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Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary information

Advances in Atmospheric sciences 2025年第1期42卷 53-66页

作者： Jing HU Jialing MU Xiaomeng HUANG Xi WU Department of Computer science Chengdu University of Information TechnologyChengdu 610225China Department of earth System science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China

Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale *** article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate *** a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the *** dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping *** findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and ***,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation *** summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.

关键词： downscaling unsupervised deep learning dual learning auxiliary information

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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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science China(information sciences) 2025年第2期68卷 89-102页

作者： Meiting SUN Fangyu LI Honggui HAN School of information science and technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder(RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

关键词： unsupervised feature selection fractal autoencoder correspondence neural network selection neural network redundancy regularization strategy

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An embedded electron current layer observed at the edge of the plasma sheet in the earth’s magnetotail

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earth and Planetary Physics 2025年第1期9卷 148-158页

作者： ChenChen Zhan RongSheng Wang QuanMing Lu San Lu XinMin Li Deep Space Exploration Laboratory/School of earth and Space sciences University of Science and Technology of ChinaHefei 230026China Chinese Academy of sciences(CAS)center for Excellence in Comparative Planetology/CAS Key Laboratory of Geospace Environment/Anhui Mengcheng National Geophysical Observatory University of Science and Technology of ChinaHefei 230026China Collaborative Innovation center of Astronautical science and technology Harbin 150001China

The formation of an embedded electron current sheet within the magnetotail plasma sheet has been poorly *** this article,we present an electron current layer detected at the edge of the magnetotail plasma *** ions were demagnetized inside the electron current layer,but the electrons were still frozen in with the magnetic field ***,this decoupling of ions and electrons gave rise to a strong Hall electric field,which could be the reason for the formation of the embedded thin current *** magnetized electrons,the absence of the nongyrotropic electron distribution,and negligible energy dissipation in the layer indicate that magnetic reconnection had not been triggered within the embedded thin current *** highly asymmetric plasma on the two sides of the current layer and low magnetic shear across it could suppress magnetic *** observations indicate that the embedded electric current layer,probably generated by the Hall electric field,even down to electron scale,is not a sufficient condition for magnetic reconnection.

关键词： magnetotail plasma sheet magnetic reconnection

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Shear softening of earth's inner core as indicated by its high poisson ratio and elastic anisotropy

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Fundamental Research 2025年第1期5卷 264-268页

作者： Zhongqing Wu Wenzhong Wang Laboratory of Seismology and Physics of earth's Interior School of Earth and Space ScienceUniversity of Science and Technology of ChinaHefei 230026China Mengcheng National Geophysical Observatory University of Science and Technology of ChinaHefei 230026China CAS center for Excellence in Comparative Planetology University of Science and Technology of ChinaHefei 230026China

earth's inner core exhibits an unusually high Poisson ratio and noticeable elastic *** mechanisms responsible for these features are crucial to understanding the nature of earth's *** different mechanisms have been proposed,few can explain both observations *** results of this study indicated that the crystal with noticeable shear softening would have strong anisotropy and an exceptionally high Poisson ratio ***-centered-cubic(bcc)iron exhibits shear instability at inner-core pressures and can be dynamically stabilized by high temperature and the presence of light *** bcc-lattice iron alloy could have strong anisotropy and a Poisson ratio of earth's inner core before shear *** which light elements can stabilize the bcc-lattice iron alloy will provide an independent constraint on the chemical composition of the inner core.

