arXiv subjects

Zhenxiang Gao

Publications and source records attributed to Zhenxiang Gao.

8 recordsLinked to original sources

arXiv2026

Signatures of a ferro-Josephson effect in twisted graphene

When a spin-polarized current is driven across a magnetic domain wall, the resulting spin-transfer torque may, beyond a critical threshold, set the wall's moments into precession. This precession modulates the Berry curvature experienced by electrons traversing the wall, producing an electromotive force that is topological in nature and proportional to the precession frequency, mapping precisely onto the DC Josephson effect and leading to the name ferro-Josephson effect. We report signatures consistent with this effect in a twisted graphene van der Waals heterostructure, where spin and valley textures are linked by exchange, Hund's coupling, and spin-orbit interactions. Tuned to fillings where the isospin degeneracy is spontaneously broken, the samples develop a sharp peak in the longitudinal resistance within a fraction of a millitesla of $B_\parallel=0$---a peak that disappears as the current is reduced toward zero. In differential resistance the feature resolves into sharp resonances that disperse with $B_\parallel$ on microtesla and picoampere scales. We argue that these arise from the current-driven precession of spin-domain-wall moments, in competition with the in-plane anisotropy set by a minuscule applied field, and that they establish nonlinear transport as a sensitive probe of isospin domain-wall dynamics at energy scales far below $k_BT$.

cond-mat.mes-hall↗

arXiv2026

Metastable magnetic domains and the anomalous $B_\parallel=0$ resistance peak in twisted double bilayer graphene

In graphene moirés, valley polarization gives rise to orbital magnetism, manifested as an anomalous Hall effect and resulting in Barkhausen jumps in longitudinal resistance when changing domain configurations modify quasiparticle scattering. Beyond a simple picture of polarized domains, however, spin and valley textures within and between the domains are less well understood, as is the effect of these textures on transport. In the valley-polarized quarter-metal state of twisted double bilayer graphene, a sharp and metastable peak in longitudinal resistance often appears at zero in-plane magnetic field, whose microscopic origin has yet to be identified. Here, we show that this peak depends on the configuration of domains of orbital magnetism, which is itself set by the gate-voltage trajectory used to enter the ordered state and by the magnetic field --- particularly the in-plane component --- present during that trajectory. The sensitivity of the effect to in-plane magnetic field components points to spin, linked to valley polarization through spin-orbit coupling, as the key degree of freedom in both the domain formation and the resistance peak.

cond-mat.mes-hall↗

arXiv2024

A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs)

Electronic Health Records (EHRs) play an important role in the healthcare system. However, their complexity and vast volume pose significant challenges to data interpretation and analysis. Recent advancements in Artificial Intelligence (AI), particularly the development of Large Language Models (LLMs), open up new opportunities for researchers in this domain. Although prior studies have demonstrated their potential in language understanding and processing in the context of EHRs, a comprehensive scoping review is lacking. This study aims to bridge this research gap by conducting a scoping review based on 329 related papers collected from OpenAlex. We first performed a bibliometric analysis to examine paper trends, model applications, and collaboration networks. Next, we manually reviewed and categorized each paper into one of the seven identified topics: named entity recognition, information extraction, text similarity, text summarization, text classification, dialogue system, and diagnosis and prediction. For each topic, we discussed the unique capabilities of LLMs, such as their ability to understand context, capture semantic relations, and generate human-like text. Finally, we highlighted several implications for researchers from the perspectives of data resources, prompt engineering, fine-tuning, performance measures, and ethical concerns. In conclusion, this study provides valuable insights into the potential of LLMs to transform EHR research and discusses their applications and ethical considerations.

cs.ET↗

arXiv2023

Graph Agent: Explicit Reasoning Agent for Graphs

Graph embedding methods such as Graph Neural Networks (GNNs) and Graph Transformers have contributed to the development of graph reasoning algorithms for various tasks on knowledge graphs. However, the lack of interpretability and explainability of graph embedding methods has limited their applicability in scenarios requiring explicit reasoning. In this paper, we introduce the Graph Agent (GA), an intelligent agent methodology of leveraging large language models (LLMs), inductive-deductive reasoning modules, and long-term memory for knowledge graph reasoning tasks. GA integrates aspects of symbolic reasoning and existing graph embedding methods to provide an innovative approach for complex graph reasoning tasks. By converting graph structures into textual data, GA enables LLMs to process, reason, and provide predictions alongside human-interpretable explanations. The effectiveness of the GA was evaluated on node classification and link prediction tasks. Results showed that GA reached state-of-the-art performance, demonstrating accuracy of 90.65%, 95.48%, and 89.32% on Cora, PubMed, and PrimeKG datasets, respectively. Compared to existing GNN and transformer models, GA offered advantages of explicit reasoning ability, free-of-training, easy adaption to various graph reasoning tasks

