Boys and Girls on the Playground: Sex Differences in Social Development Are Not Stable across Early Childhood

Sex differences in human social behaviors and abilities have long been a question of public and scientific interest. Females are usually assumed to be more socially oriented and skilful than males. However, despite an extensive literature, the very existence of sex differences remains a matter of discussion while some studies found no sex differences whereas others reported differences that were either congruent or not with gender stereotypes. Moreover, the magnitude, consistency and stability across time of the differences remain an open question, especially during childhood. As play provides an excellent window into children's social development, we investigated whether and how sex differences change in social play across early childhood. Following a cross-sectional design, 164 children aged from 2 to 6 years old, divided into four age groups, were observed during outdoor free play at nursery school. We showed that sex differences are not stable over time evidencing a developmental gap between girls and boys. Social and structured forms of play emerge systematically earlier in girls than in boys leading to subsequent sex differences in favor of girls at some ages, successively in associative play at 3–4 years, cooperative play at 4–5 years, and social interactions with peers at 5–6 years. Preschool boys also display more solitary play than preschool girls, especially when young. Nevertheless, while boys catch up and girls move on towards more complex play, sex differences in social play patterns are reversed in favor of boys at the following ages, such as in associative play at 4–5 years and cooperative play at 5–6 years. This developmental perspective contributes to resolve apparent discrepancies between single-snapshot studies. A better understanding of the dynamics of sex differences in typical social development should also provide insights into atypical social developments which exhibit sex differences in prevalence, such as autism

Deep Learning Approaches for Dynamic Mechanical Analysis of Viscoelastic Fiber Composites

The increased adoption of reinforced polymer (RP) composite materials, driven by eco-design standards, calls for a fine balance between lightness, stiffness, and effective vibration control. These materials are integral to enhancing comfort, safety, and energy efficiency. Dynamic Mechanical Analysis (DMA) characterizes viscoelastic behavior, yet there's a growing interest in using Machine Learning (ML) to expedite the design and understanding of microstructures. In this paper we aim to map microstructures to their mechanical properties using deep neural networks, speeding up the process and allowing for the generation of microstructures from desired properties.Comment: 12 pages, 5 figures, https://hal.science/hal-0425055

Damage Detection in Metallic Beams from Dynamic Strain Measurements under Different Load Cases by Using Automatic Clustering and Pattern Recognition Techniques

International audienceIn general, the change in the local strain field or global stiffness caused by damage in a structure is very small and the strain field tends to homogenize very quickly in the field close to the defect. Moreover, other environmental effects can fade the slight changes in the strain field. Only by comparing the response of the structure at several points some information about damage may be unveiled. By means of pattern recognition techniques based on the strain field, this task can be achieved. This is the basis of the strain measurements data-driven models. The main limitation of the strain field pattern recognition techniques lies in the susceptibility of the strain field to change depending on the load conditions. In the case of dynamic loads, this may reflect even a greater limitation. Robust automated techniques are required to manage these limitations. In first instance, automatic clustering techniques are needed so that data can be classified according to the load conditions and secondly, a dimensional reduction technique is needed in order to obtain patterns that often underlie from data. Within the context of this paper, a combination of Local Density-based Simultaneous Two-Level (DS2L-SOM) Clustering based on Self-Organizing Maps (SOM) and Principal Components Analysis (PCA) is proposed in order to firstly, classify load conditions and secondly, perform strain field pattern recognition. The clustering technique is the basis for an Optimal Baseline Selection. An experimental validation of the technique is discussed in this paper, comparing damages of different sizes and positions in an aluminum beam, under a set of combined loads under dynamic conditions. Strains were measured at several points by using Fiber Bragg Gratings

A Pattern Recognition Approach for Damage Detection and Temperature Compensation in Acousto-Ultrasonics

International audienceThe global trends in the construction of modern structures require the integration of sensors together with data recording and analysis modules so that their integrity can be continuously monitored for safe-life, economic and ecological reasons. This process of measuring and analysing the data from a distributed sensor network all over a structural system in order to quantify its condition is known as structural health monitoring (SHM). Guided ultrasonic wave-based techniques are increasingly being adapted and used in several SHM systems which benefit from built&#8208,in transduction, large inspection ranges, and high sensitivity to small flaws. However, for reliable health monitoring, much information regarding the innate characteristics of the sources and their propagation is essential. Moreover, any SHM system which is expected to transition to field operation must take into account the influence of environmental and operational changes which cause modifications in the stiffness and damping of the structure and consequently modify its dynamic behaviour. On that account, special attention is paid in this paper to the development of an efficient SHM methodology where robust signal processing and pattern recognition techniques are integrated for the correct interpretation of complex ultrasonic waves within the context of damage detection and identification. The methodology is based on an acousto-ultrasonics technique where the discrete wavelet transform is evaluated for feature extraction and selection, linear principal component analysis for data-driven modelling and self-organizing maps for a two-level clustering under the principle of local density. At the end, the methodology is experimentally demonstrated and results show that all the damages were detectable and i

Structural health monitoring by means of strain field pattern recognition on the basis of PCA and automatic clustering techniques based on SOM

