Parallel Hybrid Monte Carlo Algorithms for Matrix Computations

In this paper we consider hybrid (fast stochastic approximation and deterministic refinement) algorithms for Matrix Inversion (MI) and Solving Systems of Linear Equations (SLAE). Monte Carlo methods are used for the stochastic approximation, since it is known that they are very efficient in finding a quick rough approximation of the element or a row of the inverse matrix or finding a component of the solution vector. We show how the stochastic approximation of the MI can be combined with a deterministic refinement procedure to obtain MI with the required precision and further solve the SLAE using MI. We employ a splitting A = D – C of a given non-singular matrix A, where D is a diagonal dominant matrix and matrix C is a diagonal matrix. In our algorithm for solving SLAE and MI different choices of D can be considered in order to control the norm of matrix T = D –1C, of the resulting SLAE and to minimize the number of the Markov Chains required to reach given precision. Further we run the algorithms on a mini-Grid and investigate their efficiency depending on the granularity. Corresponding experimental results are presented.

Parallel Monte Carlo algorithms for information retrieval

In any data mining applications, automated text and text and image retrieval of information is needed. This becomes essential with the growth of the Internet and digital libraries. Our approach is based on the latent semantic indexing (LSI) and the corresponding term-by-document matrix suggested by Berry and his co-authors. Instead of using deterministic methods to find the required number of first "k" singular triplets, we propose a stochastic approach. First, we use Monte Carlo method to sample and to build much smaller size term-by-document matrix (e.g. we build k x k matrix) from where we then find the first "k" triplets using standard deterministic methods. Second, we investigate how we can reduce the problem to finding the "k"-largest eigenvalues using parallel Monte Carlo methods. We apply these methods to the initial matrix and also to the reduced one. The algorithms are running on a cluster of workstations under MPI and results of the experiments arising in textual retrieval of Web documents as well as comparison of the stochastic methods proposed are presented. (C) 2003 IMACS. Published by Elsevier Science B.V. All rights reserved.

Complexity of Monte Carlo algorithms for a class of integral equations

In this work we study the computational complexity of a class of grid Monte Carlo algorithms for integral equations. The idea of the algorithms consists in an approximation of the integral equation by a system of algebraic equations. Then the Markov chain iterative Monte Carlo is used to solve the system. The assumption here is that the corresponding Neumann series for the iterative matrix does not necessarily converge or converges slowly. We use a special technique to accelerate the convergence. An estimate of the computational complexity of Monte Carlo algorithm using the considered approach is obtained. The estimate of the complexity is compared with the corresponding quantity for the complexity of the grid-free Monte Carlo algorithm. The conditions under which the class of grid Monte Carlo algorithms is more efficient are given.

Robustness and applicability of Markov chain Monte Carlo algorithms for eigenvalue problems

In this paper we analyse applicability and robustness of Markov chain Monte Carlo algorithms for eigenvalue problems. We restrict our consideration to real symmetric matrices. Almost Optimal Monte Carlo (MAO) algorithms for solving eigenvalue problems are formulated. Results for the structure of both - systematic and probability error are presented. It is shown that the values of both errors can be controlled independently by different algorithmic parameters. The results present how the systematic error depends on the matrix spectrum. The analysis of the probability error is presented. It shows that the close (in some sense) the matrix under consideration is to the stochastic matrix the smaller is this error. Sufficient conditions for constructing robust and interpolation Monte Carlo algorithms are obtained. For stochastic matrices an interpolation Monte Carlo algorithm is constructed. A number of numerical tests for large symmetric dense matrices are performed in order to study experimentally the dependence of the systematic error from the structure of matrix spectrum. We also study how the probability error depends on the balancing of the matrix. (c) 2007 Elsevier Inc. All rights reserved.

Modified radial basis function neural network using output transformation

A modified radial basis function (RBF) neural network and its identification algorithm based on observational data with heterogeneous noise are introduced. The transformed system output of Box-Cox is represented by the RBF neural network. To identify the model from observational data, the singular value decomposition of the full regression matrix consisting of basis functions formed by system input data is initially carried out and a new fast identification method is then developed using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator (MLE) for a model base spanned by the largest eigenvectors. Finally, the Box-Cox transformation-based RBF neural network, with good generalisation and sparsity, is identified based on the derived optimal Box-Cox transformation and an orthogonal forward regression algorithm using a pseudo-PRESS statistic to select a sparse RBF model with good generalisation. The proposed algorithm and its efficacy are demonstrated with numerical examples.

