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X-ray absorption spectroscopy is a versatile tool to investigate oxidation state and molecular structure or uranium in aqueous and solid phases and at their common interface. However, the structural analysis is often hampered by limited resolution and range. I will show recent advances to overcome these limitations, including improved XAFS data analysis approaches like Monte-Carlo and Landweber methods; coupling to XAFS-independent methods like DFT and surface complexation modeling as well as to other x-ray methods like HEXS which have a longer detection range. I will demonstrate the usefulness of these methods by showing recent results on uranyl sorption to mineral surfaces, with a focus on polynuclear and carbonate complexes; Fe(II)-driven interfacial redox processes of uranyl, also in comparison to other actinoides; colloid formation processes of U(IV) and its tetravalent actinoide neighbors.

У статті наведені результати аналізу фінансового стану банків за допомогою нейронної мережевої моделі кластеризації – карти Кохонена. За наслідками групування банків зі спільними характеристиками встановлено основні напрямки розвитку, кожний з яких пов’язаний із конкретними профілями ризиків і стратегіями управління. The article presents the results of the analysis of financial condition of banks by the neural network model for clustering – Kohonen maps. As a result, groups of banks with general characteristics identified major areas of development that are associated with specific risk profiles and strategies management.

Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization., {"references":["TR Circular, \"Use of Artificial Neural Networks in Geomechanical and\nPavement Systems\", Transportation Research Circular No. E-C012,\nTRB, Washington, DC, 1999.","K. Gopalakrishnan, M.R. Thompson, and A. Manik, \"Rapid Finite-\nElement Based Airport Pavement Moduli Solutions using Neural\nNetworks\", Int J. Comp. Intelligence, 3 (1), 2006, pp. 63-71.","S. Frias, J.E. Conde, M.A. Rodriguez, V. Dohnal, and J.P. Perez-\nTrujillo, \"Metallic content of wines from the Canary Islands (Spain).\nApplication of artificial neural networks to the data analysis\",\nNahrung/Food, 46(5), 2002, pp. 370-375.","SM.J. Benito, M.C. Ortiz, M. Sagrario, L. Sarabia, and M. Iniguez,\nAnalyst, 1999, 124, pp. 547-552.","M.C. Garcia-Parrilla, G.A. Gonzalez, F.J. Heredia, and A.M. Troncoso,\nJ. Agric. Food Chem., 1997, 45, pp. 3487-3492.","M.C. Garcia-Parrilla, F.J. Heredia, A.M. Troncoso, Food Res. Int., 1999,\n32, pp. 433-440.","M.C. Ortiz, A. Herrero, M.S. Sanchez, L. Sarabia, and M. Iniiguez,\nChemom. Intell. Lab. Syst., 1995, 28, pp. 273-285.","S. Vlassides, J.G. Ferrier, D.E. Block, Biotechnol. Bioeng., 2001, 73, pp.\n55-68.","E. Marengo, M. Aceto, V.J. Maurino, Chromatogr. A, 2001, 94, pp.\n123-137.\n[10] L.X. Sun, K. Danzer, G. Thiel, J. Fresenius, Anal. Chem., 1997, 359, pp.\n143-149.\n[11] C.G. Raptis, C.I. Siettos, C.T. Kiranoudis, G.V. Bafas, J. Food Eng.,\n2000, 46, pp. 267-275.\n[12] T. Kohonen, \"Self-Organized Formation of Topologically Correct\nFeature Maps\", Biological Cybernetics, Vol. 43, 1982, pp. 59-69.\n[13] SDL Component Suite, \"Kohonen Network - Background Information\",\nhttp://www.lohninger.com/helpcsuite/kohonen_network_-\n_background_information.htm. Accessed online March 13, 2007.\n[14] T. Kohonen, Self Organization and Associative Memory, Springer\nVerlag, Berlin, 1989.\n[15] G. Deichsel and H.J. Trampisch, Clusteranalyse und\nDiskriminanzanalyse, Gustav Fischer Verlag, Stuttgart, New York,\n1985.