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Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-13页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Dept. of Math and Computer Science Brandon University Canada

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE

关键词： Decision trees

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Adaptive particle swarm optimization model for resource leveling

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Evolving Systems 2023年第4期14卷 593-604页

作者： Lin, Jerry Chun-Wei Lv, Qing Yu, Dehu Srivastava, Gautam Chen, Chun-Hao School of Information and Control Engineering Qingdao University of Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway School of Civil Engineering Qingdao University of Technology Qingdao China School of Civil Engineering Shandong Jianzhu University Jinan China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan

In the process of engineering project construction, the balanced allocation of resources has an important impact on the purchase of actual materials, the progress of the site construction and the arrangement of temporary facilities. Although there have been many studies on the use of evolutionary computation (EC) to solve the fixed duration of the resource-leveling problem, the optimization effect of standard EC to solve the resource-balancing problem in large-scale network design planning is reduced due to the phenomenon of premature convergence of PSO and the complexity of large-scale network planning. Therefore, in this paper, an adaptive dissipative particle swarm optimization (ADPSO) algorithm is used to solve the resource balancing optimization problem for different network-plans scales, and the feasibility of the developed model is verified using four different scaled network plans (databases). At the same time, a solution to the dynamic resource-leveling problem with optimization for a fixed duration is proposed. The results show that the proposed models are suitable for solving the problem of resource-leveling with a fixed duration of optimization of different scale network plans, and their accuracy and stability are significantly improved compared to the state-of-the-art approaches. Moreover, the proposed models are accurate and suitable for combining the calculation method of network planning time parameters with the ADPSO algorithm to realize dynamic resource balancing optimization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Particle swarm optimization (PSO)

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Collaborative Control of Aerial Robots for Inferring Human Intent from Gaze Following

Collaborative Control of Aerial Robots for Inferring Human I...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Chipade, Vishnu S. Gilbert, Alia Harari, Daniel Panagou, Dimitra University of Michigan Department of Aerospace Engineering Ann ArborMI United States University of Michigan Department of Robotics Ann ArborMI United States Weizmann Institute of Science Weizmann Ai Center Department of Computer Science and Applied Math Israel

ISBN: (纸本)9798350335446

In an ideal human-robot collaboration, autonomous robots work side-by-side with humans in a joint workspace, often performing complementary tasks to the humans. A robotic ability to infer human intention and goals directly from human behavior will facilitate the collaboration and maximize its efficiency. In this paper, we focus on inferring which object the human wants picked up next, based on what the human is looking at, by visually following the human gaze and head orientation. We develop a coordination protocol for a team of aerial robots to extract effective human head and gaze cues. The aerial robots are controlled to navigate around the human and collect data that improves the detection of the human's gaze and hence the intended object to be picked up. The effectiveness of the approach is shown using simulations in AirSim, a photo-realistic simulator. © 2023 IEEE.

关键词： Antennas

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First-Order Federated Bilevel Learning 39

First-Order Federated Bilevel Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Yifan Xiao, Peiyao Ma, Shiqian Ji, Kaiyi Department of Computer Science and Engineering University at Buffalo United States Department of Computational Applied Math and Operations Research Rice University United States

ISBN: (纸本)157735897X

Federated bilevel optimization (FBO) has garnered significant attention lately, driven by its promising applications in meta-learning and hyperparameter optimization. Existing algorithms generally aim to approximate the gradient of the upper-level objective function (hypergradient) in the federated setting. However, because of the nonlinearity of the hypergradient and client drift, they often involve complicated computations. These computations, like multiple optimization sub-loops and second-order derivative evaluations, end up with significant memory consumption and high computational costs. In this paper, we propose a computationally and memory-efficient FBO algorithm named MemFBO. MemFBO features a fully single-loop structure with all involved variables updated simultaneously, and uses only first-order gradient information for all local updates. We show that MemFBO exhibits a linear convergence speedup with milder assumptions in both partial and full client participation scenarios. We further implement MemFBO in a novel FBO application for federated data cleaning. Our experiments, conducted on this application and federated hyper-representation, demonstrate the effectiveness of the proposed algorithm. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Riemannian Bilevel Optimization

arXiv

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arXiv 2024年

作者： Li, Jiaxiang Ma, Shiqian Department of Electrical and Computer Engineering University of Minnesota Twin Cities United States Department of Computational Applied Math and Operations Research Rice University United States

In this work, we consider the bilevel optimization problem on Riemannian manifolds. We inspect the calculation of the hypergradient of such problems on general manifolds and thus enable the utilization of gradient-based algorithms to solve such problems. The calculation of the hypergradient requires utilizing the notion of Riemannian cross-derivative and we inspect the properties and the numerical calculations of Riemannian cross-derivatives. Algorithms in both deterministic and stochastic settings, named respectively RieBO and RieSBO, are proposed that include the existing Euclidean bilevel optimization algorithms as special cases. Numerical experiments on robust optimization on Riemannian manifolds are presented to show the applicability and efficiency of the proposed *** Codes 90C26 Copyright © 2024, The Authors. All rights reserved.

