Showing papers by "University of Grenoble published in 2018"
TL;DR: In this paper, the cosmological parameter results from the final full-mission Planck measurements of the CMB anisotropies were presented, with good consistency with the standard spatially-flat 6-parameter CDM cosmology having a power-law spectrum of adiabatic scalar perturbations from polarization, temperature, and lensing separately and in combination.
Abstract: We present cosmological parameter results from the final full-mission Planck measurements of the CMB anisotropies. We find good consistency with the standard spatially-flat 6-parameter $\Lambda$CDM cosmology having a power-law spectrum of adiabatic scalar perturbations (denoted "base $\Lambda$CDM" in this paper), from polarization, temperature, and lensing, separately and in combination. A combined analysis gives dark matter density $\Omega_c h^2 = 0.120\pm 0.001$, baryon density $\Omega_b h^2 = 0.0224\pm 0.0001$, scalar spectral index $n_s = 0.965\pm 0.004$, and optical depth $\tau = 0.054\pm 0.007$ (in this abstract we quote $68\,\%$ confidence regions on measured parameters and $95\,\%$ on upper limits). The angular acoustic scale is measured to $0.03\,\%$ precision, with $100\theta_*=1.0411\pm 0.0003$. These results are only weakly dependent on the cosmological model and remain stable, with somewhat increased errors, in many commonly considered extensions. Assuming the base-$\Lambda$CDM cosmology, the inferred late-Universe parameters are: Hubble constant $H_0 = (67.4\pm 0.5)$km/s/Mpc; matter density parameter $\Omega_m = 0.315\pm 0.007$; and matter fluctuation amplitude $\sigma_8 = 0.811\pm 0.006$. We find no compelling evidence for extensions to the base-$\Lambda$CDM model. Combining with BAO we constrain the effective extra relativistic degrees of freedom to be $N_{\rm eff} = 2.99\pm 0.17$, and the neutrino mass is tightly constrained to $\sum m_
u< 0.12$eV. The CMB spectra continue to prefer higher lensing amplitudes than predicted in base -$\Lambda$CDM at over $2\,\sigma$, which pulls some parameters that affect the lensing amplitude away from the base-$\Lambda$CDM model; however, this is not supported by the lensing reconstruction or (in models that also change the background geometry) BAO data. (Abridged)
3,077 citations
TL;DR: The aim of this review is to compare synthetic (engineered) and naturally occurring nanoparticles (NPs) and nanostructured materials (NSMs) to identify their nanoscale properties and to define the specific knowledge gaps related to the risk assessment of NPs and NSMs in the environment.
Abstract: Nanomaterials (NMs) have gained prominence in technological advancements due to their tunable physical, chemical and biological properties with enhanced performance over their bulk counterparts. NMs are categorized depending on their size, composition, shape, and origin. The ability to predict the unique properties of NMs increases the value of each classification. Due to increased growth of production of NMs and their industrial applications, issues relating to toxicity are inevitable. The aim of this review is to compare synthetic (engineered) and naturally occurring nanoparticles (NPs) and nanostructured materials (NSMs) to identify their nanoscale properties and to define the specific knowledge gaps related to the risk assessment of NPs and NSMs in the environment. The review presents an overview of the history and classifications of NMs and gives an overview of the various sources of NPs and NSMs, from natural to synthetic, and their toxic effects towards mammalian cells and tissue. Additionally, the types of toxic reactions associated with NPs and NSMs and the regulations implemented by different countries to reduce the associated risks are also discussed.
1,976 citations
Tufts Medical Center1, Northeastern University2, McGill University3, Johns Hopkins University4, Utrecht University5, Vanderbilt University Medical Center6, Brigham and Women's Hospital7, New York University8, McMaster University9, Ohio State University10, Radboud University Nijmegen11, University of Western Ontario12, London Health Sciences Centre13, University of Montpellier14, RMIT University15, University of Poitiers16, Maine Medical Center17, University of Washington18, University of Chicago19, Intermountain Healthcare20, Deakin University21, Johns Hopkins University School of Medicine22, Yale University23, University of Grenoble24, University of California, San Francisco25, Monash University26, Case Western Reserve University27, New York Medical College28, University of Toronto29, Stanford University30
TL;DR: Substantial agreement was found among a large, interdisciplinary cohort of international experts regarding evidence supporting recommendations, and the remaining literature gaps in the assessment, prevention, and treatment of Pain, Agitation/sedation, Delirium, Immobility (mobilization/rehabilitation), and Sleep (disruption) in critically ill adults.
Abstract: Objective:To update and expand the 2013 Clinical Practice Guidelines for the Management of Pain, Agitation, and Delirium in Adult Patients in the ICU.Design:Thirty-two international experts, four methodologists, and four critical illness survivors met virtually at least monthly. All section groups g
1,935 citations
TL;DR: This analysis expands upon previous analyses by working under the hypothesis that both bodies were neutron stars that are described by the same equation of state and have spins within the range observed in Galactic binary neutron stars.
