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Research Square 2021年

作者： dane, Andrew Allmaras, Jason Zhu, di Onen, Murat Colangelo, Marco Baghdadi, Reza Tambasco, Jean-luc Morimoto, Yukimi Forno, Ignacio Estay Charaev, Ilya Zhao, Qingyuan Skvortsov, Mikhail Kozorezov, Alexander Berggren, Karl Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Applied Physics and Materials Science California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Skolkovo Institute of Science and Technology Moscow143026 Russia L. D. Landau Institute for Theoretical Physics Moscow119334 Russia Department of Physics Lancaster University Lancaster United Kingdom

Controlling thermal transport is important for a range of devices and technologies, from phase change memories to next-generation electronics. This is especially true in nano-scale devices where thermal transport is altered by the influence of surfaces and changes in dimensionality. In superconducting nanowire single-photon detectors, the thermal boundary conductance (TBC) between the nanowire and the substrate it is fabricated on influences most of the performance metrics that make these detectors attractive for applications. This includes the maximum count rate, latency, jitter, and quantum efficiency. despite its importance, the study of TBC in superconducting nanowire devices has not been done systematically, primarily due to the lack of a straightforward characterization method. Here, we show that simple electrical measurements can be used to estimate the TBC between nanowires and substrates and that these measurements match acoustic mismatch theory across a variety of substrates. Numerical simulations allow us to refine our understanding, however, open questions remain. This work should enable thermal engineering in superconducting nanowire electronics and cryogenic detectors for improved device performance. © 2021, CC BY.

关键词： Nanowires

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An artificial intelligence accelerated ab initio molecular dynamics dataset for electrochemical interfaces

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Scientific data 2025年第1期12卷 997页

作者： Yong-Bin Zhuang Chang liu Jia-Xin Zhu Jin-Yuan Hu Jia-Bo le Jie-Qiong li Xiao-Jian Wen Xue-Ting Fan Mei Jia Xiang-Ying li Ao Chen lang li Zhi-li lin Wei-Hong Xu Jun Cheng State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM Department of Chemistry College of Chemistry & Chemical Engineering Xiamen University Xiamen 361005 China. yongbin.zhuang@epfl.ch. Chaire de Simulation à l'Echelle Atomique (CSEA) Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne CH-1015 Switzerland. yongbin.zhuang@epfl.ch. State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM Department of Chemistry College of Chemistry & Chemical Engineering Xiamen University Xiamen 361005 China. College of Chemistry and Chemical Engineering Shangqiu Normal University Shangqiu 476000 China. Department of Theory Fritz-Haber-Institut der Max-Planck-Gesellschaft Faradayweg 4-6 14195 Berlin Germany. Laboratory of AI for Electrochemistry (AI4EC) IKKEM Xiamen 361005 China. State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM Department of Chemistry College of Chemistry & Chemical Engineering Xiamen University Xiamen 361005 China. chengjun@***. Laboratory of AI for Electrochemistry (AI4EC) IKKEM Xiamen 361005 China. chengjun@***. Institute of Artificial Intelligence Xiamen University Xiamen 361005 China. chengjun@***.

Understanding atomic-scale structures at electrochemical interfaces is essential for advancing research and applications in electrochemistry. While experiments can provide detailed microscopic insights, their complexity and inefficiency often limit the large-scale generation of data. Complementing experimental approaches, computational methods, such as ab initio molecular dynamics and machine learning-accelerated molecular dynamics, offer an efficient means of obtaining microscopic information. However, despite these advancements, computational studies of interfaces have typically shared research data in isolation, often through private repositories. This practice has led to fragmented knowledge, reduced data accessibility, and limited opportunities for cross-study comparisons or large-scale meta-analyses. To overcome these challenges, we introduce ElectroFace, an artificial intelligence-accelerated ab initio molecular dynamics dataset for electrochemical interfaces. ElectroFace is designed to compile, visualize, and provide open access to interface data, fostering collaboration and accelerating progress in the field.

