Zobrazeno 1 - 10
of 16 506
pro vyhledávání: '"A , RUSCH"'
Incorporating equivariance as an inductive bias into deep learning architectures to take advantage of the data symmetry has been successful in multiple applications, such as chemistry and dynamical systems. In particular, roto-translations are crucia
Externí odkaz:
http://arxiv.org/abs/2410.17878
Autor:
Rusch, T. Konstantin, Rus, Daniela
We propose Linear Oscillatory State-Space models (LinOSS) for efficiently learning on long sequences. Inspired by cortical dynamics of biological neural networks, we base our proposed LinOSS model on a system of forced harmonic oscillators. A stable
Externí odkaz:
http://arxiv.org/abs/2410.03943
Sampling-based motion planning methods, while effective in high-dimensional spaces, often suffer from inefficiencies due to irregular sampling distributions, leading to suboptimal exploration of the configuration space. In this paper, we propose an a
Externí odkaz:
http://arxiv.org/abs/2410.03909
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, 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 enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challenges in c
Externí odkaz:
http://arxiv.org/abs/2311.16896
Autor:
Zhongjin Lin, Bhavin J. Shastri, Shangxuan Yu, Jingxiang Song, Yuntao Zhu, Arman Safarnejadian, Wangning Cai, Yanmei Lin, Wei Ke, Mustafa Hammood, Tianye Wang, Mengyue Xu, Zibo Zheng, Mohammed Al-Qadasi, Omid Esmaeeli, Mohamed Rahim, Grzegorz Pakulski, Jens Schmid, Pedro Barrios, Weihong Jiang, Hugh Morison, Matthew Mitchell, Xun Guan, Nicolas A. F. Jaeger, Leslie A. Rusch, Sudip Shekhar, Wei Shi, Siyuan Yu, Xinlun Cai, Lukas Chrostowski
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-10 (2024)
Abstract Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challe
Externí odkaz:
https://doaj.org/article/569dcefade71466695c6c55b8ed64eca
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