19,031 research outputs found
Green galaxies in the COSMOS field
We present a research of morphologies, spectra and environments of
2350 "green valley" galaxies at in the COSMOS field. The bimodality
of dust-corrected \nuvr\ color is used to define "green valley" (thereafter,
GV), which removes dusty star-forming galaxies from truly transiting galaxies
between blue cloud and red sequence. Morphological parameters of green galaxies
are intermediate between those of blue and red galaxy populations, both on the
Gini--Asymmetry and the Gini--M planes. Approximately 60% to 70%
green disk galaxies have intermediate or big bulges, and only 5% to 10% are
pure disk systems, based on the morphological classification with Zurich
Estimator of Structural Types (ZEST). The obtained average spectra of green
galaxies are intermediate between blue and red ones in terms of \oii\,,
H and H emission lines. Stellar population synthesis on the
average spectra show that green galaxies are averagely older than blue
galaxies, but younger than red galaxies. Green galaxies have similar projected
galaxy density () distribution with blue galaxies at . At
, the fractions of M_{\ast}<10^{10.0}M_{\sun} green galaxies located
in dense environment are found to be significantly larger than those of blue
galaxies. The morphological and spectral properties of green galaxies are
consistent with the transiting population between blue cloud and red sequence.
The possible mechanisms for quenching star formation activities in green
galaxies are discussed. The importance of AGN feedback cannot be well
constrained in our study. Finally, our findings suggest that environment
conditions, most likely starvation and harassment, significantly affect the
transformation of M_{\ast}<10^{10.0}M_{\sun} blue galaxies into red galaxies,
especially at .Comment: 45 pages, 13 figures, ApJ accepte
The Chen-Ruan Cohomology of Almost Contact Orbifolds
Comparing to the Chen-Ruan cohomology theory for the almost complex
orbifolds, we study the orbifold cohomology theory for almost contact
orbifolds. We define the Chen-Ruan cohomology group of any almost contact
orbifold. Using the methods for almost complex orbifolds (see [2]), we define
the obstruction bundle for any 3-multisector of the almost contact orbifolds
and the Chen-Ruan cup product for the Chen-Ruan cohomology. We also prove that
under this cup product the direct sum of all dimensional orbifold cohomology
groups constitutes a cohomological ring. Finally we calculate two examples.Comment: 11 page
Buckled honeycomb lattice and unconventional magnetic response
We study the magnetic response of buckled honeycomb-lattice materials. The
buckling breaks the sublattice symmetry, enhances the spin-orbit coupling, and
allows the tuning of a topological quantum phase transition. As a result, there
are two doubly degenerate spin-valley coupled massive Dirac bands, which
exhibit an unconventional Hall plateau sequence under strong magnetic fields.
We show how to externally control the splitting of anomalous zeroth Landau
levels, the prominent Landau level crossing effects, and the polarizations of
spin, valley, and sublattice degrees of freedom. In particular, we reveal that
in a p-n junction, spin-resolved fractionally quantized conductance appears in
a two-terminal measurement with a spin-polarized current propagating along the
interface. In the low-field regime where the Landau quantization is not
applicable, we provide a semiclassical description for the anomalous Hall
transport. We comment briefly on the effects of electron-electron interactions
and Zeeman couplings to electron spins and to atomic orbitals
Symmetric identities on Bernoulli polynomials
In this paper, we obtain a generalization of an identity due to Carlitz on
Bernoulli polynomials. Then we use this generalized formula to derive two
symmetric identities which reduce to some known identities on Bernoulli
polynomials and Bernoulli numbers, including the Miki identity
Dirac and Weyl Superconductors in Three Dimensions
We introduce the concept of 3D Dirac (Weyl) superconductors (SC), which have
protected bulk four(two)-fold nodal points and surface Andreev arcs at zero
energy. We provide a sufficient criterion for realizing them in centrosymmetric
SCs with odd-parity pairing and mirror symmetry, e.g., the nodal phases of
CuBiSe. Pairs of Dirac nodes appear in a mirror-invariant plane
when the mirror winding number is nontrivial. Breaking mirror symmetry may gap
Dirac nodes producing a topological SC. Each Dirac node evolves to a nodal ring
when inversion-gauge symmetry is broken. A Dirac node may split into a pair of
Weyl nodes, only when time-reversal symmetry is broken.Comment: 5 pages and 2 figure
Chirality Hall Effect in Weyl Semimetals
We generalize a semiclassical theory and use the argument of angular momentum
conservation to examine the ballistic transport in lightly-doped Weyl
semimetals, taking into account various phase-space Berry curvatures. We
predict universal transverse shifts of the wave-packet center in transmission
and reflection, perpendicular to the direction in which the Fermi energy or
velocities change adiabatically. The anomalous shifts are opposite for
electrons with different chirality, and can be made imbalanced by breaking
inversion symmetry. We discuss how to utilize local gates, strain effects, and
circularly polarized lights to generate and probe such a chirality Hall effect
Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios
In this paper, we present a decentralized sensor-level collision avoidance
policy for multi-robot systems, which shows promising results in practical
applications. In particular, our policy directly maps raw sensor measurements
to an agent's steering commands in terms of the movement velocity. As a first
step toward reducing the performance gap between decentralized and centralized
methods, we present a multi-scenario multi-stage training framework to learn an
optimal policy. The policy is trained over a large number of robots in rich,
complex environments simultaneously using a policy gradient based reinforcement
learning algorithm. The learning algorithm is also integrated into a hybrid
control framework to further improve the policy's robustness and effectiveness.
