Article
Multidisciplinary Sciences
Cristina Cornelio, Sanjeeb Dash, Vernon Austel, Tyler R. Josephson, Joao Goncalves, Kenneth L. Clarkson, Nimrod Megiddo, Bachir El Khadir, Lior Horesh
Summary: Automatic extraction of consistent governing laws from data is a challenging problem. The authors propose a method that combines symbolic regression with logical reasoning to obtain scientifically meaningful symbolic formulas.
NATURE COMMUNICATIONS
(2023)
Article
Engineering, Multidisciplinary
F. Lambinet, Z. Sharif Khodaei
Summary: This paper reports on the development and testing of a software platform for structural health monitoring (SHM) of composite fuselage panels. The platform has been shown to be reliable, user friendly and scalable to large sub-components, with optimized data acquisition and handling protocols.
Article
Quantum Science & Technology
Yuri Alexeev, Dave Bacon, Kenneth R. Brown, Robert Calderbank, Lincoln D. Carr, Frederic T. Chong, Brian DeMarco, Dirk Englund, Edward Farhi, Bill Fefferman, Alexey Gorshkov, Andrew Houck, Jungsang Kim, Shelby Kimmel, Michael Lange, Seth Lloyd, Mikhail D. Lukin, Dmitri Maslov, Peter Maunz, Christopher Monroe, John Preskill, Martin Roetteler, Martin J. Savage, Jeff Thompson
Summary: The development of quantum computers and the discovery of scientific applications should be considered together by co-designing full-stack quantum computer systems and applications to accelerate their development. In the next 2-10 years, quantum computers for science face significant challenges and opportunities.
Article
Computer Science, Information Systems
Mingfan Li, Han Lin, Junshi Chen, Jose Monsalve Diaz, Qian Xiao, Rongfen Lin, Fei Wang, Guang R. Gao, Hong An
Summary: This paper introduces a large-scale distributed framework swFLOW for deep learning tasks on Sunway TaihuLight. By analyzing and optimizing the performance of convolutional neural networks, the processing speed has been significantly improved. The use of Elastic Averaging Stochastic Gradient Descent (EASGD) algorithm reduces communication overhead and achieves high parallel efficiency.
INFORMATION SCIENCES
(2021)
Article
History & Philosophy Of Science
Elinor Clark, Donal Khosrowi
Summary: This paper investigates the use of AI's intuitions about scientific discovery to improve our understanding of scientific discovery. It argues that a collective-centred view is superior for understanding discovery with and without AI, as it recognizes the contributions of a collective of agents and entities and attributes credit based on finer-grained properties of those contributions.
Article
Computer Science, Hardware & Architecture
Yuxuan Li, Xiaohui Duan, Lin Gan, Wubing Wan, Yuhu Chen, Kai Xu, Jinzhe Yang, Weiguo Liu, Wei Xue, Haohuan Fu, Guangwen Yang
Summary: The Community Atmosphere Model (CAM) has been successfully ported and optimized on the Sunway TaihuLight system, achieving high-performance climate modeling capabilities.
IEEE TRANSACTIONS ON COMPUTERS
(2022)
Article
Computer Science, Interdisciplinary Applications
Amir H. Gandomi, Kalyanmoy Deb, Ronald C. Averill, Shahryar Rahnamayan, Mohammad Nabi Omidvar
Summary: To solve complex real-world problems, a concept-based approach called variable functioning (Fx) is introduced to reduce optimization variables and narrow down the search space. By using problem structure analysis and engineering expert knowledge, the Fx method enhances the steel frame design optimization process. Coupled with particle swarm optimization and differential evolution algorithms, the proposed approach improves the convergence rate and final design of frame structures.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2023)
Article
Chemistry, Physical
Chunyang Zhou, Yiwei Song, Xiuyan Jin, Bei Li, Chunying Pang
Summary: The predictability of Watson-Crick base pairing in DNA allows for unique structural programmability, but current reaction systems in DNA-based biocomputing have limited operation scale. This study presents a multifunctional DNA-nanostructure-based reaction platform that successfully calculates square roots and cube roots with high scalability and integration.
