检索结果-内蒙古大学图书馆

The understanding of congruent and incongruent referential gaze in 17-month-old infants: an eye-tracking study comparing human and robot (vol 10, pg 11918, 2020)

引用

SCIENTIFIC REPORTS 2020年第1期10卷 1-10页

作者： Manzi, F. Ishikawa, M. Di Dio, C. Itakura, S. Kanda, T. Ishiguro, H. Massaro, D. Marchetti, A. Research Unit on Theory of Mind Department of Psychology Università Cattolica del Sacro Cuore Milan Italy School of Graduated Letter Department of Psychology Kyoto University Kyoto Japan Centre for Baby Science Doshisha University Kyoto Japan Human-Robot Interaction Laboratory Department of Computer Science Kyoto University Kyoto Japan Advanced Telecommunications Research Institute International IRC/HIL Keihanna Science City Kyoto Japan Department of Systems Innovation Osaka University Toyonaka Japan

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dose-response effect of pre-exercise carbohydrates under muscle glycogen unavailability:Insights from McArdle disease

引用

Journal of Sport and Health science 2024年第3期13卷 398-408页

作者： Pedro L.Valenzuela Alfredo Santalla Lidia B.Alejo Andrea Merlo Asuncion Bustos Laura Castellote-Belles Roser Ferrer-Costa Nicola A.Maffiuletti David Barranco-Gil Tomas Pinos Alejandro Lucia Physical Activity and Health Research Group(“PaHerg”) Research Institute of Hospital“12 de Octubre”(“imas12”)Madrid 28041Spain Department of Systems Biology University of AlcalaMadrid 28871Spain Department of Sport and Computer Science Section of Physical Education and SportsFaculty of SportUniversidad Pablo de OlavideSevilla 41013Spain EVOPRED Research Group Universidad Europea de CanariasTenerife 38300Spain Faculty of Sport Sciences Universidad Europea de MadridMadrid 28670Spain Gait&Motion Analysis Laboratory Sol et Salus HospitalTorre Pedrera di Rimini(RN)47922Italy Department of Clinical Biochemistry Laboratoris ClınicsHospital Universitari Vall d’HebronBarcelona 08035Spain Human Performance Lab Schulthess ClinicZurich 8008Switzerland Biomedical Research Networking Center on Rare Disorders(CIBERER) Barcelona 08035Spain Mitochondrial and Neuromuscular Disorders Unit Vall d’Hebron Institut de RecercaUniversitat Autonoma de BarcelonaBarcelona 08035Spain

Background:This study aimed to determine the effect of different carbohydrate(CHO)doses on exercise capacity in patients with McArdle disease—the paradigm of“exercise intolerance”,characterized by complete muscle glycogen unavailability—and to determine whether higher exogenous glucose levels affect metabolic responses at the McArdle muscle cell(in vitro)***:Patients with McArdle disease(n=8)and healthy controls(n=9)underwent a 12-min submaximal cycling constant-load bout followed by a maximal ramp test 15 min after ingesting a non-caloric *** a randomized,double-blinded,cross-over design,patients repeated the tests after consuming either 75 g or 150 g of CHO(glucose:fructose=2:1).Cardiorespiratory,biochemical,perceptual,and electromyographic(EMG)variables were ***,glucose uptake and lactate appearance were studied in vitro in wild-type and McArdle mouse myotubes cultured with increasing glucose concentrations(0.35,1.00,4.50,and 10.00 g/L).Results:Compared with controls,patients showed the“classical”second-wind phenomenon(after prior disproportionate tachycardia,myalgia,and excess electromyographic activity during submaximal exercise,all p<0.05)and an impaired endurance exercise capacity(-51%ventilatory threshold and55%peak power output,both p<0.001).Regardless of the CHO dose(p<0.05 for both doses compared with the placebo),CHO intake increased blood glucose and lactate levels,decreased fat oxidation rates,and attenuated the second wind in the ***,only the higher dose increased ventilatory threshold(+27%,p=0.010)and peak power output(+18%,p=0.007).In vitro analyses revealed no differences in lactate levels across glucose concentrations in wild-type myotubes,whereas a doseresponse effect was observed in McArdle ***:CHO intake exerts beneficial effects on exercise capacity in McArdle disease,a condition associated with total muscle glycogen *** of these benefits are dose dependent.

