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Artificial intelligence in pediatrics

Chinese Medical Journal 2020年第3期133卷 358-360页

作者： Ya-Wen Li Fang Liu Tian-Nan Zhang Fang Xu Yu-Chen Gao Tian Wu School of Economics and Management Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Stomatology Beijing Children's HospitalCapital Medical UniversityNational Center for Children's HealthBeijing 100045China Department of Pediatrics Peking Union Medical College HospitalChinese Academy of Medical SciencesBeijing 100730China School of Banking Finance University of International Business and EconomicsBeijing 100029China School of Economics and Management Tsinghua UniversityBeijing 100084China NCMIS Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China.

The rapid development of information technology has involved advances in artificial intelligence(AI),big data processing,and cloud computing,with significant and farreaching effects on the structure and efficiency of the traditional healthcare industry,as well as the establishment and maintenance of modern medical management information *** solutions for handling data in the medical field,such as electronic medical records,medical imaging technology,medical big data,intelligent drug design,and smart health management systems have emerged,which improve the standardization and accuracy of clinical decision making,while providing more dimensions of data accumulation for medical knowledge-based *** developments can also support physicians and researchers in the optimization of treatment plans,and decision making about optimal treatment *** review aims to summarize recent advances in the research and clinical use of AI in pediatrics.

关键词： dimensions intelligence advances

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BAR: Blockwise adaptive recoding for batched network coding

arXiv

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arXiv 2021年

作者： Yin, Hoover H.F. Yang, Shenghao Zhou, Qiaoqiao Yung, Lily M.L. Ng, Ka Hei The Institute of Network Coding The Chinese University of Hong Kong Hong Kong Hong Kong The School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Shenzhen Research Institute of Big Data Shenzhen China The Department of Information Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong The Department of Physics The Chinese University of Hong Kong Hong Kong Hong Kong

Multi-hop networks become popular network topologies in various emerging Internet of things applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging to the same batch, BNC has a much smaller computational and storage requirements at the intermediate nodes compared with a direct application of random linear network coding. In this paper, we propose a practical recoding scheme called blockwise adaptive recoding (BAR) which learns the latest channel knowledge from short observations so that BAR can adapt to the fluctuation of channel conditions. We focus on investigating practical concerns such as the design of efficient BAR algorithms. We also design and investigate feedback schemes for BAR under imperfect feedback systems. Our numerical evaluations show that BAR has significant throughput gain for small batch size compared with the existing baseline recoding scheme. More importantly, this gain is insensitive to inaccurate channel knowledge. This encouraging result suggests that BAR is suitable to be realized in practice as the exact channel model and its parameters could be unknown and subject to change from time to time. Copyright © 2021, The Authors. All rights reserved.

关键词： Network coding

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Deformation And Penetration Hybrid Detection-Net For Parcels Inspection In Industrial Supply Chain

Deformation And Penetration Hybrid Detection-Net For Parcels...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhi Chen Cuifeng Du Xiujie Huang Zelong Lin Yuyu Zhou Quanlong Guan Zhefu Li Shuanghuan Lv Xiaofeng Wu Xiaotian Zhuang College of Information Science and Technology Jinan University Guangzhou China Guangdong Institute of Smart Education Jinan University Guangzhou China Cetc Potevio Science&Technology Co. Ltd Guangzhou China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China School of Intelligent Systems Science and Engineering Jinan University Zhuhai China Guangzhou Polytechnic of Sports Guangzhou China Beijing JD Zhenshi Information Technology Co. Ltd Beijing China

The express delivery industry has become integral to modern social life, but supply chain parcels, especially those made of corrugated cardboard, are at risk of damage during transportation. Although corrugated cardboard boxes offer some impact resistance, they can still experience deformation and penetration damage. To address this issue, we propose a hybrid model called Parcels-DNet. Parcels-DNet adopts a lightweight feature extraction backbone network, enabling deployment in resource-constrained scenarios like mobile or embedded devices. Additionally, our experimental results demonstrate that Parcels-DNet effectively captures the features of parcel deformation and penetration damage. This improves the safety and efficiency of express supply chain parcel transportation, offering greater convenience and economic benefits for the logistics industry.

