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A new tool for cme arrival time prediction using machine learning algorithms: Cat-puma

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arXiv 2018年

作者： Liu, Jiajia Ye, Yudong Shen, Chenglong Wang, Yuming Erdélyi, Robert School of Mathematics and Statistics University of Sheffield SheffieldS3 7RH United Kingdom State Key Laboratory of Space Weather National Space Science Center Sigma Weather Group Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Geophysics and Planetary Sciences Cas Key Laboratory of Geospace Environment University of Science and Technology of China Hefei Anhui230026 China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China Department of Astronomy Eötvös Loránd University BudapestH-1117 Hungary

Coronal Mass Ejections (CMEs) are arguably the most violent eruptions in the Solar System. CMEs can cause severe disturbances in the interplanetary space and even affect human activities in many respects, causing damages to infrastructure and losses of revenue. Fast and accurate prediction of CME arrival time is then vital to minimize the disruption CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full-halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full-halo CMEs and using algorithms of the Support Vector Machine (SVM). We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions after applying CAT-PUMA to a test set, that is unknown to the engine, show a mean absolute prediction error ∼5.9 hours of the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hours. Comparison with other models reveals that CATPUMA has a more accurate prediction for 77% of the events investigated;and can be carried out very fast, i.e. within minutes after providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA. Copyright © 2018, The Authors. All rights reserved.

关键词： Engines

Structuring interactive artificial bee colony forecasting model in foreign exchange rate forecasting with consumer confidence index and conventional microeconomics factors

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ICIC Express Letters, Part B: Applications 2016年第4期7卷 895-901页

作者： Tsai, Pei-Wei Liu, Chia-Han Zhang, Jilin Chang, Jui-Fang College of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town MinhouFuzhou350118 China Key Laboratory for Automotive Electronics and Electric Drive of Fujian Province Fujian University of Technology No. 3 Xueyuan Road University Town MinhouFuzhou350118 China Department of International Business National Kaohsiung University of Applied Sciences No. 415 Chien-Kong Road Kaohsiung City80778 Taiwan Department of Mathematics and Physics Fujian University of Technology No. 3 Xueyuan Road University Town MinhouFuzhou350118 China

Existing methods used in the foreign exchange rate forecasting are mostly in the branch of the time series methods. The time series models usually do not focus on the short-term forecasting, but the chronic forecasting. Models for the short-term forecasting still remains blank. In addition, conventional foreign exchange rate forecasting methods only consider the factors in microeconomics. No psychological factors are collected in the forecasting process. The forecasting method, which simultaneously considers both the technical and the psychological factors, is not revealed. Answering the needs of short- term foreign exchange rate forecasting and the comprehensive consideration of technical and psychological factors, the new foreign exchange rate forecasting model based on Interactive Artificial Bee Colony (IABC) is proposed. In this paper, IABC algorithm is employed to create a foreign exchange rate forecasting model. Our proposed forecasting model not only utilizes the conventional factors in microeconomics, but also includes the Consumer Confidence Index (CCI) in the consideration as one of the reference data for estimating the foreign exchange rate. In our proposed forecasting model, the reference data include indices announced daily, weekly, and monthly. The output is composed of all reference indices and a set of weighting, which is corresponding to the indices. In our design, IABC plays the role to properly rate the significance of each index and to provide a set of weighting to combine the indices, together, to form a single forecasting value. The final output of our proposed forecasting model is the forecasted foreign exchange rate for the next day. In order to test the accuracy of our proposed method, 4 years of the historical data in 2011 to 2014 are included in the experiments. The forecasting error is about 0:07‰ in average, the maximum forecasting error over all test periods is 0:21‰, and the minimum forecasting error over all test periods is zero. In a

关键词： Optimization

Tensor-networks-based learning of probabilistic cellular automata dynamics

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Physical Review Research 2024年第4期6卷 043202-043202页

作者： Heitor P. Casagrande Bo Xing William J. Munro Chu Guo Dario Poletti Science Mathematics and Technology Cluster Singapore University of Technology and Design 8 Somapah Road 487372 Singapore Okinawa Institute of Science and Technology Graduate University Onna-son Okinawa 904-0495 Japan Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China EPD Pillar Singapore University of Technology and Design 8 Somapah Road Singapore 487372 Centre for Quantum Technologies National University of Singapore Singapore 117543 MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit UMI 3654 Singapore

Algorithms developed to solve many-body quantum problems, like tensor networks, can turn into powerful quantum-inspired tools to tackle issues in the classical domain. This work focuses on matrix product operators, a prominent numerical technique to study many-body quantum systems, especially in one dimension. It has been previously shown that such a tool can be used for classification, learning of deterministic sequence-to-sequence processes, and generic quantum processes. We further develop a matrix product operator algorithm to learn probabilistic sequence-to-sequence processes and apply this algorithm to probabilistic cellular automata. This new approach can accurately learn probabilistic cellular automata processes in different conditions, even when the process is a probabilistic mixture of different chaotic rules. In addition, we find that the ability to learn these dynamics is a function of the bitwise difference between the rules and whether one is much more likely than the other.

