检索结果-内蒙古大学图书馆

Science Bulletin 2025年第6期 934-942页

作者：李灏萱江泽铧管洲榆包玉倩刘月星胡婷婷李佳佳刘茹涵吴量程棣纪宏伟王勇王亚星 Carol Y.Cheung 郑颖丰王继红李震吴卫兵 Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan 程景煜黄天荫李华婷蔡淳毛丽娟 Lee-Ling Lim 覃宇宗盛斌贾伟平 Shanghai University of Sport School of Clinical Medicine Beijing Tsinghua Changgung Hospital Tsinghua Medicine Tsinghua University Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua Medicine Tsinghua University Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Department of Cardiology the Affiliated Hospital of Qingdao University CEMS NCMIS HCMS MADIS Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics University of Chinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Beijing Institute of Ophthalmology Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular Diseases Department of Renal Medicine Singapore General Hospital Sing Health-Duke Academic Medical Centre Department of Endocrinology Singapore General Hospital Department of Endocrinology Austin Health Department of Medicine The University of Melbourne(Austin Health) Australian Centre for Accelerating Diabetes Innovations(ACADI) The Univ

Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models(LLMs)provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: Chat GPT-3.5, Chat GPT-4.0, Google Bard, Lla MA-7B, Lla MA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, Med GPT, Huatuo GPT, and Chinese Lla MA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians(PCPs) without and with the assistance of Chat GPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that Chat GPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, Lla MA-7B, and Lla MA2-7B. For the NCE-CPFC examination, Chat GPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, Med GPT, and Chat GPT-3.5 successfully passed, whereas Lla MA2-7B, Huatuo GPT,Chinese LLa MA2-7B, and Lla MA-7B failed. Chat GPT-4.0(84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Early Stage researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

引用

Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

End-to-end face parsing via interlinked convolutional neural networks

arXiv

引用

arXiv 2020年

作者： Yin, Zi Yiu, Valentin Hu, Xiaolin Tang, Liang School of Technology Beijing Forestry University Beijing100083 China State Key Laboratory of Intelligent Technology and Systems Institute for Artificial Intelligence Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology THBI Tsinghua University CentraleSuplec Gif-Sur-Yvette91190 France

Face parsing is an important computer vision task that requires accurate pixel segmentation of facial parts (such as eyes, nose, mouth, etc.), providing a basis for further face analysis, modification, and other applications. In this paper, we introduce a simple, end-to-end face parsing framework: STN-aided iCNN (STN-iCNN), which extends interlinked Convolutional Neural Network (iCNN) by adding a Spatial Transformer Network (STN) between the two isolated stages. The STN-iCNN uses the STN to provide a trainable connection to the original two-stage iCNN pipeline, making end-to-end joint training possible. Moreover, as a by-product, STN also provides more precise cropped parts than the original cropper. Due to the two advantages, our approach significantly improves the accuracy of the original model. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Improve Docker Performance 16th

N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Impro...

引用

16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Gu, Lin Tang, Qizhi Wu, Song Jin, Hai Zhang, Yingxi Shi, Guoqiang Lin, Tingyu Rao, Jia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China The University of Texas at Arlington ArlingtonTX76019 United States

ISBN: (纸本)9783030307080

Docker has been widely adopted in production environment, but unfortunately deployment and cold-start of container are limited by the low speed of disk. The emerging non-volatile memory (NVM) technology, which has high speed and can store data permanently, brings a new chance to accelerate the deployment and cold-start of container. However, it is expensive to replace the whole hard disk driver (HDD) with NVM. To achieve the fastest deployment and cold-start with lowest cost, we conduct in-depth analysis on the Top-134 images in Docker Hub and obtain two main insights as: (1) the storing latency of layered image has become the bottleneck of container deployment;(2) only a few image layers are required for container cold-start. Based on these two findings, we propose a NVM-HDD hybrid docker storage framework as N-Docker. It can effectively accelerate container cold-start by detecting the bottleneck layers as well as cold-start required layers and storing them into NVM for faster container startup with limited NVM capacity. Experimental results show that N-Docker can accelerate the container deployment by 1.21X and cold-start by 2.96X. Compared to NVM-Docker, which stores all images into NVM, N-Docker achieves the same performance improvements while reducing the usage of NVM by 88.22%. © 2019, IFIP International Federation for Information Processing.

