用快速自动驾驶汽车导航越野，取决于强大的感知系统，该系统与不可传输的地形区分开来。通常，这取决于语义理解，该语义理解基于人类专家注释的图像的监督学习。这需要对人类时间进行大量投资，假定正确的专家分类，并且小细节可能导致错误分类。为了应对这些挑战，我们提出了一种方法，可以以一种自我监督的方式从过去的车辆体验中预测高风险的地形。首先，我们开发了一种将车辆轨迹投射到前摄像头图像中的工具。其次，在地形的3D表示中的遮挡被过滤掉。第三，在蒙面车辆轨迹区域训练的自动编码器根据重建误差确定低风险和高风险地形。我们通过两种型号和不同的瓶颈评估了我们的方法，并使用了两个不同的训练站点和四轮越野车。与来自类似地形的两个独立的语义标签的独立测试集比较，表明能够将地面作为低风险和植被为高风险，精度为81.1％和85.1％。

本文介绍了Cerberus机器人系统系统，该系统赢得了DARPA Subterranean挑战最终活动。出席机器人自主权。由于其几何复杂性，降解的感知条件以及缺乏GPS支持，严峻的导航条件和拒绝通信，地下设置使自动操作变得特别要求。为了应对这一挑战，我们开发了Cerberus系统，该系统利用了腿部和飞行机器人的协同作用，再加上可靠的控制，尤其是为了克服危险的地形，多模式和多机器人感知，以在传感器退化，以及在传感器退化的条件下进行映射以及映射通过统一的探索路径计划和本地运动计划，反映机器人特定限制的弹性自主权。 Cerberus基于其探索各种地下环境及其高级指挥和控制的能力，表现出有效的探索，对感兴趣的对象的可靠检测以及准确的映射。在本文中，我们报告了DARPA地下挑战赛的初步奔跑和最终奖项的结果，并讨论了为社区带来利益的教训所面临的亮点和挑战。

贝叶斯过滤器的测量更新规则通常包含手工制作的启发式，以计算高维传感器数据的观察概率，如图像。在这项工作中，我们提出了新颖的方法深度测量更新（DMU）作为广泛系统的一般更新规则。 DMU具有条件编码器 - 解码器神经网络结构，用于处理深度图像作为原始输入。尽管网络仅在合成数据上培训，但该模型在真实数据上的评估时间下显示出良好的性能。通过我们提出的培训计划引发数据培训，我们展示了如何有效地培训DMU模型对条件变量敏感，而无需依赖于随机信息瓶颈。我们在多种情况下验证提出的方法，从而增加了复杂性的姿势，从单个物体的姿势估计开始到姿势的关节估计和铰接系统的内部状态。此外，我们在RBO数据集上提供反对铰接的符号距离功能（A-SDF）的基准，作为关节状态估计的基线比较。

Some of the most challenging environments on our planet are accessible to quadrupedal animals but remain out of reach for autonomous machines. Legged locomotion can dramatically expand the operational domains of robotics. However, conventional controllers for legged locomotion are based on elaborate state machines that explicitly trigger the execution of motion primitives and reflexes. These designs have escalated in complexity while falling short of the generality and robustness of animal locomotion. Here we present a radically robust controller for legged locomotion in challenging natural environments. We present a novel solution to incorporating proprioceptive feedback in locomotion control and demonstrate remarkable zero-shot generalization from simulation to natural environments. The controller is trained by reinforcement learning in simulation. It is based on a neural network that acts on a stream of proprioceptive signals. The trained controller has taken two generations of quadrupedal ANYmal robots to a variety of natural environments that are beyond the reach of prior published work in legged locomotion. The controller retains its robustness under conditions that have never been encountered during training: deformable terrain such as mud and snow, dynamic footholds such as rubble, and overground impediments such as thick vegetation and gushing water. The presented work opens new frontiers for robotics and indicates that radical robustness in natural environments can be achieved by training in much simpler domains.

State-of-the-art language models are often accurate on many question-answering benchmarks with well-defined questions. Yet, in real settings questions are often unanswerable without asking the user for clarifying information. We show that current SotA models often do not ask the user for clarification when presented with imprecise questions and instead provide incorrect answers or "hallucinate". To address this, we introduce CLAM, a framework that first uses the model to detect ambiguous questions, and if an ambiguous question is detected, prompts the model to ask the user for clarification. Furthermore, we show how to construct a scalable and cost-effective automatic evaluation protocol using an oracle language model with privileged information to provide clarifying information. We show that our method achieves a 20.15 percentage point accuracy improvement over SotA on a novel ambiguous question-answering answering data set derived from TriviaQA.

