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A new nano-scale authentication architecture for improving the security of human-computer interaction systems based on quantum computing

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Analog Integrated Circuits and Signal Processing 2025年第1期124卷 1-15页

作者： Ahmadpour, Seyed-Sajad Zohaib, Muhammad Navimipour, Nima Jafari Misra, Neeraj Kumar Rasmi, Hadi Salahov, Huseyn Avval, Danial Bakhshayeshi Navin, Ahmad Habibizad Hosseinzadeh, Mehdi Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Department of Electrical and Electronics Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan School of Electronics Engineering VIT-AP University Amaravathi India Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Systems Engineering Sakarya University Sakarya Turkey Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Republic of Korea

Human-computer Interaction (HCI) is an interdisciplinary area of study focusing on the interaction of users and computers by scheming interactive computer interfaces. In addition, HCI systems need security to confirm user authentication, which is a crucial issue in these systems. Hence, user authentication is vital, allowing only authorized users to access data. Authentication is critical to the digital world since it provides security and safety for digital data. Moreover, a digital signature is an authentication method to confirm the accuracy and reliability of digital documents or communications. In addition, designing the circuit based on the complementary metal-oxide semiconductor (CMOS) technology can affect the security and safety of digital data due to the excessive heat dissipation of circuits. On the other hand, quantum-dot cellular automata (QCA) and reversible logic as alternative technologies to CMOS address these problems. Since QCA and reversible logic circuits have minimal energy dissipation, which is considered nearly zero, approaching these technologies proves extremely difficult for any hacker. This work presents an effective structure for the authenticator and human-computer interaction using QCA and IBM quantum computing with Qiskit simulations. The proposed structure has outperformed current circuits in terms of area, cell count, and latency. The paper demonstrates the QCA reversible logic layout of the proposed HCI authenticator and integrates IBM quantum computing simulations using Qiskit for validation. The implementation and testing of results are performed utilizing QCADesigner-2.0.3 and Qiskit simulation tools. The accuracy and efficiency of the proposed design are validated through simulation-derived comparison values, and energy dissipation simulations prove that the suggested circuit dissipates minimal energy.

关键词： Qubits

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High-resolution simulations of nonlinear electromagnetic turbulence in tokamak devices

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The European Physical Journal Special Topics 2025年 1-14页

作者： Liu, Yanjun Liu, Zixi Liu, Jian Gao, Baofeng Zhang, Wei Xia, Tianyang Liu, Haiqing Zhuang, Ge Gao, Xiang Institute of Plasma Physics Chinese Academy of Sciences Anhui China KTX Laboratory and Department of Engineering and Applied Physics University of Science and Technology of China Anhui China Weihai Institute for Interdisciplinary Research Shandong University Shandong China SDU-ANU Joint Science College Shandong University Shandong China Shandong Computer Science Center (National Supercomputing Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong China Advanced Algorithm Joint Lab National Supercomputing Center in Jinan Shandong China

To achieve long-pulse steady operation, the physical mechanisms of boundary turbulence need further investigation. We employ the two-fluid model with flute reduction on BOUT++ to simulate the boundary plasma in Tokamaks. The space and time scales of turbulence reproduced by our simulations closely relate to the spatial mesh size and time step size, respectively. As an inherent time scale, the Alfven time is sufficient to resolve MHD instabilities. The spatial scale can be refined by increasing mesh resolutions, which necessitates larger scale parallel computing resources. We have conducted nonlinear simulations using more than 33 million spatial meshes with 16,384 CPU processors in parallel. The results indicate that while the decrease in parallel efficiency with an increase in core numbers does not necessarily lead to shorter runtimes, higher computational complexity improves parallel efficiency for the same number of cores. In addition, the mesh resolution required for convergence conditions differs between linear and nonlinear simulations, with nonlinear simulations demanding higher resolution. Besides finer structure obtained, the fluctuation characteristic of density similar to WCM, which is more consistent with the experimental observation, also shows the requirement for high-resolution meshes and large-scale computing in the future.

