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Lecture Notes in Networks and Systems 2023年 725 LNNS卷 xiii-xiv页

作者： Das, Asit Kumar Nayak, Janmenjoy Naik, Bighnaraj Vimal, S. Pelusi, Danilo Department of Computer Science and Technology Indian Institute of Engineering Science and Technology West Bengal Howrah India Department of Computer Applications Veer Surendra Sai University of Technology Odisha Burla India Department of Communication Sciences University of Teramo Teramo Italy Odisha Baripada India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Tamil Nadu Rajapalayam India

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ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis

arXiv

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arXiv 2023年

作者： Zheng, Zhiling Zhang, Oufan Borgs, Christian Chayes, Jennifer T. Yaghi, Omar M. Department of Chemistry University of California BerkeleyCA94720 United States Kavli Energy Nanoscience Institute University of California BerkeleyCA94720 United States Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Mathematics University of California BerkeleyCA94720 United States Department of Statistics University of California BerkeleyCA94720 United States School of Information University of California BerkeleyCA94720 United States KACST-UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh11442 Saudi Arabia

We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information-an issue that previously made the use of Large Language Models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different tradeoffs between labor, speed, and accuracy. We deploy this system to extract 26,257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the dataset built by text mining, we constructed a machine-learning model with over 87% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions on chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format, while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry subdisciplines. Copyright © 2023, The Authors. All rights reserved.

关键词： data mining

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Robust frequent directions with application in online learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2019年第1期20卷

作者： Luo Luo Cheng Chen Zhihua Zhang Wu-Jun Li Tong Zhang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University Beijing China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University Nanjing China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms.

关键词： frequent directions matrix approximation online Newton algorithm online convex optimization sketching

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RETRACTED ARTICLE: An enhanced pairing-based authentication scheme for smart grid communications

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Journal of Ambient Intelligence and Humanized Computing 2021年第1期15卷 165-165页

作者： Wu, Tsu-Yang Lee, Yu-Qi Chen, Chien-Ming Tian, Yuan Al-Nabhan, Najla Abdulrahman College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Computer Engineering Nanjing Institute of Technology Nanjing China Computer Science Department College of Computer and Information Sciences King Saud University Riyadh Kingdom of Saudi Arabia

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Deep learning with quantized neural networks for gravitational wave forecasting of eccentric compact binary coalescence

arXiv

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arXiv 2020年

作者： Wei, Wei Huerta, E.A. Yun, Mengshen Loutrel, Nicholas Shaikh, Md Arif Kumar, Prayush Haas, Roland Kindratenko, Volodymyr National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States NCSA Center for Artificial Intelligence Innovation University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States University of Chicago ChicagoIL60637 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics Princeton University PrincetonNJ08544 United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bangalore560089 India Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States

We present the first application of deep learning forecasting for binary neutron stars, neutron star-black hole systems, and binary black hole mergers that span an eccentricity range e ≤ 0.9. We train neural networks that describe these astrophysical populations, and then test their performance by injecting simulated eccentric signals in advanced LIGO noise available at the Gravitational Wave Open science Center to: 1) quantify how fast neural networks identify these signals before the binary components merge;2) quantify how accurately neural networks estimate the time to merger once gravitational waves are identified;and 3) estimate the time-dependent sky localization of these events from early detection to merger. Our findings show that deep learning can identify eccentric signals from a few seconds (for binary black holes) up to tens of seconds (for binary neutron stars) prior to merger. A quantized version of our neural networks achieves 4x reduction in model size, and up to 2.5x inference speed up. These novel algorithms may be used to facilitate time-sensitive multi-messenger astrophysics observations of compact binaries in dense stellar environments. Copyright © 2020, The Authors. All rights reserved.

关键词： Gravity waves

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Probing nuclear physics with supernova gravitational waves and machine learning

arXiv

引用

arXiv 2023年

作者： Mitra, Ayan Orel, Daniil Abylkairov, Y. Sultan Shukirgaliyev, Bekdaulet Abdikamalov, Ernazar Center for Astrophysical Surveys National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Physics Nazarbayev University 53 Kabanbay Batyr ave Astana010000 Kazakhstan Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States Kazakh-British Technical University 59 Tole Bi Street Almaty050000 Kazakhstan Department of Computer Science Nazarbayev University 53 Kabanbay Batyr ave Astana010000 Kazakhstan Energetic Cosmos Laboratory Nazarbayev University 53 Kabanbay Batyr ave Astana010000 Kazakhstan Heriot-Watt International Faculty Zhubanov University 263 Zhubanov brothers str Aktobe030000 Kazakhstan Fesenkov Astrophysical Institute 23 Observatory str Almaty050020 Kazakhstan Department of Computation and Data Science Astana IT University 55/11 Mangilik El ave Astana010000 Kazakhstan

Core-collapse supernovae are sources of powerful gravitational waves (GWs). We assess the possibility of extracting information about the equation of state (EOS) of high density matter from the GW signal. We use the bounce and early post-bounce signals of rapidly rotating supernovae. A large set of GW signals is generated using general relativistic hydrodynamics simulations for various EOS models. The uncertainty in the electron capture rate is parametrized by generating signals for six different models. To classify EOSs based on the GW data, we train a convolutional neural network (CNN) model. Even with the uncertainty in the electron capture rates, we find that the CNN models can classify the EOSs with an average accuracy of about 87 percent for a set of four distinct EOS models. © 2023, CC BY.

