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Incompressible Limit of the Oldroyd-B Model with Density-Dependent Viscosity

Journal of Applied Mathematics and Physics 2023年第4期11卷 949-971页

作者： Qingliu Li Dandan Ren School of Mathematics and Big Data Anhui University of Science and Technology Huainan China

This paper studies the existence and uniqueness of local strong solutions to an Oldroyd-B model with density-dependent viscosity in a bounded domain Ω ⊂ Rd, d = 2 or 3 via incompressible limit, in which the initial d... 详细信息

This paper studies the existence and uniqueness of local strong solutions to an Oldroyd-B model with density-dependent viscosity in a bounded domain Ω ⊂ R^d, d = 2 or 3 via incompressible limit, in which the initial data is “well-prepared” and the velocity field enjoys the slip boundary conditions. The main idea is to derive the uniform energy estimates for nonlinear systems and corresponding incompressible limit.

关键词： Incompressible Limit Oldroyd-B Model Slip Boundary Condition Density-Dependent Viscosity

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Few-Shot Image Classification Based on Ensemble Metric Learning 2

Few-Shot Image Classification Based on Ensemble Metric Learn...

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2nd International Conference on Computer, big data and Artificial Intelligence, ICCBDAI 2021

作者： Wang, Hang Chen, Duanbing Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Chengdu Union Big Data Tech. Inc. Chengdu610041 China

In the case of few labelled image data samples, image classification is a difficult challenge, which is called few-shot image classification. Recently, many methods based on metric learning have been proposed. Most of these methods mainly focus on the representations of global image-level features or local feature-level descriptors. However, these methods calculate similarity from a single metric learning perspective. Motivated by ensemble learning, a novel Ensemble Metric Learning (EML) method for few-shot image classification is proposed, which not only utilizes label propagation, but also considers image-level and local feature-level descriptor metrics. The experimental results show that the proposed method can effectively improve the classification accuracy by ensemble learning. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Image classification

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Fast Parallel Recovery for Transactional Stream Processing on Multicores 40

Fast Parallel Recovery for Transactional Stream Processing o...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhao, Jianjun Liu, Haikun Zhang, Shuhao Duan, Zhuohui Liao, Xiaofei Jin, Hai Zhang, Yu School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350317152

Transactional stream processing engines (TSPEs) have gained increasing attention due to their capability of processing real-time stream applications with transactional semantics. However, TSPEs remain susceptible to system failures and power outages. Existing TSPEs mainly focus on performance improvement, but still face a significant challenge to guarantee fault tolerance while offering high-performance services. We revisit commonly-used fault tolerance approaches in stream processing and database systems, and find that these approaches do not work well on TSPEs due to complex data dependencies. In this paper, we propose a novel TSPE called MorphStreamR to achieve fast failure recovery while guaranteeing low performance overhead at runtime. The key idea of MorphStreamR is to record intermediate results of resolved dependencies at runtime, and thus eliminate data dependencies to improve task parallelism during failure recovery. MorphStreamR further mitigates the runtime overhead by selectively tracking data dependencies and incorporating workload-aware log commitment. Experimental results show that MorphStreamR can significantly reduce the recovery time by up to 3.1 x while experiencing much less performance slowdown at runtime, compared with other applicable fault tolerance approaches. © 2024 IEEE.

关键词： Fault tolerance

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Research on Probability of Discharge in Emergency Departments Based on Stochastic Timed Petri Nets and Machine Learning 21

Research on Probability of Discharge in Emergency Department...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Yinglong Yu, Wangyang Fang, Xianwen Meng, Lei Cheng, Yumeng Zhang, Jing School of Computer Science Shaanxi Normal University Xi'an China School of Mathematics and Big Data Anhui University of Science and Technology Huainan China Qinghai Normal University Information Network Center Xining China

