On the sample-dependent minimal conductivity in weakly disordered graphene

We present a unified understanding for experimentally observed minimal dc conductivity at the Dirac point in weak disordered graphene. First of all, based on the linear response theory, we unravel that randomness or disorder, inevitably inducing momentum dependent corrections in electron self-energy function, naturally yields a sample-dependent minimal conductivity. Taking the long-ranged Gaussian potential as an example, we demonstrate the momentum dependent self-energy function within the Born approximation, and further validate it via numerical simulation using large-scale Lanczos algorithm. The explicit dependence of self-energy on the intensity, concentration and range of potential is critically addressed. Therefore, our results provide a reasonable interpretation of the sample-dependent minimal conductivity observed in graphene samples.

GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks

In this paper, we present GCN-Denoiser, a novel feature-preserving mesh denoising method based on graph convolutional networks (GCNs). Unlike previous learning-based mesh denoising methods that exploit hand-crafted or voxel-based representations for feature learning, our method explores the structure of a triangular mesh itself and introduces a graph representation followed by graph convolution operations in the dual space of triangles. We show such a graph representation naturally captures the geometry features while being lightweight for both training and inference. To facilitate effective feature learning, our network exploits both static and dynamic edge convolutions, which allow us to learn information from both the explicit mesh structure and potential implicit relations among unconnected neighbors. To better approximate an unknown noise function, we introduce a cascaded optimization paradigm to progressively regress the noise-free facet normals with multiple GCNs. GCN-Denoiser achieves the new state-of-the-art results in multiple noise datasets, including CAD models often containing sharp features and raw scan models with real noise captured from different devices. We also create a new dataset called PrintData containing 20 real scans with their corresponding ground-truth meshes for the research community. Our code and data are available in https://github.com/Jhonve/GCN-Denoiser.

UPDesc: Unsupervised Point Descriptor Learning for Robust Registration

In this work, we propose UPDesc, an unsupervised method to learn point descriptors for robust point cloud registration. Our work builds upon a recent supervised 3D CNN-based descriptor extraction framework, namely, 3DSmoothNet, which leverages a voxel-based representation to parameterize the surrounding geometry of interest points. Instead of using a predefined fixed-size local support in voxelization, which potentially limits the access of richer local geometry information, we propose to learn the support size in a data-driven manner. To this end, we design a differentiable voxelization module that can back-propagate gradients to the support size optimization. To optimize descriptor similarity, the prior 3D CNN work and other supervised methods require abundant correspondence labels or pose annotations of point clouds for crafting metric learning losses. Differently, we show that unsupervised learning of descriptor similarity can be achieved by performing geometric registration in networks. Our learning objectives consider descriptor similarity both across and within point clouds without supervision. Through extensive experiments on point cloud registration benchmarks, we show that our learned descriptors yield superior performance over existing unsupervised methods.

Light Bridges Can Suppress the Formation of Coronal Loops

A light bridge is a magnetic intrusion into a sunspot, it interacts with the main magnetic field and excites a variety of dynamical processes. In the letter, we studied magnetic connectivity between a light bridge and coronal loops rooted at the sunspot. We used the data of the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory (SDO) to study the features of sunspots with light bridges. It is found that if a light bridge anchors at the umbra-penumbra boundary, the coronal loops could not be formed around the anchoring point. If the a light bridge become detached from the penumbra, the coronal loop starts to form again. The vector magnetogram provided by the Helioseismic Magnetic Imager onboard SDO shows that the anchoring region of a light bridge usually have an accompanying opposite minor-polarities. We conjugate that the magnetic field line could connect to these opposite polarities and form short-range magnetic loops, and therefore, coronal loops that extend to long-range could not be formed. A model of light bridge is proposed to explain the magnetic connectivity between a light bridge and the coronal loops. This model could explain many physical processes associated with light bridges.

Searching for Dark Matter Particles with Compton Scattering Approach

The dark matter puzzle is one of the most important fundamental physics questions in 21 century. There is no doubt that solving the puzzle will be a new milestone for human beings in the way of deeper understanding the mother nature. Here we propose to use the Shanghai laser electron gamma source (SLEGS) to search for dark matter candidates particles, including dark pseudo scalar particles, dark scalar particles, and dark photons. Our simulations show that electron facilities like SLEGS with some upgrading could be competitive platforms in searching for light dark matter particles with mass under tens of keV.

