Curso de Flautas Dolce

VII Seminário de Extensão Universitária da UNILA (SEUNI); VIII Encontro de Iniciação Científica e IV Encontro de Iniciação em Desenvolvimento Tecnológico e Inovação (EICTI 2019) e Seminário de Atividades Formativas da UNILA (SAFOR)El curso de flauta dulce ofrece un acercamiento inicial al instrumento como herramienta de musicalización. A través de clases prácticas se abordan cuestiones técnicas (respiración, digitación, escalas, entrenamiento rítmico) y el aprendizaje del repertorio popular. Se ofrece en dos espacios: Alliance Fraternity Association (proyecto social) y Campus AlmadaAgradezco a la Universidad de Integración Latino-Americana (UNILA) por el financiamiento para este proyecto, los equipos y recursos necesarios para desarrollar el proyecto del curso de flautas dulce. Al orientador Me. Danilo Bogo, al colaborador Prof. Dr. Marcelo R. Villena y a los voluntarios del proyect

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

Exploiting temporal information for 3D pose estimation

In this work, we address the problem of 3D human pose estimation from a sequence of 2D human poses. Although the recent success of deep networks has led many state-of-the-art methods for 3D pose estimation to train deep networks end-to-end to predict from images directly, the top-performing approaches have shown the effectiveness of dividing the task of 3D pose estimation into two steps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from images and then mapping them into 3D space. They also showed that a low-dimensional representation like 2D locations of a set of joints can be discriminative enough to estimate 3D pose with high accuracy. However, estimation of 3D pose for individual frames leads to temporally incoherent estimates due to independent error in each frame causing jitter. Therefore, in this work we utilize the temporal information across a sequence of 2D joint locations to estimate a sequence of 3D poses. We designed a sequence-to-sequence network composed of layer-normalized LSTM units with shortcut connections connecting the input to the output on the decoder side and imposed temporal smoothness constraint during training. We found that the knowledge of temporal consistency improves the best reported result on Human3.6M dataset by approximately $12.2\%$ and helps our network to recover temporally consistent 3D poses over a sequence of images even when the 2D pose detector fails

CONSTRUCTAL DESIGN OF FINS IN COOLED CAVITIES BY NON-NEWTONIAN FLUIDS

The present work investigates the Construtal Design of fins inserted in cavities submitted to mixed convection by non-Newtonian fluids. The objective is to obtain the optimum aspect ratio for the fin considering different flow conditions and variations in the rheological parameters of the fluid. The phenomena of flow and heat transfer are modeled by mass balance, momentum and energy equations, and by the generalized Newtonian liquid constitutive equation. The viscosity is modeled as that of a pseudoplastic fluid, using the Carreau function. The optimization problem consists in maximizing heat transfer from the fin using the average Nusselt number. The investigated project variable is the aspect ratio between the edges of the rectangular plane fin profile. The restrictions are the volume of the cavity and the fin. The results are obtained numerically using a finite volume code and a two-dimensional geometry, through exhaustive searching. The results show that the fin geometry influences the maximum Nusselt number mainly for the cases with high Reynolds and Rayleigh numbers, such as was shown in previous studies. The results show that the fin geometry influences the maximum Nusselt number mainly for the cases with high Reynolds and Rayleigh numbers, as was shown in previous studies. It was also found that the Nusselt number increases as the increase in flow intensity, represented by the parameter p, and that the result of the maximum Nusselt number does not change monotonically with the non-Newtonian dimensionless viscosity and with the flow index, showing that the pseudoplasticity of the fluid implies optimal configurations very different from those predicted for Newtonian fluids

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

Influence of Quince rootstocks on Entomosporium Leaf Spot (Entomosporium mespili) susceptibility in European Pear cv. Abate Fetel

Entomosporium leaf spot (ELS) is caused by the fungus Fabraea maculata (anamorph: Entomosporium mespili) and affects most pear cultivars and quince rootstocks in Brazil. The aim of this study was to characterize the effect of Adams, EMA and EMC quince rootstocks on ELS in European pear cultivar “Abate Fetel” in Southern Brazil, during the 2009/2010, 2010/2011 and 2011/2012 growing season. The incidence and severity of disease was quantified weekly in 100 randomly leaves distributed in four medium-height branches per plant with eight replications. Disease progress curves of ELS were constructed and the epidemics compared according to: (1) the beginning of symptoms appearance (BSA); (2) the time to reach the maximum disease incidence and severity (TRMDI and TRMDS); (3) area under the incidence and severity disease progress curve (AUIDPC and AUSDPC). The data were analyzed by linear regression and adjusted for three empirical models: Logistic, Monomolecular and Gompertz. The Abate Fetel cultivar under all rootstocks evaluated was susceptible to E. mespili. However, there were significant differences in ELS intensity among rootstocks evaluated. The highest ELS intensities were observed in combinations with EMA and Adams quince rootstock. Abate Fetel cultivar grafted on EMC quince rootstock showed all epidemiological variables results significantly different when compared with EMA quince rootstock. EMC quince rootstock induced late resistance compared with the other considerated rootstocks. The Logistic model was the most appropriates to describe the ELS progress of Abate Fetel cultivar under all rootstocks evaluated in the edafoclimatic conditions of Southern Brazil, during the 2009/2010, 2010/2011 and 2011/2012 growing season

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

NASA: Neural Articulated Shape Approximation

Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. To efficiently simulate deformation, existing approaches represent 3D objects using polygonal meshes and deform them using skinning techniques. This paper introduces neural articulated shape approximation (NASA), an alternative framework that enables efficient representation of articulated deformable objects using neural indicator functions that are conditioned on pose. Occupancy testing using NASA is straightforward, circumventing the complexity of meshes and the issue of water-tightness. We demonstrate the effectiveness of NASA for 3D tracking applications, and discuss other potential extensions.Comment: ECCV 202

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