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Secondary spin current driven efficient THz spintronic emitters

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arXiv 2022年

作者： Agarwal, Piyush Yang, Yingshu Medwal, Rohit Asada, Hironori Fukuma, Yasuhiro Battiato, Marco Singh, Ranjan Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore637371 Singapore Center for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University Singapore639798 Singapore Department of Physics Indian Institute of Technology Uttar Pradesh Kanpur208016 India Department of Electronic Devices and Engineering Graduate School of Science and Engineering Yamaguchi University Ube755-8611 Japan Department of Physics and Information Technology Faculty of Computer Science and System Engineering Kyushu Institute of Technology Iizuka820-8502 Japan

Femtosecond laser-induced photoexcitation of ferromagnet (FM)/heavy metal (HM) heterostructures have attracted attention by emitting broadband terahertz frequencies. The phenomenon relies on the formation of ultrafast spin current, which is largely attributed to the direct photoexcitation of the FM layer. However, we reveal that during the process, the FM layer also experiences a secondary excitation led by the hot electrons from the HM layer that travel across the FM/HM interface and transfer additional energy in the FM. Thus, the generated secondary spins enhance the total spin current formation and lead to amplified spintronic terahertz emission. The results also emphasize the significance of the secondary spin current, which even exceeds the primary spin currents when FM/HM heterostructures with thicker HM are used. An analytical model is developed to provide deeper insights into the microscopic processes within the individual layers, underlining the generalized ultrafast superdiffusive spin-transport mechanism. Copyright © 2022, The Authors. All rights reserved.

关键词： Hot electrons

Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

Active learning for linear parameter-varying system identification

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arXiv 2020年

作者： Chin, Robert Maass, Alejandro I. Ulapane, Nalika Manzie, Chris Shames, Iman Nešić, Dragan Rowe, Jonathan E. Nakada, Hayato Department of Electrical & Electronic Engineering University of Melbourne Australia & School of Computer Science University of Birmingham United Kingdom Department of Electrical & Electronic Engineering University of Melbourne Australia School of Computer Science University of Birmingham United Kingdom Alan Turing Institute United Kingdom Advanced Unit Management System Development Division Toyota Motor Corporation Japan

Active learning is proposed for selection of the next operating points in the design of experiments, for identifying linear parameter-varying systems. We extend existing approaches found in literature to multiple-input multiple-output systems with a multivariate scheduling parameter. Our approach is based on exploiting the probabilistic features of Gaussian process regression to quantify the overall model uncertainty across locally identified models. This results in a flexible framework which accommodates for various techniques to be applied for estimation of local linear models and their corresponding uncertainty. We perform active learning in application to the identification of a diesel engine air-path model, and demonstrate that measures of model uncertainty can be successfully reduced using the proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Diesel engines

Efficient MedSAMs: Segment Anything in Medical Images on Laptop

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

Tracking and Failure Analysis of Explanted Neuromodulation Devices

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Neuromodulation : journal of the International Neuromodulation Society 2025年第4期28卷 700-702页

作者： Tess L Veuthey Deborah Duricka Dario J Englot David A Provenzano David M Dickerson Sanket Dhruva Christopher Miranda Loftus Nina Meizu-Nwaba David Prescott McMullen Richard B North Pierre D'Haese Peter E Konrad Lawrence Poree Department of Neurology and Neurological Sciences Stanford Medicine Stanford CA USA. Washington Wyoming Alaska Montana and Idaho School of Medical Education Anchorage AK USA. Departments of Neurological Surgery & Electrical Engineering Neurology Radiology & Radiological Sciences Computer Science and Biomedical Engineering Vanderbilt Health Nashville TN USA. Pain Diagnostics and Interventional Care Pittsburgh PA USA. Anesthesia Pain Management Services NorthShore University Health System Chicago IL USA. Department of Internal Medicine University for California San Francisco San Francisco CA USA. Office of Neurological and Physical Medicine Devices OHT5 OPEQ CDRH FDA Silver Springs MD USA. Department of Neurosurgery Anesthesiology and Critical Care Medicine (ret.) Johns Hopkins University School of Medicine Baltimore MD USA. Rockefeller Neuroscience Institute West Virginia University Morgantown WV USA. Dept of Neurosurgery West Virginia University Morgantown WV USA. Department of Anesthesia and Perioperative Care Division of Pain Medicine University of California San Francisco CA USA. Electronic address: Lawrence.Poree@UCSF.edu.

Observation of the dual quantum spin Hall insulator by density-tuned correlations in a van der Waals monolayer

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arXiv 2024年

作者： Tang, Jian Ding, Thomas Siyuan Chen, Hongyu Gao, Anyuan Qian, Tiema Huang, Zumeng Sun, Zhe Han, Xin Strasser, Alex Li, Jiangxu Geiwitz, Michael Shehabeldin, Mohamed Belosevich, Vsevolod Wang, Zihan Wang, Yiping Watanabe, Kenji Taniguchi, Takashi Bell, David C. Wang, Ziqiang Fu, Liang Zhang, Yang Qian, Xiaofeng Burch, Kenneth S. Shi, Youguo Ni, Ni Chang, Guoqing Xu, Su-Yang Ma, Qiong Department of Physics Boston College Chestnut HillMA United States Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore Department of Chemistry and Chemical Biology Harvard University CambridgeMA United States Department of Physics and Astronomy and California NanoSystems Institute University of California Los Angeles Los AngelesCA United States Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Department of Materials Science and Engineering Texas A&M University College StationTX United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN United States Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN United States Research Center for Electronic and Optical Materials National Institute for Materials Science Tsukuba Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba Japan Harvard John A. Paulson School of Engineering and Applied Sciences The Center for Nanoscale system Harvard University CambridgeMA United States Department of Physics Massachusetts Institute of Technology CambridgeMA United States CIFAR Azrieli Global Scholars program CIFAR Toronto Canada

