Boundary effect and dressed states of a giant atom in a topological waveguide

The interaction between the quantum emitter and topological photonic system makes the photon behave in exotic ways. We here study the properties of a giant atom coupled to two sites of a one-dimensional topological waveguide, which is described by the Su-Schrieffer-Heeger (SSH) chain. We find that the giant atom can act as an effective boundary and induce the chiral zero modes, which are similar to those in the SSH model with open boundary, for the waveguide under the periodical boundary. Except for the boundary effect, we also find that the giant atom can lift energy degeneracy inside the energy bands of the SSH chain and adjust spatial symmetry of the photon distributions for the states of the dressed giant atom and waveguide. That is, the giant atom can be used to change the properties of the topological environment. Our work may stimulate more studies on the interaction between matter and topological environment.Comment: 7 Pages, 4 Figure

Sequential optimization for efficient high-quality object proposal generation

We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING ++, which inherits the virtue of good computational efficiency of BING [1] but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5 and 16.7 percent on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster