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Enhanced Privacy-Preserving WiFi Fingerprint Localization from CL Encryption

Chinese Journal of Electronics 2024年第6期33卷 1435-1446页

作者： Zhiwei WANG Qiuchi ZHU Zhenqi ZHANG School of Computer Nanjing University of Posts and Telecommunications State Key Laboratory of Public Big Data Guizhou University Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Engineering Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University

The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a wireless fidelity(WiFi) fingerprint localization system based on Paillier encryption, which is claimed to protect both client C 's location privacy and service provider S's database privacy. However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain S's database. We propose a novel WiFi fingerprint localization system based on CastagnosLaguillaumie(CL) encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption. To prevent Yang et al.'s attack, the system requires that S selects only the locations from its database that can receive the nonzero signals from all the available access points in C 's nonzero fingerprint in order to determine C's location. Security analysis shows that our scheme is secure under Li et al.'s threat model. Furthermore, to enhance the security level of privacy-preserving WiFi fingerprint localization scheme based on CL encryption, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in C's encrypted localization queries.

关键词： Location awareness Threat modeling data privacy Protocols databases Fingerprint recognition Trustless services Encryption Servers Wireless fidelity

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Spectroscopy and molecule opacity investigation on excited states of SiS

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Chinese Physics B 2024年第5期33卷 381-387页

作者：李瑞吕浩男桑纪群刘晓华梁桂颖吴勇 College of Teacher Education Qiqihar UniversityQiqihar 161006China National Key Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Department of Physics College of ScienceQiqihar UniversityQiqihar 161006China School of Data Science and Artificial Intelligence Jilin Engineering Normal UniversityChangchun 130052China HEDPS Center for Applied Physics and TechnologyPeking UniversityBeijing 100084China

The SiS molecule,which plays a significant role in space,has attracted a great deal of attention for many *** to complex interactions among its low-lying electronic states,precise information regarding the molecular structure of SiS is *** obtain accurate information about the structure of its excited states,the high-precision multireference configuration interaction(MRCI)method has been *** method is used to calculate the potential energy curves(PECs)of the 18Λ–S states corresponding to the lowest dissociation limit of *** core–valence correlation effect,Davidson’s correction and the scalar relativistic effect are also included to guarantee the precision of the MRCI *** on the calculated PECs,the spectroscopic constants of quasi-bound and bound electronic states are calculated and they are in accordance with previous experimental *** transition dipole moments(TDMs)and dipole moments(DMs)are determined by the MRCI *** addition,the abrupt variations of the DMs for the 1^(5)Σ^(+)and 2^(5)Σ^(+)states at the avoided crossing point are attributed to the variation of the electronic *** opacity of SiS at a pressure of 100 atms is presented across a series of *** increasing temperature,the expanding population of excited states blurs the band boundaries.

关键词： SiS opacity excited state spectroscopic constant configuration interaction

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Fully-Decoupled and Second-Order Time-Accurate Scheme for the Cahn–Hilliard Ohta–Kawaski Phase-Field Model of Diblock Copolymer Melt Confined in Hele–Shaw Cell

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Communications in Mathematics and Statistics 2024年第3期12卷 479-504页

作者： Junying Cao Jun Zhang Xiaofeng Yang School of Data Science and Information Engineering Guizhou Minzu UniversityGuiyang 550025People’s Republic of China Guizhou Key Laboratory of Big Data Statistics Analysis Computational Mathematics Research CenterGuizhou University of Finance and EconomicsGuiyang 550025People’s Republic of China Department of Mathematics University of South CarolinaColumbiaSCUSA

In this work,we consider numerical approximations of the phase-field model of diblock copolymer melt confined in Hele–Shaw cell,which is a very complicated coupled nonlinear system consisting of the Darcy equations and the Cahn–Hilliard type equations with the Ohta–Kawaski *** the combination of a novel explicit-Invariant Energy Quadratization approach and the projection method,we develop the first full decoupling,energy stable,and second-order time-accurate numerical *** introduction of two auxiliary variables and the design of two auxiliary ODEs play a vital role in obtaining the full decoupling structure while maintaining energy *** scheme is also linear and unconditional energy stable,and the practical implementation efficiency is also very high because it only needs to solve a few elliptic equations with constant coefficients at each time *** strictly prove that the scheme satisfies the unconditional energy stability and give a detailed implementation *** experiments further verify the convergence rate,energy stability,and effectiveness of the developed algorithm.

