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arXiv 2021年

作者： Guo, di Tu, Zhangren Guo, Yi Zhou, Yirong Wang, Jian Wang, Zi Qiu, Tianyu Xiao, Min Chen, Yinran Feng, Liubin Huang, Yuqing Lin, donghai Hong, Qing Goldbourt, Amir Lin, Meijin Qu, Xiaobo The School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen361024 China Department of Computer Science School of Information Xiamen University Xiamen361102 China College of Chemistry and Chemical Engineering Key Laboratory for Chemical Biology of Fujian Province High-field NMR Center Xiamen University Xiamen361005 China China Mobile Group Xiamen361005 China School of Chemistry Tel Aviv University Tel Aviv6997801 Israel Department of Applied Marine Physics and Engineering College of Ocean and Earth Sciences Xiamen University Xiamen361102 China

—Nuclear Magnetic resonance (NMr) spectroscopy is an important bio-engineering tool to determine the metabolic concentrations, molecule structures and so on. The data acquisition time, however, is very long in multi-dimensional NMr. To accelerate data acquisition, non-uniformly sampling is an effective way but may encounter severe spectral distortions and unfaithful quantitative measures when the acceleration factor is high. By modelling the acquired signal as the superimposed exponentials, we proposed a virtual peak (VIP) approach to self-adapt the prior spectral information, such as the resonance frequency and peak lineshape, and then feed these information into the reconstruction. The proposed method is further implemented with cloud computing to facilitate online, open, and easy access. results on simulated and experimental data demonstrate that, compared with the low-rank Hankel matrix method, the new approach reconstructs high-fidelity NMr spectra from highly undersampled data and achieves more accurate quantification. The maximum quantitative errors of distances between nuclear pairs and concentrations of metabolites in mixtures have been reduced by 61.1% and 57.7%, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance spectroscopy

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1364P ctdNA analysis of SAF-189s efficacy in ALK+ advanced non-small cell lung cancer (NSCLC)

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Annals of Oncology 2023年 34卷 S784-S784页

作者： J-J. Yang J. Zhou Y. Cheng M. Li Q. Zhao Z-Y. Zhang A. Zang Y. Fan Y. Zhou J. Guo Y. Zheng Z. Wu J. Sun X. Xiang X. Wang Y-L. Wu Guangdong Lung Cancer Institute Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences Southern Medical University Guangzhou China Respiratory medicine The First Affiliated Hospital Zhejiang University School Of Medicine Hangzhou China Oncology Jilin Cancer Hospital Changchun China Medical Oncology The First Affiliated Hospital of Zhengzhou University Zhengzhou China Thoracic Oncology Shulan Hospital Hangzhou China Oncology The First Affiliated Hospital of Henan University of Science and Technology Luoyang China Oncology North Hospital Affiliated Hospital of Hebei University Baoding China Thoracic Oncology Zhejiang Cancer Hospital Hangzhou China R&D Wanbang Biopharmaceuticals Jiangsu China Translational Medicine Beijing Fosun Pharmaceutical Research and Development Co. Ltd. Shanghai China Clinical Oncology Beijing Fosun Pharmaceutical Research and Development Co. Ltd. Beijing China Biometrics and Data Science Beijing Fosun Pharmaceutical Technology Development Co. Ltd. Beijing China Global R&D Center Shanghai Fosun Pharmaceutical Development Co. Ltd Shanghai China

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Skin disease detection using artificial intelligence

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AIP Conference Proceedings 2022年第1期2393卷

作者： S. Kuzhaloli L. M. Varalakshmi Kamal Gulati Makarand Upadhyaya Narinder Kumar Bhasin Vijayakumar Peroumal 1EEE Department Vel Tech RangarajanDr.Sagunthala R&D Institute of Science and Technology Chennai India 2Dept of ICE Sri ManakulaVinayagar Engineering College Puducherry India 3Data Analytics Amity School of Insurance Banking and Actuarial Science Amity University Noida India 4Department of Management & Marketing College of Business Administration Kingdom of Bahrain Bahrain 5CSIT Amity School of Insurance Banking and Actuarial Science Amity University Noida India 6School of Electronics Engineering Vellore Institute of Technology Chennai India

Artificial intelligence (AI) algorithms for automated classification of skin diseases are available to the consumer market. Studies of their diagnostic accuracy are rare. We assessed the diagnostic accuracy of an open-access AI application for recognition of skin diseases. The AI algorithm classified the images giving 5 differential diagnoses, which were then compared to the diagnoses made clinically by the dermatologists and/or histological. The level of diagnostic accuracy varied considerably for diagnostic groups. The online application demonstrated low diagnostic accuracy compared to a dermatologist evaluation and needs further development. Input signs have been developed to classify the disorder. With the aid of experts in the area, we received symptoms of 10 skin diseases. The symptom data were trained by various classifiers. We observed that high quality AI-based support for clinical decision making enhances the accurate diagnosis of either AI or doctors alone and that less skilled physicians are better served by AI.

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Benchmarking the variational quantum eigensolver through simulation of the ground state energy of prebiotic molecules on high-performance computers

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AIP Conference Proceedings 2021年第1期2362卷

作者： P. Lolur M. rahm M. Skogh L. García-Álvarez G. Wendin 1)Department of Chemistry and Chemical Engineering Chalmers University of Technology SE-412 96 Gothenburg Sweden 2)Data Science & Modelling Pharmaceutical Science R&D AstraZeneca Gothenburg Sweden 3)Applied Quantum Physics Laboratory Department of Microtechnology and Nanoscience-MC2 Chalmers University of Technology SE-412 96 Gothenburg Sweden 4)Quantum Technology Laboratory Department of Microtechnology and Nanoscience-MC2 Chalmers University of Technology SE-412 96 Gothenburg Sweden

We use the Variational Quantum Eigensolver (VQE) as implemented in the Qiskit software package to compute the ground state energy of small molecules derived from water, H2O, and hydrogen cyanide, HCN. The work aims to benchmark algorithms for calculating the electronic structure and energy surfaces of molecules of relevance to prebiotic chemistry, beginning with water and hydrogen cyanide, and to run them on the available simulated and physical quantum hardware. The numerical calculations of the algorithms for small quantum processors allow us to design more efficient protocols to be run in real hardware, as well as to analyze their performance. Future implementations on accessible quantum processing prototypes will benchmark quantum computers and provide tests of quantum advantage with heuristic quantum algorithms.

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Machine learning for modelling unstructured grid data in computational physics: a review

arXiv

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arXiv 2025年

作者： Cheng, Sibo Bocquet, Marc ding, Weiping Finn, Tobias Sebastian Fu, rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China Centre for Health Informatics Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Department of Community Health Sciences Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1E 6BT United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G 1M8 Canada School of Mechanical Medical and Process Engineering Faculty of Engineering Queensland University of Technology BrisbaneQLD Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. Key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025, CC BY-NC-SA.

关键词： Mesh generation

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Optimizing Laser Pulses for Narrow-Band Inverse Compton Sources in the High-Intensity regime

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Physical review Letters 2019年第20期122卷 204802-204802页

作者： daniel Seipt Vasily Yu. Kharin Sergey G. rykovanov Helmholtz-Institut Jena Fröbelstieg 3 07743 Jena Germany Center for Ultrafast Optical Science University of Michigan Ann Arbor Michigan 48109 USA Research Institute Moscow R&D Lab Moscow Bersenevskaya nab. 6 119072 Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Nobel Str. 3 Skolkovo Russia

Scattering of ultraintense short laser pulses off relativistic electrons allows one to generate a large number of X- or gamma-ray photons with the expense of the spectral width—temporal pulsing of the laser inevitable leads to considerable spectral broadening. In this Letter, we describe a simple method to generate optimized laser pulses that compensate the nonlinear spectrum broadening and can be thought of as a superposition of two oppositely linearly chirped pulses delayed with respect to each other. We develop a simple analytical model that allows us to predict the optimal parameters of such a two-pulse—the delay, amount of chirp, and relative phase—for generation of a narrow-band γ-ray spectrum. Our predictions are confirmed by numerical optimization and simulations including three-dimensional effects.

关键词： Gamma-ray generation in plasmas High intensity laser-plasma interactions Strong electromagnetic field effects radiation from moving charges

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Advancing COVId-19 diagnosis with privacy-preserving collaboration in artificial intelligence (vol 3, pg 1081, 2021)

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NATUrE MACHINE INTELLIGENCE 2022年第4期4卷 413-413页

