Investigation and analysis of patient outcomes, including in-hospital mortality and length of stay, are crucial for assisting clinicians in determining a patient's result at the outset of their hospitalization and for assisting hospitals in allocating their resources. This paper proposes an approach based on combining the well-known gray wolf algorithm with frequent items extracted by association rule mining algorithms. First, original features are combined with the discriminative extracted frequent items. The best subset of these features is then chosen, and the parameters of the used classification algorithms are also adjusted, using the gray wolf algorithm. This framework was evaluated using a real dataset made up of 2816 patients from the Imam Ali Kermanshah Hospital in Iran. The study's findings indicate that low Ejection Fraction, old age, high CPK values, and high Creatinine levels are the main contributors to patients' mortality. Several significant and interesting rules related to mortality in hospitals and length of stay have also been extracted and presented. Additionally, the accuracy, sensitivity, specificity, and auroc of the proposed framework for the diagnosis of mortality in the hospital using the SVM classifier were 0.9961, 0.9477, 0.9992, and 0.9734, respectively. According to the framework's findings, adding frequent items as features considerably improves classification accuracy.

我们介绍了Amstertime：一个具有挑战性的数据集，可在存在严重的域移位的情况下基准视觉位置识别（VPR）。 Amstertime提供了2500张曲式曲目的图像，这些图像匹配了相同的场景，从街景与来自阿姆斯特丹市的历史档案图像数据相匹配。图像对将同一位置与不同的相机，观点和外观捕获。与现有的基准数据集不同，Amstertime直接在GIS导航平台（Mapillary）中众包。我们评估了各种基准，包括在不同相关数据集上预先培训的非学习，监督和自我监督的方法，以进行验证和检索任务。我们的结果将在地标数据集中预先培训的RESNET-101模型的最佳准确性分别验证和检索任务分别为84％和24％。此外，在分类任务中收集了阿姆斯特丹地标子集以进行特征评估。分类标签进一步用于使用Grad-CAM提取视觉解释，以检查深度度量学习模型中学习的类似视觉效果。

三维（3D）建筑模型在许多现实世界应用中发挥着越来越竞触的作用，同时获得紧凑的建筑物的表现仍然是一个公开的问题。在本文中，我们提出了一种从点云中重建紧凑，水密的多边形建筑模型的新框架。我们的框架包括三个组件：（a）通过自适应空间分区生成一个单元复合物，该分区提供了作为候选集的多面体嵌入; （b）由深度神经网络学习隐式领域，促进建立占用估计; （c）配制马尔可夫随机场，通过组合优化提取建筑物的外表面。我们在形状重建，表面逼近和几何简化中评估和比较我们的最先进方法的方法。综合性和现实世界点云的实验表明，通过我们的神经引导策略，可以获得高质量的建筑模型，在保真度，紧凑性和计算效率方面具有显着的优势。我们的方法显示了对噪声和测量不足的鲁棒性，并且可以从合成扫描到现实世界测量中直接概括。