NANOSCALE HORIZONS
(2023)
Review
Agriculture, Multidisciplinary
Xu Ren, Bo Huang, Hesheng Yin
Summary: With the disappearance of the demographic dividend and labor shortage due to aging population, achieving high automation in agricultural scenarios is necessary and urgent. The development of science and technology has made certain progress, with autonomous mobility platforms playing a crucial role.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Mathematical & Computational Biology
Wenlong Ma, Siyuan Chen, Yuhong Qi, Minggui Song, Jingjing Zhai, Ting Zhang, Shang Xie, Guifeng Wang, Chuang Ma
Summary: In this study, we developed easyMF, a web platform that utilizes matrix factorization algorithms for functional gene discovery from large-scale transcriptome data. Compared with existing software, easyMF offers greater functionality, flexibility, and ease of use. The platform is equipped with user-friendly graphic user interfaces and supports various analyses, including transcriptome analysis, multiple-scenario matrix factorization analysis, and multiple-way gene discovery. We applied easyMF to maize RNA-Seq datasets and successfully identified numerous seed-specific genes. Additionally, easyMF outperformed other systems in gene prioritization.
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
(2022)
Article
Physics, Multidisciplinary
Yanfen Zhou, Wei Wang, Tingting Song, Xutong Wang, Qiqi Zhu, Kai Zhang, Shengshuai Liu, Jietai Jing
Summary: Quantum entanglement is crucial for quantum information processing, and the scale of quantum entanglement directly affects its processing capability. Generating ultra-large-scale (ULS) quantum entanglement is of great importance for the development of quantum information science and technology. This study introduces time-domain multiplexing into continuous-variable quantum systems to significantly increase the scale of quantum entanglement. By using a time-delayed quantum interferometer, the researchers propose and demonstrate a scheme for generating ULS continuous-variable deterministic entanglement with 2 x 20 400 optical modes, including 81 596 squeezed modes. This provides a new platform for ULS continuous-variable quantum information processing.
PHYSICAL REVIEW LETTERS
(2023)
Article
Multidisciplinary Sciences
Adam G. M. Lewis, Jackson Beall, Martin Ganahl, Markus Hauru, Shrestha Basu Mallick, Guifre Vidal
Summary: Researchers have repurposed Google's TPUs to create large-scale dense linear algebra supercomputers, which can rapidly compute large matrices in less than two minutes using distributed matrix multiplication algorithms.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Chemistry, Multidisciplinary
Zetian Mao, Chi Chen, Yucheng Zhang, Kuniko Suzuki, Yuji Suzuki
Summary: Ionization potential is discovered as a descriptor to quantify the charging performance of amorphous fluorinated polymer electrets. Using this descriptor and deep learning models, 3 promising electrets are identified and successfully synthesized. The films of these electrets exhibit excellent bipolar surface potentials and longevity, showing great potential for power generation in electret-based vibration energy harvesters. This work demonstrates the application of deep learning in accelerating practical materials discovery.
ADVANCED MATERIALS
(2023)
Article
Computer Science, Theory & Methods
Ye Tian, Langchun Si, Xingyi Zhang, Ran Cheng, Cheng He, Kay Chen Tan, Yaochu Jin
Summary: This article provides a comprehensive survey of state-of-the-art MOEAs for solving large-scale multi-objective optimization problems, categorizing them into different types and discussing their strengths and weaknesses. It also reviews benchmark problems for performance assessment and important applications, while also addressing remaining challenges and future research directions in evolutionary large-scale multi-objective optimization.
ACM COMPUTING SURVEYS
(2021)
Article
Energy & Fuels
Biao Li, Jie Liu, Xiaoxiong Zhu, Shengjie Ding
Summary: The paper presents a new method for large-scale heterogeneous computing on the Tianhe-2A supercomputer, achieving significant acceleration by optimizing the particle transport algorithm. On the Tianhe-2A supercomputer, the parallel efficiency of 1.01 million cores compared with 170 thousand cores is 52%.