Experimental design and model construction algorithms for radial basis function networks

New construction algorithms for radial basis function (RBF) network modelling are introduced based on the A-optimality and D-optimality experimental design criteria respectively. We utilize new cost functions, based on experimental design criteria, for model selection that simultaneously optimizes model approximation, parameter variance (A-optimality) or model robustness (D-optimality). The proposed approaches are based on the forward orthogonal least-squares (OLS) algorithm, such that the new A-optimality- and D-optimality-based cost functions are constructed on the basis of an orthogonalization process that gains computational advantages and hence maintains the inherent computational efficiency associated with the conventional forward OLS approach. The proposed approach enhances the very popular forward OLS-algorithm-based RBF model construction method since the resultant RBF models are constructed in a manner that the system dynamics approximation capability, model adequacy and robustness are optimized simultaneously. The numerical examples provided show significant improvement based on the D-optimality design criterion, demonstrating that there is significant room for improvement in modelling via the popular RBF neural network.

Are differences in life history parameters of the pine beauty moth Panolis flammea modified by host plant quality or gender?

Pine beauty moth (Panolis flammea D&S, Lepidoptera: Noctuidae) were reared individually from egg hatch to pupation on one of three host plants, Pinus sylvestris (native host plant), Pinus contorta (Central Interior seed origin - good quality introduced host) and P. contorta (Alaskan seed origin - poor quality introduced host). After emerging from the pupae the adult moths were confined to a Skeena River seed origin of P. contorta. Female pupal weight and adult life span were significantly higher on P. sylvestris than on the two lodgepole pine seed origins. Development time was, however, not significantly different between treatments, but larval mean relative growth rate was found to be negatively correlated with birth weight and positively correlated with pupal weight. The time to emerge from the pupa was also not significantly different between treatments. However, there were marked differences between the genders. Male moths lost a significantly greater proportion of their weight over the pupal stage but lived significantly longer as adults than the females. Female moths emerged from the pupal stage significantly sooner than male moths. There was no apparent advantage of lai-ge birth size when looked at in terms of subsequent performance. These results are discussed in light of current life history theory.

Self-assembly of PEGylated peptide conjugates containing a modified amyloid β-peptide fragment

The self-assembly of PEGylated peptides containing a modified sequence from the amyloid beta peptide, FEK LVFF, has been studied in aqueous solution. PEG molar masses PEG1k, PEG2k, and PEG10k were used in the conjugates. It is shown that the three FFK LVFF-PEG hybrids form fibrils comprising a FFKLVFF core and a PEG corona. The beta-sheet secondary structure of the peptide is retained in the FFK LVFF fibril core. At sufficiently high concentrations, FEK LVFF-PEG1k and FEK LVFF-PEG2k form a nema tic phase, while PEG10k-FEK LVFF exhibits a hexagonal columnar phase. Simultaneous small angle neutron scattering/shear flow experiments were performed to study the shear flow alignment of the nematic and hexagonal liquid crystal phases. On drying, PEG crystallization occurs without disruption of the FFK LVFF beta-sheet structure leading to characteristic peaks in the X-ray diffraction pattern and FTIR spectra. The stability of beta-sheet structures was also studied in blends of FFKLVFF-PEG conjugates with poly(acrylic acid) (PAA). While PEG crystallization is only observed up to 25% PAA content in the blends, the FFK LVFF beta-sheet structure is retained up to 75% PAA.

A β-amino acid modified heptapeptide containing a designed recognition element disrupts fibrillization of the amyloid β-peptide

We study the complex formation of a peptide betaAbetaAKLVFF, previously developed by our group, with Abeta(1–42) in aqueous solution. Circular dichroism spectroscopy is used to probe the interactions between betaAbetaAKLVFF and Abeta(1–42), and to study the secondary structure of the species in solution. Thioflavin T fluorescence spectroscopy shows that the population of fibers is higher in betaAbetaAKLVFF/Abeta(1–42) mixtures compared to pure Abeta(1–42) solutions. TEM and cryo-TEM demonstrate that co-incubation of betaAbetaAKLVFF with Abeta(1–42) causes the formation of extended dense networks of branched fibrils, very different from the straight fibrils observed for Abeta(1–42) alone. Neurotoxicity assays show that although betaAbetaAKLVFF alters the fibrillization of Abeta(1–42), it does not decrease the neurotoxicity, which suggests that toxic oligomeric Abeta(1–42) species are still present in the betaAbetaAKLVFF/Abeta(1–42) mixtures. Our results show that our designed peptide binds to Abeta(1–42) and changes the amyloid fibril morphology. This is shown to not necessarily translate into reduced toxicity.

Self-assembly of a modified amyloid peptide fragment: pH-responsiveness and nematic phase formation

The self-assembly of peptide YYKLVFFC based on a fragment of the amyloid beta (A) peptide, A beta 16-20, KLVFF has been studied in aqueous solution. The peptide is designed with multiple functional residues to examine the interplay between aromatic interactions and charge on the self-assembly, as well as specific transformations such as the pH-induced phenol-phenolate transition of the tyrosine residue. Circular dichroism (CD) and Fourier-transform infrared (FTIR) spectroscopies are used to investigate the conditions for beta-sheet self-assembly and the role of aromatic interactions in the CD spectrum as a function of pH and concentration. The formation of well-defined fibrils at pH 4.7 is confirmed by cryo-TEM (transmission electron microscope) and negative stain TEM. The morphology changes at higher pH, and aggregates of short twisted fibrils are observed at pH 11. Polarized optical microscopy shows birefringence at a low concentration (1 wt.-%) of YYKLVFFC in aqueous solution, and small-angle X-ray scattering was used to probe nematic phase formation in more detail. A pH-induced transition from nematic to isotropic phases is observed on increasing pH that appears to be correlated to a reduction in aggregate anisotropy upon increasing pH.

