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Proximity-induced magnetic order in topological insulator on ferromagnetic semiconductor

science China(Information sciences) 2023年第12期66卷 267-274页

作者： Hangtian WANG Koichi MURATA Weiran XIE Jing LI Jie ZHANG Kang L.WANG Weisheng ZHAO Tianxiao NIE School of Integrated Circuit Science and Engineering and Advanced Innovation Center for Big Data and Brain Computing Beihang University Beihang-Goertek Joint Microelectronics Institute Qingdao Research InstituteBeihang University Department of Electrical Engineering University of California

Introducing magnetic order into topological insulator（TI） to break the time-reversal symmetry can yield numerous fascinating physical phenomena,which brings new hope for the emerging spintronic *** proximity effect is regarded as a promising strategy that could advance the step for realistic application by choosing a suitable ferromagnetic layer with the merits of high Curie temperature and high compatibility with mainstream semiconductor ***,we prepare a Bi2Se3thin film on Sicompatible ferromagnetic semiconductor（FMS） of MnxGe1-xby molecular beam *** integration,the nonmagnetic Bi2Se3exhibits an anomalous Hall signal and a clear weak localization cusp in magnetoresistance until 150 K,confirming that a high-temperature magnetism can be induced by the proximity *** investigation of the magnetoconductance quantitatively indicates that the Bi2Se3conductance suffers a transition from weak antilocalization to weak localization behavior after integrating with MnxGe1-x,and an 80 meV bandgap is predicted to be opened in the surface states in Bi2Se3layer due to the proximityinduced *** results prove that the proximity effect could be an important method to achieve topological magnetism at high temperatures,and reveals its potential for the manipulation of the topological surface states.

关键词： topological insulator ferromagnetic semiconductor proximity effect quantum interference

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Copper-Catalyzed Asymmetric Three-Component Radical 1,2-Carboamination of Acrylamides with Arylamines: Access to Chiral α-Tertiary N-Arylamines

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CCS Chemistry 2024年第11期6卷 2652-2661页

作者： Jia-Heng Fang Ji-Jun Chen Xuan-Yi Du Zhe Dong Run-Yan Tian Chang-Jiang Yang Fu-Li Wang Cheng Luan Zhong-Liang Li Xin-Yuan Liu School of Chemistry and Chemical Engineering Harbin Institute of TechnologyHarbin 150001 Shenzhen Grubbs Institute Department of ChemistryGuangming Advanced Research InstituteShenzhen Key Laboratory of CrossCoupling ReactionsSouthern University of Science and TechnologyShenzhen 518055 Department of Chemistry and Dongguan Key Laboratory for Data Science and Intelligent Medicine Great Bay UniversityDongguan 523000 Dongguan Key Laboratory of Interdisciplinary Science for Advanced Materials and Large-Scale Scientific Facilities School of Physical SciencesGreat Bay UniversityDongguan 523000

The asymmetric radical carboamination of 1,1-disubstituted alkenes from readily available alkyl halides and arylamines provides expedient access to valueadded chiralα-tertiary N-arylamines but has been less recognized.A challenge arises mainly from the difficult reaction initiation inherent in alkyl halides and the construction of fully substituted chiral C–N bonds from sterically congested tertiary alkyl ***,we report a copper-catalyzed asymmetric three-component radical carboamination of acrylamides utilizing an anionic chiral N,N,N-ligand under mild *** ligand was essential for the reaction initiation by enhancing the reducing capability of copper and enabling the enantiocontrol over tertiary alkyl *** substrate scope was broad,covering an array of acrylamides,aryl-and heteroaryl-amines,as well as alkyl halides and sulfonyl chlorides,enabling good functional group *** combined with the follow-up transformation,this strategy provides a versatile platform for accessing structurally diverse chiralα-tertiary N-arylamine building blocks of interest in organic synthesis.

关键词： asymmetric catalysis copper alkenes radical reactions chiral amines

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TSDDISCOVER: Discovering data Dependency for Time Series data

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

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International Conference on data engineering

作者： Xiaoou Ding Yingze Li Hongzhi Wang Chen Wang Yida Liu Jianmin Wang School of Computer Science and Technology Harbin Institute of Technology National Engineering Research Center for Big Data Software EIRI Tsinghua University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods.

关键词： data integrity Time series analysis Semantics Maintenance engineering data engineering Cognition Cleaning

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intelligent Control of an Integrated Energy System: Low Carbon Modeling and intelligent Optimization

Intelligent Control of an Integrated Energy System: Low Carb...

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International Conference on Electronic engineering and Informatics (EEI)

作者： Zezheng Fu Wenting Lin Degang Yang College of Computer and Information Science Chongqing Normal University Chongqing China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

An integrated energy system is a complex system that requires intelligent control to optimize its operation. This paper proposes an intelligent control method for integrated energy systems based on a low carbon model. The model integrates a variety of low carbon technologies such as carbon capture systems, demand response, carbon emissions trading, and clean energy generation. The effect of the proposed intelligent control methods on reducing system costs and carbon emissions is verified through a case study. The results show that the combination of the intelligent control method and the low carbon model leads to a significant reduction in system operating costs and carbon emissions. This provides an important reference for intelligent control of the existing integrated energy system from a low-carbon approach.

