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engineering 2024年第3期34卷 60-69页

作者： Bingxi He Yu Guo Yongbei Zhu Lixia Tong Boyu Kong Kun Wang Caixia Sun Hailin Li Feng Huang Liwei Wu Meng Wang Fanyang Meng Le Dou Kai Sun Tong Tong Zhenyu Liu Ziqi Wei Wei Mu Shuo Wang Zhenchao Tang Shuaitong Zhang Jingwei Wei Lizhi Shao Mengjie Fang Juntao Li Shouping Zhu Lili Zhou Shuo Wang Di Dong Huimao Zhang Jie Tian Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Radiology The First Hospital of Jilin UniversityChangchun 130021China Neusoft Medical Systems Co.Ltd. Shenyang 110167China Neusoft Research of Intelligent Healthcare Technology Co.Ltd. Shenyang 110167China School of Life Science and Technology Xidian UniversityXi’an 710126China Engineering Research Center of Molecular and Neuro Imaging Ministry of EducationXi’an 710126China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Encouraging and astonishing developments have recently been achieved in image-based diagnostic *** medical care and imaging technology are becoming increasingly ***,the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction(from signal to image).Artificial intelligence(AI)technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established *** this prospective study,for the first time,we develop an AI-based signal-toknowledge diagnostic scheme for lung nodule classification directly from the computed tomography(CT)raw data(the signal).We find that the raw data achieves almost comparable performance with CT,indicating that it is possible to diagnose diseases without reconstructing ***,the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts(with a gain ranging from 0.01 to 0.12),demonstrating that raw data contains diagnostic information that CT does not *** results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.

关键词： Computed tomography Diagnosis Deep learning Lung cancer Raw data

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The Class B Heat Shock Factor Rchsf17 from Rosa Chinensis Enhances Basal Thermotolerance in Rosa Rugosa

SSRN

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SSRN 2024年

作者： Ding, Minghai Xing, Wen Li, Zeqing Jin, Xi Yu, Qiuxiu Sun, Jiahui College of Landscape Architecture Central South University of Forestry and Technology Changsha410004 China National Forestry and Grassland Administration Engineering Research Center for Osmanthus Fragrans Hubei University of Science and Technology Xianning437100 China Hunan Big Data Engineering Technology Research Center of Natural Protected Areas Landscape Resources Central South University of Forestry and Technology Changsha410004 China Yuelushan Laboratory Carbon Sinks Forests Variety Innovation Center Changsha China

Rosa rugosa, a species with considerable economic value, encounters cultivation impediments due to its susceptibility to heat stress. This study delineates the cloning and functional characterization of RcHsf17, a member of the B2 group Heat shock transcription factor (Hsf), isolated from the stress-tolerant Chinese rose cultivar, R. chinensis ‘Slater's crimson China’. RcHsf17 functions as a nuclear transcription factor and demonstrates reactivity to heat stress and a spectrum of stress-related treatments, including abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA), and hydrogen peroxide (H2O2). The overexpression of RcHsf17 in transgenic Arabidopsis thaliana and R. rugosa enhanced their heat tolerance. Specifically, in R. rugosa, RcHsf17 overexpression resulted in the thickening of the cell wall, maintenance of photosynthetic efficiency, and a reduction in the levels of reactive oxygen species (ROS) and malondialdehyde (MDA). In addition, transcriptomic analysis indicated that under ambient conditions, RcHsf17 elevates the expression of HsfA3, HsfB1, small heat shock protein genes (sHSPs), and genes associated with cell wall formation. Under heat stress, there is a notable upregulation of HsfA6B, genes related to photosynthesis, and antioxidant genes. Collectively, these findings highlighted the significant function of RcHsf17 in enhancing heat resistance in R. rugosa by facilitating cell wall biosynthesis, reducing heat-induced impairment to photosynthesis, and improving ROS scavenging mechanisms. © 2024, The Authors. All rights reserved.