Abstract: On 17 August 2017, the LIGO and Virgo observatories made the first direct detection of gravitational waves from the coalescence of a neutron star binary system. The detection of this gravitational-wave signal, GW170817, offers a novel opportunity to directly probe the properties of matter at the extreme conditions found in the interior of these stars. The initial, minimal-assumption analysis of the LIGO and Virgo data placed constraints on the tidal effects of the coalescing bodies, which were then translated to constraints on neutron star radii. Here, we expand upon previous analyses by working under the hypothesis that both bodies were neutron stars that are described by the same equation of state and have spins within the range observed in Galactic binary neutron stars. Our analysis employs two methods: the use of equation-of-state-insensitive relations between various macroscopic properties of the neutron stars and the use of an efficient parametrization of the defining function pðρÞ of the equation of state itself. From the LIGO and Virgo data alone and the first method, we measure the two neutron star radii as R1 ¼ 10.8 þ2.0 −1.7 km for the heavier star and R2 ¼ 10.7 þ2.1 −1.5 km for the lighter star at the 90% credible level. If we additionally require that the equation of state supports neutron stars with masses larger than 1.97 M⊙ as required from electromagnetic observations and employ the equation-of-state parametrization, we further constrain R1 ¼ 11.9 þ1.4 −1.4 km and R2 ¼ 11.9 þ1.4 −1.4 km at the 90% credible level. Finally, we obtain constraints on pðρÞ at supranuclear densities, with pressure at twice nuclear saturation density measured at 3.5 þ2.7 −1.7 × 1034 dyn cm−2 at the 90% level.
1,595 citations
TL;DR: The JASPAR 2018 CORE vertebrate collection of PFMs was used to predict TF-binding sites in the human genome and this update comes with a new web framework with an interactive and responsive user-interface, along with new features.
Abstract: JASPAR (http://jaspar.genereg.net) is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) and TF flexible models (TFFMs) for TFs across multiple species in six taxonomic groups. In the 2018 release of JASPAR, the CORE collection has been expanded with 322 new PFMs (60 for vertebrates and 262 for plants) and 33 PFMs were updated (24 for vertebrates, 8 for plants and 1 for insects). These new profiles represent a 30% expansion compared to the 2016 release. In addition, we have introduced 316 TFFMs (95 for vertebrates, 218 for plants and 3 for insects). This release incorporates clusters of similar PFMs in each taxon and each TF class per taxon. The JASPAR 2018 CORE vertebrate collection of PFMs was used to predict TF-binding sites in the human genome. The predictions are made available to the scientific community through a UCSC Genome Browser track data hub. Finally, this update comes with a new web framework with an interactive and responsive user-interface, along with new features. All the underlying data can be retrieved programmatically using a RESTful API and through the JASPAR 2018 R/Bioconductor package.
1,282 citations
Harvard University1, University of Chile2, Rice University3, Heidelberg University4, Pontifical Catholic University of Chile5, University of Nevada, Las Vegas6, Ludwig Maximilian University of Munich7, Tsinghua University8, University of Grenoble9, Wesleyan University10, California State University, Northridge11
TL;DR: The Disk Substructures at High Angular Resolution Project (DSHARP) as mentioned in this paper was the first large-scale project to find and characterize substructures in the spatial distributions of solid particles for a sample of 20 nearby protoplanetary disks, using very high resolution (similar to 0'' 035 or 5 au, FWHM) observations of their 240 GHz (1.25 mm) continuum emission.
Abstract: We introduce the Disk Substructures at High Angular Resolution Project (DSHARP), one of the initial Large Programs conducted with the Atacama Large Millimeter/submillimeter Array (ALMA). The primary goal of DSHARP is to find and characterize substructures in the spatial distributions of solid particles for a sample of 20 nearby protoplanetary disks, using very high resolution (similar to 0.'' 035, or 5 au, FWHM) observations of their 240 GHz (1.25 mm) continuum emission. These data provide a first homogeneous look at the small-scale features in disks that are directly relevant to the planet formation process, quantifying their prevalence, morphologies, spatial scales, spacings, symmetry, and amplitudes, for targets with a variety of disk and stellar host properties. We find that these substructures are ubiquitous in this sample of large, bright disks. They are most frequently manifested as concentric, narrow emission rings and depleted gaps, although large-scale spiral patterns and small arc-shaped azimuthal asymmetries are also present in some cases. These substructures are found at a wide range of disk radii (from a few astronomical units to more than 100 au), are usually compact (less than or similar to 10 au), and show a wide range of amplitudes (brightness contrasts). Here we discuss the motivation for the project, describe the survey design and the sample properties, detail the observations and data calibration, highlight some basic results, and provide a general overview of the key conclusions that are presented in more detail in a series of accompanying articles. The DSHARP data-including visibilities, images, calibration scripts, and more-are released for community use at https://almascience.org/alma-data/lp/DSHARP.
822 citations
Nagoya University1, University of Grenoble2, University of Padua3, University of Liverpool4, Hong Kong University of Science and Technology5, Massachusetts Institute of Technology6, HRL Laboratories7, University of Sheffield8, Katholieke Universiteit Leuven9, Fraunhofer Society10, Nagoya Institute of Technology11, University of Notre Dame12, Virginia Tech13, Infineon Technologies14, University of Glasgow15, University of Texas at Austin16, University of Bristol17, National Institute of Advanced Industrial Science and Technology18, Cardiff University19, University of Cambridge20, Zhejiang University21
TL;DR: This collection of GaN technology developments is not itself a road map but a valuable collection of global state-of-the-art GaN research that will inform the next phase of the technology as market driven requirements evolve.
Abstract: Gallium nitride (GaN) is a compound semiconductor that has tremendous potential to facilitate economic growth in a semiconductor industry that is silicon-based and currently faced with diminishing returns of performance versus cost of investment. At a material level, its high electric field strength and electron mobility have already shown tremendous potential for high frequency communications and photonic applications. Advances in growth on commercially viable large area substrates are now at the point where power conversion applications of GaN are at the cusp of commercialisation. The future for building on the work described here in ways driven by specific challenges emerging from entirely new markets and applications is very exciting. This collection of GaN technology developments is therefore not itself a road map but a valuable collection of global state-of-the-art GaN research that will inform the next phase of the technology as market driven requirements evolve. First generation production devices are igniting large new markets and applications that can only be achieved using the advantages of higher speed, low specific resistivity and low saturation switching transistors. Major investments are being made by industrial companies in a wide variety of markets exploring the use of the technology in new circuit topologies, packaging solutions and system architectures that are required to achieve and optimise the system advantages offered by GaN transistors. It is this momentum that will drive priorities for the next stages of device research gathered here.