We validate the learned sensor-level collision avoidance policy in a variety
of simulated and real-world scenarios with thorough performance evaluations for
large-scale multi-robot systems. The generalization of the learned policy is
verified in a set of unseen scenarios including the navigation of a group of
heterogeneous robots and a large-scale scenario with 100 robots. Although the
policy is trained using simulation data only, we have successfully deployed it
on physical robots with shapes and dynamics characteristics that are different
from the simulated agents, in order to demonstrate the controller's robustness
against the sim-to-real modeling error. Finally, we show that the
collision-avoidance policy learned from multi-robot navigation tasks provides
an excellent solution to the safe and effective autonomous navigation for a
single robot working in a dense real human crowd. Our learned policy enables a
robot to make effective progress in a crowd without getting stuck. Videos are
available at https://sites.google.com/view/hybridmrc
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Point cloud analysis is very challenging, as the shape implied in irregular
points is difficult to capture. In this paper, we propose RS-CNN, namely,
Relation-Shape Convolutional Neural Network, which extends regular grid CNN to
irregular configuration for point cloud analysis. The key to RS-CNN is learning
from relation, i.e., the geometric topology constraint among points.
Specifically, the convolutional weight for local point set is forced to learn a
high-level relation expression from predefined geometric priors, between a
sampled point from this point set and the others. In this way, an inductive
local representation with explicit reasoning about the spatial layout of points
can be obtained, which leads to much shape awareness and robustness. With this
convolution as a basic operator, RS-CNN, a hierarchical architecture can be
developed to achieve contextual shape-aware learning for point cloud analysis.
Extensive experiments on challenging benchmarks across three tasks verify
RS-CNN achieves the state of the arts.Comment: Accepted to CVPR 2019 as an oral presentation. Project page at
https://yochengliu.github.io/Relation-Shape-CN
Magnon properties of random alloys
We study magnon properties in terms of spin stiffness, Curie temperatures and
magnon spectrum of Fe-Ni, Co-Ni and Fe-Co random alloys using a combination of
electronic structure calculations and atomistic spin dynamics simulations.
Influence of the disorder are studied in detail by use of large supercells with
random atomic arrangement. It is found that disorder affects the magnon
spectrum in vastly different ways depending on the system. Specifically, it is
more pronounced in Fe-Ni alloys compared to Fe-Co alloys. In particular, the
magnon spectrum at room temperature in Permalloy (FeNi) is found
to be rather diffuse in a large energy interval while in FeCo it
forms sharp branches. Fe-Co alloys are very interesting from a technological
point of view due to the combination of large Curie temperatures and very low
calculated Gilbert damping of 0.0007 at room temperature for Co
concentrations around 20--30\%
Planecell: Representing the 3D Space with Planes
Reconstruction based on the stereo camera has received considerable attention
recently, but two particular challenges still remain. The first concerns the
need to aggregate similar pixels in an effective approach, and the second is to
maintain as much of the available information as possible while ensuring
sufficient accuracy. To overcome these issues, we propose a new 3D
representation method, namely, planecell, that extracts planarity from the
depth-assisted image segmentation and then projects these depth planes into the
3D world. An energy function formulated from Conditional Random Field that
generalizes the planar relationships is maximized to merge coplanar segments.
We evaluate our method with a variety of reconstruction baselines on both KITTI
and Middlebury datasets, and the results indicate the superiorities compared to
other 3D space representation methods in accuracy, memory requirements and
further applications
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