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Tensor Graph Convolutional Network for Dynamic Graph Representation Learning

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arXiv 2024年

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network and sequence neural network, which model spatial-temporal dependencies through two different types of neural networks. However, this hybrid design cannot well capture the spatial-temporal continuity of a DG. In this paper, we propose a tensor graph convolutional network to learn DG representations in one convolution framework based on the tensor product with the following two-fold ideas: a) representing the information of DG by tensor form;b) adopting tensor product to design a tensor graph convolutional network modeling spatial-temporal feature simultaneously. Experiments on real-world DG datasets demonstrate that our model obtains state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

GrabDAE: An Innovative Framework for Unsupervised Domain Adaptation Utilizing Grab-Mask and Denoise Auto-Encoder

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arXiv 2024年

作者： Chen, Junzhou Wen, Xuan Zhang, Ronghui Ren, Bingtao Wu, Di Xu, Zhigang Wang, Danwei The Guangdong Provincial Key Laboratory of Intelligent Transport System School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China The School of Transportation Science and Engineering Beihang University State Key Lab of Intelligent Transportation System Beijing100191 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangdong510006 China The School of Information Engineering Chang’an University Shaanxi Xi’an710064 China The School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore

Unsupervised Domain Adaptation (UDA) aims to adapt a model trained on a labeled source domain to an unlabeled target domain by addressing the domain shift. Existing Unsupervised Domain Adaptation (UDA) methods often fall short in fully leveraging contextual information from the target domain, leading to suboptimal decision boundary separation during source and target domain alignment. To address this, we introduce GrabDAE, an innovative UDA framework designed to tackle domain shift in visual classification tasks. GrabDAE incorporates two key innovations: the Grab-Mask module, which blurs background information in target domain images, enabling the model to focus on essential, domain-relevant features through contrastive learning;and the Denoising Auto-Encoder (DAE), which enhances feature alignment by reconstructing features and filtering noise, ensuring a more robust adaptation to the target domain. These components empower GrabDAE to effectively handle unlabeled target domain data, significantly improving both classification accuracy and robustness. Extensive experiments on benchmark datasets, including VisDA-2017, OfficeHome, and Office31, demonstrate that GrabDAE consistently surpasses state-of-the-art UDA methods, setting new performance benchmarks. By tackling UDA’s critical challenges with its novel feature masking and denoising approach, GrabDAE offers both significant theoretical and practical advancements in domain adaptation. © 2024, CC BY-NC-SA.

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An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis

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arXiv 2024年

作者： Li, Jinli Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

High-dimensional and incomplete (HDI) matrix contains many complex interactions between numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model is remarkably effective in extracting valuable information from an HDI matrix. However, such a model commonly encounters the problem of slow convergence because a standard SGD algorithm only considers the current learning error to compute the stochastic gradient without considering the historical and future state of the learning error. To address this critical issue, this paper innovatively proposes an ADRC-incorporated SGD (ADS) algorithm by refining the instance learning error by considering the historical and future state by following the principle of an ADRC controller. With it, an ADS-based LFA model is further achieved for fast and accurate latent factor analysis on an HDI matrix. Empirical studies on two HDI datasets demonstrate that the proposed model outperforms the state-of-the-art LFA models in terms of computational efficiency and accuracy for predicting the missing data of an HDI matrix. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

Remote Image Acquisition System Based on STM32 for Spodoptera frugiperda

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Remote Image Acquisition System Based on STM32 for Spodopter...

IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Jumei Chang Quanyuan Xu Ying Lu Hanrui Zhang College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Key Laboratory of Forestry and Ecological Big Data State Forestry Administration Southwest Forestry University China

ISBN: (纸本)9781665479691

To enable remote image acquisition and wireless transmission for field sex lure monitoring of Spodoptera frugiperda, the design consists of a CPU processor module, power supply module, image acquisition module, 4G wireless network transmission module, and external SRAM module. The system employs the STM32F407 control core, the OV2640 camera for image acquisition, the JPEG algorithm to compress and decode the image data collected by the camera, and automatically restore the image collected at the monitoring point, the EC20 as a 4G wireless communication network for image data transmission, and the transmission of real-time image data to the server side of the upper computer to achieve human-computer interaction and real-time interaction of system daemons. The experimental results show that the system achieves remote image acquisition of grassland moths and reliably transmits the collected image data to the server side of the upper computer via a 4G wireless network, allowing the monitor to view images of grassland moth monitoring points collected by the system in real-time and save the collected image data locally. This system accomplishes the automation and intelligence of the grassland moth monitoring system, prepares the data for subsequent grassland moth research, and has a broad application promise for grassland moth population monitoring.

