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data augmentation in microscopic images for material data mining

npj Computational Materials 2020年第1期6卷 601-609页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China School of Materials Science and Technology Liaoning Technical UniversityLiaoning 114051China The Institute of Statistical Mathematics Research Organization of Information and SystemsTachikawaTokyo 190-8562Japan National Intellectual Property Administration Beijing 100088China Faculty of Engineering Sciences University of BurundiBujumburaBurundi Faculty of Engineering and Technology Palestine Technical University–KadoorieTulkaremPalestine College of Information Science and Engineering China University of PetroleumBeijingChina Key Lab of Petroleum Data Mining China University of PetroleumBeijingChina

Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise ***,we develop a novel transfer learning strategy to address problems of small or insufficient *** strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining *** a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real *** results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real *** the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.

关键词： mining instance competitive

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Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication 28

Keep it Consistent: Topic-Aware Storytelling from an Image S...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Wang, Ruize Wei, Zhongyu Cheng, Ying Li, Piji Shan, Haijun Zhang, Ji Zhang, Qi Huang, Xuanjing Academy for Engineering and Technology Fudan University China School of Data Science Fudan University China Research Institute of Intelligent and Complex Systems Fudan University China Tencent AI Lab. China Zhejiang Lab. China School of Computer Science Fudan University China

ISBN: (纸本)9781952148279

Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically. Existing approaches construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. In this paper, we propose a new way for visual storytelling by introducing a topic description task to detect the global semantic context of an image stream. A story is then constructed with the guidance of the topic description. In order to combine the two generation tasks, we propose a multi-agent communication framework that regards the topic description generator and the story generator as two agents and learn them simultaneously via iterative updating mechanism. We validate our approach on VIST dataset, where quantitative results, ablations, and human evaluation demonstrate our method’s good ability in generating stories with higher quality compared to state-of-the-art methods. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Iterative methods

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Communication Experiment of Iridium Satellite and Super Pressure Balloon over Antarctica

Communication Experiment of Iridium Satellite and Super Pres...

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Asia-Pacific Conference on Microwave

作者： Takana Kaho Yoshitaka Saito Yoshihiro Tomikawa Faculty of Engineering Shonan Institute of Technology Fujisawa Kanagawa Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Sagamihara City Kanagawa Japan National Institute of Polar Research Tokyo Japan Department of Polar Science The Graduate University for Advanced Studies SOKENDAI Tokyo Japan Polar Environment Data Science Center Research Organization of Information and Systems Tokyo Japan

ISBN: (数字)9784902339567

ISBN: (纸本)9781665451086

An experiment (LODEWAVE) was conducted from January to February 2022 to measure the atmospheric gravity wave over the Antarctica using a super pressure balloon and the PANSY radar located at Syowa Station in the Antarctica. The short burst communication of the Iridium satellite was used for communication with the balloon. In this paper, we report the experimental results on the error of the position information of the balloon and the communication quality due to the use of Iridium over Antarctica.

关键词： Satellites Thermometers Atmospheric measurements Spaceborne radar Redundancy Extraterrestrial measurements Antarctica

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Synchronization cluster bursting in adaptive oscillators networks

arXiv

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arXiv 2024年

作者： Wei, Mengke Amann, Andreas Burylko, Oleksandr Han, Xiujing Yanchuk, Serhiy Kurths, Jürgen School of Mathematical Science Yangzhou University Yangzhou225002 China Potsdam Institute for Climate Impact Research Telegrafenberg Potsdam14473 Germany Faculty of Civil Engineering and Mechanics Jiangsu University Zhenjiang212013 China School of Mathematical Sciences University College Cork Cork Ireland Institute of Mathematics National Academy of Sciences of Ukraine Kyiv01024 Ukraine Institute of Mathematics Humboldt University Berlin Berlin12489 Germany Fudan University Institute Intelligent Complex Systems Shanghai China