788 citations
TL;DR: Gaia DR2 as mentioned in this paper is the second Gaia data release, consisting of astrometry, photometry, radial velocities, and information on astrophysical parameters and variability, for sources brighter than magnitude 21.
Abstract: We present the second Gaia data release, Gaia DR2, consisting of astrometry, photometry, radial velocities, and information on astrophysical parameters and variability, for sources brighter than magnitude 21. In addition epoch astrometry and photometry are provided for a modest sample of minor planets in the solar system.
A summary of the contents of Gaia DR2 is presented, accompanied by a discussion on the differences with respect to Gaia DR1 and an overview of the main limitations which are still present in the survey. Recommendations are made on the responsible use of Gaia DR2 results.
Gaia DR2 contains celestial positions and the apparent brightness in G for approximately 1.7 billion sources. For 1.3 billion of those sources, parallaxes and proper motions are in addition available. The sample of sources for which variability information is provided is expanded to 0.5 million stars. This data release contains four new elements: broad-band colour information in the form of the apparent brightness in the $G_\mathrm{BP}$ (330--680 nm) and $G_\mathrm{RP}$ (630--1050 nm) bands is available for 1.4 billion sources; median radial velocities for some 7 million sources are presented; for between 77 and 161 million sources estimates are provided of the stellar effective temperature, extinction, reddening, and radius and luminosity; and for a pre-selected list of 14000 minor planets in the solar system epoch astrometry and photometry are presented. Finally, Gaia DR2 also represents a new materialisation of the celestial reference frame in the optical, the Gaia-CRF2, which is the first optical reference frame based solely on extragalactic sources. There are notable changes in the photometric system and the catalogue source list with respect to Gaia DR1, and we stress the need to consider the two data releases as independent.
761 citations
University of Leeds1, California Institute of Technology2, University of California, Irvine3, University of Washington4, Durham University5, University of Grenoble6, Goddard Space Flight Center7, University of Bristol8, University of Colorado Boulder9, Geological Survey of Denmark and Greenland10, University at Buffalo11, National Space Institute12, University of South Florida13, University of Texas at Austin14, University College London15, Dresden University of Technology16, Georgia Institute of Technology17, University of Lincoln18, University of Arizona19, Alfred Wegener Institute for Polar and Marine Research20, Technische Universität München21, Danish Meteorological Institute22, Memorial University of Newfoundland23, National Institute of Geophysics and Volcanology24, Bergen University College25, University of Magallanes26, Remote Sensing Center27, Newcastle University28, University of Toronto29, University of Bonn30, Delft University of Technology31, Seoul National University32, University of Urbino33, University of Stuttgart34
TL;DR: This work combines satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that the Antarctic Ice Sheet lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6‚¬3.9 millimetres.
Abstract: The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992–2017 (5 ± 46 billion tonnes per year) being the least certain.
725 citations
Goethe University Frankfurt1, University of Maryland, College Park2, University of Guelph3, Duke University4, Radboud University Nijmegen5, Swedish University of Agricultural Sciences6, Federal University of Mato Grosso do Sul7, University of Alberta8, Royal Veterinary College9, Wildlife Conservation Society10, Mississippi State University11, Sao Paulo State University12, Michigan Department of Natural Resources13, University of California, Davis14, Aarhus University15, Max Planck Society16, University of Potsdam17, Middle Tennessee State University18, Mammal Research Institute19, Harvard University20, Edmund Mach Foundation21, Smithsonian Conservation Biology Institute22, University of Évora23, University of Montpellier24, Monash University25, Parks Victoria26, Ohio State University27, Fiji National University28, University of Massachusetts Amherst29, United States Geological Survey30, University of Oxford31, Save the Elephants32, German Primate Center33, Technische Universität München34, Institute of Ecosystem Studies35, University of British Columbia36, University of Zurich37, University of Wyoming38, University of Washington39, University of Montana40, University of Freiburg41, Bavarian Forest National Park42, University of Toulouse43, University of Veterinary Medicine Vienna44, University College Cork45, North Carolina State University46, North Carolina Museum of Natural Sciences47, Karatina University48, University of Lethbridge49, Lamont–Doherty Earth Observatory50, University of Valencia51, Stony Brook University52, International Union for Conservation of Nature and Natural Resources53, University of Alicante54, Empresa Brasileira de Pesquisa Agropecuária55, University of Glasgow56, New York University57, University of Oslo58, Hebrew University of Jerusalem59, Norwegian University of Science and Technology60, Field Museum of Natural History61, University of Grenoble62, University of Bayreuth63, University of New South Wales64, Pennsylvania Game Commission65, Princeton University66, University of Konstanz67, University of Haifa68, Polish Academy of Sciences69, Instituto Superior de Agronomia70, University of Lisbon71, University of Porto72, University of California, Santa Cruz73, University of Pretoria74, Colorado State University75
TL;DR: Using a unique GPS-tracking database of 803 individuals across 57 species, it is found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in area with a low human footprint.
Abstract: Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.
719 citations
European Southern Observatory1, University of Lisbon2, University of Porto3, Max Planck Society4, University of Grenoble5, University of Geneva6, Paris Diderot University7, Leiden University8, University of Cologne9, Dublin Institute for Advanced Studies10, University of California, Berkeley11, Space Telescope Science Institute12
TL;DR: Eisenhauer et al. as mentioned in this paper detect the combined gravitational redshift and relativistic transverse Doppler effect for S2 of z = Δλ / λ ≈ 200 km s−1/c with different statistical analysis methods.
