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Coherent H ∞ control for Markovian jump linear quantum systems

IFAC-PapersOnLine 2020年第2期53卷 269-274页

作者： Yanan Liu Daoyi Dong Ian R. Petersen Qing Gao Steven X. Ding Hidehiro Yonezawa School of Engineering and Information Technology University of New South Wales Canberra ACT 2600 Australia Center for Quantum Computation and Communication Technology Australian Research Council Canberra ACT 2600 Australia Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen. 47057 Duisburg Germany Research School of Electrical Energy and Materials Engineering The Australian National University Canberra ACT 2601 Australia School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing and also with Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

The purpose of this paper is to design a coherent feedback controller for a Markovian jump linear quantum system suffering from a fault signal. The control objective is to bound the effect of the disturbance input on the output for the time-varying quantum system. We prove the relation between the H ∞ control problem, the dissipation properties, and the solutions of Riccati differential equations, by which the H ∞ controller of the Markovian jump linear quantum system is given by the solutions of Linear Matrix Inequalities (LMIs).

关键词： Coherent quantum feedback H∞ control fault-tolerant control Markovian jump linear quantum systems

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Optimal control and comprehensive cost-effectiveness analysis for COVID-19

arXiv

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arXiv 2021年

作者： Asamoah, Joshua Kiddy Kwasi Okyere, Eric Abidemi, Afeez Moore, Stephen E. Sun, Gui-Quan Jin, Zhen Acheampong, Edward Gordon, Joseph Frank Complex Systems Research Center Shanxi University Taiyuan030006 China Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Shanxi University Taiyuan030006 China Department of Mathematics and Statistics University of Energy and Natural Resources Sunyani Ghana Department of Mathematical Sciences Federal University of Technology Akure Ondo StatePMB 704 Nigeria Department of Mathematics North University of China Taiyuan Shanxi030051 China Department of Statistics and Actuarial Science University of Ghana P.O. Box LG 115 Legon Ghana Department of Mathematics Education Akenten Appiah Menka University of Skills Training and Entrepreneurial Development Kumasi Ghana

Cost-effectiveness analysis is a mode of determining both the cost and economic health outcomes of one or more control interventions. In this work, we have formulated a non-autonomous nonlinear deterministic model to study the control of COVID-19 to unravel the cost and economic health outcomes for the autonomous nonlinear model proposed for the Kingdom of Saudi Arabia. The optimal control model captures four time-dependent control functions, thus, u1-practising physical or social distancing protocols;u2-practising personal hygiene by cleaning contaminated surfaces with alcohol-based detergents;u3-practising proper and safety measures by exposed, asymptomatic and symptomatic infected individuals;u4-fumigating schools in all levels of education, sports facilities, commercial areas and religious worship centres. We proved the existence of the proposed optimal control model. The optimality system associated with the non-autonomous epidemic model is derived using Pontryagin’s maximum principle. We have performed numerical simulations to investigate extensive cost-effectiveness analysis for fourteen optimal control strategies. Comparing the control strategies, we noticed that;Strategy 1 (practising physical or social distancing protocols) is the most cost-saving and most effective control intervention in Saudi Arabia in the absence of vaccination. But, in terms of the infection averted, we saw that strategy 6, strategy 11, strategy 12, and strategy 14 are just as good in controlling COVID-19. © 2021, CC BY.

关键词： Cost effectiveness

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Transfer learning versus multi-agent learning regarding distributed decision-making in highway traffic 10

Transfer learning versus multi-agent learning regarding dist...

引用

10th International Workshop on Agents in Traffic and Transportation, ATT 2018

作者： Schutera, Mark Goby, Niklas Neumann, Dirk Reischl, Markus Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Chair for Information Systems Research University of Freiburg Germany IT Innovation Chapter Data Science ZF Friedrichshafen AG Germany Research and Development ZF Friedrichshafen AG Germany

Transportation and traffic are currently undergoing a rapid increase in terms of both scale and complexity. At the same time, an increasing share of traffic participants are being transformed into agents driven or supported by artificial intelligence resulting in mixed-intelligence traffic. This work explores the implications of distributed decision-making in mixed-intelligence traffic. The investigations are carried out on the basis of an online-simulated highway scenario, namely the MIT DeepTraffic simulation. In the first step traffic agents are trained by means of a deep reinforcement learning approach, being deployed inside an elitist evolutionary algorithm for hyperparameter search. The resulting architectures and training parameters are then utilized in order to either train a single autonomous traffic agent and transfer the learned weights onto a multi-agent scenario or else to conduct multi-agent learning directly. Both learning strategies are evaluated on different ratios of mixed-intelligence traffic. The strategies are assessed according to the average speed of all agents driven by artificial intelligence. Traffic patterns that provoke a reduction in traffic flow are analyzed with respect to the different strategies. © 2018 CEUR-WS. All rights reserved.

