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Effects of nitrogen deposition on the carbon budget and water stress in Central Asia under climate change

Journal of Arid Land 2024年第8期16卷 1118-1129页

作者： HAN Qifei XU Wei LI Chaofan School of Geographical Sciences Nanjing University of Information Science&TechnologyNanjing 210044China

Atmospheric deposition of nitrogen(N)plays a significant role in shaping the structure and functioning of various terrestrial ecosystems ***,the magnitude of N deposition on grassland ecosystems in Central Asia still remains highly *** this study,a multi-data approach was adopted to analyze the distribution and amplitude of N deposition effects in Central Asia from 1979 to 2014 using a process-based denitrification decomposition(DNDC)*** showed that total vegetation carbon(C)in Central Asia was 0.35(±0.09)Pg C/a and the averaged water stress index(WSI)was 0.20(±0.02)for the whole *** N deposition led to an increase in the vegetation C of 65.56(±83.03)Tg C and slightly decreased water stress in Central *** of this study will expand both our understanding and predictive capacity of C characteristics under future increases in N deposition,and also serve as a valuable reference for decision-making regarding water resources management and climate change mitigation in arid and semi-arid areas globally.

关键词： carbon dynamics climate change grassland ecosystems nitrogen deposition water stress index

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Mitigating the negative catalytic effect of CuO by FAS-17 coated Al nanopowder:Isothermal ageing of Al/CuO nanothermite at 71°C and 60%relative humidity

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Defence technology（防务技术） 2024年第4期34卷 156-167页

作者： Fuwei Li Qian Wang Jian Cheng Zehua Zhang Yuxuan Zhou Keer Ouyang Jianbing Xu Yinghua Ye Ruiqi Shen School of Chemistry and Chemical Engineering Nanjing University of Science&TechnologyNanjing 210094China Micro-Nano Energetic Devices Key Laboratory Ministry of Industry and Information TechnologyNanjing 210094JiangsuChina Institute of Space Propulsion Nanjing University of Science and TechnologyNanjing 210094China

The safety and reliability of weapon systems would be significantly affected by changes in the performance of energetic materials due to ambient temperature and *** have promising applications due to their excellent *** it becomes extremely important to understand their aging and failure process in the environment before using ***,the aging and failure process of Al/CuO in 71°C/60%RH were investigated,and showed that CuO nanoparticles negatively catalyze Al nanopowders,resulting in rapid *** anti-aging effect of FAS-17-coated Al nanopowder was also *** aging process of Al,Al/CuO,and Al@FAS-17/CuO in high humidity and heat environment were revealed by quasi-in situ SEM and TEM *** with the aging of pure Al,the Al nanopowder in the nanothermites strongly agglomerated with the CuO nanopowder and hydrated *** may be caused by CuO catalyzed hydration of Al *** energy release experiments showed that the performance of Al/CuO decreased rapidly and failed to ignite after 4 h of *** contrast,the Al@FAS-17/CuO thermite can achieve long-term stability of up to 60 h in the same environment by simple cladding of *** is found that FAS-17 coated Al nanopowder can prevent both particle agglomeration and water erosion,which is an effective means to make nanothermites application in high humidity and heat environment.

关键词： Al/CuO nanothermite FAS-17 coating Aging and failure process

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Explainable Business Process Remaining Time Prediction Using Reachability Graph

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Chinese Journal of Electronics 2023年第3期32卷 625-639页

作者： CAO Rui ZENG Qingtian NI Weijian LU Faming LIU Cong DUAN Hua College of Computer Science and Engineering Shandong University of Science and Technology School of Computer Science and Technology Shandong University of Technology

With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictive results. However, existing time prediction methods based on deep learning have poor interpretability, an explainable business process remaining time prediction method is proposed using reachability graph,which consists of prediction model construction and visualization. For prediction models, a Petri net is mined and the reachability graph is constructed to obtain the transition occurrence vector. Then, prefixes and corresponding suffixes are generated to cluster into different transition partitions according to transition occurrence vector. Next,the bidirectional recurrent neural network with attention is applied to each transition partition to encode the prefixes, and the deep transfer learning between different transition partitions is performed. For the visualization of prediction models, the evaluation values are added to the sub-processes of a Petri net to realize the visualization of the prediction models. Finally, the proposed method is validated by publicly available event logs.

