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Thermochromic Smart Windows with Ultra-High Solar Modulation and Ultra-Fast Responsive Speed Based on Solid–Liquid Switchable Hydrogels

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Research 2024年第4期2024卷 2-13页

作者： Guangjun Zhu Gang Xu Yu Zhang Guo Lu Xuan Cai Wei Zhang Wei She Changwen Miao State Key Laboratory of High Performance Civil Engineering Materials Southeast UniversityNanjing 211189China School of Materials Science and Engineering Southeast UniversityNanjing 211189China Jiangsu Key Laboratory of Advanced Metallic Materials Southeast UniversityNanjing 211189China School of Civil Engineering and Architecture Shandong University of Science and TechnologyQingdao 266590China Wuhan National Laboratory for Optoelectronics School of Optical and Electronic InformationHuazhong University of Science and TechnologyWuhan 430074China

Thermo-responsive hydrogels can dynamically modulate incident light,providing a broad prospect for development of smart windows,which are of pivotal importance for energy conservation in ***,these hydrogels normally exhibit slow response speed and tend to contract over extended phase transition,compromising structural integrity of smart *** this study,a solid–liquid switchable thermochromic hydrogel,denoted as SL-PNIPAm,was synthesized by cross-linking PNIPAm with AMEO through dynamic imine *** to its distinctive solid–liquid transformation characteristics,SL-PNIPAm demonstrates rapid response time(within 5 s)and retains structural integrity without undergoing shrinkage during heating/cooling and freezing/thawing cycles.

关键词： bonds linking freezing

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C2P-Net: Comprehensive Depth Map to Planar Depth Conversion for Room Layout Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5350-5367页

作者： Zhang, Weidong Zhou, Mengjie Cheng, Jiyu Liu, Ying Zhang, Wei Xi'an University of Posts & Telecommunications School of Communications and Information Engineering China Xi'an Key Laboratory of Image Processing Technology and Applications for Public Security China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China

Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requires the reconstruction of these planes. Some studies reconstruct indoor planes from perspective images by learning pixel-level or instance-level plane parameters. However, directly learning these parameters has the problems of susceptibility to occlusions and position dependency. In this paper, we introduce the Comprehensive depth map to Planar depth (C2P) conversion, which reformulates planar depth reconstruction into the prediction of a comprehensive depth map and planar visibility confidence. Based on the parametric representation of planar depth we propose, the C2P conversion is applicable to both panoramic and perspective images. Accordingly, we present an effective framework for room layout estimation that jointly learns the comprehensive depth map and planar visibility confidence. Due to the differentiability of the C2P conversion, our network autonomously learns planar visibility confidence by constraining the estimated plane parameters and reconstructed planar depth map. We further propose a novel approach for 3D layout generation through sequential planar depth map integration. Experimental results demonstrate the superiority of our method across all evaluated panoramic and perspective datasets. © 1979-2012 IEEE.

关键词： Layout Three Dimensional Displays Image Reconstruction Estimation Cameras Floors Cognition Transforms Training Semantics Room Layout Estimation Monocular Depth Estimation Planar Depth Reconstruction Planar Visibility Confidence Function Of Depth Comprehensive Function Room Layout Depth Planes Depth Conversion Layout Estimation Sequence Mapping Integration Of Sequences Joint Learning Panoramic Images Planar Graphs Parameter Plane Visible Light Validation Set Intersection Over Union Probability Function Training Images Weight Coefficient Output Channels Depth Values Adjacent Walls Planar Regions Semantic Labels Pixel Coordinates Dice Loss Non Planar Surfaces Depth Estimation Global Vision Camera Pose Camera Coordinate system

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Transient receptor potential vanilloid-1 (TRPV1) is a mediator of noise-induced neural damage in zebrafish and mice

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science China Life sciences 2025年第7期68卷 2028-2042页

作者： Ruicun Liu Boyu Luo Honglu Yan Qing Lin Wei Liu Xiaowei Hao Shuai Huang Zhenjun Luo Tuoyu Liu Jinyu Li Zhiyuan Shi Songzuo Liu Qing Yuan Yue Teng State Key Laboratory of Pathogen and Biosecurity Beijing Institute of Microbiology and Epidemiology National Key Laboratory of Underwater Acoustic Technology Harbin Engineering University Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University) Ministry of Industry and Information Technology College of Underwater Acoustic Engineering Harbin Engineering University School of Medicine South China University of Technology Laboratory Department in Second Medical Center of PLA General Hospital Department of Urology the Third Medical Center of Chinese PLA General Hospital School of Pharmaceutical Science and Technology Faculty of Medicine Tianjin University

