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Whole-disk sampling of molecular clouds in M83

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arXiv 2024年

作者： Hirota, Akihiko Koda, Jin Egusa, Fumi Sawada, Tsuyoshi Sakamoto, Kazushi Heyer, Mark Lee, Amanda M. Maeda, Fumiya Boissier, Samuel Calzetti, Daniela Elmegreen, Bruce G. Harada, Nanase Ho, Luis C. Kobayashi, Masato I.N. Kuno, Nario Madore, Barry F. Martín, Sergio Meyer, Jennifer Donovan Muraoka, Kazuyuki Watanabe, Yoshimasa Joint ALMA Observatory Alonso de Córdova 3107 Vitacura Santiago763-0355 Chile National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Physics and Astronomy Stony Brook University Stony BrookNY11794-3800 United States Institute of Astronomy Graduate School of Science The University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015 Japan Academia Sinica Institute of Astronomy and Astrophysics Taipei10617 Taiwan Department of Astronomy University of Massachusetts Amherst 710 North Pleasant Street AmherstMA01003 United States Research Center for Physics and Mathematics Osaka Electro-Communication University 18-8 Hatsucho Osaka Neyagawa572-8530 Japan Aix Marseille Univ. CNRS CNES Laboratoire d'Astrophysique de Marseille Marseille France Katonah NY10536 United States 2-21-1 Osawa Mitaka Tokyo181-1855 Japan Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Department of Astronomy School of Physics Peking University Beijing100871 China Universität zu Köln I. Physikalisches Institut Zülpicher Str. 77 KölnD-50937 Germany Division of Physics Faculty of Pure and Applied Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8571 Japan Tomonaga Center for the History of the Universe University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8571 Japan The Observatories Carnegie Institution for Science 813 Santa Barbara Street PasadenaCA91101 United States Department of Astronomy and Astrophysics University of Chicago 5640 S. Ellis Ave. ChicagoIL60637 United States European Southern Observatory Alonso de Córdova 3107 Vitacura Santiago763-0355 Chile National Radio Astronomy Observatory 520 Edgemont Road CharlottesvilleVA22903 United States Department of Physics Graduate School of Science Osaka Metropolitan University 1-1 Gakuen-cho Naka-ku Osaka Sakai599-8531 Japan Materials Science and Engineering College of Engineering Shibaura Institute of T

We present a catalog of clouds identified from the 12CO (1-0) data of M83, which was observed using Atacama Large Millimeter/submillimeter Array (ALMA) with a spatial resolution of ∼46 pc and a mass sensitivity of ∼104 M☉ (3 σ). The almost full-disk coverage and high sensitivity of the data allowed us to sample 5724 molecular clouds with a median mass of ∼ 1.9 × 105 M☉, which is comparable to the most frequently sampled mass of Giant Molecular Clouds (GMCs) by surveys in the Milky Way. About 60 percent of the total CO luminosity in M83's disk arises from clouds more massive than 106 M☉. Such massive clouds comprise 16 percent of the total clouds in number and tend to concentrate toward the arm, bar, and center, while smaller clouds are more prevalent in inter-arm regions. Most > 106 M☉ clouds have peak brightness temperatures Tpeak above 2 K with the current resolution. Comparing the observed cloud properties with the scaling relations determined by Solomon et al. 1987 (S87), Tpeak> 2 K clouds follow the relations, but Tpeakvir ∼ 2.7) and low surface mass densities (median Σ ∼22 M☉ pc−2) for the entire cloud samples, which are similar to values found for the Milky Way clouds by Rice et al. (2016) and Miville-Deschenes et al. (2017). However, once beam dilution is taken into account, the observed αvir and Σ for a majority of the clouds (mostly Tpeak ☉ pc−2 and αvir of ∼1, which are similar to the clouds of S87. © 2024, CC BY.

