2,953 research outputs found
Graph Neural Networks with Generated Parameters for Relation Extraction
Recently, progress has been made towards improving relational reasoning in
machine learning field. Among existing models, graph neural networks (GNNs) is
one of the most effective approaches for multi-hop relational reasoning. In
fact, multi-hop relational reasoning is indispensable in many natural language
processing tasks such as relation extraction. In this paper, we propose to
generate the parameters of graph neural networks (GP-GNNs) according to natural
language sentences, which enables GNNs to process relational reasoning on
unstructured text inputs. We verify GP-GNNs in relation extraction from text.
Experimental results on a human-annotated dataset and two distantly supervised
datasets show that our model achieves significant improvements compared to
baselines. We also perform a qualitative analysis to demonstrate that our model
could discover more accurate relations by multi-hop relational reasoning
Contrasting the Implicit Method in Incoherent Lagrangian and the Correction Map Method in Hamiltonian
The equations of motion for a Lagrangian mainly refer to the acceleration
equations, which can be obtained by the Euler--Lagrange equations. In the
post-Newtonian Lagrangian form of general relativity, the Lagrangian systems
can only maintain a certain post-Newtonian order and are incoherent Lagrangians
since the higher-order terms are omitted. This truncation can cause some
changes in the constant of motion. However, in celestial mechanics,
Hamiltonians are more commonly used than Lagrangians. The conversion from
Lagrangian to Hamiltonian can be achieved through the Legendre transformation.
The coordinate momentum separable Hamiltonian can be computed by the symplectic
algorithm, whereas the inseparable Hamiltonian can be used to compute the
evolution of motion by the phase-space expansion method. Our recent work
involves the design of a multi-factor correction map for the phase-space
expansion method, known as the correction map method. In this paper, we compare
the performance of the implicit algorithm in post-Newtonian Lagrangians and the
correction map method in post-Newtonian Hamiltonians. Specifically, we
investigate the extent to which both methods can uphold invariance of the
motion's constants, such as energy conservation and angular momentum
preservation. Ultimately, the results of numerical simulations demonstrate the
superior performance of the correction map method, particularly with respect to
angular momentum conservation
Black hole scalarizations induced by parity violations
It is well-known that parity symmetry is broken in the weak interaction but
conserved for Einstein's general relativity and Maxwell's electromagnetic
theory. Nevertheless, parity symmetry could also be violated in the
gravitational/electromagnetic sectors if a fundamental scalar field couples to
the parity-violating gravitational/electromagnetic curvature terms. Such
parity-violating terms, which flip signs under reversed spatial directions, can
inevitably lead to a negative effective mass squared for the scalar field
perturbations near nonspherically symmetric black holes and thus are expected
to trigger tachyonic instability. As illustrative examples, we show that the
scalar field coupled to gravitational/electromagnetic Chern-Simons terms near a
Kerr-Newmann spacetime can develop tachyonic instabilities, leading to
equilibrium scalar field configurations in certain parameter regions of black
holes. This instability, which is an indication of the black hole scalarization
process, can occur in a broad class of nonspherically symmetric black holes and
parity-violating theories.Comment: 9 pages, 3 figures, 1 tabl
Photoproduction of in peripheral isobar collisions
We investigate the photoproduction of di-electrons in peripheral collisions
of and
at 200 GeV. With the charge and
mass density distributions given by the calculation of the density functional
theory, we calculate the spectra of transverse momentum, invariant mass and
azimuthal angle for di-electrons at 40-80\% centrality. The ratios of these
spectra in Ru+Ru collisions over to Zr+Zr collisions are shown to be smaller
than (the ratio of for Ru and Zr) at low transverse
momentum. The deviation arises from the different mass and charge density
distributions in Ru and Zr. So the photoproduction of di-leptons in isobar
collisions may provide a new way to probe the nuclear structure.Comment: 17 pages, 6 figure
Re-examining the premise of isobaric collisions and a novel method to measure the chiral magnetic effect
In these proceedings we show that the premise of the isobaric and collisions to search for the chiral magnetic effect (CME) may not hold as originally anticipated due to large uncertainties in the isobaric nuclear structures. We demonstrate this using Woods-Saxon densities and the proton and neutron densities calculated by the density functional theory. Furthermore, a novel method is proposed to gauge background and possible CME contributions in the same system, intrinsically better than the isobaric collisions of two different systems. We illustrate the method with Monte Carlo Glauber and AMPT (A Multi-Phase Transport) simulations
SDM-NET: Deep Generative Network for Structured Deformable Mesh
We introduce SDM-NET, a deep generative neural network which produces
structured deformable meshes. Specifically, the network is trained to generate
a spatial arrangement of closed, deformable mesh parts, which respect the
global part structure of a shape collection, e.g., chairs, airplanes, etc. Our
key observation is that while the overall structure of a 3D shape can be
complex, the shape can usually be decomposed into a set of parts, each
homeomorphic to a box, and the finer-scale geometry of the part can be
recovered by deforming the box. The architecture of SDM-NET is that of a
two-level variational autoencoder (VAE). At the part level, a PartVAE learns a
deformable model of part geometries. At the structural level, we train a
Structured Parts VAE (SP-VAE), which jointly learns the part structure of a
shape collection and the part geometries, ensuring a coherence between global
shape structure and surface details. Through extensive experiments and
comparisons with the state-of-the-art deep generative models of shapes, we
demonstrate the superiority of SDM-NET in generating meshes with visual
quality, flexible topology, and meaningful structures, which benefit shape
interpolation and other subsequently modeling tasks.Comment: Conditionally Accepted to Siggraph Asia 201
TM-NET: Deep Generative Networks for Textured Meshes
We introduce TM-NET, a novel deep generative model for synthesizing textured
meshes in a part-aware manner. Once trained, the network can generate novel
textured meshes from scratch or predict textures for a given 3D mesh, without
image guidance. Plausible and diverse textures can be generated for the same
mesh part, while texture compatibility between parts in the same shape is
achieved via conditional generation. Specifically, our method produces texture
maps for individual shape parts, each as a deformable box, leading to a natural
UV map with minimal distortion. The network separately embeds part geometry
(via a PartVAE) and part texture (via a TextureVAE) into their respective
latent spaces, so as to facilitate learning texture probability distributions
conditioned on geometry. We introduce a conditional autoregressive model for
texture generation, which can be conditioned on both part geometry and textures
already generated for other parts to achieve texture compatibility. To produce
high-frequency texture details, our TextureVAE operates in a high-dimensional
latent space via dictionary-based vector quantization. We also exploit
transparencies in the texture as an effective means to model complex shape
structures including topological details. Extensive experiments demonstrate the
plausibility, quality, and diversity of the textures and geometries generated
by our network, while avoiding inconsistency issues that are common to novel
view synthesis methods
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