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An information model for highway operational risk management based on the IFC-Brick schema

International Journal of Transportation Science and Technology 2023年第3期12卷 878-890页

作者： Bencheng Zhu Fujin Hou Tao Feng Tao Li Cancan Song The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji UniversityShanghai 201804China Shandong Hi-Speed Construction Management Group Co. Ltd.Jinan 250014ShandongChina Urban and Data Science Lab Graduate School of Advanced Science and EngineeringHiroshima University7390046 Higashi-HiroshimaJapan Department of the Built Environment Eindhoven University of TechnologyEindhoven 5600 MBthe Netherlands School of Civil Engineering Shanghai Normal UniversityNo.100Haisi RoadShanghai 201418China

With the development of highways,new technologies should be continuously introduced to improve highway traffic *** twin(DT)has been an emerging field of research in recent *** develop a digital twin management system,a data model is *** the field of highway operational risk management(HORM),however,the development of data models is still in its *** by the concept of linked data,in this paper,we attempt to propose an information model for *** main achievements of this paper include data architecture,identification and classification code methods,data interaction method,and the developed *** on data needs analysis,the highway information model architecture for risk management is defined as five layers:basic highway products,traffic sensors and equipment,traffic rules,traffic flow,and ***,according to the concepts of semantic data,these five layers can be classified into three categories:highway product data,topology data,and sensor *** the Industry Foundation Classes(IFC)standard and Brick schema were first proposed and applied in the building domain,some of their entities and relationships can also be applied to *** this end,we defined some new classes,a specific ontology,and an integrated framework for ***,a case study was carried *** such information model to highways has broad *** changes the file-based exchange method to the data-based one,which can promote highway data exchange and *** proposed information model could be of great significance for HORM.

关键词： Highway Information Model Operational Risk Management IFC-Brick Digital Twin

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Retrieving chlorophyll content and equivalent water thickness of Moso bamboo(Phyllostachys pubescens) forests under Pantana phyllostachysae Chao-induced stress from Sentinel-2A/B images in a multiple LUTs-based PROSAIL framework

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Forest Ecosystems 2023年第2期10卷 252-267页

作者： Zhanghua Xu Anqi He Yiwei Zhang Zhenbang Hao Yifan Li Songyang Xiang Bin Li Lingyan Chen Hui Yu Wanling Shen Xuying Huang Xiaoyu Guo Zenglu Li College of Environment and Safety Engineering Academy of Geography and Ecological EnvironmentFuzhou UniversityFuzhou350108China Fujian Provincial Key Laboratory of Resources and Environment Monitoring&Sustainable Management and Utilization Sanming365004China Key Laboratory of Spatial Data Mining&Information Sharing Ministry of EducationThe Academy of Digital ChinaFuzhou UniversityFuzhou350108China College of Forestry Fujian Agriculture and Forestry UniversityFuzhou350002China International Institute for Earth System Science Nanjing UniversityNanjing210023China

Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the *** coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content ***,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be ***,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation *** inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were *** scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were *** results showed that LAI,CCC,and CEWT were inversely related with PPC-induced *** applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),*** to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for *** feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.

关键词： Moso bamboo Chlorophyll content Equivalent water thickness PROSAIL model Multiple LUTs Pantana phyllostachysae Chao Sentinel-2A/B images

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Learning generalizable agents via saliency-guided features decorrelation 23

Learning generalizable agents via saliency-guided features d...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Sili Huang Yanchao Sun Jifeng Hu Siyuan Guo Hechang Chen Yi Chang Lichao Sun Bo Yang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education and School of Artificial Intelligence Jilin University China Department of Computer Science University of Maryland College Park School of Artificial Intelligence Jilin University China Lehigh University Bethlehem Pennsylvania Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

In visual-based Reinforcement Learning (RL), agents often struggle to generalize well to environmental variations in the state space that were not observed during training. The variations can arise in both task-irrelevant features, such as background noise, and task-relevant features, such as robot configurations, that are related to the optimal decisions. To achieve generalization in both situations, agents are required to accurately understand the impact of changed features on the decisions, i.e., establishing the true associations between changed features and decisions in the policy model. However, due to the inherent correlations among features in the state space, the associations between features and decisions become entangled, making it difficult for the policy to distinguish them. To this end, we propose Saliency-Guided Features Decorrelation (SGFD) to eliminate these correlations through sample reweighting. Concretely, SGFD consists of two core techniques: Random Fourier Functions (RFF) and the saliency map. RFF is utilized to estimate the complex non-linear correlations in high-dimensional images, while the saliency map is designed to identify the changed features. Under the guidance of the saliency map, SGFD employs sample reweighting to minimize the estimated correlations related to changed features, thereby achieving decorrelation in visual RL tasks. Our experimental results demonstrate that SGFD can generalize well on a wide range of test environments and significantly outperforms state-of-the-art methods in handling both task-irrelevant variations and task-relevant variations.