Abstract: The highly elliptical, 16-year-period orbit of the star S2 around the massive black hole candidate Sgr A✻ is a sensitive probe of the gravitational field in the Galactic centre. Near pericentre at 120 AU ≈ 1400 Schwarzschild radii, the star has an orbital speed of ≈7650 km s−1, such that the first-order effects of Special and General Relativity have now become detectable with current capabilities. Over the past 26 years, we have monitored the radial velocity and motion on the sky of S2, mainly with the SINFONI and NACO adaptive optics instruments on the ESO Very Large Telescope, and since 2016 and leading up to the pericentre approach in May 2018, with the four-telescope interferometric beam-combiner instrument GRAVITY. From data up to and including pericentre, we robustly detect the combined gravitational redshift and relativistic transverse Doppler effect for S2 of z = Δλ / λ ≈ 200 km s−1/c with different statistical analysis methods. When parameterising the post-Newtonian contribution from these effects by a factor f , with f = 0 and f = 1 corresponding to the Newtonian and general relativistic limits, respectively, we find from posterior fitting with different weighting schemes f = 0.90 ± 0.09|stat ± 0.15|sys. The S2 data are inconsistent with pure Newtonian dynamics.Key words: Galaxy: center / gravitation / black hole physics⋆ This paper is dedicated to Tal Alexander, who passed away about a week before the pericentre approach of S2.⋆⋆ GRAVITY is developed in a collaboration by the Max Planck Institute for extraterrestrial Physics, LESIA of Paris Observatory/CNRS/Sorbonne Universite/Univ. Paris Diderot and IPAG of Universite Grenoble Alpes/CNRS, the Max Planck Institute for Astronomy, the University of Cologne, the CENTRA – Centro de Astrofisica e Gravitacao, and the European Southern Observatory.⋆⋆⋆ Corresponding author: F. Eisenhauer e-mail: eisenhau@mpe.mpg.de
TL;DR: In this paper, the authors provide guidelines on how to use parallaxes more efficiently to estimate distances by using Bayesian methods, and provide examples that show more generally how to combine proper motions and paralaxes and the handling of covariances in the uncertainties.
Abstract: Context. The second Gaia data release (Gaia DR2) provides precise five-parameter astrometric data (positions, proper motions, and parallaxes) for an unprecedented number of sources (more than 1.3 billion, mostly stars). This new wealth of data will enable the undertaking of statistical analysis of many astrophysical problems that were previously infeasible for lack of reliable astrometry, and in particular because of the lack of parallaxes. However, the use of this wealth of astrometric data comes with a specific challenge: how can the astrophysical parameters of interest be properly inferred from these data?Aims. The main focus of this paper, but not the only focus, is the issue of the estimation of distances from parallaxes, possibly combined with other information. We start with a critical review of the methods traditionally used to obtain distances from parallaxes and their shortcomings. Then we provide guidelines on how to use parallaxes more efficiently to estimate distances by using Bayesian methods. In particular we also show that negative parallaxes, or parallaxes with relatively large uncertainties still contain valuable information. Finally, we provide examples that show more generally how to use astrometric data for parameter estimation, including the combination of proper motions and parallaxes and the handling of covariances in the uncertainties.Methods. The paper contains examples based on simulated Gaia data to illustrate the problems and the solutions proposed. Furthermore, the developments and methods proposed in the paper are linked to a set of tutorials included in the Gaia archive documentation that provide practical examples and a good starting point for the application of the recommendations to actual problems. In all cases the source code for the analysis methods is provided.Results. Our main recommendation is to always treat the derivation of (astro-)physical parameters from astrometric data, in particular when parallaxes are involved, as an inference problem which should preferably be handled with a full Bayesian approach.Conclusions. Gaia will provide fundamental data for many fields of astronomy. Further data releases will provide more data, and more precise data. Nevertheless, to fully use the potential it will always be necessary to pay careful attention to the statistical treatment of parallaxes and proper motions. The purpose of this paper is to help astronomers find the correct approach.
TL;DR: This review establishes detailed best practices, methods and techniques for characterizing CNM particle morphology, surface chemistry, surface charge, purity, crystallinity, rheological properties, mechanical properties, and toxicity for two distinct forms of CNMs: cellulose nanocrystals and cellulose Nanofibrils.
Abstract: A new family of materials comprised of cellulose, cellulose nanomaterials (CNMs), having properties and functionalities distinct from molecular cellulose and wood pulp, is being developed for applications that were once thought impossible for cellulosic materials. Commercialization, paralleled by research in this field, is fueled by the unique combination of characteristics, such as high on-axis stiffness, sustainability, scalability, and mechanical reinforcement of a wide variety of materials, leading to their utility across a broad spectrum of high-performance material applications. However, with this exponential growth in interest/activity, the development of measurement protocols necessary for consistent, reliable and accurate materials characterization has been outpaced. These protocols, developed in the broader research community, are critical for the advancement in understanding, process optimization, and utilization of CNMs in materials development. This review establishes detailed best practices, methods and techniques for characterizing CNM particle morphology, surface chemistry, surface charge, purity, crystallinity, rheological properties, mechanical properties, and toxicity for two distinct forms of CNMs: cellulose nanocrystals and cellulose nanofibrils.
TL;DR: Targeting the VN, for example through VN stimulation which has anti-inflammatory properties, would be of interest to restore homeostasis in the microbiota-gut-brain axis.
