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arXiv 2025年

作者： Chen, Luxi Zhou, Zihan Zhao, Min Wang, Yikai Zhang, Ge Huang, Wenhao Sun, Hao Wen, Ji-Rong Li, Chongxuan Gaoling School of AI Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China Dept. of Comp. Sci. & Tech. BNRist Center THU-Bosch MLCenter Tsinghua University China School of Artificial Intelligence Beijing Normal University China ByteDance Seed

Generating flexible-view 3D scenes, including 360° rotation and zooming, from single images is challenging due to a lack of 3D data. To this end, we introduce FlexWorld, a novel framework consisting of two key components: (1) a strong video-to-video (V2V) diffusion model to generate high-quality novel view images from incomplete input rendered from a coarse scene, and (2) a progressive expansion process to construct a complete 3D scene. In particular, leveraging an advanced pre-trained video model and accurate depth-estimated training pairs, our V2V model can generate novel views under large camera pose variations. Building upon it, FlexWorld progressively generates new 3D content and integrates it into the global scene through geometry-aware scene fusion. Extensive experiments demonstrate the effectiveness of FlexWorld in generating high-quality novel view videos and flexible-view 3D scenes from single images, achieving superior visual quality under multiple popular metrics and datasets compared to existing state-of-the-art methods. Qualitatively, we highlight that FlexWorld can generate high-fidelity scenes with flexible views like 360° rotations and zooming. Project page: https://***/FlexWorld. Copyright © 2025, The Authors. All rights reserved.

关键词： 3D modeling

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RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers

arXiv

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arXiv 2025年

作者： Zhao, Min He, Guande Chen, Yixiao Zhu, Hongzhou Li, Chongxuan Zhu, Jun Dept. of Comp. Sci. & Tech. BNRist Center THU-Bosch ML Center Tsinghua University China ShengShu China The University of Texas Austin United States Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods China China

Recent advancements in video generation have enabled models to synthesize high-quality, minute-long videos. However, generating even longer videos with temporal coherence remains a major challenge and existing length extrapolation methods lead to temporal repetition or motion deceleration. In this work, we systematically analyze the role of frequency components in positional embeddings and identify an intrinsic frequency that primarily governs extrapolation behavior. Based on this insight, we propose RIFLEx, a minimal yet effective approach that reduces the intrinsic frequency to suppress repetition while preserving motion consistency, without requiring any additional modifications. RIFLEx offers a true free lunch—achieving high-quality 2× extrapolation on state-of-the-art video diffusion transformers in a completely training-free manner. Moreover, it enhances quality and enables 3× extrapolation by minimal fine-tuning without long videos. Project page and codes: https://***/. © 2025, CC BY.

关键词： Extrapolation

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Unsupervised social network embedding via adaptive specific mappings

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Frontiers of Computer Science 2024年第3期18卷 61-71页

作者： Youming GE Cong HUANG Yubao LIU Sen ZHANG Weiyang KONG School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006China

In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction ***,the non-linear property of node attributes and network structure is not efficiently fused in existing methods,which is potentially helpful in learning a better network *** this end,in this paper,we propose a novel model called ASM(Adaptive Specific Mapping)based on encoder-decoder *** encoder,we use the kernel mapping to capture the non-linear property of both node attributes and network *** particular,we adopt two feature mapping functions,namely an untrainable function for node attributes and a trainable function for network *** the mapping functions,we obtain the low dimensional feature vectors for node attributes and network structure,***,we design an attention layer to combine the learning of both feature vectors and adaptively learn the node *** encoder,we adopt the component of reconstruction for the training process of learning node attributes and network *** conducted a set of experiments on seven real-world social network *** experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.

关键词： network embedding specific kernel mapping attention mechanism

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A Causal Adversarial Graph Neural Network for Multi-Center Autism Spectrum Disorder Identification

