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SSRN 2023年

作者： Du, Wei Huo, Yongkang Tang, Shiyi Chen, Liang Li, Ying Li, Gaoyang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China College of Engineering Shantou University Shantou515063 China Translational Medical Center for Stem Cell Therapy Institute for Regenerative Medicine Shanghai East Hospital Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai200092 China

Medical image segmentation is a crucial computer-aided diagnosis (CAD) procedure in various clinical applications. However, most existing methods rely on pixel-level annotations, which are costly and time-consuming. We propose a novel contrastive fusion model of generative and discriminative lesion maps for weakly supervised medical image segmentation to address this limitation. The generative lesion map is generated by a lesion region-guided cycle-consistent adversarial network (CCANet), and the discriminative lesion map is produced by a class-aware-based discriminative lesion localization network (DLLNet). CCANet and DLLNet share weights at the encoder layer, which contains skip connections and channel attention modules to enhance feature embedding. In order to fully utilize and integrate the generative and discriminative lesion maps, and improve the segmentation accuracy, we design a fusion method based on contrastive loss to jointly train CCANet and DLLNet. The final lesion segmentation result is obtained by applying dense conditional random fields (Dense CRF) to the fused lesion map. Extensive experiments on three benchmark medical image datasets show that our model outperforms the state-of-the-art methods on weakly supervised medical image segmentation, especially in datasets with few samples, validating the proposed model's good generalization ability and robustness in various medical image segmentation scenarios. The source code and experiment datasets are available on GitHub: https://***/mlcb-jlu/GDC4MedSeg. © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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A New Multiphase Process Monitoring Method with Application to a Hot Rolling Mill Process

A New Multiphase Process Monitoring Method with Application ...

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Chinese Automation Congress (CAC)

作者： Yongbo Zhang Kai Zhang Kaixiang Peng Pengcheng Yang Shunde Graduate School School of Automation and Electrical Engineering University of Science and Technology Beijing Foshan China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

A joint knowledge and data-driven process monitoring approach is proposed for batch courses with multipahse characteristics. In a hot rolling mill process, there is a transition area between two batches of steel plates, which has strong time-varying characteristics. At present, the monitoring methods for the hot strip mill process usually only consider the rolling phase, and do not take the intermediate transition area into account. Firstly, a method based on summation of first-order differences is developed for multiphase partitioning. Secondly, a combined method of slow feature analysis and cointegration analysis enable to be introduced to monitor the faults in both transition phase and stable phase. The newly proposed method is applied to a real HRM *** practical performance of the application demonstrates that the proposed methodology can accurately detect failure in the HRM process..

关键词： Process monitoring Strips Automation Steel

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Probing the Formation Mechanism of Hierarchical Microstructures in a Ni-Based Superalloy

SSRN

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SSRN 2024年

作者： Hou, Bolong Yu, Suyang Shi, Rongpei AECC Beijing Institute of Aeronautical Materials Beijing100095 China Key Laboratory of Advanced High-temperature Structural Materials AECC Beijing Institute of Aeronautical Materials Beijing100095 China College of Materials Science and Engineering Institute of Materials Genome and Big Data Harbin Institute of Technology Shenzhen518055 China

Hierachical microstructures, consisting of a γ matrix with γ’ precipitates that contain embeded finer-scale γ precipitate, have been found to possess improved strength and enhanced coarsening resistance of ordered γ’ precipitates in several nickel superalloys. There is, however, a lacking understanding of the origin and thus design principles of such a hierarchical microstructure. Herein, we empoly a combined experimental and computational thermodynamic and kinetic modeling approach to explore the formation mechanism of the hierarchical microstructure in an experimental, multi-component nickel-base superalloy. Chemical mapping via atom probe tomograph togeter with nucleation driving force calculations suggest that the enrichment of Co, Cr along with the depletion of Al in the γ’ phase drives its phase separation, leading to the formation of this hierarchical microstructure. The alloy and heat treatment strategies discussed here may enable an effective design of a hierarchical γ/γ’/γ microstructure. © 2024, The Authors. All rights reserved.

关键词： Phase separation

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Dissipative State Feedback of General Linear Time-Delay Systems: Applications of the Kronecker-Seuret Decomposition Concept

SSRN

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SSRN 2024年

作者： Feng, Qian Zheng, Wei Xing Wang, Xiao Yu Xiao, Feng School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

Stabilizing autonomous linear time delay systems, particularly when addressing an unlimited number of pointwise and distributed delays (DDs) under dissipative constraints, poses a significant challenge. Existing solutions are often hindered by theoretical limitations, numerical obstacles, or an inability to address the complexities of the delay integral kernels. In this paper, we propose a unified framework to tackle the above problem by employing the concept of the Kronecker-Seuret decomposition (KSD) for matrix-valued functions, which we recently have developed for the analysis of complex delay structures in coordination with the Krasovski\u{\i} functional approach. Our strategy can simultaneously address two distinct control problems, where the matrix kernels of DDs can contain an unlimited number of square-integrable functions. We show in detail how the KSD can factorize and approximate different kernel functions simultaneously without introducing conservatism. Furthermore, the use of KSD also enables us to construct complete-type functionals, whose integral kernels can include any number of weakly differentiable and linearly independent functions, underpinned by the utilization of novel integral inequalities derived from the least-squares principle. The solution to each synthesis problem comprises two theorems accompanied by an iterative algorithm, which can be utilized as a single package to compute controller gains, thus eliminating the need for nonlinear solvers. We present the testing results of two challenging examples, which could not be addressed by existing methods, to demonstrate the effectiveness of our methodology. Additionally, the paper reviews recent advancements in the research of time-delay systems, providing a valuable reference for both emerging and established researchers. © 2024, The Authors. All rights reserved.

