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International Conference on Communication technology (ICCT)

作者： Kuang Luo Lu Ou Chuangfeng Zhang Ming Zhang Jinxin Li Shaolin Liao School of Electronics and Information Technology Sun Yat-sen University (SYSU) Guangzhou Guangdong China Hunan Province Key Laboratory of Big Data Research and Applications School of Journalism and Communication Hunan University Changsha Hunan China College of Computer Science and Electronic Engineering Hunan University Changsha Hunan China Electrical and Computer Engineering Illinois Institute of Technology Chicago IL USA

Efficient Numerical Singular Integral (NSI) methods have been developed for both 1D and 2D electromagnetics problems, in both the Cartesian and polar coordinates. Through a special type of integration by parts, rigorous mathematics derivation has been performed to decompose the original singular integrals into sum of non-singular integrals that can be evaluated numerically. It can be shown that it is more convenient to evaluate the line-type singularity in the Cartesian coordinate; while it is better to evaluate the point-type singularity in the polar coordinate. Also, numerical schemes to find the order of singularity has been presented. Finally, to validate the 2D numerical singular integral methods, the electromagnetic scattering problem of a 2D reflecting ellipse antenna has been simulated through the Ping-Pong algorithm developed by the authors.

关键词： Three-dimensional displays Electromagnetic scattering Reflector antennas Computational electromagnetics Numerical simulation Mathematics Communications technology

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LLMs Can Simulate Standardized Patients via Agent Coevolution

arXiv

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arXiv 2024年

作者： Du, Zhuoyun Zheng, Lujie Hu, Renjun Xu, Yuyang Li, Xiawei Sun, Ying Chen, Wei Wu, Jian Cai, Haolei Ying, Haochao State Key Lab of CAD & CG Zhejiang University China Zhejiang Polytechnic Institute Polytechnic Institute Zhejiang University China College of Computer Science & Technology Zhejiang University China Alibaba Group China Sun Yat-sen University Cancer Center China School of Public Health Zhejiang University China Second Affiliated Hospital Zhejiang University School of Medicine China Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence China

Training medical personnel using standardized patients (SPs) remains a complex challenge, requiring extensive domain expertise and role-specific practice. Most research on Large Language Model (LLM)-based simulated patients focuses on improving data retrieval accuracy or adjusting prompts through human feedback. However, this focus has overlooked the critical need for patient agents to learn a standardized presentation pattern that transforms data into human-like patient responses through unsupervised simulations. To address this gap, we propose EvoPatient, a novel simulated patient framework in which a patient agent and doctor agents simulate the diagnostic process through multi-turn dialogues, simultaneously gathering experience to improve the quality of both questions and answers, ultimately enabling human doctor training. Extensive experiments on various cases demonstrate that, by providing only overall SP requirements, our framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference, while achieving an optimal balance of resource consumption after evolving over 200 cases for 10 hours, with excellent generalizability. The code will be available at https://***/ZJUMAI/EvoPatient. Copyright © 2024, The Authors. All rights reserved.

关键词： Personnel training

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Model Error Correction in data Assimilation by Integrating Neural Networks

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big data Mining and Analytics 2019年第2期2卷 83-91页

作者： Jiangcheng Zhu Shuang Hu Rossella Arcucci Chao Xu Jihong Zhu Yi-ke Guo State Key Laboratory of Industrial Control Technology Zhejiang University Data Science Institute Imperial College London Department of Computer Science and Technology Tsinghua University

In this paper, we suggest a new methodology which combines Neural Networks(NN) into data Assimilation(DA). Focusing on the structural model uncertainty, we propose a framework for integration NN with the physical models by DA algorithms, to improve both the assimilation process and the forecasting results. The NNs are iteratively trained as observational data is updated. The main DA models used here are the Kalman filter and the variational approaches. The effectiveness of the proposed algorithm is validated by examples and by a sensitivity study.

关键词： data assimilation deep learning neural networks Kalman filter variational approach

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MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection

arXiv

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arXiv 2025年

作者： Cheng, Jiayi Gao, Can Zhou, Jie Wen, Jiajun Dai, Tao Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D Anomaly Detection (AD) is a promising means of controlling the quality of manufactured products. However, existing methods typically require carefully training a task-specific model for each category independently, leading to high cost, low efficiency, and weak generalization. Therefore, this paper presents a novel unified model for Multi-Category 3D Anomaly Detection (MC3D-AD) that aims to utilize both local and global geometry-aware information to reconstruct normal representations of all categories. First, to learn robust and generalized features of different categories, we propose an adaptive geometry-aware masked attention module that extracts geometry variation information to guide mask attention. Then, we introduce a local geometry-aware encoder reinforced by the improved mask attention to encode group-level feature tokens. Finally, we design a global query decoder that utilizes point cloud position embeddings to improve the decoding process and reconstruction ability. This leads to local and global geometry-aware reconstructed feature tokens for the AD task. MC3D-AD is evaluated on two publicly available Real3D-AD and Anomaly-ShapeNet datasets, and exhibits significant superiority over current state-of-the-art single-category methods, achieving 3.1% and 9.3% improvement in object-level AUROC over Real3D-AD and Anomaly-ShapeNet, respectively. The source code will be released upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

