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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Guangfeng Zhang Zuyin Zhang Wei Guo Key Laboratory of State Education Ministry for Image Information Processing and Intelligence Control Department of Electronics and Information Huazhong University of Science and Technology Wuhan Hubei China

ISBN: (纸本)0780384016

On basis of studying the fusion algorithm of multiple sensors, a new approach is presented to fuse the millimeter wave radiometric image and the optical-based simulated image by wavelet transform. The 8mm simulation result shows that fusion image has more complementary information and possesses higher resolution than the measuring image. The fusion image can help to improve the scientific decision-making.

关键词： Microwave radiometry Optical sensors Image resolution Millimeter wave technology Millimeter wave measurements Optical imaging Geometrical optics Wavelet transforms Intelligent sensors Infrared detectors

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Multi-Centroid Representation Network for Domain Adaptive Person Re-ID

arXiv

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arXiv 2021年

作者： Wu, Yuhang Huang, Tengteng Yao, Haotian Zhang, Chi Shao, Yuanjie Han, Chuchu Gao, Changxin Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Megvii Technology China

Recently, many approaches tackle the Unsupervised Domain Adaptive person re-identification (UDA re-ID) problem through pseudo-label-based contrastive learning. During training, a uni-centroid representation is obtained by simply averaging all the instance features from a cluster with the same pseudo label. However, a cluster may contain images with different identities (label noises) due to the imperfect clustering results, which makes the uni-centroid representation inappropriate. In this paper, we present a novel Multi-Centroid Memory (MCM) to adaptively capture different identity information within the cluster. MCM can effectively alleviate the issue of label noises by selecting proper positive/negative centroids for the query image. Moreover, we further propose two strategies to improve the contrastive learning process. First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intra-domain information by comparing samples only from the same domain. Second, we propose Second-Order Nearest Interpolation (SONI) to obtain abundant and informative negative samples. We integrate MCM, DSCL, and SONI into a unified framework named Multi-Centroid Representation Network (MCRN). Extensive experiments demonstrate the superiority of MCRN over state-of-the-art approaches on multiple UDA re-ID tasks and fully unsupervised re-ID tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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HyRSM++: Hybrid relation guided temporal set matching for few-shot action recognition

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Pattern Recognition 2024年 147卷

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Zuo, Zhengrong Gao, Changxin Jin, Rong Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China Meta AI United States

Few-shot action recognition is a challenging but practical problem aiming to learn a model that can be easily adapted to identify new action categories with only a few labeled samples. However, existing attempts still suffer from two drawbacks: (i) learning individual features without considering the entire task may result in limited representation capability, and (ii) existing alignment strategies are sensitive to noises and misaligned instances. To handle the two limitations, we propose a novel Hybrid Relation guided temporal Set Matching (HyRSM++) approach for few-shot action recognition. The core idea of HyRSM++ is to integrate all videos within the task to learn discriminative representations and involve a robust matching technique. To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric. Given the basic representations from the feature extractor, the hybrid relation module is introduced to fully exploit associated relations within and cross videos in an episodic task and thus can learn task-specific embeddings. Subsequently, in the temporal set matching metric, we carry out the distance measure between query and support videos from a set matching perspective and design a bidirectional Mean Hausdorff Metric to improve the resilience to misaligned instances. Furthermore, we extend the proposed HyRSM++ to deal with the more challenging semi-supervised few-shot action recognition and unsupervised few-shot action recognition tasks. Experimental results on multiple benchmarks demonstrate that our method consistently outperforms existing methods and achieves state-of-the-art performance under various few-shot settings. The source code is available at https://***/alibaba-mmai-research/HyRSMPlusPlus. © 2023 Elsevier Ltd

关键词： Benchmarking

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Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline

arXiv

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arXiv 2020年

作者： Wu, Dongrui Jiang, Xue Peng, Ruimin Kong, Wanzeng Huang, Jian Zeng, Zhigang Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Key Laboratory for Brain-Machine Collaborative Intelligence Hangzhou Dianzi University Hangzhou310018 China

Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject, and demonstrated promising performance. While a closed-loop MI-based BCI system, after electroencephalogram (EEG) signal acquisition and temporal filtering, includes spatial filtering, feature engineering, and classification blocks before sending out the control signal to an external device, previous approaches only considered TL in one or two such components. This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs. Furthermore, it is also very important to specifically add a data alignment component before spatial filtering to make the data from different subjects more consistent, and hence to facilitate subsequential TL. Offline calibration experiments on two MI datasets verified our proposal. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduces the calibration effort. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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基于质谱的多尺度代谢组学技术进展及其在生命医学研究中的应用

引用

Science Bulletin 2023年第19期68卷 2268-2284,M0004页

作者：汪子怡朱洪影熊伟 Department of Neurology The First Affiliated Hospital of USTCDivision of Life Sciences and MedicineHefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei 230026China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefei 230088China CAS Key Laboratory of Brain Function and Disease Hefei 230026China Anhui Province Key Laboratory of Biomedical Aging Research Hefei 230026China

代谢组学是20世纪后期出现的一个新兴研究领域,主要关注各类生命体中代谢物的全面分析.随着分析手段的飞速发展,这一领域在过去几十年取得了显著进步.精密的分析技术,如液相色谱-质谱(LCMS)、气相色谱-质谱(GC-MS)、毛细管电泳-质谱(CE-... 详细信息

代谢组学是20世纪后期出现的一个新兴研究领域,主要关注各类生命体中代谢物的全面分析.随着分析手段的飞速发展,这一领域在过去几十年取得了显著进步.精密的分析技术,如液相色谱-质谱(LCMS)、气相色谱-质谱(GC-MS)、毛细管电泳-质谱(CE-MS)以及核磁共振等构成了代谢组分析的技术基础.基于这些技术手段的一系列全新改进和融合不断推动着代谢组学的发展.在近几十年里,单细胞或单细胞器水平上进行代谢组学检测一直是一个具有挑战性的任务,该领域近年来出现了重要突破,如单细胞质谱技术和单细胞器质谱技术实现了对细胞和细胞器水平上代谢组成分的检测,并深入揭示了代谢组在细胞或细胞器水平的异质性及其在生命活动中的重要意义;空间代谢组学使得原位代谢组分析成为可能,并结合三维重构技术清晰地展示了代谢物在组织内含量和分布情况.代谢组学技术的不断进步对于生物学和临床研究都具有重要意义,例如新型代谢通路的发现、细胞分化命运与代谢之间关系、以及病理诊断相关生物标志物等.本文总结了传统代谢组学技术和新型多尺度代谢组学技术的最新进展,以及这些技术在生物医学中的应用.

关键词：代谢组学技术质谱技术代谢通路细胞分化病理诊断生物标志物核磁共振毛细管电泳

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A Recipe for Scaling up Text-to-Video Generation with Text-free Videos

A Recipe for Scaling up Text-to-Video Generation with Text-f...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiang Wang Shiwei Zhang Hangjie Yuan Zhiwu Qing Biao Gong Yingya Zhang Yujun Shen Changxin Gao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group Zhejiang University Ant Group

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Diffusion-based text-to-video generation has witnessed impressive progress in the past year yet still falls behind text-to-image generation. One of the key reasons is the limited scale of publicly available data (e.g., 10M video-text pairs in WebVid10m vs. 5B image-text pairs in LAION), considering the high cost of video captioning. Instead, it could be far easier to collect unlabeled clips from video platforms like YouTube. Motivated by this, we come up with a novel text-to-video generation framework, termed TF-T2V, which can directly learn with text-free videos. The rationale behind is to separate the process of text decoding from that of temporal modeling. To this end, we employ a content branch and a motion branch, which are jointly optimized with weights shared. Following such a pipeline, we study the effect of doubling the scale of training set (i.e., video-only WebVid10M) with some randomly collected text-free videos and are encouraged to observe the performance improvement (FID from 9.67 to 8.19 and FVD from 484 to 441), demonstrating the scalability of our approach. We also find that our model could enjoy sustainable performance gain (FID from 8.19 to 7.64 and FVD from 441 to 366) after reintroducing some text labels for training. Finally, we validate the effectiveness and generalizability of our ideology on both native text-to-video generation and compositional video synthesis paradigms. Code and models will be publicly available at here.

