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Artificial intelligence for geoscience:Progress,challenges,and perspectives

The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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FINANCIAL VISION BASED DIFFERENTIAL PRIVACY APPLICATIONS

arXiv

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arXiv 2021年

作者： Chen, Jun-Hao Tsai, Yun-Cheng Wang, Yi-Jen Chen, Samuel Yen-Chi Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Data Science Soochow University China Computational Science Initiative Brookhaven National Laboratory

The importance of deep learning data privacy has gained significant attention in recent years. It is probably to suffer data breaches when applying deep learning to cryptocurrency that lacks supervision of financial regulatory agencies. However, there is little relative research in the financial area to our best knowledge. We apply two representative deep learning privacy-privacy frameworks proposed by Google to financial trading data. We designed the experiments with several different parameters suggested from the original studies. In addition, we refer the degree of privacy to Google and Apple companies to estimate the results more reasonably. The results show that DP-SGD performs better than the PATE framework in financial trading data. The tradeoff between privacy and accuracy is low in DP-SGD. The degree of privacy also is in line with the actual case. Therefore, we can obtain a strong privacy guarantee with precision to avoid potential financial loss. © 2021, CC BY.

关键词： Deep learning

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Nomad Platform Revisited for Reliable and Simplified Hull Construction

SSRN

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SSRN 2022年

作者： Uzunoğlu, Bahri Özçelik, Serdal Department of Engineering Sciences Department of Electrical Engineering Computational and data-enabled science and engineering in energy systems Uppsala University The Ångström Laboratory Box 534 Uppsala751 21 Sweden Department of Mathematics Florida State University The Ångström Laboratory Box 534 TallahasseeFL32310 United States Computational Renewables LLC The Ångström Laboratory Box 534 TallahasseeFL32309-3267 United States

The U.S. Navy (USN) in the late ’40s developed Navy Oceanographic Meteorological AutomaticDevice (NOMAD) platform for offshore autonomous meteorological measurements which has seen widespread usage since then because of its performance characteristics as meteorological and oceanographic data buoys, as energy system platform testing, as rocket data acquisition platform from Pacific to Atlantic. This article revisits the NOMAD hulls for engineering design to achieve simpler and less costly construction while this article focuses on hull construction and aspects and seakeeping are investigated in further work. Two hull forms namely NOMAD and HACIYATMAZ are analyzed. The HACIYATMAZ hull which is built from flat surfaces achieve similar structural performance to a streamlined NOMAD hull while simplifying the hull construction significantly with similar stability characteristics. Shell element-based finite element approach is employed for computational analysis. © 2022, The Authors. All rights reserved.

关键词： Rockets

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Maximum Effort Consensus Modeling Under Social Network Considering Individual Emotions 22nd

Maximum Effort Consensus Modeling Under Social Network Consi...

引用

22nd International Symposium on Knowledge and Systems sciences, KSS 2023

作者： Xu, Mengru Jing, Fuying Chao, Xiangrui Herrera-viedma, Enrique School of Management Science and Engineering Chongqing Technology and Business University Chongqing400067 China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing400067 China School of Business Sichuan University Chengdu610065 China Andalusian Research Institute in Data Science and Computational Intelligence Department of Computer Science and Artificial Intelligence University of Granada Granada18071 Spain

ISBN: (纸本)9789819983179

It’s beyond disputed that everyone has their own unique understandings of words, which induces personalized individual semantics (PISs) attached to linguistic expressions. Social network illustrates trust relationships among group members. In linguistic social network decision making (SNGDM), social network analysis usually aids in determining the importance weights of decision makers (DMs). Actually, emotions may have an impact on trust propagation. For instance, positive emotions would strengthen trust whereas negative emotions work in a reverse way. Thus, a social network with individual emotions is firstly developed. Generally, DMs are frequently required to modify their opinions in consensus reaching process (CRP). To some extent, emotions also reflect their adjusted probabilities. Meanwhile, DM’s willingness and attitude to making necessary modifications for better agreement, that is measured by effort degree, will bring to different consensus results. Under a limited cost budget, a maximum effort consensus model driven by maximizing all DMs’ effort degree is proposed for instructing feedback regulation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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RL Perceptron: Generalization Dynamics of Policy Learning in High Dimensions

