Approximate Gauss-Newton methods for nonlinear least squares problems

The Gauss–Newton algorithm is an iterative method regularly used for solving nonlinear least squares problems. It is particularly well suited to the treatment of very large scale variational data assimilation problems that arise in atmosphere and ocean forecasting. The procedure consists of a sequence of linear least squares approximations to the nonlinear problem, each of which is solved by an “inner” direct or iterative process. In comparison with Newton’s method and its variants, the algorithm is attractive because it does not require the evaluation of second-order derivatives in the Hessian of the objective function. In practice the exact Gauss–Newton method is too expensive to apply operationally in meteorological forecasting, and various approximations are made in order to reduce computational costs and to solve the problems in real time. Here we investigate the effects on the convergence of the Gauss–Newton method of two types of approximation used commonly in data assimilation. First, we examine “truncated” Gauss–Newton methods where the inner linear least squares problem is not solved exactly, and second, we examine “perturbed” Gauss–Newton methods where the true linearized inner problem is approximated by a simplified, or perturbed, linear least squares problem. We give conditions ensuring that the truncated and perturbed Gauss–Newton methods converge and also derive rates of convergence for the iterations. The results are illustrated by a simple numerical example. A practical application to the problem of data assimilation in a typical meteorological system is presented.

Implicit and explicit knowledge bases in artificial grammar learning

Two experiments examined the claim for distinct implicit and explicit learning modes in the artificial grammar-learning task (Reber, 1967, 1989). Subjects initially attempted to memorize strings of letters generated by a finite-state grammar and then classified new grammatical and nongrammatical strings. Experiment 1 showed that subjects' assessment of isolated parts of strings was sufficient to account for their classification performance but that the rules elicited in free report were not sufficient. Experiment 2 showed that performing a concurrent random number generation task under different priorities interfered with free report and classification performance equally. Furthermore, giving different groups of subjects incidental or intentional learning instructions did not affect classification or free report.

Genetic diversity and structure of natural and managed populations of Cedrus atlantica (Pinaceae) assessed using random amplified polymorphic DNA

Cedrus atlantica (Pinaceae) is a large and exceptionally long-lived conifer native to the Rif and Atlas Mountains of North Africa. To assess levels and patterns of genetic diversity of this species. samples were obtained throughout the natural range in Morocco and from a forest plantation in Arbucies, Girona (Spain) and analyzed using RAPD markers. Within-population genetic diversity was high and comparable to that revealed by isozymes. Managed populations harbored levels of genetic variation similar to those found in their natural counterparts. Genotypic analyses Of Molecular variance (AMOVA) found that most variation was within populations. but significant differentiation was also found between populations. particularly in Morocco. Bayesian estimates of F,, corroborated the AMOVA partitioning and provided evidence for Population differentiation in C. atlantica. Both distance- and Bayesian-based Clustering methods revealed that Moroccan populations comprise two genetically distinct groups. Within each group, estimates of population differentiation were close to those previously reported in other gymnosperms. These results are interpreted in the context of the postglacial history of the species and human impact. The high degree of among-group differentiation recorded here highlights the need for additional conservation measures for some Moroccan Populations of C. atlantica.

EfeUOB (YcdNOB) is a tripartite, acid-induced and CpxAR-regulated, low-pH Fe2+ transporter that is cryptic in Escherichia coli K-12 but functional in E-coli O157 : H7

Escherichia coli possesses iron transporters specific for either Fe2+ or Fe3+. Although Fe2+ is far more soluble than Fe3+, it rapidly oxidizes aerobically at pH >= 7. Thus, FeoAB, the major Fe2+ transporter of E. coli, operates anaerobically. However, Fe2+ remains stable aerobically under acidic conditions, although a low-pH Fe2+ importer has not been previously identified. Here we show that ycdNOB (efeUOB) specifies the first such transporter. efeUOB is repressed at high pH by CpxAR, and is Fe2+-Fur repressed. EfeU is homologous to the high-affinity iron permease, Ftr1p, of Saccharomyces cerevisiae and other fungi. EfeO is periplasmic with a cupredoxin N-terminal domain; EfeB is also periplasmic and is haem peroxidase-like. All three Efe proteins are required for Efe function. The efeU gene of E. coli K-12 is cryptic due to a frameshift mutation - repair of the single-base-pair deletion generates a functional EfeUOB system. In contrast, the efeUOB operon of the enterohaemorrhagic strain, O157:1147, lacks any frameshift and is functional. A 'wild-type' K-12 strain bearing a functional EfeUOB displays a major growth advantage under aerobic, low-pH, low-iron conditions when a competing metal is provided. Fe-55 transport assays confirm the ferrous iron specificity of EfeUOB.

