34 resultados para self-organizing maps of Kohonen


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Intensities and self-broadening coefficients are presented for about 460 of the strongest water vapour lines in the spectral regions 1400–1840 cm−1 and 3440–3970 cm−1 at room temperature, obtained from rather unique measurements using a 5-mm-path-length cell. The retrieved spectral line parameters are compared with those in the HITRAN database ver. 2008 and 2012 and with recent ab-initio calculations. Both the retrieved intensities and half-widths are on average in reasonable agreement with those in HITRAN-2012. Maximum systematic differences do not exceed 4% for intensities (1600 cm−1 band) and 7% for self-broadening coefficients (3600 cm−1 band). For many lines however significant disagreements were detected with the HITRAN-2012 data, exceeding the average uncertainty of the retrieval. In addition, water vapour line parameters for 5300 cm−1 (1.9 μm) band reported by us in 2005 were also compared with HITRAN-2012, and show average differences of 4–5% for both intensities and half-widths.

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This paper explores the identities projected in advertisements directed towards HIV positive individuals and people with AIDS. Fifty such advertisements were collected from three popular American magazines for gay men over a period of seven months. Analysis of the ads reveals a paradoxical presentation of people with HIV/AIDS, which offers simultaneous conflicting images of hope and fear, power and weakness, innocence and guilt. An interactive sociolinguistic model through which this contradictory discourse might be understood is presented, drawing on Goffman’s insights on stigma management and the presentation of the self in social interaction. Advertisements directed towards people with HIV/AIDS, it is suggested, present a contradictory discourse in which the advertisers are positioned as ‘the wise’, offering to mediate the conflicting identities of the stigmatized. The identity values enacted in this contradictory discourse are further measured against American conceptions of communication and the self as observed by Carbaugh and others. The possible consequences of these positionings on the roles made available to people with HIV/AIDS in the wider social context are discussed.

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Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. This paper presents a description of the HLS architecture and an analysis of the modified feature map implementing associative memory. Initial results are presented that demonstrate the behaviour of the system on a simple control task.

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It has been shown through a number of experiments that neural networks can be used for a phonetic typewriter. Algorithms can be looked on as producing self-organizing feature maps which correspond to phonemes. In the Chinese language the utterance of a Chinese character consists of a very simple string of Chinese phonemes. With this as a starting point, a neural network feature map for Chinese phonemes can be built up. In this paper, feature map structures for Chinese phonemes are discussed and tested. This research on a Chinese phonetic feature map is important both for Chinese speech recognition and for building a Chinese phonetic typewriter.

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The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensional data. Several experiments are used to compare the proposed approach with the original algorithm and some of its modification and speed-up techniques.

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This article explores the Foucauldian notions of practices of the self and care of the self, read via Deleuze, in the context of Iyengar yoga (one of the most popular forms of yoga currently). Using ethnographic and interview research data the article outlines the Iyengar yoga techniques which enable a focus upon the self to be developed, and the resources offered by the practice for the creation of ways of knowing, experiencing and forming the self. In particular, the article asks whether Iyengar yoga offers possibilities for freedom and liberation, or whether it is just another practice of control and management. Assessing Iyengar yoga via a ‘critical function’, a function of ‘struggle’ and a ‘curative and therapeutic function’, the article analyses whether the practice might constitute a mode of care of the self, and what it might offer in the context of the contemporary need to live better, as well as longer.

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Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions.

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Single crystal X-ray diffraction studies and solvent dependent NMR titration reveal that the designed pepticles I and 11, Boc-Xx(1)-Aib(2)-Yy(3)-NH(CH2)(2)NH-Yy(3)-Aib(2)-Xx(1)-Boc, where Xx and Yy are lie and Leu in peptide I and Leu and Val in peptide 11, respectively, fold into a turn-linker-turn (T-L-T) conformation both in the solid state and in solution. In the crystalline state the T-L-T foldamers; of peptide I and II self-assemble to form a three-dimensional framework of channels. The insides of the channels are hydrophilic and found to contain solvent CHCl3 hydrogen bonded to exposed C=O of Aib located at the turn regions. (c) 2008 Elsevier B.V. All rights reserved.

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We have described here the self-assembling properties of the synthetic tripeptides Boc-Ala(1)-Aib(2) -Val (3)-OMe 1, BocAla(l)-Aib(2)-Ile(3)-OMe 2 and Boc-Ala(l)-Gly(2)-Val(3)-OMe 3 (Aib=alpha-arnino isobutyric acid, beta-Ala=beta-alanine) which have distorted beta-turn conformations in their respective crystals. These turn-forming tripeptides self-assemble to form supramolecular beta-sheet structures through intermolecular hydrogen bonding and other noncovalent interactions. The scanning electron micrographs of these peptides revealed that these compounds form amyloid-like fibrils, the causative factor for many neurodegenerative diseases including Alzheimer's disease, Huntington's disease and Prion-related encephalopathies. (C) 2004 Elsevier Ltd. All rights reserved.