关键词： Green energy Costs Carbon dioxide Emissions trading Mathematical models Demand response Sustainable development Informatics Optimization intelligent control

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Dual Contrastive Learning Model for Representation Learning and Background Debiasing in SAR Object Classification

Dual Contrastive Learning Model for Representation Learning ...

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2023 SAR in big data Era, bigSARdata 2023

作者： Wang, Jing Tian, Sirui Feng, Xiaolin Zhang, Bo Wu, Fan Wang, Chao School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Digital Earth Science Beijing100094 China

ISBN: (纸本)9798350326239

As a popular representation learning framework, contrastive learning has been widely used in SAR image interpretation for feature extraction. Recent studies have shown that some features obtained by the model are related to the background information due to the spurious correlation of the backgrounds during the SAR data collection. As a result, the generalization ability of the representation learning model will be greatly affected by the background information and cannot satisfy the requirements of practical applications. This paper constructed a multi-branch dual contrastive learning model that simultaneously compares the similarities between objects and backgrounds of various images. The motivation is to alleviate the impact of the spurious correlation between background information and the object category. The experimental results and interpretable Shapley value demonstrated that the proposed method significantly improved the generalization performance of the SAR object classification algorithm. © 2023 IEEE.

关键词： Learning systems

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Channel Gain Map Estimation for Wireless Networks Based on Scatterer Model

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IEEE Transactions on Wireless Communications 2025年

作者： Sun, He Zhu, Lipeng Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Channel gain map (CGM) contains crucial large-scale fading information regarding wireless channels at specific frequency bands in wireless networks. Traditional CGM construction methods require either detailed propagation environment information or numerous channel measurements, rendering them practically cumbersome to implement. To overcome such difficulties, we propose in this paper a novel scatterer-based CGM construction framework to characterize the large-scale channel fading in a spatial region by estimating the scatterer responses from a few channel gain measurements at designated locations in the region. In particular, the region is divided into equally-spaced grids so as to smooth out the small-scale fading by averaging the channel power gain within each grid. As the average channel power gain within each grid can be expressed as a function of large-scale multi-path channel parameters such as the scatterer response coefficients and path-loss coefficients, an iterative algorithm is proposed to estimate these parameters based on only a sufficient number of channel gain measurements taken at random locations around each scatterer. Specifically, the proposed algorithm decomposes the parameter estimation problem into a set of univariate or linear estimation subproblems, which can be efficiently solved by one-dimensional (1D) line search and least-squares methods. Based on the estimated channel parameters, the CGM over the whole region can be estimated by calculating the average channel power gain for each grid. Simulation results under both the two-dimensional (2D) multipath channel model and the practical three-dimensional (3D) ray-tracing channel model verify that the proposed scatterer-based channel gain characterization can accurately capture the large-scale channel gain spatial distribution within a region, and the proposed CGM estimation framework outperforms other channel-measurement-based benchmarks in terms of estimation accuracy, while requiring a sign

关键词： Least squares approximations

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Listen to the Speaker in Your Gaze

Listen to the Speaker in Your Gaze

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IEEE Conference on Cybernetics and intelligent Systems

作者： Hongli Yang Xinyi Chen Junjie Li Hao Huang Siqi Cai Haizhou Li Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Technology Xinjiang University Urumqi China Department of Electrical and Computer Engineering National University of Singapore Singapore School of Data Science Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Attending to one’s voice in a cocktail party is notably challenging, particularly for individuals with hearing impairments. This paper proposes a novel eye-controlled target speaker extraction system, which consists of an eye-tracker, face detection model, Active Speaker Detection (ASD), and Target Speaker Extraction (TSE) model. The system employs the eye-tracker to capture real-time video together with the listener’s gaze. This gaze data then allows the face detection model to locate and isolate the target speaker’s face within the video on a frame-by-frame basis. Using the speaker’s face as the reference cue, the system can discern and separate his/her speech from a mixture of multi-talk. The experiments show that the system effectively extracts the target speaker’s speech in complex auditory environments, providing both real-time performance and accuracy. A demonstration of our system is available on our website 1 .

