检索结果-内蒙古大学图书馆

Tuning the Legacy Survey of Space and Time Observing Strategy for Solar System science: Incremental Templates in Year 1

学校读者我要写书评

暂无评论

The Astrophysical Journal Supplement Series 2025年第1期279卷 9-9页

作者： James E. Robinson Megan E. Schwamb R. Lynne Jones Mario Jurić Peter Yoachim Bryce T. Bolin Colin O. Chandler Steven R. Chesley Grigori Fedorets Wesley C. Fraser Sarah Greenstreet Henry H. Hsieh Lauren J. McGinley Stephanie R. Merritt Cyrielle Opitom John K. Parejko Institute for Astronomy The University of Edinburgh Royal Observatory Edinburgh Blackford Hill Edinburgh EH9 3HJ UK james.robinson@ed.ac.uk Astrophysics Research Centre School of Mathematics and Physics Queen’s University Belfast Belfast BT7 1NN UK Rubin Observatory 950 N. Cherry Ave. Tucson AZ 85719 USA Aerotek Suite 150 4321 Still Creek Drive Burnaby BC V5C 6S7 Canada Department of Astronomy & the DiRAC Institute University of Washington 3910 15th Ave. NE Seattle WA 98195 USA Eureka Scientific Inc. 2452 Delmer Street Suite 100 Oakland CA 94602-3017 USA LSST Interdisciplinary Network for Collaboration and Computing 933 N. Cherry Ave. Tucson AZ 85721 USA Department of Astronomy & Planetary Science Northern Arizona University P.O. Box 6010 Flagstaff AZ 86011 USA Raw Data Speaks Initiative USA Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Dr. Pasadena CA 91109 USA Finnish Centre for Astronomy with ESO University of Turku FI-20014 Turku Finland Department of Physics University of Helsinki P.O. Box 64 00014 Helsinki Finland National Research Council Herzberg Astronomy and Astrophysics Research Centre 5071 W. Saanich Rd. Victoria BC V9E 2E7 Canada Rubin Observatory/NSF NOIRLab 950 N. Cherry Ave. Tucson AZ 85719 USA Planetary Science Institute 1700 East Fort Lowell Rd. Suite 106 Tucson AZ 85719 USA Department of Astronomy University of Washington 3910 15th Ave. NE Seattle WA 98195 USA

The Vera C. Rubin Observatory is due to commence the 10 yr Legacy Survey of Space and Time (LSST) at the end of 2025. To detect transient/variable sources and identify solar system objects (SSOs), the processing pipelines require templates of the static sky to perform difference imaging. During the first year of the LSST, templates must be generated as the survey progresses; otherwise, SSOs cannot be discovered nightly. The incremental template generation strategy has not been finalized; therefore, we use the Metric Analysis Framework (MAF) and a simulation of the survey cadence (one_snap_v4.0_10yrs) to explore template generation in Year 1. We have assessed the effects of generating templates over timescales of days–weeks, when at least four images of sufficient quality are available for ≥90% of the visit. We predict that SSO discoveries will begin ∼2–3 months after the start of the survey. We find that the ability of the LSST to discover SSOs in real time is reduced in Year 1. This is especially true for detections in areas of the sky that receive fewer visits, such as the North Ecliptic Spur (NES), and in less commonly used filters, such as the u and g bands. The lack of templates in the NES dominates the loss of real-time SSO discoveries; across the whole sky the MAF main-belt asteroid (MBA) discovery metric decreases by up to 63% compared to the baseline observing strategy, whereas the metric decreases by up to 79% for MBAs in the NES alone.

