侧can声纳是一种小型且具有成本效益的传感溶液，可以轻松地安装在大多数船上。从历史上看，它一直用于生产高清图像，专家可能用来识别海底或水柱上的目标。虽然已提出溶液仅从侧扫或与Multibeam结合使用，但影响有限。这部分是由于主要仅限于单侧扫描线的结果。在本文中，我们提出了一种现代可口的解决方案，以从许多侧扫线中创建高质量的测量规模测深。通过合并对同一位置的多个观察结果，可以改善结果，因为估计值相互加强。我们的方法基于正弦表示网络，这是神经表示学习的最新进展。我们通过从大型侧扫调查中产生测深，证明了该方法的可伸缩性。通过与高精度多光束传感器收集的数据进行比较，可以证明所得的质量。

可区分渲染的最新进展，可以将相对于3D对象模型计算2D像素值的梯度，可以通过仅在2D监督下通过基于梯度的优化来估计模型参数。将深度神经网络纳入这样的优化管道很容易，从而可以利用深度学习技术。这也大大减少了收集和注释3D数据的要求，例如，在2D传感器构造几何形状时，这对于应用程序非常困难。在这项工作中，我们为侧can声纳图像提出了一个可区分的渲染器。我们进一步证明了它可以解决仅从2D侧can声纳数据直接重建3D海底网眼的反问题的能力。

侧扫声纳强度编码有关海床表面正常变化的信息。但是，其他因素（例如海底几何形状及其材料组成）也会影响回流强度。可以建模这些强度从向前方向上的变化从从测深图和物理特性到测量强度的表面正常的变化，或者可以使用逆模型，该模型从强度开始并模拟表面正常。在这里，我们使用一个逆模型，该模型利用深度学习能够从数据中学习的能力；卷积神经网络用于估计侧扫的正常表面。因此，海床的内部特性仅是隐式学习的。一旦估算了此信息，就可以通过优化框架重建测深图，该框架还包括高度计读数，以提供稀疏的深度轮廓作为约束。最近提出了隐式神经表示学习，以代表这种优化框架中的测深图。在本文中，我们使用神经网络来表示地图并在高度计点的约束和侧can的估计表面正常状态下进行优化。通过从几个侧扫线的不同角度融合多个观测值，通过优化改善了估计的结果。我们通过使用大型侧扫调查的侧扫数据重建高质量的测深，通过重建高质量的测深，证明了该方法的效率和可伸缩性。我们比较了提出的数据驱动的逆模型方法，该方法将侧扫形成前向兰伯特模型。我们通过将每个重建的质量与由多光束传感器构建的数据进行比较来评估它的质量。因此，我们能够讨论每种方法的优点和缺点。

我们提出了一种新型的数据驱动方法，用于从侧扫而言高分辨率测深的重建。侧面声纳（SSS）强度随范围的函数确实包含有关海底斜率的一些信息。但是，必须推断该信息。此外，导航系统提供了估计的轨迹，通常也可以使用沿该轨迹的高度。通过这些，我们获得了非常粗糙的海床测深，作为输入。然后将其与从侧扫的间接但高分辨率的海床信息结合在一起，以估计完整的测深。这个稀疏的深度可以通过单光束回声声音，多普勒速度日志（DVL），其他底部跟踪传感器或底部跟踪算法从侧can本身获得。在我们的工作中，使用一个完全卷积的网络来估算侧扫图像中的深度轮廓及其不确定性，并以端到端的方式稀疏深度。然后将估计的深度与范围一起使用，以计算海底上点的3D位置。可以在融合深度预测和来自神经网络的相应置信度度量后重建高质量的测深图。我们显示了通过使用侧扫而言，仅与侧扫相比，通过使用侧扫而获得的稀疏深度获得了测得图的改进。当将多个测深估计值融合到单个地图中时，我们还显示了置信度加权的好处。