关键词： Photosynthesis

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Temporal Hierarchical Graph Attention Network for Traffic Prediction with Prompt Learning

Temporal Hierarchical Graph Attention Network for Traffic Pr...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Cheng Li Pei-Yuan Lai Yu-Xuan Zhou Chang-Dong Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China South China Technology Commercialization Center Guangzhou China School of Information Engineering Guangdong University of Technology Guangzhou Guangdong China Beijing Normal University - Hong Kong Baptist University United International College Zhuhai China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Traffic prediction is a complex task. In the traditional forecasting methodologies, there are limitations regarding generalization, as specifically observed in the pretraining approaches. This is particularly evident in models involving temporal and spatial attributes, where finetuning often encounters numerous challenges. With the emergence of graph prompt learning, a new finetuning paradigm has gained increasing application. In the context of the traffic prediction methods involving spatio-temporal attributes, we endeavor to enhance its performance in the downstream tasks through graph pretraining and prompt-based finetuning. In particular, we propose a traffic prediction model based on graph prompt learning. This model deeply mines the hierarchical information of traffic data and its temporal attributes, by employing the method of graph pretraining and prompt learning to enhance the model’s generalizability and accuracy. Extensive experiments conducted on two real-world traffic prediction datasets confirm the effectiveness of the proposed method.

关键词： Wireless communication Accuracy Computational modeling Predictive models data models Task analysis Forecasting

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CLDG: Contrastive Learning on Dynamic Graphs

CLDG: Contrastive Learning on Dynamic Graphs

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International Conference on data engineering

作者： Yiming Xu Bin Shi Teng Ma Bo Dong Haoyi Zhou Qinghua Zheng Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG.

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TVEG: Model Selection of the Time-Varying Exponential Family Distributions Graphical Models

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IEEE Transactions on Network science and engineering 2025年

作者： Liu, Juan Mei, Guofeng Xia, Yuanqing Wu, Xiaoqun Lu, Jinhu Beijing Forestry University School of Technology Beijing100107 China Italy Zhongyuan University of Technology Henan 451191 China Wuhan University School of Mathematics and Statistics Wuhan430072 China Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence School of Automation Science and Electrical Engineering Beijing100191 China

The undirected graphical model, a popular class of statistical model, offers a way to describe and explain the relationships among a set of variables. However, it remains a challenge to choose a certain graphical model to explain the relationships of variables adequately, especially when the relationships of variables are rewiring over time. This paper proposes the Time-Varying Exponential Family Distributions Graphical (TVEG) models, with time-varying structures and exponential family node-wise conditional distributions. TVEG models extend the scope of available graph models and can be applied to time-varying and exponential family distribution observation data in reality. We propose the Temporally Smoothed L1regularized exponential family graphical estimator (TSLEG), an estimator to infer the structure of TVEG from observations. We derive sufficient conditions for the TSLEG to recover the block partition and sparse pattern with high probability. We derive a message-passing optimization method to solve the TSLEG for time-varying Ising, Gaussian, exponential, and Poisson graphs based on the ADMM. The synthetic network simulations corroborate the theoretical analysis. Analysing of real data of stocks and the US Senate by the time-varying exponential model and Poisson model indicates the effectiveness and practicality of TVEG models. © 2025 IEEE. All rights reserved.

关键词： ADMM exponential family distributions Graphical model pseudo maximum likelihood estimation

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Closer Twins Model: Consistent Design of Modem Scheme and Channel Estimation under High-Mobility Scenarios

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4564-4580页

作者： Zhang, Hengyu Wang, Xuehan Tan, Jingbo Wang, Jintao Yang, Zhaohui Ai, Bo Tsinghua University Department of Electronic Engineering Beijing100084 China China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University International Joint Innovation Center Haining314400 China Hangzhou310007 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China PengCheng Laboratory Shenzhen518052 China Zhengzhou University Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou450001 China