Sound pollution（noise） is an increasing environmental concern, particularly associated with neurological and neurobehavioral abnormalities. However, the molecular mechanisms underlying noise-induced neural damage remain unclear. In this study, we conducted transcriptional profiling of zebrafish to investigate the mechanisms underlying acoustic stimulation（1,000 Hz, 130 d B）. RNA sequencing and subsequent experiments revealed that TRPV1 is an important mediator of noise-induced neural damage in Hu C（elavl3）-GFP transgenic zebrafish. The results demonstrated that inhibiting TRPV1 significantly mitigated noise-induced neural damage in zebrafish with trpv1 gene RNAi and in mice with Trpv1 knockout(Trpv1-/-). Specifically, TRPV1 antagonism significantly reduced neural damage in zebrafish and mice under noise exposure. Furthermore, activated TRPV1 could induce endoplasmic reticulum stress, leading to apoptosis and resulting in neural damage in mice and HEK293T cells. The findings of this study not only enhance our understanding of the molecular mechanisms underlying sound-induced neural damage but also highlight a novel target for drug intervention.

关键词： noise pollution TRPV1 neural damage mechanisms apoptosis

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Driving into Danger: Adversarial Patch Attack on End-To-End Autonomous Driving systems Using Deep Learning 28

Driving into Danger: Adversarial Patch Attack on End-To-End ...

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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Wang, Tong Kuang, Xiaohui Li, Hu Du, Qianjin Hu, Zhanhao Deng, Huan Zhao, Gang Institute of System Engineering Academy of Military Sciences Beijing China National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (纸本)9798350300482

Deep learning-based autonomous driving systems have been extensively researched due to their superior performance compared to traditional methods. Specifically, end-To-end deep learning systems have been developed, which directly output control signals for vehicles using various sensor inputs. However, deep learning techniques are vulnerable to security issues, generating adversarial examples that can attack the output of the relevant model. This paper proposes an adversarial example generation method that applies a patch to pedestrians' clothing, which can generate dangerous behaviors when the pedestrian appears within the camera lens, thereby attacking the end-To-end autonomous driving system. The proposed method is validated using the CARLA simulator, and the results demonstrate successful attacks in various weather and lighting conditions, exposing the security vulnerabilities of this type of system. This study highlights the need for further research to address these vulnerabilities and ensure the safety of autonomous driving systems. © 2023 IEEE.

关键词： Deep learning

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Multi-level Multi-task representation learning with adaptive fusion for multimodal sentiment analysis

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Neural Computing and Applications 2025年第3期37卷 1491-1508页

作者： Zhu, Chuanbo Chen, Min Li, Haomin Zhang, Sheng Liang, Han Sun, Chao Liu, Yifan Chen, Jincai Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan430074 China School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510641 China Pazhou Laboratory Guangdong Guangzhou510640 China School of Computer Science and Technology Huazhong University of Science and Technology Hubei Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China Hubei Wuhan430074 China

Multimodal sentiment analysis is an active task in multimodal intelligence, which aims to compute the user’s sentiment tendency from multimedia data. Generally, each modality is a specific and necessary perspective to express human sentiment, providing complementary and consensus information unavailable in a single modality. Nevertheless, the heterogeneous multimedia data often contain inconsistent and conflicting sentiment semantics that limits the model performance. In this work, we propose a Multi-level Multi-task Representation Learning with Adaptive Fusion (MuReLAF) network to bridge the semantic gap among different modalities. Specifically, we design a modality adaptive fusion block to adjust modality contributions dynamically. Besides, we build a multi-level multimodal representations framework to obtain modality-specific and modality-shared semantics by the multi-task learning strategy, where modality-specific semantics contain complementary information and modality-shared semantics include consensus information. Extensive experiments are conducted on four publicly available datasets: MOSI, MOSEI, SIMS, and SIMSV2(s), demonstrating that our model exhibits superior or comparable performance to state-of-the-art models. The achieved accuracies are 86.28%, 86.07%, 84.46%, and 82.78%, respectively, showcasing improvements of 0.82%, 0.84%, 1.75%, and 1.83%. Further analyses also indicate the effectiveness of our model in sentiment analysis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Multi-task learning

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A Unified Framework for Analysis and Optimization of RIS-Assisted MIMO Multiple Access Networks

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IEEE Internet of Things Journal 2025年第13期12卷 25224-25239页

作者： Wang, Siqiang Zheng, Zhong Guo, Jing Fei, Zesong Sun, Zhi Zhang, Qin Beijing Institute of Technology School of Information and Electronics Beijing China Beijing Institute of Technology School of Information and Electronics National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China Tsinghua University Department of Electronic Engineering Beijing China