A carbene insertion approach to functionalised poly(ethylene oxide)-based gels

Carbenes photogenerated from the novel bisdiazirine, 1, 3-bis(3-(trifluoromethyl)diazirin-3-yl) benzene 1, have been applied successfully to cross-linking of mono-methyl poly(ethylene oxide) (MePEO5000) in the presence of dichloromethane, leading to the simultaneous incorporation of alkylhalide functionalities. The PEO-based gels swell in a wide range of solvents with polarity index values varying from 3.1 to 9.0. Reaction of the alkylhalide functionalities present in the gels with 4-phenylazophenol provided loading capacities of up to 0.20 mmol g(-1) and demonstrated the potential of these materials for gel-phase synthesis applications. (C) 2008 Elsevier Ltd. All rights reserved.

Comparative analysis of four beta-galactosidases from Bifidobacterium bifidum NCIMB41171: purification and biochemical characterisation

Four different beta-galactosidases (previously named BbgI, BbgII, BbgIII and BbgIV) from Bifidobacterium bifidum NCIMB41171 were overexpressed in Escherichia coli, purified to homogeneity and their biochemical properties and substrate preferences comparatively analysed. BbgI was forming a hexameric protein complex of 875 kDa, whereas BbgII, BbgIII and BbgIV were dimers with native molecular masses of 178, 351 and 248 kDa, respectively. BbgII was the only enzyme that preferred acidic conditions for optimal activity (pH 5.4-5.8), whereas the other three exhibited optima in more neutral pH ranges (pH 6.4-6.8). Na+ and/or K+ ions were prerequisite for BbgI and BbgIV activity in Bis-Tris-buffered solutions, whereas Mg++ was strongly activating them in phosphate-buffered solutions. BbgII and BbgIII were slightly influenced from the presence or absence of cations, with Mg++, Mn++ and Ca++ ions exerting the most positive effect. Determination of the specificity constants (k(cat)/K-m) clearly indicated that BbgI (6.11 x 10(4) s(-1) M-1), BbgIII (2.36 x 10(4) s(-1) M-1) and especially BbgIV (4.01 x 10(5) s(-1) M-1) are highly specialised in the hydrolysis of lactose, whereas BbgII is more specific for beta-D-(1 -> 6) galactobiose (5.59 x 10(4) s(-1) M-1) than lactose (1.48 x 10(3) s(-1) M-1). Activity measurements towards other substrates (e. g. beta-D-(1 -> 6) galactobiose, beta-D-(1 -> 4) galactobiose, beta-D-(1 -> 4) galactosyllactose, N-acetyllactosamine, etc.) indicated that the beta-galactosidases were complementary to each other by hydrolysing different substrates and thus contributing in a different way to the bacterial physiology.

Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms

Objective: This paper presents a detailed study of fractal-based methods for texture characterization of mammographic mass lesions and architectural distortion. The purpose of this study is to explore the use of fractal and lacunarity analysis for the characterization and classification of both tumor lesions and normal breast parenchyma in mammography. Materials and methods: We conducted comparative evaluations of five popular fractal dimension estimation methods for the characterization of the texture of mass lesions and architectural distortion. We applied the concept of lacunarity to the description of the spatial distribution of the pixel intensities in mammographic images. These methods were tested with a set of 57 breast masses and 60 normal breast parenchyma (dataset1), and with another set of 19 architectural distortions and 41 normal breast parenchyma (dataset2). Support vector machines (SVM) were used as a pattern classification method for tumor classification. Results: Experimental results showed that the fractal dimension of region of interest (ROIs) depicting mass lesions and architectural distortion was statistically significantly lower than that of normal breast parenchyma for all five methods. Receiver operating characteristic (ROC) analysis showed that fractional Brownian motion (FBM) method generated the highest area under ROC curve (A z = 0.839 for dataset1, 0.828 for dataset2, respectively) among five methods for both datasets. Lacunarity analysis showed that the ROIs depicting mass lesions and architectural distortion had higher lacunarities than those of ROIs depicting normal breast parenchyma. The combination of FBM fractal dimension and lacunarity yielded the highest A z value (0.903 and 0.875, respectively) than those based on single feature alone for both given datasets. The application of the SVM improved the performance of the fractal-based features in differentiating tumor lesions from normal breast parenchyma by generating higher A z value. Conclusion: FBM texture model is the most appropriate model for characterizing mammographic images due to self-affinity assumption of the method being a better approximation. Lacunarity is an effective counterpart measure of the fractal dimension in texture feature extraction in mammographic images. The classification results obtained in this work suggest that the SVM is an effective method with great potential for classification in mammographic image analysis.

