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Observation of Negative Terahertz Photoconductivity in Large Area Type-II Dirac Semimetal PtTe2

Physical Review Letters 2021年第22期126卷 227402-227402页

作者： Peng Suo Huiyun Zhang Shengnan Yan Wenjie Zhang Jibo Fu Xian Lin Song Hao Zuanming Jin Yuping Zhang Chao Zhang Feng Miao Shi-Jun Liang Guohong Ma Department of Physics Shanghai University Shanghai 200444 China College of Electronics and Information Engineering Shandong University of Science and Technology Qingdao 266590 China Institute of Brain-inspired Intelligence National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China Terahertz Technology Innovation Research Institute Terahertz Spectrum and Imaging Technology Cooperative Innovation Center Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology 516 JunGong Road Shanghai 200093 China STU & SIOM Joint Laboratory for Superintense Lasers and the Applications Shanghai 201210 China School of Physics University of Wollongong Wollongong New South Wales 2522 Australia

As a newly emergent type-II Dirac semimetal, platinum telluride (PtTe2) stands out from other two dimensional noble-transition-metal dichalcogenides for the unique band structure and novel physical properties, and has been studied extensively. However, the ultrafast response of low energy quasiparticle excitation in terahertz frequency remains nearly unexplored yet. Herein, we employ optical pump-terahertz probe (OPTP) spectroscopy to systematically study the photocarrier dynamics of PtTe2 thin films with varying pump fluence, temperature, and film thickness. Upon photoexcitation the terahertz photoconductivity (PC) of PtTe2 films shows abrupt increase initially, while the terahertz PC changes into negative value in a subpicosecond timescale, followed by a prolonged recovery process that lasted a few nanoseconds. The magnitude of both positive and negative terahertz PC response shows strongly pump fluence dependence. We assign the unusual negative terahertz PC to the formation of small polaron due to the strong electron-phonon (e−ph) coupling, which is further substantiated by temperature and film thickness dependent measurements. Moreover, our investigations give a subpicosecond timescale of simultaneous carrier cooling and polaron formation. The present study provides deep insights into the underlying dynamics evolution mechanisms of photocarrier in type-II Dirac semimetal upon photoexcitation, which is of crucial importance for designing PtTe2-based optoelectronic devices.

关键词： Photoinduced effect Polarons

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Predictability of real temporal networks

arXiv

引用

arXiv 2020年

作者： Tang, Disheng Du, Wenbo Shekhtman, Louis Wang, Yijie Havlin, Shlomo Cao, Xianbin Yan, Gang School of Electronic and Information Engineering Beihang University Beijing100191 China School of Physics Science and Engineering Tongji University Shanghai200092 China National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation Beijing100191 China Network Science Institute Northeastern University BostonMA02115 United States Department of Physics Bar Ilan University Ramat Gan5290002 Israel Shanghai Institute of Intelligence Science and Technology Tongji University Shanghai200092 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and function. Empirical evidence has shown that the temporal nature of links in many real-world networks is not random. Nonetheless, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here, we propose an entropy-rate-based framework, based on combined topological–temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological–temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly, we find that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combined topological–temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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Cataloguing MoSi2N4and WSi2N4van der Waals Heterostructures: An Exceptional Material Platform for Excitonic Solar Cell applications

arXiv

引用

arXiv 2022年

作者： Tho, Che Chen Yu, Chenjiang Tang, Qing Wang, Qianqian Su, Tong Feng, Zhuoer Wu, Qingyun Nguyen, C.V. Ong, Wee-Liat Liang, Shi-Jun Guo, San-Dong Cao, Liemao Zhang, Shengli Yang, Shengyuan A. Ang, Lay Kee Wang, Guangzhao Ang, Yee Sin Science Mathematics and Technology Cluster Singapore University of Technology and Design 487372 Singapore School of Electronic Information Engineering Key Laboratory of Extraordinary Bond Engineering and Advanced Materials Technology of Chongqing Yangtze Normal University Chongqing408100 China Department of Materials Science and Engineering Le Quy Don Technical University Hanoi100000 Viet Nam ZJU-UIUC Institute College of Energy Engineering Zhejiang University Haining Zhejiang Jiaxing314400 China State Key Laboratory of Clean Energy Utilization Zhejiang University Zhejiang Hangzhou310027 China Institute of Brain-Inspired Intelligence National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China School of Electronic Engineering Xi'an University of Posts and Telecommunications Xi’an710121 China College of Physics and Electronic Engineering Hengyang Normal University Hengyang421002 China MIIT Key Laboratory of Advanced Display Materials and Devices School of Materials Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Two-dimensional (2D) materials van der Waals heterostructures (vdWHs) provides a revolutionary route towards high-performance solar energy conversion devices beyond the conventional silicon-based pn junction solar cells. Despite tremendous research progress accomplished in recent years, the searches of vdWHs with exceptional excitonic solar cell conversion efficiency and optical properties remain an open theoretical and experimental quest. Here we show that the vdWH family composed of MoSi2N4and WSi2N4monolayers provides a compelling material platform for developing high-performance ultrathin excitonic solar cells and photonics devices. Using first-principle calculations, we construct and classify 51 types of MoSi2N4and WSi2N4-based [(Mo,W)Si2N4] vdWHs composed of various metallic, semimetallic, semiconducting, insulating and topological 2D materials. Intriguingly, MoSi2N4/(InSe, WSe2) are identified as Type-II vdWHs with exceptional excitonic solar cell power conversion efficiency reaching well over 20%, which are competitive to state-of-art silicon solar cells. The (Mo,W)Si2N4vdWH family exhibits strong optical absorption in both the visible and ultraviolet regimes. Exceedingly large peak ultraviolet absorptions over 40%, approaching the maximum absorption limit of a free-standing 2D material, can be achieved in (Mo,W)Si2N4/α2-(Mo,W)Ge2P4vdWHs. Our findings unravel the enormous potential of (Mo,W)Si2N4vdWHs in designing ultimately compact excitonic solar cell device technology. Copyright © 2022, The Authors. All rights reserved.

