手动检查粪便涂片样品以鉴定寄生卵的存在非常耗时，只能由专家进行。因此，需要自动化系统来解决此问题，因为它可以与严重的肠道寄生虫感染有关。本文回顾了微观图像中关于寄生卵检测和分类的ICIP 2022挑战。我们描述了此应用程序的新数据集，该数据集是同类数据集的最大数据集。参与者在挑战中使用的方法及其结果及其结果进行了汇总和讨论。

Sunquakes are seismic emissions visible on the solar surface, associated with some solar flares. Although discovered in 1998, they have only recently become a more commonly detected phenomenon. Despite the availability of several manual detection guidelines, to our knowledge, the astrophysical data produced for sunquakes is new to the field of Machine Learning. Detecting sunquakes is a daunting task for human operators and this work aims to ease and, if possible, to improve their detection. Thus, we introduce a dataset constructed from acoustic egression-power maps of solar active regions obtained for Solar Cycles 23 and 24 using the holography method. We then present a pedagogical approach to the application of machine learning representation methods for sunquake detection using AutoEncoders, Contrastive Learning, Object Detection and recurrent techniques, which we enhance by introducing several custom domain-specific data augmentation transformations. We address the main challenges of the automated sunquake detection task, namely the very high noise patterns in and outside the active region shadow and the extreme class imbalance given by the limited number of frames that present sunquake signatures. With our trained models, we find temporal and spatial locations of peculiar acoustic emission and qualitatively associate them to eruptive and high energy emission. While noting that these models are still in a prototype stage and there is much room for improvement in metrics and bias levels, we hypothesize that their agreement on example use cases has the potential to enable detection of weak solar acoustic manifestations.

In a typical car-following scenario, target vehicle speed fluctuations act as an external disturbance to the host vehicle and in turn affect its energy consumption. To control a host vehicle in an energy-efficient manner using model predictive control (MPC), and moreover, enhance the performance of an ecological adaptive cruise control (EACC) strategy, forecasting the future velocities of a target vehicle is essential. For this purpose, a deep recurrent neural network-based vehicle speed prediction using long-short term memory (LSTM) and gated recurrent units (GRU) is studied in this work. Besides these, the physics-based constant velocity (CV) and constant acceleration (CA) models are discussed. The sequential time series data for training (e.g. speed trajectories of the target and its preceding vehicles obtained through vehicle-to-vehicle (V2V) communication, road speed limits, traffic light current and future phases collected using vehicle-to-infrastructure (V2I) communication) is gathered from both urban and highway networks created in the microscopic traffic simulator SUMO. The proposed speed prediction models are evaluated for long-term predictions (up to 10 s) of target vehicle future velocities. Moreover, the results revealed that the LSTM-based speed predictor outperformed other models in terms of achieving better prediction accuracy on unseen test datasets, and thereby showcasing better generalization ability. Furthermore, the performance of EACC-equipped host car on the predicted velocities is evaluated, and its energy-saving benefits for different prediction horizons are presented.

Traditionally, robots are regarded as universal motion generation machines. They are designed mainly by kinematics considerations while the desired dynamics is imposed by strong actuators and high-rate control loops. As an alternative, one can first consider the robot's intrinsic dynamics and optimize it in accordance with the desired tasks. Therefore, one needs to better understand intrinsic, uncontrolled dynamics of robotic systems. In this paper we focus on periodic orbits, as fundamental dynamic properties with many practical applications. Algebraic topology and differential geometry provide some fundamental statements about existence of periodic orbits. As an example, we present periodic orbits of the simplest multi-body system: the double-pendulum in gravity. This simple system already displays a rich variety of periodic orbits. We classify these into three classes: toroidal orbits, disk orbits and nonlinear normal modes. Some of these we found by geometrical insights and some by numerical simulation and sampling.

This paper presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments -- outdoors, from urban to woodland, and indoors in warehouses and mines - without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach CFEAR, we present an in-depth investigation on a wider range of data sets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar SLAM and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160Hz.

