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Exploring implicit influence for social recommendation based on GNN

Soft computing 2024年第19期28卷 11001-11014页

作者： Liu, Zhewei Yang, Peilin Hao, Qingbo Zheng, Wenguang Xiao, Yingyuan Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

In recent years, the method of using graph neural networks (GNN) to learn users’ social influence has been widely applied to social recommendation and has shown effectiveness, but several important challenges have not been well addressed: (i) Most work fails to consider the user interests (historical user-item interactions) when first building user-user social relationships, which can make it difficult to capture accurate user embedding and thus prevent the model from better exploring the users’ social influence;(ii) Most of the current methods do not build social neighbors (with the same item-user interaction) that belong to the item and do not aggregate information from the perspective of social neighbors, which makes it possible for the item to lose a lot of details when expressing the user’s interest factors. Therefore, to address the above challenges, we propose Exploring Implicit Influence for Social Recommendation Based on GNN (EIIGNN). First, we construct the initial user embedding with user-item interaction information and use the implicit modeling module in user modeling to explore the implicit influence of interest factors on users. In addition, EIIGNN models the social graph structure of item (an item-item graph) so that item can aggregate information from the perspective of their social neighbors, which helps the model learn a more accurate representation of the item. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of EIIGNN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graph neural networks

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Elimination of Interference Analysis for RIS Element Demage 7

Elimination of Interference Analysis for RIS Element Demage

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Min, Sipei Yu, Canping Li, Liping Li, Yingsong Zlatanov, Nikola Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China Innopolis University Faculty of Computer and Engineering Sciences Russia

ISBN: (纸本)9798350384437

This paper aims to investigate the ability of reconfigurable intelligent surfaces (RIS) damaged in multi-user environments to eliminate interference. Our research found that even if the RIS is damaged, interference can still be eliminated in the absence of a direct path and when the number of RIS is sufficient. In order to solve interference cancellation, we adopted the alternating projection algorithm, which ensures local convergence and good convergence speed. © 2024 IEEE.

关键词： Interference suppression

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Real-to-Sim Grasp: Rethinking the Gap between Simulation and Real World in Grasp Detection 8

Real-to-Sim Grasp: Rethinking the Gap between Simulation and...

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8th Conference on Robot Learning, CoRL 2024

作者： Cai, Jia-Feng Chen, Zibo Wu, Xiao-Ming Jiang, Jian-Jian Wei, Yi-Lin Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works bridge this gap with a sim-to-real way1. However, this way explicitly or implicitly forces the simulated data to adapt to the noisy real data when training grasp detectors, where the positional drift and structural distortion within the camera noise will harm the grasp learning. In this work, we propose a Real-to-Sim framework for 6-DoF Grasp detection, named R2SGrasp, with the key insight of bridging this gap in a real-to-sim way2, which directly bypasses the camera noise in grasp detector training through an inference-time real-to-sim adaption. To achieve this real-to-sim adaptation, our R2SGrasp designs the Real-to-Sim Data Repairer (R2SRepairer) to mitigate the camera noise of real depth maps in data-level, and the Real-to-Sim Feature Enhancer (R2SEnhancer) to enhance real features with precise simulated geometric primitives in feature-level. To endow our framework with the generalization ability, we construct a large-scale simulated dataset cost-efficiently to train our grasp detector, which includes 64,000 RGB-D images with 14.4 million grasp annotations. Sufficient experiments show that R2SGrasp is powerful and our real-to-sim perspective is effective. The real-world experiments further show great generalization ability of R2SGrasp. Project page is available on https://***/R2SGrasp. © 2024 Proceedings of Machine Learning Research.

关键词： Image annotation

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Parallel finite element computation of incompressible magnetohydrodynamics based on three iterations

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Applied Mathematics and Mechanics(English Edition) 2022年第1期43卷 141-154页

作者： Qili TANG Yunqing HUANG Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationSchool of Mathematics and Computational ScienceXiangtan UniversityXiangtanHunan Province411105China

Based on local algorithms,some parallel finite element(FE)iterative methods for stationary incompressible magnetohydrodynamics(MHD)are *** approaches are on account of two-grid skill include two major phases:find the FE solution by solving the nonlinear system on a globally coarse mesh to seize the low frequency component of the solution,and then locally solve linearized residual subproblems by one of three iterations(Stokes-type,Newton,and Oseen-type)on subdomains with fine grid in parallel to approximate the high frequency *** error estimates with regard to two mesh sizes and iterative steps of the proposed algorithms are *** numerical examples are implemented to verify the algorithm.

关键词： local and parallel algorithm finite element(FE)method iteration stationary incompressible magnetohydrodynamics(MHD)

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MS-SAM: Multi-scale SAM Based on Dynamic Weighted Agent Attention 31st

MS-SAM: Multi-scale SAM Based on Dynamic Weighted Agent Att...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Yang, Enhui Zhang, Zhibin Engineering Research Center of Ecological Big Data Ministry of Education Key Laboratory of Wireless Networks and Mobile Computing Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9789819620630

The Segment Anything Model (SAM), introduced in 2023, has made significant advancements in the field of computer vision. However, SAM faces two major challenges: limitations related to single-scale processing and high computational demands for large-scale data handling, which restrict its widespread adoption in practical applications. Therefore, we propose MS-SAM to achieve better segmentation performance at a lower cost. We introduce a multi-scale image encoder to handle images of varying scales and complexities. Additionally, we propose a dynamic weighted agent attention, which can flexibly and efficiently adjust attention distribution while reducing computational costs. We train MS-SAM on a 1/20 subset of the SA-1B dataset for instance segmentation using points or boxes as prompts. The results show that we maintain the accuracy with the SAM method while reducing the parameters by about 6 times. We give sufficient experimental results to demonstrate its effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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A novel policy iteration algorithm for solving the optimal consensus control problem of a discrete-time multiagent system with unknown dynamics