关键词： Luminance

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

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SSRN

SSRN 2023年

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

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

关键词： Forecasting

JWST and ALMA Multiple-Line Study in and around a Galaxy at z = 8.496: Optical to FIR Line Ratios and the Onset of an Outflow Promoting Ionizing Photon Escape

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arXiv 2022年

作者： Fujimoto, Seiji Ouchi, Masami Nakajima, Kimihiko Harikane, Yuichi Isobe, Yuki Brammer, Gabriel Oguri, Masamune Giménez-Arteaga, Clara Heintz, Kasper E. Kokorev, Vasily Bauer, Franz E. Ferrara, Andrea Kojima, Takashi Lagos, Claudia del P. Laura, Sommovigo Schaerer, Daniel Shimasaku, Kazuhiro Hatsukade, Bunyo Kohno, Kotaro Sun, Fengwu Valentino, Francesco Watson, Darach Fudamoto, Yoshinobu Inoue, Akio K. González-López, Jorge Koekemoer, Anton M. Knudsen, Kirsten Lee, Minju M. Magdis, Georgios E. Richard, Johan Strait, Victoria B. Sugahara, Yuma Tamura, Yoichi Toft, Sune Umehata, Hideki Walth, Gregory Department of Astronomy The University of Texas at Austin AustinTX78712 United States Denmark Niels Bohr Institute University of Copenhagen Lyngbyvej 2 Copenhagen ØDK2100 Denmark National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwanoha Chiba Kashiwa277-8582 Japan University of Tokyo Chiba Kashiwa277-8583 Japan Department of Physics Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo Tokyo113-0033 Japan Center for Frontier Science Chiba University 1-33 Yayoi-cho Inage-ku Chiba263-8522 Japan Department of Physics Graduate School of Science Chiba University 1-33 Yayoi-Cho Inage-Ku Chiba263-8522 Japan Niels Bohr Institute University of Copenhagen Blegdamsvej 17 Copenhagen ØDK2100 Denmark Niels Bohr Institute University of Copenhagen Jagtvej 128 Copenhagen NDK-2200 Denmark Instituto de Astrofísica Facultad de Física Pontificia Universidad Católica de Chile Campus San Joaquín Av. Vicuña Mackenna 4860 Macul Santiago7820436 Chile Centro de Astroingeniería Facultad de Física Pontificia Universidad Católica de Chile Campus San Joaquín Av. Vicuña Mackenna 4860 Macul Santiago7820436 Chile Millennium Institute of Astrophysics Nuncio Monseñor Sótero Sanz 100 Of 104 Providencia Santiago Chile Scuola Normale Superiore Piazza dei Cavalieri 7 Pisa50126 Italy M468 University of Western Australia 35 Stirling Hwy CrawleyWA6009 Australia Australia Observatoire de Genève Université de Genève Chemin Pegasi 51 Versoix1290 Switzerland CNRS IRAP 14 Avenue E. Belin Toulouse31400 France Department of Astronomy School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Institute of Astronomy Graduate School of Science The University of Tokyo 2-21-1 Osawa Mitaka Tokyo181-0015

We present ALMA deep spectroscopy for a lensed galaxy at zspec = 8.496 with log(Mstar/M) ∼ 7.8 whose optical nebular lines and stellar continuum are detected by JWST/NIRSpec and NIRCam Early Release Observations in SMACS0723. Our ALMA spectrum shows [O iii]88 µm and [C ii]158 µm line detections at 4.0σ and 4.5σ, respectively. The redshift and position of the [O iii] line coincide with those of the JWST source, while the [C ii] line is blue-shifted by 90 km s−1 with a spatial offset of 0′′ . 5 (≈ 0.5 kpc in source plane) from the centroid of the JWST source. The NIRCam F444W image, including [O iii]λ5007 and Hβ line emission, spatially extends beyond the stellar components by a factor of > 8. This indicates that the z = 8.5 galaxy has already experienced strong outflows as traced by extended [O iii]lambda5007 and offset [C ii] emission, which would promote ionizing photon escape and facilitate reionization. With careful slit-loss corrections and removals of emission spatially outside the galaxy, we evaluate the [O iii]88µm/λ5007 line ratio, and derive the electron density ne by photoionization modeling to be 220+230−130 cm−3, which is comparable with those of z ∼ 2–3 galaxies. We estimate an [O iii]88µm/[C ii]158µm line ratio in the galaxy of > 4, as high as those of known z ∼6–9 galaxies. This high [O iii]88 µm/[C ii]158 µm line ratio is generally explained by the high ne as well as the low metallicity (Zgas/Z = 0.04+0.02−0.02), high ionization parameter (log U > −2.27), and low carbon-to-oxygen abundance ratio (log(C/O) = [−0.52: −0.24]) obtained from the JWST/NIRSpec data;further [C ii] follow-up observations will constrain the covering fraction of photodissociation regions. © 2022, CC BY.

