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arXiv 2024年

作者： Du, Zhuoyun Zheng, Lujie Hu, Renjun Xu, Yuyang Li, Xiawei Sun, Ying Chen, Wei Wu, Jian Cai, Haolei Ying, Haochao State Key Lab of CAD & CG Zhejiang University China Zhejiang Polytechnic Institute Polytechnic Institute Zhejiang University China College of Computer Science & Technology Zhejiang University China Alibaba Group China Sun Yat-sen University Cancer Center China School of Public Health Zhejiang University China Second Affiliated Hospital Zhejiang University School of Medicine China Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence China

Training medical personnel using standardized patients (SPs) remains a complex challenge, requiring extensive domain expertise and role-specific practice. Most research on Large Language Model (LLM)-based simulated patients focuses on improving data retrieval accuracy or adjusting prompts through human feedback. However, this focus has overlooked the critical need for patient agents to learn a standardized presentation pattern that transforms data into human-like patient responses through unsupervised simulations. To address this gap, we propose EvoPatient, a novel simulated patient framework in which a patient agent and doctor agents simulate the diagnostic process through multi-turn dialogues, simultaneously gathering experience to improve the quality of both questions and answers, ultimately enabling human doctor training. Extensive experiments on various cases demonstrate that, by providing only overall SP requirements, our framework improves over existing reasoning methods by more than 10% in requirement alignment and better human preference, while achieving an optimal balance of resource consumption after evolving over 200 cases for 10 hours, with excellent generalizability. The code will be available at https://***/ZJUMAI/EvoPatient. Copyright © 2024, The Authors. All rights reserved.

关键词： Personnel training

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Electro-optic tuning of a single-frequencyultranarrow linewidth microdisk laser

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Advanced Photonics 2022年第3期4卷 105-113页

作者： Jintian Lin Saeed Farajollahi Zhiwei Fang Ni Yao Renhong Gao Jianglin Guan Li Deng Tao Lu Min Wang Haisu Zhang Wei Fang Lingling Qiao Ya Cheng Chinese Academy of Sciences(CAS) Shanghai Institute of Optics and Fine Mechanics(SIOM)State Key Laboratory of High Field Laser Physics and CAS Center for Excellence in Ultra-Intense Laser ScienceShanghaiChina University of Chinese Academy of Sciences Center of Materials Science and Optoelectronics EngineeringBeijingChina University of Victoria Department of Electrical and Computer EngineeringVictoriaBritish ColumbiaCanada East China Normal University School of Physics and Electronic ScienceXXL—The Extreme Optoelectromechanics LaboratoryShanghaiChina Research Center for Intelligent Sensing Zhejiang LabHangzhouChina Zhejiang University College of Optical Science and EngineeringThe Interdisciplinary Center for Quantum InformationState Key Laboratory of Modern Optical InstrumentationHangzhouChina East China Normal University State Key Laboratory of Precision SpectroscopyShanghaiChina Jiaxing Institute of Zhejiang University Intelligent Optics&Photonics Research CenterJiaxingChina Shanxi University Collaborative Innovation Center of Extreme OpticsTaiyuanChina Shandong Normal University Collaborative Innovation Center of Light Manipulations and ApplicationsJinanChina Shanghai Research Center for Quantum Sciences Shang

Single-frequency ultranarrow linewidth on-chip microlasers with a fast wavelength tunability play a game-changing role in a broad spectrum of applications ranging from coherent communication,light detection and ranging,to metrology and *** and fabrication of such light sources remain a challenge due to the difficulties in making a laser cavity that has an ultrahigh optical quality(Q)factor and supports only a single lasing frequency ***,we demonstrate a unique single-frequency ultranarrow linewidth lasing mechanism on an erbium ion-doped lithium niobate(LN)microdisk through simultaneous excitation of high-Q polygon modes at both pump and laser *** the polygon modes are sparse within the optical gain bandwidth compared with the whispering gallery mode counterpart,while their Q factors(above 10 million)are even higher due to the significantly reduced scattering on their propagation paths,single-frequency lasing with a linewidth as narrow as 322 Hz is *** measured linewidth is three orders of magnitude narrower than the previous record in on-chip LN ***,enabled by the strong linear electro-optic effect of LN,real-time electro-optical tuning of the microlaser with a high tuning efficiency of∼50 pm∕100 V is demonstrated.

