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2021 International Conference on Culture-Oriented Science and technology, ICCST 2021

作者： Zhang, Jinguo Zhang, Shuwu Wang, Zhi Ministry of Science Technology High-tech Research Development Center Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Xi'an Jiaotong University School of Software Engineering Xi'an China

ISBN: (纸本)9781665442541

In the field of the integration of culture and scitech covering such important areas as media integration, digital culture, cultural and travel integration, Chinese civilization tracing and cultural heritage protection, China will carry out in-depth researches on some commonly basic and restrictive key technologies to realize core technical breakthroughs and their integrative applications. An integrated system of cultural services and smart governance technologies is established to solve outstanding problems of cultural creation workshopization, propaganda stylization, performance stereotyping, resource islanding, and supervision artificialization, aiming to improve the efficiency of cultural creation and cultural resource utilization, the ability of cultural communication and performance, and the level of cultural smart management, so that with all-media technology as the foundation and its service innovation to transform and upgrade traditional cultural activities, this system could further vigorously help develop the new economic formats of digital cultural services, promoting digital creativity and design to empower the real economy, advancing cultural heritage protection, and realizing the deep integration of culture and sci-tech. © 2021 IEEE.

关键词： Integration

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A differentiable programming framework for spin models

arXiv

引用

arXiv 2023年

作者： Farias, Tiago S. Schultz, Vitor V. Mombach, José C.M. Maziero, Jonas Physics Deparament Center for Exact Sciences and Technology Federal University of São Carlos Rod. Washington Luís km 235 SP São Carlos13565-905 Brazil Physics Department Center for Natural and Exact Sciences Federal University of Santa Maria Roraima Avenue 1000 RS Santa Maria97105-900 Brazil Institute and Center for Development and Research in Software Technology - ICTS Governor Danilo de Matos Areosa Avenue 1199 AM Manaus69075-904 Brazil

We introduce a novel framework for simulating spin models using differentiable programming, an approach that leverages the advancements in machine learning and computational efficiency. We focus on three distinct spin systems: the Ising model, the Potts model, and the Cellular Potts model, demonstrating the practicality and scalability of our framework in modeling these complex systems. Additionally, this framework allows for the optimization of spin models, which can adjust the parameters of a system by a defined objective function. In order to simulate these models, we adapt the Metropolis-Hastings algorithm to a differentiable programming paradigm, employing batched tensors for simulating spin lattices. This adaptation not only facilitates the integration with existing deep learning tools but also significantly enhances computational speed through parallel processing capabilities, as it can be implemented on different hardware architectures, including GPUs and TPUs. © 2023, CC BY.

关键词： Ising model

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Investigation of Rounding Algorithms Combining Data Distribution Characteristics

Investigation of Rounding Algorithms Combining Data Distribu...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Mengyuan Zhu Qian Zhang Yunwei Zhang Tao Shen Baochang Zhang Software Development Center China Information Technology Designing & Consulting Institute Co. Ltd. Beijing China Artificial Intelligence Research Institute Beihang University Beijing China

ISBN: (纸本)9781665409858

The default ratings for the scenarios like user ratings or recommendation systems are usually expressed as integers. However, the prediction results of regression models based on machine learning or deep learning are usually floating-point decimals. The bias between integer ratings and floating-point decimals expanded the mean absolute error (MAE). In this paper, we first compared the results among three conventional rounding methods, i.e., rounding up, rounding down, and rounding off. The results show that conventional rounding method does not consider the information of original data distribution. Therefore, we established two novel rounding algorithms combining the real-world data distribution, which aim to reduce the MAE of the LightGBM regression outputs. First, an adjacent rounding algorithm combining the data distribution of adjacent labels was proposed. By this means, the predicted value should be the one with the higher distribution frequency between the two integer labels. Then, we extended the rounding algorithm to the top-n label values with higher distribution frequency than others. Moreover, a global optimal rounding algorithm is proposed by taking the distance information between each predicted value and the first n labels as another influencing factor. The proposed method has demonstrated potential applications in recommender systems, user ratings and other scenarios.

关键词： Industrial electronics Deep learning Electric potential Predictive models Prediction algorithms Data models Recommender systems

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Cloud-based storage and computing for remote sensing big data:a technical review

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International Journal of Digital Earth 2022年第1期15卷 1417-1445页

作者： Chen Xu Xiaoping Du Xiangtao Fan Gregory Giuliani Zhongyang Hu Wei Wang Jie Liu Teng Wang Zhenzhen Yan Junjie Zhu Tianyang Jiang Huadong Guo Key Lab of Digital Earth Sciences Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China International Research Center of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China College of Resources and Environment University of Chinese Academy of SciencesBeijingPeople’s Republic of China Institute for Environmental Sciences/GRID-Geneva University of GenevaGenevaSwitzerland Institute for Marine and Atmospheric research Utrecht (IMAU) Utrecht UniversityUtrechtThe Netherlands State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijingPeople’s Republic of China Tianyi Cloud People’s Republic of China Telecom Co.LtdGuangzhouPeople’s Republic of China Department of Computer Science and Technology Tsinghua UniversityBeijingPeople's Republic of China