\n[16] A. Ultsch and C. Vetter, \"Self-Organizing-Feature-Maps versus\nStatistical Clustering Methods: A Benchmark\", Research Report 0994,\nFG Neuroinformatik & K├╝nstliche Intelligenz, University of Marburg,\nDenmark, 1995.\n[17] .A. Hartigan, Clustering Algorithms, Wiley and Sons, New York, 1975.\n[18] H. Sp├ñth, Cluster Analysis Algorithms, Chichester, UK, 1980.\n[19] M.R. Andernberg, Cluster Analysis for Applications, New York,\nAcademic Press, 1973.\n[20] G.J. McLachlan and K.E. Basford, Mixture Models, New York: Marcel\nDekker, Inc., 1988.\n[21] Murtagh F. and Hern├índez-Pajares M. (1995). The Kohonen Self-\nOrganizing Map Method: An Assesment. Journal of Classification, 12,\n165-190.\n[22] L. Leinonen, T. Hiltunen, K. Torkkola, and J. Kangas, \"Self-organized\nacoustic feature map in detection of fricative-vowel coarticulation\", J.\nAcoust. Soc. Am., 93 (6), 1993, pp. 3468-3474.\n[23] C.N. Manikopoulos, \"Finite state vector quantisation with neural\nnetwork classification of states\", IEEE Proc.-F, 140 (3), 1993, pp. 153-\n161.\n[24] A.D. Bimbo, L. Landi, S. Santini, \"Three-dimensional planar-faced\nobject classification with Kohonen maps\", Opt. Eng., 32 (6), 1993, pp.\n1222-1234.\n[25] M. Sabourin, A. Mitiche, \"Modeling and classi6cation of shape using a\nKohonen associative memory with selective multiresolution\", Neural\nNetworks 6, 1993, pp. 275-283.\n[26] J.A. Walter, K.J. Schulten, \"Implementation of self-organizing neural\nnetworks for visuo-motor control of an industrial robot\", IEEE Trans.\nNeural Networks 4 (1), 1993, pp. 86-95.\n[27] H. Ritter, T. Martinetz, K. Schulten, \"Topology-conserving maps for\nlearning visuo-motorcoordination\", Neural Networks 2, 1989, pp. 159-\n168.\n[28] L. Vercauteren, G. Sieben, M. Praet, G. Otte, R. Vingerhoeds, L.\nBoullart, L. Calliauw, H. Roels, \"The classi6cation of brain tumours by a\ntopological map\", in Proc. of the International Neural Networks\nConference, Paris, 1990, pp. 387-391.\n[29] M. Y. Kiang (2001). \"Extending the Kohonen self-organizing map\nnetworks for clustering analysis\", Computational Statistics & Data\nAnalysis, Vol. 38, pp. 161-180.\n[30] UCI Machine Learning Repository, Wine recognition data,\nftp://ftp.ics.uci.edu/pub/machine-learning-databases/wine/, Apr 16th,\n2005.\n[31] M. Cottrell and J.C. Fort, \"A stochastic model of retinotopy: a selforganizing\nprocess\", Biol. Cybern., 53, 1986, pp. 405-411.\n[32] H. Ritter and K. Schulten, \"On the stationary state of Kohonen-s selforganizing\nsensory mapping\", Biol. Cybern., 54, 1986, pp. 99-106.\n[33] Z.-P. Lo and B. Bavarian, \"On the rate of convergence in topology\npreserving neural networks\", Biol. Cybern., 65, 1991, pp. 55-63.\n[34] S. Mitra and S.K. Pal, \"Self-organizing neural network as a fuzzy\nclassifier\", IEEE Trans. Systems, Man, Cybernetics, 24 (3), 1994, pp.\n385-399.\n[35] D. DeSieno, \"Adding a conscience to competitive learning\", in Proc. of\nthe International Conference on Neural Networks, Vol. I, IEEE Press,\n1988, New York, pp. 117-124.\n[36] D. Merkl and A. Rauber, \"Uncovering the hierarchical structure of text\narchives by using an unsupervised neural network with adaptive\narchitecture\", PADKK, LNAI 1805, 2000, pp. 384-395.\n[37] J. Vesanto and E. Alhoniemi, \"Clustering of the self-organizing map\",\nIEEE Trans. Neural Networks, 11 (3), 2000, pp. 586-600.\n[38] F. Murtagh, \"Interpreting the Kohonen self-organizing feature map using\ncontiguity-constrained clustering\", Pattern Recognition Lett., 16, 1995,\npp. 399-408."]}

