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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

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

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

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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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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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Approaching ocular risks during spaceflight with 3D printing: Technical strategies to protect astronaut vision

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Life sciences in Space Research 2025年 47卷 98-104页

作者： Saif Pasha Joshua Ong Yannie Guo Ryung Lee Ethan Waisberg Andrew G. Lee Prithul Sarker Alireza Tavakkoli Virginia Tech Carilion School of Medicine Roanoke VA USA Department of Ophthalmology and Visual Sciences University of Michigan Kellogg Eye Center Ann Arbor MI USA Baylor College of Medicine Houston TX USA Touro College of Osteopathic Medicine New York NY USA Department of Clinical Neuroscience University of Cambridge Cambridge UK Center for Space Medicine Baylor College of Medicine Houston TX USA Department of Ophthalmology Blanton Eye Institute Houston Methodist Hospital Houston TX USA The Houston Methodist Research Institute Houston Methodist Hospital Houston TX USA Departments of Ophthalmology Neurology and Neurosurgery Weill Cornell Medicine New York New York USA Department of Ophthalmology University of Texas Medical Branch Galveston TX USA University of Texas MD Anderson Cancer Center Houston TX USA Texas A&M School of Medicine Bryan TX USA Department of Ophthalmology The University of Iowa Hospitals and Clinics IA City IA USA Human-Machine Perception Laboratory Department of Computer Science and Engineering University of Nevada Reno NV USA Department of Computer Science University of Nevada Reno Reno NV USA

Long-duration spaceflight poses significant risks to ocular health due to prolonged microgravity exposure and space environmental stressors, contributing to conditions such as Spaceflight-Associated Neuro-ocular Syndrome (SANS) and Spaceflight-Associated Dry Eye Syndrome (SADES). These conditions, along with radiation exposure and risk of ocular trauma in resource-limited environments, necessitate development of innovative countermeasures to safeguard astronauts' vision, particularly for future planetary missions such as Mars. Traditional ophthalmic care depends on specialized equipment and materials impractical for cargo limitations and transport in space, highlighting the need for adaptive solutions. Advances in 3D printing and bioprinting offer an innovative approach to space ophthalmology by enabling on-demand fabrication of customized eyewear, contact lenses, moisture chambers, radiation-shielding lenses, and surgical tools. Furthermore, emerging bioprinting capabilities may facilitate production of biocompatible tissues for ocular repair. The precision, adaptability, and mission-specific applicability of 3D printing provide a strategic advantage to address preventive and therapeutic ocular health needs. However, challenges include optimizing biocompatible materials, refining high-resolution printing techniques, and ensuring structural and functional viability of printed tissues in space conditions. Further research is required to improve material durability, integrate protective elements such as boron nitride nanotubes, and adapt 3D printing processes to the constraints of microgravity. Beyond space medicine, 3D printing applications in space can drive innovations for ophthalmic care on Earth, from customized intraocular lenses to regenerative therapies. This review highlights the critical role of 3D printing in space ophthalmology and need for continued development and deployment to ensure success of future deep-space missions.

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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

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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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CapST: An Enhanced and Lightweight Method for Deepfake Video Classification

arXiv

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arXiv 2023年

作者： Ahmad, Wasim Peng, Yan-Tsung Chang, Yuan-Hao Ganfure, Gaddisa Olani Khan, Sarwar Shahzad, Sahibzada Adil Institute of Information Science Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei116 Taiwan Social Networks Human-Centered Computing Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan Dire Dawa University Ethiopia Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