The convergence of topology and correlations represents a highly coveted realm in the pursuit of novel quantum states of matter [1, 2]. Introducing electron correlations to a quantum spin Hall (QSH) insulator can lead to the emergence of a fractional topological insulator and other exotic time-reversal-symmetric topological order [3- 10], not possible in quantum Hall and Chern insulator systems. However, the QSH insulator with quantized edge conductance remains rare, let alone that with significant correlations. In this work, we report a novel dual QSH insulator within the intrinsic monolayer crystal of TaIrTe4, arising from the interplay of its single-particle topology and density-tuned electron correlations. At charge neutrality, monolayer TaIrTe4 demonstrates the QSH insulator that aligns with single-particle band structure calculations, manifesting enhanced nonlocal transport and quantized helical edge conductance. Interestingly, upon introducing electrons from charge neutrality, TaIrTe4 only shows metallic behavior in a small range of charge densities but quickly goes into a new insulating state, entirely unexpected based on TaIrTe4's single-particle band structure. This insulating state could arise from a strong electronic instability near the van Hove singularities (VHS), likely leading to a charge density wave (CDW). Remarkably, within this correlated insulating gap, we observe a resurgence of the QSH state, marked by the revival of nonlocal transport and quantized helical edge conduction. Our observation of helical edge conduction in a CDW gap could bridge spin physics and charge orders. The discovery of a dual QSH insulator introduces a new method for creating topological flat minibands via CDW superlattices, which offer a promising platform for exploring time-reversal-symmetric fractional phases and electromagnetism [3-5, 11, 12]. Copyright © 2024, The Authors. All rights reserved.

关键词： Charge density waves

Priming with specific context improves large language model performance on nuclear cardiology board preparation test

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Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology 2025年 102269页

作者： Waseem Hijazi Valerie Builoff Aditya Killekar Aakash Shanbhag Robert Jh Miller Damini Dey Joanna X Liang Kathleen Flood Daniel Berman Jamieson M Bourque Lawrence M Phillips Panithaya Chareonthaitawee Piotr J Slomka Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA USA. Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA USA Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA. Department of Cardiac Sciences University of Calgary Calgary AB Canada. American Society of Nuclear Cardiology Fairfax Virginia USA. Division of Cardiovascular Medicine and Radiology University of Virginia Health System Charlottesville VA USA. Leon H. Charney Division of Cardiology Department of Medicine NYU Grossman School of Medicine New York NY USA. Department of Cardiovascular Medicine Mayo Clinic Rochester MN USA. Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA USA. Electronic address: Piotr.Slomka@***.

Dividing-and-Kriging Method for Wireless RSS Fingerprint Based Indoor Localization

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Dividing-and-Kriging Method for Wireless RSS Fingerprint Bas...

IEEE Global Communications Conference

作者： Yue Li Yoshiaki Nishikawa Takahiro Nobukiyo Dept. of Electronic & Computer Engineering The Hong Kong University of Science and Technology Hong Kong China System Platform Research Laboratories NEC Corporation Kawasaki Japan Research Planning Division NEC Corporation Kawasaki Japan

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Received Signal Strength (RSS) fingerprints methods is an effective technique to localize a device at indoor environment. However, to construct a fine grained fingerprint database with lower facilities costs and shorter time, RSS estimations technique with limited number of measurements are necessary. In this paper, a Dividing-and-Kriging algorithm is proposed based on Kriging, a widely used spatial interpolation algorithm, to estimate RSS of unmeasured locations. The diversity of propagation model at indoor environment is concerned in this paper. The room is divided into several area and then Kriging method is conducted separately with different measured data. Experimental results in a working office show Dividing-and-Kriging method can achieve around 10%-20% accuracy improvement for RSS estimation.

关键词： Estimation Wireless sensor networks Wireless communication Databases Indoor environments Interpolation Data models

Super-resolution fluorescence polarization microscopy

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Journal of Innovative Optical Health Sciences 2018年第1期11卷 1-12页

作者： Karl Zhanghao Juntao Gao Dayong Jin Xuedian Zhang Peng Xi Department of Biomedical Engineering College of Engineering Peking UniversityP.R.China School of Optical-Electronic and Computer Engineering Shanghai University of Science and Technology P.R.China Department of Automation Tsinghua University Beijing 100084P.R.China Bioinfomatics Division TNLIST MOE Key Laboratory of Bioinformatics and Center for Synthetic&System Biology Tsinghua UniversityBeijing 100084P.R.China Faculty of Science Institute for Biomedical Materials and Devices(IBMD)University of TechnologyAustralia

Fluorescence polarization is related to the dipole orientation of chromophores,making fuores-cence polarization microscopy possible to_reveal structures and functions of tagged cellularorganelles and biological *** recent super resolution techniques have beenapplied to fluorescence polarization microscopy,achieving dipole measurement at *** this review,we summarize both difraction limited and super resolution fluorescence polari-zation microscopy techniques,as well as their applications in biological imaging.

关键词： Fluorescence polarization microscopy super resolution fluorescence anisotropy linear dichroism polarization modulation