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Comprehensive Architecture Search for Deep Graph Neural Networks

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IEEE Transactions on Big Data 2025年

作者： Dong, Yukang Pan, Fanxing Gui, Yi Jiang, Wenbin Wan, Yao Zheng, Ran Jin, Hai National Engineering Research Center for Big Data Technology Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Service Computing Technology and System Laboratory Wuhan430074 China Huazhong University of Science and Technology Cluster and Grid Computing Laboratory Wuhan430074 China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

In recent years, Neural Architecture Search (NAS) has emerged as a promising approach for automatically discovering superior model architectures for deep Graph Neural Networks (GNNs). Different methods have paid attention to different types of search spaces. However, due to the time-consuming nature of training deep GNNs, existing NAS methods often fail to explore diverse search spaces sufficiently, which constrains their effectiveness. To crack this hard nut, we propose CAS-DGNN, a novel comprehensive architecture search method for deep GNNs. It encompasses four kinds of search spaces that are the composition of aggregate and update operators, different types of aggregate operators, residual connections, and hyper-parameters. To meet the needs of such a complex situation, a phased and hybrid search strategy is proposed to accommodate the diverse characteristics of different search spaces. Specifically, we divide the search process into four phases, utilizing evolutionary algorithms and Bayesian optimization. Meanwhile, we design two distinct search methods for residual connections (All-connected search and Initial Residual search) to streamline the search space, which enhances the scalability of CAS-DGNN. The experimental results show that CAS-DGNN achieves higher accuracy with competitive search costs across ten public datasets compared to existing methods. © 2015 IEEE.

关键词： Neural network models

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A Large-Scale Study of Failures on Petascale Supercomputers

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Journal of computer Science & technology 2018年第1期33卷 24-41页

作者： Rui-Tao Liu Zuo-Ning Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 213215 China National Research Center of Parallel Computer Engineering and Technology Beijing 100190 China

With the rapid development of supercomputers, the scale and complexity are ever increasing, and the reliability and resilience are faced with larger challenges. There are many important technologies in fault tolerance, such as proacrive failure avoidance technologies based on fault prediction, reactive fault tolerance based on checkpoint, and scheduling technologies to improve reliability. Both qualitative and quantitative descriptions on characteristics of system faults are very critical for these technologies, This study analyzes the source of failures on two typical petascale supercomputers called Sunway BlueLight （based on multi-core CPUs） and Sunway TaihuLight （based on heterogeneous manycore CPUs）. It uncovers some interesting fault characteristics and finds unknown correlation relationship among main components＇ faults. Finally the paper analyzes the failure time of the two supercomputers in various grains of resource and different time spans, and builds a uniform multi-dimensional failure time model for petascale supereomputers.

关键词： petascale supercomputer fault characteristic correlation relationship multi-dimension failure time model

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Characteristics of nanoepoxy composite through structural, thermal and machine learning-enhanced dielectric analysis

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Journal of Polymer research 2025年第5期32卷 1-13页

作者： Thakor, Sanketsinh Jain, Prince Joshi, Anand Vaja, Chandan R. Parikh, Swapnil Panchal, Piyush Department of Applied Science and Humanities Parul Institute of Technology Parul University Vadodara India Micro Nano Research and Development Center Parul University Vadodara India Department of Mechatronics Engineering Parul Institute of Technology Parul University Vadodara India Department of Physics School of Sciences Gujarat University Ahmedabad India Department of Computer Science Engineering Parul Institute of Technology Parul University Vadodara India Department of Physics Sardar Vallabhbhai National Institute of Technology Surat India