关键词： Phase-field Explicit-IEQ Darcy Decoupled Energy Stability Diblock copolymer melt

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QoS-Based Beamforming and Compression Design for Cooperative Cellular Networks via Lagrangian Duality

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IEEE Transactions on Signal Processing 2025年 73卷 2070-2086页

作者： Fan, Xilai Liu, Ya-Feng Liu, Liang Chang, Tsung-Hui Chinese Academy of Sciences State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong The Chinese University of HongKong School of Science and Engineering Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China

This paper considers the quality-of-service (QoS)-based joint beamforming and compression design problem in the downlink cooperative cellular network, where multiple relay-like base stations (BSs), connected to the central processor via ratelimited fronthaul links, cooperatively transmit messages to the users. The problem of interest is formulated as the minimization of the total transmit power of the BSs, subject to all users' signal-to-interference-plus-noise ratio (SINR) constraints and all BSs' fronthaul rate constraints. In this paper, we first show that there is no duality gap between the considered joint optimization problem and its Lagrangian dual by showing the tightness of its semidefinite relaxation (SDR). Then, we propose an efficient algorithm based on the above duality result for solving the considered problem. The proposed algorithm judiciously exploits the special structure of an enhanced Karush-Kuhn-Tucker (KKT) conditions of the considered problem and approaches the solution that satisfies the enhanced KKT conditions via two fixed point iterations. Two key features of the proposed algorithm are: (1) it is able to detect whether the considered problem is feasible or not and find its globally optimal solution when it is feasible;(2) it is highly efficient because both of the fixed point iterations in the proposed algorithm are linearly convergent and function evaluations in the fixed point iterations are computationally cheap. Numerical results show the global optimality and efficiency of the proposed algorithm. © 1991-2012 IEEE.

关键词： Lagrange multipliers

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Unusual metallic state in superconducting A15-type La4H23

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National science Review 2024年第12期11卷 94-103页

作者： Jianning Guo Dmitrii Semenok Grigoriy Shutov Di Zhou Su Chen Yulong Wang Kexin Zhang Xinyue Wu Sven Luther Toni Helm Xiaoli Huang Tian Cui State Key Laboratory of Superhard Materials College of Physics Jilin University Center for High Pressure Science and Technology Advanced Research (HPSTAR) Department of High-Temperature Superconductivity Skolkovo Institute of Science and Technology Skolkovo Innovation CenterComputational and Data Science and Engineering HochfeldMagnetlabor Dresden(HLD-EMFL) and Würzburg-Dresden Cluster of Excellence Helmholtz-Zentrum Dresden-Rossendorf(HZDR) School of Physical Science and Technology Ningbo University

Hydride superconductors continue to fascinate the communities of condensed matter physics and material scientists because they host the promising near room-temperature superconductivity. Current research has concentrated on the new hydride superconductors with the enhancement of the superconducting transition temperature（Tc）. The multiple extreme conditions（high pressure/temperature and magnetic field） will introduce new insights into hydride superconductors. The study of transport properties under very high magnetic fields facilitates the understanding of superconductivity in conventional hydride *** the present work, we report experimental evidence of an unusual metal state in a newly synthesized cubic A15-type La4H23that exhibits superconductivity with a Tc reaching 105 K at 118 GPa. A large negative magnetoresistance is observed in strong pulsed magnetic fields in the non-superconducting state of this compound below 40 K. Moreover, we construct the full magnetic phase diagram of La4H23up to 68 T at high pressure. The present work reveals anomalous electronic structural properties of A15-La4H23under high magnetic fields, and therefore has great importance with regard to advancing the understanding of quantum transport behaviors in hydride superconductors.

