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TSC-PCAC: Voxel Transformer and Sparse Convolution-Based Point Cloud Attribute Compression for 3D Broadcasting

IEEE TRANSACTIONS ON BROADCASTING 2025年第1期71卷 154-166页

作者： Guo, Zixi Zhang, Yun Zhu, Linwei Wang, Hanli Jiang, Gangyi Sun Yat sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen 518017 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai 200092 Peoples R China Ningbo Univ Fac Informat & Sci & Engn Ningbo 315211 Peoples R China

Point cloud has been the mainstream representation for advanced 3D applications, such as virtual reality and augmented reality. However, the massive data amounts of point clouds is one of the most challenging issues for transmission and storage. In this paper, we propose an end-to-end voxel Transformer and Sparse Convolution based Point Cloud Attribute Compression (TSC-PCAC) for 3D broadcasting. Firstly, we present a framework of the TSC-PCAC, which includes Transformer and Sparse Convolutional Module (TSCM) based variational autoencoder and channel context module. Secondly, we propose a two-stage TSCM, where the first stage focuses on modeling local dependencies and feature representations of the point clouds, and the second stage captures global features through spatial and channel pooling encompassing larger receptive fields. This module effectively extracts global and local inter-point relevance to reduce informational redundancy. Thirdly, we design a TSCM based channel context module to exploit inter-channel correlations, which improves the predicted probability distribution of quantized latent representations and thus reduces the bitrate. Experimental results indicate that the proposed TSC-PCAC method achieves an average of 38.53%, 21.30%, and 11.19% bitrate reductions on datasets 8iVFB, Owlii, 8iVSLF, Volograms, and MVUB compared to the Sparse-PCAC, NF-PCAC, and G-PCC v23 methods, respectively. The encoding/decoding time costs are reduced 97.68%/98.78% on average compared to the Sparse-PCAC. The source code and the trained TSC-PCAC models are available at https://***/igizuxo/TSC-PCAC.

关键词： Point cloud compression Image coding Convolution Three-dimensional displays Geometry Transforms Transformers voxel transformer sparse convolution variational autoencoder channel context module

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A generalized zero-shot semantic learning model for batch process fault diagnosis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第1期36卷

作者： Liu, Kai Zhao, Xiaoqiang Mou, Miao Hui, Yongyong Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou Peoples R China Gansu Key Lab Adv Control Ind Proc Lanzhou Peoples R China Lanzhou Univ Technol Natl Expt Teaching Ctr Elect & Control Engn Lanzhou Peoples R China

In industrial monitoring, although zero-shot learning successfully solves the problem of diagnosing unseen faults, it is difficult to diagnose both unseen and seen faults. Motivated by this, we propose a generalized zero-shot semantic learning fault diagnosis model for batch processes called joint low-rank manifold distributional semantic embedding and multimodal variational autoencoder (mVAE). Firstly, joint low-rank representation and manifold learning makes the training samples map to the low-rank space, which obtains the global-local features of the samples while reducing the redundancy in the inputs for the training model;secondly, the bias of human-defined semantic attributes is corrected by predicting the attribute error rate;then, fault samples and corrected semantic vectors are embedded into the consistency space, in which the samples are reconstructed using the mVAE to fully integrate the cross-modal information, meanwhile, Barlow matrix is designed to measure the consistency between the fault samples and the attribute vectors, the higher the consistency, the higher the learning efficiency of attribute classifiers;finally, the generalized zero-shot fault diagnosis experiments are designed and conducted on the penicillin fermentation process and the semiconductor etching process to validate the effectiveness, the results show that the proposed model is indeed possible to diagnose target faults without their samples.

关键词： fault diagnosis batch process generalized zero-shot semantic correction variational autoencoder distributed semantic embedding

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A 1T1M-based efficient probability storage and computing cell for the VAE-SBN hybrid model

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PHYSICA SCRIPTA 2025年第1期100卷

作者： Dai, Yuehua Tan, Su Feng, Zhe Zou, Jianxun Guo, Wenbin Yu, Ruihan Wang, Zeqing Hu, Yang Wang, Haochen Ruan, Hao Hao, Yang Lin, Zhihao Xu, Zuyu Zhu, Yunlai Wu, Zuheng Anhui Univ Sch Integrated Circuits Hefei 230601 Anhui Peoples R China

