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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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A community-powered search of machine learning strategy space to find NMR property prediction models

arXiv

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arXiv 2020年

作者： Bratholm, Lars A. Gerrard, Will Anderson, Brandon Bai, Shaojie Choi, Sunghwan Dang, Lam Hanchar, Pavel Howard, Addison Huard, Guillaume Kim, Sanghoon Kolter, Zico Kondor, Risi Kornbluth, Mordechai Lee, Youhan Lee, Youngsoo Mailoa, Jonathan P. Nguyen, Thanh Tu Popovic, Milos Rakocevic, Goran Reade, Walter Song, Wonho Stojanovic, Luka Thiede, Erik H. Tijanic, Nebojsa Torrubia, Andres Willmott, Devin Butts, Craig P. Glowacki, David R. Participants, Kaggle School of Chemistry University of Bristol Cantock’s Close BristolBS8 1TS United Kingdom School of Mathematics University of Bristol Fry Building Woodland Road BristolBS1 1UG United Kingdom Dept. of Computer Science University of Bristol Merchant Venturer’s Building BristolBS8 1UB United Kingdom Intangible Realities Laboratory University of Bristol Cantock’s Close BristolBS8 1TS United Kingdom Bosch Center for Artificial Intelligence PittsburghPA15222 United States Carnegie Mellon University PittsburghPA15213 United States National Institute of Supercomputing and Network Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of BNP Paribas Cardif Fyusion Inc. Kaggle Google Inc. Mountain View United States Ebay Korea 34F Gangnam Finance Center 152 Teheran Ro Gangnam Gu Seoul Korea Republic of Department of Computer Science University of Chicago 5730 S Ellis Ave ChicagoIL60637 United States Department of Statistics University of Chicago 5747 S Ellis Ave ChicagoIL60637 United States Center for Computational Mathematics Flatiron Institute 162 5th Ave. New YorkNY10010 United States Bosch Research and Technology Center CambridgeMA02139 United States Department of Chemical and Biomolecular Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of 111 Daedeok-daero 989beon-gil Yuseong-gu Daejeon Korea Republic of MINDS AND COMPANY 2621 Nambusunhwan-ro Gangnam-gu Seoul06267 Korea Republic of Totient Inc Sindjeliceva 9 Belgrade11000 Serbia KAIST Web Security & Privacy Lab 291 Daehak-ro Yuseong-guDaejeon34141 Korea Republic of *** Alicante Spain

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published ‘in-house’ efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties. Copyright © 2020, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance

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The messy death of a multiple star system and the resulting planetary nebula as observed by JWST

arXiv

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arXiv 2023年

作者： De Marco, Orsola Akashi, Muhammad Akras, Stavros Alcolea, Javier Aleman, Isabel Amram, Philippe Balick, Bruce De Beck, Elvire Blackman, Eric G. Boffin, Henri M.J. Boumis, Panos Bublitz, Jesse Bucciarelli, Beatrice Bujarrabal, Valentin Cami, Jan Chornay, Nicholas Chu, You-Hua Corradi, Romano L.M. Frank, Adam García-Segura, Guillermo García-Hernández, D.A. García-Rojas, Jorge Gómez-Llanos, Veronica Gonçalves, Denise R. Guerrero, Martín A. Jones, David Karakas, Amanda I. Kastner, Joel H. Kwok, Sun Lykou, Foteini Manchado, Arturo Matsuura, Mikako McDonald, Iain Monreal-Ibero, Ana Monteiro, Hektor Baez, Paula Moraga Morisset, Christophe Miszalski, Brent Mohamed, Shazrene S. Montez, Rodolfo Nordhaus, Jason de Oliveira, Claudia Mendes Osborn, Zara Otsuka, Masaaki Parker, Quentin A. Peeters, Els Quint, Bruno C. Quintana-Lacaci, Guillermo Redman, Matt Ruiter, Ashley J. Sabin, Laurence Contreras, Carmen Sánchez Santander-García, Miguel Seitenzahl, Ivo Sahai, Raghvendra Soker, Noam Speck, Angela K. Stanghellini, Letizia Steffen, Wolfgang Toalá, Jesús A. Ueta, Toshiya Van de Steene, Griet Villaver, Eva Ventura, Paolo Vlemmings, Wouter Walsh, Jeremy R. Wesson, Roger van Winckel, Hans Zijlstra, Albert A. School of Mathematical and Physical Sciences Macquarie University SydneyNSW2109 Australia Astronomy Astrophysics and Astrophotonics Research Centre Macquarie University SydneyNSW2109 Australia Department of Physics Technion Haifa3200003 Israel Kinneret College on the Sea of Galilee Samakh 15132 Institute for Astronomy Astrophysics Space Applications and Remote Sensing National Observatory of Athens PenteliGR 15236 Greece Alfonso XII 3 Madrid28014 Spain Instituto de Física e Química Universidade Federal de Itajubá Av. BPS 1303 Pinheirinho Itajubá37500-903 Brazil Marseille France Astronomy Department University of Washington SeattleWA98105-1580 United States Department of Space Earth and Environment Chalmers University of Technology GothenburgS-41296 Sweden Department of Physics and Astronomy University of Rochester RochesterNY14627 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623 United States European Southern Observatory Karl-Schwarzschild Strasse 2 GarchingD-85748 Germany Green Bank Observatory 155 Observatory Road PO Box 2 Green Bank WV24944 United States INAF Osservatorio Astrofisico di Torino Via Osservatorio 20 Pino Torinese 10023 Italy Department of Physics & Astronomy University of Western Ontario LondonONN6A 3K7 Canada Institute for Earth and Space Exploration University of Western Ontario LondonONN6A 3K7 Canada SETI Institute 399 Bernardo Avenue Suite 200 Mountain ViewCA94043 United States Institute of Astronomy University of Cambridge Madingley Road CambridgeCB3 0HA United Kingdom No. 1 Section 4 Roosevelt Road Taipei10617 Taiwan GRANTECAN Cuesta de San José s/n La Palma Breña BajaE-38712 Spain Instituto de Astrofísica de Canarias Tenerife La LagunaE-38205 Spain Instituto de Astronomía Universidad Nacional Autónoma de México Km. 107 Carr. Tijuana-Ensenada B. C. Ensenada22860 Mexico Departamento de Astrofísica Universidad de La Laguna Tenerife La La

