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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Deng, Hao Jing, Kunlei Cheng, Shengmei Liu, Cheng Ru, Jiawei Bo, Jiang Wang, Lin State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University China Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality China School of Software Engineering Xi'an Jiaotong University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of downsampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7%, 81.4%, and 69.1% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available at https://***/DengH293/LinNet. © 2024 Neural information processing systems foundation. All rights reserved.

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The Propagation Strategy Model of Taint Analysis 4

The Propagation Strategy Model of Taint Analysis

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2019 4th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2019

作者： Ren, Yuzhu Zhao, Jiangtao Zhang, Cao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450000 China

The taint propagation strategy is the core of the taint analysis technology. When the taint analysis toolsanalyses the target program, it needs to mark the target data according to the formulatedtaint propagation strategy. Formulating a reasonable strategy for taint propagation can effectively improve the accuracy of taint analysis. There are two difficulties in developing the taint propagation strategy, namely, alias analysis in static taint analysis and indirect jump in dynamic taint analysis. In our paper, we propose a taint propagation strategy at the intermediate representation language level, which can effectively improve the accuracy of taint propagation. © Published under licence by IOP Publishing Ltd.

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Harnessing Graphene Plasmons by Accessing the Retardation Regime

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ACS Applied Materials and Interfaces 2025年第22期17卷 32782-32789页

作者： Zhang, Jiasheng Xing, Qiaoxia Zhao, Tuoyu Wang, Shunjia Ma, Junwei Wang, Chong Wang, Jiajun Xie, Yuangang Huang, Shenyang Song, Chaoyu Wan, Quan Shi, Lei Tao, Zhensheng Shi, Wu Li, Xuesong Zhou, Lei Yan, Hugen State Key Laboratory of Surface Physics Key Laboratory of Micro- and Nano-Photonic Structures Ministry of Education Shanghai Key Laboratory of Metasurfaces for Light Manipulation Department of Physics Fudan University Shanghai 200433 China Inner Mongolia Key Laboratory of Microscale Physics and Atom Innovation School of Physical Science and Technology Inner Mongolia University Hohhot 010021 China State Key Laboratory of Surface Physics Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai 200433 China Zhangjiang Fudan International Innovation Center Fudan University Shanghai 201210 China Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE) School of Physics Beijing Institute of Technology Beijing 100081 China Beijing Key Lab of Nanophotonics & Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing 100081 China School of Integrated Circuit Science and Engineering Exemplary School of Microelectronics University of Electronic Science and Technology of China Chengdu 611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen 518110 China

Plasmons in graphene are highly tunable, mainly by leveraging the carrier density and Drude scattering. Here, we introduce another scheme, i.e., accessing the retardation regime (originated from the finite speed of light), to engineer the lifetime and dispersion of plasmons in graphene in the terahertz regime. We find that the retardation regime can be approached by reducing plasmon momentum in artificially stacked multilayer graphene systems with large Drude weight, and this can significantly increase the plasmon lifetime. In addition, an explicit theoretical model along with finite-element simulation was given, consistent with the experimental findings. Our work opens another avenue to manipulate terahertz plasmons in graphene. © 2025 American Chemical Society.

关键词： Drude weight graphene plasmon retardation effects terahertz

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GLCF: A Global-Local Multimodal Coherence Analysis Framework for Talking Face Generation Detection

arXiv

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arXiv 2024年

作者： Chen, Xiaocan Yin, Qilin Liu, Jiarui Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China Alibaba Group Hangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China

Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating the urgent need for research into corresponding detection methods. However, research in this field has been hindered by the lack of public datasets. In this paper, we construct the first large-scale multi-scenario talking face dataset (MSTF), which contains 22 audio and video forgery techniques, filling the gap of datasets in this field. The dataset covers 11 generation scenarios and more than 20 semantic scenarios, closer to the practical application scenario of TFG. Besides, we also propose a TFG detection framework, which leverages the analysis of both global and local coherence in the multimodal content of TFG videos. Therefore, a region-focused smoothness detection module (RSFDM) and a discrepancy capture-time frame aggregation module (DCTAM) are introduced to evaluate the global temporal coherence of TFG videos, aggregating multi-grained spatial information. Additionally, a visual-audio fusion module (V-AFM) is designed to evaluate audiovisual coherence within a localized temporal perspective. Comprehensive experiments demonstrate the reasonableness and challenges of our datasets, while also indicating the superiority of our proposed method compared to the state-of-the-art deepfake detection approaches. © 2024, CC BY-NC-ND.