Abstract: The microbiota, the gut, and the brain communicate through the microbiota-gut-brain axis in a bidirectional way that involves the autonomic nervous system. The vagus nerve (VN), the principal component of the parasympathetic nervous system, is a mixed nerve composed of 80% afferent and 20% efferent fibers. The VN, because of its role in interoceptive awareness, is able to sense the microbiota metabolites through its afferents, to transfer this gut information to the central nervous system where it is integrated in the central autonomic network, and then to generate an adapted or inappropriate response. A cholinergic anti-inflammatory pathway has been described through VN's fibers, which is able to dampen peripheral inflammation and to decrease intestinal permeability, thus very probably modulating microbiota composition. Stress inhibits the VN and has deleterious effects on the gastrointestinal tract and on the microbiota, and is involved in the pathophysiology of gastrointestinal disorders such as irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) which are both characterized by a dysbiosis. A low vagal tone has been described in IBD and IBS patients thus favoring peripheral inflammation. Targeting the VN, for example through VN stimulation which has anti-inflammatory properties, would be of interest to restore homeostasis in the microbiota-gut-brain axis.
TL;DR: In this paper, the authors describe the processing of the Gaia DR2 data, and describe the criteria used to select the sample published in Gaia DR 2, and explore the data set to assess its quality.
Abstract: Context. The Gaia spacecraft of the European Space Agency (ESA) has been securing observations of solar system objects (SSOs) since the beginning of its operations. Data Release 2 (DR2) contains the observations of a selected sample of 14,099 SSOs. These asteroids have been already identified and have been numbered by the Minor Planet Center repository. Positions are provided for each Gaia observation at CCD level. As additional information, complementary to astrometry, the apparent brightness of SSOs in the unfiltered G band is also provided for selected observations.Aims. We explain the processing of SSO data, and describe the criteria we used to select the sample published in Gaia DR2. We then explore the data set to assess its quality.Methods. To exploit the main data product for the solar system in Gaia DR2, which is the epoch astrometry of asteroids, it is necessary to take into account the unusual properties of the uncertainty, as the position information is nearly one-dimensional. When this aspect is handled appropriately, an orbit fit can be obtained with post-fit residuals that are overall consistent with the a-priori error model that was used to define individual values of the astrometric uncertainty. The role of both random and systematic errors is described. The distribution of residuals allowed us to identify possible contaminants in the data set (such as stars). Photometry in the G band was compared to computed values from reference asteroid shapes and to the flux registered at the corresponding epochs by the red and blue photometers (RP and BP).Results. The overall astrometric performance is close to the expectations, with an optimal range of brightness G ~ 12 − 17. In this range, the typical transit-level accuracy is well below 1 mas. For fainter asteroids, the growing photon noise deteriorates the performance. Asteroids brighter than G ~ 12 are affected by a lower performance of the processing of their signals. The dramatic improvement brought by Gaia DR2 astrometry of SSOs is demonstrated by comparisons to the archive data and by preliminary tests on the detection of subtle non-gravitational effects.
TL;DR: In this article, an analysis of the kinematics, chemistry, age and spatial distribution of stars that are mainly linked to two major Galactic components: the thick disk and the stellar halo.
Abstract: The assembly of our Galaxy can be reconstructed using the motions and chemistry of individual stars1,2. Chemo-dynamical studies of the stellar halo near the Sun have indicated the presence of multiple components3, such as streams4 and clumps5, as well as correlations between the stars’ chemical abundances and orbital parameters6–8. Recently, analyses of two large stellar surveys9,10 revealed the presence of a well populated elemental abundance sequence7,11, two distinct sequences in the colour–magnitude diagram12 and a prominent, slightly retrograde kinematic structure13,14 in the halo near the Sun, which may trace an important accretion event experienced by the Galaxy15. However, the link between these observations and their implications for Galactic history is not well understood. Here we report an analysis of the kinematics, chemistry, age and spatial distribution of stars that are mainly linked to two major Galactic components: the thick disk and the stellar halo. We demonstrate that the inner halo is dominated by debris from an object that at infall was slightly more massive than the Small Magellanic Cloud, and which we refer to as Gaia–Enceladus. The stars that originate in Gaia–Enceladus cover nearly the full sky, and their motions reveal the presence of streams and slightly retrograde and elongated trajectories. With an estimated mass ratio of four to one, the merger of the Milky Way with Gaia–Enceladus must have led to the dynamical heating of the precursor of the Galactic thick disk, thus contributing to the formation of this component approximately ten billion years ago. These findings are in line with the results of galaxy formation simulations, which predict that the inner stellar halo should be dominated by debris from only a few massive progenitors2,16.
08 Sep 2018
TL;DR: In this article, a loss composed of a distillation measure to retain the knowledge acquired from the old classes and a cross-entropy loss to learn the new classes is proposed.
Abstract: Although deep learning approaches have stood out in recent years due to their state-of-the-art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in overall performance when training with new classes added incrementally. This is due to current neural network architectures requiring the entire dataset, consisting of all the samples from the old as well as the new classes, to update the model—a requirement that becomes easily unsustainable as the number of classes grows. We address this issue with our approach to learn deep neural networks incrementally, using new data and only a small exemplar set corresponding to samples from the old classes. This is based on a loss composed of a distillation measure to retain the knowledge acquired from the old classes, and a cross-entropy loss to learn the new classes. Our incremental training is achieved while keeping the entire framework end-to-end, i.e., learning the data representation and the classifier jointly, unlike recent methods with no such guarantees. We evaluate our method extensively on the CIFAR-100 and ImageNet (ILSVRC 2012) image classification datasets, and show state-of-the-art performance.