关键词： Endurance Glycogen storage disease Glycogenosis Nutrition Supplement

来源：评论

学校读者我要写书评

暂无评论

A Precise Hip Protection System with Multi-scale Fall Warning Algorithm Based on Offset Displacement

A Precise Hip Protection System with Multi-scale Fall Warnin...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Qiangqiang Chen Yanan Diao Yaping Wang Yumin Chen Yunkun Ning Guanglin Li Guoru Zhao CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Research Center for Neural Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China College of Computer Science and Technology Xiamen University of Technology China Department of Neurology Shenzhen Second People's Hospital Shenzhen China

Falls occur frequently in daily life and the damage to the body is irreversible. Therefore, it is crucial to implement timely and effective warning and protection systems for falls to minimize the damage caused by falls. Currently, the fall warning algorithm has shortcomings such as low recognition rates for falls and fall-risk movements and insufficient lead-time, the time before the subject impacts the floor, making it difficult for falling protection devices to function effectively. In this study, a multi-scale falls warning algorithm based on offset displacement is built, and a hip protection system is designed. The performance of the algorithm and the system is validated using 150 falling and 500 fall-risk actions from 10 volunteers. The results showed that the recognition accuracy for falling actions is 98.7% and the recognition accuracy for fall-risk actions is 99.4%, with an average lead-time of 402ms. The protection rate for falling movements reached 98.7%. This proposed algorithm and hip protection system have the potential to be applied in elderly communities, hospitals, and homes to reduce the damage caused by *** Relevance— This study provides important reference for clinicians in analyzing fall behaviors to patients at risk of falls in clinical settings, offering valuable technical support for ensuring the safety of patients in danger of falling. It also contributes to further promoting the development of falling-prevention medical devices.

关键词：

来源：评论

学校读者我要写书评

暂无评论

human Subjects Research in the Age of Generative AI: Opportunities and Challenges of Applying LLM-Simulated Data to HCI Studies

Human Subjects Research in the Age of Generative AI: Opportu...

引用

2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Hwang, Angel Hsing-Chi Bernstein, Michael S. Sundar, S. Shyam Zhang, Renwen Ribeiro, Manoel Horta Lu, Yingdan Chang, Serina Wu, Tongshuang Yang, Aimei Williams, Dmitri Park, Joon Sung Ognyanova, Katherine Xiao, Ziang Shaw, Aaron Shamma, David A. University of Southern California Los AngelesCA United States Stanford University StanfordCA United States Media Effects Research Laboratory Penn State University University ParkPA United States Sungkyunkwan University Seoul Korea Republic of Department of Communications and New Media National University of Singapore Singapore Singapore Princeton University PrincetonNJ United States Department of Communication Studies School of Communication Northwestern University EvanstonIL United States EECS & Computational Precision Health University of California Berkeley BerkeleyCA United States Microsoft Research New York CityNY United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Computer Science Department Stanford University StanfordCA United States School of Communication & Information Rutgers University New BrunswickNJ United States Johns Hopkins University BaltimoreMD United States Northwestern University EvanstonIL United States Toyota Research Institute Los AltosCA United States

ISBN: (纸本)9798400713958

Rapid advances in generative artificial intelligence suggest new possibilities for how human subjects research can be conducted in HCI studies. The panel invites both computer and social scientists to discuss future directions for applying simulated responses from large language models (LLM) for human subjects research. We discuss current challenges and opportunities in LLM simulations and brainstorm how insights across different disciplines might inform breakthroughs. We pay close attention to when and how applications of LLM simulations might augment human subjects research instead of steering it toward unintended directions. Discussions from the panel will provide preliminary ideas for when and how HCI researchers can apply LLM simulations to human subjects research pipelines. Through this engagement, we also aim to build a research community with shared interests. © 2025 Copyright held by the owner/author(s).

