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2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

作者： Li, Yanan Yu, Jun Zhan, Yibing Chen, Zhi Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781450383080

Visual relationship detection aims to predict the relationships between detected object pairs. It is well believed that the correlations between image components (i.e., objects and relationships between objects) are significant considerations when predicting objects' relationships. However, most current visual relationship detection methods only exploited the correlations among objects, and the correlations among objects' relationships remained underexplored. This paper proposes a relationship graph learning network (RGLN) to explore the correlations among objects' relationships for visual relationship detection. Specifically, RGLN obtains image objects using an object detector, and then, every pair of objects constitutes a relationship proposal. All relationship proposals construct a relationship graph, in which the proposals are treated as nodes. Accordingly, RGLN designs bi-stream graph attention subnetworks to detect relationship proposals, in which one graph attention subnetwork analyzes correlations among relationships based on visual and spatial information, and the other analyzes correlations based on semantic and spatial information. Besides, RGLN exploits a relationship selection subnetwork to ignore redundant information of object pairs with no relationships. We conduct extensive experiments on two public datasets: the VRD and the VG datasets. The experimental results compared with the state-of-the-art demonstrate the competitiveness of RGLN. © 2021 ACM.

关键词： Semantics

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Superacid assisted high-throughput production and solution-processing of pristine two-dimensional materials

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Materials Today 2025年 86卷 228-237页

作者： Deng, Qixuan Ma, Jiabin Liu, Minsu Zhou, Yicong Ding, Siyuan Zhan, Ke Zhu, Yuan Xiong, Zhiyuan Su, Yang Yu, Kuang Cheng, Hui-Ming Qiu, Ling Shenzhen Geim Graphene Center Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Laboratory for Simulation and Modelling of Particulate Systems Department of Chemical Engineering Department of Materials Science and Engineering Monash University VIC3800 Australia Monash Suzhou Research Institute Monash University Suzhou Suzhou SIP215000 China School of Microelectronics Southern University of Science and Technology Shenzhen518055 China School of Light Industry and Engineering South China University of Technology Guangzhou510640 China Shenzhen Key Lab of Energy Materials for Carbon Neutrality Shenzhen Institute of Advanced Technology Chinese Academy of Sciences 1068 Xueyuan Road Shenzhen518055 China Faculty of Materials Science and Energy Engineering Shenzhen University of Advanced Technology 291 Louming Road Shenzhen518107 China

Pristine two-dimensional (2D) materials have a multitude of interesting properties, promising a broad spectrum of applications. However, their poor affinity for common solvents and tendency to aggregate impede their effective exfoliation and solution-processing into various structures, such as fibers and membranes, thus limiting their practical use. We report a high-efficiency method for producing pristine 2D nanosheets by direct high-speed shearing exfoliation in trifluoromethanesulfonic acid (TfOH). Typically, this method produces hexagonal boron nitride nanosheets (BNNSs) with an unprecedented throughput of 38.8 mg mL-1h−1. The effective exfoliation is attributed to the strong binding affinity, large dipole moment and steric repulsion of TfOH, which collectively introduce layer edge distortion in hexagonal boron nitride (hBN), facilitating its exfoliation. The mechanical exfoliation and reversible edge interaction introduce no additional functional groups to the exfoliated BNNSs. The resulting pristine BNNS exhibit remarkable stability in TfOH solutions at ultrahigh concentrations of 200 mg mL−1 for over 28 days. The viscosity of the highly concentrated BNNS/TfOH dispersion can be adjusted, enabling techniques to produce BNNS-based spheres, fibers, and membranes with high thermal conductivity and mechanical strength, suitable for advanced thermal management applications. Room temperature processed BNNS-based spheres exhibit superior thermal conductivity compared to high temperature processed commercially available hBN or Al2O3 spheres. Moreover, TfOH can also be used to assist the efficient production and solution-processing of other 2D nanosheets, including graphene, molybdenum disulfide and tungsten disulfide, enabling the fabrication of high-quality bulk assemblies. © 2025 Elsevier Ltd

关键词： Molybdenum disulfide

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HRSTORY: Historical News Review Based Online Story Discovery 25

HRSTORY: Historical News Review Based Online Story Discovery

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Renjie Zhou Haoran Ye Jian Wan Yong Liao Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Hangzhou Dianzi University Hangzhou Zhejiang China Zhejiang University of Science and Technology Hangzhou Zhejiang China University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9798400712456

Story discovery on news streams can help people quickly find story from vast amounts of news, improving the efficiency of information acquisition. Recent online story discovery methods encode text topics and then cluster articles into stories based on similarity. However, the results obtained by these methods are one-time, and clustered news cannot adaptively update in a continuous news stream. Additionally, the inadequate quality of article encoding and the presence of noise data deteriorate the performance of story discovery. To this end, we propose HRSTORY for online story discovery on news streams, which employs a historical news review method to enable news to continuously adapt to the latest environment in the stream data and make corrections and updates. Furthermore, HRSTORY captures better article embeddings through modeling multi-layer relational dependencies within the text. By using sentence-level noise masking, HRSTORY improves the relevance of news article representation to core topics and reduces the interference of noise data. Experiments on real news datasets show that HRSTORY outperforms the state-of-the-art algorithms in unsupervised online story discovery performance.

