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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D'Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Spyridon Ca Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IndianapolisIN United States Children's Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children's National Hospital WashingtonDC United States George Washington University WashingtonDC United State

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, T2/FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Tumors

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Virtual reality learning environments for vocational education: a comparative study with conventional instructional media on two-stroke engine

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IOP Conference Series: Materials science and Engineering 2020年第1期979卷

作者： F Sholichin N M Suaib D A Irawati Sutiman M Solikin A Yudantoko A Yudianto I W Adiyasa A J Sihes H A Sulaiman School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. UTM Big Data Center Ibnu Sina Institute of Scientific and Industrial Research Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. Automotive Engineering of Education Faculty of Engineering Universitas Negeri Yogyakarta School of Education Faculty of Social Science and Humanities Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka

Virtual reality technology has been adopted as a modern learning media in vocational education. Nowadays, the practical learning needs complete equipment and tools to achieve the goal study. However, the conventional media requirement is a challenge for academician for learning process, specifically in automotive and machine areas. Virtual reality provides a solution for this problem, but still needs proper analysis on the impact of usability between conventional and VR media to which each learning objective is achieved. The case study used in this research is a two-stroke engine, which is currently a rare item in the real world. We replicated this two-stroke engine in the developed VR system using Unity game engine and Oculus quest. A comparison on the usability effectiveness on conventional and virtual reality methods was conducted using cognitive walkthrough, which follows the four steps of learning objective training. The result shows that three out of four learning objectives are similar with conventional objective training. One of the process on preparation for disassembly machine has slight difference with conventional training; faulty engine due from environment elements (liquid, rusty iron) was not included.

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Parkes transient events: I. database of single pulses, initial results and missing frbs

arXiv

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arXiv 2020年

作者： Zhang, S.-B. Hobbs, G. Russell, C.J. Toomey, L. Dai, S. Dempsey, J. Manchester, R.N. Johnston, S. Staveley-Smith, L. Wu, X.-F. Li, D. Yang, Y.-Y. Wang, S.-Q. Qiu, H. Luo, R. Wang, C. Zhang, C. Zhang, L. Mandow, R. Purple Mountain Observatory Chinese Academy of Sciences Nanjing210008 China University of Chinese Academy of Sciences Beijing100049 China CSIRO Astronomy and Space Science PO Box 76 EppingNSW1710 Australia International Centre for Radio Astronomy Research University of Western Australia CrawleyWA6009 Australia CSIRO Scientific Computing Services Australian Technology Park Locked Bag 9013 AlexandriaNSW1435 Australia CSIRO Information Management & Technology Box 225 DicksonACT2602 Australia National Astronomical Observatories Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China FAST Collaboration CAS Key Laboratory of FAST NAOC Chinese Academy of Sciences Beijing100101 China School of Physics and Electronic Science Guizhou Education University Guiyang550018 China Xinjiang Astronomical Observatory Chinese Academy of Sciences Urumqi Xinjiang830011 China Sydney Institute for Astronomy School of Physics University of Sydney SydneyNSW2006 Australia CSIRO Data61 SydneyNSW2015 Australia Centre for Astrophysics and Supercomputing Swinburne University of Technology Mail H30 PO Box 218 HawthornVIC3122 Australia

A large number of observations from the Parkes 64 m-diameter radio telescope, recorded with high time resolution, are publicly available. We have re-processed all of the observations obtained during the first four years (from 1997 to 2001) of the Parkes Multibeam receiver system in order to identify transient events and have built a database that records the 568,736,756 pulse candidates generated during this search. We have discovered a new fast radio burst (FRB), FRB 010305, with a dispersion measure of 350±5 cm−3 pc and explored why so few FRBs have been discovered in data prior to 2001. After accounting for the dispersion smearing across the channel bandwidth and the sky regions surveyed, the number of FRBs is found to be consistent with model predictions. We also present five single pulse candidates from unknown sources, but with Galactic dispersion measures. We extract a diverse range of sources from the database, which can be used, for example, as a training set of data for new software being developed to search for FRBs in the presence of radio frequency interference. Copyright © 2020, The Authors. All rights reserved.

