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Two-step electronic response to magnetic ordering in a van der Waals ferromagnet

Physical Review B 2024年第4期109卷 045416-045416页

作者： Han Wu Jian-Xin Zhu Lebing Chen Matthew W. Butcher Ziqin Yue Dongsheng Yuan Yu He Ji Seop Oh Bin Gao Jianwei Huang Shan Wu Cheng Gong Yucheng Guo Sung-Kwan Mo Jonathan Denlinger Donghui Lu Makoto Hashimoto Matthew B. Stone Alexander I. Kolesnikov Songxue Chi Junichiro Kono Andriy H. Nevidomskyy Robert J. Birgeneau Pengcheng Dai Ming Yi Department of Physics and Astronomy and Rice Center for Quantum Materials Rice University Houston Texas 77005 USA Theoretical Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Center for Integrated Nanotechnologies Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Physics University of California at Berkeley Berkeley California 94720 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Department of Applied Physics Yale University New Haven Connecticut 06511 USA Department of Electrical and Computer Engineering and Quantum Technology Center University of Maryland College Park Maryland 20742 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park California 94025 USA Neutron Scattering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Smalley-Curl Institute Rice University Houston Texas 77005 USA Department of Material Science and Nanoengineering Rice University Houston Texas 77005 USA Department of Materials Science and Engineering University of California Berkeley California 94720 USA

The two-dimensional material Cr2Ge2Te6 is a member of the class of insulating van der Waals (vdW) magnets. Here, using high resolution angle-resolved photoemission spectroscopy in a detailed temperature dependence study, we identify a clear response of the electronic structure to a dimensional crossover in the form of two distinct temperature scales marking onsets of modifications in the electronic structure. Specifically, we observe Te p-orbital-dominated bands to undergo changes at the Curie transition temperature TC while the Cr d-orbital-dominated bands begin evolving at a higher temperature scale. Combined with neutron scattering, density functional theory calculations, and Monte Carlo simulations, we find that the electronic system can be consistently understood to respond sequentially to the distinct temperatures at which in-plane and out-of-plane spin correlations exceed a characteristic length scale. Our findings reveal the sensitivity of the orbital-selective electronic structure for probing the dynamical evolution of local moment correlations in vdW insulating magnets.

关键词： Electronic structure Magnetic order Magnetic phase transitions Van der Waals systems Angle-resolved photoemission spectroscopy

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Identifying and Anticipating Cyberattacks That Could Cause Physical Damage to Industrial Control Systems

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IEEE Power and Energy technology Systems Journal 2019年第4期6卷 172-182页

作者： Angle, Matthew G. Madnick, Stuart Kirtley, James L. Khan, Shaharyar Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Sloan School of Management School of Engineering Massachusetts Institute of Technology CambridgeMA02139 United States System Design and Management Program Massachusetts Institute of Technology CambridgeMA02139 United States

Physical control systems are increasingly controlled by reconfigurable, network-enabled devices to increase flexibility and ease commissioning and maintenance. Such capability creates vulnerabilities. Devices may be remotely reprogrammed by a malicious actor to act in unintended ways, causing physical damage to mechanical equipment, infrastructure, and life and limb. In this paper, past examples of actual damage to cyber-physical systems are shown, threats posed by software-controlled variable frequency drives (VFDs) are analyzed, and a small-scale version of an attack on ubiquitous VFD equipment is demonstrated. © 2014 IEEE.

关键词： Embedded systems

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Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective

arXiv

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arXiv 2024年

作者： Restrepo, David Wu, Chenwei Vásquez-Venegas, Constanza Matos, João Gallifant, Jack Celi, Leo Anthony Bitterman, Danielle S. Nakayama, Luis Filipe Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Departamento de Telematica Universidad del Cauca Cauca Popayán Colombia Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Scientific Image Analysis Lab Faculty of Medicine Universidad de Chile RM Santiago Chile Department of Computer Science Faculty of Engineering Universidad de Concepción Biobio Concepción Chile Department of Biostatistics Harvard TH Chan School of Public Health BostonMA United States Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Ophthalmology São Paulo Federal University São Paulo São Paulo Brazil