关键词： Biographies

来源：评论

学校读者我要写书评

暂无评论

Theory-based habit modeling for enhancing behavior prediction

arXiv

引用

arXiv 2021年

作者： Zhang, Chao Vanschoren, Joaquin van Wissen, Arlette Lakens, Daniël de Ruyter, Boris IJsselsteijn, Wijnand A. Human-Technology Interaction Group Eindhoven University of Technology Eindhoven Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Brain Behavior and Cognition Philips Research Eindhoven Netherlands

Psychological theories of habit posit that when a strong habit is formed through behavioral repetition, it can trigger behavior automatically in the same environment. Given the reciprocal relationship between habit and behavior, changing lifestyle behaviors (e.g., toothbrushing) is largely a task of breaking old habits and creating new and healthy ones. Thus, representing users’ habit strengths can be very useful for behavior change support systems (BCSS), for example, to predict behavior or to decide when an intervention reaches its intended effect. However, habit strength is not directly observable and existing self-report measures are taxing for users. In this paper, built on recent computational models of habit formation, we propose a method to enable intelligent systems to compute habit strength based on observable behavior. The hypothesized advantage of using computed habit strength for behavior prediction was tested using data from two intervention studies, where we trained participants to brush their teeth twice a day for three weeks and monitored their behaviors using accelerometers. Through hierarchical cross-validation, we found that for the task of predicting future brushing behavior, computed habit strength clearly outperformed self-reported habit strength (in both studies) and was also superior to models based on past behavior frequency (in the larger second study). Our findings provide initial support for our theory-based approach of modeling user habits and encourages the use of habit computation to deliver personalized and adaptive interventions. © 2021, CC BY.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

An Empirical Study on the Fairness of Foundation Models for Multi-Organ Image Segmentation

arXiv

引用

arXiv 2024年

作者： Li, Qing Zhang, Yizhe Li, Yan Lyu, Jun Liu, Meng Sun, Longyu Sun, Mengting Li, Qirong Mao, Wenyue Wu, Xinran Zhang, Yajing Chu, Yinghua Wang, Shuo Wang, Chengyan Human Phenome Institute Fudan University Shanghai China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Psychiatry Brigham and Women's Hospital Harvard Medical School BostonMA United States School of Computer Science Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China MR Business Unit Philips Healthcare Suzhou China Simens Healthineers Ltd. China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China

The segmentation foundation model, e.g., Segment Anything Model (SAM), has attracted increasing interest in the medical image community. Early pioneering studies primarily concentrated on assessing and improving SAM's performance from the perspectives of overall accuracy and efficiency, yet little attention was given to the fairness considerations. This oversight raises questions about the potential for performance biases that could mirror those found in task-specific deep learning models like nnU-Net. In this paper, we explored the fairness dilemma concerning large segmentation foundation models. We prospectively curate a benchmark dataset of 3D MRI and CT scans of the organs including liver, kidney, spleen, lung and aorta from a total of 1056 healthy subjects with expert segmentations. Crucially, we document demographic details such as gender, age, and body mass index (BMI) for each subject to facilitate a nuanced fairness analysis. We test state-of-the-art foundation models for medical image segmentation, including the original SAM, medical SAM and SAT models, to evaluate segmentation efficacy across different demographic groups and identify disparities. Our comprehensive analysis, which accounts for various confounding factors, reveals significant fairness concerns within these foundational models. Moreover, our findings highlight not only disparities in overall segmentation metrics, such as the Dice Similarity Coefficient but also significant variations in the spatial distribution of segmentation errors, offering empirical evidence of the nuanced challenges in ensuring fairness in medical image segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Improving AlphaFLOW for Efficient Protein Ensembles Generation

arXiv

引用

arXiv 2024年

作者： Li, Shaoning Li, Mingyu Wang, Yusong He, Xinheng Zheng, Nanning Zhang, Jian Heng, Pheng Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Shanghai Institute of Materia Medica Chinese Academy of Sciences China

Investigating conformational landscapes of proteins is a crucial way to understand their biological functions and properties. AlphaFLOW stands out as a sequence-conditioned generative model that introduces flexibility into structure prediction models by fine-tuning AlphaFold under the flow-matching framework. Despite the advantages of efficient sampling afforded by flow-matching, AlphaFLOW still requires multiple runs of AlphaFold to finally generate one single conformation. Due to the heavy consumption of AlphaFold, its applicability is limited in sampling larger set of protein ensembles or the longer chains within a constrained timeframe. In this work, we propose a feature-conditioned generative model called AlphaFLOW-Lit to realize efficient protein ensembles generation. In contrast to the full fine-tuning on the entire structure, we focus solely on the light-weight structure module to reconstruct the conformation. AlphaFLOW-Lit performs on-par with AlphaFLOW and surpasses its distilled version without pretraining, all while achieving a significant sampling acceleration of around 47 times. The advancement in efficiency showcases the potential of AlphaFLOW-Lit in enabling faster and more scalable generation of protein ensembles. © 2024, CC BY.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