FRONTIERS IN ENERGY RESEARCH
(2021)
Article
Imaging Science & Photographic Technology
Tiechui Yao, Li Xiao, Di Zhao, Yuzhong Sun
IMAGING SCIENCE JOURNAL
(2018)
Article
Energy & Fuels
Dazhi Yang, Wenting Wang, Tao Hong
Summary: Weather is a crucial factor for power generation and energy consumption, and energy forecasting models often rely on numerical weather prediction. This article offers an NWP forecast dataset from ECMWF for the energy forecasting community, along with case studies on post-processing of solar forecasts.
Article
Energy & Fuels
Tiechui Yao, Jue Wang, Haoyan Wu, Pei Zhang, Shigang Li, Yangang Wang, Xuebin Chi, Min Shi
Summary: The power output of PV systems is influenced by climate and weather conditions. The scarcity of datasets combining power and weather data hinders progress in solar PV research. The PVOD dataset provides multi-source high-quality data for solar energy research.
Article
Green & Sustainable Science & Technology
Tiechui Yao, Jue Wang, Haoyan Wu, Pei Zhang, Shigang Li, Ke Xu, Xiaoyan Liu, Xuebin Chi
Summary: This paper proposes a data-driven forecasting framework based on deep learning to enhance the accuracy of PV generation prediction by integrating multiple data sources such as time series records and satellite images.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2022)
Article
Chemistry, Multidisciplinary
Haizhou Cao, Jing Yang, Xuemeng Zhao, Tiechui Yao, Jue Wang, Hui He, Yangang Wang
Summary: The penetration of photovoltaic (PV) energy has significantly increased in recent years due to its sustainable and clean characteristics. However, accurate short-term prediction of PV power is challenging due to the uncertainty caused by variable weather. Existing methods focus on utilizing deep neural networks to extract features from satellite images and ground measurements, but a flexible predictive framework that can handle both data structures is lacking. Therefore, this study proposes a novel dual-encoder transformer (DualET) for short-term PV power prediction, which utilizes wavelet transform and series decomposition blocks to extract informative features from image and sequence data, respectively. Additionally, a cross-domain attention module is introduced to learn the correlation between temporal features and cloud information, and attention modules are modified using sparse form and Fourier transform to improve performance. Experimental results on real-world datasets demonstrate that the proposed model outperforms other models in short-term PV power prediction.
APPLIED SCIENCES-BASEL
(2023)
Proceedings Paper
Computer Science, Information Systems
He Li, Rongqiang Cao, Hanwen Xiu, Meng Wan, Kai Li, Xiaoguang Wang, Yangang Wang, Jue Wang
Summary: SSHRA is a secure shell remote access information system for virtualized computing environment, which provides more convenient remote login and enhances the security through certificate-based authentication.
SMART COMPUTING AND COMMUNICATION
(2022)
Proceedings Paper
Computer Science, Information Systems
Meng Wan, Jiaheng Wang, Jue Wang, Rongqiang Cao, Yangang Wang, He Li
Summary: This paper discusses the importance of scientific software in the process of open science and Open research, and proposes a design scheme for a safe and controllable support platform for open-source scientific software. China has made breakthroughs in theory and technology, support platform, ecosystem, and operation system, and created a proven scientific software ecosystem. The progress of research transparency and maturity of scientific software contribute to the collaboration between community developers and the cultivation of scientific research talents.
SMART COMPUTING AND COMMUNICATION
(2022)
Proceedings Paper
Computer Science, Information Systems
Jing Wang, Meng Wan, Jue Wang, Xiaoguang Wang, Yangang Wang, Fang Liu, Weixiao Min, He Lei, Lihua Wang
Summary: This paper presents a surface defect detection system for medical gloves based on deep learning, achieving high efficiency and accuracy through improved real-time performance, a dual model detection strategy, and the use of auxiliary models.
SMART COMPUTING AND COMMUNICATION
(2022)