关键词： Training Video on demand Scalability Pipelines Text to image Performance gain Market research

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Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation

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Light(Science & Applications) 2023年第12期12卷 2826-2844页

作者： Weisong Zhao Xiaoshuai Huang Jianyu Yang Liying Qu Guohua Qiu Yue Zhao Xinwei Wang Deer Su Xumin Ding Heng Mao Yaming Jiu Ying Hu Jiubin Tan Shiqun Zhao Leiting Pan Liangyi Chen Haoyu Li Innovation Photonics and Imaging Center School of Instrumentation Science and EngineeringHarbin Institute of TechnologyHarbinChina Key Laboratory of Ultra-Precision Intelligent Instrumentation of Ministry of Industry and Information Technology Harbin Institute of TechnologyHarbinChina Biomedical Engineering Department International Cancer InstitutePeking University Cancer Hospital and InstituteHealth Science CenterPeking UniversityBeijingChina The Key Laboratory of Weak-Light Nonlinear Photonics of Education Ministry School of Physics and TEDA Institute of Applied PhysicsFrontiers Science Center for Cell ResponsesNankai UniversityTianjinChina State Key Laboratory of Membrane Biology Beijing Key Laboratory of Cardiometabolic Molecular MedicineInstitute of Molecular MedicineNational Biomedical Imaging CenterSchool of Future TechnologyPeking UniversityBeijingChina Department of Control Science and Engineering Harbin Institute of TechnologyHarbinChina School of Mathematical Sciences Peking UniversityBeijingChina Unit of Cell Biology and Imaging Study of Pathogen Host Interaction The Center for MicrobesDevelopment and HealthKey Laboratory of Molecular Virology and ImmunologyShanghai Institute of Immunity and InfectionChinese Academy of SciencesShanghaiChina School of Life Science and Technology Harbin Institute of TechnologyHarbinChina PKU-IDG/McGovern Institute for Brain Research BeijingChina Beijing Academy of Artificial Intelligence BeijingChina Frontiers Science Center for Matter Behave in Space Environment Harbin Institute of TechnologyHarbinChina Key Laboratory of Micro-Systems and Micro-Structures Manufacturing of Ministry of Education Harbin Institute of TechnologyHarbinChina

In fluorescence microscopy,computational algorithms have been developed to suppress noise,enhance contrast,and even enable super-resolution(SR).However,the local quality of the images may vary on multiple scales,and these differences can lead to *** mapping methods fail to finely estimate the local quality,challenging to associate the SR scale ***,we develop a rolling Fourier ring correlation(rFRC)method to evaluate the reconstruction uncertainties down to SR *** visually pinpoint regions with low reliability,a filtered rFRC is combined with a modified resolution-scaled error map(RSM),offering a comprehensive and concise map for further *** demonstrate their performances on various SR imaging modalities,and the resulting quantitative maps enable better SR images integrated from different ***,we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.

关键词： resolution local enable

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Bioluminescence tomography based on Bayesian approach

Bioluminescence tomography based on Bayesian approach

引用

Medical imaging 2009: Biomedical Applications in Molecular, Structural, and Functional imaging

作者： Feng, Jinchao Jia, Kebin Tian, Jie Yan, Guorui Qin, Chenghu The College of Electronic Information and Control Engineering Beijing University of Technology Beijing 100022 China Medical Image Processing Group Key Laboratory of Complex Systems and Intelligence Science Institute of Automation P. O. Box 2728 Beijing 100190 China Life Science Research Center Xidian University Xi'an 710071 China

