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pro vyhledávání: '"T Nathan"'
Autor:
Chandrakumar Bogguri, Vivek Kurien George, Beheshta Amiri, Alexander Ladd, Nicholas R. Hum, Aimy Sebastian, Heather A. Enright, Carlos A. Valdez, T. Nathan Mundhenk, Jose Cadena, Doris Lam
Publikováno v:
Frontiers in Cellular Neuroscience, Vol 18 (2024)
Organophosphorus nerve agents (OPNA) are hazardous environmental exposures to the civilian population and have been historically weaponized as chemical warfare agents (CWA). OPNA exposure can lead to several neurological, sensory, and motor symptoms
Externí odkaz:
https://doaj.org/article/2c2b58c2460a469e8a5d95815e53ca1f
Autor:
Lee, Woojoo, Wang, Yuanxi, Qin, Wei, Kim, Hyunsue, Liu, Mengke, Nunley, T. Nathan, Fang, Bin, Maniyara, Rinu, Dong, Chengye, Robinson, Joshua A., Crespi, Vincent, Li, Xiaoqin, MacDonald, Allan H., Shih, Chih-Kang
2D materials have intriguing quantum phenomena that are distinctively different from their bulk counterparts. Recently, epitaxially synthesized wafer-scale 2D metals, composed of elemental atoms, are attracting attention not only for their potential
Externí odkaz:
http://arxiv.org/abs/2201.01701
We recently developed a deep learning method that can determine the critical peak stress of a material by looking at scanning electron microscope (SEM) images of the material's crystals. However, it has been somewhat unclear what kind of image featur
Externí odkaz:
http://arxiv.org/abs/2111.03729
Autor:
Mundhenk, T. Nathan, Landajuela, Mikel, Glatt, Ruben, Santiago, Claudio P., Faissol, Daniel M., Petersen, Brenden K.
Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process. It is a discrete optimization problem generally believed to be NP-hard. Prior approaches to solving the problem include neur
Externí odkaz:
http://arxiv.org/abs/2111.00053
Autor:
Landajuela, Mikel, Petersen, Brenden K., Kim, Soo K., Santiago, Claudio P., Glatt, Ruben, Mundhenk, T. Nathan, Pettit, Jacob F., Faissol, Daniel M.
Publikováno v:
1st Mathematical Reasoning in General Artificial Intelligence Workshop, ICLR 2021
Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols. In contrast to traditional evolutionary approaches, using a neural network at the core of
Externí odkaz:
http://arxiv.org/abs/2107.09158
Autor:
Petersen, Brenden K., Landajuela, Mikel, Mundhenk, T. Nathan, Santiago, Claudio P., Kim, Soo K., Kim, Joanne T.
Publikováno v:
International Conference on Learning Representations, 2021
Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of $\textit{symbolic regression}$. Despite recent advances in training neural networks to solve complex task
Externí odkaz:
http://arxiv.org/abs/1912.04871
We describe an explainable AI saliency map method for use with deep convolutional neural networks (CNN) that is much more efficient than popular fine-resolution gradient methods. It is also quantitatively similar or better in accuracy. Our technique
Externí odkaz:
http://arxiv.org/abs/1911.11293
Autor:
Gallagher, Brian, Rever, Matthew, Loveland, Donald, Mundhenk, T. Nathan, Beauchamp, Brock, Robertson, Emily, Jaman, Golam G., Hiszpanski, Anna M., Han, T. Yong-Jin
We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g. compressive strength) based on SEM images. We show that it's possible to train ML models to predict materials performance based on
Externí odkaz:
http://arxiv.org/abs/1906.02130
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Autor:
David Lujan, Jeongheon Choe, Martin Rodriguez-Vega, Zhipeng Ye, Aritz Leonardo, T. Nathan Nunley, Liang-Juan Chang, Shang-Fan Lee, Jiaqiang Yan, Gregory A. Fiete, Rui He, Xiaoqin Li
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-7 (2022)
MnBi2Te4, referred to as MBT, is a van der Waals material combining topological electron bands with magnetic order. Here, Lujan et al study collective spin excitations in MBT, and show that magnetic fluctuations increase as samples reduce in thicknes
Externí odkaz:
https://doaj.org/article/748bcb3b3ac6442ea11a2ad887ebeeba