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arXiv 2025年

作者： He, Yuting Wang, Boyu Ge, Rongjun Chen, Yang Yang, Guanyu Li, Shuo Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Nanjing China School of Instrument Science and Engineering Southeast University Nanjing China Department of Computer Science Western University LondonONN6A 3K7 Canada Department of Biomedical Engineering Department of Computer and Data Science Case Western Reserve University ClevelandOH44106 United States

Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, bringing an open problem of large-scale false positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior to DCRL and enables a reliable correspondence discovery for effective dense contrast. We proposes a deformable homeomorphism learning (DHL) which models the homeomorphism of medical images and learns to estimate a deformable mapping to predict the pixels’ correspondence under the condition of topological preservation. It effectively reduces the searching space of pairing and drives an implicit and soft learning of negative pairs via gradient. We also proposes a geometric semantic similarity (GSS) which extracts semantic information in features to measure the alignment degree for the correspondence learning. It will promote the learning efficiency and performance of deformation, constructing positive pairs reliably. We implement two practical variants on two typical representation learning tasks in our experiments. Our promising results on seven datasets which outperform the existing methods show our great superiority. We will release our code at a companion website. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Implicit Relative Labeling-Importance Aware Multi-Label Metric Learning 39

Implicit Relative Labeling-Importance Aware Multi-Label Metr...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Mao, Jun-Xiang Rui, Yong Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing 210096 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China Information Technology and Data Management Department China Mobile Communications Group Zhejiang Co. Ltd. China Lenovo Research Lenovo Group Ltd. Beijing China

ISBN: (纸本)157735897X

Multi-label metric learning, as an extension of metric learning to multi-label scenarios, aims to learn better similarity metrics for objects with rich semantics. Existing multi-label metric learning approaches employ the common assumption of equal labeling-importance, i.e., all associated labels are considered relevant to the training instance, while there is no differentiation in the relative importance of their semantics. However, this common assumption does not reflect the fact that the importance of each relevant label is generally different, even though such importance information is not directly accessible from the training examples. In this paper, we claim that it is beneficial to leverage the implicit Relative Labeling-Importance (RLI) information to facilitate multi-label metric learning. Specifically, the manifold structure within the feature space is exploited by local linear reconstruction, and then the RLIs are recovered by transferring such structure to the label space. Subsequently, a discrimiative multi-label metric learning framework is introduced to align the predictive modeling outputs with the recovered RLIs, under which instances with similar RLI are implicitly pulled closer to each other, while those with dissimilar RLI are pushed further apart. Comprehensive experiments on benchmark multi-label datasets validate the superiority of our proposed approach in learning effective similarity metrics between multi-label examples. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Nameplates

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A Method of Realizing External Network Access to Intranet for Embedded Web Server

A Method of Realizing External Network Access to Intranet fo...

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Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Xian Zhang Taiguo Qu Pengju Tang Yiwen Liu School of Computer Science and Engineering Huaihua University Huaihua China Key Laboratory of Intelligent Control Technology for Wuling Mountain Ecological Agriculture in Hunan Province Huaihua China Key Laboratory of Wuling Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua China College of Computer Science and Electronic Engineering Hunan University ChangSha China

ISBN: (纸本)9781665464024

Based on the application requirements of the embedded Web server for external network access to the internal network, this paper designs and builds an embedded Web server scheme for external network access to the internal network based on the embedded Boa server and phddns program. This solution mainly transplants the Boa server and phddns program in embedded devices. Boa provides a Web server application function, and phddns software realizes the mapping and binding mapping between the IP address and the domain name of embedded devices in the intranet. An experiment is designed to test this scheme's connection success rate, response time, stable running time, domain name mapping success rate, CPU utilization and other performance indicators. The test results show that this scheme can better meet the system requirements of external network access to the internal network. Therefore, this scheme can support the information and intelligent monitoring and control of embedded devices and has good practical value and application prospects.

关键词： Performance evaluation Uniform resource locators Smart agriculture Web servers Software Real-time systems Organ transplantation

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Insight-HXMT observations of the extremely bright GRB 221009A

arXiv

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arXiv 2025年

作者： Zhang, Wen-Long Xue, Wang-Chen Li, Cheng-Kui Xiong, Shao-Lin Li, Gang Chen, Yong Cui, Wei-Wei Li, Xiao-Bo Liu, Cong-Zhan Ge, Ming-Yu Tan, Wen-Jun Liu, Jia-Cong Wang, Chen-Wei Zheng, Chao Zhang, Yan-Qiu Wang, Yue Zhang, Zhen Yi, Shu-Xu Xiao, Shuo Cai, Ce Yi, Shuang-Xi Song, Li-Ming Tao, Lian Zhang, Shu Zhang, Shuang-Nan Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China School of Physics and Physical Engineering Qufu Normal University Shandong Qufu273165 China University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China