关键词： Costs Automation Wireless networks data visualization Cameras Real-time systems Hardware

Circular Decomposition and Cross-Modal Recombination for Multimodal Sentiment Analysis

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Circular Decomposition and Cross-Modal Recombination for Mul...

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

作者： Haijian Liang Weicheng Xie Xilin He Siyang Song Linlin Shen School of Computer Science & Software Engineering Computer Vision Institue Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Computing and Mathematical Sciences University of Leicester National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

Multimodal Sentiment Analysis is a burgeoning research area, leveraging various modalities to predict the sentiment score. Nevertheless, previous studies have disregarded the impact of noise interference on specific modal sentiments during video recording, thereby compromising the accuracy of sentiment prediction. In this paper, we propose the Guided Circular Decomposition and Cross-Modal Recombination (GCD-CMR) model, which aims to eliminate contaminated sentiment features in a fine-grained way. To achieve this, we utilize tailored global information specific to each modality to guide the circular decomposing process in the GCD module, to produce a set of sentiment prototypes. Subsequently, in the CMR module, we align cross-modal sentiment prototypes and remove the contaminated prototypes for recombination. Experimental results on two publicly available datasets demonstrate that our model surpasses state-of-the-art models, confirming the effectiveness of our proposed method. We release the code at: https://***/nianhua20/GCD-CMR.

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Trim21 depletion alleviates bone loss in osteoporosis via activation of YAP1/β-catenin signaling

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Bone Research 2023年第4期11卷 819-833页

作者： Ri-Xu Liu Rong-He Gu Zhi-Peng Li Zhi-Quan Hao Qin-Xiao Hu Zhen-Yan Li Xiao-Gang Wang Wang Tang Xiao-He Wang Yu-Kai Zeng Zhen-Wei Li Qiu Dong Xiao-Feng Zhu Di Chen Ke-Wei Zhao Rong-Hua Zhang Zhen-Gang Zha Huan-Tian Zhang Department of Bone and Joint Surgery the First Affiliated Hospital of Jinan UniversityKey Laboratory of Regenerative Medicine of Ministry of EducationJinan UniversityGuangzhou 510630 GuangdongChina Department of Orthopedic and Spine Surgery The First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou 510120 GuangdongChina School of Basic Medical Sciences of Guangxi Medical University the Fifth Affiliated Hospital of Guangxi Medical UniversityNanning 530022 GuangxiChina Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang University100191 BeijingChina Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization College of PharmacyJinan UniversityGuangzhou 510630 GuangdongChina Research Center for Computer-aided Drug Discovery Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences518005 ShenzhenChina Guangzhou Key Laboratory of Chinese Medicine Research on Prevention and Treatment of Osteoporosis the Third Affiliated Hospital of Guangzhou University of Chinese Medicine510375 GuangzhouChina

Despite the diverse roles of tripartite motif(Trim)-containing proteins in the regulation of autophagy,the innate immune response,and cell differentiation,their roles in skeletal diseases are largely *** recently demonstrated that Trim21 plays a crucial role in regulating osteoblast(OB)differentiation in ***,how Trim21 contributes to skeletal degenerative disorders,including osteoporosis,remains ***,human and mouse bone specimens were evaluated,and the results showed that Trim21 expression was significantly elevated in bone tissues obtained from osteoporosis ***,we found that global knockout of the Trim21 gene(KO,Trim2^(1-/-))resulted in higher bone mass compared to that of the control *** further demonstrated that loss of Trim21 promoted bone formation by enhancing the osteogenic differentiation of bone marrow mesenchymal stem cells(BMSCs)and elevating the activity of OBs;moreover,Trim21 depletion suppressed osteoclast(OC)formation of RAW264.7 *** addition,the differentiation of OCs from bone marrow-derived macrophages(BMMs)isolated from Trim21^(-/-)and Ctsk-cre;Trim21^(f/f)mice was largely compromised compared to that of the littermate control ***,YAP1/β-catenin signaling was identified and demonstrated to be required for the Trim21-mediated osteogenic differentiation of *** importantly,the loss of Trim21 prevented ovariectomy(OVX)-and lipopolysaccharide(LPS)-induced bone loss in vivo by orchestrating the coupling of OBs and OCs through YAP1 *** current study demonstrated that Trim21 is crucial for regulating OB-mediated bone formation and OC-mediated bone resorption,thereby providing a basis for exploring Trim21 as a novel dual-targeting approach for treating osteoporosis and pathological bone loss.

