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arXiv 2019年

作者： Liao, Hao Wu, Xing-Tong Vidmer, Alexandre Huang, Xiao-Min Liu, Ming-Kai Zhou, Mingyang Zhang, Yi-Cheng National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Physics University of Fribourg Fribourg1700 Switzerland

Prediction is one of the major challenges in complex systems. The prediction methods have shown to be effective predictors of the evolution of networks. These methods can help policy makers to solve practical problems successfully and make better strategy for the future. In this work, we focus on exporting countries' data of the international trading network. A recommendation system is then used to identify the products corresponding to the production capacity of each individual country, but are somehow overlook by the country. Then, we simulate the evolution of the country's fitness if it would have followed the recommendations. The result of this work is the combination combine these two methods to provide insights to countries on how to enhance the diversification of their exported products in a scientific way and improve national competitiveness significantly, especially for developing countries. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems

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Parallelization of protein clustering algorithm using OpenMP 1

引用

2nd International Conference on Advances in computing and Data sciences, ICACDS 2018

作者： Dhar, Dhruv Hegde, Lakshana Patil, Mahesh S. Chickerur, Satyadhyan Department of Computer Science and Engineering B.V.B College of Engineering and Technology Centre for High Performance Computing KLE Technological University HubballiKarnataka580031 India

ISBN: (数字)9789811318139

ISBN: (纸本)9789811318122

Proteins are the building blocks of all living organisms and its analysis can help us to understand the bimolecular mechanics of living organisms. Protein clustering attempts to group similar protein sequences and has diverse applications in bioinformatics. However, this operation faces various computational challenges because of dependency on complex data structures, high memory usage and irregular memory access patterns. In genome studies, the time consideration for alignment is also an important parameter and should be minimized. Conventional solutions have rather been unsuccessful in achieving decent runtime performance because these algorithms are designed for serial computation which means that they use a single processor to perform computations. These algorithms can be improved upon by modifying them to use multiple processing elements. The purpose of this research is to modify existing protein clustering algorithm and apply parallelization techniques on them in order to optimize protein sequencing operation for faster results without sacrificing accuracy. © Springer Nature Singapore Pte Ltd 2018.

关键词： Proteins

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Faking photon number on a transition-edge sensor

arXiv

引用

arXiv 2021年

作者： Chaiwongkhot, Poompong Zhong, Jiaqiang Huang, Anqi Qin, Hao Shi, Sheng-Cai Makarov, Vadim Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand Bangkok10110 Thailand Purple Mountain Observatory and Key Laboratory of Radio Astronomy Chinese Academy of Sciences 10 Yuanhua road Nanjing210033 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China CAS Quantum Network Co. Ltd. 99 Xiupu road Shanghai201315 China Russian Quantum Center Skolkovo Moscow121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

We study potential security vulnerabilities of a single-photon detector based on superconducting transition-edge sensor. In a simple experiment, we show that an adversary could fake a photon number result at a certain wavelength by sending a larger number of photons at a longer wavelength. In another experiment, we show that the detector can be blinded by bright continuous-wave light and then, a controlled response simulating single-photon detection can be produced by applying a bright light pulse. We model an intercept- and-resend attack on a quantum key distribution system that exploits the latter vulnerability and, under certain assumptions, succeeds to steal the key. © 2021, CC BY.

关键词： Photons

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Empirical topological investigation of practical supply chains based on complex networks

引用

Chinese Physics B 2017年第11期26卷 144-150页

作者：廖好沈婧吴兴桐陈博奎周明洋 National Engineering Laboratory for Big Data Computing Systems Guangdong Province Key Laboratory of Popular High Performance ComputersCollege of Computer Science and Software Engineering Shenzhen University Department of Computer Science School of Computing National University of Singapore

The industrial supply chain networks basically capture the circulation of social resource, dominating the stability and efficiency of the industrial system. In this paper, we provide an empirical study of the topology of smartphone supply chain network. The supply chain network is constructed using open online data. Our experimental results show that the smartphone supply chain network has small-world feature with scale-free degree distribution, in which a few high degree nodes play a key role in the function and can effectively reduce the communication cost. We also detect the community structure to find the basic functional unit. It shows that information communication between nodes is crucial to improve the resource utilization. We should pay attention to the global resource configuration for such electronic production management.

