Examining relationships between food environments and adult obesity in Mexico using geographical information systems

Mexico has one of the highest rates of obesity and overweight worldwide: 73% of the population is overweight or obese. The country has experienced a dietary and food retail transition involving increased high-calorie-dense food and beverage availability: 163 L of sugar-sweetened beverages (SSB) were consumed on average per person in 2012. The obesity epidemic in Mexico increased significantly which suggests that the contributing risk factors are likely to be influenced by the environment. Therefore, this study aimed to assess the relationship between the food environment and obesity in Mexico. Food outlet geolocation was obtained from the National Institute of Statistics and Geography in Mexico; anthropometric measurements and socio-economic characteristics of adult participants came from the National Survey on Health and Nutrition in Mexico (ENSANUT) 2012. I calculated the density of supermarkets, restaurants, fast-food outlets, chain convenience stores (CCS) and non-chain convenience stores (NCCS), and fruit and vegetable stores separately and overall. The retail food environment index (RFEI), and the density of ‘unhealthy’ and ‘healthy’ food outlets were also calculated per CTA using ArcGIS. I then analysed the relationship between food outlet density types and obesity through five models which controlled for different covariates including gender, age, socioeconomic status and physical activity, using multilevel linear regression in STATA 14. Results indicated that density of NCCS [β=3.10, 95% CI: 0.97-5.23, p=0.004] and CCS [β=19.11, 95% CI: 1.59-36.63, p=0.003] and the RFEI [β=0.015, 95%CI: 0.049-0.0001, p<0.05] were significantly directly associated with obesity whilst total, healthy and unhealthy food outlet density showed no significant associations. This study showed strong associations between both high densities of convenience stores and a higher proportion of unhealthy food outlets with higher levels of BMI in Mexican adults living in urban areas. Policy makers in Mexico should consider interventions aimed at tackling the obesogenic food environment in the country

Cross-Paced Representation Learning with Partial Curricula for Sketch-based Image Retrieval

In this paper we address the problem of learning robust cross-domain representations for sketch-based image retrieval (SBIR). While most SBIR approaches focus on extracting low- and mid-level descriptors for direct feature matching, recent works have shown the benefit of learning coupled feature representations to describe data from two related sources. However, cross-domain representation learning methods are typically cast into non-convex minimization problems that are difficult to optimize, leading to unsatisfactory performance. Inspired by self-paced learning, a learning methodology designed to overcome convergence issues related to local optima by exploiting the samples in a meaningful order (i.e. easy to hard), we introduce the cross-paced partial curriculum learning (CPPCL) framework. Compared with existing self-paced learning methods which only consider a single modality and cannot deal with prior knowledge, CPPCL is specifically designed to assess the learning pace by jointly handling data from dual sources and modality-specific prior information provided in the form of partial curricula. Additionally, thanks to the learned dictionaries, we demonstrate that the proposed CPPCL embeds robust coupled representations for SBIR. Our approach is extensively evaluated on four publicly available datasets (i.e. CUFS, Flickr15K, QueenMary SBIR and TU-Berlin Extension datasets), showing superior performance over competing SBIR methods

Viraliency: Pooling Local Virality

In our overly-connected world, the automatic recognition of virality - the quality of an image or video to be rapidly and widely spread in social networks - is of crucial importance, and has recently awaken the interest of the computer vision community. Concurrently, recent progress in deep learning architectures showed that global pooling strategies allow the extraction of activation maps, which highlight the parts of the image most likely to contain instances of a certain class. We extend this concept by introducing a pooling layer that learns the size of the support area to be averaged: the learned top-N average (LENA) pooling. We hypothesize that the latent concepts (feature maps) describing virality may require such a rich pooling strategy. We assess the effectiveness of the LENA layer by appending it on top of a convolutional siamese architecture and evaluate its performance on the task of predicting and localizing virality. We report experiments on two publicly available datasets annotated for virality and show that our method outperforms state-of-the-art approaches.Comment: Accepted at IEEE CVPR 201

Every Smile is Unique: Landmark-Guided Diverse Smile Generation

Each smile is unique: one person surely smiles in different ways (e.g., closing/opening the eyes or mouth). Given one input image of a neutral face, can we generate multiple smile videos with distinctive characteristics? To tackle this one-to-many video generation problem, we propose a novel deep learning architecture named Conditional Multi-Mode Network (CMM-Net). To better encode the dynamics of facial expressions, CMM-Net explicitly exploits facial landmarks for generating smile sequences. Specifically, a variational auto-encoder is used to learn a facial landmark embedding. This single embedding is then exploited by a conditional recurrent network which generates a landmark embedding sequence conditioned on a specific expression (e.g., spontaneous smile). Next, the generated landmark embeddings are fed into a multi-mode recurrent landmark generator, producing a set of landmark sequences still associated to the given smile class but clearly distinct from each other. Finally, these landmark sequences are translated into face videos. Our experimental results demonstrate the effectiveness of our CMM-Net in generating realistic videos of multiple smile expressions.Comment: Accepted as a poster in Conference on Computer Vision and Pattern Recognition (CVPR), 201

How to Make an Image More Memorable? A Deep Style Transfer Approach

Recent works have shown that it is possible to automatically predict intrinsic image properties like memorability. In this paper, we take a step forward addressing the question: "Can we make an image more memorable?". Methods for automatically increasing image memorability would have an impact in many application fields like education, gaming or advertising. Our work is inspired by the popular editing-by-applying-filters paradigm adopted in photo editing applications, like Instagram and Prisma. In this context, the problem of increasing image memorability maps to that of retrieving "memorabilizing" filters or style "seeds". Still, users generally have to go through most of the available filters before finding the desired solution, thus turning the editing process into a resource and time consuming task. In this work, we show that it is possible to automatically retrieve the best style seeds for a given image, thus remarkably reducing the number of human attempts needed to find a good match. Our approach leverages from recent advances in the field of image synthesis and adopts a deep architecture for generating a memorable picture from a given input image and a style seed. Importantly, to automatically select the best style a novel learning-based solution, also relying on deep models, is proposed. Our experimental evaluation, conducted on publicly available benchmarks, demonstrates the effectiveness of the proposed approach for generating memorable images through automatic style seed selectionComment: Accepted at ACM ICMR 201

SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

Identificación de la prueba adecuada para determinar el retardo mental en el delito de violación sexual

La presente investigación tiene como finalidad Identificar la prueba pericial&nbsp;adecuada que permita acreditar fehacientemente la existencia del supuesto&nbsp;retardo mental en los delitos de violación de persona en incapacidad deresistencia a losfines de su correcta valoración en el proceso penal.&nbsp;Para ello,se realizará un análisis dogmático delretardomental, del cualse&nbsp;adviertequetienecomocausaorgánica.Paraluegotomarenconsideración&nbsp;la valoración de la prueba se adecuada (pertinente, conducente y útil) a fin&nbsp;de determinar el retardo mental vulnera el derecho a la prueba.Concluyéndose, que la prueba pericial adecuada para determinar&nbsp;fehacientemente elsupuesto de retardo mental esla evaluación psiquiátrica,&nbsp;la misma que garantiza el derecho a la prueba a las partes y su valoración&nbsp;racional enelprocesopenal antela emisióndeunasentencia, condenatorio&nbsp;o absolutoria

Deep Variational Generative Models for Audio-visual Speech Separation

In this paper, we are interested in audio-visual speech separation given a single-channel audio recording as well as visual information (lips movements) associated with each speaker. We propose an unsupervised technique based on audio-visual generative modeling of clean speech. More specifically, during training, a latent variable generative model is learned from clean speech spectrograms using a variational auto-encoder (VAE). To better utilize the visual information, the posteriors of the latent variables are inferred from mixed speech (instead of clean speech) as well as the visual data. The visual modality also serves as a prior for latent variables, through a visual network. At test time, the learned generative model (both for speaker-independent and speaker-dependent scenarios) is combined with an unsupervised non-negative matrix factorization (NMF) variance model for background noise. All the latent variables and noise parameters are then estimated by a Monte Carlo expectation-maximization algorithm. Our experiments show that the proposed unsupervised VAE-based method yields better separation performance than NMF-based approaches as well as a supervised deep learning-based technique

Association of the retail food environment, BMI, dietary patterns, and socioeconomic position in urban areas of Mexico

Copyright: © 2023 Pineda et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The retail food environment is a key modifiable driver of food choice and the risk of non-communicable diseases (NCDs). This study aimed to assess the relationship between the density of food retailers, body mass index (BMI), dietary patterns, and socioeconomic position in Mexico. Cross-sectional dietary data, BMI and socioeconomic characteristics of adult participants came from the nationally representative 2012 National Health and Nutrition Survey in Mexico. Geographical and food outlet data were obtained from official statistics. Densities of food outlets per census tract area (CTA) were calculated. Dietary patterns were determined using exploratory factor analysis and principal component analysis. The association of food environment variables, socioeconomic position, BMI, and dietary patterns was assessed using two-level multilevel linear regression models. Three dietary patterns were identified-the healthy, the unhealthy and the carbohydrates-and-drinks dietary pattern. Lower availability of fruit and vegetable stores was associated with an unhealthier dietary pattern whilst a higher restaurant density was associated with a carbohydrates-and-drinks pattern. A graded and inverse association was observed for fruit and vegetable store density and socioeconomic position (SEP)-lower-income populations had a reduced availability of fruit and vegetable stores, compared with higher-income populations. A higher density of convenience stores was associated with a higher BMI when adjusting for unhealthy dietary patterns. Upper-income households were more likely to consume healthy dietary patterns and middle-upper-income households were less likely to consume unhealthy dietary patterns when exposed to high densities of fruit and vegetable stores. When exposed to a high concentration of convenience stores, lower and upper-lower-income households were more likely to consume unhealthy dietary patterns. Food environment and sociodemographic conditions within neighbourhoods may affect dietary behaviours. Food environment interventions and policies which improve access to healthy foods and restrict access to unhealthy foods may facilitate healthier diets and contribute to the prevention of NCDs.publishersversionpublishe

Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction

Multi-scale representations deeply learned via convolutional neural networks have shown tremendous importance for various pixel-level prediction problems. In this paper we present a novel approach that advances the state of the art on pixel-level prediction in a fundamental aspect, i.e. structured multi-scale features learning and fusion. In contrast to previous works directly considering multi-scale feature maps obtained from the inner layers of a primary CNN architecture, and simply fusing the features with weighted averaging or concatenation, we propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs) model for learning and fusing multi-scale representations in a principled manner. In order to further improve the learning capacity of the network structure, we propose to exploit feature dependant conditional kernels within the deep probabilistic framework. Extensive experiments are conducted on four publicly available datasets (i.e. BSDS500, NYUD-V2, KITTI, and Pascal-Context) and on three challenging pixel-wise prediction problems involving both discrete and continuous labels (i.e. monocular depth estimation, object contour prediction, and semantic segmentation). Quantitative and qualitative results demonstrate the effectiveness of the proposed latent AG-CRF model and the overall probabilistic graph attention network with feature conditional kernels for structured feature learning and pixel-wise prediction.Comment: Regular paper accepted at TPAMI 2020. arXiv admin note: text overlap with arXiv:1801.0052