Zobrazeno 1 - 10
of 323
pro vyhledávání: '"Chen, Qijin"'
We investigate the finite-temperature superfluid behavior of ultracold atomic Fermi gases in quasi-two-dimensional Lieb lattices with a short-range attractive interaction, using a pairing fluctuation theory within the BCS-BEC crossover framework. We
Externí odkaz:
http://arxiv.org/abs/2401.02990
Autor:
Li, Xi, Wang, Shuai, Luo, Xiang, Zhou, Yu-Yang, Xie, Ke, Shen, Hong-Chi, Nie, Yu-Zhao, Chen, Qijin, Hu, Hui, Chen, Yu-Ao, Yao, Xing-Can, Pan, Jian-Wei
The nature of pseudogap lies at the heart of strongly-interacting superconductivity and superfluidity. With known pairing interactions, unitary Fermi gases provide an ideal testbed to verify whether a pseudogap can arise from many-body pairing. Here
Externí odkaz:
http://arxiv.org/abs/2310.14024
Publikováno v:
Ann. Phys. 463, 169639 (2024)
The ground-state superfluid behavior of ultracold atomic Fermi gases with a short-range attractive interaction in a quasi-two-dimensional Lieb lattice is studied using BCS mean-field theory, within the context of BCS-BEC crossover. We find that the f
Externí odkaz:
http://arxiv.org/abs/2310.12944
Bundle recommendation aims to provide a bundle of items to satisfy the user preference on e-commerce platform. Existing successful solutions are based on the contrastive graph learning paradigm where graph neural networks (GNNs) are employed to learn
Externí odkaz:
http://arxiv.org/abs/2307.13468
Publikováno v:
npj Quantum Materials 9, 27 (2024)
In this paper we address the question of whether high-temperature superconductors have anything in common with BCS-BEC crossover theory. Towards this goal, we present a proposal and related predictions which provide a concrete test for the applicabil
Externí odkaz:
http://arxiv.org/abs/2307.08611
In this paper, we present the ``joint pre-training and local re-training'' framework for learning and applying multi-source knowledge graph (KG) embeddings. We are motivated by the fact that different KGs contain complementary information to improve
Externí odkaz:
http://arxiv.org/abs/2306.02679
Joint representation learning over multi-sourced knowledge graphs (KGs) yields transferable and expressive embeddings that improve downstream tasks. Entity alignment (EA) is a critical step in this process. Despite recent considerable research progre
Externí odkaz:
http://arxiv.org/abs/2306.02622
Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertil
Externí odkaz:
http://arxiv.org/abs/2304.04389
Publikováno v:
Phys. Rev. A 107, 023321 (2023)
Experimental evidence of the Higgs mode in strongly interacting superfluid Fermi gases had not been observed until recently [Behrle et al., Nat. Phys. 14, 781 (2018)]. Due to the coupling with other collective modes and quasiparticle excitations, gen
Externí odkaz:
http://arxiv.org/abs/2210.09829
Publikováno v:
Rev. Mod. Phys. 96, 025002 (2024)
New developments in superconductivity, particularly through unexpected and often astonishing forms of superconducting materials, continue to excite the community and stimulate theory. It is now becoming clear that there are two distinct platforms for
Externí odkaz:
http://arxiv.org/abs/2208.01774