检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Karim, Mohammad Ramezani, Mahin Sunbury, Tenaya Ohsfeldt, Robert Kum, Hye-Chung Population Informatics Lab Department of Health Policy and Management Texas A&M University 1266 TAMU College StationTX77843 United States Washington State Department of Social and Health Services United States Department of Health Policy and Management Texas A&M University College StationTX77843 United States Population Informatics Lab Department of Health Policy and Management Department of Computer Science & Engineering Texas A&M University 1266 TAMU College StationTX77843 United States

Objective: To design and evaluate a general framework for interactive record linkage using a convenient algorithm combined with tractable Human Intelligent Tasks (HITs;i.e. micro tasks requiring human judgment) that can support reproducible data science. Materials and Methods: Accurate linkage of real data requires both automatic processing of well-defined tasks and human processing of tasks that require human judgment (i.e., HITs) on messy data. We present a reproducible, interactive, and iterative framework for record linkage called VIEW (Visual Interactive Entity-resolution Workbench). We implemented and evaluated VIEW by integrating two commonly used hospital databases, the American Hospital Association (AHA) Annual Survey of Hospitals and the Medicare Cost Reports for Hospitals from CMS. Results: Using VIEW to iteratively standardize and clean the data, we linked all Texas hospitals common in both databases with 100% precision by confirming 78 approximate linkages using HITs and manually linking 28 hospitals using HITs. Discussion: Similarities in hospital names and addresses and the dynamic nature of hospital attributes over time make it impossible to build a fully automated linkage system for hospitals that can be maintained over time. VIEW is a software that supports a reproducible semi-automated process that can generate and track HITs to be reviewed and linked manually for messy data elements such as hospitals that have been merged. Conclusion: Effective software that can support the interactive and iterative process of record linkage, and well-designed HITs can streamline the linkage processes to support high quality replicable research using messy real data. © 2021, CC BY.

关键词： Data handling

来源：评论

学校读者我要写书评

暂无评论

A mobile robot hand-arm teleoperation system by vision and IMU

arXiv

引用

arXiv 2020年

作者： Li, Shuang Jiang, Jiaxi Ruppel, Philipp Liang, Hongzhuo Ma, Xiaojian Hendrich, Norman Sun, Fuchun Zhang, Jianwei Department of Informatics Universität Hamburg Department of Computer Science RWTH Aachen University Center for Vision Cognition Learning and Autonomy Department of Statistics University of California Los Angeles United States State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University

In this paper, we present a multimodal mobile teleoperation system that consists of a novel vision-based hand pose regression network (Transteleop) and an IMU-based arm tracking method. Transteleop observes the human hand through a low-cost depth camera and generates not only joint angles but also depth images of paired robot hand poses through an image-to-image translation process. A keypoint-based reconstruction loss explores the resemblance in appearance and anatomy between human and robotic hands and enriches the local features of reconstructed images. A wearable camera holder enables simultaneous hand-arm control and facilitates the mobility of the whole teleoperation system. Network evaluation results on a test dataset and a variety of complex manipulation tasks that go beyond simple pick-and-place operations show the efficiency and stability of our multimodal teleoperation system. Copyright © 2020, The Authors. All rights reserved.

关键词： Robotic arms

来源：评论

学校读者我要写书评

暂无评论

Longitudinal Complex Dynamics of labour Markets Reveal Increasing Polarisation

arXiv

引用

arXiv 2022年

作者： Althobaiti, Shahad Alabdulkareem, Ahmad Shen, Judy Hanwen Rahwan, Iyad Frank, Morgan Moro, Esteban Rutherford, Alex Riyadh Saudi Arabia Intelmatix Intelligent Informatics Technologies CambridgeMA United States Computer Science Department Stanford University CA United States Center for Humans and Machines Max Planck Institute for Human Development Berlin Germany Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA United States Digital Economy Lab Institute for Human-Centered Artificial Intelligence Stanford University StanfordCA United States Media Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics & GISC Universidad Carlos III de Madrid Leganes Spain Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States

In this paper we conduct a longitudinal analysis of the structure of labour markets in the US over 7 decades of technological, economic and policy change. We make use of network science, natural language processing and machine learning to uncover structural changes in the labour market over time. We find a steady rate of both disappearance of jobs and a shift in the required work tasks, despite much technological and economic change over this time period. Machine learning is used to classify jobs as being predominantly cognitive or physical based on the textual description of the workplace tasks. We also measure increasing polarisation between these two classes of jobs, linked by the similarity of tasks, over time that could constrain workers wishing to move to different jobs. © 2022, CC BY.