Planetary nebulae (PNe), the ejected envelopes of red giant stars, provide us with a history of the last, mass-losing phases of 90% of stars initially more massive than the Sun. Here, we analyse James Webb Space Telescope (JWST) Early Release Observation (ERO) images of the PN NGC 3132. A structured, extended H2 halo surrounding an ionised central bubble is imprinted with spiral structures, likely shaped by a low-mass companion orbiting the central star at ∼40–60 AU. The images also reveal a mid-IR excess at the central star interpreted as a dusty disk, indicative of an interaction with another, closer companion. Including the previously known, A-type visual companion, the progenitor of the NGC 3132 PN must have been at least a stellar quartet. The JWST images allow us to generate a model of the illumination, ionisation and hydrodynamics of the molecular halo, demonstrating the power of JWST to investigate complex stellar outflows. Further, new measurements of the A-type visual companion allow us to derive the value for the mass of the progenitor of a central star to date with excellent precision: 2.86 ± 0.06 M☉. These results serve as pathfinders for future JWST observations of PNe providing unique insight into fundamental astrophysical processes including colliding winds, and binary star interactions, with implications for supernovae and gravitational wave systems. © 2023, CC BY.

关键词： Orbits

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Rare SLC13A1 variants associate with intervertebral disc disorder highlighting role of sulfate in disc pathology (vol 13, 634, 2022)

