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arXiv 2024年

作者： Choi, Bo-Eun Werk, Jessica K. Tchernyshyov, Kirill Prochaska, J. Xavier Zheng, Yong Putman, Mary E. Fielding, Drummond B. Strader, Jay Department of Astronomy University of Washington SeattleWA98195 United States Department of Astronomy and Astrophysics University of California Santa CruzCA95064 United States 5-1-5 Kashiwanoha Kashiwa277-8583 Japan Division of Science National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute TroyNY12180 United States Department of Astronomy Columbia University New YorkNY10027 United States Department of Astronomy Cornell University IthacaNY14853 United States Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY10010 United States Center for Data Intensive and Time Domain Astronomy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States

Metals in the diffuse, ionized gas at the boundary between the Milky Way’s interstellar medium (ISM) and circumgalactic medium (CGM), known as the disk-halo interface (DHI), are valuable tracers of the feedback processes that drive the Galactic fountain. However, metallicity measurements in this region are challenging due to obscuration by the Milky Way ISM and uncertain ionization corrections that affect the total hydrogen column density. In this work, we constrain the ionization corrections to neutral hydrogen column densities using precisely measured electron column densities from the dispersion measure of pulsars that lie in the same globular clusters as UV-bright targets with high-resolution absorption spectroscopy. We address the blending of absorption lines with the ISM by jointly fitting Voigt profiles to all absorption components. We present our metallicity estimates for the DHI of the Milky Way based on detailed photoionization modeling to the absorption from ionized metal lines and ionization-corrected total hydrogen columns. Generally, the gas clouds show a large scatter in metallicity, ranging between 0.04 − 3.2 Z, implying that the DHI consists of a mixture of gaseous structures having multiple origins. We estimate the inflow and outflow timescales of the DHI ionized clouds to be 6 − 35 Myr. We report the detection of an infalling cloud with super-solar metallicity that suggests a Galactic fountain mechanism, whereas at least one low-metallicity outflowing cloud (Z © 2024, CC BY.

关键词： Photoionization

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MatterChat: A Multi-Modal LLM for Material science

arXiv

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arXiv 2025年

作者： Tang, Yingheng Xu, Wenbin Cao, Jie Gao, Weilu Farrell, Steven Erichson, Benjamin Mahoney, Michael W. Nonaka, Andy Yao, Zhi Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States NSF National AI Institute for Student-AI Teaming University of Colorado at Boulder Boulder United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States International Computer Science Institute BerkeleyCA United States Department of Statistics University of California at Berkeley BerkeleyCA United States

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material structure data with language-based information through multi-modal large language models (LLMs) offers great potential to support these efforts by enhancing human–AI interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multi-modal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat employs a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat significantly improves performance in material property prediction and human-AI interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis. © 2025, CC BY.

关键词： Structure (composition)

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Fault Diagnosis and Fault-Tolerant Attitude Control of Spacecraft Based on Combined Observer

Fault Diagnosis and Fault-Tolerant Attitude Control of Space...

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第40届中国控制会议

作者： Yuandong Li Yizhong Fang Menghan Shao Xuning Chen Xiaodong Shao Qinglei Hu Jianying Zheng School of Automation Science and Electrical Engineering Beihang University The State Key Laboratory of Experiment Physics & Computational Mathematic Beijing Institute of Space Long March Vehicle Beijing Aerospace Automatic Control Institute China Academy of Launch Vehicle Technology School of Aeronautic Science and Engineering Beihang University Beijing Advanced Innovation Center for Big-Data Based Precision Medicine Beihang University

Reaction wheels as the main actuators for attitude control of spacecraft are very prone to malfunction,which may significantly degrade the performance of the spacecraft attitude control *** this paper a novel active fault-tolerant control scheme is proposed to react the reaction wheel faults actively.A combined observer is first designed to detect the reaction wheel faults,by combining a traditional adaptive Luenberger observer and a newly designed neural network（NN） *** inherits the advantages of the low computational cost of the Luenberger observer and the high performance of the NN ***,a fault isolation scheme is given to locate ***,an adaptive fault-tolerant controller is designed to achieve graceful performance degradation under reaction wheel faults,based on the diagnosis results of the combined observer and the judgment results of the fault isolation ***,simulation results show that the proposed scheme is able to achieve precise fault diagnosis and fault-tolerant control of spacecraft in the presence of external interference and limited computation.