关键词： YAP1 osteoporosis depletion

Realizing Emotional Interactions to Learn User Experience and Guide Energy Optimization for Mobile Architectures 22

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Realizing Emotional Interactions to Learn User Experience an...

Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Xueliang Li Zhuobin Shi Junyang Chen Yepang Liu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation and Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781665462723

In the age of AI, mobile architectures such as smartphones are still "cold machines"; machines do not feel. If the architecture is able to feel users' feelings and runtime user experience (UX), it will accordingly adapt performance/energy to find the optimal system-operating state that consumes the least energy to satisfy users. In this paper, we will utilize users' facial expressions (FEs) to learn their runtime UX. We know that FEs are the natural and direct way for humans to convey their emotions and feelings. Our study reveals that FEs also reflect UX. Our research for the first time quantifies the link between FEs and UX. Leveraging this link, the architecture will be able to use the front camera to see FEs and feel users' UX. Based on UX, the architecture can appropriately provision computing resources. We propose Vi-energy system to realize the above idea. Our evaluation shows that Vi-energy reduces energy consumption by 52.9% at maximum and secures UX.

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A Fast and Effective Multiple Kernel Clustering Method on Incomplete data

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Computers, Materials & Continua 2021年第4期67卷 267-284页

作者： Lingyun Xiang Guohan Zhao Qian Li Gwang-Jun Kim Osama Alfarraj Amr Tolba Changsha Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha410114China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems Changsha University of Science and TechnologyChangsha410114China Faculty of Engineering and Information Technology Global Big Data Technologies CentreUniversity of Technology SydneyUltimoNSW2007Australia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoua UniversityShebin-El-kom32511Egyp

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be ***,multiple kernel clustering for incomplete data is a critical yet challenging *** the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local *** address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete *** AMKC method rst clusters the initialized incomplete ***,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination ***,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel *** three stages in this process are carried out simultaneously until the convergence condition is *** on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art ***,the proposed method gains fast convergence speed.

关键词： Multiple kernel clustering absent-kernel imputation incomplete data kernel k-means clustering

GLCONet: Learning Multi-source Perception Representation for Camouflaged Object Detection

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arXiv 2024年

作者： Sun, Yanguang Xuan, Hanyu Yang, Jian Luo, Lei The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China The School of Big Data and Statistics Anhui University Hefei230601 China

Recently, biological perception has been a powerful tool for handling the camouflaged object detection (COD) task. However, most existing methods are heavily dependent on the local spatial information of diverse scales from convolutional operations to optimize initial features. A commonly neglected point in these methods is the long-range dependencies between feature pixels from different scale spaces that can help the model build a global structure of the object, inducing a more precise image representation. In this paper, we propose a novel Global-Local Collaborative Optimization Network, called GLCONet. Technically, we first design a collaborative optimization strategy from the perspective of multi-source perception to simultaneously model the local details and global long-range relationships, which can provide features with abundant discriminative information to boost the accuracy in detecting camouflaged objects. Furthermore, we introduce an adjacent reverse decoder that contains cross-layer aggregation and reverse optimization to integrate complementary information from different levels for generating high-quality representations. Extensive experiments demonstrate that the proposed GLCONet method with different backbones can effectively activate potentially significant pixels in an image, outperforming twenty state-of-the-art methods on three public COD datasets. The source code is available at: https://***/CSYSI/GLCONet. © 2024, CC BY.

关键词： Object detection