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pro vyhledávání: '"Merkel, P A"'
Autor:
Merkel, Romy Marie
We study special Lagrangian submanifolds in the Calabi-Yau manifold $T^*S^n$ with the Stenzel metric, as well as calibrated submanifolds in the $\text{G}_2$-manifold $\Lambda^2_-(T^*X)$ $(X^4 = S^4, \mathbb{CP}^2)$ and the $\text{Spin}(7)$-manifold $
Externí odkaz:
http://arxiv.org/abs/2411.17648
Neuromorphic computing offers a low-power, parallel alternative to traditional von Neumann architectures by addressing the sequential data processing bottlenecks. Electric double layer-gated transistors (EDLTs) resemble biological synapses with their
Externí odkaz:
http://arxiv.org/abs/2410.08978
Understanding the structural dynamics of many-particle glassy systems remains a key challenge in statistical physics. Over the last decade, glassy dynamics has also been reported in biological tissues, but is far from being understood. It was recentl
Externí odkaz:
http://arxiv.org/abs/2409.18949
We consider the Merton problem of optimizing expected power utility of terminal wealth in the case of an unobservable Markov-modulated drift. What makes the model special is that the agent is allowed to purchase costly expert opinions of varying qual
Externí odkaz:
http://arxiv.org/abs/2409.11569
Graph neural networks (GNNs) are a type of neural network capable of learning on graph-structured data. However, training GNNs on large-scale graphs is challenging due to iterative aggregations of high-dimensional features from neighboring vertices w
Externí odkaz:
http://arxiv.org/abs/2409.11129
Autor:
Merkel, Cory, Ororbia, Alexander
Spiking neural networks, the third generation of artificial neural networks, have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks, including their hig
Externí odkaz:
http://arxiv.org/abs/2409.10887
Autor:
Govia, L. C. G., Majumder, S., Barron, S. V., Mitchell, B., Seif, A., Kim, Y., Wood, C. J., Pritchett, E. J., Merkel, S. T., McKay, D. C.
Quantum error mitigation is a promising route to achieving quantum utility, and potentially quantum advantage in the near-term. Many state-of-the-art error mitigation schemes use knowledge of the errors in the quantum processor, which opens the quest
Externí odkaz:
http://arxiv.org/abs/2408.10985
We consider a continuous-time linear-quadratic Gaussian control problem with partial observations and costly information acquisition. More precisely, we assume the drift of the state process to be governed by an unobservable Ornstein--Uhlenbeck proce
Externí odkaz:
http://arxiv.org/abs/2408.09693
The simulation of electromagnetic devices with complex geometries and large-scale discrete systems benefits from advanced computational methods like IsoGeometric Analysis and Domain Decomposition. In this paper, we employ both concepts in an Isogeome
Externí odkaz:
http://arxiv.org/abs/2407.21707
Autor:
Asai, Shoji, Ballarino, Amalia, Bose, Tulika, Cranmer, Kyle, Cyr-Racine, Francis-Yan, Demers, Sarah, Geddes, Cameron, Gershtein, Yuri, Heeger, Karsten, Heinemann, Beate, Hewett, JoAnne, Huber, Patrick, Mahn, Kendall, Mandelbaum, Rachel, Maricic, Jelena, Merkel, Petra, Monahan, Christopher, Murayama, Hitoshi, Onyisi, Peter, Palmer, Mark, Raubenheimer, Tor, Sanchez, Mayly, Schnee, Richard, Seidel, Sally, Seo, Seon-Hee, Thaler, Jesse, Touramanis, Christos, Vieregg, Abigail, Weinstein, Amanda, Winslow, Lindley, Yu, Tien-Tien, Zwaska, Robert
This is the report from the 2023 Particle Physics Project Prioritization Panel (P5) approved by High Energy Physics Advisory Panel (HEPAP) on December 8, 2023. The final version was made public on May 8, 2024 and submitted to DOE SC and NSF MPS.
Externí odkaz:
http://arxiv.org/abs/2407.19176