Max Planck Society1, University of Grenoble2, University of Chile3, Leiden University4, European Southern Observatory5, University of Oxford6, Paris Diderot University7, INAF8, École normale supérieure de Lyon9, Aix-Marseille University10, University of Tübingen11, University of Bern12, Hungarian Academy of Sciences13, ETH Zurich14, Diego Portales University15, Ludwig Maximilian University of Munich16, Kavli Institute for Theoretical Physics17, California Institute of Technology18, Rice University19, Stockholm University20, University of Cambridge21, Centre national de la recherche scientifique22, Valparaiso University23, University of Arizona24, Monash University, Clayton campus25, University of Geneva26, University of Hawaii at Manoa27, University of Atacama28, Heidelberg University29, University of Michigan30
TL;DR: In this article, the authors detect a point source within the gap of the transition disk at about 195 mas (~22 au) projected separation and detect a signal from an inner disk component.
Abstract: Context. Young circumstellar disks are the birthplaces of planets. Their study is of prime interest to understand the physical and chemical conditions under which planet formation takes place. Only very few detections of planet candidates within these disks exist, and most of them are currently suspected to be disk features.Aims. In this context, the transition disk around the young star PDS 70 is of particular interest, due to its large gap identified in previous observations, indicative of ongoing planet formation. We aim to search for the presence of an embedded young planet and search for disk structures that may be the result of disk–planet interactions and other evolutionary processes.Methods. We analyse new and archival near-infrared images of the transition disk PDS 70 obtained with the VLT/SPHERE, VLT/NaCo, and Gemini/NICI instruments in polarimetric differential imaging and angular differential imaging modes.Results. We detect a point source within the gap of the disk at about 195 mas (~22 au) projected separation. The detection is confirmed at five different epochs, in three filter bands and using different instruments. The astrometry results in an object of bound nature, with high significance. The comparison of the measured magnitudes and colours to evolutionary tracks suggests that the detection is a companion of planetary mass. The luminosity of the detected object is consistent with that of an L-type dwarf, but its IR colours are redder, possibly indicating the presence of warm surrounding material. Further, we confirm the detection of a large gap of ~54 au in size within the disk in our scattered light images, and detect a signal from an inner disk component. We find that its spatial extent is very likely smaller than ~17 au in radius, and its position angle is consistent with that of the outer disk. The images of the outer disk show evidence of a complex azimuthal brightness distribution which is different at different wavelengths and may in part be explained by Rayleigh scattering from very small grains.Conclusions. The detection of a young protoplanet within the gap of the transition disk around PDS 70 opens the door to a so far observationally unexplored parameter space of planetary formation and evolution. Future observations of this system at different wavelengths and continuing astrometry will allow us to test theoretical predictions regarding planet–disk interactions, planetary atmospheres, and evolutionary models.
Richard A. Klein1, Michelangelo Vianello2, Fred Hasselman3, Byron G. Adams4 +187 more•Institutions (118)
TL;DR: This paper conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings, and found that very little heterogeneity was attributable to the order in which the tasks were performed or whether the task were administered in lab versus online.
Abstract: We conducted preregistered replications of 28 classic and contemporary published findings, with protocols that were peer reviewed in advance, to examine variation in effect magnitudes across samples and settings. Each protocol was administered to approximately half of 125 samples that comprised 15,305 participants from 36 countries and territories. Using the conventional criterion of statistical significance (p < .05), we found that 15 (54%) of the replications provided evidence of a statistically significant effect in the same direction as the original finding. With a strict significance criterion (p < .0001), 14 (50%) of the replications still provided such evidence, a reflection of the extremely high-powered design. Seven (25%) of the replications yielded effect sizes larger than the original ones, and 21 (75%) yielded effect sizes smaller than the original ones. The median comparable Cohen’s ds were 0.60 for the original findings and 0.15 for the replications. The effect sizes were small (< 0.20) in 16 of the replications (57%), and 9 effects (32%) were in the direction opposite the direction of the original effect. Across settings, the Q statistic indicated significant heterogeneity in 11 (39%) of the replication effects, and most of those were among the findings with the largest overall effect sizes; only 1 effect that was near zero in the aggregate showed significant heterogeneity according to this measure. Only 1 effect had a tau value greater than .20, an indication of moderate heterogeneity. Eight others had tau values near or slightly above .10, an indication of slight heterogeneity. Moderation tests indicated that very little heterogeneity was attributable to the order in which the tasks were performed or whether the tasks were administered in lab versus online. Exploratory comparisons revealed little heterogeneity between Western, educated, industrialized, rich, and democratic (WEIRD) cultures and less WEIRD cultures (i.e., cultures with relatively high and low WEIRDness scores, respectively). Cumulatively, variability in the observed effect sizes was attributable more to the effect being studied than to the sample or setting in which it was studied.
TL;DR: It is demonstrated that the inner halo of the Galaxy is dominated by debris from an object that at infall was slightly more massive than the Small Magellanic Cloud, and which the authors refer to as Gaia–Enceladus, which is in line with the results of galaxy formation simulations.
