In solution, biopolymers commonly fold into well-defined three-dimensional structures, but only recently has analogous behavior been explored in synthetic chain molecules. An aromatic hydrocarbon backbone is described that spontaneously acquires a stable helical conformation having a large cavity. The chain does not form intramolecular hydrogen bonds, and solvophobic interactions drive the folding transition, which is sensitive to chain length, solvent quality, and temperature.

Saturation remains a problematic concept within the field of qualitative research, particularly with regard to issues of definition and process. This article sets out some of the common problems with saturation and, with reference to one research study, assesses the value of adopting a range of 'conceptual depth criteria' to address problems of definition and process when seeking to establish saturation within a grounded theory approach. It is suggested that the criteria can act as a test to measure the progress of the theoretical sampling and thus ascertain the readiness of the research for the final analytical stages and theory building. Moreover, the application of 'conceptual depth criteria' provides the researcher with an evaluative framework and a tool for producing a structured evidence base to substantiate choices made during the theoretical sampling process.

Abstract-Spectral band selection is a fundamental problem in hyperspectral data processing. In this paper, a new bandselection method based on mutual information (MI) is proposed. MI measures the statistical dependence between two random variables and can therefore be used to evaluate the relative utility of each band to classification. A new strategy is described to estimate the mutual information using a priori knowledge of the scene, reducing reliance on a 'ground truth' reference map, by retaining bands with high associated MI values (subject to certain so-called 'complementary' conditions). Simulations of classification performance on 16 classes of vegetation from the AVIRIS 92AV3C dataset show the effectiveness of the method, which outperforms an MI-based method using the associated reference map, an entropy-based method and a correlation-based method. It is also competitive with the steepest ascent (SA) algorithm at much lower computational cost.

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Abstract-Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms. However, few efforts have been made to extend SVMs to cover the specific requirements of hyperspectral image classification, for example, by building tailor-made kernels. Observation of real-life spectral imagery from the AVIRIS hyperspectral sensor shows that the useful information for classification is not equally distributed across bands, which provides potential to enhance the SVM's performance through exploring different kernel functions. Spectrally weighted kernels are, therefore, proposed, and a set of particular weights is chosen by either optimizing an estimate of generalization error or evaluating each band's utility level. To assess the effectiveness of the proposed method, experiments are carried out on the publicly available 92AV3C dataset collected from the 220-dimensional AVIRIS hyperspectral sensor. Results indicate that the method is generally effective in improving performance: spectral weighting based on learning weights by gradient descent is found to be slightly better than an alternative method based on estimating "relevance" between band information and ground truth.Index Terms-Hyperspectral image processing, mutual information (MI), remote sensing, support vector machines (SVMs).

Sequence-specific phenylacetylene oligomers consisting of functionalized monomers (hexyl benzoate, hexyl phenyl ether, benzonitrile, and tert-butylphenyl) are synthesized in gram quantities using solid-phase methods. Growing oligomers are attached to a divinylbenzene cross-linked polystyrene support by the 1-aryl-3-propyl-3-(benzyl-supported) triazene moiety. This linkage is obtained by reaction of arenediazonium tetrafluoroborate salts with a n-propylamino-modified Merrifield resin. Condensation strategies are described, producing oligomers with higher yields and simplified procedures compared to solution-phase methods. Terminal acetylene is protected with a trimethylsilyl group. After deprotection of the resin-bound terminal acetylene, an aryl iodide monomer or an aryl iodide-terminated oligomer is coupled to the supported oligomer using a palladium(0) catalyst. The cycle can be repeated to produce sequence-specific oligomers of varying length and functionality. The resulting oligomers are liberated from the polymer support by cleavage of the 1-aryl-3-propyl-3-(benzyl-supported) triazene group by reaction with iodomethane producing an aryl iodide.

Because of the difficulty of obtaining an analytic expression for Bayes error, a wide variety of separability measures has been proposed for feature selection. In this paper, we show that there is a general framework based on the criterion of mutual information (MI) that can provide a realistic solution to the problem of feature selection for high-dimensional data. We give a theoretical argument showing that the mutual information of multidimensional data can be broken down into several one-dimensional components, which makes numerical evaluation much easier and more accurate. It also reveals that selection based on the simple criterion of only retaining features with high associated MI values may be problematic when the features are highly correlated. Although there is a direct way of selecting features by jointly maximising mutual information, this suffers from combinatorial explosion. Hence, we propose a fast feature selection scheme based on a 'greedy' optimisation strategy. To confirm the effectiveness of this scheme, simulations are carried out on 16 land-cover classes using the 92AV3C dataset collected from the 220-dimensional AVIRIS hyperspectral sensor. We replicate our earlier positive results (which used an essentially heuristic method for MI-based band-selection) but with much reduced computational cost and a much sounder theoretical basis.

The time-evolving precision matrix of a piecewise-constant Gaussian graphical model encodes the dynamic conditional dependency structure of a multivariate time- is drawn identically from a generating distribution. Introducing sparsity and sparsedifference inducing priors we relax these assumptions and propose a novel regularized M-estimator to jointly estimate both the graph and changepoint structure. The resulting estimator possesses the ability to therefore favor sparse dependency structures and/or smoothly evolving graph structures, as required. Moreover, our approach extends current methods to allow estimation of changepoints that are grouped across multiple dependencies in a system. An efficient algorithm for estimating structure is proposed. We study the empirical recovery properties in a synthetic setting. The qualitative effect of grouped changepoint estimation is then demonstrated by applying the method on a genetic time-course data-set.