关键词： Elasticity Crystal structure Iron alloy earth’s interior Composition

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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science China(information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research center for Big Data technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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Unsupervised multiplex graph diffusion networks with multi-level canonical correlation analysis for multiplex graph representation learning

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science China(information sciences) 2025年第03期68卷 66-79页

作者： Sichao FU Qinmu PENG Yange HE Baokun DU Bin ZOU Xiao-Yuan JING Xinge YOU School of Electronic information and Communications Huazhong University of Science and Technology Platform Operation and Marketing center JD Retail Faculty of Mathematics and Statistics Hubei Key Laboratory of Applied Mathematics Hubei University School of Computer science Wuhan University

In recent years, unsupervised multiplex graph representation learning(UMGRL) has received increasing research interest, which aims to learn discriminative node features from the multiplex graphs supervised by data without the guidance of labels. Although these designed UMGRL methods have obtained great success in various graph-related tasks, most existing UMGRL models still have the following issues: highly depending on complex self-supervised strategies(i.e., data augmentation,pretext tasks, and negative pairs sampling), restricted receptive fields, and only aggregating low-frequency information between nodes. In this paper, we propose a simple unsupervised multiplex graph diffusion network(UMGDN) with the aid of multi-level canonical correlation analysis to solve the above issues. Specifically, we first decouple the feature transform and propagation processes of the graph convolution layer to further improve the generalization of the learnable parameters. And then, we propose adaptive diffusion propagation to capture long-range dependency relationships between nodes, not the local neighborhood interactions. Finally, a multi-level canonical correlation analysis loss on both the feature transform and propagation processes is proposed to maximize the correlation of the same node features from multiple graphs for guiding model optimization. Compared to the existing UMGRL models, our proposed UMGDN does not need to introduce any data augmentation, negative pairs sampling techniques, complex pretext tasks, and also adaptively aggregates the optimal frequency information between nodes to generate more robust node embeddings. Extensive experiments on four popular datasets and two graph-related tasks demonstrate the effectiveness of the proposed method.

关键词： unsupervised multiplex graph representation learning graph neural networks node classification node clustering

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Energy injection, transport, and dissipation in earth's magnetosphere

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science China earth sciences 2025年第2期68卷 363-388页

作者： Huishan FU Jinbin CAO School of Space and earth sciences Beihang UniversityBeijing 100191China Key Laboratory of Space Environment Monitoring and information Processing Ministry of Industry and Information TechnologyBeijing 100191China

The earth's magnetosphere is a region occupied by many artificial satellites, and also is a region that spacecraft must cruise during the deep-space exploration. In this sense, the earth's magnetosphere is closely related to human activity and is a candidate for us to expand our living space. Generally, the earth's magnetosphere can preclude most energetic particles from the Sun and the interstellar space, effectively protecting human beings on the earth from being attacked and thus making the earth to be a habitable planet. However, in some conditions, the earth's magnetosphere becomes dynamic and energetic, and consequently may damage the artificial satellites, threaten the astronauts' health, and disrupt the ground infrastructure, which leads to a decline in the national economy. Therefore, investigating how energy is injected into the magnetosphere, how it is transported in the magnetosphere, and how it is ultimately dissipated in the magnetosphere are the key issues in space physics. Targeting these key issues, in this paper, we review the recent progress on them. Particularly, we introduce the relevant scientific questions,models, methods, and spacecraft missions, for better building a physical link among the energy injection, transport, and dissipation in the magnetosphere, present an energy chain of the magnetosphere, reveal the relationship between such energy chain and the space weather events, and discuss the forecasting and warning methods for energetic-particle events in the magnetosphere. The magnetospheric energy chain discussed in this paper will help us reveal the mechanisms of space weather events, establish the models of space environment, and forecast the disastrous space weather events.

关键词： earth's magnetosphere Energy injection Energy transport Energy dissipation

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First observation results of Macao science Satellite-1 on lightning-induced electron precipitation

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science China earth sciences 2025年第1期68卷 1-10页

作者： Yongping WANG Yixin SUN Qiugang ZONG Gaopeng LU Xudong GU Ze-Jun HU Bin LI Key Laboratory for Polar science Ministry of Natural ResourcesPolar Research Institute of ChinaShanghai 200136China Institute of Space Physics and Applied technology Peking UniversityBeijing 100871China State Key Laboratory of Lunar and Planetary science Macao University of Science and TechnologyMacao 999078China School of earth and Space sciences University of Science and Technology of ChinaHefei 230026China School of Electronic information Wuhan UniversityWuhan 430072China