关键词： Density functional theory

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Current Progress and Challenges in Large-scale 3D Mitochondria Instance Segmentation

TechRxiv

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TechRxiv 2023年

作者： Franco-Barranco, Daniel Lin, Zudi Jang, Won-Dong Wang, Xueying Shen, Qijia Yin, Wenjie Fan, Yutian Li, Mingxing Chen, Chang Xiong, Zhiwei Xin, Rui Liu, Hao Chen, Huai Li, Zhili Zhao, Jie Chen, Xuejin Pape, Constantin Conrad, Ryan De Folter, Jozefus Nightingale, Luke Jones, Martin L. Liu, Yanling Ziaei, Dorsa Huschauer, Stephan Arganda-Carreras, Ignacio Pfister, Hanspeter Wei, Donglai The Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain San Sebastian Spain Ikerbasque Basque Foundation for Science Bilbao Spain Biofisika Institute CSIC UPV/EHU Bilbao Spain Harvard University All-ston MA United States The Department of Molecular and Cellular Biology Harvard University CambridgeMA United States The Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom University of Science and Technology of China Anhui China The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China The National Engineering Laboratory for Brain-inspired Intelligence Technology and Application University of Science and Technology of China Anhui China The Georg-August University Goettingen Germany The Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda United States The Cancer Research Technology Program Frederick National Laboratory for Cancer Research Frederick United States The Francis Crick Institute London United Kingdom The Advanced Biomedical Computational Science Group Frederick National Laboratory for Cancer Research FrederickMD United States The Computer Science Department Boston College Chestnut Hill MA United States

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 3,600 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that (1) the challenge metric was permissive with the false positive predictions and (2) the size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download. © 2023, CC BY.

关键词： Electron microscopy

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Two-dimensional materials for future information technology: status and prospects

引用

Science China(Information Sciences) 2024年第6期67卷 1-147页

作者： Hao QIU Zhihao YU Tiange ZHAO Qi ZHANG Mingsheng XU Peifeng LI Taotao LI Wenzhong BAO Yang CHAI Shula CHEN Yiqi CHEN Hui-Ming CHENG Daoxin DAI Zengfeng DI Zhuo DONG Xidong DUAN Yuhan FENG Yu FU Jingshu GUO Pengwen GUO Yue HAO Jun HE Xiao HE Jingyi HU Weida HU Zehua HU Xinyue HUANG Ziyang HUANG Ali IMRAN Ziqiang KONG Jia LI Qian LI Weisheng LI Lei LIAO Bilu LIU Can LIU Chunsen LIU Guanyu LIU Kaihui LIU Liwei LIU Sheng LIU Yuan LIU Donglin LU Likuan MA Feng MIAO Zhenhua NI Jing NING Anlian PAN Tian-Ling REN Haowen SHU Litao SUN Yue SUN Quanyang TAO Zi-Ao TIAN Dong WANG Hao WANG Haomin WANG Jialong WANG Junyong WANG Wenhui WANG Xingjun WANG Yeliang WANG Yuwei WANG Zhenyu WANG Yao WE... Department of Applied Physics The Hong Kong Polytechnic University Key Laboratory for Micro-Nano Physics and Technology of Hunan Province College of Materials Science and EngineeringHunan University College of Integrated Circuits Zhejiang University Shenzhen Geim Graphene Center Shenzhen Key Laboratory of Layered Materials for Value-Added ApplicationsTsinghua-Berkeley Shenzhen Institute & Institute of Materials ResearchTsinghua Shenzhen International Graduate SchoolTsinghua University State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZijingang CampusZhejiang University National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences CAS Key Laboratory of Nanophotonic Materials and Devices & Key Laboratory of Nanodevices and Applications i-LabSuzhou Institute of Nano-Tech and Nano-Bionics (SINANO)Chinese Academy of Sciences College of Chemistry and Chemical Engineering Hunan University Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education) Department of PhysicsRenmin University of China School of Integrated Circuits Beijing National Research Center for Information Science and Technology (BNRist)Tsinghua University School of Electronic Science and Engineering Nanjing University The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology Shaanxi Joint Key Laboratory of GrapheneSchool of MicroelectronicsXidian University Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education School of Physics and TechnologyWuhan University School of Integrated Circuits Huazhong University of Science and Technology Center for Nanochemistry (CNC) Beijing Science and Engineering Center for NanocarbonsBeijing National Laboratory for Molecular SciencesCollege of Chemistry and Molecular EngineeringPeking University State Key Laboratory for Artificial Microstruct

