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Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Demodulation method combining virtual reference interferometry and minimum mean square error for fiber-optic Fabry–Perot sensors

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Chinese Optics Letters 2018年第1期16卷 30-33页

作者：桂新旺 Michael Anthony Galle 钱黎梁伟龙周次明欧艺文范典 National Engineering Laboratory for Fiber Optic Sensing Technology Wuhan University of Technology Key Laboratory of Fiber Optic Sensing Technology and Information Processing Ministry of EducationWuhan University of Technology Department of Electrical and Computer Engineering University of Toronto

We propose a cavity length demodulation method that combines virtual reference interferometry（VRI） and minimum mean square error（MMSE） algorithm for fiber-optic Fabry–Perot（F-P） sensors. In contrast to the conventional demodulating method that uses fast Fourier transform（FFT） for cavity length estimation,our method employs the VRI technique to obtain a raw cavity length, which is further refined by the MMSE algorithm. As an experimental demonstration, a fiber-optic F-P sensor based on a sapphire wafer is fabricated for temperature sensing. The VRI-MMSE method is employed to interrogate cavity lengths of the sensor under different temperatures ranging from 28°C to 1000°C. It eliminates the ＂mode jumping＂ problem in the FFT-MMSE method and obtains a precision of 4.8 nm, corresponding to a temperature resolution of 2.0°C over a range of 1000°C. The experimental results reveal that the proposed method provides a promising, high precision alternative for demodulating fiber-optic F-P sensors.

关键词： VRI Demodulation method combining virtual reference interferometry and minimum mean square error for fiber-optic Fabry Perot sensors

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Characterization of laser wakefield acceleration efficiency with octave spanning near-IR spectrum measurements

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Physical Review Accelerators and Beams 2022年第10期25卷 101302-101302页

作者： M. J. V. Streeter Y. Ma B. Kettle S. J. D. Dann E. Gerstmayr F. Albert N. Bourgeois S. Cipiccia J. M. Cole I. Gallardo González A. E. Hussein D. A. Jaroszynski K. Falk K. Krushelnick N. Lemos N. C. Lopes C. Lumsdon O. Lundh S. P. D. Mangles Z. Najmudin P. P. Rajeev R. Sandberg M. Shahzad M. Smid R. Spesyvtsev D. R. Symes G. Vieux A. G. R. Thomas The Cockcroft Institute Keckwick Lane Daresbury WA4 4AD United Kingdom Physics Department Lancaster University Lancaster LA1 4YB United Kingdom The John Adams Institute for Accelerator Science Imperial College London London SW7 2AZ United Kingdom School of Mathematics and Physics Queen’s University Belfast Belfast BT7 1NN United Kingdom Center for Ultrafast Optical Science University of Michigan Ann Arbor Michigan 48109-2099 USA Lawrence Livermore National Laboratory (LLNL) P.O. Box 808 Livermore California 94550 USA Central Laser Facility STFC Rutherford Appleton Laboratory Didcot OX11 0QX United Kingdom Diamond Light Source Harwell Science and Innovation Campus Fermi Avenue Didcot OX11 0DE United Kingdom Department of Physics Lund University P.O. Box 118 S-22100 Lund Sweden Department of Electrical and Computer Engineering University of Alberta 9211 116 Street NW Edmonton Alberta T6G 1H9 Canada SUPA Department of Physics University of Strathclyde Glasgow G4 0NG United Kingdom Helmholtz-Zentrum Dresden-Rossendorf Bautzner Landstrasse 400 01328 Dresden Germany Technische Universitat Dresden 01062 Dresden Germany Institute of Physics of the ASCR 182 21 Prague Czech Republic GoLP/Instituto de Plasmas e Fusão Nuclear Instituto Superior Técnico Universidade de Lisboa Lisboa 1049-001 Portugal York Plasma Institute Department of Physics University of York York YO10 5DD United Kingdom

We report on experimental measurements of energy transfer efficiencies in a GeV-class laser wakefield accelerator. Both the transfer of energy from the laser to the plasma wakefield and from the plasma to the accelerated electron beam was diagnosed by simultaneous measurement of the deceleration of laser photons and the acceleration of electrons as a function of plasma length. The extraction efficiency, which we define as the ratio of the energy gained by the electron beam to the energy lost by the self-guided laser mode, was maximized at 19±3% by tuning the plasma density and length. The additional information provided by the octave-spanning laser spectrum measurement allows for independent optimization of the plasma efficiency terms, which is required for the key goal of improving the overall efficiency of laser wakefield accelerators.

