Upcoming astronomical surveys will observe billions of galaxies across cosmic time, providing a unique opportunity to map the many pathways of galaxy assembly to an incredibly high resolution. However, the huge amount of data also poses an immediate computational challenge: current tools for inferring parameters from the light of galaxies take $\gtrsim 10$ hours per fit. This is prohibitively expensive. Simulation-based Inference (SBI) is a promising solution. However, it requires simulated data with identical characteristics to the observed data, whereas real astronomical surveys are often highly heterogeneous, with missing observations and variable uncertainties determined by sky and telescope conditions. Here we present a Monte Carlo technique for treating out-of-distribution measurement errors and missing data using standard SBI tools. We show that out-of-distribution measurement errors can be approximated by using standard SBI evaluations, and that missing data can be marginalized over using SBI evaluations over nearby data realizations in the training set. While these techniques slow the inference process from $\sim 1$ sec to $\sim 1.5$ min per object, this is still significantly faster than standard approaches while also dramatically expanding the applicability of SBI. This expanded regime has broad implications for future applications to astronomical surveys.

X射线荧光光谱（XRF）在广泛的科学领域，尤其是在文化遗产中，在元素分析中起重要作用。使用栅格扫描来获取跨艺术品的光谱的XRF成像为基于其元素组成的颜料分布的空间分析提供了机会。然而，常规的基于XRF的色素识别依赖于耗时的元素映射，该元素映射通过测量光谱的专家解释。为了减少对手动工作的依赖，最近的研究应用了机器学习技术，以在数据分析中聚集相似的XRF光谱并确定最可能的颜料。然而，对于自动色素识别策略，直接处理真实绘画的复杂结构，例如色素混合物和分层色素。此外，与平均光谱相比，基于XRF成像的像素颜料识别仍然是障碍物。因此，我们开发了一个基于深度学习的端到端色素识别框架，以完全自动化色素识别过程。特别是，它对浓度较低的颜料具有很高的敏感性，因此可以使令人满意的结果基于单像素XRF光谱映射颜料。作为案例研究，我们将框架应用于实验室准备的模型绘画和两幅19世纪的绘画：Paul Gauguin的Po \'Emes Barbares（1896），其中包含带有底层绘画的分层颜料，以及Paul Cezanne的沐浴者（1899--1899-- 1904）。色素鉴定结果表明，我们的模型通过元素映射获得了与分析的可比结果，这表明我们的模型的概括性和稳定性。

联合学习（FL）以来已提议已应用于许多领域，例如信用评估，医疗等。由于网络或计算资源的差异，客户端可能不会同时更新其渐变可能需要花费等待或闲置的时间。这就是为什么需要异步联合学习（AFL）方法。AFL中的主要瓶颈是沟通。如何在模型性能和通信成本之间找到平衡是AFL的挑战。本文提出了一种新的AFL框架VAFL。我们通过足够的实验验证了算法的性能。实验表明，VAFL可以通过48.23 \％的平均通信压缩速率降低约51.02 \％的通信时间，并允许模型更快地收敛。代码可用于\ url {https://github.com/robai-lab/vafl}