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International Conference on computer and Communications (ICCC)

作者： Tao Ma Hangbiao Zhu Weijian Huang Pei An Chun Wang Kun Yu Institute of Computer Application China Academy of Engineering Physics Mianyang China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China National Key Laboratory of Science and Technology on Electromagnetic Energy Naval University of Engineering Wuhan China

Dense image matching plays a important role in stereo vision and remote sensing. Semi-global matching (SGM) is a widely used dense image matching framwork, which uses dynamic programming calculation strategy to optimize the global energy function. SGM has the problem of wrong disparity accumulation along the path. To solve the problem, this paper proposes a new dense image matching method based on feature matching and SGM. First, the feature matching is used to extract the reliable reference points. And then, reference points are used as additional constraints in matching cost aggregation to cut off the wrong disparity delivering along the path. In order to evaluate our method, qualitative and quantitative experiments are carried out on three stereo image pairs. The experimental results show that our method can achieve high matching accuracy by effectively reducing the error propagation.

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Self-supervised Heterogeneous Graph Neural Network Based on Deep and Broad Neighborhood Encoding 1st

Self-supervised Heterogeneous Graph Neural Network Based on...

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1st Conference on Blockchain and Web3.0 technology Innovation and application, BWTAC 2024

作者： Li, Chao Song, Qianyu Fu, Jinhu Xie, Nengfu Zeng, Qingtian College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Key Laboratory of Agricultural Blockchain Application Ministry of Agriculture and Rural Affairs Agricultural Information Institute Chinese Academy of Agricultural Sciences Beijing China

ISBN: (纸本)9789819794119

Self-supervised heterogeneous graph neural networks have demonstrated remarkable effectiveness in addressing the challenge of limited labeled data. However, current contrastive learning methods face limitations in exploiting neighborhood information for each node. Some methods only utilize the local information of the target node, ignoring useful signals from deeper neighborhoods. On the other hand, certain methods fail to consider the differences among neighbors at various distances. In this paper, we propose HGNN-DB, a self-supervised heterogeneous graph neural network based on depth and breadth neighborhood encoding. Specifically, HGNN-DB aims to effectively capture features from deep and broad neighborhoods. We introduce a deep neighborhood encoder using a distance-weighted strategy to capture deep features of target nodes. Additionally, we utilize a single-layer graph convolutional network for the broad neighborhood encoder to aggregate broad features of target nodes. We furthermore employ a collaborative contrastive algorithm to learn the complementarity and potential invariance between the two views of neighborhood information. The results demonstrate that HGNN-DB performs better than other state-of-the-art methods on various tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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CrossDiff: Exploring Self-Supervised Representation of Pansharpening via Cross-Predictive Diffusion Model

arXiv

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arXiv 2024年

作者： Xing, Yinghui Qu, Litao Zhang, Shizhou Zhang, Kai Zhang, Yanning Ran, Lingyan Zhang, Xiuwei Yin, Hanlin The Shaanxi Provincial Key Laboratory of Speech and Image Information Processing The National Engineering Laboratory for Integrated Aerospace-GroundOcean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China The Research Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China

Fusion of a panchromatic (PAN) image and corresponding multispectral (MS) image is also known as pansharpening, which aims to combine abundant spatial details of PAN and spectral information of MS. Due to the absence of high-resolution MS images, available deep-learning-based methods usually follow the paradigm of training at reduced resolution and testing at both reduced and full resolution. When taking original MS and PAN images as inputs, they always obtain sub-optimal results due to the scale variation. In this paper, we propose to explore the self-supervised representation of pansharpening by designing a cross-predictive diffusion model, named CrossDiff. It has two-stage training. In the first stage, we introduce a cross-predictive pretext task to pre-train the UNet structure based on conditional DDPM, while in the second stage, the encoders of the UNets are frozen to directly extract spatial and spectral features from PAN and MS, and only the fusion head is trained to adapt for pansharpening task. Extensive experiments show the effectiveness and superiority of the proposed model compared with state-of-the-art supervised and unsupervised methods. Besides, the cross-sensor experiments also verify the generalization ability of proposed self-supervised representation learners for other satellite’s datasets. We will release our code for reproducibility. © 2024, CC BY.