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Phyllotaxis-inspired nanosieves with multiplexed orbital angular momentum

arXiv

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arXiv 2021年

作者： Jin, Zhongwei Janoschka, david deng, Junhong Ge, lin dreher, Pascal Frank, Bettina Hu, Guangwei Ni, Jincheng Yang, Yuanjie li, Jing Yu, Changyuan lei, dangyuan li, Guixin Xiao, Shumin Mei, Shengtao Giessen, Harald zu Heringdorf, Frank Meyer Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore College of Optical and Electronic Technology China Jiliang University Hangzhou310018 China University of Duisburg–Essen Lotharstrasse 1-21 Duisburg47057 Germany Department of Materials Science and Engineering Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China Beijing Qianjunyide Technology Co. Ltd. Beijing100031 China University of Stuttgart StuttgartD-70569 Germany School of Physics University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Optoelectronic Materials Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing100190 China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Hong Kong Department of Materials Science and Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong Hong Kong State Key Laboratory on Tunable Laser Technology Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Shenzhen Graduate School Harbin Institute of Technology Shenzhen518055 China

Nanophotonic platforms such as metasurfaces, achieving arbitrary phase profiles within ultrathin thickness, emerge as miniaturized, ultracompact and kaleidoscopic optical vortex generators. However, it is often required to segment or interleave independent sub-array metasurfaces to multiplex optical vortices in a single nano-device, which in turn affects the device’s compactness and channel capacity. Here, inspired by phyllotaxis patterns in pine cones and sunflowers, we theoretically prove and experimentally report that multiple optical vortices can be produced in a single compact phyllotaxis nanosieve, both in free space and on a chip, where one meta-atom may contribute to many vortices simultaneously. The time-resolved dynamics of on-chip interference wavefronts between multiple plasmonic vortices was revealed by ultrafast time-resolved photoemission electron microscopy. Our nature-inspired optical vortex generator would facilitate various vortex-related optical applications, including structured wavefront shaping, free-space and plasmonic vortices, and high-capacity information metaphotonics. Copyright © 2021, The Authors. All rights reserved.

关键词： Plasmonics

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Three-dimensional modelling of the shock-turbulence interaction

arXiv

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arXiv 2023年

作者： Trotta, domenico Pezzi, Oreste Burgess, david Preisser, luis Blanco-Cano, Xochitl Kajdic, Primoz Hietala, Heli Horbury, Timothy S. Vainio, Rami dresing, Nina Retino', Alessandro Marcucci, Maria Federica Sorriso-Valvo, luca Servidio, Sergio Valentini, Francesco The Blackett Laboratory Department of Physics Imperial College London LondonSW7 2AZ United Kingdom Consiglio Nazionale delle Ricerche Via Amendola 122/D BariI-70126 Italy Department of Physics and Astronomy Queen Mary University of London E1 4NS United Kingdom Space Research Institute Austrian Academy of Sciences Graz Austria Departamento de Ciencias Espaciales Instituto de Geofísica Universidad Nacional Autónoma de México Ciudad Universitaria Ciudad de México Mexico Department of Physics and Astronomy University of Turku TurkuFI-20014 Finland LPP Ecole Polytechnique CNRS UPMC Université Paris Sud Palaiseau France INAF-Istituto di Astrofisica e Planetologia Spaziali Rome Italy Space and Plasma Physics School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Ångström Laboratory Lägerhyddsvägen 1 Uppsala75121 Sweden Dipartimento di Fisica Università della Calabria Rende87036 Italy

The complex interaction between shocks and plasma turbulence is extremely important to address crucial features of energy conversion in a broad range of astrophysical systems. We study the interaction between a supercritical, perpendicular shock and pre-existing, fully-developed plasma turbulence, employing a novel combination of magnetohydrodynamic (MHd) and small-scale, hybrid-kinetic simulations where a shock is propagating through a turbulent medium. The variability of the shock front in the unperturbed case and for two levels of upstream fluctuations is addressed. We find that the behaviour of shock ripples, i.e., shock surface fluctuations with short (a few ion skin depths, ) wavelengths, is modified by the presence of pre-existing turbulence, which also induces strong corrugations of the shock front at larger scales. We link this complex behaviour of the shock front and the shock downstream structuring with the proton temperature anisotropies produced in the shock-turbulence system. Finally, we put our modelling effort in the context of spacecraft observations, elucidating the role of novel cross-scale, multi-spacecraft measurements in resolving shock front irregularities at different scales. These results are relevant for a broad range of astrophysical systems characterised by the presence of shock waves interacting with plasma turbulence. © 2023, CC BY.