关键词： Deep learning Training Visualization Recurrent neural networks Petri nets Transfer learning Process control

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Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition

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International Journal of Crowd science 2024年第3期8卷 140-148页

作者： Lyu, Shengfei Sun, Linghao Yi, Huixiong Liu, Yong Chen, Huanhuan Miao, Chunyan School of Computer Science and Technology University of Science and Technology of China Hefei230027 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

In recent years, great success has been achieved in many tasks of natural language processing (NLP), e.g., named entity recognition (NER), especially in the high-resource language, i.e., English, thanks in part to the considerable amount of labeled resources. More labeled resources, better word representations. However, most low-resource languages do not have such an abundance of labeled data as high-resource English, leading to poor performance of NER in these low-resource languages due to poor word representations. In the paper, we propose converse attention network (CAN) to augment word representations in low-resource languages from the high-resource language, improving the performance of NER in low-resource languages by transferring knowledge learned in the high-resource language. CAN first translates sentences in low-resource languages into high-resource English using an attention-based translation module. In the process of translation, CAN obtains the attention matrices that align word representations of high-resource language space and low-resource language space. Furthermore, CAN augments word representations learned in low-resource language space with word representations learned in high-resource language space using the attention matrices. Experiments on four low-resource NER datasets show that CAN achieves consistent and significant performance improvements, which indicates the effectiveness of CAN. © The author(s) 2024.

关键词： Labeled data

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Hyperspectral Image Classification Based on A Multi-Scale Weighted Kernel Network

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Chinese Journal of Electronics 2022年第5期31卷 832-843页

作者： SUN Le XU Bin LU Zhenyu School of Computer Science Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing University of Information Science and Technology

Recently, many deep learning models have shown excellent performance in hyperspectral image(HSI) classification. Among them, networks with multiple convolution kernels of different sizes have been proved to achieve richer receptive fields and extract more representative features than those with a single convolution kernel. However, in most networks, different-sized convolution kernels are usually used directly on multibranch structures, and the image features extracted from them are fused directly and simply. In this paper, to fully and adaptively explore the multiscale information in both spectral and spatial domains of HSI, a novel multi-scale weighted kernel network(MSWKNet) based on an adaptive receptive field is proposed. First, the original HSI cubic patches are transformed to the input features by combining the principal component analysis and one-dimensional spectral convolution. Then, a three-branch network with different convolution kernels is designed to convolve the input features, and adaptively adjust the size of the receptive field through the attention mechanism of each branch. Finally, the features extracted from each branch are fused together for the task of *** on three well-known hyperspectral data sets show that MSWKNet outperforms many deep learning networks in HSI classification.

关键词： multibranch structures image features convolution kernels multiple convolution kernels adaptive receptive field image classification feature extraction HSI classification single convolution kernel convolutional neural nets hyperspectral image classification representative features hyperspectral imaging geophysical image processing deep learning (artificial intelligence) multiscale weighted kernel network deep learning networks hyperspectral data sets three-branch network HSI cubic patches principal component analysis 1D spectral convolution different-sized convolution kernels

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Expert Recommendation in Community Question Answering via Heterogeneous Content Network Embedding

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computers, Materials & Continua 2023年第4期75卷 1687-1709页

作者： Hong Li Jianjun Li Guohui Li Rong Gao Lingyu Yan School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China College of Computer Science and Technology Hubei University of TechnologyWuhan 430068China

ExpertRecommendation(ER)aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering(CQA)web *** to model questions and users in the heterogeneous content network is critical to this *** traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity *** approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for ***,they often fail to distinguish the nodes’personalized preferences and only consider the textual content of a part of the nodes in network embedding learning,while ignoring the semantic relevance of *** this paper,we propose a novel framework that jointly considers the structural proximity relations and textual semantic relevance to model users and questions more ***,we learn topology-based embeddings through a hierarchical attentive network learning strategy,in which the proximity information and the personalized preference of nodes are encoded and ***,we utilize the node’s textual content and the text correlation between adjacent nodes to build the content-based embedding through a meta-context-aware skip-gram *** addition,the user’s relative answer quality is incorporated to promote the ranking *** results show that our proposed framework consistently and significantly outperforms the state-of-the-art baselines on three real-world datasets by taking the deep semantic understanding and structural feature learning *** performance of the proposed work is analyzed in terms of MRR,P@K,and MAP and is proven to be more advanced than the existing methodologies.