Augmented reality and robotics in education: A systematic literature review

Computers in Human Behavior: Artificial Humans

引用

computers in human Behavior: Artificial humans 2025年 4卷

作者： Christina Pasalidou Chris Lytridis Avgoustos Tsinakos Nikolaos Fachantidis Laboratory of Informatics & Robotic Applications in Education and Society Department of Educational and Social Policy University of Macedonia Thessaloniki Greece Human-Machines Interaction (HUMAIN) Lab Department of Computer Science Democritus University of Thrace Kavala Greece Advanced Educational Technologies and Mobile Applications (AETMA) Lab Department of Computer Science Democritus University of Thrace Kavala Greece

Integrating cutting-edge technologies into education has been a continuous goal to enhance teaching and learning experiences. Augmented Reality (AR) and robotics are two emerging technologies that have shown promise in transforming educational environments. This paper presents a systematic review of the literature on the combination of AR and robotics for educational purposes, identifying key applications, benefits, and trends. Using the PRISMA methodology, 69 relevant studies from five major databases were analysed and categorised into three themes: (a) AR and Socially Assistive Robots (SAR), (b) AR-assisted educational robotics, and (c) AR in robotics/engineering education. The review provides insights into how different AR-enhanced robotics applications across primary, secondary, and higher education, provide visualizations, multimodal feedback, and immersive experiences. Key findings suggest that while interactive features of AR and the embodiment of robots show promising results for learning, fostering motivation, excitement, positive attitudes, and enriched educational experiences, challenges such as technological complexity and cost remain barriers to widespread adoption. Future research should focus on pedagogical frameworks and large-scale implementations to optimize AR-robotics integration in diverse educational settings.

关键词： Robotics Augmented reality HRI Education

来源：评论

学校读者我要写书评

暂无评论

Building Expertise on FAIR Through Evolving Bring Your Own Data(BYOD) Workshops: Describing the Data, Software, and Management-focused Approaches and Their Evolution

引用

Data Intelligence 2024年第2期6卷 429-456页

作者： César H.Bernabé Lieze Thielemans Rajaram Kaliyaperumal Claudio Carta Shuxin Zhang Celia W.G.van Gelder Nirupama Benis Luiz Olavo Bonino da Silva Santos Ronald Cornet Bruna dos Santos Vieira Nawel Lalout Ines Henriques Alberto Camara Ballesteros Kees Burger Martijn G.Kersloot Friederike Ehrhart Esther van Enckevort Chris T.Evelo Alasdair J.G.Gray Marc Hanauer Kristina Hettne Joep de Ligt Arnaldo Pereira Nuria Queralt-Rosinach Erik Schultes Domenica Taruscio Andra Waagmeester Mark D.Wilkinson Egon L.Willighagen Mascha Jansen Barend Mons Marco Roos Annika Jacobsen Biosemantics Group Department of Human GeneticsLeiden University Medical CentreLeidenThe Netherlands NIHR Academic Fellow in General Practice University College London and The Royal Free NHS Foundation Trust National Centre for Rare Diseases Istituto Superiore di SanitaRomeItaly Department of Medical Informatics Amsterdam UMC location University of AmsterdamMeibergdreef 9AmsterdamThe Netherlands Amsterdam Public Health Methodology&Digital HealthAmsterdamThe Netherlands Health-Rl UtrechtThe Netherlands Semantics Services and Cybersecurity groupUniversity of TwenteEnschedeThe Netherlands Department of Medical Imaging Radboud Institute for Health SciencesRadboud University Medical CenterNijmegenThe Netherlands Department of Medical BioSciences Radboud University Medical CenterNijmegenThe Netherlands The Duchenne Parent Project VeneendaalThe Netherlands Departamento de Biotecnologia-Biologia Vegetal Escuela Tecnica Superior de Ingenieria AgronomicaAlimentaria y de BiosistemasCentro de Biotecnologia y Genomica de PlantasUniversidad Politecnica de Madrid(UPM)-Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria-CSIC(INIA-CSIC).Campus MontegancedoPozuelo de Alarcon(Madrid)Spain Castor AmsterdamThe Netherlands Department of Bioinformatics-BiGCaT Maastricht UniversityMaastrichtThe Netherlands University Medical Center Groningen University of GroningenGroningenThe Netherlands Maastricht Centre for Systems Biology(MaCSBio) Maastricht UniversityMaastrichtThe Netherlands Department of Computer Science Heriot-Watt UniversityEdinburghUK INSERM US14-OrphanetPlateforme Maladies RaresParisFrance Centre for Digital Scholarship Leiden University LibrariesThe Netherlands Center for Molecular Medicine Cancer Genomics NetherlandsDepartment of GeneticsUniversity Medical Center UtrechtUniversity UtrechtThe Netherlands University of Aveiro DETI/IEETAAveiroPortugal GO FAIR Foundation LeidenThe Netherlands The Leiden Academic Center for Drug Research LeidenThe Netherlands Micel