关键词： Shock waves

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deep Separable Spatiotemporal learning for Fast dynamic Cardiac MRI

arXiv

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arXiv 2024年

作者： Wang, Zi Xiao, Min Zhou, Yirong Wang, Chengyan Wu, Naiming li, Yi Gong, Yiwen Chang, Shufu Chen, Yinyin Zhu, liuhong Zhou, Jianjun Cai, Congbo Wang, He Guo, di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Institute of Artificial Intelligence Xiamen University China Human Phenome Institute Fudan University China Department of Imaging Xiamen Cardiovascular Hospital of Xiamen University School of Medicine Xiamen University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University Department of Medical Imaging Shanghai Medical School Shanghai Institute of Medical Imaging China Fujian Province Key Clinical Specialty Construction Project Medical Imaging Department Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering and Imperial-X Imperial College London United Kingdom Department of Bioengineering Imperial College London United Kingdom

dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data. We design this new approach by incorporating spatiotemporal priors into the development of a deep Separable Spatiotemporal learning network (deepSSl), which unrolls an iteration process of a 2d spatiotemporal reconstruction model with both temporal low-rankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability. Extensive results on cardiac cine datasets demonstrate that the proposed deepSSl surpasses state-of-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, deepSSl enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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CrossModA 2021 challenge: Benchmark of Cross-Modality domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

arXiv

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arXiv 2022年

作者： dorent, Reuben Kujawa, Aaron Ivory, Marina Bakas, Spyridon Rieke, Nicola Joutard, Samuel Glocker, Ben Cardoso, Jorge Modat, Marc Batmanghelich, Kayhan Belkov, Arseniy Calisto, Maria Baldeon Choi, Jae Won dawant, Benoit M. dong, Hexin Escalera, Sergio Fan, Yubo Hansen, lasse Heinrich, Mattias P. Joshi, Smriti Kashtanova, Victoriya Kim, Hyeon Gyu Kondo, Satoshi Kruse, Christian N. lai-Yuen, Susana K. li, Hao liu, Han ly, Buntheng Oguz, Ipek Shin, Hyungseob Shirokikh, Boris Su, Zixian Wang, Guotai Wu, Jianghao Xu, Yanwu Yao, Kai Zhang, li Ourselin, Sébastien Shapey, Jonathan Vercauteren, Tom School of Biomedical Engineering & Imaging Sciences King's College London London United Kingdom University of Pennsylvania Philadelphia United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States NVIDIA United States Department of Computing Imperial College London Department of Computing London United Kingdom School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Center for Data Science Peking University Beijing China Department of Radiology Armed Forces Yangju Hospital Yangju Korea Republic of Vanderbilt University Nashville United States University of Liverpool Liverpool United Kingdom School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou China Inria Université Côte d'Azur Sophia Antipolis France Department of Biomedical Informatics University of Pittsburgh Pittsburgh United States School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Universitat de Barcelona Barcelona Spain Institute of Medical Informatics Universität zu Lübeck Germany Universidad San Francisco de Quito Quito Ecuador University of South Florida Tampa United States Muroran Institute of Technology Muroran Japan Moscow Institute of Physics and Technology Moscow Russia Skolkovo Institute of Science and Technology Moscow Russia Moscow Russia Department of Neurosurgery King's College Hospital London United Kingdom

domain Adaptation (dA) has recently been of strong interest in the medical imaging community. While a large variety of dA techniques have been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality domain Adaptation (crossModA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI 2021). CrossModA is the first large and multi-class benchmark for unsupervised cross-modality domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT1) MR imaging. However, there is growing interest in using non-contrast imaging sequences such as high-resolution T2 (hrT2) imaging. For this reason, we established an unsupervised cross-modality segmentation benchmark. The training dataset provides annotated ceT1 scans (N=105) and unpaired non-annotated hrT2 scans (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 scans as provided in the testing set (N=137). This problem is particularly challenging given the large intensity distribution gap across the modalities and the small volume of the structures. A total of 55 teams from 16 countries submitted predictions to the validation leaderboard. Among them, 16 teams from 9 different countries submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median dice score-VS: 88.4%;Cochleas: 85.7%) and close to full supervision (median dice score-VS: 92.5%;Cochleas: 87.7%). All top-pe

