Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Donald Loveland"'
Autor:
Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Xiaoting Zhong, T. Yong-Jin Han
Publikováno v:
ACS Omega, Vol 7, Iss 3, Pp 2624-2637 (2022)
Externí odkaz:
https://doaj.org/article/b3746167135a4ba89a10291ecb901fa8
Autor:
Brian Gallagher, Matthew Rever, Donald Loveland, T. Nathan Mundhenk, Brock Beauchamp, Emily Robertson, Golam G. Jaman, Anna M. Hiszpanski, T. Yong-Jin Han
Publikováno v:
Materials & Design, Vol 190, Iss , Pp - (2020)
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 o
Externí odkaz:
https://doaj.org/article/b0c0c231d88d41bd920defda87945393
Autor:
Joanne Taery Kim, T. Yong-Jin Han, Anna M. Hiszpanski, Phan Nguyen, Piyush Karande, Donald Loveland
Publikováno v:
Journal of Chemical Information and Modeling. 61:2147-2158
To expedite new molecular compound development, a long-sought goal within the chemistry community has been to predict molecules' bulk properties of interest a priori to synthesis from a chemical structure alone. In this work, we demonstrate that mach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d6b9a3f8d0d91eed8ff804532cdd69d
https://doi.org/10.1021/acs.jcim.0c01318
https://doi.org/10.1021/acs.jcim.0c01318
Publikováno v:
Journal of chemical information and modeling. 60(12)
Packing motifs-patterns in how molecules orient relative to one another in a crystal structure-are an important concept in many subdisciplines of materials science because of correlations observed between specific packing motifs and properties of int
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3fee2e316b4b6cea95d37df392247c3d
https://pubmed.ncbi.nlm.nih.gov/33245232
https://pubmed.ncbi.nlm.nih.gov/33245232
Autor:
Vivian U, Aaron J. Barth, H. Alexander Vogler, Hengxiao Guo, Tommaso Treu, Vardha N. Bennert, Gabriela Canalizo, Alexei V. Filippenko, Elinor Gates, Frederick Hamann, Michael D. Joner, Matthew A. Malkan, Anna Pancoast, Peter R. Williams, Jong-Hak Woo, Bela Abolfathi, L. E. Abramson, Stephen F. Armen, Hyun-Jin Bae, Thomas Bohn, Benjamin D. Boizelle, Azalee Bostroem, Andrew Brandel, Thomas G. Brink, Sanyum Channa, M. C. Cooper, Maren Cosens, Edward Donohue, Sean P. Fillingham, Diego González-Buitrago, Goni Halevi, Andrew Halle, Carol E. Hood, Keith Horne, J. Chuck Horst, Maxime de Kouchkovsky, Benjamin Kuhn, Sahana Kumar, Douglas C. Leonard, Donald Loveland, Christina Manzano-King, Ian McHardy, Raúl Michel, Melanie Kae B. Olaes, Daeseong Park, Songyoun Park, Liuyi Pei, Timothy W. Ross, Jordan N. Runco, Jenna Samuel, Javier Sánchez, Bryan Scott, Remington O. Sexton, Jaejin Shin, Isaac Shivvers, Chance L. Spencer, Benjamin E. Stahl, Samantha Stegman, Isak Stomberg, Stefano Valenti, L. Villafaña, Jonelle L. Walsh, Heechan Yuk, WeiKang Zheng
Publikováno v:
The astrophysical journal / 2 925(1), 52 (2022). doi:10.3847/1538-4357/ac3d26
The astrophysical journal 925(1), 52 (2022). doi:10.3847/1538-4357/ac3d26
We carried out spectroscopic monitoring of 21 low-redshift Seyfert 1 galaxies using the Kast double spectrograph on the 3 m Shane telescope at Lick Observatory from 2016 A
We carried out spectroscopic monitoring of 21 low-redshift Seyfert 1 galaxies using the Kast double spectrograph on the 3 m Shane telescope at Lick Observatory from 2016 A
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f50635ad26351074c56be531f72dd06a
https://doi.org/10.3847/1538-4357/ac3d26
https://doi.org/10.3847/1538-4357/ac3d26
Autor:
Kelsi Flatland, Daesong Park, Nathan Milgram, Sean Lewis, Maren Cosens, Matthew W. Auger, Edward Donohue, Matthew A. Malkan, Vardha N. Bennert, Mariana S. Lazarova, Tommaso Treu, S. Komossa, Donald Loveland
Publikováno v:
Monthly Notices of the Royal Astronomical Society. 481:138-152
Author(s): Bennert, Vardha N; Loveland, Donald; Donohue, Edward; Cosens, Maren; Lewis, Sean; Komossa, S; Treu, Tommaso; Malkan, Matthew A; Milgram, Nathan; Flatland, Kelsi; Auger, Matthew W; Park, Daeseong; Lazarova, Mariana S | Abstract: For a sampl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8dd84579f50f7816613460aee402a588
https://doi.org/10.1093/mnras/sty2236
https://doi.org/10.1093/mnras/sty2236
Publikováno v:
GlobalSIP
In this work, we propose an introspection technique for deep neural networks that relies on a generative model to instigate salient editing of the input image for model interpretation. Such modification provides the fundamental interventional operati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::139a76c92f675ae366f0107306466c5e
https://doi.org/10.1109/globalsip45357.2019.8969491
https://doi.org/10.1109/globalsip45357.2019.8969491
Autor:
T. Nathan Mundhenk, Brian Gallagher, Brock Beauchamp, T. Yong-Jin Han, Anna M. Hiszpanski, Emily Robertson, Matthew Rever, Donald Loveland, Golam G. Jaman
Publikováno v:
Materials & Design, Vol 190, Iss, Pp-(2020)
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 o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6945238baead4c71dd0da7c5823adc6
http://arxiv.org/abs/1906.02130
http://arxiv.org/abs/1906.02130
Publikováno v:
Communications of the ACM. 5:394-397
The programming of a proof procedure is discussed in connection with trial runs and possible improvements.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c98aced0727b10e01f9cdba0425d7f4
https://doi.org/10.1145/368273.368557
https://doi.org/10.1145/368273.368557
Publikováno v:
Automated Theorem Proving: After 25 Years. :71-72
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d919596cd9babdd7cb49bbc7c2244a4
https://doi.org/10.1090/conm/029/04
https://doi.org/10.1090/conm/029/04