Bayesian network are crucial components for uncertainty reasoning and knowledge representation in probabilistic graphical models. As model complexity increases, traditional digital methods face challenges in efficiency and resource utilization. In this work, a novel Bayesian network optimization method based on one-transistor one-memristor (1T1M) units for storing and accessing probability values was proposed. This method leverages the sigmoid-like characteristics of 1T1M for computational optimization, integrates probability storage into sigmoid belief network (SBN), and extends to the variational autoencoder (VAE) framework. By storing and manipulating probabilities at hardware level, this approach enhances storage efficiency and access speed while enhancing computational precision and model expressiveness through the sigmoid-like space of 1T1M. Validation experiments using the MNIST digit generation task demonstrate that this method exhibits unique advantages in SBN structures, particularly in handling complex conditional probabilities and inference tasks. Compared to traditional digital implementations, the proposed method exhibits potential advantages in inference speed and hardware utilization.

关键词： memristor probability storage bayesian network sigmoid belief network variational autoencoder image generation

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A hierarchical and interlamination graph self-attention mechanism-based knowledge graph reasoning architecture

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INFORMATION SCIENCES 2025年 686卷

作者： Wu, Yuejia Zhou, Jian-tao Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Infor Hohhot Peoples R China Minist Educ Engn Res Ctr Ecol Big Data Hohhot Peoples R China Inner Mongolia Engn Lab Cloud Comp & Serv Softwar Hohhot Peoples R China Inner Mongolia Key Lab Social Comp & Data Proc Hohhot Peoples R China Inner Mongolia Key Lab Discipline Inspect & Super Hohhot Peoples R China Inner Mongolia Engn Lab Big Data Anal Technol Hohhot Peoples R China

Knowledge Graph (KG) is an essential research field in graph theory, but its inherent incompleteness and sparsity influence its performance in several fields. Knowledge Graph Reasoning (KGR) aims to ameliorate those problems by mining new knowledge from subsistent knowledge. As one of the downstream tasks of KGR, link prediction is of great significance for improving the quality of KG. Recently, the Graph Neural Network (GNN)-based method became the most effective way to achieve the link prediction task. However, it still suffers from problems such as incomplete neighbor and relation-level information aggregation and unstable learning of the entity's features. To improve those issues, a Hierarchical and Interlamination Graph Self-attention Mechanism- based (HIGSM) plug-and-play architecture is proposed for KGR in this paper. It is composed of three-level layers: feature extractor, encoder, and decoder. The feature extractor makes our architecture more effective and stable for the retrieval of new features. The encoder is equipped with a two-stage encoding mechanism accompanied by two mixture-of-expert strategies, which enables our architecture to capture more practical reasoning information to improve prediction accuracy and generalization of the model. The decoder can use existing KGR models and compute the scores of triples in KG. The extensive experimental results and ablation studies on four KGs unambiguously demonstrate the state-of-the-art prediction performance of the proposed HIGSM architecture compared to current GNN-based methods.

关键词： Knowledge graph reasoning Graph self-attention mechanism variational autoencoder Mixture-of-expert Link prediction

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Application of Artificial Intelligence Virtual Image Technology in Photography Art Creation Under Deep Learning

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IEEE ACCESS 2025年 13卷 14542-14556页

作者： Yao, Qiong Mahasarakham Univ Fac Fine Appl Arts & Cultural Sci Maha Sarakham 44150 Thailand

With the continuous advancement of artificial intelligence (AI) and deep learning technologies, virtual image generation exhibits significant potential for application in photographic art creation. The primary objective of this study is to investigate the use of AI virtual image technology in photography, particularly focusing on achieving creative expression and artistic style transfer through deep learning models. Consequently, this study proposes a novel model that integrates conditional generative adversarial networks (cGANs) with variational autoencoders (VAEs). This model aims to effectively address the challenges associated with image generation and style conversion in photographic art by leveraging the realistic generation capabilities of cGANs alongside the diversity maintenance features of VAEs. In the experimental section, the proposed cGANs + VAEs model is systematically compared with traditional Deep Convolutional GANs (DCGAN) and Pix2Pix models through empirical analysis. The experimental results indicate that the cGANs + VAEs model significantly outperforms traditional models in terms of image quality, artistic expression, and user satisfaction. Expert reviews further confirm the model's superiority in artistic style imitation and creative generation. Additionally, user surveys reveal that most participants are highly satisfied with the images generated by the model, particularly regarding artistic perception and visual effects. Moreover, the cGANs + VAEs model demonstrates strong performance in Frechet Inception Distance (FID) and Inception Score (IS) across multiple datasets, yielding FID values of 13.67, 9.45, and 11.90 on the COCO, CelebA, and WikiArt datasets, respectively. In summary, the proposed cGANs + VAEs model not only achieves remarkable advancements in the technical performance of image generation but also exhibits considerable potential for practical applications in photographic art creation.

