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Gravitational wave search by time-scale-recursive denoising and matched filtering

Science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 166-182页

作者： Cunliang Ma Chenyang Ma Zhoujian Cao Mingzhen Jia School of Information Engineering Jiangxi University of Science and TechnologyGanzhou341000China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing102206China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced StudyUCASHangzhou310024China Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Ganzhou341000China

In our previous work [Physical Review D,2024,109(4):043009],we introduced MSNRnet,a framework integrating deep learning and matched filtering methods for gravitational wave(GW) *** with end-to-end classification methods,MSNRnet is physically *** denoising models and astrophysical discrimination models corresponding to different parameter space were operated independently for the template prediction and *** the MSNRnet has a lot of computational *** this study,we propose a new framework for template prediction,which significantly improves our previous *** new framework consists of the recursive application of denoising models and waveform classification models,which solve the problem of computational *** waveform classification network categorizes the denoised output based on the signal's time *** enhance the denoising performance for long-time-scale data,we upgrade the denoising model by incorporating Transformer and ResNet ***,we introduce a novel training approach that allows for the simultaneous training of the denoising network and waveform classification network,eliminating the need for manual annotation of the waveform dataset required in our previous ***-data analysis results demonstrate that our new method decreases the false alarm rate by approximately 25%,boosts the detection rate by roughly 5%,and slashes the computational cost by around 90%.The new method holds potential for future application in online GW data processing.

关键词： gravitational wave matched filtering deep learning

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Few-Shot Partial Multi-View Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 11824-11841页

作者： Zhou, Yuan Guo, Yanrong Hao, Shijie Hong, Richang Luo, Jiebo Ministry of Education Hefei University of Technology The Key Laboratory of Knowledge Engineering with Big Data Hefei230009 China Hefei University of Technology The School of Computer Science and Information Engineering Hefei230009 China University of Rochester The Department of Computer Science RochesterNY14627 United States

It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views;(ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness. The codes will be released at https://***/zhouyuan888888/UGDA. © 1979-2012 IEEE.

关键词： Neural networks

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Multi-Class Token Attention Learning for Weakly Supervised Semantic Segmentation 23

Multi-Class Token Attention Learning for Weakly Supervised S...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Zeng, Zhen Luo, Huilan School of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Ganzhou341000 China

ISBN: (纸本)9798331528041

Weakly supervised semantic segmentation (WSSS) using only image-level class labels is a challenging task. Due to the local receptive fields of convolutional neural networks (CNNs), CAMs applied to CNNs often suffer from partial activation-highlighting the most discriminative parts of the image. To capture both local features and global representations, we propose an approach called MCTRM, which is a WSSS solution based on an improved Conformer as backbone. To ensure each class token's discriminative ability for the respective target class, we design a class-aware training strategy (CAT) to establish a one-to-one correspondence between the output class tokens and the ground-truth class labels. To enhance the class discrimination ability between each class token, we propose a class token regularization module (CTRM) to enhance the learning of discriminative class tokens so that the model can better capture the unique features and attributes of each class. To obtain a fine-grained CAM, we propose the refinement module (RM) to combine the class-specific attention and inherent patch-level pairwise affinity between image patches learned by the Transformer branch with the CAM of the CNN branch. Despite its simplicity, MCTRM achieves the state-of-the-art performance of 69.0% and 68.4% on the PASCAL VOC 2012 validation and test sets, respectively. © 2024 IEEE.

关键词： Semantic Segmentation

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Noise-Aware Intermediary Fusion Network for Off-Road Freespace Detection

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Lv, Ying Liu, Zhi Li, Gongyang Chang, Xiaojun Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Engineering and Information Technology University of Technology Sydney Australia

Freespace detection holds a crucial role in autonomous driving technology, particularly in unstructured offroad scenarios that present additional challenges compared to structured road environments. Multimodal fusion methods are widely recognized as effective strategies for addressing these challenges. However, current fusion methods often overlook the critical issue of noise interference in multimodal data. To address this issue, we propose a novel Noise-Aware Intermediary Fusion Network for off-road freespace detection, named NAIFNet. This framework is specifically designed to mitigate noise interference during multimodal fusion. The key component of NAIFNet is the Noise-Aware Intermediary Interaction (NAII) module, which incorporates a denoising template as an intermediary during the fusion process. The NAII module employs multimodal features as query vectors, while the key and value vectors are derived from the search region features of the denoising template. At the same time, noise-aware interaction ensures effective denoising for data in each modality. Furthermore, in the decoding phase, we introduce the Denoising-Guided Decoder (DGD). Leveraging the denoising template, this decoder achieves more precise feature restoration and effectively mitigates the impact of noise. Extensive experiments on the popular benchmark of the offroad freespace detection dataset (ORFD) demonstrate that the proposed NAIFNet achieves state-of-the-art performance. IEEE

