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7th International Symposium on Autonomous Systems, ISAS 2024

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 College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9798350363173

Dynamic graphs (DG) represent evolving interactions between entities in various real-world scenarios. Many existing DG representation learning models employ a combination of graph convolutional networks and sequence neural networks to capture spatial-temporal dependencies. However, this hybrid approach often fails to effectively capture the spatial-temporal continuity inherent in DGs. In this paper, we propose a novel approach called the Tensor Graph Convolutional Network (TGCN) to learn DG representations within a unified convolution framework based on tensor products. Our approach is founded on two key principles: (a) representing DG information in tensor form, and (b) leveraging tensor product operations to design a graph convolutional network that simultaneously models spatial and temporal features. Experimental results on three real-world DG datasets demonstrate that our proposed model achieves state-of-the-art performance. © 2024 IEEE.

关键词： Tensors

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Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution 27

Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Xu, Tianyi Zhou, Yijie Hu, Xiaotao Zhang, Kai Zhang, Anran Qiu, Xingye Xu, Jun School of Statistics and Data Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China School of Intelligence Science and Technology Nanjing University Suzhou China Tencent Data Platform Beijing China Zhejiang University Hangzhou China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781643685489

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code is available at https://***/Tillyhere/TADT. © 2024 The Authors.

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Measurement of Br(n,γ)cross sections up to stellar s-process temperatures at the CSNS Back‑n

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Nuclear science and Techniques 2023年第11期34卷 226-241页

作者： Gao‑Le Yang Zhen‑Dong An Wei Jiang Xian‑Kai Li Wei‑Wei Qiu Zheng‑Fa Liao Zi‑Yue Zhuang Xiao‑Ping Zhang Sheng‑Li Chen Chen‑Chen Guo Er‑Xi Xiao Xiao Fang Xin‑Xiang Li Xin‑Rong Hu Bing Jiang Jin‑Cheng Wang Jie Ren Wen Luo Zhi‑Chao Zhu Hao‑Yang Lan Zong‑Wei Cao Xu Ma Ying‑Du Liu Pu‑Sen Wang Yi Yang Ping Su Xian‑Gai Deng Wan‑Bing He Chun‑Wang Ma Yu‑Ting Wang Zhi‑Tao Dai Peng‑Qin He Ren‑Guang Tang Tao Zhou Jing Wang Han Yi Yue Zhang Yong‑Hao Chen Rui‑Rui Fan Ke‑Qing Gao Qiang Li Kang Sun Zhi‑Xin Tan Min‑Hao Gu Han‑Tao Jing Jing‑Yu Tang School of Physics and Astronomy Sun Yat-sen UniversityZhuhai 519082China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China State Key Laboratory of Lunar and Planetary Sciences Macao University of Science and TechnologyMacao 999078China CNSA Macao Center for Space Exploration and Science Macao 999078China Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China Spallation Neutron Source Science Center Dongguan 523803China School of Nuclear Science and Technology University of South ChinaHengyang 421001China Sino‑French Institute of Nuclear Engineering and Technology Sun Yat-sen UniversityZhuhai 519082China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China School of Materials Science and Engineering Xiangtan UniversityXiangtan 411100China Key Laboratory of Nuclear Physics and Ion‑beam Application(MOE) Institute of Modern PhysicsDepartment of Nuclear Science and TechnologyFudan UniversityShanghai 200433China Institute of Particle and Nuclear Physics Henan Normal UniversityXinxiang 453007China School of Physics Henan Normal UniversityXinxiang 453007China Shenzhen Center Cancer hospitalChinese Academy of Medical SciencesShenzhen 518100China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230027China

The neutron capture cross sections(n,γ)of bromine were obtained using the time-of-flight technique at the Back-n facility of the China Spallation Neutron ***γ-rays originating from neutron-induced capture events were detected using four C_(6)D_(6) *** pulse-height weighting technique and double-bunch unfolding method based on Bayesian theory were used in the data *** deductions,normalization,and corrections were carefully considered to obtain reliable measurement *** multilevel R-matrix Bayesian code SAMMY was used to extract the resonance parameters in the resolved resonance region(RRR).The average cross sections in the unresolved resonance region(URR)were obtained from 10 to 400 *** experimental results were compared with data from several evaluated libraries and previous experi-ments in the RRR and *** TALYS code was used to describe the average cross sections in the *** astrophysical Maxwell average cross sections(MACSs)of ^(79,81)Br from kT=5 to 100 keV were calculated over a sufficiently wide range of neutron *** a thermal energy of kT=30 keV,the MACS value for ^(79)Br 682±68 mb was in good agreement with the KADoNiS v1.0 recommended *** contrast,the value of 293±29 mb for ^(81)Br was substantially higher than that of the evaluated database and the KADoNiS v1.0 recommended value.

