检索结果-内蒙古大学图书馆

Nonconvex optimization for optimum retrieval of the transmission matrix of a multimode fiber

Advanced Photonics Nexus 2023年第6期2卷 40-50页

作者： Shengfu Cheng Xuyu Zhang Tianting Zhong Huanhao Li Haoran Li Lei Gong Honglin Liu Puxiang Lai The Hong Kong Polytechnic University Department of Biomedical EngineeringHong KongChina The Hong Kong Polytechnic University Shenzhen Research InstituteShenzhenChina Chinese Academy of Sciences Shanghai Institute of Optics and Fine MechanicsKey Laboratory for Quantum OpticsShanghaiChina University of Shanghai for Science and Technology School of Optical-Electrical and Computer EngineeringShanghaiChina University of Science and Technology of China Department of Optics and Optical EngineeringHefeiChina University of Chinese Academy of Sciences Center of Materials Science and Optoelectronics EngineeringBeijingChina The Hong Kong Polytechnic University Photonics Research InstituteHong KongChina

Transmission matrix(TM)allows light control through complex media,such as multimode fibers(MMFs),gaining great attention in areas,such as biophotonics,over the past *** have been taken to retrieve a complex-valued TM directly from intensity measurements with several representative phase-retrieval algorithms,which still see limitations of slow or suboptimum recovery,especially under noisy ***,we propose a modified nonconvex optimization *** numerical evaluations,it shows that the optimum focusing efficiency is approached with less running time or sampling *** comparative tests under different signal-to-noise levels further indicate its improved ***,the superior focusing performance of our algorithm is collectively validated by single-and multispot focusing;especially with a sampling ratio of 8,it achieves a 93.6%efficiency of the gold-standard holography *** on the recovered TM,image transmission through an MMF is realized with high *** to parallel operation and GPU acceleration,our nonconvex approach retrieves a 8685×1024 TM(sampling ratio is 8)with 42.3 s on average on a regular *** proposed method provides optimum efficiency and fast execution for TM retrieval that avoids the need for an external reference beam,which will facilitate applications of deep-tissue optical imaging,manipulation,and treatment.

关键词： transmission matrix phase retrieval multimode fiber imaging wavefront shaping

来源：评论

学校读者我要写书评

暂无评论

Multi-scale fusion clustering algorithm for urban travel features 3

Multi-scale fusion clustering algorithm for urban travel fea...

引用

2023 3rd International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2023

作者： Zhang, Zixiang Zhao, Zhilong Deng, Jiaming Zhao, Yingying Xu, Tao Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China School of Computer and Information Engineering Henan University Kaifeng475004 China International Education College Henan University Zhengzhou450046 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

ISBN: (纸本)9781510672963

Urban travel data typically encompass various temporal and spatial scales and features. Nonetheless, existing traffic travel feature analysis often focuses on a single spatial scale, which may overlook crucial information and hinder features at different scales. Consequently, this could reduce the accuracy of travel pattern recognition. To solve this problem, we introduce a multi-scale fusion transformation algorithm. In our approach, we first establish a multi-level taxi travel data grid system. Then, we apply the I-Clique clustering algorithm to perform grid clustering at each level of the system to identify taxi travel feature regions of different scales. Subsequently, a multi-scale fusion pyramid model is constructed utilizing the acquired travel feature regions at various scales. Finally, we determine the transformation relationships between scales in the pyramid model, enabling the identification of travel feature areas across both known and unknown scales. This approach facilitates the recognition of travel features within cities at any scale. Experimental results demonstrate that the multi-scale fusion transformation algorithm enhances our understanding of spatial aggregation patterns and the hierarchical structure of geographical entities, offering a novel approach for multi-scale urban traffic analysis. © 2024 SPIE. All rights reserved.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

MassSpecGym: a benchmark for the discovery and identification of molecules 24

MassSpecGym: a benchmark for the discovery and identificatio...

