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.

In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward neural network (NN)-based equalizers for nonlinearity compensation in coherent optical transmission systems. First, we present a realization pipeline showing the conversion of the models from Python libraries to the FPGA chip synthesis and implementation. Then, we review the main alternatives for the hardware implementation of nonlinear activation functions. The main results are divided into three parts: a performance comparison, an analysis of how activation functions are implemented, and a report on the complexity of the hardware. The performance in Q-factor is presented for the cases of bidirectional long-short-term memory coupled with convolutional NN (biLSTM + CNN) equalizer, CNN equalizer, and standard 1-StpS digital back-propagation (DBP) for the simulation and experiment propagation of a single channel dual-polarization (SC-DP) 16QAM at 34 GBd along 17x70km of LEAF. The biLSTM+CNN equalizer provides a similar result to DBP and a 1.7 dB Q-factor gain compared with the chromatic dispersion compensation baseline in the experimental dataset. After that, we assess the Q-factor and the impact of hardware utilization when approximating the activation functions of NN using Taylor series, piecewise linear, and look-up table (LUT) approximations. We also show how to mitigate the approximation errors with extra training and provide some insights into possible gradient problems in the LUT approximation. Finally, to evaluate the complexity of hardware implementation to achieve 400G throughput, fixed-point NN-based equalizers with approximated activation functions are developed and implemented in an FPGA.

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。

FPGA中首次实施了针对非线性补偿的经常性和前馈神经网络均衡器，其复杂度与分散均衡器的复杂度相当。我们证明，基于NN的均衡器可以胜过1个速度的DBP。

许多现实世界的科学和工业应用都需要优化多个竞争的黑盒目标。当目标是昂贵的评估时，多目标贝叶斯优化（BO）是一种流行的方法，因为其样品效率很高。但是，即使有了最近的方法学进步，大多数现有的多目标BO方法在具有超过几十个参数的搜索空间上的表现较差，并且依赖于随着观测值数量进行立方体扩展的全局替代模型。在这项工作中，我们提出了Morbo，这是高维搜索空间上多目标BO的可扩展方法。 Morbo通过使用协调策略并行在设计空间的多个局部区域中执行BO来确定全球最佳解决方案。我们表明，Morbo在几种高维综合问题和现实世界应用中的样品效率中的最新效率显着提高，包括光学显示设计问题和146和222参数的车辆设计问题。在这些问题上，如果现有的BO算法无法扩展和表现良好，Morbo为从业者提供了刻度级别的效率，则在当前方法上可以提高样本效率。

当前的量子点（QD）设备的自动传动方法在显示出一些成功的同时，缺乏对数据可靠性的评估。当自主系统处理嘈杂或低质量数据时，这会导致意外的失败。在这项工作中，我们为QD设备的强大自动调整提供了一个框架，该QD设备将机器学习（ML）状态分类器与数据质量控制模块结合在一起。数据质量控制模块充当“守门人”系统，确保只有国家分类器处理可靠的数据。较低的数据质量会导致设备重新校准或终止。为了训练两个ML系统，我们通过结合QD实验的典型合成噪声来增强QD仿真。我们确认，在状态分类器的训练中包含合成噪声可以显着提高性能，在测试实验数据时，准确性为95.0（9）％。然后，我们通过表明状态分类器的性能随着预期的数据质量而恶化，从而验证数据质量控制模块的功能。我们的结果为嘈杂的QD设备的自动调整建立了强大而灵活的ML框架。

在本文中，我们考虑了增强学习（RL）中对风险敏感的顺序决策。我们的贡献是两个方面。首先，我们介绍了一种新颖而连贯的风险量化，即复合风险，该风险量化了学习过程中综合和认知风险的关节作用。现有的作品单独被视为综合性或认知风险，或作为添加剂组合。我们证明，当认知风险措施被期望取代时，添加剂配方是复合风险的特殊情况。因此，综合风险比单个和添加剂配方对伴侣和认知不确定性更敏感。我们还基于集合引导和分布RL提出了一种算法，Sentinel-K，分别代表认知和差异不确定性。 K Learners的合奏使用遵循正规领导者（FTRL）来汇总分布并获得综合风险。我们通过实验验证了Sentinel-K可以更好地估计回报分布，并且与复合风险估计相比，与最新风险敏感和分布RL算法相比，对风险敏感的性能更高。