关键词： Time-of-flight technique Neutron capture cross sections Maxwell average cross sections

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DVasMesh: Deep Structured Mesh Reconstruction from Vascular Images for Dynamics Modeling of Vessels 6th

DVasMesh: Deep Structured Mesh Reconstruction from Vascular...

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6th International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2024

作者： Jia, Dengqiang Yang, Xinnian Xiong, Xiaosong Huang, Shijie Hou, Feiyu Qin, Li Sun, Kaicong Chan, Kannie Wai Yan Shen, Dinggang Hong Kong Centre for Cerebro-Cardiovascular Health Engineering China School of Data Science City University of Hong Kong China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai201210 China School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai201620 China JinFeng Laboratory Chongqing401329 China Department of Biomedical Engineering City University of Hong Kong China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200230 China Shanghai Clinical Research and Trial Center Shanghai201210 China

ISBN: (纸本)9783031832420

Vessel dynamics simulation is vital in studying the relationship between geometry and vascular disease progression. Reliable dynamics simulation relies on high-quality vascular meshes. Most of the existing mesh generation methods highly depend on manual annotation, which is time-consuming and laborious, usually facing challenges such as branch merging and vessel disconnection. This will hinder vessel dynamics simulation, especially for the population study. To address this issue, we propose a deep learning-based method, dubbed as DVasMesh to directly generate structured hexahedral vascular meshes from vascular images. Our contributions are threefold. First, we propose to formally formulate each vertex of the vascular graph by a four-element vector, including coordinates of the centerline point and the radius. Second, a vectorized graph template is employed to guide DVasMesh to estimate the vascular graph. Specifically, we introduce a sampling operator, which samples the extracted features of the vascular image (by a segmentation network) according to the vertices in the template graph. Third, we employ a graph convolution network (GCN) and take the sampled features as nodes to estimate the deformation between vertices of the template graph and target graph, and the deformed graph template is used to build the mesh. Taking advantage of end-to-end learning and discarding direct dependency on annotated labels, our DVasMesh demonstrates outstanding performance in generating structured vascular meshes on cardiac and cerebral vascular images. It shows great potential for clinical applications by reducing mesh generation time from 2 h (manual) to 30 s (automatic). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mesh generation

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SQLStateGuard: Statement-Level SQL Injection Defense Based on Learning-Driven Middleware 15

SQLStateGuard: Statement-Level SQL Injection Defense Based o...

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15th Annual ACM Symposium on Cloud Computing, SoCC 2024

作者： Liu, Xin Huang, Yuanyuan Wang, Tianyi Li, Song Niu, Weina Shen, Jun Zhou, Qingguo Zhou, Xiaokang School of Information Science and Engineering Lanzhou University Gansu Lanzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China School of Computing and Information Technology University of Wollongong WollongongNSW Australia Kansai University Osaka Suita Japan

ISBN: (纸本)9798400712869

SQL injection is a significant and persistent threat to web services. Most existing protections against SQL injections rely on traffic-level anomaly detection, which often results in high false-positive rates and can be easily bypassed by attackers. This paper introduces SQLStateGuard, the world's first middleware-driven statement-level SQL injection defense approach, to address these issues. The SQLStateGuard uses a custom SQL middleware based on the idea of Runtime Application Self-Protection to capture raw SQL statements. These statements are then analyzed by SQLSG-Net, a database-oriented detection network based on gated linear units. If SQLSG-Net detects malicious SQL statements, the SQL middleware will block them. Experiments show that the detection accuracy of SQLStateGuard exceeds 99%, outperforming existing approaches, and it can identify the type of a specific SQL injection. Additionally, SQLStateGuard has no fingerprint and does not respond to SQL syntax errors, making it more challenging for attackers to gather information. This paper also presents a novel dataset generation process for SQLStateGuard and shares two statement-level SQL injection datasets with the research community, including over 145,000 malicious SQL statements categorized by the type of SQL injection. © 2024 ACM.