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A novel method for driving path planning with spark

TechRxiv

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TechRxiv 2021年

作者： Li, Leixiao Lin, Hao Wan, Jianxiong Wang, Yongsheng Gao, Jing College of Data Science and Application Inner Mongolia University of Technology China School of Computer Science and Engineering Tianjin University of Technology China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service China College of Computer and Information Engineering Inner Mongolia Agricultural University China Inner Mongolia Autonomous Region Key Laboratory of big data research and application for agriculture and animal husbandry China

Efficient and accurate driving path planning can help drivers drive. To solve the problem of low efficiency of traditional heuristic algorithms such as PSO and GA in solving driving path planning, we introduce Excellence Coefficient into heuristic algorithms and make a parallel design based on Spark, which called EC-SPPSOGA. Excellence Coefficient can increase the probability of good edges being left, simultaneously, preserves the possibility of longer side being selected. The parallel design is based on time-consuming analysis of heuristic algorithms. We validate the performance of EC-SPPSOGA based on the data in TSPLIB. It is verified that the EC-SPPSOGA can improve efficiency of driving path planning and has good scalability. © 2021, CC BY.

关键词： Heuristic algorithms

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Fault Diagnosis of Modular Multilevel Converter Based on RNN and Wavelet Analysis

Fault Diagnosis of Modular Multilevel Converter Based on RNN...

引用

Chinese Automation Congress (CAC)

作者： Zhiqiang Geng Qi Wang Yongming Han Kai Chen Feng Xie Yajie Wang Guizhou Provincial Key laboratory of Public Big Data Guiyang China College of information Science and Technology Beijing University of Chemical Technology Beijing China Guizhou Provincial Key Laboratory of Public Big Data Guiyang China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Modular multilevel converter (MMC) is very common in DC transmission system. But the power electronic components in the MMC sub-module usually fail and affect the operation of the circuit. Consequently, a method of MMC fault diagnosis based on recurrent neural network (RNN) and wavelet analysis is proposed. This proposed method combines the wavelet transform and the energy spectrum entropy to reduce noise interference and extract characteristic signals more effectively. During the experiment, the wavelet transform is used for signal processing, and then the fault signal is extracted by the wavelet entropy. After the fault feature data are obtained, the fault diagnosis model is established by using the RNN. Finally verified by experiment, the validity and the high accuracy of the new method are showed by comparing with the method based on the RNN and the BP with wavelet analysis.

关键词： Wavelet transforms Fault diagnosis Circuit faults Entropy Wavelet analysis Feature extraction Training

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Message Authentication with a New Quantum Hash Function

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computers, Materials & Continua 2019年第5期59卷 635-648页

作者： Yalan Wang Yuling Chen Haseeb Ahmad Zhanhong Wei GuiZhou University State Key Laboratory of Public Big DataGuiZhou Guiyang550025China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science National Textile University Faisalabad37610Pakistan College of Information Engineering Beijing Institute of Petrochemical TechnologyBeijing102617China

To ensure the security during the communication,we often adopt different ways to encrypt the messages to resist various ***,with the computing power improving,the existing encryption and authentication schemes are being faced with big *** take the message authentication as an example into a careful ***,we proposed a new message authentication scheme with the Advanced Encryption Standard as the encryption function and the new quantum Hash function as the authentication ***,the Advanced Encryption Standard algorithm is used to encrypt the result of the initial message cascading the corresponding Hash values,which ensures that the initial message can resist eavesdropping ***,utilizing the new quantum Hash function with quantum walks can be much more secure than traditional classical Hash functions with keeping the common properties,such as one-wayness,resisting different collisions and easy *** on these two points,the message authentication scheme can be much more secure than previous ***,it is a new way to design the message authentication scheme,which provides a new thought for other researchers in the *** works will contribute to the study on the new encryption and authentication functions and the combination of quantum computing with traditional cryptology in the future.

关键词： Message authentication symmetric encryption quantum Hash function quantum walk

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PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implicit Variables 2nd

PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implic...

