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Hundredfold increase of stimulated Brillouin-scattering bandwidth in whispering-gallery mode resonators

Photonics Research 2023年第6期11卷 917-924页

作者： GUOPING LIN JINGYI TIAN TANG SUN QINGHAI SONG YANNE K.CHEMBO Ministry of Industry and Information Technology Key Laboratory of Micro-Nano Optoelectronic Information System School of ScienceHarbin Institute of TechnologyShenzhen 518055China Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems Shenzhen 518055China Department of Electrical and Computer Engineering&Institute for Research in Electronics and Applied Physics(IREAP) University of MarylandCollege ParkMaryland 20742USA

Backward stimulated Brillouin scattering(SBS)is widely exploited for various applications in optics and *** typically features a narrow gain bandwidth of a few tens of megahertz in fluoride *** we report a hundredfold increase of SBS bandwidth in whispering-gallery mode *** crystalline orientation results in a large variation of the acoustic phase velocity upon propagation along the periphery,from which a broad Brillouin gain is *** 2.5 GHz wide Brillouin gain profile is theoretically found and experimentally *** phenomena with Brillouin shift frequencies ranging from 11.73 to 14.47 GHz in ultrahigh Q Z-cut magnesium fluoride cavities pumped at the telecommunication wavelength are ***,the Brillouin-Kerr comb in this device is *** 400 comb lines spanning across a spectral window of 120 nm are *** finding paves a new way for tailoring and harnessing the Brillouin gain in crystals.

关键词： scattering pumped Brillouin

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Dynamic Graph Neural Networks for Joint Terahertz based Sensing and Communication Optimization in Vehicular Networks

Dynamic Graph Neural Networks for Joint Terahertz based Sens...

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IEEE Conference on Wireless Communications and Networking

作者： Xuefei Li Mingzhe Chen Ye Hu Zhilong Zhang Danpu Liu Shiwen Mao Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijina China Department-of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Industrial and System Engineering University of Miami Coral Gables FL USA Department of Electrical and Computer Engineering Auburn University Auburn AL USA

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based scheme can improve the number of successfully served vehicles by up to 17% compared to a GNN based algorithm with a fixed neural network model.

关键词： Network topology Simulation Heuristic algorithms Graph neural networks Sensors Data mining Vehicle dynamics

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(θ,θ∗)-Operator-Driven Intuitionistic Fuzzy Matrix Composite Operation and Its Optimal Application in Medical Diagnosis 11th

(θ,θ∗)-Operator-Driven Intuitionistic Fuzzy Matrix Compos...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Kang, Yuqi Feng, Huarong Zhang, Xianyong Yang, Jilin Liang, Qian School of Mathematical Sciences Sichuan Normal University Chengdu610066 China National-Local Joint Engineering Laboratory of System Credibility Automatic Verification Research Center of Sichuan Normal University Chengdu610066 China College of Science Xihua University Chengdu610039 China College of Computer Science Sichuan Normal University Chengdu610066 China

ISBN: (纸本)9783031927430

Intuitionistic fuzzy sets (IFSs) extend fuzzy sets, and they provide a more accurate representation of uncertainty in realistic scenarios. Fuzzy implication operators play a crucial role in fuzzy reasoning. Accordingly, this paper introduces fuzzy implication operators θ and θ∗ to IFSs, and it correspondingly proposes two types of intuitionistic fuzzy matrix composite operations, where the (θ, θ∗) induction is comparatively accompanied with a direct formulation from (∧,∨). Firstly, we investigate concrete properties of fuzzy implication operators θ,θ∗. Then, two IFSs matrix composite operations are proposed, respectively from (∧,∨) and (θ, θ∗). Finally, the two composite operations of IFSs matrices are applied to medical diagnosis, and their application feasibility is verified through a practical medical example;by comparative analyses, the (θ,θ∗)-driven operation better provides right diagnosis results in contrast to previous studies. This study resorts to specific fuzzy implication operators to enrich theoretical research and practical applications of IFSs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fuzzy rules

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Modeling Method of Aircraft Simulation Based on Formal Description of Petri Net 18th

Modeling Method of Aircraft Simulation Based on Formal Des...