关键词： Image synthesis Deep learning Art Artificial intelligence Photography Training Diversity reception Data models Analytical models Translation conditional generative adversarial networks variational autoencoder photography artistic creation virtual image technology

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Asynchronous multimodal PINN pre-train framework based on TransVNet(MPP-TV) and its application in numerical solutions of the Cauchy problem for the Hamilton-Jacobi equation

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2025年 187卷 203-230页

作者： Chen, Tianhao Li, Zeyu Xu, Pengbo Zheng, Haibiao East China Normal Univ Sch Math Sci Key Lab MEA Shanghai Key Lab PMMPMinist Educ Shanghai 200241 Peoples R China Sanda Univ Innovat Res Ctr AI Finance Shanghai 201209 Peoples R China Shanghai Zhangjiang Inst Math Shanghai 201203 Peoples R China

The Hamilton-Jacobi(HJ) equation represents a class of highly nonlinear partial differential equations. Classical numerical techniques, such as finite element methods, face significant challenges when addressing the numerical solutions of such nonlinear HJ equations. However, recent advances in neural network-based approaches, particularly Physics-Informed Neural Networks (PINNs) and neural operator methods, have ushered in innovative paradigms for numerically solving HJ equations. In this work, we leverage the PINN approach, infused with the concept of neural operators. By encoding and extracting features from the discretized images of functions through TransVNet, which is a novel autoencoder architecture proposed in this paper, we seamlessly integrate Hamiltonian information into PINN training, thereby establishing a novel scientific computation framework. Additionally, we incorporate the vanishing viscosity method, introducing viscosity coefficients in our model, which equips our model to tackle potential singularities in nonlinear HJ equations. These attributes signify that our MPP-TV framework paves new avenues and insights for the generalized solutions of nonlinear HJ equations.

关键词： Physics-informed neural network Numerical methods for PDE Hamilton-Jacobi equation variational autoencoder

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Counterfactual experience augmented off-policy reinforcement learning

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NEUROCOMPUTING 2025年 637卷

作者： Lee, Sunbowen Gong, Yicheng Deng, Chao Wuhan Univ Sci & Technol Coll Sci Wuhan 430065 Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Prov Key Lab Syst Sci Met Proc Wuhan 430065 Hubei Peoples R China Wuhan Univ Sci & Technol Intelligent Automobile Engn Res Inst Sch Automobile & Traff Engn Wuhan 430065 Hubei Peoples R China

Reinforcement learning control algorithms face significant challenges due to out-of-distribution and inefficient exploration problems. While model-based reinforcement learning enhances the agent's reasoning and planning capabilities by constructing virtual environments, training such virtual environments can be very complex. In order to build an efficient inference model and enhance the representativeness of learning data, we propose the Counterfactual Experience Augmentation (CEA) algorithm. CEA leverages variational autoencoders to model the dynamic patterns of state transitions and introduces randomness to model non-stationarity. This approach focuses on expanding the learning data in the experience pool through counterfactual inference and performs exceptionally well in environments that follow the bisimulation assumption. Environments with bisimulation properties are usually represented by discrete observation and action spaces, we propose a sampling method based on maximum kernel density estimation entropy to extend CEA to various environments. By providing reward signals for counterfactual state transitions based on real information, CEA constructs a complete counterfactual experience to alleviate the out-of-distribution problem of the learning data, and outperforms general SOTA algorithms in environments with difference properties. Finally, we discuss the similarities, differences and properties of generated counterfactual experiences and real experiences. The code is available at https://***/Aegis1863/CEA.

