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pro vyhledávání: '"Vincent, Joshua L."'
In this work, we employ first-principles simulations to investigate the spin polarization of CeO$_2$-(111) surface and its impact on interactions between a ceria support and Pt nanoparticles. For the first time, we report that the CeO$_2$-(111) surfa
Externí odkaz:
http://arxiv.org/abs/2110.00710
Autor:
Mohan, Sreyas, Vincent, Joshua L., Manzorro, Ramon, Crozier, Peter A., Simoncelli, Eero P., Fernandez-Granda, Carlos
Publikováno v:
Adv. Neural Information Processing Systems (NeurIPS), vol.35 Dec 2021
Deep convolutional neural networks (CNNs) for image denoising are usually trained on large datasets. These models achieve the current state of the art, but they have difficulties generalizing when applied to data that deviate from the training distri
Externí odkaz:
http://arxiv.org/abs/2107.12815
In this work, an optic fiber based $\textit{in situ}$ illumination system integrated into an aberration-corrected environmental transmission electron microscope (ETEM) is designed, built, characterized and applied. With this illumination system, the
Externí odkaz:
http://arxiv.org/abs/2104.02006
Autor:
Vincent, Joshua L., Crozier, Peter A.
Reducible oxides are widely used catalyst supports that can increase oxidation reaction rates by transferring their lattice oxygen at the metal-support interface. The interfacial oxidation process is typically described in terms of a Mars-van Krevele
Externí odkaz:
http://arxiv.org/abs/2104.00821
Autor:
Vincent, Joshua L., Manzorro, Ramon, Mohan, Sreyas, Tang, Binh, Sheth, Dev Y., Simoncelli, Eero P., Matteson, David S., Fernandez-Granda, Carlos, Crozier, Peter A.
A deep convolutional neural network has been developed to denoise atomic-resolution TEM image datasets of nanoparticles acquired using direct electron counting detectors, for applications where the image signal is severely limited by shot noise. The
Externí odkaz:
http://arxiv.org/abs/2101.07770
Autor:
Sheth, Dev Yashpal, Mohan, Sreyas, Vincent, Joshua L., Manzorro, Ramon, Crozier, Peter A., Khapra, Mitesh M., Simoncelli, Eero P., Fernandez-Granda, Carlos
Deep convolutional neural networks (CNNs) for video denoising are typically trained with supervision, assuming the availability of clean videos. However, in many applications, such as microscopy, noiseless videos are not available. To address this, w
Externí odkaz:
http://arxiv.org/abs/2011.15045
Autor:
Mohan, Sreyas, Manzorro, Ramon, Vincent, Joshua L., Tang, Binh, Sheth, Dev Yashpal, Simoncelli, Eero P., Matteson, David S., Crozier, Peter A., Fernandez-Granda, Carlos
Publikováno v:
IEEE Trans. Computational Imaging, vol.8 pp. 585--597, Jul 2022
Denoising is a fundamental challenge in scientific imaging. Deep convolutional neural networks (CNNs) provide the current state of the art in denoising natural images, where they produce impressive results. However, their potential has barely been ex
Externí odkaz:
http://arxiv.org/abs/2010.12970
Autor:
Vincent, Joshua L., Vance, Jarod W., Langdon, Jayse T., Miller, Benjamin K., Crozier, Peter A.
$\textit{In situ}$ environmental transmission electron microscopy (ETEM) is a powerful tool for observing structural modifications taking place in heterogeneous catalysts under reaction conditions. However, to strengthen the link between catalyst str
Externí odkaz:
http://arxiv.org/abs/2003.10426
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