Communication objectives with high mobility bring severe Doppler shifts, causing the inter-carrier interference of orthogonal frequency division multiplexing (OFDM) system, which raises the requirements of novel modem schemes. However, existing modem schemes for high-mobility communications face challenges in adapting to diverse channel environments, involving complex channel estimation and etc. Fortunately, the potential of deep learning (DL) has been exploited in various communication applications. In order to design the consistent and robust modem scheme for different channel environments, we propose the DL-based architecture termed the closer twins (CTs) model, which borrows the idea from the Siamese structure in contrastive learning. In specific, two identical network backbones like twins can simultaneously process different channel inputs and make outputs consistent. We design a convlutional neural network called modem network (ModNet) as the backbone for the design of consistent and robust modem scheme. Moreover, to make traditional channel estimation and interpolation methods applicable to the designed modem scheme, a training-aided strategy called random-pilot (R-P) is proposed. In R-P strategy, we simulate the process of conventional channel estimation to modify the objective function of the modem scheme design. Furthermore, the performance of traditional channel estimation can be further improved by DL-based methods. We utilize the CTs model and design the backbone called estimation matrix network (EMNet) to optimize a linear channel estimation method, who outperforms the traditional methods with a similar complexity. Simulation results demonstrate that the proposed modem scheme outperforms OFDM, especially with high Doppler spread. The channel estimation strategy, supported by the R-P strategy and EMNet, achieves lower normalized mean square error compared with traditional methods, contributing to more reliable transmission. © 2025 IEEE. All rights reser

关键词： Orthogonal frequency division multiplexing

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An application-oriented multi-granularity vehicle trajectory data indexing structure based on concept lattice 5

An application-oriented multi-granularity vehicle trajectory...

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5th International Conference on Computer Information science and Application Technology, CISAT 2022

作者： Xu, Tao Zhang, Zixiang Ma, Xiaqing Deng, Jiaming Zhao, Yingying Li, Xiang Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 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)9781510660076

Massive amount of vehicle trajectory data is an important data source and has been widely used in many research fields. However, due to the huge volume and variety of application scenarios, it is still not easy to achieve the application-oriented and efficient retrieval of vehicle trajectory. The paper proposes a multi-granularity vehicle trajectory index method based on the concept lattice model. The method identifies the multi-granularity feature regions for application requirements to segment vehicle trajectory and employs the concept lattice model to maintain the complex relationships between vehicle trip and the feature regions, to realize the vehicle travel trajectory data retrieval for the multi-granularity feature regions. In this experiment, shanghai city is divided into 21 feature regions according to the county-level area of Shanghai, and the concept lattice index structure of vehicle trips is constructed. The availability and efficiency of the proposed method are verified by the comparative experimental results. © 2022 SPIE.

关键词： Vehicles

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Enrichment, Borrowing, And Mining: A data-Driven Approach To Colonoscopic Lesion Classification

Enrichment, Borrowing, And Mining: A Data-Driven Approach To...

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IEEE International Symposium on Biomedical Imaging

作者： Shuangyi Tan Yuhao Du ZhenHua Wu Dejun Fan Xutao Lin Zhen Li Xiang Wan Guanbin Li The Chinese University of Hong Kong(Shenzhen) Shenzhen Research Institute of Big Data School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Department of Gastrointestinal Endoscopy Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases Biomedical Innovation Center The Sixth Affiliated Hospital Sun Yat-Sen University

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The automatic detection and diagnosis of colorectal lesions during colonoscopy are vital for the prevention and treatment of colorectal cancer. To date, numerous ROI-based neural network models have been proposed to classify colonoscopic lesions. However, they commonly encounter two main issues: 1) Their performance is limited by the dataset size and the diversity of lesion types included; 2) They tend to directly extract deep features from the original Region of Interests (ROIs) of the lesion, neglecting features of the lesion’s edge background as well as detailed texture and color information. To address these issues, we first propose a publicly available colonoscopic lesion classification dataset that includes all major categories of intestinal lesions. Secondly, we innovatively utilize additional colonoscopy video detection data with consistency between frames to enhance the lesion classification model by leveraging an online-target network. Simultaneously, to better mine the characteristics of lesions in data, we propose an ROI broaden strategy and a deep-fusion module, enabling the model to learn lesion-related information such as lesion edges, morphology, texture, and color. Experiments show that our method outperforms existing supervised and semi-supervised state-of-the-art methods, demonstrating the effectiveness of our approach. The GitHub link for our work is https://***/Zoe-TAN/EBM-polyp-cls.