Compartmental models are a tool commonly used in epidemiology for the mathematical modelling of the spread of infectious diseases, with their most popular representative being the Susceptible-Infected-Removed (SIR) model and its derivatives. However, current SIR models are bounded in their capabilities to model government policies in the form of non-pharmaceutical interventions (NPIs) and weather effects and offer limited predictive power. More capable alternatives such as agent based models (ABMs) are computationally expensive and require specialized hardware. We introduce a neural network augmented SIR model that can be run on commodity hardware, takes NPIs and weather effects into account and offers improved predictive power as well as counterfactual analysis capabilities. We demonstrate our models improvement of the state-of-the-art modeling COVID-19 in Austria during the 03.2020 to 03.2021 period and provide an outlook for the future up to 01.2024.

Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. However, current CNN approaches largely remain vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to the human eye. In recent years, various approaches have been proposed to defend CNNs against such attacks, for example by model hardening or by adding explicit defence mechanisms. Thereby, a small "detector" is included in the network and trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. In this work, we propose a simple and light-weight detector, which leverages recent findings on the relation between networks' local intrinsic dimensionality (LID) and adversarial attacks. Based on a re-interpretation of the LID measure and several simple adaptations, we surpass the state-of-the-art on adversarial detection by a significant margin and reach almost perfect results in terms of F1-score for several networks and datasets. Sources available at: https://github.com/adverML/multiLID

在本文中，我们考虑了通过风险最小化监督学习中变异模型的问题。我们的目标是通过双层优化和通过算法展开对学习变异模型的两种方法进行更深入的了解。前者将变分模型视为低于风险最小化问题的较低级别优化问题，而后者将较低级别优化问题替换为解决上述问题的算法。两种方法都在实践中使用，但是从计算的角度来看，展开要简单得多。为了分析和比较两种方法，我们考虑了一个简单的玩具模型，并明确计算所有风险和各自的估计器。我们表明，展开可能比双重优化方法更好，而且展开的性能可以显着取决于进一步的参数，有时会以意外的方式：虽然展开的算法的步骤大小很重要，但展开的迭代数量只有很重要如果数字是偶数或奇数，并且这两种情况截然不同。

我们提出了一种使用合理的心形和现实外观合成心脏MR图像的方法，目的是生成标记的数据进行深度学习（DL）训练。它将图像合成分解为标签变形和标签到图像翻译任务。前者是通过VAE模型中的潜在空间插值来实现的，而后者是通过条件GAN模型完成的。我们设计了一种在受过训练的VAE模型的潜在空间中的标记操纵方法，即病理合成，旨在合成一系列具有所需心脏病特征的伪病理合成受试者。此外，我们建议通过估计潜在矢量之间的相关系数矩阵来对2D切片之间的关系进行建模，并利用它在解码到图像空间之前将样品随机绘制的元素关联。这种简单而有效的方法导致从2D片段产生3D一致的受试者。这种方法可以提供一种解决方案，以多样化和丰富心脏MR图像的可用数据库，并为开发基于DL的图像分析算法的开发铺平道路。该代码将在https://github.com/sinaamirrajab/cardiacpathologysynthesis中找到。

收集和注释面向任务的对话框数据很困难，尤其是对于需要专家知识的高度特定领域。同时，非正式的沟通渠道（例如即时使者）在工作中越来越多地使用。这导致了许多与工作相关的信息，这些信息通过这些渠道传播，需要由员工进行后处理。为了减轻这个问题，我们提出了TexPrax，这是一种消息传递系统，以收集和注释与工作有关的聊天中发生的问题，原因和解决方案。 TexPrax使用聊天机器人直接吸引员工，以提供对话的轻量级注释并简化文档工作。为了遵守数据隐私和安全法规，我们使用端到端消息加密，并使用户完全控制其数据，该数据比常规注释工具具有各种优势。我们与德国工厂员工一起在用户研究中评估TexPrax，他们要求同事提供有关日常工作中出现的问题的解决方案。总体而言，我们收集201个面向任务的德语对话，其中包含1,027个句子，并带有句子级专家注释。我们的数据分析还表明，现实世界对话经常包含具有代码转换，对同一实体的缩写的实例，以及NLP系统应该能够处理的方言。