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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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NANOGrav 15-year gravitational-wave background methods

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Physical Review D 2024年第10期109卷 103012-103012页

作者： Aaron D. Johnson Patrick M. Meyers Paul T. Baker Neil J. Cornish Jeffrey S. Hazboun Tyson B. Littenberg Joseph D. Romano Stephen R. Taylor Michele Vallisneri Sarah J. Vigeland Ken D. Olum Xavier Siemens Justin A. Ellis Rutger van Haasteren Sophie Hourihane Gabriella Agazie Akash Anumarlapudi Anne M. Archibald Zaven Arzoumanian Laura Blecha Adam Brazier Paul R. Brook Sarah Burke-Spolaor Bence Bécsy J. Andrew Casey-Clyde Maria Charisi Shami Chatterjee Katerina Chatziioannou Tyler Cohen James M. Cordes Fronefield Crawford H. Thankful Cromartie Kathryn Crowter Megan E. DeCesar Paul B. Demorest Timothy Dolch Brendan Drachler Elizabeth C. Ferrara William Fiore Emmanuel Fonseca Gabriel E. Freedman Nate Garver-Daniels Peter A. Gentile Joseph Glaser Deborah C. Good Kayhan Gültekin Ross J. Jennings Megan L. Jones Andrew R. Kaiser David L. Kaplan Luke Zoltan Kelley Matthew Kerr Joey S. Key Nima Laal Michael T. Lam William G. Lamb T. Joseph W. Lazio Natalia Lewandowska Tingting Liu Duncan R. Lorimer Jing Luo Ryan S. Lynch Chung-Pei Ma Dustin R. Madison Alexander McEwen James W. McKee Maura A. McLaughlin Natasha McMann Bradley W. Meyers Chiara M. F. Mingarelli Andrea Mitridate Cherry Ng David J. Nice Stella Koch Ocker Timothy T. Pennucci Benetge B. P. Perera Nihan S. Pol Henri A. Radovan Scott M. Ransom Paul S. Ray Shashwat C. Sardesai Carl Schmiedekamp Ann Schmiedekamp Kai Schmitz Brent J. Shapiro-Albert Joseph Simon Magdalena S. Siwek Ingrid H. Stairs Daniel R. Stinebring Kevin Stovall Abhimanyu Susobhanan Joseph K. Swiggum Jacob E. Turner Caner Unal Haley M. Wahl Caitlin A. Witt Olivia Young Center for Gravitation Cosmology and Astrophysics Department of Physics University of Wisconsin-Milwaukee P.O. Box 413 Milwaukee Wisconsin 53201 USA Division of Physics Mathematics and Astronomy California Institute of Technology Pasadena California 91125 USA Department of Physics and Astronomy Widener University One University Place Chester Pennsylvania 19013 USA Department of Physics Montana State University Bozeman Montana 59717 USA Department of Physics Oregon State University Corvallis Oregon 97331 USA NASA Marshall Space Flight Center Huntsville Alabama 35812 USA Department of Physics Texas Tech University Box 41051 Lubbock Texas 79409 USA Department of Physics and Astronomy Vanderbilt University 2301 Vanderbilt Place Nashville Tennessee 37235 USA Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive Pasadena California 91109 USA Institute of Cosmology Department of Physics and Astronomy Tufts University Medford Massachusetts 02155 USA Department of Astronomy University of Maryland College Park Maryland 20742 Center for Research and Exploration in Space Science and Technology NASA/GSFC Greenbelt Maryland 20771 NASA Goddard Space Flight Center Greenbelt Maryland 20771 USA Max-Planck-Institut für Gravitationsphysik (Albert-Einstein-Institut) Callinstrasse 38 D-30167 Hannover Germany Newcastle University NE1 7RU Newcastle United Kingdom X-Ray Astrophysics Laboratory NASA Goddard Space Flight Center Code 662 Greenbelt Maryland 20771 USA Physics Department University of Florida Gainesville Florida 32611 USA Cornell Center for Astrophysics and Planetary Science and Department of Astronomy Cornell University Ithaca New York 14853 USA Cornell Center for Advanced Computing Cornell University Ithaca New York 14853 USA Institute for Gravitational Wave Astronomy and School of Physics and Astronomy University of Birmingham Edgbaston Birmingham B15 2TT United Kingdom Department of Physics and Astronomy West

