Zobrazeno 1 - 10
of 92 327
pro vyhledávání: '"Cheong BE"'
Autor:
Tyson, T. A., Amarasinghe, S., Abeykoon, AM M., Lalancette, R., Du, S. K., Fang, X., Cheong, S. -W., Al-Mahboob, A., Sadowski, J. T.
The surface magnetization of Fe$_3$GeTe$_2$ was examined by low-energy electron microscopy (LEEM) using an off-normal incidence electron beam. We found that the 180$^o$ domain walls are of Bloch type. Temperature-dependent LEEM measurements yield a s
Externí odkaz:
http://arxiv.org/abs/2409.03565
Autor:
Gao, Bin, Chen, Tong, Liu, Chunxiao, Klemm, Mason L., Zhang, Shu, Ma, Zhen, Xu, Xianghan, Won, Choongjae, McCandless, Gregory T., Murai, Naoki, Ohira-Kawamura, Seiko, Moxim, Stephen J., Ryan, Jason T., Huang, Xiaozhou, Wang, Xiaoping, Chan, Julia Y., Cheong, Sang-Wook, Tchernyshyov, Oleg, Balents, Leon, Dai, Pengcheng
In magnetically ordered insulators, elementary quasiparticles manifest as spin waves - collective motions of localized magnetic moments propagating through the lattice - observed via inelastic neutron scattering. In effective spin-1/2 systems where g
Externí odkaz:
http://arxiv.org/abs/2408.15957
In the information era, how learners find, evaluate, and effectively use information has become a challenging issue, especially with the added complexity of large language models (LLMs) that have further confused learners in their information retriev
Externí odkaz:
http://arxiv.org/abs/2408.08894
We outline a fundamentally quantum description of bosonic dark matter (DM) from which the conventional classical-wave picture emerges in the limit $m \ll 10~\textrm{eV}$. As appropriate for a quantum system, we start from the density matrix which enc
Externí odkaz:
http://arxiv.org/abs/2408.04696
Social agents and robots are increasingly being used in wellbeing settings. However, a key challenge is that these agents and robots typically rely on machine learning (ML) algorithms to detect and analyse an individual's mental wellbeing. The proble
Externí odkaz:
http://arxiv.org/abs/2408.04026
Autor:
Na, Woongki, Park, Pyeongjae, Oh, Siwon, Kim, Junghyun, Scheie, Allen, Tennant, David Alan, Lee, Hyun Cheol, Park, Je-Geun, Cheong, Hyeonsik
Van der Waals (vdW) magnets have rapidly emerged as a fertile playground for novel fundamental physics and exciting applications. Despite the impressive developments over the past few years, technical limitations pose a severe challenge to many other
Externí odkaz:
http://arxiv.org/abs/2407.20480
Publikováno v:
Expert Systems with Applications, Volume 255, 2024, Article 124742
Random forests are considered a cornerstone in machine learning for their robustness and versatility. Despite these strengths, their conventional centralized training is ill-suited for the modern landscape of data that is often distributed, sensitive
Externí odkaz:
http://arxiv.org/abs/2407.19193
Autor:
Ma, Jiabo, Guo, Zhengrui, Zhou, Fengtao, Wang, Yihui, Xu, Yingxue, Cai, Yu, Zhu, Zhengjie, Jin, Cheng, Lin, Yi, Jiang, Xinrui, Han, Anjia, Liang, Li, Chan, Ronald Cheong Kin, Wang, Jiguang, Cheng, Kwang-Ting, Chen, Hao
Foundation models pretrained on large-scale datasets are revolutionizing the field of computational pathology (CPath). The generalization ability of foundation models is crucial for the success in various downstream clinical tasks. However, current f
Externí odkaz:
http://arxiv.org/abs/2407.18449
Autor:
Jung, Inkee, Lau, Siu-Cheong
We present a local-to-global and measure-theoretical approach to understanding datasets. The core idea is to formulate a logifold structure and to interpret network models with restricted domains as local charts of datasets. In particular, this provi
Externí odkaz:
http://arxiv.org/abs/2407.16177
Autor:
Xu, Yingxue, Wang, Yihui, Zhou, Fengtao, Ma, Jiabo, Yang, Shu, Lin, Huangjing, Wang, Xin, Wang, Jiguang, Liang, Li, Han, Anjia, Chan, Ronald Cheong Kin, Chen, Hao
Remarkable strides in computational pathology have been made in the task-agnostic foundation model that advances the performance of a wide array of downstream clinical tasks. Despite the promising performance, there are still several challenges. Firs
Externí odkaz:
http://arxiv.org/abs/2407.15362