The Theory of Social Representations in Brazilian Health Researches: a Bibliometric Profile

The study aimed to characterizing the production of national articles on health, the time frame of the past 10 years, available in the database LILACS and MEDLINE Virtual Health Library that used the Theory of Social Representations in its searches, using as descriptors the words: social representations and health. It is a descriptive study, developed in the context of ibliometrics. Of the 158 units found, 122 were considered and analyzed after removal of those that did not include the stablished inclusion criteria: articles in Portuguese,available in full and that mentioned the expression "social representations", either in the title or abstract. The journal that most published researches about the Theory of Social Representations was Science & Public Health; being the largest number of articles published in 2011. The most frequent area of knowledge covering about the Theory of Social Representations was the Public Health, with the participant group most cited health professionals. Among the data collection instruments used, the semi-structured interview was the most frequent and the kind of qualitative analysis the content analysis was the most common. Noteworthy is the growing interest for the theory and the need for greater criteria in the preparation of abstracts, considering its importance in the spread of scientific production.

SOCIAL REPRESENTATIONS OF BASICS LIFE SUPPORT

Victims of cardiac arrest need immediate Basic Life Support, in order to preserve as much as possible, the flow of blood to the brain and heart and other vital organs, it is essential to gain time pending differentiated help, performing simple acts and practical (BLS) to save lives. Learn how to perform RPC is an interactive process that requires knowledge and skills, but at the same time an act of solidarity, social responsibility, civic consciousness, and a duty of citizenship. Because no one revives alone, it requires a coordinated work of a team, all citizens must join forces in a single goal: Save Lives, the massification of the BLS (RPC, 2014). We conducted an exploratory study that aimed to identify the social representations of basic life support in the general population. We used the technique of free association of words through a short questionnaire, we obtained a sample of 45 participants. The results show that participants were mostly female and 27 that fashion of age was in the age group 40 to 59 years. With regard to social representations, we find an organized structure follows the core: help, help to revive, and save is giving life, are in fact structural and consensual elements in basic life support. In more peripheral elements we find extremely important elements, which can be worked in a way so that the core is more efficient such as to act coordinately as a team in face of an accident, it can thus be successful in practice. The social representation of basic life support does not differ from that referred in the literature on the subject, but it is common knowledge that these skills can only be acquired if they are systematically trained, because they obey an algorithm that if it is not settled theoretical and instrumentally it is not effective in practice.

Embedded representations of social interactions

Social interactions have been the focus of social science research for a century, but their study has recently been revolutionized by novel data sources and by methods from computer science, network science, and complex systems science. The study of social interactions is crucial for understanding complex societal behaviours. Social interactions are naturally represented as networks, which have emerged as a unifying mathematical language to understand structural and dynamical aspects of socio-technical systems. Networks are, however, highly dimensional objects, especially when considering the scales of real-world systems and the need to model the temporal dimension. Hence the study of empirical data from social systems is challenging both from a conceptual and a computational standpoint. A possible approach to tackling such a challenge is to use dimensionality reduction techniques that represent network entities in a low-dimensional feature space, preserving some desired properties of the original data. Low-dimensional vector space representations, also known as network embeddings, have been extensively studied, also as a way to feed network data to machine learning algorithms. Network embeddings were initially developed for static networks and then extended to incorporate temporal network data. We focus on dimensionality reduction techniques for time-resolved social interaction data modelled as temporal networks. We introduce a novel embedding technique that models the temporal and structural similarities of events rather than nodes. Using empirical data on social interactions, we show that this representation captures information relevant for the study of dynamical processes unfolding over the network, such as epidemic spreading. We then turn to another large-scale dataset on social interactions: a popular Web-based crowdfunding platform. We show that tensor-based representations of the data and dimensionality reduction techniques such as tensor factorization allow us to uncover the structural and temporal aspects of the system and to relate them to geographic and temporal activity patterns.

Deploying and processing neural representations of signals

Neural representations (NR) have emerged in the last few years as a powerful tool to represent signals from several domains, such as images, 3D shapes, or audio. Indeed, deep neural networks have been shown capable of approximating continuous functions that describe a given signal with theoretical infinite resolution. This finding allows obtaining representations whose memory footprint is fixed and decoupled from the resolution at which the underlying signal can be sampled, something that is not possible with traditional discrete representations, e.g., grids of pixels for images or voxels for 3D shapes. During the last two years, many techniques have been proposed to improve the capability of NR to approximate high-frequency details and to make the optimization procedures required to obtain NR less demanding both in terms of time and data requirements, motivating many researchers to deploy NR as the main form of data representation for complex pipelines. Following this line of research, we first show that NR can approximate precisely Unsigned Distance Functions, providing an effective way to represent garments that feature open 3D surfaces and unknown topology. Then, we present a pipeline to obtain in a few minutes a compact Neural Twin® for a given object, by exploiting the recent advances in modeling neural radiance fields. Furthermore, we move a step in the direction of adopting NR as a standalone representation, by considering the possibility of performing downstream tasks by processing directly the NR weights. We first show that deep neural networks can be compressed into compact latent codes. Then, we show how this technique can be exploited to perform deep learning on implicit neural representations (INR) of 3D shapes, by only looking at the weights of the networks.

