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  • A large collection of synthetic, path-traced renderings for use in multi-view stereopsis and 3D reconstruction applications. The material properties are primarily non-Lambertian (reflective metals). Ground truth depth maps, model geometry, object masks and camera extrinsic and intrinsic data is provided together with the rendered images. A total of 18,000 samples are provided (45 camera views for 400 scenes), varying the illumination using five, high-definition HDR environemnt textures, four models (teapot, bunny, armadillo and dragon), ten material properties (bricks, brushed metal, ceramic, checkerboard, concrete, copper, grunge metal, marble, piano/ivory, and steel) and two camera forcal lengths (35 mm and 50 mm). File descriptions: + ShinySMVS_sample.jpg - a visual collage of 8 scenes, illustrating the variability introduced by using different models, illumination, material properties and camera focal lengths. + sample_armadillo1bricks50mm.zip - A single ShinySMVS scene sample for easy viewing and download (31Mb). + ShinySMVS_576p.tar.xz - The full ShinySMVS dataset containing all 400 scenes (768x576 pixel resolution). Note that tar.xz archive used due to file size limitations (8.6Gb). + PFMdemo.zip - Python example source code for loading ground truth depth map (PFM file format) as a numpy array. + modelSTL - Stereolithography (STL) file for the models used in the dataset.
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  • Ab initio calculation files
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  • In the study, a high-performance controlled short-circuiting metal transfer process at a wire feed rate of 12 m/min for WAAM with an Al-Mg-Mn alloying system was developed. The arc current and voltage waveforms were studied by oscillograms processing and then modified to reduce energy input in comparison with a self-regulated gas metal arc welding process. The newly developed process was implemented in manufacturing of the sample parts at a travel speed of up to 150 cm/min. Modified waveforms in a combination with an increased travel speed led to a decrease in heat input, which appeared to be 16% lower than that of a conventional self-regulated process. Decreased heat input lead to an improvement of the geometry preservation stability at high process rates (up to 2.2 kg/h). The mechanical properties study showed that the elongation of the tensile samples was up to 41%, the increase in elongation was explained through macro- and microstructure analysis. The provided data shows remarkable tensile test results and remarkable macro- and microstructure images, which were used to evaluate porosity and grain size in the study of the deposited metal properties. High-speed video of a metal transfer during deposition using the newly developed process along with a high-speed video of a wire feeding process are also presented in this dataset.
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  • Retinal blood vessel segmentation(Retinopathy of Prematurity).
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  • In 2018, a new Mercosur license plate standard was published, unifing the identification of vehicles and replacing the present license plate standards of five South America countries. In this new scenario, automatic license plate recognition (ALPR) systems built upon supervised learning algorithms could not be trained due to the lack of available data in real scenarios. So, in order to create a dataset without real samples of the new license plates standard, enabling the trainment of these models, a Mercosur license plate generator was developed to generate artificial license plates images with shadow, occlusion and other variations to mimetize real conditions, and a embeding system with license plate detection (LPD) that detects old 3-letter license plates in images of real scenarios and overwrite it with an artificially generated license plates. The dataset contains images of real scenarios where 3-letter license plates were detected using YOLOv3 (http://arxiv.org/abs/1804.02767) and overwritten by artificially generated images of the new mercosur license plates. It is organized in two folders: images - containing the images (JPEG) of the dataset; and labels - containing text files with the class identification number and the coordinates of the detected license plates in the image, following the Yolo_mark annotation specification (https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects). The images are separated in five classes, identified by a prefix in the filename: 1) monitoring_system_ - 2925 images acquired by a license plate detection model applied in a public videomonitoring system; 2) parking_lot1_ - 566 images of cars in a parking lot acquired using a smartphone camera; 3) parking_lot2_ containing 23 images of the same as parking_lot1_ but acquired using a tablet camera; 4) parking_lot3_ 11 images same as parking_lot1_ but acquired using a different smartphone model; and 5) cropped_parking_lot containing 315 images cropped in the license plate area from parking_lot_ images; Also, there is a CSV file listing all license plates detected in all images, organized in seven columns: image, label, class, x_center, y_center, width and height. The image column is the filename of the image containing the license plate, label is the filename of its recpective annotation, class is the class of the object (in this case, always zero, the index of the license plate object), and x_center, y_center, width and height, the coordinates of the rectangle of the set of pixels representing the license plate in the image, following YOLO annotation standard.
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  • Optic disc segmentation(ROP)
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  • COVID-CTset: A Large COVID-19 CT Scans dataset containing 63849 images from 377 patients Some samples of the dataset are uploaded here. For more details and using the Full dataset refer to https://github.com/mr7495/COVID-CTset The shared links are available at the GitHub profile. Please cite this dataset by: @article {Rahimzadeh2020.06.08.20121541, author = {Rahimzadeh, Mohammad and Attar, Abolfazl and Sakhaei, Seyed Mohammad}, title = {A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset}, elocation-id = {2020.06.08.20121541}, year = {2020}, doi = {10.1101/2020.06.08.20121541}, publisher = {Cold Spring Harbor Laboratory Press}, URL = {https://www.medrxiv.org/content/early/2020/06/12/2020.06.08.20121541}, eprint = {https://www.medrxiv.org/content/early/2020/06/12/2020.06.08.20121541.full.pdf}, journal = {medRxiv} }
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  • The experimental data were used to study effects of raindrop impact on the resistance characteristics of sheet flow. The flume was 6.0 m long, 0.25 m wide, and 0.3 m deep. The bed surface of the flume was covered by a smooth and uniform layer of mortar (sand to cement mixing ratio 2:1). The flume surface was then sheltered with a nylon gauze sheet, which was fixed at 0.4 m above the flume surface to reduce the raindrop impact. Flume tests with four rainfall intensities and eight slope angles were simulated for each scenario. The four simulated raindrop characteristics of with and without gauze screen are located in the folder. The experimental data mainly include mean flow velocity and depth of whole flume. The surface flow velocity was measured in each segment by using a dye tracing method with KMnO4. The whole flume was equally divided into three segments: downslope (0-2 m), middle slope (2-4 m) and upslope (4-6 m). The surface flow velocity and flow depth were measured in each segment. The surface velocity of the whole flume was mean surface velocity of the three segments. Flow depth was measured in each 1 m by using a water level measuring needle. There were six observation points for measuring flow depth, positioned at 0.5, 1.5, 2.5, 3.5, 4.5, 5.5 m along the flume center line. The mean flow depth of the whole flume was the average of all measured points.
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  • Data regarding the perceived air pollution are collected by means of an online survey distributed between 11-05-2020 and 31-05-2020 in Australia, Brazil, China, Ghana, Italy, India, Iran, Norway, South Africa and the United States. The survey respondents are asked to evaluate the perceived air pollution quality before and during the COVID-19 restrictions according to a Likert scale varying from "1=extremely low/absent" to "7= extremely high". Overall, the data shows that an improvement in air quality is perceived by the respondents in all the ten investigated countries. The online survey has been created with Google Forms and WenJuanXing and conveniently translated into Chinese, English, Italian, Norwegian, Persian, Portuguese and has been distributed via email, social media and professional networks. Overall, the total number of respondents is approximately equal to 10 000.
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  • Supplemental Figures
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