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A progressive prediction model towards home-based stroke rehabilitation programs

Smart Health 2022年 23卷

作者： Bo, Wei Cavuoto, Lora Langan, Jeanne Subryan, Heamchand Bhattacharjya, Sutanuka Huang, Ming-Chun Xu, Wenyao Department of Computer Science and Engineering University at Buffalo (SUNY) Buffalo 14260 NY United States Department of Industrial and Systems Engineering University at Buffalo (SUNY) Buffalo 14260 NY United States Department of Rehabilitation Science University at Buffalo (SUNY) Buffalo 14214 NY United States School of Architecture and Planning University at Buffalo (SUNY) Buffalo 14214 NY United States Department of Occupational Therapy Georgia State University Atlanta 30303 GA United States Department of Data and Computational Science Duke Kunshan University Jiangsu 215316 China

More than 795,000 people suffer from stroke each year in the United States. Rehabilitation is crucial to help restore mobility function in activities of daily life. Many research works manifest that effective prediction of rehabilitation outcomes can significantly improve personalized rehabilitation strategies and maximize health outcomes. Existing studies on predicting stroke rehabilitation outcomes are primarily based on clinical or inpatient data, and there are few in-depth investigations in home-based rehabilitation prediction. In this work, we perform a retrospective study on mRehab (mobile rehabilitation), a six-week home-based rehabilitation program, where we investigated the in-program user data across 12 different rehabilitation activities performed by 16 stroke survivors. We proposed a new progressive prediction framework that constructs the multiple linear regression (MLR) and the random forest (RF) models, and found that combining the clinical and demographic data of stroke survivors with movement phenotyping data will significantly improve the prediction model's performance. Specifically, our proposed model can accurately predict the outcome only with the first two weeks’ data with the root mean square error (RMSE) of 0.1050 in the MLR model and 0.0011 in the RF model. Moreover, we identify the features that significantly contribute to the prediction outcomes in home-based rehabilitation programs through feature correlation analyses. To the best of our knowledge, this work is the first study towards the rehabilitation outcome prediction in a home-based program. © 2021

关键词： Mobile health Movement phenotyping Prediction Stroke rehabilitation

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A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models

arXiv

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arXiv 2021年

作者： Chae, Minwoo Kim, Dongha Kim, Yongdai Lin, Lizhen Department of Industrial and Management Engineering Pohang University of Science and Technology Gyeongbuk Pohang37673 Korea Republic of School of Mathematics Statistics and Data Science Data Science Center Sungshin Women’s University Seoul02844 Korea Republic of Department of Statistics Seoul National University Seoul08826 Korea Republic of Department of Applied and Computational Mathematics and Statistics University of Notre Dame South BendIN46556 United States

We investigate statistical properties of a likelihood approach to nonparametric estimation of a singular distribution using deep generative models. More specifically, a deep generative model is used to model high-dimensional data that are assumed to concentrate around some low-dimensional structure. Estimating the distribution supported on this low-dimensional structure, such as a low-dimensional manifold, is challenging due to its singularity with respect to the Lebesgue measure in the ambient space. In the considered model, a usual likelihood approach can fail to estimate the target distribution consistently due to the singularity. We prove that a novel and effective solution exists by perturbing the data with an instance noise, which leads to consistent estimation of the underlying distribution with desirable convergence rates. We also characterize the class of distributions that can be efficiently estimated via deep generative models. This class is sufficiently general to contain various structured distributions such as product distributions, classically smooth distributions and distributions supported on a low-dimensional manifold. Our analysis provides some insights on how deep generative models can avoid the curse of dimensionality for nonparametric distribution estimation. We conduct a thorough simulation study and real data analysis to empirically demonstrate that the proposed data perturbation technique improves the estimation performance *** Codes 62G05 (Primary), 62G20 (Secondary) © 2021, CC BY-NC-SA.

关键词： Perturbation techniques

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Neural Brain: A Neuroscience-inspired Framework for Embodied Agents

arXiv

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arXiv 2025年

作者： Liu, Jian Shi, Xiongtao Nguyen, Thai Duy Zhang, Haitian Zhang, Tianxiang Sun, Wei Li, Yanjie Vasilakos, Athanasios V. Iacca, Giovanni Khan, Arshad Ali Kumar, Arvind Cho, Jae Won Mian, Ajmal Xie, Lihua Cambria, Erik Wang, Lin School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Robotics Hunan University China School of Intelligence Science and Engineering Harbin Institute of Technology Shenzhen China Department of Information and Communication Technology University of Agder Norway Department of Information Engineering and Computer Science University of Trento Italy Elm Company London United Kingdom Division of Computational Science and Technology KTH Royal Institute of Technology Sweden School of Artificial Intelligence and Data Science Sejong University Korea Republic of Department of Computer Science the University of Western Australia Australia College of Computing and Data Science Nanyang Technological University Singapore