关键词：

Max-min fairness user scheduling and power allocation in full-duplex OFDMA systems

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Zhang, Xiaozhou Chang, Tsung-Hui Liu, Ya-Feng Shen, Chao Zhu, Gang State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Science and Engineering Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In a full-duplex (FD) multi-user network, the system performance is not only limited by the self-interference but also by the co-channel interference due to the simultaneous uplink and downlink transmissions. Joint design of the uplink/downlink transmission direction of users and the power allocation is crucial for achieving high system performance in the FD multi-user network. In this paper, we investigate the joint uplink/downlink transmission direction assignment (TDA), user paring (UP) and power allocation problem for maximizing the system max-min fairness (MMF) rate in a FD multi-user orthogonal frequency division multiple access (OFDMA) system. The problem is formulated with a two-time-scale structure where the TDA and the UP variables are for optimizing a long-term MMF rate while the power allocation is for optimizing an instantaneous MMF rate during each channel coherence interval. We show that the studied joint MMF rate maximization problem is NP-hard in general. To obtain high-quality suboptimal solutions, we propose efficient methods based on simple relaxation and greedy rounding techniques. Simulation results are presented to show that the proposed algorithms are effective and achieve higher MMF rates than the existing heuristic methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Optimization

Learning from learning machines: A new generation of AI technology to meet the needs of science

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

A compact frozen-spin trap for the search for the electric dipole moment of the muon

学校读者我要写书评

暂无评论

The European physical journal. C, Particles and fields 2025年第6期85卷 622页

作者： A Adelmann A R Bainbridge I Bailey A Baldini S Basnet N Berger L Bianco C Calzolaio L Caminada G Cavoto F Cei R Chakraborty C Barajas Chavez M Chiappini R Chislett A Crivellin C Dutsov A Ebrahimi M Francesconi L Galli G Gallucci M Giovannozzi H Goyal M Grassi A Gurgone G Hesketh M Hildebrandt M Hoferichter S Y Hoh D Höhl T Hu T Hume J A Jaeger P Juknevicius H C Kästli A Keshavarzi K S Khaw K Kirch A Kozlinskiy M Lancaster F Leonetti B Märkisch L Morvaj A Papa M Paraliev D Pasciuto J Price F Renga M Sakurai D Sanz-Becerra P Schmidt-Wellenburg Y Z Shang Y Takeuchi M E Tegano T Teubner F Trillaud D Uglietti D Vasilkova A Venturini B Vitali C Voena J Vossebeld F Wauters G M Wong Y Zeng ETH Zürich 8093 Zurich Switzerland. PSI Center for Scientific Computing Theory and Data 5232 Villigen PSI Switzerland. ASTeC STFC Daresbury Laboratory Sci-Tech Daresbury Warrington WA4 4AD UK. Cockcroft Institute Sci-Tech Daresbury Warrington WA4 4AD UK. Lancaster University Lancaster UK. INFN Pisa Pisa Italy. Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai China. School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China. PRISMA+ Cluster of Excellence and Institute of Nuclear Physics Johannes Gutenberg University Mainz Mainz Germany. Physics Department Pisa Pisa Italy. PSI Center for Accelerator Science and Engineering 5232 Villigen PSI Switzerland. Physik-Institut Universität Zürich Winterthurerstrasse 190 8057 Zurich Switzerland. PSI Center for Neutron and Muon Sciences 5232 Villigen PSI Switzerland. Sapienza Università di Roma Dip. di Fisica P.le A. Moro 2 00185 Rome Italy. INFN Sez. di Roma P. le A. Moro 2 00185 Rome Italy. University of Liverpool Liverpool UK. University College London London UK. CERN Beams Department 1211 Geneva Switzerland. Albert Einstein Center for Fundamental Physics Institute for Theoretical Physics University of Bern Sidlerstrasse 5 3012 Bern Switzerland. Instituto de Ingeniería Universidad Nacional Autónoma de México Mexico Mexico. Department of Physics and Astronomy University of Manchester Manchester UK. School of Natural Sciences Technical University of Munich Garching Germany. Swiss Plasma Center École Polytechnique Fédérale de Lausanne Villigen Switzerland.