关键词： Numerical methods

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Critically-Damped Third-Order Langevin Dynamics

Critically-Damped Third-Order Langevin Dynamics

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Benjamin Sterling Mónica F. Bugallo Department of Applied Math & Statistics Stony Brook University Stony Brook NY USA Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

While systems analysis has been studied for decades in the context of control theory, it has only been recently used to improve the convergence of Denoising Diffusion Probabilistic Models. This work describes a novel improvement to Third-Order Langevin Dynamics (TOLD), a recent diffusion method that performs better than its predecessors. This improvement, abbreviated TOLD++, is carried out by critically damping the TOLD forward transition matrix similarly to Dockhorn’s Critically-Damped Langevin Dynamics (CLD). Specifically, it exploits eigen-analysis of the forward transition matrix to derive the optimal set of dynamics under the original TOLD scheme. TOLD++ is theoretically guaranteed to converge faster than TOLD, and its faster convergence is verified on the Swiss Roll toy dataset and CIFAR-10 dataset according to the FID metric.

关键词： Damping Measurement Training Toy manufacturing industry Noise reduction Signal processing Diffusion models Speech processing System analysis and design Convergence

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Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

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npj Computational Materials 2023年第1期9卷 1402-1410页

作者： Ying Huang David Perlmutter Andrea Fei-Huei Su Jerome Quenum Pavel Shevchenko Dilworth Y.Parkinson Iryna V.Zenyuk Daniela Ushizima Department of Materials Science&Engineering University of California IrvineIrvineCaliforniaUSA National Fuel Cell Research Center University of California IrvineIrvineCaliforniaUSA Applied Math and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Department of Chemical&Biomolecular Engineering University of California IrvineIrvineCaliforniaUSA Department of Electrical Engineering and Computer Sciences College of EngineeringUniversity of California BerkeleyBerkeleyCaliforniaUSA Advanced Photon Source Argonne National Laboratory9700 South Cass AvenueLemontIllinoisUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Berkeley Institute for Data Science University of California BerkeleyBerkeleyCaliforniaUSA

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch *** is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the ***,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li *** resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the *** addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited *** also report discoveries about the spatial relationships between these *** approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

关键词： battery lithium state

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Critically Damped Third-Order Langevin Dynamics

arXiv

引用

arXiv 2024年

作者： Sterling, Benjamin Bugallo, Mónica F. Department of Applied Math & Statistics Stony Brook University Stony BrookNY United States Department of Electrical and Computer Engineering Stony Brook University Stony BrookNY United States

While systems analysis has been studied for decades in the context of control theory, it has only been recently used to improve the convergence of Denoising Diffusion Probabilistic Models. This work describes a novel improvement to Third-Order Langevin Dynamics (TOLD), a recent diffusion method that performs better than its predecessors. This improvement, abbreviated TOLD++, is carried out by critically damping the TOLD forward transition matrix similarly to Dockhorn's Critically-Damped Langevin Dynamics (CLD). Specifically, it exploits eigen-analysis of the forward transition matrix to derive the optimal set of dynamics under the original TOLD scheme. TOLD++ is theoretically guaranteed to converge faster than TOLD, and its faster convergence is verified on the Swiss Roll toy dataset and CIFAR-10 dataset according to the FID metric. Copyright © 2024, The Authors. All rights reserved.

关键词： Damping

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Tuning-Free Bilevel Optimization: New Algorithms and Convergence Analysis

arXiv

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arXiv 2024年

作者： Yang, Yifan Ban, Hao Huang, Minhui Ma, Shiqian Ji, Kaiyi Department of Computer Science and Engineering University at Buffalo United States AI Research for Monetization Meta United States Department of Computational Applied Math and Operations Research Rice University United States

Bilevel optimization has recently attracted considerable attention due to its abundant applications in machine learning problems. However, existing methods rely on prior knowledge of problem parameters to determine stepsizes, resulting in significant effort in tuning stepsizes when these parameters are unknown. In this paper, we propose two novel tuning-free algorithms, D-TFBO and S-TFBO. D-TFBO employs a double-loop structure with stepsizes adaptively adjusted by the "inverse of cumulative gradient norms" strategy. S-TFBO features a simpler fully single-loop structure that updates three variables simultaneously with a theory-motivated joint design of adaptive stepsizes for all variables. We provide a comprehensive convergence analysis for both algorithms and show that D-TFBO and S-TFBO respectively require (Equation presented) and (Equation presented) iterations to find an ϵ-accurate stationary point, (nearly) matching their well-tuned counterparts using the information of problem parameters. Experiments on various problems show that our methods achieve performance comparable to existing well-tuned approaches, while being more robust to the selection of initial stepsizes. To the best of our knowledge, our methods are the first to completely eliminate the need for stepsize tuning, while achieving theoretical guarantees. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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Problem-Parameter-Free Decentralized Nonconvex Stochastic Optimization

arXiv

引用

arXiv 2024年

作者： Li, Jiaxiang Chen, Xuxing Ma, Shiqian Hong, Mingyi Department of Electrical and Computer Engineering University of Minnesota Twin Cities United States Department of Mathematics University of California Davis United States Department of Computational Applied Math and Operations Research Rice University United States

Existing decentralized algorithms usually require knowledge of problem parameters for updating local iterates. For example, the hyperparameters (such as learning rate) usually require the knowledge of Lipschitz constant of the global gradient or topological information of the communication networks, which are usually not accessible in practice. In this paper, we propose D-NASA, the first algorithm for decentralized nonconvex stochastic optimization that requires no prior knowledge of any problem parameters. We show that D-NASA has the optimal rate of convergence for nonconvex objectives under very mild conditions and enjoys the linear-speedup effect, i.e. the computation becomes faster as the number of nodes in the system increases. Extensive numerical experiments are conducted to support our findings. Copyright © 2024, The Authors. All rights reserved.

关键词： NASA

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