The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits

Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)

Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis

One of the main findings of cognitive sciences is that automatic processes of which we are unaware shape, to a significant extent, our perception of the environment. The phenomenon applies not only to the real world, but also to multimedia data we consume every day. Whenever we look at pictures, watch a video or listen to audio recordings, our conscious attention efforts focus on the observable content, but our cognition spontaneously perceives intentions, beliefs, values, attitudes and other constructs that, while being outside of our conscious awareness, still shape our reactions and behavior. So far, multimedia technologies have neglected such a phenomenon to a large extent. This paper argues that taking into account cognitive effects is possible and it can also improve multimedia approaches. As a supporting proof-of-concept, the paper shows not only that there are visual patterns correlated with the personality traits of 300 Flickr users to a statistically significant extent, but also that the personality traits (both self-assessed and attributed by others) of those users can be inferred from the images these latter post as "favourite"

What your Facebook Profile Picture Reveals about your Personality

People spend considerable effort managing the impressions they give others. Social psychologists have shown that people manage these impressions differently depending upon their personality. Facebook and other social media provide a new forum for this fundamental process; hence, understanding people's behaviour on social media could provide interesting insights on their personality. In this paper we investigate automatic personality recognition from Facebook profile pictures. We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals. For example, extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits, mirroring their inclination to socialize; while neurotic ones have a prevalence of pictures of indoor places. Then, we propose a classification approach to automatically recognize personality traits from these visual features. Finally, we compare the performance of our classification approach to the one obtained by human raters and we show that computer-based classifications are significantly more accurate than averaged human-based classifications for Extraversion and Neuroticism

A COMPREENSÃO DA MORTE E DO MORRER

Introdução: A morte e o morrer são assuntos ligados ao medo do desconhecido, expressos na concepção de finitude da vida, evitados por grande parte da população. Existem algumas diferenças entre suas definições que são de suma importância ao entendimento deste processo. Pode-se falar da morte como o fim da vida, do corpo biológico, uma condição inerente ao ser vivo. O morrer remete ao evento que precede a morte. É um processo que ocorre ao longo da vida, um evento familiar e compartilhado. Objetivo: Compreender os conceitos da morte e do morrer e os estigmas que envolvem tais processos. Metodologia: Trata-se de um estudo de reflexão sobre a compreensão da morte e do morrer. A reflexão e construção do trabalho emergiram a partir de discussões suscitadas em aula durante a disciplina de Ética e Bioética em Enfermagem do Curso de Enfermagem da UNOESC. Resultados: A morte e morrer são processos que precisam ser compreendidos existencialmente, embora ainda são tidos como um "tabu" na sociedade. A morte é consequência da vida, é algo natural, como o nascer. O ser humano é um ser finito e morrer significa que chegou a sua hora de partir. Se morre em corpo, porém a essência sempre estará com quem ama, em forma de lembrança, carinho e amor. Conclusão: Existe uma dificuldade na compreensão e aceitação da morte e do morrer, pois se tratam de processos que acarretam profundas reações emocionais. Contudo, é necessário entender tais processos que permeiam a vida, por meio da discussão e reflexão que possam contribuir para uma melhoria da qualidade de vida e de morte

UTILIZAÇÃO DA ESCALA DE EVARUCI PARA AVALIAR O RISCO DE LESÃO POR PRESSÃO

INTRODUÇÃO: A lesão por pressão é definida como o dano nos tecidos moles e pele, mais presente sobre proeminências ósseas ou relacionada ao uso de dispositivos médicos. Com o avanço da medicina e da grande prevalência de lesão por pressão foram criados métodos para avaliar o desenvolvimento dessas lesões, como a escala de EVARUCI. METODOLOGIA: Trata-se de uma pesquisa bibliográfica. RESULTADOS: A escala de avaliação de risco de lesão por pressão EVARUCI é um instrumento favorável para a prevenção de lesões, contata-se que o percentual de valor preditivo da escala de Evaruci sobressai a escala de Braden durante um estudo realizado, sendo considerada uma ferramenta válida para análise do risco de desenvolvimento de lesão por pressão em clientes que permanecem na Unidade de Terapia Intensiva. Analisa também outros parâmetros essenciais como temperatura, estado da pele, mobilidade, nível de consciência, pressão arterial, saturação, situação hemodinâmica do paciente, se esses parâmetros estão alterados consequentemente o risco de lesão por pressão será maior, visto que o agente causal de lesão é o estado de hipoperfusão da pele. CONCLUSÃO: O olhar clínico do enfermeiro em conjunto com conhecimento científico é fundamental para uma boa avaliação, a habilidade de mensurar o risco de desenvolver lesão por pressão aliados com instrumentos com boa efetividade mostram melhores resultados no processo de cuidado