关键词： Reinforcement learning variational autoencoder Counterfactual inference Bisimulation

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Multi-agent reinforcement learning for cooperative search under aperiodically intermittent communication

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 280卷

作者： Fu, Longyue Wang, Jiao Luo, Hongchen Northeastern Univ Coll Informat Sci & Engn 3-11 Wenhua Rd Shenyang 110819 Liaoning Peoples R China

Aperiodically Intermittent Communication (AIC) refers to the unpredictable irregular disruptions in exchange of information among agents. These disruptions pose significant challenges to multi-agent cooperative search tasks. The challenges are information loss, increased uncertainty, and reduced collaboration efficiency, thus limiting overall search performance. The Aperiodically Intermittent Communication Reinforcement Learning (AICoRL) algorithm is proposed to achieve multi-agent cooperative search under AIC to address these challenges. Firstly, AICoRL integrates an Adaptive Predictive variational autoencoder (AP-VAE) to reconstruct lost information during communication interruptions. Predicting uncertain information through adaptive communication states and global features enables agents to maintain an approximate global perspective. Secondly, to enhance agents' adaptability in complex dynamic environments, the policy network employs multi-agent reinforcement learning with noisy parameterization. It integrates individual observations with reconstructed features and encourages a more diverse exploration of the action space. Extensive experimental results demonstrate that AICoRL significantly outperforms traditional methods in the search task under AIC. Our algorithm achieves higher search efficiency, better reward performance, and stronger robustness. This framework demonstrates its potential for enhancing the performance of multi-agent systems operating under AIC.

关键词： Aperiodically intermittent communication Multi-agent reinforcement learning Multi-agent cooperative search variational autoencoder

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The Application of Lite-GRU Embedding and VAE-Augmented Heterogeneous Graph Attention Network in Friend Link Prediction for LBSNs

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4585-4585页

作者： Yang, Ziteng Li, Boyu Wang, Yong Liu, Aoxue China Univ Geosci Wuhan Sch Comp Sci Wuhan 430078 Peoples R China

Friend link prediction is an important issue in recommendation systems and social network analysis. In Location-Based Social Networks (LBSNs), predicting potential friend relationships faces significant challenges due to the diversity of user behaviors, along with the high dimensionality, sparsity, and complex noise in the data. To address these issues, this paper proposes a Heterogeneous Graph Attention Network (GEVEHGAN) model based on Lite Gate Recurrent Unit (Lite-GRU) embedding and variational autoencoder (VAE) enhancement. The model constructs a heterogeneous graph with two types of nodes and three types of edges;combines Skip-Gram and Lite-GRU to learn Point of Interest (POI) and user node embeddings;introduces VAE for dimensionality reduction and denoising of the embeddings;and employs edge-level attention mechanisms to enhance information propagation and feature aggregation. Experiments are conducted on the publicly available Foursquare dataset. The results show that the GEVEHGAN model outperforms other comparative models in evaluation metrics such as AUC, AP, and Top@K accuracy, demonstrating its superior performance in the friend link prediction task.

关键词： friend link prediction gated recurrent unit variational autoencoder heterogeneous graph attention mechanism

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A semi-supervised multiscale generalized-VAE framework for one-class classification

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NEUROCOMPUTING 2025年 620卷

作者： Sharma, Renuka Awate, Suyash P. Commonwealth Sci & Ind Res Org CSIRO Data61 Brisbane Australia Indian Inst Technol IIT Bombay Comp Sci & Engn CSE Dept Mumbai India IITB Monash Res Acad Mumbai India

Deep-learning based approaches for unsupervised anomaly detection typically learn either a generative model of the inlier class or a decision boundary to encapsulate the inlier class. In addition to the training data from the inlier class, the availability of a small amount of training data from the outlier class can aid in refining the classifier model using principles of semi-supervised learning. This paper proposes a novel end-to-end deep semi-supervised variational framework for one-class classification of images, leveraging data-adaptive generalized- Gaussian (GG) models leading to effective modeling of distributions in both latent space and image space. The framework proposes a novel variational encoder that models a distribution on a multiscale (here, "scale"refers to spatial resolution) latent-space encoding, together with a generalized reparameterization scheme for the GG model's sampling at each such scale. While the multiscale latent-space helps effective feature learning at coarse and fine spatial scales, the semi-supervision helps tune the feature learning to improve separability between the inliers and the outliers. Results on several publicly available industrial-imaging and medical-imaging datasets show the benefits of our framework's novel components over existing approaches.

关键词： One-class classification variational autoencoder Semi-supervision Multiscale latent space Generalized-Gaussian Robustness Uncertainty

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