关键词： historical news

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Design of a lightweight and easy-to-wear hand glove with multi-modal tactile perception for digital human

Design of a lightweight and easy-to-wear hand glove with mul...

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作者： Pan, Zhigeng Ren, Hongyi Liu, Chang Chen, Ming Mukherjee, Mithun Yang, Wenzhen School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Center for Space-based Computing Systems Zhejiang Laboratory Hangzhou China College of Mechanical Engineering Zhejiang Sci-Tech University Zhejiang Sci-Tech University Hangzhou China Center for Intelligent Manufacturing Computing Zhejiang Laboratory Hangzhou China

Within the field of human–computer interaction, data gloves play an essential role in establishing a connection between virtual and physical environments for the realization of digital human. To enhance the credibility of human-virtual hand interactions, we aim to develop a system incorporating a data glove-embedded technology. Our proposed system collects a wide range of information (temperature, bending, and pressure of fingers) that arise during natural interactions and afterwards reproduce them within the virtual environment. Furthermore, we implement a novel traversal polling technique to facilitate the streamlined aggregation of multi-channel sensors. This mitigates the hardware complexity of the embedded system. The experimental results indicate that the data glove demonstrates a high degree of precision in acquiring real-time hand interaction information, as well as effectively displaying hand posture in real-time using Unity3D. The data glove's lightweight and compact design facilitates its versatile utilization in virtual reality interactions. © 2024 John Wiley & Sons Ltd.

关键词： Virtual reality

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A Ga-Bp Compensated Pedot:Pss/Mxene Wearable Nh3 Detection Device for Food Safety, Environmental Monitoring, and Disease Diagnosis

SSRN

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SSRN 2025年

作者： Zhang, Xinru Wang, Yijie Zhang, Lei Zhang, Xiaohai Hao, Bao Liang, Xiaorui Dong, Helei Qin, Yongqiang Tan, Qiulin Key Laboratory of Micro/Nano Devices and Systems Ministry of Education North University of China Tai Yuan030051 China Science and Technology on Space Physics Technology Laboratory Beijing100076 China

The real-time and accurate detection of ammonia is critical for food freshness testing, agricultural monitoring, and respiratory health diagnostics. Recently, flexible gas sensors have garnered attention owing to their potential for integration into smart wearable electronics and display devices. However, the development of high-performance, low-power, and stable devices remains challenging. In this study, a chemoresistive sensor based on poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)/MXene composites is designed, wherein the application of a genetic algorithm-optimized back propagation neural network to compensate for humidity leads to a significantly enhanced detection accuracy and sensor lifespan. A wireless Bluetooth monitoring system is integrated to enable real-time data transmission and remote monitoring. This sensor detects ammonia within the range of 0.6–1000 ppm, demonstrating an excellent stability, selectivity, and flexibility, thereby confirming the synergistic effects between PEDOT:PSS and MXene. Importantly, in application tests, the developed system reliably assesses meat spoilage and farm ventilation levels, indicating its high applicability and reliability. Successful human breath tests also open new possibilities for the application of this sensor in respiratory health monitoring. Ultimately, this study offers a convenient and efficient approach for food quality assurance, disease diagnosis, and environmental monitoring. © 2025, The Authors. All rights reserved.

关键词： Diseases

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Spontaneous magnetization and magnetic domain texture of strontium hexaferrite in equilibrium state

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Rare Metals 2022年第9期41卷 3019-3026页

作者： Dan Liu Si-Da Jiang Li-Chen Wang Ruo-Shui Liu Ming Zhang Tong-Yun Zhao Feng-Xia Hu Ji-Rong Sun Bao-Gen Shen School of Artificial Intelligence Beijing Technology and Business UniversityBeijing100048China Space Environment Simulation Research Infrastructure Harbin Institute of TechnologyHarbin150001China Ganjiang Innovation Academy Chinese Academy of SciencesGanzhou341000China School of Physics Inner Mongolia University of Science and TechnologyBaotou014010China State Key Laboratory of Magnetism Institute of PhysicsChinese Academy of SciencesBeijing100190China

The spontaneous magnetization and equilibrium domain of SrFeOunder zero external field by micromagnetic simulation and experimental measurements were *** was found that the magnetic moment distribution was extremely sensitive to the grain size of strontium *** critical diameter from single-domain to multi-domain can be controlled by changing the thickness and diameter,which is the key to improve the permanent magnet properties.

