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A Novel Framework for Learning and Classifying the Imbalanced Multi-Label data

Computer Systems Science & engineering 2024年第5期48卷 1367-1385页

作者： P.K.A.Chitra S.Appavu alias Balamurugan S.Geetha Seifedine Kadry Jungeun Kim Keejun Han Department of Computer Science and Engineering SRM Institute of Science and TechnologyTiruchirappalliTamil Nadu603203India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&Technology(Deemed to be University)ThanjavurTamil Nadu613403India School of Computer Science and Engineering ChennaiTamil Nadu600048India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346AjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos10150Lebanon Department of Software Kongju National UniversityCheonan31080Republic of Korea Division of Computer Engineering Hansung UniversitySeoul02876Republic of Korea

A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label *** main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label *** work proposes a framework of two *** imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase ***,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase *** proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional *** minority labels in these samples are called difficult minority labels and are more prone to penalize classification *** concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision *** phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic *** on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.

关键词： Multi-label imbalanced data multi-label learning Borderline MLSMOTE concurrent multi-label adaptive weighted multi-label elastic net difficult minority label

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Development and testing of a vehicle management system for autonomous spacecraft habitat operations

Development and testing of a vehicle management system for a...

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AIAA Space and Astronautics Forum and Exposition, 2018

作者： Aaseng, Gordon B. Frank, Jeremy D. Iatauro, Michael Knight, C. Levinson, Richard Ossenfort, John Scott, Michael Sweet, Adam Csank, Jeffrey Soeder, James Carrejo, Daniel Loveless, Andrew T. Ngo, Tam Greenwood, Zachary NASA Ames Research Center Moffett Field Intelligent Systems Division M/S/269 CA94035-1000 United States NASA Ames Research Center Moffett Field Intelligent Systems Division M/S 269-1 CA94035-1000 United States NASA Glenn Research Center Power Management and Distribution M/S 301-5 21000 Brookpark Rd ClevelandOH44135 United States NASA Glenn Research Center Power Division M/S 301-5 21000 Brookpark Rd ClevelandOH44135 United States NASA Johnson Space Center Systems Engineering and Test Branch 2101 E NASA Pkwy HoustonTX77058 United States NASA Johnson Space Center Command and Data Handling Branch 2101 E NASA Pkwy HoustonTX77058 United States NASA Johnson Space Center Spacecraft Software Engineering Branch 2101 E NASA Pkwy HoustonTX77058 United States NASA Marshall Space Flight Center Redstone Arsenal Environmental Control and Life Support Systems Branch ES62 HuntsvilleAL35812 United States

ISBN: (数字)9781624105753

ISBN: (纸本)9781624105753

As the increased distance between Earth-based mission control and the spacecraft results in increasing communication delays, small crews cannot take on all functions performed by ground today, and so vehicles must be more automated to reduce the crew workload for such missions. In addition, both near-term and future missions will feature significant periods when crew is not present, meaning the vehicles will need to operate themselves autonomously. NASA’s Advanced Exploration Systems Program pioneers new approaches for rapidly developing prototype systems, demonstrating key capabilities, and validating operational concepts for future human missions beyond low-Earth orbit. Under this program, NASA has developed and demonstrated multiple technologies to enable the autonomous operation of a dormant space habitat. These technologies included a fault-tolerant avionics architecture, novel spacecraft power system and power system controller, and autonomy software to control the habitat. The demonstration involved simulation of the habitat and multiple spacecraft sub-systems (power storage and distribution, avionics, and air-side life-support) during a multi-day test at NASA’s Johnson Space Center. The foundation of the demonstration was ‘quiescent operations’ of a habitat during a 55 minute eclipse period. For this demonstration, the spacecraft power distribution system and air-side life support system were simulated at a high level of fidelity;additional systems were managed, but with lower fidelity operational constraints and system behavior. Operational constraints for real and simulated loads were developed by analyzing on-orbit hardware and evaluating future Exploration capable technology. A total of 13 real and simulated loads were used during the test. Eight scenarios including both nominal and off-nominal conditions were performed. Over the course of the test, every application performed its desired functions successfully during the simulated tests. The results wil

关键词： NASA

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Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism

arXiv

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arXiv 2023年

作者： Yang, Han Wang, Qiuli Zhang, Yue An, Zhulin Chen, Liu Zhang, Xiaohong Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China The Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Radiology The First Affiliated Hospital of Army Medical University Chongqing400032 China School of Big Data & Software Engineering Chongqing University Chongqing401331 China Institute of Computing Technology Beijing100190 China

Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations. However, these approaches fail to leverage the valuable information inherent in the consensus and disagreements among the multiple annotations. In this paper, we propose an Uncertainty-Aware Attention Mechanism (UAAM) that utilizes consensus and disagreements among multiple annotations to facilitate better segmentation. To this end, we introduce the Multi-Confidence Mask (MCM), which combines a Low-Confidence (LC) Mask and a High-Confidence (HC) Mask. The LC mask indicates regions with low segmentation confidence, where radiologists may have different segmentation choices. Following UAAM, we further design an Uncertainty-Guide Multi-Confidence Segmentation Network (UGMCS-Net), which contains three modules: a Feature Extracting Module that captures a general feature of a lung nodule, an Uncertainty-Aware Module that produces three features for the annotations’ union, intersection, and annotation set, and an Intersection-Union Constraining Module that uses distances between the three features to balance the predictions of final segmentation and MCM. To comprehensively demonstrate the performance of our method, we propose a Complex Nodule Validation on LIDC-IDRI, which tests UGMCS-Net’s segmentation performance on lung nodules that are difficult to segment using common methods. Experimental results demonstrate that our method can significantly improve the segmentation performance on nodules that are difficult to segment using conventional methods. © 2023, CC BY-NC-ND.

关键词： Computerized tomography

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APPLYING VECTOR PROCESSING TO ACTIVE SONARS FOR INCREASED ACCURACY AND AN EXPANDED OPERATING ENVELOPE

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NAVAL ENGINEERS JOURNAL 1991年第3期103卷 231-239页

作者： WHITNEY, DA BENEDICT, TJ David A. Whitneyis a division staff analyst in the Strategic Systems Division at TASC Reading Mass. He has B.S. and M.S. degrees in mathematics and statistics from Princeton University and the University of Illinois and has completed additional graduate work toward a Ph.D. at MIT. He has directed a number of algorithm development and data analysis activities in support of the U.S. Navy's Trident programs and has authored several technical papers. Mr. Whitney is currently involved in development and field testing of passive sonar signal processing algorithms and directs a R&D activity in the area of parallel processing applications for ASW. Lt Terry J. Benedict USNis an engineering duty officer in the U.S. Navy and is currently assigned as the assistant section head for missile engineering at Strategic Systems Programs Washington D.C. He has a B.S. degree in management from the U.S. Naval Academy and M.S. degree in weapons systems engineering from the Naval Postgraduate School. He formerly served as the system software development officer for the Navigation Branch at Strategic Systems Programs and shared responsibilities for the design development testing and configuration control of the Navigation Subsystem software.

Advanced computing technology development now allows considerable computational power to be packaged in small, modular components for low costs. In particular, vector and array processor add-on boards offer a way to incorporate modern parallel capabilities into existing hardware systems. In addition to cost efficiency, the fact that these boards may be able to plug-in to existing computer systems provides both design flexibility and a more rapid way of upgrading deployed computer hardware. To take advantage of such processing capabilities, however, algorithm analysis must be performed to determine what functions can be efficiently off-loaded to the vector processing board, and to decide what parts of the original serial algorithm should be redesigned to take advantage of the new hardware architecture. This paper presents a case study of how these issues are being addressed in a real-time active sonar data processing system. Vector processing technologies are being analyzed for application to correlation sonar systems used for estimating ship velocity on submarines, surface ships, and autonomous underwater vehicles. Such a system is deployed on Trident II SSBNs and is meeting its current design objectives. However, real-time operation of the correlation sonar is computationally intensive and fully utilizes the processing power of the fielded computing hardware. Expansions in the accuracy and operating envelope of the system are desirable and are not limited by the physics or practical operation of the sonar system, but rather by the speed at which processing of hydrophone returns can be performed. This analytic feasibility study investigates the possibility of expanding the operating envelope through the use of vector processing technology, within the constraint that any hardware or software changes would have minimal impact on the deployed system. It is predicted that significant speedups and operating envelope expansions can be achieved by introducing vectorized ha

关键词： Sonar

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Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenergetic Positron Beams 30

Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenerg...

