22,381 research outputs found
Clustering by Hierarchical Nearest Neighbor Descent (H-NND)
Previously in 2014, we proposed the Nearest Descent (ND) method, capable of
generating an efficient Graph, called the in-tree (IT). Due to some beautiful
and effective features, this IT structure proves well suited for data
clustering. Although there exist some redundant edges in IT, they usually have
salient features and thus it is not hard to remove them.
Subsequently, in order to prevent the seemingly redundant edges from
occurring, we proposed the Nearest Neighbor Descent (NND) by adding the
"Neighborhood" constraint on ND. Consequently, clusters automatically emerged,
without the additional requirement of removing the redundant edges. However,
NND proved still not perfect, since it brought in a new yet worse problem, the
"over-partitioning" problem.
Now, in this paper, we propose a method, called the Hierarchical Nearest
Neighbor Descent (H-NND), which overcomes the over-partitioning problem of NND
via using the hierarchical strategy. Specifically, H-NND uses ND to effectively
merge the over-segmented sub-graphs or clusters that NND produces. Like ND,
H-NND also generates the IT structure, in which the redundant edges once again
appear. This seemingly comes back to the situation that ND faces. However,
compared with ND, the redundant edges in the IT structure generated by H-NND
generally become more salient, thus being much easier and more reliable to be
identified even by the simplest edge-removing method which takes the edge
length as the only measure. In other words, the IT structure constructed by
H-NND becomes more fitted for data clustering. We prove this on several
clustering datasets of varying shapes, dimensions and attributes. Besides,
compared with ND, H-NND generally takes less computation time to construct the
IT data structure for the input data.Comment: 19 pages, 9 figure
Clustering by Descending to the Nearest Neighbor in the Delaunay Graph Space
In our previous works, we proposed a physically-inspired rule to organize the
data points into an in-tree (IT) structure, in which some undesired edges are
allowed to occur. By removing those undesired or redundant edges, this IT
structure is divided into several separate parts, each representing one
cluster. In this work, we seek to prevent the undesired edges from arising at
the source. Before using the physically-inspired rule, data points are at first
organized into a proximity graph which restricts each point to select the
optimal directed neighbor just among its neighbors. Consequently, separated
in-trees or clusters automatically arise, without redundant edges requiring to
be removed.Comment: 7 page
Carbon Nanotube Initiated Formation of Carbon Nanoscrolls
The unique topology and exceptional properties of carbon nanoscrolls (CNSs)
have inspired unconventional nano-device concepts, yet the fabrication of CNSs
remains rather challenging. Using molecular dynamics simulations, we
demonstrate the spontaneous formation of a CNS from graphene on a substrate,
initiated by a carbon nanotube (CNT). The rolling of graphene into a CNS is
modulated by the CNT size, the carbon-carbon interlayer adhesion, and the
graphene-substrate interaction. A phase diagram emerging from the simulations
can offer quantitative guideline toward a feasible and robust physical approach
to fabricating CNSs.Comment: 12 pages, 3 figure
IT-Dendrogram: A New Member of the In-Tree (IT) Clustering Family
Previously, we proposed a physically-inspired method to construct data points
into an effective in-tree (IT) structure, in which the underlying cluster
structure in the dataset is well revealed. Although there are some edges in the
IT structure requiring to be removed, such undesired edges are generally
distinguishable from other edges and thus are easy to be determined. For
instance, when the IT structures for the 2-dimensional (2D) datasets are
graphically presented, those undesired edges can be easily spotted and
interactively determined. However, in practice, there are many datasets that do
not lie in the 2D Euclidean space, thus their IT structures cannot be
graphically presented. But if we can effectively map those IT structures into a
visualized space in which the salient features of those undesired edges are
preserved, then the undesired edges in the IT structures can still be visually
determined in a visualization environment. Previously, this purpose was reached
by our method called IT-map. The outstanding advantage of IT-map is that
clusters can still be found even with the so-called crowding problem in the
embedding.
In this paper, we propose another method, called IT-Dendrogram, to achieve
the same goal through an effective combination of the IT structure and the
single link hierarchical clustering (SLHC) method. Like IT-map, IT-Dendrogram
can also effectively represent the IT structures in a visualization
environment, whereas using another form, called the Dendrogram. IT-Dendrogram
can serve as another visualization method to determine the undesired edges in
the IT structures and thus benefit the IT-based clustering analysis. This was
demonstrated on several datasets with different shapes, dimensions, and
attributes. Unlike IT-map, IT-Dendrogram can always avoid the crowding problem,
which could help users make more reliable cluster analysis in certain problems.Comment: 13 pages, 6 figures. IT-Dendrogram: An Effective Method to Visualize
the In-Tree structure by Dendrogra
Nonparametric Nearest Neighbor Descent Clustering based on Delaunay Triangulation
In our physically inspired in-tree (IT) based clustering algorithm and the
series after it, there is only one free parameter involved in computing the
potential value of each point. In this work, based on the Delaunay
Triangulation or its dual Voronoi tessellation, we propose a nonparametric
process to compute potential values by the local information. This computation,
though nonparametric, is relatively very rough, and consequently, many local
extreme points will be generated. However, unlike those gradient-based methods,
our IT-based methods are generally insensitive to those local extremes. This
positively demonstrates the superiority of these parametric (previous) and
nonparametric (in this work) IT-based methods.Comment: 7 pages; 6 figure
MsCGAN: Multi-scale Conditional Generative Adversarial Networks for Person Image Generation
To synthesize high-quality person images with arbitrary poses is challenging.