关键词： Galaxies

Towards Neural Charged Particle Tracking in Digital Tracking Calorimeters with Reinforcement Learning

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TechRxiv

TechRxiv 2022年

作者： Kortus, Tobias Keidel, Ralf Gauger, Nicolas R. Aehle, Max Alme, Johan Barnaföldi, Gergely Gábor Bodova, Tea Borshchov, Vyacheslav van den Brink, Anthony Chaar, Mamdouh Eikeland, Viljar Feofilov, Gregory Garth, Christoph Genov, Georgi Grøttvik, Ola Helstrup, Håvard Igolkin, Sergey Keidel, Ralf Kobdaj, Chinorat Leonhardt, Viktor Mehendale, Shruti Mulawade, Raju Ningappa Odland, Odd Harald O’Neill, George Papp, Gábor Peitzmann, Thomas Pettersen, Helge Egil Seime Piersimoni, Pierluigi Protsenko, Maksym Rauch, Max Rehman, Attiq Ur Richter, Matthias Röhrich, Dieter Santana, Joshua Schilling, Alexander Seco, Joao Songmoolnak, Arnon Sudár, Ákos Sølie, Jarle Rambo Tambave, Ganesh Tymchuk, Ihor Ullaland, Kjetil Varga-Kofarago, Monika Volz, Lennart Wagner, Boris Wendzel, Steffen Wiebel, Alexander Xiao, RenZheng Yang, Shiming Yokoyama, Hiroki Zillien, Sebastian University of Applied Sciences Worms Worms67549 Germany Chair for Scientific Computing TU Kaiserslautern Kaiserslautern67663 Germany Department of Physics and Technology University of Bergen Bergen5007 Norway Wigner Research Centre for Physics Budapest Hungary Kharkiv Ukraine Institute for Subatomic Physics Utrecht University/Nikhef Utrecht Netherlands St. Petersburg University St. Petersburg Russia Scientific Visualization Lab TU Kaiserslautern Kaiserslautern67663 Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway Institute of Science Suranaree University of Technology Nakhon Ratchasima Thailand Department of Oncology and Medical Physics Haukeland University Hospital Bergen5021 Norway Institute for Physics Eötvös Loránd University 1/A Pázmány P. Sétány BudapestH-1117 Hungary UniCamillus Saint Camillus International University of Health Sciences Rome Italy Department of Physics University of Oslo Oslo0371 Norway Department of Biomedical Physics in Radiation Oncology DKFZ—German Cancer Research Center Heidelberg Germany Department of Physics and Astronomy Heidelberg University Heidelberg Germany Budapest University of Technology and Economics Budapest Hungary Department of Diagnostic Physics Division of Radiology and Nuclear Medicine Oslo University Hospital Oslo Norway Bhubaneswar India Biophysics GSI Helmholtz Center for Heavy Ion Research GmbH Darmstadt Germany Department of Medical Physics and Biomedical Engineering University College London London United Kingdom College of Mechanical & Power Engineering China Three Gorges University Yichang China

We propose a novel technique for reconstructing charged particles in digital tracking calorimeters using reinforcement learning aiming to benefit from the rapid progress and success of neural network architectures without the dependency on simulated or manually-labeled data. Here we optimize by trial-and-error a behavior policy acting as an approximation to the full combinatorial optimization problem, maximizing the physical plausibility of sampled trajectories. In modern processing pipelines used in high energy physics and related applications, tracking plays an essential role allowing to identify and follow charged particle trajectories traversing particle detectors. Due to the high multiplicity of charged particles and their physical interactions, randomly deflecting the particles, the reconstruction is a challenging undertaking, requiring fast, accurate and robust algorithms. Our approach works on graph-structured data, capturing track hypotheses through edge connections between particles in the detector layers. We demonstrate in a comprehensive study on simulated data for a particle detector used for proton computed tomography, the high potential as well as the competitiveness of our approach compared to a heuristic search algorithm and a model trained on ground truth. Finally, we point out limitations of our approach, guiding towards a robust foundation for further development of reinforcement learning based tracking. © 2022, CC BY.