The proliferation of deepfake videos, synthetic media produced through advanced Artificial Intelligence techniques, has raised significant concerns across various sectors, encompassing realms such as politics, entertainment, and security. In response, this research introduces an innovative and streamlined model designed to classify deepfake videos generated by five distinct encoders adeptly. Our approach not only achieves state-of-the-art performance but also optimizes computational resources. At its core, our solution employs part of a VGG19_bn as a backbone to efficiently extract features, a strategy proven effective in image-related tasks. We integrate a Capsule Network coupled with a Spatial-Temporal attention mechanism to bolster the model’s classification capabilities while conserving resources. This combination captures intricate hierarchies among features, facilitating robust identification of deepfake attributes. Delving into the intricacies of our innovation, we introduce an existing video-level fusion technique that artfully capitalizes on temporal attention mechanisms. This mechanism serves to handle concatenated feature vectors, capitalizing on the intrinsic temporal dependencies embedded within deepfake videos. By aggregating insights across frames, our model gains a holistic comprehension of video content, resulting in more precise predictions. Experimental results on an extensive benchmark dataset of deepfake videos (DFDM) showcase the efficacy of our proposed method. Notably, our approach achieves up to a 4% improvement in accurately categorizing deepfake videos compared to baseline models, all while demanding fewer computational resources. Copyright © 2023, The Authors. All rights reserved.

关键词： Routing algorithms

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R2 Indicator and Deep Reinforcement Learning Enhanced Adaptive Multi-Objective Evolutionary Algorithm

arXiv

引用

arXiv 2024年

作者： Tahernezhad-Javazm, Farajollah Rankin, Debbie Bois, Naomi Du Smith, Alice E. Coyle, Damien Intelligent Systems Research Center School of Computing Engineering and Intelligent Systems Ulster University LondonderryBT48 7JL United Kingdom The Bath Institute for Augmented Human University of Bath BathBA2 7AY United Kingdom The Department of Industrial and Systems Engineering Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States

Choosing an appropriate optimization algorithm is essential to achieving success in optimization challenges. Here we present a new evolutionary algorithm structure that utilizes a reinforcement learning-based agent aimed at addressing these issues. The agent employs a double deep q-network to choose a specific evolutionary operator based on feedback it receives from the environment during optimization. The algorithm’s structure contains five single-objective evolutionary algorithm operators. This single-objective structure is transformed into a multi-objective one using the R2 indicator. This indicator serves two purposes within our structure: first, it renders the algorithm multi-objective, and second, provides a means to evaluate each algorithm’s performance in each generation to facilitate constructing the reinforcement learning-based reward function. The proposed R2-reinforcement learning multi-objective evolutionary algorithm (R2-RLMOEA) is compared with six other multiobjective algorithms that are based on R2 indicators. These six algorithms include the operators used in R2-RLMOEA as well as an R2 indicator-based algorithm that randomly selects operators during optimization. We benchmark performance using the CEC09 functions, with performance measured by inverted generational distance and spacing. The R2-RLMOEA algorithm outperforms all other algorithms with strong statistical significance (p © 2024, CC BY.

关键词： Reinforcement learning

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Heart-brain axis:low blood pressure during off-pump CABG surgery is associated with postoperative heart failure

引用

Military Medical Research 2025年第5期 799-802页

作者： Xiu-Yun Liu Jing-Jing Mu Jian-Ge Han Mei-Jun Pang Kuo Zhang Wen-Qian Zhai Nan Su Guang-Jian Ni Zhi-Gang Guo Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin University State Key Laboratory of Advanced Medical Materials and Devices Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Department of Anesthesiology Tianjin University Chest HospitalTianjin Key Laboratory of Cardiovascular Emergency and Critical CareTianjin Municipal Science and Technology Bureau Department of Cardiac Surgery Tianjin University Chest HospitalTianjin Key Laboratory of Cardiovascular Emergency and Critical CareTianjin Municipal Science and Technology Bureau

Dear Editor,The primary objective of the letter is to emphasize the importance of personalized management of arterial blood pressure (ABP) in the context of off-pump coronary artery bypass grafting (CABG) *** artery disease,a leading global cause of mortality,necessitates a substantial number of cardiac surgeries,with approximately 400,000CABG operations conducted annually in the United *** heart failure (HF) is a common occurrence a?t er CABG surgery,with readmission rates within 30 d due to HF ranging from 12%to 16%.

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