Understanding the socio-economic distribution of multiple deprivation: An application of self-organising maps

In this paper we seek to contribute to recent efforts to develop and implement multi-dimensional approaches to social exclusion by applying self-organising maps (SOMs) to a set of material deprivation indicators from the Irish component of EU-SILC. The first stage of our analysis involves the identification of sixteen clusters that confirm the multi-dimensional nature of deprivation in contemporary Ireland and the limitations of focusing solely on income. In going beyond this mapping stage, we consider both patterns of socio-economic differentiation in relation to cluster membership and the extent to which such membership contributes to our understanding of economic stress. Our analysis makes clear the continuing importance of traditional forms of stratification relating to factors such as income, social class and housing tenure in accounting for patterns of multiple deprivation. However, it also confirms the role of acute life events and life cycle and location influences. Most importantly, it demonstrates that conclusions relating to the relative impact of different kinds of socio-economic influences are highly dependent on the form of deprivation being considered. Our analysis suggests that debates relating to the extent to which poverty and social exclusion have become individualized should take particular care to distinguish between different kinds of outcomes. Further analysis demonstrates that the SOM approach is considerably more successful than a comparable latent class analysis in identifying those exposed to subjective economic stress. (C) 2010 International Sociological Association Research Committee 28 on Social Stratification and Mobility. Published by Elsevier Ltd. All rights reserved.

Mapping patterns of multiple deprivation using self-organising maps: An application to EU-SILC data for Ireland

The development of conceptual frameworks for the analysis of social exclusion has somewhat out-stripped related methodological developments. This paper seeks to contribute to filling this gap through the application of self-organising maps (SOMs) to the analysis of a detailed set of material deprivation indicators relating to the Irish case. The SOM approach allows us to offer a differentiated and interpretable picture of the structure of multiple deprivation in contemporary Ireland. Employing this approach, we identify 16 clusters characterised by distinct profiles across 42 deprivation indicators. Exploratory analyses demonstrate that, controlling for equivalised household income, SOM cluster membership adds substantially to our ability to predict subjective economic stress. Moreover, in comparison with an analogous latent class approach, the SOM analysis offers considerable additional discriminatory power in relation to individuals' experience of their economic circumstances. The results suggest that the SOM approach could prove a valuable addition to a 'methodological platform' for analysing the shape and form of social exclusion. (c) 2009 Elsevier Inc. All rights reserved.

Reduction of invasive tests in chagasic patients with a modified self-organizing map

The applicability of AI methods to the Chagas' disease diagnosis is carried out by the use of Kohonen's self-organizing feature maps. Electrodiagnosis indicators calculated from ECG records are used as features in input vectors to train the network. Cross-validation results are used to modify the maps, providing an outstanding improvement to the interpretation of the resulting output. As a result, the map might be used to reduce the need for invasive explorations in chronic Chagas' disease.

Simulated annealing technique for fast learning of SOM networks

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.

Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means

Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.

Algoritmo SOM com estrutura hierárquica e dinâmica aplicado a compressão de imagens

ln this work the implementation of the SOM (Self Organizing Maps) algorithm or Kohonen neural network is presented in the form of hierarchical structures, applied to the compression of images. The main objective of this approach is to develop an Hierarchical SOM algorithm with static structure and another one with dynamic structure to generate codebooks (books of codes) in the process of the image Vector Quantization (VQ), reducing the time of processing and obtaining a good rate of compression of images with a minimum degradation of the quality in relation to the original image. Both self-organizing neural networks developed here, were denominated HSOM, for static case, and DHSOM, for the dynamic case. ln the first form, the hierarchical structure is previously defined and in the later this structure grows in an automatic way in agreement with heuristic rules that explore the data of the training group without use of external parameters. For the network, the heuristic mIes determine the dynamics of growth, the pruning of ramifications criteria, the flexibility and the size of children maps. The LBO (Linde-Buzo-Oray) algorithm or K-means, one ofthe more used algorithms to develop codebook for Vector Quantization, was used together with the algorithm of Kohonen in its basic form, that is, not hierarchical, as a reference to compare the performance of the algorithms here proposed. A performance analysis between the two hierarchical structures is also accomplished in this work. The efficiency of the proposed processing is verified by the reduction in the complexity computational compared to the traditional algorithms, as well as, through the quantitative analysis of the images reconstructed in function of the parameters: (PSNR) peak signal-to-noise ratio and (MSE) medium squared error

Constrained self-organizing feature map to preserve feature extraction topology

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.

Magnification factors for the SOM and GTM algorithms

Magnification factors specify the extent to which the area of a small patch of the latent (or `feature') space of a topographic mapping is magnified on projection to the data space, and are of considerable interest in both neuro-biological and data analysis contexts. Previous attempts to consider magnification factors for the self-organizing map (SOM) algorithm have been hindered because the mapping is only defined at discrete points (given by the reference vectors). In this paper we consider the batch version of SOM, for which a continuous mapping can be defined, as well as the Generative Topographic Mapping (GTM) algorithm of Bishop et al. (1997) which has been introduced as a probabilistic formulation of the SOM. We show how the techniques of differential geometry can be used to determine magnification factors as continuous functions of the latent space coordinates. The results are illustrated here using a problem involving the identification of crab species from morphological data.

