Zobrazeno 1 - 10
of 4 972
pro vyhledávání: '"Rusch, P."'
Autor:
Hu, Yang, Lorenzin, Giacomo, Yeom, Jeyun, Liyanage, Manura, Curtin, William A., Jeurgens, Lars P. H., Janczak-Rusch, Jolanta, Cancellieri, Claudia, Turlo, Vladyslav
The intrinsic stress in nanomultilayers (NMLs) is typically dominated by interface stress, which is particularly high in immiscible Cu/W NMLs. Here, atomistic simulations with a chemically-accurate neural network potential reveal the role of interfac
Externí odkaz:
http://arxiv.org/abs/2406.14959
Discrepancy is a well-known measure for the irregularity of the distribution of a point set. Point sets with small discrepancy are called low-discrepancy and are known to efficiently fill the space in a uniform manner. Low-discrepancy points play a c
Externí odkaz:
http://arxiv.org/abs/2405.15059
Autor:
Omidi, Amir, Banawan, Mai, Weckenmann, Erwan, Paquin, Benoit, Geravand, Alireza, Zheng, Zibo, Shi, Wei, Zeng, Ming, Rusch, Leslie A.
We examine pulse amplitude modulation (PAM) for intensity modulation and direct detection systems. Using a straight-forward, mixed noise model, we optimize the constellations with an autoencoder-based neural network (NN), an improve required signal-t
Externí odkaz:
http://arxiv.org/abs/2402.04395
Publikováno v:
Physical Review E 109, 015303 (2024)
We investigate the usage of a recently introduced noise-cancellation algorithm for Brownian simulations to enhance the precision of measuring transport properties such as the mean-square displacement or the velocity-autocorrelation function. The algo
Externí odkaz:
http://arxiv.org/abs/2401.12577
Autor:
Lin, Zhongjin, Shastri, Bhavin J., Yu, Shangxuan, Song, Jingxiang, Zhu, Yuntao, Safarnejadian, Arman, Cai, Wangning, Lin, Yanmei, Ke, Wei, Hammood, Mustafa, Wang, Tianye, Xu, Mengyue, Zheng, Zibo, Al-Qadasi, Mohammed, Esmaeeli, Omid, Rahim, Mohamed, Pakulski, Grzegorz, Schmid, Jens, Barrios, Pedro, Jiang, Weihong, Morison, Hugh, Mitchell, Matthew, Qiang, Xiaogang, Guan, Xun, Jaeger, Nicolas A. F., Rusch, Leslie A. n, Shekhar, Sudip, Shi, Wei, Yu, Siyuan, Cai, Xinlun, Chrostowski, Lukas
Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by providing low latency, high bandwidth, and energy-efficient computations. Here, we introduce a photonic tensor core processor enabled by time-mul
Externí odkaz:
http://arxiv.org/abs/2311.16896
Autor:
Karina Zitta, Lars Hummitzsch, Frank Lichte, Fred Fändrich, Markus Steinfath, Christine Eimer, Sebastian Kapahnke, Matthias Buerger, Katharina Hess, Melanie Rusch, Rene Rusch, Rouven Berndt, Martin Albrecht
Publikováno v:
Journal of Translational Medicine, Vol 22, Iss 1, Pp 1-10 (2024)
Abstract Background Macrophages are involved in tissue homeostasis, angiogenesis and immunomodulation. Proangiogenic and anti-inflammatory macrophages (regulatory macrophages, Mreg) can be differentiated in-vitro from CD14+ monocytes by using a defin
Externí odkaz:
https://doaj.org/article/51f12bd03bc74df9971446b0755322a3
Autor:
Di Giovanni, Francesco, Rusch, T. Konstantin, Bronstein, Michael M., Deac, Andreea, Lackenby, Marc, Mishra, Siddhartha, Veličković, Petar
Graph Neural Networks (GNNs) are the state-of-the-art model for machine learning on graph-structured data. The most popular class of GNNs operate by exchanging information between adjacent nodes, and are known as Message Passing Neural Networks (MPNN
Externí odkaz:
http://arxiv.org/abs/2306.03589
Coupled oscillators are being increasingly used as the basis of machine learning (ML) architectures, for instance in sequence modeling, graph representation learning and in physical neural networks that are used in analog ML devices. We introduce an
Externí odkaz:
http://arxiv.org/abs/2305.08753
Node features of graph neural networks (GNNs) tend to become more similar with the increase of the network depth. This effect is known as over-smoothing, which we axiomatically define as the exponential convergence of suitable similarity measures on
Externí odkaz:
http://arxiv.org/abs/2303.10993
We propose a novel multi-scale message passing neural network algorithm for learning the solutions of time-dependent PDEs. Our algorithm possesses both temporal and spatial multi-scale resolution features by incorporating multi-scale sequence models
Externí odkaz:
http://arxiv.org/abs/2302.03580