关键词： database systems

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Fast 3D Map Reconstruction Using Dense Visual Simultaneous Localization and Mapping Based on Unmanned Aerial Vehicle

Fast 3D Map Reconstruction Using Dense Visual Simultaneous L...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Siyuan Peng Fang Huang Jian Tao Bo Tie Jun Lu Xiaodong Zhang School of Resources & Environment University of Electronic Science and Technology of China Chengdu P.R. China Institute of Remote Sensing Big Data University of Electronic Science and Technology of China Chengdu P.R. China Texas A&M Engineering Experiment Station and High Performance Research Computing Texas A&M University College Station TX USA

Traditional 3D map reconstruction methods based on unmanned aerial (UA) always relies on multi-camera optical equipment or additional space positioning equipment. All of these restrict the UA's application scenarios to some extent. Visual simultaneous localization and mapping (VSLAM), using the camera as the only external sensor, can construct a 3D map of its spatial environment while recording its own localization. This paper proposes a technique of fast 3D map reconstruction based on UAs by using VSLAM, with parallel computing based on CUDA, to achieve fast dense reconstruction based on a UA platform. This research belongs to one of the emerging but very exciting application areas using UAs. It helps to expand the application scope of UAs to some extent. From the experiments, it is demonstrated that the methodology presented in this paper works well, and could be applied to real world applications.

关键词： Unmanned aerial Visual SLAM 3D map High performance computing Dense reconstruction

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Searching for GRBs at VHE with MAGIC: The status before CTA

arXiv

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arXiv 2019年

作者： Berti, Alessio Antonelli, Lucio Angelo Bošnjak, Zeljka Cortina, Juan Covino, Stefano D’Elia, Valerio do Souto Espiñeira, Elia Fukami, Satoshi Inoue, Susumu Longo, Francesco Miceli, Davide Moretti, Elena Nava, Lara Noda, Koji Nozaki, Seya Peresano, Michele Suda, Yusuke Will, Martin University of Torino INFN Torino Torino Italy INAF National Institute for Astrophysics Rome Italy Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia CIEMAT Madrid Spain INAF Osservatorio Astronomico di Brera Merate Italy Space Science Data Center - Agenzia Spaziale Italiana Rome Italy Spain ICRR University of Tokyo Kashiwa Chiba Japan RIKEN Wako Saitama Japan University of Trieste INFN Trieste Trieste Italy University of Udine INFN Trieste Udine Italy Department of Physics Kyoto University Kyoto Japan Max-Planck-Institut für Physik Munich Germany

Gamma-Ray Bursts (GRBs) are one of the main targets for current and next generation Imaging Atmospheric Cherenkov Telescopes (IACTs). Given their transient behavior, especially in the case of their prompt emission phase, performing fast follow-up observations is challenging for IACTs, which have a narrow field of view and limited duty cycle. Despite this, MAGIC plays a major role in the search for Very High Energy (VHE, E>100 GeV) gamma-ray emission from GRBs: this is possible thanks to its fast repositioning speed, low energy threshold and high sensitivity at the lowest energies. In 2013 the MAGIC GRB automatic procedure was upgraded, increasing the number of GRBs followed in the prompt and early afterglow phases and decreasing dramatically hardware failures during fast repositioning. Currently, only GRB 190114C was firmly detected in the VHE band, while for other GRBs no significant detection was achieved. In such a case, upper limits (ULs) can give insight into the physics driving such eluding sources, especially on their emission mechanisms. In this contribution we report on the status of the GRB follow-up with MAGIC and focus on the ULs and results obtained from a sample of GRBs observed between 2013 and 2018. This GRB catalog is the result of the MAGIC well-designed and tested follow-up procedure, and it serves as a precursor of GRBs observation with the next generation IACT system, the Cherenkov Telescope Array (CTA). Copyright © 2019, The Authors. All rights reserved.

关键词： Gamma rays

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Digital is the New Blood: Enabling the Present and the Future

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Alzheimer's & Dementia 2023年第S21期19卷