Learning representations for graph-structured socio-technical systems

The recent widespread use of social media platforms and web services has led to a vast amount of behavioral data that can be used to model socio-technical systems. A significant part of this data can be represented as graphs or networks, which have become the prevalent mathematical framework for studying the structure and the dynamics of complex interacting systems. However, analyzing and understanding these data presents new challenges due to their increasing complexity and diversity. For instance, the characterization of real-world networks includes the need of accounting for their temporal dimension, together with incorporating higher-order interactions beyond the traditional pairwise formalism. The ongoing growth of AI has led to the integration of traditional graph mining techniques with representation learning and low-dimensional embeddings of networks to address current challenges. These methods capture the underlying similarities and geometry of graph-shaped data, generating latent representations that enable the resolution of various tasks, such as link prediction, node classification, and graph clustering. As these techniques gain popularity, there is even a growing concern about their responsible use. In particular, there has been an increased emphasis on addressing the limitations of interpretability in graph representation learning. This thesis contributes to the advancement of knowledge in the field of graph representation learning and has potential applications in a wide range of complex systems domains. We initially focus on forecasting problems related to face-to-face contact networks with time-varying graph embeddings. Then, we study hyperedge prediction and reconstruction with simplicial complex embeddings. Finally, we analyze the problem of interpreting latent dimensions in node embeddings for graphs. The proposed models are extensively evaluated in multiple experimental settings and the results demonstrate their effectiveness and reliability, achieving state-of-the-art performances and providing valuable insights into the properties of the learned representations.

Latent representations for traditional music analysis and generation

We create and study a generative model for Irish traditional music based on Variational Autoencoders and analyze the learned latent space trying to find musically significant correlations in the latent codes' distributions in order to perform musical analysis on data. We train two kinds of models: one trained on a dataset of Irish folk melodies, one trained on bars extrapolated from the melodies dataset, each one in five variations of increasing size. We conduct the following experiments: we inspect the latent space of tunes and bars in relation to key, time signature, and estimated harmonic function of bars; we search for links between tunes in a particular style (i.e. "reels'") and their positioning in latent space relative to other tunes; we compute distances between embedded bars in a tune to gain insight into the model's understanding of the similarity between bars. Finally, we show and evaluate generative examples. We find that the learned latent space does not explicitly encode musical information and is thus unusable for musical analysis of data, while generative results are generally good and not strictly dependent on the musical coherence of the model's internal representation.

Voice conversion with pre-trained representations for audio anonymization

The recording and processing of voice data raises increasing privacy concerns for users and service providers. One way to address these issues is to move processing on the edge device closer to the recording so that potentially identifiable information is not transmitted over the internet. However, this is often not possible due to hardware limitations. An interesting alternative is the development of voice anonymization techniques that remove individual speakers characteristics while preserving linguistic and acoustic information in the data. In this work, a state-of-the-art approach to sequence-to-sequence speech conversion, ini- tially based on x-vectors and bottleneck features for automatic speech recognition, is explored to disentangle the two acoustic information using different pre-trained speech and speakers representation. Furthermore, different strategies for selecting target speech representations are analyzed. Results on public datasets in terms of equal error rate and word error rate show that good privacy is achieved with limited impact on converted speech quality relative to the original method.

Políticas sociais de atendimento às crianças e aos adolescentes no Brasil

This article analyses the emergence and development of social policies for children and adolescents attendance that are in line with the development process of the Brazilian social protection system, focusing on some of the main representations attributed to childhood, according to the historical and political periods. It seeks to present the notion of childhood instituted under the constitution of the Brazilian welfare state, in such a way as to place it within the broader context of the historical and political transformations that involved the emergence and consolidation of the social policies directed towards children and adolescents in Brazil in the 20th century and the beginning of the 21st.

Descrições estruturais cristalinas de zeólitos

Crystalline structures of zeolites can be studied using different representations: the internal symmetry obtained by X-Ray or neutron diffraction crystallography techniques or a systematic analysis of the basic structural units which can be arranged to build the geometries of each kind of zeolite. In this work the basic concepts of three building units, SBU (Secondary Building Units), SSU (Structural SubUnits) and PBU (Periodic Building Units) are presented. The properties of the resulting crystalline structures are discussed (pores, cavities, channels), describing the influence of each one of these properties in processes of physical-chemical interest. Representative case studies of known zeolite crystalline structures are also discussed in terms of their space group classification.

A formação de personalidades éticas: representações de si e moral

The article presents studies of a current investigation among 75 adolescents from 12 to 15 years old, students of private schools of Campinas city, that have as main objective to notice a possible correspondence among the moral judgments and the representation that individuals have about themselves. From a questionnaire, the studies bring out the representations of these individuals and answer a questioning if they would have an ethical character or not and if these individuals would correspond to their moral judgments. The results point out to a correspondence among those whose self representations are characterized by more evolved ethical contents and judgments related to sensitivity and to the characters feelings involved in the situations described. Such studies validate the intention of this article to discuss the correspondences between ethics (how the individual sees himself/herself) and moral (how he/she judges the situations moral).

Andarilhos do bem alimentar : o trabalho, a devoção e o lazer

Universidade Estadual de Campinas . Faculdade de Educação Física

A constituição da intervenção profissional em Educação Fisica : interações entre o conhecimento formalizado e a noção de competencia

Universidade Estadual de Campinas. Faculdade de Educação Física

Torcidas organizadas e seus jovens torcedores : diversidades e normativas do torcer = Torcidas organizadas and their young supporters : diversities and normatives of supporting

Universidade Estadual de Campinas . Faculdade de Educação Física

Educação física e inclusão educacional : entender para atender = Physical education and educational inclusion : understanding to attend

Universidade Estadual de Campinas . Faculdade de Educação Física

Democracia Corinthiana : sentidos e significados da participação dos jogadores = Corinthians democracy : meanings and significance of the players' participation

Universidade Estadual de Campinas . Faculdade de Educação Física