关键词： Deep learning

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Prediction of chilling damage risk in maize growth period based on probabilistic neural network approach

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International Journal of Agricultural and Biological Engineering 2021年第2期14卷 120-125页

作者： Chunqiao Mi Changhua Zhao Qingyou Deng Xiaowu Deng School of Computer Science and Engineering Huaihua UniversityHuaihua 418000HunanChina Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture Huaihua 418000HunanChina Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua 418000HunanChina Human Resource Department Huaihua UniversityHuaihua 418000HunanChina

Low temperature chilling damage is one of the most serious disasters in maize production,which is a typical non-linear complex issue with numerous influencing factors and strong *** to predict it is not only a hot theoretical research topic,but also an urgent practical problem to be ***,most of the current researches are focusing on post-disaster static descriptive assessment rather than pre-disaster dynamic predictive analysis,resulting in the problems such as no indicative result and low *** this study,the satisfaction rate of environmental accumulated temperature for maize production was used to measure the chilling damage risk,and a model for maize chilling damage risk prediction based on probabilistic neural network was *** model was composed of input layer,pattern layer,summation layer and output *** obtained results showed that the prediction accuracy for the most serious risk level was as high as 0.91,and the rates of the Type I Error and Type II Error made by the model were 0.1 and 0.09,*** indicated that the model employed was promising with good *** results of this research are of both theoretical significance for providing a new reference method of pre-disaster prediction to study maize chilling disaster risk and practical significance for reducing maize production risk and ensuring yield safety.

关键词： maize chilling damage risk prediction probabilistic neural network

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Review of Nodule Mineral Image Segmentation Algorithms for Deep-Sea Mineral Resource Assessment

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computers, Materials & Continua 2022年第10期73卷 1649-1669页

作者： Wei Song Lihui Dong Xiaobing Zhao Jianxin Xia Tongmu Liu Yuxi Shi School of Information and Engineering Minzu University of ChinaBeijing100081China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural ResourceGuangzhou510300China National Language Resource Monitoring&Research Center of Minority Languages Minzu University of ChinaBeijing100081China School of Ocean Science China University of GeosciencesBeijing100191China Department of Buoy Engineering South China Sea Marine Survey and Technology Center of State Oceanic AdministrationGuangzhou510310China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ07102USA

A large number of nodule minerals exist in the deep *** on the factors of difficulty in shooting,high economic cost and high accuracy of resource assessment,large-scale planned commercial mining has not yet been *** experimental mining has been carried out in areas with high mineral density and obvious benefits after mineral resource *** an efficient method for deep-sea mineral resource assessment,the deep towing system is equipped with a visual system for mineral resource analysis using collected images and videos,which has become a key component of resource ***,high accuracy in deep-sea mineral image segmentation is the primary goal of the segmentation *** this paper,the existing deep-sea nodule mineral image segmentation algorithms are studied in depth and divided into traditional and deep learning-based segmentation methods,and the advantages and disadvantages of each are compared and *** deep learning methods show great advantages in deep-sea mineral image segmentation,and there is a great improvement in segmentation accuracy and efficiency compared with the traditional ***,the mineral image dataset and segmentation evaluation metrics are ***,possible future research topics and improvement measures are discussed for the reference of other researchers.

关键词： Polymetallic nodule deep-sea mining image segmentation deep learning

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Semantic Tagging Algorithm for Tibetan La Case Function Words Oriented to Classification of La Case

Semantic Tagging Algorithm for Tibetan La Case Function Word...

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computer Engineering and Intelligent Communications (ISCEIC), International Symposium on

作者： Mabao Ban Jiecairang Duo Rui Zhang Maojia Yang Rangjia Cai College of Computer Science and Technology Qinghai Normal University Xining China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining China Tibetan Information Processing Engineering Technology and Research Center of Qinghai Province Xining China

Automatic classification of Tibetan La case () is a challenging and significant task in Tibetan natural language processing. Compared with other short text classification tasks, the automatic classification of La case depend more on the semantic information of context and function words of La case. Based on the definition about La case classification task, this paper designs a semantic labeling algorithm for Tibetan La case function words by analyzing their usage and semantic features. When training La case classification model, calling this algorithm can integrate the semantic information about La case function words, thus increasing the diversity of input features and enriching the semantic information of embedded vectors. Experiments on eight commonly used short text classification models show that the classification accuracy (ACC) after fusing the semantic information on La case function words is improved by 2.1 percentage points on average, which verifies the effectiveness and universality of the algorithm.

