arXiv subjects

Chenglong Shi

Publications and source records attributed to Chenglong Shi.

6 recordsLinked to original sources

arXiv2026

Quantum Machine Learning-based 6G edge Network: Enabling Adaptive Communication and Model Aggregation

With the advent of sixth-generation (6G) mobile communication technology, vehicle-to-everything (V2X) communication faces unprecedented challenges in communication efficiency, system generalization capabilities, and model collaboration. Conventional machine learning struggles with high-dimensional state spaces, slow convergence, and poor generalization under heterogeneous V2X nodes, rapidly varying channels, and multimodal sensing data in V2X systems. To address these issues, we propose a quantum-enhanced framework for V2X communication and model aggregation that targets efficient, robust, and intelligent transportation in 6G, which includes four modules: the channel-adaptive semantic communication module, the multimodal fusion module, the model transfer module, and the federated aggregation module. Specifically, the channel-adaptive semantic communication module leverages quantum convolutional neural networks (CNN) and quantum distortion metrics to enable efficient transmission and strong generalization across diverse conditions. The multimodal fusion module exploits quantum attention and entanglement to compress features and associate semantics across heterogeneous data. The model transfer module employs quantum reinforcement learning to model decision-making and improve adaptability in dynamic environments. The federated aggregation module integrates quantum tensor decomposition with backpropagation-based corrections to provide privacy preservation with low overhead and to strengthen global model robustness. This work outlines a new paradigm for communication and model collaboration in future 6G intelligent transportation.

quant-ph↗

arXiv2025

GraphEdge: Dynamic Graph Partition and Task Scheduling for GNNs Computing in Edge Network

With the exponential growth of Internet of Things (IoT) devices, edge computing (EC) is gradually playing an important role in providing cost-effective services. However, existing approaches struggle to perform well in graph-structured scenarios where user data is correlated, such as traffic flow prediction and social relationship recommender systems. In particular, graph neural network (GNN)-based approaches lead to expensive server communication cost. To address this problem, we propose GraphEdge, an efficient GNN-based EC architecture. It considers the EC system of GNN tasks, where there are associations between users and it needs to take into account the task data of its neighbors when processing the tasks of a user. Specifically, the architecture first perceives the user topology and represents their data associations as a graph layout at each time step. Then the graph layout is optimized by calling our proposed hierarchical traversal graph cut algorithm (HiCut), which cuts the graph layout into multiple weakly associated subgraphs based on the aggregation characteristics of GNN, and the communication cost between different subgraphs during GNN inference is minimized. Finally, based on the optimized graph layout, our proposed deep reinforcement learning (DRL) based graph offloading algorithm (DRLGO) is executed to obtain the optimal offloading strategy for the tasks of users, the offloading strategy is subgraph-based, it tries to offload user tasks in a subgraph to the same edge server as possible while minimizing the task processing time and energy consumption of the EC system. Experimental results show the good effectiveness and dynamic adaptation of our proposed architecture and it also performs well even in dynamic scenarios.

cs.LG↗

arXiv2025

Homophily-aware Heterogeneous Graph Contrastive Learning

Heterogeneous graph pre-training (HGP) has demonstrated remarkable performance across various domains. However, the issue of heterophily in real-world heterogeneous graphs (HGs) has been largely overlooked. To bridge this research gap, we proposed a novel heterogeneous graph contrastive learning framework, termed HGMS, which leverages connection strength and multi-view self-expression to learn homophilous node representations. Specifically, we design a heterogeneous edge dropping augmentation strategy that enhances the homophily of augmented views. Moreover, we introduce a multi-view self-expressive learning method to infer the homophily between nodes. In practice, we develop two approaches to solve the self-expressive matrix. The solved self-expressive matrix serves as an additional augmented view to provide homophilous information and is used to identify false negatives in contrastive loss. Extensive experimental results demonstrate the superiority of HGMS across different downstream tasks.

cs.LG↗

arXiv2018

Probing lattice vibration at surface and interface of SiO$_2$/Si with nanometer resolution

Recent advances in monochromatic aberration corrected electron microscopy make it possible to detect the lattice vibration with both high-energy resolution and high spatial resolution. Here, we use sub-10 meV electron energy loss spectroscopy to investigate the local vibrational properties at surface and interface of an amorphous SiO$_2$ (a-SiO$_2$) thin film on Si substrate. We find that each optical mode splits into three sub-modes, i.e., surface mode, bulk mode and interface mode, which can be measured from different locations. The pure surface modes can be measured in the vacuum near the surface, and the pure interface modes are expected to be obtained either at the interface location or in the Si, while inside the SiO$_2$ the measured signal is a mixture of bulk, surface, and interface modes. The bulk mode has the largest vibration energy and surface mode has the lowest. The energy of surface mode is thickness dependent, showing a blue-shift as z-thickness (parallel to fast electron beam) of SiO$_2$ film increases, while the bulk and interface modes have constant vibration energy. The intensity of bulk mode linearly increases with thickness being increased, and it drops steeply to zero near the surface and interface (within a few nanometers). The surface modes decay slowly in the vacuum following a Bessel function. The mechanism of the observed spatially dependent vibration behavior is discussed and quantitatively compared with dielectric response theory analysis. Our nanometer scale measurements of vibrations properties provide useful information about the bonding conditions at the surface and interface and thus may help to design better silicon-based electronic devices via surface and interface treatments

cond-mat.mtrl-sci↗

arXiv2016

NMR Evidence for the Topologically Nontrivial Nature in a Family of Half-Heusler Compounds

Spin-orbit coupling (SOC) is expected to partly determine the topologically nontrivial electronic structure of heavy half-Heusler ternary compounds. However, to date, attempts to experimentally observe either the strength of SOC or how it modifies the bulk band structure have been unsuccessful. By using bulk-sensitive nuclear magnetic resonance (NMR) spectroscopy combined with first-principles calculations, we reveal that 209Bi NMR isotropic shifts scale with relativity in terms of the strength of SOC and average atomic numbers, indicating strong relativistic effects on NMR parameters. According to first-principles calculations, we further claim that nuclear magnetic shieldings from relativistic p1/2 states and paramagnetic contributions from low-lying unoccupied p3/2 states are both sensitive to the details of band structures tuned by relativity, which explains why the hidden relativistic effects on band structure can be revealed by 209Bi NMR isotropic shifts in topologically nontrivial half-Heusler compounds. Used in complement to surface-sensitive methods, such as angle resolved photon electron spectroscopy and scanning tunneling spectroscopy, NMR can provide valuable information on bulk electronic states.

cond-mat.str-el↗

arXiv2014

NMR evidence for enhanced orbital diamagnetism in topologically nontrivial half-Heusler semimetals

209Bi nuclear magnetic resonance (NMR) spectroscopy was employed to probe potential spin-orbit effects on orbital diamagnetism in YPtBi and YPdBi crystals. The observed opposite sign and temperature dependent magnitude of 209Bi NMR shifts of both crystals reveal experimental signatures of enhanced orbital diamagnetism induced by spin-orbit interactions. This investigation indicates that NMR isotropic shifts might be beneficial in search of interesting spin-electronic phases among a vast number of topological nontrivial half-Heusler semimetals.

cond-mat.str-el↗