Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation

Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Adaptation (ProCA), a simple and efficient contrastive learning method for unsupervised domain adaptive semantic segmentation. Previous domain adaptation methods merely consider the alignment of the intra-class representational distributions across various domains, while the inter-class structural relationship is insufficiently explored, resulting in the aligned representations on the target domain might not be as easily discriminated as done on the source domain anymore. Instead, ProCA incorporates inter-class information into class-wise prototypes, and adopts the class-centered distribution alignment for adaptation. By considering the same class prototypes as positives and other class prototypes as negatives to achieve class-centered distribution alignment, ProCA achieves state-of-the-art performance on classical domain adaptation tasks, {\em i.e., GTA5 $\to$ Cityscapes \text{and} SYNTHIA $\to$ Cityscapes}. Code is available at \href{https://github.com/jiangzhengkai/ProCA}{ProCA

The electromagnetic decays of X(3823)X(3823) as the ψ2(13D2)\psi_2(1^{3}D_{2}) state and its radial excited states

We study the electromagnetic (EM) decays of $X(3823)$ as the $\psi_2(1^{3}D_{2})$ state by using the relativistic Bethe-Salpeter method. Our results are $\Gamma[X(3823)\rightarrow\chi_{_{c0}}\gamma]=1.6$ keV, $\Gamma[X(3823)\rightarrow\chi_{_{c1}}\gamma]=265$ keV, $\Gamma[X(3823)\rightarrow\chi_{_{c2}}\gamma]=57$ keV and $\Gamma[X(3823)\rightarrow\eta_{_c}\gamma]=1.3$ keV. The ratio ${\cal B}[X(3823)\rightarrow\chi_{_{c2}}\gamma]/{\cal B}[X(3823)\rightarrow\chi_{_{c1}}\gamma]=0.22$, agrees with the experimental data. Similarly, the EM decay widths of $\psi_{_2}(n^{3}D_{_2})$, $n=2,3$, are predicted, and we find the dominant decays channels are $\psi_{_2}(n^{3}D_{_2})\rightarrow\chi_{_{c1}}(nP)\gamma$, where $n=1,2,3$. The wave function include different partial waves, which means the relativistic effects are considered. We also study the contributions of different partial waves.Comment: 20 pages, 6 figures, 9 table

Learning Styles of Undergraduate and Graduate Physical Therapy Students in Taiwan

AbstractThe research was conducted to identify the learning styles of undergraduate and graduate physical therapy students in Taiwan and to examine the associations between learning style and academic performance. Basic data and Kolb's Learning Style Inventory were obtained from 52 participants from one university. The most commonly occurring style of learner was assimilator (44%), followed by diverger (23%), accommodator (15%), and converger (17%). There was no significant difference in academic performance among the four different styles of learners. Qualitative analyses provided further understanding of the preferred learning and teaching strategies. The different strategies are recommended to meet students’ learning preferences