In this paper, we propose a novel Multi-scale Conditional Generative
Adversarial Networks (MsCGAN), aiming to convert the input conditional person
image to a synthetic image of any given target pose, whose appearance and the
texture are consistent with the input image. MsCGAN is a multi-scale
adversarial network consisting of two generators and two discriminators. One
generator transforms the conditional person image into a coarse image of the
target pose globally, and the other is to enhance the detailed quality of the
synthetic person image through a local reinforcement network. The outputs of
the two generators are then merged into a synthetic, discriminant and
high-resolution image. On the other hand, the synthetic image is downsampled to
multiple resolutions as the input to multi-scale discriminator networks. The
proposed multi-scale generators and discriminators handling different levels of
visual features can benefit to synthesizing high-resolution person images with
realistic appearance and texture. Experiments are conducted on the Market-1501
and DeepFashion datasets to evaluate the proposed model, and both qualitative
and quantitative results demonstrate the superior performance of the proposed
MsCGAN
The limit to rarefaction wave with vacuum for 1D compressible fluids with temperature-dependent viscosities
In this paper we study the zero dissipation limit of the one-dimensional full
compressible Navier-Stokes(CNS) equations with temperature-dependent viscosity
and heat-conduction coefficient. It is proved that given a rarefaction wave
with one-side vacuum state to the full compressible Euler equations, we can
construct a sequence of solutions to the full CNS equations which converge to
the above rarefaction wave with vacuum as the viscosity and the heat conduction
coefficient tend to zero. Moreover, the uniform convergence rate is obtained.
The main difficulty in our proof lies in the degeneracies of the density, the
temperature and the temperature-dependent viscosities at the vacuum region in
the zero dissipation limit.Comment: 31 pages. arXiv admin note: text overlap with arXiv:1011.199
Stability of the Superposition of a Viscous Contact Wave with two Rarefaction Waves to the bipolar Vlasov-Poisson-Boltzmann System
We investigate the nonlinear stability of the superposition of a viscous
contact wave and two rarefaction waves for one-dimensional bipolar
Vlasov-Poisson-Boltzmann (VPB) system, which can be used to describe the
transportation of charged particles under the additional electrostatic
potential force. Based on a new micro-macro type decomposition around the local
Maxwellian related to the bipolar VPB system in our previous work [26], we
prove that the superposition of a viscous contact wave and two rarefaction
waves is time-asymptotically stable to 1D bipolar VPB system under some
smallness conditions on the initial perturbations and wave strength, which
implies that this typical composite wave pattern is nonlinearly stable under
the combined effects of the binary collisions, the electrostatic potential
force, and the mutual interactions of different charged particles. Note that
this is the first result about the nonlinear stability of the combination of
two different wave patterns for the Vlasov-Poisson-Boltzmann system.Comment: 45 pages. arXiv admin note: text overlap with arXiv:1710.0308
Stability of planar rarefaction wave to 3D full compressible Navier-Stokes equations
We prove the time-asymptotic stability toward planar rarefaction wave for the
three-dimensional full compressible Navier-Stokes equations in an infinite long
flat nozzle domain . Compared with
one-dimensional case, the proof here is based on our new observations on the
cancellations on the flux terms and viscous terms due to the underlying wave
structures, which are crucial to overcome the difficulties due to the wave
propagation along the transverse directions and and its
interactions with the planar rarefaction wave in direction.Comment: 21 pages. Published on ARM
Programmable Extreme Pseudomagnetic Fields in Graphene by a Uniaxial Stretch
Many of the properties of graphene are tied to its lattice structure,
allowing for tuning of charge carrier dynamics through mechanical strain. The
graphene electro-mechanical coupling yields very large pseudomagnetic fields
for small strain fields, up to hundreds of Tesla, which offer new scientific
opportunities unattainable with ordinary laboratory magnets. Significant
challenges exist in investigation of pseudomagnetic fields, limited by the
non-planar graphene geometries in existing demonstrations and the lack of a
viable approach to controlling the distribution and intensity of the
pseudomagnetic field. Here we reveal a facile and effective mechanism to
achieve programmable extreme pseudomagnetic fields with uniform distributions
in a planar graphene sheet over a large area by a simple uniaxial stretch. We
achieve this by patterning the planar graphene geometry and graphene-based
hetero-structures with a shape function to engineer a desired strain gradient.
Our method is geometrical, opening up new fertile opportunities of strain
engineering of electronic properties of 2D materials in general.Comment: 3 figure
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