cs.AI↗

arXiv2023

Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking

The biomedical field relies heavily on concept linking in various areas such as literature mining, graph alignment, information retrieval, question-answering, data, and knowledge integration. Although large language models (LLMs) have made significant strides in many natural language processing tasks, their effectiveness in biomedical concept mapping is yet to be fully explored. This research investigates a method that exploits the in-context learning (ICL) capabilities of large models for biomedical concept linking. The proposed approach adopts a two-stage retrieve-and-rank framework. Initially, biomedical concepts are embedded using language models, and then embedding similarity is utilized to retrieve the top candidates. These candidates' contextual information is subsequently incorporated into the prompt and processed by a large language model to re-rank the concepts. This approach achieved an accuracy of 90.% in BC5CDR disease entity normalization and 94.7% in chemical entity normalization, exhibiting a competitive performance relative to supervised learning methods. Further, it showed a significant improvement, with an over 20-point absolute increase in F1 score on an oncology matching dataset. Extensive qualitative assessments were conducted, and the benefits and potential shortcomings of using large language models within the biomedical domain were discussed. were discussed.

cs.CL↗

arXiv2022

Spontaneous time-reversal symmetry breaking in twisted double bilayer graphene

Twisted double bilayer graphene (tDBG) comprises two Bernal-stacked bilayer graphene sheets with a twist between them. Gate voltages applied to top and back gates of a tDBG device tune both the flatness and topology of the electronic bands, enabling an unusual level of experimental control. Broken spin/valley symmetry metallic states have been observed in tDBG devices with twist angles $\sim $ 1.2-1.3$^\circ$, but the topologies and order parameters of these states have remained unclear. We report the observation of an anomalous Hall effect in the correlated metal state of tDBG, with hysteresis loops spanning 100s of mT in out-of-plane magnetic field ($B_{\perp}$) that demonstrate spontaneously broken time-reversal symmetry. The $B_{\perp}$ hysteresis persists for in-plane fields up to several Tesla, suggesting valley (orbital) ferromagnetism. At the same time, the resistivity is strongly affected by even mT-scale values of in-plane magnetic field, pointing to spin-valley coupling or to a direct orbital coupling between in-plane field and the valley degree of freedom.

cond-mat.mes-hall↗

arXiv2020

Flat bands and $Z_2$ topological phases in a non-Abelian kagome lattice

We introduce a non-Abelian kagome lattice model that has both time-reversal and inversion symmetries and study the flat band physics and topological phases of this model. Due to the coexistence of both time-reversal and inversion symmetries, the energy bands consist of three doubly degenerate bands whose energy and conditions for the presence of flat bands could be obtained analytically, allowing us to tune the flat band with respect to the other two dispersive bands from the top to the middle and then to the bottom of the three bands. We further study the gapped phases of the model and show that they belong to the same phase as the band gaps only close at discrete points of the parameter space, making any two gapped phases adiabatically connected to each other without closing the band gap. Using the Pfaffian approach based on the time-reversal symmetry and parity characterization from the inversion symmetry, we calculate the bulk topological invariants and demonstrate that the unique gapped phases belong to the $Z_2$ quantum spin Hall phase, which is further confirmed by the edge state calculations.

cond-mat.str-el↗

arXiv2019

SoReC: A Social-Relation Based Centrality Measure in Mobile Social Networks

Mobile Social Networks (MSNs) have been evolving and enabling various fields in recent years. Recent advances in mobile edge computing, caching, and device-to-device communications, can have significant impacts on 5G systems. In those settings, identifying central users is crucial. It can provide important insights into designing and deploying diverse services and applications. However, it is challenging to evaluate the centrality of nodes in MSNs with dynamic environments. In this paper, we propose a Social-Relation based Centrality (SoReC) measure, in which social network information is used to quantify the influence of each user in MSNs. We first introduce a new metric to estimate direct social relations among users via direct contacts, and then extend the metric to explore indirect social relations among users bridging by the third parties. Based on direct and indirect social relations, we detect the influence spheres of users and quantify their influence in the networks. Simulations on real-world networks show that the proposed measure can perform well in identifying future influential users in MSNs.

cs.SI↗