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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

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

arXiv

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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

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Improving AlphaFLOW for Efficient Protein Ensembles Generation

arXiv

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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

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

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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

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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

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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

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Time-to-Event Pretraining for 3D Medical Imaging

arXiv

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arXiv 2024年

作者： Huo, Zepeng Fries, Jason Alan Lozano, Alejandro Valanarasu, Jeya Maria Jose Steinberg, Ethan Blankemeier, Louis Chaudhari, Akshay S. Langlotz, Curtis Shah, Nigam H. Center for Biomedical Informatics Research Stanford University United States Department of Biomedical Data Science Stanford University United States Department of Computer Science Stanford University United States Stanford Medical Center Stanford Health Care United States Stanford Center for Artificial Intelligence in Medicine and Imaging United States Prealize Health United States Technology and Digital Solutions Stanford Health Care United States Department of Medicine Stanford School of Medicine United States Human-Centered Artificial Intelligence Institute Stanford University United States Department of Radiology Stanford University United States Clinical Excellence Research Center Stanford School of Medicine United States

With the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predictive of future disease risk. While current self-supervised methods for 3D medical imaging models capture local structural features like organ morphology, they fail to link pixel biomarkers with long-term health outcomes due to a missing context problem. Current approaches lack the temporal context necessary to identify biomarkers correlated with disease progression, as they rely on supervision derived only from images and concurrent text descriptions. To address this, we introduce time-to-event pretraining, a pretraining framework for 3D medical imaging models that leverages large-scale temporal supervision from paired, longitudinal electronic health records (EHRs). Using a dataset of 18,945 CT scans (4.2 million 2D images) and time-to-event distributions across thousands of EHR-derived tasks, our method improves outcome prediction, achieving an average AUROC increase of 23.7% and a 29.4% gain in Harrell’s C-index across 8 benchmark tasks. Importantly, these gains are achieved without sacrificing diagnostic classification performance. This study lays the foundation for integrating longitudinal EHR and 3D imaging data to advance clinical risk prediction. © 2024, CC BY.

关键词： Electronic health record

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Walking-in-Place Characteristics-Based Geriatric Assessment Using Deep Convolutional Neural Networks 42

Walking-in-Place Characteristics-Based Geriatric Assessment ...

引用

42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

作者： Jung, Dawoon Nguyen, Mau Dung Park, Mina Kim, Miji Won, Chang Won Kim, Jinwook Mun, Kyung-Ryoul Center for Imaging Media Research Kist Seoul Korea Republic of University of Science and Technology Department of Human Computer Interface Robotics Engineering Daejeon Korea Republic of

ISBN: (纸本)9781728119908

The world population is aging, and this phenomenon is expected to continue for the next decades. This study aimed to propose a simple and reliable method that can be used for daily in-home monitoring of frailty and cognitive dysfunction in the elderly based on their walking-in-place characteristics. Fifty-four community-dwelling elderly people aged 65 years or older participated in this study. The participants were categorized into the robust and the non-robust groups according to the FRAIL scale. The mini-mental state examination was used to classify the cognitive impairment and the non-cognitive impairment groups. The 3-axis acceleration and the 3-axis angular velocity signals were measured using the inertial measurement units attached to the foot, shank, thigh, and posterior pelvis while each participant was walking in place for 20 seconds. The walking-in-place spectrograms were acquired by applying time-frequency analysis to the lower body movement signals measured in one stride. Four-fold cross-validation was applied to 80% of the total samples and the remaining 20% were used as test data. The deep convolutional neural network-based classifiers trained with the walking-inplace spectrograms enabled to categorize the robust and the non-robust groups with 94.63% accuracy and classify the cognitive impairment and the non-cognitive impairment groups with 97.59% accuracy. This study suggests that the walking-in-place spectrograms, which can be obtained without spacious experimental space, cumbersome equipment, and laborious processes, are effective indicators of frailty and cognitive dysfunction in the elderly. © 2020 IEEE.

关键词： Convolutional neural networks

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Characterising high-order interdependence via entropic conjugation

arXiv

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arXiv 2024年

作者： Rosas, Fernando E. Gutknecht, Aaron Mediano, Pedro A.M. Gastpar, Michael Sussex AI and Sussex Centre for Consciousness Science Department of Informatics University of Sussex United Kingdom Centre for Complexity Science Center for Psychedelic Research Department of Brain Science Imperial College London United Kingdom Center for Eudaimonia and Human Flourishing University of Oxford United Kingdom United Kingdom Campus Institute for Dynamics of Biological Networks Georg-August University Göttingen Germany Department of Computing Imperial College London United Kingdom Division of Psychology and Language Sciences University College London United Kingdom School of Computer and Communication Sciences École Polytechnique Fédérale de Lausanne Switzerland

High-order phenomena play crucial roles in many systems of interest, but their analysis is often highly nontrivial. There is a rich literature providing a number of alternative information-theoretic quantities capturing high-order phenomena, but their interpretation and relationship with each other is not well understood. The lack of principles unifying these quantities obscures the choice of tools for enabling specific type of analyses. Here we show how an entropic conjugation provides a theoretically grounded principle to investigate the space of possible high-order quantities, clarifying the nature of the existent metrics while revealing gaps in the literature. This leads to identify novel notions of symmetry and skew-symmetry as key properties for guaranteeing a balanced account of high-order interdependencies and enabling broadly applicable analyses across physical systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Clutter (information theory)

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Social Choice Should Guide AI Alignment in Dealing with Diverse human Feedback

arXiv

引用

arXiv 2024年

作者： Conitzer, Vincent Freedman, Rachel Heitzig, Jobst Holliday, Wesley H. Jacobs, Bob M. Lambert, Nathan Mossé, Milan Pacuit, Eric Russell, Stuart Schoelkopf, Hailey Tewolde, Emanuel Zwicker, William S. Foundations of Cooperative AI Lab Computer Science Department Carnegie Mellon University Pittsburgh United States Institute for Ethics in AI University of Oxford Oxford United Kingdom Center for Human-Compatible AI Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Potsdam Institute for Climate Impact Research Brandenburg Potsdam Germany Department of Philosophy University of California Berkeley United States Department of Philosophy and Moral Sciences Ghent University Ghent Belgium Allen Institute for AI BerkeleyCA United States Department of Philosophy University of Maryland College Park United States EleutherAI Greece Department of Mathematics Union College Schenectady United States Murat Sertel Center for Advanced Economic Studies Istanbul Bilgi University Istanbul Turkey

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans’ expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about "collective" preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December *** Codes 68T01, 68T50, 91B14, 91B12 Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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