The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes. However, the underrepresentation of diverse groups in the development and application of these models can perpetuate biases, leading to inequitable healthcare delivery. This paper presents a comprehensive scientometric analysis of LLM research for healthcare, including data from January 1, 2021, to July 1, 2024. By analyzing metadata from PubMed and Dimensions, including author affiliations, countries, and funding sources, we assess the diversity of contributors to LLM research. Our findings highlight significant gender and geographic disparities, with a predominance of male authors and contributions primarily from high-income countries (HICs). We introduce a novel journal diversity index based on Gini diversity to measure the inclusiveness of scientific publications. Our results underscore the necessity for greater representation in order to ensure the equitable application of LLMs in healthcare. We propose actionable strategies to enhance diversity and inclusivity in artificial intelligence research, with the ultimate goal of fostering a more inclusive and equitable future in healthcare innovation. © 2024, CC BY.

关键词： Decision making

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Unbiased Estimation using a Class of Diffusion Processes

arXiv

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arXiv 2022年

作者： Ruzayqat, Hamza Beskos, Alexandros Crisan, Dan Jasra, Ajay Kantas, Nikolas Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 KSA Saudi Arabia Department of Statistical Science University College London LondonWC1E 6BT United Kingdom Department of Mathematics Imperial College London LondonSW7 2AZ United Kingdom

We study the problem of unbiased estimation of expectations with respect to (w.r.t.) π a given, general probability measure on (d, B(d)) that is absolutely continuous with respect to a standard Gaussian measure. We focus on simulation associated to a particular class of diffusion processes, sometimes termed the Schrödinger-Föllmer Sampler, which is a simulation technique that approximates the law of a particular diffusion bridge process {Xt}tϵ[0,1]on d, d ϵ 0. This latter process is constructed such that, starting at X0= 0, one has X1∼ π. Typically, the drift of the diffusion is intractable and, even if it were not, exact sampling of the associated diffusion is not possible. As a result, [10, 16] consider a stochastic Euler-Maruyama scheme that allows the development of biased estimators for expectations w.r.t. π. We show that for this methodology to achieve a mean square error of O(ϵ2), for arbitrary ϵ > 0, the associated cost is O(ϵ-5). We then introduce an alternative approach that provides unbiased estimates of expectations w.r.t. π, that is, it does not suffer from the time discretization bias or the bias related with the approximation of the drift function. We prove that to achieve a mean square error of O(ϵ2), the associated cost (which is random) is, with high probability, O(ϵ-2| log(ϵ)|2+δ), for any δ > 0. We implement our method on several examples including Bayesian inverse *** Codes 60J60, 62D05, 65C40 © 2022, CC BY.

关键词： Diffusion

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Neuromuscular fatigue detection by mechanomyography in people with complete spinal cord injury

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Research on Biomedical engineering 2020年第3期36卷 203-212页

作者： Krueger, Eddy Popović-Maneski, Lana Neto, Guilherme Nunes Nogueira Mendonça Scheeren, Eduardo Fiusa, Jessika Mehret Nohama, Percy Neural Engineering and Rehabilitation Laboratory Universidade Estadual de Londrina LondrinaPR Brazil Graduate Program in Rehabilitation Sciences UEL-UNOPAR Campus Universitário s/n zipcode LondrinaPR86051-990 Brazil Institute of Technical Sciences of the Serbian Academy of Sciences and Arts KnezMihailova 35/IV zipcode Belgrade11000 Serbia Graduate Program in Health Technology Pontifícia Universidade Católica do Paraná Rua Imaculada Conceição 1155 Prado Velho zipcode CuritibaPR80215-901 Brazil Graduate Program in Electrical and Computer Engineering Universidade Tecnológica Federal do Paraná Av. Sete de Setembro 3165 zipcode CuritibaPR80230-901 Brazil