关键词： lasers lithium niobate microcavities integrated opti

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Dynamic neural network with memristive CIM and CAM for 2D and 3D vision

arXiv

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arXiv 2024年

作者： Zhang, Yue Zhang, Woyu Wang, Shaocong Lin, Ning Yu, Yifei He, Yangu Wang, Bo Jiang, Hao Lin, Peng Xu, Xiaoxin Qi, Xiaojuan Wang, Zhongrui Zhang, Xumeng Shang, Dashan Liu, Qi Cheng, Kwang-Ting Liu, Ming Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Microelectronic Devices & Integrated Technology Institute of Microelectronics Chinese Academy of Sciences Beijing100049 China ACCESS – AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong Institute of the Mind The University of Hong Kong Hong Kong Key Lab of Fabrication Technologies for Integrated Circuits Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Integrated Chips and Systems Frontier Institute of Chip and System Fudan University Shanghai200433 China University of Chinese Academy of Sciences Beijing100049 China College of Computer Science and Technology Zhejiang University Zhejiang310027 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

The brain is dynamic, associative and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network (DNN) using memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based Computing-In-Memory (CIM) and Content-Addressable Memory (CAM) circuits, respectively. We validate our co-designs, using a 40nm memristor macro, on ResNet and PointNet++ for classifying images and 3D points from the MNIST and ModelNet datasets, which not only achieves accuracy on par with software but also a 48.1% and 15.9% reduction in computational budget. Moreover, it delivers a 77.6% and 93.3% reduction in energy consumption. © 2024, CC BY.

关键词： Memristors

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Deep Unfolding Neural Networks for Fluid Antenna-Enhanced Vehicular Communication

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IEEE Transactions on Vehicular Technology 2025年

作者： Feng, Biqian Feng, Chenyuan Wong, Kai-Kit Quek, Tony Q. S. University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China EURECOM Sophia Antipolis 06410 France University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore

Fluid antenna (FA) technology has emerged as a promising technology to achieve higher spectral and energy efficiency by introducing a new dimension. However, the antenna position configuration inevitably increases computational complexity, presenting challenges under real-time configuration requirements, especially in vehicular communication systems characterized by rapidly time-varying channels. To address these issues, this paper investigates the classical weighted sum rate maximization problem and proposes an optimization-empowered neural network framework designed to accelerate convergence without compromising accuracy. Extensive simulations demonstrate that the proposed approach effectively mitigates the computational burdens associated with FAs, delivering superior performance in terms of convergence rate and system performance, thus paving the way for the deployment of next-generation FA-enabled communication systems. © 2025 IEEE.

关键词： Deep neural networks

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Efficient Adaptive label Refinement for label Noise Learning

arXiv

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arXiv 2025年

作者： Zhang, Wenzhen Cheng, Debo Lu, Guangquan Zhou, Bo Li, Jiaye Zhang, Shichao School of Computer Science and Engineering Guangxi Normal University Guangxi Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guangxi Guilin541004 China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guangxi Guilin541004 China UniSA STEM University of South Australia AdelaideSA5095 Australia Guangxi Collaborative Innovation Center of Modern Sericulture and Silk Hechi University Guangxi Hechi546300 China The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou310027 China

Deep neural networks are highly susceptible to overfitting noisy labels, which leads to degraded performance. Existing methods address this issue by employing manually defined criteria, aiming to achieve optimal partitioning in each iteration to avoid fitting noisy labels while thoroughly learning clean samples. However, this often results in overly complex and difficult-to-train models. To address this issue, we decouple the tasks of avoiding fitting incorrect labels and thoroughly learning clean samples and propose a simple yet highly applicable method called Adaptive label Refinement (ALR). First, inspired by label refurbishment techniques, we update the original hard labels to soft labels using the model’s predictions to reduce the risk of fitting incorrect labels. Then, by introducing the entropy loss, we gradually ‘harden’ the high-confidence soft labels, guiding the model to better learn from clean samples. This approach is simple and efficient, requiring no prior knowledge of noise or auxiliary datasets, making it more accessible compared to Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Constrained maximum weighted bipartite matching:a novel approach to radio broadcast scheduling