关键词： Middleware

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Unified Graph Augmentations for Generalized Contrastive Learning on Graphs 38

Unified Graph Augmentations for Generalized Contrastive Lear...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhuo, Jiaming Lu, Yintong Ning, Hui Fu, Kun Niu, Bingxin He, Dongxiao Wang, Chuan Guo, Yuanfang Wang, Zhen Cao, Xiaochun Yang, Liang Hebei Province Key Laboratory of Big Data Calculation School of Artificial Intelligence Hebei University of Technology Tianjin China College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Technology Beijing JiaoTong University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Cybersecurity Northwestern Polytechnical University Xi'an China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China

In real-world scenarios, networks (graphs) and their tasks possess unique characteristics, requiring the development of a versatile graph augmentation (GA) to meet the varied demands of network analysis. Unfortunately, most Graph Contrastive Learning (GCL) frameworks are hampered by the specificity, complexity, and incompleteness of their GA techniques. Firstly, GAs designed for specific scenarios may compromise the universality of models if mishandled. Secondly, the process of identifying and generating optimal augmentations generally involves substantial computational overhead. Thirdly, the effectiveness of the GCL, even the learnable ones, is constrained by the finite selection of GAs available. To overcome the above limitations, this paper introduces a novel unified GA module dubbed UGA after reinterpreting the mechanism of GAs in GCLs from a message-passing perspective. Theoretically, this module is capable of unifying any explicit GAs, including node, edge, attribute, and subgraph augmentations. Based on the proposed UGA, a novel generalized GCL framework dubbed Graph cOntrastive UnifieD Augmentations (GOUDA) is proposed. It seamlessly integrates widely adopted contrastive losses and an introduced independence loss to fulfill the common requirements of consistency and diversity of augmentation across diverse scenarios. Evaluations across various datasets and tasks demonstrate the generality and efficiency of the proposed GOUDA over existing state-of-the-art GCLs. © 2024 Neural information processing systems foundation. All rights reserved.

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An integrative profiling of metabolome and transcriptome in the plasma and skeletal muscle following an exercise intervention in diet-induced obese mice

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Journal of Molecular Cell Biology 2023年第3期15卷 18-31页

作者： Shuang Han Qingqian Wu Mengying Wang Miqi Yang Chen Sun Jiaqi Liang Xiaozhen Guo Zheyu Zhang Jingya Xu Xinyuan Qiu Cen Xie Siyu Chen Yue Gao Zhuo-Xian Meng Department of Pathology and Pathophysiology and Department of Cardiology of the Second Affiliated Hospital Zhejiang University School of MedicineHangzhou 310058China Department of Geriatrics Affiliated Hangzhou First People’s HospitalZhejiang University School of MedicineHangzhou 310006China Key Laboratory of Disease Proteomics of Zhejiang Province Zhejiang University School of MedicineHangzhou 310058China Department of Big Data in Health Science School of Public Health Zhejiang University School of MedicineHangzhou 310003China State Key Laboratory of Natural Medicines and School of Life Science and Technology China Pharmaceutical UniversityNanjing 211198China State Key Laboratory of Drug Research Shanghai Institute of Material MedicalChinese Academy of SciencesShanghai 201203China Department of Biology and Chemistry College of Liberal Arts and SciencesNational University of Defense TechnologyChangsha 410073China

Exercise intervention at the early stage of type 2 diabetes mellitus(T2DM)can aid in the maintenance of blood glucose homeostasis and prevent the development of macrovascular and microvascular ***,the exercise-regulated pathways that prevent the development of T2DM remain largely *** this study,two forms of exercise intervention,treadmill training and voluntary wheel running,were conducted for high-fat diet(HFD)-induced obese *** observed that both forms of exercise intervention alleviated HFD-induced insulin resistance and glucose *** muscle is recognized as the primary site for postprandial glucose uptake and for responsive alteration beyond exercise *** profiling of the plasma and skeletal muscle in Chow,HFD,and HFD-exercise groups revealed robust alterations in metabolic pathways by exercise intervention in both *** analysis identified nine metabolites,including beta-alanine,leucine,valine,and tryptophan,which were reversed by exercise treatment in both the plasma and skeletal *** analysis of gene expression profiles in the skeletal muscle revealed several key pathways involved in the beneficial effects of exercise on metabolic *** addition,integrative transcriptomic and metabolomic analyses uncovered strong correlations between the concentrations of bioactive metabolites and the expression levels of genes involved in energy metabolism,insulin sensitivity,and immune response in the skeletal *** work established two models of exercise intervention in obese mice and provided mechanistic insights into the beneficial effects of exercise intervention on systemic energy homeostasis.