The rapid evolution of artificial intelligence (AI) has shifted from static, data-driven models to dynamic systems capable of perceiving and interacting with real-world environments. Despite advancements in pattern recognition and symbolic reasoning, current AI systems, such as large language models, remain disembodied, unable to physically engage with the world. This limitation has driven the rise of embodied AI, where autonomous agents, such as humanoid robots, must navigate and manipulate unstructured environments with human-like adaptability. At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability. A Neural Brain must seamlessly integrate multimodal sensing and perception with cognitive capabilities. Achieving this also requires an adaptive memory system and energy-efficient hardware-software co-design, enabling real-time action in dynamic environments. This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges: (1) defining the core components of Neural Brain and (2) bridging the gap between static AI models and the dynamic adaptability required for real-world deployment. To this end, we propose a biologically inspired architecture that integrates multimodal active sensing, perception-cognition-action function, neuroplasticity-based memory storage and updating, and neuromorphic hardware/software optimization. Furthermore, we also review the latest research on embodied agents across these four aspects and analyze the gap between current AI systems and human intelligence. By synthesizing insights from neuroscience, we outline a roadmap towards the development of generalizable, autonomous agents capable of human-level intelligence in real-world scenarios. Our project page is at Neural-Brain-for-Embodied-Agents. Copyright © 2025, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Prediction of Diblock Copolymer Morphology via Machine Learning

arXiv

引用

arXiv 2023年

作者： Park, Hyun Yu, Boyuan Park, Juhae Sun, Ge Tajkhorshid, Emad de Pablo, Juan J. Schneider, Ludwig Theoretical and Computational Biophysics Group NIH Resource Center for Macromolecular Modeling and Bioinformatics Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Pritzker School of Molecular Engineering University of Chicago 5640 Ellis Ave ChicagoIL60637 United States Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States

A machine learning approach is presented to accelerate the computation of block polymer morphology evolution for large domains over long timescales. The strategy exploits the separation of characteristic times between coarse-grained particle evolution on the monomer scale and slow morphological evolution over mesoscopic scales. In contrast to empirical continuum models, the proposed approach learns stochastically driven defect annihilation processes directly from particle-based simulations. A UNet architecture that respects different boundary conditions is adopted, thereby allowing periodic and fixed substrate boundary conditions of arbitrary shape. Physical concepts are also introduced via the loss function and symmetries are incorporated via data augmentation. The model is validated using three different use cases. Explainable artificial intelligence methods are applied to visualize the morphology evolution over time. This approach enables the generation of large system sizes and long trajectories to investigate defect densities and their evolution under different types of confinement. As an application, we demonstrate the importance of accessing late-stage morphologies for understanding particle diffusion inside a single block. This work has implications for directed self-assembly and materials design in micro-electronics, battery materials, and membranes. Copyright © 2023, The Authors. All rights reserved.

关键词： Block copolymers

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An Approach to Prevent Weight Manipulation by Minimum Adjustment and Maximum Entropy Method in Large-Scale Group Decision Making

SSRN

引用

SSRN 2022年

作者： Sun, Qi Wu, Jian Chiclana, Francisco Wang, Sha Herrera-Viedma, Enrique School of Economics and Management Shanghai Maritime University Shanghai201306 China Center for Artificial Intelligence and Decision Sciences Shanghai Maritime University Shanghai201306 China Institute of Artificial Intelligence Faculty of Computing Engineering and Media De Montfort University Leicester United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence Dept. of Computer Science and AI University of Granada Granada18071 Spain Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

This paper proposes a theoretical framework to prevent weight manipulation in large-scale group decision making (LSGDM). Firstly, a community detection based method is used to cluster the large group. The power relations of subgroups are measured by the rights index (RI), which depends on the subgroups size and cohesion. Then, a minimum adjustment feedback model with maximum entropy is proposed to prevent subgroups’manipulation behavior. The minimum adjustment rule aims for ‘effificiency’ while the maximum entropy rule aims for ‘justice’. Consequently, by keeping a balance between ‘effificiency’ and ‘justice’ in the mechanism of assigning weights, the rationality of weight allocation to reach consensus effificiently can be ensured. Detailed numerical and simulation analyses are carried out to verify the validity of the proposed method. © 2022, The Authors. All rights reserved.