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science China(Information sciences) 2019年第7期62卷 156-169页

作者： Shaojiang WANG Tianyong WU Yuan YAO Dongbo BU Shaowei CAI State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing Key Lab of Human-Computer Interaction Institute of SoftwareChinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences

Given a set of radio broadcast programs, the radio broadcast scheduling problem is to allocate a set of devices to transmit the programs to achieve the optimal sound quality. In this article, we propose a complete algorithm to solve the problem, which is based on a branch-and-bound(BnB) algorithm. We formulate the problem with a new model, called constrained maximum weighted bipartite matching(CMBM),i.e., the maximum matching problem on a weighted bipartite graph with constraints. For the reduced matching problem, we propose a novel BnB algorithm by introducing three new strategies, including the highest quality first, the least conflict first and the more edge first. We also establish an upper bound estimating function for pruning the search space of the algorithm. The experimental results show that our new algorithm can quickly find the optimal solution for the radio broadcast scheduling problem at small scales, and has higher scalability for the problems at large scales than the existing complete algorithm.

关键词： radio broadcast scheduling branch-and-bound algorithm constrained maximum weighted bipartite matching Kuhn-Munkres algorithm strategy combinations

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Doping Shortens the Metal/Metal Distance and Promotes OH Coverage in Non-Noble Acidic Oxygen Evolution Reaction Catalysts

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Journal of the American Chemical Society 2023年第14期145卷 7829-7836页

作者： Wang, Ning Ou, Pengfei Miao, Rui Kai Chang, Yuxin Wang, Ziyun Hung, Sung-Fu Abed, Jehad Ozden, Adnan Chen, Hsuan-Yu Wu, Heng-Liang Huang, Jianan Erick Zhou, Daojin Ni, Weiyan Fan, Lizhou Yan, Yu Peng, Tao Sinton, David Liu, Yongchang Liang, Hongyan Sargent, Edward H. Department of Electrical and Computer Engineering University of Toronto 35 St George Street TorontoONM5S 1A4 Canada School of Materials Science and Engineering Key Laboratory of Efficient Utilization of Low and Medium Grade Energy Ministry of Education Tianjin University Tianjin300350 China Department of Mechanical and Industrial Engineering University of Toronto 5 King’s College Road TorontoONM5S 3G8 Canada School of Chemical Sciences The University of Auckl Auckland1010 New Zealand Department of Applied Chemistry National Yang Ming Chiao Tung University Hsinchu300 Taiwan Center for Condensed Matter Sciences National Taiwan University Taipei10617 Taiwan Center of Atomic Initiative for New Materials National Taiwan University Taipei10617 Taiwan State Key Lab of Hydraulic Engineering Simulation and Safety School of Materials Science & Engineering Tianjin University Tianjin300354 China

Acidic water electrolysis enables the production of hydrogen for use as a chemical and as a fuel. The acidic environment hinders water electrolysis on non-noble catalysts, a result of the sluggish kinetics associated with the adsorbate evolution mechanism, reliant as it is on four concerted proton-electron transfer steps. Enabling a faster mechanism with non-noble catalysts will help to further advance acidic water electrolysis. Here, we report evidence that doping Ba cations into a Co3O4 framework to form Co3-xBaxO4 promotes the oxide path mechanism and simultaneously improves activity in acidic electrolytes. Co3-xBaxO4 catalysts reported herein exhibit an overpotential of 278 mV at 10 mA/cm2 in 0.5 M H2SO4 electrolyte and are stable over 110 h of continuous water oxidation operation. We find that the incorporation of Ba cations shortens the Co-Co distance and promotes OH adsorption, findings we link to improved water oxidation in acidic electrolyte. © 2023 American Chemical Society.