Adaptive dynamical networks are ubiquitous in real-world systems. This paper aims to explore the synchronization dynamics in networks of adaptive oscillators based on a paradigmatic system of adaptively coupled phase oscillators. Our numerical observations reveal the emergence of synchronization cluster bursting, characterized by periodic transitions between cluster synchronization and global synchronization. By investigating a reduced model, the mechanisms underlying synchronization cluster bursting are clarified. We show that a minimal model exhibiting this phenomenon can be reduced to a phase oscillator with complex-valued adaptation. Furthermore, the adaptivity of the system leads to the appearance of additional symmetries and thus to the coexistence of stable bursting solutions with very different Kuramoto order parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Synchronization

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The bulk motion of gas in the core of the Centaurus galaxy cluster

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Nature 2025年第8050期638卷 365-369页

作者： Majumder, Anwesh Hosogi, Kokoro Sun, Ming Plšek, Tomáš Werner, Norbert Kondo, Marie Zhuravleva, Irina Yukita, Mihoko Yoshida, Tessei Yoneyama, Tomokage Yaqoob, Tahir Yamauchi, Shigeo Yamauchi, Makoto Yamasaki, Noriko Y. Yamaoka, Kazutaka Yamaguchi, Hiroya Yamada, Shinya Yamada, Satoshi Williams, Brian J. Watanabe, Shin Vink, Jacco Uno, Shinichiro Ueda, Yoshihiro Uchiyama, Hideki Uchida, Yuusuke Uchida, Nagomi Uchida, Hiroyuki Tsuru, Takeshi G. Tsunemi, Hiroshi Tsujimoto, Masahiro Tsuboi, Yohko Trigo, María Díaz Terashima, Yuichi Terada, Yukikatsu Tashiro, Makoto Tanimoto, Atsushi Tanaka, Takaaki Tamura, Keisuke Tamagawa, Toru Takeo, Mai Takahashi, Hiromitsu Szymkowiak, Andrew Suzuki, Hiromasa Smith, Randall Simionescu, Aurora Shidatsu, Megumi Seta, Hiromi Sawada, Makoto Sato, Toshiki Sato, Kosuke Pottschmidt, Katja Porter, Frederick Scott Plucinsky, Paul Petre, Robert Paltani, Stephane Ota, Naomi Okajima, Takashi Ogorzalek, Anna Ogawa, Shoji Odaka, Hirokazu Noda, Hirofumi Nobukawa, Masayoshi Nobukawa, Kumiko Ness, Jan-Uwe Nakazawa, Kazuhiro Nakajima, Hiroshi Mushotzky, Richard Murakami, Hiroshi Mukai, Koji Mori, Koji Mizuno, Tsunefumi Mizumoto, Misaki Mitsuishi, Ikuyuki Miller, Jon M. Miller, Eric D. Mernier, François McNamara, Brian McCammon, Dan Matsushita, Kyoko Matsumoto, Hironori Markevitch, Maxim Maeda, Yoshitomo Loewenstein, Michael Leutenegger, Maurice Kubota, Aya Kohmura, Takayoshi Kobayashi, Shogo Kitamoto, Shunji Kilbourne, Caroline Kelley, Richard Kanemaru, Yoshiaki Katsuda, Satoru Kara, Erin Kallman, Timothy Kaastra, Jelle Ishisaki, Yoshitaka Ishikawa, Kumi Ishida, Manabu Ichinohe, Yuto Hornschemeier, Ann Hodges-Kluck, Edmund Hell, Natalie Hayashi, Takayuki Hayashi, Katsuhiro Hatsukade, Isamu Hamaguchi, Kenji Hagino, Kouichi Guainazzi, Matteo Gu, Liyi García, Javier A. Gallo, Luigi Furuzawa, Akihiro Fukushima, Kotaro Fukazawa, Yasushi Fujita, Yutaka Fujimoto, Ryuichi Foster, Adam Ezoe, Yuichiro Eguchi, Satoshi Enoto, Teruaki Eckert, Dominique Eckart, Megan E. Ebisawa, Ken Dotani, Tadayasu Done, Chris Department of Physics Ehime University Matsuyama Japan Department of Astronomy University of Maryland College Park MD United States NASA/Goddard Space Flight Center Greenbelt MD United States Center for Research and Exploration in Space Science and Technology NASA/GSFC (CRESST II) Greenbelt MD United States Department of Physics University of Tokyo Tokyo Japan Department of Physics Technion Haifa Israel Center for Space Science and Technology University of Maryland Baltimore County Baltimore MD United States Center for Astrophysics | Harvard-Smithsonian Cambridge MA United States Lawrence Livermore National Laboratory Livermore CA United States Department of Astronomy University of Michigan Ann Arbor MI United States SRON Netherlands Institute for Space Research Leiden Netherlands Centre for Extragalactic Astronomy Department of Physics University of Durham Durham United Kingdom Institute of Space and Astronautical Science (ISAS) Japan Aerospace Exploration Agency (JAXA) Kanagawa Japan Department of Physics Kyoto University Kyoto Japan Department of Economics Kumamoto Gakuen University Kumamoto Japan Department of Physics Tokyo Metropolitan University Tokyo Japan Department of Physics Hiroshima University Hiroshima Japan Department of Physics Fujita Health University Toyoake Japan Department of Astronomy and Physics Saint Mary’s University Halifax Nova Scotia Canada Cahill Center for Astronomy and Astrophysics California Institute of Technology Pasadena CA United States European Space Agency (ESA) European Space Research and Technology Centre (ESTEC) Noordwijk Netherlands Faculty of Engineering University of Miyazaki Miyazaki Japan RIKEN Nishina Center Saitama Japan Leiden Observatory University of Leiden Leiden Netherlands Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology Cambridge MA United States Department of Physics Saitama University Saitama Japan Department of Physics Rikkyo Universi