关键词： Containers

来源：评论

学校读者我要写书评

暂无评论

Cyber-Physical-Social systems for Smart City: An Implementation Based on intelligent Loop

引用

IFAC-PapersOnLine 2020年第5期53卷 501-506页

作者： Gang Xiong Xiaoyu Chen Nan Shuo Yisheng Lv Fenghua Zhu Tianci Qu Peijun Ye State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China Graduate School of System Design Tokyo Metropolitan University Tokyo 191-0065 Japan

Cyber-Physical-Social systems (CPSS) provides a novel perspective for constructing “Smart City”, which is also known as the Human-Machine-Things-System (HMTS), focusing on the fusion of ternary space: social network of human society, network of machines and the Internet of things. In this paper, we propose a specific implementation framework of CPSS for Smart City based on intelligent loops, including basic modeling and interactive fusion, state perception and cognition, and adaptive learning. On this basis, an overall architecture of the CPSS platform is designed, which is applied in the urban transportation management in Hangzhou. The application results demonstrate that the intelligent loop could optimize the control and management strategies for actual urban transportation.

关键词： CPSS intelligent Loop Human-Cyber-Physical Ternary Space

来源：评论

学校读者我要写书评

暂无评论

Collaborative Optimization of Cyber Physical Social systems for Urban Transportation Based on Knowledge Automation

引用

IFAC-PapersOnLine 2020年第5期53卷 572-577页

The integration of signals from physical, social and cyber spaces, known as Cyber-Physical-Social systems (CPSS), is a new research paradigm for urban transportation, where the traffic control and management (C&M) is collaborative optimized among the three sub-systems. Though some technologies and optimization methods have been studied since its proposition, there is a lack of a systemic architecture as well as an overall implementation about how to efficiently exploit the social signals. For this reason, this paper proposes a general framework of CPSS for urban transportation and presents a feasible solution for traffic optimization based on knowledge automation. The specific implementation includes basic modeling of CPSS, knowledge evolution and reasoning, and collaborative optimization of C&P strategies. As a remarkable highlight, the influence of both individual activities and social learning is concerned during knowledge evolution and reasoning part. A case study from the application in the city of Dongguan is also given to validate our proposed framework and methods, showing that they can efficiently improve the average speed of the actual transportation.

关键词： CPSS knowledge automation urban transportation Collaborative Optimization

来源：评论

学校读者我要写书评

暂无评论

Accurate RNA 3D structure prediction using a language model-based deep learning approach

arXiv

引用

arXiv 2022年

作者： Shen, Tao Hu, Zhihang Sun, Siqi Liu, Di Wong, Felix Wang, Jiuming Chen, Jiayang Wang, Yixuan Hong, Liang Xiao, Jin Zheng, Liangzhen Krishnamoorthi, Tejas King, Irwin Wang, Sheng Yin, Peng Collins, James J. Li, Yu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Zelixir Biotech Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA United States Department of Systems Biology Harvard Medical School BostonMA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Synthetic Biology Center Massachusetts Institute of Technology CambridgeMA United States Integrated Biosciences Redwood CityCA United States OneAIM Ltd. Hong Kong Shanghai AI Laboratory Shanghai China The CUHK Shenzhen Research Institute Hi-Tech Park Shenzhen Nanshan China Shenzhen Institute of Advanced Technology Guangdong Shenzhen China School of Molecular Sciences Center for Molecular Design and Biomimetics the Biodesign Institute Arizona State University TempeAZ United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States

Accurate prediction of RNA three-dimensional (3D) structure remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The structural flexibility of RNA, which leads to scarcity of experimentally determined data, complicates computational prediction efforts. Here, we present RhoFold+, an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences. By integrating an RNA language model pre-trained on ∼23.7 million RNA sequences and leveraging techniques to address data scarcity, RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction. Retrospective evaluations on RNA-Puzzles and CASP15 natural RNA targets demonstrate RhoFold+’s superiority over existing methods, including human expert groups. Its efficacy and generalizability are further validated through cross-family and cross-type assessments, as well as time-censored benchmarks. Additionally, RhoFold+ predicts RNA secondary structures and inter-helical angles, providing empirically verifiable features that broaden its applicability to RNA structure and function studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

SenseMag: Enabling low-cost traffic monitoring using non-invasive magnetic sensing

arXiv

引用

arXiv 2021年

作者： Wang, Kafeng Xiong, Haoyi Zhang, Jie Chen, Hongyang Dou, Dejing Xu, Cheng-Zhong Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Shenzhen China Big Data Lab Baidu Inc. Haidian Beijing China Key Laboratory of High Confidence Software Technologies Peking University Haidian Beijing China Research Center for Intelligent Network Zhejiang Lab Zhejiang Hangzhou China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Tapia Macau China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