关键词： Medical imaging

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lithography-free, planar IR polarization filters and converters via biaxial phonons in a-MoO3 flakes integrated into Fabry-Perot cavities

arXiv

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arXiv 2020年

作者： dereshgi, Sina Abedini Folland, Thomas G. Murthy, Akshay A. Song, Xianglian Tanriover, Ibrahim dravid, Vinayak P. Caldwell, Joshua d. Aydin, Koray Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Department of Physics and Astronomy University of Iowa Iowa CityIA52242 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States International Institute for Nanotechnology Northwestern University EvanstonIL60208 United States International Collaborative Laboratory of 2D Materials for Optoelectronic Science & Technology of Ministry of Education Engineering Technology Research Center for 2D material Information Function Devices and Systems of Guangdong Province College of Optoelectronic Engineering Shenzhen University Shenzhen518060 China Center Northwestern University EvanstonIL60208 United States

Exploiting polaritons in natural vdW materials has been successful in achieving extreme light confinement and low-loss optical interactions and enabling inherent simplified device integration. Recently, a-MoO3 has been reported as a semiconducting biaxial vdW material capable of sustaining naturally anisotropic phonon polariton modes in IR. In this study, we investigate the polarization-dependent optical characteristics of cavities formed using a-MoO3 to extend the degrees of freedom in the design of IR photonic components by tailoring the in-plane anisotropy of this material. Polarization-dependent absorption over 80% in a multilayer Fabry-Perot structure with a-MoO3 is reported without the need for nanoscale fabrication on the a-MoO3. We observe coupling between the a-MoO3 optical phonons and the Fabry-Perot cavity resonances. Using cross-polarized reflectance spectroscopy we show that the strong birefringence results in 15% of the total power converted into the orthogonal polarization with respect to incident wave. These findings can open new avenues in the quest for polarization filters and low-loss, integrated planar IR photonics and in dictating polarization control. Copyright © 2020, The Authors. All rights reserved.

关键词： Photons

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A blockchains security architecture for confidential applications

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AIP Conference Proceedings 2022年第1期2519卷

作者： Revathy Krishnamurthy K. deepa S. Alagumuthukrishnan A. Karthikram 1MEASI Institute of Information Technology Chennai Tamil Nadu India 2Sri Ramakrishna Engineering College Vattamalaipalayam Coimbatore Tamil Nadu India 3CMR Institute of Technology Hyderabad Telangana India 4Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

Blockchain technologies have been prominent due to their unique functionality. Blockchains are layered networks in which the secret to their effectiveness is stability. Blockchains were originally suggested as open cryptocurrencies but were still desirable for other implementations because of their features. A distributed consensus protocol offering resilience to errors provides Blockchains with decentralization. The members are generally equal, and there is no jurisdiction of any particular organization. The key goal of this paper is to systematize details on blockchains’ protection and privacy concerns. In this regard, we suggest a protection architecture based on models that display the stacked hierarchy of different threats and danger estimation. We are focused on categorizing protection vulnerabilities depending on their sources and the design suggested compared to the previous surveys. We are presenting current preventive and mitigation strategies. The focus of our work encompasses facets of the design of blockchains primarily, although only tangentially do we discuss organizational safety concerns and initiatives.

