Zobrazeno 1 - 10
of 28 823
pro vyhledávání: '"Holbrook A."'
Autor:
Devarakonda, Aravind, Koay, Christie S., Chica, Daniel G., Thinel, Morgan, Kundu, Asish K., Lin, Zhi, Georgescu, Alexandru B., Rossi, Sebastian, Han, Sae Young, Ziebel, Michael E., Holbrook, Madisen A., Rajapitamahuni, Anil, Vescovo, Elio, Watanabe, K., Taniguchi, T., Delor, Milan, Zhu, Xiaoyang, Pasupathy, Abhay N., Queiroz, Raquel, Dean, Cory R., Roy, Xavier
Materials hosting flat electronic bands are a central focus of condensed matter physics as promising venues for novel electronic ground states. Two-dimensional (2D) geometrically frustrated lattices such as the kagome, dice, and Lieb lattices are att
Externí odkaz:
http://arxiv.org/abs/2408.01512
Hawkes stochastic point process models have emerged as valuable statistical tools for analyzing viral contagion. The spatiotemporal Hawkes process characterizes the speeds at which viruses spread within human populations. Unfortunately, likelihood-ba
Externí odkaz:
http://arxiv.org/abs/2407.11349
Bayesian multidimensional scaling (BMDS) is a probabilistic dimension reduction tool that allows one to model and visualize data consisting of dissimilarities between pairs of objects. Although BMDS has proven useful within, e.g., Bayesian phylogenet
Externí odkaz:
http://arxiv.org/abs/2406.15573
Autor:
Kujawa-Holbrook, Sheryl A.
Publikováno v:
Anglican and Episcopal History, 2024 Jun 01. 93(2), 401-413.
Externí odkaz:
https://www.jstor.org/stable/27316619
Autor:
Constantinides, Marios, Tahaei, Mohammad, Quercia, Daniele, Stumpf, Simone, Madaio, Michael, Kennedy, Sean, Wilcox, Lauren, Vitak, Jessica, Cramer, Henriette, Bogucka, Edyta, Baeza-Yates, Ricardo, Luger, Ewa, Holbrook, Jess, Muller, Michael, Blumenfeld, Ilana Golbin, Pistilli, Giada
With the upcoming AI regulations (e.g., EU AI Act) and rapid advancements in generative AI, new challenges emerge in the area of Human-Centered Responsible Artificial Intelligence (HCR-AI). As AI becomes more ubiquitous, questions around decision-mak
Externí odkaz:
http://arxiv.org/abs/2403.00148
Autor:
Smalley, Darian, Lough, Stephanie D., Holtzman, Luke, Xu, Kaikui, Holbrook, Madisen, Rosenberger, Matthew R., Hone, J. C., Barmak, Katayun, Ishigami, Masahiro
Atomic-scale defect detection is shown in scanning tunneling microscopy images of single crystal WSe2 using an ensemble of U-Net-like convolutional neural networks. Standard deep learning test metrics indicated good detection performance with an aver
Externí odkaz:
http://arxiv.org/abs/2312.05160
Autor:
Lin, Chin-Yi, Chen, Kuo-Chin, Lemey, Philippe, Suchard, Marc A., Holbrook, Andrew J., Hsieh, Min-Hsiu
Multiproposal Markov chain Monte Carlo (MCMC) algorithms choose from multiple proposals at each iteration in order to sample from challenging target distributions more efficiently. Recent work demonstrates the possibility of quadratic quantum speedup
Externí odkaz:
http://arxiv.org/abs/2312.01402
Autor:
Darlington, Thomas P., Rahaman, Mahfujur, Kwock, Kevin W. C., Yanev, Emanuil, Wu, Xuehao, Holtzman, Luke N., Holbrook, Madisen, Kim, Gwangwoo, Ma, Kyung Yeol, Shin, Hyeon Suk, Krayev, Andrey, Strasbourg, Matthew, Borys, Nicholas J., Basov, D. N., Barmak, Katayun, Hone, James C., Pasupathy, Abhay N., Jariwala, Deep, Schuck, P. James
The advancement of quantum photonic technologies relies on the ability to precisely control the degrees of freedom of optically active states. Here, we realize real-time, room-temperature tunable strong plasmon-exciton coupling in 2D semiconductor mo
Externí odkaz:
http://arxiv.org/abs/2311.02513
Large language models (LLMs) are playing an increasingly important role in science and engineering. For example, their ability to parse and understand human and computer languages makes them powerful interpreters and their use in applications like co
Externí odkaz:
http://arxiv.org/abs/2310.11458
Autor:
Didier, Gustavo, Glatt-Holtz, Nathan E., Holbrook, Andrew J., Magee, Andrew F., Suchard, Marc A.
The continuous-time Markov chain (CTMC) is the mathematical workhorse of evolutionary biology. Learning CTMC model parameters using modern, gradient-based methods requires the derivative of the matrix exponential evaluated at the CTMC's infinitesimal
Externí odkaz:
http://arxiv.org/abs/2306.15841