关键词： neural network observer fault-tolerant control fault diagnosis spacecraft attitude control

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A comparative analysis of thermal and pressure distribution in rotating flows of von Karman and Bodewadt using multivariate regression models: A case of machine learning techniques

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International Communications in Heat and Mass Transfer 2025年 166卷

作者： Himanshu Upreti Alok Kumar Pandey Ankita Pandey Priya Bartwal School of Engineering and Technology BML Munjal University Gurugram Haryana India Center for Advanced Data and Computational Science BML Munjal University Gurugram Haryana India Department of Mathematics Graphic Era (Deemed to be University) Dehradun Uttarakhand India Department of Mathematics GLA University Mathura Uttar Pradesh India

This work is related to fluid flow over a rotating disk, which has attracted significant interest due to its applications in engineering, aerodynamics, and industrial processes. This research presents a detailed numerical solution of rotating flows of von Karman and Bodewadt. The system of boundary layer approximation consists of the external forces i.e. Hall current, magnetic field, thermal radiation, Darcy-Forchheimer porous model and slip mechanism. The governing equations are boundary value problem which is solved using bvp4c solver, that efficiently handles non-linear differential equations with boundary conditions at two different points. To further analyze the flow characteristics, we employ multivariate linear and polynomial regression models, providing data driven perspective on the dependency of SFC (skin friction coefficient) and LNN (local Nusselt number) on governing parameters. The study reports that, multivariate linear regression and polynomial regression are the machine learning approaches used to estimate the SFC and LNN values for the von Karman and Bodewadt flows, respectively. The MAE (mean absolute error), MSE (mean square error), and R 2 are used to evaluate the prediction's performance. The highest R 2 values for the implemented method is obtained as 0.98905073. The results obtained demonstrate the efficacy of machine learning techniques in this field.

关键词： Bodewadt flow Corcione model Hall current Machine learning techniques Von Karman flow

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Interplay Between Residential Nature Exposure and Walkability and Their Association with Cardiovascular Health

JACC: Advances

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JACC: Advances 2025年第1期4卷 101457-101457页

作者： Makram, Omar M. Nwana, Nwabunie Pan, Alan Nicolas, Juan C. Gullapelli, Rakesh Bose, Budhaditya Sabharwal, Ashutosh Chang, Jenny Javed, Zulqarnain Kash, Bita Maddock, Jay E. Nasir, Khurram Al-Kindi, Sadeer Center for Health & Nature Houston Methodist Research Institute Houston TX United States Department of Medicine Medical College of Georgia at Augusta University Augusta GA United States Center for Health Data Science and Analytics Houston Methodist Research Institute Houston TX United States Department of Electrical and Computer Engineering Rice University Houston TX United States Houston Methodist Neal Cancer Center Houston Methodist Hospital Houston TX United States Department of Environmental and Occupational Health School of Public Health Texas A&M University College Station TX United States Division of Cardiovascular Prevention and Wellness Department of Cardiology Houston Methodist DeBakey Heart and Vascular Center Houston TX United States Center for Cardiovascular Computational Health & Precision Medicine (C3-PH) Houston Methodist Hospital Houston TX United States

Background: Green space has been linked with cardiovascular (CV) health. Nature access and quality may have significant impact on CV risk factors and health. Objectives: The authors aimed to investigate the relationship between NatureScore, a composite score for natural environment exposure and quality of green spaces, with CV risk factors and atherosclerotic cardiovascular diseases (ASCVD). Methods: A cross-sectional study including one million adult patients from the Houston Methodist Learning Health System Outpatient Registry (2016-2022). NatureScore is a composite measure of natural environment exposure and quality (0-100) calculated for each patient based on residential address. NatureScores was divided into 4 categories: nature deficient/light (0-39), nature adequate (40-59), nature rich (60-79), and nature utopia (80-100). CV risk factors included hypertension, diabetes, dyslipidemia, and obesity. Results: Among 1.07 million included patients (mean age 52 years, female 59%, Hispanic 16%, Non-Hispanic Black 14%), median NatureScore was 69.4. After adjusting for neighborhood walkability, patients living in highest NatureScore neighborhoods had lower prevalence of CV risk factors (OR: 0.91, 95% CI: 0.90-0.93) and ASCVD (OR: 0.96, 95% CI: 0.93-0.98) than those in lowest NatureScore neighborhoods. A significant interaction existed between NatureScore and Walkability (P < 0.001), where those in high NatureScore (≥60) high walkability (≥40) areas had lower prevalence of CV risk factors (OR: 0.93, 95% CI: 0.90-0.97, P < 0.001) and were more likely to have optimal CV risk profile (relative risk ratio: 1.09, 95% CI: 1.04-1.14, P = 0.001). Conclusions: These findings suggest that while green spaces benefit health, their accessibility through walkable environments is crucial for cardiovascular disease protection. © 2024 The Authors

关键词： cardiovascular diabetes hypertension nature walkability

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Single-cell technologies:From research to application

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The Innovation 2022年第6期3卷 92-108页