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Self-report measures of obsessive-compulsive disorder (OCD) in children and adolescents are needed for practical evaluation of severity and treatment response. We compared the self- and parent-report Obsessional Compulsive Inventory Revised (CHOCI-R) to the interview-based Child Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) in a clinical sample of 285 children and adolescents with OCD. Classical test theory and item-response theory were applied to compare the instruments. The self- and parent-report CHOCI-R had good internal consistency and were strongly related to each other. The self- and parent-report CHOCI-R severity scores correlated with the CY-BOCS (Pearson's r 0.55 and 0.45 respectively). The CY-BOCS discriminated better at the severe end of the spectrum. The CHOCI-R provided better discrimination in the mild to moderate range. The time-efficient self- and parent-report alternatives will enable routine measurement of OCD severity in clinical practice. Estimates of equivalent summed scores are provided to facilitate comparison. (c) 2007 Elsevier Ltd. All rights reserved.

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A novel framework for multimodal semantic-associative collateral image labelling, aiming at associating image regions with textual keywords, is described. Both the primary image and collateral textual modalities are exploited in a cooperative and complementary fashion. The collateral content and context based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix, of the visual keywords, A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. Finally, we use Self Organising Maps to examine the classification and retrieval effectiveness of the proposed high-level image feature vector model which is constructed based on the image labelling results.

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We have discovered a novel approach of intrusion detection system using an intelligent data classifier based on a self organizing map (SOM). We have surveyed all other unsupervised intrusion detection methods, different alternative SOM based techniques and KDD winner IDS methods. This paper provides a robust designed and implemented intelligent data classifier technique based on a single large size (30x30) self organizing map (SOM) having the capability to detect all types of attacks given in the DARPA Archive 1999 the lowest false positive rate being 0.04 % and higher detection rate being 99.73% tested using full KDD data sets and 89.54% comparable detection rate and 0.18% lowest false positive rate tested using corrected data sets.

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The identification and visualization of clusters formed by motor unit action potentials (MUAPs) is an essential step in investigations seeking to explain the control of the neuromuscular system. This work introduces the generative topographic mapping (GTM), a novel machine learning tool, for clustering of MUAPs, and also it extends the GTM technique to provide a way of visualizing MUAPs. The performance of GTM was compared to that of three other clustering methods: the self-organizing map (SOM), a Gaussian mixture model (GMM), and the neural-gas network (NGN). The results, based on the study of experimental MUAPs, showed that the rate of success of both GTM and SOM outperformed that of GMM and NGN, and also that GTM may in practice be used as a principled alternative to the SOM in the study of MUAPs. A visualization tool, which we called GTM grid, was devised for visualization of MUAPs lying in a high-dimensional space. The visualization provided by the GTM grid was compared to that obtained from principal component analysis (PCA). (c) 2005 Elsevier Ireland Ltd. All rights reserved.

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The influence of charge and aromatic stacking interactions on the self-assembly of a series of four model amyloid peptides has been examined. The four model peptides are based on the KLVFF motif from the amyloid Beta peptide, ABeta(16-20) extended at the N terminus with two Beta-alanine residues. We have studied NH2-BetaABetaAKLVFF-COOH (FF), NH2-BetaABetaAKLVFCOOH (F), CH3CONH-BetaABetaAKLVFF-CONH2 (CapF), and CH3CONH-BetaABetaAKLVFFCONH2 (CapFF). The former two are uncapped (net charge plus 2) and differ by one hydrophobic phenylalanine residue; the latter two are the analogous capped peptides (net charge plus 1). The self-assembly characteristics of these peptides are remarkably different and strongly dependent on concentration. NMR shows a shift from carboxylate to carboxylic acid forms upon increasing concentration. Saturation transfer measurements of solvent molecules indicate selective involvement of phenylalanine residues in driving the self-assembly process of CapFF due presumably to the effect of aromatic stacking interactions. FTIR spectroscopy reveals beta-sheet features for the two peptides containing two phenylalanine residues but not the single phenylalanine residue, pointing again to the driving force for self-assembly. Circular dichroism (CD) in dilute solution reveals the polyproline II conformation, except for F which is disordered. We discuss the relationship of this observation to the significant pH shift observed for this peptide when compared the calculated value. Atomic force microscopy and cryogenic-TEM reveals the formation of twisted fibrils for CapFF, as previously also observed for FF. The influence of salt on the self-assembly of the model beta-sheet forming capped peptide CapFF was investigated by FTIR. Cryo-TEM reveals that the extent of twisting decreases with increased salt concentration, leading to the formation of flat ribbon structures. These results highlight the important role of aggregation-induced pKa shifts in the self-assembly of model beta-sheet peptides.