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Prediction of solar energetic events impacting space weather conditions

Advances in Space Research 2024年

作者： Georgoulis, Manolis K. Yardley, Stephanie L. Guerra, Jordan A. Murray, Sophie A. Ahmadzadeh, Azim Anastasiadis, Anastasios Angryk, Rafal Aydin, Berkay Banerjee, Dipankar Barnes, Graham Bemporad, Alessandro Benvenuto, Federico Bloomfield, D. Shaun Bobra, Monica Campi, Cristina Camporeale, Enrico DeForest, Craig E. Emslie, A. Gordon Falconer, David Feng, Li Gan, Weiqun Green, Lucie M. Guastavino, Sabrina Hapgood, Mike Kempton, Dustin Kitiashvili, Irina Kontogiannis, Ioannis Korsos, Marianna B. Leka, K.D. Massa, Paolo Massone, Anna Maria Nandy, Dibyendu Nindos, Alexander Papaioannou, Athanasios Park, Sung-Hong Patsourakos, Spiros Piana, Michele Rawafi, Nour E. Sadykov, Viacheslav M. Toriumi, Shin Vourlidas, Angelos Wang, Haimin L. Wang, Jason T. Whitman, Kathryn Yan, Yihua Zhukov, Andrei N. Research Center for Astronomy and Applied Mathematics of the Academy of Athens Athens11527 Greece Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Department of Meteorology University of Reading ReadingRG6 6ET United Kingdom Paseo Manuel de Lardizabal 4 San Sebastián20018 Spain CIRES University of Colorado BoulderCO80309 United States NOAA Space Weather Prediction Center BoulderCO80305 United States School of Cosmic Physics Dublin Institute for Advanced Studies Dunsink Observatory DublinD15 XR2R Ireland Computer Science Department Georgia State University AtlantaGA30303 United States National Observatory of Athens Penteli15236 Greece Uttarakhand Nainital263001 India NorthWest Research Associates BoulderCO80301 United States INAF Turin Astrophysical Observatory via Osservatorio 20 10025 Pino Torinese Torino Italy MIDA Dipartimento di Matematica Università di Genova via Dodecaneso 35 16146 Genova Italy State of California Office of Data and Innovation 401 I Street Ste 200 SacramentoCA95814 United States Southwest Research Institute BoulderCO80302 United States Department of Physics & Astronomy Western Kentucky University Bowling GreenKY42101 United States Center for Space Plasma and Aeronomic Research University of Alabama in Huntsville HuntsvilleAL35899 United States NASA/Marshall Space Flight Center HuntsvilleAL35812 United States Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China Mullard Space Science Laboratory University College London Holmbury St. MaryRH5 6NT United Kingdom University of Chinese Academy of Sciences Nanjing211135 China Visiting Scientist RAL Space STFC Rutherford Appleton Laboratory Oxfordshire DidcotOX11 0QX United Kingdom NASA Ames Research Center Moffett FieldCA94035 United States An der Sternwarte 16 Potsdam14482 Germany Dipartime

Aiming to assess the progress and current challenges on the formidable problem of the prediction of solar energetic events since the COSPAR/ International Living With a Star (ILWS) Roadmap paper of Schrijver et al. (2015), we attempt an overview of the current status of global research efforts. By solar energetic events we refer to flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. The emphasis, therefore, is on the prediction methods of solar flares and eruptions, as well as their associated SEP manifestations. This work complements the COSPAR International Space Weather Action Teams (ISWAT) review paper on the understanding of solar eruptions by Linton et al. (2023) (hereafter, ISWAT review papers are conventionally referred to as ’Cluster’ papers, given the ISWAT structure). Understanding solar flares and eruptions as instabilities occurring above the nominal background of solar activity is a core solar physics problem. We show that effectively predicting them stands on two pillars: physics and statistics. With statistical methods appearing at an increasing pace over the last 40 years, the last two decades have brought the critical realization that data science needs to be involved, as well, as volumes of diverse ground- and space-based data give rise to a Big data landscape that cannot be handled, let alone processed, with conventional statistics. Dimensionality reduction in immense parameter spaces with the dual aim of both interpreting and forecasting solar energetic events has brought artificial intelligence (AI) methodologies, in variants of machine and deep learning, developed particularly for tackling Big data problems. With interdisciplinarity firmly present, we outline an envisioned framework on which statistical and AI methodologies should be verified in terms of performance and validated against each other. We emphasize that a homogenized and streamlined method validation is another open challenge. The performance of the

