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Dilute MnBi2Te4-alloying enables high-performance GeTe thermoelectrics

Applied Physics Letters 2025年第19期126卷

作者： Zhang, Dan Wang, Hongli Liu, Jiandong Zhao, Manzhe Liu, Guannan Yang, Junyou Luo, Yubo Wang, Shufang Key Laboratory of High-precision Computation Application of Quantum Field Theory of Hebei Province College of Physics Science and Technology Hebei University Baoding071002 China State Key Laboratory of Materials Processing and Die & Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

As an attractive lead-free thermoelectric material, GeTe has gained widespread interest. However, the extremely high hole concentration seriously limits the thermoelectric performance of pristine GeTe. In this work, dilute MnBi2Te4-alloying is utilized to synergically optimize electrical- and thermal-transport properties of GeTe for thermoelectric performance improvement. It can not only decrease the precipitation of Ge to optimize carrier concentration, but also promote multi-band convergence to enlarge the density of state effective mass. Furthermore, the MnBi2Te4-alloyed samples also maintain a moderate carrier mobility. In addition, the increased point-defect scattering for phonons leads to the significant reduction in lattice thermal conductivity. Consequently, a high peak thermoelectric figure of merit (ZT) of ∼1.8 at 723 K and a large average ZT of ∼1.0 from 300 to 723 K are achieved in 4% MnBi2Te4-alloyed GeTe, showing the potential of dilute compound alloying to enhance the thermoelectric performance of GeTe by the concurrent regulation of electrical- and thermal-transport properties. © 2025 Author(s).

关键词： Point defects

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Towards practical data alignment in production federated learning

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Frontiers of Computer science 2025年第1期19卷 167-169页

作者： Yexuan SHI Wei YU Yuanyuan ZHANG Chunbo XUE Yuxiang ZENG Zimu ZHOU Manxue GUO Lun XIN Wenjing NIE State Key Laboratory of Complex&Critical Software Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang UniversityBeijing 100191China Zhongguancun Pan Connected Mobile Communication Technology Innovation and Application Research Institute Beijing 100088China China Mobile Research Institute Beijing 100053China School of Data Science City University of Hong KongHong Kong 999077China

1 Introduction Federated learning has emerged as a promising par-adigm for collaborative model training that facilitates cooperation among multiple parties while ensuring data privacy[1].Successful alignment of data across parties is crucial for effective federated learning[2].This alignment involves harmonizing heterogeneous data from different parties to identify shared data for joint model *** set intersection(PSI)is a technique that allows the alignment of common entities between parties without revealing additional ***,efficiently performing data alignment with PSI in federated learning[3],especially when dealing with highly unbalanced data,remains challenging due to the low efficiency.

关键词： alignment dealing parties

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Location Privacy Preservation Crowdsensing With Federated Reinforcement Learning

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 1877-1894页

作者： You, Zhichao Dong, Xuewen Liu, Ximeng Gao, Sheng Wang, Yongzhi Shen, Yulong Xidian University School of Computer Science and Technology The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi'an710071 China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Fuzhou University College of Computer and Data Science Fuzhou350025 China Central University of Finance and Economics School of Information Beijing102206 China California State Polytechnic University Pomona Department of Computer Information Systems PomonaCA91768 United States Xidian University School of Computer Science and Technology Xi'an710071 China Shaanxi Key Laboratory of Network and System Security Xi'an710071 China

Crowdsensing has become a popular method of sensing data collection while facing the problem of protecting participants' location privacy. Existing location-privacy crowdsensing mechanisms focus on static tasks and participants without considering sensing tasks' time requirements and participants' mobility, which cannot achieve satisfactory collected data quality and task completion in crowdsensing with dynamic tasks and participants. Inspired by this, we proposed a location-preservation crowdsensing mechanism, FedSense, considering dynamic tasks and participants based on federated learning (FL) and reinforcement learning (RL). In FedSense, through RL's outstanding decision-making ability, participants select sensing tasks to perform by well-trained RL models without uploading location information to servers for task allocation. We propose an independent tasks selection environment that defines actions, states, and rewards of RL to enable FedSense to achieve satisfactory task completion and data quality while preserving location privacy. Besides, FedSense applies an asynchronous FL aggregation algorithm that reduces participants' network stabilization and device computing ability requirements. Analysis proves that participants' location information does not leave the local device during the model training and task selection process, effectively avoiding privacy leakage. Simulation shows that compared with existing location-preservation crowdsensing mechanisms, FedSense achieves the highest task completion and sensing accuracy for dynamic tasks and participants. © 2024 IEEE.

