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arXiv 2021年

作者： Luo, Luyang Chen, Hao Xiao, Yongjie Zhou, Yanning Wang, Xi Vardhanabhuti, Varut Wu, Mingxiang Han, Chu Liu, Zaiyi Fang, Xin Hao Benjamin Tsougenis, Efstratios Lin, Huangjing Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong AI Research Lab Imsight Technology Co. Ltd. Shenzhen China Department of Diagnostic Radiology Li Ka Shing Faculty of Medicine The University of Hong Kong Hong Kong Department of Radiology Shenzhen People's Hospital Shenzhen Luohu China Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Department of Radiology Queen Marry Hospital Hong Kong Artificial Intelligence Lab Head Office Information Technology and Health Informatics Division Hospital Authority Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Hong Kong

Purpose To evaluate the ability of fine-grained annotations to overcome shortcut learning in deep learning (DL)-based diagnosis using chest radiographs. Materials and Methods Two DL models were developed using radiograph-level annotations (yes or no disease) and fine-grained lesion-level annotations (lesion bounding boxes), respectively named CheXNet and CheXDet. A total of 34,501 chest radiographs from January 2005-September 2019 were retrospectively collected and annotated with cardiomegaly, pleural effusion, mass, nodule, pneumonia, pneumothorax, tuberculosis, fracture, and aortic calcification. The models' internal classification performance and lesion localization performance were compared on an testing set (n=2,922), external classification performance was compared on NIH-Google (n=4,376) and PadChest (n=24,536) datasets, and external lesion localization performance was compared on NIH-ChestX-ray14 dataset (n=880). The models were also compared to radiologists on a subset of the internal testing set (n=496). Performance was evaluated using receiver operating characteristic curve (ROC) analysis. Results Given sufficient training data, both models performed comparably to radiologists. CheXDet achieved significant improvement for external classification, such as in classifying fracture on NIH-Google (CheXDet area under the ROC curve [AUC]: 0.67, CheXNet AUC: 0.51;p © 2021, CC BY.

关键词： Classification (of information)

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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Low-Velocity-Favored Transition Radiation

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Physical Review Letters 2023年第11期131卷 113002-113002页

作者： Jialin Chen Ruoxi Chen Fuyang Tay Zheng Gong Hao Hu Yi Yang Xinyan Zhang Chan Wang Ido Kaminer Hongsheng Chen Baile Zhang Xiao Lin Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science & Electronic Engineering Zhejiang University Hangzhou 310027 China International Joint Innovation Center the Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa 32000 Israel Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore Department of Physics University of Hong Kong Hong Kong 999077 China Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua 321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing 312000 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore 637371 Singapore

When a charged particle penetrates through an optical interface, photon emissions emerge—a phenomenon known as transition radiation. Being paramount to fundamental physics, transition radiation has enabled many applications from high-energy particle identification to novel light sources. A rule of thumb in transition radiation is that the radiation intensity generally decreases with the decrease of particle velocity v; as a result, low-energy particles are not favored in practice. Here, we find that there exist situations where transition radiation from particles with extremely low velocities (e.g., v/c<10−3) exhibits comparable intensity as that from high-energy particles (e.g., v/c=0.999), where c is the light speed in free space. The comparable radiation intensity implies an extremely high photon extraction efficiency from low-energy particles, up to 8 orders of magnitude larger than that from high-energy particles. This exotic phenomenon of low-velocity-favored transition radiation originates from the interference of the excited Ferrell-Berreman modes in an ultrathin epsilon-near-zero slab. Our findings may provide a promising route toward the design of integrated light sources based on low-energy electrons and specialized detectors for beyond-standard-model particles.

关键词： Atom or molecule scattering from surfaces Atomic & molecular collisions Cherenkov radiation Crystal optics Electron beams & optics Light-matter interaction Optical sources & detectors Optical transient phenomena Plasmonics

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Kramers nodal lines in intercalated TaS2 superconductors

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Nature Communications 2025年第1期16卷 1-10页

