Transverse Goldstone mode in holographic fluids with broken translations

Zhong, Yuanyuan; Li, Weijia

doi:10.1140/epjc/s10052-022-10430-w

Cited by 3 publications

(2 citation statements)

References 81 publications

(80 reference statements)

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“…According to the previous work on the holographic model with gauge-axion coupling [19][20][21][22][23][24][25][26][27], we consider the following two-current model in 4-dimensional spacetime with a gauge-axion coupling term between the two U(1) gauge fields, denoted by A µ , B µ , and the axion fields, denoted by φ I , I = x, y:…”

Section: Holographic Modelmentioning

confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

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Section: Holographic Modelmentioning

confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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“…In parameter selection, Z is set to 1 based on [21- 23,25,29,37,39,40] practices for consistency and comparability. Subsequently, k 2 is determined as 1000μm −2 using the temperature expression Eq.…”

Section: Comparing With Experimentsmentioning

confidence: 99%

Investigation of transport properties of graphene Dirac fluid by holographic two-current axion coupling model

Liu,

Zhang

2024

Eur. Phys. J. C

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Recently, there has been great interest in the phenomenon of severe violation of the Wiedemann–Franz law in graphene Dirac fluids around 75 K, due to the strong coupling relativistic plasma near the neutral point, where traditional perturbation theory fails. To explain this phenomenon, this article proposes a holographic dual two-current axion coupling model, describing the interaction between electrons and holes in graphene near the charge neutrality point (CNP) and revealing the related physical mechanism. The study shows that the holographic two-current model aligns with experimental results at 100 $$\upmu $$ μ m$$^{-2}$$ - 2 , and correctly predicts conductivity as temperature increases. Additionally, the article explores the behavior of $$\beta +\gamma $$ β + γ and its impact on conductivity and thermal conductivity. The results suggest a frictional effect between electrons and holes. Consequently, this study provides us with a clearer understanding of the properties of graphene Dirac fluids and further confirms the reliability of the holographic duality method.

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Holographic superfluid with gauge–axion coupling

Liu,

Wang,

et al. 2023

Eur. Phys. J. C

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We have constructed a holographic superfluid with gauge–axion coupling. Depending on whether the coupling is positive or negative, the system displays metallic or insulating behavior in its normal state. A significant feature of the system is the appearance of a mid-IR peak in the alternating current (AC) conductivity in a certain range of parameters. This peak arises due to competition between explicit symmetry breaking (ESB) and spontaneous symmetry breaking (SSB), which results in the presence of a pseudo-Goldstone mode. Moreover, a dip in low-frequency AC conductivity is observed, stemming from the excitation of the SSB Goldstone mode. In the superfluid phase, the effect of gauge–axion coupling on the condensation or superfluid energy gap is only amplified in the presence of strong momentum dissipation. Notably, for the case with negative gauge–axion coupling, a hard-gap-like behavior at low frequency and a pronounced peak at intermediate frequency are observed, indicating that the evolution of the superfluid component is distinct from that of positive coupling.

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Transverse Goldstone mode in holographic fluids with broken translations

Cited by 3 publications

References 81 publications

Geography of Technology Transfer in China

Geography of Technology Transfer in China

Investigation of transport properties of graphene Dirac fluid by holographic two-current axion coupling model

Holographic superfluid with gauge–axion coupling

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