Integral Human Pose Regression

State-of-the-art human pose estimation methods are based on heat map representation. In spite of the good performance, the representation has a few issues in nature, such as not differentiable and quantization error. This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues. It is differentiable, efficient, and compatible with any heat map based methods. Its effectiveness is convincingly validated via comprehensive ablation experiments under various settings, specifically on 3D pose estimation, for the first time

BodyNet: Volumetric Inference of 3D Human Body Shapes

Human shape estimation is an important task for video editing, animation and fashion industry. Predicting 3D human body shape from natural images, however, is highly challenging due to factors such as variation in human bodies, clothing and viewpoint. Prior methods addressing this problem typically attempt to fit parametric body models with certain priors on pose and shape. In this work we argue for an alternative representation and propose BodyNet, a neural network for direct inference of volumetric body shape from a single image. BodyNet is an end-to-end trainable network that benefits from (i) a volumetric 3D loss, (ii) a multi-view re-projection loss, and (iii) intermediate supervision of 2D pose, 2D body part segmentation, and 3D pose. Each of them results in performance improvement as demonstrated by our experiments. To evaluate the method, we fit the SMPL model to our network output and show state-of-the-art results on the SURREAL and Unite the People datasets, outperforming recent approaches. Besides achieving state-of-the-art performance, our method also enables volumetric body-part segmentation.Comment: Appears in: European Conference on Computer Vision 2018 (ECCV 2018). 27 page

ITS-rDNA phylogeny of Colletotrichum spp. causal agent of apple glomerella leaf spot.

Several diseases have affected apple production, among them there is Glomerella leaf spot (GLS) caused by Colletotrichum spp. The first report of this disease in apple was in plants nearby citrus orchards in São Paulo State, Brazil. The origin of this disease is still not clear, and studies based on the molecular phylogeny could relate the organisms evolutionarily and characterize possible mechanisms of divergent evolution. The amplification of 5.8S-ITS (Internal Transcribed Spacer) of rDNA of 51 pathogenic Colletotrichum spp. isolates from apples, pineapple guava and citrus produced one fragment of approximately 600 bases pairs (bp) for all the isolates analyzed. The amplified fragments were cleaved with restriction enzymes, and fragments from 90 to 500bp were obtained. The sequencing of this region allowed the generation of a phylogenetic tree, regardless of their hosts, and 5 isolated groups were obtained. From the "in silico" comparison, it was possible to verify a variation from 93 to 100% of similarity between the sequences studied and the Genbank data base. The causal agent of GLS is nearly related (clustered) to isolates of pineapple guava and to the citrus isolates used as control

Component-aware Orchestration of Cloud-based Enterprise Applications, from TOSCA to Docker and Kubernetes

Enterprise IT is currently facing the challenge of coordinating the management of complex, multi-component applications across heterogeneous cloud platforms. Containers and container orchestrators provide a valuable solution to deploy multi-component applications over cloud platforms, by coupling the lifecycle of each application component to that of its hosting container. We hereby propose a solution for going beyond such a coupling, based on the OASIS standard TOSCA and on Docker. We indeed propose a novel approach for deploying multi-component applications on top of existing container orchestrators, which allows to manage each component independently from the container used to run it. We also present prototype tools implementing our approach, and we show how we effectively exploited them to carry out a concrete case study

Características e controle da podridão "olho de boi" nas maçãs do sul do Brasil.

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Learning 3D Human Pose from Structure and Motion

3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose two anatomically inspired loss functions and use them with a weakly-supervised learning framework to jointly learn from large-scale in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal network that exploits temporal and structural cues present in predicted pose sequences to temporally harmonize the pose estimations. We carefully analyze the proposed contributions through loss surface visualizations and sensitivity analysis to facilitate deeper understanding of their working mechanism. Our complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics card.Comment: ECCV 2018. Project page: https://www.cse.iitb.ac.in/~rdabral/3DPose

Developing effective practice learning for tomorrow's social workers

This paper considers some of the changes in social work education in the UK, particularly focusing on practice learning in England. The changes and developments are briefly identified and examined in the context of what we know about practice learning. The paper presents some findings from a small scale qualitative study of key stakeholders involved in practice learning and education in social work and their perceptions of these anticipated changes, which are revisited at implementation. The implications for practice learning are discussed

Optimal conditions for conidial germination and infection of European pear leaves by Diplocarpon mespili.

The epidemiology of Entomosporium leaf spot (ELS) affecting European pear is poorly understood, which limits the development of an effective management strategy. In vitro assays were conducted to study the effect of temperature levels (5, 10, 15, 20, 25, and 30 °C) on Diplocarpon mespili conidial germination evaluated at different incubation times (0, 2, 4, 6, 8, 12, 24, and 48 h). Inoculation experiments were conducted to assess the effect of leaf wetness duration (0, 6, 12, 24, and 48 h) under constant temperature (20 °C) on ELS disease severity on leaves of cultivar ?Rocha?. The temperature × time interaction significantly affected conidial germination in both experiments and a response surface model was fitted to percent conidial germination data. The optimal temperature for conidial germination was estimated at 20 °C. The incubation period was estimated at 4 days for all leaf wetness durations, excepting the ?zero? duration for which no infection occurred. A minimum of 6 h of leaf wetness duration was required for D. mespili infection. Severity reached maximum values after 24 h of leaf wetness duration. A linear regression model described ELS severity increase over time in the absence of reinfection conditions and a monomolecular model described the increase of disease severity influenced by leaf wetness duration in both experiments

Crescimento micelial e produção de conídios de Cryptosporiosis perennans, agente causal da mancha foliar da 'Gala', em diferentes meios de cultura.

Vários fatores interferem no crescimento micelial e produção de conídios para a correta identificação de espécies de Cryptosporiopsis perennans. Avaliou-se o crescimento micelial c a esporulação de conídios de 9 isolados (Embrapa I a 9) de C. perennans em 3 meio de cultura, BDA (Batata-dextrose-ágar), Extrato de Malte (malte) e V8 ágar sob regime de fotoperíodo de 12 horas.Resumo 286