关键词： Employment

来源：评论

学校读者我要写书评

暂无评论

Semi-Decentralized Model for Drone Collaboration on Secure Measurement of Positions 2021

Semi-Decentralized Model for Drone Collaboration on Secure M...

引用

The 5th International Conference on Future Networks & Distributed Systems

作者： Mostefa Kara Abdelkader Laouid Ahcene Bounceur Mohammad Hammoudeh Muath Alshaikh Romaissa Kebache LIAP Laboratory El Oued University PO Box 789 El Oued 39000 El Oued Algeria Algeria Lab-STICC UMR CNRS University of Western Brittany UBO Brest 6285 France France School of Computing Mathematics and Digital Technology Manchester Metropolitan University Manchester M1 5GD UK UK Computer Science Department College of Computing and Informatics Saudi Electronic University 11673 Riyadh Kingdom of Saudi Arabia Saudi Arabia

ISBN: (纸本)9781450387347

The importance of drone use is increasing in all fields due to several characteristics and advantages they provide, such as saving lives, quality imaging, and being easily controllable. In addition, Unmanned Aerial Vehicle (UAV) prices are going to decrease, this has contributed greatly to an increase in demand for them. However, many challenges face the widespread use of UAVs. One of the most important challenges is his vulnerability to hacking. This paper presents a semi-decentralized collaboration model for the secure measurement of drone positions. The proposed model is based on choosing a leader for each specified period of time by a lightweight consensus algorithm. The leader gets his position using satellites, other drone team members are based on the leader’s safe position to find their locations in a way that is immune to GPS spoofing. Our proposal study shows the extent of the security level it provides against spoofing attacks.

关键词： Secure communication UAV Consensus Data Collection. Drone

来源：评论

学校读者我要写书评

暂无评论

Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

arXiv

引用

arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic available energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

来源：评论

学校读者我要写书评

暂无评论

Skeletonization Quality Evaluation: Geometric Metrics for Point Cloud Analysis in Robotics

arXiv

引用

arXiv 2025年

作者： Wen, Qingmeng Lai, Yu-Kun Ji, Ze Tafrishi, Seyed Amir School of Engineering Cardiff University CF24 3AA United Kingdom School of Computer Science and Informatics Cardiff University CF24 4AG United Kingdom Lab School of Engineering Cardiff University Queen’s Buildings The Parade CardiffCF24 3AA United Kingdom

Skeletonization is a powerful tool for shape analysis, rooted in the inherent instinct to understand an object’s morphology. It has found applications across various domains, including robotics. Although skeletonization algorithms have been studied in recent years, their performance is rarely quantified with detailed numerical evaluations. This work focuses on defining and quantifying geometric properties to systematically score the skeletonization results of point cloud shapes across multiple aspects, including topological similarity, boundedness, centeredness, and smoothness. We introduce these representative metric definitions along with a numerical scoring framework to analyze skeletonization outcomes concerning point cloud data for different scenarios, from object manipulation to mobile robot navigation. Additionally, we provide an open-source tool to enable the research community to evaluate and refine their skeleton models. Finally, we assess the performance and sensitivity of the proposed geometric evaluation methods from various robotic applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Geometry

来源：评论

学校读者我要写书评

暂无评论

Presenting Artificial Intelligence Predictions Based on Electronic Medical Records to Clinicians in Hospitals: A Scoping Review

