Current technological advances open up new opportunities for bringing human-machine interaction to a new level of human-centered cooperation. In this context, a key issue is the semantic understanding of the environment in order to enable mobile robots more complex interactions and a facilitated communication with humans. Prerequisites are the vision-based registration of semantic objects and humans, where the latter are further analyzed for potential interaction partners. Despite significant research achievements, the reliable and fast registration of semantic information still remains a challenging task for mobile robots in real-world scenarios. In this paper, we present a vision-based system for mobile assistive robots to enable a semantic-aware environment perception without additional a-priori knowledge. We deploy our system on a mobile humanoid robot that enables us to test our methods in real-world applications.

在本文中，我们专注于改进二进制2D实例细分，以帮助人类用多边形标记地面真相数据集。人类的标签只需要在物体周围绘制盒子，然后自动生成多边形。为了有用，我们的系统必须实时运行CPU。二进制实例细分的最常见方法涉及编码器折叠网络。本报告评估了最先进的编码器 - 码头网络，并提出了一种使用这些网络改善实例分割质量的方法。除了网络体系结构的改进之外，我们提出的方法还依靠为网络输入，所谓的极端点（即对象轮廓上的最外部点）提供额外的信息。用户可以几乎尽快给它们标记它们，而不是边界框。边界框也可以从极端点推导。与其他最先进的编码器网络相比，此方法可产生更好的IOU，并且在将其部署在CPU上时也足够快。

In this paper, we present a method for unconstrained end-to-end head pose estimation. We address the problem of ambiguous rotation labels by introducing the rotation matrix formalism for our ground truth data and propose a continuous 6D rotation matrix representation for efficient and robust direct regression. This way, our method can learn the full rotation appearance which is contrary to previous approaches that restrict the pose prediction to a narrow-angle for satisfactory results. In addition, we propose a geodesic distance-based loss to penalize our network with respect to the SO(3) manifold geometry. Experiments on the public AFLW2000 and BIWI datasets demonstrate that our proposed method significantly outperforms other state-of-the-art methods by up to 20\%. We open-source our training and testing code along with our pre-trained models: https://github.com/thohemp/6DRepNet.

A default assumption in reinforcement learning and optimal control is that experience arrives at discrete time points on a fixed clock cycle. Many applications, however, involve continuous systems where the time discretization is not fixed but instead can be managed by a learning algorithm. By analyzing Monte-Carlo value estimation for LQR systems in both finite-horizon and infinite-horizon settings, we uncover a fundamental trade-off between approximation and statistical error in value estimation. Importantly, these two errors behave differently with respect to time discretization, which implies that there is an optimal choice for the temporal resolution that depends on the data budget. These findings show how adapting the temporal resolution can provably improve value estimation quality in LQR systems from finite data. Empirically, we demonstrate the trade-off in numerical simulations of LQR instances and several non-linear environments.

Text-based personality computing (TPC) has gained many research interests in NLP. In this paper, we describe 15 challenges that we consider deserving the attention of the research community. These challenges are organized by the following topics: personality taxonomies, measurement quality, datasets, performance evaluation, modelling choices, as well as ethics and fairness. When addressing each challenge, not only do we combine perspectives from both NLP and social sciences, but also offer concrete suggestions towards more valid and reliable TPC research.

Stance detection (SD) can be considered a special case of textual entailment recognition (TER), a generic natural language task. Modelling SD as TER may offer benefits like more training data and a more general learning scheme. In this paper, we present an initial empirical analysis of this approach. We apply it to a difficult but relevant test case where no existing labelled SD dataset is available, because this is where modelling SD as TER may be especially helpful. We also leverage measurement knowledge from social sciences to improve model performance. We discuss our findings and suggest future research directions.

The detection of state-sponsored trolls acting in information operations is an unsolved and critical challenge for the research community, with repercussions that go beyond the online realm. In this paper, we propose a novel AI-based solution for the detection of state-sponsored troll accounts, which consists of two steps. The first step aims at classifying trajectories of accounts' online activities as belonging to either a state-sponsored troll or to an organic user account. In the second step, we exploit the classified trajectories to compute a metric, namely "troll score", which allows us to quantify the extent to which an account behaves like a state-sponsored troll. As a study case, we consider the troll accounts involved in the Russian interference campaign during the 2016 US Presidential election, identified as Russian trolls by the US Congress. Experimental results show that our approach identifies accounts' trajectories with an AUC close to 99\% and, accordingly, classify Russian trolls and organic users with an AUC of 97\%. Finally, we evaluate whether the proposed solution can be generalized to different contexts (e.g., discussions about Covid-19) and generic misbehaving users, showing promising results that will be further expanded in our future endeavors.

高级驾驶员辅助系统（ADA）旨在提高车辆安全性。但是，如果不了解当前ADA及其可能的解决方案的原因和局限性，就很难获得此类收益。这项研究1）通过文献综述研究了ADA的局限性和解决方案，2）通过使用自然语言处理模型来确定ADA通过消费者投诉的原因和影响，3）比较了两者之间的主要差异。这两条研究线确定了类似的ADA原因类别，包括人为因素，环境因素和车辆因素。但是，学术研究更多地集中在ADA问题的人为因素上，并提出了高级算法来减轻此类问题，而驾驶员抱怨ADAS失败的更多车辆因素，这导致了最大的后果。这两个来源的发现倾向于相互补充，并为未来的改善ADA提供了重要意义。

我们提出了一个具有物理信息的神经网络，作为生物样品层析成像重建的正向模型。我们证明，通过用Helmholtz方程训练该网络作为物理损失，我们可以准确预测散射场。可以证明，可以对不同的样本进行微调的验证网络，并用于与其他数值解决方案更快地解决散射问题。我们通过数值和实验结果评估我们的方法。我们的物理知识神经网络可以推广到任何前进和反向散射问题。

本文介绍了用于自动赛车的多层运动计划和控制架构，能够避免静态障碍，进行主动超越并达到75 $ m/s $以上的速度。使用的脱机全局轨迹生成和在线模型预测控制器高度基于车辆的优化和动态模型，在该模型中，在基本的Pacejka Magic公式的扩展版本中，轮胎和弯曲效果表示。使用多体汽车运动库鉴定并验证了所提出的单轨模型，这些模型允许正确模拟车辆动力学，在丢失实际实验数据时尤其有用。调整了控制器的基本正规化项和约束，以降低输入的变化速率，同时确保可接受的速度和路径跟踪。运动计划策略由一个基于Fren \'ET框架的计划者组成，该计划者考虑了Kalman过滤器产生的对手的预测。策划者选择了无碰撞路径和速度轮廓要在3秒钟的视野中跟踪，以实现不同的目标，例如跟随和超车。该提议的解决方案已应用于达拉拉AV-21赛车，并在椭圆形赛道上进行了测试，可实现高达25 $ m/s^{2} $的横向加速度。