关键词： Mechatronics Random access memory Auditory system Streaming media Real-time systems data models Face detection

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An Unsupervised Network Architecture Search Method for Solving Partial Differential Equations

arXiv

引用

arXiv 2025年

作者： Li, Qing Chen, Jingrun School of Artificial Intelligence and Data Science University of Science and Technology of China China School of Mathematical Sciences Suzhou Institute for Advanced Research University of Science and Technology of China China Suzhou Big Data & AI Research and Engineering Center China

Solving partial differential equations (PDEs) has been indispensable in scientific and engineering applications. Recently, deep learning methods have been widely used to solve high-dimensional problems. The interplay between deep learning and numerical PDEs has resulted in a variety of new methods, one of which is the physics-informed neural network (PINN). Typically, a deep learning method has three main components: a neural network, a loss function, and an optimizer. While the construction of the loss function is rooted in the definition of solution space, how to choose a optimal neural network is somewhat ad hoc, leaving much room for improvement. In the framework of PINN, we propose an unsupervised network architecture search method for solving PDEs, termed PINN-DARTS, which applies the differentiable architecture search (DARTS) to find the optimal network architecture structure in a given set of neural networks. In this set, the number of layers and the number of neurons in each layer can change. In the searching phase, both network and architecture parameters are updated simultaneously, so the running time is close to that of PINN with a pre-determined network structure. Unlike available works, our approach is unsupervised and purely based on the PDE residual without any prior usage of solutions. PINN-DARTS outputs the optimal network structure as well as the associated numerical solution. The performance of PINN-DARTS is verified on several benchmark PDEs, including elliptic, parabolic, wave, and Burgers’ equations. Compared to traditional architecture search methods, PINN-DARTS achieves significantly higher architectural accuracy. Another interesting observation is that both the solution complexity and the PDE type have a prominent impact on the optimal network architecture. Our study suggests that architectures with uneven widths from layer to layer may have superior performance across different solution complexities and different PDE types. Copyright © 202

关键词： Wave equations

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The Processes-Based Attributes of Four Major Surface Melting Events over the Antarctic Ross Ice Shelf

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Advances in Atmospheric sciences 2023年第9期40卷 1662-1670页

作者： Wenyi LI Yuting WU Xiaoming HU School of Atmospheric Sciences Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519000China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China Chongqing Research Institute of Big Data Peking UniversityChongqing 401333China Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen UniversityZhuhai 519000China

The Ross-Amundsen sector is experiencing an accelerating warming trend and a more intensive advective influx of marine air *** a result,massive surface melting events of the ice shelf are occurring more frequently,which puts the West Antarctica Ice Sheet at greater risk of *** study shows the connection between surface melting and the prominent intrusion of warm and humid air flows from lower *** applying the Climate Feedback-Response Analysis Method(CFRAM),the temporal surge of the downward longwave(LW)fluxes over the surface of the Ross Ice Shelf(RIS)and adjacent regions are identified for four historically massive RIS surface melting *** melting events are decomposed to identify which physical mechanisms are the main *** found that intrusions of warm and humid airflow from lower latitudes are conducive to warm air temperature and water vapor anomalies,as well as cloud *** changes exert a combined impact on the abnormal enhancement of the downward LW surface radiative fluxes,significantly contributing to surface warming and the resultant massive melting of ice.

关键词： Ross Ice Shelf(RIS) surface melting warm and humid air advection downward longwave radiation Climate Feedback-Response Analysis Method(CFRAM)

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DiffusionClusNet: Deep Clustering-Driven Diffusion Models for Ultrasound Image Enhancement

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Nuo Zhang, Yongquan Fan, Chenchen Zhao, Wei Wang, Changmiao Wang, Hai Zhejiang University of Finance and Economics School of Data Science Hangzhou310018 China Shenzhen Research Institute of Big Data Shenzhen518172 China Murdoch University School of Engineering and Energy Harry Butler Institute PerthWA6150 Australia

In modern medical diagnostics, high-quality ultrasound images are essential because they are cost-effective, non-invasive, and capable of providing dynamic recordings. Nevertheless, obtaining such high-quality images is challenging, especially in resource-limited areas, which negatively impacts diagnostic accuracy. To address these issues, we propose a novel method for enhancing ultrasound images using deep clustering-enhanced diffusion models. Our proposed method consists of two main components: an image enhancement pathway and an Auxiliary Classification Pathway (ACP), which are integrated through a Fusion of Image and Classification (FIC) module. The image enhancement pathway employs a structure that includes a Variational Autoencoder (VAE) encoder, a UNet denoising network, and a VAE decoder. This structure progressively reduces noise and generates high-quality images. Simultaneously, the ACP utilizes a convolutional neural network, a transformer encoder, and a clustering module to extract classification information, which supports the enhancement process. The FIC module uses a cross-attention mechanism to merge the image and classification features, thus enhancing the overall performance of image enhancement. To ensure the generated images retain their structural integrity, Structural Similarity (SSIM) loss is employed. Experiments conducted on multiple ultrasound datasets reveal that our method surpasses existing techniques in terms of peak signal-to-noise ratio and SSIM scores. Clinically, our approach significantly improves image contrast and structural detail, leading to more accurate diagnoses. This diffusion-based strategy for image enhancement and classification feature fusion introduces a fresh perspective on preserving structure and enhancing detail in medical image processing. Our Code is available at https://***/ichbincn/Ultrasound-Enhancement. © 1975-2011 IEEE.

关键词： Ultrasonic imaging

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