关键词： Solar energy

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Adaptive unimodal cost volume filtering for deep stereo matching

arXiv

引用

arXiv 2019年

作者： Zhang, Youmin Chen, Yimin Bai, Xiao Yu, Suihanjin Yu, Kun Li, Zhiwei Yang, Kuiyuan State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China DeepMotion

State-of-the-art deep learning based stereo matching approaches treat disparity estimation as a regression problem, where loss function is directly defined on true disparities and their estimated ones. However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost volume driven by disparity regression is prone to overfitting since the cost volume is under constrained. In this paper, we propose to directly add constraints to the cost volume by filtering cost volume with unimodal distribution peaked at true disparities. In addition, variances of the unimodal distributions for each pixel are estimated to explicitly model matching uncertainty under different contexts. The proposed architecture achieves state-of-the-art performance on Scene Flow and two KITTI stereo benchmarks. In particular, our method ranked the 1st place of KITTI 2012 evaluation and the 4th place of KITTI 2015 evaluation (recorded on 2019.8.20). The codes of AcfNet are available at: https://***/DeepMotionAIResearch/ DenseMatchingBenchmark. Copyright © 2019, The Authors. All rights reserved.

关键词： Uncertainty analysis

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DDK system in finite volume

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Physical Review D 2020年第11期102卷 114515-114515页

作者： Jin-Yi Pang Jia-Jun Wu Li-Sheng Geng College of Science University of Shanghai for Science and Technology Shanghai 200093 China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Physics Beihang University Beijing 100191 China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and Engineering Beihang University Beijing 100191 China School of Physics and Microelectronics Zhengzhou University Zhengzhou Henan 450001 China

The DDK three-body system is supposed to be bound due to the strongly attractive interaction between the D meson and the K meson in the isospin zero channel. The minimum quark content of this three-body bound state is ccq¯s¯ with q=u, d. It will be an explicitly exotic tetraquark state once discovered. In order to confirm the phenomenological study of the DDK system, we can refer to lattice QCD as a powerful theoretical tool parallel to the experiment measurement. In this paper, a three-body quantization condition scheme is derived via the nonrelativistic effective theory and the particle-dimer picture in a finite volume. The lattice spectrum of this three-body system is calculated within the existing model inputs. The spectrum shows various interesting properties of the DDK system, and it may reveal the nature of the D*(2317). This predicated spectrum is expected to be tested in future lattice simulations.

关键词： Bound states Effective field theory Hadron-hadron interactions Lattice QCD

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Adaptive NMS: Refining pedestrian detection in a crowd

arXiv

引用

arXiv 2019年

作者： Liu, Songtao Huang, Di Wang, Yunhong Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing100191

Pedestrian detection in a crowd is a very challenging issue. This paper addresses this problem by a novel NonMaximum Suppression (NMS) algorithm to better refine the bounding boxes given by detectors. The contributions are threefold: (1) we propose adaptive-NMS, which applies a dynamic suppression threshold to an instance, according to the target density;(2) we design an efficient subnetwork to learn density scores, which can be conveniently embedded into both the single-stage and two-stage detectors;and (3) we achieve state of the art results on the CityPersons and CrowdHuman benchmarks. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Learning continuous face age progression: A pyramid of GANs

arXiv

引用

arXiv 2019年

作者： Yang, Hongyu Huang, Di Wang, Yunhong Jain, Anil K. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. This paper presents a novel generative adversarial network based approach to address the issues in a coupled manner. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. To ensure photo-realistic facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects with finer details. Further, an adversarial learning scheme is introduced to simultaneously train a single generator and multiple parallel discriminators, resulting in smooth continuous face aging sequences. The proposed method is applicable even in the presence of variations in pose, expression, makeup, etc., achieving remarkably vivid aging effects. Quantitative evaluations by a COTS face recognition system demonstrate that the target age distributions are accurately recovered, and 99.88% and 99.98% age progressed faces can be correctly verified at 0.001% FAR after age transformations of approximately 28 and 23 years elapsed time on the MORPH and CACD databases, respectively. Both visual and quantitative assessments show that the approach advances the state-of-the-art. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Single Cell Sequencing Analysis Identifies Genetics-Modulated ORMDL3+ Cholangiocytes Having Higher Metabolic Effects On Primary Biliary Cholangitis