Over the past 70 years, the semiconductor industry has undergone transformative changes,largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional(2D) materials like transition metal dichalcogenides(TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

关键词： two-dimensional (2D) materials information technology production technology standardization of characterization microelectronic devices optoelectronic devices multi-functional integration

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Comprehensive histological imaging of native microbiota in human glioma

Research Square

引用

Research Square 2021年

作者： Zhao, Jiajia He, Dian Lai, Hei Ming Xu, Yingying Luo, Yunhao Li, Ting Liang, Jianhao Yang, Xiaodu Guo, Linlang Ke, Yiquan Zhou, Hongwei Wu, Wutian Guo, Hongbo Sun, Haitao Microbiome Medicine Center Clinical Biobank Center Department of Laboratory Medicine Zhujiang Hospital Southern Medical University Guangzhou510280 China Neurosurgery Center The National Key Clinical Specialty The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration The Neurosurgery Institute of Guangdong Province Zhujiang Hospital Southern Medical University Guangzhou510280 China Department of Psychiatry Faculty of Medicine The Chinese University of Hong Kong Shatin Hong Kong School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou510515 China Department of Pathology Zhujiang Hospital Southern Medical University Guangzhou510280 China Jinan University Guangzhou510632 China Re-Stem Biotechnology Co. Ltd Suzhou215300 China Key Laboratory of Mental Health of the Ministry of Education Guangdong–Hong Kong–Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence Southern Medical University Guangzhou510515 China

Mounting evidence suggests that distinct microbial communities reside in tumors and play important roles in tumor physiology. Recently, Nejman et al. profiled the composition and localization of intratumoral bacteria using 16S DNA sequencing and histological visualization methods across seven tumor types, including human glioblastoma. However, considering potential contamination in their sample origins and processing, the results based on traditional histological methods need to be validated. Here, we propose a three-dimensional (3D) intratumoral microbiota visualization and quantification protocol to observe microbiota in intact tumor tissues on the premise of avoiding possible contamination in the surface of tissues, based on tissue clearing, immunofluorescent labeling, microscopy imaging, and image processing. For the first time, we have achieved 3D quantitative imaging of bacterial LPS fluorescent signals deep in gliomas in a contamination-free manner, which was founded mostly localized near nuclear membranes or in the intercellular space. Through an automated statistical algorithm, reliable signals can be distinguished for further analysis of their sizes, distribution, and fluorescence intensities. Combining two-dimensional images from multiple thin-section histological methods, including immunochemistry and fluorescence in situ hybridization, we provide a comprehensive histological investigation of the morphology and distribution of these signals on human glioma samples. We expect that this multi-evidence chain will provide supporting proof for the presence of intratumoral bacteria in human glioma and that the integrated pipeline can be applied to investigate the native bacteria within diverse tumors and contribute to the interpretation of their direct roles in the tumor microenvironment. © 2021, CC BY.

关键词： Tumors

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LAENet: Light-weight asymmetric encoder-decoder network for semantic segmentation

引用

Journal of Physics: Conference Series 2021年第1期1966卷

作者： Liangyi Hong Shukai Duan Lidan Wang Yongbin Pan College of Electronic and Information Engineering Southwest University Chongqing China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing China Chongqing Brain Science Collaborative Innovation Center Chongqing China College of Artificial Intelligence Southwest University Chongqing China

Encode-decoder structure is used in deep learning for real-time dense segmentation task. On account of the limitation of calculation burden on mobile devices, we present a light-weight asymmetric encoder-decoder network in this paper, namely LAENet, which quickly and efficiently accomplish the task of real-time semantic segmentation. We employ an asymmetric convolution and group convolution structure combined with dilated convolution and dense connectivity to reduce computation cost and model size, which can guarantee adequate receptive field and enhance the model learning ability in encoder. On the other hand, feature pyramid networks (FPN) structure combine attention mechanism and ECRE block are utilized in the decoder to strike a balance between the network complexity and segmentation performance. Our approach achieves only have 0.84M parameters, and is able to reach 66 FPS in a single GTX 1080Ti GPU. Experiments on Cityscapes datasets demonstrate that superior performance of LAENet is better than the existing segmentation network, in terms of speed and accuracy trade-off without any post-processing.

