Search results for "Approx"

Is There Anything New to Say About SIFT Matching?

2020

SIFT is a classical hand-crafted, histogram-based descriptor that has deeply influenced research on image matching for more than a decade. In this paper, a critical review of the aspects that affect SIFT matching performance is carried out, and novel descriptor design strategies are introduced and individually evaluated. These encompass quantization, binarization and hierarchical cascade filtering as means to reduce data storage and increase matching efficiency, with no significant loss of accuracy. An original contextual matching strategy based on a symmetrical variant of the usual nearest-neighbor ratio is discussed as well, that can increase the discriminative power of any descriptor. Th…

Approximation through suffixation:-ḍḍu/-a in Sicilian

2023

This article investigates the use of the Sicilian suffix -ḍḍu/-afor the expression of ap-proximation. On the basis of a survey of a corpus of ethnotexts and the outputs of a translation questionnaire, we propose that approximation is a core value in the semantic network of the suffix, expressing a certain distance fromthe default values conveyed by the base. In terms of prototypi-cality, this distance may occur from the categorial centre (internal approximation): the suffix mod-ifies the semantics of the base but does not alter the categorial status of the referent. Alternatively, the suffix may impact on categorial membership tout court (external approximation), questioning the categorial …

MR2410211 (2009b:47107) Păcurar, Mădălina Viscosity approximation of fixed points with $\phi$-contractions. Carpathian J. Math. 24 (2008), no. 1, 88-…

2009

Let T be a nonexpansive self-mapping of a closed bounded convex subset Y of a Hilbert space. For l in (0, 1), the author considers the iteration xl = lf(xl)+(1−l)Txl, where f from Y to Y is a $\phi$-contraction. Then, the author proves that (xl)l converges strongly as l goes to 0 to the unique fixed point of the $\phi$-contraction Pof, where P is the metric projection of Y onto the set FT of fixed points of T. The viscosity approximation method of the paper is obtained from the method proposed by A. Moudafi [J. Math. Anal. Appl. 241 (2000), no. 1, 46–55; MR1738332 (2000k:47085)] for mappings in Hilbert spaces, and by H. K. Xu [J. Math. Anal. Appl. 298 (2004), no. 1, 279–291; MR2086546 (2005…

ADVANCED MESHLESS NUMERICAL METHODS AND APPLICATIONS

Consistency Restoring in SPH for Trigonometric Functions Approximation

2009

Analysis of the Allee threshold via moving least square approximation

2016

Cooperation is a common behavior between the members of predators species, because it can improve theirs skill in hunt, especially in endangered eco-systems. This behavior it is well known to induce the Strong Allee effect, that can induce the extinction when the initial populations’ is under a critical density called ”Allee threshold ”. Here we investigate the impact of the pack hunting in a predator-prey system in which the predator suffers of an infectious disease with frequency and vertical transmission. The result is a three dimensional system with the predators population divided into susceptible and infected individuals. Studying the system dynamics a scenario was identified in which…

Accretion shock on CTTSs and its X-ray emission

2009

High spectral resolution X-ray observations of classical T Tauri stars (CTTSs) demonstrate the presence of plasma at T~2-3×10^6 K and ne~10^11-10^13 cm-3. Stationary models suggest that this emission is due to shock-heated accreting material. We address this issue by a 1-D hydrodynamic model of the impact of the accretion flow onto a chromosphere of a CTTS with the aim of investigating the stability of accretion shock and the role of the chromosphere. Our simulations include the effects of gravity, radiative losses from optically thin plasma, the thermal conduction and a detailed modeling of the stellar chromosphere. Here we present the results of a simulation based on the parameters of the…

Probabilistic response of linear structures equipped with nonlinear dampers devices (PIS method)

2008

Passive control introducing energy absorbing devices into the structure has received considerable attention in recent years. Unfortunately the constitutive law of viscous fluid dampers is highly nonlinear, and even supposing that the structure behaves linearly, the whole system has inherent nonlinear properties. Usually the analysis is performed by a stochastic linearization technique (SLT) determining a linear system equivalent to the nonlinear one, in a statistical sense. In this paper the effect of the non-Gaussianity of the response due to the inherent nonlinearity of the damper device will be studied in detail via the Path Integral Solution (PIS) method. A systematic study is conducted…

Random Feature Approximation for Online Nonlinear Graph Topology Identification

2021

Online topology estimation of graph-connected time series is challenging, especially since the causal dependencies in many real-world networks are nonlinear. In this paper, we propose a kernel-based algorithm for graph topology estimation. The algorithm uses a Fourier-based Random feature approximation to tackle the curse of dimensionality associated with the kernel representations. Exploiting the fact that the real-world networks often exhibit sparse topologies, we propose a group lasso based optimization framework, which is solve using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. The experiments con…

A probabilistic compressive sensing framework with applications to ultrasound signal processing

2019

Abstract The field of Compressive Sensing (CS) has provided algorithms to reconstruct signals from a much lower number of measurements than specified by the Nyquist-Shannon theorem. There are two fundamental concepts underpinning the field of CS. The first is the use of random transformations to project high-dimensional measurements onto a much lower-dimensional domain. The second is the use of sparse regression to reconstruct the original signal. This assumes that a sparse representation exists for this signal in some known domain, manifested by a dictionary. The original formulation for CS specifies the use of an l 1 penalised regression method, the Lasso. Whilst this has worked well in l…