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A decouple-decomposition noise analysis model for closed-loop mode-localized tilt sensors

Microsystems & Nanoengineering 2023年第6期9卷 297-307页

作者： Kunfeng Wang XingYin Xiong Zheng Wang Liangbo Ma BoWen Wang WuHao Yang Xiaorui Bie ZhiTian Li XuDong Zou The State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina Ming Hsieh Department of Electrical and Computer Engineering University of Southern CaliforniaLos AngelesCAUSA Shangdong Key Laboratory of Low-altitude Airspace Surveillance Network Technology QiLu Aerospace Information Research InstituteJinanChina

The development of mode-localized sensors based on amplitude output metrics has attracted increasing attention in recent years due to the potential of such sensors for high sensitivity and ***-localization phenomena leverage the interaction between multiple coupled resonant modes to achieve enhanced performance,providing a promising solution to overcome the limitations of traditional sensing *** noise plays a key role in determining the resolution of mode-localized sensors,as the output metric is derived from the measured AR(amplitude ratio)within the weakly coupled resonator ***,the amplitude noise originating from the weakly coupled resonator's closed-loop circuit has not yet been fully *** paper presents a decouple-decomposition(DD)noise analysis model,which is applied to achieve high resolution in a mode-localized tilt sensor based on a weakly coupled resonator closed-loop *** DD noise model separates the weakly coupled resonators using the decoupling method considering the nonlinearity of the *** integrating the decoupled weakly coupled resonators,the model decomposes the weakly coupled resonator's closed-loop circuit into distinct paths for amplitude and phase noise *** DD noise model reveals noise effects at various circuit nodes and models the system noise in the closed-loop circuit of the weakly coupled ***/Simulink simulations verify the model's accuracy when compared to theoretical *** the optimal operating point,the mode-localized tilt sensor achieves an input-referred instability of 3.91×10^(-4°)and an input-referred AR of PSD of 2.01×10^(-4°)⁄√Hz using the closed-loop noise *** model is also applicable to other varieties of mode-localized sensors.

关键词： weakly couple overcome

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Transmissive reconfigurable metasurface enabling independent control of active and passive modules through weak coupling

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Photonics Research 2024年第7期12卷 1449-1456页

作者： KUN XUE HENG WEI CILEI ZHANG YONGHAO ZHANG HAOLIANG SUN SHAOHUA DONG Peng Cheng Laboratory Shenzhen 518055China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore State Key Laboratory of Radio Frequency Heterogeneous Integration College of Electronics and Information EngineeringShenzhen UniversityShenzhen 518060China

Metasurfaces have demonstrated rich electromagnetic control capabilities and degrees of freedom in past years. As is well known, for passive metasurfaces, their functionalities cannot be further expanded accordingly once prototypes are established. Therefore, reconfigurable metasurfaces, utilizing active devices to replace geometric changes in passive structures, have received widespread attention, especially with the development of wireless communication recently. In reconfigurable metasurfaces, artificial meta-atoms are composed of active devices and passive structures combined together. However, these two modules are usually utilized as a whole due to the tight coupling of the active devices and the passive structures, which results in passive structures not receiving sufficient attention and being utilized as independent degrees of freedom. In this article, we propose the concept of weakly coupled reconfigurable metasurfaces in transmissive systems, enabling independent control of active and passive modules through weak coupling. As the proof of concept, a simple weakly coupled system is proposed, which can realize the transmission wavefront engineering through the geometric changes of meta-structures in passive mode, while achieving switching between transmission and reflection states in active mode, respectively. Our exploration lies in making use of the physical structure, which is easily neglected in traditional reconfigurable metasurface design, emphasizing the collaborative work of active and passive modules, exploring more available variables within the same aperture, and providing a potential solution for balancing functionality and resource consumption in practical applications.

关键词： modules coupling weakly

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Partial multi-label learning with probabilistic graphical disambiguation 23

Partial multi-label learning with probabilistic graphical di...

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Proceedings of the 37th International Conference on Neural information Processing Systems

作者： Jun-Yi Hang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China

In partial multi-label learning (PML), each training example is associated with a set of candidate labels, among which only some labels are valid. As a common strategy to tackle PML problem, disambiguation aims to recover the ground-truth labeling information from such inaccurate annotations. However, existing approaches mainly rely on heuristics or ad-hoc rules to disambiguate candidate labels, which may not be universal enough in complicated real-world scenarios. To provide a principled way for disambiguation, we make a first attempt to explore the probabilistic graphical model for PML problem, where a directed graph is tailored to infer latent ground-truth labeling information from the generative process of partial multi-label data. Under the framework of stochastic gradient variational Bayes, a unified variational lower bound is derived for this graphical model, which is further relaxed probabilistically so that the desired prediction model can be induced with simultaneously identified ground-truth labeling information. Comprehensive experiments on multiple synthetic and real-world data sets show that our approach outperforms the state-of-the-art counterparts.