关键词： data privacy

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application of Copula in S&P 500 Index Option Pricing with non-Gaussian Stochastic Volatility Models

Application of Copula in S&P 500 Index Option Pricing with n...

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International Symposium on Computational and Business Intelligence (ISCBI)

作者： Yongbo Sun Guangdong Provincial/Zhuhai Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China

ISBN: (数字)9798331533786

ISBN: (纸本)9798331533793

In this paper, a newly derived quasi closed-form dynamic European option pricing approach using copula methods is proposed, extending the traditional Gaussian stochastic volatility models to non-Gaussian stochastic volatility models (with fat tails). This approach can save more computing costs compared to simulation techniques (e.g., Monte Carlo method). By integrating non-Gaussian stochastic volatility models with copula methods, we can more accurately model the underlying dependence structures between asset price and volatility. It is quite crucial for option pricing in real financial markets. Specifically, we apply the Heston model under CIR process and a log-volatility model under the Ornstein-Uhlenbeck process to construct the stochastic volatility models, which are also among the best-known stochastic volatility models in the field of stochastic processes and mathematical finance. Furthermore, we conduct empirical analysis based on S&P500 Index prices and the related European-style call option price to test the accuracy of our method and evaluate its robustness and efficiency using appropriate benchmarks.

关键词： Monte Carlo methods Computational modeling Stochastic processes Europe Finance Pricing Mathematical models Robustness Fats Indexes

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Experimental and Modeling Study on Void Fraction Using Thermal Distribution Sensor in Horizontal Annular Flow

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IEEE Sensors Journal 2025年第10期25卷 17640-17649页

作者： Zhao, Ning Chen, Xiangyi Sun, Mingcong Song, Yajing Fang, Lide Li, Zhibin Hebei University College of Quality and Technical Supervision Hebei Baoding071000 China National and Local Joint Engineering Research Center of Metrology Instrument and System Hebei Baoding071002 China CCIC North China Metrology Company Ltd. Beijing100070 China Chengdu University of Information Technology School of Software Engineering Chengdu610225 China Sichuan University of Arts and Science Dazhou Key Laboratory of Government Data Security Sichuan Dazhou635000 China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi830011 China

The void fraction is an important parameter to characterize the characteristics of annular flow, which is an important precondition for calculating the flow velocity, average density, pressure gradient, and analyzing the flow status. Based on the principle of convective heat transfer, the effects of the void fraction α and the liquid Froude number Frl on the heat transfer coefficient h of annular flow are analyzed. The thermal distribution sensor of constant power type is designed in this article. Air-water annular flow experiments have been conducted in a stainless-steel tubular test section having an inside diameter of 17.0 mm on the high-precision small-diameter two-phase flow experimental device of hebei University. The 75 sets of temperature T at four angles (0°, 90°, 180°, and 270°) of pipe circumference and entrainment E under different flow conditions were collected. Utilizing the S. L. Smith correlation, the void fraction is calculated. By introducing dimensionless heat transfer coefficient h+ , the prediction correlation of void fraction α , which could acceptably handle the data, is proposed. laboratory results indicate that the relative error of 99% of the data is within the ±3% error band, the mean absolute percentage error (MAPE) of the correlation is 0.63%, and the root-mean-square error (RMSE) is 0.76%. © 2001-2012 IEEE.

关键词： Void fraction

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Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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Knowledge Transfer Across Modalities for Weakly Supervised Point Cloud Semantic Segmentation

Knowledge Transfer Across Modalities for Weakly Supervised P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zihan Wang Yunhang Shen Mengtian Li Ke Li Xing Sun Shaohui Lin Lizhuang Ma East China Normal University Tencent Youtu Lab Shanghai University Key Laboratory of Advanced Theory and Application in Statistics and Data Science