Purpose: Functional electrical stimulation (FES) is a method of activating paralyzed muscles. During FES application, fast muscle fatigue can occur (the inability of stimulated muscles to generate force). Therefore, it is beneficial to estimate the muscle fatigue for FES closed-loop control for walking to prevent unexpected muscle collapse and adapt the FES strategy in real time. Mechanomyography (MMG) is a noninvasive technique for registering myofiber vibrations, representing an ideal candidate for the provision of feedback. The hypothesis was that MMG signals could effectively detect muscle fatigue and, thus, provide feedback. Methods: We tested this hypothesis by analyzing the wavelet transform of signals from an MMG sensor positioned over the rectus femoris muscle during electrically evoked contractions in subjects with spinal cord injury (SCI). The signals were collected from a total of 24 lower limb muscles. We investigated both legs of 15 participants with spinal cord injury (male, YOA = 27.13 ± 5.05, M = 75.8 ± 10.35 kg, and H = 1.78 ± 0.07 m, American Spinal Injury Impairment Scale (AIS) A and B). All MMG signals were analyzed in 12 frequency bands from 5 to 53 Hz. Results: We found different trends in the magnitudes in different frequency bands. The magnitude decreased in 13, 16, 20, 25, and 35 Hz bands in correlation with fatigue. The greatest statistical difference was found at 20 Hz and 25 Hz. Conclusion: This result suggests that processed MMG signals indicate muscle fatigue and can, thus, be used as the feedback in FES systems. © 2020, Sociedade Brasileira de Engenharia Biomedica.

关键词： Muscle

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Transformation between elastic dipoles, quadrupoles, octupoles and hexadecapoles driven by surfactant self-assembly in nematic emulsion

arXiv

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arXiv 2022年

作者： Senyuk, Bohdan Mozaffari, Ali Crust, Kevin Zhang, Rui de Pablo, Juan J. Smalyukh, Ivan I. Department of Physics and Soft Materials Research Center University of Colorado Boulder CO United States Pritzker School of Molecular Engineering The University of Chicago ChicagoIL United States Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Center for Molecular Engineering Argonne National Laboratory LemontIL United States Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program University of Colorado Boulder CO United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado Boulder CO United States

Emulsions comprising isotropic fluid drops within a nematic host are of interest for applications ranging from biodetection to smart windows, which rely on changes of molecular alignment structures around the drops in response to chemical, thermal, electric and other stimuli. We show that absorption or desorption of trace amounts of common surfactants can drive continuous transformations of elastic multipoles induced by the droplets within the uniformly aligned nematic host. Out-of-equilibrium dynamics of director structures emerge from a controlled self-assembly or desorption of different surfactants at the drop-nematic interfaces, with ensuing forward and reverse transformations between elastic dipoles, quadrupoles, octupoles and hexadecapoles. We characterize inter-transformations of droplet-induced surface and bulk defects, probe elastic pair interactions and discuss emergent prospects for fundamental science and applications of the reconfigurable nematic emulsions. © 2022, CC BY-NC-ND.

关键词： Surface active agents

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Visualizing Field-free Deterministic Magnetic Switching of all–van der Waals Spin-Orbit Torque System Using Spin Ensembles in Hexagonal Boron Nitride

arXiv

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arXiv 2025年

作者： Zhang, Xi Zhou, Jingcheng Hu, Chaowei Deng, Kuangyin Wang, Chuangtang Agarwal, Nishkarsh Jin, Hanshang Al-Matouq, Faris A. Xu, Stelo Trivedi, Roshan S. Li, Senlei Rathi, Sumedh Lu, Hanyi Jiang, Zhigang Taufour, Valentin Hovden, Robert Zhao, Liuyan Cheng, Ran Xu, Xiaodong Chu, Jiun-Haw Du, Chunhui Rita Wang, Hailong School of Physics Georgia Institute of Technology AtlantaGA30332 United States Department of Physics University of Washington SeattleWA98105 United States Department of Electrical and Computer Engineering University of California RiversideCA92521 United States Department of Physics University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Department of Physics and Astronomy University of California DavisCA95616 United States Department of Physics University of California San Diego La Jolla CA92093 United States Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Materials Science and Engineering University of Washington SeattleWA98105 United States

