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32nd International Conference on Information Modeling and Knowledge Bases, EJC 2022

作者： Sornlertlamvanich, Virach Iamtrakul, Pawinee Horanont, Teerayuth Hnoohom, Narit Wongpatikaseree, Konlakorn Yuenyong, Sumeth Angkapanichkit, Jantima Piyapasuntra, Suthasinee Lopkerd, Prittipoen Prasertsuk, Santirak Busayarat, Chawee Raungratanaamporn, I-Soon Deepaisarn, Somrudee Charoenporn, Thatsanee Faculty of Data Science Musashino University Japan Faculty of Engineering Thammasat University Thailand Center of Excellence in Urban Mobility Research and Innovation Faculty of Architecture and Planning Thammasat University Thailand Sirindhorn International Institute of Technology Thammasat University Thailand Faculty of Engineering Mahidol University Thailand Faculty of Liberal Arts Thammasat University Thailand Faculty of Architecture and Planning Thammasat University Thailand School of Transportation Engineering Institute of Engineering Suranaree University of Technology Thailand Faculty of Engineering Thammasat University 99 Moo 18 Paholyothin Road Klong Nueng Klong Luang Pathumthani12120 Thailand

ISBN: (纸本)9781643683706

This research proposes an AI platform for data sharing across multiple domains. Since the data in the smart city concept are domain-specific processed, the existing smart city architecture is suffered from cross-domain data interpretation. To go beyond the digital transformation efforts in smart city development, the AI city is created on the architecture of cross-domain data connectivity and transform learning in the machine learning paradigm. In this research, the health and human behavioral data are targeted on human traceability and contactless technologies. To measure the inhabitants quality of life (QoL), the primary emotion expression study is conducted to interpret the emotional states and the mental health of people in the urbanized city. The results of information augmentation draw attention to the immersive visualization of the Thammasat model. © 2023 The authors and IOS Press.

关键词： Metadata

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Size-Dependent Strength Superiority in Multi-Principal Element Alloys Versus Constituent Metals: Insights from Machine Learning Potential and Embedded-Atom Model

SSRN

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SSRN 2024年

作者： Shuang, Fei Ji, Yucheng Laurenti, Luca Dey, Poulumi Department of Materials Science and Engineering Faculty of Mechanical Engineering Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands Beijing Advanced Innovation Center for Materials Genome Engineering National Materials Corrosion and Protection Data Center Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing100083 China Delft Centre of System and Control Faculty of Mechanical Engineering Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands

Multi-principal element alloys (MPEAs) are renowned for their enhanced mechanical strength compared to their constituent metals, as evidenced by various experimental techniques such as tension/compression tests and instrumental indentation. Nevertheless, atomistic simulations sometimes produce conflicting results, casting doubt on the consistently superior mechanical properties of MPEAs. To address these discrepancies, we develop new machine-learning interatomic potentials (MLIPs) for body-centered cubic refractory MoNbTaW MPEAs, supported by a comprehensive dataset that encompasses extensive defects. We also benchmark the established embedded-atom model (EAM) potential against this dataset and our new MLIPs. Our simulations, covering diverse MoNbTaW compositions, confirm that both MLIPs and EAM accurately capture the critical strengthening mechanisms associated with plastic deformation in MPEAs. Utilizing the EAM potential, we perform reliable large-scale atomistic simulations to investigate various plastic mechanisms. Our findings suggest that generally, MPEAs exhibit superior mechanical strength compared to their constituent metals in macroscale specimens, primarily due to solid solution strengthening and dislocation motion. However, at the nanoscale where plasticity is mainly governed by dislocation nucleation and grain boundary deformation, the constituent metals may outperform MPEAs. This study identifies a critical length scale above which MPEAs demonstrate enhanced mechanical strength relative to their constituent elements. Below this scale, the advantage diminishes, underscoring a significant size-dependent effect that is crucial for optimizing MPEA applications, particularly at the nanoscale. © 2024, The Authors. All rights reserved.