关键词： type 2 diabetes exercise intervention metabolomics transcriptomics

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CITE: Compact Interactive TransformEr for Multilingual Image Captioning 23

CITE: Compact Interactive TransformEr for Multilingual Image...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Xu, Yueyuan Hu, Zhenzhen Zhou, Yuanen Hao, Shijie Hong, Richang Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9781450398572

Current state-of-the-art image captioning models generate captions in a single language, requiring a combination of multiple language specific models to build a multilingual image captioning system. However, as the number of supported languages increases, it leads to the parameters of the multilingual image captioning system grow linearly. To tackle this issue, we propose a single Compact Interactive TransformEr (CITE) model, which can describe an image in multiple languages simultaneously, making the captioning system more compact. Specifically, based on the standard Transformer model, we share the encoder and decoder backbone parameters and replace the self-attention sub-layer in the decoder with the interactive attention sub-layer. In addition, we extend the traditional monolingual reinforcement learning mechanism to a multilingual version to promote better description generation. Due to the wide use of Chinese and English, we evaluate the performance of our CITE model by simultaneously generating English and Chinese captions. We expand the image captions of the whole MSCOCO dataset and release a COCO-EN-CN dataset. Extensive experiments on the COCO-EN-CN dataset show that our single CITE model with more parameter-efficient can maintain the competitive performance or even better than the monolingual captioning models. © 2023 ACM.

关键词： Reinforcement learning

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Sparrow Search Algorithm with Adaptive Collaborative Updating

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1706-1724页

作者： Hong, Chuanbo Liang, Quan Liang, Qiaoxin Yu, Wenjie Yu, Wenze Xiong, Neng School of Mechanical and Electrical Engineering Quanzhou University of Information Engineering Quanzhou362000 China College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Quanzhou Management Branch Fujian Expressway Group Cp. Ltd Quanzhou362000 China

The research of swarm intelligence shows a rapid development trend. People have studied a lot of swarm intelligence algorithms inspired by biology. Sparrow search algorithm is concerned by many scholars because of its powerful search performance. However, in the case of high-dimension optimization problems, if only a single sparrow search algorithm is used to solve optimization problems, the optimal solution often cannot be found, and the phenomenon of dimension disaster will occur or it is easy to fall into the local optimal solution. In order to make sparrow search algorithm have better performance in solving high-dimensional complex problems, In this paper, we propose a new Sparrow Search Algorithm with Adaptive Collaborative Updating (SSA-ACU). The SSA-ACU algorithm initializes the population through the chaotic search strategy to make the population distribution more diverse. Then, adaptive parameters are introduced to update the position of the leader to provide a more reliable direction for particle search. Finally, in order to enhance the development ability of the population, the position of the adjoint is updated through the pseudo-affine transformation mechanism to change the mode of its position update. Then CEC2013 was used to test and compare SSA-ACU algorithm with other sparrow search algorithm variants and classical algorithms, and it was verified that SSA-ACU algorithm has good performance in convergence speed and optimization ability. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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Kernel Fence GAN: Unsupervised Anomaly Detection Model Based on Kernel Function 1

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14th International Symposium on Intelligence Computation and Applications, ISICA 2023

作者： Niu, Lu Li, Shaobo Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610041 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China

ISBN: (数字)9789819743933

ISBN: (纸本)9789819743926

data imbalance and difficult construction of high-dimensional data cause the low accuracy of anomaly detection. This paper proposes an unsupervised anomaly detection model Kernel Fence GAN to increase the accuracy. In this adversarial generative network, the generator's loss function consists of three items: the encirclement loss limits the generated samples to lie at the decision boundary of normal samples, the central dispersion loss maximizes the range of the decision boundary, and the enhanced dispersion loss uses kernel function to nonlinearly map the generated samples to RKHS space, which can maximize sample differences and avoid outliers. The purpose of training the discriminator is to accurately identify whether the input sample is a normal sample in the original dataset or an abnormal sample generated by the generator. In this paper, validation experiments are performed on two datasets: on two-dimensional synthetic dataset, the experiment result shows that the model has faster convergence rate than Fence GAN. On the KDD99 dataset, compared with traditional and advanced deep learning methods, both precision and recall have been correspondingly improved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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