9,560 research outputs found
Unconstrained Face Verification using Deep CNN Features
In this paper, we present an algorithm for unconstrained face verification
based on deep convolutional features and evaluate it on the newly released
IARPA Janus Benchmark A (IJB-A) dataset. The IJB-A dataset includes real-world
unconstrained faces from 500 subjects with full pose and illumination
variations which are much harder than the traditional Labeled Face in the Wild
(LFW) and Youtube Face (YTF) datasets. The deep convolutional neural network
(DCNN) is trained using the CASIA-WebFace dataset. Extensive experiments on the
IJB-A dataset are provided
A Proximity-Aware Hierarchical Clustering of Faces
In this paper, we propose an unsupervised face clustering algorithm called
"Proximity-Aware Hierarchical Clustering" (PAHC) that exploits the local
structure of deep representations. In the proposed method, a similarity measure
between deep features is computed by evaluating linear SVM margins. SVMs are
trained using nearest neighbors of sample data, and thus do not require any
external training data. Clusters are then formed by thresholding the similarity
scores. We evaluate the clustering performance using three challenging
unconstrained face datasets, including Celebrity in Frontal-Profile (CFP),
IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3)
datasets. Experimental results demonstrate that the proposed approach can
achieve significant improvements over state-of-the-art methods. Moreover, we
also show that the proposed clustering algorithm can be applied to curate a set
of large-scale and noisy training dataset while maintaining sufficient amount
of images and their variations due to nuisance factors. The face verification
performance on JANUS CS3 improves significantly by finetuning a DCNN model with
the curated MS-Celeb-1M dataset which contains over three million face images
Systematic study of proton radioactivity of spherical proton emitters within various versions of proximity potential formalisms
In this work we present a systematic study of the proton radioactivity
half-lives of spherical proton emitters within the Coulomb and proximity
potential model. We investigate 28 different versions of the proximity
potential formalisms developed for the description of proton radioactivity,
decay and heavy particle radioactivity. It is found that 21
of them are not suitable to deal with the proton radioactivity, because the
classical turning points cannot be obtained due to the fact
that the depth of the total interaction potential between the emitted proton
and the daughter nucleus is above the proton radioactivity energy. Among the
other 7 versions of the proximity potential formalisms, it is Guo2013 which
gives the lowest rms deviation in the description of the experimental
half-lives of the known spherical proton emitters. We use this proximity
potential formalism to predict the proton radioactivity half-lives of 13
spherical proton emitters, whose proton radioactivity is energetically allowed
or observed but not yet quantified, within a factor of 3.71.Comment: 10 pages, 5 figures. This paper has been accepted by The European
Physical Journal A (in press 2019
Advantages of the multinucleon transfer reactions based on 238U target for producing neutron-rich isotopes around N = 126
The mechanism of multinucleon transfer (MNT) reactions for producing
neutron-rich heavy nuclei around N = 126 is investigated within two different
theoretical frameworks: dinuclear system (DNS) model and isospin-dependent
quantum molecular dynamics (IQMD) model. The effects of mass asymmetry
relaxation, N=Z equilibration, and shell closures on production cross sections
of neutron-rich heavy nuclei are investigated. For the first time, the
advantages for producing neutron-rich heavy nuclei around N = 126 is found in
MNT reactions based on 238U target. We propose the reactions with 238U target
for producing unknown neutron-rich heavy nuclei around N = 126 in the future.Comment: 6 pages, 6 figure
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