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120 results for “GAN”
Figure 4. Sphaerassiminea brevicula. A. front view B in Brackish water snails from Qi'ao-Dan'gan Island in the Pearl River estuary, China
Figure 4. Sphaerassiminea brevicula. A. front view B. back view. Scale bar = 1 mm.
Figure 2. Assiminea estuarina. A. front view B in Brackish water snails from Qi'ao-Dan'gan Island in the Pearl River estuary, China
Figure 2. Assiminea estuarina. A. front view B. back view. Scale bar = 1 mm.
Figure 16. I in Brackish water snails from Qi'ao-Dan'gan Island in the Pearl River estuary, China
Figure 16. I. (Fairbankia) cochinchinensis. Radular teeth.
GANS, 1975
<p>CARL GANS, Tetrapod Limblessness: Evolution and Functional Corollaries, American Zoologist, Volume 15, Issue 2, May 1975, Pages 455–467,</p> <p>https://doi.org/10.1093/icb/15.2.455</p>
Dataset of "Some effects of limited wall-sensor availability on flow estimation with 3D-GANs"
<p>Dataset of the article 'Some effects of limited wall-sensor availability on flow estimation with 3D-GANs' (https://doi.org/10.1007/s00162-024-00718-w). The codes processing data here are on https://github.com/erc-nextflow/3D-GAN.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 949085, NEXTFLOW). Views and opinions expressed are, however, those of the authors only, and do not necessarily reflect those of the European Union or the ERC. Neither the European Union nor the granting authority can be held responsible for them. A.C.M. acknowledges financial support from the Spanish Ministry of Universities under the Formación de Profesorado Universitario (FPU) programme 2020.</p>
pGAN Synthetic Dataset: A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs
<p>Synthetic dataset for <strong>A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs</strong></p> <p><strong> Dataset specification:</strong></p> <ul> <li>MRI images of Vertebral Units labelled based on region</li> <li>Dataset is comprised of 10000 pairs of images and labels</li> <li>Image and label pair number k can be selected by: synthetic_dataset['images'][k] and synthetic_dataset['regions'][k]</li> <li>Images are 3D of size (9, 64, 64)</li> <li>Regions are stored as an integer. Mapping is 0: cervical, 1: thoracic, 2: lumbar</li> </ul> <p>Arxiv paper: <a href="https://arxiv.org/abs/2106.13199">https://arxiv.org/abs/2106.13199</a><br> Github code: <a href="https://github.com/tcoroller/pGAN/">https://github.com/tcoroller/pGAN/</a></p> <p>Abstract:</p> <p>Sharing data from clinical studies can facilitate innovative data-driven research and ultimately lead to better public health. However, sharing biomedical data can put sensitive personal information at risk. This is usually solved by anonymization, which is a slow and expensive process. An alternative to anonymization is sharing a synthetic dataset that bears a behaviour similar to the real data but preserves privacy. As part of the collaboration between Novartis and the Oxford Big Data Institute, we generate a synthetic dataset based on COSENTYX Ankylosing Spondylitis (AS) clinical study. We apply an Auxiliary Classifier GAN (ac-GAN) to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs). The images are conditioned on the VU location (cervical, thoracic and lumbar). In this paper, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties of along three key metrics: image fidelity, sample diversity and dataset privacy.</p>
Miniature optical fiber curvature sensor via integration with GaN optoelectronics
<p>Raw data of publication "<strong>Miniature optical fiber curvature sensor via integration with GaN optoelectronics</strong>"</p>
Data set of simulated rimed aggregates for "A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh-Gans approximation"
<p><strong>Simulated rimed aggregates</strong> generated with https://github.com/jleinonen/aggregation in setting "aggregation followed by riming".</p> <p>Aggregates were built from between 10 to 700 monomer crystals of <strong>columns, dendrites, needles, plates or rosettes</strong> with mean sizes of 100 or 200 micrometer. Then they were exposed to ELWP = 2.0 kg m⁻². Monomer crystals are composed of cubical elements with resolution 20 micrometer. Frozen rime droplets are also represented by 20 micrometer cubes.</p> <p>The data set contains folders with <strong>evolution (evol) and shape files for each monomer crystal type</strong>. For each particle one evolution and one corresponding shape file exists. The evolution (evol) file contains particle mass, rime mass, area, size, fall speed (Heymsfield&Westbrook, 2010), fall speed (Khvorostyanov&Curry, 2005) for each step during the aggregation and riming process. The corresponding shape file contains the x,y,z positions of the cubical elements that compose the particle for each step. <strong>For further documentation see readme.</strong></p>
Gan & Lee Insulin Glargine Target Type (2) Evaluating Research
ClinicalTrials.gov study NCT03371108. IPD Sharing: NO. Countries: 1. Publications: 0.
AVIRIS-NG-like smart virtual remote sensing via spectra-aware physics informed GANs
Open the record for dataset details and reuse information.