This study aims to comprehensively explore the characteristics of hybrid nano fillers incorporated epoxy composites (NFIEC) using various analytical techniques. The research encompasses machine learning-enhanced dielectric analysis across a wide frequency spectrum ranging from 100 Hz to 2 MHz. The structural properties of the nano-fillers, pristine epoxy (PE), and NFIEC are investigated through X-ray Diffraction (XRD). Hybrid nano fillers, including combinations like SiO2 + Fe3O4, ZnO + Fe3O4, TiO2 + Fe3O4 and Al2O3 + Fe3O4, are added to the PE at a concentration of 1 wt.% to form NFIEC. Additionally, Differential Scanning Calorimetry are employed to gain further insights into the thermal behaviour of the NFIEC. This study also demonstrates the effectiveness of machine learning models, particularly Extra Trees, CatBoost, XGBoost, and Ensemble Averaging, in predicting dielectric properties (ε′ and ε′′) of NFIEC using data from LCR and VNA measurements. The models exhibited high predictive accuracy, with Ensemble Averaging consistently delivering robust performance across datasets, highlighting the potential of ML techniques in advancing material science by reducing experimental efforts and enhancing predictive precision.

关键词： Dielectric Epoxy Hybrid nano filler Machine learning

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Passive non-line-of-sight pedestrian imaging based on light transport matrix decomposition

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Optics and Lasers in engineering 2025年 192卷

作者： Chen, Mingyang Wang, Jiyue Liu, Mengge Xu, Ziqin Liu, Hao Xu, Mingliang School of Computer Science and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China College of Advanced Interdisciplinary Studies National University of Defense Technology Hunan Changsha410073 China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou University Henan Zhengzhou450001 China National Supercomputing Center in Zhengzhou Zhengzhou University Henan Zhengzhou450001 China

Passive non-line-of-sight (NLOS) imaging extends the observer's perceptual range, offering promising applications in fields such as autonomous driving and emergency rescue. However, mainstream passive NLOS imaging methods are generally limited to distances of less than 6 meters and often rely on a single simulated light transmission matrix, overlooking brightness and reflection characteristics. To overcome these limitations, we employ an infrared camera to capture relayed signals from long distances and introduce the NLOS Pedestrian Imaging Algorithm based on Light Transport Matrix Decomposition (NLOS-LTMD). This innovative algorithm simulates the light transport matrix in two parts: an illumination transport matrix for reconstructing brightness and a reflection transport matrix for enhancing contours. In addition, we propose an Illumination-Oriented Transformer (IO-Transformer) that utilizes threshold segmentation to identify the most informative regions based on the intensity of illumination information addressing the issue of introducing noise and low-SNR regions during contextual modeling, which can degrade imaging quality. Quantitative and qualitative experiments demonstrate that NLOS-LTMD markedly surpasses existing methods in both robustness and reconstruction quality. © 2025 Elsevier Ltd

关键词： Light transmission

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A deep learning-based targeted interpolation method for seismic data: a consecutively missing trace VSP case

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IEEE Geoscience and Remote Sensing Letters 2025年 22卷

作者： Yang, Wen Song, Qianggong Li, Le Li, Xiaobin Cao, Zhonglin Duan, Pengfei China National Petroleum Corporation BGP Inc Zhuozhou 072751 China National Engineering Research Center of Oil and Gas Exploration Computer Software Zhuozhou 072751 China Chengdu University of Technology College of Geophysics Chengdu 610059 China

Seismic data interpolation is an important processing method for improving the quality of seismic data. Traditional interpolation methods often face limitations due to their dependence on prior information and their challenges in processing continuous missing data. Vertical seismic profiling (VSP) data, owing to its unique acquisition approach, generally do not suffer from missing receivers but can have missing shots, with the locations of these missing shots being known. To address this specific issue of missing shots, a specialized interpolation technique has been proposed for targeted missing data. This technique involves creating datasets from the original complete data that are tailored to fixed missing shot scenarios, allowing for a more effective application of the trained network to field data. Additionally, we have optimized the network structure based on UNet to meet the specific requirements for handling consecutive gaps. Both synthetic and field data demonstrate the effectiveness of this targeted interpolation method. © 2004-2012 IEEE.