The role of competition and state aid policy in financial and monetary law

During the financial crisis, companies and lenders found themselves in distressed situations. Competition authorities across the globe had to deal with controversial issues such as the application of the failing firm defence in merger transactions as well as assessment of emergency aid granted by states. This article considers competition policy in periods of crisis, in particular the failing firm defence in merger control and its state aid policy.

New algorithms for synthesized design of optical filters

Chebyshev optical-filter algorithms for low-cost microcomputers have been improved. An offset ripple is now used for better transmission/matching in low-pass stacks. A prototype for narrowband filters is now more general and nearer practicability.

Conscientiousness predicts greater recovery from negative emotion

Greater levels of conscientiousness have been associated with lower levels of negative affect. We focus on one mechanism through which conscientiousness may decrease negative affect: effective emotion regulation, as reflected by greater recovery from negative stimuli. In 273 adults who were 35 - 85 years old, we collected self-report measures of personality including conscientiousness and its self-control facet, followed on average 2 years later by psychophysiological measures of emotional reactivity and recovery. Among middle-aged adults (35 - 65 years old), the measures of conscientiousness and self-control predicted greater recovery from, but not reactivity to, negative emotional stimuli. The effect of conscientiousness and self-control on recovery was not driven by other personality variables or by greater task adherence on the part of high conscientiousness individuals. In addition, the effect was specific to negative emotional stimuli and did not hold for neutral or positive emotional stimuli.

Prediction of a typhoon using a fine-mesh NWP model

The ECMWF operational grid point model (with a resolution of 1.875° of latitude and longitude) and its limited area version (with a resolution of !0.47° of latitude and longitude) with boundary values from the global model have been used to study the simulation of the typhoon Tip. The fine-mesh model was capable of simulating the main structural features of the typhoon and predicting a fall in central pressure of 60 mb in 3 days. The structure of the forecast typhoon, with a warm core (maximum potential temperature anomaly 17 K). intense swirling wind (maximum 55 m s-1 at 850 mb) and spiralling precipitation patterns is characteristic of a tropical cyclone. Comparison with the lower resolution forecast shows that the horizontal resolution is a determining factor in predicting not only the structure and intensity but even the movement of these vortices. However, an accurate and refined initial analysis is considered to be a prerequisite for a correct forecast of this phenomenon.

An effective approach for selection of terrain modelling method

This letter presents an effective approach for selection of appropriate terrain modeling methods in forming a digital elevation model (DEM). This approach achieves a balance between modeling accuracy and modeling speed. A terrain complexity index is defined to represent a terrain's complexity. A support vector machine (SVM) classifies terrain surfaces into either complex or moderate based on this index associated with the terrain elevation range. The classification result recommends a terrain modeling method for a given data set in accordance with its required modeling accuracy. Sample terrain data from the lunar surface are used in constructing an experimental data set. The results have shown that the terrain complexity index properly reflects the terrain complexity, and the SVM classifier derived from both the terrain complexity index and the terrain elevation range is more effective and generic than that designed from either the terrain complexity index or the terrain elevation range only. The statistical results have shown that the average classification accuracy of SVMs is about 84.3% ± 0.9% for terrain types (complex or moderate). For various ratios of complex and moderate terrain types in a selected data set, the DEM modeling speed increases up to 19.5% with given DEM accuracy.