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Harmonic motions of a weakly forced autoparametric vibrating system

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Journal of Physics: Conference Series 2018年第1期1053卷

作者： Zhanyong Li Qihuai Liu Kelei Zhang School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin 541004 China

Based on the method of averaging, in this paper we investigate the continuation of harmonic motions for a weakly forced autoparametric vibrating system which models the dynamics of a forced pendulum positioned on a vertically excited mass. The result shows that when the very weakly force is imposed on the pendulum, a state of dynamic balance (a harmonic motion) is preserving.

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Cross View Capture for Distributed Image Compression with Decoder Side Information

Cross View Capture for Distributed Image Compression with De...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Yankai Yin Zhe Sun Peiying Ruan Feng Duan Ruidong Li Chi Zhu Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China Institute of Natural Sciences Kanazawa University Ishikawa Japan Faculty of Health Data Science and Graduate School of Medicine Juntendo University Chiba Japan NVIDIA AI Technology Center NVIDIA Japan Tokyo Japan Department of Systems Life Engineering Maebashi Institute of Technology Maebashi Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Image compression is increasingly important in applications like intelligent driving and smart surveillance systems. This study presents a novel cross view capture distributed image compression network (CVCDIC) to improve the compression quality by using decoder side information. The CVCDIC’s decoder utilizes feature extraction networks to extract features from both the primary image and the side information. Furthermore, a multi-level cross view attention module is designed to capture interrelated details between images at multiple hierarchical levels. Finally, a spatial refinement module, constructed on the foundation of information distillation networks, is designed to further refine the quality of reconstructed images. The results show that CVCDIC can achieve an MS-SSIM of 0.978 at 0.15 bpp, surpassing DSIN (0.925), NDIC (0.956), and ATN (0.955) on the KITTI Stereo dataset.

关键词： Measurement Image coding Fuses Surveillance Rate-distortion Feature extraction Decoding

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Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

arXiv

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arXiv 2021年

作者： Li, Yue Zhou, Xuyang Colnaghi, Timoteo Wei, Ye Marek, Andreas Li, Hongxiang Bauer, Stefan Rampp, Markus Stephenson, Leigh T. Max-Planck Institut für Eisenforschung GmbH Max-Planck-Straße 1 Düsseldorf40237 Germany Max Planck Computing and Data Facility Gießenbachstraße 2 Garching85748 Germany State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing100083 China Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen72076 Germany

Nanoscale L12-type ordered structures are widely used in face-centred cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyse the complete point cloud (>10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type d¢–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization

arXiv

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arXiv 2023年

作者： Wei, Feng-Feng Chen, Wei-Neng Guo, Xiao-Qi Zhao, Bowen Jeon, Sang-Woon Zhang, Jun The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The State Key Laboratory of Subtropical Building Science With The Guangdong-Hong Kong Joint Innovative Platform of Big Data and Computational Intelligence South China University of Technology Guangzhou510006 China The Pazhou Lab Guangzhou510006 China Guangzhou Institute of Technology Xidian University Guangzhou510006 China The Department of Electronics Engineering and Communication Engineering Hanyang University Ansan15588 Korea Republic of

Crowdsourcing is an emerging computing paradigm that takes advantage of the intelligence of a crowd to solve complex problems effectively. Besides collecting and processing data, it is also a great demand for the crowd to conduct optimization. Inspired by this, this paper intends to introduce crowdsourcing into evolutionary computation (EC) to propose a crowdsourcing-based evolutionary computation (CEC) paradigm for distributed optimization. EC is helpful for optimization tasks of crowdsourcing and in turn, crowdsourcing can break the spatial limitation of EC for large-scale distributed optimization. Therefore, this paper firstly introduces the paradigm of crowdsourcing-based distributed optimization. Then, CEC is elaborated. CEC performs optimization based on a server and a group of workers, in which the server dispatches a large task to workers. Workers search for promising solutions through EC optimizers and cooperate with connected neighbors. To eliminate uncertainties brought by the heterogeneity of worker behaviors and devices, the server adopts the competitive ranking and uncertainty detection strategy to guide the cooperation of workers. To illustrate the satisfactory performance of CEC, a crowdsourcing-based swarm optimizer is implemented as an example for extensive experiments. Comparison results on benchmark functions and a distributed clustering optimization problem demonstrate the potential applications of CEC. Copyright © 2023, The Authors. All rights reserved.

