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SSRN 2023年

作者： Liu, Lixiang Chen, Shanshan Small, Michael Moore, Jack Murdoch Shang, Keke Department of Computer Science School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Complex Systems Group Department of Mathematics and Statistics The University of Western Australia Perth6009 Australia Mineral Resources Commonwealth Scientific and Industrial Research Organisation Kensington6151 Australia MOE Key Laboratory of Advanced Micro-Structured Materials School of Physics Science and Engineering Tongji University Shanghai200092 China Computational Communication Collaboratory Nanjing University Nanjing210093 China

During the outbreak of infectious diseases, individual always make some adaptive response to avoid infected the diseases. In this paper, we introduce SIRS model on scale-free networks that take into account behavioral memory and time delay to depict individuals’ adaptive feedback, which is better characterize the actual spread of epidemics. We address uniform persistence and find that the model has the sharp threshold property, that is, for the basic reproduction number less than one, the disease-free equilibrium is globally asymptotically stable, while for the basic reproduction number is above one, the endemic equilibrium is globally stable. We further analyze the effects of two control strategies (the vaccination of susceptible people and treatment of infected people) on epidemic dynamics by optimal control strategy. The existence and uniqueness of the optimal control strategy are obtained. Finally, we perform some stochastic network simulations that yield quantitative agreement with the deterministic mean-field approach. The final results show that the effect of time delay does not change the basic reproduction number, but it plays an important role in the process of disease transmission. The behavioral memory is able to affect the basic reproduction number, and it has important and positive implications for disease transmission and control. The work of this paper can provide a theoretical basis and reference for the prediction and prevention of infectious diseases. © 2023, The Authors. All rights reserved.

关键词： Diseases

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Neural Signal Compression System with Spike Detection Using Compressed Sensing

Neural Signal Compression System with Spike Detection Using ...

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Cross Strait Quad-Regional Radio science and Wireless Technology Conference (CSRWTC)

作者： Ruihan Zheng Yu Xia Dongming Li Liyang Wang Hung Chun Li Peng Un Mak Mang I. Vai Sio Hang Pun State Key Laboratory of Analog and Mixed-Signal VLSL University of Macau Macau China Institution of Microelectronics University of Macau Macau China Joint Laboratory of Zhuhai UM Science and Technology Research Institution – Lingyange Semiconductor Incorporated Zhuhai Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China

This article aims to demonstrate a signal compression method for the wireless invasive neural recording system. A compression system with spike detection for neural signals is proposed. The input signal is firstly detected in the spike detection part and then the intercepted spike segments are sent to the compression part. A compressed sensing technique is applied in the compression part, and the Minimum Euclidean or Manhattan Distance Cluster-based (MDC) matrix is adopted for compressing neural spike segments. During simulation, the compression rate can surpass 99% and the signal-to-noise distortion ratio is around 37 dB. Moreover, the proposed method is also contrasted with the direct compression of input neural signals. When the input neural signal is directly compressed, the compression ratio is 98%, and the signal-to-noise ratio distortion rate is about 28 dB. By employing spike detection and utilizing this MDC matrix, it becomes possible to compress non-sparse spike segments in the time domain, resulting in a higher compression rate and improved reconstruction performance.

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Tunable Magnetically Induced Transparency Spectra in Magnon-Magnon Coupled Y3Fe5O12/Permalloy Bilayers

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Physical Review Applied 2022年第4期17卷 044010-044010页

作者： Yuzan Xiong Jerad Inman Zhengyi Li Kaile Xie Rao Bidthanapally Joseph Sklenar Peng Li Steven Louis Vasyl Tyberkevych Hongwei Qu Zhili Xiao Wai K. Kwok Valentine Novosad Yi Li Fusheng Ma Wei Zhang Department of Physics Oakland University Rochester Michigan 48309 USA Department of Electronic and Computer Engineering Oakland University Rochester Michigan 48309 USA Materials Science Division Argonne National Laboratory Argonne Illinois 60439 USA Jiangsu Key Laboratory of Opto-Electronic Technology School of Physics and Technology Nanjing Normal University Nanjing 210046 China Department of Physics and Astronomy Wayne State University Detroit Michigan 48201 USA Department of Electrical and Computer Engineering Auburn University Auburn Alabama 36849 USA