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ESNet: Evolution and Succession network for High-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-instance partial-label learning with margin adjustment 24

Multi-instance partial-label learning with margin adjustment

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Proceedings of the 38th International Conference on Neural information Processing Systems

作者： Wei Tang Yin-Fang Yang Zhaofei Wang Weijia Zhang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China School of Information and Physical Sciences The University of Newcastle Callaghan NSW 2308 Australia

ISBN: (纸本)9798331314385

Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on a non-candidate label. In this paper, we propose an algorithm named MIPLMA, i.e., Multi-Instance Partial-Label learning with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce a margin-aware attention mechanism to dynamically adjust the margins for attention scores and propose a margin distribution loss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior performance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms.

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Identity-based threshold proxy re-encryption scheme from lattices and its applications

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Frontiers of information Technology & Electronic Engineering 2022年第2期23卷 258-277页

作者： Liqiang WU Yiliang HAN Xiaoyuan YANG Minqing ZHANG Key Laboratory of Network and Information Security Engineering University of People’s Armed PoliceXi'an 710086China MOE Key Laboratory of Computer Network and Information Security Xidian UniversityXi'an 710071China

Threshold proxy re-encryption(TPRE)can prevent collusion between a single proxy and a delegatee from converting arbitrary files against the wishes of the delegator through multiple proxies,and can also provide normal services even when certain proxy servers are paralyzed or damaged.A non-interactive identity-based TPRE(IB-TPRE)scheme over lattices is proposed which removes the public key *** accomplish this scheme,Shamir’s secret sharing is employed twice,which not only effectively hides the delegator’s private key information,but also decentralizes the proxy power by splitting the re-encryption *** means that a combiner can detect a misbehaving proxy server that has sent an invalid transformed ciphertext *** property is achieved by lattice-based fully homomorphic *** a result,the whole scheme is thoroughly capable of resisting quantum attacks even when they are *** security of the proposed scheme is based on the decisional learning with error hardness assumption in the standard *** typical application scenarios,including a file-sharing system based on a blockchain network and a robust key escrow system with threshold cryptography,are presented.

关键词： Post-quantum cryptography Threshold proxy re-encryption Lattices Robustness Decentralization

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Community Evolution Tracking Based on Core Node Extension and Edge Variation Discerning 17th

Community Evolution Tracking Based on Core Node Extension a...

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Zhuang, Qifeng Yu, Zhiyong Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Communities exist anywhere in various complex networks, and community evolution tracking is one of the most well-liked areas of inquiry in the study of dynamic complex networks. Community evolution tracking has many applications in daily life, such as predicting social network behaviors or analyzing the spread of infectious illnesses. However, the majority of existing evolution tracking algorithms obtain community detection results before matching the community in tracking evolution events, making it difficult to trace the whole evolution of communities because of community matching errors. In addition, the majority of evolution tracking algorithms do not adequately account for the potential scenarios in community evolution, resulting in erroneous detection of evolution events. In this research, a community evolution tracking algorithm based on edge variation discerning and core node extension is proposed. First, we detect communities based on the core node extension strategy, which avoids the problem of community matching errors. Second, we track community evolution based on the edge variation discerning strategy, which fully considers various situations that may occur during the community evolution process. According to the outputs of our experiments, our system can effectively track the evolution of communities in synthetic dynamic networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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A k-nearest neighbor query method based on trust and location privacy protection

A k-nearest neighbor query method based on trust and locatio...

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作者： Guo, Liangmin Zhu, Ying Yang, Hao Luo, Yonglong Sun, Liping Zheng, Xiaoyao School of Computer and Information Anhui Normal University Wuhu China Anhui Provincial Key Laboratory of Network and Information Security Wuhu China

Spatial query is an important supporting technology in the Internet of Things (IoT) and location-based services (LBS). The k-nearest neighbor query is widely used for spatial queries. However, user location privacy may be leaked in the query. In addition, some users are malicious or uncooperative. With the objective of overcoming these problems, a k-nearest neighbor query method based on trust and location privacy protection is proposed. First, we employ a new K-anonymity method based on cooperation to protect a query user's location privacy. In this method, the query user constructs an anonymous group by introducing a trust mechanism to incentivize cooperation among users. Then, according to the different radii of the selection area set by the query user, agent users with higher reputation values who send query requests for the query user are selected. Finally, the agent users obtain the query results from the LBS server and forward them to the query user, and the query user screens the results according to his or her real location. The experiments show that our method can effectively stimulate users to cooperate, better exclude malicious users, and improve the accuracy of the query results while protecting the privacy of the query user. © 2020 John Wiley & Sons, Ltd.

关键词： Location based services

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Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models

arXiv

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arXiv 2025年

作者： Zhang, Yanyue He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute

Personalized opinion summarization is crucial as it considers individual user interests while generating product summaries. Recent studies show that although large language models demonstrate powerful text summarization and evaluation capabilities without the need for training data, they face difficulties in personalized tasks involving long texts. To address this, Rehearsal, a personalized opinion summarization framework via LLMs-based role-playing is proposed. Having the model act as the user, the model can better understand the user’s personalized needs. Additionally, a role-playing supervisor and practice process are introduced to improve the role-playing ability of the LLMs, leading to a better expression of user needs. Furthermore, through suggestions from virtual users, the summary generation is intervened, ensuring that the generated summary includes information of interest to the user, thus achieving personalized summary generation. Experiment results demonstrate that our method can effectively improve the level of personalization in large model-generated summaries. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

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