The binary pnictide semimetals have attracted considerable attention due to their fantastic physical properties that include topological effects, negative magnetoresistance, Weyl fermions and large non-saturation magnetoresistance. In this paper, we have successfully grown the high-quality V1-deltaSb2 single crystals by Sb flux method and investigated their electronic transport properties. A large positive magnetoresistance that reaches 477% under a magnetic field of 12 T at T = 1.8 K was observed. Notably, the magnetoresistance showed a cusp-like feature at the low magnetic fields and such feature weakened gradually as the temperature increased, which indicated the presence of weak antilocalization effect (WAL). The angle-dependent magnetoconductance and the ultra-large prefactor alpha extracted from the Hikami-Larkin-Nagaoka equation revealed that the WAL effect is a 3D bulk effect originated from the three-dimensional bulk spin-orbital coupling.
We present a novel high-fidelity generative adversarial network (GAN) inversion framework that enables attribute editing with image-specific details well-preserved (e.g., background, appearance and illumination). We first formulate GAN inversion as a lossy data compression problem and carefully discuss the Rate-Distortion-Edit trade-off. Due to this trade-off, previous works fail to achieve high-fidelity reconstruction while keeping compelling editing ability with a low bit-rate latent code only. In this work, we propose a distortion consultation approach that employs the distortion map as a reference for reconstruction. In the distortion consultation inversion (DCI), the distortion map is first projected to a high-rate latent map, which then complements the basic low-rate latent code with (lost) details via consultation fusion. To achieve high-fidelity editing, we propose an adaptive distortion alignment (ADA) module with a self-supervised training scheme. Extensive experiments in the face and car domains show a clear improvement in terms of both inversion and editing quality.
Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. We begin by collecting a new dataset of news articles with questions as titles and pairing them with summaries of varying length. This dataset is used to learn a QA pair generation model producing summaries as answers that balance brevity with sufficiency jointly with their corresponding questions. We then reinforce the QA pair generation process with a differentiable reward function to mitigate exposure bias, a common problem in natural language generation. Both automatic metrics and human evaluation demonstrate these QA pairs successfully capture the central gists of the articles and achieve high answer accuracy.
We present a visible-infrared imaging study of young planetary nebula (PN) Hubble 12 (Hb 12; PN G111.8-02.8) obtained with Hubble Space Telescope (HST) archival data and our own Canada-France-Hawaii Telescope (CFHT) measurements. Deep HST and CFHT observations of this nebula reveal three pairs of bipolar structures and an arc-shaped filament near the western waist of Hb 12. The existence of nested bipolar lobes together with the presence of H2 knots suggests that these structures originated from several mass-ejection events during the pre-PN phase. To understand the intrinsic structures of Hb 12, a three-dimensional model enabling the visualisation of this PN at various orientations was constructed. The modelling results show that Hb 12 may resemble other nested hourglass nebulae, such as Hen 2-320 and M 2-9, suggesting that this type of PN may be common and the morphologies of PNs are not so diverse as is shown by their visual appearances. The infrared spectra show that this PN has a mixed chemistry. We discuss the possible material that may cause the unidentified infrared emissions. The analyses of the infrared spectra and the spectral energy distribution suggest the existence of a cool companion in the nucleus of this object.
In this paper, we develop a novel method for fast geodesic distance queries. The key idea is to embed the mesh into a high-dimensional space, such that the Euclidean distance in the high-dimensional space can induce the geodesic distance in the original manifold surface. However, directly solving the high-dimensional embedding problem is not feasible due to the large number of variables and the fact that the embedding problem is highly nonlinear. We overcome the challenges with two novel ideas. First, instead of taking all vertices as variables, we embed only the saddle vertices, which greatly reduces the problem complexity. We then compute a local embedding for each non-saddle vertex. Second, to reduce the large approximation error resulting from the purely Euclidean embedding, we propose a cascaded optimization approach that repeatedly introduces additional embedding coordinates with a non-Euclidean function to reduce the approximation residual. Using the precomputation data, our approach can determine the geodesic distance between any two vertices in near-constant time. Computational testing results show that our method is more desirable than previous geodesic distance queries methods.
