Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling

Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieved promising results by directly learning on point sets. However, it does not take full advantage of a point's local neighborhood that contains fine-grained structural information which turns out to be helpful towards better semantic learning. In this regard, we present two new operations to improve PointNet with a more efficient exploitation of local structures. The first one focuses on local 3D geometric structures. In analogy to a convolution kernel for images, we define a point-set kernel as a set of learnable 3D points that jointly respond to a set of neighboring data points according to their geometric affinities measured by kernel correlation, adapted from a similar technique for point cloud registration. The second one exploits local high-dimensional feature structures by recursive feature aggregation on a nearest-neighbor-graph computed from 3D positions. Experiments show that our network can efficiently capture local information and robustly achieve better performances on major datasets. Our code is available at http://www.merl.com/research/license#KCNetComment: Accepted in CVPR'18. *indicates equal contributio

Observation of Quantized Hall Effect and Shubnikov-de Hass Oscillations in Highly Doped Bi2Se3: Evidence for Layered Transport of Bulk Carriers

Bi2Se3 is an important semiconductor thermoelectric material and a prototype topological insulator. Here we report observation of Shubnikov-de Hass (SdH) oscillations accompanied by quantized Hall resistances (Rxy) in highly-doped n-type Bi2Se3 with bulk carrier concentrations of few 10^19 cm^-3. Measurements under tilted magnetic fields show that the magnetotransport is 2D-like, where only the c-axis component of the magnetic field controls the Landau level formation. The quantized step size in 1/Rxy is found to scale with the sample thickness, and average ~e2/h per quintuple layer (QL). We show that the observed magnetotransport features do not come from the sample surface, but arise from the bulk of the sample acting as many parallel 2D electron systems to give a multilayered quantum Hall effect. Besides revealing a new electronic property of Bi2Se3, our finding also has important implications for electronic transport studies of topological insulator materials.Comment: accepted by Physical Review Letters (2012

Experimental study on discretely modulated continuous-variable quantum key distribution

We present a discretely modulated continuous-variable quantum key distribution system in free space by using strong coherent states. The amplitude noise in the laser source is suppressed to the shot-noise limit by using a mode cleaner combined with a frequency shift technique. Also, it is proven that the phase noise in the source has no impact on the final secret key rate. In order to increase the encoding rate, we use broadband homodyne detectors and the no-switching protocol. In a realistic model, we establish a secret key rate of 46.8 kbits/s against collective attacks at an encoding rate of 10 MHz for a 90% channel loss when the modulation variance is optimal.Comment: 7 pages,6 figure

Quantum Hall effect on centimeter scale chemical vapor deposited graphene films

We report observations of well developed half integer quantum Hall effect (QHE) on mono layer graphene films of 7 mm \times 7 mm in size. The graphene films are grown by chemical vapor deposition (CVD) on copper, then transferred to SiO_{2} /Si substrates, with typical carrier mobilities \approx 4000 cm^{2} /Vs. The large size graphene with excellent quality and electronic homogeneity demonstrated in this work is promising for graphene-based quantum Hall resistance standards, and can also facilitate a wide range of experiments on quantum Hall physics of graphene and practical applications exploiting the exceptional properties of graphene