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Institution

Max Planck Society

NonprofitMunich, Germany
About: Max Planck Society is a nonprofit organization based out in Munich, Germany. It is known for research contribution in the topics: Galaxy & Population. The organization has 148289 authors who have published 406224 publications receiving 19522268 citations. The organization is also known as: Max-Planck-Gesellschaft zur Förderung der Wissenschaften e. V. & MPG.


Papers
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Journal ArticleDOI
13 Feb 2019-Nature
TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.
Abstract: Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.

2,014 citations

Journal ArticleDOI
29 Sep 2005-Nature
TL;DR: This work presents an autonomous ordering and assembly of atoms and molecules on atomically well-defined surfaces that combines ease of fabrication with exquisite control over the shape, composition and mesoscale organization of the surface structures formed.
Abstract: The fabrication methods of the microelectronics industry have been refined to produce ever smaller devices, but will soon reach their fundamental limits. A promising alternative route to even smaller functional systems with nanometre dimensions is the autonomous ordering and assembly of atoms and molecules on atomically well-defined surfaces. This approach combines ease of fabrication with exquisite control over the shape, composition and mesoscale organization of the surface structures formed. Once the mechanisms controlling the self-ordering phenomena are fully understood, the self-assembly and growth processes can be steered to create a wide range of surface nanostructures from metallic, semiconducting and molecular materials.

2,013 citations

Journal ArticleDOI
TL;DR: A focus of this review is nuclear export of messenger RNA, which apparently largely relies on export mediators distinct from importin beta-related factors.
Abstract: ▪ Abstract The compartmentation of eukaryotic cells requires all nuclear proteins to be imported from the cytoplasm, whereas, for example, transfer RNAs, messenger RNAs, and ribosomes are made in the nucleus and need to be exported to the cytoplasm. Nuclear import and export proceed through nuclear pore complexes and can occur along a great number of distinct pathways, many of which are mediated by importin β-related nuclear transport receptors. These receptors shuttle between nucleus and cytoplasm, and they bind transport substrates either directly or via adapter molecules. They all cooperate with the RanGTPase system to regulate the interactions with their cargoes. Another focus of our review is nuclear export of messenger RNA, which apparently largely relies on export mediators distinct from importin β-related factors. We discuss mechanistic aspects and the energetics of transport receptor function and describe a number of pathways in detail.