关键词： Gravity waves

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An Integrated Tone Mapping for High Dynamic Range Image Visualization

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Journal of Physics: Conference Series 2018年第1期960卷

作者： Lei Liang Jeng-Shyang Pan Yongjun Zhuang Department of Computer Science and Technology Shenzhen Graduate School Harbin Institute of Technology Shenzhen China Qihan Research Qihan Technology Co. LTD Shenzhen China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Techology Fujian China

There are two type tone mapping operators for high dynamic range (HDR) image visualization. HDR image mapped by perceptual operators have strong sense of reality, but will lose local details. Empirical operators can maximize local detail information of HDR image, but realism is not strong. A common tone mapping operator suitable for all applications is not available. This paper proposes a novel integrated tone mapping framework which can achieve conversion between empirical operators and perceptual operators. In this framework, the empirical operator is rendered based on improved saliency map, which simulates the visual attention mechanism of the human eye to the natural scene. The results of objective evaluation prove the effectiveness of the proposed solution.

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Estimating Canopy Interception Water Storage with GNSS-Transmissometry

Estimating Canopy Interception Water Storage with GNSS-Trans...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Konstantin Schellenberg Thomas Jagdhuber David Chaparro Oliver Binks Florian Hellwig Clémence Dubois Mehmet Kurum Adriano Camps Henrik Hartmann Christiane Schmullius Department for Earth Observation Friedrich Schiller University Jena Jena Germany Department Biogeochemical Processes Max Planck Institute for Biogeochemistry Jena Germany German Aerospace Center (DLR) Microwaves and Radar Institute (HR) Wessling Germany Institute of Geography University of Augsburg Augsburg Germany Centre for Ecological and Forestry Applications (CREAF) Cerdanyola del Vallès Spain German Aerospace Center (DLR) Institute of Data Science Jena Germany School of Electrical and Computer Engineering University of Georgia Athens GA USA Department of Signal Theory and Communications CommSensLab and Institut d’Estudis Espacials de Catalunya Universitat Politècnica de Catalunya Barcelona Spain Julius Kühn-Institute for Forest Protection Federal Research Institute for Cultivated Plants Quedlinburg Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Storage of interception water in the canopy (S c ) heavily affects measurements of vegetation optical depth (VOD) from rain, dew and fog, impeding the direct retrieval of tree physiological parameters such as biomass and plant water content. This study presents a time series decomposition of VOD from Global Navigation Satellite System-Transmissometry (GNSS-T) into biomass, plant moisture content (M g ) and S c . The experiment was conducted at eddy covariance (EC) towers in two temperate forest types in Germany, over the entire vegetation period of 2023 and under fairly wet conditions. S c -values were 1.5 times (needleleaf) to two times (broadleaf) higher than the average diurnal M g cycle, allowing partitioning of interception water storage from plant water. Furthermore, we found indications that S c maxima did not linearly increase with precipitation, suggesting sensitivity of VOD to saturation effects when canopy interception storage reaches a maximum during strong precipitation events. Results indicate the sensitivity of VOD from GNSS-T to canopy wetness. This allows partitioning of canopy water storage from other VOD components and improves the usefulness of VOD as a remote sensing metric for forest canopy water relations. Moreover, it opens pathways to quantify S c and evaporation fluxes independently from EC measurements and field experiments.

关键词： Water storage Sensitivity Biomedical optical imaging Vegetation mapping Forestry Vegetation Optical variables measurement

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Synergy of Binary Substitutions for Improving the Cycle Performance in LiNiO2 Revealed by Ab Initio Materials Informatics

arXiv

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arXiv 2020年

作者： Yoshida, Tomohiro Maezono, Ryo Hongo, Kenta Department of Computer-Aided Engineering and Development Sumitomo Metal Mining Co. Ltd. 3-5 Sobiraki-cho NiihamaEhime792-0001 Japan School of Information Science JAIST Asahidai 1-1 IshikawaNomi923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa SaitamaWako351-0198 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen TsukubaIbaraki305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho SaitamaKawaguchi-shi322-0012 Japan

We explore LiNiO2-based cathode materials with two-element substitutions by an ab initio simulation based materials informatics (AIMI) approach. According to our previous study, a higher cycle performance strongly correlates with less structural change during charge–discharge cycles;the latter can be used for evaluating the former. However, if we target the full substitution space, full simulations are infeasible even for all binary combinations. To circumvent such an exhaustive search, we rely on Bayesian optimization. Actually, by searching only 4% of all the combinations, our AIMI approach discovered two promising combinations, Cr-Mg and Cr-Re, whereas each atom itself never improved the performance. We conclude that the synergy never emerges from a common strategy restricted to combinations of "good" elements that individually improve the performance. In addition, we propose a guideline for the binary substitutions by elucidating the mechanism of crystal structure change. Copyright © 2020, The Authors. All rights reserved.

关键词： Binary alloys

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A GPT-4 Reticular Chemist for Guiding MOF Discovery**

引用

Angewandte Chemie 2023年第46期135卷

作者： Zhiling Zheng Zichao Rong Dr. Nakul Rampal Prof. Dr. Christian Borgs Prof. Dr. Jennifer T. Chayes Prof. Dr. Omar M. Yaghi Department of Chemistry Kavli Energy Nanoscience Institute and Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley Berkeley CA-94720 United States Department of Electrical Engineering and Computer Sciences and Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley Berkeley CA-94720 United States Department of Electrical Engineering and Computer Sciences Department of Statistics Department of Mathematics School of Information and Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley Berkeley CA-94720 United States KACST—UC Berkeley Center of Excellence for Nanomaterials for Clean Energy Applications King Abdulaziz City for Science and Technology Riyadh 11442 Saudi Arabia

We present a new framework integrating the AI model GPT-4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT-4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in-context learning of AI in the next iteration. This iterative human-AI interaction enabled GPT-4 to learn from the outcomes, much like an experienced chemist, by a prompt-learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine-tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT-4 to enhance the feasibility and efficiency of research activities.

关键词： Artificial Intelligence Crystals Large Language Model Metal–Organic Frameworks Synthesis

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