腿部运动中的弹簧基于弹簧的执行器可提供能量效率和提高的性能，但增加了控制器设计的难度。尽管以前的作品集中在广泛的建模和模拟上，以找到此类系统的最佳控制器，但我们建议直接在真实机器人上学习无模型控制器。在我们的方法中，步态首先是由中央模式发电机（CPG）合成的，其参数被优化以快速获得可实现有效运动的开环控制器。然后，为了使该控制器更强大并进一步提高性能，我们使用强化学习来关闭循环，以在CPG之上学习纠正措施。我们评估了DLR弹性四足动物BERT中提出的方法。我们在学习小跑和前进步态方面的结果表明，对弹簧执行动力学的开发自然而然地从对动态运动的优化中出现，尽管没有模型，但仍会产生高性能的运动。整个过程在真正的机器人上不超过1.5小时，并导致自然步态。

社会机器人的快速发展刺激了人类运动建模，解释和预测，主动碰撞，人类机器人相互作用和共享空间中共同损害的积极研究。现代方法的目标需要高质量的数据集进行培训和评估。但是，大多数可用数据集都遭受了不准确的跟踪数据或跟踪人员的不自然的脚本行为。本文试图通过在语义丰富的环境中提供运动捕获，眼睛凝视跟踪器和板载机器人传感器的高质量跟踪信息来填补这一空白。为了诱导记录参与者的自然行为，我们利用了松散的脚本化任务分配，这使参与者以自然而有目的的方式导航到动态的实验室环境。本文介绍的运动数据集设置了高质量的标准，因为使用语义信息可以增强现实和准确的数据，从而使新算法的开发不仅依赖于跟踪信息，而且还依赖于移动代理的上下文提示，还依赖于跟踪信息。静态和动态环境。

在这项工作中，我们提出了一种神经方法，用于重建描述层次相互作用的生根树图，使用新颖的表示，我们将其称为最低的共同祖先世代（LCAG）矩阵。这种紧凑的配方等效于邻接矩阵，但是如果直接使用邻接矩阵，则可以单独从叶子中学习树的结构，而无需先前的假设。因此，采用LCAG启用了第一个端到端的可训练解决方案，该解决方案仅使用末端树叶直接学习不同树大小的层次结构。在高能量粒子物理学的情况下，粒子衰减形成了分层树结构，只能通过实验观察到最终产物，并且可能的树的大型组合空间使分析溶液变得很棘手。我们证明了LCAG用作使用变压器编码器和神经关系编码器编码器图神经网络的模拟粒子物理衰减结构的任务。采用这种方法，我们能够正确预测LCAG纯粹是从叶子特征中的LCAG，最大树深度为$ 8 $ in $ 92.5 \％\％的树木箱子，最高$ 6 $叶子（包括）和$ 59.7 \％\％\％\％的树木$在我们的模拟数据集中$ 10 $。

机器人越来越多地部署在与人类共享的空间中，包括家庭环境和工业环境。在这些环境中，人与机器人之间的相互作用（HRI）对于安全性，可读性和效率至关重要。 HRI的一个关键因素是信任，它调节了系统的接受。已显示拟人化可以调节机器人的信任发展，但工业环境中的机器人通常不是拟人化的。我们在工业环境中设计了一个简单的互动，在该环境中，拟人化模拟驱动器（ARMOD）机器人模拟了自动驾驶汽车（AGV）。该任务由与AGV的人类交叉路径组成，有或不带有狭窄的走廊上安装在顶部。人类和系统在越过路径时需要协商轨迹，这意味着人必须关注机器人的轨迹，以避免与它发生碰撞。在存在ARMOD的情况下，报告的信任评分有显着的增长，表明拟人化机器人的存在足以调节信任，即使在有限的相互作用中，就像我们在这里提出的相互作用一样。

PyStacked通过Python的Scikit-Lear}实现了堆积的概括（Wolpert，1992），以进行回归和二进制分类。堆叠将多个监督的机器学习者（“基础”或“级别”学习者）结合到一个学习者中。当前支持的基础学习者包括正规化回归，随机森林，梯度增强的树木，支撑矢量机和前馈神经网（多层感知器）。PyStacked也可以用作“常规”机器学习程序，以适合单个基础学习者，因此为Scikit-Learn的机器学习算法提供了易于使用的API。