The Mouse Action Recognition System (MARS): a software pipeline for automated analysis of social behaviors in mice

The study of social behavior requires scoring the animals' interactions. This is generally done by hand-- a time consuming, subjective, and expensive process. Recent advances in computer vision enable tracking the pose (posture) of freely-behaving laboratory animals automatically. However, classifying complex social behaviors such as mounting and attack remains technically challenging. Furthermore, the extent to which expert annotators, possibly from different labs, agree on the definitions of these behaviors varies. There is a shortage in the neuroscience community of benchmark datasets that can be used to evaluate the performance and reliability of both pose estimation tools and manual and automated behavior scoring. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely behaving mice. We compare MARS's annotations to human annotations and find that MARS's pose estimation and behavior classification achieve human-level performance. As a by-product we characterize the inter-expert variability in behavior scoring. The two novel datasets used to train MARS were collected from ongoing experiments in social behavior, and identify the main sources of disagreement between annotators. They comprise 30,000 frames of manual annotated mouse poses and over 14 hours of manually annotated behavioral recordings in a variety of experimental preparations. We are releasing this dataset alongside MARS to serve as community benchmarks for pose and behavior systems. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (Bento), a graphical interface that allows users to quickly browse, annotate, and analyze datasets including behavior videos, pose estimates, behavior annotations, audio, and neural recording data. We demonstrate the utility of MARS and Bento in two use cases: a high-throughput behavioral phenotyping study, and exploration of a novel imaging dataset. Together, MARS and Bento provide an end-to-end pipeline for behavior data extraction and analysis, in a package that is user-friendly and easily modifiable

The Mouse Action Recognition System (MARS): a software pipeline for automated analysis of social behaviors in mice

The study of social behavior requires scoring the animals' interactions. This is generally done by hand-- a time consuming, subjective, and expensive process. Recent advances in computer vision enable tracking the pose (posture) of freely-behaving laboratory animals automatically. However, classifying complex social behaviors such as mounting and attack remains technically challenging. Furthermore, the extent to which expert annotators, possibly from different labs, agree on the definitions of these behaviors varies. There is a shortage in the neuroscience community of benchmark datasets that can be used to evaluate the performance and reliability of both pose estimation tools and manual and automated behavior scoring. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely behaving mice. We compare MARS's annotations to human annotations and find that MARS's pose estimation and behavior classification achieve human-level performance. As a by-product we characterize the inter-expert variability in behavior scoring. The two novel datasets used to train MARS were collected from ongoing experiments in social behavior, and identify the main sources of disagreement between annotators. They comprise 30,000 frames of manual annotated mouse poses and over 14 hours of manually annotated behavioral recordings in a variety of experimental preparations. We are releasing this dataset alongside MARS to serve as community benchmarks for pose and behavior systems. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (Bento), a graphical interface that allows users to quickly browse, annotate, and analyze datasets including behavior videos, pose estimates, behavior annotations, audio, and neural recording data. We demonstrate the utility of MARS and Bento in two use cases: a high-throughput behavioral phenotyping study, and exploration of a novel imaging dataset. Together, MARS and Bento provide an end-to-end pipeline for behavior data extraction and analysis, in a package that is user-friendly and easily modifiable

Mouse Academy: high-throughput automated training and trial-by-trial behavioral analysis during learning

Progress in understanding how individual animals learn will require high-throughput standardized methods for behavioral training but also advances in the analysis of the resulting behavioral data. In the course of training with multiple trials, an animal may change its behavior abruptly, and capturing such events calls for a trial-by-trial analysis of the animal's strategy. To address this challenge, we developed an integrated platform for automated animal training and analysis of behavioral data. A low-cost and space-efficient apparatus serves to train entire cohorts of mice on a decision-making task under identical conditions. A generalized linear model (GLM) analyzes each animal's performance at single-trial resolution. This model infers the momentary decision-making strategy and can predict the animal's choice on each trial with an accuracy of ~80%. We also introduce automated software to assess the animal's detailed trajectories and body poses within the apparatus. Unsupervised analysis of these features revealed unusual trajectories that represent hesitation in the response. This integrated hardware/software platform promises to accelerate the understanding of animal learning

Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change

Understanding how changes in climate will affect terrestrial ecosystems is particularly important in tropical forest regions, which store large amounts of carbon and exert important feedbacks onto regional and global climates. By combining multiple types of observations with a state-of-the-art terrestrial ecosystem model, we demonstrate that the sensitivity of tropical forests to changes in climate is dependent on the length of the dry season and soil type, but also, importantly, on the dynamics of individual-level competition within plant canopies. These interactions result in ecosystems that are more sensitive to changes in climate than has been predicted by traditional models but that transition from one ecosystem type to another in a continuous, non–tipping-point manner.Organismic and Evolutionary Biolog

Phylogenetic diversity of Amazonian tree communities

This is the peer reviewed version of the following article: Honorio Coronado, E. N., Dexter, K. G., Pennington, R. T., Chave, J., Lewis, S. L., Alexiades, M. N., Alvarez, E., Alves de Oliveira, A., Amaral, I. L., Araujo-Murakami, A., Arets, E. J. M. M., Aymard, G. A., Baraloto, C., Bonal, D., Brienen, R., Cerón, C., Cornejo Valverde, F., Di Fiore, A., Farfan-Rios, W., Feldpausch, T. R., Higuchi, N., Huamantupa-Chuquimaco, I., Laurance, S. G., Laurance, W. F., López-Gonzalez, G., Marimon, B. S., Marimon-Junior, B. H., Monteagudo Mendoza, A., Neill, D., Palacios Cuenca, W., Peñuela Mora, M. C., Pitman, N. C. A., Prieto, A., Quesada, C. A., Ramirez Angulo, H., Rudas, A., Ruschel, A. R., Salinas Revilla, N., Salomão, R. P., Segalin de Andrade, A., Silman, M. R., Spironello, W., ter Steege, H., Terborgh, J., Toledo, M., Valenzuela Gamarra, L., Vieira, I. C. G., Vilanova Torre, E., Vos, V., Phillips, O. L. (2015), Phylogenetic diversity of Amazonian tree communities. Diversity and Distributions, 21: 1295–1307. doi: 10.1111/ddi.12357, which has been published in final form at 10.1111/ddi.12357Aim: To examine variation in the phylogenetic diversity (PD) of tree communities across geographical and environmental gradients in Amazonia. Location: Two hundred and eighty-three c. 1 ha forest inventory plots from across Amazonia. Methods: We evaluated PD as the total phylogenetic branch length across species in each plot (PDss), the mean pairwise phylogenetic distance between species (MPD), the mean nearest taxon distance (MNTD) and their equivalents standardized for species richness (ses.PDss, ses.MPD, ses.MNTD). We compared PD of tree communities growing (1) on substrates of varying geological age; and (2) in environments with varying ecophysiological barriers to growth and survival. Results: PDss is strongly positively correlated with species richness (SR), whereas MNTD has a negative correlation. Communities on geologically young- and intermediate-aged substrates (western and central Amazonia respectively) have the highest SR, and therefore the highest PDss and the lowest MNTD. We find that the youngest and oldest substrates (the latter on the Brazilian and Guiana Shields) have the highest ses.PDss and ses.MNTD. MPD and ses.MPD are strongly correlated with how evenly taxa are distributed among the three principal angiosperm clades and are both highest in western Amazonia. Meanwhile, seasonally dry tropical forest (SDTF) and forests on white sands have low PD, as evaluated by any metric. Main conclusions: High ses.PDss and ses.MNTD reflect greater lineage diversity in communities. We suggest that high ses.PDss and ses.MNTD in western Amazonia results from its favourable, easy-to-colonize environment, whereas high values in the Brazilian and Guianan Shields may be due to accumulation of lineages over a longer period of time. White-sand forests and SDTF are dominated by close relatives from fewer lineages, perhaps reflecting ecophysiological barriers that are difficult to surmount evolutionarily. Because MPD and ses.MPD do not reflect lineage diversity per se, we suggest that PDss, ses.PDss and ses.MNTD may be the most useful diversity metrics for setting large-scale conservation priorities.FINCyT - PhD studentshipSchool of Geography of the University of LeedsRoyal Botanic Garden EdinburghNatural Environment Research Council (NERC)Gordon and Betty Moore FoundationEuropean Union's Seventh Framework ProgrammeERCCNPq/PELDNSF - Fellowshi