Electric dipole moments (EDM) of fundamental particles inherently violate time-reversal (T) and the combined charge-conjugation and parity symmetry (CP). We aim to measure the EDM of the muon using the frozen-spin technique within a compact storage trap. This method exploits the high effective electric field, , experienced in the rest frame of the muon with a momentum of about when it passes through a solenoidal magnetic field of . In this paper, we outline the fundamental considerations for a muon EDM search and present a conceptual design for a demonstration experiment to be conducted at secondary muon beamlines of the Paul Scherrer institute in Switzerland. In Phase I, with an anticipated data acquisition period of 200 days, the expected sensitivity to a muon EDM is . In a subsequent phase, Phase II, we propose to improve the sensitivity to using a dedicated instrument installed on a different beamline that produces muons of momentum 125 .

关键词：

First principles calculations of superconducting critical temperature of ThCr2Si2-type structure

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Sinaga, Gewinner Senderanto Utimula, Keishu Nakano, Kousuke Hongo, Kenta Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan PRESTO JST Kawaguchi Saitama332-0012 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

High critical temperature (Tc) superconductor has a great potential in many industrial applications. However, discovering a compound having high Tc is still remaining a big challenge for experimental approach due to time-consuming and high cost. In this paper, we investigated the critical temperature (Tc) of several compounds of ThCr2Si2-type structure (space group I4/mmm) since some of them had already been investigated and trusted as the potential candidates for superconductivity. First principle calculation was performed to compute the critical temperature (Tc) based on allen-dynes equation modification of McMillian formula. In order to confirm our calculation scheme, we compared our result with compounds which had been experimentally determined obtained from NIMS database. The result showed a very good agreement with experimental data. Based on this scheme, finally, we found ThCu2Si2 and ThAu2Si2 which were most likely to exhibit a superconductivity around 3.88 K and 4.27. Since, these compounds have not been studied experimentally, then we futher investigated their electronic properties showing non-negative phonons which indicate the dynamical stability of the compounds at ambient condition. Band structure and DOS analysis showed metallic nature where the highest contributor come from d-orbital of rare-earth element and p-orbital of Si emerging a mixture of covalent, ionic and metallic bonds exists in both the compounds. Finally, we calculated the electron phonon coupling coefficient showing a quite large different (27%) leading to the increment of Tc, however, both of compounds possess three dimensional electron-like and hole-like multi-band fermi surface which is related to the phonon mediated which is still under electron-phonon mediated superconductivity. Copyright © 2019, The Authors. All rights reserved.

关键词： Temperature

Creating bus timetables with maximal synchronization by a new algorithm based on the PSO 17

学校读者我要写书评

暂无评论

Creating bus timetables with maximal synchronization by a ne...

17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation, CICTP 2017

作者： Tian, Daxin Liu, Wenhao Guo, Peng Rong, Hui Wang, Wenyang Gong, Jinfeng Wang, Yunpeng Liu, He Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing100191 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Nanjing210096 China Quartermaster Equipment Institute of the General Logistics Dept. of CPLA Beijing100010 China

ISBN: (数字)9780784480915

ISBN: (纸本)9780784480915

This paper addresses the problem of receiving a timetable for a settled network with maximal synchronization. In this paper, a model has been developed to solve the problem which attaches importance to creating a timetable for maximizing their synchronization and enables the transfer of passengers from one route to another with least waiting times at transfer nodes. Next, this problem is formulated as a mixed integer linear programming problem, and a new algorithm based on the traditional particle swarm optimization (PSO) has been developed to solve the problem in a short time. Finally, the relationship of iterative time and the complexity of the given network is qualitatively established. © ASCE.