The operation and management of intelligent transportation systems (ITS), such as traffic monitoring, relies on real-time data aggregation of vehicular traffic information, including vehicular types (e.g., cars, trucks, and buses), in the critical roads and highways. While traditional approaches based on vehicular-embedded GPS sensors or camera networks would either invade drivers’ privacy or require high deployment cost, this paper introduces a low-cost method, namely SenseMag, to recognize the vehicular type using a pair of non-invasive magnetic sensors deployed on the straight road section. SenseMag filters out noises and segments received magnetic signals by the exact time points that the vehicle arrives or departs from every sensor node. Further, SenseMag adopts a hierarchical recognition model to first estimate the speed/velocity, then identify the length of vehicle using the predicted speed, sampling cycles, and the distance between the sensor nodes. With the vehicle length identified and the temporal/spectral features extracted from the magnetic signals, SenseMag classify the types of vehicles accordingly. Some semi-automated learning techniques have been adopted for the design of filters, features, and the choice of hyper-parameters. Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i.e., 7 types by SenseMag versus 4 types by the existing work in comparisons). To be specific, our field experiment results validate that SenseMag is with at least 90% vehicle type classification accuracy and less than 5% vehicle length classification error. Copyright © 2021, The Authors. All rights reserved.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Optimizing Multi-Dimensional Packet Classification for Multi-Core systems

引用

Journal of computer Science & Technology 2018年第5期33卷 1056-1071页

作者： Tong Shen Da-Fang Zhang Gao-Gang Xie Xin-Yi Zhang College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Network Technology Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale the five-tuple model to multi-tuple. In general, packet classification on multiple fields is a complex problem. Although most existing software-based algorithms have been proved extraordinary in practice, they are only suitable for the classic five-tuple model and difficult to be scaled up. Meanwhile, hardware-specific solutions are inflexible and expensive, and some of them are power consuming. In this paper, we propose a universal multi-dimensional packet classification approach for multi-core systems. In our approach, novel data structures and four decomposition-based algorithms are designed to optimize the classification and updating of rules. For multi-field rules, a rule set is cut into several parts according to the number of fields. Each part works independently. In this way, the fields are searched in parallel and all the partial results are merged together at last. To demonstrate the feasibility of our approach, we implement a prototype and evaluate its throughput and latency. Experimental results show that our approach achieves a 40% higher throughput than that of other decomposed-based algorithms and a 43% lower latency of rule incremental update than that of the other algorithms on average. Furthermore, our approach saves 39% memory consumption on average and has a good scalability.

关键词： multi-dimensional multi-core packet classification

来源：评论

学校读者我要写书评

暂无评论

Improved impossible differential cryptanalysis of large-block Rijndael

引用

Science China(Information Sciences) 2019年第3期62卷 5-18页

作者： Ya LIU Yifan SHI Dawu GU Bo DAI Fengyu ZHAO Wei LI Zhiqiang LIU Zhiqiang ZENG Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University School of Computer Science and Technology Donghua University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Science and Technology on Information Assurance Laboratory

Rijndael is a substitution-permutation network（SPN） block cipher for the AES development process. Its block and key sizes range from 128 to 256 bits in steps of 32 bits, which can be denoted by Rijndael-b-k, where b and k are the block and key sizes, respectively. Among them, Rijndael-128-128/192/256,that is, AES, has been studied by many researchers, and the security of other large-block versions of Rijndael has been exploited less frequently. However, more attention has been paid to large-block versions of block ciphers with the fast development of quantum computers. In this paper, we propose improved impossible differential attacks on 10-round Rijndael-256-256, 10-round Rijndael-224-256, and 9-round Rijndael-224-224 using precomputation tables, redundancies of key schedules, and multiple impossible differentials. For 10-round Rijndael-256-256, the data, time, and memory complexities of our attack were approximately 2244.4chosen plaintexts, 2240.1encryptions, and 2181.4blocks, respectively. For 10-round Rijndael-224-256, the data, time, and memory complexities of our attack were approximately 2214.4chosen plaintexts, 2241.3encryptions, and 2183.4blocks, respectively. For 9-round Rijndael-224-224, the data, time, and memory complexities of our attack are approximately 2214.4chosen plaintexts, 2113.4encryptions, and 287.4blocks,respectively, or 2206.6chosen plaintexts, 2153.6encryptions, and 2111.6blocks, respectively. To the best of our knowledge, our results are currently the best on Rijndael-256-256 and Rijndael-224-224/256.

关键词： block cipher Rijndael precomputation tables impossible di?erentials multiple impossible differential attacks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：