作者： Bai, Xiang Wang, Hanchen Ma, Liya Xu, Yongchao Gan, Jiefeng Fan, Ziwei Yang, Fan Ma, Ke Yang, Jiehua Bai, Song Shu, Chang Zou, Xinyu Huang, renhao Zhang, Changzheng Liu, Xiaowu Tu, dandan Xu, Chuou Zhang, Wenqing Wang, Xi Chen, Anguo Zeng, Yu Yang, dehua Wang, Ming-Wei Holalkere, Nagaraj Halin, Neil J. Kamel, Ihab r. Wu, Jia Peng, Xuehua Wang, Xiang Shao, Jianbo Mongkolwat, Pattanasak Zhang, Jianjun Liu, Weiyang roberts, Michael Teng, Zhongzhao Beer, Lucian Sanchez, Lorena E. Sala, Evis rubin, daniel L. Weller, Adrian Lasenby, Joan Zheng, Chuansheng Wang, Jianming Li, Zhen Schonlieb, Carola Xia, Tian Department of Radiology Tongji Hospital and Medical College Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Engineering University of Cambridge Cambridge UK Alan Turing Institute London UK Department of Radiology Union Hospital of Tongji Medical College Huazhong University of Science and Technology Wuhan China HUST-HW Joint Innovation Lab Wuhan China CalmCar Inc Suzhou China Wuhan Blood Centre Wuhan China MSA Capital Beijing China The National Centre for Drug Screening Shanghai Institute of Materia Medica Chinese Academy of Sciences Shanghai China CardioVascular and Interventional Radiology Radiology for Quality and Operations The Cardiovascular Centre at Tufts Medical Centre Radiology Tufts University School of Medicine Medford OR USA Russell H Morgan Department of Radiology & Radiologic Science Johns Hopkins Hospital & Medicine Institute Baltimore MD USA Department of Radiation Oncology School of Medicine Stanford University Palo Alto CA USA Department of Radiology Wuhan Central Hospital Wuhan China Department of Radiology Wuhan Children’s Hospital Wuhan China Faculty of Information and Communication Technology Mahidol University Salaya Thailand Thoracic/Head and Neck Medical Oncology University of Texas MD Anderson Cancer Centre Houston TX USA Translational Molecular Pathology University of Texas MD Anderson Cancer Centre Houston TX USA Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge UK Oncology R&D at AstraZeneca Cambridge UK Department of Radiology University of Cambridge Cambridge UK Department of Biomedical Data Science Radiology and Medicine Stanford University Palo Alto USA Department of Hepatobiliary Pancreatic Surgery Affiliated Tianyou Hospital Wuhan University of Science and Technology Wuhan China

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Enabling AI in future wireless networks: A data life cycle perspective

arXiv

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arXiv 2020年

作者： Nguyen, dinh C. Cheng, Peng ding, Ming Lopez-Perez, david Pathirana, Pubudu N. Li, Jun Seneviratne, Aruna Li, Yonghui Poor, H. Vincent School of Engineering Deakin University Australia Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Data61 CSIRO Australia Huawei France R&D Boulogne-Billancourt France School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk634050 Russia NSW Australia School of Electrical and Information Engineering University of Sydney Australia Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems. Big data analysis, pervasive computing, and eventually artificial intelligence (AI) are envisaged to be deployed on top of the IoT and create a new world featured by data-driven AI. In this context, a novel paradigm of merging AI and wireless communications, called Wireless AI that pushes AI frontiers to the network edge, is widely regarded as a key enabler for future intelligent network evolution. To this end, we present a comprehensive survey of the latest studies in wireless AI from the data-driven perspective. Specifically, we first propose a novel Wireless AI architecture that covers five key data-driven AI themes in wireless networks, including Sensing AI, Network device AI, Access AI, User device AI and data-provenance AI. Then, for each data-driven AI theme, we present an overview on the use of AI approaches to solve the emerging data-related problems and show how AI can empower wireless network functionalities. Particularly, compared to the other related survey papers, we provide an in-depth discussion on the Wireless AI applications in various data-driven domains wherein AI proves extremely useful for wireless network design and optimization. Finally, research challenges and future visions are also discussed to spur further research in this promising area. © 2020, CC BY.

关键词： Wireless networks

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Toward a Smart Platform for data Science Career

Toward a Smart Platform for Data Science Career

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International Conference on Knowledge and Systems Engineering

作者： Phuong N. Y. Le Tinh H. Nguyen Linh V. Nguyen Khoi M. Vo Suong N. Hoang Data Advising Ho Chi Minh City Vietnam DI Lab Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Vietnam R&D Department Kyanon Digital Ho Chi Minh City Vietnam Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Vietnam

In this article, we aim to describe our data Advising system. It is a smart platform to provide users with insight into the data science labor market. It connects learners/applicants with employers/businesses and educational providers via an intelligent recommender system. The system will constantly learn from data and domain expertise in order to get smarter and catch up with the trending in data Science.

关键词： data science Facebook Urban areas Engineering profession Business Ontologies data collection

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Correction to "Green-Light-Selective Organic Photodiodes with High detectivity for CMOS Color Image Sensors"

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ACS applied materials & interfaces 2021年第8期13卷 10664页

作者： Younhee Lim Sungyoung Yun daiki Minami Taejin Choi Hyesung Choi Jisoo Shin Sungjun Park Chul-Joon Heo dong-Seok Leem Tadao Yagi Kyung-Bae Park Sunghan Kim Organic Materials Laboratory Samsung Advanced Institute of Technology (SAIT) Samsung Electronics Co. Ltd. Suwon-si Gyeonggi-do 16678 Republic of Korea. Data & Information Technology (DIT) Center Samsung Electronics Co. Ltd Hwaseong-si Gyeonggido 18448 Korea. MD-2 Lab Samsung R&D Institute Japan-Yokohama (SRJ-Y) Samsung Electronics Co. Ltd. 2-7 Sugasawa-cho Tsurumi-ku Yokohama 230-0027 Japan.

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