引用

2nd International Conference on Applied Intelligence, ICAI 2024

作者： Liu, Yixin Fan, Yueqin Li, Zhipeng Zhang, Qinhu Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China School of Mechanical Engineering Guangxi University Nanning530004 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China College of Advanced Agricultural Sciences Zhejiang Agriculture and Forestry University Hangzhou311300 China

ISBN: (纸本)9789819619061

In the field of drug discovery and development, deep learning techniques have become a powerful tool to accelerate the discovery and development of new drugs. In the design and optimization of lead molecules, generative adversarial network and graph neural network are one of the prime techniques for accelerating research and development, because the former is based on the idea of two-player game to learn drug features to generate effective molecules, while the unstructured inputs of the latter are well-suited for the analysis of molecular data. Therefore, this study develops a new graph generative adversarial model, called PreZ-DGGAN, by inserting the graph neural network layer into the generative adversarial network. Within PreZ-DGGAN, the latent variable input of the generator is pre-learned by an improved graph variational autoencoder, which effectively avoids the mode collapse of the generative adversarial network, whereas the hidden layer of the discriminator is graph neural network layer, which is able to better learn the real molecules and judge the generated molecules as true or false. Experiments on two molecule datasets show that the molecules generated by PreZ-DGGAN have higher validity (100% on average) and uniqueness (91.5% on average), and the generated molecules are more diverse in structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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A survey of Few-Shot Relation Extraction combining meta-learning with prompt learning

引用

Neurocomputing 2025年 647卷

作者： Hang, Tingting Wu, Wei Feng, Jun Djigal, Hamza Huang, Jun School of Computer Science and Technology Anhui University of Technology Ma'anshan243032 China Key Laboratory of Water Big Data Technology of Ministry of Water Resource Hohai University Nanjing211100 China School of Computer Science and Technology Hohai University Nanjing211100 China Department of Computer Science College of Science Mathematics and Technology Wenzhou-Kean University Wenzhou325060 China

Few-shot Relation Extraction (FSRE) is challenging in Natural Language Processing (NLP). It requires models to accurately identify and categorize the relation between entities in texts based on limited annotated samples. Researchers have explored meta-learning and prompt-learning methods to address this challenge. Meta-learning aims to accelerate the model's learning rate for new tasks by training across different tasks, while prompt learning seeks to enhance the model's understanding of context. However, both methods face limitations in few-shot scenarios. To overcome these restrictions, studies combining meta-learning with prompt learning have emerged, garnering widespread academic interest. This paper presents a comprehensive survey of FSRE combining Meta-Learning with Prompt Learning. We first categorize existing research into three main groups: meta-learning-based, prompt learning-based, and hybrid approaches. Then, we provide a detailed analysis and discussion of the latest advancements in each category. Moreover, we extensively describe datasets commonly used in the FSRE task, evaluation metrics, and comparisons among various models. Finally, we explore current challenges in the field and forecast potential future research trends, offering insights for subsequent studies. © 2025 Elsevier B.V.

关键词： Natural language processing systems

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StarGAT: Star-Shaped Hierarchical Graph Attentional Network for Heterogeneous Network Representation Learning

StarGAT: Star-Shaped Hierarchical Graph Attentional Network ...

引用

IEEE International Conference on data Mining (ICDM)

作者： Wen-Zhi Li Ling Huang Chang-Dong Wang Yu-Xin Ye School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Computational Science Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China College of Mathematics and Informatics South China Agricultural University Guangzhou China Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application Guangzhou China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781665423991

Many real-world graphs can be viewed as Heterogeneous Networks or Heterogeneous Information Networks (HINs) for that they comprise a diversity of node types and relation types. Due to the efficient representation ability of Graph Neural Network and the idea of random walk, many recent studies apply graph representation learning to HINs and achieve satisfactory results. However, these works either treat different node types in a metapath equally, which is inconsistent with the original graph semantic information for that different node types should have different statuses, or only consider the first-order (node-level) and second-order (metapath-level, a.k.a. link-level) information aggregation while ignoring the higher-order relations. To tackle these two problems, we propose a novel Star-Shaped Hierarchical Graph Attentional Network (StarGAT) model to boost representation learning in HINs. Specifically, we assume nodes in HINs can be categorized into a star-shaped structure including one center node type and a bunch of auxiliary node types in a specific task; and we encode node-level, link-level and motif-level attentions in a hierarchical manner to capture richer semantic information. Extensive experiments on three datasets illustrate the model effectiveness.

关键词： Representation learning Conferences Semantics Heterogeneous networks Graph neural networks data mining Task analysis

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