关键词： Noise abatement

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Privacy-Preserving Multi-keyword Fuzzy Adjacency Search Strategy for Encrypted Graph in Cloud Environment

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Computers, Materials & Continua 2024年第3期78卷 3177-3194页

作者： Bin Wu Xianyi Chen Jinzhou Huang Caicai Zhang Jing Wang Jing Yu Zhiqiang Zhao Zhuolin Mei School of Computer and Big Data Science Jiujiang UniversityJiujiang332005China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Engineering Hubei University of Arts and ScienceXiangyang441053China School of Modern Information Technology Zhejiang Institute of Mechanical and Electrical EngineeringHangzhou310053China Information Center Jiangxi Changjiang Chemical Co.Ltd.Jiujiang332005China School of Mathematics and Computer Science Ningxia Normal UniversityGuyuan756099China

In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so *** owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud *** on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure *** issue of privacy data protection has become an important obstacle to data sharing and *** to query outsourcing graph data safely and effectively has become the focus of *** query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same *** work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a *** our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud *** proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental *** research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.

关键词： Privacy-preserving adjacency query multi-keyword fuzzy search encrypted graph

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XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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Science China(information Sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of Software Nanjing University of Information Science and Technology School of Computing and Communications Lancaster University School of Computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

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Optimized Design of Temperature Field in GaN Crystal Growth by the Na-flux Method Using the Simulation Technique 21

Optimized Design of Temperature Field in GaN Crystal Growth ...

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21st China International Forum on Solid State Lighting and 10th International Forum on Wide Bandgap Semiconductors, SSLCHINA: IFWS 2024

作者： Yang, Rui Wu, Wenxiao Yao, Jineng Zhao, Juanli Kuang, Hai Zhou, Mingbin Xiong, Zhihua Key Laboratory for Optoelectronics and Communication of Jiangxi Province Jiangxi Science & Technology Normal University Nanchang330038 China Key Laboratory of Advanced Photonic and Electronic Materials School of Electronic Science and Engineering Nanjing University Nanjing210093 China School of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China

ISBN: (纸本)9798331541149

In the Na-flux method for growing gallium nitride (GaN), the temperature field within the growth furnace is crucial for crystal quality. An appropriate temperature field facilitates nitrogen transport, enhances crystal growth rate, reduces defects and dislocations, and improves the optical and electrical properties of GaN crystals. Therefore, optimizing the temperature field is essential for the Na-flux method. This study employs a two-heater furnace to optimize the internal temperature field of the reaction chamber. Compared to the traditional single-heater furnace, the two-heater setup provides a more suitable temperature gradient, aiding in nitrogen transport and crystal deposition. By combining experimental data with finite element numerical simulations, the distributions of the temperature field, flow field, concentration field, and supersaturation in the crucible's melt are analyzed. Changes in upper and lower heater and temperature gradients are investigated to observe variations in mass and heat transfer within the melt. This approach aims to identify the optimal temperature curve for the two-heater furnace, achieving effective temperature field optimization. The findings provide significant theoretical support and practical guidance for future crystal growth efforts. ©2024 IEEE.