Pulsar timing arrays (PTAs) use an array of millisecond pulsars to search for gravitational waves in the nanohertz regime in pulse time of arrival data. This paper presents rigorous tests of PTA methods, examining their consistency across the relevant parameter space. We discuss updates to the 15-year isotropic gravitational-wave background analyses and their corresponding code representations. Descriptions of the internal structure of the flagship algorithms enterprise and ptmcmcsampler are given to facilitate understanding of the PTA likelihood structure, how models are built, and what methods are currently used in sampling the high-dimensional PTA parameter space. We introduce a novel version of the PTA likelihood that uses a two-step marginalization procedure that performs much faster in gravitational wave searches, reducing the required resources facilitating the computation of Bayes factors via thermodynamic integration and sampling a large number of realizations for computing Bayesian false-alarm probabilities. We perform stringent tests of consistency and correctness of the Bayesian and frequentist analysis methods. For the Bayesian analysis, we test prior recovery, simulation recovery, and Bayes factors. For the frequentist analysis, we test that the optimal statistic, when modified to account for a non-negligible gravitational-wave background, accurately recovers the amplitude of the background. We also summarize recent advances and tests performed on the optimal statistic in the literature from both gravitational wave background detection and parameter estimation perspectives. The tests presented here validate current analyses of PTA data.

关键词： Gravitational waves Gravitational wave detection

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Self-Triggered Distributed Optimization for Multi-Agent Systems with Periodic Sampling Schemes

Self-Triggered Distributed Optimization for Multi-Agent Syst...

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Chinese Automation Congress (CAC)

作者： Shuling Li Wu Ai Jian Wu College of Science Center for Data analysis and Algorithm Technology Guilin University of Technology Guilin P. R. China College of Science Guilin University of Technology Guangxi Key Laboratory of Spatial Information and Geomatics Guilin P. R. China Perception and Computing of Anhui University Key Laboratory of Intelligent Province Anqing P. R. China

This work is the first to consider the self-triggered distributed optimization problem for multi-agent systems with periodic sampling communication. Each agent computes its next update time instance at the previous time. The self-triggered distributed protocols are proposed to reduce the load of in-network communication. It is proved that these self-triggered protocol achieves optimal consensus exponentially under a undirected and connected network topology.

关键词： Multi-agent systems Cost function Protocols Eigenvalues and eigenfunctions Geomagnetism Network topology

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THE ARCTIC

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Bulletin of the American Meteorological Society 2024年第8期105卷 S277-S330页

作者： Druckenmiller, M.L. Thoman, R.L. Moon, T.A. National Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Boulder Colorado International Arctic Research Center University of Alaska Fairbanks Fairbanks Alaska National Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Colorado Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff Arizona Biospherical Instruments Inc. San Diego California Geophysical Institute University of Alaska Fairbanks Fairbanks Alaska Plant Soils and Climate Department Utah State University Logan Utah Norwegian Institute for Nature Research Trondheim Norway FRAM – High North Research Centre for Climate and the Environment Tromsø Norway Geological Survey of Denmark and Greenland (GEUS) Copenhagen Denmark NOAA/NWS Alaska Region Anchorage Alaska Alaska Native Tribal Health Consortium Anchorage Alaska Geological Survey of Canada Natural Resources Canada Ottawa Canada NOAA Chemical Sciences Laboratory Boulder Colorado Arctic Geophysics Department University Centre in Svalbard Longyearbyen Norway Geology Department University Centre in Svalbard Longyearben Norway Centre National de Recherches Météorologiques Météo-France/CNRS Toulouse France Climate Research Division Environment and Climate Change Canada Toronto Canada Norwegian Polar Institute Fram Centre Tromsø Norway Danish Meteorological Institute Copenhagen Denmark Department of Physics University of Toronto Toronto Canada Department of Environmental Sciences University of Virginia Charlottesville Virginia Department of Geographical Sciences University of Maryland College Park Maryland University of Liège Liège Belgium Environment and Climate Change Canada Toronto Canada Northern Rocky Mountain Science Center U.S. Geological Survey Bozeman Montana Arctic C

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An Algorithm for Describing the Convex and Concave Shape of Protein Surface

An Algorithm for Describing the Convex and Concave Shape of ...