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Enriched Second-Harmonic Generation in Meta-Phase Barium Titanate Nanostructures Synthesized by Sol-Gel Hydrothermal Method

SSRN

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SSRN 2022年

作者： Mahalakshmi, Selvaraj Mayandi, Jeyanthinath Suresh, Sagadevan Venkatesan, Ragavendran Manikandan, K. Arumugam, Sonachalam Pearce, Joshua M. Venkatachalapathy, Vishnukanthan Department of Physics R.D. Govt. Arts College Tamilnadu Sivaganga630561 India Department of Material Science School of Chemistry Madurai Kamaraj University Tamilnadu Madurai625 021 India Nanotechnology and Catalysis Research Centre University of Malaya Kuala Lumpur50603 Malaysia Centre for High Pressure Research School of Physics Bharathidasan University Palkalaiperur Campus Tamilnadu Tiruchirappalli620 024 India Department of Electrical & Computer Engineering Western University LondonONN6A 3K7 Canada Department of Physics Centre for Materials Science and Nanotechnology University of Oslo P.O Box 1048 Blindern OsloNO-0316 Norway

This study reports on the second harmonic generation (SHG) of barium titanate (BaTiO 3 ) nanoparticles synthesized by a low-cost and easily-accessible sol-gel hydrothermal method at different temperatures (100-250 °C). According to the powder X-ray diffraction (XRd) pattern, all of the prepared samples had meta-phase without a centre of symmetry and the crystal structure changes from simple cubic to rhombohedral at different temperatures. In addition to XRd, differential scanning calorimetry (dSC) confirms the meta-phase in BaTiO 3 by showing a significant phase shift indicating structural distortion. The variation in particle size was observed from transmission electron microscope (TEM) images and the selected area electron diffraction (SAEd) pattern as well supports the presence of structural distortion in the nanomaterials. The phase variation in BaTiO 3 has not only affected its structure, but also creates a disorder in its magnetic properties by changing it from paramagnetic to ferromagnetic for different synthesizing temperatures. This was determined with a vibrating sample magnetometer (VSM) and electron paramagnetic resonance (EPR) techniques. As a consequence of this structural deformation, particle size variation, and magnetic phase transition the second harmonic generation (SHG) conversion efficiency of BaTiO 3 has been improved. Hence this technique for synthesizing nanomaterials could be a promising candidate for fabricating bio-compatible imaging probes and coherent bio-markers in the field of medicine and electronics. © 2022, The Authors. All rights reserved.

关键词： Harmonic generation

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An Application of Using Support Vector Machine Based on Classification Technique for Predicting Medical data Sets 15th

An Application of Using Support Vector Machine Based on Clas...

引用

15th International Conference on Intelligent Computing, ICIC 2019

作者： Khalaf, Mohammed Hussain, Abir Jaafar Alafandi, Omar Al-Jumeily, dhiya Alloghani, Mohamed Alsaadi, Mahmood dawood, Omar A. Abd, dhafar Hamed Department of Computer Science Al-Maarif University College RamadiAnbar31001 Iraq Faculty of Engineering and Technology Liverpool John Moores University Byrom Street LiverpoolL3 3AF United Kingdom College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Sultan Bin Zayed Street PO Box 109090 Abu Dhabi United Arab Emirates College of Computer Science and Information Technology University of Anbar Ramadi31001 Iraq

ISBN: (纸本)9783030269685

This paper illustrates the utilise of various kind of machine learning approaches based on support vector machines for classifying Sickle Cell disease data set. It has demonstrated that support vector machines generate an essential enhancement when applied for the pre-processing of clinical time-series data set. In this aspect, the objective of this study is to present discoveries for a number of classes of approaches for therapeutically associated problems in the purpose of acquiring high accuracy and performance. The primary case in this study includes classifying the dosage necessary for each patient individually. We applied a number of support vector machines to examine sickle cell data set based on the performance evaluation metrics. The result collected from a number of models have indicated that, support vector Classifier demonstrated inferior outcomes in comparison to Radial Basis Support Vector Classifier. For our Sickle cell data sets, it was found that the Parzen Kernel Support Vector Classifier produced the highest levels of performance and accuracy during training procedure accuracy 0.89733, AUC 0.94267. Where the testing set process, accuracy 0.81778, the area under the curve with 0.86556. © 2019, Springer Nature Switzerland AG.

关键词： Support vector machines

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