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arXiv 2024年

作者： Yang, Xinquan Li, Xuguang Luo, Xiaoling Zeng, Leilei Zhang, Yudi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China

Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been applied to assist the dentist quickly locates the implant position, most of them are not able to determine the implant depth. Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone. TPNet consists of an implant region detector (IRD) and an implant depth prediction network (IDPNet). IRD is an object detector designed to crop the candidate implant volume from the CBCT, which greatly saves the computation resource. IDPNet takes the cropped CBCT data to predict the implant depth. A Texture Perceive Loss (TPL) is devised to enable the encoder of IDPNet to perceive the texture variation among slices. Extensive experiments on a large dental implant dataset demonstrated that the proposed TPNet achieves superior performance than the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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BurstBalancer: Do Less, Better Balance for Large-scale data Center Traffic

BurstBalancer: Do Less, Better Balance for Large-scale Data ...

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International Conference on Network Protocols

作者： Zirui Liu Yikai Zhao Zhuochen Fan Tong Yang Xiaodong Li Ruwen Zhang Kaicheng Yang Zheng Zhong Yi Huang Cong Liu Jing Hu Gaogang Xie Bin Cui National Engineering Laboratory for Big Data Analysis Technology and Application School of Computer Science Peking University Beijing China Peng Cheng Laboratory Shenzhen China Huawei Technologies CNIC CAS

ISBN: (数字)9781665482349

ISBN: (纸本)9781665482356

Layer-3 load balancing is a key topic in the networking field. It is well acknowledged that flowlet is the most promising solution because of its good trade-off between load balance and packet reordering. However, we find its one significant limitation: it makes the forwarding paths of flows unpredictable. To address this limitation, this paper presents BurstBalancer, a simple yet efficient load balancing system with a sketch, named BalanceSketch. Our design philosophy is doing less changes to keep the forwarding path of most flows fixed, which guides the design of BalanceSketch and balance operations. We have fully implemented BurstBalancer in a small-scale testbed built with Tofino switches, and conducted large-scale NS-2 simulations. Our results show that BurstBalancer achieves 5%∼35% smaller FCT than LetFlow in symmetric topology and up to 30× smaller FCT in asymmetric topology, while 58× fewer flows suffer from path changing. All related codes are open-sourced at Github 2 2 https://***/BurstBalancer/Burst-Balancer.

关键词： data centers Philosophical considerations Protocols Codes Network topology Load management Topology

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arXiv

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arXiv 2024年

作者： Wang, Yuhe Wang, Yating National & Local Joint Engineering Laboratory for Big Data Analysis and Computing Technology Beijing100190 China Institute for Scientific Computation Texas A&M University College StationTX77843 United States School of Mathematics and Statistics Xi’an Jiaotong University Shaanxi Xi’an710049 China

Fluid flow through bimodal porous media, characterized by a distinct separation in pore size distribution, is critical in various scientific and engineering applications, including groundwater management, oil and gas production, and carbon sequestration. This note delves into the physical similarity of fluid flow within such media, bridging the gap between microscale phenomena and macroscale observations. We present a representative mathematical model that conceptualizes bimodal porous media as a double-continuum system, distinguishing between macroporous and microporous regions. The model captures the complex interactions between these regions, particularly focusing on the challenges of modeling fluid flow when there is significant disparity in pore sizes. By employing a heuristic approach grounded in pore-scale tomography, we derive governing equations that describe fluid flow and analyze the solution characteristics. The results reveal unique features of the fluid flow in bimodal systems, such as the occurrence of boundary discontinuities and the delayed transient response, which are not observed in conventional porous media. This work provides ground for further studies in bimodal porous media, offering insights that could enhance predictive modeling and optimization in various applications concerning porous media with similar bimodal pore size distributions. © 2024, CC BY.

关键词： Transient analysis

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Model Degradation Hinders Deep Graph Neural Networks

arXiv

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arXiv 2022年

作者： Zhang, Wentao Sheng, Zeang Yin, Ziqi Jiang, Yuezihan Xia, Yikuan Gao, Jun Yang, Zhi Cui, Bin School of CS & Key Laboratory of High Confidence Software Technologies Peking University China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Beijing Institute of Technology China

Graph Neural Networks (GNNs) have achieved great success in various graph mining tasks. However, drastic performance degradation is always observed when a GNN is stacked with many layers. As a result, most GNNs only have shallow architectures, which limits their expressive power and exploitation of deep neighborhoods. Most recent studies attribute the performance degradation of deep GNNs to the over-smoothing issue. In this paper, we disentangle the conventional graph convolution operation into two independent operations: Propagation (P) and Transformation (T). Following this, the depth of a GNN can be split into the propagation depth (Dp ) and the transformation depth (Dt ). Through extensive experiments, we find that the major cause for the performance degradation of deep GNNs is the model degradation issue caused by large Dt rather than the over-smoothing issue mainly caused by large Dp. Further, we present Adaptive Initial Residual (AIR), a plug-and-play module compatible with all kinds of GNN architectures, to alleviate the model degradation issue and the over-smoothing issue simultaneously. Experimental results on six real-world datasets demonstrate that GNNs equipped with AIR outperform most GNNs with shallow architectures owing to the benefits of both large Dp and Dt, while the time costs associated with AIR can be ignored. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced Graph Sample and Aggregate Inductive Learning Algorithm

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

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Research of mask wearing detection method using Improved YOLO network

Research of mask wearing detection method using Improved YOL...

