3,715 research outputs found
Nuclear effects in neutrino and antineutrino CCQE scattering at MINERvA kinematics
We compare the charged-current quasielastic neutrino and antineutrino
observables obtained in two different nuclear models, the phenomenological
SuperScaling Approximation and the Relativistic Mean Field approach, with the
recent data published by the MINERvA Collaboration. Both models provide a good
description of the data without the need of an ad hoc increase in the mass
parameter in the axial-vector dipole form factor. Comparisons are also made
with the MiniBooNE results where different conclusions are reached.Comment: 6 pages, 7 figures, Accepted for publication in Physical Review
Estimate of the theoretical uncertainty of the cross sections for nucleon knockout in neutral-current neutrino-oxygen interactions
Free nucleons propagating in water are known to produce gamma rays, which
form a background to the searches for diffuse supernova neutrinos and sterile
neutrinos carried out with Cherenkov detectors. As a consequence, the process
of nucleon knockout induced by neutral-current quasielastic interactions of
atmospheric (anti)neutrinos with oxygen needs to be under control at the
quantitative level in the background simulations of the ongoing and future
experiments. In this paper, we provide a quantitative assessment of the
uncertainty associated with the theoretical description of the nuclear cross
sections, estimating it from the discrepancies between the predictions of
different models.Comment: 7 pages, 2 figure
Protocolo de recuperaciĂłn funcional de una lesiĂłn ligamentosa de rodilla en baloncesto
A face recognition system for assistive robots
Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience.
To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance.
In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots.
Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet.
We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS).
The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.This work is supported by the Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech, the research projects WISER ([DPI2017-84827-R]),funded by the Spanish Government, and financed by European RegionalDevelopment’s funds (FEDER), and MoveCare ([ICT-26-2016b-GA-732158]), funded by the European H2020 program, and by a postdoc contract from the I-PPIT-UMA program financed by the University of Málaga
Determination of Arsenic, Mercury and Barium in herbarium mount paper using dynamic ultrasound-assisted extraction prior to atomic fluorescence and absorption spectrometry
A dynamic ultrasound-assisted extraction method using Atomic Absorption and Atomic Flourescence spectrometers as detectors was developed to analyse mercury, arsenic and barium from herbarium mount paper originating from the herbarium collection of the National Museum of Wales. The variables influencing extraction were optimised by a multivariate approach. The optimal conditions were found to be 1% HNO3 extractant solution used at a flow rate of 1 mL min-1. The duty cycle and amplitude of the ultrasonic probe was found to be 50% in both cases with an ultrasound power of 400 W. The optimal distance between the probe and the top face of the extraction chamber was found to be 0 cm. Under these conditions the time required for complete extraction of the three analytes was 25 min. Cold vapour and hydride generation coupled to atomic fluorescence spectrometry was utilized to determine mercury and arsenic, respectively. The chemical and instrumental conditions were optimized to provide detection limits of 0.01ng g-1 and 1.25 ng g-1 for mercury and arsenic, respectively. Barium was determined by graphite-furnace atomic absorption spectrometry, with a detection limit of 25 ng g-1. By using 0.5 g of sample, the concentrations of the target analytes varied for the different types of paper and ranged between 0.4–2.55 µg g-1 for Ba, 0.035–10.47 µg g-1 for As and 0.0046–2.37 µg g-1 for Hg
On the ubiquity of trivial torsion on elliptic curves
The purpose of this paper is to give a "down--to--earth" proof of the
well--known fact that a randomly chosen elliptic curve over the rationals is
most likely to have trivial torsion
Testing the chemical tagging technique with open clusters
Context. Stars are born together from giant molecular clouds and, if we
assume that the priors were chemically homogeneous and well-mixed, we expect
them to share the same chemical composition. Most of the stellar aggregates are
disrupted while orbiting the Galaxy and most of the dynamic information is
lost, thus the only possibility of reconstructing the stellar formation history
is to analyze the chemical abundances that we observe today.
Aims. The chemical tagging technique aims to recover disrupted stellar
clusters based merely on their chemical composition. We evaluate the viability
of this technique to recover co-natal stars that are no longer gravitationally
bound.
Methods. Open clusters are co-natal aggregates that have managed to survive
together. We compiled stellar spectra from 31 old and intermediate-age open
clusters, homogeneously derived atmospheric parameters, and 17 abundance
species, and applied machine learning algorithms to group the stars based on
their chemical composition. This approach allows us to evaluate the viability
and efficiency of the chemical tagging technique.
Results. We found that stars at different evolutionary stages have distinct
chemical patterns that may be due to NLTE effects, atomic diffusion, mixing,
and biases. When separating stars into dwarfs and giants, we observed that a
few open clusters show distinct chemical signatures while the majority show a
high degree of overlap. This limits the recovery of co-natal aggregates by
applying the chemical tagging technique. Nevertheless, there is room for
improvement if more elements are included and models are improved.Comment: accepted for publication in Astronomy and Astrophysics. Corrected
typo
Electron- versus neutrino-nucleus scattering
We illustrate the connection between electron and neutrino scattering off
nuclei and show how the former process can be used to constrain the description
of the latter. After reviewing some of the nuclear models commonly used to
study lepton-nucleus reactions, we describe in detail the SuSAv2 model and show
how its predictions compare with the available electron- and
neutrino-scattering data over the kinematical range going from the
quasi-elastic peak to pion-production and highly inelastic scattering.Comment: Shortened version, 71 pages, review article, 52 figure
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