作者： Rhoda Au Zachary T Popp Spencer Low Phillip H Hwang Ileana De Anda-Duran Stephanie Li Salman Rahman Huitong Ding Akwaugo Igwe Cody Karjadi Ting Fang Alvin Ang Sherral A. Devine Ashita S Gurnani Jesse B. Mez Lindsay A. Farrer Preeti Sunderaraman Honghuang Lin Vijaya B. Kolachalama Boston University School of Public Health Boston MA USA Framingham Heart Study Boston University School of Medicine Boston MA USA Davos Alzheimer’s Collaborative Philadelphia PA USA Department of Epidemiology Boston University School of Public Health Boston MA USA Boston University Chobanian & Avedisian School of Medicine Boston MA USA Department of Neurology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University Alzheimer’s Disease Research Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Department of Anatomy and Neurobiology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University School of Public Heatlh Boston MA USA Tulane School of Public Health and Tropical Medicine New Orleans LA USA Slone Epidemiology Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Framigham Heart Study - Boston University Framingham MA USA Boston University Bioinformatics Program Boston MA USA Departments of Medicine (Biomedical Genetics) Neurology Ophthalmology Biostatistics and Epidemiology Boston University Schools of Medicine and Public Health Boston MA USA Department of Neurology - Boston University Chobanian & Avedisian School of Medicine Boston MA USA University of Massachusetts Medical School Worcester MA USA Framingham Heart Study/Boston University BOSTON MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA College of Arts & Sciences Boston University Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Boston University Alzheimer’s Disease Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Whitaker Cardiovascular Institute Boston University Chobanian & Avedisian School of Medicine Boston MA USA

Background Digital is the new blood, where collection, use, and storage of raw digital data streams enabled by technology-embedded sensors are replacing analog methods. Analyses of digital data extend beyond current applications, analogous to previously stored blood currently be used for omics/biomarker research. Today digital technologies are used to measure Alzheimer’s disease (AD)-related clinical symptoms (e.g., cognition, function, and behavior), but future possibilities remain to be determined. Importantly, digital data collection can go beyond current intermittent sampling by employing internet-connected applications and devices that allow real-time monitoring and accurate detection of changes, even within a widely heterogeneous spectrum. Method Beginning in 2005, the Framingham Heart Study (FHS) began digitally recording participants’ spoken responses to neuropsychological (NP) test questions, and in 2011 extended to a digital pen to record written responses. The emergence of wearable devices and smartphone applications has led to an expanded use of digital technologies beyond recordings during NP assessments. The Boston University Alzheimer’s Disease Research Center (BU ADRC) leverages digital sensors in multiple internet-connected devices that longitudinally collect digital data, in both in-person and remote settings. Result The first publication involving FHS digital data was in 2016; the remaining (n> 17) have been from 2020-current. Digital data measuring cognition, function and behavior are being collected through the BU ADRC, where results to date indicate nearly 100% and 75% uptake of active and passive engagement applications, respectively, and 95% uptake for wearable devices (n = 60). Longitudinal assessments include > 3 months for ∼50 participants; > 1 year for n∼21 participants. Conclusion Opportunities for improved trial outcome measurement are evident in the broad range of outcomes digital sensors capture beyond traditional research methodologi

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Hierarchical and modularly-minimal vertex colorings

arXiv

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arXiv 2020年

作者： Valdivia, Dulce I. Geiß, Manuela Rosales, Maribel Hernández Stadler, Peter F. Hellmuth, Marc Km. 9.6 Libramiento Norte Carretera Irapuato-León Irapuato GTOMX-36821 Mexico Bioinformatics Group Department of Computer Science & Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Software Competence Center Hagenberg GmbH Softwarepark 21 HagenbergA-4232 Austria CONACYT-Instituto de Matemáticas UNAM Juriquilla Blvd. Juriquilla 3001 Juriquilla Querétaro QRO76230 Mexico Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine at Leipzig University Universität Leipzig Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States School of Computing University of Leeds EC Stoner Building LeedsLS2 9JT United Kingdom

Cographs are exactly the hereditarily well-colored graphs, i.e., the graphs for which a greedy vertex coloring of every induced subgraph uses only the minimally necessary number of colors χ(G). We show that greedy colorings are a special case of the more general hierarchical vertex colorings, which recently were introduced in phylogenetic combinatorics. Replacing cotrees by modular decomposition trees generalizes the concept of hierarchical colorings to arbitrary graphs. We show that every graph has a modularly-minimal coloring σ satisfying |σ(M)| = χ(M) for every strong module M of G. This, in particular, shows that modularly-minimal colorings provide a useful device to design efficient coloring algorithms for certain hereditary graph classes. For cographs, the hierarchical colorings coincide with the modularly-minimal coloring. As a by-product, we obtain a simple linear-time algorithm to compute a modularly-minimal coloring of P4-sparse graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Coloring