ISBN: (纸本)9798350365221

Currently, factors such as hidden health hazards, unhealthy lifestyles, and environmental pollution have led to the frequent occurrence of diseases, causing a common problem - a sharp increase in hospital admissions. However, for emergency departments (EDs), their medical resources and scale are insufficient to meet the challenges of this conventional hospital level. Thus, modeling the diagnostic process of the ED, and applying an appropriate method to predict the probability of discharges within a certain time, is of significant importance for achieving rational resource allocation. Effective allocation of medical resources can ensure that patients receive timely and effective medical care, improve the quality and level of medical services, and thereby enhance patient satisfaction with medical care. Therefore, the modeling tool for this paper is stochastic timed Petri nets (STPN), in order to more accurately simulate various random events and delays that occur within the ED. Additionally, for better analysis of the model, we will convert the time interval values assigned to transitions into time interval values assigned to places, creating a Petri net known as TSdPN. Using Net learning method to predict patient discharge probabilities helps healthcare providers allocate beds, resources, and follow-up medical services more accurately, thereby improving the efficiency of medical resource utilization. Combining these two approaches makes ED operations more intelligent and efficient, providing patients with a better quality medical experience. © 2024 IEEE.

关键词： Diagnosis

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EEG-Based Joint Semi-supervised Learning for major Depressive Disorder Detection 26th

EEG-Based Joint Semi-supervised Learning for Major Depressi...

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Chen, Tao Zheng, Tong Shi, Jinlong Guo, Yanrong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9783031376597

major Depressive Disorder (MDD) has been a major mental disease in recent years, imposing huge negative impacts on both our society and individuals. The current clinical MDD detection methods, such as self-report scales and interviews, often suffer from their subjectiveness. In contrast, electroencephalography (EEG) signals reflect brain activity and can be used as an effective information source for MDD detection. However, the learning-based MDD detection methods with EEG data usually neglect the valuable information contained in the unlabeled data, therefore possibly suffering from the label scarcity issue. In this context, we propose a simple EEG-based Joint Semi-supervised Learning (JSL) model for the MDD detection task, which fully leverages the information from unlabeled data. Specifically, by equipping the unlabeled samples with the intermediately predicted pseudo labels, the accurate position of the class-specific centroids in the feature space can be gradually refined. Based on that, the auxiliary labels based on a heuristic centroid-based classifier can be obtained, which plays an effective complementary role in predicting the sample labels. The experiments on a public dataset demonstrate the effectivenes s of our method, showing an obvious performance gain via using the proposed semi-supervised strategy. © 2023, Springer Nature Switzerland AG.

关键词： Electroencephalography

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Interactive Visualization of Temporal Brain Connectivity data based-on Frequent Feature Mining 35

Interactive Visualization of Temporal Brain Connectivity Dat...

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35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Zhang, Guangwei Jing, Ming Liu, Yunjing Zhang, Li Dong, Anming Yu, Jiguo School of Computer Science and Technology Qilu University of Technology Jinan China Big Data Institute Qilu University of Technology Jinan China Shandong Fundamental Research Center for Computer Science Jinan China

Medical data visualization is instrumental in assisting disease diagnosis and exploring brain function and structure. In this paper, we constructed a brain connectivity network using changes in BOLD signals at different time intervals and identified frequent characteristics to help doctors quickly pinpoint areas of interest. To study the changes in connectivity between brain regions, we visualize frequent sequences and compare them, highlighting important temporal features of patient brain areas. This makes the study and analysis of fMRI data more convenient and assists doctors in investigating abnormalities in the connections between brain functional areas. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Brain

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A Framework for Multiclass Contour Visualization

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IEEE Transactions on Visualization and Computer Graphics 2023年第1期29卷 353-362页

作者： Li, Sihang Yu, Jiacheng Li, Mingxuan Liu, Le Zhang, Xiaolong Luke Yuan, Xiaoru Laboratory of Machine Perception Ministry of Education School of AI Peking University China Center for Data Science Academy for Advanced Interdisciplinary Studies Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China School of Computer Science Northwestern Polytechnical University China College of Information Sciences and Technology Pennsylvania State University United States

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization. © 2022 IEEE.