作者： Lu Wen Guoqiang Li Tao Huang Wei Geng Hao Pei Jialiang Yang Miao Zhu Pengfei Zhang Rui Hou Geng Tian Wentao Su Jian Chen Dake Zhang Pingan Zhu Wei Zhang Xiuxin Zhang Ning Zhang Yunlong Zhao Xin Cao Guangdun Peng Xianwen Ren Nan Jiang Caihuan Tian Zi-Jiang Chen Biomedical Pioneering Innovation Centre(BIOPIC) Peking UniversityBeijing 100871China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghai 200031China School of Chemical Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China Mozhuo Biotech(Zhejiang)Co. Ltd.TongxiangJiaxing 314500China Geneis(Beijing)Co. Ltd.Beijing 100102China Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhou 510530China Department of Medical Oncology Zhongshan HospitalFudan UniversityShanghai 200032China School of Food Science and Technology Dalian Polytechnic UniversityDalian 116034China State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationBeijing Advanced Innovation Center for Biomedical EngineeringSchool of Engineering MedicineBeihang UniversityBeijing 100083China Department of Mechanical Engineering City University of Hong KongHong Kong 999077China Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China Center of Peony Institute of Vegetables and FlowersChinese Academy of Agricultural SciencesKey Laboratory of Biology and Genetic Improvement of Flower Crops(North China)Key Laboratory of Biology and Genetic Improvement of Horticultural CropsMinistry of Agriculture and Rural AffairsBeijing 100081China Department of Hepatic Surgery Fudan University Shanghai Cancer CenterShanghai 200032China Advanced Technology Institute University of SurreyGuildfordSurreyGU27XHUK National Physical Laboratory TeddingtonMiddlesex TW110LWUK Institute of Clinical Science Zhongshan HospitalFudan UniversityShanghai 200032China West China School of Basic Medical Sciences&Forensic Medicine Sichuan UniversityChengdu 610041China Jinfeng Laboratory Chongqing 401329China Center for Reproduc

In recent years,more and more single-cell technologies have been developed.A vast amount of single-cell omics data has been generated by large projects,such as the Human Cell Atlas,the Mouse Cell Atlas,the Mouse RNA Atlas,the Mouse ATAC Atlas,and the Plant Cell *** on these single-cell big data,thousands of bioinformatics algorithms for quality control,clustering,cell-type annotation,developmental inference,cell-cell transition,cell-cell interaction,and spatial analysis are *** powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence,the molecular landscape at the single-cell level can be revealed.

关键词： Atlas landscape application

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Stabilization of continuous-time Markov/semi-Markov jump linear systems via finite data-rate feedback

arXiv

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arXiv 2021年

作者： Wang, Jingyi Feng, Jianwen Xu, Chen Wu, Xiaoqun Lü, Jinhu College of Mathematics and Statistics Shenzhen University Shenzhen518060 China School of Mathematics and Statistics Hubei Key Laboratory of Computational Science Wuhan University Wuhan China State Key Laboratory of Software Development Environment School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

This paper investigates almost sure exponential stabilization of continuous-time Markov jump linear systems (MJLSs) under communication data-rate constraints by introducing sampling and quantization into the feedback control. Different from previous works, the sampling times and the jump times are independent of each other in this paper. The quantization is recursively adjusted on the sampling time, and its updating strategy does not depend on the switching in a sampling interval. In other words, the explicit value of the switching signal in a sampling interval is not necessary. The numerically testable condition is developed to ensure almost sure exponential stabilization of MJLSs under the proposed communication and control protocols. We also drop the assumption of stabilizability of all individual modes required in previous works about the switched systems. Moreover, we extend the result to the case of continuous-time semi-Markov jump linear systems (semi-MJLSs) via the semi-Markov kernel approach. Finally, some numerical examples are presented to illustrate the effectiveness on stabilization of the proposed communication and control *** Codes 93D15 © 2021, CC BY.

关键词： Markov processes

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Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review

arXiv

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arXiv 2023年

作者： Cheng, Sibo Quilodrán-Casas, César Ouala, Said Farchi, Alban Liu, Che Tandeo, Pierre Fablet, Ronan Lucor, Didier Iooss, Bertrand Brajard, Julien Xiao, Dunhui Janjic, Tijana Ding, Weiping Guo, Yike Carrassi, Alberto Bocquet, Marc Arcucci, Rossella Data Science Institute Department of Computing Imperial College London LondonSW7 2AZ United Kingdom Department of Earth Science and Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Computer Science and Engineering Hong Kong University of Science and Technology 999077 Hong Kong IMT Atlantique Lab-STICC UMR CNRS 6285 France and Odyssey Inria/IMT France RIKEN Center for Computational Science Kobe Japan CEREA École des Ponts and EDF R&D île-de-France France The Laboratoire Interdisciplinaire des Sciences du Numérique CNRS Paris-Saclay University OrsayF-91403 France 78401 Chatou France Institut de Mathématiques de Toulouse Toulouse31062 France SINCLAIR AI Lab Saclay France Bergen Norway School of Mathematical Sciences Tongji University Shanghai200092 China Mathematical Institute for Machine Learning and Data Science KU Eichstätt-Ingolstadt Bavaria Germany School of Information Science and Technology Nantong University Nantong226019 China Department of Physics and Astronomy Augusto Righi University of Bologna Bologna40124 Italy

