我们旨在借助一些观察数据，将随机对照试验（RCT）的结果推广到目标人群。这是多个数据源的因果效应识别的问题。当RCT在与目标人群不同的情况下进行时，就会出现挑战。较早的研究集中在可以通过观察数据调整RCT的估计值以消除选择偏差和其他域特定差异的情况。我们考虑了无法通过调整来概括实验发现的示例，并表明可以通过应用DO-Calculus得出的其他识别策略仍然可以进行概括。这些示例的获得的识别功能包含新类型的陷阱变量。陷阱变量的值需要在估计中固定，并且值的选择可能会对估计值的偏见和准确性产生重大影响，这在模拟中也可以看到。提出的结果扩大了实验发现的概括是可行的设置范围

常用图是表示和可视化因果关系的。对于少量变量，这种方法提供了简洁和清晰的方案的视图。随着下属的变量数量增加，图形方法可能变得不切实际，并且表示的清晰度丢失。变量的聚类是减少因果图大小的自然方式，但如果任意实施，可能会错误地改变因果关系的基本属性。我们定义了一种特定类型的群集，称为Transit Cluster，保证在某些条件下保留因果效应的可识别性属性。我们提供了一种用于在给定图中查找所有传输群集的声音和完整的算法，并演示集群如何简化因果效应的识别。我们还研究了逆问题，其中一个人以群集的图形开始，寻找扩展图，其中因果效应的可识别性属性保持不变。我们表明这种结构稳健性与过境集群密切相关。

Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural networks which were trained to classify colorectal cancer tissue in hematoxylin-eosin stained samples into three classes: stroma, tumor and other. The models were trained using a data set that consists of 1343 whole slide images. Three different training setups were applied with a transfer learning approach using domain-specific data i.e. an external colorectal cancer histopathological data set. The three most accurate models were chosen as a classifier, TSR values were predicted and the results were compared to a visual TSR estimation made by a pathologist. The results suggest that classification accuracy does not improve when domain-specific data are used in the pre-training of the convolutional neural network models in the task at hand. Classification accuracy for stroma, tumor and other reached 96.1$\%$ on an independent test set. Among the three classes the best model gained the highest accuracy (99.3$\%$) for class tumor. When TSR was predicted with the best model, the correlation between the predicted values and values estimated by an experienced pathologist was 0.57. Further research is needed to study associations between computationally predicted TSR values and other clinicopathological factors of colorectal cancer and the overall survival of the patients.

Robustness of different pattern recognition methods is one of the key challenges in autonomous driving, especially when driving in the high variety of road environments and weather conditions, such as gravel roads and snowfall. Although one can collect data from these adverse conditions using cars equipped with sensors, it is quite tedious to annotate the data for training. In this work, we address this limitation and propose a CNN-based method that can leverage the steering wheel angle information to improve the road area semantic segmentation. As the steering wheel angle data can be easily acquired with the associated images, one could improve the accuracy of road area semantic segmentation by collecting data in new road environments without manual data annotation. We demonstrate the effectiveness of the proposed approach on two challenging data sets for autonomous driving and show that when the steering task is used in our segmentation model training, it leads to a 0.1-2.9% gain in the road area mIoU (mean Intersection over Union) compared to the corresponding reference transfer learning model.

在本文中，新方法被认为可以检测双侧PA中的膝关节区域固定屈曲膝关节X射线图像。该方法是模板匹配类型的，其中距离标准基于负归一化互相关。 The manual annotations are made on only one side of a single bilateral image when the templates are selected.最好的匹配贴片搜索被配制为无约束的连续域最小化问题。对于最小化问题，考虑了不同的优化方法。本文的主要方法是一种可训练的优化器，其中教授该方法可以从看起来像模板的输入图像中获取缩放和可能旋转的补丁。在实验中，我们比较不同优化方法发现的最小值。我们还查看了一些测试图像，以检查最小值与膝盖区域局部程度之间的对应关系。似乎只对单个图像进行注释才能非常精确地检测膝关节区域。