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Texture Features and Machine Learning Based Environmental Microorganism Microscopic Image Classification

Texture Features and Machine Learning Based Environmental Mi...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Ouyang, Xinyu Yuan, Huaqian Zhou, Shangqi Fu, Yanyu Yang, Jinzhu Grzegorzek, Marcin Wang, Yuexi Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Department of Rehabilitation Shengjing Hospital of China Medical University Shenyang China

ISBN: (纸本)9789819708543

In recent years, with the continuous development of molecular biology technology, the effective use of environmental microorganisms to solve the problem of pollution in the ecological environment has been widely used. However, the original microbial cultivation, environmental application and monitoring have the disadvantages of high degree of specialization, long time and high cost. In this case, the use of computer image processing and machine learning techniques for microbial identification and classification becomes a more efficient, economical and necessary method. This paper presents an environmental microorganism microscopic image classification method based on texture features and machine learning. In the research and experiments, microscopic images of microorganisms are cropped, grayed, data augmented, target areas segmented and extracted four types of texture features. The training set and test set are divided in a 1:1 ratio, and different recognition results are obtained based on different texture feature vectors and classifier models. Ultimately, the gradient-gray co-occurrence matrix feature combined with the Random Forest algorithm classifier achieves an F1-measure value of nearly 40%, which improves the work efficiency of environmental microbial researchers and related field workers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Microorganisms

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Few-Shot data Completion for New Tasks in Sparse Crowdsensing

Few-Shot Data Completion for New Tasks in Sparse Crowdsensin...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： En Wang Mijia Zhang Bo Yang Yang Xu Zixuan Song Yongjian Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Electronic Engineering Hunan University China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Mobile Crowdsensing is a type of technology that utilizes mobile devices and volunteers to gather data about specific topics at large scales in real-time. However, in practice, limited participation leads to missing data, i.e., the collected data may be sparse, which makes it difficult to perform accurate analysis. A possible technique called sparse crowdsensing incorporates the sparse case with data completion, where unsensed data could be estimated through inference. However, sparse crowdsensing typically suffers from poor performance during the data completion stage due to various challenges: the sparsity of the sensed data, reliance on numerous timeslots, and uncertain spatiotemporal connections. To resolve such few-shot issues, the proposed solution uses the Correlated data Fusion for Matrix Completion (CDFMC) approach, which leverages a small amount of objective data to retrain an auxiliary dataset-based pre-trained model that can estimate unsensed data efficiently. CDFMC is trained using a combination of the traditional Deep Matrix Factorization and the Kalman Filtering, which not only enables the efficient representation and comparison of data samples but also fuses the objective data and auxiliary data effectively. Evaluation results show that the proposed CDFMC outperforms baseline techniques, achieving high accuracy in completing unsensed data with minimal training data.

关键词： Performance evaluation Accuracy Crowdsensing Filtering Training data data models Real-time systems

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RGB-D Visual Odometry Based on Semantic Feature Points in Dynamic Environments 1st

RGB-D Visual Odometry Based on Semantic Feature Points in ...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Wang, Hao Wang, Yincan Fang, Baofu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Anhui230009 China School of Computer Science and Information Engineering HeFei University of Technology Anhui Hefei230009 China

ISBN: (纸本)9783030930486

Various algorithms of traditional visual Simultaneous Localization and Mapping (SLAM) can well match with static scenes, but mismatches will occur in dynamic scenes, which makes the positioning and mapping of the SLAM system produce large errors. Therefore, this paper proposed a visual odometry algorithm based on semantic feature points, which can improve the positioning accuracy in dynamic scenes. The algorithm combined semantic information to detect dynamic objects, and then detects and eliminates dynamic feature points. This paper conducted an extensive evaluation of the system and compared it with ORB-SLAM3 and other dynamic scene SLAM systems. The experimental results show that this method greatly improves the positioning accuracy of the camera and the robustness of the system in a highly complex dynamic environment, which verifies the advancement and effectiveness of the algorithm in this paper. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on data engineering, ICDE 2023

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

ISBN: (纸本)9798350322279

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. © 2023 IEEE.