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Interfacial magnetic spin Hall effect in van der Waals Fe3GeTe2/MoTe2 heterostructure

arXiv

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arXiv 2024年

作者： Dai, Yudi Xiong, Junlin Ge, Yanfeng Cheng, Bin Wang, Lizheng Wang, Pengfei Liu, Zenglin Yan, Shengnan Zhang, Cuiwei Xu, Xianghan Shi, Youguo Cheong, Sang-Wook Xiao, Cong Yang, Shengyuan A. Liang, Shi-Jun Miao, Feng National Laboratory of Solid State Microstructures Institute of Brain-Inspired Intelligence School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Research Laboratory for Quantum Materials Singapore University of Technology and Design Singapore Singapore Institute of Interdisciplinary Physical Sciences School of Science Nanjing University of Science and Technology Nanjing210094 China Institute of Physics Chinese Academy of Sciences Beijing100190 China Center for Quantum Materials Synthesis Department of Physics and Astronomy Rutgers The State University of New Jersey PiscatawayNJ08854 United States Institute of Applied Physics and Materials Engineering University of Macau Taipa Macau China Department of Physics University of Hong Kong Hong Kong Hong Kong HKU-UCAS Joint Institute of Theoretical and Computational Physics at Hong Kong Hong Kong Hong Kong

The spin Hall effect (SHE) allows efficient generation of spin polarization or spin current through charge current and plays a crucial role in the development of spintronics. While SHE typically occurs in non-magnetic materials and is time-reversal even, exploring time-reversal-odd (T-odd) SHE, which couples SHE to magnetization in ferromagnetic materials, offers a new charge-spin conversion mechanism with new functionalities. Here, we report the observation of giant Todd SHE in Fe3GeTe2/MoTe2 van der Waals heterostructure, representing a previously unidentified interfacial magnetic spin Hall effect (interfacial-MSHE). Through rigorous symmetry analysis and theoretical calculations, we attribute the interfacial-MSHE to a symmetry-breaking induced spin current dipole at the vdW interface. Furthermore, we show that this linear effect can be used for implementing multiply-accumulate operations and binary convolutional neural networks with cascaded multi-terminal devices. Our findings uncover an interfacial T-odd charge-spin conversion mechanism with promising potential for energy-efficient in-memory computing. © 2024, CC BY-NC-SA.

关键词： Spin Hall effect

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Robust correlation filter that combined temporal regularization with spatial weight L1 constraint

引用

Journal of Physics: Conference Series 2020年第4期1550卷

作者： Xin He Shukai Duan Lidan Wang College of Electronic and Information Engineering Southwest University Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China

The parameter optimization of the Object tracking algorithms has achieved excellent performance based on parameter regularization. Adaptive spatial regularization correlation filter can obtain the optimal weight coefficient for a specific target. inspired by this, we propose a filter combined temporal regularity with spatial L1 regularization (LTASRCF). The time regularizer is used to remember the data of the previous frame to update the current target position. It performs well in the process of occlusion, deformation and motion blur. Meanwhile, L1 regularization of spatial weights reduces the computational complexity of the coefficients, and improves the robustness of the spatial weight matrix. We have conducted comparative experiments on the OTB-2015 dataset, and the proposed LTASRCF model ranks first among the five popular algorithms, which outperforms by 9.2% and 18.9% than KCF, respectively, and reaches real-time performance.

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Car Plate Detection Based on Yolov3

引用

Journal of Physics: Conference Series 2020年第1期1544卷

作者： Siyuan Shen Lidan Wang Shukai Duan Xin He College of Electronic and Information Engineering Southwest University Chongqing 400715 China Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China College of Artificial Intelligence Southwest University Chongqing 400715 China

An intelligent car plate detection method can make the travel more convenient and efficient. However, traditional methods are reasonably effective under the specific circumstances or strong assumptions only, and there are few databases for car plate detection. Therefore, a novel real-time car plate detection method based on improved Yolov3 has been proposed. In order to select the more precise number of candidate anchor boxed and aspect ratio dimensions, the K-Means algorithm is utilized. To solve the short of the available car plate database, a car plate database which has 6668 pictures has been established. As shown in the experimental results, the method which is proposed by this paper is better than original Yolov3. Thanks to the car plate database, the proposed method obtained better results even in the situation of inclination, too bright or too dark, different weather and so on.

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