关键词： China supply chain networks complex networks data science network science

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A multi-cubic-kilometre neutrino telescope in the western Pacific Ocean

arXiv

引用

arXiv 2022年

作者： Ye, Z.P. Hu, F. Tian, W. Chang, Q.C. Chang, Y.L. Cheng, Z.S. Gao, J. Ge, T. Gong, G.H. Guo, J. Guo, X.X. He, X.G. Huang, J.T. Jiang, K. Jiang, P.K. Jing, Y.P. Li, H.L. Li, J.L. Li, L. Li, W.L. Li, Z. Liao, N.Y. Lin, Q. Lin, J. Liu, F. Liu, J.L. Liu, X.H. Miao, P. Mo, C. Morton-Blake, I. Peng, T. Sun, Z.Y. Tang, J.N. Tang, Z.B. Tao, C.H. Tian, X.L. Wang, M.X. Wang, Y. Wang, Y. Wei, H.D. Wei, Z.Y. Wu, W.H. Xian, S.S. Xiang, D. Xu, D.L. Xue, Q. Yang, J.H. Yang, J.M. Yu, W.B. Zeng, C. Zhang, F.Y.D. Zhang, T. Zhang, X.T. Zhang, Y.Y. Zhi, W. Zhong, Y.S. Zhou, M. Zhu, X.H. Zhuang, G.J. Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai200240 China School of Physics and Astronomy Shanghai Jiao Tong University MOE Key Laboratory for Particle Astrophysics and Cosmology Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Department of Astronomy School of Physics Peking University Being 100871 China State Key Laboratory of Ocean Engineering School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Sanya Yazhou Bay Institute of Deep Sea Technology Sanya572024 China Center for High Energy Physics Peking University Beijing100871 China Department of Engineering Physics Tsinghua University Beijing100084 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei230026 China Department of Modern Physics University of Science and Technology of China Hefei230026 China Shanghai Key Lab of Electrical Insulation and Thermal Aging School of Chemistry and Chemical Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Marine Ecosystem Dynamics Second Institute of Oceanography MNR Hangzhou310012 China Kavli Institute for Astronomy and Astrophysics Peking University Bejing100871 China Center for High Performance Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China School of Oceanography Shanghai Jiao Tong University Shanghai200030 China Key Laboratory of Submarine Geosciences Second Institute of Oceanography MNR Hangzhou310012 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China MOE Key Laboratory for Laser Plasmas School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China Zhangjiang Institute for Advanced Study

Next-generation neutrino telescopes with significantly improved sensitivity are required to pinpoint the sources of the diffuse astrophysical neutrino flux detected by IceCube and uncover the century-old puzzle of cosmic ray origins. A detector near the equator will provide a unique viewpoint of the neutrino sky, complementing IceCube and other neutrino telescopes in the Northern Hemisphere. Here we present results from an expedition to the north-eastern region of the South China Sea, in the western Pacific Ocean. A favorable neutrino telescope site was found on an abyssal plain at a depth of ∼ 3.5 km. At depths below 3 km, the sea current speed, water absorption and scattering lengths for Cherenkov light, were measured to be vc © 2022, CC BY-NC-ND.

关键词： Cosmic rays

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Variational quantum circuits for quantum state tomography

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Physical Review A 2020年第5期101卷 052316-052316页

作者： Yong Liu Dongyang Wang Shichuan Xue Anqi Huang Xiang Fu Xiaogang Qiang Ping Xu He-Liang Huang Mingtang Deng Chu Guo Xuejun Yang Junjie Wu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 China National Innovation Institute of Defense Technology AMS 100071 Beijing China Henan Key Laboratory of Quantum Information and Cryptography IEU Zhengzhou 450001 China Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 China Quantum Intelligence Lab Supremacy Future Technologies Guangzhou 511340 China

Quantum state tomography is a key process in most quantum experiments. In this work, we employ quantum machine learning for state tomography. Given an unknown quantum state, it can be learned by maximizing the fidelity between the output of a variational quantum circuit and this state. The number of parameters of the variational quantum circuit grows linearly with the number of qubits and the circuit depth, so that only polynomial measurements are required, even for highly entangled states. After that, a subsequent classical circuit simulator is used to transform the information of the target quantum state from the variational quantum circuit into a familiar format. We demonstrate our method by performing numerical simulations for the tomography of the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator. Our method is suitable for near-term quantum computing platforms, and could be used for relatively large-scale quantum state tomography for experimentally relevant quantum states.