ISBN: (纸本)9780819475138

As a new mode of molecular imaging, bioluminescence tomography (BLT) will have *** *** on revealing the molecular and cellular information in vivo at the whole-body small animal level because of its high sensitive detection and facile operation. However, BLT is an ill-posed problem, it is necessary to incorporate a priori knowledge into the tomographic algorithm. In this paper, a novel Bayesian reconstruction algorithm for BLT is firstly proposed. In the algorithm, a priori permissible source region strategy is incorporated into the Bayesian network to reduce the ill-posedness of BLT. Then a generalized adaptive Gaussian Markov random field (GAGMRF) prior model for unknown source density estimation is developed to further reduce the ill-posedness of BLT on the basis of adaptive finite element analysis. Finally, the algorithm maximizes the log posterior probability with respect to a noise parameter and the unknown source density, the distribution of bioluminescent source can be reconstructed. In addition, the novel tomography algorithm based adaptive finite element makes the method more appropriate for complex phantom such as real mouse. In the numerical simulation, a heterogeneous phantom is used to evaluate the performance of the proposed algorithm with the Monte Carlo based synthetic data. The accurate localization of bioluminescent source and quantitative results show the effectiveness and potential of the tomographic algorithm for BLT. © 2009 SPIE.

关键词： Monte Carlo methods

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Performing DNA Strand Displacement with DNA Polymerase 14th

Performing DNA Strand Displacement with DNA Polymerase

引用

14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Wang, Zhiyu Hu, Yingxin Chen, Zhekun Liao, Sulin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan730074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan730074 China Department of Pathology Renmin Hospital of Wuhan University Wuhan430060 China

ISBN: (纸本)9789811534140

Significant improvements to the dynamic DNA nano science have emerged in recent years, primarily due to the elaborate design of toehold-mediated DNA strand displacement (TMSD). However, it remains an ongoing challenge to design huge-scaled TMSD based sophisticated dynamic DNA structures without base-pair mismatching or unwanted secondary structures, because the sequence design complexity of TMSD will dramatically increase as the scale increases. Here we report a new polymerase triggered strand displacement (PTSD) mechanism to realize DNA strand displacement with the potential of constructing complicated dynamic DNA nanostructures. By employing proper polymerase with strand displacement activity, the target strand in a duplex will be displaced and freed during the polymerization. In this manner, DNA strand displacement takes place without considering the design complexity of branch migration domain. The mechanism was successfully applied in constructing one-layer and two-layer circuits in which the parameters involved were explored, suggesting that PTSD is feasible and able to be harnessed to construct cascading DNA circuits with high complexity. © 2020, Springer Nature Singapore Pte Ltd.

关键词： DNA

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A novel deep learning framework with variational auto-encoder for indoor air quality prediction

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Frontiers of Environmental Science & Engineering 2024年第1期18卷 97-109页

作者： Qiyue Wu Yun Geng Xinyuan Wang Dongsheng Wang ChangKyoo Yoo Hongbin Liu Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry UniversityNanjing 210037China College of Automation&College of Artificial Intelligence Nanjing University of Posts and TelecommunicationsNanjing 210023China Guangxi Key Laboratory of Clean Pulp&Papermaking and Pollution Control College of Light Industry and Food EngineeringGuangxi UniversityNanning 530004China Laboratory for Comprehensive Utilization of Paper Waste of Shandong Province Shandong Huatai Paper Co.Ltd.Dongying 257335China Department of Environmental Science and Engineering College of EngineeringKyung Hee UniversityYongin 446701Republic of Korea

Exposure to poor indoor air conditions poses significant risks to human health, increasing morbidity and mortality rates. Soft measurement modeling is suitable for stable and accurate monitoring of air pollutants and improving air quality. Based on partial least squares (PLS), we propose an indoor air quality prediction model that utilizes variational auto-encoder regression (VAER) algorithm. To reduce the negative effects of noise, latent variables in the original data are extracted by PLS in the first step. Then, the extracted variables are used as inputs to VAER, which improve the accuracy and robustness of the model. Through comparative analysis with traditional methods, we demonstrate the superior performance of our PLS-VAER model, which exhibits improved prediction performance and stability. The root mean square error (RMSE) of PLS-VAER is reduced by 14.71%, 26.47%, and 12.50% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. Additionally, the coefficient of determination (R2) of PLS-VAER improves by 13.70%, 30.09%, and 11.25% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. This research offers an innovative and environmentally-friendly approach to monitor and improve indoor air quality.

关键词： Indoor air quality PM_(2.5)concentration Variational auto-encoder Latent variable Soft measurement modeling

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