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Instituto Superior Técnico
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About: Instituto Superior Técnico is a based out in . It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 10085 authors who have published 30226 publications receiving 667524 citations. The organization is also known as: IST & Instituto Superior Tecnico.
Topics: Catalysis, Finite element method, Population, Black hole, Ionic liquid
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University of California, Berkeley1, Lawrence Berkeley National Laboratory2, Instituto Superior Técnico3, Pierre-and-Marie-Curie University4, Stockholm University5, European Southern Observatory6, Collège de France7, University of Cambridge8, University of Barcelona9, Yale University10, Space Telescope Science Institute11, European Space Agency12, University of New South Wales13
TL;DR: In this paper, the mass density, Omega_M, and cosmological-constant energy density of the universe were measured using the analysis of 42 Type Ia supernovae discovered by the Supernova Cosmology project.
Abstract: We report measurements of the mass density, Omega_M, and
cosmological-constant energy density, Omega_Lambda, of the universe based on
the analysis of 42 Type Ia supernovae discovered by the Supernova Cosmology
Project. The magnitude-redshift data for these SNe, at redshifts between 0.18
and 0.83, are fit jointly with a set of SNe from the Calan/Tololo Supernova
Survey, at redshifts below 0.1, to yield values for the cosmological
parameters. All SN peak magnitudes are standardized using a SN Ia lightcurve
width-luminosity relation. The measurement yields a joint probability
distribution of the cosmological parameters that is approximated by the
relation 0.8 Omega_M - 0.6 Omega_Lambda ~= -0.2 +/- 0.1 in the region of
interest (Omega_M <~ 1.5). For a flat (Omega_M + Omega_Lambda = 1) cosmology we
find Omega_M = 0.28{+0.09,-0.08} (1 sigma statistical) {+0.05,-0.04}
(identified systematics). The data are strongly inconsistent with a Lambda = 0
flat cosmology, the simplest inflationary universe model. An open, Lambda = 0
cosmology also does not fit the data well: the data indicate that the
cosmological constant is non-zero and positive, with a confidence of P(Lambda >
0) = 99%, including the identified systematic uncertainties. The best-fit age
of the universe relative to the Hubble time is t_0 = 14.9{+1.4,-1.1} (0.63/h)
Gyr for a flat cosmology. The size of our sample allows us to perform a variety
of statistical tests to check for possible systematic errors and biases. We
find no significant differences in either the host reddening distribution or
Malmquist bias between the low-redshift Calan/Tololo sample and our
high-redshift sample. The conclusions are robust whether or not a
width-luminosity relation is used to standardize the SN peak magnitudes.
16,838 citations
TL;DR: A new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA), which competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
Abstract: Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
2,422 citations
University of Manchester1, KEK2, CERN3, Complutense University of Madrid4, SLAC National Accelerator Laboratory5, Toyama College6, Lebedev Physical Institute7, Fermilab8, University of Paris-Sud9, Lawrence Livermore National Laboratory10, National Research Nuclear University MEPhI11, Queen's University Belfast12, Korea Institute of Science and Technology Information13, Istituto Nazionale di Fisica Nucleare14, Northeastern University15, University of Seville16, National University of Cordoba17, Saint Joseph University18, Joint Institute for Nuclear Research19, Illawarra Health & Medical Research Institute20, University of Wollongong21, Hampton University22, TRIUMF23, ETH Zurich24, University of Bordeaux25, Centre national de la recherche scientifique26, University of Helsinki27, Johns Hopkins University School of Medicine28, National Technical University of Athens29, University of Notre Dame30, Ashikaga Institute of Technology31, Kobe University32, Intelligence and National Security Alliance33, University of Trieste34, University of Warwick35, University of Belgrade36, Instituto Superior Técnico37, European Space Agency38, Varian Medical Systems39, George Washington University40, Ritsumeikan University41, Ton Duc Thang University42, Université Paris-Saclay43, Idaho State University44, Naruto University of Education45
01 Nov 2016-Nuclear Instruments & Methods in Physics Research Section A-accelerators Spectrometers Detectors and Associated Equipment
TL;DR: Geant4 as discussed by the authors is a software toolkit for the simulation of the passage of particles through matter, which is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection.
Abstract: Geant4 is a software toolkit for the simulation of the passage of particles through matter. It is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection. Over the past several years, major changes have been made to the toolkit in order to accommodate the needs of these user communities, and to efficiently exploit the growth of computing power made available by advances in technology. The adaptation of Geant4 to multithreading, advances in physics, detector modeling and visualization, extensions to the toolkit, including biasing and reverse Monte Carlo, and tools for physics and release validation are discussed here.
2,260 citations
TL;DR: In this comprehensive survey, a large number of existing approaches to biclustering are analyzed, and they are classified in accordance with the type of biclusters they can find, the patterns of bIClusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications.
Abstract: A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications.
2,123 citations
University of Chicago1, Pierre-and-Marie-Curie University2, Lawrence Berkeley National Laboratory3, University of Pennsylvania4, Argonne National Laboratory5, Fermilab6, University of Cape Town7, African Institute for Mathematical Sciences8, Texas A&M University9, University of Cambridge10, University of Portsmouth11, University of Toronto12, Wayne State University13, University of Colorado Boulder14, University of Tokyo15, California Institute of Technology16, University of Victoria17, University of California, Berkeley18, University of Illinois at Urbana–Champaign19, University of Chile20, Autonomous University of Barcelona21, Stockholm University22, University of Texas at Austin23, Princeton University24, University of Oxford25, University of California, Santa Barbara26, Las Cumbres Observatory Global Telescope Network27, Rutgers University28, University of Copenhagen29, Australian Astronomical Observatory30, Instituto Superior Técnico31, University of Utah32, Rochester Institute of Technology33, Space Telescope Science Institute34, Johns Hopkins University35, Pennsylvania State University36, University of the Western Cape37, University of Southampton38
TL;DR: In this article, the authors presented cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations.
Abstract: Aims. We present cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations. The dataset includes several low-redshift samples (z< 0.1), all three seasons from the SDSS-II (0.05
1,939 citations
Authors
Showing all 10288 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Daniel E. Otzen | 69 | 384 | 17465 |
| Angela Casini | 69 | 270 | 15127 |
| P. Abreu | 69 | 557 | 23647 |
| Peter B. Hitchcock | 68 | 1313 | 28245 |
| Joao P. S. Catalao | 68 | 1039 | 19348 |
| Erwin Reisner | 68 | 211 | 12598 |
| Massimo Olivucci | 67 | 292 | 14880 |
| George Jackson | 67 | 276 | 19329 |
| Marta Kwiatkowska | 67 | 399 | 19657 |
| Paolo Pani | 66 | 302 | 14022 |
| David F. Mota | 65 | 276 | 13979 |
| Maria A.M. Reis | 65 | 358 | 16343 |
| Jose M. Bioucas-Dias | 65 | 326 | 27010 |
| M. Pimenta | 64 | 395 | 17405 |
| Sofia Andringa | 64 | 264 | 16966 |