486 research outputs found
Structure-Aware Shape Synthesis
We propose a new procedure to guide training of a data-driven shape
generative model using a structure-aware loss function. Complex 3D shapes often
can be summarized using a coarsely defined structure which is consistent and
robust across variety of observations. However, existing synthesis techniques
do not account for structure during training, and thus often generate
implausible and structurally unrealistic shapes. During training, we enforce
structural constraints in order to enforce consistency and structure across the
entire manifold. We propose a novel methodology for training 3D generative
models that incorporates structural information into an end-to-end training
pipeline.Comment: Accepted to 3DV 201
Proton mass decomposition
We report the results on the proton mass decomposition and also on the
related quark and glue momentum fractions. The results are based on overlap
valence fermions on four ensembles of DWF configurations with three
lattice spacings and volumes, and several pion masses including the physical
pion mass. With 1-loop perturbative calculation and proper normalization of the
glue operator, we find that the and quark masses contribute 9(2)\%
to the proton mass. The quark energy and glue field energy contribute 31(5)\%
and 37(5)\% respectively in the scheme at GeV. The
trace anomaly gives the remaining 23(1)\% contribution. The and glue
momentum fractions in the scheme are consistent with the global
analysis at GeV.Comment: 6 pages. Proceedings of the 35th International Symposium on Lattice
Field Theory (Lattice2017), Granada, Spai
Anatomy of the resonance from lattice QCD at the physical point
We propose a strategy to access the component of the
resonance in lattice QCD. Through a mixed action formalism (overlap valence on
domain wall sea), the energy of the component is derived at
different valence quark masses, and shows a linear dependence on . The
slope is determined to be , from which the
valence sigma term is extracted to be MeV using the Feynman-Hellman theorem. At the physical pion
mass, the mass of the component is interpolated to be
MeV, which is close to the resonance mass.
We also obtain the leptonic decay constant of the component to be
MeV, which can be compared with the
experimental value MeV through the relation
with being
the on-shell wavefunction renormalization of owing to the
interaction. We emphasize that and of the
component, which are obtained for the first time from QCD, can be taken as the
input parameters of in effective field theory studies where acts
as a fundamental degree of freedom.Comment: 7 pages, 4 figures. Considerably modified, more discussions, matching
to the published versio
Proton Mass Decomposition from the QCD Energy Momentum Tensor
We report results on the proton mass decomposition and also on related quark
and glue momentum fractions. The results are based on overlap valence fermions
on four ensembles of DWF configurations with three lattice spacings
and three volumes, and several pion masses including the physical pion mass.
With fully non-perturbative renormalization (and universal normalization on
both quark and gluon), we find that the quark energy and glue field energy
contribute 33(4)(4)\% and 37(5)(4)\% respectively in the scheme
at GeV. A quarter of the trace anomaly gives a 23(1)(1)\%
contribution to the proton mass based on the sum rule, given 9(2)(1)\%
contribution from the and quark scalar condensates. The and
glue momentum fractions in the scheme are in good agreement
with global analyses at GeV.Comment: 10 pages, 6 figure
Enhancing Cardiac MRI Segmentation via Classifier-Guided Two-Stage Network and All-Slice Information Fusion Transformer
Cardiac Magnetic Resonance imaging (CMR) is the gold standard for assessing
cardiac function. Segmenting the left ventricle (LV), right ventricle (RV), and
LV myocardium (MYO) in CMR images is crucial but time-consuming. Deep
learning-based segmentation methods have emerged as effective tools for
automating this process. However, CMR images present additional challenges due
to irregular and varying heart shapes, particularly in basal and apical slices.
In this study, we propose a classifier-guided two-stage network with an
all-slice fusion transformer to enhance CMR segmentation accuracy, particularly
in basal and apical slices. Our method was evaluated on extensive clinical
datasets and demonstrated better performance in terms of Dice score compared to
previous CNN-based and transformer-based models. Moreover, our method produces
visually appealing segmentation shapes resembling human annotations and avoids
common issues like holes or fragments in other models' segmentations.Comment: Accepted by 2023 MICCAI AMAI worksho
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