The paper deals with the study of labor market dynamics, and aims to characterize its equilibriums and possible trajectories. The theoretical background is the theory of the segmented labor market. The main idea is that this theory is well adapted to interpret the observed trajectories, due to the heterogeneity of the work situations., Comment: accepted to the WSOM 2007 Conference (Bielefield)

Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety of problems, but with limited applications in time series forecasting context. In this paper, we propose a forecasting method specifically designed for multi-dimensional long-term trends prediction, with a double application of the Kohonen algorithm. Practical applications of the method are also presented., \`{a} la suite de la conf\'{e}rence ANNPR, Florence 2003

Chip form classification during longitudinal turning of carbon steel with coated carbide inserts, yielding different chip forms, was performed using cutting force sensor signals. Advanced signal analysis was carried out by spectral estimation through a parametric method. In this approach, the signal spectrum is assumed to take on a specific functional form, the parameters of which are unknown. The spectral estimation problem becomes one of estimating the unknown parameters of the spectrum model, rather than the spectrum itself. From the cutting force signal, a set of features, corresponding to the characteristic parameters of the spectrum model, were obtained by linear predictive analysis. Decision making on chip form through the analysis of sensor signal features was performed using an unsupervised neural network methodology based on Kohonen maps. The selection of the parameters and variants of the map during the training phase are studied to improve the quality of the Kohonen maps.

With the help of a Kohonen self-organising algorithm, this paper presents a mapping and analysis of the global digital divide along with its main drivers. Several broad groups and subgroups are identified, consisting of countries that are similar in their digital development and in a number of other attributes. We find that the digital divide seems to occur synchronously with divisions in income, social, demographic and infrastructure measures. By examining a large dataset of 160 countries over a short period of three years, we find evidence of both convergence and divergence among the countries over time. We expect these findings to inform the ongoing debate on drivers of the International Digital Divide (IDD). In addition, this paper provides a novel visualisation of the digital divide and its predictors on a two-dimensional grid. Extensions of this work, with the availability of more years of data, could investigate the potential convergence of countries to particular patterns of digital development. © 2007 Inderscience Enterprises Ltd.

Proceedings of the 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebastián, Spain, June 20-22, 2007.; We propose an easy-to-use methodology to allocate one of the groups which have been previously built from a complete learning data base, to new individuals. The learning data base contains continuous and categorical variables for each individual. The groups (clusters) are built by using only the continuous variables and described with the help of the categorical ones. For the new individuals, only the categorical variables are available, and it is necessary to define a model which computes the probabilities to belong to each of the clusters, by using only the categorical variables. Then this model provides a decision rule to assign the new individuals and gives an efficient tool to decision-makers. This tool is shown to be very efficient for customers allocation in consumer clusters for marketing purposes, for example.

It is well known that the SOM algorithm achieves a clustering of data which can be interpreted as an extension of Principal Component Analysis, because of its topology-preserving property. But the SOM algorithm can only process real-valued data. In previous papers, we have proposed several methods based on the SOM algorithm to analyze categorical data, which is the case in survey data. In this paper, we present these methods in a unified manner. The first one (Kohonen Multiple Correspondence Analysis, KMCA) deals only with the modalities, while the two others (Kohonen Multiple Correspondence Analysis with individuals, KMCA\_ind, Kohonen algorithm on DISJonctive table, KDISJ) can take into account the individuals, and the modalities simultaneously., Special Issue apr\`{e}s WSOM 03 \`{a} Kitakiushu