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2018国际计算机前沿大会（原国际青年计算机大会）

作者： Wei Wang Keliang Li Hehe Lv Lin Sun Hongjun Zhang Jinling Shi Shiguang Zhang Yun Zhou Yuan Zhao Jingjing Xv Department of Computer Science and Technology College of Computer and Information Engineering Henan Normal University Laboratory of Computation Intelligence and Information Processing Engineering Technology Research Center for Computing Intelligence and Data Mining School of Aviation Engineering Anyang University School of International Education Xuchang University

Protein surface plays a key role in many biological *** proteins participate in the life activities of cells via binding to other proteins or ligand molecules. It is an important work to study protein structure and function by analyzing the protein surface shape. Based on the CX algorithm and the 2 D fngerprint-base method, we proposed a FCX method to identify the morphology of bulges and depressions on the protein surface. The experimental results show that the FCX algorithm has a more desirable outcome than CX algorithm. The FCX algorithm has a higher correlation with the convex and concave features than CX values with solvent accessibility, solvent accessibility, and Bfactor's Pearson correlation coefficient. This result shows that the FCX algorithm can describe the shape of the protein surface residues more accurately than the CX algorithm.

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Dual encoding for zero-example video retrieval

arXiv

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arXiv 2018年

作者： Dong, Jianfeng Li, Xirong Xu, Chaoxi Ji, Shouling He, Yuan Yang, Gang Wang, Xun College of Computer and Information Engineering Zhejiang Gongshang University Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China College of Computer Science Zhejiang University Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies Alibaba Group

This paper attacks the challenging problem of zero-example video retrieval. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described in natural language text with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is required. The majority of existing methods are concept based, extracting relevant concepts from queries and videos and accordingly establishing associations between the two modalities. In contrast, this paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Dual encoding is conceptually simple, practically effective and end-to-end. As experiments on three benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the proposed solution establishes a new state-of-the-art for zero-example video retrieval. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal encoding

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The Future of Astronomical data Infrastructure: Meeting Report

arXiv

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arXiv 2023年

作者： Blanton, Michael R. Evans, Janet D. Norman, Dara O'Mullane, William Price-Whelan, Adrian Rizzi, Luca Accomazzi, Alberto Ansdell, Megan Bailey, Stephen Barrett, Paul Berukoff, Steven Bolton, Adam Borrill, Julian Cruz, Kelle Dalcanton, Julianne Desai, Vandana Dubois-Felsmann, Gregory P. Economou, Frossie Ferguson, Henry Field, Bryan Foreman-Mackey, Dan Forero-Romero, Jaime Gaffney, Niall Gillies, Kim Graham, Matthew J. Gwyn, Steven Hennawi, Joseph Hughes, Anna L.H. Jaffe, Tess Jagannathan, Preshanth Jenness, Tim Jurić, Mario Kavelaars, J.J. Kee, Kerk Kern, Jeff Kremin, Anthony Labrie, Kathleen Lacy, Mark Law, Casey Martínez-Galarza, Rafael McCully, Curtis McEnery, Julie Miller, Bryan Moriarty, Christopher Muench, August Muna, Demitri Murillo, Angela Narayan, Gautham Neill, James D. Nikutta, Robert Ojha, Roopesh Olsen, Knut O'Meara, John Rusholme, Ben Seaman, Robert Starkman, Nathaniel Still, Martin Stoehr, Felix Swinbank, John D. Teuben, Peter Toledo, Ignacio Tollerud, Erik Turk, Matthew D. Turner, James Vacca, William Vieira, Joaquin Weaver, Benjamin Weiner, Benjamin Weiss, Jason Westfall, Kyle Willman, Beth Zhao, Lily Center for Cosmology & Particle Physics New York University 726 Broadway New York10003 United States Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States NSF's National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States Rubin Observatory Project Office 950 N. Cherry Ave. TucsonAZ85719 United States Center for Computational Astrophysics Flatiron Institute 162 Fifth Ave New YorkNY10010 United States National Science Foundation 2415 Eisenhower Avenue AlexandriaVA22314 United States National Aeronautics and Space Administration Goddard Space Flight Center GreenbeltMD United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States US Naval Observatory Flagstaff Station 10391 Naval Observatory Road FlagstaffAZ86001 United States City University of New York Hunter College Hunter North Building 695 Park Ave. New YorkNY10065 United States IPAC California Institute of Technology MS 100-22 PasadenaCA91125 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Office of Science U.S. Department of Energy 1000 Independence Ave. SW WashingtonDC20585 United States Universidad de los Andes Cra 1 Num. 18A - 12 Bogotá Colombia Texas Advanced Computing Center The University of Texas Austin United States TMT International Observatory United States Astronomy Department California Institute of Technology 1200 East California Blvd. PasadenaCA91125 United States National Research Council Canada 5071 West Saanich Road Victoria Canada University of California Santa BarbaraCA93106-9530 United States National Solar Observatory 3665 Discovery Drive BoulderCO80303 United States National Radio Astronomy Observstory 520 Edgemont Road CharlottesvilleVA22903 United States Department of Astronomy The DIRAC Institute University of Washington 3910 15th Avenue NE SeattleWA98195 Unite