The Hard X-ray Modulation Telescope (Insight-HXMT) detected GRB 221009A, the brightest gamma-ray burst observed to date, with all its three telescopes, i.e. High Energy telescope (HE, 20-250 keV), Medium Energy telescope (ME, 5-30 keV), and Low Energy telescope (LE, 1-10 keV). Here we present the detailed observation results of all three telescopes of Insight-HXMT on the prompt emission of GRB 221009A. After dead-time and data saturation correction, we recovered the light curves of HE, ME and LE telescopes and find that they generally track the GECAM-C low gain light curves that are free of data saturation issues. Particularly, the ME light curve matches the GECAM-C light curve in low gain mode above 400 keV, while the LE light curve is more consistent with the GECAM-C above 1.5 MeV. Based on simulation, we find that the signals recorded by the ME and LE are actually caused by the secondary particles produced by the interaction between GRB gamma-ray photons and the material of the satellite. Interestingly, the consistency between ME and LE light curves and GECAM-C demonstrates that ME and LE data could be used to characterize the GRB properties. Espeically, the high time resolution light curve of ME allowed us, for the first time, to calculate the minimum variability timescale (MVT = 0.10 s) of the main burst episode of GRB 221009A. Copyright © 2025, The Authors. All rights reserved.

关键词： Gamma radiography

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Transformer-Based Hierarchical Dynamic Decoders for Salient Object Detection

SSRN

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SSRN 2023年

作者： Zheng, Qingping Zheng, Ling Deng, Jiankang Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northewestern Polytechnical University Shaanxi Xi’an710129 China Fuzhou Institute of Data Technology Fujian350200 China Department of Computing Imperial College London LondonSW7 2AZ United Kingdom

HighlightsTransformer-Based Hierarchical Dynamic Decoders for Salient Object DetectionQingping Zheng, Ling Zheng, Jiankang Deng, Ying Li, Changjing Shang, Qiang Shen• T-HDDNet employs dynamic upsampling and fusion for data-driven salient object detection.• T-HDDNet uses a spatially sensitive loss to learn salient object structures, yielding a high-resolution map.• Our method exceeds benchmarks at 22.19G MACs and 27M Params, reducing costs compared to the top model. © 2023, The Authors. All rights reserved.

关键词： Object detection

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A Unified Modeling Framework for Automated Penetration Testing

arXiv

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arXiv 2025年

作者： Wang, Yunfei Liu, Shixuan Wang, Wenhao Zhou, Changling Zhang, Chao Jin, Jiandong Zhu, Cheng National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan China Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Hunan China National University of Defense Technology Hunan China Tsinghua University Beijing China Computer Center Peking University Beijing China

The integration of artificial intelligence into automated penetration testing (AutoPT) has highlighted the necessity of simulation modeling for the training of intelligent agents, due to its cost-efficiency and swift feedback capabilities. Despite the proliferation of AutoPT research, there is a recognized gap in the availability of a unified framework for simulation modeling methods. This paper presents a systematic review and synthesis of existing techniques, introducing MDCPM to categorize studies based on literature objectives, network simulation complexity, dependency of technical and tactical operations, and scenario feedback and variation. To bridge the gap in unified method for multi-dimensional and multi-level simulation modeling, dynamic environment modeling, and the scarcity of public datasets, we introduce AutoPT-Sim, a novel modeling framework that based on policy automation and encompasses the combination of all sub dimensions. AutoPT-Sim offers a comprehensive approach to modeling network environments, attackers, and defenders, transcending the constraints of static modeling and accommodating networks of diverse scales. We publicly release a generated standard network environment dataset and the code of Network Generator. By integrating publicly available datasets flexibly, support is offered for various simulation modeling levels focused on policy automation in MDCPM and the network generator help researchers output customized target network data by adjusting parameters or fine-tuning the network generator. Copyright © 2025, The Authors. All rights reserved.

关键词： Intelligent agents

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Non-small-molecule therapeutics for drug addiction:From pharmaco-kinetics modulating to synthetic biology

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Fundamental Research 2024年第6期4卷 1398-1400页

作者： Yuanpeng Li Yibo Wang Lin Lu Jie Shi Xiaohui Wang Laboratory of Chemical Biology Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun 130022China School of Applied Chemistry and Engineering University of Science and Technology of ChinaHefei 230026China Institute of Mental Health Peking University Sixth Hospital and National Clinical Research Center for Mental DisordersPeking UniversityBeijing 100191China Peking-Tsinghua Center for Life Sciences Peking UniversityBeijing 100191China International Data Group/McGovern Institute for Brain Research at Peking University Peking UniversityBeijing 100191China National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Peking UniversityBeijing 100191China Beijing National Laboratory for Molecular Sciences Beijing 100190China

Drug addiction is a pervasive problem *** not only affects the abuser’s life,but also poses a serious threat to public ***,there is a strong demand for the development of novel and effective therapies for drug *** that small-molecule drugs have only had limited success,there is great interest in developing alternative strategies that extend the reach of small-molecule-based *** antibody-based trapping approach and the enzyme catalytic strategy have been considered as promising ways to reduce the euphoria of drug users by altering drug pharmacokinetics and decreasing drug concentrations in the central nervous ***,these biological macromolecules are generally unstable and their in vivo half-lives are *** the rapid development of gene editing technologies,it is possible to perform ex vivo gene therapy for the long-term and stable delivery of enzymes and other effector proteins,which could free abusers from frequent injections of *** constructing programmed gene switches that regulate spatiotemporal gene expression in response to illicit drugs,this perspective proposed the concept of preventing drug addiction by the drug *** strategy enables the controlled release of therapeutic proteins in vivo and is expected to improve patient safety and *** will open up new opportunities for next-generation medicine and hold great promise for expanding our ability to treat drug addiction.

