本文讨论了最近鉴于最新的机器人抓握和操纵竞争（RGMC）的机器人抓握和操纵中的研究进展。我们首先概述了与机器人操纵领域相关的过去的基准和竞争。然后，我们讨论在RGMC中设计操纵任务的方法。我们对每个任务的关键挑战提供详细分析，并确定近年来竞争团队表现的最困难方面。我们认为，这种分析是富有魅力的，可以确定确定机器人操纵领域的未来研究方向。

这项工作使用来自建设性模拟的可靠数据比较了监督的机器学习方法，以估算空袭期间发射导弹的最有效时刻。我们采用了重采样技术来改善预测模型，分析准确性，精度，召回和F1得分。的确，我们可以根据决策树以及其他算法对重采样技术的显着敏感性来确定模型的显着性能。最佳F1分数的模型的值分别为0.379和0.465，而没有重新采样技术，这一值分别增加了22.69％。因此，如果理想，重新采样技术可以改善模型的召回率和F1得分，而准确性和精确度略有下降。因此，通过通过建设性模拟获得的数据，可以根据机器学习模型开发决策支持工具，从而可以提高BVR空中战斗的飞行质量，从而提高进攻任务的有效性以达到特定目标。

这项工作调查了使用深神经网络（DNN）来执行武器接触区域（WEZ）最大发射范围的估计。韦茨允许飞行员识别空域，其中可用导弹具有更大的成功参与特定目标的概率，即围绕着对手易受射击群体的飞机的假设区域。我们提出了一种方法来确定使用50,000个变化条件下的模拟发射的给定导弹的韦茨。这些模拟用于训练当飞机在不同的烧制条件下发现自身时，可以预测韦茨的DNN，其测定系数为0.99。它提供了另一种关于前面研究的程序，因为它采用了非离散化模型，即，它立即考虑了WEZ的所有方向，以前尚未完成。此外，所提出的方法使用实验设计，允许较少的模拟运行，提供更快的模型训练。

这项工作旨在在防御柜台（DCA）任务的背景下提供超出视觉范围（BVR）空战的参与决策支持工具。在BVR AIR作战中，接合判决是指通过假设令人反感的姿态和执行相应的演示来选择导频的时刻。为了模拟这一决定，我们使用巴西空军航空航天仿真环境（\ {Ambiente de Simula \ C {C} \〜a \〜a \〜ao ao aeroispacial - Asa}在葡萄牙语中，它产生了3,729个建设性模拟，每个建设性模拟持续12分钟，总共10,316场比赛。我们通过称为DCA指数的操作性标准分析了所有样本，这些标准基于主题专家的经验，这类使命的成功程度代表。该公制考虑了同一团队和对方团队的飞机的距离，对抗空气巡逻的点以及所使用的导弹数。通过在整个参与过程中开始和DCA指数的平均值之前定义参与状态，我们创建了一个监督的学习模型，以确定新的参与的质量。一种基于决策树的算法，与XGBoost库一起使用，提供了一种回归模型，以预测具有接近0.8的确定系数的DCA索引和0.05的根均方误差，可以为BVR飞行员提供参数以决定是否或不要搞。因此，使用通过仿真获得的数据，这项工作通过基于BVR Air战斗的机器学习构建决策支持系统而有贡献。

Variational inference uses optimization, rather than integration, to approximate the marginal likelihood, and thereby the posterior, in a Bayesian model. Thanks to advances in computational scalability made in the last decade, variational inference is now the preferred choice for many high-dimensional models and large datasets. This tutorial introduces variational inference from the parametric perspective that dominates these recent developments, in contrast to the mean-field perspective commonly found in other introductory texts.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5$\rightarrow$Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

The performance of inertial navigation systems is largely dependent on the stable flow of external measurements and information to guarantee continuous filter updates and bind the inertial solution drift. Platforms in different operational environments may be prevented at some point from receiving external measurements, thus exposing their navigation solution to drift. Over the years, a wide variety of works have been proposed to overcome this shortcoming, by exploiting knowledge of the system current conditions and turning it into an applicable source of information to update the navigation filter. This paper aims to provide an extensive survey of information aided navigation, broadly classified into direct, indirect, and model aiding. Each approach is described by the notable works that implemented its concept, use cases, relevant state updates, and their corresponding measurement models. By matching the appropriate constraint to a given scenario, one will be able to improve the navigation solution accuracy, compensate for the lost information, and uncover certain internal states, that would otherwise remain unobservable.