Hybrid magnonic systems host a variety of characteristic phenomena such as the magnetically induced transparency (MIT) and Purcell effect, which are considered useful for future coherent quantum-information processing. In this work, we experimentally demonstrate a tunable MIT effect in the Y3Fe5O12(YIG)/Permalloy(Py) magnon-magnon coupled system via changing the magnetic field orientations. By probing the magneto-optic effects of Py and YIG thin films, we identify clear features of MIT spectra induced by the mode hybridization between the uniform mode of Py and the perpendicular standing spin-wave modes of YIG. By changing the external magnetic field orientations, we observe a tunable coupling strength between the YIG’s spin-wave modes and the Py’s uniform mode, upon the application of an out-of-plane magnetic field. This observation is theoretically interpreted by a geometrical consideration of the Py and YIG magnetization under the oblique magnetic field even at a constant interfacial exchange coupling. Our findings show high promise for investigating tunable coherent phenomena with hybrid magnonic platforms.

关键词： Electromagnetically induced transparency Magneto-optical effect Magnons Quantum information with hybrid systems Spintronics Ferromagnets Magnetic insulators Magnetic multilayers

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Efficient simulations of Hartree–Fock equations by an accelerated gradient descent method

arXiv

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arXiv 2024年

作者： Ohno, Y. Del Maestro, A. Lakoba, T.I. Department of Mathematics and Statistics University of Vermont BurlingtonVT05405 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Institute for Advanced Materials & Manufacturing University of Tennessee KnoxvilleTN37920 United States

We develop convergence acceleration procedures that enable a gradient descent-type iteration method to efficiently simulate Hartree–Fock equations for atoms interacting both with each other and with an external potential. Our development focuses on three aspects: (i) optimization of a parameter in the preconditioning operator;(ii) adoption of a technique that eliminates the slowest-decaying mode to the case of many equations (describing many atoms);and (iii) a novel extension of the above technique that allows one to eliminate multiple modes simultaneously. We illustrate performance of the numerical method for the 2D model of the first layer of helium atoms above a graphene sheet. We demonstrate that incorporation of aspects (i) and (ii) above into the "plain" gradient descent method accelerates it by at least two orders of magnitude, and often by much more. Aspect (iii) — a multiple-mode elimination — may bring further improvement to the convergence rate compared to aspect (ii), the single-mode elimination. Both single- and multiple-mode elimination techniques are shown to significantly outperform the well-known Anderson Acceleration. We believe that our acceleration techniques can also be gainfully employed by other numerical methods, especially those handling hard-core-type interaction potentials. Copyright © 2024, The Authors. All rights reserved.

关键词： Numerical methods

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Unification of Finite Symmetries in Simulation of Many-body Systems on Quantum computers

arXiv

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arXiv 2024年

作者： Bastidas, Victor M. Fitzpatrick, Nathan Joven, K.J. Rossi, Zane M. Islam, Shariful Van Voorhis, Troy Chuang, Isaac L. Liu, Yuan Department of Chemistry Massachusetts Institute of Technology CambridgeMA02139 United States Physics and Informatics Laboratory NTT Research Inc. 940 Stewart Dr. SunnyvaleCA94085 United States Quantinuum Terrington House 13-15 Hills Road CambridgeCB2 1NL United Kingdom Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics North Carolina State University RaleighNC27606 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC27606 United States Department of Computer Science North Carolina State University RaleighNC27606 United States

Symmetry is fundamental in the description and simulation of quantum systems. Leveraging symmetries in classical simulations of many-body quantum systems often results in an exponential overhead due to the exponentially growing size of some symmetry groups as the number of particles increases. Quantum computers hold the promise of achieving exponential speedup in simulating quantum many-body systems;however, a general method for utilizing symmetries in quantum simulations has not yet been established. In this work, we present a unified framework for incorporating symmetry groups into the simulation of many-body systems on quantum computers. The core of our approach lies in the development of efficient quantum circuits for symmetry-adapted projection onto irreducible representations of a group or pairs of commuting groups. We provide resource estimations for common groups, including the cyclic and permutation groups. Our algorithms demonstrate the capability to prepare coherent superpositions of symmetry-adapted states and to perform quantum evolution across a wide range of models in condensed matter physics and ab initio electronic structure in quantum chemistry. Specifically, we execute a symmetry-adapted quantum subroutine for small molecules in first quantization on noisy hardware, and demonstrate the emulation of symmetry-adapted quantum phase estimation for preparing coherent superpositions of quantum states in various irreps of a symmetry group. In addition, we present a discussion of major open problems regarding the use of symmetries in digital quantum simulations of many-body systems, paving the way for future systematic investigations into leveraging symmetries for practical quantum advantage. The broad applicability and rigorous resource estimation for symmetry transformations make our framework appealing for achieving provable quantum advantage on fault-tolerant quantum computers. The efficiency of the proposed symmetry-adapted subroutine holds the promi

