Neural Networks for Complex Data

Artificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved to address more and more difficulties of complex real world problems, ranging from time evolving data to sophisticated data structures such as graphs and functions. This paper summarizes advances on those themes from the last decade, with a focus on results obtained by members of the SAMM team of Universit\'e Paris

Which dissimilarity is to be used when extracting typologies in sequence analysis? A comparative study

International audienceOriginally developed in bioinformatics, sequence analysis is being increasingly used in social sciences for the study of life-course processes. The methodology generally employed consists in computing dissimilarities between the trajectories and, if typologies are sought, in clustering the trajectories according to their similarities or dissemblances. The choice of an appropriate dissimilarity measure is a major issue when dealing with sequence analysis for life sequences. Several dissimilarities are available in the literature, but neither of them succeeds to become indisputable. In this paper, instead of deciding upon one dissimilarity measure, we propose to use an optimal convex combination of different dissimilarities. The optimality is automatically determined by the clustering procedure and is defined with respect to the within-class variance

Career-path analysis using drifting Markov models (DMM) and self-organizing maps

Analyzing school-to-work transitions is an important challenge for the specialists of the labor-market. The aim of this paper is to study the insertion of graduates and to identify the main career-paths typologies. We introduce a new methodology for clustering career-paths by combining statistical estimation of non-homogeneous Markov chains with self-organizing maps. The proposed methodology is tested on real-life data issued from the survey ''Generation 98'' elaborated by CEREQ, France (http://www.cereq.fr/)Career paths, categorical data, drifting Markov model, self organizing maps

On-line relational and multiple relational SOM

International audienceIn some applications and in order to address real-world situations better, data may be more complex than simple numerical vectors. In some examples, data can be known only through their pairwise dissimilarities or through multiple dissimilarities, each of them describing a particular feature of the data set. Several variants of the Self Organizing Map (SOM) algorithm were introduced to generalize the original algorithm to the framework of dissimilarity data. Whereas median SOM is based on a rough representation of the prototypes, relational SOM allows representing these prototypes by a virtual linear combination of all elements in the data set, referring to a pseudo-euclidean framework. In the present article, an on-line version of relational SOM is introduced and studied. Similarly to the situation in the Euclidean framework, this on-line algorithm provides a better organization and is much less sensible to prototype initialization than standard (batch) relational SOM. In a more general case, this stochastic version allows us to integrate an additional stochastic gradient descent step in the algorithm which can tune the respective weights of several dissimilarities in an optimal way: the resulting \emph{multiple relational SOM} thus has the ability to integrate several sources of data of different types, or to make a consensus between several dissimilarities describing the same data. The algorithms introduced in this manuscript are tested on several data sets, including categorical data and graphs. On-line relational SOM is currently available in the R package SOMbrero that can be downloaded at http://sombrero.r-forge.r-project.org or directly tested on its Web User Interface at http://shiny.nathalievilla.org/sombrero

Typology of early professional careers and perceived discrimination for young people of foreign origin

This research focuses on individuals who consider they have been victims of discrimination. The aim is to look at the feeling of discrimination and to assess its effects on career paths seven years after leaving school. Taking data from the Class of 98 (Génération 98) survey by the Céreq, we used the method for grouping self-organising maps (Kohonen's algorithm), supplemented by an econometric analysis to distinguish eight major classes of career paths. In parallel, an interview survey was conducted. The results show a segmentation of career paths at two levels. On the one hand, young people of foreign origin who experienced discrimination are over-represented in certain paths, characterised by unemployment, temping or precarious work (inter- class segmentation). On the other hand, strong inequalities exist within those paths which provide rapid access to stabe employment, as persons obtain lower-quality jobs (intra-class segmentation).Labor economics, segmentation, discrimination, youth, France.

Work lives amid social change and continuity: occupational trajectories in Monterrey, Mexico

In this paper we use sequence analysis to study the occupational trajectories between the ages 14 and 30 for men in Monterrey, the third largest city of Mexico. We build typologies of trajectories based on life-history data and then explore changes in the frequency of these ´typical´ trajectories over time as well as differences across socioeconomic groups. Cohort trends reveal more continuities than changes in occupational trajectories, despite the structural changes experienced by the city in the last two decades. Career patterns are closely related to family origins and educational attainment, thus suggesting the continuing importance of both ascribed and attained characteristics on occupational outcomes.

Typology of early professional careers and perceived discrimination for young people of foreign origin

URL des Documents de travail : http://centredeconomiesorbonne.univ-paris1.fr/bandeau-haut/documents-de-travail/Documents de travail du Centre d'Economie de la Sorbonne 2011.41 - ISSN : 1955-611XThis research focuses on individuals who consider they have been victims of discrimination. The aim is to look at the feeling of discrimination and to assess its effects on career paths seven years after leaving school. Taking data from the Class of 98 (Génération 98) survey by the Céreq, we used the method for grouping self-organising maps (Kohonen's algorithm), supplemented by an econometric analysis to distinguish eight major classes of career paths. In parallel, an interview survey was conducted. The results show a segmentation of career paths at two levels. On the one hand, young people of foreign origin who experienced discrimination are over-represented in certain paths, characterised by unemployment, temping or precarious work (inter- class segmentation). On the other hand, strong inequalities exist within those paths which provide rapid access to stabe employment, as persons obtain lower-quality jobs (intra-class segmentation).Cette étude porte sur les jeunes qui estiment avoir été victimes de discrimination en raison de leur origine étrangère et/ou de leur couleur de peau. L'enquête Génération 98 du Céreq à 7 ans est mobilisée afin de construire une typologie (méthode de regroupement des cartes d'auto-organisation, algorithme de Kohonen), complétée par une analyse économétrique (modèle probit bivarié) et par une enquête qualitative par entretiens. Nous cherchons à montrer en quoi le fait de se déclarer victime de discrimination a une influence sur la position professionnelle de ces jeunes adultes. Les résultats montrent l'effet complexe de cette déclaration de discrimination sur les trajectoires d'insertion sept ans après la sortie du système éducatif. La segmentation des trajectoires semble exister à deux niveaux : inter-classes et intra-classe

Deployment of drone-based small cells for public safety communication system

In the event of a natural disaster, communications infrastructure plays an important role in organizing effective rescue services. However, the infrastructure-based communications are often affected during severe disaster events such as earthquakes, landslides, floods, and storm surges. Addressing this issue, the article proposes a novel drone-based cellular infrastructure to revive necessary communications for out-of-coverage user equipment (UE) which is in the disaster area. In particular, a matching game algorithm is proposed using one-to-many approach wherein several drone small cells (DSCs) are deployed to match different UEs to reach a stable connection with optimal throughput. In addition, a medium access control framework is then developed to optimize emergency and high priority communications initiated from the rescue workers and vulnerable individuals. The simulation results show that the throughput for the out-of-coverage UEs are significantly improved when the DSCs are deployed in public safety network while the channel access delay is also notably reduced for emergency communications within the affected areas