Special issue of Neural Networks Journal after the WSOM 05 Conference; Special issue of Neural Networks Journal after the WSOM 05 Conference; Neural Networks Volume 19, Issues 6-7, Pages 721-976 (July-August 2006) Advances in Self Organising Maps - WSOM'05 Edited by Marie Cottrell and Michel Verleysen 1. Advances in Self-Organizing Maps Pages 721-722 Marie Cottrell and Michel Verleysen 2. Self-organizing neural projections Pages 723-733 Teuvo Kohonen 3. Homeostatic synaptic scaling in self-organizing maps Pages 734-743 Thomas J. Sullivan and Virginia R. de Sa 4. Topographic map formation of factorized Edgeworth-expanded kernels Pages 744-750 Marc M. Van Hulle 5. Large-scale data exploration with the hierarchically growing hyperbolic SOM Pages 751-761 Jörg Ontrup and Helge Ritter 6. Batch and median neural gas Pages 762-771 Marie Cottrell, Barbara Hammer, Alexander Hasenfuß and Thomas Villmann 7. Fuzzy classification by fuzzy labeled neural gas Pages 772-779 Th. Villmann, B. Hammer, F. Schleif, T. Geweniger and W. Herrmann 8. On the equivalence between kernel self-organising maps and self-organising mixture density networks Pages 780-784 Hujun Yin 9. Adaptive filtering with the self-organizing map: A performance comparison Pages 785-798 Guilherme A. Barreto and Luís Gustavo M. Souza 10. The Self-Organizing Relationship (SOR) network employing fuzzy inference based heuristic evaluation Pages 799-811 Takanori Koga, Keiichi Horio and Takeshi Yamakawa 11. SOM's mathematics Pages 812-816 J.C. Fort 12. Performance analysis of LVQ algorithms: A statistical physics approach Pages 817-829 Anarta Ghosh, Michael Biehl and Barbara Hammer 13. Self-organizing map algorithm and distortion measure Pages 830-837 Joseph Rynkiewicz 14. Understanding and reducing variability of SOM neighbourhood structure Pages 838-846 Patrick Rousset, Christiane Guinot and Bertrand Maillet 15. Assessing self organizing maps via contiguity analysis Pages 847-854 Ludovic Lebart 16. Fast algorithm and implementation of dissimilarity self-organizing maps Pages 855-863 Brieuc Conan-Guez, Fabrice Rossi and Aïcha El Golli 17. Graph-based normalization and whitening for non-linear data analysis Pages 864-876 Catherine Aaron 18. Unfolding preprocessing for meaningful time series clustering Pages 877-888 Geoffroy Simon, John A. Lee and Michel Verleysen 19. Local multidimensional scaling Pages 889-899 Jarkko Venna and Samuel Kaski 20. Spherical self-organizing map using efficient indexed geodesic data structure Pages 900-910 Yingxin Wu and Masahiro Takatsuka 21. Advanced visualization of Self-Organizing Maps with vector fields Pages 911-922 Georg Pölzlbauer, Michael Dittenbach and Andreas Rauber 22. Online data visualization using the neural gas network Pages 923-934 Pablo A. Estévez and Cristián J. Figueroa 23. TreeSOM: Cluster analysis in the self-organizing map Pages 935-949 Elena V. Samsonova, Joost N. Kok and Ad P. IJzerman 24. Self-organizing neural networks to support the discovery of DNA-binding motifs Pages 950-962 Shaun Mahony, Panayiotis V. Benos, Terry J. Smith and Aaron Golden 25. A descriptive method to evaluate the number of regimes in a switching autoregressive model Pages 963-972 Madalina Olteanu