The rapid growth of remote sensing big data(RSBD)has attracted considerable attention from both academia and *** the progress of computer technologies,conventional computing implementations have become technically inefficient for processing *** computing is effective in activating and mining large-scale heterogeneous data and has been widely applied to RSBD over the past *** study performs a technical review of cloud-based RSBD storage and computing from an interdisciplinary viewpoint of remote sensing and computer ***,we elaborate on four critical technical challenges resulting from the scale expansion of RSBD applications,*** storage,metadata management,data homogeneity,and computing ***,we introduce state-of-the-art cloud-based data management technologies for RSBD *** unit for manipulating remote sensing data has evolved due to the scale expansion and use of novel technologies,which we name the RSBD data *** data models are suggested,***,ARD,data cubes,and composite ***,we summarize recent research on the application of various cloud-based parallel computing technologies to RSBD computing ***,we categorize the architectures of mainstream RSBD *** research provides a comprehensive review of the fundamental issues of RSBD for computing experts and remote sensing researchers.

关键词： Remote sensing big data cloud computing data cube analysis ready data parallel computing data model

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Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenergetic Positron Beams

Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenerg...

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Joint e-Manufacturing and Design Collaboration Symposium (eMDC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Akira Uedono Kazuya Kawakami Tatsunori Kuribara Ryu Hasunuma Yosuke Harashima Yasuteru Shigeta Zeyuan Ni Ruolin Yan Hideo Nakamura Akira Notake Tsuyoshi Moriya Koji Michishio Shoji Ishibashi Faculty of Pure and Applied Science University of Tsukuba Tsukuba Ibaraki Japan Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Nara Japan S-Technology Development Center Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Development Department 1 Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Digital Design Center Tokyo Electron Ltd. Sapporo Hokkaido Japan Software and Data Engineering Division Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Research Institute for Measurement and Analytical Instrumentation National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki Japan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki Japan

ISBN: (数字)9798331510589

ISBN: (纸本)9798331510596

Behaviors of vacancies during the amorphous-to-crystalline transition of thin HfO 2 layers were studied by monoenergetic positron beams. The HfO 2 layers with 4‒30 nm thickness were fabricated on TiN/Si templates by atomic layer deposition technique. The major vacancy-type defects in the layers were identified as (i) Hf vacancies (V Hf ) coupled with oxygen vacancies (V O s) and (ii) vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters increased, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to crystalline phases. The phase transition started near the interface between the HfO 2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased.

关键词： Electrodes Annealing Atomic layer deposition Positrons Crystallization Tin Semiconductor device manufacture Hafnium compounds

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Driving at Realistic Urban Intersections with Adversarial Reinforcement Learning

Driving at Realistic Urban Intersections with Adversarial Re...

引用

Automated Vehicle Validation Conference (IAVVC), IEEE International

作者： Yangang Ren Letian Tao Jingliang Duan Honglin Li Beiyan Jiang Shengbo Eben Li State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing Changan Automobile CO. Ltd. Chongqing China School of Vehicle and Mobility Tsinghua University Beijing China School of Mechanical Engineering University of Science and Technology Beijing Beijing China Center of Software Engineering Research Dongfeng Motor Corporation Research and Development Institute Wuhan China

ISBN: (数字)9798350354072

ISBN: (纸本)9798350354089

Reinforcement learning (RL) has been widely studied in the decision-making of automated vehicles and demonstrated a promising self-evolution ability. However, these RL-driven methods are usually carried out based on simulation due to safety concerns of the interaction with the real world. This results in the policy well-trained in simulation struggles to remain effective in realistic environments. In this paper, we propose an adversarial training method to improve the practical performance of driving policy at urban roads. The basic idea is to proactively model the discrepancy between simulation and reality as the adversarial noise, and design offline competitive learning to enhance the robustness against environment variation. The driving policy jointly minimizes the path tracking and safety cost, while the adversary generates refined disturbances on the simulation model to increase the collision risk. The smooth adversarial actor-critic algorithm is adopted to update these policies iteratively with a gradient-based manner. Then, the trained policy is deployed on a full-size vehicle to verify its effectiveness at an urban intersection. Experiment results show that the policy can not only control the vehicle smoothly and efficiently, but also deal with the abnormal or unseen behaviors from surrounding traffic actors. Our work provides a feasibility for the application of RL-based methods in real-world driving.