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18th Chinese Intelligent systems Conference, CISC 2022

作者： Zhan, Tianming Huang, Yu Geng, Huapin Tong, Jiahui TAIJI Computer Co. Ltd Beijing100102 China Peking University Beijing100871 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100190 China Beijing Electro-Mechanical Engineering Institute Beijing102299 China

ISBN: (纸本)9789811962028

The simulation of aircraft guidance and control system has characteristics such as multiple model classification, large parameter influence,complex information interaction and so on. The traditional code-level model development method has many limitations, such as time consuming, low reusability, low modeling efficiency, and model quality can not be guaranteed. Based on the concurrency, graphic representation, mathematics and simulation characteristics of Petri net, this paper studies the aircraft simulation modeling method of formal description based on Petri net, constructs a general modeling development and integration software tool that can carry out component, parametric and graphical modeling, solves the problems of multi granularity model modeling and automatic generation of real-time simulation model code, and realizes the visual graphic modeling and comprehensive integration of different granularity models such as components, modules and meta models. The research method has been successfully applied in the simulation modeling of an aircraft. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Petri nets

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A Robot Pick and Place Skill Learning Method Based on Maximum Entropy and DDQN Algorithm

A Robot Pick and Place Skill Learning Method Based on Maximu...

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2021 International Conference on Robotics Automation and Intelligent Control, ICRAIC 2021

作者： Wu, Peiliang Zhang, Yan Li, Yao Mao, Bingyi Chen, Wenbai Gao, Guowei School of Information Science and Engineering Yanshan University Hebei Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Hebei Qinhuangdao066004 China School of Automation Beijing Information Science and Technology University Beijing100192 China

Pick and place (PAP) skill learning is a fundamental ability of intelligent robots, such as home service robot. Due to the NP-hard nature of the PAP problem, it takes a long time for an intelligent robot to learn the PAP skill based on current methods. In order to improve the learning efficiency of robot PAP skills, this paper proposes a Soft-DDQN-based PAP skill learning method. Firstly, the Soft-DDQN is proposed by introducing maximum entropy into robot DDQN framework, and the learning goal of Soft-DDQN is to maximize reward and information entropy. Secondly, PAP problem is modelled as a discrete form and Soft-DDQN is applied to solve the PAP problem. Finally, in order to verify the efficiency of Soft-DDQN-based PAP skill learning, comparisons have been given from two standard perspectives and shown that Soft-DDQN improves efficiency of PAP skill learning evidently. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Intelligent robots

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DarkFed: A Data-Free Backdoor Attack in Federated Learning

arXiv

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arXiv 2024年

作者： Li, Minghui Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Zhang, Leo Yu Wang, Yichen School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Federated learning (FL) has been demonstrated to be susceptible to backdoor attacks. However, existing academic studies on FL backdoor attacks rely on a high proportion of real clients with main task-related data, which is impractical. In the context of real-world industrial scenarios, even the simplest defense suffices to defend against the state-of-the-art attack, 3DFed. A practical FL backdoor attack remains in a nascent stage of development. To bridge this gap, we present DarkFed. Initially, we emulate a series of fake clients, thereby achieving the attacker proportion typical of academic research scenarios. Given that these emulated fake clients lack genuine training data, we further propose a data-free approach to backdoor FL. Specifically, we delve into the feasibility of injecting a backdoor using a shadow dataset. Our exploration reveals that impressive attack performance can be achieved, even when there is a substantial gap between the shadow dataset and the main task dataset. This holds true even when employing synthetic data devoid of any semantic information as the shadow dataset. Subsequently, we strategically construct a series of covert backdoor updates in an optimized manner, mimicking the properties of benign updates, to evade detection by defenses. A substantial body of empirical evidence validates the tangible effectiveness of DarkFed. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature

arXiv

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arXiv 2024年

作者： Wang, Yichen Chou, Yuxuan Zhou, Ziqi Zhang, Hangtao Wan, Wei Hu, Shengshan Li, Minghui National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of School of Software Engineering Huazhong University of Science and Technology China

As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations to inputs and cause the model’s incorrect outputs. However, existing PAEs face two challenges: unsatisfactory attack performance (i.e., poor transferability and insufficient robustness to environment conditions), and difficulty in balancing attack effectiveness with stealthiness, where better attack effectiveness often makes PAEs more perceptible. In this paper, we explore a novel perturbation-based method to overcome the challenges. For the first challenge, we introduce a strategy Deceptive RF injection based on robust features (RFs) that are predictive, robust to perturbations, and consistent across different models. Specifically, it improves the transferability and robustness of PAEs by covering RFs of other classes onto the predictive features in clean images. For the second challenge, we introduce another strategy Adversarial Semantic Pattern Minimization, which removes most perturbations and retains only essential adversarial patterns in AEs. Based on the two strategies, we design our method Robust Feature Coverage Attack (RFCoA), comprising Robust Feature Disentanglement and Adversarial Feature Fusion. In the first stage, we extract target class RFs in feature space. In the second stage, we use attention-based feature fusion to overlay these RFs onto predictive features of clean images and remove unnecessary perturbations. Experiments show our method’s superior transferability, robustness, and stealthiness compared to existing state-of-the-art methods. Additionally, our method’s effectiveness can extend to Large Vision-Language Models (LVLMs), indicating its potential applicability to more complex tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors

arXiv

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arXiv 2024年

作者： Zhou, Ziqi Li, Bowen Song, Yufei Yu, Zhifei Hu, Shengshan Wan, Wei Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (e.g., NMS and RPN), yielding some results but simultaneously constraining their scalability. Moreover, most efforts against ODs stem from image-level attacks originally designed for classification tasks, resulting in redundant computations and disturbances in object-irrelevant areas (e.g., background). Consequently, how to design a model-agnostic efficient attack to comprehensively evaluate the vulnerabilities of ODs remains challenging and unresolved. In this paper, we propose NumbOD, a brand-new spatial-frequency fusion attack against various ODs, aimed at disrupting object detection within images. We directly leverage the features output by the OD without relying on its internal structures to craft adversarial examples. Specifically, we first design a dual-track attack target selection strategy to select high-quality bounding boxes from OD outputs for targeting. Subsequently, we employ directional perturbations to shift and compress predicted boxes and change classification results to deceive ODs. Additionally, we focus on manipulating the high-frequency components of images to confuse ODs' attention on critical objects, thereby enhancing the attack efficiency. Our extensive experiments on nine ODs and two datasets show that NumbOD achieves powerful attack performance and high stealthiness. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Pointsurface: Discriminative Point Cloud Surface Feature Extraction for 3d Face Recognition

SSRN

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SSRN 2024年

作者： Yang, Junpeng Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Guangdong Shenzhen518172 China

3D face recognition performs better than 2D face recognition, in terms of robustness against lighting and digital attacks. Compared to 2D data, the rich geometric information in 3D data could be very useful to improve the performance of 3D face recognition. This paper introduces PointSurFace, a surface feature extraction integrated model tailored for point cloud based 3D face recognition. PointSurFace adopts a specially designed surface feature extraction module, which explores information about the position and normals of the 3D face surface to explicitly extract local discriminative features from 3D point cloud faces. Subsequently, it alternates between set abstraction (SA) layers and Inverted Residual MLP (InvResMLP) modules to effectively extract features from the point cloud data. To better handle the spatial relationships in the point cloud data, PointSurFace integrates coordinate and feature data within each SA layer and InvResMLP module. It also applies channel fusion operations to align these different types of data, thereby more effectively extracts point cloud features. Experimental results in 3D facial recognition and verification across multiple datasets demonstrate that PointSurFace achieves the best 3D face recognition performance to date. For example, on Lock3DFace, it achieves the state-of-the-art recognition and verification accuracy of 90.03% and 80.01%, respectively, surpassing previous methods by 2.02% and 3.19%. Results on cross-quality datasets also affirm PointSurFace’s strong generalization capabilities. Ablation experiments validate the effectiveness of each module within PointSurFace, which suggests that the proposed surface feature extraction module significantly enhances the point cloud based 3D facial recognition performance. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Identification Algorithm Framework and Structural model on Input Pattern of Metamorphic Relations 9

Identification Algorithm Framework and Structural model on I...

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9th International Conference on Dependable systems and Their Applications, DSA 2022

作者： Yan, Shiyu Yang, Xiaohua Lu, Zhongjian Li, Meng Gong, Helin Liu, Jie Computer School University of South China Hunan Hengyang China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China Cnnc Key Laboratory on High Trusted Computing Hunan Hengyang China Nuclear Power Institute of China Science and Technology on Reactor System Design Technology Laboratory Chengdu China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Metamorphic Relation (MR) plays a crucial role in Metamorphic Testing (MT), which can be regarded as the implication relation from the input pattern to the output pattern. Based on MR's properties, the identification of Metamorphic Relations (MRs) can be from its input pattern and output pattern, respectively. However, the finding of the effective input pattern is very first and important. Especially for complicated mathematical function includes more than two input variables, we systematically analyze the structural model of input pattern in MR, propose the concept of the class of basic input pattern, as well as prove that any one input pattern can be expressed as a combination of basic input patterns in scientific computing programs. Moreover, the structural model of input pattern in MR is established based on previous proof. Finally, we put forward an identification algorithm framework and analysis its complexity. The core contribution of our method is to decompose the input pattern identification problem in multi-dimensional space into a single one-dimensional. For this reason, the examples show that the complexity of the algorithm is reduced significantly. © 2022 IEEE.

关键词： Computational complexity

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