Zobrazeno 1 - 10
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pro vyhledávání: '"Cooley AS"'
Autor:
Cooley Asya
Publikováno v:
Nonprofit Policy Forum, Vol 14, Iss 1, Pp 1-23 (2022)
This study attempts to understand the role of the nonprofit sector within the climate change discourse in Russian news media. It explores the news media coverage of climate change and nonprofit sector through the quantitative review of Russian news a
Externí odkaz:
https://doaj.org/article/6cee48ba64024bda948ef53ef9f37352
Physics-informed neural networks (PINNs) are an increasingly popular class of techniques for the numerical solution of partial differential equations (PDEs), where neural networks are trained using loss functions regularized by relevant PDE terms to
Externí odkaz:
http://arxiv.org/abs/2410.03573
Interest is rising in Physics-Informed Neural Networks (PINNs) as a mesh-free alternative to traditional numerical solvers for partial differential equations (PDEs). However, PINNs often struggle to learn high-frequency and multi-scale target solutio
Externí odkaz:
http://arxiv.org/abs/2410.03496
Autor:
SuperCDMS Collaboration, Albakry, M. F., Alkhatib, I., Alonso-González, D., Amaral, D. W. P., Anczarski, J., Aralis, T., Aramaki, T., Arnquist, I. J., Langroudy, I. Ataee, Azadbakht, E., Bathurst, C., Bhattacharyya, R., Biffl, A. J., Brink, P. L., Buchanan, M., Bunker, R., Cabrera, B., Calkins, R., Cameron, R. A., Cartaro, C., Cerdeño, D. G., Chang, Y. -Y., Chaudhuri, M., Chen, J. -H., Chen, R., Chott, N., Cooley, J., Coombes, H., Cushman, P., Cyna, R., Das, S., De Brienne, F., Dharani, S., di Vacri, M. L., Diamond, M. D., Elwan, M., Fascione, E., Figueroa-Feliciano, E., Fouts, K., Fritts, M., Germond, R., Ghaith, M., Golwala, S. R., Hall, J., Harms, S. A. S., Harris, K., Hassan, N., Hong, Z., Hoppe, E. W., Hsu, L., Huber, M. E., Iyer, V., Jardin, D., Kashyap, V. K. S., Keller, S. T. D., Kelsey, M. H., Kennard, K. T., Kubik, A., Kurinsky, N. A., Lee, M., Leyva, J., Liu, J., Liu, Y., Loer, B., Asamar, E. Lopez, Lukens, P., MacFarlane, D. B., Mahapatra, R., Mammo, J. S., Mast, N., Mayer, A. J., Theenhausen, H. Meyer zu, Michaud, É., Michielin, E., Mirabolfathi, N., Mirzakhani, M., Mohanty, B., Monteiro, D., Nelson, J., Neog, H., Novati, V., Orrell, J. L., Osborne, M. D., Oser, S. M., Pandey, L., Pandey, S., Partridge, R., Pedreros, D. S., Peng, W., Perna, L., Perry, W. L., Podviianiuk, R., Poudel, S. S., Pradeep, A., Pyle, M., Rau, W., Reid, E., Ren, R., Reynolds, T., Rios, M., Roberts, A., Robinson, A. E., Ryan, J. L., Saab, T., Sadek, D., Sadoulet, B., Sahoo, S. P., Saikia, I., Sander, J., Sattari, A., Schmidt, B., Schnee, R. W., Scorza, S., Serfass, B., Simchony, A., Sincavage, D. J., Sinervo, P., Street, J., Sun, H., Tanner, E., Terry, G. D., Toback, D., Verma, S., Villano, A. N., von Krosigk, B., Watkins, S. L., Wen, O., Williams, Z., Wilson, M. J., Winchell, J., Wykoff, K., Yellin, S., Young, B. A., Yu, T. C., Zatschler, B., Zatschler, S., Zaytsev, A., Zhang, E., Zheng, L., Zuniga, A., Zurowski, M. J.
This article presents constraints on dark-matter-electron interactions obtained from the first underground data-taking campaign with multiple SuperCDMS HVeV detectors operated in the same housing. An exposure of 7.63 g-days is used to set upper limit
Externí odkaz:
http://arxiv.org/abs/2407.08085
We present polynomial-augmented neural networks (PANNs), a novel machine learning architecture that combines deep neural networks (DNNs) with a polynomial approximant. PANNs combine the strengths of DNNs (flexibility and efficiency in higher-dimensio
Externí odkaz:
http://arxiv.org/abs/2406.02336
It is well-known that the $d$-dimensional hypercube contains a Hamilton cycle for $d\ge 2$. In this paper we address the analogous problem in the $3$-uniform cube hypergraph, a $3$-uniform analogue of the hypercube: for simple parity reasons, the $3$
Externí odkaz:
http://arxiv.org/abs/2406.00401
Publikováno v:
The Twelfth International Conference on Learning Representations (ICLR 2024)
Machine learning based solvers have garnered much attention in physical simulation and scientific computing, with a prominent example, physics-informed neural networks (PINNs). However, PINNs often struggle to solve high-frequency and multi-scale PDE
Externí odkaz:
http://arxiv.org/abs/2311.04465
Autor:
Cooley, Joya A., Dairaghi, Gregor, Moore, Guy C., Horton, Matthew K., Schueller, Emily C., Persson, Kristin A., Seshadri, Ram
Co$_{1-x}$Mn$_x$Cr$_2$O$_4$ crystallizes as a normal spinel in the cubic $Fd \overline{3}m$ space group, and the end members have been reported to display a region of collinear ferrimagnetism as well as a low-temperature spin-spiral state with variab
Externí odkaz:
http://arxiv.org/abs/2309.16168
Autor:
Mhatre, Nehali, Cooley, Daniel
The innovations algorithm is a classical recursive forecasting algorithm used in time series analysis. We develop the innovations algorithm for a class of nonnegative regularly varying time series models constructed via transformed-linear arithmetic.
Externí odkaz:
http://arxiv.org/abs/2309.10061
Stakeholders representing concerns of national and global leadership, industries that use superconducting magnets in products, manufacturers of superconducting wires and tapes that supply to industries, and innovation generators from small businesses
Externí odkaz:
http://arxiv.org/abs/2308.03808