The astronomical community is grappling with the increasing volume and complexity of data produced by modern telescopes, due to difficulties in reducing, accessing, analyzing, and combining archives of data. To address this challenge, we propose the establishment of a coordinating body, an "entity," with the specific mission of enhancing the interoperability, archiving, distribution, and production of both astronomical data and software. This report is the culmination of a workshop held in February 2023 on the Future of Astronomical data Infrastructure. Attended by 70 scientists and software professionals from ground-based and space-based missions and archives spanning the entire spectrum of astronomical research, the group deliberated on the prevailing state of software and data infrastructure in astronomy, identified pressing issues, and explored potential solutions. In this report, we describe the ecosystem of astronomical data, its existing flaws, and the many gaps, duplication, inconsistencies, barriers to access, drags on productivity, missed opportunities, and risks to the long-term integrity of essential data sets. We also highlight the successes and failures in a set of deep dives into several different illustrative components of the ecosystem, included as an appendix. The report also puts forth a roadmap for the development of the proposed coordination entity, offering four potential structural models for consideration. Alongside, we present preliminary case studies scrutinizing their associated costs, advantages, disadvantages, and risks. We advocate for the establishment of a Steering Committee, bolstered by support from federal funding agencies, to take the lead in defining the entity's scope, requirements, and structural framework. This committee should actively engage with and solicit feedback from the broader astronomical community and the expert cadre specializing in astronomical data, culminating in the formulation of an actionable implementation p

关键词： Ecosystems

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Pan-genome and Haplotype Map of Cassava Cultivars and Wild Ancestors Provide Insights into its Adaptive Evolution and Domestication