Recently, reconfigurable intelligent surfaces (RISs) are gaining increasing attention in communication systems due to their ability to adapt themself according to the wireless environment. Therefore, the communication systems with massive RIS deployments are able to extend high-quality coverage ubiquitously. This is especially beneficial for Internet of Things (IoT) systems as the IoT devices can be located in hard-to-reach area. On the other hand, current methods to analyze and optimize the performance of communication systems with multiple RISs deployment are ad hoc, which depends on the specific system configurations. There lacks a unified theoretical framework that provides general analytical treatment for both passive and active RIS-assisting systems, possibly having multiple concatenated reflections via RISs, which is typical in IoT communication scenarios. Thus, we investigate general uplink RIS-assisted multi-user multiple-input multiple-output (MU-MIMO) communication systems under general Rician fading channels, where the number of cascaded RIS panels are arbitrary and the RIS elements can be active or passive. By utilizing the linearization trick and the operator-valued free probability theory, a unified analytic expression of the ergodic sum rate for the considered MU-MIMO systems is derived. Then, we further propose a low-complexity optimization approach to design the RISs’ phase shifts, thus enhancing the ergodic sum rate. Numerical results verify the accuracy of the analytical results for RIS-assisted systems. In addition, the convergence and effectiveness of the proposed optimization algorithm are demonstrated. © 2014 IEEE.

关键词： Fading channels

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Stochastic Gradient Compression for Federated Learning over Wireless Network

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China Communications 2024年第4期21卷 230-247页

作者： Lin Xiaohan Liu Yuan Chen Fangjiong Huang Yang Ge Xiaohu School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyNanjing University of Aeronautics and AstronauticsNanjing 210016China School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhan 430074China

As a mature distributed machine learning paradigm,federated learning enables wireless edge devices to collaboratively train a shared AI-model by stochastic gradient descent(SGD).However,devices need to upload high-dimensional stochastic gradients to edge server in training,which cause severe communication *** address this problem,we compress the communication by sparsifying and quantizing the stochastic gradients of edge *** first derive a closed form of the communication compression in terms of sparsification and quantization ***,the convergence rate of this communicationcompressed system is analyzed and several insights are ***,we formulate and deal with the quantization resource allocation problem for the goal of minimizing the convergence upper bound,under the constraint of multiple-access channel *** show that the proposed scheme outperforms the benchmarks.

关键词： federated learning gradient compression quantization resource allocation stochastic gradient descent(SGD)

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Multi-sensor missile-borne LiDAR point cloud data augmentation based on Monte Carlo distortion simulation

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CAAI Transactions on Intelligence technology 2025年第1期10卷 300-316页

作者： Luda Zhao Yihua Hu Fei Han Zhenglei Dou Shanshan Li Yan Zhang Qilong Wu College of Electronic Engineering National University of Defense TechnologyHefeiChina State Key Laboratory of Pulsed Power Laser Technology National University of Defense TechnologyHefeiChina Anhui Province Key Laboratory of Electronic Restriction National University of Defense TechnologyHefeiChina Information Security Research Center Hefei Comprehensive National Science CenterHefeiChina Anhui Provincial Military Region Chinese PLAHefeiChina

Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing *** to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and ***,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding *** address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical ***,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud *** effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this *** experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.

关键词： data augmentation LiDAR missile-borne imaging Monte Carlo simulation point cloud

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Slippery quartz surfaces for anti‐fouling optical windows

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Droplet 2023年第1期2卷 47-55页

作者： Minjing Li Tongzhen Yang Qing Yang Shaokun Wang Zheng Fang Yang Cheng Xun Hou Feng Chen School of Mechanical Engineering Xi'an Jiaotong UniversityXi'anChina State Key Laboratory for Manufacturing System Engineering and Shaanxi Key Laboratory of Photonics Technology for Information School of Electronic Science and EngineeringXi'an Jiaotong UniversityXi'anChina

The surface of camera‐based medical devices is easily smeared by blood and fog during the surgical procedure,causing visual field loss and bringing great distress to both doctors and *** this article,a slippery liquid‐infused porous surface(SLIPS)on a quartz window surface that can repel various liquids,especially blood droplets is reported.A femtosecond laser pulse train was used to create periodic microhole structures on the silica *** subsequent low surface energy treatment and lubricant infusion led to the successful preparation of a slippery *** blood‐repellent windows exhibited high transparency,great antifogging,and antibacterial *** addition,the slippery ability of the as‐prepared surface exhibited outstanding stability since the surface could withstand harsh treatments/environments,such as repeated pipette scratches and immersion in different pH *** as‐prepared millimeter‐sized quartz samples with SLIPS were attached to the endoscope lens as a protective coating and could maintain high visibility after repeated immersion in *** believe that the coating developed in this study will provide inspiration for the design of next‐generation endoscopes or other camera‐guided devices that will resist fouling,keep clear vision,and reduce operation time,thus offering great potential applications in lesion diagnosis and therapy.

关键词： quartz fouling visibility

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Automated object recognition in high-resolution optical remote sensing imagery

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national science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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