Research Square

引用

Research Square 2021年

作者： Xiang, Bingyu Deng, Chunyu Qiu, Fei Li, Jingjing Li, Shanshan Zhang, Huifang Lin, Xiuli Huang, Yukuan Zhou, Yijun Su, Jianzhong Lu, Mingqin Ma, Yunlong Department of Infectious Diseases The First Affiliated Hospital of Wenzhou Medical University Zhejiang Wenzhou325000 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Institute of Biomedical Big Data School of Ophthalmology & Optometry and Eye Hospital School of Biomedical Engineering Wenzhou Medical University Zhejiang Wenzhou325027 China State Key Laboratory for Diagnosis and Treatment of Infectious Diseases The First Affiliated Hospital Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases Zhejiang University School of Medicine Zhejiang Hangzhou310003 China Wenzhou Institute University of Chinese Academy of Sciences Zhejiang Wenzhou325011 China

Background: Primary biliary cholangitis (PBC) is a classical autoimmune disease, which is highly influenced by genetic determinants. Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with PBC susceptibility. However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear. Results: We constructed a powerful computational framework to integrate GWAS summary statistics with scRNA-seq data to uncover genetics-modulated liver cell subpopulations for PBC. Based on our multi-omics integrative analysis, 29 risk genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. By combining GWAS summary statistics with scRNA-seq data, we found that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals (Permuted P + cholangiocytes have prominently higher metabolism activity score than ORMDL3- cholangiocytes (P = 1.383×10-15). Compared with ORMDL3- cholangiocytes, there were 77 significantly differentially expressed genes among ORMDL3+ cholangiocytes (FDR + cholangiocytes exhibited relatively high communications with macrophage and monocyte. Compared with ORMDL3cholangiocytes, the VEGF signaling pathway is specific for ORMDL3+ cholangiocytes to interact with other cell populations. Conclusions: To the best of our knowledge, this is the first study to integrate genetic information with single cell sequencing data for parsing genetics-influenced liver cells for PBC risk. We identified that ORMDL3+ cholangiocytes with higher metabolism activity play important immune-modulatory roles in the etiology of PBC. © 2021, CC BY.

关键词： Metabolism

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Self-Assembly of Size-Controlledm-Pyridine–Urea Oligomers and Their Biomimetic Chloride Ion Channels

引用

Angewandte Chemie 2021年第19期133卷

作者： Dr. Hualong Chen Dr. Yajing Liu Dr. Xuebo Cheng Dr. Senbiao Fang Yuli Sun Zequn Yang Wei Zheng Prof. Xunming Ji Prof. Zehui Wu Beijing Institute of Brain Disorders Laboratory of Brain Disorders Ministry of Science and Technology Collaborative Innovation Center for Brain Disorders Beijing Advanced Innovation Center for Big Data-based Precision Medicine Capital Medical University Beijing 100069 China School of Pharmaceutical Science Capital Medical University Beijing 100069 China School of Computer Science and Engineering Central South University Changsha 410012 China Institute of Hypoxia Medicine Xuanwu Hospital Capital Medical University Beijing 100053 China

The m-pyridine urea (mPU) oligomer was constructed by using the intramolecular hydrogen bond formed by the pyridine nitrogen atom and the NH of urea and the intermolecular hydrogen bond of the terminal carbonyl group and the NH of urea. Due to the synergistic effect of hydrogen bonds, mPU oligomer folds and exhibits strong self-assembly behaviour. Affected by folding, mPU oligomer generates a twisted plane, and one of its important features is that the carbonyl group of the urea group orientates outwards from the twisted plane, while the NHs tend to direct inward. This feature is beneficial to NH attraction for electron-rich species. Among them, the trimer self-assembles into helical nanotubes, and can efficiently transport chloride ions. This study provides a novel and efficient strategy for constructing self-assembled biomimetic materials for electron-rich species transmission.

关键词： chloride ion channels hydrogen bonds m-pyridine–urea oligomers pseudomacrocycles self-assembly

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Observation of magnetic droplets in magnetic tunnel junctions

arXiv

引用

arXiv 2020年

作者： Shi, Kewen Cai, Wenlong Jiang, Sheng Zhu, Daoqian Cao, Kaihua Guo, Zongxia Wei, Jiaqi Du, Ao Li, Zhi Huang, Yan Yin, Jialiang Åkerman, Johan Zhao, Weisheng Fert Beijing Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao266000 China Department of Physics University of Gothenburg Gothenburg412 96 Sweden Department of Applied Physics School of Engineering Sciences KTH Royal Institute of Technology Electrum 229 KistaSE-16440 Sweden