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Molecular plant 2025年

作者： Zhiqiang Xia Zhenglin Du Xincheng Zhou Sirong Jiang Tingting Zhu Le Wang Fei Chen Luiz Carvalho Meiling Zou Luis Augusto Becerra López-Lavalle Xiaofei Zhang Liangye Xu Zhenyu Wang Meili Chen Xin Guo Shujuan Wang Mengtao Li Yuanchao Li Haiyan Wang Shisheng Liu Yuting Bao Long Zhao Chenji Zhang Jianjia Xiao Fengguang Guo Xu Shen Haozheng Li Cheng Lu Fei Qiao Hernan Ceballos Huabing Yan Xiaochun Qin Ling Ma Huaifang Zhang Shuang He Wenming Zhao Yinglang Wan Yinhua Chen Dongyi Huang Kaimian Li Bin Liu Ming Peng Weixiong Zhang Birger Lindberg Møller Xin Chen Ming-Cheng Luo Jingfa Xiao Wenquan Wang Hainan University Haikou 570228 China National Key Laboratory of Biotechnology and Breeding of Tropical Crops. National Genomics Data Center Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation Beijing 100101 China. Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agricultural Sciences (CATAS) Haikou 571101 China. Department of Plant Sciences University of California Davis California 95616 USA. Brazilian Enterprise for Agricultural Research (EMBRAPA) Genetic Resources and Biotechnology Brasilia 70770 Brazil. International Center for Tropical Agriculture (CIAT) Cali 6713 Colombia. Institute of Nanfan & Seed Industry Guangdong Academy of Sciences Guangzhou 510316 China. State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources/College of Life Science and Technology Guangxi University Nanning 530004 China. The Academy of Agriculture and Forestry Sciences Qinghai University Xining 810016 China. Tropical Crop Genetic Resources Institute Chinese Academy of Tropical Agricultural Sciences (CATAS) Haikou 571700 China. Economic Crop Institute Guangxi Academy of Agricultural Sciences Nanning China. School of Biological Science and Technology University of JINAN Jinan China. Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agricultural Sciences (CATAS) Haikou 571101 China Department of Health Technology and Informatics Department of Computing The Hong Kong Polytechnic University Hong Kong China. Plant Biochemistry Laboratory Department of Plant and Environmental Sciences University of Copenhagen DK-1871 Frederiksberg C Denmark. Institute of Tropical Bioscience and Biotechnology Chinese Academy of Tropical Agricultural Sciences (CATAS) Haikou 571101 China. Electronic address: xiaojingfa@***. National Genomics Data Center Beijing Institute of Genomics Chinese Academy of Sciences and China National Center for Bioinformation Beijing 100101

Cassava is an important, resilient tropical crop that produces large starchy storage roots and high biomass. But how did cassava´s remarkable environmental adaptability and key economic traits evolve from its wild species? We constructed nearly T2T genomes and their haplotype forms for the cultivar AM560, wild ancestors FLA4047 and W14, a graphic pan-genome of 30 representatives with 1.15 Gb, and built a clarified evolutionary tree of 486 accessions. A comparison of structural variations (SVs) and single-nucleotide variations (SNVs) between the ancestors and cultivated cassavas reveals predominant expansions and contractions of genes and gene families, which are mainly driven by transposons. Significant selective sweeping occurred in 122 footprints of genomes and affects 1,519 domesticated genes. We identify selective mutations in MeCSK and MeFNR2 that could promote photoreactions associated with MeNADP-ME in C photosynthesis in modern cassava. Co-evolution of retard floral primordia and initiation of storage roots may arise from MeCOL5 variants with altered bindings to MeFT1, MeFT2, and MeTFL2. Mutations of MeMATE1 and MeGTR occur in sweet cassava, and MeAHL19 has evolved to regulate the biosynthesis, transport, and endogenous remobilization of cyanogenic glucosides in cassava. These extensive genomic and gene resources provided here and the findings of the evolutionary mechanisms responsible for beneficial traits in modern cultivars represent a stepping-stone in future breeding of cassava.

关键词： Cassava Evolution and Domestication Genome Pan-genome Selective sweeping Structural Variations

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Activity Discovered on Mars-Crossing Jupiter Family Comet 2018 OR by Citizen Scientists

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Research Notes of the AAS 2024年第1期8卷 18-18页