Since 2014,"Bring Your Own Data"workshops(BYODs)have been organised to inform people about the process and benefits of making resources Findable,Accessible,Interoperable,and Reusable(FAIR,and the FAIRification process).The BYOD workshops'content and format differ depending on their goal,context,and the background and needs of ***-focused BYODs educate domain experts on how to make their data FAIR to find new answers to research ***-focused BYODs promote the benefits of making data FAIR and instruct project managers and policy-makers on the characteristics of FAIRification ***-focused BYODs gather software developers and experts on FAIR to implement or improve software resources that are used to support ***,these BYODs intend to foster collaboration between different types of stakeholders involved in data management,curation,and reuse(*** experts,trainers,developers,data owners,data analysts,FAIR experts).The BYODs also serve as an opportunity to learn what kind of support for FAIRification is needed from different communities and to develop teaching materials based on practical examples and *** this paper,we detail the three different structures of the BYODs and describe examples of early BYODs related to plant breeding data,and rare disease registries and biobanks,which have shaped the structure of the *** discuss the latest insights into making BYODs more productive by leveraging our almost ten years of training experience in these workshops,including successes and encountered ***,we examine how the participants'feedback has motivated the research on FAIR,including the development of workflows and software.

关键词： FAIR FAIRification FAIR expertise Bring Your Own Data Workshop BYOD

来源：评论

学校读者我要写书评

暂无评论

MILD: Multimodal Interactive Latent Dynamics for Learning human-Robot interaction

MILD: Multimodal Interactive Latent Dynamics for Learning Hu...

引用

IEEE-RAS International Conference on humanoid Robots

作者： Vignesh Prasad Dorothea Koert Ruth Stock-Homburg Jan Peters Georgia Chalvatzaki Department of Computer Science TU Darmstadt Germany Department of Law and Economics Chair for Marketing and Human Resource Management TU Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Research Department: Systems AI for Robot Learning German Research Center for AI (DFKI) Hessian.AI

ISBN: (纸本)9798350309805

Modeling interaction dynamics to generate robot trajectories that enable a robot to adapt and react to a human's actions and intentions is critical for efficient and effective collaborative human-Robot interactions (HRI). Learning from Demonstration (LfD) methods from human-human interactions (HHI) have shown promising results, especially when coupled with representation learning techniques. However, such methods for learning HRI either do not scale well to high dimensional data or cannot accurately adapt to changing via-poses of the interacting partner. We propose Multimodal Interactive Latent Dynamics (MILD), a method that couples deep representation learning and probabilistic machine learning to address the problem of two-party physical HRIs. We learn the interaction dynamics from demonstrations, using Hidden Semi-Markov Models (HSMMs) to model the joint distribution of the interacting agents in the latent space of a Variational Autoencoder (VAE). Our experimental evaluations for learning HRI from HHI demonstrations show that MILD effectively captures the multimodality in the latent representations of HRI tasks, allowing us to decode the varying dynamics occurring in such tasks. Compared to related work, MILD generates more accurate trajectories for the controlled agent (robot) when conditioned on the observed agent's (human) trajectory. Notably, MILD can learn directly from camera-based pose estimations to generate trajectories, which we then map to a humanoid robot without the need for any additional training. Supplementary Material: https://***/MILD-HRI.

关键词： Representation learning Training Adaptation models Pose estimation human-robot interaction humanoid robots Collaboration

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：