引用

Physical Review X 2025年第2期15卷 021051-021051页

作者： Nishil Patel Sebastian Lee Stefano Sarao Mannelli Sebastian Goldt Andrew Saxe Gatsby Computational Neuroscience Unit University College London Gower Street London WC1E 6BT United Kingdom Imperial College London Exhibition Road South Kensington London SW7 2AZ United Kingdom Data Science and AI Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Gothenburg Sweden School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa International School of Advanced Studies (SISSA) Via Bonomea 265 34136 Trieste Italy Sainsbury Wellcome Centre University College London 25 Howland Street London W1T 4JG United Kingdom

Reinforcement learning (RL) algorithms have transformed many domains of machine learning. To tackle real-world problems, RL often relies on neural networks to learn policies directly from pixels or other high-dimensional sensory input. By contrast, many theories of RL have focused on discrete state spaces or worst-case analysis, and fundamental questions remain about the dynamics of policy learning in high-dimensional settings. Here, we propose a solvable high-dimensional RL model that can capture a variety of learning protocols, and we derive its typical policy learning dynamics as a set of closed-form ordinary differential equations. We obtain optimal schedules for the learning rates and task difficulty—analogous to annealing schemes and curricula during training in RL—and show that the model exhibits rich behavior, including delayed learning under sparse rewards, a variety of learning regimes depending on reward baselines, and a speed-accuracy trade-off driven by reward stringency. Experiments on variants of the Procgen game “Bossfight” and Arcade Learning Environment game “Pong” also show such a speed-accuracy trade-off in practice. Together, these results take a step toward closing the gap between theory and practice in high-dimensional RL.

关键词： Differential equations

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Entanglement and coherence in pure and doped Posner molecules

arXiv

引用

arXiv 2023年

作者： Adams, Betony Sinayskiy, Ilya Agarwal, Shivang Petruccione, Francesco Quantum Research Group University of KwaZulu-Natal South Africa National Institute for Theoretical and Computational Sciences South Africa The Guy Foundation Dorset United Kingdom The Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States School of Data Science and Computational Thinking Department of Physics Stellenbosch University South Africa

The potential role of spin in biological systems is a primary topic in quantum biology. However, much of this research focuses on electron spin. A recent hypothesis suggests that nuclear spin may be better suited to biological processes, being less sensitive to decoherence. The hypothesis details how phosphorus nuclei might be prepared in a spin entangled state, how this entanglement is protected by assembly into calcium phosphate (Posner) molecules, and how this entanglement might modulate calcium ion production and concomitant neural activation. In this paper we investigate the robustness of quantum effects such as coherence and entanglement in Posner molecules. We investigate how these effects are directly dependent on specific parameters such as spin-spin coupling strengths and Posner molecule symmetry. We also investigate how lithium isotope doped Posner molecules differentially modulate quantum resources such as coherence and entanglement and whether this is a viable explanation for lithium's mechanism of action in bipolar disease. Finally we illustrate how entanglement might possibly be preserved through exploitation of the biological environment. Copyright © 2023, The Authors. All rights reserved.

关键词： Molecules

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Iceland wind farm assessment case study and development: An empirical data from wind and wind turbine

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Cleaner Energy Systems 2023年 4卷

作者： Hassanian, Reza Helgadóttir, Ásdís Riedel, Morris The Faculty of Industrial Engineering Mechanical Engineering and Computer Science University of Iceland Reykjavík 102 Iceland Computational Fluid Dynamics Simulation and Data Lab National Competence Centre of Iceland University of Iceland Reykjavík 102 Iceland Juelich Supercomputing Centre Jülich 52428 Germany

This study aimed to apply empirical data to assess wind energy production at the Búrfell site in Iceland based on the E44 turbine model. The empirical data are 5 years of recordings at the site location by the Iceland Metrological office. The wind speed data are measured at a 10 m height from 2017 to 2021. There are two E44 wind turbines test installed on the site. In the previous studies, the wind farm capacity and Levelized cost of energy (LCOE) were reported without investigating the wake loss model and its impacts on LCOE and have an estimation applied. The previous research was based on the two installed wind turbines at the site, which are located in a straight line and perpendicular to the prevailing wind speed. This study applies the Jensen-Katic model to investigate wake loss. Downwind and crosswind ten-rotor diameters and five-rotor diameters are calculated respectively as the best options. Afterward, an appropriate number of wind turbines is suggested for 80MW production. In addition, this study's optimum capacity factor (CF) is 26.08%, which was reported at 37.9% - 38.38% before. On average, the turbines produce less than 30% of their rated power, which has been reported at 38.15% in prior studies. This study presents the LCOE as equal to 0.0659 USD/kWh, which is less than 0.0703 USD/kWh in the previous studies and the LCOE reported by the 2020 LCOE European report. The obtained LCOE in this study is based on the weighted average cost of capital in the energy project by Landsvirkjun, the national power company of Iceland. The obtained result from the model used, which matched the empirical measurements, displays Iceland's best rank for wind energy LCOE metric among European countries. The proposed method provides a vision to use the wake loss model output in deep learning training to predict power production, leading to a sustainable and reliable power grid. © 2023 The Author(s)

关键词： Energy Experiments Levelized cost Turbine Wake loss model Wind

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Study of City Crimes with Smart Solutions Integrating into Smart Cities, Internet of Things (IoT), and Cybersecurity Systems

Study of City Crimes with Smart Solutions Integrating into S...