关键词： Reinforcement learning

MODL-41. LOSS OF DISTAL CHROMOSOME 6Q AND ASSOCIATEDPDE10AINDUCES AN AGGRESSIVE GLIOMA PHENOTYPE BY FACILITATING PRONEURAL TO MESENCHYMAL TRANSITION

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Neuro-Oncology 2023年第SUPPLEMENT_5期25卷 v307–v308页

作者： Nuechterlein, Nicholas Shelbourn, Allison Alvinez, Neriah Szulzewsky, Frank Arora, Sonali Paddison, Patrick Merino-Galan, Leyre Pattwell, Siobhan Sulman, Erik Jung, Mi-Yeon Brown, Desmond Banasavadi-Siddegowda, Yeshavanth Kumar Tang, Kayen Jackson, Sadhana Stoica, Stefan Chittaboina, Prashant Patel, Anoop Holland, Eric Cimino, Patrick Shapiro, Linda Surgical Neurology Branch National Institutes of Neurological Disorders and Stroke NIH Bethesda MD USA Division of Human Biology Fred Hutchinson Cancer Center Seattle WA USA Ben Towne Center for Childhood Cancer Research Seattle Children's Research Institute Seattle WA USA New York University Grossman School of Medicine Laura and Isaac Perlmutter Cancer Center at NYU Langone Health New York NY USA Neurosurgical Oncology Unit Surgical Neurology Branch NINDS NIH Bethesda MD USA Department of Neurosurgery Duke University Durham NC USA Department of Computer Science and Engineering University of Washington Seattle WA USA

Somatic copy number alterations (SCNAs) are common in glioblastoma, with some SCNAs having prognostic implications. Identifying and characterizing SCNA-related genes acting as oncogenes or tumor suppressors may potentially provide new therapeutic avenues for gliomas. In this study, we sought to uncover novel SCNA-related genes by employing an unbiased log rank analysis screen of genomic regions associated with glioblastoma survival. We identified 6q27 loss as an independent poorly-prognostic SCNA across three separate glioblastoma cohorts. Anin vitroCRISPR screen using human neural stem cells identified five genes in the 6q27 region that caused increased cellular proliferation. One of these genes, phosphodiesterase 10A (PDE10A), had a gene dosage effect on expression and decreased expression in human and mouse glioma tumor tissue compared to normal brain, which are both common tumor suppressor phenotypes. Knockdown of Pde10a in mouse neural stem cellsin vitrovalidated the human cell CRISPR screen phenotype. Suppression of Pde10a in RCAS/Tva mouse gliomain vivocaused decreased survival with increased tumor grade, mitotic activity, necrosis, Vegfa expression, and AKT/pS6 signaling. Single cell RNA-sequencing of mouse gliomas revealed that Pde10a suppression results in a shift from proneural to mesenchymal-like state. In human glioma tissue, PDE10A loss is also associated with an increased mesenchymal signature. Top activated pathways in Pde10a suppressed gliomas involved cell-cell interaction, including upregulation of cell adhesion molecules and downregulation of focal adhesion complex disassembly genes. Functional analysisin vitroconfirmed that Pde10a suppression results in increased extracellular matrix adhesion and decreased migration. Relevant to standard glioblastoma treatment, PDE10A loss was associated with decreasedMGMTpromoter methylation, decreased DNA methyltransferase gene expression, and it potentiated radiation resistancein vitro. In summary, we leverag

关键词：

Performance analysis of fog-aided D2D networks with multicast-based opportunistic content delivery

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arXiv 2019年

作者： Song, Xiaoshi Yuan, Mengying Zhou, Huan Zhang, Haijun The School of Computer Science and Engineering Northeastern University Shenyang110819 China The College of Computer and Information Technology China Three Gorges University Yichang443002 China The Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China

In this paper, we develop a comprehensive and tractable analytical framework based on stochastic geometry to evaluate the performance of large-scale fog-aided device-to-device (F-D2D) networks with opportunistic content multicasting. As a part of the analysis, to resolve the contentions of file requests from the cache-incapable conventional user equipments (C-UEs), two simple yet typical candidate file selection schemes for cache-enabled fog user equipments (F-UEs), namely the random file selection (RFS) scheme and the most requested file selection (MRFS) scheme, are considered. Further, to suppress the harmful interference among the concurrent transmissions of F-UEs, a multicast-based opportunistic content delivery strategy is proposed by exploring the idea of opportunistic spectrum access (OSA). Assuming decentralized probabilistic caching, we first derive the activation probability of F-UEs. Then, by adopting an appropriate approximation, the cache-hit probability, the coverage probability, and thereby the successful content delivery probability (SCDP) of the F-D2D network are evaluated. We also develop an iterative algorithm based on the gradient projection method to obtain a suboptimal caching policy for the maximization of SCDP. Extensive simulation and numerical results are presented to verify our analysis and demonstrate the superior performance of the proposed multicast-based opportunistic content delivery strategy. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