关键词： Consecutively Missing Deep-learning Interpolation VSP

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Adaptive Sparse Channel Estimation for RIS-Assisted Mmwave Massive MIMO Systems

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IEEE Transactions on Vehicular technology 2025年

作者： Shao, Shuying Lv, Tiejun Du, Siying Zeng, Jie Tan, Fangqing Lin, Zhipeng School of Information and Communication Engineering Beijing100876 China Shandong Institute of Petroleum and Chemical Technology Department of Computer Science and Engineering Dongying257061 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Guilin University of Electronic Technology School of Information and Communication Guilin541004 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China

The integration of reconfigurable intelligent surfaces (RISs) with millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems holds significant promise for future wireless communication methods aiming to achieve high-speed data transmission and expand communication coverage. However, channel estimation (CE) in RIS-assisted wireless communication systems faces many challenges due to the limited signal processing capability of the RIS. Existing methods often fail to fully capture the non-uniform sparsity of mmWave channels. To address this limitation, we propose an adaptive sparse CE scheme involving two progressive algorithms, i.e., normalized least mean squares–log sum (NLMS-LOG) and normalized least mean squares–normalized least mean fourth–log sum (NLMS-NLMF-LOG), for RIS-assisted mmWave massive MIMO systems. The proposed scheme selectively adjusts the channel coefficients to improve the accuracy of the estimation, enabling more efficient exploitation of the inherent non-uniform sparsity in mmWave channels. The NLMS-LOG algorithm innovatively incorporates a log-sum penalty term into the cost function of the NLMS algorithm, thereby increasing the convergence rate. Building upon NLMS-LOG, the NLMS-NLMF-LOG algorithm employs a mixed error function to achieve superior performance across different SNR conditions. The simulation results demonstrate that the proposed scheme outperforms existing methods, such as the NLMS, sparse exponential forgetting window least mean square, and sparse hybrid adaptive filtering algorithms, in terms of CE accuracy and convergence speed. © 1967-2012 IEEE.

关键词： Cost functions

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Autonomous dynamic formation for maritime target tracking using multi-agent reinforcement learning

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engineering Applications of Artificial Intelligence 2025年 154卷

作者： Wang, Hua Li, Jiaxin Tao, Hao Liu, Junnan Li, Chaochao Wang, Ke Xu, Mingliang School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou450001 China National Supercomputing Center in Zhengzhou Zhengzhou450001 China China Ship Development and Design Center Wuhan430064 China School of Geo-Science & Technology Zhengzhou University Zhengzhou450001 China

In various maritime missions such as escort and roundup, dynamic formation target tracking plays a crucial role. Most existing dynamic formation methods require user intervention before formation changes, resulting in poor flexibility and low automation. And they do not consider variations in the abilities of individual members. To address the above issue, we propose an autonomous dynamic formation planning method based on multi-agent reinforcement learning, integrating formation configuration into the strategy. This method can automatically adjust the formation based on the current state of the formation, providing greater flexibility and adaptability. Simultaneously, a staged reward function is devised for the training process to guide agents in progressively learning dynamic formation tasks. Finally, we validate the effectiveness and generalization of our proposed method through various experiments. © 2025 Elsevier Ltd

关键词： Reinforcement learning

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Quantum Linear Multistep Method for Using a Quantum Oracle with Differential Equations

arXiv

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arXiv 2025年

作者： Park, Kyoung Keun Choi, Kwangyeul Kim, Minwoo Song, Giwon Kim, Taehyun Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Automation and System Research Institute Seoul National University Seoul08826 Korea Republic of NextQuantum Seoul National University Seoul08826 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Institute of Computer Technology Seoul National University Seoul08826 Korea Republic of Inter university Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of

Differential equations are a crucial mathematical tool used in a wide range of applications. If the solution to an initial value problem (IVP) can be transformed into an oracle, it can be utilized in various fields such as search and optimization, achieving quadratic speedup with respect to the number of candidates compared to its classical counterpart. In the past, attempts have been made to implement such an oracle using the Euler method. In this study, we propose a quantum linear multistep method (QLMM) that applies the linear multistep method, commonly used to numerically solve IVPs on classical computers, to generate a numerical solution of the IVP for use in a quantum oracle. We also propose a method to find the optimal form of QLMM for a given IVP. Finally, through computer simulations, we derive the QLMM formulation for an example IVP and show that the solution from the optimized QLMM can be used in an optimization problem. © 2025, CC BY.

关键词： Initial value problems

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