引用

IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Noselli, Monica Van Der Vegt, Anton Cordeil, Maxime Campbell, Victoria Wang, Audrey P. Shetty, Amith Scott, Ian A. The University of Queensland Faculty of Medicine BrisbaneQLD Australia Centre for Health Service Research Brisbane Australia University of Queensland BrisbaneQLD Australia The University of Queensland Human-Centred Computing discipline School of Electrical Engineering and Computer Science BrisbaneQLD Australia Clinical Excellence Queensland QLD Australia Queensland Health BrisbaneQLD Australia Lab Australia University of Sydney Biomedical Informatics and Digital Informatics division NSW Australia NSW Ministry of Health NSW Australia Princess Alexandra Hospital Metro South Digital Health and Informatics clinical consultant in AI BrisbaneQLD Australia

Our objective was to investigate how artificial intelligence (AI) predictions calculated on structured hospital data are presented to clinicians. We performed a scoping review of 5 databases and 9 other reviews, identifying 31 studies on 21 implemented clinical AI systems. We report current approaches to presenting AI predictions to clinicians, whether and how interaction on the user interface (UI) is used, how UIs have been evaluated and the extent to which clinicians have been involved in UI design and testing. The results indicate variation across systems in presentation content and styles, evaluation methods, and interaction approaches. Half of the systems implemented a co-design approach to UI development. Our findings provide valuable insights for future AI-based clinical decision support system designers, clinical AI researchers and healthcare organisations seeking to implement clinical AI solutions. © 2013 IEEE.

关键词： Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration

arXiv

引用

arXiv 2024年

作者： He, Sunan Nie, Yuxiang Wang, Hongmei Yang, Shu Wang, Yihui Cai, Zhiyuan Chen, Zhixuan Xu, Yingxue Luo, Luyang Xiang, Huiling Lin, Xi Wu, Mingxiang Peng, Yifan Shih, George Xu, Ziyang Wu, Xian Wang, Qiong Chan, Ronald Cheong Kin Vardhanabhuti, Varut Chu, Winnie Chiu Wing Zheng, Yefeng Rajpurkar, Pranav Zhang, Kang Chen, Hao Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Biomedical Informatics Harvard University Boston United States Department of Ultrasound Sun Yat-sen University Cancer Center Guangzhou China Department of Radiology Shenzhen People’s Hospital Shenzhen China Population Health Sciences Weill Cornell Medicine New York United States Department of Radiology Weill Cornell Medicine New York United States Perelman Department of Dermatology New York Langone Health New York United States Jarvis Research Center Tencent YouTu Lab Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Anatomical and Cellular Pathology The Chinese University of Hong Kong Hong Kong State Key Laboratory of Translational Oncology The Chinese University of Hong Kong Hong Kong Department of Diagnostic Radiology The University of Hong Kong Hong Kong Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong Medical Artificial Intelligence Laboratory Westlake University Hangzhou China Faculty of Medicine The Macau University of Science and Technology China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Division of Life Science The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology Hong Kong Shenzhen-Hong Kong Collaborative Innovation Research Institute The Hong Kong University of Science and Technology Shenzhen China

Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in effectively generalizing across diverse tasks and modalities. In the field of medicine, while GFMs exhibit superior generalizability based on their extensive intrinsic knowledge as well as proficiency in instruction following and in-context learning, specialist models excel in precision due to their in-depth domain-specific knowledge. In this work, for the first time, we explore the synergy between the GFM and specialist models, to enable precise medical image analysis on a broader scope. Specifically, we propose a novel cooperative framework, Generalist-Specialist Collaboration (GSCo), which consists of two stages, namely the construction of GFM and specialists, and collaborative inference on downstream tasks. In the construction stage, we develop MedDr, the largest open-source GFM tailored for medicine, showcasing exceptional instruction-following and in-context learning capabilities. Meanwhile, a series of lightweight specialists are crafted for specific downstream tasks with low computational overhead. In the collaborative inference stage, we introduce two cooperative mechanisms, Mixture-of-Expert Diagnosis (MoED) and Retrieval-Augmented Diagnosis (RAD), to harvest the generalist’s in-context learning abilities alongside the specialists’ domain expertise. Concretely, MoED incorporates predictions from specialists as references, while RAD employs specialists to retrieve similar cases, collectively providing MedDr with much more contextual guidance. For a comprehensive evaluation, we curate a large-scale benchmark featuring 28 datasets and about 250,000 images across a wide range of medical modalities, including radiology, pathology, dermatology, ophthalmology, and gastroenterology. Extensive experimental results demonstrate that MedDr consistently outperforms state-of-the-art GFMs on downstream datasets. Furthermore, GSCo exceeds both GFMs and specialists across a