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Euclid preparation. 3-dimensional galaxy clustering in configuration space Part I: 2-point correlation function estimation

arXiv

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arXiv 2025年

作者： de la Torre, S. Marulli, F. Keihänen, E. Viitanen, A. Viel, M. Veropalumbo, A. Branchini, E. Tavagnacco, D. Rizzo, F. Valiviita, J. Lindholm, V. Allevato, V. Parimbelli, G. Sarpa, E. Z., Ghaffari A., Amara S., Andreon N., Auricchio C., Baccigalupi M., Baldi S., Bardelli A., Basset D., Bonino M., Brescia J., Brinchmann A., Caillat S., Camera V., Capobianco C., Carbone J., Carretero S., Casas F.J., Castander M., Castellano G., Castignani S., Cavuoti A., Cimatti C., Colodro-Conde G., Congedo C.J., Conselice L., Conversi Y., Copin F., Courbin H.M., Courtois M., Crocce A., Da Silva H., Degaudenzi G., De Lucia A.M., Di Giorgio J., Dinis F., Dubath C.A.J., Duncan X., Dupac S., Dusini M., Farina S., Farrens F., Faustini S., Ferriol N., Fourmanoit M., Frailis E., Franceschi P., Franzetti M., Fumana S., Galeotta K., George W., Gillard B., Gillis C., Giocoli P., Gómez-Alvarez B.R., Granett A., Grazian F., Grupp L., Guzzo S.V.H., Haugan W., Holmes F., Hormuth A., Hornstrup S., Ilić K., Jahnke M., Jhabvala B., Joachimi S., Kermiche A., Kiessling M., Kilbinger B., Kubik M., Kunz H., Kurki-Suonio S., Ligori P.B., Lilje I., Lloro G., Mainetti D., Maino E., Maiorano O., Mansutti O., Marggraf K., Markovic M., Martinelli N., Martinet R., Massey S., Maurogordato E., Medinaceli S., Mei M., MELMhior Y., Mellier M., Meneghetti E., Merlin G., Meylan M., Moresco B., Morin L., Moscardini E., Munari C., Neissner S.-M., Niemi C., Padilla S., Paltani F., Pasian K., Pedersen W.J., Percival V., Pettorino S., Pires G., Polenta M., Poncet L., Pozzetti F., Raison A., Renzi J., Rhodes G., Riccio E., Romelli M., Roncarelli E., Rossetti R., Saglia Z., Sakr A.G., Sánchez D., Sapone B., Sartoris P., Schneider T., Schrabback M., Scodeggio A., Secroun E., Sefusatti G., Seidel M., Seiffert S., Serrano C., Sirignano G., Sirri L., Stanco J., Steinwagner C., Surace P., Tallada-Crespí A.N., Taylor I., Tereno R., Toledo-Moreo Aix-Marseille Université CNRS CNES LAM Marseille France Dipartimento di Fisica e Astronomia Augusto Righi Alma Mater Studiorum Università di Bologna via Piero Gobetti 93/2 Bologna40129 Italy INAF-Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 93/3 Bologna40129 Italy INFN-Sezione di Bologna Viale Berti Pichat 6/2 Bologna40127 Italy Department of Physics Helsinki Institute of Physics University of Helsinki Gustaf Hällströmin katu 2 00014 Finland INAF-Osservatorio Astronomico di Roma Via Frascati 33 Monteporzio Catone00078 Italy IFPU Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy INAF-Osservatorio Astronomico di Trieste Via G. B. Tiepolo 11 Trieste34143 Italy SISSA International School for Advanced Studies Via Bonomea 265 TS Trieste34136 Italy INFN Sezione di Trieste Via Valerio 2 TS Trieste34127 Italy ICSC - Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy INAF-Osservatorio Astronomico di Brera Via Brera 28 Milano20122 Italy INFN-Sezione di Genova Via Dodecaneso 33 Genova16146 Italy Dipartimento di Fisica Università degli studi di Genova INFN-Sezione di Genova via Dodecaneso 33 Genova16146 Italy Dipartimento di Fisica Università di Genova Via Dodecaneso 33 Genova16146 Italy Department of Physics University of Helsinki P.O. Box 64 00014 Finland Helsinki Institute of Physics University of Helsinki Gustaf Hällströmin katu 2 Helsinki Finland INAF-Osservatorio Astronomico di Capodimonte Via Moiariello 16 Napoli80131 Italy Campus UAB Carrer de Can Magrans s/n Barcelona08193 Spain School of Mathematics and Physics University of Surrey Guild-ford SurreyGU2 7XH United Kingdom Dipartimento di Fisica e Astronomia Università di Bologna Via Gobetti 93/2 Bologna40129 Italy Centre National d’Etudes Spatiales Centre spatial de Toulouse 18 avenue Edouard Belin Toulouse31401Cedex 9 France Pino

