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A Real-time Cutting Model Based on Finite Element and Order Reduction

computer Systems science & engineering 2022年第10期43卷 1-15页

作者： Xiaorui Zhang Wenzheng Zhang Wei Sun Hailun Wu Aiguo Song Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in ***,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time *** proposed model uses the finite element model to construct a deformation model of the virtual ***,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation *** addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual ***,the Bezier curve is adopted to draw the incision outline after the virtual lung has been ***,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual *** results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

关键词： Virtual surgery cutting model finite element model model order reduction Bezier curve

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Boundary Guided Context Aggregation for Semantic Segmentation 32

Boundary Guided Context Aggregation for Semantic Segmentatio...

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32nd British Machine Vision Conference, BMVC 2021

作者： Ma, Haoxiang Yang, Hongyu Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China

The recent studies on semantic segmentation are starting to notice the significance of the boundary information, where most approaches see boundaries as the supplement of semantic details. However, simply combing boundaries and the mainstream features cannot ensure a holistic improvement of semantics modeling. In contrast to the previous studies, we exploit boundary as a significant guidance for context aggregation to promote the overall semantic understanding of an image. To this end, we propose a Boundary guided Context Aggregation Network (BCANet), where a Multi-Scale Boundary extractor (MSB) borrowing the backbone features at multiple scales is specifically designed for accurate boundary detection. Based on which, a Boundary guided Context Aggregation module (BCA) improved from Non-local network is further proposed to capture long-range dependencies between the pixels in the boundary regions and the ones inside the objects. By aggregating the context information along the boundaries, the inner pixels of the same category achieve mutual gains and therefore the intra-class consistency is enhanced. We conduct extensive experiments on the Cityscapes and ADE20K databases, and comparable results are achieved with the state-of-the-art methods, clearly demonstrating the effectiveness of the proposed one. Our code is available at https://***/mahaoxiang822/Boundary-Guided-Context-Aggregation. © 2021. The copyright of this document resides with its authors.

关键词： Semantic Segmentation

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Boosting performance of virtualized desktop infrastructure with physical GPU and SPICE

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science China(Information sciences) 2020年第7期63卷 236-238页

作者： Shupan LI Limin XIAO Chungang SHI Liequan CHE Changyou ZHANG Yuanzhang LI State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing Jinghang Computation and Communication Research Institute Institute of Software Chinese Academy of Sciences School of Computer Science and Technology Beijing Institute of Technology

Dear editor,The virtual desktop infrastructure (VDI) is widely used by enterprises, as it can be easily deployed,has a reduced management cost, improves resource utilization, and can be accessed at any location by the network. Currently, there is a demand for the VDIs to support applications, such as playing 3D games, playing HD videos, and using 3D design-

关键词： visualized desktop infrastructure SPICE physical GPU The virtual machine (VM) virtual machine monitor (VMM)

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PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing

arXiv

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arXiv 2025年

作者： Zhang, Yuwei Jin, Zhi Xing, Ying Li, Ge Liu, Fang Zhu, Jiaxin Dou, Wensheng Wei, Jun Affiliated with Nanjing Institute of Software Technology University of Chinese Academy of Sciences Nanjing China Key Laboratory of System Software Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China School of Computer Science Wuhan University Wuhan China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Beihang University Beijing China

Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词： computer software maintenance

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Relation-Aware Reasoning with Graph Convolutional Network 11th

Relation-Aware Reasoning with Graph Convolutional Network

引用

11th International Conference on Image and Graphics, ICIG 2021

作者： Zhou, Lei Liu, Yang Bai, Xiao Wang, Xiang Wang, Chen Zhang, Liang Gu, Lin School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University Beijing China RIKEN AIP Tokyo Japan The University of Tokyo Tokyo Japan

ISBN: (纸本)9783030873547

Semantic dependencies among objects are crucial for the recognition system to enhance performance. However, utilizing object-object relationships is a non-trivial task as objects are of various scales and locations, leading to irregular relationships. In this paper, we present a novel visual reasoning framework that incorporates both semantic and spatial relationships to improve the recognition system. We at first construct a knowledge graph to represent the co-occurrence frequency and relative position among categories. Based on this knowledge graph, we are able to enhance the original regional features by a Graph Convolutional Network (GCN) that encodes the high-level semantic contexts. Experiments show that our framework manages to outperform the baselines and state-of-the-art on different backbones in terms of both per-instance and per-class classification accuracy. © 2021, Springer Nature Switzerland AG.

关键词： Knowledge graph

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A dual plasmonic core-shell Pt/[TiN@TiO_(2)]catalyst for enhanced photothermal synergistic catalytic activity of VOCs abatement

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Nano Research 2022年第8期15卷 7071-7080页

作者： Anqi Li Qianpeng Zhang Shuaiqi Zhao Yanan Chong Peng Wu Yifei Li Xiaojing Jin Guangxu Chen Yongcai Qiu Shihe Yang Daiqi Ye Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control School of Environment and EnergySouth China University of TechnologyGuangzhou 510006China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Guangzhou HKUST Fok Ying Tung Research Institute NanshaGuangzhou 511458China State Key Laboratory of Luminescent Materials and Devices South China University of TechnologyGuangzhou 510640China Guangdong Key Lab of Nano-Micro Material Research School of Chemical Biology and Biotechnology Shenzhen Graduate SchoolPeking UniversityShenzhen 518055China

