科学和工程学的一个主要挑战是设计实验，以了解一些未知数的兴趣。经典的实验设计最佳地分配了实验预算，以最大程度地提高实用性概念（例如，降低对未知数量的不确定性）。我们考虑一个丰富的设置，其中实验与{\ em Markov链}中的状态相关联，我们只能通过选择控制状态转换的{\ em策略}来选择它们。该问题从勘探学习中的探索到空间监视任务，从而捕获了重要的应用。我们提出了一种算法 - \ textsc {markov-design} - 有效地选择了其测量分配\ emph {可证明收敛到最佳One}的策略。该算法在本质上是顺序的，可以调整其过去测量所告知的策略（实验）的选择。除了我们的理论分析外，我们还展示了我们在生态监测和药理学中应用的框架。
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We study the problem of explaining link predictions in the Knowledge Graph Embedding (KGE) models. We propose an example-based approach that exploits the latent space representation of nodes and edges in a knowledge graph to explain predictions. We evaluated the importance of identified triples by observing progressing degradation of model performance upon influential triples removal. Our experiments demonstrate that this approach to generate explanations outperforms baselines on KGE models for two publicly available datasets.
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The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients with COVID-19 or other-type pneumonia, and healthy individuals gathered from 15 Polish hospitals. The original radiographs are accompanied by the preprocessed images limited to the lung area and the corresponding lung masks obtained with the segmentation model. Moreover, the manually created lung masks are provided for a part of POLCOVID dataset and the other four publicly available CXR image collections. POLCOVID dataset can help in pneumonia or COVID-19 diagnosis, while the set of matched images and lung masks may serve for the development of lung segmentation solutions.
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