在这项工作中，我们介绍了我们的实时自我分割算法。由于我们在Thundernet的架构中灵感的浅网络，我们的算法对于640x480的输入分辨率达到了66 fps的帧速率。此外，我们非常重视培训数据的可变性。更具体地说，我们描述了我们的自我中心物体（Egobodies）数据集的创建过程，该数据集由来自三个数据集的近10,000张图像组成，这些图像既来自综合方法和真实捕获。我们进行实验以了解各个数据集的贡献；比较用自行车训练的Thundernet模型，并以更简单，更复杂的先前方法进行比较，并在分段质量和推理时间上以现实生活设置进行了相应的性能。所描述的经过训练的语义分割算法已经集成到混合现实的端到端系统中，使用户有可能在沉浸在MR场景中时看到自己的身体。

机器学习模型的培训和部署之间的分离意味着，在培训期间，并非所有部署中遇到的场景都可以预期，因此仅依靠培训的进步都有其限制。分布（OOD）检测是一个重要领域，强调模型处理看不见情况的能力：模型知道何时不知道吗？现有的OOD检测方法要么引起额外的训练步骤，其他数据或对训练的网络进行非平凡的修改。相比之下，在这项工作中，我们提出了一种非常简单的事后，即时激活塑形方法，灰分，其中大部分（例如90％）的样本激活在后层中被删除，然后删除休息（例如10％）简化或轻微调整。该塑形在推理时间应用，不需要根据培训数据计算出的任何统计数据。实验表明，这种简单的治疗可以增强分布和分布样本的区别，从而允许在ImageNet上进行最新的OOD检测，并且不会显着恶化分布的准确性。我们与论文一起释放了两个呼吁解释和验证的呼吁，他们相信集体权力进一步验证和理解这一发现。可以在：https：//andrijazz.github.io/ash上找到电话，视频和代码

The viral load of patients infected with SARS-CoV-2 varies on logarithmic scales and possibly with age. Controversial claims have been made in the literature regarding whether the viral load distribution actually depends on the age of the patients. Such a dependence would have implications for the COVID-19 spreading mechanism, the age-dependent immune system reaction, and thus for policymaking. We hereby develop a method to analyze viral-load distribution data as a function of the patients' age within a flexible, non-parametric, hierarchical, Bayesian, and causal model. The causal nature of the developed reconstruction additionally allows to test for bias in the data. This could be due to, e.g., bias in patient-testing and data collection or systematic errors in the measurement of the viral load. We perform these tests by calculating the Bayesian evidence for each implied possible causal direction. The possibility of testing for bias in data collection and identifying causal directions can be very useful in other contexts as well. For this reason we make our model freely available. When applied to publicly available age and SARS-CoV-2 viral load data, we find a statistically significant increase in the viral load with age, but only for one of the two analyzed datasets. If we consider this dataset, and based on the current understanding of viral load's impact on patients' infectivity, we expect a non-negligible difference in the infectivity of different age groups. This difference is nonetheless too small to justify considering any age group as noninfectious.