Author Correction: Hidden chemical order in disordered Ba7Nb4MoO20 revealed by resonant X-ray diffraction and solid-state NMR

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Nature Communications 2023年第1期14卷 1页

作者： Yuta Yasui Kotaro Fujii Yuichi Sakuda Masatomo Yashima Masataka Tansho Atsushi Goto Shinobu Ohki Yuuki Mogami Takahiro Iijima Shintaro Kobayashi Shogo Kawaguchi Keiichi Osaka Kazutaka Ikeda Toshiya Otomo Department of Chemistry School of Science Tokyo Institute of Technology 2-12-1-W4-17 O-okayama Meguro-ku Tokyo 152-8551 Japan NMR Station National Institute for Materials Science (NIMS) 3-13 Sakura Tsukuba Ibaraki 305-0003 Japan Institute of Arts and Sciences Yamagata University 1-4-12 Kojirakawa-machi Yamagata Yamagata 990-8560 Japan Diffraction and Scattering Division Japan Synchrotron Radiation Research Institute (JASRI) SPring-8 1-1-1 Kouto Sayo-cho Sayo-gun Hyogo 679-5198 Japan Industrial Application and Partnership Division Japan Synchrotron Radiation Research Institute (JASRI) SPring-8 1-1-1 Kouto Sayo-cho Sayo-gun Hyogo 679-5198 Japan Institute of Materials Structure Science High Energy Accelerator Research Organization (KEK) 203-1 Shirakata Tokai Ibaraki 319-1106 Japan J-PARC Center High Energy Accelerator Research Organization (KEK) 2-4 Shirakata-Shirane Tokai Ibaraki 319-1106 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies 203-1 Shirakata Tokai Ibaraki 319-1106 Japan Graduate School of Science and Engineering Ibaraki University 162-1 Shirakata Tokai Ibaraki 319-1106 Japan

The Palomar twilight survey of 'Ayló'chaxnim, Atiras, and comets

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arXiv 2024年

作者： Bolin, B.T. Masci, F.J. Coughlin, M.W. Duev, D.A. Ivezić, Ž. Jones, R.L. Yoachim, P. Ahumada, T. Bhalerao, V. Choudhary, H. Contreras, C. Cheng, Y.-C. Copperwheat, C.M. Deshmukh, K. Fremling, C. Granvik, M. Hardegree-Ullman, K.K. Ho, A.Y.Q. Jedicke, R. Kasliwal, M. Kumar, H. Lin, Z.-Y. Mahabal, A. Monson, A. Neill, J.D. Nesvorný, D. Perley, D.A. Purdum, J.N. Quimby, R. Serabyn, E. Sharma, K. Swain, V. Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States IPAC California Institute of Technology PasadenaCA91125 United States School of Physics and Astronomy University of Minnesota Twin Cities MinneapolisMN55455 United States Weights & Biases San FranciscoCA94103 United States Department of Astronomy University of Maryland College ParkMD20740 United States Department of Physics Indian Institute of Technology Bombay Powai400 076 India Las Campanas Observatory Casilla 601 La Serena Chile Department of Physics National Taiwan Normal University Taipei City116325 Taiwan Center of Astronomy and Gravitation National Taiwan Normal University Taipei City116325 Taiwan Astrophysics Research Institute IC2 Liverpool Science Park 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Institute of Astronomy Katholieke Universiteit Leuven Leuven3000 Belgium Department of Metallurgical Engineering and Materials Science Indian Institute of Technology Bombay Powai400 076 India Caltech Optical Observatories California Institute of Technology PasadenaCA91125 United States Department of Physics University of Helsinki Helsinki00014 Finland Asteroid Engineering Lab Luleå University of Technology KirunaSE-981 28 Sweden Steward Observatory University of Arizona TucsonAZ85721 United States Department of Astronomy Cornell University IthacaNY14853 United States Department of Astronomy DiRAC Institute University of Washington SeattleWA98195 United States Institute for Astronomy University of Hawai’i 2680 Woodlawn Dr. HonoluluHI96822 United States Aerotek and Rubin Observatory TucsonAZ United States Department of Space Studies Southwest Research Institute BoulderCO80302 United States Department of Astronomy San Diego State University 5500 Campanile Dr San DiegoCA92182 United States The University of Tokyo In