关键词： Cellular automata Machine learning Tensor network methods

Preparation of logically labeled pure states with only two turns for bulk quantum computation

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arXiv 2017年

作者： Xin, Tao Hao, Liang Hou, Shi-Yao Feng, Guan-Ru Long, Gui-Lu State Key Laboratory of Low-dimensional Quantum Physics and Department of Physics Tsinghua University Beijing100084 China Tsinghua National Laboratory of Information Science and Technology Beijing100084 China Institute of Applied Physics and Computational Mathematics Beijing100094 China Microsystem and Terahertz Research Center China Academy of Engineering Physics Chengdu610200 China Institute of Electronic Engineering China Academy of Engineering Physics Mianyang621999 China Institute for Quantum Computing WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Innovative Center of Quantum Matter Beijing100084 China

Quantum state preparation plays an equally important role with quantum operations and measurements in quantum information processing. The previous methods of preparing initial state for bulk quantum computation all have inevitable disadvantages, such as, requiring multiple experiments, causing loss of signals, or requiring molecules with restrictive structure. In this work, three kinds of quantum circuits are introduced to prepare the pseudo-pure states of (n − 1) qubits in the Hilbert space of n coupled spins which merely need the assist of one ancilla spin and two experiments independent of n. Being without gradient fields effectively avoids the reduction of the signals. Our methods have no special requirements on the structure of the used molecules. To test these methods more comprehensively, we experimentally demonstrate the preparation of the labeled pseudo-pure states using heteronuclear 2-qubit and homonuclear 4-qubit nuclear magnetic resonance quantum information processor. Copyright © 2017, The Authors. All rights reserved.

关键词： Molecules

Dual Ligand-Induced Photoelectrochemical Sensing by Integrating Pt/MoS2 Heterostructure and AU Polyhedra for Sensitive Detection of SARS-CoV-2

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SSRN

SSRN 2022年

作者： Li, Haolin Zhao, Jialin Wu, Ting Fu, Zhao Zhang, Wei Lian, Zheng Cai, Shuangfei Yang, Rong CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety Center of Materials Science and Optoelectronics Engineering CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology University of Chinese Academy of Sciences Beijing100190 China Sino-Danish College Sino-Danish Center for Education and Research University of Chinese Academy of Sciences Beijing100049 China Institute of Applied Physics and Computational Mathematics Beijing100088 China

The continuous evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with discovery of multiple mutants, has caused widespread panic and concern worldwide. The rapid antigen detection method via a single ligand recognition, although currently implemented in many countries, remains challenging for mutated antigens. Herein, we present a novel strategy using a dual recognition by two types of targeted ligands, based on photoelectrochemical (PEC) sensing for detection of SARS-CoV-2 spike protein. To demonstrate this strategy, the specific antibodies are modified onto the photoactive material with a supported nanostructure, created by loading the Pt nanoparticles onto MoS2 nanosheets (Pt/MoS2) to boost photon-to-electricity conversion efficiency. By subsequent binding of the targeted aptamers to the Au polyhedra, which act as a signal amplifier to suppress PEC photocurrent by competing with the Pt/MoS2 for the absorption of excitation light energy, the dual recognition is successfully achieved. The constructed biosensor not only shows satisfactory stability, and high sensitivity, but is effective for test of the pseudovirus of SARS-CoV-2. The work provides useful advance for the development of PEC biosensors for sensitive detection of SARS-CoV-2. © 2022, The Authors. All rights reserved.

关键词： Coronavirus

lMFF: Efficient and Scalable layered Materials Force Field on Heterogeneous Many-Core Processors

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lMFF: Efficient and Scalable layered Materials Force Field o...

Supercomputing Conference

作者： Ping Gao Xiaohui Duan Jiaxu Guo Jin Wang Zhenya Song Lizhen Cui Xiangxu Meng Xin Liu Wusheng Zhang Ming Ma Guohui Li Dexun Chen Haohuan Fu Wei Xue Weiguo Liu Guangwen Yang School of Software Shandong University Jinan China National Supercomputing Center in Wuxi Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China College of Computer Science and Technology Jilin University Changchun China Department of Engineering Mechanics Center for Nano and Micro Mechanics Tsinghua University Beijing China Ministry of Natural Resources First Institute of Oceanography and Key Laboratory of Marine Science and Numerical Modeling Qingdao China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Jinan China Engineering Research Center of Digital Media Technology Ministry of Education Jinan China Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China Department of Earth System Science Ministry of Education Key Lab for Earth System Modeling Tsinghua University Beijing China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

LAMMPS is one of the most popular Molecular Dynamic (MD) packages and is widely used in the field of physics, chemistry and materials simulation. Layered Materials Force Field (LMFF) is our expansion of the LAMMPS potential function based on the Tersoff potential and inter-layer potential (ILP) in LAMMPS. LMFF is designed to study layered materials such as graphene and boron hexanitride. It is universal and does not depend on any platform. We have also carried out a series of optimizations on LMFF and the optimization work is carried out on the new generation of Sunway supercomputer, called SWLMFF. Experiments show that our implementation is efficient, scalable and portable. When generic LMFF is ported to Intel Xeon Gold 6278C, $2\times$ performance improvement is achieved. For the optimized SWLMFF, the overall performance improvement is nearly $200-330\times$ compared to the original ILP and Tersoff potentials. And SWLMFF has good parallel efficiency of 95%-100% under weak scaling with 2.7 million atoms on a single process. The maximum atomic system simulated by SWLMFF is close to $2^{31}$ atoms. And nanosecond simulations in one day can be realized.