关键词： Gallium nitride

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Resource Allocation for Underwater Acoustic Sensor Networks With Partial Spectrum Sharing: When Optimization Meets Deep Reinforcement Learning

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2715-2730页

作者： Tang, Rui Zhang, Ruizhi Xu, Yongjun Liu, Chuan Huang, Chongwen Yuen, Chau China West Normal University School of Electronic Information Engineering Key Laboratory of Optimization Theory and Applications Internet of Things Perception and Big Data Analysis Key Laboratory of Nanchong City Sichuan Nanchong637002 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400064 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Nanyang Technological University School of Electrical and Electronics Engineering Jurong West 639798 Singapore

To utilize the limited acoustic spectrum while combating the harsh underwater propagation, we incorporate partial spectrum sharing into an underwater acoustic sensor network and aim to maximize the minimum data collection rate among all underwater sensor nodes through joint power allocation and spectrum assignment. To cope with the non-convex optimization problem, we propose a Hybrid Model-based and data-based Resource Allocation (HMDRA) scheme: 1) Under any given spectrum assignment strategy, we analyze the impact of the partial spectrum sharing and imperfect successive interference cancellation on baseband signal processing, and formulate a power allocation problem that is solved by the bisection method and Lagrange dual theory. 2) Based on the optimal power allocation strategy, the gradient-free genetic algorithm (GA) is first adopted to approach the optimal solution of the model-less spectrum assignment problem by nearly enumerating the solution space. To reduce complexity, we further propose a deep reinforcement learning (DRL)-based algorithm and obtain an efficient solution by traversing a deep neural network-based policy learned from the training stage. Simulation results show that compared with the GA-based algorithm, the average execution time of the DRL-based algorithm is substantially reduced by 5 orders of magnitude to 0.7076 seconds at the cost of approximately 6 percent performance loss. © 2004-2012 IEEE.

关键词： Deep reinforcement learning

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Numerical Simulation of the Influence of Various Crucible Sizes and Shapes on the Growth of GaN Crystals by the Na-flux Method 21

Numerical Simulation of the Influence of Various Crucible Si...

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21st China International Forum on Solid State Lighting and 10th International Forum on Wide Bandgap Semiconductors, SSLCHINA: IFWS 2024

作者： Yao, Jineng Wu, Wenxiao Yang, Rui Zhao, Juanli Kuang, Hai Zhou, Mingbin Xiong, Zhihua Key Laboratory for Optoelectronics and Communication of Jiangxi Province Jiangxi Science & Technology Normal University Nanchang330038 China Key Laboratory of Advanced Photonic and Electronic Materials School of Electronic Science and Engineering Nanjing University Nanjing210093 China School of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China

ISBN: (纸本)9798331541149

This study employs numerical simulation to comprehensively investigate the impact of crucible size and shape on the heat transfer, mass transfer, and convective dynamics involved in GaN crystal growth using the sodium flux method. The findings demonstrate that larger crucibles, owing to their smoother temperature gradients, facilitate enhanced convective flow and more efficient solute transport, leading to improved uniformity in the crystal growth process. Smaller crucibles, by contrast, exhibit steeper temperature and concentration gradients, which may negatively impact both solute transport and growth rates. In terms of crucible shape, steeper wall angles increase the temperature gradient in the upper regions of the melt, promoting nitrogen transport but potentially leading to localized overheating and solute accumulation. Conversely, gently sloped crucibles provide a more uniform heat distribution, creating more stable conditions conducive to homogeneous crystal growth. The optimization of crucible geometry, in terms of both size and shape, is thus critical for achieving higher-quality, larger GaN crystals. This research offers valuable theoretical insights that can guide the design of future experimental setups, contributing to advancements in crystal growth techniques and material quality. ©2024 IEEE.

关键词： Gallium nitride

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A survey on graph neural network-based next POI recommendation for smart cities

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Journal of Reliable Intelligent Environments 2024年第3期10卷 299-318页

作者： Yu, Jian Guo, Lucas Zhang, Jiayu Wang, Guiling Department of Computer Science and Soft Engineering Auckland University of Technology Auckland New Zealand School of Information Science and Technology Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of Technology Beijing China

Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there’s an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially pivotal in smart cities, these systems aim to enhance user experiences by offering location recommendations tailored to past check-ins and visited POIs. Distinguishing itself from traditional POI recommendations, the next POI approach emphasizes predicting the immediate subsequent location, factoring in both geographical attributes and temporal patterns. This approach, while promising, faces with challenges like capturing evolving user preferences and navigating data biases. The introduction of Graph Neural Networks (GNNs) brings forth a transformative solution, particularly in their ability to capture high-order dependencies between POIs, understanding deeper relationships and patterns beyond immediate connections. This survey presents a comprehensive exploration of GNN-based next POI recommendation approaches, delving into their unique characteristics, inherent challenges, and potential avenues for future research. © The Author(s) 2024.

关键词： Location based services

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