关键词： Quantum computers

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Shear strain alters the structure and migration mechanism of self-interstitial atoms in copper

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Physical Review Materials 2022年第5期6卷 053605-053605页

作者： B. Zhang C. Wheatley P. Chen X. Qian M. J. Demkowicz Department of Materials Science and Engineering Texas A&M University College Station Texas 77845 USA Department of Materials Science and Engineering Penn State University State College Pennsylvania 16801 USA Key Laboratory of Automobile Materials of Ministry of Education School of Materials Science and Engineering Jilin University Changchun People's Republic of China Department of Electrical and Computer Engineering Texas A&M University College Station Texas 77845 USA

We use atomistic modeling to show that externally applied shear strain causes the lowest energy self-interstitial atom (SIA) structure in copper (Cu) to change from a ⟨100⟩-type dumbbell to a ⟨110⟩-type dumbbell. Concurrently, SIA migration switches from the three-dimensional (3D) random walk characteristic of ⟨100⟩-type dumbbells to a 1D mechanism analogous to that of crowdion SIAs. Furthermore, the relative energies of these two dumbbell structures as a function of strain are well predicted using elastic dipole tensors computed at zero strain, indicating that examination of these tensors may be used to assess the likelihood of strain-induced SIA structure transitions in other materials. Changes in lowest energy SIA structures and associated migration mechanisms stand to impact predictions of SIA behavior in irradiated solids.

关键词： Interstitials Face-centered cubic Transition metals Density functional calculations

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The establishment of static digital humans and the integration with spinal models

arXiv

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arXiv 2025年

作者： Ju, Fujiao Wang, Yuxuan Wang, Shuo Wang, Chengyin Chen, Yinbo Li, Jianfeng Dong, Mingjie Fang, Bin Zhuang, Qianyu College of Computer Science Beijing University of Technology Beijing100124 China Department of Engineering Physics Tsinghua University Beijing100084 China Beijing Key Laboratory of Advanced Manufacturing Technology College of Mechanical & Energy Engineering Beijing University of Technology Beijing100124 China Department of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Department of Orthopedics Peking Union Medical College Hospital Beijing100730 China

Adolescent idiopathic scoliosis (AIS) is a common spinal deformity that has a profound impact on health and quality of life. Although conventional imaging techniques, e.g., X-rays, computed tomography (CT), and magnetic resonance imaging (MRIs), provide static views of the spine, they are limited in capturing the dynamic changes in the spine and its interactions with overall body motion. Therefore, it has become particularly important to develop new techniques to fill these gaps. Dynamic digital human modeling is a major advancement in digital medicine that allows the spine to be viewed in three dimensions (3D) as it changes during daily activities, helping clinicians identify deformities that may be overlooked in static imaging. Although dynamic modeling has great potential, constructing an accurate static digital human model is an essential first step to achieve high-precision simulations. In this study, we focus on constructing an accurate static digital human model integrating the spine, which is crucial for subsequent dynamic digital human research in AIS. Initially, human point cloud data is generated by combining the 3D Gaussian method with the Skinned Multi-Person Linear (SMPL) from the patient’s multi-view images. Subsequently, a standard skeletal model is fitted to the generated human model. Next, the real spine model reconstructed from CT images is aligned with the standard skeletal model. The resulting personalized spine model has been validated using X-ray data from six patients with AIS, with Cobb angles (measuring the severity of the scoliosis) as evaluation metrics. The results show that the error of the model was within 1◦ of the actual measurements. This study provides an important method for constructing digital humans. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Semi-parametric inference based on adaptively collected data

arXiv

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arXiv 2023年

作者： Lin, Licong Khamaru, Koulik Wainwright, Martin J. Department of Electrical Engineering and Computer Sciences UC Berkeley United States Department of Statistics UC Berkeley United States Department of Statistics Rutgers University United States Laboratory for Information and Decision Systems Statistics and Data Science Center EECS and Mathematics Massachusetts Institute of Technology United States