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Edit-History Vis: An Interactive Visual Exploration and Analysis on Wikipedia Edit History

Edit-History Vis: An Interactive Visual Exploration and Anal...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Yuhan Guo Qin Han Yuke Lou Yiming Wang Can Liu Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of Intelligence Science and Technology Peking University China School of Electronic Engineering and Computer Science Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China

We propose Edit-History Vis, a visual analytics system designed to facilitate interactive exploration on Wikipedia edit history at a fine-grained level. The examination of detailed changes in Wikipedia articles is crucial for understanding how authors’ perspectives vary and conflict during the collaborative editing process. However, it is challenging to reveal the details while preserving the heterogeneous attributes of revisions, namely the time, content, and editor. The Edit-History Vis system integrates editor and textual changes of revisions by utilizing a force-directed revision graph that groups revisions based on standpoints. Through this revision graph, users can identify and analyze editing events such as edit wars, vandalism, repair, and normal updates. The effectiveness of the system in analyzing the edit history is validated through a qualitative comparison with prior work and a quantitative rating from a user study.

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Two-in-One Solution: Simultaneously Enhancing Security and Privacy for Data-Driven Models in Mobile Edge Computing

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IEEE Transactions on Consumer Electronics 2024年

作者： Liu, Pengrui Yuan, Xiaohan Wang, Wei Xu, Xiangrui Li, Tao Wang, Junyong Wang, Bin Pedrycz, Witold Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation 3 Shangyuancun Beijing100044 China Haihe Lab of ITAI Tianjin300450 China Zhejiang Key Laboratory of Multi-dimensional Perception Technology Application and Cybersecurity Hangzhou310053 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Data-driven models are widely employed in Mobile Edge Computing to satisfy the demands of Emerging Consumer applications. However, previous work demonstrates that data-driven models are susceptible to security threats like Backdoor and Evasion Attacks or privacy threats like Membership Inference Attacks. Numerous existing methods for mitigating these threats have been proposed. However, these methods focus solely on enhancing model security or only on improving model privacy. Ideally, data-driven models should enhance model security and privacy simultaneously. In this paper, we propose methods that combine individual security-enhancing and privacy-enhancing methods to mitigate the security and privacy threats of data-driven models simultaneously. We evaluate the effectiveness of individual security-enhancing methods, individual privacy-enhancing methods, and our methods in simultaneously enhancing model security and privacy. Our comprehensive experimental analysis reveals two-fold insights. First, individual security-enhancing methods can either enhance or diminish model privacy, while individual privacy-enhancing methods face challenges in enhancing model security. Second, our methods improve the effectiveness of simultaneously enhancing model security and privacy compared to the individual methods. © 1975-2011 IEEE.

关键词： Differential privacy

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Insulin-resistance and depression cohort data mining to identify nutraceutical related DNA methylation biomarker for type 2 diabetes

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Genes & Diseases 2021年第5期8卷 669-676页

作者： Fengji Liang Yuan Quan Andong Wu Ying Chen Ruifeng Xu Yuexing Zhu Jianghui Xiong Lab of Epigenetics and Advanced Health Technology SPACEnter Space Science and Technology InstituteShenzhenGuangdong Province518117PR China State Key Laboratory of Space Medicine Fundamentals and Application China Astronaut Research and Training CenterBeijing100094PR China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate SchoolShenzhenGuangdong Province518055PR China

Insulin-resistance(IR)is one of the most important precursors of type 2 diabetes(T2D).Recent evidence suggests an association of depression with the onset of ***-lating evidence shows that depression and T2D share common biological origins,and DNA methylation examination might reveal the link between lifestyle,disease risk,and potential therapeutic targets for *** we hypothesize that integrative mining of IR and depression cohort data will facilitate predictive biomarkers identification for *** utilized a newly proposed method to extract gene-level information from probe level data on genome-wide DNA methylation *** identified a set of genes associated with IR and depression in clin-ical *** overlapping the IR-related nutraceutical-gene network with depression net-works,we identified a common subnetwork centered with Vitamin D Receptor(VDR)*** clinical validation of gene methylation set in a small cohort of T2D patients and controls was established using the Sequenome matrix-assisted laser desorption ionization-time flight mass spectrometry.A set of sites in the promoter regions of VDR showed a signifi-cant difference between T2D patients and *** a logistic regression model,the optimal prediction performance of these sites was AUC Z 0.902,and an odds ratio Z ***,monitoring the methylation status of specific VDR promoter region might help stratify the high-risk individuals who could potentially benefit from vitamin D dietary *** results highlight the link between IR and depression,and the DNA methyl-ation analysis might facilitate the search for their shared mechanisms in the etiology of T2D.

关键词： Depression DNA methylation Insulin resistance Nutraceuticals Type 2 Diabetes Vitamin D receptor

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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