167 citations · 849 across the 39 of their papers we have counts for
39 papers
Direct observation of topological magnon polarons in a multiferroic material
Song Bao, Zhao-Long Gu, Yanyan Shangguan +12
Magnon polarons are novel elementary excitations possessing hybrid magnonic and phononic signatures, and are responsible for many exotic spintronic and magnonic phenomena. Despite…
Observation of a 1/3 Magnetisation Plateau Phase as Evidence for the Kitaev Interaction in a Honeycomb-Lattice Antiferromagnet
Yanyan Shangguan, Song Bao, Zhao-Yang Dong +24
Fractional magnetisation plateaus, in which the magnetisation is pinned at a fraction of its saturated value within a range of external magnetic field, are spectacular macroscopic…
Dual-Gated Fusion with Prefix-Tuning for Multi-Modal Relation Extraction
Qian Li, Shu Guo, Cheng Ji +3
Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues. Rich visual content is valuable for the MMRE task,…
Learning Music Sequence Representation from Text Supervision
Tianyu Chen, Yuan Xie, Shuai Zhang +3
Music representation learning is notoriously difficult for its complex human-related concepts contained in the sequence of numerical signals. To excavate better MUsic SEquence Repr…
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification
Xingcheng Fu, Yuecen Wei, Qingyun Sun +4
Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topo…
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment
Qian Li, Shu Guo, Yangyifei Luo +4
The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable…
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
Dongcheng Zou, Hao Peng, Xiang Huang +5
Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it is susceptible to low-quality and unreliable structure, which has been a norm rather th…
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
Ce Zhou, Qian Li, Chen Li +16
Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on…
Unbiased and Efficient Self-Supervised Incremental Contrastive Learning
Cheng Ji, Jianxin Li, Hao Peng +4
Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning. Howeve…
Self-organization Preserved Graph Structure Learning with Principle of Relevant Information
Qingyun Sun, Jianxin Li, Beining Yang +3
Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption ca…
Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems
Qian Li, Jianxin Li, Lihong Wang +6
Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power…
BadRes: Reveal the Backdoors through Residual Connection
Mingrui He, Tianyu Chen, Haoyi Zhou +2
Generally, residual connections are indispensable network components in building CNNs and Transformers for various downstream tasks in CV and VL, which encourages skip shortcuts be…
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing
Qingyun Sun, Jianxin Li, Haonan Yuan +5
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topolog…
Defect-induced edge ferromagnetism and fractional spin excitations of the SU(4) -flux Hubbard model on honeycomb lattice
Yang Xie, Cheng-Ping He, Zhao-Yang Dong +1
Recently, a SU(4) -flux Hubbard model on the honeycomb lattice has been proposed to study the spin-orbit excitations of -ZrCl [Phys.~Rev.~Lett. 121.097201~(2017)]. Based…
Confinement and oscillating deconfinement crossover of two-magnon excitations in quantum spin chains quantified by spin entanglement entropy
Zhao-Yang Dong, Jian-Xin Li
We introduce the spin entanglement entropy (EE) to characterize spin excitations. The scalings of EEs are elaborated as , and for the single-magnon st…
Task-Specific Expert Pruning for Sparse Mixture-of-Experts
Tianyu Chen, Shaohan Huang, Yuan Xie +5
The sparse Mixture-of-Experts (MoE) model is powerful for large-scale pre-training and has achieved promising results due to its model capacity. However, with trillions of paramete…
THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption
Tianyu Chen, Hangbo Bao, Shaohan Huang +6
As more and more pre-trained language models adopt on-cloud deployment, the privacy issues grow quickly, mainly for the exposure of plain-text user data (e.g., search history, medi…
Spin dynamics and continuum spectra of the honeycomb J1-J2 antiferromagnetic Heisenberg model
Cheng Gu, Shun-Li Yu, Jian-Xin Li
We employ the spin cluster perturbation theory to investigate the dynamical properties of the antiferromagnetic - Heisenberg model on the honeycomb lattice. We obtain…
MTTrans: Cross-Domain Object Detection with Mean-Teacher Transformer
Jinze Yu, Jiaming Liu, Xiaobao Wei +7
Recently, DEtection TRansformer (DETR), an end-to-end object detection pipeline, has achieved promising performance. However, it requires large-scale labeled data and suffers from…
Neutron spectroscopy evidence for a possible magnetic-field-induced gapless quantum-spin-liquid phase in a Kitaev material -RuCl
Xiaoxue Zhao, Kejing Ran, Jinghui Wang +15
As one of the most promising Kitaev quantum-spin-liquid (QSL) candidates, -RuCl has received a great amount of attention. However, its ground state exhibits a long-range zig…
Curvature Graph Generative Adversarial Networks
Jianxin Li, Xingcheng Fu, Qingyun Sun +4
Generative adversarial network (GAN) is widely used for generalized and robust learning on graph data. However, for non-Euclidean graph data, the existing GAN-based graph represent…
Evidence for strong correlations at finite temperatures in the dimerized magnet NaCuTeO
Yanyan Shangguan, Song Bao, Zhao-Yang Dong +12
Dimerized magnets forming alternating Heisenberg chains exhibit quantum coherence and entanglement and thus can find potential applications in quantum information and computation.…
Disorder-induced broadening of the spin waves in a triangular-lattice quantum-spin-liquid candidate YbZnGaO
Zhen Ma, Zhao-Yang Dong, Jinghui Wang +15
Disorder is important in the study of quantum spin liquids, but its role on the spin dynamics remains elusive. Here, we explore the disorder effect by investigating the magnetic-fi…
Neutron spectroscopy evidence on the dual nature of magnetic excitations in a van der Waals metallic ferromagnet FeGeTe
Song Bao, Wei Wang, Yanyan Shangguan +12
In the local or itinerant extreme, magnetic excitations can be described by the Heisenberg model which treats electron spins as localized moments, or by the itinerant-electron mode…