关键词： Synchronization

Extractive Adversarial Networks for Network Embedding

学校读者我要写书评

暂无评论

Extractive Adversarial Networks for Network Embedding

International Conference on Behavior, Economic and Social computing (BESC)

作者： Runxuan Chen Haoran Xie Jiaxing Chen Yanghui Rao Yingchao Zhao Fu Lee Wang School of Data and Computer Science Sun Yat-sen University Guangzhou China Department of Mathematics and Information Technology The Education University of Hong Kong Tai Po Hong Kong School of Computing and Information Sciences Caritas Institute of Higher Education Tseung kwan O Hong Kong School of Science and Technology The Open University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9781728102085;9781728102078

Network embedding has attracted more and more researchers recently. Although many algorithms focus on topological information, there exists a disadvantage. Nodes in many real-world network often have their own attributes, which are potentially valuable information, but algorithms focusing on structure ignore these messages, which will decrease the accuracy of the embedding space. To solve this problem, we consider a deep learning model. In this paper, we propose a novel model named Extractive Adversarial Networks for network embedding, which aims to extract the latent space from labels, attributed information and topological structure (e.g. low-order proximity), based on generative adversarial network model in Domain-Adversarial Training of Neural Networks. The goal of EAN is extracting the same features from the topological structure and attributed information according to our extractive network, while distinguishing the extractive features from either the topological structure or attributed information, which becomes an adversarial models. First, we train our extractive network with labels information, which is feeded with attributed information and nodes structure in vector forms. The second step is training the adversarial network to distinguish the embedding representation from either the topological structure or attributed information, where the embedding representation is from the hidden layer in extractive network. We conduct experiments on six real-world datasets, which illustrates that the proposed models outperforms state-of-the-art embedding algorithms.

关键词： Predictive models data mining Feature extraction data models Mathematical model Neural networks Computer science

Ti interstitial flows giving rutile TiO2 reoxidation process enhanced in (001) surface

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Ichibha, Tom Benali, Anouar Hongo, Kenta Maezono, Ryo School of Information Science JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Computational Science Division Argonne National Laboratory 9700 Cass Avenue LemontIL60439 United States Research Center for Advanced Computing Infrastructure JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

We revisited ab initio evaluations of the energy barriers along the possible diffusion paths of the defects in rutile TiO2. By using a method carefully considering the cancellation of the self-interaction, Ti interstitials hopping along c-axis are identified as the major diffusion directing to [001] surface. The conclusion is contradicting to any of previous theoretical works, and the discrepancy is explained by the overestimation of the radius of defects due to the poor cancellations in the previous works. The updated prediction here can explain the superior photocatalysis activity in [001] surface to [110]. Copyright © 2019, The Authors. All rights reserved.

关键词： Titanium dioxide

A novel data quality assessment framework for vehicular network testbeds 12

学校读者我要写书评

暂无评论

A novel data quality assessment framework for vehicular netw...

12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TRIDENTCOM 2017

作者： Tian, Daxin Zhu, Yukai Zhou, Jianshan Duan, Xuting Wang, Yunpeng Song, Jeungeun Rong, Hui Guo, Peng Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing XueYuan Road No.37 Beijing100191 China China Automotive Technology and Research Center Automotive Engineering Research Institute East Xianfeng Road No.68 Tianjin300300 China Beihang University Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering XueYuan Road No.37 Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Si Pai Lou. 2 Nanjing210096 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781631901577

Big data technique is considered as a powerful tool to exploit all the potential of the Internet of Things and the smart cities. The development of internet of Vehicles (IoV) and wireless communication technologies have boosted diverse applications related to smart cities and Cyber-Physical Systems, but the data quality of vehicular sensors is an important issue due to the high-speed mobile wireless communication environment and physical sensor noise. This paper presents our experiences for big data analytics based on a vehicular network testbed, in terms of sensors data management, multi-dimension data fusion and data quality assessment for the vehicular sensor data. The proposed data quality assessment framework consist of feature extraction based on multi-sensor data fusion and multi-level wavelet transform, as well as a semi-supervised learning based classification algorithm. The comparison experiment shows that the proposed framework and approaches can extract feasible features and solve the unbalanced label problems, which achieve a better assessment effect. Copyright © 2018 EAI.

关键词： Smart city