Search results for "vector [form factor]"

Vector FWM in optical fibers: tuning techniques and applications

2022

Recientemente, el efecto no lineal de mezcla de cuatro ondas (FWM) en fibras ópticas ha atraído un gran interés para el desarrollo de nuevas fuentes de luz de fibra óptica debido a la emisión de luz múltiple producida por este efecto no lineal. En los últimos años, estas fuentes de luz basadas en FWM han demostrado una gran utilidad en áreas como la óptica cuántica y la microscopía avanzada basada en efecto Raman. Además, según el estado de polarización de la luz de bombeo responsable del efecto FWM y la birrefringencia de la fibra, la luz producida por FWM puede presentar diferentes propiedades de polarización dada la naturaleza vectorial de FWM. Esto posibilita el diseño y desarrollo de f…

Measurement of spin-orbital angular momentum interactions in relativistic heavy-ion collisions

2020

The first evidence of spin alignment of vector mesons ($K^{*0}$ and $\phi$) in heavy-ion collisions at the Large Hadron Collider (LHC) is reported. The spin density matrix element $\rho_{00}$ is measured at midrapidity ($|y| <$ 0.5) in Pb-Pb collisions at a center-of-mass energy ($\sqrt{s_{\rm NN}}$) of 2.76 TeV with the ALICE detector. $\rho_{00}$ values are found to be less than 1/3 (1/3 implies no spin alignment) at low transverse momentum ($p_{\rm T} <$ 2 GeV/$c$) for $K^{*0}$ and $\phi$ at a level of 3$\sigma$ and 2$\sigma$, respectively. No significant spin alignment is observed for the $K^0_S$ meson (spin = 0) in Pb-Pb collisions and for the vector mesons in $pp$ collisions. The meas…

Vektoru rēķini

1942

A generalization of Françoise's algorithm for calculating higher order Melnikov functions

2002

Abstract In [J. Differential Equations 146 (2) (1998) 320–335], Francoise gives an algorithm for calculating the first nonvanishing Melnikov function Ml of a small polynomial perturbation of a Hamiltonian vector field and shows that Ml is given by an Abelian integral. This is done under the condition that vanishing of an Abelian integral of any polynomial form ω on the family of cycles implies that the form is algebraically relatively exact. We study here a simple example where Francoise's condition is not verified. We generalize Francoise's algorithm to this case and we show that Ml belongs to the C [ log t,t,1/t] module above the Abelian integrals. We also establish the linear differentia…

Abelian integrals and limit cycles

2006

Abstract The paper deals with generic perturbations from a Hamiltonian planar vector field and more precisely with the number and bifurcation pattern of the limit cycles. In this paper we show that near a 2-saddle cycle, the number of limit cycles produced in unfoldings with one unbroken connection, can exceed the number of zeros of the related Abelian integral, even if the latter represents a stable elementary catastrophe. We however also show that in general, finite codimension of the Abelian integral leads to a finite upper bound on the local cyclicity. In the treatment, we introduce the notion of simple asymptotic scale deformation.

Two New Alternatives to the Conventional Arm-in-Cage Test for Assessing Topical Repellents

2021

Abstract European guidelines for testing attractant and repellent efficacy (i.e., Product type 19 [PT19]) have been in revision since 2017. A key topic of discussion is the current approach to evaluating topical repellents. The European Chemical Agency has stated field testing should be avoided because of mosquito-borne disease risks. However, the most common laboratory method, the arm-in-cage (AIC) test, may limit the reliable extrapolation of lab results to field conditions. This study’s main goal was to assess alternative laboratory methods for evaluating topical mosquito repellents that use mosquito landing rates more representative of those in the field. The study took place at three E…

Active Learning for Monitoring Network Optimization

2012

Kernel-based active learning strategies were studied for the optimization of environmental monitoring networks. This chapter introduces the basic machine learning algorithms originated in the statistical learning theory of Vapnik (1998). Active learning is closer to an optimization done using sequential Gaussian simulations. The chapter presents the general ideas of statistical learning from data. It derives the basics of kernel-based support vector algorithms. The active learning framework is presented and machine learning extensions for active learning are described in the chapter. Kernel-based active learning strategies are tested on real case studies. The chapter explores the use of a c…

Remote sensing image segmentation by active queries

2012

Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained with reduced active datasets become faster and more robust. Strategies for intelligent sampling have been proposed with model-based heuristics aiming at the search of the most informative pixels to optimize model's performance. Unlike standard methods that concentrate on model optimization, here we propose a method inspired in the cluster assumption that holds in most of the remote sensing data. Starting from a complete hierarchical descri…

Discovering single classes in remote sensing images with active learning

2012

When dealing with supervised target detection, the acquisition of labeled samples is one of the most critical phases: the samples must be yet representative of the class of interest, but must also be found among a vast majority of non-target examples. Moreover, the efficiency of the search is also an issue, since the samples labeled as background are not used by target detectors such as the support vector data description (SVDD). In this work we propose a competitive and effective approach to identify the most relevant training samples for one-class classification based on the use of an active learning strategy. The SVDD classifier is first trained with insufficient target examples. It is t…

Improving active learning methods using spatial information

2011

Active learning process represents an interesting solution to the problem of training sample collection for the classification of remote sensing images. In this work, we propose a criterion based on the spatial information that can be used in combination with a spectral criterion in order to improve the selection of training samples. Experimental results obtained on a very high resolution image show the effectiveness of regularization in spatial domain and open challenging perspectives for terrain campaigns planning. © 2011 IEEE.