Zobrazeno 1 - 10
of 35 517
pro vyhledávání: '"JOHNSON, A. R."'
Autor:
Mangu, Aashrita, Westbrook, Benjamin, Beckman, Shawn, Corbett, Lance, Crowley, Kevin T., Dutcher, Daniel, Johnson, Bradley R., Lee, Adrian T., Kabra, Varun, Prasad, Bhoomija, Staggs, Suzanne T., Suzuki, Aritoki, Wang, Yuhan, Zheng, Kaiwen
Publikováno v:
J Low Temp Phys (2024)
The Simons Observatory (SO) is a cosmic microwave background (CMB) experiment located in the Atacama Desert in Chile that will make precise temperature and polarization measurements over six spectral bands ranging from 27 to 285 GHz. Three small aper
Externí odkaz:
http://arxiv.org/abs/2412.01204
Autor:
Gerras, Remington G., Alford, Thomas, Randall, Michael J., Seibert, Joseph, Chesmore, Grace, Crowley, Kevin T., Galitzki, Nicholas, Gudmundsson, Jon, Harrington, Kathleen, Johnson, Bradley R., Lloyd, J. B., Miller, Amber D., Silva-Feaver, Max
Publikováno v:
SPIE Proceedings Volume 13094, Ground-based and Airborne Telescopes X; 130945M (2024)
The Simons Observatory is a ground-based telescope array located at an elevation of 5200 meters, in the Atacama Desert in Chile, designed to measure the temperature and polarization of the cosmic microwave background. It comprises four telescopes: th
Externí odkaz:
http://arxiv.org/abs/2411.07318
Autor:
Viswanath, Siddharth, Bhaskar, Dhananjay, Johnson, David R., Rocha, Joao Felipe, Castro, Egbert, Grady, Jackson D., Grigas, Alex T., Perlmutter, Michael A., O'Hern, Corey S., Krishnaswamy, Smita
Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond
Externí odkaz:
http://arxiv.org/abs/2410.20317
Autor:
Johnson, David R., Chew, Joyce, Viswanath, Siddharth, De Brouwer, Edward, Needell, Deanna, Krishnaswamy, Smita, Perlmutter, Michael
In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). The filter-combine framework parallels the popular aggregate-combine paradigm for graph neural networks (GNNs) and naturally suggest
Externí odkaz:
http://arxiv.org/abs/2410.14639
Autor:
Bunne, Charlotte, Roohani, Yusuf, Rosen, Yanay, Gupta, Ankit, Zhang, Xikun, Roed, Marcel, Alexandrov, Theo, AlQuraishi, Mohammed, Brennan, Patricia, Burkhardt, Daniel B., Califano, Andrea, Cool, Jonah, Dernburg, Abby F., Ewing, Kirsty, Fox, Emily B., Haury, Matthias, Herr, Amy E., Horvitz, Eric, Hsu, Patrick D., Jain, Viren, Johnson, Gregory R., Kalil, Thomas, Kelley, David R., Kelley, Shana O., Kreshuk, Anna, Mitchison, Tim, Otte, Stephani, Shendure, Jay, Sofroniew, Nicholas J., Theis, Fabian, Theodoris, Christina V., Upadhyayula, Srigokul, Valer, Marc, Wang, Bo, Xing, Eric, Yeung-Levy, Serena, Zitnik, Marinka, Karaletsos, Theofanis, Regev, Aviv, Lundberg, Emma, Leskovec, Jure, Quake, Stephen R.
The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intell
Externí odkaz:
http://arxiv.org/abs/2409.11654
Autor:
Johnson, Charles R., Paparella, Pietro
The longstanding nonnegative inverse eigenvalue problem (NIEP) is to determine which multisets of complex numbers occur as the spectrum of an entry-wise nonnegative matrix. Although there are some well-known necessary conditions, a solution to the NI
Externí odkaz:
http://arxiv.org/abs/2409.07682
Autor:
Ouermi, Timbwaoga A. J., Li, Jixian, Morrow, Zachary, Waanders, Bart van Bloemen, Johnson, Chris R.
Uncertainty is inherent to most data, including vector field data, yet it is often omitted in visualizations and representations. Effective uncertainty visualization can enhance the understanding and interpretability of vector field data. For instanc
Externí odkaz:
http://arxiv.org/abs/2409.00042
Isosurface visualization is fundamental for exploring and analyzing 3D volumetric data. Marching cubes (MC) algorithms with linear interpolation are commonly used for isosurface extraction and visualization. Although linear interpolation is easy to i
Externí odkaz:
http://arxiv.org/abs/2409.00043
Autor:
Saklani, Shanu, Goel, Chitwan, Bansal, Shrey, Wang, Zhe, Dutta, Soumya, Athawale, Tushar M., Pugmire, David, Johnson, Christopher R.
The increasing adoption of Deep Neural Networks (DNNs) has led to their application in many challenging scientific visualization tasks. While advanced DNNs offer impressive generalization capabilities, understanding factors such as model prediction q
Externí odkaz:
http://arxiv.org/abs/2408.06018
Autor:
Furtado, S., Johnson, C. R.
In models using pair-wise (ratio) comparisons among alternatives, a cardinal ranking vector should be deduced from a reciprocal matrix. The right Perron eigenvector (RP) was traditionally used, though several other options have emerged. We consider s
Externí odkaz:
http://arxiv.org/abs/2408.00454