Institution
University of Valencia
Education•Valencia, Spain•
About: University of Valencia is a education organization based out in Valencia, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 27096 authors who have published 65669 publications receiving 1765689 citations. The organization is also known as: Universitat de València & UV.
Topics: Population, Context (language use), Neutrino, Medicine, Catalysis
Papers published on a yearly basis
Papers
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TL;DR: The data recorded by these instruments during their first and second observing runs are described, including the gravitational-wave strain arrays, released as time series sampled at 16384 Hz.
320 citations
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TL;DR: This work presents a new lattice-based perfect reconstruction and critically sampled anisotropic M-DIR WT, which provides an efficient tool for nonlinear approximation of images, achieving the approximation power O(N/sup -1.55/), which, while slower than the optimal rate O-2/, is much better than O-1/ achieved with wavelets, but at similar complexity.
Abstract: In spite of the success of the standard wavelet transform (WT) in image processing in recent years, the efficiency of its representation is limited by the spatial isotropy of its basis functions built in the horizontal and vertical directions. One-dimensional (1-D) discontinuities in images (edges and contours) that are very important elements in visual perception, intersect too many wavelet basis functions and lead to a nonsparse representation. To efficiently capture these anisotropic geometrical structures characterized by many more than the horizontal and vertical directions, a more complex multidirectional (M-DIR) and anisotropic transform is required. We present a new lattice-based perfect reconstruction and critically sampled anisotropic M-DIR WT. The transform retains the separable filtering and subsampling and the simplicity of computations and filter design from the standard two-dimensional WT, unlike in the case of some other directional transform constructions (e.g., curvelets, contourlets, or edgelets). The corresponding anisotropic basis functions (directionlets) have directional vanishing moments along any two directions with rational slopes. Furthermore, we show that this novel transform provides an efficient tool for nonlinear approximation of images, achieving the approximation power O(N/sup -1.55/), which, while slower than the optimal rate O(N/sup -2/), is much better than O(N/sup -1/) achieved with wavelets, but at similar complexity.
320 citations
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TL;DR: In this article, the authors use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate global gridded net radiation, latent and sensible heat and their uncertainties.
Abstract: Although a key driver of Earth’s climate system, global land-atmosphere energy fluxes are poorly constrained. Here we use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate global gridded net radiation, latent and sensible heat and their uncertainties. The resulting FLUXCOM database comprises 147 products in two setups: (1) 0.0833° resolution using MODIS remote sensing data (RS) and (2) 0.5° resolution using remote sensing and meteorological data (RS + METEO). Within each setup we use a full factorial design across machine learning methods, forcing datasets and energy balance closure corrections. For RS and RS + METEO setups respectively, we estimate 2001–2013 global (±1 s.d.) net radiation as 75.49 ± 1.39 W m−2 and 77.52 ± 2.43 W m−2, sensible heat as 32.39 ± 4.17 W m−2 and 35.58 ± 4.75 W m−2, and latent heat flux as 39.14 ± 6.60 W m−2 and 39.49 ± 4.51 W m−2 (as evapotranspiration, 75.6 ± 9.8 × 103 km3 yr−1 and 76 ± 6.8 × 103 km3 yr−1). FLUXCOM products are suitable to quantify global land-atmosphere interactions and benchmark land surface model simulations. Machine-accessible metadata file describing the reported data (ISA-Tab format)
319 citations
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TL;DR: Primary colon cancer: ESMO Clinical Practice Guidelines for diagnosis, adjuvant treatment and follow-up
319 citations
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TL;DR: In this paper, a class of models which extend the standard model by adding new neutral fermions is analyzed. But the authors do not consider the effect of neutrinos on the superstring.
319 citations
Authors
Showing all 27402 results
Name | H-index | Papers | Citations |
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H. S. Chen | 179 | 2401 | 178529 |
Alvaro Pascual-Leone | 165 | 969 | 98251 |
Sabino Matarrese | 155 | 775 | 123278 |
Subir Sarkar | 149 | 1542 | 144614 |
Carlos Escobar | 148 | 1184 | 95346 |
Marco Costa | 146 | 1458 | 105096 |
Carmen García | 139 | 1503 | 96925 |
Javier Cuevas | 138 | 1689 | 103604 |
M. I. Martínez | 134 | 1251 | 79885 |
Marco Aurelio Diaz | 134 | 1015 | 93580 |
Avelino Corma | 134 | 1049 | 89095 |
Kevin Lannon | 133 | 1652 | 95436 |
Marina Cobal | 132 | 1078 | 85437 |
Mogens Dam | 131 | 1109 | 83717 |
Marcel Vos | 131 | 993 | 85194 |