关键词： High intensity laser-plasma interactions Laser light absorption in plasmas Laser wakefield acceleration Laser-plasma interactions Particle acceleration in plasmas plasma acceleration & new acceleration techniques plasma-beam interactions Self-focusing & filamentation in plasmas

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Beamforming Design and Association Scheme for Multi-RIS Multi-User mmWave Systems Through Graph Neural Networks

arXiv

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arXiv 2025年

作者： Liu, Mengbing Huang, Chongwen Alhammadi, Ahmed Di Renzo, Marco Debbah, Mérouane Yuen, Chau School of Electrical and Electronics Engineering Nanyang Technological University 639798 Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France King’s College London Centre for Telecommunications Research Department of Engineering LondonWC2R 2LS United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Reconfigurable intelligent surface (RIS) is emerging as a promising technology for next-generation wireless communication networks, offering a variety of merits such as the ability to tailor the communication environment. Moreover, deploying multiple RISs helps mitigate severe signal blocking between the base station (BS) and users, providing a practical and efficient solution to enhance the service coverage. However, fully reaping the potential of a multi-RIS aided communication system requires solving a non-convex optimization problem. This challenge motivates the adoption of learning-based methods for determining the optimal policy. In this paper, we introduce a novel heterogeneous graph neural network (GNN) to effectively leverage the graph topology of a wireless communication environment. Specifically, we design an association scheme that selects a suitable RIS for each user. Then, we maximize the weighted sum rate (WSR) of all the users by iteratively optimizing the RIS association scheme, and beamforming designs until the considered heterogeneous GNN converges. Based on the proposed approach, each user is associated with the best RIS, which is shown to significantly improve the system capacity in multi-RIS multiuser millimeter wave (mmWave) communications. Specifically, simulation results demonstrate that the proposed heterogeneous GNN closely approaches the performance of the high-complexity alternating optimization (AO) algorithm in the considered multi-RIS aided communication system, and it outperforms other benchmark schemes. Moreover, the performance improvement achieved through the RIS association scheme is shown to be of the order of 30%. Copyright © 2025, The Authors. All rights reserved.

关键词： Beamforming

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Ai driven heterogeneous MEC system for dynamic environment - Challenges and solutions

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource schEduling (ARE) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Correction to: Path-based estimation for link prediction

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International Journal of Machine Learning and Cybernetics 2021年第9期12卷 2459-2459页

作者： Ma, Guoshuai Yan, Hongren Qian, Yuhua Wang, Lingfeng Dang, Chuangyin Zhao, Zhongying Institute of Big Data Science and Industry Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee Milwaukee USA Department of Manufacture Engineering and Engineering Management City University of Hong Kong Kowloon Tong Hong Kong The School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

A correction to this paper has been published: https://***/10.1007/s13042-021-01350-4

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Outside Front Cover: plasma Process. Polym. 11/2021

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plasma Processes and Polymers 2021年第11期18卷

作者： Dingxin Liu Zifeng Wang Zeyu Chen Dehui Xu Min Chen Jinkun Chen Mengying Zhu Hao Zhang Xiaohua Wang Michael G. Kong Mingzhe Rong State Key Laboratory of Electrical Insulation and Power Equipment Center for Plasma Biomedicine Xi'an Jiaotong University Xi'an China National Key Laboratory of Science and Technology on Space Microwave Xi'an China Frank Reidy Center for Bioelectrics Old Dominion University Norfolk Virginia USA Department of Electrical and Computer Engineering Old Dominion University Norfolk Virginia USA

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Publisher Correction: Twisted moiré conductive thermal metasurface

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Nature communications 2024年第1期15卷 2876页

作者： Huagen Li Dong Wang Guoqiang Xu Kaipeng Liu Tan Zhang Jiaxin Li Guangming Tao Shuihua Yang Yanghua Lu Run Hu Shisheng Lin Ying Li Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore. State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China. International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining 314400 China. Wuhan National Laboratory for Optoelectronics and State Key Laboratory of Material Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China. Smart Materials for Architecture Research Lab Innovation Center of Yangtze River Delta Zhejiang University Jiaxing 314100 China. State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan 430074 China. Chongqing 2D Materials Institute Chongqing 400015 China. State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou 310027 China. eleying@***. International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy of Zhejiang University Zhejiang University Haining 314400 China. eleying@***. Department of Electrical and Computer Engineering National University of Singapore Kent Ridge 117583 Republic of Singapore. chengwei.qiu@nus.edu.sg.

