我们提出了一种几何多级优化方法，该方法平滑地包含了框约束。给定一个受限的优化问题，我们考虑了具有不同离散水平的模型的层次结构。更精细的型号准确但计算昂贵，而更粗的型号则不太准确，但计算便宜。在良好级别上工作时，多级优化将基于搜索方向计算搜索方向，该模型会加快良好级别的更新。此外，利用层次结构引起的几何形状保留了更新的可行性。特别是，我们的方法扩展了多移民方法的经典组成部分，例如限制和延长延长我们约束的riemannian结构。

Deep supervised models have an unprecedented capacity to absorb large quantities of training data. Hence, training on multiple datasets becomes a method of choice towards strong generalization in usual scenes and graceful performance degradation in edge cases. Unfortunately, different datasets often have incompatible labels. For instance, the Cityscapes road class subsumes all driving surfaces, while Vistas defines separate classes for road markings, manholes etc. Furthermore, many datasets have overlapping labels. For instance, pickups are labeled as trucks in VIPER, cars in Vistas, and vans in ADE20k. We address this challenge by considering labels as unions of universal visual concepts. This allows seamless and principled learning on multi-domain dataset collections without requiring any relabeling effort. Our method achieves competitive within-dataset and cross-dataset generalization, as well as ability to learn visual concepts which are not separately labeled in any of the training datasets. Experiments reveal competitive or state-of-the-art performance on two multi-domain dataset collections and on the WildDash 2 benchmark.

Crop type maps are critical for tracking agricultural land use and estimating crop production. Remote sensing has proven an efficient and reliable tool for creating these maps in regions with abundant ground labels for model training, yet these labels remain difficult to obtain in many regions and years. NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar instrument, originally designed for forest monitoring, has shown promise for distinguishing tall and short crops. In the current study, we leverage GEDI to develop wall-to-wall maps of short vs tall crops on a global scale at 10 m resolution for 2019-2021. Specifically, we show that (1) GEDI returns can reliably be classified into tall and short crops after removing shots with extreme view angles or topographic slope, (2) the frequency of tall crops over time can be used to identify months when tall crops are at their peak height, and (3) GEDI shots in these months can then be used to train random forest models that use Sentinel-2 time series to accurately predict short vs. tall crops. Independent reference data from around the world are then used to evaluate these GEDI-S2 maps. We find that GEDI-S2 performed nearly as well as models trained on thousands of local reference training points, with accuracies of at least 87% and often above 90% throughout the Americas, Europe, and East Asia. Systematic underestimation of tall crop area was observed in regions where crops frequently exhibit low biomass, namely Africa and South Asia, and further work is needed in these systems. Although the GEDI-S2 approach only differentiates tall from short crops, in many landscapes this distinction goes a long way toward mapping the main individual crop types. The combination of GEDI and Sentinel-2 thus presents a very promising path towards global crop mapping with minimal reliance on ground data.

Multilevel Stein variational gradient descent is a method for particle-based variational inference that leverages hierarchies of approximations of target distributions with varying costs and fidelity to computationally speed up inference. This work provides a cost complexity analysis of multilevel Stein variational gradient descent that applies under milder conditions than previous results, especially in discrete-in-time regimes and beyond the limited settings where Stein variational gradient descent achieves exponentially fast convergence. The analysis shows that the convergence rate of Stein variational gradient descent enters only as a constant factor for the cost complexity of the multilevel version, which means that the costs of the multilevel version scale independently of the convergence rate of Stein variational gradient descent on a single level. Numerical experiments with Bayesian inverse problems of inferring discretized basal sliding coefficient fields of the Arolla glacier ice demonstrate that multilevel Stein variational gradient descent achieves orders of magnitude speedups compared to its single-level version.

We apply Physics Informed Neural Networks (PINNs) to the problem of wildfire fire-front modelling. The PINN is an approach that integrates a differential equation into the optimisation loss function of a neural network to guide the neural network to learn the physics of a problem. We apply the PINN to the level-set equation, which is a Hamilton-Jacobi partial differential equation that models a fire-front with the zero-level set. This results in a PINN that simulates a fire-front as it propagates through a spatio-temporal domain. We demonstrate the agility of the PINN to learn physical properties of a fire under extreme changes in external conditions (such as wind) and show that this approach encourages continuity of the PINN's solution across time. Furthermore, we demonstrate how data assimilation and uncertainty quantification can be incorporated into the PINN in the wildfire context. This is significant contribution to wildfire modelling as the level-set method -- which is a standard solver to the level-set equation -- does not naturally provide this capability.