关键词： Semantics

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MC-RGCN: A Multi-Channel Recurrent Graph Convolutional Network to Learn High-Order Social Relations for Diffusion Prediction

MC-RGCN: A Multi-Channel Recurrent Graph Convolutional Netwo...

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IEEE International Conference on Data Mining (ICDM)

作者： Ningbo Huang Gang Zhou Mengli Zhang Meng Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665423991

Information diffusion prediction aims to predict the tendency of information spreading in the network. Previous methods focus on extracting chronological features from diffusion paths and leverage relations in social graph as side information to facilitate diffusion prediction. However, abundant high-order social relations in information diffusion have not been sufficiently utilized, such as co-repose and co-following which can further mine potential user common preferences. In this paper, we construct a heterogeneous diffusion network (HDN) from the social graph and information cascades to model the high-order social relations in information diffusion. Then, we design a novel model named Multi-Channel Recurrent Graph Convolutional Network (MC-RGCN), which can extract high-order social relation semantics from the channels of HDN to promote prediction performance. In each channel, we depict a specific social relations from the views of global topology, pairwise strength, and local structure. Finally, we conduct extensive experiments on three real-world datasets, and the results show that our proposed method outperforms the state-of-the-art models on diffusion prediction.

关键词： Network topology Conferences Semantics Predictive models Feature extraction Data models Topology

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ZeroDVS: Trace-Ability and Security Detection of Container Image Based on Inheritance Graph

ZeroDVS: Trace-Ability and Security Detection of Container I...

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Cryptography, Security and Privacy (CSP), IEEE International Conference on

作者： Yan Zheng Weiyu Dong Jiangtao Zhao State Key Laboratory of Mathematical Engineering and Advanced Computing ZhengZhou China

ISBN: (数字)9781728186214

ISBN: (纸本)9781728186221

Docker image is the foundation for container operation. Docker Hub is the largest online repository of public container images. Users can upload and download any random image file to the hub due to the absence of adequate security scanning and detection, potentially causing substantial security risks. This paper introduces ZeroDVS, a container image traceability and security detection system based on inheritance graphs. In ZeroDVS, a basic image inheritance graph is built with 160 official images published by Docker Hub. Then, the basic image source of the downloaded image can be identified, and the public vulnerability database is used to scan for public vulnerabilities in image files. ZeroDVS scans the public container images of Docker Hub and identifies the inheritance relationship of public container images to detect public vulnerabilities in the image layer above the parent image simultaneously. Docker image is the foundation for container operation. Docker Hub is the largest online repository of public container images. Users can upload and download any random image file to the hub due to the absence of adequate security scanning and detection, potentially causing substantial security risks. This paper introduces ZeroDVS, a container image traceability and security detection system based on inheritance graphs. In ZeroDVS, a basic image inheritance graph is built with 160 official images published by Docker Hub. Then, the basic image source of the downloaded image can be identified, and the public vulnerability database is used to scan for public vulnerabilities in image files. ZeroDVS scans the public container images of Docker Hub and identifies the inheritance relationship of public container images to detect public vulnerabilities in the image layer above the parent image simultaneously.

关键词： Privacy Databases Operating systems Containers Security Reliability Cryptography

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ThicknesVAE: Learning a Latera Prior for Clothed Human Body Reconsuction

ThicknesVAE: Learning a Latera Prior for Clothed Human Body ...

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

作者： Wu, Xiaotao Fan, Zhaoxin He, Huiguang Shen, Dinggang School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai201210 China NeuBCI Group State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Institute of Artificial Intelligence Beihang University Beijing100191 China Beijing Academy of Blockchain and Edge Computing China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 China Shanghai Clnical Research and Trial Center Shanghai201210 China

ISBN: (纸本)9798350368741

Sandwich-like structures have shown remarkable efficacy in clothed human reconstruction. However, these approaches often generate unrealistic side geometries due to inadequate handling of lateral regions. This paper addresses this limitation by incorporating the side geometry of clothed humans as a prior. We propose ThicknessVAE, a novel two-stage method that makes two key contributions: (1) We learn a prototype from point clouds for the lateral regions of clothed humans to extract common and detailed geometric features. (2) We utilize this prototype as a prior to transform geometric features into a thickness map associated with clothed human images, enabling refined normal integration for sandwich-like reconstruction methods. By seamlessly integrating our model into the sandwich-like reconstruction pipeline, we achieve highly realistic side views. Both qualitative and quantitative experiments demonstrate that our approach is comparable to state-of-the-art methods in terms of side-view realism. © 2025 IEEE.