Near-sun sky twilight observations allow for the detection of asteroids interior to the orbit of Venus (Aylos) and the Earth (Atiras) and comets. We present the results of observations with the Palomar 48-inch telescope (P48)/Zwicky Transient Facility (ZTF) camera in 30 s r-band exposures taken during evening astronomical twilight from 2019 Sep 20 to 2022 March 7 and during morning astronomical twilight sky from 2019 Sep 21 to 2022 Sep 29. More than 21,940 exposures were taken in evening astronomical twilight within 31◦ and 66◦ from the Sun with an r-band limiting magnitude between 18.0 and 20.8 (5th to 95th percentile), and more than 24,370 exposures were taken in morning astronomical twilight within 31◦ and 65◦ from the Sun with an r-band limiting magnitude between 18.2 and 20.9 (5th to 95th percentile). The morning and evening twilight pointings show a slight seasonal dependence in limiting magnitude and ability to point closer towards the Sun, with limiting magnitude improving by 0.5 magnitudes during the summer months and Sun-centric angular distances as small as 31-32◦ during the spring and fall months. In total, the one Aylo, (594913) 'Ayló'chaxnim, and 4 Atiras, 2020 OV1, 2021 BS1, 2021 PB2, and 2021 VR3, were discovered in evening and morning twilight observations. Additional twilight survey discoveries also include 6 long period comets: C/2020 T2, C/2020 V2, C/2021 D2, C/2021 E3, C/2022 E3 and C/2022 P3, and two short period comets: P/2021 N1 and P/2022 P2 using deep learning comet detection pipelines. The P48/ZTF twilight survey also recovered 11 known Atiras, one Aylo, three short period comes, two long period comets, one interstellar object, 45,536 Main Belt asteroids, and 265 near-Earth objects. Additionally, observations from the GROWTH network of telescopes were used to recover the Aylo, Atira, and comet discoveries made during the ZTF twilight survey. Lastly, we discuss the future twilight surveys for the discovery of Aylos such as with the Vera Rub

关键词： Asteroids

Computing Image Intersection and Union Regions for Drosophila Neurons Based on Multi-core CPUs 16th

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Computing Image Intersection and Union Regions for Drosophil...

16th International Symposium on Pervasive Systems, Algorithms and Networks, I-SPAN 2019

作者： Guo, Ming-Yan Cheng, Hui-Jun Lin, Chun-Yuan Lin, Yen-Jen Chiang, Ann-Shyn Department of Computer Science and Information Engineering Chang Gung University Taoyuan33302 Taiwan AI Innovation Research Center Chang Gung University Taoyuan33302 Taiwan Division of Rheumatology Allergy and Immunology Chang Gung Memorial Hospital Taoyuan33302 Taiwan National Center for High-Performance Computing Hsinchu30076 Taiwan Brain Research Center National Tsing Hua University Hsinchu30013 Taiwan Institute of Biotechnology and Department of Life Science National Tsing Hua University Hsinchu30013 Taiwan Kavli Institute for Brain and Mind University of California at San Diego La Jolla San DiegoCA United States Department of Biomedical Science and Environmental Biology Kaohsiung Medical University Kaohsiung80708 Taiwan Genomics Research Center Academia Sinica NankangTaipei11529 Taiwan

ISBN: (纸本)9783030301422

With more and more Drosophila Driver and Neuron images, it is an important work to find the similarity relationships among them as the functional inference. There is a general problem that how to find a Drosophila Driver image, which can cover a set of Drosophila Neuron images. In order to solve this problem, the intersection/union region for a set of Drosophila Neuron images should be computed at first, then a comparison work is used to calculate the similarities between the region and Drosophila Driver image(s). In this paper, three encoding schemes, namely Integer, Boolean, Decimal, are proposed to encode each Drosophila Driver and Neuron image as a one-dimensional structure, respectively. Then, the intersection/union region from these images can be computed by using the various operations, such as Boolean operators and lookup-table search method. Finally, the comparison work is done as the union region computation, and the similarity score can be calculated by the definition of Tanimoto coefficient. The above methods for the region computation are also implemented in the multi-core CPUs environment with the OpenMP. From the experimental results, in the encoding phase, the performance by Boolean scheme is the best than that by others;in the region computation phase, the performance by Decimal is the best when the number of images is large. The speedup ratio can achieve 13 based on 16 CPUs. © 2019, Springer Nature Switzerland AG.

关键词： Neurons