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An innovative Galerkin scheme based on anisotropic trilinear immersed finite elements for the magnetized plasma diffusion problem with plasma sheath interface

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Computers & Mathematics with Applications 2025年 192卷 155-171页

作者： Ziping Wang Guangqing Xia Yajie Han Chang Lu Lin Zhang Gang Xu State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Dalian 116024 China Key Laboratory of Advanced Technology for Aerospace Vehicles of Liaoning Province Dalian University of Technology Dalian 116024 China Collaborative Innovation Center of Micro & Nano Satellites of Hebei Province North China Institute of Aerospace Engineering Langfang 065000 China Suzhou Industry CFD Development Central China Co. Ltd. &CISC Suzhou 215100 China School of Mathematical Sciences and Mathematical Institute Nanjing Normal University Nanjing 210023 China

Via introducing the Robin flux jump into the Galerkin scheme, this paper develops a new anisotropic trilinear immersed finite element (IFE) method for solving the magnetized plasma diffusion problem with plasma sheath interface condition under Cartesian meshes. The three-dimensional (3D) diffusion process of magnetized plasma is anisotropic and highly sensitive to magnetic fields, making it difficult to efficiently solve by commonly used body fitted mesh methods when the simulation domain has complex boundary conditions. Even worse, the plasma sheath boundary will further exacerbates its solving difficulty. The presented method first utilizes the anisotropic trilinear IFE basis functions describing the diffusion of magnetized plasma in interface elements. Then the trilinear IFE basis functions are used to handle the plasma sheath interface conditions, i.e. the Robin flux jump conditions. As for the other non interface elements, the traditional trilinear basis functions are used. On this basis, a new Galerkin scheme is derived and applied to efficiently solving the plasma anisotropic diffusion problem with the Robin flux jump interfaces in Cartesian meshes. The proposed method can also solve other types of elliptical interface problems via controlling the coefficients. Moreover, the orthogonal meshes makes it convenient to couple other Cartesian mesh based methods, such as the particle-in-cell method, which provides an advanced tool for solving plasma transport problems. Numerical experiments are provided to demonstrate the proposed method and show the applicability in the simulations of actual engineering issues.

关键词： Plasma sheaths

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Software Vulnerability Mining and Analysis Based on Deep Learning

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Computers, Materials & Continua 2024年第8期80卷 3263-3287页

作者： Shibin Zhao Junhu Zhu Jianshan Peng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China

In recent years,the rapid development of computer software has led to numerous security problems,particularly software *** flaws can cause significant harm to users’privacy and *** security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection *** intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false ***,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)***,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency *** feature vector is then used as the learning target for the neural *** types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection *** results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

关键词： Vulnerability mining software security deep learning static analysis

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Research Progress of Reverse Monte Carlo and Its Application in Josephson Junction Barrier Layer

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Computer Modeling in engineering & Sciences 2023年第12期137卷 2077-2109页

作者： Junling Qiu Huihui Sun Shuya Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China

As indispensable components of superconducting circuit-based quantum computers,Josephson junctions determine how well superconducting qubits *** Monte Carlo(RMC)can be used to recreate Josephson junction’s atomic structure based on experimental data,and the impact of the structure on junctions’properties can be investigated by combining different analysis *** order to build a physical model of the atomic structure and then analyze the factors that affect its performance,this paper briefly reviews the development and evolution of the RMC *** also summarizes the modeling process and structural feature analysis of the Josephson junction in combination with different feature extraction techniques for electrical characterization ***,the obstacles and potential directions of Josephson junction modeling,which serves as the theoretical foundation for the production of superconducting quantum devices at the atomic level,are discussed.