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30th International Symposium on Semiconductor Manufacturing, ISSM 2024

作者： Uedono, Akira Kawakami, Kazuya Kuribara, Tatsunori Hasunuma, Ryu Harashima, Yosuke Shigeta, Yasuteru Ni, Zeyuan Yan, Ruolin Nakamura, Hideo Notake, Akira Moriya, Tsuyoshi Michishio, Koji Ishibashi, Shoji University of Tsukuba Faculty of Pure and Applied Science Ibaraki Tsukuba305-8573 Japan Nara Institute of Science and Technology Graduate School of Science and Technology Nara Ikoma630-0192 Japan Tokyo Electron Technology Solutions Ltd. S-Technology Development Center Yamanashi Nirasaki407-0192 Japan Tokyo Electron Technology Solutions Ltd. Development Department 1 Yamanashi Nirasaki407-0192 Japan Tokyo Electron Ltd. Digital Design Center Hokkaido Sapporo060-0003 Japan Tokyo Electron Technology Solutions Ltd. Software and Data Engineering Division Yamanashi Nirasaki407-0192 Japan National Institute of Advanced Industrial Science and Technology Research Institute for Measurement and Analytical Instrumentation Ibaraki Tsukuba305-8568 Japan National Institute of Advanced Industrial Science and Technology Research Center for Computational Design of Advanced Functional Materials Ibaraki Tsukuba305-8568 Japan

ISBN: (纸本)9798331510589

Behaviors of vacancies during the amorphous-to-crystalline transition of thin HfO2 layers were studied by monoenergetic positron beams. The HfO2 layers with 430 nm thickness were fabricated on TiN/Si templates by atomic layer deposition technique. The major vacancy-type defects in the layers were identified as (i) Hf vacancies (VHf) coupled with oxygen vacancies (VOs) and (ii) vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters increased, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to crystalline phases. The phase transition started near the interface between the HfO2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased. © 2024 IEEE.

关键词： Positron annihilation

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Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenergetic Positron Beams

Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenerg...

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Joint e-Manufacturing and Design Collaboration Symposium (eMDC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Akira Uedono Kazuya Kawakami Tatsunori Kuribara Ryu Hasunuma Yosuke Harashima Yasuteru Shigeta Zeyuan Ni Ruolin Yan Hideo Nakamura Akira Notake Tsuyoshi Moriya Koji Michishio Shoji Ishibashi Faculty of Pure and Applied Science University of Tsukuba Tsukuba Ibaraki Japan Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Nara Japan S-Technology Development Center Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Development Department 1 Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Digital Design Center Tokyo Electron Ltd. Sapporo Hokkaido Japan Software and Data Engineering Division Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Research Institute for Measurement and Analytical Instrumentation National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki Japan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki Japan

ISBN: (数字)9798331510589

ISBN: (纸本)9798331510596

Behaviors of vacancies during the amorphous-to-crystalline transition of thin HfO 2 layers were studied by monoenergetic positron beams. The HfO 2 layers with 4‒30 nm thickness were fabricated on TiN/Si templates by atomic layer deposition technique. The major vacancy-type defects in the layers were identified as (i) Hf vacancies (V Hf ) coupled with oxygen vacancies (V O s) and (ii) vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters increased, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to crystalline phases. The phase transition started near the interface between the HfO 2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased.

关键词： Electrodes Annealing Atomic layer deposition Positrons Crystallization Tin Semiconductor device manufacture Hafnium compounds

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A comparison of deep learning-based techniques for solving partial differential equations

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AIP Conference Proceedings 2024年第1期2905卷

作者： Rabiu Bashir Yunus Nooraini Zainuddin Afza Shafie Muhammad Izzatullah Samsul Ariffin Abdul Karim Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS Bandar Seri Iskandar 32610 Seri Iskandar Perak Darul Ridzuan Malaysia King Abdullah University of Science and Technology (KAUST) Division of Physical Sciences and Engineering Bldg. 1 Desk #0202-WS023 Thuwal 23955-6900 Kingdom of Saudi Arabia Software Engineering Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia Data Technologies and Applications (DaTA) Research Group Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia

Obtaining the solutions of high-dimensional partial differential equations (PDEs) seems to be difficult by utilizing the classical numerical methods. Recently, deep neural networks (DNNs) techniques have received special attentions in solving high–dimensional problems in PDEs. In this study, our quest is to investigate some newly introduced data-driven deep learning-based approaches and compare their performance in terms of their efficiency and faster training towards high-dimensional PDEs. However, the comparison is carried out based on different activation functions, number of layers and gradient based optimizers. We consider some benchmark problems in our numerical experiments which includes Burgers equation, Diffusion-reaction equation and Allen-Cahn Equations.