引用

NATURE COMMUNICATIONS 2022年第1期13卷 1-13页

作者： Bjornsdottir, Gyda Stefansdottir, Lilja Thorleifsson, Gudmar Sulem, Patrick Norland, Kristjan Ferkingstad, Egil Oddsson, Asmundur Zink, Florian Lund, Sigrun H. Nawaz, Muhammad S. Walters, G. Bragi Skuladottir, Astros Th. Gudjonsson, Sigurjon A. Einarsson, Gudmundur Halldorsson, Gisli H. Bjarnadottir, Valgerdur Sveinbjornsson, Gardar Helgadottir, Anna Styrkarsdottir, Unnur Gudmundsson, Larus J. Pedersen, Ole B. Hansen, Thomas Folkmann Werge, Thomas Banasik, Karina Troelsen, Anders Skou, Soren T. Thorner, Lise Wegner Erikstrup, Christian Nielsen, Kaspar Rene Mikkelsen, Susan Jonsdottir, Ingileif Bjornsson, Aron Olafsson, Ingvar H. Ulfarsson, Elfar Blondal, Josep Vikingsson, Arnor Brunak, Soren Ostrowski, Sisse R. Ullum, Henrik Thorsteinsdottir, Unnur Stefansson, Hreinn Gudbjartsson, Daniel F. Thorgeirsson, Thorgeir E. Stefansson, Kari deCODE Genetics/Amgen Inc. Reykjavik Iceland (GRID:grid.421812.c) (ISNI:0000 0004 0618 6889) deCODE Genetics/Amgen Inc. Reykjavik Iceland (GRID:grid.421812.c) (ISNI:0000 0004 0618 6889) University of Iceland Faculty of Medicine School of Health Sciences Reykjavik Iceland (GRID:grid.14013.37) (ISNI:0000 0004 0640 0021) deCODE Genetics/Amgen Inc. Reykjavik Iceland (GRID:grid.421812.c) (ISNI:0000 0004 0618 6889) University of Iceland School of Engineering and Natural Sciences Reykjavik Iceland (GRID:grid.14013.37) (ISNI:0000 0004 0640 0021) Landspitali University Hospital Reykjavik Iceland (GRID:grid.410540.4) (ISNI:0000 0000 9894 0842) Zealand University Hospital Department of Clinical Immunology Køge Denmark (GRID:grid.512923.e) (ISNI:0000 0004 7402 8188) University of Copenhagen Department of Clinical Medicine Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Rigshospitalet-Glostrup Danish Headache Center Dept. Neurology Glostrup Denmark (GRID:grid.475435.4) University of Copenhagen Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Department of Clinical Medicine Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Copenhagen University Hospital Institute of Biological Psychiatry Mental Health Services Copenhagen Denmark (GRID:grid.4973.9) (ISNI:0000 0004 0646 7373) University of Copenhagen Lundbeck Foundation for GeoGenetics GLOBE Institute Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Department of Clinical Medicine Faculty of Health and Medical Sciences Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001

Back pain is a common and debilitating disorder with largely unknown underlying biology. Here we report a genome-wide association study of back pain using diagnoses assigned in clinical practice; dorsalgia (119,100 cases, 909,847 controls) and intervertebral disc disorder (IDD) (58,854 cases, 922,958 controls). We identify 41 variants at 33 loci. The most significant association (ORIDD = 0.92, P = 1.6 × 10−39; ORdorsalgia = 0.92, P = 7.2 × 10−15) is with a 3’UTR variant (rs1871452-T) in CHST3, encoding a sulfotransferase enzyme expressed in intervertebral discs. The largest effects on IDD are conferred by rare (MAF = 0.07 − 0.32%) loss-of-function (LoF) variants in SLC13A1, encoding a sodium-sulfate co-transporter (LoF burden OR = 1.44, P = 3.1 × 10−11); variants that also associate with reduced serum sulfate. Genes implicated by this study are involved in cartilage and bone biology, as well as neurological and inflammatory *** is known about the biology of back pain, a leading cause of disability. Here the authors report 30 new back pain loci, implicating genes involved in cartilage/bone biology, as well as neurological and inflammatory processes.

关键词： Bone Genome-wide association studies Pain

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Collaborative workspaces for pathway curation

Collaborative workspaces for pathway curation

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2016 Joint International Conference on Biological Ontology and BioCreative - Food, Nutrition, Health and Environment for the 9 Billion, ICBO-BioCreative 2016

作者： Durupinar-Babur, Funda Siper, Metin Can Dogrusoz, Ugur Bahceci, Istemi Babur, Ozgun Demir, Emek Oregon Health and Science University Computational Biology Program PortlandOR United States Bilkent University Dept. of Comp Engineering Ankara Turkey

We present a web based visual biocuration workspace, focusing on curating detailed mechanistic pathways. It was designed as a flexible platform where multiple humans, NLP and AI agents can collaborate in real-time on a common model using an event driven API. We will use this platform for exploring disruptive technologies that can scale up biocuration such as NLP, human-computer collaboration, crowd-sourcing, alternative publishing and gamification. As a first step, we are designing a pilot to include an author-curation step into the scientific publishing, where the authors of an article create formal pathway fragments representing their discovery- heavily assisted by computer agents. We envision that this "microcuration" use-case will create an excellent opportunity to integrate multiple NLP approaches and semi-automated curation. © 2016, CEUR-WS. All rights reserved.

关键词： Natural language processing systems

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Denoising in a Jiffy: A fast and GPU friendly algorithm for image denoising

Denoising in a Jiffy: A fast and GPU friendly algorithm for ...