关键词： Catalysts

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Scalable Electrophysiology of Millimeter-Scale Animals with Electrode Devices

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Biomedical Engineering Frontiers 2023年第1期4卷 3-20页

作者： Kairu Dong Wen-Che Liu Yuyan Su Yidan Lyu Hao Huang Nenggan Zheng John A.Rogers Kewang Nan College of Pharmaceutical Sciences Zhejiang UniversityHangzhou 310058China National Key Laboratory of Advanced Drug Delivery and Release Systems Zhejiang UniversityHangzhou 310058China College of Biomedical Engineering&Instrument Science Zhejiang UniversityHangzhou310027China Department of Gastroenterology Brigham and Women’s HospitalHarvard Medical SchoolBostonMA 02115USA College of Chemical and Biological Engineering Zhejiang UniversityHangzhou 310058China Qiushi Academy for Advanced Studies Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhou 310058China CCAI by MOE and Zhejiang Provincial Government(ZJU) Hangzhou 310027China Querrey Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Department of Biomedical Engineering Northwestern UniversityEvanstonIL 60208USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL 60208USA Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA Jinhua Institute of Zhejiang University Jinhua 321299China

Millimeter-scale animals such as Caenorhabditis elegans,Drosophila larvae,zebrafish,and bees serve as powerful model organisms in the fields of neurobiology and *** methods exist for recording large-scale electrophysiological signals from these *** approaches often lack,however,real-time,uninterrupted investigations due to their rigid constructs,geometric constraints,and mechanical mismatch in integration with soft *** recent research establishes the foundations for 3-dimensional flexible bioelectronic interfaces that incorporate microfabricated components and nanoelectronic function with adjustable mechanical properties and multidimensional variability,offering unique capabilities for chronic,stable interrogation and stimulation of millimeter-scale animals and miniature tissue *** review summarizes the most advanced technologies for electrophysiological studies,based on methods of 3-dimensional flexible bioelectronics.A concluding section addresses the challenges of these devices in achieving freestanding,robust,and multifunctional biointerfaces.

关键词： offering recording millimeter

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Progressive frequency-decoupled network with neighbor-aware self-evolving convolution kernels for pansharpening

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Pattern Recognition 2026年 169卷

作者： Rui Miao Fengguang Peng Dong Dong Hang Shi Zhejiang Lab Hangzhou China State Key Laboratory of Ocean Sensing Hangzhou China School of Computer Science and Engineering Beihang University China Department of Computer Science and Technology Tsinghua University China

Pansharpening uses the spatial details of the panchromatic image (PAN) to sharpen the multispectral image (MS), so as to obtain a higher-resolution multispectral image for subsequent vision tasks. Existing methods focus more on high-level semantic feature extraction, the diverse features cannot be effectively distinguished and fully utilized. In this paper, we propose a novel strategy to generate neighbor-aware self-evolving convolution kernels (NSConv) and introduce a frequency-decoupled block (FDBlock) for deep fusion. The proposed NSConv can dynamically optimize in multiple dimensions and enhance the ability to extract locally correlated features. Furthermore, the FDBlock realizes the interactive fusion of dual-domain features based on NSConv to learn the complementary representation, making the overall network have a more diverse representation and strong neighborhood feature extraction ability. We conduct extensive qualitative and quantitative experiments to validate the effectiveness of the proposed network and demonstrate its significant performance against other state-of-the-art methods.

关键词：

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Resolution Switchable Networks for Runtime Efficient Image Recognition 16th

Resolution Switchable Networks for Runtime Efficient Image R...

引用

16th European Conference on computer Vision, ECCV 2020

作者： Wang, Yikai Sun, Fuchun Li, Duo Yao, Anbang State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Cognitive Computing Laboratory Intel Labs China Beijing China

ISBN: (纸本)9783030585549

We propose a general method to train a single convolutional neural network which is capable of switching image resolutions at inference. Thus the running speed can be selected to meet various computational resource limits. Networks trained with the proposed method are named Resolution Switchable Networks (RS-Nets). The basic training framework shares network parameters for handling images which differ in resolution, yet keeps separate batch normalization layers. Though it is parameter-efficient in design, it leads to inconsistent accuracy variations at different resolutions, for which we provide a detailed analysis from the aspect of the train-test recognition discrepancy. A multi-resolution ensemble distillation is further designed, where a teacher is learnt on the fly as a weighted ensemble over resolutions. Thanks to the ensemble and knowledge distillation, RS-Nets enjoy accuracy improvements at a wide range of resolutions compared with individually trained models. Extensive experiments on the ImageNet dataset are provided, and we additionally consider quantization problems. Code and models are available at https://***/yikaiw/RS-Nets. © 2020, Springer Nature Switzerland AG.

关键词： Distillation

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