Green galaxies in the COSMOS field

We present a research of morphologies, spectra and environments of $\approx$ 2350 "green valley" galaxies at $0.2<z<1.0$ 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$_{\rm 20}$ 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$\alpha$ and H$\beta$ 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 ($\Sigma_{10}$) distribution with blue galaxies at $z>0.7$. At $z<0.7$, 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 $z<0.5$.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 Cu$_x$Bi$_2$Se$_3$. 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 (Fe$_{20}$Ni$_{80}$) is found to be rather diffuse in a large energy interval while in Fe$_{75}$Co$_{25}$ 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 $\sim$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