引用

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

作者： Roman Bushuiev Anton Bushuiev Niek F. de Jonge Adamo Young Fleming Kretschmer Raman Samusevich Janne Heirman Fei Wang Luke Zhang Kai Dührkop Marcus Ludwig Nils A. Haupt Apurva Kalia Corinna Brungs Robin Schmid Russell Greiner Bo Wang David S. Wishart Li-Ping Liu Juho Rousu Wout Bittremieux Hannes Rost Tytus D. Mak Soha Hassoun Florian Huber Justin J.J. van der Hooft Michael A. Stravs Sebastian Böcker Josef Sivic Tomáš Pluskal Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Bioinformatics Group Wageningen University & Research Department of Computer Science University of Toronto Chair for Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Department of Computer Science University of Antwerp Department of computing science University of Alberta and Alberta Machine Intelligence Institute Department of Molecular Genetics University of Toronto Bright Giant GmbH Department of Computer Science Tufts University Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Department of computing science and Department of Biological Sciences University of Alberta Department of Computer Science Aalto University Mass Spectrometry Data Center National Institute of Standards and Technology Department of Computer Science and Department of Chemical and Biological Engineering Tufts University Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Bioinformatics Group Wageningen University & Research and Department of Biochemistry University of Johannesburg Eawag: Swiss Federal Institute of Aquatic Science and Technology

ISBN: (纸本)9798331314385

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MoRe: Class Patch Attention Needs Regularization for Weakly Supervised Semantic Segmentation 39

MoRe: Class Patch Attention Needs Regularization for Weakly ...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Zhiwei Meng, Yucong Fu, Kexue Wang, Shuo Song, Zhijian Academy for Engineering and Technology Fudan University Shanghai200433 China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Shanghai200032 China Shandong Computer Science Center National Supercomputer Center in Jinan China

ISBN: (纸本)157735897X

Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically uses Class Activation Maps (CAM) to achieve dense predictions. Recently, Vision Transformer (ViT) has provided an alternative to generate localization maps from class-patch attention. However, due to insufficient constraints on modeling such attention, we observe that the Localization Attention Maps (LAM) often struggle with the artifact issue, i.e., patch regions with minimal semantic relevance are falsely activated by class tokens. In this work, we propose MoRe to address this issue and further explore the potential of LAM. Our findings suggest that imposing additional regularization on class-patch attention is necessary. To this end, we first view the attention as a novel directed graph and propose the Graph Category Representation module to implicitly regularize the interaction among class-patch entities. It ensures that class tokens dynamically condense the related patch information and suppress unrelated artifacts at a graph level. Second, motivated by the observation that CAM from classification weights maintains smooth localization of objects, we devise the Localization-informed Regularization module to explicitly regularize the class-patch attention. It directly mines the token relations from CAM and further supervises the consistency between class and patch tokens in a learnable manner. Extensive experiments on PASCAL VOC and MS COCO validate that MoRe effectively addresses the artifact issue and achieves state-of-the-art performance, surpassing recent single-stage and even multi-stage methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Flow graphs

来源：评论

学校读者我要写书评

暂无评论

Efficient Reconstruction of Spatial Features for Remote Sensing Image-Text Retrieval

引用

Transactions of Nanjing University of Aeronautics and Astronautics 2025年第1期42卷 101-111页

作者： ZHANG Weihang CHEN Jialiang ZHANG Wenkai LI Xinming GAO Xin SUN Xian Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100190P.R.China Key Laboratory of Target Cognition and Application Technology(TCAT) Chinese Academy of SciencesBeijing 100190P.R.China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100190P.R.China School of Computer Science and Artificial Intelligence Aerospace Information Technology UniversityJinan 250299P.R.China

Remote sensing cross-modal image-text retrieval(RSCIR)can flexibly and subjectively retrieve remote sensing images utilizing query text,which has received more researchers’attention ***,with the increasing volume of visual-language pre-training model parameters,direct transfer learning consumes a substantial amount of computational and storage ***,recently proposed parameter-efficient transfer learning methods mainly focus on the reconstruction of channel features,ignoring the spatial features which are vital for modeling key entity *** address these issues,we design an efficient transfer learning framework for RSCIR,which is based on spatial feature efficient reconstruction(SPER).A concise and efficient spatial adapter is introduced to enhance the extraction of spatial *** spatial adapter is able to spatially reconstruct the features in the backbone with few parameters while incorporating the prior information from the channel *** conduct quantitative and qualitative experiments on two different commonly used RSCIR *** with traditional methods,our approach achieves an improvement of 3%-11% in sumR *** with methods finetuning all parameters,our proposed method only trains less than 1% of the parameters,while maintaining an overall performance of about 96%.