关键词： Machine learning Quantum algorithms Quantum tomography

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Optimization of laser-driven quantum beam generation and the applications with artificial intelligence

引用

Physics of Plasmas 2024年第5期31卷

作者： Kuramitsu, Y. Taguchi, T. Nikaido, F. Minami, T. Hihara, T. Suzuki, S. Oda, K. Kuramoto, K. Yasui, T. Abe, Y. Ibano, K. Takabe, H. Chu, C.M. Wu, K.T. Woon, W.Y. Chen, S.H. Jao, C.S. Chen, Y.C. Liu, Y.L. Morace, A. Yogo, A. Arikawa, Y. Kohri, H. Tokiyasu, A. Kodaira, S. Kusumoto, T. Kanasaki, M. Asai, T. Fukuda, Y. Kondo, K. Kiriyama, H. Hayakawa, T. Tanaka, S.J. Isayama, S. Watamura, N. Suzuki, H. Kumar, H.S. Ohnishi, N. Pikuz, T. Filippov, E. Sakai, K. Yasuhara, R. Nakata, M. Ishikawa, R. Hoshi, T. Mizuta, A. Bolouki, N. Saura, N. Benkadda, S. Koenig, M. Hamaguchi, S. Graduate School of Engineering Osaka University 2-1 Yamadaoka Osaka Suita565-0871 Japan Institute of Laser Engineering Osaka University 2-6 Yamadaoka Osaka Suita565-0871 Japan 8-1-7 Umemidai Kyoto Kizugawa619-0215 Japan Department of Physics National Central University No. 300 Jhongda Rd. Taoyuan Jhongli320 Taiwan Department of Physics National Cheng Kung University Tainan701 Taiwan National Center for High-Performance Computing National Applied Research Laboratories Tainan City711010 Taiwan Institute of Space and Plasma Sciences National Cheng Kung University Tainan701 Taiwan Research Center for Nuclear Physics Osaka University 10-1 Mihogaoka Osaka Ibaraki567-0047 Japan Research Center for Accelerator and Radioisotope Science Tohoku University 1-2-1 Mikamine Taihaku-ku Miyagi Sendai982-0826 Japan Inage Chiba263-8555 Japan Graduate School of Maritime Sciences Kobe University Kobe658-0022 Japan Department of Physical Sciences Aoyama Gakuin University 5-10-1 Fuchinobe Kanagawa Sagamihara252-5258 Japan Department of Advanced Environmental Science and Engineering Kyushu University 6-1 Kasuga-Kohen Fukuoka Kasuga816-8580 Japan Graduate School of Information Science and Technology Osaka University 2-1 Yamadaoka Suita565-0871 Japan Department of Aerospace Engineering Tohoku University 6-6-01 Aramakiazaaoba Aoba-ku Sendai980-8579 Japan Institute for Open and Transdisciplinary Research Initiatives Osaka University 2-1 Yamadaoka Osaka Suita565-0871 Japan Villamayor Salamanca37185 Spain National Institute for Fusion Science 322-6 Oroshicho Gifu Toki509-5292 Japan Faculty of Arts and Sciences Komazawa University 1-23-1 Komazawa Setagaya-ku Tokyo154-8525 Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako351-0198 Japan Department of Plasma Physics and Technology Faculty of Science Masaryk University Kotlářská 2 Brno611 37 Czech Republic Aix-Marseille Université CNRS PIIM UMR 7345 Marseille France LULI CN

