Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Xu, Youjiang"'
Video try-on stands as a promising area for its tremendous real-world potential. Prior works are limited to transferring product clothing images onto person videos with simple poses and backgrounds, while underperforming on casually captured videos.
Externí odkaz:
http://arxiv.org/abs/2405.18326
Autor:
Xu, Youjiang, Hofstetter, Walter
Topological invariants, including the Chern numbers, can topologically classify parameterized Hamiltonians. We find that topological invariants can be properly defined and calculated even if the parameter space is discrete, which is done by geodesic
Externí odkaz:
http://arxiv.org/abs/2311.11618
Publikováno v:
Phys. Rev. B 106, L220407 (2022)
We prove that invisible bands associated with zeros of the single-particle Green's function exist ubiquitously at topological interfaces of 2D Chern insulators, dual to the chiral edge/domain-wall modes. We verify this statement in a repulsive Hubbar
Externí odkaz:
http://arxiv.org/abs/2204.11946
It has been shown that deep neural networks are prone to overfitting on biased training data. Towards addressing this issue, meta-learning employs a meta model for correcting the training bias. Despite the promising performances, super slow training
Externí odkaz:
http://arxiv.org/abs/2104.15092
We consider an important generalization of the Dicke model in which multi-level atoms, instead of two-level atoms as in conventional Dicke model, interact with a single photonic mode. We explore the phase diagram of a broad class of atom-photon coupl
Externí odkaz:
http://arxiv.org/abs/2011.07342
Autor:
Xu, Youjiang, Pu, Han
Publikováno v:
Phys. Rev. A 102, 053305 (2020)
We propose a powerful and convenient method to systematically design flat-band lattice models, which overcomes the difficulties underlying the previous method. Especially, our method requires no elaborate calculations, applies to arbitrary spatial di
Externí odkaz:
http://arxiv.org/abs/2002.06767
Autor:
Xu, Youjiang, Duan, Jiaqi, Kuang, Zhanghui, Yue, Xiaoyu, Sun, Hongbin, Guan, Yue, Zhang, Wayne
Large geometry (e.g., orientation) variances are the key challenges in the scene text detection. In this work, we first conduct experiments to investigate the capacity of networks for learning geometry variances on detecting scene texts, and find tha
Externí odkaz:
http://arxiv.org/abs/1909.00794
Autor:
Xu, Youjiang, Pu, Han
We propose a generalized Dicke model which supports a quantum tricritical point. We map out the phase diagram and investigate the critical behaviors of the model through exact low-energy effective Hamiltonian in the thermodynamic limit. As predicted
Externí odkaz:
http://arxiv.org/abs/1904.10576
Movies provide us with a mass of visual content as well as attracting stories. Existing methods have illustrated that understanding movie stories through only visual content is still a hard problem. In this paper, for answering questions about movies
Externí odkaz:
http://arxiv.org/abs/1804.09412
Publikováno v:
Phys. Rev. B 96, 115110 (2017)
We present a method to construct number-conserving Hamiltonians whose ground states exactly reproduce an arbitrarily chosen BCS-type mean-field state. Such parent Hamiltonians can be constructed not only for the usual $s$-wave BCS state, but also for
Externí odkaz:
http://arxiv.org/abs/1703.01249