关键词： remote sensing cross-modal image-text retrieval(RSCIR) spatial features channel features contrastive learning parameter effective transfer learning

来源：评论

学校读者我要写书评

暂无评论

Event Stream-Based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline

Event Stream-Based Visual Object Tracking: A High-Resolution...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiao Wang Shiao Wang Chuanming Tang Lin Zhu Bo Jiang Yonghong Tian Jin Tang School of Computer Science and Technology Anhui University Hefei China University of Chinese Academy of Sciences Beijing China Institute of Optics and Electronics CAS Chengdu China Beijing Institute of Technoloay Beijing China Peng Cheng Laboratory Shenzhen China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Tracking with bio-inspired event cameras has garnered increasing interest in recent years. Existing works either utilize aligned RGB and event data for accurate tracking or directly learn an event-based tracker. The former incurs higher inference costs while the latter may be susceptible to the impact of noisy events or sparse spatial resolution. In this paper, we propose a novel hierarchical knowledge distillation framework that can fully utilize multimodal / multi-view information during training to facilitate knowledge transfer, enabling us to achieve high-speed and low-latency visual tracking during testing by using only event signals. Specifically, a teacher Transformer-based multimodal tracking framework is first trained by feeding the RGB frame and event stream simultaneously. Then, we design a new hierarchical knowledge distillation strategy which includes pairwise similarity, feature representation, and response maps-based knowledge distillation to guide the learning of the student Transformer network. In particular, since existing event-based tracking datasets are all low-resolution (346 × 260), we propose the first large-scale high-resolution (1280 × 720) dataset named EventVOT. It contains 1141 videos and covers a wide range of categories such as pedestrians, vehicles, UAVs, ping pong, etc. Ex-tensive experiments on both low-resolution (FE240hz, Vi-sEvent, COESOT), and our newly proposed high-resolution EventVOT dataset fully validated the effectiveness of our proposed method.

关键词： Knowledge engineering Training Visualization Pedestrians Self-supervised learning Transformers Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Retraction Note: Unlocking the power of recalling enhanced recurrent neural network: channel estimation and data analysis for 5G MIMO communication system

引用

Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Alqahtani, Abdullah Saleh Pathak, Pankaj Pandiaraj, Saravanan Indrapriyadarsini, H. Arun Kumar, U. Nataraju, Susheela Department of Self-Development Skills-IT CFY Deanship King Saud University Riyadh Saudi Arabia Symbiosis Institute of Digital and Telecom Management (SIDTM) Symbiosis International (Deemed University) Lavale Pune India Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India Department of Electrical and Electronics Engineering Faculty of Engineering Karpagam Academy of Higher Education (Deemed to Be University) Coimbatore India Department of Computer Science Engineering G Madegowda Institute of Technology Bharathinagara (K M Doddi) India

来源：评论

学校读者我要写书评

暂无评论

MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework

arXiv

引用

arXiv 2024年

作者： Xu, Xusheng Cui, Jiangyu Cui, Zidong He, Runhong Li, Qingyu Li, Xiaowei Lin, Yanling Liu, Jiale Liu, Wuxin Lu, Jiale Luo, Maolin Lyu, Chufan Pan, Shijie Pavel, Mosharev Shu, Runqiu Tang, Jialiang Xu, Ruoqian Xu, Shu Yang, Kang Yu, Fan Zeng, Qingguo Zhao, Haiying Zheng, Qiang Zhou, Junyuan Zhou, Xu Zhu, Yikang Zou, Zuoheng Bayat, Abolfazl Cao, Xi Cui, Wei Li, Zhendong Long, Guilu Su, Zhaofeng Wang, Xiaoting Wang, Zizhu Wei, Shijie Wu, Re-Bing Zhang, Pan Yung, Man-Hong MindSpore Quantum Special Interest Group Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610051 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Department of Physical Chemistry University of the Basque Country UPV/EHU Apartado 644 Bilbao48080 Spain School of Physics and Astronomy Sun Yat-sen University Zhuhai519082 China Key Laboratory of Quantum Physics and Photonic Quantum Information Ministry of Education University of Electronic Science and Technology of China Chengdu611731 China Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Theoretical and Computational Photochemistry Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Beijing Academy of Quantum Information Sciences Beijing100193 China State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China CAS Key Laboratory for Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China Shenzhen International Quantum Academy Shenzhen518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China