Explainable artificial intelligence (XAI) provides explanations for not interpretable machine learning (ML) models. While many technical approaches exist, there is a lack of validation of these techniques on real-world datasets. In this work, we present a use-case of XAI: an ML model which is trained to estimate electrification rates based on mobile phone data in Senegal. The data originate from the Data for Development challenge by Orange in 2014/15. We apply two model-agnostic, local explanation techniques and find that while the model can be verified, it is biased with respect to the population density. We conclude our paper by pointing to the two main challenges we encountered during our work: data processing and model design that might be restricted by currently available XAI methods, and the importance of domain knowledge to interpret explanations.

人工智能的最新发展提高了其在很大程度上自主和协作背景下创建艺术的能力。在这两种情况下，AI旨在模仿，结合和扩展现有的艺术风格，并可以改变创造性实践。在我们正在进行的研究中，我们从可持续性和道德角度研究了这种创意-AI。这两个主要重点领域是了解涉及创意-AI的艺术过程中的环境可持续性方面（材料，实践），以及与谁参与创建过程（权力，作者身份，所有权）有关的道德问题。本文概述了我们在这两个方向上正在进行的研究。我们将介绍我们的跨学科方法，该方法结合了访谈，讲习班，在线人种志和能源测量，以解决我们的研究问题：艺术家社区当前如何使用Creative-AI，以及艺术家想象的未来应用？当AI应用于创建艺术时，它将如何影响经济和环境？而且，如何回答这些问题指导创意ai的知识产权制度的要求？

本文介绍了针对自闭症儿童的社会辅助机器人技术，旨在使用机器人疗法进行自闭症。该项目的目的是测试自闭症儿童与社会机器人NAO的互动。特别是机器人将在其工作中支持操作员（心理学家，教育者，言语治疗师等）。该项目的创新方面是，儿童机器人互动将考虑孩子的情绪和特定特征，机器人将相应地适应其行为。

病变分割是放射线工作流程的关键步骤。手动分割需要长时间的执行时间，并且容易发生可变性，从而损害了放射线研究及其鲁棒性的实现。在这项研究中，对非小细胞肺癌患者的计算机断层扫描图像进行了深入学习的自动分割方法。还评估了手动与自动分割在生存放射模型的性能中的使用。方法总共包括899名NSCLC患者（2个专有：A和B，1个公共数据集：C）。肺部病变的自动分割是通过训练先前开发的建筑NNU-NET进行的，包括2D，3D和级联方法。用骰子系数评估自动分割的质量，以手动轮廓为参考。通过从数据集A的手动和自动轮廓中提取放射性的手工制作和深度学习特征来探索自动分割对患者生存的放射素模型对患者生存的性能的影响。评估并比较模型的精度。结果通过平均2D和3D模型的预测以及应用后处理技术来提取最大连接的组件，可以实现具有骰子= 0.78 +（0.12）的自动和手动轮廓之间的最佳一致性。当使用手动或自动轮廓，手工制作或深度特征时，在生存模型的表现中未观察到统计差异。最好的分类器显示出0.65至0.78之间的精度。结论NNU-NET在自动分割肺部病变中的有希望的作用已得到证实，从而大大降低了时必的医生的工作量，而不会损害基于放射线学的生存预测模型的准确性。

快速，可靠地找到准确的逆运动学（IK）解决方案仍然是机器人操纵的挑战性问题。现有的数值求解器广泛适用，但依赖于本地搜索技术来管理高度非关键目标函数。最近，基于学习的方法已显示出有望作为生成快速准确的IK结果的一种手段。可以轻松地将学习的求解器与端到端系统中的其他学习算法集成在一起。但是，基于学习的方法具有致命的脚跟：每个感兴趣的机器人都需要一个专门的模型，必须从头开始训练。为了解决这一关键缺点，我们研究了一种新颖的距离几何机器人表示，并与图形结构相结合，使我们能够利用图形神经网络（GNNS）的灵活性。我们使用这种方法来训练第一个学到的生成图形逆运动学（GGIK）求解器，它至关重要的是，“机器人 - 敏捷” - 单个模型能够为各种不同的机器人提供IK解决方案。此外，GGIK的生成性质使求解器可以同时生产大量不同的解决方案，并与最小的额外计算时间同行，使其适用于诸如基于采样的运动计划之类的应用。最后，GGIK可以通过提供可靠的初始化来补充本地IK求解器。这些优势以及使用与任务相关的先验并通过新数据不断改进的能力表明，GGIK有可能成为灵活的，基于学习的机器人操作系统的关键组成部分。