关键词： Graph neural networks

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Semi-Supervised Image Manipulation Localization with Residual Enhancement

SSRN

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SSRN 2024年

作者： Zeng, Qiang Wang, Hongxia Zhou, Yang Zhang, Rui Meng, Sijiang School of Cyber Science and Engineering Sichuan University Chengdu610065 China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China

Images have become a significant medium for information transmission, while image forensics has garnered widespread attention from researchers. Due to the scarcity of finely annotated images in the field of image manipulation detection and localization, existing methods for locating manipulated images can only utilize a limited amount of data for model training. However, deep learning models typically require a large number of finely annotated images to fully leverage their powerful fitting capabilities. In this paper, we propose a semi-supervised image manipulation localization framework, employing semi-supervised learning to use unannotated images for deep learning model training. To achieve this goal, we first design a residual enhancement module that contains an encoding-decoding structure. The regression target of this branch is the pristine regions in the images to generate the reconstructed images. Next, we perform a residual operation between the reconstructed and original images to roughly locate the anomalous regions and refine the features extracted by the encoder. Secondly, we employ weakly-supervised learning by adding an image-level classification head to the final layer of the encoder. The classification head generates a class active map based on image-level classification results, which further guides the model's feature augmentation. Finally, we formulate a specialized semi-supervised framework tailored for image manipulation detection, enabling the utilization of a large volume of unannotated data for model training. Extensive experimental results highlight the notable efficacy of the proposed approach, and outperforms state-of-the-art approaches by a significant margin of 22.5 on the mean of F1 metrics across various benchmark datasets. © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Immunometabolism:a new dimension in immunotherapy resistance

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Frontiers of Medicine 2023年第4期17卷 585-616页

作者： Chaoyue Xiao Wei Xiong Yiting Xu Ji’an Zou Yue Zeng Junqi Liu Yurong Peng Chunhong Hu Fang Wu Department of Oncology The Second Xiangya HospitalCentral South UniversityChangsha 410011China NHC Key Laboratory of Carcinogenesis and Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education Cancer Research InstituteCentral South UniversityChangsha 410078China Xiangya School of Medicine Central South UniversityChangsha 410013China Hunan Cancer Mega-Data Intelligent Application and Engineering Research Centre Changsha 410011China Hunan Key Laboratory of Early Diagnosis and Precision Therapy in Lung Cancer The Second Xiangya HospitalCentral South UniversityChangsha 410011China Hunan Key Laboratory of Tumor Models and Individualized Medicine The Second Xiangya HospitalCentral South UniversityChangsha 410011China

Immune checkpoint inhibitors(ICIs)have demonstrated unparalleled clinical responses and revolutionized the paradigm of tumor treatment,while substantial patients remain unresponsive or develop resistance to ICIs as a single agent,which is traceable to cellular metabolic *** dysregulated metabolism has long been adjudged as a hallmark of tumor,it is now increasingly accepted that metabolic reprogramming is not exclusive to tumor cells but is also characteristic of ***,people used to pay more attention to the effect of tumor cell metabolism on immunocytes,but in practice immunocytes interact intimately with their own metabolic function in a way that has never been realized before during their activation and differentiation,which opens up a whole new frontier called *** metabolic intervention for tumor-infiltrating immunocytes could offer fresh opportunities to break the resistance and ameliorate existing ICI immunotherapy,whose crux might be to ascertain synergistic combinations of metabolic intervention with ICIs to reap synergic benefits and facilitate an adjusted anti-tumor immune ***,we elaborate potential mechanisms underlying immunotherapy resistance from a novel dimension of metabolic reprogramming in diverse tumor-infiltrating immunocytes,and related metabolic intervention in the hope of offering a reference for targeting metabolic vulnerabilities to circumvent immunotherapeutic resistance.

关键词： immune cell immunometabolism metabolic reprogramming immunotherapy resistance tumor microenvironment immune checkpoint inhibitor

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A Semantic-Aware and Multi-Guided Network for Infrared-Visible Image Fusion

arXiv

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arXiv 2024年

作者： Zhang, Xiaoli Wang, Liying Zhao, Libo Li, Xiongfei Ma, Siwei The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China

Multi-modality image fusion aims at fusing modality-specific (complementarity) and modality-shared (correlation) information from multiple source images. To tackle the problem of the neglect of inter-modal information, high-frequency information loss and limited attention to downstream tasks, this paper focuses on how to model correlation-driven decomposing features and reason high-level graph representation by efficiently extracting complementary features and aggregating multi-guided features. We propose a three-branch encoder-decoder architecture along with corresponding fusion layers as the fusion strategy. The transformer with Multi-Dconv Head Transposed Attention and Local-enhanced Feed Forward network is used to extract shallow features after the depthwise convolution. In the three parallel branches of the encoder, Cross Attention and Invertible Block (CAI) enables to extract local features and preserve high-frequency texture details. Base Feature Extraction Module (BFE) with residual connections can capture long-range dependencies and enhance modality-shared information. Graph Reasoning Module (GR) is introduced to reason high-level cross-modality relations and simultaneously extract low-level detail features as CAI’s modality-specific complementary information. Experiments demonstrate that our method obtains competitive results compared with state-of-the-art methods in visible/infrared image fusion and medical image fusion tasks. Moreover, we surpass other fusion methods in terms of subsequent tasks, averagely scoring 8.27% mAP@0.5 higher in object detection and 5.85% mIoU higher in semantic segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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