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SSRN 2022年

作者： Zhang, Ting Cui, Shuqi Hong, Ning Shi, Baochang College of Sciences Wuhan University of Science and Technology Wuhan430081 China School of General Education Wuchang University of Technology Wuhan430223 China Institute of Interdisciplinary Research for Mathematics and Applied Science School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China

In this paper, an exponential transformation based lattice Boltzmann (LB) model for solving the n -dimensional ( n D) convection-diffusion equation(CDE) is developed. Firstly, a class of exponential transformation is proposed to convert the n D CDE into a diffusion equation. Then, the converted diffusion equation is solved by the LB model. So, compared to the available LB models for CDE, the present LB model can eliminate the difficulty in treating the convection term. With the direct Taylor expansion method, it is shown that the CDE can be exactly derived from the exponential transformation based LB model. Finally, a variety of numerical tests have been conducted to validate the present LB model. It can be found that the numerical results agree well with the analytical solutions. Moreover, we also find that the present LB model has a second-order convergence rate in space, and it is more effective and more stable than the previous LB model for the CDE. © 2022, The Authors. All rights reserved.

关键词： Boltzmann equation

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Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

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Computers, Materials & Continua 2022年第7期72卷 1323-1338页

作者： Mahmoud Ragab Samah Alshehri Hani A.Alhadrami Faris Kateb Ehab Bahaudien Ashary SAbdel-khalek Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah21589Saudi Arabia Molecular Diagnostic Lab King Abdulaziz University HospitalKing Abdulaziz UniversityJeddah21589Saudi Arabia Special Infectious Agent Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohag82524Egypt

Rapid advancements of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)pose serious security issues by revealing secret ***,security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real ***,AI techniques can be utilized to design image steganographic techniques in *** addition,encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized *** order to accomplish secure data transmission in IIoT environment,this study presents novel encryption with image steganography based data hiding technique(EISDHT)for IIoT *** proposed EIS-DHT technique involves a new quantum black widow optimization(QBWO)to competently choose the pixel values for hiding secrete data in the cover *** addition,the multi-level discrete wavelet transform(DWT)based transformation process takes ***,the secret image is divided into three R,G,and B bands which are then individually encrypted using Blowfish,Twofish,and Lorenz Hyperchaotic *** last,the stego image gets generated by placing the encrypted images into the optimum pixel locations of the cover *** order to validate the enhanced data hiding performance of the EIS-DHT technique,a set of simulation analyses take place and the results are inspected interms of different *** experimental outcomes stated the supremacy of the EIS-DHT technique over the other existing techniques and ensure maximum security.

关键词： IIoT security data hiding technique image steganography encryption secure communication

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Preparing for HPC on RISC-V: Examining Vectorization and Distributed Performance of an Astrophysics Application with HPX and Kokkos 24

Preparing for HPC on RISC-V: Examining Vectorization and Dis...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and Analysis

作者： Patrick Diehl Panagiotis Syskakis Gregor Daiß Steven R. Brandt Alireza Kheirkhahan Srinivas Yadav Singanaboina Dominic Marcello Chris Taylor John Leidel Hartmut Kaiser Applied Computer Science (CCS-7) Los Alamos National Laboratory Los Alamos NM U.S.A. LSU Center for Computation & Technology Louisiana State University Baton Rouge LA U.S.A. IPVS University of Stuttgart Stuttgart Stuttgart Germany Tactical Computing Labs 1001 Pecan St. Lindsay TX U.S.A. Department of Physics and Astronomy Louisiana State University Baton Rouge LA U.S.A.