SSRN

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SSRN 2025年

作者： Zhang, Zhuan Wang, Lihui Chen, Qijian Wang, Li Jian, Caiqing Zhu, Yue-Min Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Due to the heterogeneous distributions of multi-center rs-fMRI data, currently it is still challenging to identify accurately autism spectrum disorder (ASD) patients from these heterogenous data. To deal with the influence of heterogeneous distributions on multi-center ASD diagnosis, a novel causal adversarial graph neural network (CAG) for multi-center ASD prediction is proposed in this work. CAG mainly consists of a graph filtering (GF) module and a learnable causal graph predictor (CGP). Specifically, the brain connection graph is first constructed from rs-fMRI data, after passing a GF module, the filtered graph is further divided by a causal subgraph and causally irrelevant subgraph. The ground-truth environmental labels and ASD labels are predicted from causal subgraph by a clustering method and a classification block, respectively. In addition, from the filtered graph and causally irrelevant subgraph, the environmental labels are predicted, while from the causally irrelevant subgraph the ASD labels are predicted. Assuming that the predicted environmental labels are independent to causal subgraph, while the predicted ASD labels are independent to causally irrelevant subgraph, accordingly, several adversarial losses are implemented by reversing the gradient. Through this adversarial learning strategy, the derived causal graph can predict multi-center ASD. The comparison experiments on both ABIDE-I and ABIDE-II datasets demonstrate that, the ACC and AUC of CAG has been improved by 3.78\% and 7.38\% at least comparing against the existing methods. Moreover, some abnormal functional connectivities in ASD group are identified from the causal subgraph which may provide useful imaging biomarkers for the early diagnosis of ASD. © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Demand Learning and Cooperative Deployment of UAV Networks

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Chinese Journal of Electronics 2022年第3期31卷 408-415页

作者： ZHANG Xiao WANG Xuehe XU Xinping ZHAO Yingchao South-Central University for Nationalities Caritas Institute of Higher Education Sun Yat-sen University & Guangdong Key Laboratory of Big Data Analysis and Processing Nanyang Technological University

Unmanned aerial vehicle(UAV) as a powerful tool has found its applicability in assisting mobile users to deal with computation-intensive and delaysensitive applications(e.g., edge computing, high-speed Internet access, and local caching). However, deployment of UAV-aided mobile services(UMS) faces challenges due to the UAV limitation in wireless coverage and energy storage. Aware of such physical limitations, a future UMS system should be intelligent enough to self-plan trajectories and best offer computational capabilities to mobile users. There are important issues regarding the UAV-user interaction, UAV-UAV cooperation for sustainable service provision, and dynamic UMS pricing. These networking and resource management issues are largely overlooked in the literature and this article presents intelligent solutions for cooperative UMS deployment and operation. Mobile users' locations are generally private information and changing over time. How to learn on-demand users' truthful location reporting is important for determining optimal UAV deployment in serving all the users fairly. After addressing the truthful UAV-user interaction issue via game theory, we further study the UAV network sustainability for UMS provision by minimizing the energy consumption cost during deployment and seeking UAV-UAV cooperation. Finally, for profit-maximizing purpose, we analyze the cooperative UAVs' deployment, capacity allocation, and dynamic service pricing.

关键词： UAV network sustainability UMS provision local caching path planning delay-sensitive applications future UMS system UAV-UAV cooperation Internet access wireless coverage mobile robots energy storage optimal UAV deployment Internet control engineering computing autonomous aerial vehicles unmanned aerial vehicle dynamic service pricing learning (artificial intelligence) game theory mobile computing UAV-aided mobile services edge computing UMS deployment sustainable service provision resource allocation UAV-user interaction issue on-demand users resource management issues dynamic UMS pricing computation-intensive applications

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Leapfrog Polymorphic Neural Ordinary Differential Equation

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IEEE Signal processing Letters 2025年 32卷 2139-2143页

作者： Zhang, Xiao Wei, Wei Zhang, Zhen Zhang, Lei Northwestern Polytechnical University School of Software Xi'an710072 China Northwestern Polytechnical University Shaanxi Provincial Key Laboratory of Speech and Image Information Processing School of Computer Science Xi'an710072 China Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Xi'an710072 China The University of Adelaide school of computer science AdelaideSA5005 Australia

Neural Ordinary Differential Equations (NODEs) revolutionize the way we view residual networks as solvers for initial value problems (IVPs), with layer depth serving as the time step. In this study, we propose a more efficient extension of NODEs called Leap-Frog Polymorphic Neural ODEs (LF-NODEs). LF-NODEs introduce the leap-frog updating scheme, breaking free from the specific structure of time-evolving mixtures of multiple dynamical systems. In each time step (corresponding to each residual network layer), LF-NODEs integrate the features of multiple dynamical systems (residual network layers) by employing time-variant weights from different dynamical systems. Our LF-NODEs not only overcome the limitations in the representation power of individual residual networks but also provide a more flexible and effective structure for residual networks based on multiple dynamical systems. The efficiency of the leap-frog updating scheme is theoretically derived and demonstrated. Furthermore, we expand the model by incorporating a multi-scale approach (LF-MSNODEs) to achieve even more accurate model representations. Empirical results showcase the improvements provided by our proposed methods in 2D concentric spheres task, irregularly sampled time series prediction, and classification tasks. Additionally, the results provide empirical evidence that the learned feature space enhances system efficiency with fewer parameters and function evaluations compared to the baseline methods. © 1994-2012 IEEE.