Abstract: The assembly process of our Galaxy can be retrieved using the motions and chemistry of individual stars. Chemo-dynamical studies of the nearby halo have long hinted at the presence of multiple components such as streams, clumps, duality and correlations between the stars' chemical abundances and orbital parameters. More recently, the analysis of two large stellar surveys have revealed the presence of a well-populated chemical elemental abundance sequence, of two distinct sequences in the colour-magnitude diagram, and of a prominent slightly retrograde kinematic structure all in the nearby halo, which may trace an important accretion event experienced by the Galaxy. Here report an analysis of the kinematics, chemistry, age and spatial distribution of stars in a relatively large volume around the Sun that are mainly linked to two major Galactic components, the thick disk and the stellar halo. We demonstrate that the inner halo is dominated by debris from an object which at infall was slightly more massive than the Small Magellanic Cloud, and which we refer to as Gaia-Enceladus. The stars originating in Gaia-Enceladus cover nearly the full sky, their motions reveal the presence of streams and slightly retrograde and elongated trajectories. Hundreds of RR Lyrae stars and thirteen globular clusters following a consistent age-metallicity relation can be associated to Gaia-Enceladus on the basis of their orbits. With an estimated 4:1 mass-ratio, the merger with Gaia-Enceladus must have led to the dynamical heating of the precursor of the Galactic thick disk and therefore contributed to the formation of this component approximately 10 Gyr ago. These findings are in line with simulations of galaxy formation, which predict that the inner stellar halo should be dominated by debris from just a few massive progenitors.
TL;DR: This work proposes a novel spectral mixture model, called the augmented LMM, to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing and introduces a spectral variability dictionary.
Abstract: Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing. The proposed approach models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity, atmospheric effects) and instrumental configurations (e.g., sensor noise), as well as material nonlinear mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unmixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.
TL;DR: It is found that, depending on the New Physics scenario under consideration, the effect of a proper treatment of statistics on the predicted dark matter abundance can range from a few percent up to a factor of two, or more.
Proceedings Article•
15 Feb 2018TL;DR: It is found that replacing the conventional exploration heuristics for A3C, DQN and dueling agents with NoisyNet yields substantially higher scores for a wide range of Atari games, in some cases advancing the agent from sub to super-human performance.
Abstract: We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights. NoisyNet is straightforward to implement and adds little computational overhead. We find that replacing the conventional exploration heuristics for A3C, DQN and dueling agents (entropy reward and $\epsilon$-greedy respectively) with NoisyNet yields substantially higher scores for a wide range of Atari games, in some cases advancing the agent from sub to super-human performance.
Peking University1, University of Arizona2, Leiden University3, University of Leicester4, University of Cambridge5, University of Milan6, Vassar College7, Smith College8, University of Grenoble9, University of Victoria10, Max Planck Society11, Chinese Academy of Sciences12, Rice University13, University of Paris14, European Southern Observatory15, University of Chicago16, INAF17, Space Telescope Science Institute18, Ames Research Center19
TL;DR: In this article, the authors identify the most frequently revealed substructure in ALMA dust observations of protoplanetary disks, and measure their properties to investigate how they form, including axisymmetric rings and gaps.
Abstract: Rings are the most frequently revealed substructure in ALMA dust observations of protoplanetary disks, but their origin is still hotly debated. In this paper, we identify dust substructures in 12 disks and measure their properties to investigate how they form. This subsample of disks is selected from a high-resolution ($\sim0.12''$) ALMA 1.33 mm survey of 32 disks in the Taurus star-forming region, which was designed to cover a wide range of sub-mm brightness and to be unbiased to previously known substructures. While axisymmetric rings and gaps are common within our sample, spiral patterns and high contrast azimuthal asymmetries are not detected. Fits of disk models to the visibilities lead to estimates of the location and shape of gaps and rings, the flux in each disk component, and the size of the disk. The dust substructures occur across a wide range of stellar mass and disk brightness. Disks with multiple rings tend to be more massive and more extended. The correlation between gap locations and widths, the intensity contrast between rings and gaps, and the separations of rings and gaps could all be explained if most gaps are opened by low-mass planets (super-Earths and Neptunes) in the condition of low disk turbulence ($\alpha=10^{-4}$). The gap locations are not well correlated with the expected locations of CO and N$_2$ ice lines, so condensation fronts are unlikely to be a universal mechanism to create gaps and rings, though they may play a role in some cases.
TL;DR: The main recommendation is to always treat the derivation of (astro-)physical parameters from astrometric data, in particular when parallaxes are involved, as an inference problem which should preferably be handled with a full Bayesian approach.
Abstract: The second Gaia data release (GDR2) provides precise five-parameter astrometric data (positions, proper motions and parallaxes) for an unprecedented amount of sources (more than $1.3$ billion, mostly stars). The use of this wealth of astrometric data comes with a specific challenge: how does one properly infer from these data the astrophysical parameters of interest?
The main - but not only - focus of this paper is the issue of the estimation of distances from parallaxes, possibly combined with other information. We start with a critical review of the methods traditionally used to obtain distances from parallaxes and their shortcomings. Then we provide guidelines on how to use parallaxes more efficiently to estimate distances by using Bayesian methods. In particular also we show that negative parallaxes, or parallaxes with relatively larger uncertainties still contain valuable information. Finally, we provide examples that show more generally how to use astrometric data for parameter estimation, including the combination of proper motions and parallaxes and the handling of covariances in the uncertainties.
The paper contains examples based on simulated Gaia data to illustrate the problems and the solutions proposed. Furthermore, the developments and methods proposed in the paper are linked to a set of tutorials included in the Gaia archive documentation that provide practical examples and a good starting point for the application of the recommendations to actual problems. In all cases the source code for the analysis methods is provided. Our main recommendation is to always treat the derivation of (astro-) physical parameters from astrometric data, in particular when parallaxes are involved, as an inference problem which should preferably be handled with a full Bayesian approach.
Harvard University1, Heidelberg University2, Rice University3, University of Chile4, Pontifical Catholic University of Chile5, University of Nevada, Las Vegas6, Tsinghua University7, University of Grenoble8, Ludwig Maximilian University of Munich9, Wesleyan University10, California State University, Northridge11
TL;DR: In this article, the authors present a systematic analysis of annular substructures in the 18 single-disk systems targeted in this survey, and find that annular structures can occur at virtually any radius where millimeter continuum emission is detected and range in widths from a few astronomical units to tens of astronomical units.
