1,174 research outputs found
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
(radius of a puffy DM particle) and (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
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