The Medium-energy Electron Spectrometers(MES)is a space-borne instrument onboard Macao science Satellite-1(MSS-1)dedicated to monitoring the typical charged particle radiation characteristics in the satellite orbit and the process of their occurrence and development,including short bursts of lightning-induced electron precipitation(LEP).This paper presents the first results of the LEP measurements by the MSS-1.47 LEP events are identified with the routine operation for 3 months since satellite launch,all within the range of 1.5

关键词： Lightning-induced electron precipitation Inner radiation belts Energetic electrons Macao science Satellite-1

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Beyond Performance of Learning Control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 198-214页

作者： Fazhi Song Ning Cui Shuaiqi Chen Kai Zhang Yang Liu Xinkai Chen Jiubin Tan IEEE the center of Ultra-precision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technology the State Key Laboratory of Precision Electronic Manufacturing technology and Equipment Guangdong University of Technology the center of Ultraprecision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of UltraPrecision Intelligent Instrumentation Ministry of Industry and Information Technology the Department of Electronic and information Systems Shibaura Institute of Technology

The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the accuracy in terms of nanometers. This demanding requirement witnesses a widespread application of iterative learning control(ILC), given the repetitive nature of wafer scanning. ILC enables substantial performance improvement by using past measurement data in combination with the system model knowledge. However, challenges arise in cases where the data is contaminated by the stochastic noise, or when the system model exhibits significant uncertainties, constraining the achievable performance. In response to this issue, an extended state observer(ESO) based adaptive ILC approach is proposed in the frequency *** being model-based, it utilizes only a rough system model and then compensates for the resulting model uncertainties using an ESO, thereby achieving high robustness against uncertainties with minimal modeling effort. Additionally, an adaptive learning law is developed to mitigate the limited performance in the presence of stochastic noise, yielding high convergence accuracy yet without compromising convergence speed. Simulation and experimental comparisons with existing model-based and data-driven inversion-based ILC validate the effectiveness as well as the superiority of the proposed method.

关键词： Extended state observer learning control model uncertainties motion control stochastic noise

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Simultaneous immobilization of multiple heavy metal(loid)s in contaminated water and alkaline soil inoculated Fe/Mn oxidizing bacterium

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Journal of Environmental sciences 2025年第1期147卷 370-381页

作者： Yi Wu Shengli Wang Jun Xu Fei Zang Song Long Yining Wu Yuqing Wang Zhongren Nan technology Research center for Pollution Control and Remediation of Northwest Soil and Groundwater College of Earth and Environmental SciencesLanzhou UniversityLanzhou 730000China State Key Laboratory of Grassland Agro-ecosystems College of Pastoral Agriculture Science and TechnologyLanzhou UniversityLanzhou 730000China

Two strains of Fe/Mn oxidizing bacteria tolerant to high concentrations of multiple heavy metal(loid)s and efficient decontamination for them were *** surface of the bio-Fe/Mn oxides produced by the oxidation of Fe(II)and Mn(II)by Pseudomonas taiwanensis(marked as P4)and Pseudomonas plecoglossicida(marked as G1)contains rich reactive oxygen functional groups,which play critical roles in the removal efficiency and immobilization of heavymetal(loid)s in co-contamination *** isolated strains P4 and G1 can growwell in the following environments:pH 5-9,NaCl 0-4%,and temperature 20-30℃.The removal efficiencies of Fe,Pb,As,Zn,Cd,Cu,and Mn are effective after inoculation of the strains P4 and G1 in the simulated water system(the initial concentrations of heavy metal(loid)were 1 mg/L),approximately reaching 96%,92%,85%,67%,70%,54%and 15%,*** exchangeable and carbonate bound As,Cd,Pb and Cu are more inclined to convert to the Fe-Mn oxide bound fractions in P4 and G1 treated soil,thereby reducing the phytoavailability and bioaccessible of heavy metal(loid)*** research provides alternatives method to treat water and soil containing high concentrations of multi-heavy metal(loid)s.

关键词： Bio-Fe/Mn oxides Pseudomonas Heavy metal(loid)s Removal and immobilization

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