2,012 citations

Journal ArticleDOI
Anton M. Koekemoer1, Sandra M. Faber2, Henry C. Ferguson1, Norman A. Grogin1, Dale D. Kocevski2, David C. Koo2, Kamson Lai2, Jennifer M. Lotz1, Ray A. Lucas1, Elizabeth J. McGrath2, Sara Ogaz1, Abhijith Rajan1, Adam G. Riess3, S. Rodney3, L. G. Strolger4, Stefano Casertano1, Marco Castellano, Tomas Dahlen1, Mark Dickinson, Timothy Dolch3, Adriano Fontana, Mauro Giavalisco5, Andrea Grazian, Yicheng Guo5, Nimish P. Hathi6, Kuang-Han Huang1, Kuang-Han Huang3, Arjen van der Wel7, Hao Jing Yan8, Viviana Acquaviva9, David M. Alexander10, Omar Almaini11, Matthew L. N. Ashby12, Marco Barden13, Eric F. Bell14, Frédéric Bournaud15, Thomas M. Brown1, Karina Caputi16, Paolo Cassata5, Peter Challis17, Ranga-Ram Chary18, Edmond Cheung2, Michele Cirasuolo16, Christopher J. Conselice11, Asantha Cooray19, Darren J. Croton20, Emanuele Daddi15, Romeel Davé21, Duilia F. de Mello22, Loic de Ravel16, Avishai Dekel23, Jennifer L. Donley1, James Dunlop16, Aaron A. Dutton24, David Elbaz25, Giovanni Fazio12, Alexei V. Filippenko26, Steven L. Finkelstein27, Chris Frazer19, Jonathan P. Gardner22, Peter M. Garnavich28, Eric Gawiser9, Ruth Gruetzbauch11, Will G. Hartley11, B. Haussler11, Jessica Herrington14, Philip F. Hopkins26, J.-S. Huang29, Saurabh Jha9, Andrew Johnson2, Jeyhan S. Kartaltepe3, Ali Ahmad Khostovan19, Robert P. Kirshner12, Caterina Lani11, Kyoung-Soo Lee30, Weidong Li26, Piero Madau2, Patrick J. McCarthy6, Daniel H. McIntosh31, Ross J. McLure, Conor McPartland2, Bahram Mobasher32, Heidi Moreira9, Alice Mortlock11, Leonidas A. Moustakas18, Mark Mozena2, Kirpal Nandra33, Jeffrey A. Newman34, Jennifer L. Nielsen31, Sami Niemi1, Kai G. Noeske1, Casey Papovich27, Laura Pentericci, Alexandra Pope, Joel R. Primack2, Swara Ravindranath35, Naveen A. Reddy, Alvio Renzini, Hans Walter Rix7, Aday R. Robaina, David J. Rosario2, Piero Rosati7, S. Salimbeni5, Claudia Scarlata18, Brian Siana18, Luc Simard36, Joseph Smidt19, D. Snyder2, Rachel S. Somerville1, Hyron Spinrad26, Amber N. Straughn22, Olivia Telford34, Harry I. Teplitz18, Jonathan R. Trump2, Carlos J. Vargas9, Carolin Villforth1, C. Wagner31, P. Wandro2, Risa H. Wechsler37, Benjamin J. Weiner21, Tommy Wiklind1, Vivienne Wild, Grant W. Wilson5, Stijn Wuyts12, Min S. Yun5 
TL;DR: In this paper, the authors describe the Hubble Space Telescope imaging data products and data reduction procedures for the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS).
Abstract: This paper describes the Hubble Space Telescope imaging data products and data reduction procedures for the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS). This survey is designed to document the evolution of galaxies and black holes at z 1.5-8, and to study Type Ia supernovae at z > 1.5. Five premier multi-wavelength sky regions are selected, each with extensive multi-wavelength observations. The primary CANDELS data consist of imaging obtained in the Wide Field Camera 3 infrared channel (WFC3/IR) and the WFC3 ultraviolet/optical channel, along with the Advanced Camera for Surveys (ACS). The CANDELS/Deep survey covers ~125 arcmin2 within GOODS-N and GOODS-S, while the remainder consists of the CANDELS/Wide survey, achieving a total of ~800 arcmin2 across GOODS and three additional fields (Extended Groth Strip, COSMOS, and Ultra-Deep Survey). We summarize the observational aspects of the survey as motivated by the scientific goals and present a detailed description of the data reduction procedures and products from the survey. Our data reduction methods utilize the most up-to-date calibration files and image combination procedures. We have paid special attention to correcting a range of instrumental effects, including charge transfer efficiency degradation for ACS, removal of electronic bias-striping present in ACS data after Servicing Mission 4, and persistence effects and other artifacts in WFC3/IR. For each field, we release mosaics for individual epochs and eventual mosaics containing data from all epochs combined, to facilitate photometric variability studies and the deepest possible photometry. A more detailed overview of the science goals and observational design of the survey are presented in a companion paper.

2,011 citations

Journal ArticleDOI
TL;DR: In this article, the assembly of a massive rich cluster and the formation of its constituent galaxies in a flat, low-density universe is simulated, and the most accurate model follows the collapse, the star formation history and the orbital motion of all galaxies more luminous than the Fornax dwarf spheroidal, while dark halo structure is tracked consistently throughout the cluster.
Abstract: ABSTRA C T We simulate the assembly of a massive rich cluster and the formation of its constituent galaxies in a flat, low-density universe. Our most accurate model follows the collapse, the star formation history and the orbital motion of all galaxies more luminous than the Fornax dwarf spheroidal, while dark halo structure is tracked consistently throughout the cluster for all galaxies more luminous than the SMC. Within its virial radius this model contains about 2 10 7 dark matter particles and almost 5000 distinct dynamically resolved galaxies. Simulations of this same cluster at a variety of resolutions allow us to check explicitly for numerical convergence both of the dark matter structures produced by our new parallel N-body and substructure identification codes, and of the galaxy populations produced by the phenomenological models we use to follow cooling, star formation, feedback and stellar aging. This baryonic modelling is tuned so that our simulations reproduce the observed properties of isolated spirals outside clusters. Without further parameter adjustment our simulations then produce a luminosity function, a mass-to-light ratio, luminosity, number and velocity dispersion profiles, and a morphology ‐radius relation which are similar to those observed in real clusters. In particular, since our simulations follow galaxy merging explicitly, we can demonstrate that it accounts quantitatively for the observed cluster population of bulges and elliptical galaxies.

2,011 citations


Authors

Showing all 148365 results

NameH-indexPapersCitations
Ronald C. Kessler2741332328983
Albert Hofman2672530321405
Graham A. Colditz2611542256034
Michael Grätzel2481423303599
Guido Kroemer2361404246571
George Davey Smith2242540248373
Matthias Mann221887230213
Yi Chen2174342293080
Eric N. Olson206814144586
Ronald M. Evans199708166722
Hans Clevers199793169673
Raymond J. Dolan196919138540
David J. Schlegel193600193972
Simon D. M. White189795231645
George Efstathiou187637156228
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202334
2022371
202114,895
202016,697
201916,602
201816,160