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Science China(Information Sciences) 2023年第8期66卷 269-270页

作者： Wenkai XU Li WANG Shiwen SUN Chengyi XIA Zengqiang CHEN Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology The Engineering Research Center of Learning-Based Intelligent System Ministry of Education College of Artificial Intelligence Nankai University

At present, an increasing number of researchers have noticed the importance of optimal consensus control(OCC) of multiagent systems(MASs) because of their rich practical applications in various areas [1–4]. To accomp...

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Harnessing Dimensional Contrast and Information Compensation for Sentence Embedding Enhancement

Harnessing Dimensional Contrast and Information Compensation...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： He, Kang Ding, Yuzhe Li, Bobo Wang, Haining Li, Fei Teng, Chong Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China

ISBN: (纸本)9798350368741

Unsupervised sentence embedding learning excels through positive sample construction and instance-level contrastive learning (ICL). However, this approach can lead to over-compression and dimensional contamination from noisy data augmentation and unconstrained ICL processes. To mitigate these issues, we propose a novel enhancement method, MSSE, which incorporates an Information Compensation Mechanism (ICM) and a Dimensional-Level Contrastive Learning Mechanism (DCM). ICM, inspired by the information bottleneck principle, prevents excessive compression in representation learning. DCM constrains ICL and minimizes information leakage across dimensions. Experimental results show that MSSE surpasses existing competitive baselines across seven STS tasks, including unsupervised and few-shot scenarios. The source code is available at https://***/Hekang001/MSSE. © 2025 IEEE.

关键词： contrastive learning natural language processing Sentence embedding unsupervised learning

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Intracellularly synthesized ssDNA for continuous genome engineering

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Trends in Biotechnology 2024年第6期43卷 1356-1370页

作者： Liu, Wenqian Pan, Yingjia Zhang, Yu Dong, Chang Huang, Lei Lian, Jiazhang Key Laboratory of Biomass Chemical Engineering of Ministry of Education and State Key Laboratory of Biobased Transportation Fuel Technology College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China BGI Research Hangzhou310030 China ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310000 China

Despite the prevalence of genome editing tools, there are still some limitations in dynamic and continuous genome editing. In vivo single-stranded DNA (ssDNA)-mediated genome mutation has emerged as a valuable and promising approach for continuous genome editing. In this review, we summarize the various types of intracellular ssDNA production systems and notable achievements in genome engineering in both prokaryotic and eukaryotic cells. We also review progress in the development of applications based on retron-based systems, which have demonstrated significant potential in molecular recording, multiplex genome editing, high-throughput functional variant screening, and gene-specific continuous in vivo evolution. Furthermore, we discuss the major challenges of ssDNA-mediated continuous genome editing and its prospects for future applications. © 2024 Elsevier Ltd

关键词： Genome

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Two-Stage Submodular Maximization Under Knapsack Problem

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Tsinghua Science and Technology 2024年第6期29卷 1703-1708页

作者： Zhicheng Liu Jing Jin Donglei Du Xiaoyan Zhang Beijing Institute for Scientific and Engineering Computing Beijing University of TechnologyBeijing 100124China College of Taizhou Nanjing Normal UniversityTaizhou 225300China Faculty of Management University of New BrunswickFrederictonE3B 5A3Canada School of Mathematical Science Institute of Mathematicsand Ministry of Education Key Laboratory of NSLSCSNanjing Normal UniversityNanjing 210023China

Two-stage submodular maximization problem under cardinality constraint has been widely studied in machine learning and combinatorial *** this paper,we consider knapsack *** this problem,we give n articles and m categories,and the goal is to select a subset of articles that can maximize the function F(S).Function F(S)consists of m monotone submodular functions fj,j=1,2,…,m,and each fj measures the similarity of each article in category *** present a constant-approximation algorithm for this problem.

关键词： submodular function knapsack constraint matroid

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Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data 38

Revealing Distribution Discrepancy by Sampling Transfer in U...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhao, Zhilin Cao, Longbing Fan, Xuhui Zheng, Wei-Shi School of Computing Macquarie University Australia School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

There are increasing cases where the class labels of test samples are unavailable, creating a significant need and challenge in measuring the discrepancy between training and test distributions. This distribution discrepancy complicates the assessment of whether the hypothesis selected by an algorithm on training samples remains applicable to test samples. We present a novel approach called Importance Divergence (I-Div) to address the challenge of test label unavailability, enabling distribution discrepancy evaluation using only training samples. I-Div transfers the sampling patterns from the test distribution to the training distribution by estimating density and likelihood ratios. Specifically, the density ratio, informed by the selected hypothesis, is obtained by minimizing the Kullback-Leibler divergence between the actual and estimated input distributions. Simultaneously, the likelihood ratio is adjusted according to the density ratio by reducing the generalization error of the distribution discrepancy as transformed through the two ratios. Experimentally, I-Div accurately quantifies the distribution discrepancy, as evidenced by a wide range of complex data scenarios and tasks. © 2024 Neural information processing systems foundation. All rights reserved.

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