ISBN: (纸本)9798350355543

In recent years, interest in RISC-V computing architectures has moved from academic to mainstream, especially in the field of High Performance computing where energy limitations are increasingly a concern. As of this year, the first single board RISC-V CPUs implementing the finalized ratified vector specification are being released. The RISC-V vector specification follows in the tradition of vector processors found in the CDC STAR-100, the Cray-1, the Convex C-Series, and the NEC SX machines and accelerators. The family of vector processors offers support for variable-length array processing as opposed to the fixed-length processing functionality offered by SIMD. Vector processors offer opportunities to perform vector-chaining which allows temporary results to be used without the need to resolve memory *** this work, we use the Octo-Tiger multi-physics, multi-scale, 3D adaptive mesh refinement astrophysics application to study these early RISC-V chips with vector machine support. We report on our experience in porting this modern C++ code (which is built upon several open-source libraries such as HPX and Kokkos) to RISC-V. In addition, we show the impact of the RISC-V Vector extension on a RISC-V single board computer by implementing the std: experimental:simd interface and integrating it with our code. We also compare the application's performance, scalability, and power consumption on desktop-grade RISC-V computer to an A64FX *** results presented in this paper are part of a longer-term evaluation of RISC-V's viability for HPC applications.

关键词： HPX

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Dense Outputs from Quantum Simulations

SSRN

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SSRN 2023年

作者： Liu, Jin-Peng Lin, Lin Department of Mathematics University of California Berkeley United States Simons Institute for the Theory of Computing University of California Berkeley United States Center for Theoretical Physics Massachusetts Institute of Technology Cambridge United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Challenge Institute for Quantum Computation University of California Berkeley Berkeley United States

The quantum dense output problem is the process of evaluating time-accumulated observables from time-dependent quantum dynamics using quantum computers. This problem arises frequently in applications such as quantum control and spectroscopic computation. We present a range of algorithms designed to operate on both early and fully fault-tolerant quantum platforms. These methodologies draw upon techniques like amplitude estimation, Hamiltonian simulation, quantum linear Ordinary Differential Equation (ODE) solvers, and quantum Carleman linearization. We provide a comprehensive complexity analysis with respect to the evolution time $T$ and error tolerance $\epsilon$. Our results demonstrate that the linearization approach can nearly achieve optimal complexity $\mathcal{O}(T/\epsilon)$ for a certain type of low-rank dense outputs. Moreover, we provide a linearization of the dense output problem that yields an exact and finite-dimensional closure which encompasses the original states. This formulation is related to the Koopman Invariant Subspace theory and may be of independent interest in nonlinear control and scientific machine learning. © 2023, The Authors. All rights reserved.

关键词： Linearization

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Observation of giant nonlinear valley Hall effect

arXiv

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arXiv 2025年

作者： He, Pan Zhang, Min Cao, Jin Li, Jingru Liu, Hao Zhai, Jinfeng Wang, Ruibo Xiao, Cong Yang, Shengyuan A. Shen, Jian State Key Laboratory of Surface Physics Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Hefei National Laboratory Hefei230088 China Institute of Applied Physics and Materials Engineering Faculty of Science and Technology University of Macau China Fudan University Shanghai200433 China Department of Physics Fudan University Shanghai China Shanghai Research Center for Quantum Sciences Shanghai China Zhangjiang Fudan International Innovation Center Fudan University Shanghai201210 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China

The valley Hall effect (VHE) holds great promise for valleytronic applications by leveraging the valley degree of freedom. To date, research on VHE has focused on its linear response to an applied current, leaving nonlinear valley responses undetected and nonlinear valleytronic devices undeveloped. Here, we report the experimental observation of a nonlinear VHE in a graphene-hBN moiré superlattice, evidenced by the generation of second-harmonic nonlocal voltages under AC currents. Remarkably, the nonlinear VHE has magnitude surpassing the linear VHE and is highly tunable via a gate voltage, which exhibits a pair of opposite peaks on the two sides of a Dirac gap. The nonlinear signal shows quadratic scaling with driving current and quartic scaling with local resistance, setting it apart from the linear counterpart. These experimental features are consistent with the theoretical picture of nonlocal transport mediated by nonlinear VHE and linear inverse VHE. We further reveal a nonlinear inverse VHE by observing the third- and fourth-harmonic nonlocal voltages. The nonlinear VHE provides a novel mechanism for valley manipulation and enables a novel valleytronic device—the valley rectifier—that converts AC charge current into DC valley current. Copyright © 2025, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Dense outputs from quantum simulations