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On the Conformation of Dimeric Acceptors and Their Polymer Solar Cells with Efficiency over 18 %

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Angewandte Chemie 2023年第45期135卷

作者： Jingnan Wu Zhaoheng Ling Leandro R. Franco Sang Young Jeong Zewdneh Genene Josué Mena Si Chen Cailing Chen Prof. C. Moyses Araujo Prof. Cleber F. N. Marchiori Joost Kimpel Xiaoming Chang Furkan H. Isikgor Qiaonan Chen Hendrik Faber Prof. Yu Han Frédéric Laquai Prof. Maojie Zhang Prof. Han Young Woo Prof. Donghong Yu Prof. Thomas D. Anthopoulos Prof. Ergang Wang Department of Chemistry and Chemical Engineering Chalmers University of Technology 41296 Göteborg Sweden Department of Chemistry and Bioscience Aalborg University 9220 Aalborg Denmark Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Writing - original draft (lead) Writing - review & editing (lead) King Abdullah University of Science and Technology (KAUST) KAUST Solar Center Thuwal 23955 Saudi Arabia Contribution: Data curation (lead) Formal analysis (lead) Writing - original draft (equal) Writing - review & editing (equal) Department of Engineering and Physics Karlstad University 65188 Karlstad Sweden Contribution: Data curation (equal) Writing - review & editing (equal) Department of Chemistry Korea University Seoul 02841 (Republic of Korea Contribution: Data curation (equal) Formal analysis (equal) Advanced Membranes and Porous Materials Center Physical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Contribution: Data curation (equal) Methodology (equal) Materials Theory Division Department of Physics and Astronomy Uppsala University 75120 Uppsala Sweden Contribution: Data curation (equal) Funding acquisition (equal) Software (equal) Writing - review & editing (equal) Contribution: Data curation (equal) Methodology (equal) Writing - review & editing (equal) Contribution: Data curation (equal) Writing - review & editing (supporting) Contribution: Data curation (equal) Methodology (supporting) Contribution: Data curation (equal) Contribution: Data curation (supporting) Funding acquisition (supporting) National Engineering Research Center for Colloidal Materials School of Chemistry & Chemical Engineering Shandong University Jinan Shandong 250100 China Contribution: Data curation (equal) Funding acquisition (supporting) Contribution: Data curation (equal) Funding acquisition (supporting) Writing - review & editing (supporting) Contribution: Data curation (equal) Fu

The determination of molecular conformations of oligomeric acceptors (OAs) and their impact on molecular packing are crucial for understanding the photovoltaic performance of their resulting polymer solar cells (PSCs) but have not been well studied yet. Herein, we synthesized two dimeric acceptor materials, DIBP3F-Se and DIBP3F-S, which bridged two segments of Y6-derivatives by selenophene and thiophene, respectively. Theoretical simulation and experimental 1D and 2D NMR spectroscopic studies prove that both dimers exhibit O -shaped conformations other than S - or U -shaped counter-ones. Notably, this O -shaped conformation is likely governed by a distinctive “conformational lock” mechanism, arising from the intensified intramolecular π–π interactions among their two terminal groups within the dimers. PSCs based on DIBP3F-Se deliver a maximum efficiency of 18.09 %, outperforming DIBP3F-S-based cells (16.11 %) and ranking among the highest efficiencies for OA-based PSCs. This work demonstrates a facile method to obtain OA conformations and highlights the potential of dimeric acceptors for high-performance PSCs.

关键词： Acceptor Molecular Conformation Oligomer Selenophene Solar Cells

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Dynamical clustering of U.S. states reveals four distinct infection patterns that predict SARS-CoV-2 pandemic behavior