Improving self organizing capabilities of local communities for sustainable development in decentralized Indonesia : the roles of procedural justice and social learning

The development planning process introduced under Law No. 25/2004 is said to be a better approach to increase public participation in decentralised Indonesia. This Law has introduced planning mechanisms, called Musyawarah Perencanaan Pembangunan (musrenbang), to provide a forum for development planning. In spite of the expressed intention of these mechanisms to improve public participation, some empirical observations have cast doubt on the outcomes. As a result, some local governments have tried to provide alternative mechanisms for participatory local development planning processes. Since planning constitutes one of the most effective ways to improve community empowerment, this paper aims to examine the extent to which the alternative local development planning process in Indonesia provides sufficient opportunities to improve the self organising capabilities of communities to sustain development programs to meet local needs. In so doing, this paper explores the key elements and approaches of the concept of community empowerment and shows how they can be incorporated within planning processes. Based on this, it then examines the problems encountered by musrenbang in increasing community empowerment. Having done this, it is argued that to change current unfavourable outcomes, procedural justice and social learning approaches need to be incorporated as pathways to community empowerment. Lastly the capacity of an alternative local planning process, called Sistem Dukungan (SISDUK), introduced in South Sulawesi, offering scope to incorporate procedural justice and social learning is explored as a means to improve the self organizing capabilities of local communities.

The roles of procedural justice and social learning in improving self organizing capabilities of local communities for sustainable development in decentralized Indonesia

The development planning process under Law No. 25/2004 is said to be a new approach to increase public participation in decentralised Indonesia. This Law has introduced planning mechanisms, called Musyawarah Perencanaan Pembangunan (Musrenbang), to provide a forum for development planning. In spite of the expressed intention of these mechanisms to improve public participation, some empirical observations have cast doubt on the outcomes. As a result, some local governments have tried to provide alternative mechanisms to promote for participation in local development planning. Since planning is often said to be one of the most effective ways to improve community empowerment, it is of particular concern, to examine the extent to which the current local development planning processes in Indonesia provide sufficient opportunities to improve the self organising capabilities of communities to sustain development programs to meet local needs. With this objective in mind, this paper examines problems encountered by the new local planning mechanism (Musrenbang) in increasing local community empowerment particularly regarding their self organising capabilities. The concept of community empowerment as a pathway to social justice is explored to identify its key elements and approaches and to show how they can be incorporated within planning processes. Having discussed this, it is then argued that to change current unfavorable outcomes, procedural justice and social learning approaches need to be adopted as pathways to community empowerment. Lastly it is also suggested that an alternative local planning process, called Sistem Dukungan (SISDUK), introduced in South Suluwezi in collaboration with JAICA in 2006 (?) offers scope to incorporate such procedural justice and social learning approaches to improve the self organizing capabilities of local communities.

Impressive Gelation in Organic Solvents by Synthetic, Low Molecular Mass, Self-Organizing Urethane Amides of L-Phenylalanine

Synthetic routes leading to 12 L-phenylalanine based mono- and bipolar derivatives (1-12) and an in-depth study of their structure-property relationship with respect to gelation have been presented. These include monopolar systems such as N-[(benzyloxy)carbonyl]-L-phenylalanine-N-alkylamides and the corresponding bipolar derivatives with flexible and rigid spacers such as with 1,12-diaminododecane and 4,4'-diaminodiphenylmethane, respectively. The two ends of the latter have been functionalized with N-[(benzyloxy)carbonyl]-L-phenylalanine units via amide connection. Another bipolar molecule was synthesized in which the middle portion of the hydrocarbon segment contained polymerizable diacetylene unit. To ascertain the role of the presence of urethane linkages in the gelator molecule protected L-phenylalanine derivatives were also synthesized in which the (benzyloxy)carbonyl group has been replaced with (tert-butyloxy)carbonyl, acetyl, and benzoyl groups, respectively. Upon completion of the synthesis and adequate characterization of the newly described molecules, we examined the aggregation and gelation properties of each of them in a number of solvents and their mixtures. Optical microscopy and electron microscopy further characterized the systems that formed gels. Few representative systems, which showed excellent gelation behavior was, further examined by FT-IR, calorimetric, and powder X-ray diffraction studies. To explain the possible reasons for gelation, the results of molecular modeling and energy-minimization studies were also included. Taken together these results demonstrate the importance of the presence of (benzyloxy)carbonyl unit, urethane and secondary amide linkages, chiral purities of the headgroup and the length of the alkyl chain of the hydrophobic segment as critical determinants toward effective gelation.

A Self-Organizing Multiple-View Representation of 3D Objects

We explore representation of 3D objects in which several distinct 2D views are stored for each object. We demonstrate the ability of a two-layer network of thresholded summation units to support such representations. Using unsupervised Hebbian relaxation, we trained the network to recognise ten objects from different viewpoints. The training process led to the emergence of compact representations of the specific input views. When tested on novel views of the same objects, the network exhibited a substantial generalisation capability. In simulated psychophysical experiments, the network's behavior was qualitatively similar to that of human subjects.