关键词： Program processors Atomic layer deposition High performance computing Force Graphene Supercomputers Optimization

Fourth Hankel Determinant for a Subclass of Starlike Functions Based on Modified Sigmoid

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Journal of Function Spaces 2021年第1期2021卷

作者： Mashwani, Wali Khan Ahmad, Bakhtiar Khan, Nazar Khan, Muhammad Ghaffar Arjika, Sama Khan, Bilal Chinram, Ronnason Institute of Numerical Sciences Kohat University of Science and Technology Kohat Pakistan Govt. Degree College Mardan Mardan 23200 Pakistan Department of Mathematics Abbottabad University of Science and Technology Abbottabad 22010 Pakistan Department of Mathematics and Informatics University of Agadez Agadez Niger International Chair of Mathematical Physics and Applications (ICMPA-UNESCO Chair) University of Abomey-Calavi Post Box 072 50 Cotonou Benin School of Mathematical Sciences Shanghai Key Laboratory of PMMP East China Normal University 500 Dongchuan Road Shanghai 200241 China Algebra and Applications Research Unit Division of Computational Science Faculty of Science Prince of Songkla University Hat Yai Songkhla 90110 Thailand

In our present investigation, we obtain the improved third-order Hankel determinant for a class of starlike functions connected with modified sigmoid functions. Further, we investigate the fourth-order Hankel determinant, Zalcman conjecture, and also evaluate the fourth-order Hankel determinants for 2-fold, 3-fold, and 4-fold symmetric starlike functions. © 2021 Wali Khan Mashwani et al.

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No constraints for f(T) gravity from gravitational waves induced from primordial black hole fluctuations

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arXiv 2022年

作者： Papanikolaou, Theodoros Tzerefos, Charalampos Basilakos, Spyros Saridakis, Emmanuel N. National Observatory of Athens Lofos Nymfon Athens11852 Greece Department of Physics National & Kapodistrian University of Athens Zografou Campus AthensGR 157 73 Greece Academy of Athens Research Center for Astronomy and Applied Mathematics Soranou Efesiou 4 Athens11527 Greece School of Sciences European University Cyprus Diogenes Street Engomi Nicosia1516 Cyprus CAS Key Laboratory for Researches in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Anhui Hefei230026 China School of Astronomy School of Physical Sciences University of Science and Technology of China Hefei230026 China

Primordial black hole (PBH) fluctuations can induce a stochastic gravitational wave background at second order, and since this procedure is sensitive to the underlying gravitational theory it can be used as a novel tool to test general relativity and extract constraints on possible modified gravity deviations. We apply this formalism in the framework of f(T) gravity, considering three viable mono-parametric models. In particular, we investigate the induced modifications at the level of the gravitational-wave source, which is encoded in terms of the power spectrum of the PBH gravitational potential, as well as at the level of their propagation, described in terms of the Green function which quantifies the propagator of the tensor perturbations. We find that, within the observationally allowed range of the f(T) model-parameters, the obtained deviations from general relativity, both at the levels of source and propagation, are practically negligible. Hence, we conclude that realistic and viable f(T) theories can safely pass the primordial black hole constraints, which may offer an additional argument in their favor. Copyright © 2022, The Authors. All rights reserved.

关键词： Gravity waves

Observing geometry of quantum states in a three-level system

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arXiv 2019年

作者： Xie, Jie Zhang, Aonan Cao, Ningping Xu, Huichao Zheng, Kaimin Poon, Yiu-Tung Sze, Nung-Sing Xu, Ping Zeng, Bei Zhang, Lijian National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences School of Physics Nanjing University Nanjing210093 China Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China Department of Mathematics & Statistics University of Guelph GuelphONN1G 2W1 Canada Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Mathematics Iowa State University AmesIA50011 United States Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Physics Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

Geometric characters of physical objects greatly facilitate our understanding of the world. Along with the founding of quantum mechanics, of particular interest is the geometry of quantum systems from which many non-classical behaviors arise and exotic properties of the system can be inferred. However, there is always the difficulty to illustrate geometry of quantum systems beyond the simplest quantum bit model. One approach is to project the state space of quantum systems to the familiar Euclidean space via measurements of observables on the system. Here we report the first experiment measuring the geometry of such projections beyond one quantum bit. Specifically, we observe the joint numerical ranges of a triple of observables in a three-level photonic system, providing complete classification of the geometric objects. We further show that the geometry of different classes reveals ground-state degeneracies of corresponding Hamiltonians, which is related to quantum phases in the thermodynamic limit. Our results offer a versatile geometric approach for exploring and utilizing the higher dimensional quantum systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum optics