关键词： Decision making

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Epidemic dynamics on metapopulation networks with node2vec mobility

arXiv

引用

arXiv 2021年

作者： Meng, Lingqi Masuda, Naoki Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York BuffaloNY14260-5030 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Metapopulation models have been a powerful tool for both theorizing and simulating epidemic dynamics. In a metapopulation model, one considers a network composed of subpopulations and their pairwise connections, and individuals are assumed to migrate from one subpopulation to another obeying a given mobility rule. While how different mobility rules affect epidemic dynamics in metapopulation models has been studied, there have been relatively few efforts on comparison of the effects of simple (i.e., unbiased) random walks and more complex mobility rules. Here we study a susceptible-infectious-susceptible (SIS) dynamics in a metapopulation model, in which individuals obey a parametric second-order random-walk mobility rule called the node2vec. We map the second-order mobility rule of the node2vec to a first-order random walk in a network whose each node is a directed edge connecting a pair of subpopulations and then derive the epidemic threshold. For various networks, we find that the epidemic threshold is large (therefore, epidemic spreading tends to be suppressed) when the individuals infrequently backtrack or infrequently visit the common neighbors of the currently visited and the last visited subpopulations than when the individuals obey the simple random walk. The amount of change in the epidemic threshold induced by the node2vec mobility is in general not as large as, but is sometimes comparable with, the one induced by the change in the diffusion rate for individuals. Copyright © 2021, The Authors. All rights reserved.

关键词： Random processes

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Trend analysis of the proportional allocation of funding by gender in Spanish National Research Projects: A study by disciplines and staff positions

引用

Journal of Informetrics 2025年第3期19卷

作者： Thamyres T. Choji Jose A. Moral-Munoz Manuel J. Cobo Department of Computer Science and Engineering University of Cádiz Puerto Real 11519 Spain Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada 18071 Spain Department of Nursing and Physiotherapy Faculty of Nursing and Physiotherapy University of Cádiz Cádiz 11009 Spain Biomedical Research and Innovation Institute of Cádiz (INiBICA) University of Cádiz Cádiz 11002 Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada 18071 Spain Unit for Computational Humanities and Social Sciences (U-CHASS) University of Granada Granada 18071 Spain

Given the critical role of research funding in driving knowledge production and promoting gender parity in academia, this study aimed to analyze gender differences in funding allocation over time across different disciplines or areas of knowledge and research staff categories. We analyzed data from 20,843 Spanish grants awarded between 2015 and 2022, matching these records with data on tenured research staff. Considering that in scientific systems, women represent a lower workforce, we analyzed the proportion of grants awarded relative to their presence as associate and full professors. To quantify these differences, we employed proportional gender funding and the women/men ratio. This approach was used to analyze whether the amount of grants awarded was proportional to their presence as tenured staff members. Our findings reveal significant disparities in grant allocation and tenured staff, with women receiving approximately 33% of the grants and representing 43% and 38% of associate and full professors, respectively. This difference was higher in terms of areas of knowledge, with engineering and Architecture having the lowest women/men ratio and Arts and Humanities having the highest ratio. This pattern was repeated among associate and full professors with pronounced differences in engineering and Architecture . Despite this, the longitudinal analysis showed that the differences decreased over time, showing a positive trend for both staff categories and across different areas of knowledge. Regarding proportionality, we observed that the proportion of grants awarded to women agreed with their representation as tenured staff, reaching its highest value by 2022. In areas where women received fewer grants and were fewer associate and full professors, such as engineering and Architecture , the proportion of grants awarded to women was similar to that of men. Although our findings indicate a positive trend favoring women, more action needs to be taken. Future research c

关键词： Gender-differences SDG-5 Grants Research-staff Trend-analysis

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GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging

arXiv

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arXiv 2024年

作者： Pati, Sarthak Mazurek, Szymon Bakas, Spyridon Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning Indiana University School of Medicine IndianapolisIN United States Medical Working Group MLCommons San FranciscoCA United States AGH University of Krakow Academic Computer Centre Cyfronet Krakow Poland Indiana University Melvin and Bren Simon Comprehensive Cancer Center IndianapolisIN United States Department of Radiology & Imaging Sciences Indiana University School of Medicine IndianapolisIN United States Department of Biostatistics & Health Data Science Indiana University School of Medicine IndianapolisIN United States Department of Neurological Surgery Indiana University School of Medicine IndianapolisIN United States Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IndianapolisIN United States

Generative Artificial Intelligence (GenAI) is a field of AI that creates new data samples from existing ones. It utilizing deep learning to overcome the scarcity and regulatory constraints of healthcare data by generating new data points that integrate seamlessly with original datasets. This paper explores the background and motivation for GenAI, and introduces the Generally Nuanced Deep Learning Framework for Synthesis (GaNDLF-Synth) to address a significant gap in the literature and move towards democratizing the implementation and assessment of image synthesis tasks in healthcare. GaNDLF-Synth describes a unified abstraction for various synthesis algorithms, including autoencoders, generative adversarial networks, and diffusion models. Leveraging the GANDLF-core framework, it supports diverse data modalities and distributed computing, ensuring scalability and reproducibility through extensive unit testing. The aim of GaNDLF-Synth is to lower the entry barrier for GenAI, and make it more accessible and extensible by the wider scientific community. © 2024, CC BY.

关键词： Generative adversarial networks

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