Galaxy clusters contain vast amounts of hot ionized gas known as the intracluster medium (ICM). In relaxed cluster cores, the radiative cooling time of the ICM is shorter than the age of the cluster. However, the absence of line emission associated with cooling suggests heating mechanisms that offset the cooling, with feedback from active galactic nuclei (AGNs) being the most likely source^1,2. Turbulence and bulk motions, such as the oscillating (‘sloshing’) motion of the core gas in the cluster potential well, have also been proposed as mechanisms for heat distribution from the outside of the core^3,4. Here we present X-ray spectroscopic observations of the Centaurus galaxy cluster with the X-Ray Imaging and Spectroscopy Mission satellite. We find that the hot gas flows along the line of sight relative to the central galaxy, with velocities from 130 km s⁻¹ to 310 km s⁻¹ within about 30 kpc of the centre. This indicates bulk flow consistent with core gas sloshing. Although the bulk flow may prevent excessive accumulation of cooled gas at the centre, it could distribute the heat injected by the AGN and bring in thermal energy from the surrounding ICM. The velocity dispersion of the gas is found to be only ≲120 km s⁻¹ in the core, even within about 10 kpc of the AGN. This suggests that the influence of the AGN on the surrounding ICM motion is limited in the cluster. © The Author(s), under exclusive licence to Springer Nature Limited 2025.

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare

Artificial Intelligence-based Service Chains Scheduling for ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mukesh Soni Maher Ali Rusho Haewon Byeon Azzah AlGhamdi Shaikh Abdul Hannan Parth Rameshchandra Dave Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Tathawade Pune Marashtra Department of Lockheed Martin Engineering Management University of Colorado Boulder Colorado Department of AI and Software & Medical Big Data Research Centre Inje University Gimhae Republic of Korea Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Faculty of Computing and Information AlBaha University AlBaha Kingdom of Saudi Arabia Department of Computer Engineering L. D. College of Engineering Ahmedabad Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare.