关键词： Image color analysis Image edge detection Prevention and mitigation Colonoscopy Morphology Feature extraction data models

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XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Adversarial Network with Explainable AI System for NIR-II Fluorescence Molecular Imaging 22

XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Ad...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Fu, Lidan Lu, Binchun Li, Lingbing Shi, Xiaojing Tian, Jie Hu, Zhenhua Cas Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Precision Instrument Tsinghua University Beijing China Interventional Radiology Department Chinese Pla General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (纸本)9798331520526

Fluorescence imaging in the second near-infrared window (NIR-II) facilitates the real-time optical contrast for in vivo biomedical imaging. However, the detection noise is an inevitable byproduct of the real-time imaging process. Additionally, fluorescent probes currently approved for clinical use remain limited within the wavelength of NIR-IIa (1000-1300 nm). NIR-IIb (1500-1700 nm) imaging has the potential to deliver higher resolution than NIR-IIa, but relies on toxic fluorescent probes not approved for human use. To support clinical high-precision imaging, we propose a NIR-II fluorescence enhanced imaging method based on perception-enhanced spectrum shift generative adversarial network with explainable AI systems (xAI-PSSGAN). The proposed method tackles the problem of limited unpaired data training, and generates NIR-IIb resembling images with enhanced resolution and reduced noise. Experimental results demonstrate that proposed method effectively improves the imaging quality and surpasses competing alternatives, which is of great significance to promote the high-quality NIR-II imaging in clinical practice. © 2025 IEEE.

关键词： Biomedical image enhancement cycle-consistent generative adversarial network explainability generative AI NIR - II imaging

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Dynamic Signal State Estimation of Electromagnetic Flowmeter Measurement Based on Variance Compensation Kalman Filter

Dynamic Signal State Estimation of Electromagnetic Flowmeter...

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Chinese Control Conference (CCC)

作者： Xinyu Zhai Wei Zhang School of Artificial Intelligence Hebei University of Technology Tianjin China Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin P. R. China School of Artificial Intelligent and Data Science Hebei University of Technology Tianjin China

Electromagnetic flowmeter is widely applied in single-phase flow, solid-liquid two-phase flow and solid-liquid-gas three-phase flow measurement due to the advantages of high accuracy and high stability. However, existing electromagnetic flowmeter inherit the shortcomings of limited applicability and slow dynamic response speed. In order to solve the problem above, this paper make several modifications on the traditional Kalman filter to form an adaptive algorithm. The traditional Kalman filter method is difficult to track the changing flow signal. In order to solve the problem that the flow state changes with the flow state in electromagnetic flowmeter, the adaptive estimation considering the change of real-time flow state is introduced, which provides a solution to remove invalid signal more effectively, make rapid changes according to the change of flow state, and reduce the actual error of filtering. The adaptive Kalman estimation model under continuous flow condition is established, which makes the scope of application wider, improves the signal-to-noise ratio and greatly improves the signal accuracy. Through simulation, the results obtained by Kalman filter are more close to the real value than the measured value, indicating that the variance compensation adaptive Kalman filter has the effect of eliminating noise. At the same time, with the passage of time, the filtering gain $K$ slowly decreases until it becomes stable to a constant. When the adaptive Kalman gain is stable, the adaptive Kalman filtering is equivalent to low-pass filtering, and the system reaches steady state. The adaptive Kalman filtering solves the problem that the traditional Kalman filtering method is difficult to track the changing flow signal.

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