Zobrazeno 1 - 10
of 31 515
pro vyhledávání: '"Kim, Eun A."'
Motivated by the rapid experimental progress in twisted van der Waals materials, we study the triangular trimer model as a representative framework for extended Wannier orbitals in twisted bilayer graphene at 1/3-filling. This deceptively simple mode
Externí odkaz:
http://arxiv.org/abs/2410.00092
Autor:
Plumb, Jayden, Salinas, Andrea Capa, Mallayya, Krishnanand, Kisiel, Elliot, Carneiro, Fellipe B., Gomez, Reina, Pokharel, Ganesh, Kim, Eun-Ah, Sarker, Suchismita, Islam, Zahirul, Daly, Sam, Wilson, Stephen D.
Publikováno v:
Phys. Rev. Materials, 8, 093601 (2024)
We present X-ray scattering studies resolving structural twinning and phase separation in the charge density wave (CDW) state of the kagome superconductor CsV$_3$Sb$_5$. The three-dimensional CDW state in CsV$_3$Sb$_5$ is reported to form a complex s
Externí odkaz:
http://arxiv.org/abs/2408.08842
Autor:
Minev, Zlatko K., Najafi, Khadijeh, Majumder, Swarnadeep, Wang, Juven, Stern, Ady, Kim, Eun-Ah, Jian, Chao-Ming, Zhu, Guanyu
Fibonacci string-net condensate, a complex topological state that supports non-Abelian anyon excitations, holds promise for fault-tolerant universal quantum computation. However, its realization by a static-lattice Hamiltonian has remained elusive du
Externí odkaz:
http://arxiv.org/abs/2406.12820
Autor:
Ahn, Jinwoo, Park, Junhyeok, Kim, Min-Jun, Kim, Kang-Hyeon, Sohn, So-Yeong, Lee, Yun-Ji, Chang, Du-Seong, Heo, Yu-Jung, Kim, Eun-Sol
In this paper, the solution of HYU MLLAB KT Team to the Multimodal Algorithmic Reasoning Task: SMART-101 CVPR 2024 Challenge is presented. Beyond conventional visual question-answering problems, the SMART-101 challenge aims to achieve human-level mul
Externí odkaz:
http://arxiv.org/abs/2406.05963
We propose a novel method for fast and accurate training of physics-informed neural networks (PINNs) to find solutions to boundary value problems (BVPs) and initial boundary value problems (IBVPs). By combining the methods of training deep neural net
Externí odkaz:
http://arxiv.org/abs/2406.05290
Artificial neural networks (ANNs) are powerful tools capable of approximating any arbitrary mathematical function, but their interpretability remains limited, rendering them as black box models. To address this issue, numerous methods have been propo
Externí odkaz:
http://arxiv.org/abs/2406.05295
Autor:
Kim, Junlee, Park, Jaebeom, Hong, Byungsik, Hong, Juhee, Kim, Eun-Joo, Kim, Yongsun, Kweon, MinJung, Lee, Su Houng, Lim, Sanghoon, Seo, Jinjoo
The primary purpose of studying quarkonium production in relativistic heavy-ion collisions is to understand the properties of the quark-gluon plasma. At various collision systems, measurements of quarkonium states of different binding energies, such
Externí odkaz:
http://arxiv.org/abs/2405.11689
Autor:
Kim, Hyejin, Zhou, Yiqing, Xu, Yichen, Varma, Kaarthik, Karamlou, Amir H., Rosen, Ilan T., Hoke, Jesse C., Wan, Chao, Zhou, Jin Peng, Oliver, William D., Lensky, Yuri D., Weinberger, Kilian Q., Kim, Eun-Ah
The imminent era of error-corrected quantum computing urgently demands robust methods to characterize complex quantum states, even from limited and noisy measurements. We introduce the Quantum Attention Network (QuAN), a versatile classical AI framew
Externí odkaz:
http://arxiv.org/abs/2405.11632
An out-of-plane magnetic field can always suppress superconductivity. In Bernal-stacked bilayer graphene (BBG), recently observed activation of superconductivity (SC) through either in-plane magnetic fields or proximate spin-orbit coupling (SOC) offe
Externí odkaz:
http://arxiv.org/abs/2405.05442
Accurate classification of fine-grained images remains a challenge in backbones based on convolutional operations or self-attention mechanisms. This study proposes novel dual-current neural networks (DCNN), which combine the advantages of convolution
Externí odkaz:
http://arxiv.org/abs/2405.04093