Software-related platforms have enabled their users to collaboratively label software entities with topics. Tagging software repositories with relevant topics can be exploited for facilitating various downstream tasks. For instance, a correct and complete set of topics assigned to a repository can increase its visibility. Consequently, this improves the outcome of tasks such as browsing, searching, navigation, and organization of repositories. Unfortunately, assigned topics are usually highly noisy, and some repositories do not have well-assigned topics. Thus, there have been efforts on recommending topics for software projects, however, the semantic relationships among these topics have not been exploited so far. We propose two recommender models for tagging software projects that incorporate the semantic relationship among topics. Our approach has two main phases; (1) we first take a collaborative approach to curate a dataset of quality topics specifically for the domain of software engineering and development. We also enrich this data with the semantic relationships among these topics and encapsulate them in a knowledge graph we call SED-KGraph. Then, (2) we build two recommender systems; The first one operates only based on the list of original topics assigned to a repository and the relationships specified in our knowledge graph. The second predictive model, however, assumes there are no topics available for a repository, hence it proceeds to predict the relevant topics based on both textual information of a software project and SED-KGraph. We built SED-KGraph in a crowd-sourced project with 170 contributors from both academia and industry. The experiment results indicate that our solutions outperform baselines that neglect the semantic relationships among topics by at least 25% and 23% in terms of ASR and MAP metrics.

在面部识别领域，一方面猕猴神经生理学与人类电生理学之间存在令人困惑的时序差异。猕猴中的单个单位记录已显示出100毫秒刺激发作以内的外部视觉皮层中的面部身份特定响应。但是，在人类的脑电图和梅格实验中，据报道，与不熟悉和熟悉的面孔相对应的神经活动之间存在一致的区别，大约在250毫秒内出现。这表明可能存在迄今未发现的人类电生理痕迹的面部熟悉感的早期相关性。我们在这里报告了使用模式分类技术在密集的MEG录音中成功搜索这种相关性。我们的分析表明，早在刺激发作后85毫秒内，面部熟悉程度的标记。图像的低级属性（例如亮度和颜色分布）无法解释这种早期新兴响应差异。这些结果有助于调和人类和猕猴的数据，并提供有关熟悉面部感知的神经机制的线索。

在自动驾驶汽车和自动驾驶系统的视觉系统中，交通标志检测是至关重要的任务。最近，基于变压器的新型模型为各种计算机视觉任务取得了令人鼓舞的结果。我们仍然观察到，香草VIT无法在交通符号检测中产生令人满意的结果，因为数据集的整体大小非常小，交通标志的类分布非常不平衡。为了克服这个问题，本文提出了一种具有局部机制的新型金字塔变压器。具体而言，金字塔变压器具有几个空间金字塔还原层，可通过使用严重的卷积将输入图像缩小并嵌入具有丰富多尺度上下文的令牌中。此外，它继承了固有的量表不变性归纳偏差，并能够在各种尺度上学习对象的本地功能表示，从而增强了网络的鲁棒性，以与流量标志的大小差异。实验是在德国交通标志基准（GTSDB）上进行的。结果证明了交通符号检测任务中提出的模型的优势。更具体地说，当将金字塔变压器应用于级联RCNN中时，将金字塔变压器在GTSDB中获得75.6％的地图，并超过了最知名和广泛使用的SOTA。