关键词： Reverse Monte Carlo Josephson junction alumina barrier layer

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Visual Class Incremental Learning with Textual Priors Guidance based on an Adapted Vision-Language Model

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Wentao Yu, Tong Wang, Ruixuan Xie, Jianhui Trucco, Emanuele Zheng, Wei-Shi Yang, Xiaobo Sun Yat-sen Univerisity School of Computer Science and Engineering Guangzhou510275 China Peng Cheng Laboratory Shenzhen518066 China MOE Key Laboratory of Machine Intelligence and Advanced Computing Guangzhou510275 China Second Affiliated Hospital Guangzhou University of Chinese Medicine State Key Laboratory of Dampness Syndrome of Chinese Medicine Guangzhou510260 China Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome Guangzhou510120 China University of Dundee School of Science and Engineering DundeeDD1 4HN United Kingdom

An ideal artificial intelligence (AI) system should have the capability to continually learn like humans. However, when learning new knowledge, AI systems often suffer from catastrophic forgetting of old knowledge. Although many continual learning methods have been proposed, they often ignore the issue of misclassifying similar classes and make insufficient use of textual priors of visual classes to improve continual learning performance. In this study, we propose a continual learning framework based on a pre-trained vision-language model (VLM) that does not require storing old class data. This framework utilizes parameter-efficient fine-tuning of the VLM's text encoder for constructing a shared and consistent semantic textual space throughout the continual learning process. The textual priors of visual classes are encoded by the adapted VLM's text encoder to generate discriminative semantic representations, which are then used to guide the learning of visual classes. Additionally, fake out-of-distribution (OOD) images constructed from each training image further assist in the learning of visual classes. Extensive empirical evaluations on three natural datasets and one medical dataset demonstrate the superiority of the proposed framework. The source code is available at https://***/OpenMedIA/CIL_Adapterd_VLM. © 1999-2012 IEEE.

关键词： Contrastive Learning

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Low-power technologies in high-performance computer: trends and perspectives

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National Science Review 2016年第1期3卷 23-25页

作者： Xianghui Xie State Key Laboratory of Mathematical Engineering and Advanced Computing

INTRODUCTION With the breakdown of Dennard’s scaling rule,chip power for a given area size is not constant as transistors shrink from generation to generation[1].In particular,the power wall problem worsens with the ... 详细信息

关键词： HPC high trends and perspectives Low-power technologies in high-performance computer GPU CPU

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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ThicknessVAE: Learning a Lateral Prior for Clothed Human Body Reconstruction

ThicknessVAE: Learning a Lateral Prior for Clothed Human Bod...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaotao Wu Zhaoxin Fan Huiguang He Dinggang Shen School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China NeuBCI Group State Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Institute of Artificial Intelligence Beihang University Beijing China Beijing Academy of Blockchain and Edge Computing China Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Shanghai Clinical Research and Trial Center Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Sandwich-like structures have shown remarkable efficacy in clothed human reconstruction. However, these approaches often generate unrealistic side geometries due to inadequate handling of lateral regions. This paper addresses this limitation by incorporating the side geometry of clothed humans as a prior. We propose ThicknessVAE, a novel two-stage method that makes two key contributions: (1) We learn a prototype from point clouds for the lateral regions of clothed humans to extract common and detailed geometric features. (2) We utilize this prototype as a prior to transform geometric features into a thickness map associated with clothed human images, enabling refined normal integration for sandwich-like reconstruction methods. By seamlessly integrating our model into the sandwich-like reconstruction pipeline, we achieve highly realistic side views. Both qualitative and quantitative experiments demonstrate that our approach is comparable to state-of-the-art methods in terms of side-view realism.

关键词： Geometry Surface reconstruction Three-dimensional displays Pipelines Prototypes Transforms Feature extraction Speech processing Image reconstruction Surface treatment

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Combo Packet:An Encryption Traffic Classification Method Based on Contextual Information

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Computers, Materials & Continua 2024年第4期79卷 1223-1243页

作者： Yuancong Chai Yuefei Zhu Wei Lin Ding Li State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou450001China

With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this *** methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and *** with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.