关键词： 3D Human Reconsruction Normal Integraton Prior Learning

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mathematical modeling of lunar soft landing —Take chang'e-3 as an example

Mathematical modeling of lunar soft landing —Take chang'e-3...

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Benchao Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665428408

In this paper, the three-dimensional sphere dynamic model of lunar precise soft landing of the probe is discussed. Taking the National College Students' mathematical modeling competition as the background and Chang'e 3 lunar landing as an example, the lunar center inertial coordinate system and orbital coordinate system are established respectively to better describe the positional relationship between the moon and aircraft and the dynamic changes of aircraft. The coordinates of the near lunar point and the far lunar point are obtained. Through the changes of altitude, speed, pitch angle, yaw angle, longitude and latitude during landing, the landing preparation orbit of Chang'e-3 is determined. Then we analyze the six stages of chang'e-3 landing process one by one, and discuss the results.

关键词： Space vehicles Machine learning algorithms Moon Clustering algorithms mathematical models Data models Partitioning algorithms

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SUMI-IFL: An Information-Theoretic Framework for Image Forgery Localization with Sufficiency and Minimality Constraints

arXiv

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arXiv 2024年

作者： Sheng, Ziqi Lu, Wei Luo, Xiangyang Zhou, Jiantao Cao, Xiaochun School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University China State Key Laboratory of Mathematical Engineering and Advanced Computing China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus China

Image forgery localization (IFL) is a crucial technique for preventing tampered image misuse and protecting social safety. However, due to the rapid development of image tampering technologies, extracting more comprehensive and accurate forgery clues remains an urgent challenge. To address these challenges, we introduce a novel information-theoretic IFL framework named SUMI-IFL that imposes sufficiency-view and minimality-view constraints on forgery feature representation. First, grounded in the theoretical analysis of mutual information, the sufficiency-view constraint is enforced on the feature extraction network to ensure that the latent forgery feature contains comprehensive forgery clues. Considering that forgery clues obtained from a single aspect alone may be incomplete, we construct the latent forgery feature by integrating several individual forgery features from multiple perspectives. Second, based on the information bottleneck, the minimality-view constraint is imposed on the feature reasoning network to achieve an accurate and concise forgery feature representation that counters the interference of task-unrelated features. Extensive experiments show the superior performance of SUMI-IFL to existing state-of-the-art methods, not only on in-dataset comparisons but also on cross-dataset comparisons. © 2024, CC BY-NC-ND.

关键词： Clutter (information theory)

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A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

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Plant Phenomics 2023年第2期5卷 209-225页

作者： Xiaojun Xie Fei Xia Yufeng Wu Shouyang Liu Ke Yan Huanliang Xu Zhiwei Ji College of Artificial Intelligence Nanjing Agricultural UniversityNanjingJiangsu 210095China Center for Data Science and Intelligent Computing Nanjing Agricultural UniversityNanjingJiangsu 210095China State Key Laboratory for Crop Genetics and Germplasm Enhancement Bioinformatics CenterAcademy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjingJiangsu 210095China Academy for Advanced Interdisciplinary Studies Nanjing Agricultural UniversityNanjingJiangsu 210095China Department of the Built Environment College of Design and EngineeringNational University of Singapore4 Architecture DriveSingapore 117566Singapore

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern ***,it has limited interpretability for deep *** the transfer of expert knowledge,handcrafted features provide a new way for personalized diagnosis of plant ***,irrelevant and redundant features lead to high *** this study,we proposed a swarm intelligence algorithm for feature selection[salp swarm algorithm for feature selection(SSAFS)]in image-based plant disease *** is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of *** verify the effectiveness of the developed SSAFS algorithm,we conducted experimental studies using SSAFS and 5 metaheuristic *** evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from *** results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms,confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image *** computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.

关键词： Plant image redundant

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