关键词： data visualization

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DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment 38

DFA-GNN: Forward Learning of Graph Neural Networks by Direct...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Gongpei Wang, Tao Lang, Congyan Jin, Yi Li, Yidong Ling, Haibin Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education China School of Computer Science & Technology Beijing Jiaotong University China Department of Computer Science Stony Brook University United States

Graph neural networks (GNNs) are recognized for their strong performance across various applications, with the backpropagation (BP) algorithm playing a central role in the development of most GNN models. However, despite its effectiveness, BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. While several non-backpropagation (non-BP) training algorithms, such as the direct feedback alignment (DFA), have been successfully applied to fully-connected and convolutional network components for handling Euclidean data, directly adapting these non-BP frameworks to manage non-Euclidean graph data in GNN models presents significant challenges. These challenges primarily arise from the violation of the independent and identically distributed (i.i.d.) assumption in graph data and the difficulty in accessing prediction errors for all samples (nodes) within the graph. To overcome these obstacles, in this paper we propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning. The proposed method breaks the limitations of BP by using a dedicated forward training mechanism. Specifically, DFA-GNN extends the principles of DFA to adapt to graph data and unique architecture of GNNs, which incorporates the information of graph topology into the feedback links to accommodate the non-Euclidean characteristics of graph data. Additionally, for semi-supervised graph learning tasks, we developed a pseudo error generator that spreads residual errors from training data to create a pseudo error for each unlabeled node. These pseudo errors are then utilized to train GNNs using DFA. Extensive experiments on 10 public benchmarks reveal that our learning framework outperforms not only previous non-BP methods but also the standard BP methods, and it exhibits excellent robustness against various types of noise and attacks. © 2024 Neura

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An activated variable parameter gradient‐based neural network for time‐variant constrained quadratic programming and its applications

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CAAI Transactions on Intelligence technology 2023年第3期8卷 670-679页

作者： Guancheng Wang Zhihao Hao Haisheng Li Bob Zhang PAMI Research Group Department of Computer and Information ScienceUniversity of MacaoTaipaMacaoChina China Industrial Control Systems Cyber Emergency Response Team BeijingChina Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijingChina

This study proposes a novel gradient‐based neural network model with an activated variable parameter,named as the activated variable parameter gradient‐based neural network(AVPGNN)model,to solve time‐varying constrained quadratic programming(TVCQP)*** with the existing models,the AVPGNN model has the following advantages:(1)avoids the matrix inverse,which can significantly reduce the computing complexity;(2)introduces the time‐derivative of the time‐varying param-eters in the TVCQP problem by adding an activated variable parameter,enabling the AVPGNN model to achieve a predictive calculation that achieves zero residual error in theory;(3)adopts the activation function to accelerate the convergence *** solve the TVCQP problem with the AVPGNN model,the TVCQP problem is transformed into a non‐linear equation with a non‐linear compensation problem function based on the Karush Kuhn Tucker ***,a variable parameter with an activation function is employed to design the AVPGNN *** accuracy and convergence rate of the AVPGNN model are rigorously analysed in ***,numerical experiments are also executed to demonstrate the effectiveness and superiority of the proposed ***,to explore the feasibility of the AVPGNN model,appli-cations to the motion planning of a robotic manipulator and the portfolio selection of marketed securities are illustrated.

关键词： computational intelligence mathematics computing optimisation

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Car Quality Prediction Based on Multiple Machine Learning Algorithms

Car Quality Prediction Based on Multiple Machine Learning Al...

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Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC), Asia-Pacific Conference on

作者： Jiayu Liu Department of Data Science and Big Data Technology with Contemporary Entrepreneurialism Xi’an Jiaotong-liverpool University Suzhou China

ISBN: (数字)9798350393682

ISBN: (纸本)9798350393699

As technology advances, the role that cars play in our daily lives increases. Both car manufactures and customers are eager to know about the car quality to help them choose the car they like. In this paper, three models were used to train and find the relationship between the car attributes and labels to predict the car quality. The dataset was found in the UCI dataset at first. Next the attributes and label of the data were changed into string type to fulfill the requirement of training the machine learning model. Then the dataset was divided into the training set and testing set to fit the model. The machine learning algorithms used in the article includes the Decision Tree model, Random Forest model, Extra Tree model. These models are used to train and test the dataset respectively and the accuracy of each model can be obtained. Finally, the most suitable hyperparameters in each model were found by using the validation curve. Experimental results indicate that the Random Forest model’s precision can reach to 85.66% and it is the most accurate one among three models. Thus, The Random Forest model would help the customers and manufacturers to have the better car.

关键词： Training Machine learning algorithms Accuracy Predictive models data models Automobiles Decision trees

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