The Self-Organizing Map (SOM) with its related extensions is the most popular artificial neural algorithm for use in unsupervised learning, clustering, classification and data visualization. Over 5,000 publications have been reported in the open literature, and many commercial projects employ the SOM as a tool for solving hard real-world problems. Each two years, the "Workshop on Self-Organizing Maps" (WSOM) covers the new developments in the field. The WSOM series of conferences was initiated in 1997 by Prof. Teuvo Kohonen, and has been successfully organized in 1997 and 1999 by the Helsinki University of Technology, in 2001 by the University of Lincolnshire and Humberside, and in 2003 by the Kyushu Institute of Technology. The Universit\'{e} Paris I Panth\'{e}on Sorbonne (SAMOS-MATISSE research centre) organized WSOM 2005 in Paris on September 5-8, 2005., Comment: Special Issue of the Neural Networks Journal after WSOM 05 in Paris

Reprend l'article publié en 2003 dans la revue de la SFDS : Journal de la Société Française de Statistique

Conference WSOM 05 5-8 September 2005; International audience; The paper presents a method to allocate a class of a Kohonen map to a new customer without knowing anything about the variables used for the classification. In this study, a classification of daily gas load profiles is performed on a panel of Gaz de France customers. Then, we use a multinomial logit model to allocate a class to a new customer according to its additional variables. With this model, it is also possible to infer the probability to belong to each of the Kohonen classes. The main application for Gaz de France is the inference of the daily gas load for any customer.

Seeking to determine which working patterns have a specific effect on part-time work, in 1998-99 France's INSEE statistical agency carried out a Timetable survey that questioned the homogeneity of this form of employment (again in terms of the working patterns upon which it is based). A neuronal method was used to classify an entire sample of part-time employees according to their weekly working patterns -the end result being that part-time work was shown to be a very heterogeneous form of employment. This was not only reflected by the existence of many different groups of part-time employees, each with highly differentiated individual and professional characteristics, but also (and above all) by the diversity of their weekly working patterns., Comment: ACSEG 2002, publi\'{e} EJESS

A la suite de la conférence WSOM 03 à Kitakiushu; International audience; The Kohonen self-organization map is usually considered as a classification or clustering tool, with only a few applications in time series prediction. In this paper, a particular time series forecasting method based on Kohonen maps is described. This method has been specifically designed for the prediction of long-term trends. The proof of the stability of the method for long-term forecasting is given, as well as illustrations of the utilization of the method both in the scalar and vectorial cases.

Chapitre 5, à la suite de la conférence ACSEG 2001 à Rennes; In the present article, we attempt to devise a typology of forms of part-time employment by applying a widely used neuronal methodology called Kohonen maps. Starting out with data that we describe using category-specific variables, we show how it is possible to represent observations and the modalities of the variables that define them simultaneously, on a single map. This allows us to ascertain, and to try to describe, the main categories of part-time employment.

In the analysis of poverty and social exclusion, indicators of living conditions are some interesting non-monetary complements to the usual measurements in terms of current or annual income. Living conditions depend in fact on longer term factors than income, and provide further information on households' actual resources that allow to compare more accurately between living standards. But in counterpart, a difficulty comes from the qualitative nature of the information, and the large number of dimensions and items that may be taken into account; in other words, living conditions are difficult to "measure". A consequence is that very often, the information is either used only partly, or reduced into a global score of (bad) living conditions, that results from counting "negative" items, and the qualitative dimension is lost. In this paper, we propose to use the Kohonen algorithm first to describe how the elements of living conditions are combined, and secondly to classify households according to their living conditions. The main interest of a classification is to make appear not only quantitative differences in the "levels" of living conditions, but also qualitative differences within similar "levels"., Comment: Extended version of a paper presented in ACSEG 2000 conference in Paris (dec 2000)

Article de synthèse sur les applications de l'algorithme de Kohonen pour la visualisation et l'analyse de données; This paper shows how to use the Kohonen algorithm to represent multidimensional data, by exploiting the self-organizing property. It is possible to get such maps as well for quantitative variables as for qualitative ones, or for a mixing of both. The contents of the paper come from various works by SAMOS-MATISSE members, in particular by E. de Bodt, B. Girard, P. Letrémy, S. Ibbou, P. Rousset. Most of the examples have been studied with the computation routines written by Patrick Letrémy, with the language IML-SAS, which are available on the WEB page http://samos.univ-paris1.fr.