关键词： Heterogeneous network learning expert recommendation semantic representation community question answering

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Recommendation Algorithm Based on Social Influence Diffusion 17

Recommendation Algorithm Based on Social Influence Diffusion

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Jin, Xin Chen, Shiyu Nanjing University of Science and Technology School of Mechanical Engineering Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351637

Social influence is everywhere in people's lives. It controls users' activities in social networks. Understanding the mechanism of interactions between users in social networks can benefit various applications, such as recommendations. Traditional recommendation systems based on collaborative filtering suppose that the behaviors of users are independent of each other and ignore the influence relationships among users. However, different historical behaviors and social relationships of users result in different influences among users. First, the complete user behavior sequences are fed into a recurrent neural network to capture long term patterns of user interest in items. Secondly, a self-attention network is used to obtain short lived interests of users. Then the outputs of the recurrent neural network as queries are fed into another self-attention network to obtain user representations that combine transient effect of and long term patterns of users' interests, thereby predicting the ratings of users to items. Finally, the proposed recommendation system is used to recommend products that need to be promoted to the maximum number of users. The proposed method is verified to outperform the state-of-the-art algorithms on three real-world datasets. © 2024 IEEE.

关键词： Chatbots

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Deep Learning-Driven Analysis of Tumor Regions in Magnetic Resonance Imaging 24

Deep Learning-Driven Analysis of Tumor Regions in Magnetic R...

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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Cao, Bingyu School of Computer Science Nanjing University of Information Science and Technology Jiangsu Nanjing China

ISBN: (纸本)9798400717826

The early diagnosis of tumor is very important to improve the survival rate of patients. Magnetic resonance imaging (MRI), as a non-invasive advanced imaging technology, has a wide range of potential applications in tumor detection and evaluation. However, MRI images are often complex and susceptible to noise, and traditional manual analysis methods have limitations in accuracy and efficiency. In recent years, deep learning technology has made a breakthrough in the field of image recognition, which brings a new method and perspective for the intelligent analysis of MRI images. This paper studied the application of several classical deep learning models in MRI image tumor region recognition, including Convolutional Neural Network (CNN) models such as VGG, GoogLeNet, ResNet, EfficientNet and Vision Transformer model, combined with data processing technology and parameter adjustment technology. The performance of these models on data sets is discussed, and a high classification accuracy is finally achieved. This study not only provides an effective solution for automated tumor region detection, but also provides important technical support and reference for clinical tumor diagnosis, and further promotes the intelligent development of imaging diagnosis. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Deep Contrastive Clustering for Protocol Analysis in 5G Network Engineering 4

Deep Contrastive Clustering for Protocol Analysis in 5G Netw...

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4th International Conference on Communication technology and Information technology, ICCTIT 2024

作者： Lu, Jianhong Zhang, Kun Nanjing University of Science and Technology School of Cyber Science and Engineering Nanjing China Nanjing University of Science and Technology School of Computer and Engineering Nanjing China

ISBN: (纸本)9798331528973

With the widespread adoption of 5G networks in satellite communication and vehicular communication systems, the complexity of communication and signaling interactions has significantly increased. Traditional protocol analysis methods primarily focus on high-level structured data, often neglecting the low-level binary data critical for understanding 5G-specific protocols. This paper proposes a novel approach that employs a deep contrastive clustering framework to analyze and classify unknown protocols in 5G communication networks. The proposed method leverages depthwise separable convolutions for feature extraction, transforming binary protocol data into structured feature representations while significantly reducing computational overhead. Additionally, by integrating contrastive learning into the clustering process, the framework enhances the discrimination of protocol embeddings through a positive-negative sample feedback mechanism, ensuring more accurate clustering results. Additionally, this paper collected and analyzed a comprehensive dataset of 5 G protocols, which includes wireless access layer protocols and core network communication protocols. © 2024 IEEE.

关键词： Satellite communication systems

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Clustered Reinforcement Learning

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Frontiers of computer science 2025年第4期19卷 43-57页

作者： Xiao MA Shen-Yi ZHAO Zhao-Heng YIN Wu-Jun LI National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing 210023China Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720-1770USA

Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse *** exploration,the agent tries to discover unexplored(novel)areas or high reward(quality)*** existing methods perform exploration by only utilizing the novelty of *** novelty and quality in the neighboring area of the current state have not been well utilized to simultaneously guide the agent’s *** address this problem,this paper proposes a novel RL framework,called clustered reinforcement learning(CRL),for efficient exploration in *** adopts clustering to divide the collected states into several clusters,based on which a bonus reward reflecting both novelty and quality in the neighboring area(cluster)of the current state is given to the *** leverages these bonus rewards to guide the agent to perform efficient ***,CRL can be combined with existing exploration strategies to improve their performance,as the bonus rewards employed by these existing exploration strategies solely capture the novelty of *** on four continuous control tasks and six hard-exploration Atari-2600 games show that our method can outperform other state-of-the-art methods to achieve the best performance.

关键词： deep reinforcement learning exploration count-based method clustering K-means

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