缺失值的插补代表了许多现实世界数据分析管道的重要障碍。在这里，我们专注于时间序列数据，并提出SSSD，这是一个依赖两种新兴技术的插图模型，（条件）扩散模型是最先进的生成模型，结构化状态空间模型作为内部模型体系结构，是特别适合捕获时间序列数据中的长期依赖性。我们证明，在广泛的数据集和不同的丢失方案（包括具有挑战性的停电失误的情况）上，SSSD匹配甚至超过了最先进的概率插补和预测性能，在这些情况下，先前的方法未能提供有意义的结果。

临床12-铅心电图（ECG）是遇到的最广泛的生物信息之一。尽管公共ECG数据集的可用性增加，但标签稀缺仍然是该领域的中央挑战。自我监督的学习代表了缓解这个问题的有希望的方式。在这项工作中，我们提出了从临床12引导ECG数据的自我监督代表学习的第一次全面评估。为此，我们基于对ECG域的实例辨别和潜在预测来适应最先进的自我监督方法。在第一步中，我们基于最近成立，全面的临床ECG分类任务的线性评估性能来学习对比表征并评估其质量。在第二步中，与纯粹监督性能相比，我们分析了自我监督预先训练对Fineetuned ECG分类器的影响。对于最佳性能的方法，对比预测性编码的适应性，我们发现线性评估性能下降低于监督性能的0.5％。对于FineTuned模型，与监督性能，标签效率以及对生理噪声的鲁棒性相比，我们发现下游性能大约1％的下游性能。这项工作明确建立了通过自我监督的学习和众多优势来提取从心电图数据提取歧视性表现的可行性，与纯粹的监督培训相比，在下游任务上的这种代表性上进行了多种优势。作为对其在公开可用的数据集的ECG域中进行的第一次全面评估，我们希望在生物资料中快速发展的代表学习领域建立一个可重复进展的第一步。

Preddiff是一种模型不合时宜的局部归因方法，牢固地植根于概率理论。它的简单直觉是在边缘化特征时测量预测变化。在这项工作中，我们阐明了Preddiff的属性及其与Shapley值的密切联系。我们强调分类和回归之间的重要差异，这在两种形式主义中都需要特定的治疗方法。我们通过引入一种新的，有充分的基础的措施来扩展Preddiff，以实现任意特征子集之间的相互作用效果。对互动效应的研究代表了对黑盒模型的全面理解的不可避免的一步，对于科学应用尤其重要。Preddiff配备了我们的新型交互度量，是一种有前途的模型无关方法，用于获得可靠的，数值廉价和理论上声音的归因。

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).

People are not very good at detecting lies, which may explain why they refrain from accusing others of lying, given the social costs attached to false accusations - both for the accuser and the accused. Here we consider how this social balance might be disrupted by the availability of lie-detection algorithms powered by Artificial Intelligence. Will people elect to use lie detection algorithms that perform better than humans, and if so, will they show less restraint in their accusations? We built a machine learning classifier whose accuracy (67\%) was significantly better than human accuracy (50\%) in a lie-detection task and conducted an incentivized lie-detection experiment in which we measured participants' propensity to use the algorithm, as well as the impact of that use on accusation rates. We find that the few people (33\%) who elect to use the algorithm drastically increase their accusation rates (from 25\% in the baseline condition up to 86% when the algorithm flags a statement as a lie). They make more false accusations (18pp increase), but at the same time, the probability of a lie remaining undetected is much lower in this group (36pp decrease). We consider individual motivations for using lie detection algorithms and the social implications of these algorithms.