引用

International Conference on Signal Processing and Communications, SPCOM

作者： Nithish Divakar R. Venkatesh Babu Dept. of Computational and Data Sciences Indian Institute of Science Bangalore

ISBN: (纸本)9781509017478

Non-local means is a state-of-the-art image denoising algorithm which uses weighted contribution of similar patches to denoise images. But its asymptotic complexity upper bounds the degree to which the algorithm can be accelerated. In this paper, we present an approximate version of the same which uses Locality Sensitive Hashing to reduce the complexity. Finally, we show that the new proposed approximate algorithm is magnitudes faster that the original algorithm while maintaining 90% of the denoising performance. A GPU implementation of the algorithm which obtains over 100 times speedup is also presented.

关键词： Approximation algorithms Graphics processing units Noise reduction Instruction sets Computer architecture Image denoising Acceleration

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Explaining first impressions: Modeling, recognizing, and explaining apparent personality from videos

arXiv

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arXiv 2018年

作者： Escalante, Hugo Jair Kaya, Heysem Salah, Albert Ali Escalera, Sergio Güçlütürk, Yağmur Güçlü, Umut Baró, Xavier Guyon, Isabelle Jacques, Julio Madadi, Meysam Ayache, Stephane Viegas, Evelyne Gürpınar, Furkan Wicaksana, Achmadnoer Sukma Liem, Cynthia C.S. van Gerven, Marcel A.J. van Lier, Rob INAOE Mexico ChaLearn United States Namık Kemal University Department of Computer Engineering Turkey Bogaziçi University Dept. of Computer Engineering Turkey and Nagoya University FCVRC Japan Bogaziçi University Computational Science and Engineering Turkey University of Barcelona and Computer Vision Center Spain Computer Vision Center Spain Radboud University Donders Institute for Brain Cognition and Behaviour Nijmegen Netherlands Universitat Oberta de Catalunya and Computer Vision Center Spain UPSud/INRIA Université Paris-Saclay France Aix Marseille Univ CNRS LIF Marseille France Microsoft Research United States Multimedia Computing Group Delft University of Technology Delft Netherlands

Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field. Copyright © 2018, The Authors. All rights reserved.

关键词： Decision support systems

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Sensor network based solar forecasting using a local vector autoregressive ridge framework 16

Sensor network based solar forecasting using a local vector ...

引用

31st Annual ACM Symposium on Applied Computing, SAC 2016

作者： Xu, Jin Yoo, Shinjae Heiser, John Kalb, Paul Electrical and Computer Engineering Dept. Stony Brook University United States Computational Science Center Brookhaven National Lab United States Environmental Sciences Dept. Brookhaven National Lab United States

ISBN: (纸本)9781450337397

The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset. © 2016 ACM.

关键词： Sensor networks

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MHONGOOSE — A MeerKAT nearby galaxy H i survey

arXiv

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arXiv 2024年

作者： de Blok, W.J.G. Healy, J. Maccagni, F.M. Pisano, D.J. Bosma, A. English, J. Jarrett, T. Marasco, A. Meurer, G.R. Veronese, S. Bigiel, F. Chemin, L. Fraternali, F. Holwerda, B.W. Kamphuis, P. Klöckner, H.R. Kleiner, D. Leroy, A.K. Mogotsi, M. Oman, K.A. Schinnerer, E. Verdes-Montenegro, L. Westmeier, T. Wong, O.I. Zabel, N. Amram, P. Carignan, C. Combes, F. Brinks, E. Dettmar, R.J. Gibson, B.K. Jozsa, G.I.G. Koribalski, B.S. McGaugh, S.S. Oosterloo, T.A. Spekkens, K. Schröder, A.C. Adams, E.A.K. Athanassoula, E. Bershady, M.A. Beswick, R.J. Blyth, S. Elson, E.C. Frank, B.S. Heald, G. Henning, P.A. Kurapati, S. Loubser, S.I. Lucero, D. Meyer, M. Namumba, B. Oh, S.-H. Sardone, A. Sheth, K. Smith, M.W.L. Sorgho, A. Walter, F. Williams, T. Woudt, P.A. Zijlstra, A. Oude Hoogeveensedijk 4 Dwingeloo7991 PD Netherlands Dept. of Astronomy Univ. of Cape Town Private Bag X3 Rondebosch7701 South Africa Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands INAF Osservatorio Astronomico di Cagliari Via della Scienza 5 CA Selargius09047 Italy Green Bank Observatory 155 Observatory Road Green BankWV24944 United States Aix Marseille Univ CNRS CNES LAM Marseille France Department of Physics and Astronomy University of Manitoba Manitoba WinnipegR3T 2N2 Canada INAF – Padova Astronomical Observatory Vicolo dell’Osservatorio 5 PadovaI-35122 Italy International Centre for Radio Astronomy Research The University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia Argelander-Institut für Astronomie Auf dem Hügel 71 Bonn53121 Germany Instituto de Astrofísica Departamento de Ciencias Físicas Universidad Andrés Bello Fernandez Concha 700 Las Condes Santiago Chile Department of Physics and Astronomy University of Louisville 102 Natural Science Building LouisvilleKY40292 United States Universitätsstrasse 150 Bochum44801 Germany Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany Department of Astronomy The Ohio State University 140 West 18th Avenue ColumbusOH43210 United States Center for Cosmology and Astroparticle Physics 191 West Woodruff Avenue ColumbusOH43210 United States P.O. Box 9 Cape Town Observatory7935 South Africa P.O. Box 9 Cape Town Observatory7935 South Africa Institute for Computational Cosmology Department of Physics Durham University South Road DurhamDH1 3LE United Kingdom Centre for Extragalactic Astronomy Department of Physics Durham University South Road DurhamDH1 3LE United Kingdom Max Planck Institute for Astronomy Königstuhl 17 Heidelberg69117 Germany Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain CSIRO Space & Astronomy PO Box 1130 Bent