Peer ReviewedPostprint (published version

Classification non supervise à deux niveaux guidée par le voisinage et la densité

Le travail de recherche exposé dans cette thèse concerne le développement d'approches à base de Cartes Auto-Organisatrices (SOM) pour la découverte et le suivi de structures de classes dans les données par apprentissage non supervisé. Nous proposons des méthodes de classification à deux niveaux simultanés qui se basent sur l'estimation, à partir des données, de valeurs de connectivité et de densité des prototypes de la SOM. Le nombre de clusters est détecté automatiquement et la complexité est linéaire selon le nombre de données. Nous montrons aussi qu il est relativement simple et efficace d adapter ces algorithmes aux variantes de l algorithme SOM, de façon à obtenir une méthode très polyvalente capable par exemple d analyser différents types de données. Nous proposons en outre une amélioration de la qualité de la SOM en utilisant les valeurs de connectivité lors de l'apprentissage des prototypes. Nous décrivons une nouvelle méthode de description condensée de la distribution des données, ainsi qu une mesure heuristique de similarité entre ces modèles. Par ailleurs, nous proposons un algorithme de suivi des données d'un flux. Ces algorithmes se basent sur une estimation de la densité sous-jacente des données pendant l'apprentissage d'une SOM modifiée. Enfin, nous présentons deux applications réelles pour le suivi d'individus dans un dispositif RFID. La première application est une étude du comportement d'une colonie de fourmis pendant un déménagement. La deuxième est une étude commerciale nécessitant le suivi de clients dans un magasin pendant leurs achats.The research outlined in this thesis concerns the development of approaches based on self-organizing maps (SOM) for the discovery and the monitoring of class structures in the data through unsupervised learning. We propose a simultaneously two levels clustering method. This method is based on the estimate, from the data, of connectivity and density values of the SOM's prototypes. The number of clusters is detected automatically. Moreover, the complexity is linear with the number of data. We show that it is relatively simple and efficient to adapt these algorithms to variants of the SOM in order to obtain a versatile method capable of analyzing different data types. We also propose an improvement of the quality of the SOM using the connectivity values during the learning of the prototypes. We describe a new method of condensed description of the data distribution and a heuristic measure of similarity between these models. These algorithms are based on an estimate of the underlying density for learning a modified SOM. In addition, we combine the clustering algorithm to measure similarity between distributions for the analysis of evolutionary data, and we propose an algorithm for monitoring data stream. Finally, we present two applications for tracking individuals in a RFID device. The first application is a study of the behavior of a colony of ants while moving. The second application, require tracking of customers in a store.PARIS13-BU Sciences (930792102) / SudocSudocFranceF

Unsupervised Topographic Learning for Spatiotemporal Data Mining

International audienceIn recent years, the size and complexity of datasets have shown an exponential growth. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we propose a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency IDentification (RFID) data. Two real applications show that this algorithm is an efficient data-mining tool for behavioral studies based on RFID technology. It allows discovering and comparing stable patterns in an RFID signal and is suitable for continuous learning

Study on the Influence of Diversity and Quality in Entropy Based Collaborative Clustering

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Mining RFID Behavior Data using Unsupervised Learning

International audienceRadio Frequency IDentification (RFID) is an advanced tracking technology that can be used to study the spatial organization of individual's spatio-temporal activity. The aim of this work is firstly to build a new RFID-based autonomous system which can follow individuals' spatio-temporal activity, a tool not currently available. Secondly, we aim to develop new tools for automatic data mining. In this paper, we study how to transform these data to investigate the division of labor, the intra-colonial cooperation and conflict in an ant colony. We also develop a new unsupervised learning data mining method (DS2L-SOM: Density-based Simultaneous Two-Level-Self Organizing Map) to find homogeneous clusters (i.e., sets of individual which share a similar behavior). According to the experimental results, this method is very fast and efficient. It also allows a very useful visualization of the results. Keywords: RFID; spatio-temporal data; automatic data mining; unsupervised learning; ants behavior. INTRODUCTION Radio Frequency IDentification (RFID) is an advanced tracking technology. The RFID tags, which consist of a microchip and an antenna, must be used with a reader that can detect simultaneously a lot of tags in a single scan. A computer has to be used to store the data about the position of each tag for each scan in a database. This allows different analyses. RFID systems can be used to study animal societies. Animal societies are dynamic complex systems characterized by numerous interactions between individual members. Such dynamic structures stem from the synergy of these interactions, the individual capacities in information processing and the diversity of individual responses (Fresneau et al., 1989). RFID, thanks to miniaturization, offers the advantage of automation and overcomes the constraints imposed by video analyzes. Indeed, video recording allows long-duration tracking, however the time for analyzes highly increases with the number of individuals monitored. It also imposes strong constraints (as the need of a minimum illumination and high contrast between the animals and the environment) and it does not work when the ant is moving in a reverse position which doesn't allow individual identification. The aim of this work is to develop a new RFID-based autonomous system to follow the spatio-temporal activity of groups, which is currently very difficult to study in its entirety and to develop new tools for automatic data processing. These objectives have necessarily led to an interdisciplinary project combining behavioral and complex systems sciences with computer and engineering sciences