作者： Kennedy A. Farrell Colin Orion Chandler William J. Oldroyd Chadwick A. Trujillo William A. Burris Henry H. Hsieh Jay K. Kueny Jarod A. DeSpain Mark Jesus Mendoza Magbanua Nima Sedaghat Scott S. Sheppard Michele T. Mazzucato Milton K. D. Bosch Tiffany Shaw-Diaz Virgilio Gonano Al Lamperti José A. da Silva Campos Brian L. Goodwin Ivan A. Terentev Charles J. A. Dukes Dept. of Astronomy & Planetary Science Northern Arizona University P.O. Box 6010 Flagstaff AZ 86011 USA kaf435@nau.edu Dept. of Astronomy & the DiRAC Institute University of Washington 3910 15th Ave NE Seattle WA 98195 USA LSST Interdisciplinary Network for Collaboration and Computing 933 N. Cherry Avenue Tucson AZ 85721 USA Raw Data Speaks Initiative Dept. of Physics San Diego State University 5500 Campanile Drive San Diego CA 92182 USA Planetary Science Institute 1700 East Fort Lowell Rd. Suite 106 Tucson AZ 85719 USA Institute of Astronomy and Astrophysics Academia Sinica P.O. Box 23-141 Taipei 10617 Taiwan Steward Observatory 933 North Cherry Avenue Rm. N204 Tucson AZ 85721 USA Wyant College of Optical Sciences University of Arizona 1630 E. University Blvd. Tucson AZ 85721 USA Department of Laboratory Medicine University of California San Francisco 2340 Sutter Street San Francisco CA 94143 USA Department of Terrestrial Magnetism Carnegie Institution for Science 5241 Broad Branch Road. NW Washington DC 20015 USA Active Asteroids Citizen Scientist Royal Astronomical Society Burlington House Piccadilly London W1J 0BQ UK Physical Sciences Group Siena Academy of Sciences Piazzetta Silvio Gigli 2 I-53100 Siena Italy Delaware Valley Amateur Astronomers 112 Pebble Beach Drive Royersford PA 19468 USA Astronomical Society of Southern Africa P.O. Box 9 Observatory 7935 Cape Town South Africa

We present the discovery of cometary activity on 2018 OR as part of our Active Asteroids project, a NASA Partner Program fueled by Zooniverse Citizen Scientists. Volunteers found 2018 OR with a long, diffuse tail in a...

We present the discovery of cometary activity on 2018 OR as part of our Active Asteroids project, a NASA Partner Program fueled by Zooniverse Citizen Scientists. Volunteers found 2018 OR with a long, diffuse tail in archival images from the Dark Energy Camera on the Blanco 4 m telescope at the Cerro Tololo Inter-American Observatory in Chile. Our team identified additional Canada–France–Hawaii–Telescope MegaCam and Zwicky Transient Facility archival data after classification by Citizen Scientists. Activity originating from 2018 OR and directed in the anti-solar and anti-velocity directions was visible in archival images between UT 2018 September 5–18. Our dynamical simulations indicate 2018 OR experiences close encounters with Jupiter over hundred-year timescales. The orbital period and dynamics suggest 2018 OR is a Jupiter Family Comet, and we recommend further observations from the community to reduce observational uncertainties and investigate activity patterns.

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Pivot-based metric indexing 43

Pivot-based metric indexing

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43rd International Conference on Very Large data Bases, VLDB 2017

作者： Chen, Lu Gao, Yunjun Zheng, Baihua Jensen, Christian S. Yang, Hanyu Yang, Keyu College of Computer Science Zhejiang University Hangzhou China The Key Lab of Big Data Intelligent Computing of Zhejiang Province Zhejiang University Hangzhou China School of Information Systems Singapore Management University Singapore Department of Computer Science Aalborg University Denmark

The general notion of a metric space encompasses a diverse range of data types and accompanying similarity measures. Hence, metric search plays an important role in a wide range of settings, including multimedia retrieval, data mining, and data integration. With the aim of accelerating metric search, a collection of pivotbased indexing techniques for metric data has been proposed, which reduces the number of potentially expensive similarity comparisons by exploiting the triangle inequality for pruning and validation. However, no comprehensive empirical study of those techniques exists. Existing studies each offers only a narrower coverage, and they use different pivot selection strategies that affect performance substantially and thus render cross-study comparisons difficult or impossible. We offer a survey of existing pivot-based indexing techniques, and report a comprehensive empirical comparison of their construction costs, update efficiency, storage sizes, and similarity search performance. As part of the study, we provide modifications for two existing indexing techniques to make them more competitive. The findings and insights obtained from the study reveal different strengths and weaknesses of different indexing techniques, and offer guidance on selecting an appropriate indexing technique for a given setting. © 2017 VLDB Endowment.

关键词： data integration

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