We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of MindSpore, an advanced open-source deep learning training/inference framework, MindSpore Quantum exhibits exceptional efficiency in the design and training of variational quantum algorithms on both CPU and GPU platforms, delivering remarkable performance. Furthermore, this framework places a strong emphasis on enhancing the operational efficiency of quantum algorithms when executed on real quantum hardware. This encompasses the development of algorithms for quantum circuit compilation and qubit mapping, crucial components for achieving optimal performance on quantum processors. In addition to the core framework, we introduce QuPack-a meticulously crafted quantum computing acceleration engine. QuPack significantly accelerates the simulation speed of MindSpore Quantum, particularly in variational quantum eigensolver (VQE), quantum approximate optimization algorithm (QAOA), and tensor network simulations, providing astonishing speed. This combination of cutting-edge technologies empowers researchers and practitioners to explore the frontiers of quantum computing with unprecedented efficiency and performance. © 2024, CC BY.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Global Challenge for Safe and Secure LLMs Track 1

arXiv

引用

arXiv 2024年

作者： Jia, Xiaojun Huang, Yihao Liu, Yang Tan, Peng Yan Yau, Weng Kuan Mak, Mun-Thye Sim, Xin Ming Ng, Wee Siong Ng, See Kiong Liu, Hanqing Zhou, Lifeng Yan, Huanqian Sun, Xiaobing Liu, Wei Wang, Long Qian, Yiming Liu, Yong Yang, Junxiao Zhang, Zhexin Lei, Leqi Chen, Renmiao Lu, Yida Cui, Shiyao Wang, Zizhou Li, Shaohua Wang, Yan Goh, Rick Siow Mong Zhen, Liangli Zhang, Yingjie Zhao, Zhe Nanyang Technological University Singapore AI Singapore Singapore Hangzhou Innovation Institute Beihang University China School of Computer Science and Technology Anhui University China Qiyuan laboratory China IHPC Agency for Science Technology and Research Singapore CoAI Group Tsinghua University China Singapore Institute of Information Engineering Chinese Academy of Sciences China RealAI China

This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks. With the increasing integration of LLMs in critical sectors such as healthcare, finance, and public administration, ensuring these models are resilient to adversarial attacks is vital for preventing misuse and upholding ethical standards. This competition focused on two distinct tracks designed to evaluate and enhance the robustness of LLM security frameworks. Track 1 tasked participants with developing automated methods to probe LLM vulnerabilities by eliciting undesirable responses, effectively testing the limits of existing safety protocols within LLMs. Participants were challenged to devise techniques that could bypass content safeguards across a diverse array of scenarios, from offensive language to misinformation and illegal activities. Through this process, Track 1 aimed to deepen the understanding of LLM vulnerabilities and provide insights for creating more resilient models. Copyright © 2024, The Authors. All rights reserved.

关键词： Public administration

来源：评论

学校读者我要写书评

暂无评论

TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems

arXiv

引用

arXiv 2024年

作者： Yang, Jing Zhao, Yu Yang, Linyao Wang, Xiao Chen, Long Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha410000 China Zhejiang Lab Hangzhou311121 China School of Artificial Intelligence Anhui University Hefei266114 China Department of Computer and Information Science University of Macau 999078 China Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

Temporal relation extraction (TRE) aims to grasp the evolution of events or actions, and thus shape the workflow of associated tasks, so it holds promise in helping understand task requests initiated by requesters in crowdsourcing systems. However, existing methods still struggle with limited and unevenly distributed annotated data. Therefore, inspired by the abundant global knowledge stored within pre-trained language models (PLMs), we propose a multi-task prompt learning framework for TRE (TemPrompt), incorporating prompt tuning and contrastive learning to tackle these issues. To elicit more effective prompts for PLMs, we introduce a task-oriented prompt construction approach that thoroughly takes the myriad factors of TRE into consideration for automatic prompt generation. In addition, we design temporal event reasoning in the form of masked language modeling as auxiliary tasks to bolster the model’s focus on events and temporal cues. The experimental results demonstrate that TemPrompt outperforms all compared baselines across the majority of metrics under both standard and few-shot settings. A case study on designing and manufacturing printed circuit boards is provided to validate its effectiveness in crowdsourcing scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：