关键词： properties. magnetization magnetic

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"Why not looking backward?" a robust two-step method to automatically terminate Bayesian optimization 23

"Why not looking backward?" a robust two-step method to auto...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Shuang Li Ke Li Wei Li Control and Simulation Center Harbin Institute of Technology China and National Key Laboratory of Modeling and Simulation for Complex Systems China Department of Computer Science University of Exeter Exeter UK

Bayesian Optimization (BO) is a powerful method for tackling expensive blackbox optimization problems. As a sequential model-based optimization strategy, BO iteratively explores promising solutions until a predetermined budget, either iterations or time, is exhausted. The decision on when to terminate BO significantly influences both the quality of solutions and its computational efficiency. In this paper, we propose a simple, yet theoretically grounded, two-step method for automatically terminating BO. Our core concept is to proactively identify if the search is within a convex region by examining previously observed samples. BO is halted once the local regret within this convex region falls below a predetermined threshold. To enhance numerical stability, we propose an approximation method for calculating the termination indicator by solving a bilevel optimization problem. We conduct extensive empirical studies on diverse benchmark problems, including synthetic functions, reinforcement learning, and hyperparameter optimization. Experimental results demonstrate that our proposed method saves up to ≈ 80% computational budget yet is with an order of magnitude smaller performance degradation, comparing against the other peer methods. In addition, our proposed termination method is robust in terms of the setting of its termination criterion.

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An Anomaly Detection Method Based on Adaptive Log and Dual Feature Fusion Analysis for Distributed systems

An Anomaly Detection Method Based on Adaptive Log and Dual F...

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Information systems and Computing technology (ISCTech), International Conference on

作者： Yijiang Jia Kefei Li Ping Lu Baodi Xie Huayang Wang Jincai Chen School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China National Key Laboratory of Modeling and Simulation for Complex Systems Beijing China

In this study, we present a middleware-based approach for detecting anomalies in distributed systems. Our method facilitates the dynamic collection of logs at various levels of detail and incorporates an a priori dictionary-based compression strategy to process and transmit logs, minimizing the performance impact on distributed systems. Additionally, we utilize a dual feature fusion technique to analyze the logs. We evaluate the effectiveness of our approach by performing anomaly detection in a publish/subscribe distributed system and comparing it with existing methods. The results illustrate that our method outperforms other approaches, demonstrating superior performance.

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FedECCR: Federated Learning Method with Encoding Comparison and Classification Rectification 19th

FedECCR: Federated Learning Method with Encoding Comparison...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yan Zheng, Hui Wang, Xin Zhang, Beibei Zhou, Mingyao Zhang, Jilin Ren, YongJian School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Zhejiang Lab Hangzhou311100 China Hangzhou Huawei Communication Technology Co. Ltd. Hangzhou310052 China

ISBN: (纸本)9783031545306

Federated learning is a distributed training method that integrates multi-party data information using privacy-preserving technologies through dispersed client data sets to jointly construct a global model under the coordination of a central server. However, in practical applications, there is a high degree of data distribution skewness among clients, which causes the optimization direction of the client models to diverge, resulting in model bias and reducing the accuracy of the global model. Existing methods require the calculation and transmission of much information to correct the optimization direction of the client models, or only roughly limit the deviation of the client models end-to-end, ignoring targeted processing of the internal structure of the model, resulting in unclear improvement effects. To address these problems, we propose a federated optimization algorithm FedECCR based on encoding contrast and classification correction. This algorithm divides the model into an encoder and a classifier. It utilizes prototype contrastive training of the model encoder and unbiased classification correction of the classifier. This approach notably improves the accuracy of the global model while maintaining low communication costs. We conducted experiments on multiple data sets to evaluate the validity of our method, and the quantified results showed that FedECCR can improve the global model classification accuracy by approximately 1% to 6% compared to FedAvg, FedProx, and MOON. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Classification (of information)

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Comprehensive understanding of microplastics in compost: Ecological risks and degradation mechanisms

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science of the Total Environment 2025年 958卷 178104页

作者： Dong, Dazhuang Gao, Wenjing Li, Lele Liu, Yuxin Dai, Yaodan State Key Laboratory of Water Environment Simulation School of Environment Beijing Normal University Beijing100875 China Department of Earth and Space Science University of Science and Technology of China Anhui Hefei230026 China School of Resources and Environmental Engineering Hefei University of Technology Hefei230009 China

The introduction of microplastics (MPs) into soil ecosystems via compost application has emerged as a critical environmental concern. However, the ecological risks and degradation behavior of MPs in compost remain insufficiently understood. This review addresses these gaps by synthesizing recent findings on MPs in composting systems, focusing on their sources, impacts on compost quality, ecological risks, and degradation mechanisms. MP sources vary significantly across compost matrices-domestic waste, sludge, and agricultural waste‑leading to differences in their types and quantities. MPs adversely impact compost quality by disrupting its physical structure and impairing fertility, aeration, and water retention. Furthermore, their persistence after compost application can result in long-term environmental accumulation, posing risks to soil ecosystems and biological health. This review also explores the aging and degradation of MPs during composting, a complex process influenced by physical, chemical, and biological mechanisms. Finally, we propose future research directions, emphasizing the development of standardized methodologies to assess MP behavior in compost and strategies to mitigate associated risks. These insights contribute to advancing sustainable waste management and environmental protection practices. © 2024 Elsevier B.V.

关键词： Compost Degradation mechanisms Ecological risks Influencing factors Microplastics

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