Abstract: The Disk Substructures at High Angular Resolution Project (DSHARP) used ALMA to map the 1.25 mm continuum of protoplanetary disks at a spatial resolution of similar to 5 au. We present a systematic analysis of annular substructures in the 18 single-disk systems targeted in this survey. No dominant architecture emerges from this sample;instead, remarkably diverse morphologies are observed. Annular substructures can occur at virtually any radius where millimeter continuum emission is detected and range in widths from a few astronomical units to tens of astronomical units. Intensity ratios between gaps and adjacent rings range from near-unity to just a few percent. In a minority of cases, annular substructures coexist with other types of substructures, including spiral arms (3/18) and crescent-like azimuthal asymmetries (2/18). No clear trend is observed between the positions of the substructures and stellar host properties. In particular, the absence of an obvious association with stellar host luminosity (and hence the disk thermal structure) suggests that substructures do not occur preferentially near major molecular snowlines. Annular substructures like those observed in DSHARP have long been hypothesized to be due to planet-disk interactions. A few disks exhibit characteristics particularly suggestive of this scenario, including substructures in possible mean-motion resonance and "double gap" features reminiscent of hydrodynamical simulations of multiple gaps opened by a planet in a low-viscosity disk.
Posted Content•
TL;DR: This work proposes an approach to learn deep neural networks incrementally, using new data and only a small exemplar set corresponding to samples from the old classes, based on a loss composed of a distillation measure to retain the knowledge acquired from theold classes, and a cross-entropy loss to learn the new classes.
Abstract: Although deep learning approaches have stood out in recent years due to their state-of-the-art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in overall performance when training with new classes added incrementally. This is due to current neural network architectures requiring the entire dataset, consisting of all the samples from the old as well as the new classes, to update the model -a requirement that becomes easily unsustainable as the number of classes grows. We address this issue with our approach to learn deep neural networks incrementally, using new data and only a small exemplar set corresponding to samples from the old classes. This is based on a loss composed of a distillation measure to retain the knowledge acquired from the old classes, and a cross-entropy loss to learn the new classes. Our incremental training is achieved while keeping the entire framework end-to-end, i.e., learning the data representation and the classifier jointly, unlike recent methods with no such guarantees. We evaluate our method extensively on the CIFAR-100 and ImageNet (ILSVRC 2012) image classification datasets, and show state-of-the-art performance.
Florence Demenais1, Florence Demenais2, Patricia Margaritte-Jeannin1, Patricia Margaritte-Jeannin2 +213 more•Institutions (79)
TL;DR: A meta-analysis of GWAS studies for asthma from multiancestral cohorts identifies five new loci and finds that the asthma-associated loci are enriched near enhancer marks in immune cells, suggesting a major role of these loci in the regulation of immunologically related mechanisms.
Abstract: We examined common variation in asthma risk by conducting a meta-analysis of worldwide asthma genome-wide association studies (23,948 asthma cases, 118,538 controls) of individuals from ethnically diverse populations. We identified five new asthma loci, found two new associations at two known asthma loci, established asthma associations at two loci previously implicated in the comorbidity of asthma plus hay fever, and confirmed nine known loci. Investigation of pleiotropy showed large overlaps in genetic variants with autoimmune and inflammatory diseases. The enrichment in enhancer marks at asthma risk loci, especially in immune cells, suggested a major role of these loci in the regulation of immunologically related mechanisms.
University of Queensland1, University of Salford2, University of Western Australia3, Queensland University of Technology4, University of Lausanne5, Wildfowl & Wetlands Trust6, Canadian Parks and Wilderness Society7, University of Évora8, University of Freiburg9, University of Melbourne10, University of Plymouth11, Mississippi State University12, Australian Institute of Marine Science13, Griffith University14, Zurich University of Applied Sciences/ZHAW15, University of British Columbia16, Duke University17, Finnish Environment Institute18, University of Adelaide19, King Fahd University of Petroleum and Minerals20, IFREMER21, University of California, Davis22, Technical University of Madrid23, National Institute of Water and Atmospheric Research24, Memorial University of Newfoundland25, Royal Society for the Protection of Birds26, University of Kansas27, University of California, Merced28, University of the Sunshine Coast29, Nelson Mandela Metropolitan University30, Swedish University of Agricultural Sciences31, University of Grenoble32, Oregon State University33, University of Toronto34, Gulf of Maine Research Institute35, University of Georgia36, Newcastle University37, Parks Canada38, Humboldt University of Berlin39
TL;DR: Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.
Abstract: Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world However, limited understanding of the accuracy and precision of models transferred to novel conditions (their ‘transferability’) undermines confidence in their predictions Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions
TL;DR: In this paper, a search for new phenomena in final states with an energetic jet and large missing transverse momentum is reported, and the results are translated into exclusion limits in models with pair-produced weakly interacting dark-matter candidates, large extra spatial dimensions, and supersymmetric particles in several compressed scenarios.
Abstract: Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses proton-proton collision data corresponding to an integrated luminosity of 36.1 fb−1 at a centre-of-mass energy of 13 TeV collected in 2015 and 2016 with the ATLAS detector at the Large Hadron Collider. Events are required to have at least one jet with a transverse momentum above 250 GeV and no leptons (e or μ). Several signal regions are considered with increasing requirements on the missing transverse momentum above 250 GeV. Good agreement is observed between the number of events in data and Standard Model predictions. The results are translated into exclusion limits in models with pair-produced weakly interacting dark-matter candidates, large extra spatial dimensions, and supersymmetric particles in several compressed scenarios.