Many standard estimators, when applied to adaptively collected data, fail to be asymptotically normal, thereby complicating the construction of confidence intervals. We address this challenge in a semi-parametric context: estimating the parameter vector of a generalized linear regression model contaminated by a non-parametric nuisance component. We construct suitably weighted estimating equations that account for adaptivity in data collection, and provide conditions under which the associated estimates are asymptotically normal. Our results characterize the degree of "explorability" required for asymptotic normality to hold. For the simpler problem of estimating a linear functional, we provide similar guarantees under much weaker assumptions. We illustrate our general theory with concrete consequences for various problems, including standard linear bandits and sparse generalized bandits, and compare with other methods via simulation studies. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Effects of substrate corrugation during helium adsorption on graphene in the grand canonical ensemble

arXiv

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arXiv 2023年

作者： Erwin, Gage Maestro, Adrian Del Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Institute for Advanced Materials and Manufacturing University of Tennessee KnoxvilleTN37996 United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States

Adsorption of 4He on graphene substrates has been a topic of great interest due to the intriguing effects of graphene corrugation on the manifestation of commensurate solid and exotic phases in low-dimensional systems. In this study, we employ worm algorithm quantum Monte Carlo to study helium adsorbed on a graphene substrate to explore corrugation effects in the grand canonical ensemble. We utilized a Szalewicz potential for helium-helium interactions and a summation of isotropic interactions between helium and carbon atoms to construct a helium-graphene potential. We implement different levels of approximation to achieve a smooth potential, three partially corrugated potentials, and a fully ab initio potential to test the effects of corrugation on the first and second layers. We demonstrate that the omission of corrugation within the helium-graphene potential could lead to finite-size effects in both the first and second layers. Thus, a fully corrugated potential should be used when simulating helium in this low-dimensional regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Helium

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Merging synthetic and real embryo data for advanced AI predictions

arXiv

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arXiv 2024年

作者： Presacan, Oriana Dorobanţiu, Alexandru Thambawita, Vajira Riegler, Michael A. Stensen, Mette H. Iliceto, Mario Aldea, Alexandru C. Sharma, Akriti Faculty of Electronics Telecommunications and Information Technology National University of Science and Technology Politehnica Bucharest Bucharest061071 Romania Department of Computer Science and Electrical Engineering Lucian Blaga University of Sibiu Sibiu550024 Romania Department of Holistic Systems SimulaMet Stensberggata 27 Oslo0170 Norway Department of Computer Science Oslo Metropolitan University Oslo0167 Norway Volvat Spiren Oslo0176 Norway Faculty of Biotechnologies University of Agronomic Sciences and Veterinary Medicine Bucharest011464 Romania Department of Validation Intelligence for Autonomous Software Systems Simula Research Laboratory Oslo0164 Norway Cyber Security Simula Research Laboratory Oslo0164 Norway

Accurate embryo morphology assessment is essential in assisted reproductive technology for selecting the most viable embryo. Artificial intelligence has the potential to enhance this process. However, the limited availability of embryo data presents challenges for training deep learning models. To address this, we trained two generative models using two datasets—one we created and made publicly available, and one existing public dataset—to generate synthetic embryo images at various cell stages, including 2-cell, 4-cell, 8-cell, morula, and blastocyst. These were combined with real images to train classification models for embryo cell stage prediction. Our results demonstrate that incorporating synthetic images alongside real data improved classification performance, with the model achieving 97% accuracy compared to 94.5% when trained solely on real data. This trend remained consistent when tested on an external Blastocyst dataset from a different clinic. Notably, even when trained exclusively on synthetic data and tested on real data, the model achieved a high accuracy of 92%. Furthermore, combining synthetic data from both generative models yielded better classification results than using data from a single generative model. Four embryologists evaluated the fidelity of the synthetic images through a Turing test, during which they annotated inaccuracies and offered feedback. The analysis showed the diffusion model outperformed the generative adversarial network, deceiving embryologists 66.6% versus 25.3% and achieving lower Fréchet inception distance scores. © 2024, CC BY-SA.

关键词： Generative adversarial networks

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