arXiv

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arXiv 2023年

The quantum dense output problem is the process of evaluating time-accumulated observables from time-dependent quantum dynamics using quantum computers. This problem arises frequently in applications such as quantum control and spectroscopic computation. We present a range of algorithms designed to operate on both early and fully fault-tolerant quantum platforms. These methodologies draw upon techniques like amplitude estimation, Hamiltonian simulation, quantum linear Ordinary Differential Equation (ODE) solvers, and quantum Carleman linearization. We provide a comprehensive complexity analysis with respect to the evolution time T and error tolerance ǫ. Our results demonstrate that the linearization approach can nearly achieve optimal complexity O(T/ǫ) for a certain type of low-rank dense outputs. Moreover, we provide a linearization of the dense output problem that yields an exact and finite-dimensional closure which encompasses the original states. This formulation is related to the Koopman Invariant Subspace theory and may be of independent interest in nonlinear control and scientific machine learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Linearization

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Machine learning framework for computing the most probable paths of stochastic dynamical systems

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Physical Review E 2021年第1期103卷 012124-012124页

作者： Yang Li Jinqiao Duan Xianbin Liu State Key Laboratory of Mechanics and Control of Mechanical Structures College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics 29 Yudao Street Nanjing 210016 China Department of Applied Mathematics College of Computing Illinois Institute of Technology Chicago Illinois 60616 USA

The emergence of transition phenomena between metastable states induced by noise plays a fundamental role in a broad range of nonlinear systems. The computation of the most probable paths is a key issue to understanding the mechanism of transition behaviors. The shooting method is a common technique for this purpose to solve the Euler-Lagrange equation for the associated action functional, while losing its efficacy in high-dimensional systems. In the present work, we develop a machine learning framework to compute the most probable paths in the sense of Onsager-Machlup action functional theory. Specifically, we reformulate the boundary value problem of a Hamiltonian system and design a neural network to remedy the shortcomings of the shooting method. The successful applications of our algorithms to several prototypical examples demonstrate its efficacy and accuracy for stochastic systems with both (Gaussian) Brownian noise and (non-Gaussian) Lévy noise. This approach is effective in exploring the internal mechanisms of rare events triggered by random fluctuations in various scientific fields.

关键词： Diffusion Fluctuations & noise Neuronal networks

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Black Box Watermarking for DNN Model Integrity Detection Using Label Loss

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Journal of Computers (Taiwan) 2024年第4期35卷 277-290页

作者： Song, Yunfei Zhu, Yujia Yang, Liu Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University Guiyang550000 China Guizhou Key Laboratory of Information and Computing Science Guizhou Normal University Guiyang550000 China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang550000 China

After significant investments of time and resources, the accuracy of deep neural network (DNN) models has reached commercially viable levels, leading to their increasing deployment on cloud platforms for commercial services. However, ongoing research indicates that the challenges facing deep neural network models are continually evolving, particularly with various attacks emerging to compromise their integrity. Deep neural networks are susceptible to poisoning attacks and backdoor attacks, both of which involve malicious fine-tuning of the deep models. Malicious fine-tuning can lead to unpredictable outputs from deep neural network models. Although at-tempts have been made to address this issue, these solutions often increase model complexity or diminish model performance. We propose a black-box watermarking technique based on trigger image sets, which can effectively detect malicious fine-tuning while also enabling copyright authentication. This watermarking technique builds upon black-box watermarking methods, leveraging trig-ger image sets and utilizing a two-stage alternating training approach to fine-tune the model. During training, a novel loss function is employed to optimize the trigger images, thereby embedding the watermark while preserving the model’s original classification capabilities. The proposed watermarking model is highly sensitive to malicious fine-tuning, resulting in unstable classification outcomes for trigger images. Ultimately, by inputting trigger image sets and analyzing the output of the watermarking model, the integrity of the deep neural network model can be verified. Experimental results demonstrate the effectiveness of this approach in detecting the integrity of DNN models. © 2024 Codon Publications. All rights reserved.