arXiv

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arXiv 2021年

作者： Natale, Joseph L. Viswanath, Varun Acevedo, Oscar Trujillo Giottonini, Sophia Perez Hernández, Sandy Ihuiyan Romero Cruz Millán, Diana G. Montserrat Palacios-Puga, A. Mandvi, Ammar Khan, Brian M. Lilik, Martin Park, Jay Smarr, Benjamin L. Halicioǧlu Data Science Institute University of California La Jolla San DiegoCA90293 United States Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA90293 United States Division de Ingeniera Bioqumica Instituto Tecnologico Superior of Atlixco C. Heliotropo No.1201 Unidad 8 Norte Nueva Xalpatlaco Atlixco74210 Mexico Colegio de Bachilleres del Estado de Sonora plantel Villa de Seris SON Hermosillo Mexico Instituto Tecnológico Superior de Atlixco C. Heliotropo No.1201 Unidad 8 Norte Nueva Xalpatlaco Atlixco Mexico Department of Health Sciences Universidad de Sonora campus Cajeme Cd. Obregon Son. Mexico Department of Biochemical and Chemical Engineering Instituto Tecnológico de Tijuana Tecnológico Nacional de Mexico B.C Tijuana Mexico Department of Emergency Medicine Department of Family Medicine University of California San DiegoCA United States Department of Emergency Medicine Department of Internal Medicine University of California San DiegoCA United States Department of Software Engineering Park Media Semarang Indonesia Department of Emergency Medicine University of California La Jolla San DiegoCA92037 United States Department of Bioengineering Halicioǧlu Data Science Institute University of California La Jolla San DiegoCA90293 United States

The SARS-CoV-2 pandemic has so far unfolded diversely across the fifty United States of America, reflected both in different time progressions of infection "waves" and in magnitudes of local infection rates. Despite a marked diversity of presentations, most U.S. states experienced their single greatest surge in daily new cases during the transition from Fall 2020 to Winter 2021. Popular media also cite additional similarities between states - often despite disparities in governmental policies, reported mask-wearing compliance rates, and vaccination percentages. Here, we identify a set of robust, low-dimensional clusters that 1) summarize the timings and relative heights of four historical COVID-19 "wave opportunities" accessible to all 50 U.S. states, 2) correlate with geographical and intervention patterns associated with those groups of states they encompass, and 3) predict aspects of the "fifth wave" of new infections in the late Summer of 2021. In particular, we argue that clustering elucidates a negative relationship between vaccination rates and subsequent case-load variabilities within state groups. We advance the hypothesis that vaccination acts as a "seat belt," in effect constraining the likely range of new-case upticks, even in the context of the Summer 2021, variant-driven surge. © 2021, CC BY.

关键词： Vaccines

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Generating Approximate Ground States of Molecules Using Quantum Machine Learning

arXiv

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arXiv 2022年

作者： Ceroni, Jack Stetina, Torin F. Kieferová, Mária Marrero, Carlos Ortiz Arrazola, Juan Miguel Wiebe, Nathan Xanadu TorontoONM5G 2C8 Canada Department of Mathematics University of Toronto TorontoONM5S 3E1 Canada Simons Institute for the Theory of Computing BerkeleyCA94704 United States Berkeley Quantum Information and Computation Center University of California BerkeleyCA94720 United States Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia AI & Data Analytics Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Electrical & Computer Engineering North Carolina State University RaleighNC27607 United States Department of Computer Science University of Toronto ONM5S 1A1 Canada High Performance Computing Group Pacific Northwest National Laboratory RichlandWA99354 United States

The potential energy surface (PES) of molecules with respect to their nuclear positions is a primary tool in understanding chemical reactions from first principles. However, obtaining this information is complicated by the fact that sampling a large number of ground states over a high-dimensional PES can require a vast number of state preparations. In this work, we propose using a generative quantum machine learning model to prepare quantum states at arbitrary points on the PES. The model is trained using quantum data consisting of ground-state wavefunctions associated with different classical nuclear coordinates. Our approach uses a classical neural network to convert the nuclear coordinates of a molecule into quantum parameters of a variational quantum circuit. The model is trained using a fidelity loss function to optimize the neural network parameters. We show that gradient evaluation is efficient and numerically demonstrate our method’s ability to prepare wavefunctions on the PES of hydrogen chains, water, and beryllium hydride. In all cases, we find that a small number of training points are needed to achieve very high overlap with the groundstates in practice. From a theoretical perspective, we further prove limitations on these protocols by showing that if we were able to learn across an avoided crossing using a small number of samples, then we would be able to violate Grover’s lower bound. Additionally, we prove lower bounds on the amount of quantum data needed to learn a locally optimal neural network function using arguments from quantum Fisher information. This work further identifies that quantum chemistry can be an important use case for quantum machine learning. © 2022, CC BY.

关键词： Molecules

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