关键词： high-pressure superconductivity hydrides negative magnetoresistance

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Correction to: Chaos suppression through Chaos enhancement (Nonlinear Dynamics, (2024), 10.1007/s11071-024-10426-z)

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Nonlinear Dynamics 2024年第10期113卷 12349-12350页

作者： Li, Lin Li, Jizhou Miyoshi, Takemasa Prediction Science Laboratory RIKEN Cluster for Pioneering Research Kobe Japan Data Assimilation Research Team RIKEN Center for Computational Science Kobe Japan Failure Mechanics and Engineering Disaster Prevention Key Laboratory of Sichuan Province College of Architecture and Environment Sichuan University Chengdu China Wako Japan

The article "Chaos suppression through Chaos enhancement", written by Li, L., Li, J. & Miyoshi, T., was originally published electronically on the publisher’s internet portal on 06 October 2024 without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 07 November 2024 to © The Author(s) 2024 and the article is forthwith distributed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://***/licenses/by/4.0. Open access funding enabled and organized by Projekt DEAL. © The Author(s) 2024.

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A Convergence Analysis of a Structure-Preserving Gradient Flow Method for the All-Electron Kohn-Sham Model

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Numerical Mathematics(Theory,Methods and Applications) 2023年第3期16卷 597-621页

作者： Yedan Shen Ting Wang Jie Zhou Guanghui Hu School of Mathematics and Information Science Guangzhou UniversityGuangzhouChina Faculty of Science and Technology University of MacaoMacao SARChina Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan UniversityXiangtanChina School of Mathematical Computational Sciences Xiangtan UniversityXiangtanChina Zhuhai UM Science&Technology Research Institute ZhuhaiChina Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications University of MacaoMacao SARChina

In[Dai et al.,***.18(4)(2020)],a structure-preserving gradient flow method was proposed for the ground state calculation in Kohn-Sham density functional theory,based on which a linearized method was developed in[Hu et al.,EAJAM.13(2)(2023)]for further improving the numerical *** this paper,a complete convergence analysis is delivered for such a linearized method for the all-electron Kohn-Sham ***,the convergence,the asymptotic stability,as well as the structure-preserving property of the linearized numerical scheme in the method is discussed following previous works,while spatially,the convergence of the h-adaptive mesh method is demonstrated following[Chen et al.,***.12(2014)],with a key study on the boundedness of the Kohn-Sham potential for the all-electron Kohn-Sham *** examples confirm the theoretical results very well.

关键词： Kohn-Sham density functional theory gradient flow model structure-preserving linear scheme convergence analysis

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Machine learning for automated experimentation in scanning transmission electron microscopy

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npj computational Materials 2023年第1期9卷 25-40页

作者： Sergei V.Kalinin Debangshu Mukherjee Kevin Roccapriore Benjamin J.Blaiszik Ayana Ghosh Maxim A.Ziatdinov Anees Al-Najjar Christina Doty Sarah Akers Nageswara S.Rao Joshua C.Agar Steven R.Spurgeon Department of Materials Science and Engineering University of TennesseeKnoxvilleTN 37996USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Argonne National Laboratory Data Science and Learning DivisionChicagoIL 60439USA University of Chicago GlobusChicagoIL 60637USA National Security Directorate Pacific Northwest National LaboratoryRichlandWA 99352USA Department of Materials Science and Engineering Drexel UniversityPhiladelphiaPA 19104USA Department of Physics University of WashingtonSeattleWA 98195USA

Machine learning(ML)has become critical for post-acquisition data analysis in(scanning)transmission electron microscopy,(S)TEM,imaging and *** emerging trend is the transition to real-time analysis and closed-loop microscope *** effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and ***,we discuss the associated challenges with the transition to active ML,including sequential data analysis and out-of-distribution drift effects,the requirements for edge operation,local and cloud data storage,and theory in the loop ***,we discuss the relative contributions of human scientists and ML agents in the ideation,orchestration,and execution of experimental workflows,as well as the need to develop universal hyper languages that can apply across multiple *** considerations will collectively inform the operationalization of ML in next-generation experimentation.