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current weakly supervised point cloud semantic segmentation struggles with insufficient utilization of limited annotations in unimodal representation learning due to the sparse and textureless nature of point clouds. In this work, we leverage cross-modality information by transferring knowledge from image and text sources to the point cloud network. The intuition is that images contribute rich texture, color, and discriminative information, complementing point clouds to boost semantic segmentation performance. To reduce extensive computational resources for cross-modality fusion, we introduce the Multi-Scale Deformable Knowledge Transfer, an innovative training scheme that optimizes and extends the one-to-one mapping to flexible one-to-many relations between multi-modal data. Furthermore, we employ pre-trained image-text models to generate pseudo labels for point clouds and construct positive and negative samples for semantic contrastive regularization, facilitating the full exploitation of unlabeled data. The experimental results evaluated on SemanticKITTI and nuScenes demonstrate substantial improvements, achieving an average gain of 3.8% over the previous weakly supervised methods, and comparable performances to fully supervised approaches.

关键词： Point cloud compression Training Representation learning Weak supervision Three-dimensional displays Semantic segmentation Semantics Signal processing Speech processing Knowledge transfer

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Mutable hierarchy feature selection based on generalized fuzzy rough sets

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Applied Soft Computing 2025年 177卷

作者： Lin, Zilong Lin, Yaojin Wang, Chenxi Chen, Jinkun School of Computer Science Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou Fujian363000 China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Wuyishan Fujian354300 China School of Mathematics and Statistics Minnan Normal University Zhangzhou Fujian363000 China

Hierarchical classification divides data into correlated sub-tasks from coarse to fine. Compared to flat classification, it is more complex and suffers from the curse of dimensionality. Existing hierarchical Fuzzy Rough Sets (FRS) methods only stay at the fine-grained to select the features, which is a fine-grained search strategy. Thus, we propose a coarse-grained search strategy and use Generalized Fuzzy Rough Sets (GFRS) to enhance its robustness. Furthermore, we introduce the concept of tree fragmentation. Though assessing the degree of fragmentation in the tree structure, it selects an appropriate granularity search strategy for hierarchical tree-structured datasets. Finally, we combine both granularity search strategies and collectively named - Mutable Hierarchy Search Strategy (MHSS);the entire proposed algorithm is named - Mutable Hierarchy Feature Selection Based on Generalized Fuzzy Rough Sets (MHFS). We compare two FRS-based feature selection algorithms, three hierarchical optimization methods, and six flat feature selection methods. Extensive experiments demonstrate the performance of our method. © 2025 Elsevier B.V.

关键词： Feature extraction

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Origin of the tiny energy gap and Dirac points in monoclinic trilayer nickelate La4Ni3O10

arXiv

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arXiv 2025年

作者： Zhang, Hu Hebei Research Center of The Basic Discipline for Computational Physics Hebei Key Laboratory of High-precision Computation and Application of Quantum Field Theory College of Physics Science and Technology Hebei University Baoding071002 China

Superconductivity was recently found in trilayer nickelate La4Ni3O10 under high pressure with a phase transition from the monoclinic P21/a structure to the tetragonal I4/mmm structure. Previous experimental works have confirmed the existence of a tiny energy gap formed with Ni 3dz2 orbitals in monoclinic La4Ni3O10. Here we investigate the physical origin of this gap by analyzing symmetry properties of energy bands based on the group theory. The tiny gap comes from energy bands with opposite parity at the Brillouin zone center. In addition, we also find previously unknown Dirac points in some momentum directions around the Fermi level. An effective Hamiltonian is constructed to describe low energy physics of the tiny energy gap and Dirac points. Due to the low crystal symmetry of monoclinic La4Ni3O10, its energy bands display strong anisotropic properties. © 2025, CC BY.

关键词： Hamiltonians

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Symmetric quantum joint measurements on multiple qubits

arXiv

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arXiv 2025年

作者： Ding, Dong He, Ying-Qiu Gao, Ting Yan, Feng-Li College of Science North China Institute of Science and Technology Beijing101601 China School of Mathematical Sciences Hebei Normal University Shijiazhuang050024 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Shijiazhuang050024 China

We investigate the generalization of symmetric quantum joint measurements on multiple qubits. We first describe a method for constructing a symmetric joint measurement basis for three qubits by utilizing single-qubit states corresponding to the four vertices of a tetrahedron on the Bloch sphere. We demonstrate the expected tetrahedral symmetry of the current measurement basis and discuss its application in a trilocal star-shaped network. This architecture enables us to generalize the two-qubit symmetric joint measurement to an n-qubit version, preserving the tetrahedral or hexahedral symmetry. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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