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Chinese Control and Decision Conference, CCDC

作者： Fei Xiao Zixi Liu Shengbin Zhao Shuai Yuan School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China Daqing Oilfield Limited Company DaQing China School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Mask wearing detection technology is a practical and research-oriented area within object detection. Due to the complexity of mask wearing detection scenarios, issues such as small target detection and low image clarity often result in missed detections and false alarms. The YOLO algorithm, known for its fast detection speed, is commonly used in mask wearing detection. However, the original YOLO algorithm struggles to achieve satisfactory results in dense crowd scenarios. Therefore, this paper proposes an improved YOLOv5 algorithm based on the BiFPN structure and incorporating attention mechanisms to enhance the accuracy of mask wearing detection while maintaining real-time performance. The proposed approach firstly introduces the Bidirectional Feature Pyramid (BiFPN) structure into the backbone network to enhance the feature extraction capability. Additionally, attention mechanisms are incorporated to filter out irrelevant information, conserve computational resources, and improve the detection performance for small targets. Experimental results on a custom dataset demonstrate that the YOLOv5_BiFPN algorithm achieves a 4% improvement in accuracy and an 11.7% increase in mAP50 compared to the original YOLOv5. Moreover, the inclusion of attention mechanisms further boosts the accuracy by an additional 1.6%. These findings confirm that the improved YOLOv5s model exhibits superior performance in the task of mask wearing detection.

关键词： YOLO Attention mechanisms Accuracy Filtering algorithms Feature extraction Information filters Real-time systems

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Graph Condensation for Inductive Node Representation Learning

arXiv

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arXiv 2023年

作者： Gao, Xinyi Chen, Tong Zang, Yilong Zhang, Wentao Hung Nguyen, Quoc Viet Zheng, Kai Yin, Hongzhi The University of Queensland Brisbane Australia Kaiserslautern Germany Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China Griffith University Gold Coast Australia University of Electronic Science and Technology of China Chengdu China

Graph neural networks (GNNs) encounter significant computational challenges when handling large-scale graphs, which severely restricts their efficacy across diverse applications. To address this limitation, graph condensation has emerged as a promising technique, which constructs a small synthetic graph for efficiently training GNNs while retaining performance. However, due to the topology structure among nodes, graph condensation is limited to condensing only the observed training nodes and their corresponding structure, thus lacking the ability to effectively handle the unseen data. Consequently, the original large graph is still required in the inference stage to perform message passing to inductive nodes, resulting in substantial computational demands. To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning. This enables direct information propagation on the synthetic graph, which is much more efficient than on the original large graph. Specifically, MCond employs an alternating optimization scheme with innovative loss terms from transductive and inductive perspectives, facilitating the mutual promotion between graph condensation and node mapping learning. Extensive experiments demonstrate the efficacy of our approach in inductive inference. On the Reddit dataset, MCond achieves up to 121.5× inference speedup and 55.9× reduction in storage requirements compared with counterparts based on the original graph. Copyright © 2023, The Authors. All rights reserved.

关键词： Condensation

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Graph Condensation for Inductive Node Representation Learning

Graph Condensation for Inductive Node Representation Learnin...

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International Conference on data engineering

作者： Xinyi Gao Tong Chen Yilong Zang Wentao Zhang Quoc Viet Hung Nguyen Kai Zheng Hongzhi Yin The University of Queensland Brisbane Australia German Research Centre for Artificial Intelligence (DFKI) Kaiserslautern Germany Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China Griffith University Gold Coast Australia University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

关键词： Training Representation learning Social networking (online) Network topology Message passing data engineering Robustness

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Bayesian Optimization of Double-SAC Robotic Arm Path Planning Using Recent Prioritized Experience Replay

Bayesian Optimization of Double-SAC Robotic Arm Path Plannin...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Shuai Yuan Ke Wen Feng Zhang Jianqiang Xiong School of Computer Science and Engineering Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shengyang China School of Electrical and Control Engineering Shenyang Jianzhu University Shengyang China

As for the path planning of robotic arms, Deep Reinforcement Learning (DRL) algorithms have recently been developed. However, the high-dimensional action space and state space will result in reward sparsity and low training efficiency of robotic arm path planning. We propose a Bayesian optimization of Double-SAC algorithm using recent priority experience replay to solve these issues. We first propose the sampling method of recent prioritized experience replay (RPER), which combines recent sampling and prioritized experience replay (PER). The interval of experience pool is divided according to the recent sampling method, and sampling is performed according to the sample priority of the interval. Then we adopt a Bayesian optimization method that reduces the variance of the random strategy and improve the quality of the sampled actions. The larger variance is more beneficial for exploring the environment. Smaller variance is advantageous for exploiting good experiences to obtain higher returns. Our approach finds a good balance between exploration and exploitation. We train two Soft Actor Critic (SAC) agents to obtain two independent probability distributions, and a Bayesian optimizer is adopted to combine the two distributions to design a hybrid strategy. The hybrid strategy can output better actions according to the uncertainty estimation of the distribution, and further enhance training efficiency and stability. Finally, simulation tests are performed on the ROS-Gazebo platform, and the simulation results illustrate that the algorithm proposed in this paper converges faster and has a shorter training time than the original algorithm in terms of path planning performance.

关键词：

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LECF: recommendation via learnable edge collaborative filtering

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Science China(Information Sciences) 2022年第1期65卷 116-130页

作者： Shitao XIAO Yingxia SHAO Yawen LI Hongzhi YIN Yanyan SHEN Bin CUI School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications School of Information Technology and Electrical Engineering The University of Queensland Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Technology Peking University National Engineering Laboratory for Big Data Analysis and Applications Peking University

The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering(CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering(LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges(i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.

关键词： collaborative filtering recommender system implicit feedback edge-edge similarity line graph

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