关键词： Grain boundaries

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Observational study of the physical and chemical characteristics of the winter radiation fog in the tropical rainforest in Xishuangbanna, China

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science China Earth sciences 2021年第11期64卷 1982-1995页

作者： Yuan WANG Shengjie NIU Chunsong LU Jingjing LV Jing ZHANG Hongwei ZHANG Sirui ZHANG Naifu SHAO Wei SUN Yuchen JIN Qinghai SONG Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters and Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological AdministrationNanjing University of Information Science&Technology(NUIST)Nanjing 210044China College of Safety Science and Engineering Nanjing Tech UniversityNanjing 210009China Key Laboratory of Tropical Forest Ecology(TFE) Xishuangbanna Tropical Botanical GardenChinese Academy of Sciences(CAS)Menglun 666303China Henan Meteorological Observation and Data Center Zhengzhou 450003China

We conducted a three-month field experiment focusing on the physical and chemical characteristics of fog in a tropical rainforest in Xishuangbanna,Southwest China,in the winter of *** general,the fog would form at midnight and persist because of the increased long-wave radiative cooling combined with the high relative humidity,gentle breeze,and a relatively low aerosol number concentration in the forest;the fog would dissipate before noon due to the increasing turbulence near the *** diurnal cycle is typical for radiation *** microphysical fog properties included a relatively low number concentration of the fog droplet,large droplet size,high liquid water content,narrow droplet number-size distribution,and high *** chemical properties showed that the fog water was slightly alkaline with low electrical conductivity,whereas the highest proportions of anions and cations therein were Cl^(−)and Ca^(2+),respectively;the chemical components were enriched in small fog *** addition,we indirectly calculated the fog supersaturation according to theκ-Köhler *** found that condensation broadens the droplet number-size distribution at relatively low supersaturation,which is positively correlated with the fog-droplet number concentration and negatively correlated with the droplet mean-volume diameter;this affects the key microphysical processes of fog.

关键词： Radiation fog Formation and disappearance mechanisms Physical and chemical characteristics Supersaturation Tropical rainforest

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis

Personalized Federated Learning with Collaborative Aggregati...

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Industrial Automation, Robotics and Control engineering (IARCE), International Conference on

作者： Qian Si Yang Li School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Advanced Institute of Information Technology Peking University Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques.

关键词： Training Adaptation models data centers Federated learning Service robots Collaboration Brain modeling data models Servers Medical diagnostic imaging

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Nonlinear Model Predictive Control for Robot Manipulator with Disturbance Observer

Nonlinear Model Predictive Control for Robot Manipulator wit...

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Chinese Control Conference (CCC)

作者： Chao Zhuang Bo-Kai Xuan Yue-Xuan Xu Tian-Yi Ma Ming-Huan Yin Zhi-Feng Gu Hao Sun School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin School of Electrical and Electronic Engineering Shijiazhuang Tiedao University Hebei Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of Education Tianjin Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin

This paper investigates a variable horizon nonlinear model predictive control strategy based on disturbance observer for a three-degree-of- freedom robot manipulator which has the property of high nonlinearity and uncertainty. Firstly, a nonlinear disturbance observer is implemented to accurately estimate and compensate for uncertain factors such as external disturbances, model parameter disturbance and unmodeled part of the system. Secondly, based on the disturbance observer, the nonlinear model predictive control is utilized to handle the nonlinear model such that avoiding large model error and the predictive horizon of nonlinear model predictive control is improved to the form of dynamic optimization, and the optimal predictive horizon is selected according to the state error of each sampling period. Compared with constant predictive horizon nonlinear model predictive control, this improved strategy not only improves the system tracking performance, but also reduces the use of computing resources and shortens the calculation time. Finally, the numerical simulation and comparative experimental results are presented to verify the superiority and effectiveness of the proposed control strategy.

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III-V/Si hybrid external-cavity laser utilizing TM mode in the passive photonic circuit 27

III-V/Si hybrid external-cavity laser utilizing TM mode in t...

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27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022

作者： Guo, Yuyao Li, Xinhang Jin, Minhui Lu, Liangjun Xie, Jingya Chen, Jianping Zhou, Linjie Shanghai Institute for Advanced Communication and Data Science State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation Center University of Shanghai for Science and Technology Shanghai Key Lab of Modern Optical System Shanghai200093 China

We present a III-V/Si hybrid laser using the silicon waveguide TM mode to reduce the propagation loss, backscattering, and two-photon absorption. The TM-mode laser has superior performance than the TE-mode laser. ... 详细信息

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Photons

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Application of the path optimization method to a discrete spin system

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Physical Review D 2023年第9期108卷 094504-094504页

作者： Kouji Kashiwa Yusuke Namekawa Akira Ohnishi Hayato Takase Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka 811-0295 Japan Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima 730-0053 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto 606-8502 Japan

The path optimization method, which is proposed to control the sign problem in quantum field theories with continuous degrees of freedom by machine learning, is applied to a spin model with discrete degrees of freedom. The path optimization method is applied by replacing the spins with dynamical variables, via the Hubbard-Stratonovich transformation, and the sum with the integral. The one-dimensional (Lenz-)Ising model with a complex coupling constant is used as a laboratory for the sign problem in the spin model. The average phase factor is enhanced by the path optimization method, indicating that the method can weaken the sign problem. Our result reproduces the analytic values with controlled statistical errors.