The 2-point correlation function of the galaxy spatial distribution is a major cosmological observable that enables constraints on the dynamics and geometry of the Universe. The Euclid mission aims at performing an extensive spectroscopic survey of approximately 20–30 million Hα-emitting galaxies up to about redshift two. This ambitious project seeks to elucidate the nature of dark energy by mapping the 3-dimensional clustering of galaxies over a significant portion of the sky. This paper presents the methodology and software developed for estimating the 3-dimensional 2-point correlation function within the Euclid science Ground Segment. The software is designed to overcome the significant challenges posed by the large and complex Euclid data set, which involves millions of galaxies. Key challenges include efficient pair counting, managing computational resources, and ensuring the accuracy of the correlation function estimation. The software leverages advanced algorithms, including kd-tree, octree, and linked-list data partitioning strategies, to optimise the pair-counting process. These methods are crucial for handling the massive volume of data efficiently. The implementation also includes parallel processing capabilities using shared-memory open multi-processing to further enhance performance and reduce computation times. Extensive validation and performance testing of the software are presented. Those have been performed by using various mock galaxy catalogues to ensure that it meets the stringent accuracy requirement of the Euclid mission. The results indicate that the software is robust and can reliably estimate the 2-point correlation function, which is essential for deriving cosmological parameters with high precision. Furthermore, the paper discusses the expected performance of the software during different stages of the Euclid Wide Survey observations and forecasts how the precision of the correlation function measurements will improve over the mission’s t

关键词： Dark energy

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Discovering new strong gravitational lenses in the DESI legacy imaging surveys

arXiv

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arXiv 2020年

作者： Huang, Xiaosheng Storfer, C. Gu, A. Ravi, V. Pilon, A. Sheu, W. Venguswamy, R. Banka, S. Dey, A. Landriau, M. Lang, D. Meisner, A. Moustakas, J. Myers, A.D. Sajith, R. Schlafly, E.F. Schlegel, D.J. Department of Physics & Astronomy University of San Francisco San FranciscoCA94117-1080 United States Department of Physics University of California Berkeley BerkeleyCA94720 United States Department of Electrical Engineering & Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Computer Science University of San Francisco San FranciscoCA94117-1080 United States Department of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States NSF's National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States Physics Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Dunlap Institute University of Toronto TorontoONM5S 3H4 Canada Department of Astronomy & Astrophysics University of Toronto TorontoONM5S 3H4 Canada Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy Siena College 515 Loudon Rd. LoudonvilleNY12211 United States Department of Physics & Astronomy University of Wyoming Dept 3905 1000 E. University LaramieWY8207 United States Lawrence Livermore National Laboratory 7000 East Ave. LivermoreCA94550-9234 United States

We have conducted a search for new strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' data Release 8. We use deep residual neural networks, building on previous work presented in Huang et al. (2020). These surveys together cover approximately one third of the sky visible from the northern hemisphere, reaching a z-band AB magnitude of ∼ 22.5. We compile a training sample that consists of known lensing systems as well as non-lenses in the Legacy Surveys and the Dark Energy Survey. After applying our trained neural networks to the survey data, we visually inspect and rank images with probabilities above a threshold. Here we present 1210 new strong lens candidates. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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A deep investigation of deep IR models

arXiv

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arXiv 2017年

作者： Pang, Liang Lan, Yanyan Guo, Jiafeng Xu, Jun Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

The effective of information retrieval (IR) systems have become more important than ever. Deep IR models have gained increasing attention for its ability to automatically learning features from raw text;thus, many deep IR models have been proposed recently. However, the learning process of these deep IR models resemble a black box. Therefore, it is necessary to identify the difference between automatically learned features by deep IR models and hand-crafted features used in traditional learning to rank approaches. Furthermore, it is valuable to investigate the differences between these deep IR models. This paper aims to conduct a deep investigation on deep IR models. Specifically, we conduct an extensive empirical study on two different datasets, including Robust and LETOR4.0. We first compared the automatically learned features and handcrafted features on the respects of query term coverage, document length, embeddings and robustness. It reveals a number of disadvantages compared with hand-crafted features. Therefore, we establish guidelines for improving existing deep IR models. Furthermore, we compare two different categories of deep IR models, i.e. representation-focused models and interaction-focused models. It is shown that two types of deep IR models focus on different categories of words, including topic-related words and query-related words. Copyright © 2017, The Authors. All rights reserved.

关键词： Deep learning

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