关键词： Reinforcement learning

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Correction: Artificial intelligence for surgical safety during laparoscopic gastrectomy for gastric cancer: Indication of anatomical landmarks related to postoperative pancreatic fistula using deep learning

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Surgical endoscopy 2024年第10期38卷 6203页

作者： Yoshimasa Aoyama Yusuke Matsunobu Tsuyoshi Etoh Kosuke Suzuki Shunsuke Fujita Takayuki Aiba Hajime Fujishima Shinichiro Empuku Yohei Kono Yuichi Endo Yoshitake Ueda Hidefumi Shiroshita Toshiya Kamiyama Takemasa Sugita Kenichi Morishima Kohei Ebe Tatsushi Tokuyasu Masafumi Inomata Department of Gastroenterological and Pediatric Surgery Faculty of Medicine Oita University Oita Japan. Department of Information Systems and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka Japan. Department of Healthcare AI Data Science Faculty of Medicine Oita University Oita Japan. Department of Gastroenterological and Pediatric Surgery Faculty of Medicine Oita University Oita Japan. teto@oita-u.ac.jp. Research Center for GLOBAL and LOCAL Infectious Diseases Oita University 1-1 Idaigaoka Hasama-Machi Oita Oita 879-5593 Japan. teto@oita-u.ac.jp. Faculty of Medicine Oita University Oita Japan. Department of Comprehensive Surgery for Community Medicine Faculty of Medicine Oita University Oita Japan. Advanced AI Technology Research Advanced Software Technology Research Olympus Corporation Tokyo Japan. Information Aided Medical Solutions Development Application Software Engineering Olympus Medical Systems Corporation Tokyo Japan. Clinical Engineering Research Center Faculty of Medicine Oita University Oita Japan.

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Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

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Science China(Information Sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

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Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia

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Nature Communications 2024年第1期15卷 9402-9402页

作者： Wegmann, Rebekka Bonilla, Ximena Casanova, Ruben Chevrier, Stéphane Coelho, Ricardo Esposito, Cinzia Ficek-Pascual, Joanna Goetze, Sandra Gut, Gabriele Jacob, Francis Jacobs, Andrea Kuipers, Jack Lischetti, Ulrike Mena, Julien Milani, Emanuela S. Prummer, Michael Del Castillo, Jacobo Sarabia Singer, Franziska Sivapatham, Sujana Toussaint, Nora C. Vilinovszki, Oliver Wildschut, Mattheus H. E. Thavayogarajah, Tharshika Malani, Disha Zuend, Gregor Zoche, Martin Zimmermann, Marc Ziegler, Johanna Yu, Shuqing Wey, Norbert Wendt, Fabian Weller, Michael Weber, Walter P. Weber, Sandra Vlajnic, Tatjana Vetter, Marcus Van Drogen, Audrey Tusup, Marina Tuncel, Mustafa A. Tosevski, Vinko Tolnay, Markus Thomas, Tinu M. Tanna, Tanmay Stekhoven, Daniel J. Stark, Stefan Sreedharan, Vipin T. Sobottka, Bettina Snijder, Berend Shao, Wenguang Senti, Gabriela Schär, Tobias Schwan, Severin Schwalie, Petra C. Schlenker, Ramona Santacroce, Natascha Rätsch, Gunnar Rosano-González, María L. Rommel, Christian Ritter, Mathilde Rimmer, Natalie Prélot, Laurie Piscuoglio, Salvatore Pelkmans, Lucas Pedrioli, Patrick G. A. Lopez, Monica Nunez Nowak, Marta Nicolet, Stefan Ng, Charlotte K. Y. Murri, Riccardo Muenst, Simone Moch, Holger Miglino, Nicola Metzler, Julian M. Mehnert, Martin Markolin, Philipp Maass, Gerd Lugert, Sebastian Lombardo, Flavio C. Levesque, Mitchell Lehmann, Kjong-Van Kurzeder, Christian Kunze, Christian P. Kuebler, Werner Koelzer, Viktor H. Kahraman, Abdullah Kahles, André Jermann, Philip M. James, Alva R. Jahn, Katharina Jaeger, Tim M. Irmisch, Anja Immer, Alexander Huesser, Tamara Holtackers, Rene Herter, Sylvia Heinzelmann-Schwarz, Viola Haeuptle, Pirmin Günther, Detlef Grob, Linda Frey, Bruno Frei, Anja L. Ferreira, Pedro F. Fedier, André Eschbach, Katja Erkens, Martin Engler, Stefanie Dummer, Reinhard Drăgan, Monica-Andreea Davidson, Natalie R. Danenberg, Esther D’Costa, Maya Chicherova, Natalia Calgua, Byron Bosshard, Lars Bodenmiller, Bernd Bertolini, Anne Bernasconi, Lara Beisel, Christian Beerenwinkel, Niko Beck-Schim Institute of Molecular Systems Biology Department of Biology ETH Zurich Zurich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Biomedicine University Hospital Basel and University of Basel Basel Switzerland Institute of Molecular Life Sciences University of Zurich Zurich Switzerland Department of Health Sciences and Technology ETH Zurich Zurich Switzerland ETH PHRT Swiss Multi-Omics Center (SMOC) Lausanne Switzerland SIB Swiss Institute of Bioinformatics Lausanne Switzerland Department of Biosystems Science and Engineering ETH Zurich Zurich Switzerland NEXUS Personalized Health Technologies ETH Zurich Zurich Switzerland Swiss Data Science Center ETH Zürich Zurich Switzerland Department of Medical Oncology and Hematology University Hospital Zurich Zurich Switzerland Harvard Medical School and Dana-Farber Cancer Institute Boston United States Roche Pharmaceutical Research and Early Development Roche Innovation Center Zurich Zurich Switzerland Department of Pathology and Molecular Pathology University Hospital Zurich Zurich Switzerland University of Zurich Faculty of Medicine Zurich Switzerland Department of Dermatology University Hospital Zurich Zurich Switzerland AI Center at ETH Zurich Zurich Switzerland Institute of Medical Genetics and Pathology University Hospital Basel Basel Switzerland