关键词： Schedules Job shop scheduling Service function chaining Hospitals Processor scheduling Reinforcement learning Games Emergency services Network function virtualization Standards

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Heterogeneous influence of individuals’ behavior on mask efficacy in gathering environments

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Frontiers of engineering Management 2022年第4期9卷 550-562页

作者： Haochen SUN Xiaofan LIU Zhanwei DU Ye WU Haifeng ZHANG Xiaoke XU College of Information and Communication Engineering Dalian Minzu UniversityDalian 116600China Web Mining Laboratory Department of Media and CommunicationCity University of Hong KongHong KongChina WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong KongChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkHong KongChina Computational Communication Research Center Beijing Normal University(Zhuhai)Zhuhai 519087China School of Journalism and Communication Beijing Normal UniversityBeijing 100875China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical ScienceAnhui UniversityHefei 230601China

Wearing masks is an easy way to operate and popular measure for preventing *** masks can slow down the spread of viruses,their efficacy in gathering environments involving heterogeneous person-to-person contacts remains ***,we aim to investigate the epidemic prevention effect of masks in different real-life gathering *** study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering *** transmission of COVID-19 is simulated using the Monte Carlo simulation *** heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general ***,the heterogeneity of gathering environments causes the epidemic prevention effect of masks to *** masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools,high schools,and ***,the use of masks alone in primary schools and hospitals cannot control *** high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse,masks can meet the need for *** the heterogeneity of individual behavior,if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing,the epidemic prevention effect of masks can be ***,asymptomatic infection has varying effects on the prevention effect of masks in different *** effect can be weakened or eliminated by increasing the usage rate of masks in high schools and ***,the effect on primary schools and hospitals cannot be *** study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.

关键词： COVID-19 masks behavioral heterogeneity asymptomatic infection

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Getting Ready for the European Health data Space (Ehds): The Iderha's Plan to Align with the Ehds Requirements for the Secondary Use of Health data

SSRN

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SSRN 2024年

作者： Hussein, Rada Balaur, Irina Burmann, Anja Ćwiek-Kupczyńska, Hanna Gadiya, Yojana Ghosh, Soumyabrata Jayathissa, Prabath Katsch, Florian Kremer, Andreas Lähteenmäki, Jaakko Meng, Zhaoling Morasek, Kathrin Rancourt, Rebecca C. Satagopam, Venkata Sauermann, Stefan Scheider, Simon Stamm, Tanja A. Muehlendyck, Christian Gribbon, Philip Ludwig Boltzmann Institute for Digital Health and Prevention Salzburg Austria Luxembourg Centre for Systems Biology University of Luxembourg Luxembourg Fraunhofer Institute for Software and Systems Engineering Dortmund Germany Discovery Research Screening Port Hamburg Germany Theodor Stern Kai 7 Frankfurt60590 Germany Bonn-Aachen International Center for Information Technology University of Bonn Bonn Germany Center for Medical Data Science Medical University of Vienna Vienna Austria ITTM S.A. Luxembourg VTT Technical Research Centre of Finland Ltd Espoo Finland Clinical Modeling and Evidence Integration Sanofi CambridgeMA United States Institute of Outcomes Research Center for Medical Data Science Medical University of Vienna Austria Ludwig Boltzmann Institute for Arthritis and Rehabilitation Vienna Austria Medical School Berlin Berlin Germany Faculty Life Science Engineering FH Technikum Wien Vienna Austria TU Dortmund Dortmund Germany Johnson & Johnson Medical GmbH Norderstedt Germany

Objective: The European Health data Space (EHDS) shapes the digital transformation of healthcare in Europe. The EHDS regulations will also accelerate the use of health data for research, innovation, policy-making, and regulatory activities for secondary use of data (known as EHDS2). The Integration of heterogeneous data and Evidence towards Regulatory and HTA Acceptance (IDERHA) project builds one of the first pan-European health data spaces in alignment with the EHDS2 requirements, addressing lung cancer as a *** and Methods: In this study, we conducted a comprehensive review of the EHDS regulations and technical requirements for EHDS2. Mainly utilizing the results of the Joint Action Towards the European Health data Space (TEHDAS) project to identify the framework of IDERHA’s alignment with EHDS. We also conducted an internal webinar and an external workshop with EHDS experts to share expertise on the EHDS requirements and ***: We identified the minimum-set of requirements for aligning IDERHA infrastructure with the planned EHDS regulations, including user journey, concepts, terminologies, and standards. The future IDERHA framework (i.e., platform architecture, standardization approaches, documentation, etc.) will be developed ***: The IDERHA's alignment plan with EHDS2 necessitates the implementation of three categories of standardization for: data discoverability (DCAT-AP), enabling semantics interoperability (OMOP), and health data exchange (DICOM and FHIR). The main challenge is that some standards are still being refined, e.g., the extension of the DCAT-AP (HealthDCAT-AP). Additionally, extensions to the OHDSI OMOP CMD for representing the Patient-Generated Health data are still needed. Finally, proper mapping between standards (FHIR/OMOP) is a prerequisite for proper data ***: Collaboration, sharing lessons learned, and creating synergy among the current EHDS initiatives and projects are critical