作者： Yichen Zhang Yuxiang Gao Jianwei Huang Yucheng Guo Ziqin Yue Ji Seop Oh Junichiro Kono Emilia Morosan Ming Yi Aki Pulkkinen Alberto Marmodoro Ján Minár Xingyao Guo Xue-Jian Gao Kam Tuen Law Robert J. Birgeneau Alex Moon Luis Balicas Mohamed Oudah Alannah M. Hallas Alexei Fedorov Sung-Kwan Mo Makoto Hashimoto Donghui Lu Anil Rajapitamahuni Elio Vescovo Pavan Hosur Department of Physics and Astronomy Rice University Houston TX 77005 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX 77005 USA Department of Physics University of California Berkeley Berkeley CA 94720 USA Rice Center for Quantum Materials Rice University Houston TX 77005 USA Department of Electrical and Computer Engineering Rice University Houston TX 77005 USA Department of Materials Science and NanoEngineering Rice University Houston TX 77005 USA Smalley-Curl Institute Rice University Houston TX 77005 USA New Technologies Research Center University of West Bohemia Plzen 301 00 Czech Republic Department of Physics Hong Kong University of Science and Technology Clear Water Bay Hong Kong China Materials Science Division Lawrence Berkeley National Laboratory Berkeley Berkeley CA 94720 USA National High Magnetic Field Laboratory Tallahassee Tallahassee FL 32310 USA Physics Department Florida State University Tallahassee FL 32306 USA Stewart Blusson Quantum Matter Institute University of British Columbia Vancouver Vancouver BC V6T 1Z4 Canada Department of Physics & Astronomy University of British Columbia Vancouver Vancouver BC V6T 1Z1 Canada Canadian Institute for Advanced Research Toronto Toronto ON M5G 1M1 Canada Advanced Light Source Lawrence Berkeley National Laboratory Berkeley Berkeley CA 94720 USA Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park CA 94025 USA National Synchrotron Light Source II Brookhaven National Lab Upton NY 11973 USA Department of Physics and Texas Center for Superconductivity University of Houston Houston TX 77204 USA

Kramers degeneracy is one fundamental embodiment of the quantum mechanical nature of particles with half-integer spin under time reversal symmetry. Under the chiral and noncentrosymmetric achiral crystalline symmetries, Kramers degeneracy emerges respectively as topological quasiparticles of Weyl fermions and Kramers nodal lines (KNLs), anchoring the Berry phase-related physics of electrons. However, an experimental demonstration for ideal KNLs well isolated at the Fermi level is lacking. Here, we establish a class of noncentrosymmetric achiral intercalated transition metal dichalcogenide superconductors with large Ising-type spin-orbit coupling, represented by InxTaS2, to host an ideal KNL phase. We provide evidence from angle-resolved photoemission spectroscopy with spin resolution, angle-dependent quantum oscillation measurements, and ab-initio calculations. Our work not only provides a realistic platform for realizing and tuning KNLs in layered materials, but also paves the way for exploring the interplay between KNLs and superconductivity, as well as applications pertaining to spintronics, valleytronics, and nonlinear transport.

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Process simulation of Multi-layer and Multi-channel Selective Laser Melt of Al2O3 Ceramics Based on Temperature Field

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IOP Conference Series: Materials Science and engineering 2019年第3期688卷

作者： Xiaobing Chen Quan Sun Quanping Sun Haibing Wu Zhang Chang Key Lab of Advanced Manufacturing Technology of Jiangsu Province Huaiyin Institute of Technology Huai'an Jiangsu Province 223003 China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an Jiangsu Province 223003 China

Based on the temperature field theory and ANSYS software, the simulation model of Al2O3 ceramics selective laser melting (SLM) temperature field of multi-layer and multi-channel was established, and the analysis process of SLM temperature field simulation was designed. The temperature distribution of Al2O3 ceramics SLM temperature field and molten pool forming characteristics were researched. When the laser power is 105W, the scanning speed is 50mm/s and the thickness of powder layer is 0.25m, there is no remelting in adjacent molten channels in the same layer and multiple layers. Laser power and scanning speed are the main factors that whether remelting occurs in adjacent molten channels and multiple layers.

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Simulation on Molten Pool Characteristics of Al2O3 Laser Selective Melting Based on Energy Input Model

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IOP Conference Series: Materials Science and engineering 2020年第1期711卷

作者： Xiaobing Chen Quan Sun Quanping Sun Haibing Wu Qian Xu Key Lab of Advanced Manufacturing Technology of Jiangsu Province Huaiyin Institute of Technology Huai'an Jiangsu Province 223003 China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an Jiangsu Province 223003 China

In order to obtain the formation rule of molten pool characteristics in SLM process of ceramics, an energy input model for SLM process of Al2O3 ceramics is established. The model is related to laser power P, scanning speed v, scanning spacing s and thickness h of powder layer. The influence of process parameters in the energy model on molten pool characteristics is analyzed by simulation of temperature field of multi-layer and multi-channel SLM process. The simulation results show that when the laser power P is 105W, the scanning speed v is 50mm/s, the initial temperature of powder is 330°C and the thickness of powder layer is 0.20mm, the molten pool formed is easy to form Al2O3 ceramic specimens.

