Yi Liu

DS-SpecIT: Decomposed Spectral Inverted Transformer for Interference-Aware Spectrum Forecasting

Xi Xiao, Youchen Fan, Ming Lei, Yi Liu et autres

Large-scale spectrum monitoring infrastructures generate high-dimensional spectral time series, providing a critical data foundation for proactive spectrum management, anomaly detection, radio environment awareness, and interference-aware decision-making. In complex electromagnetic environments, real-world deployments are highly nonstationary and frequently affected by unexpected interference, which …

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Feature Fusion Network for Optical and Synthetic Aperture Radar Images Based on Residual Dense Blocks and Interactive Attention Mechanism

Jie Zhou, Lurui Xia, Sen Li, Yi Liu

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The effects of perceived stress on compassion fatigue and SWB in medical interns from vocational colleges: the mediation of social support, psychological resilience, and coping styles

Lijuan Yi, Chan Peng, Houlian Wang, Yi Liu et autres

BACKGROUND: Medical interns from vocational colleges play a crucial role as a reserve force for the primary healthcare system. However, they are particularly vulnerable to compassion fatigue (CF), which can significantly reduce their subjective well-being (SWB). Grounded in the Stress Coping Theory, …

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CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation

Yi Liu, Shengqian Li, Zuzeng Lin, Feng Wang et autres

The current conditional autoregressive image generation methods have shown promising results, yet their potential remains largely unexplored in the practical unsupervised image translation domain, which operates without explicit cross-domain correspondences. A critical limitation stems from the discrete quantization inherent in traditional Vector …

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Optimization of a Walker Constellation Using an RBF Surrogate Model for Space Target Awareness

You Fu, Zhaojing Xu, Youchen Fan, Yi Liu et autres

Designing Low Earth Orbit (LEO) constellations for the continuous, collaborative observation of space objects in MEO/GEO is a complex optimization task, frequently limited by prohibitive computational costs. This study introduces an efficient surrogate-based framework to overcome this challenge. Our approach integrates Optimized …

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LMC: Lightweight Message Collection for Distributed Training of Deep Learning Models

Y Zhang, Y. B. Wang, Jie Jia, Yi Liu

As the scale of deep learning models continues to expand, distributed training has become an inevitable trend, which is not only time-consuming but also expensive. Therefore, effective debugging and tuning are crucial for the distributed training, where the messages among training processes …

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Dual Semantic-Aware Network for Noise Suppressed Ultrasound Video Segmentation

Ling Zhou, Runtian Yuan, Yi Liu, Yuejie Zhang et autres

Ultrasound imaging is a prevalent diagnostic tool known for its simplicity and non-invasiveness. However, its inherent characteristics often introduce substantial noise, posing considerable challenges for automated lesion or organ segmentation in ultrasound video sequences. To address these limitations, we propose the Dual …

MICCAI 2023 STS Challenge: A retrospective study of semi-supervised approaches for teeth segmentation

Yaqi Wang, Yifan Zhang, Xiao-Diao Chen, Shuai Wang et autres

Computer-aided diagnosis greatly enhances personalized treatment planning and diagnostic efficiency by providing accurate dental anatomy through teeth segmentation. However, it still constrained by the scarcity of high-quality annotated dental datasets. To address this issue, this paper presents a dataset combining both 2D …

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Fine-Grained Fault Sensitivity Analysis of Vision Transformers Under Soft Errors

Jiajun He, Yi Liu, Changqing Xu, Xinfang Liao et autres

Over the past decade, deep neural networks (DNNs) have revolutionized the fields of computer vision (CV) and natural language processing (NLP), achieving unprecedented performance across a variety of tasks. The Vision Transformer (ViT) has emerged as a powerful alternative to convolutional neural …

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A Lightweight Dual-Branch Complex-Valued Neural Network for Automatic Modulation Classification of Communication Signals

Zhaojing Xu, Youchen Fan, Shengliang Fang, You Fu et autres

Currently, deep learning has become a mainstream approach for automatic modulation classification (AMC) with its powerful feature extraction capability. Complex-valued neural networks (CVNNs) show unique advantages in the field of communication signal processing because of their ability to directly process complex data …

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QuCloudSim: A Customizable Discrete Event Simulator for Quantum Cloud Computing Enviroment

Ruilin Zhou, Yi Liu, Mignot Messel

Current quantum computing service providers operate in a cloud-based model, hosting expensive quantum computers in data centers for remote access. However, in the Noisy Intermediate-Scale Quantum (NISQ) era, quantum hardware is limited in terms of qubit count and connectivity. To address scalability …

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A survey of model compression techniques: past, present, and future

Defu Liu, Yixiao Zhu, Zhe Liu, Yi Liu et autres

The exceptional performance of general-purpose large models has driven various industries to focus on developing domain-specific models. However, large models are not only time-consuming and labor-intensive during the training phase but also have very high hardware requirements during the inference phase, such …