关键词： Crowdsourcing

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Fourier Neural Operator Based Fluid-Structure Interaction for Predicting the Vesicle Dynamics

SSRN

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SSRN 2024年

作者： Xiao, Wang Gao, Ting Liu, Kai Duan, Jinqiao Zhao, Meng School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Center for Mathematical Sciences Huazhong University of Science and Technology Wuhan430074 China Steklov-Wuhan Institute for Mathematical Exploration Huazhong University of Science and Technology Wuhan430074 China College of Education for the Future Beijing Normal University Zhuhai519087 China Department of Mathematics Department of Physics Great Bay University Guangdong Dongguan523000 China Dongguan Key Laboratory for Data Science and Intelligent Medicine Guangdong Dongguan523000 China

Solving complex fluid-structure interaction (FSI) problems, characterized by nonlinear partial differential equations, is crucial in various scientific and engineering applications. Traditional computational fluid dynamics (CFD) solvers are insufficient to meet the growing requirements for large-scale and long-period simulations. Fortunately, the rapid advancement in neural networks, especially neural operator learning mappings between function spaces, has introduced novel approaches to tackle these challenges via data-driven modeling. In this paper, we propose a Fourier neural operator-based fluid-structure interaction solver (FNObased FSI solver) for efficient simulation of FSI problems, where the solid solver based on the finite difference method is seamlessly integrated with the Fourier neural operator to predict incompressible flow using the immersed boundary method. We analyze the performance of the FNO-based FSI solver in the following three situations: training data with or without the steady state, training method with one-step label or multi-step labels, and prediction in interpolation or extrapolation. We find that the best performance for interpolation is achieved by training the operator with multi-step labels using steady-state data. Finally, we train the FNO-based FSI solver using this optimal training method and apply it to vesicle dynamics. The results show that the FNO-based FSI solver is capable of capturing the variations in the fluid and the vesicle. © 2024, The Authors. All rights reserved.

关键词： Navier Stokes equations

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Ppdrm: Privacy-Preserving Drm Training and Classification on the Cloud

SSRN

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SSRN 2023年

作者： Xu, Lida Tian, Chengliang Zhang, Guoyan Li, Leibo Tian, Weizhong Zhang, Yan College of Computer Science and Technology Qingdao University Qingdao266071 China School of Cyber Science and Technology Shandong University Shandong Ji’nan250101 China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong Ji’nan250101 China Shandong Institute of Blockchain Shandong Ji’nan250101 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen518118 China College of Electromechanical Engineering Qingdao University of Science and Technology Qingdao266061 China

The prevalence of cloud computing greatly promotes the development of artificial intelligence(AI). Accurately and efficiently classify data on the cloud is a classic AI task. The recent Discriminative Ridge Machine(DRM) is a new type of supervised classifier which outperforms many other classic classifiers in accuracy and efficiency, especially for high-dimensional data or unbalanced ***, the DRM training and classification task is tedious for resource-constrained data owners. Motivated by this, in this paper, we study the outsourcing of DRM training and classification within a cloud environment and propose a quantum secure and efficient solution for this problem. Specifically, based on the two non-colluding servers model, we adopt the quantum-immune CKKS cryptosystem to design a series of homomorphic security protocols for matrix and argmin operations and realize the correct model training and data classification without leaking the privacy of training dataset, the DRM model, the query data and the classification results. We argue the $\mathcal{CPA}$ secure of our design and evaluate the accuracy and efficiency from theory and experiment perspectives. Our theoretical and experimental analysis demonstrates that our design can achieve the same classification accuracy as the origin plaintext-based DRM classifier and the query-response time is reasonable and acceptable. © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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