Search results for "Names"
showing 10 items of 6843 documents
Feigenbaum graphs: a complex network perspective of chaos
2011
The recently formulated theory of horizontal visibility graphs transforms time series into graphs and allows the possibility of studying dynamical systems through the characterization of their associated networks. This method leads to a natural graph-theoretical description of nonlinear systems with qualities in the spirit of symbolic dynamics. We support our claim via the case study of the period-doubling and band-splitting attractor cascades that characterize unimodal maps. We provide a universal analytical description of this classic scenario in terms of the horizontal visibility graphs associated with the dynamics within the attractors, that we call Feigenbaum graphs, independent of map…
Fractional Derivatives in Interval Analysis
2017
In this paper, interval fractional derivatives are presented. We consider uncertainty in both the order and the argument of the fractional operator. The approach proposed takes advantage of the property of Fourier and Laplace transforms with respect to the translation operator, in order to first define integral transform of interval functions. Subsequently, the main interval fractional integrals and derivatives, such as the Riemann–Liouville, Caputo, and Riesz, are defined based on their properties with respect to integral transforms. Moreover, uncertain-but-bounded linear fractional dynamical systems, relevant in modeling fractional viscoelasticity, excited by zero-mean stationary Gaussian…
Temperature and doping dependence of normal state spectral properties in a two-orbital model for ferropnictides
2016
Using a second-order perturbative Green's functions approach we determined the normal state single-particle spectral function $A(\vec{k},\omega)$ employing a minimal effective model for iron-based superconductors. The microscopic model, used before to study magnetic fluctuations and superconducting properties, includes the two effective tight-binding bands proposed by S.Raghu et al. [Phys. Rev. B 77, 220503 (R) (2008)], and intra- and inter-orbital local electronic correlations, related to the Fe-3d orbitals. Here, we focus on the study of normal state electronic properties, in particular the temperature and doping dependence of the total density of states, $A(\omega)$, and of $A(\vec{k},\o…
Precision Measurement of the First Ionization Potential of Nobelium
2018
One of the most important atomic properties governing an element's chemical behavior is the energy required to remove its least-bound electron, referred to as the first ionization potential. For the heaviest elements, this fundamental quantity is strongly influenced by relativistic effects which lead to unique chemical properties. Laser spectroscopy on an atom-at-a-time scale was developed and applied to probe the optical spectrum of neutral nobelium near the ionization threshold. The first ionization potential of nobelium is determined here with a very high precision from the convergence of measured Rydberg series to be 6.626 21±0.000 05 eV. This work provides a stringent benchmark for st…
The thin and medium filters of the EPIC camera on-board XMM-Newton: measured performance after more than 15 years of operation
2016
After more than 15 years of operation of the EPIC camera on board the XMM-Newton X-ray observatory, we have reviewed the status of its Thin and Medium filters. We have selected a set of Thin and Medium back-up filters among those still available in the EPIC consortium and have started a program to investigate their status by different laboratory measurements including: UV/VIS transmission, Raman scattering, X-Ray Photoelectron Spectroscopy, and Atomic Force Microscopy. Furthermore, we have investigated the status of the EPIC flight filters by performing an analysis of the optical loading in the PN offset maps to gauge variations in the optical and UV transmission. We both investigated repea…
A Survey on Gaussian Processes for Earth-Observation Data Analysis: A Comprehensive Investigation
2016
Gaussian processes (GPs) have experienced tremendous success in biogeophysical parameter retrieval in the last few years. GPs constitute a solid Bayesian framework to consistently formulate many function approximation problems. This article reviews the main theoretical GP developments in the field, considering new algorithms that respect signal and noise characteristics, extract knowledge via automatic relevance kernels to yield feature rankings automatically, and allow applicability of associated uncertainty intervals to transport GP models in space and time that can be used to uncover causal relations between variables and can encode physically meaningful prior knowledge via radiative tra…
Latent force models for earth observation time series prediction
2016
We introduce latent force models for Earth observation time series analysis. The model uses Gaussian processes and differential equations to combine data driven modelling with a physical model of the system. The LFM presented here performs multi-output structured regression, adapts to the signal characteristics, it can cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. We successfully illustrate the performance in challenging scenarios of crop monitoring from space, providing time-resolved time series predictions.
Nonlinear PCA for Spatio-Temporal Analysis of Earth Observation Data
2020
Remote sensing observations, products, and simulations are fundamental sources of information to monitor our planet and its climate variability. Uncovering the main modes of spatial and temporal variability in Earth data is essential to analyze and understand the underlying physical dynamics and processes driving the Earth System. Dimensionality reduction methods can work with spatio-temporal data sets and decompose the information efficiently. Principal component analysis (PCA), also known as empirical orthogonal functions (EOFs) in geophysics, has been traditionally used to analyze climatic data. However, when nonlinear feature relations are present, PCA/EOF fails. In this article, we pro…
Statistical biophysical parameter retrieval and emulation with Gaussian processes
2019
Abstract Earth observation from satellites poses challenging problems where machine learning is being widely adopted as a key player. Perhaps the most challenging scenario that we are facing nowadays is to provide accurate estimates of particular variables of interest characterizing the Earth's surface. This chapter introduces some recent advances in statistical bio-geophysical parameter retrieval from satellite data. In particular, we will focus on Gaussian process regression (GPR) that has excelled in parameter estimation as well as in modeling complex radiative transfer processes. GPR is based on solid Bayesian statistics and generally yields efficient and accurate parameter estimates, a…
Gradient-Based Automatic Lookup Table Generator for Radiative Transfer Models
2022
Physically based radiative transfer models (RTMs) are widely used in Earth observation to understand the radiation processes occurring on the Earth’s surface and their interactions with water, vegetation, and atmosphere. Through continuous improvements, RTMs have increased in accuracy and representativity of complex scenes at expenses of an increase in complexity and computation time, making them impractical in various remote sensing applications. To overcome this limitation, the common practice is to precompute large lookup tables (LUTs) for their later interpolation. To further reduce the RTM computation burden and the error in LUT interpolation, we have developed a method to automaticall…