Compared to regular cameras, Dynamic Vision Sensors or Event Cameras can output compact visual data based on a change in the intensity in each pixel location asynchronously. In this paper, we study the application of current image-based SLAM techniques to these novel sensors. To this end, the information in adaptively selected event windows is processed to form motion-compensated images. These images are then used to reconstruct the scene and estimate the 6-DOF pose of the camera. We also propose an inertial version of the event-only pipeline to assess its capabilities. We compare the results of different configurations of the proposed algorithm against the ground truth for sequences of two publicly available event datasets. We also compare the results of the proposed event-inertial pipeline with the state-of-the-art and show it can produce comparable or more accurate results provided the map estimate is reliable.

从演示中学习（LFD）是一种从人提供的演示中复制和概括机器人技能的流行方法。在本文中，我们提出了一种基于优化的新型LFD方法，该方法将演示描述为弹性图。弹性图是通过弹簧网格连接的节点的图。我们通过将弹性地图拟合到一组演示中来构建技能模型。我们方法中的公式优化问题包括三个具有自然和物理解释的目标。主术语奖励笛卡尔坐标中的平方误差。第二项惩罚了导致最佳轨迹总长度的点的非等应存在分布。第三学期奖励平滑度，同时惩罚非线性。这些二次目标形成了凸问题，可以通过局部优化器有效地解决。我们研究了九种用于构建和加权弹性图并研究其在机器人任务中的性能的方法。我们还使用UR5E操纵器组在几个模拟和现实世界中评估了所提出的方法，并将其与其他LFD方法进行比较，以证明其在各种指标中的好处和灵活性。

在这项工作中，我们通过用户定义的关系网络将“社交”相互作用集成到MARL设置中，并检查代理与代理关系对新兴行为兴起的影响。利用社会学和神经科学的见解，我们提出的框架模型使用奖励共享的关系网络（RSRN）的构图代理关系，其中网络边缘的权重衡量了一项代理在成功中投入多少代理（或关心“关心） '） 其他。我们构建关系奖励是RSRN相互作用权重的函数，以通过多代理增强学习算法共同训练多代理系统。该系统的性能经过了具有不同关系网络结构（例如自我利益，社区和专制网络）的3个代理方案的测试。我们的结果表明，奖励分享关系网络可以显着影响学习的行为。我们认为，RSRN可以充当一个框架，不同的关系网络会产生独特的新兴行为，通常类似于对此类网络的直觉社会学理解。

测量机器人系统的整体自主评分需要一组相关方面和系统的组合，这些方面和特征可以以不同的单位，定性和/或不和谐测量。在本文中，我们建立了现有的非语境自治框架，以衡量并结合系统的自主水平和系统的组件性能，作为整体自治分数。我们检查一些组合功能的方法，显示一些方法如何找到相同数据的不同排名，并且我们使用加权产品方法来解决此问题。此外，我们介绍了非语境自治坐标，并表示具有自主距离的系统的整体自主权。我们将我们的方法应用于一组七个无人驾驶空中系统（UAS），并获得绝对的自主评分以及与最佳系统相对得分。

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.

Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies. However, the self-attention mechanism, which is the core part of the Transformer model, usually suffers from quadratic computational complexity with respect to the number of tokens. Many architectures attempt to reduce model complexity by limiting the self-attention mechanism to local regions or by redesigning the tokenization process. In this paper, we propose DAE-Former, a novel method that seeks to provide an alternative perspective by efficiently designing the self-attention mechanism. More specifically, we reformulate the self-attention mechanism to capture both spatial and channel relations across the whole feature dimension while staying computationally efficient. Furthermore, we redesign the skip connection path by including the cross-attention module to ensure the feature reusability and enhance the localization power. Our method outperforms state-of-the-art methods on multi-organ cardiac and skin lesion segmentation datasets without requiring pre-training weights. The code is publicly available at https://github.com/mindflow-institue/DAEFormer.

A track-before-detect (TBD) particle filter-based method for detection and tracking of low observable objects based on a sequence of image frames in the presence of noise and clutter is studied. At each time instance after receiving a frame of image, first, some preprocessing approaches are applied to the image. Then, it is sent to the detection and tracking algorithm which is based on a particle filter. Performance of the approach is evaluated for detection and tracking of an object in different scenarios including noise and clutter.

Machine reading comprehension (MRC) is a long-standing topic in natural language processing (NLP). The MRC task aims to answer a question based on the given context. Recently studies focus on multi-hop MRC which is a more challenging extension of MRC, which to answer a question some disjoint pieces of information across the context are required. Due to the complexity and importance of multi-hop MRC, a large number of studies have been focused on this topic in recent years, therefore, it is necessary and worth reviewing the related literature. This study aims to investigate recent advances in the multi-hop MRC approaches based on 31 studies from 2018 to 2022. In this regard, first, the multi-hop MRC problem definition will be introduced, then 31 models will be reviewed in detail with a strong focus on their multi-hop aspects. They also will be categorized based on their main techniques. Finally, a fine-grain comprehensive comparison of the models and techniques will be presented.

Multi-hop Machine reading comprehension is a challenging task with aim of answering a question based on disjoint pieces of information across the different passages. The evaluation metrics and datasets are a vital part of multi-hop MRC because it is not possible to train and evaluate models without them, also, the proposed challenges by datasets often are an important motivation for improving the existing models. Due to increasing attention to this field, it is necessary and worth reviewing them in detail. This study aims to present a comprehensive survey on recent advances in multi-hop MRC evaluation metrics and datasets. In this regard, first, the multi-hop MRC problem definition will be presented, then the evaluation metrics based on their multi-hop aspect will be investigated. Also, 15 multi-hop datasets have been reviewed in detail from 2017 to 2022, and a comprehensive analysis has been prepared at the end. Finally, open issues in this field have been discussed.

Regular cameras and cell phones are able to capture limited luminosity. Thus, in terms of quality, most of the produced images from such devices are not similar to the real world. They are overly dark or too bright, and the details are not perfectly visible. Various methods, which fall under the name of High Dynamic Range (HDR) Imaging, can be utilised to cope with this problem. Their objective is to produce an image with more details. However, unfortunately, most methods for generating an HDR image from Multi-Exposure images only concentrate on how to combine different exposures and do not have any focus on choosing the best details of each image. Therefore, it is strived in this research to extract the most visible areas of each image with the help of image segmentation. Two methods of producing the Ground Truth were considered, as manual threshold and Otsu threshold, and a neural network will be used to train segment these areas. Finally, it will be shown that the neural network is able to segment the visible parts of pictures acceptably.