引用

Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Al-Siyam Rahman Md. Sadik Tasrif Anubhove Mohammad Zeyad S.M. Masum Ahmed Md. Abul Ala Walid Energy and Technology Research Division Advanced Bioinformatics Computational Biology and Data Science Laboratory Bangladesh (ABCD Laboratory Bangladesh) Chattogram Bangladesh Erasmus+ Joint MSC in Cybersecurity Faculty of Sciences Université libre de Bruxelles (ULB) Brussels Belgium Avenues Department of Urban Engineering Universite de Technologie de Compiègne (UTC) Compiègne France Centre for Research on Geography Resources Environment Energy & Networks (Green) Bocconi University Milano Italy Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Bangladesh

The development of smart cities enhances the quality of life for many people by promoting digital interconnection, which encourages dependability and accessibility in cities. Cities have multiple security issues to deal with, as well as kidnapping, rape, child abuse, and violence, completely of which stand on the rise globally. This kind of crime constantly disturbs the victim not only physically and mentally but also forces them to attempt suicide, which can ruin the happiness of many families in a city. Conventional methods are insufficient to safeguard a city from crime and provide a secure way of life for everyone, which is depraved for society and their day-to-day activities. However, this paper, first analyzes the different types of criminal activities in Bangladesh, such as kidnapping, rape, murder, and child abuse, and here offer various technological solutions, to protect cities against the worst crimes, using technologies like cybersecurity and the Internet of Things (IoT) based technologies. With the help of various technological solutions, a city becomes safer while also achieving Sustainable Development Goals (SDG's) number 11, which promotes sustainable cities and communities. This paper clarifies how to reduce criminal activities by making use of a range of technologies, which benefit the country and improve people's quality of life.

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Machine learning approaches and databases for prediction of drug-target interaction: a survey paper (vol 22, pg 247, 2021)

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BRIEFINGS IN BIOINFORMATICS 2021年第1期22卷 606-606页

作者： Bagherian, Maryam Sabeti, Elyas Wang, Kai Sartor, Maureen A. Nikolovska-Coleska, Zaneta Najarian, Kayvan Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI 48109 USA Michigan Institute for Data Science University of Michigan Ann Arbor MI 48109 USA Department of Biostatistics School of Public Health University of Michigan Ann Arbor MI 48109 USA Department of Pathology University of Michigan Ann Arbor MI 48109 USA Department of Emergency Medicine Medical School University of Michigan Ann Arbor MI 48109 USA Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann Arbor MI 48109 USA

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

关键词： Machine learning drug–target interaction prediction DTI software DTI database

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Cell2Sentence: teaching large language models the language of biology 24

Cell2Sentence: teaching large language models the language o...

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Proceedings of the 41st International Conference on Machine Learning

作者： Daniel Levine Syed Asad Rizvi Sacha Lévy Nazreen Pallikkavaliyaveetil David Zhang Xingyu Chen Sina Ghadermarzi Ruiming Wu Zihe Zheng Ivan Vrkic Anna Zhong Daphne Raskin Insu Han Antonio Henrique De Oliveira Fonseca Josue Ortega Caro Amin Karbasi Rahul M. Dhodapkar David Van Dijk Department of Computer Science Yale University New Haven CT School of Engineering Applied Science University of Pennsylvania Philadelphia PA School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT and Wu Tsai Institute Yale University New Haven CT Google and Yale Institute for Foundations of Data Science New Haven CT and Department of Computer Science Yale University New Haven CT and Yale School of Engineering and Applied Science New Haven CT Roski Eye Institute University of Southern California Los Angeles CA and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Yale Institute for Foundations of Data Science New Haven CT and Wu Tsai Institute Yale University New Haven CT and Cardiovascular Research Center Yale School of Medicine New Haven CT and Interdepartmental Program in Computational Biology & Bioinformatics Yale University New Haven CT and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the fine-tuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

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