关键词： Encrypted traffic classification packet-level convolutional neural network attention mechanisms

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Photonic axion insulator

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Science (New York, N.Y.) 2025年第6730期387卷 162-166页

作者： Liu, Gui-Geng Mandal, Subhaskar Xi, Xiang Wang, Qiang Devescovi, Chiara Morales-Pérez, Antonio Wang, Ziyao Yang, Linyun Banerjee, Rimi Long, Yang Meng, Yan Zhou, Peiheng Gao, Zhen Chong, Yidong García-Etxarri, Aitzol Vergniory, Maia G. Zhang, Baile Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Research Center for Industries of the Future Department of Electronic and Information Engineering School of Engineering Westlake University Hangzhou China School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan China National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing China Donostia International Physics Center Donostia-San Sebastian Spain Institute for Theoretical Physics ETH Zurich Zurich Switzerland Material and Applied Physics Department University of the Basque Country (UPV/EHU) Donostia-San Sebastián Spain Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China National Engineering Research Center of Electromagnetic Radiation Control Materials Key Laboratory of Multi-spectral Absorbing Materials and Structures of Ministry of Education University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Optical Fiber and Cable Manufacture Technology Department of Electronic and Electrical Engineering Guangdong Key Laboratory of Integrated Optoelectronics Intellisense Southern University of Science and Technology Shenzhen China Centre for Disruptive Photonic Technologies Photonics Institute Nanyang Technological University Singapore IKERBASQUE Basque Foundation for Science Bilbao Spain Max Planck Institute for Chemical Physics of Solids Dresden Germany Département de Physique et Institut Quantique Université de Sherbrooke Sherbrooke Canada

Axions, hypothetical elementary particles that remain undetectable in nature, can arise as quasiparticles in three-dimensional crystals known as axion insulators. Previous implementations of axion insulators have largely been limited to two-dimensional systems, leaving their topological properties in three dimensions unexplored in experiment. Here, we realize an axion insulator in a three-dimensional photonic crystal and probe its topological properties. Demonstrated features include half-quantized Chern numbers on each surface that resembles a fractional Chern insulator, unidirectional chiral hinge states forming topological transport in three dimensions, and arithmetic operations between fractional and integer Chern numbers. Our work experimentally establishes the axion insulator as a three-dimensional topological phase of matter and enables chiral states to form complex, unidirectional three-dimensional networks through braiding.

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MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts

arXiv

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arXiv 2025年

作者： Meng, Runqi Song, Sifan Jin, Pengfei Oh, Yujin Teng, Lin Wang, Yulin Sun, Yiqun Chen, Ling Li, Xiang Li, Quanzheng Guo, Ning Shen, Dinggang School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University China Center of Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School Harvard University United States Shanghai United Imaging Intelligence Co. Ltd China Shanghai Clinical Research and Trial Center China

Accurate tumor segmentation is crucial for cancer diagnosis and treatment. While foundation models have advanced general-purpose segmentation, existing methods still struggle with: (1) limited incorporation of medical priors, (2) imbalance between generic and tumor-specific features, and (3) high computational costs for clinical adaptation. To address these challenges, we propose MAST-Pro (Mixture-of-experts for Adaptive Segmentation of pan-Tumors with knowledge-driven Prompts), a novel framework that integrates dynamic Mixture-of-Experts (D-MoE) and knowledge-driven prompts for pan-tumor segmentation. Specifically, text and anatomical prompts provide domain-specific priors, guiding tumor representation learning, while D-MoE dynamically selects experts to balance generic and tumor-specific feature learning, improving segmentation accuracy across diverse tumor types. To enhance efficiency, we employ Parameter-Efficient Fine-Tuning (PEFT), optimizing MAST-Pro with significantly reduced computational overhead. Experiments on multi-anatomical tumor datasets demonstrate that MAST-Pro outperforms state-of-the-art approaches, achieving up to a 5.20% improvement in average DSC while reducing trainable parameters by 91.04%, without compromising accuracy. © 2025, CC BY-NC-ND.

关键词： Image segmentation

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