来自不同摄像头设备的光学相干断层扫描（OCT）成像会导致挑战域的变化，并可能导致机器学习模型的精度严重下降。在这项工作中，我们引入了基于单数值分解（SVDNA）的最小噪声适应方法，以克服视网膜OCT成像中三个不同设备制造商的目标域之间的域间隙。我们的方法利用噪声结构的差异成功地弥合了不同OCT设备之间的域间隙，并将样式从未标记的目标域图像转移到可用手动注释的源图像。我们演示了该方法尽管简单，但如何比较甚至胜过最先进的无监督域适应方法，用于在公共OCT数据集中进行语义细分。 SVDNA可以将仅几行代码集成到任何网络的增强管道中，这些网络与许多最新的域适应方法形成鲜明对比，这些方法通常需要更改基础模型体系结构或训练单独的样式转移模型。 SVDNA的完整代码实现可在https://github.com/valentinkoch/svdna上获得。

Novel topological spin textures, such as magnetic skyrmions, benefit from their inherent stability, acting as the ground state in several magnetic systems. In the current study of atomic monolayer magnetic materials, reasonable initial guesses are still needed to search for those magnetic patterns. This situation underlines the need to develop a more effective way to identify the ground states. To solve this problem, in this work, we propose a genetic-tunneling-driven variance-controlled optimization approach, which combines a local energy minimizer back-end and a metaheuristic global searching front-end. This algorithm is an effective optimization solution for searching for magnetic ground states at extremely low temperatures and is also robust for finding low-energy degenerated states at finite temperatures. We demonstrate here the success of this method in searching for magnetic ground states of 2D monolayer systems with both artificial and calculated interactions from density functional theory. It is also worth noting that the inherent concurrent property of this algorithm can significantly decrease the execution time. In conclusion, our proposed method builds a useful tool for low-dimensional magnetic system energy optimization.

The success of neural networks builds to a large extent on their ability to create internal knowledge representations from real-world high-dimensional data, such as images, sound, or text. Approaches to extract and present these representations, in order to explain the neural network's decisions, is an active and multifaceted research field. To gain a deeper understanding of a central aspect of this field, we have performed a targeted review focusing on research that aims to associate internal representations with human understandable concepts. In doing this, we added a perspective on the existing research by using primarily deductive nomological explanations as a proposed taxonomy. We find this taxonomy and theories of causality, useful for understanding what can be expected, and not expected, from neural network explanations. The analysis additionally uncovers an ambiguity in the reviewed literature related to the goal of model explainability; is it understanding the ML model or, is it actionable explanations useful in the deployment domain?

当前主导的人工智能和机器学习技术神经网络基于归纳统计学习。当今的神经网络是信息处理系统，因此无法理解和推理能力，因此他们无法以人类有效的形式解释促进决策。在这项工作中，我们将科学理论的基本哲学重新访问和使用作为一种分析镜头，目的是揭示，可以从旨在解释神经网络推动的决策的方法中揭示什么，更重要的是，而不是期望的。通过进行案例研究，我们研究了在两个平凡的领域，动物和头饰上的解释性方法的选择。通过我们的研究，我们证明这些方法的有用性取决于人类领域的知识以及我们理解，概括和理性的能力。当目标是进一步了解受过训练的神经网络的优势和劣势时，解释性方法可能很有用。如果我们的目标是使用这些解释性方法来促进可行的决策或建立对ML模型的信任，那么他们需要比今天不太模棱两可。在这项工作中，我们从研究中得出结论，基于解释性方法是对值得信赖的人工智能和机器学习的核心追求。