Using Games to Teach English in Chinese High School Classroom

In the 20th century, English played an important role in international communication as an international language. English is a bridge between countries\u27 economies, cultures, and trade. However, current English education in Chinese high schools is still test-oriented which is ineffective, and students are tired of it. Moreover, teachers also have trouble engaging students in the class. The purpose of this project is to create a curriculum for high school English teachers in China to use games to teach English language skills. Krashen’s (1982) Theory of Second Language Acquisition contains five main hypotheses which support this project. The project includes twenty-three activities to improve students’ five language skills: vocabulary, listening, reading, speaking, and writing

GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding

In this paper, we propose GPSP, a novel Graph Partition and Space Projection based approach, to learn the representation of a heterogeneous network that consists of multiple types of nodes and links. Concretely, we first partition the heterogeneous network into homogeneous and bipartite subnetworks. Then, the projective relations hidden in bipartite subnetworks are extracted by learning the projective embedding vectors. Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network. Extensive experiments are conducted on a real-life dataset. The results demonstrate that GPSP outperforms the state-of-the-art baselines in two key network mining tasks: node classification and clustering.Comment: WWW 2018 Poste

Revisiting Puffy Dark Matter with Novel Insights: Partial Wave Analysis

We present a comprehensive study on the self-interaction cross-section of puffy dark matter (DM) particles, which have a significant intrinsic size compared to their Compton wavelength. For such puffy DM self-interaction cross-section in the resonant and classical regimes, our study demonstrates the significance of the Yukawa potential and the necessity of partial wave analysis: (i) Due to the finite-size effect of puffy DM particles, the new Yukawa potential of puffy DM is found to enlarge the Born-effective regime for the self-interaction cross-section, compared with the point-like DM; (ii) Our partial wave analysis shows that depending on the value of the ratio between $R_{\chi}$ (radius of a puffy DM particle) and $1/m_{\phi}$ (force range), the three regimes (Born-effective, resonant and classical) for puffy DM self-interaction cross-section can be very different from the point-like DM; (iii) We find that to solve the small-scale anomalies via self-interacting puffy DM, the Born-effective and the resonant regimes exist for dwarf galaxies, while for the cluster and Milky Way galaxy the non-Born regime is necessary.Comment: 17 pages, 8 figures, accepted by JHE

LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation

Semantic map construction under bird's-eye view (BEV) plays an essential role in autonomous driving. In contrast to camera image, LiDAR provides the accurate 3D observations to project the captured 3D features onto BEV space inherently. However, the vanilla LiDAR-based BEV feature often contains many indefinite noises, where the spatial features have little texture and semantic cues. In this paper, we propose an effective LiDAR-based method to build semantic map. Specifically, we introduce a BEV feature pyramid decoder that learns the robust multi-scale BEV features for semantic map construction, which greatly boosts the accuracy of the LiDAR-based method. To mitigate the defects caused by lacking semantic cues in LiDAR data, we present an online Camera-to-LiDAR distillation scheme to facilitate the semantic learning from image to point cloud. Our distillation scheme consists of feature-level and logit-level distillation to absorb the semantic information from camera in BEV. The experimental results on challenging nuScenes dataset demonstrate the efficacy of our proposed LiDAR2Map on semantic map construction, which significantly outperforms the previous LiDAR-based methods over 27.9% mIoU and even performs better than the state-of-the-art camera-based approaches. Source code is available at: https://github.com/songw-zju/LiDAR2Map.Comment: Accepted by CVPR202

Direct detection of cosmic ray-boosted puffy dark matter

For the light relativistic dark matter (DM) boosted by high energy cosmic ray, its scattering cross section with the nucleon is sensitively dependent on the momentum-transfer and such an dependence is caused by the mediator in the scattering. For puffy DM particle with a size, the momentum-transfer dependence can also arise from the DM radius effect. All these momentum-transfer dependences should be considered. In this note we study the direct detection limits on the cosmic ray-boosted puffy DM for a simplified model with a light mediator. For comparison, we first re-derive the direct detection limits on the cosmic ray-boosted point-like DM. We display the limits on various planes of parameters and find that the limits for the cosmic ray-boosted puffy DM are stronger than for the point-like DM.Comment: 14 pages, 7 figure

Box-supervised Instance Segmentation with Level Set Evolution

In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently attracted a lot of research attentions. In this paper, we propose a novel single-shot box-supervised instance segmentation approach, which integrates the classical level set model with deep neural network delicately. Specifically, our proposed method iteratively learns a series of level sets through a continuous Chan-Vese energy-based function in an end-to-end fashion. A simple mask supervised SOLOv2 model is adapted to predict the instance-aware mask map as the level set for each instance. Both the input image and its deep features are employed as the input data to evolve the level set curves, where a box projection function is employed to obtain the initial boundary. By minimizing the fully differentiable energy function, the level set for each instance is iteratively optimized within its corresponding bounding box annotation. The experimental results on four challenging benchmarks demonstrate the leading performance of our proposed approach to robust instance segmentation in various scenarios. The code is available at: https://github.com/LiWentomng/boxlevelset.Comment: 17 page, 4figures, ECCV202