We have investigated space and astrophysical phenomena in nonrelativistic laboratory plasmas with long high-power lasers, such as collisionless shocks and magnetic reconnections, and have been exploring relativistic regimes with intense short pulse lasers, such as energetic ion acceleration using large-area suspended graphene. Increasing the intensity and repetition rate of the intense lasers, we have to handle large amounts of data from the experiments as well as the control parameters of laser beamlines. Artificial intelligence (AI) such as machine learning and neural networks may play essential roles in optimizing the laser and target conditions for efficient laser ion acceleration. Implementing AI into the laser system in mind, as the first step, we are introducing machine learning in ion etch pit analyses detected on plastic nuclear track detectors. Convolutional neural networks allow us to analyze big ion etch pit data with high precision and recall. We introduce one of the applications of laser-driven ion beams using AI to reconstruct vector electric and magnetic fields in laser-produced turbulent plasmas in three dimensions. © 2024 Author(s).

关键词： Ions

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Predicting Spring Phenology in Deciduous Broadleaf Forests: Neon Phenology Forecasting Community Challenge

SSRN

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SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Ross, Arun A. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chen, Min Goldstein, Benjamin Guo, Jessica Hao, Dalei Jones, Chris Kelly-Fair, Mira Liu, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Computer Science and Engineering Michigan State University 428 South Shaw Lane East LansingMI48824 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Instit 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Tech 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building Universi

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative’s National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate what factors impact current predictive skill of phenology forecasts. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge

SSRN

引用

SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chenp, Min Goldstein, Benjamin Guo, Jessica S. Haoq, Dalei Jones, Chris Kelly-Fair, Mira Liup, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Ross, Arun A. Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Institute State University 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Polytechnic Institute State University 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building University of California Berkeley BerkeleyCA94720 United States Department of For

Accurate phenology models are important to predict how global climate change will continue to alter the timing of plant phenological events, such as spring greenup in deciduous broadleaf forests. While there is merit in long-term predictions, investigating how models can predict near-term (1– 35 days) canopy greenness throughout the spring allows us to validate performance and understanding now. The Ecological Forecasting Initiative’s NEON Forecasting Challenge, is an open challenge to the community to predict daily greenness values, measured through digital images collected by the PhenoCam Network at National Ecological Observatory Network (NEON) sites. For the first round of the challenge, which is presented here, teams were tasked to forecast phenology at eight deciduous broadleaf sites. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts, which future rounds will continue to investigate. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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high-performance Mid-IR to Deep-UV van der Waals Photodetectors Capable of Local Spectroscopy at Room Temperature

arXiv

引用

arXiv 2022年

作者： Shen, Daozhi Yang, HeeBong Spudat, Christian Patel, Tarun Zhong, Shazhou Chen, Fangchu Yan, Jian Luo, Xuan Cheng, Meixin Sciaini, Germán Sun, Yuping Rhodes, Daniel A. Timusk, Thomas Zhou, Y. Norman Kim, Na Young Tsen, Adam W. Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada Centre for Advanced Materials Joining Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Institute of Electrodynamics Microwave and Circuit Engineering TU Wien Gusshausstrasse 25/354 Vienna1040 Austria Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Key Laboratory of Materials Physics Institute of Solid State Physics HFIPS Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L 3G1 Canada Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory HFIPS Chinese Academy of Sciences Hefei230031 China Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Department of Materials Science and Engineering University of Wisconsin MadisonWI United States Department of Physics and Astronomy McMaster University HamiltonONL8S 4M1 Canada

The ability to perform broadband optical spectroscopy with sub-diffraction-limit resolution is highly sought-after for a wide range of critical applications. However, sophisticated tip-enhanced techniques are currently required to achieve this goal. We bypass this challenge by demonstrating an extremely broadband photodetector based on a two-dimensional (2D) van der Waals heterostructure that is sensitive to light across over a decade in energy from the mid-infrared (MIR) to deep-ultraviolet (DUV) at room temperature. The devices feature high detectivity (> 109 cm Hz1/2 W-1) together with high bandwidth (2.1 MHz). The active area can be further miniaturized to submicron dimensions, far below the diffraction limit for the longest detectable wavelength of 4.1 µm, enabling such devices for facile measurements of local optical properties on atomic-layer-thickness samples placed in close proximity. This work can lead to the development of low-cost and high-throughput photosensors for hyperspectral imaging at the nanoscale. © 2022, CC BY.

关键词： Van der Waals forces

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