关键词： optimization automated execution

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Integrating machine learning with simultaneous quantile regression for uncertainty estimation

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Leading Edge 2025年第2期44卷 133-141页

作者： Liu, Yanhua Zhang, Xitong Tsvankin, Ilya Lin, Youzuo Los Alamos National Laboratory Theoretical Division Los AlamosNM United States Colorado School of Mines Center for Wave Phenomena GoldenCO United States Michigan State University Computational Mathematics Science and Engineering East LansingMI United States University of North Carolina at Chapel Hill School of Data Science and Society Chapel HillNC United States

Accurate velocity models are crucial for understanding subsurface structures, which is vital for various subsurface activities including hydrocarbon exploration, development, and CO2 storage. The application of data-driven techniques based on machine learning (ML) to velocity model building has garnered significant attention due to their computational efficiency. However, most existing ML implementations provide only a single prediction for a given input, which may not adequately reflect the distribution of the testing data. The simultaneous quantile regression method can estimate the entire conditional distribution of the target variable given the input using pinball loss. In this study, we incorporate this technique into seismic inversion and test the proposed method on synthetic Kimberlina data. The uncertainty map is then calculated pixel by pixel from a particular prediction interval around the median. We also introduce an innovative data-augmentation method that leverages uncertainty to enhance prediction accuracy. The results validate the reliability of the calculated uncertainty in the proposed InvNet_UQ model. The method remains robust, even if the testing data are distorted due to problems in the field data acquisition. Another test demonstrates the effectiveness of the developed data-augmentation method in increasing the spatial resolution of the estimated velocity field and in reducing the prediction error. © 2025 by The Society of Exploration Geophysicists.

关键词： Adversarial machine learning

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A genetic engineering algorithm for the generalized quadratic assignment problem

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Neural Computing and Applications 2025年第18期37卷 12253-12279页

作者： Sohrabi, Majid Fathollahi-Fard, Amir M. Gromov, Vasilii A. Dulebenets, Maxim A. School of Data Analysis and Artificial Intelligence National Research University Higher School of Economics 11 Pokrovsky Boulevard Moscow109028 Russia Laboratory for Models and Methods of Computational Pragmatics National Research University Higher School of Economics Moscow Russia Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Taipa China College of Engineering 2035 E Paul Dirac Dr. Sliger Building Suite 275 TallahasseeFL32310 United States

The generalized quadratic assignment problem (GQAP) poses a significant challenge in optimization, known for its NP-hard complexity and wide-ranging applications in supply chain and manufacturing contexts. While various metaheuristic algorithms have addressed the GQAP, this paper introduces a novel modification of the genetic algorithm (GA), namely the genetic engineering algorithm (GEA). A traditional GA initializes the population of solutions randomly and refines these solutions iteratively through crossover and mutation operators. However, its reliance on randomness often leads to premature convergence, susceptibility to local optima, and suboptimal solutions at termination of the algorithmic run. To address these drawbacks, our research presents a modified GA framework inspired by genetic engineering principles. This framework introduces novel search mechanisms mimicking gene mining processes, targeting population enhancement through insertion, purification, and genetic material exchange from elite solutions. The GEA adds three search scenarios to the traditional GA framework to exhibit desired characteristics from chromosomes, enhancing solution quality and convergence speed. This GEA demonstrates a superior performance across various search scenarios against state-of-the-art algorithms and exact solutions for the benchmark instances of the GQAP. Additionally, this study conducts comprehensive sensitivity analyses on the search scenarios of the GEA. By employing gene mining strategies, the proposed GEA offers promising solutions for effectively addressing the NP-hard optimization challenges of the GQAP on large-scale datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Sensitivity analysis

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