关键词： Drug abuse Enzymatic degradation Gene therapy Synthetic biology Pharmacokinetics

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Restoration of User Videos Shared on Social Media

arXiv

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arXiv 2022年

作者： Luo, Hongming Zhou, Fei Lam, Kin-Man Qiu, Guoping College of Electronics and Information Engineering Shenzhen University China Department of Electronic and Information Engineering The Hong Kong Polytechnic University China Peng Cheng Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Key Laboratory of Digital Creative Technology China Shenzhen Institute for Artificial Intelligence and Robotics for Society China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication China

User videos shared on social media platforms usually suffer from degradations caused by unknown proprietary processing procedures, which means that their visual quality is poorer than that of the originals. This paper presents a new general video restoration framework for the restoration of user videos shared on social media platforms. In contrast to most deep learning-based video restoration methods that perform end-to-end mapping, where feature extraction is mostly treated as a black box, in the sense that what role a feature plays is often unknown, our new method, termed Video restOration through adapTive dEgradation Sensing (VOTES), introduces the concept of a degradation feature map (DFM) to explicitly guide the video restoration process. Specifically, for each video frame, we first adaptively estimate its DFM to extract features representing the difficulty of restoring its different regions. We then feed the DFM to a convolutional neural network (CNN) to compute hierarchical degradation features to modulate an end-to-end video restoration backbone network, such that more attention is paid explicitly to potentially more difficult to restore areas, which in turn leads to enhanced restoration performance. We will explain the design rationale of the VOTES framework and present extensive experimental results to show that the new VOTES method outperforms various state-of-the-art techniques both quantitatively and qualitatively. In addition, we contribute a large scale real-world database of user videos shared on different social media platforms. Codes and datasets are available at https://***/luohongming/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Restoration

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A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility 12

A Comparison of Global and Local Statistical and Machine Lea...

引用

12th International Conference on Geographic Information Science, GIScience 2023

作者： Yao, Jing Li, Ziqi Zhang, Xiaoxiang Liu, Changjun Ren, Liliang Urban Big Data Centre School of Social and Political Sciences University of Glasgow United Kingdom Department of Geography Florida State University TallahasseeFL United States Department of Geographic Information Science College of Hydrology and Water Resources Hohai University Nanjing China Department of Flood and Drought Disaster Reduction China Institute of Water Resources and Hydropower Research Beijing China State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering College of Hydrology and Water Resources Hohai University Nanjing China

ISBN: (纸本)9783959772884

Flash floods, as a type of devastating natural disasters, can cause significant damage to infrastructure, agriculture, and people’s livelihoods. Mapping flash flood susceptibility has long been an effective measure to help with the development of flash flood risk reduction and management strategies. Recent studies have shown that machine learning (ML) techniques perform better than traditional statistical and process-based models in estimating flash flood susceptibility. However, a major limitation of standard ML models is that they ignore the local geographic context where flash floods occur. To address this limitation, we developed a local Geographically Weighted Random Forest (GWRF) model and compared its performance against other global and local statistical and ML alternatives using an empirical flash floods model of Jiangxi Province, China. © Jing Yao, Ziqi Li, Xiaoxiang Zhang, Changjun Liu, and Liliang Ren.

关键词： Machine learning

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Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Approach

arXiv

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arXiv 2025年

作者： He, Changpeng Lu, Yang Chen, Wei Ai, Bo Wong, Kai-Kit Niyato, Dusit State Key Laboratory of Advanced Rail Autonomous Operation China School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China School of Electronics and Information Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom Yonsei Frontier Laboratory The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

An emerging fluid antenna system (FAS) brings a new dimension, i.e., the antenna positions, to deal with the deep fading, but simultaneously introduces challenges related to the transmit design. This paper proposes an "unsupervised learning to optimize" paradigm to optimize the FAS. Particularly, we formulate the sum-rate and energy efficiency (EE) maximization problems for a multiple-user multiple-input single-output (MU-MISO) FAS and solved by a two-stage graph neural network (GNN) where the first stage and the second stage are for the inference of antenna positions and beamforming vectors, respectively. The outputs of the two stages are jointly input into a unsupervised loss function to train the two-stage GNN. The numerical results demonstrates that the advantages of the FAS for performance improvement and the two-stage GNN for real-time and scalable optimization. Besides, the two stages can function separately. © 2025, CC BY.

关键词： Graph neural networks

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