical parameters and an alignment procedure to prevent angular drift of the solution. A demonstration of the algorithm is provided for thin films of diblock copolymer grafted to the surface of a spherical core, in which the sequence of equilibrium morphologies is predicted as a function of diblock composition. The study reveals an array of interesting behaviors as the block copolymer pattern is forced to adapt to the finite surface area of the sphere.