Search results for "Linear"
showing 10 items of 7165 documents
Testing for Government Intertemporal Solvency: A Smooth Transition Error Correction Model Approach
2001
Applied macroeconomists have tested for the government intertemporal solvency condition by either testing for linear stationarity in the total government deficit series or testing for linear cointegration between total government spending and total tax revenues. A number of authors have focused, in particular, on structural breaks in the government deficit process. In this paper, we use a smooth transition error correction model to test and estimate a shift in the adjustment toward a linear cointegration relationship between the government spending to output ratio and the total tax revenues to output ratio. Estimation results show that government authorities react only to large (in absolute…
Spectral analysis of the Neumann-Poincaré operator and characterization of the stress concentration in anti-plane elasticity
2012
When holes or hard elastic inclusions are closely located, stress which is the gradient of the solution to the anti-plane elasticity equation can be arbitrarily large as the distance between two inclusions tends to zero. It is important to precisely characterize the blow-up of the gradient of such an equation. In this paper we show that the blow-up of the gradient can be characterized by a singular function defined by the single layer potential of an eigenfunction corresponding to the eigenvalue 1/2 of a Neumann–Poincare type operator defined on the boundaries of the inclusions. By comparing the singular function with the one corresponding to two disks osculating to the inclusions, we quant…
A sub-supersolution approach for Neumann boundary value problems with gradient dependence
2020
Abstract Existence and location of solutions to a Neumann problem driven by an nonhomogeneous differential operator and with gradient dependence are established developing a non-variational approach based on an adequate method of sub-supersolution. The abstract theorem is applied to prove the existence of finitely many positive solutions or even infinitely many positive solutions for a class of Neumann problems.
Prediction of the difficulty level in a standardized reading comprehension test : contributions from cognitive psychology and psychometrics
2013
Esta investigación busca identificar posibles variables predictoras del nivel de dificultad de los ítems de comprensión de lectura utilizados en una prueba psicométrica estandarizada para la admisión a una institución universitaria. Se propusieron varios posibles predictores del nivel de dificultad, a saber: densidad proposicional, negaciones, estructura sintáctica, dificultad del vocabulario, presencia elementos de realce (palabras resaltadas tipográficamente), abstracción del ítem y grado de similitud entre opción correcta y texto relevante para resolver el ítem. Mediante el Modelo Logístico Lineal de Rasgo Latente se encontró que la cantidad de proposiciones, la estructura sintáctica y, …
Advances in Design, Simulation and Manufacturing IV
2021
This book reports on topics at the interface between mechanical and chemical engineering, emphasizing design, simulation, and manufacturing. Specifically, it covers recent developments in the mechanics of solids and structures, numerical simulation of coupled problems, including fatigue, fluid behavior, particle movement, pressure distribution. Further, it reports on developments in chemical process technology, heat and mass transfer, energy-efficient technologies, and industrial ecology. Based on the 4th International Conference on Design, Simulation, Manufacturing: The Innovation Exchange (DSMIE-2021), held on June 8-11, 2021, in Lviv, Ukraine, this second volume of a 2-volume set provide…
Semisupervised nonlinear feature extraction for image classification
2012
Feature extraction is of paramount importance for an accurate classification of remote sensing images. Techniques based on data transformations are widely used in this context. However, linear feature extraction algorithms, such as the principal component analysis and partial least squares, can address this problem in a suboptimal way because the data relations are often nonlinear. Kernel methods may alleviate this problem only when the structure of the data manifold is properly captured. However, this is difficult to achieve when small-size training sets are available. In these cases, exploiting the information contained in unlabeled samples together with the available training data can si…
Kernel-Based Inference of Functions Over Graphs
2018
Abstract The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting—and prevalent in several fields of study—problem is that of inferring a function defined over the nodes of a network. This work presents a versatile kernel-based framework for tackling this inference problem that naturally subsumes and generalizes the reconstruction approaches put forth recently for the signal processing by the community studying graphs. Both the static and the dynamic settings are considered along with effective modeling approaches for addressing real-world problems. The analytical discussion herein is complement…
Model selection based product kernel learning for regression on graphs
2013
The choice of a suitable graph kernel is intrinsically hard and often cannot be made in an informed manner for a given dataset. Methods for multiple kernel learning offer a possible remedy, as they combine and weight kernels on the basis of a labeled training set of molecules to define a new kernel. Whereas most methods for multiple kernel learning focus on learning convex linear combinations of kernels, we propose to combine kernels in products, which theoretically enables higher expressiveness. In experiments on ten publicly available chemical QSAR datasets we show that product kernel learning is on no dataset significantly worse than any of the competing kernel methods and on average the…
Mapping nonlinear gravity into General Relativity with nonlinear electrodynamics
2018
We show that families of nonlinear gravity theories formulated in a metric-affine approach and coupled to a nonlinear theory of electrodynamics can be mapped into General Relativity (GR) coupled to another nonlinear theory of electrodynamics. This allows to generate solutions of the former from those of the latter using purely algebraic transformations. This correspondence is explicitly illustrated with the Eddington-inspired Born-Infeld theory of gravity, for which we consider a family of nonlinear electrodynamics and show that, under the map, preserve their algebraic structure. For the particular case of Maxwell electrodynamics coupled to Born-Infeld gravity we find, via this corresponden…
Incremental Gaussian Discriminant Analysis based on Graybill and Deal weighted combination of estimators for brain tumour diagnosis
2011
In the last decade, machine learning (ML) techniques have been used for developing classifiers for automatic brain tumour diagnosis. However, the development of these ML models rely on a unique training set and learning stops once this set has been processed. Training these classifiers requires a representative amount of data, but the gathering, preprocess, and validation of samples is expensive and time-consuming. Therefore, for a classical, non-incremental approach to ML, it is necessary to wait long enough to collect all the required data. In contrast, an incremental learning approach may allow us to build an initial classifier with a smaller number of samples and update it incrementally…