Data from: Study on the optimization of the deposition rate of planetary GaN-MOCVD films based on CFD simulation and the corresponding surface model
Metal-organic chemical vapour deposition (MOCVD) is a key technique for fabricating GaN thin film structures for light-emitting and semiconductor laser diodes. Film uniformity is an important index to measure equipment performance and chip processes. This paper introduces a method to improve the quality of thin films by optimizing the rotation speed of different substrates of a model consisting of a planetary with seven 6-inch wafers for the planetary GaN-MOCVD. A numerical solution to the transient state at low pressure is obtained using computational fluid dynamics. To evaluate the role of the different zone speeds on the growth uniformity, single factor analysis is introduced. The results show that the growth rate and uniformity are strongly related to the rotational speed. Next, a response surface model was constructed by using the variables and the corresponding simulation results. The optimized combination of the matching of different speeds is also proposed as a useful reference for applications in industry, obtained by a response surface model and genetic algorithm with a balance between the growth rate and the growth uniformity. This method can save time, and the optimization can obtain the most uniform and highest thin film quality.
BigVSAN: Enhancing GAN-based Neural Vocoders with Slicing Adversarial Network
<p>This repository contains a pre-trained checkpoint for BigVSAN proposed in the paper <a href="https://arxiv.org/abs/2309.02836">BigVSAN: Enhancing GAN-based Neural Vocoders with Slicing Adversarial Network</a> by Sony.</p><p>More information about BigVSAN including our code is available at <a href="https://github.com/sony/bigvsan">https://github.com/sony/bigvsan</a>.</p>
GaN metalens
Open the record for dataset details and reuse information.
SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer
<p>This repository contains a pre-trained checkpoints for StyleSAN-XL proposed in the paper <a href="https://arxiv.org/abs/2301.12811">SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer</a> by Sony.</p> <p>More information about StyleSAN-XL including our code is available at <a href="https://github.com/sony/san">https://github.com/sony/san</a>.</p>
HYPPO product files from GAN optimization for HEP event simulations
<p>Contain the entirety of all the results obtained for the optimization of Generative Adversarial Network (GAN) models for high-energy physics simulations, using the HYPPO hyperparameter optimization software. All runs were executed on the NERSC's Cori cluster.</p>
Cloud masks of GAN-CDM based models
<p>This is the result of GAN-CDM based models reported in Li, J., Wu, Z., Sheng, Q., Wang, B., Hu, Z., Zheng, S., Camps-Vall, G., Molinier, M., 2022. A hybrid generative adversarial network for weakly-supervised cloud detection in multispectral images. Remote Sens. Environ. 280, 113197. https://doi.org/10.1016/j.rse.2022.113197</p> <p>The file name means: dataset-binary mask under certain threshold-modelversion</p>
CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy
<p>This is the models as well as results for CLTS-GAN, a deep learning model that disentangles color and lighting and texture and specular information. The results for the model as well as an augmented polyp dataset are contained here.</p> <p><strong>Abstract:</strong></p> <p>Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with annotations (but expensive and time-consuming). We present CLTS-GAN, a new deep learning model that gives fine control over color, lighting, texture, and specular reflection synthesis for OC video frames. We show that adding these colonoscopy-specific augmentations to the training data can improve state-of-the-art polyp detection/segmentation methods as well as drive next generation of OC simulators for training medical students. You can find the code and additional detail about CLTS-GAN via our Computation Endoscopy Platform at <a href="https://github.com/nadeemlab/CEP">https://github.com/nadeemlab/CEP</a></p>
Machine Learning GAN Deconvolution
<p>We initially benchmarked our GAN against the LUCYD network using a Z-stack of a selection U2OS cells acquired by widefield microscopy from the Li et al dataset (Li et al., 2022) . Following this we ran another benchmark against Deconwolf to gain a direct comparison of the improvements achieved using a Z-stack image from the ChrX-36plex OligoFISSEQ dataset (Nguyen et al., 2020).</p> <p><br>Data is organised as follow:</p> <p>├───Input<br>└───Output<br> ├───Deconwolf<br> ├───GAN<br> └───LUCYD</p> <p></p>
GaN_Dislocations_1 EBSD Example data
<p>EBSD Example data from a GaN sample, measured by Naresh Gunasekar. Data conversion by Aimo Winkelmann.</p>
FIGURE 3 in Amphisbaena lumbricalis Vanzolini, 1996 is a synonym of Amphisbaena carvalhoi Gans, 1965 (Squamata, Amphisbaenidae)
FIGURE 3. Geographic distribution map of literature records Amphisbaena carvalhoi (black circles), literature records of A. lumbricalis (white circles), and new records presented in this study. Locality names are in Table 1. Acronyms: CE—Ceará state; RN—Rio Grande do Norte state; PB = Paraíba state; PE: Pernambuco state; AL = Alagoas state; SE = Sergipe state; BA = Bahia state.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.