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

This contribution demonstrates the feasibility of applying Generative Adversarial Networks (GANs) on images of EPAL pallet blocks for dataset enhancement in the context of re-identification. For many industrial applications of re-identification methods, datasets of sufficient volume would otherwise be unattainable in non-laboratory settings. Using a state-of-the-art GAN architecture, namely CycleGAN, images of pallet blocks rotated to their left-hand side were generated from images of visually centered pallet blocks, based on images of rotated pallet blocks that were recorded as part of a previously recorded and published dataset. In this process, the unique chipwood pattern of the pallet block surface structure was retained, only changing the orientation of the pallet block itself. By doing so, synthetic data for re-identification testing and training purposes was generated, in a manner that is distinct from ordinary data augmentation. In total, 1,004 new images of pallet blocks were generated. The quality of the generated images was gauged using a perspective classifier that was trained on the original images and then applied to the synthetic ones, comparing the accuracy between the two sets of images. The classification accuracy was 98% for the original images and 92% for the synthetic images. In addition, the generated images were also used in a re-identification task, in order to re-identify original images based on synthetic ones. The accuracy in this scenario was up to 88% for synthetic images, compared to 96% for original images. Through this evaluation, it is established, whether or not a generated pallet block image closely resembles its original counterpart.

Earthquakes, fire, and floods often cause structural collapses of buildings. The inspection of damaged buildings poses a high risk for emergency forces or is even impossible, though. We present three recent selected missions of the Robotics Task Force of the German Rescue Robotics Center, where both ground and aerial robots were used to explore destroyed buildings. We describe and reflect the missions as well as the lessons learned that have resulted from them. In order to make robots from research laboratories fit for real operations, realistic test environments were set up for outdoor and indoor use and tested in regular exercises by researchers and emergency forces. Based on this experience, the robots and their control software were significantly improved. Furthermore, top teams of researchers and first responders were formed, each with realistic assessments of the operational and practical suitability of robotic systems.

The Me 163 was a Second World War fighter airplane and a result of the German air force secret developments. One of these airplanes is currently owned and displayed in the historic aircraft exhibition of the Deutsches Museum in Munich, Germany. To gain insights with respect to its history, design and state of preservation, a complete CT scan was obtained using an industrial XXL-computer tomography scanner. Using the CT data from the Me 163, all its details can visually be examined at various levels, ranging from the complete hull down to single sprockets and rivets. However, while a trained human observer can identify and interpret the volumetric data with all its parts and connections, a virtual dissection of the airplane and all its different parts would be quite desirable. Nevertheless, this means, that an instance segmentation of all components and objects of interest into disjoint entities from the CT data is necessary. As of currently, no adequate computer-assisted tools for automated or semi-automated segmentation of such XXL-airplane data are available, in a first step, an interactive data annotation and object labeling process has been established. So far, seven 512 x 512 x 512 voxel sub-volumes from the Me 163 airplane have been annotated and labeled, whose results can potentially be used for various new applications in the field of digital heritage, non-destructive testing, or machine-learning. This work describes the data acquisition process of the airplane using an industrial XXL-CT scanner, outlines the interactive segmentation and labeling scheme to annotate sub-volumes of the airplane's CT data, describes and discusses various challenges with respect to interpreting and handling the annotated and labeled data.

Deep Reinforcement Learning (RL) agents are susceptible to adversarial noise in their observations that can mislead their policies and decrease their performance. However, an adversary may be interested not only in decreasing the reward, but also in modifying specific temporal logic properties of the policy. This paper presents a metric that measures the exact impact of adversarial attacks against such properties. We use this metric to craft optimal adversarial attacks. Furthermore, we introduce a model checking method that allows us to verify the robustness of RL policies against adversarial attacks. Our empirical analysis confirms (1) the quality of our metric to craft adversarial attacks against temporal logic properties, and (2) that we are able to concisely assess a system's robustness against attacks.

Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer. Similar to classical compilation, quantum compilation is a sequential process with many compilation steps and numerous possible optimization passes. Despite the similarities, the development of compilers for quantum computing is still in its infancy-lacking mutual consolidation on the best sequence of passes, compatibility, adaptability, and flexibility. In this work, we take advantage of decades of classical compiler optimization and propose a reinforcement learning framework for developing optimized quantum circuit compilation flows. Through distinct constraints and a unifying interface, the framework supports the combination of techniques from different compilers and optimization tools in a single compilation flow. Experimental evaluations show that the proposed framework-set up with a selection of compilation passes from IBM's Qiskit and Quantinuum's TKET-significantly outperforms both individual compilers in over 70% of cases regarding the expected fidelity. The framework is available on GitHub (https://github.com/cda-tum/MQTPredictor).