Recently, optically active spin defects embedded in van der Waals (vdW) crystals have emerged as a transformative quantum sensing platform to explore cutting-edge materials science and quantum physics. Taking advantage of excellent solid-state integrability, this new class of spin defects can be arranged in controllable nanoscale proximity of target materials in vdW heterostructures, showing great promise for improving spatial resolution and field sensitivity of current sensing technologies. Building on this state-of-the-art measurement platform, here we report hexagonal boron nitride-based quantum imaging of field-free deterministic magnetic switching of room-temperature two-dimensional magnet Fe3GaTe2 in an all-vdW spin-orbit torque (SOT) system. By visualizing SOT-driven variations of nanoscale Fe3GaTe2 magnetic stray field profile under different conditions, we have revealed how the observed magnetic switching evolves from deterministic to indeterministic behavior due to the interplay between out-of-plane spins, in-plane spins and Joule heating. This understanding, which is otherwise difficult to access by conventional transport measurements, offers valuable insights on material design, testing, and evaluation of next-generation vdW spintronic devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Van der Waals forces

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Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

arXiv

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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh The Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the two sensors. For example, some objects visible to lidar are invisible to radar, and some objects occluded in lidar scans are visible in radar images because of the radar's penetrating capability. These sensing differences cause false positive and penetrating capability degeneration, respectively. In this paper, we propose training data preprocessing and polar sliding window inference to solve the issues. The data preprocessing aims to reduce the effect caused by radar-invisible measurements in lidar scans. The polar sliding window inference aims to solve the limited sensing range issue by applying a near-range trained network to the long-range region. Instead of using common Cartesian representation, we propose to use polar representation to reduce the shape dissimilarity between long-range and near-range data. We find that extending a near-range trained network to long-range region inference in the polar space has 4.2 times better IoU than in Cartesian space. Besides, the polar sliding window inference can preserve the radar penetrating capability by changing the viewpoint of the inference region, which makes some occluded measurements seem non-occluded for a pretrained network. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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Machine learning approaches for fault detection and diagnosis of induction motors

Machine learning approaches for fault detection and diagnosi...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Lamia Belguesmi Mansour Hajji Majdi Mansouri Mohamed-Faouzi Harkat Abdelmalek Kouadri Hazem Nounou Mohamed Nounou Higher institute of Applied Science and Technology of Kasserine University of Kairouan Kasserine Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

This paper deals with the problem of monitoring of induction motors (IM) through the development of fault detection and diagnosis (FDD) approach. The developed FDD technique is addressed such that, the principal component analysis (PCA) technique is used for features extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach the most efficient features are extracted and selected through PCA scheme using induction motor data. Besides, their statistical characteristics (mean and variance) are also included. The ML classifiers are applied using the extracted and selected features to perform the FDD problem. The obtained results indicate that the proposed techniques have a wide application area, fast fault detection and diagnosis, making them more reliable for induction motors monitoring.

关键词： Induction motors Machine learning algorithms Fault detection Machine learning Feature extraction Monitoring Principal component analysis

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Efficient fault detection and diagnosis of wind energy converter systems

Efficient fault detection and diagnosis of wind energy conve...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Mohamed-Faouzi Harkat Abdelmalek Kouadri Hazem Nounou Mohamed Nounou Higher institute of Applied Science and Technology of Kasserine University of Kairouan Kasserine Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728110806

ISBN: (纸本)9781728110813

Fault detection and diagnosis for modern wind turbines converter (WTC) systems have been received an important measure for improving the operation of these systems, in such a way to increase their reliability, availability and required safety. Therefore, this paper deals with the problem of fault detection and diagnosis (FDD) in WTC systems. The developed FDD approach uses feature extraction and selection, and fault classification tools for monitoring WTC system under different operating conditions. The developed FDD approach is addressed such that, the principal component analysis (PCA) technique is used for feature extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach, an efficient features in PCA subspace that extract and select the most informative features from WTC data are provided. Besides, their statistical characteristics are also included. The ML classifiers are applied to the extracted and selected features to perform the fault diagnosis problem. The effectiveness and higher classification accuracy of the developed approach are demonstrated using simulated data extracted from different operating conditions of the wind turbine.

关键词： Fault diagnosis Fault detection Machine learning Feature extraction Wind turbines Monitoring Principal component analysis

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