Influence of the solvent on the self-assembly of a modified amyloid beta peptide fragment. II. NMR and computer simulation investigation

The conformation of a model peptide AAKLVFF based on a fragment of the amyloid beta peptide A beta 16-20, KLVFF, is investigated in methanol and water via solution NMR experiments and Molecular dynamics computer simulations. In previous work, we have shown that AAKLVFF forms peptide nanotubes in methanol and twisted fibrils in water. Chemical shift measurements were used to investigate the solubility of the peptide as a function of concentration in methanol and water. This enabled the determination of critical aggregation concentrations, The Solubility was lower in water. In dilute solution, diffusion coefficients revealed the presence of intermediate aggregates in concentrated solution, coexisting with NMR-silent larger aggregates, presumed to be beta-sheets. In water, diffusion coefficients did not change appreciably with concentration, indicating the presence mainly of monomers, coexisting with larger aggregates in more concentrated solution. Concentration-dependent chemical shift measurements indicated a folded conformation for the monomers/intermediate aggregates in dilute methanol, with unfolding at higher concentration. In water, an antiparallel arrangement of strands was indicated by certain ROESY peak correlations. The temperature-dependent solubility of AAKLVFF in methanol was well described by a van't Hoff analysis, providing a solubilization enthalpy and entropy. This pointed to the importance of solvophobic interactions in the self-assembly process. Molecular dynamics Simulations constrained by NOE values from NMR suggested disordered reverse turn structures for the monomer, with an antiparallel twisted conformation for dimers. To model the beta-sheet structures formed at higher concentration, possible model arrangements of strands into beta-sheets with parallel and antiparallel configurations and different stacking sequences were used as the basis for MD simulations; two particular arrangements of antiparallel beta-sheets were found to be stable, one being linear and twisted and the other twisted in two directions. These structures Were used to simulate Circular dichroism spectra. The roles of aromatic stacking interactions and charge transfer effects were also examined. Simulated spectra were found to be similar to those observed experimentally.(in water or methanol) which show a maximum at 215 or 218 nm due to pi-pi* interactions, when allowance is made for a 15-18 nm red-shift that may be due to light scattering effects.

Fibrillisation of hydrophobically modified amyloid peptide fragments in an organic solvent

The self-assembly of a hydrophobically modified fragment of the amyloid beta(A beta) peptide has been studied in methanol. The peptide FFKLVFF is based on A beta(16-20) extended at the N terminus by two phenylalanine residues. The formation of amyloid-type fibrils is confirmed by Congo Red staining, thioflavin T fluorescence and circular dichroism experiments. FTIR points to the formation of beta-sheet structures in solution and in dried films and suggests that aggregation occurs at low concentration and is not strongly affected by further increase in concentration, i.e. the peptide is a strong fibril-former in methanol. UV fluorescence experiments on unstained peptide and CD point to the importance of aromatic interactions between phenylalanine groups in driving aggregation into beta-sheets. The CD spectrum differs from that usually observed for beta-sheet assemblies formed by larger peptides or proteins and this is discussed for solutions in methanol and also trifluoroethanol. The fibril structure is imaged by transmission electron microscopy and scanning electron microscopy on dried samples and is confirmed by small-angle X-ray scattering experiments in solution.

Regression based D-optimality experimental design for sparse kernel density estimation

This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates.

Modified level set method with Canny operator for image noise removal

The level set method is commonly used to address image noise removal. Existing studies concentrate mainly on determining the speed function of the evolution equation. Based on the idea of a Canny operator, this letter introduces a new method of controlling the level set evolution, in which the edge strength is taken into account in choosing curvature flows for the speed function and the normal to edge direction is used to orient the diffusion of the moving interface. The addition of an energy term to penalize the irregularity allows for better preservation of local edge information. In contrast with previous Canny-based level set methods that usually adopt a two-stage framework, the proposed algorithm can execute all the above operations in one process during noise removal.

Silyl-Modified Analogues of 2-Tritylpyrrolidine: Synthesis and Applications in Asymmetric Organocatalysis

Silicon-based organocatalysts: In an effort to study the effects of substituting carbon by silicon within the catalyst backbone, we developed an efficient synthesis of (S)-2-triphenylsilylpyrrolidine [(S)-2]. The evaluation of (S)-2 against its carbon analogue (S)-1 in two organocatalytic reactions is complemented by computational studies.