Helical Symmetry Breaking and Quantum Anomaly in Massive Dirac Fermions

Helical symmetry of massive Dirac fermions is broken explicitly in the presence of electric and magnetic fields. Here we present two equations for the divergence of helical and axial-vector currents following the Jackiw-Johnson approach to the anomaly of the neutral axial vector current. We discover the contribution from the helical symmetry breaking is attributed to the occupancy of the two states at the top of the valence band and the bottom of the conduction band. The explicit symmetry breaking fully cancels the anomalous correction from the quantum fluctuation in the band gap. The chiral anomaly can be derived from the helical symmetry breaking. It provides an alternative route to understand the chiral anomaly from the point of view of the helical symmetry breaking. The pertinent physical consequences in condensed matter are the helical magnetic effect which means a charge current circulating at the direction of the magnetic field, and the mass-dependent positive longitudinal magnetoconductivity as a transport signature. The discovery not only reflects anomalous magneto-transport properties of massive Dirac materials but also reveals the close relation between the helical symmetry breaking and the physics of chiral anomaly in quantum field theory and high energy physics.

The Bulk-Hinge Correspondence and Three-Dimensional Quantum Anomalous Hall Effect in Second Order Topological Insulators

The chiral hinge modes are the key feature of a second order topological insulator in three dimensions. Here we propose a quadrupole index in combination of a slab Chern number in the bulk to characterize the flowing pattern of chiral hinge modes along the hinges at the intersection of the surfaces of a sample. We further utilize the topological field theory to demonstrate the correspondent connection of the chiral hinge modes to the quadrupole index and the slab Chern number, and present a picture of three-dimensional quantum anomalous Hall effect as a consequence of chiral hinge modes. The two bulk topological invariants can be measured in electric transport and magneto-optical experiments. In this way we establish the bulk-hinge correspondence in a three-dimensional second order topological insulator.

Robust localization for planar moving robot in changing environment: A perspective on density of correspondence and depth

Visual localization for planar moving robot is important to various indoor service robotic applications. To handle the textureless areas and frequent human activities in indoor environments, a novel robust visual localization algorithm which leverages dense correspondence and sparse depth for planar moving robot is proposed. The key component is a minimal solution which computes the absolute camera pose with one 3D-2D correspondence and one 2D-2D correspondence. The advantages are obvious in two aspects. First, the robustness is enhanced as the sample set for pose estimation is maximal by utilizing all correspondences with or without depth. Second, no extra effort for dense map construction is required to exploit dense correspondences for handling textureless and repetitive texture scenes. That is meaningful as building a dense map is computational expensive especially in large scale. Moreover, a probabilistic analysis among different solutions is presented and an automatic solution selection mechanism is designed to maximize the success rate by selecting appropriate solutions in different environmental characteristics. Finally, a complete visual localization pipeline considering situations from the perspective of correspondence and depth density is summarized and validated on both simulation and public real-world indoor localization dataset. The code is released on github.

Chiral Majorana Hinge Modes in Superconducting Dirac Materials

Chiral Majorana hinge modes are characteristic of a second-order topological superconductor in three dimensions. Here we systematically study pairing symmetry in the point group D_{2h}, and find that the leading pairing channels can be of s-, d-, and s+id-wave pairing in Dirac materials. Except for the odd-parity s-wave pairing superconductivity, the s+id-wave pairing superconductor is topologically nontrivial and possesses Majorana hinge and surface modes. The chiral Majorana hinge modes can be characterized by a winding number of the quadrupole moment, or quantized quadruple moment at the symmetrically invariant point. Our findings suggest the strong spin-orbital coupling, crystalline symmetries and electron-electron interaction in the Dirac materials may provide a microscopic mechanism to realize chiral Majorana hinge modes without utilizing the proximity effect or external fields.

Topological Phase Transitions in Disordered Electric Quadrupole Insulators

We investigate disorder-driven topological phase transitions in quantized electric quadrupole insulators. We show that chiral symmetry can protect the quantization of the quadrupole moment $q_{xy}$, such that the higher-order topological invariant is well-defined even when disorder has broken all crystalline symmetries. Moreover, nonvanishing $q_{xy}$ and consequent corner modes can be induced from a trivial insulating phase by disorder that preserves chiral symmetry. The critical points of such topological phase transitions are marked by the occurrence of extended boundary states even in the presence of strong disorder. We provide a systematic characterization of these disorder-driven topological phase transitions from both bulk and boundary descriptions.