关键词： Electronic data interchange

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Dengue Dashboard for Forecasting the Future Trend of Dengue Cases in Pahang

Dengue Dashboard for Forecasting the Future Trend of Dengue ...

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Software engineering & Computer systems and International Conference on Computational science and Information Management (ICSECS-ICOCSIM), International Conference on

作者： Muhammad Khairul Syafiq Mustafa Junaida Sulaiman Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan Pahang Malaysia Faculty of Computing College of Computing and Applied Sciences Centre For Data Science & Artificial Intelligence Soft Computing & Intelligent Systems (SPINT) Universiti Malaysia Pahang Pekan Pahang Malaysia

ISBN: (数字)9781665414074

ISBN: (纸本)9781665414081

The study is concerned about the rising number of dengue cases in Pahang. As a preventive solution, an analytical dashboard is proposed to forecast the trend of dengue cases in Pahang for the next two years using Auto Regressive Integrated Moving Average (ARIMA) model. Due to its efficiency to solve linear problems, ARIMA model is a popular model used by the researchers and economists to forecast a time-series data. The dashboard contains various information of dengue cases for instances the total number of dengue cases in Pahang, total number of dengue cases by district, a map that shows the green zone and the red zone which indicates the risk of the dengue infection in that area and a graph that displays the forecasted trend of dengue cases in Pahang from 2021 to 2023 using the best- fitted ARIMA model. The dashboard and the forecasted data trend of dengue is generated to assist JKN Pahang to plan a preventive measure regarding the rising number of dengue cases in thefuture.

关键词： Temperature Rain Scientific computing Meteorological factors Weather forecasting Predictive models Market research

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Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model

arXiv

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arXiv 2024年

作者： Zhou, Peng Wang, Jianmin Li, Chunyan Wang, Zixu Liu, Yiping Sun, Siqi Lin, Jianxin Wei, Leyi Cai, Xibao Lai, Houtim Liu, Wei Wang, Longyue Liu, Yuansheng Zeng, Xiangxiang College of Information Science and Engineering Hunan University Changsha China The Interdisciplinary Graduate Program in Integrative Biotechnology Yonsei University Incheon21983 Korea Republic of School of Informatics Yunnan Normal University Kunming650500 China Department of Computer Science University of Tsukuba Tsukuba3058577 Japan Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China Shanghai AI Laboratory Shanghai200232 China Centre for Artificial Intelligence driven Drug Discovery Faculty of Applied Science Macao Polytechnic University China School of Informatics Xiamen University Xiamen China Tencent AI Lab China

While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge. Here, we introduce a multi-constraint molecular generation large language model, TSMMG, which, akin to a student, incorporates knowledge from various small models and tools, namely, the 'teachers'. To train TSMMG, we construct a large set of text-molecule pairs by extracting molecular knowledge from these 'teachers', enabling it to generate novel molecules that conform to the descriptions through various text prompts. We experimentally show that TSMMG remarkably performs in generating molecules meeting complex, natural language-described property requirements across two-, three-, and four-constraint tasks, with an average molecular validity of over 99% and success ratio of 82.58%, 68.03%, and 67.48%, respectively. The model also exhibits adaptability through zero-shot testing, creating molecules that satisfy combinations of properties that have not been encountered. It can comprehend text inputs with various language styles, extending beyond the confines of outlined prompts, as confirmed through empirical validation. Additionally, the knowledge distillation feature of TSMMG contributes to the continuous enhancement of small models, while the innovative approach to dataset construction effectively addresses the issues of data scarcity and quality, which positions TSMMG as a promising tool in the domains of drug discovery and materials science. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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