关键词： Ordinary differential equations

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Tensor joint framework enhanced by game-theoretic constrained optimization for skin cancer forecasting

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Swarm and Evolutionary Computation 2025年 96卷

作者： Yang, Jinqian Wen, Jie Zhao, Haochen Cai, Jianghui Cai, Xingjuan Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan030024 China School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan030024 China School of Computer Science and Technology North University of China Taiyuan030051 China

Skin cancer is a public health concern due to its high incidence and detection challenges. While tensor decomposition is widely utilized to predict miRNA-disease associations, existing models are not optimized for skin cancer, thereby limiting their comprehensiveness and interpretability. To address these limitations, we propose a many-objective tensor joint framework aimed at enhancing prediction accuracy while maintaining comprehensiveness and interpretability. The proposed framework classifies tensors according to their similarity to skin cancer and employs a many-objective optimization algorithm to optimize weight allocation. Similarity constraints for skin cancer are applied to prioritize relevant information, effectively minimizing noise from unrelated diseases. Furthermore, we introduce a multi-stage constrained many-objective optimization algorithm based on game theory. This algorithm leverages game theory to dynamically adjust population diversity, convergence, and constraint violations throughout the evolutionary process, thereby improving the overall framework's performance. Experimental results demonstrate that the proposed algorithm outperforms existing state-of-the-art constrained many-objective optimization algorithms on benchmarks such as MW and DTLZ. Compared to other tensor decomposition methods, the proposed framework achieves a comprehensive improvement ranging from 8.49% to 11.53% across four objectives. © 2025

关键词： Cellular automata

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ZuantuSet: A Collection of Historical Chinese Visualizations and Illustrations

arXiv

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arXiv 2025年

作者： Mei, Xiyao Zhang, Yu Yang, Chaofan Shi, Rui Yuan, Xiaoru National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Department of Computer Science University of Oxford Oxford United Kingdom Center for Research on Ancient Chinese History Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China PKU-WUHAN Institute for Artificial Intelligence Wuhan China

Historical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historical visualizations within the European cultural framework, this work shifts the focus to ancient China, a cultural context that remains underexplored by visualization researchers. To this aim, we propose a semi-automatic pipeline to collect, extract, and label historical Chinese visualizations. Through the pipeline, we curate ZuantuSet, a dataset with over 71K visualizations and 108K illustrations. We analyze distinctive design patterns of historical Chinese visualizations and their potential causes within the context of Chinese history and culture. We illustrate potential usage scenarios for this dataset, summarize the unique challenges and solutions associated with collecting historical Chinese visualizations, and outline future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： data collection

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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

作者： Pei Zhang Dong Wang Chanyue Wu Jing Yang Lei Kang Zongwen Bai Ying Li Qiang Shen School of Computer Science Northwestern Polytechnical University Xi’an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an University Yan’an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd Qingdao China Department of Computer Science Aberystwyth University UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques.

关键词： Training Transfer learning Self-supervised learning Transformers Diffusion models Real-time systems Decoding Computational efficiency Speech processing Hyperspectral imaging

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Non-SPF routing algorithm based on ordered semi-group preference algebra

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The Journal of China Universities of Posts and Telecommunications 2017年第6期24卷 14-23页

作者： Zhang Yongtang Fan Bo Department of Computer Science and Technology Guangdong Neusoft Institute Foshan 528225 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006 China

Layer 2 network technology is extending beyond its traditional local area implementation and finding wider acceptance in provider＇s metropolitan area networks and large-scale cloud data center networks. This is mainly due to its plug-and-play capability and native mobility support. Many efforts have been put to increase the bisection bandwidth in layer 2 network, which has been constrained by the spanning tree protocol （STP） that layer 2 network uses for preventing looping. The recent trend is to incorporate layer 3＇s routing approach into layer 2 network so that multiple paths can be used for forwarding traffic between any source-destination （S-D） node pair. Equal cost multipath （ECMP） is one such example. However, ECMP may still be limited in generating multiple paths due to its shortest path （lowest cost） requirement. In this paper, we consider a non-shortest-path routing approach, called equal preference multipath （EPMP） based on ordered semi group theory, which can generate more paths than ECMP. In EPMP routing, all the paths with different traditionally-defined costs, such as hops, bandwidth, etc., can be determined equally now and thus they become equal candidate paths. By the comparative tests with ECMP, EPMP routing not only generates more paths, provides 15% higher bisection bandwidth, but also identifies bottleneck links in a hierarchical network when different traffic patterns are applied EPMP is more flexible in controlling the number and length of multipath generation. Simulation results indicate the effectiveness of the proposed algorithm. It is a good reference for non-blocking running of big datacenter networks.

关键词： non-SPF routing algorithm algebraic routing equal preference multipath datacenter networks

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