data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surrogate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains, resulting in the necessity for a comprehensive guide. This paper provides the first overview of the state-of-the-art researches in this interdisciplinary field, covering a wide range of applications. This review aims at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models, but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their systems. Therefore, this article has a special focus on how ML methods can overcome the existing limits of DA and UQ, and vice versa. Some exciting perspectives of this rapidly developing research field are also discussed. © 2023, CC BY-NC-SA.

关键词： Dynamical systems

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Multi-armed Bandit Experimental Design: Online Decision-Making and Adaptive Inference

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Management science 2024年第6期71卷 iv-vi, 4533-5418页

作者： David Simchi-Levi Chonghuan Wang Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States Center for Computational Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Multi-armed bandit has been well known for its efficiency in online decision-making in terms of minimizing the loss of the participants’ welfare during experiments (i.e., the regret). In clinical trials and many other scenarios, the statistical power of inferring the treatment effects (i.e., the gaps between the mean outcomes of different arms) is also crucial. Nevertheless, minimizing the regret entails harming the statistical power of estimating the treatment effect because the observations from some arms can be limited. In this paper, we investigate the trade-off between efficiency and statistical power by casting the multi-armed bandit experimental design into a minimax multi-objective optimization problem . We introduce the concept of Pareto optimality to mathematically characterize the situation in which neither the statistical power nor the efficiency can be improved without degrading the other. We derive a useful sufficient and necessary condition for the Pareto optimal solutions to the minimax multi-objective optimization problem. Additionally, we design an effective Pareto optimal multi-armed bandit experiment that can be tailored to different levels of the trade-off between the two objectives. Moreover, we extend the design and analysis to the setting where the outcome of each arm consists of an adversarial baseline reward and a stochastic treatment effect, demonstrating the robustness of our design. Finally, motivated by clinical trials, we examine the setting where the employed experiment must split the experimental units into a small number of batches, and we propose a flexible Pareto optimal design. This paper was accepted by George Shanthikumar, data science. Funding: The authors thank the Massachusetts Institute of Technology (MIT)-IBM partnership in Artificial Intelligence and the MIT data science Laboratory for support. Supplemental Material: The online appendix and data files are available at https://***/10.1287/mnsc.2023.00492

关键词： adaptive experimental design multi-armed bandit minimax multi-objective optimization Pareto optimality

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Continuous black-box optimization with an Ising machine and random subspace coding

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Physical Review Research 2022年第2期4卷 023062-023062页

作者： Syun Izawa Koki Kitai Shu Tanaka Ryo Tamura Koji Tsuda Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa 277-8561 Japan Department of Mechanical Engineering The University of Tokyo Tokyo 113-8654 Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama 223-8522 Japan Green Computing System Research Organization Waseda University Tokyo 162-0042 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0047 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0047 Japan RIKEN Center for Advanced Intelligence Project Tokyo 103-0027 Japan

A black-box optimization algorithm such as Bayesian optimization finds the extremum of an unknown function by alternating the inference of the underlying function and optimization of an acquisition function. In a high-dimensional space, such algorithms perform poorly due to the difficulty of acquisition function optimization. Herein, we apply Ising machines to overcome the difficulty in the continuous black-box optimization. As an Ising machine specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution by Ising machines has to be translated back. Our method has the following three parts: (1) random subspace coding based on axis-parallel hyperrectangles from continuous vector to binary vector, (2) a quadratic unconstrained binary optimization (QUBO) defined by the acquisition function based on the nonnegative-weighted linear regression model, which is solved by Ising machines, and (3) a penalization scheme to ensure that the solution can be translated back. It is shown with benchmark tests that its performance using the D-Wave Advantage quantum annealer and simulated annealing is competitive with a state-of-the-art method based on the Gaussian process in high-dimensional problems. Our method may open up the possibility of Ising machines and other QUBO solvers, including a quantum approximate optimization algorithm using gated-quantum computers, and may expand its range of application to continuous-valued problems.

关键词： Adiabatic quantum optimization Machine learning Quantum algorithms Quantum computation

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