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TransMut: a program to predict HLA-I peptide binding and optimize mutated peptides for vaccine design by the Transformer-derived self-attention model

Research Square

引用

Research Square 2021年

作者： Chu, Yanyi Zhang, Yan Wang, Qiankun Zhang, Lingfeng Wang, Xuhong Wang, Yanjing Wang, Jianmin Jiang, Xue Salahub, Dennis Russell Xiong, Yi Wei, Dong-Qing State Key Laboratory of Microbial Metabolism Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances Joint International Research Laboratory of Metabolic & Developmental Sciences School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200030 China Department of Chemistry CMS - Centre for Molecular Simulation IQST - Institute for Quantum Science and Technology University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada Department of Clinical Oncology The University of Hong Kong-Shenzhen Hospital Shenzhen China School of Electrical Engineering and Computer Science University of Ottawa 75 Laurier Ave. OttawaONK1N 6N5 Canada The Ministry of Education Key Laboratory of System control and information processing Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200030 China Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of Peng Cheng Laboratory Vanke Cloud City Phase I Building 8 Xili Street Nanshan District Guangdong Shenzhen518055 China

Computational prediction of the interaction between human leukocyte antigen (HLA) and peptide (pHLA) can speed up epitope screening and vaccine design. Here, we develop the TransMut framework composed of TransPHLA for pHLA binding prediction and AOMP for mutated peptide optimization, which can be generalized to any binding and mutation task of biomolecules. Firstly, TransPHLA is developed by using a Transformer-derived self-attention model to predict pHLA binding, which is significantly superior to 11 previous methods on pHLA binding prediction, neoantigen and human papilloma virus vaccine identification. For vaccine design, the AOMP program is then developed to automatically optimize mutated peptides to search for mutant peptides with higher affinity to the target HLA and with high homology to the source peptide. Among 3660 non-binding pHLAs, 3630 were successfully mutated. Of these, 94% were verified by the IEDB recommended method, and 88% have homology higher than 80% to the source peptide. © 2021, CC BY.

关键词： Peptides

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Non-Maxwellianity of electron distributions near Earth’s magnetopause

arXiv

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arXiv 2021年

作者： Graham, Daniel B. Khotyaintsev, Yu V. André, M. Vaivads, A. Chasapis, A. Matthaeus, W.H. Retino, A. Valentini, F. Gershman, D.J. Swedish Institute of Space Physics Uppsala Sweden Space and Plasma Physics School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Laboratory of Atmospheric and Space Physics University of Colorado BoulderCO United States Department of Physics and Astronomy University of Delaware NewarkDE United States Laboratoire de Physique des Plasmas CNRS Ecole Polytechnique Sorbonne Université Université Paris-Sud Observatoire de Paris Paris France Dipartimento di Fisica Università della Calabria Arcavacata di Rende Italy NASA Goddard Space Flight Center GreenbeltMD United States Department of Astronomy University of Maryland College ParkMD United States

plasmas in Earth’s outer magnetosphere, magnetosheath, and solar wind are essentially collisionless. This means particle distributions are not typically in thermodynamic equilibrium and deviate significantly from Maxwellian distributions. The deviations of these distributions can be further enhanced by plasma processes, such as shocks, turbulence, and magnetic reconnection. Such distributions can be unstable to a wide variety of kinetic plasma instabilities, which in turn modify the electron distributions. In this paper the deviations of the observed electron distributions from a bi-Maxwellian distribution function is calculated and quantified using data from the Magnetospheric Multiscale (MMS) spacecraft. A statistical study from tens of millions of electron distributions shows that the primary source of the observed non-Maxwellianity are electron distributions consisting of distinct hot and cold components in Earth’s low-density magnetosphere. This results in large non-Maxwellianities in at low densities. However, after performing a stastical study we find regions where large non-Maxwellianities are observed for a given density. Highly non-Maxwellian distributions are routinely found are Earth’s bowshock, in Earth’s outer magnetosphere, and in the electron diffusion regions of magnetic reconnection. Enhanced non-Maxwellianities are observed in the turbulent magnetosheath, but are intermittent and are not correlated with local processes. The causes of enhanced non-Maxwellianities are investigated. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetosphere

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