关键词： Neural network models

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Experimental evidence for the two-path description of neutron spin echo

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Physical Review A 2024年第4期109卷 042420-042420页

作者： S. McKay A. A. M. Irfan Q. Le Thien N. Geerits S. R. Parnell R. M. Dalgliesh N. V. Lavrik I. I. Kravchenko G. Ortiz R. Pynn Department of Physics Indiana University Bloomington Indiana 47405 USA Center for Exploration of Energy and Matter Indiana University Bloomington Indiana 47408 USA Atominstitut TU Wien Stadionallee 2 1020 Vienna Austria Faculty of Applied Sciences Delft University of Technology Mekelweg 15 2629 JB Delft The Netherlands ISIS Rutherford Appleton Laboratory Chilton Oxfordshire OX11 0QX United Kingdom Center for Nanophase Materials Science Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Quantum Science and Engineering Center Indiana University Bloomington Indiana 47408 USA Institute for Quantum Computing University of Waterloo Waterloo Ontario Canada N2L 3G1 Neutron Sciences Directorate Oak Ridge National Laboratory Oak Ridge Tennessee 37830 USA

We describe an experiment that strongly supports a two-path interferometric model in which the spin-up and spin-down components of each neutron propagate coherently along spatially separated parallel paths in a typical neutron spin-echo small-angle scattering (SESANS) experiment. Specifically, we show that the usual semi-classical, single-path treatment of Larmor precession of a polarized neutron in an external magnetic field predicts a damping as a function of the spin-echo length of the SESANS signal obtained with a periodic phase grating when the transverse width of the neutron wave packet is finite. However, no such damping is observed experimentally, implying either that the Larmor model is incorrect or that the transverse extent of the wave packet is very large. In contrast, we demonstrate theoretically that a quantum-mechanical interferometric model in which the two mode-entangled (i.e., intraparticle entangled) spin states of a single neutron are separated in space when they interact with the grating accurately predicts the measured SESANS signal, which is independent of the wave packet width.

关键词： Quantum entanglement Interferometry Neutron spin echo spectroscopy Spin echo small angle neutron scattering

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Ultrabright Multiplexed Energy-Time-Entangled Photon Generation from Lithium Niobate on Insulator Chip

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Physical Review applied 2021年第6期15卷 064059-064059页

作者： Guang-Tai Xue Yun-Fei Niu Xiaoyue Liu Jia-Chen Duan Wenjun Chen Ying Pan Kunpeng Jia Xiaohan Wang Hua-Ying Liu Yong Zhang Ping Xu Gang Zhao Xinlun Cai Yan-Xiao Gong Xiaopeng Hu Zhenda Xie Shining Zhu National Laboratory of Solid State Microstructures School of Physics School of Electronic Science and Engineering College of Engineering and Applied Sciences and Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China State Key Laboratory of Optoelectronic Materials and Technologies and School of Physics and Engineering Sun Yat-sen University Guangzhou 510275 China Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computing National University of Defense Technology Changsha 410073 China

A high-flux entangled-photon source is a key resource for quantum optical study and application. Here, it is realized in a lithium niobate on isolator (LNOI) chip, with 2.79 × 1011 Hz/mW photon-pair rate and 1.53 × 109 Hz/(nm mW) spectral brightness. These data are boosted by over 2 orders of magnitude compared with existing technologies. A 160-nm-broad bandwidth is engineered for eight-channel multiplexed energy-time entanglement. Harnessed by high-extinction-frequency correlation and Franson interferences up to 99.17% visibility, such energy-time-entanglement multiplexing further enhances the high-flux data rate and warrants broad application in quantum-information processing on a chip.

关键词： Integrated optics Optical quantum information processing Optoelectronics Photon pairs & parametric down-conversion Quantum state engineering Second order nonlinear optical processes Optical sources & detectors Waveguides

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