Magnetic droplets, a class of highly non-linear magnetodynamical solitons, can be nucleated and stabilized in nanocontact spin-torque nano-oscillators where they greatly increase the microwave output power. Here, we experimentally demonstrate magnetic droplets in magnetic tunnel junctions (MTJs). The droplet nucleation is accompanied by a power increase of over 300 times compared to its ferromagnetic resonance modes. The nucleation and stabilization of droplets are ascribed to the double-CoFeB free layer structure in the all-perpendicular MTJ which provides a low Zhang-Li torque and a high pinning field. Our results enable better electrical sensitivity in the fundamental studies of droplets and show that the droplets can be utilized in MTJ-based applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Spin dynamics

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Highly Selective Electrocatalytic Olefin Hydrogenation in Aqueous Solution

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Angewandte Chemie 2023年第41期135卷

作者： Chengyu Xing Prof. Yurui Xue Xuchen Zheng Dr. Yang Gao Siao Chen Prof. Yuliang Li Beijing Advanced Innovation Centre for Soft Matter Science and Engineering Beijing University of Chemical Technology Beijing 100029 P. R. China CAS Key Laboratory of Organic Solids Institute of Chemistry Chinese Academy of Sciences Beijing 100190 P. R. China Contribution: Data curation (lead) Formal analysis (lead) Methodology (lead) Validation (lead) Writing - original draft (equal) Shandong Provincial Key Laboratory for Science of Material Creation and Energy Conversion Science Center for Material Creation and Energy Conversion Science School of Chemistry and Chemical Engineering Shandong University Jinan 250100 P. R. China Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Methodology (lead) Supervision (lead) Writing - original draft (lead) University of Chinese Academy of Sciences Beijing 100190 P. R. China Contribution: Formal analysis (lead) Methodology (lead) Contribution: Formal analysis (equal) Methodology (equal)

Selective hydrogenation of olefins with water as the hydrogen source at ambient conditions is still a big challenge in the field of catalysis. Herein, the electrocatalytic hydrogenation of purely aliphatic and functionalized olefins was achieved by using graphdiyne based copper oxide quantum dots (Cu x O/GDY) as cathodic electrodes and water as the hydrogen source, with high activity and selectivity in aqueous solution at high current density under ambient temperature and pressure. In particular, the sp-/sp 2 -hybridized graphdiyne catalyst allows the selective hydrogenation of cis-trans isomeric olefins. The chemical and electronic structure of the GDY results in the incomplete charge transfer between GDY and Cu atoms to optimize the adsorption/desorption of the reaction intermediates and results in high reaction selectivity and activity for hydrogenation reactions.

关键词： Atomic Active Sites Electrocatalysis Graphdiyne Highly Selectivity Hydrogenation

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Forecasting Thailand’s Precipitation with Cascading Model of CNN and GRU

Forecasting Thailand’s Precipitation with Cascading Model o...

引用

International Conference on Information Technology and Electrical engineering (ICITEE)

作者： Fuenglada Manokij Kanoksri Sarinnapakorn Peerapon Vateekul Department of Computer Engineering Faculty of Engineering Chulalongkorn University Big Data Analytics and IoT Center (CUBIC) Chulalongkorn University Bangkok Thailand Hydro-Informatics Institute (Public Organization) Ministry of Higher Education Science Research and Innovation Bangkok Thailand

Precipitation prediction is necessary to use in water management, especially in Thailand, it can be applied for various ways, such as flood warning, agriculture planning, etc. There are many prior attempts to forecast rainfall from the rain-gauge station. Some deployed traditional machine learning approaches: ARIMA, k-NN, etc. Recently, deep learning approach has shown promising results in this task. However, the accuracy is still limited since the raining period throughout the year in Thailand is very scarce, so most rainfall amount is zero. In this paper, we propose to cascade two deep learning networks to tackle this problem: one is a classification model to classify whether it rains or not, and the other is a regression model to predict rainfall amount. Our network is a combination of CNN and GRU, where CNN aims to capture relationship between various sensors and GRU aims to capture time-series information. Furthermore, we also perform multi-step forecasting by applying a rolling mechanism that uses the predicted rainfall and involved features to predict the next 6 steps. The experiment was conducted on hourly rainfall dataset for 6 years (2013-2018) provided from the public government sector in Thailand. We use RMSE as performance metric to evaluate three periods of rainfall: Overall, Rain, and non-rain periods and the results show that our cascading model is the winner with only 4.53% in term of RMSE which is the average percentage of difference from over all regions.

关键词： Deep learning Rain Predictive models data models Sensors Forecasting Task analysis

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