In this paper, we consider a 1D periodic transport equation with nonlocal flux and fractional dissipation $$ u_{t}-(Hu)_{x}u_{x}+kappaLambda^{alpha}u=0,quad (t,x)in R^{+}times S, $$ where $kappageq0$, $0<alphaleq1$ and $S=[-pi,pi]$. We first establish the local-in-time well-posedness for this transport equation in $H^{3}(S)$. In the case of $kappa=0$, we deduce that the solution, starting from the smooth and odd initial data, will develop into singularity in finite time. If adding a weak dissipation term $kappaLambda^{alpha}u$, we also prove that the finite time blowup would occur.
Energy can be transferred in a radiative manner between objects with different electrical fluctuations. In this work, we consider near-field energy transfer between two separated parallel plates: one is graphene-covered boron nitride and the other a magneto-optic medium. We first study the energy transfer between the two plates having the same temperature. An electric current through the graphene gives rise to nonequilibrium fluctuations and induces the energy transfer. Both the magnitude and direction of the energy flux can be controlled by the electric current and an in-plane magnetic field in the magneto-optic medium. This is due to the interplay between nonreciprocal effective photonic temperature in graphene and nonreciprocal surface modes in the magneto-optic plate. Furthermore, we report that a tunable thermoelectric current can be generated in the graphene in the presence of a temperature difference between the two plates.
As an emerging data modal with precise distance sensing, LiDAR point clouds have been placed great expectations on 3D scene understanding. However, point clouds are always sparsely distributed in the 3D space, and with unstructured storage, which makes it difficult to represent them for effective 3D object detection. To this end, in this work, we regard point clouds as hollow-3D data and propose a new architecture, namely Hallucinated Hollow-3D R-CNN ($text{H}^2$3D R-CNN), to address the problem of 3D object detection. In our approach, we first extract the multi-view features by sequentially projecting the point clouds into the perspective view and the bird-eye view. Then, we hallucinate the 3D representation by a novel bilaterally guided multi-view fusion block. Finally, the 3D objects are detected via a box refinement module with a novel Hierarchical Voxel RoI Pooling operation. The proposed $text{H}^2$3D R-CNN provides a new angle to take full advantage of complementary information in the perspective view and the bird-eye view with an efficient framework. We evaluate our approach on the public KITTI Dataset and Waymo Open Dataset. Extensive experiments demonstrate the superiority of our method over the state-of-the-art algorithms with respect to both effectiveness and efficiency. The code will be made available at url{https://github.com/djiajunustc/H-23D_R-CNN}.
The paper is concerned with the steady-state Burgers equation of fractional dissipation on the real line. We first prove the global existence of viscosity weak solutions to the fractal Burgers equation driven by the external force. Then the existence and uniqueness of solution with finite $H^{frac{alpha}{2}}$ energy to the steady-state equation are established by estimating the decay of fractal Burgers solutions. Furthermore, we show that the unique steady-state solution is nonlinearly stable, which means any viscosity weak solution of fractal Burgers equation, starting close to the steady-state solution, will return to the steady state as $trightarrowinfty$.
Cosmic ray muon has strong penetrating power and no ionizing radiation hazards, which make cosmic ray muon an ideal probe to detect the special nuclear materials (SNM). However, the existing muon tomography experiments have the disadvantages of long imaging time and poor imaging accuracy, due to the low event rate of muons and small interaction cross section between muons and material nucleus. To optimize the imaging quality and imaging time, high spatial resolution muon tomography facility should be investigated more deeply. Micromegas with its high spatial resolution and large detection area is one of the suitable detectors for the muon tomography facility. In this paper, a high spatial muon tomography prototype was presented. The Micromegas detector was based on thermal bonding technique, which was easy to manufacture and can achieve good performance. A novel multiplexing method base on position encoding was introduced in this research to reduce the channels in an order of magnitude. Then, this paper carried out the research of a general and scalable muon imaging readout system, which employed a discrete architecture of front-end and back-end electronics and can be adapted to different scales of muon tomography experiments. Finally, a tomography prototype system was designed and implemented, including eight Micromegas detectors, four front-end electronics cards and a data acquisition board. Test results showed that this prototype can image objects with 2cm size and distinguish different materials.