关键词： Training Constraint optimization Costs Roads Noise Decision making Reinforcement learning Games Robustness Safety

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Full-Coverage Mapping of Daily High-Resolution XCO2 across China from 2015 to 2020 by Deep Learning-Based Spatio-Temporal Fusion

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Li, Yi Yan, Jining Zhong, Liheng Bao, De Sun, Leigang Li, Guangyu China University of Geosciences School of Computer Science Wuhan430074 China Ministry of Education Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Wuhan430074 China Ant Group World Financial Center Beijing100020 China Hebei Academy of Sciences Institute of Geographical Sciences Shijiazhuang050011 China Hebei Technology Innovation Center for Geographic Information Application Shijiazhuang050011 China Environmental Development Center of the Ministry of Ecology and Environment China

Carbon neutrality has become a global priority, and high spatio-temporal resolution data on the column-average dry-air mole fraction carbon dioxide (XCO2) is essential for tracking progress and guiding policy adjustments. However, satellite-derived XCO2 exhibits significant temporal and spatial gaps due to influences such as orbital dynamics and cloud cover. Additionally, the low spatial resolution of CarbonTracker (CT) is insufficient to meet the current demands for fine-scale monitoring. Current XCO2 assessment methods often rely solely on single-pixel data, overlooking the spatio-temporal correlations. In this article, we introduce a deep learning-based spatio-temporal model (DSTM) that extracts features from multiple data sources related to atmospheric transport, carbon emissions, and carbon sinks, enabling fine-scale XCO2 assessments. Additionally, XCO2 data from the Orbiting Carbon Observatory-2 (OCO-2) and CT were fused at a 0.1° spatial resolution to generate training labels with broader coverage and more samples, serving as fitting labels. Our approach produced daily, full-coverage 0.1° resolution XCO2 maps for China from 2015 to 2020, and analyzed changes in XCO2 growth trends over this period. Numerical results show that our model outperforms traditional deep learning methods. Model validation using data from four ground-based observation sites of the Total Carbon Column Observing Network (TCCON) achieved an average R2 of 0.86 and an RMSE of 2.67 ppm. The extraction and fusion of spatio-temporal features from multiple data sources provide a novel approach for reconstructing missing XCO2 data. © 1980-2012 IEEE.

关键词： Atmospheric movements

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3D Anatomical Structure-Guided Deep Learning for Accurate Diffusion Microstructure Imaging 22

3D Anatomical Structure-Guided Deep Learning for Accurate Di...

引用

22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Ma, Xinrui Cheng, Jian Fan, Wenxin Wu, Ruoyou Ye, Yongquan Wang, Shanshan Beihang University State Key Laboratory of Software Development Environment Beijing China Institute of International Innovation Beihang University Key Laboratory of Data Science and Intelligent Computing Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China Pengcheng Laboratory Guangdong Shenzhen China United Imaging Healthcare Houston TX United States

ISBN: (纸本)9798331520526

Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue microstructure information. Recent advances in deep learning have shown promise in microstructure estimation;however, accurately estimating tissue microstructure from clinically feasible dMRI scans remains challenging without appropriate constraints. This paper introduces a novel framework that achieves high-fidelity and rapid diffusion microstructure imaging by simultaneously leveraging anatomical information from macro-level priors and mutual information across parameters. This approach enhances time efficiency while maintaining accuracy in microstructure estimation. Experimental results demonstrate that our method outperforms four state-of-the-art techniques, achieving a peak signal-to-noise ratio (PSNR) of 30.51±0.58 and a structural similarity index measure (SSIM) of 0.97±0.004 in estimating parametric maps of multiple diffusion models. Notably, our method achieves a 15× acceleration compared to the dense sampling approach, which typically utilizes 270 diffusion gradients. © 2025 IEEE.

关键词： deep learning diffusion MRI microstructural estimation multiple diffusion models

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Automated Mura Defect Detection System on LCD Displays using Random Forest Classifier

Automated Mura Defect Detection System on LCD Displays using...

引用

IEEE International Conference on Consumer Electronics (ICCE)

作者： Gustavo M. Torres Adriana S. Souza David A. O. Ferreira Luiz C. S. G. Júnior Kethilen Y. Ouchi Myke D. M. Valadão Mateus O. Silva Victor L. G. Cavalcante Edma V. C. U Mattos Antônio M. C. Pereira Caio F. S. Cruz Agemilson P. Silva Ruan J. S. Belem André S. Costa Lucas G. C. Evangelista Wilson C. C. Junior Ricardo G. Paula Thiago B. Bezerra Waldir S. S. Júnior Celso B. Carvalho Federal University of Amazonas and Center for R&D in Elec. and Inf. Tech. (UFAM/CETELI) AM-Brazil ENVISION/TPV Group Technology Limited AM-Brazil ICTS Institute Center of Development Research in Software Technology AM-Brazil

Mura on LCD displays are challenging visual defects to detect, due to uneven brightness of the panel. So, automatic detection of mura defects on TVs and monitors is an important application to ensure the quality of LCDs manufactured in electronic product factories. In this work, conducted by three partners, called UFAM/CETELI, Envision (TPV Group) and icts, we propose an automated mura defect detection system on LCD displays that are based on similarities of the histograms and a machine learning (ML) algorithm called random forest. For comparison with the proposed method, we use one of the most popular methods of ML called k-Nearest Neighbors (k-NN). The experimental results show a detection rate above 99%, and processing time of 27 milliseconds per image, highlighting as contributions of our proposal, the high accuracy and reduced processing time, which allows the use of our solution in real production processes of LCD monitors.

关键词： Visualization TV Machine learning algorithms Shape Liquid crystal displays Production facilities Visual databases

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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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