Deep single-cell multi-omic profiling offers a promising approach to understand and overcome drug resistance in relapsed or refractory (rr) acute myeloid leukemia (AML). Here, we combine single-cell ex vivo drug profiling (pharmacoscopy) with single-cell and bulk DNA, RNA, and protein analyses, alongside clinical data from 21 rrAML patients. Unsupervised data integration reveals reduced ex vivo response to the Bcl-2 inhibitor venetoclax (VEN) in patients treated with both a hypomethylating agent (HMA) and VEN, compared to those pre-exposed to chemotherapy or HMA alone. Integrative analysis identifies both known and unreported mechanisms of innate and treatment-related VEN resistance and suggests alternative treatments, like targeting increased proliferation with the PLK inhibitor volasertib. Additionally, high CD36 expression in VEN-resistant blasts associates with sensitivity to CD36-targeted antibody treatment ex vivo. This study demonstrates how single-cell multi-omic profiling can uncover drug resistance mechanisms and treatment vulnerabilities, providing a valuable resource for future AML research. © 2024. The Author(s).

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Correction: The BCPM method: decoding breast cancer with machine learning

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BMC medical imaging 2024年第1期24卷 272页

作者： Badar Almarri Gaurav Gupta Ravinder Kumar Vandana Vandana Fatima Asiri Surbhi Bhatia Khan College of Computer Sciences and Information Technology King Faisal University Alhasa Saudi Arabia. baalmarri@kfu.edu.sa. Yogananda School of AI Computers and Data Sciences Shoolini University Solan 173212 Himachal Pradesh India. School of Bioengineering & Food Technology Shoolini University Solan 173212 Himachal Pradesh India. College of Computer Science Informatics and Computer Systems Department King Khalid University Abha Saudi Arabia. School of Science Engineering and Environment University of Salford Manchester UK. s.khan138@salford.ac.uk. University Centre for Research and Development Chandigarh University Punjab India. s.khan138@salford.ac.uk.

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Correction to‘Landscape dynamic network biomarker analysis reveals the tipping point of transcriptome reprogramming to prevent skin photodamage’

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Journal of Molecular Cell Biology 2022年第3期14卷 65-65页

作者： Chengming Zhang Hong Zhang Jing Ge Tingyan Mi Xiao Cui Fengjuan Tu Xuelan Gu Tao Zeng Luonan Chen State Key Laboratory of Cell Biology Shanghai Institute of Biochemistry and Cell BiologyCenter for Excellence in Molecular Cell ScienceChinese Academy of SciencesShanghai 200031China University of Chinese Academy of Sciences Chinese Academy of SciencesBeĳing 100049China Unilever Research&Development Centre Shanghai Shanghai 200335China Bio-Med Big Data Center Shanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China School of Life Science and Technology ShanghaiTech UniversityShanghai 201210China Key Laboratory of Systems Health Science of Zhejiang Province Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesChinese Academy of SciencesHangzhou 310024China Guangdong Institute of Intelligence Science and Technology Zhuhai 519031China

In the originally published version of this article(p.822),there was an error in the second affiliation of author Chengming *** erroneous affiliation was given as‘University of the Chinese Academy of Sciences’.This ... 详细信息

关键词： programming damage network

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Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

arXiv

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arXiv 2021年

作者： Ahmad, Muhammad Shabbir, Sidrah Roy, Swalpa Kumar Hong, Danfeng Wu, Xin Yao, Jing Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Chanussot, Jocelyn Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Pakistan The Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Institute of Data Science and Artificial Intelligence Innopolis University Innopolis 420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines. © 2021, CC BY.

关键词： Surveys

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LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping

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Geoderma 2025年 459卷

作者： Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Department of Agromechatronics Potsdam Germany Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany —Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Academic Coordination Santa Maria Brazil Soil Department Santa Maria Brazil Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence ‘Machine Learning for Science’ Germany Bern University of Applied Sciences Competence Center for Soils Zollikofen Switzerland Simulation and Data Science Müncheberg Germany Federal University of Jataí Institute of Agricultural Sciences Jatai Brazil Federal University of Mato Grosso Instute of Agricultural and Environmental Scinces Sinop Brazil Department of Soil and Environment Skara S

Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number ( © 2025 The Author(s)

关键词： Random forests

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