Volatile organic compounds(VOCs)are ubiquitous organic pollutants affecting atmospheric environment and human *** development of new efficient and environmentally friendly materials utilizing photothermal synergistic catalysis for purification of VOCs is still ***,we design and prepare a core–shell TiN@TiO_(2)nanostructure integrating with nanoscaled Pt(Pt/[TiN@TiO_(2)])by an attractive quenching *** strong light-harvesting capability of Pt and TiN components improve light-to-heat utilization efficiency by their intrinsic surface plasmon resonance *** TiO_(2)component upon the surface and the coexisting coupling effect of Pt0 and Pt2+enhance the photocatalytic effect of the *** a result,the catalytic performance is significantly improved with toluene(120 ppm)conversion of 100%under the gas hourly space velocity of 72,000 mL·g^(−1)·h^(−1)and light illumination of 500 mW·cm^(−2).The desired catalyst thus achieves highly efficient coupling effect of photocatalysis and light-to-heat conversion for promoting VOCs abatement.

关键词： core-shell Pt/[TiN@TiO_(2)]catalyst volatile organic compounds(VOCs)abatement photothermal synergistic catalysis full spectrum sunlight utilization

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Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis

arXiv

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arXiv 2023年

作者： Li, Jiahe Zhang, Jiawei Bai, Xiao Zhou, Jun Gu, Lin School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University China School of Information and Communication Technology Griffith University Australia RIKEN AIP Japan The University of Tokyo Japan

This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based architecture for talking portrait synthesis that can concurrently achieve fast convergence, real-time rendering, and state-of-the-art performance with small model size. Our idea is to explicitly exploit the unequal contribution of spatial regions to guide talking portrait modeling. Specifically, to improve the accuracy of dynamic head reconstruction, a compact and expressive NeRF-based Tri-Plane Hash Representation is introduced by pruning empty spatial regions with three planar hash encoders. For speech audio, we propose a Region Attention Module to generate region-aware condition feature via an attention mechanism. Different from existing methods that utilize an MLP-based encoder to learn the cross-modal relation implicitly, the attention mechanism builds an explicit connection between audio features and spatial regions to capture the priors of local motions. Moreover, a direct and fast Adaptive Pose Encoding is introduced to optimize the head-torso separation problem by mapping the complex transformation of the head pose into spatial coordinates. Extensive experiments demonstrate that our method renders better high-fidelity and audio-lips synchronized talking portrait videos, with realistic details and high efficiency compared to previous methods. Code is available at https://***/Fictionarry/ ER-NeRF. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning

arXiv

引用

arXiv 2023年

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, The Authors. All rights reserved.

关键词： Students

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Heterophily-Aware Graph Attention Network

SSRN

引用

SSRN 2023年

作者： Wang, Junfu Guo, Yuanfang Yang, Liang Wang, Yunhong State Key Laboratory of Software Development Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China School of Artificial Intelligence Hebei University of Technology Tianjin300401 China

Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning. Unfortunately, current weight assignment schemes in standard GNNs, such as the calculation based on node degrees or pair-wise representations, can hardly be effective in processing the networks with heterophily, in which the connected nodes usually possess different labels or features. Existing heterophilic GNNs tend to ignore the modeling of heterophily of each edge, which is also a vital part in tackling the heterophily problem. In this paper, we firstly propose a heterophily-aware attention scheme and reveal the benefits of modeling the edge heterophily, i.e., if a GNN assigns different weights to edges according to different heterophilic types, it can learn effective local attention patterns, which enable nodes to acquire appropriate information from distinct neighbors. Then, we propose a novel Heterophily-Aware Graph Attention Network (HA-GAT) by fully exploring and utilizing the local distribution as the underlying heterophily, to handle the networks with different homophily ratios. To demonstrate the effectiveness of the proposed HA-GAT, we analyze the proposed heterophily-aware attention scheme and local distribution exploration, by seeking for an interpretation from their mechanism. Extensive results demonstrate that our HA-GAT achieves state-of-the-art performances on eight datasets with different homophily ratios in both the supervised and semi-supervised node classification tasks. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Masked Facial Region Recognition Using Human Pose Estimation and Broad Learning System 14

Masked Facial Region Recognition Using Human Pose Estimation...

引用

14th International Conference on Digital Image Processing, ICDIP 2022

作者： Xiao, Hongli Wang, Bingshu Zheng, Jiangbin Fang, Jin Liu, Zhulin Philip Chen, C.L. School of Software Northwestern Polytechnical University Xi’an710129 China State Key Lab of IOTSC University of Macau 999078 China School of Computer Science and Engineering South China University of Technology Guangzhou510641 China Pazhou Lab Guangzhou510335 China

ISBN: (纸本)9781510657564

COVID-19 and its variants have been posing a large risk to people around the world since the outbreak of the disease. Many techniques like AI are explored to help combat epidemics. People are required or forced to wear a mask to fight against COVID-19 epidemics worldwide. It brings new challenges to the task of masked facial region recognition. When facial regions are occluded by masks, it will result in some failures of face detection algorithms. In this paper, we propose a method to recognize masked faces. It mainly includes three parts. Firstly, the human pose is estimated to produce a series of key points. It is implemented by OpenPose. Secondly, a key-points location strategy is designed to capture the masked facial regions. It can locate the positions of faces accurately. Thirdly, the broad learning system, which is also an incremental learning algorithm, is employed to recognize the classes of candidate regions. Experiments conducted on some datasets shed light on the effectiveness of the proposed method. © 2022 SPIE.

关键词： Learning systems

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