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All-Optical Signal Processing of Vortex Beams with Diffractive Deep Neural Networks

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Physical Review Applied 2021年第1期15卷 014037-014037页

作者： Zebin Huang Peipei Wang Junmin Liu Wenjie Xiong Yanliang He Jiangnan Xiao Huapeng Ye Ying Li Shuqing Chen Dianyuan Fan International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education Institute of Microscale Optoelectronics Shenzhen University Shenzhen 518060 China College of New Materials and New Energies Shenzhen Technology University Shenzhen 518118 China Terahertz Technology Innovation Research Institute Shanghai Key Lab of Modern Optical System Terahertz Science Cooperative Innovation Center School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays South China Academy of Advanced Optoelectronics South China Normal University Guangzhou 510006 China

Vortex beams (VBs), possessing a helical phase front and carrying orbital angular momentum (OAM), have attracted considerable attention in optical communications for their mode orthogonality. A platform for achieving all-optical signal processing of VBs, however, remains elusive due to the limited light-field-manipulation capability. We introduce diffractive deep neural networks (D2NNs) and their applications to process VBs. Exploiting the multiple-light-field-modulation ability of multilayer diffraction structures and the strong data-processing capability of deep neural networks, we reveal that D2NNs can manipulate multiple VBs by configuring the phase and amplitude distribution of diffractive screens. The diffraction efficiency and converted-mode purity are greater than 96%. After being trained, D2NNs with functions of hybrid-OAM-mode generation, identification, and conversion are obtained, and three typical types of all-optical signal-processing communication, (OAM-shift keying (OAM-SK), OAM multiplexing and demultiplexing, and OAM-mode switching) are successfully achieved. Our simulation results provide an approach that breaks the limitations of poor functionality and complex design in processing VBs, introducing the D2NN as a universal light-field-modulation platform.

关键词： Interference & diffraction of light Optical quantum information processing Optical vortices Quantum communication Artificial neural networks Multilayer thin films Machine learning Optical tweezers

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Spectral evidence for local-moment ferromagnetism in the van der Waals metals Fe3GaTe2 and Fe3GeTe2

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Physical Review B 2024年第10期109卷 104410-104410页

作者： Han Wu Chaowei Hu Yaofeng Xie Bo Gyu Jang Jianwei Huang Yucheng Guo Shan Wu Cheng Hu Ziqin Yue Yue Shi Rourav Basak Zheng Ren T. Yilmaz Elio Vescovo Chris Jozwiak Aaron Bostwick Eli Rotenberg Alexei Fedorov Jonathan D. Denlinger Christoph Klewe Padraic Shafer Donghui Lu Makoto Hashimoto Junichiro Kono Alex Frano Robert J. Birgeneau Xiaodong Xu Jian-Xin Zhu Pengcheng Dai Jiun-Haw Chu Ming Yi Department of Physics and Astronomy and Rice Center for Quantum Materials Rice University Houston Texas 77005 USA Department of Physics University of Washington Seattle Washington 98195 USA Department of Materials Science and Engineering University of Washington Seattle Washington 98195 USA Theoretical Division and Center for Integrated Nanotechnologies Los Alamos National Laboratory Los Alamos New Mexico USA Department of Advanced Materials Engineering for Information and Electronics Kyung Hee University Yongin 17104 Republic of Korea Department of Physics University of California at Berkeley Berkeley California 94720 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics University of California San Diego La Jolla California 92093USA National Synchrotron Light Source II Brookhaven National Lab Upton New York 11973 USA Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park California 94025 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Department of Material Science and NanoEngineering Rice University Houston Texas 77005 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Materials Science and Engineering University of California Berkeley CaliforniaUSA

Magnetism in two-dimensional (2D) materials has attracted considerable attention recently for both fundamental understanding of magnetism and its tunability towards device applications. The isostructural Fe3GeTe2 and Fe3GaTe2 are two members of the Fe-based van der Waals (vdW) ferromagnet family, but exhibit very different Curie temperatures (TC) of 210 and 360 K, respectively. Here, by using angle-resolved photoemission spectroscopy and density functional theory, we systematically compare the electronic structures of the two compounds. Qualitative similarities in the Fermi surface can be found between the two compounds, with expanded hole pockets in Fe3GaTe2 suggesting additional hole carriers compared to Fe3GeTe2. Interestingly, we observe almost no band shift in Fe3GaTe2 across its TC of 360 K, compared to a small shift in Fe3GeTe2 across its TC of 210 K. The weak temperature-dependent evolution strongly deviates from the expectations of an itinerant Stoner mechanism. Our results suggest that itinerant electrons have minimal contributions to the enhancement of TC in Fe3GaTe2 compared to Fe3GeTe2, and that the nature of ferromagnetism in these Fe-based vdW ferromagnets must be understood with considerations of the electron correlations.