关键词： Ophthalmology

来源：评论

学校读者我要写书评

暂无评论

An information-theoretic approach to hypergraph psychometrics

arXiv

引用

arXiv 2022年

作者： Marinazzo, Daniele Van Roozendaal, Jan Rosas, Fernando E. Stella, Massimo Comolatti, Renzo Colenbier, Nigel Stramaglia, Sebastiano Rosseel, Yves Department of Data Analysis Ghent University Belgium Data Science Institute Imperial College London London United Kingdom Centre for Psychedelic Research Imperial College London London United Kingdom Centre for Complexity Science Imperial College London London United Kingdom Department of Informatics University of Sussex Brighton United Kingdom CogNosco Lab Dept. of Computer Science University of Exeter United Kingdom Department of Biomedical and Clinical Sciences "L. Sacco" Università degli Studi di Milano Italy IRCCS San Camillo Hospital Via Alberoni 70 Venice30126 Italy Physics Department Universitá degli Studi di Bari Aldo Moro INFN Sezione di Bari Italy

Psychological network approaches propose to see symptoms or questionnaire items as interconnected nodes, with links between them reflecting pairwise statistical dependencies evaluated on cross-sectional, time-series, or panel data. These networks constitute an established methodology to assess the interactions and relative importance of nodes/indicators, providing an important complement to other approaches such as factor analysis. However, focusing the modelling solely on pairwise relationships can neglect potentially critical information shared by groups of three or more variables in the form of higher-order interdependencies. To overcome this important limitation, here we propose an information-theoretic framework based on hypergraphs as psychometric models. As edges in hypergraphs are capable of encompassing several nodes together, this extension can thus provide a richer representation of the interactions that may exist among sets of psychological variables. Our results show how psychometric hypergraphs can highlight meaningful redundant and synergistic interactions on either simulated or state-of-art, re-analyzed psychometric datasets. Overall, our framework extends current network approaches while leading to new ways of assessing the data that differ at their core from other methods, extending the psychometric toolbox and opening promising avenues for future investigation. © 2022, CC BY.

关键词： Information theory

来源：评论

学校读者我要写书评

暂无评论

Filter Grafting for Deep Neural Networks

Filter Grafting for Deep Neural Networks

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Fanxu Meng Hao Cheng Ke Li Zhixin Xu Rongrong Ji Xing Sun Guangming Lu School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Tencent Youtu Lab Shanghai China Department of Artificial Intelligence School of Informatics Xiamen University China Peng Cheng Laboratory China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

This paper proposes a new learning paradigm called filter grafting, which aims to improve the representation capability of Deep Neural Networks (DNNs). The motivation is that DNNs have unimportant (invalid) filters (e.g., l 1 norm close to 0). These filters limit the potential of DNNs since they are identified as having little effect on the network. While filter pruning removes these invalid filters for efficiency consideration, filter grafting re-activates them from an accuracy boosting perspective. The activation is processed by grafting external information (weights) into invalid filters. To better perform the grafting process, we develop an entropy-based criterion to measure the information of filters and an adaptive weighting strategy for balancing the grafted information among networks. After the grafting operation, the network has very few invalid filters compared with its untouched state, empowering the model with more representation capacity. We also perform extensive experiments on the classification and recognition tasks to show the superiority of our method. For example, the grafted MobileNetV2 outperforms the non-grafted MobileNetV2 by about 7 percent on CIFAR-100 dataset.

关键词： Training Information filters Filtering algorithms Filtering theory Entropy Nickel

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：