The MHONGOOSE (MeerKAT H i Observations of Nearby Galactic Objects: Observing Southern Emitters) survey maps the distribution and kinematics of the neutral atomic hydrogen (H i) gas in and around 30 nearby star-forming spiral and dwarf galaxies to extremely low H i column densities. The H i column density sensitivity (3σ over 16 km s−1) ranges from ∼ 5 · 1017 cm−2 at 90′′ resolution to ∼ 4 · 1019 cm−2 at the highest resolution of 7′′. The H i mass sensitivity (3σ over 50 km s−1) is ∼ 5.5 · 105 M☉ at a distance of 10 Mpc (the median distance of the sample galaxies). The velocity resolution of the data is 1.4 km s−1. One of the main science goals of the survey is the detection of cold accreting gas in the outskirts of the sample galaxies. The sample was selected to cover a range in H i masses from 107 M☉ to almost 1011 M☉ in order to optimally sample possible accretion scenarios and environments. The distance to the sample galaxies ranges from 3 to 23 Mpc. In this paper, we present the sample selection, survey design, and observation and reduction procedures. We compared the integrated H i fluxes based on the MeerKAT data with those derived from single-dish measurement and find good agreement, indicating that our MeerKAT observations are recovering all flux. We present H i moment maps of the entire sample based on the first ten percent of the survey data, and find that a comparison of the zeroth- and second-moment values shows a clear separation in the physical properties of the H i between areas with star formation and areas without related to the formation of a cold neutral medium. Finally, we give an overview of the H i-detected companion and satellite galaxies in the 30 fields, five of which have not previously been cataloged. We find a clear relation between the number of companion galaxies and the mass of the main target galaxy. © 2024, CC BY.

关键词： Galaxies

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An efficient distributed learning algorithm based on effective local functional approximations

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2018年第1期19卷

作者： Dhruv Mahajan Nikunj Agrawal S. Sathiya Keerthi Sundararajan Sellamanickam Léon Bottou Facebook AI Menlo Park CA Indian Institute of Technology Dept. of Computer Science & Engineering Kanpur India Office Data Science Group Microsoft Mountain View CA Microsoft Research Bangalore India Facebook AI Research New York NY

Scalable machine learning over big data is an important problem that is receiving a lot of attention in recent years. On popular distributed environments such as Hadoop running on a cluster of commodity machines, communication costs are substantial and algorithms need to be designed suitably considering those costs. In this paper we give a novel approach to the distributed training of linear classifiers (involving smooth losses and L2 regularization) that is designed to reduce the total communication costs. At each iteration, the nodes minimize locally formed approximate objective functions; then the resulting minimizers are combined to form a descent direction to move. Our approach gives a lot of freedom in the formation of the approximate objective function as well as in the choice of methods to solve them. The method is shown to have O(log(1/ε)) time convergence. The method can be viewed as an iterative parameter mixing method. A special instantiation yields a parallel stochastic gradient descent method with strong convergence. When communication times between nodes are large, our method is much faster than the Terascale method (Agarwal et al., 2011), which is a state of the art distributed solver based on the statistical query model (Chu et al., 2006) that computes function and gradient values in a distributed fashion. We also evaluate against other recent distributed methods and demonstrate superior performance of our method.

关键词： L2 regularization distributed learning example partitioning

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