关键词： State feedback

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A Study on a Reduced Indicator System for the Network Security Situational Assessment Model

A Study on a Reduced Indicator System for the Network Securi...

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International Conference on Networking and Network Applications (NaNA)

作者： Hongbin Zhang Yikang Bai Dongmei Zhao Bin Liu Ying Xu Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang China School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China

It is a challenge to monitor the network security state in a large-scale network environment because the selection of situation assessment indicators is overrun by massive observational information. To address this issue, this paper proposes a network security situational assessment model based on a reduced indicator system. The model categorises the characteristics of network assets and threats into endogenous situational factors and exogenous situational factors. By applying the multi-label extremely random tree algorithm to select the importance of network features, a reduced indicator system with independence and strong comprehensiveness is established. Additionally, the weights of the reduced indicator system are calculated using an improved analytical hierarchy process, which yields the evaluation results for network security situations. The experimental results show that the model improves the accuracy and timeliness of the situation assessment model, and the method can reflect the network situation in real time.

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Spin-down and emission variations for PSR J0742-2822

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Research in Astronomy and Astrophysics 2021年第2期21卷 142-148页

作者： Shi-Jun Dang Na Wang Hui-Hui Wang Jian-Ping Yuan Lun-Hua Shang Rai Yuen Ming-Yu Ge Xia Zhou Shuang-Qiang Wang Fei-Fei Kou Wen-Ming Yan Jing-Bo Wang Zhi-Gang Wen Jun-Tao Bai Zhi-Yong Liu Zu-Rong Zhou Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China University of Chinese Academy of Sciences 19A Yuquan RoadBeijing 100049China Key Laboratory of Radio Astronomy Chinese Academy of SciencesNanjing 210008China School of Physics Huazhong University of Science and TechnologyWuhan 430074China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang 550001China

PSR J0742-2822 is known for its quasi-periodic changes in the observed pulse profile and spindown *** this paper,we analyzed 13 years of timing data obtained with the Nanshan 25-m radio telescope and the Parkes 64-m radio *** found that the average values of the spin-down rate((v))of this pulsar changed in four different *** investigated the correlation between v and W50,and ascertained that the correlation changed in different ***,not all the changes in states and correlation can be associated with glitch *** examined the long term evolution ofγ-ray flux(0.1-300 GeV)and the pulse profiles corresponding to the four different states using Fermi-LAT Pass8(P8 R3)data from 2008 August 5 to 2019 October *** did not detect a significant change inγ-ray flux or the pulse *** results suggest that the connection between pulsar rotation and emission is more complex than previously reported for this pulsar.

关键词： pulsars general-stars pulsar-methods timing

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Consensus-Based Distributed Nonlinear Filtering with Kernel Mean Embedding

arXiv

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arXiv 2023年

作者： Guo, Liping Wang, Jimin Zhao, Yanlong Zhang, Ji-Feng The Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China The School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China The Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing100083 China

This paper proposes a consensus-based distributed nonlinear filter with kernel mean embedding (KME). This fills with gap of posterior density approximation with KME for distributed nonlinear dynamic systems. To approximate the posterior density, the system state is embedded into a higher-dimensional reproducing kernel Hilbert space (RKHS), and then the nonlinear measurement function is linearly converted. As a result, an update rule of KME of posterior distribution is established in the RKHS. To show the proposed distributed filter being capable of achieving the centralized estimation accuracy, a centralized filter, serving as an extension of the standard Kalman filter in the state space to the RKHS, is developed first. Benefited from the KME, the proposed distributed filter converges to the centralized one while maintaining the distributed pattern. Two examples are introduced to demonstrate the effectiveness of the developed filters in target tracking scenarios including nearly constantly moving target and turning target, respectively, with bearing-only, range and bearing measurements. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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High-precision FMCW ranging with a hybrid-integrated external cavity laser

High-precision FMCW ranging with a hybrid-integrated externa...

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OptoElectronics and Communications Conference, OECC

作者： Chuxin Liu Ruiyang Xu Weihan Xu Liangjun Lu Yuyao Guo Yu Li Jianping Chen Linjie Zhou Department of Electronic Engineering State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University Shanghai China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu China Shanghai China

We implement an analog EO-PLL for a III/V-Si 3 N 4 hybrid integrated ECL to generate a highly-linear FMCW signal over multiple wavelengths. A high range precision of 4.2 cm is achieved for a 200-m distance.

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Gradient Saliency-aware CutMix for Semi-Supervised Medical Image Segmentation

Gradient Saliency-aware CutMix for Semi-Supervised Medical I...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yuxuan Jiang Guobin Zhu Yi Ding Zhen Qin Minghui Pang Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Yibin GREAT Technology Co. Ltd. Yibin China Department of Geriatric General Surgery Sichuan Provincial People’s Hospital Chengdu China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

In semi-supervised medical image segmentation, the use of CutMix in the Mean Teacher architecture is considered an effective strong data augmentation strategy. However, we believe that randomly selecting patches from the source image might mislead the model into learning unexpected feature representations. Therefore, we propose Gradient Saliency-aware CutMix for semi-supervised medical image segmentation (GSC-Seg). Utilizing the gradient from pre-trained models to detect salient regions and then copies and pastes the large gradient areas from labeled data into corresponding areas of unlabeled data based on the gradient, and vice versa, guiding the model to learn more appropriate feature representations. Furthermore, we propose a gradient augmentation strategy, which generates disruptions in the gradient through the network itself and enhances the gradient representation abilities of the network. Experiment results show that our approach achieves the state-of-the-art performance on three medical image segmentation datasets. Code is available at https://***/UESTC-Med424-JYX/GSC-Seg.

关键词： Image segmentation Codes Feature extraction data augmentation data models Biomedical imaging

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