关键词： Magnetism Van der Waals systems Angle-resolved photoemission spectroscopy

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Clean quantum point contacts in an InAs quantum well grown on a lattice-mismatched InP substrate

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Physical Review B 2022年第19期105卷 195303-195303页

作者： Connie L. Hsueh Praveen Sriram Tiantian Wang Candice Thomas Geoffrey Gardner Marc A. Kastner Michael J. Manfra David Goldhaber-Gordon Department of Applied Physics Stanford University Stanford California 94305 USA Stanford Institute for Materials and Energy Sciences SLAC National Accelerator Laboratory Menlo Park California 94025 USA Geballe Laboratory for Advanced Materials Stanford University Stanford California 94305 USA Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Birck Nanotechnology Center West Lafayette Indiana 47907 USA Microsoft Quantum Lab Purdue Purdue University West Lafayette Indiana 47907 USA Department of Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics Stanford University Stanford California 94305 USA School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA School of Materials Engineering Purdue University West Lafayette Indiana 47907 USA

Strong spin-orbit coupling, the resulting large g factor, and small effective mass make InAs an attractive material platform for inducing topological superconductivity. The surface Fermi level pinning in the conduction band enables highly transparent ohmic contact without excessive doping. We investigate electrostatically defined quantum point contacts (QPCs) in a deep-well InAs two-dimensional electron gas. Despite the 3.3% lattice mismatch between the InAs quantum well and the InP substrate, we report clean QPCs with up to eight pronounced quantized conductance plateaus at zero magnetic field. Source-drain dc bias spectroscopy reveals a harmonic confinement potential with a nearly 5 meV subband spacing. We find a many-body exchange interaction enhancement for the out-of-plane g factor |g⊥*|=27±1, whereas the in-plane g factor is isotropic |gx*|=|gy*|=12±2, close to the bulk value for InAs.

关键词： Ballistic transport Exchange interaction Magnetic anisotropy Magnetotransport Quantum transport III-V semiconductors Point contacts Two-dimensional electron gas Etching Evaporation Hall bar Liquid helium cooling Lithography Molecular beam epitaxy Transport techniques

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Constant depth fault-tolerant clifford circuits for multi-qubit large block codes

arXiv

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arXiv 2020年

作者： Zheng, Yi-Cong Lai, Ching-Yi Brun, Todd A. Kwek, Leong-Chuan Tencent Quantum Lab Tencent Shenzhen Guangdong518057 China Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore Yale-NUS College Singapore138527 Singapore Institute of Communications Engineering National Chiao Tung University Hsinchu30010 Taiwan Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore Institute of Advanced Studies Nanyang Technological University Singapore639673 Singapore National Institute of Education Nanyang Technological University Singapore637616 Singapore

Fault-tolerant quantum computation (FTQC) schemes using large block codes that encode k > 1 qubits in n physical qubits can potentially reduce the resource overhead to a great extent because of their high encoding rate. However, the fault-tolerant (FT) logical operations for the encoded qubits are difficult to find and implement, which usually takes not only a very large resource overhead but also long in-situ computation time. In this paper, we focus on Calderbank-Shor-Steane Jn;k;dK (CSS) codes and their logical FT Clifford circuits. We show that the depth of an arbitrary logical Clifford circuit can be implemented fault-tolerantly in O(1) steps in-situ via either Knill or Steane syndrome measurement circuit, with the qualified ancilla states efficiently prepared. Particularly, for those codes satisfying k/n ∼ Θ(1), the resource scaling for Clifford circuits implementation on the logical level can be the same as on the physical level up to a constant, which is independent of code distance d. With a suitable pipeline to produce ancilla states, our scheme requires only a modest resource cost in physical qubits, physical gates, and computation time for very large scale FTQC. Copyright © 2020, The Authors. All rights reserved.

关键词： Timing circuits

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