关键词： Optimization problems Hybrid Monte Carlo algorithm

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation 15th

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Computerized Assessment of Motor Imitation for Distinguishing Autism in Video (CAMI-2DNet)

arXiv

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arXiv 2025年

作者： Kinfu, Kaleab A. Pacheco, Carolina Sperry, Alice D. Crocetti, Deana Tunçgenç, Bahar Mostofsky, Stewart H. Vidal, René The Center for Innovation in Data Engineering and Science The University of Pennsylvania United States The Department of Biomedical Engineering Johns Hopkins University United States The Center for Neurodevelopmental and Imaging Research The Kennedy Krieger Institute United States The Department of Neurology The Department of Psychiatry and Behavioral Sciences at the Johns Hopkins University School of Medicine United States The Department of Psychology The Nottingham Trent University United Kingdom

Motor imitation impairments are commonly reported in individuals with autism spectrum conditions (ASCs), suggesting that motor imitation could be used as a phenotype for addressing autism heterogeneity. Traditional methods for assessing motor imitation are subjective and labor-intensive, and require extensive human training. Modern Computerized Assessment of Motor Imitation (CAMI) methods, such as CAMI-3D for motion capture data and CAMI-2D for video data, are less subjective. However, they rely on labor-intensive data normalization and cleaning techniques, and human annotations for algorithm training. To address these challenges, we propose CAMI-2DNet, a scalable and interpretable deep learning-based approach to motor imitation assessment in video data, which eliminates the need for data normalization, cleaning and annotation. CAMI-2DNet uses an encoder-decoder architecture to map a video to a motion encoding that is disentangled from nuisance factors such as body shape and camera views. To learn a disentangled representation, we employ synthetic data generated by motion retargeting of virtual characters through the reshuffling of motion, body shape, and camera views, as well as real participant data. To automatically assess how well an individual imitates an actor, we compute a similarity score between their motion encodings, and use it to discriminate individuals with ASCs from neurotypical (NT) individuals. Our comparative analysis demonstrates that CAMI-2DNet has a strong correlation with human scores while outperforming CAMI-2D in discriminating ASC vs NT children. Moreover, CAMI-2DNet performs comparably to CAMI-3D while offering greater practicality by operating directly on video data and without the need for ad-hoc data normalization and human annotations. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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Spatio-temporal Dynamics and Drivers of Ecological Quality in Yulin City Using the MRSEI Model

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Journal of Resources and Ecology 2025年第2期16卷 340-355页

作者： MU Weichen HE Zhilin CHEN Yanglong GAO Dongkai YUE Tianming QIN Fen College of Geographical Sciences Faculty of Geographical Science and EngineeringHenan UniversityZhengzhou450046China Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Ministry of EducationHenan UniversityKaifengHenan 475004China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan UniversityKaifengHenan 475004China Henan Technology Innovation Center of Spatial-Temporal Big Data Henan UniversityZhengzhou 450046China.

Urbanization has resulted in growing ecological pressures on cities,necessitating assessments of urban ecological ***-term characterization of regional dynamics and drivers is critical for environmental *** study proposes an enhanced ecological quality model(MRSEI)incorporating vegetation cover and EVI rather than just *** MRSEI model was applied to analyse ecological quality in Yulin City during 2000-2018 using Landsat TM/OLI data on Google Earth *** detectors also quantified anthropo-genic and environmental influences on the study *** results are summarized as follows:(1)MRSEI showed an average correlation coefficient of 0.840 with other indices,demonstrating higher representativeness than indi-vidual *** principal component analysis indicated a 12.88%increase in explained *** also exhibited significantly improved identification of roads,villages,and unused lands over RSEI,better matching ground conditions,and suitability for regional ecological assessment.(2)During 2000-2020,the average MRSEI in Yulin City was 0.481,peaking at 0.518 in 2018,indicating general ecological improvement over ***,conditions were better in the southeast than *** 38.81%of the area showed significant improvement,10.15%exhibited significant deterioration,concentrated in western Dingbian and Jingbian counties,highlighting areas requiring enhanced protection.(3)Ecological conditions in Yulin City remained stable over ***-high clusters were concentrated in eastern counties(Qingjian,Wubao,Jia,Fugu)and central lower-altitude areas near Yokoyama and ***-low clusters predominated in the northern Yuyang desert and high-altitude western Dingbian regions.(4)Enhanced vegetation cover had the greatest influence in improving Yulin’s ecological *** was the most impactful environmental driver,while precipitation and land use change interactions showed the strongest combined *** cont

关键词： Google Earth Engine improved remote sensing-based ecological indicator temporal and spatial variation geographical detector

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