由于生产和依赖数据集以产生自动化决策系统（广告）增加，因此需要评估和询问底层数据的过程。在2018年启动数据集营养标签后，数据营养项目已对标签的设计和目的进行了重大更新，并在2020年代后期推出更新的标签，该标签在本文中预览。新标签包括通过针对数据科学家配置文件的更新的设计和用户界面提供的上下文专用用例和警报。本文讨论了标签旨在减轻的潜在培训数据的危害和偏见，包括标记，新的和现有挑战以及工作的进一步方向，以及预览新的新数据集标签。

Tomographic SAR technique has attracted remarkable interest for its ability of three-dimensional resolving along the elevation direction via a stack of SAR images collected from different cross-track angles. The emerged compressed sensing (CS)-based algorithms have been introduced into TomoSAR considering its super-resolution ability with limited samples. However, the conventional CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity, and complex parameter fine-tuning. Aiming at efficient TomoSAR imaging, this paper proposes a novel efficient sparse unfolding network based on the analytic learned iterative shrinkage thresholding algorithm (ALISTA) architecture with adaptive threshold, named Adaptive Threshold ALISTA-based Sparse Imaging Network (ATASI-Net). The weight matrix in each layer of ATASI-Net is pre-computed as the solution of an off-line optimization problem, leaving only two scalar parameters to be learned from data, which significantly simplifies the training stage. In addition, adaptive threshold is introduced for each azimuth-range pixel, enabling the threshold shrinkage to be not only layer-varied but also element-wise. Moreover, the final learned thresholds can be visualized and combined with the SAR image semantics for mutual feedback. Finally, extensive experiments on simulated and real data are carried out to demonstrate the effectiveness and efficiency of the proposed method.

Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility. However, the applicability of these models is constrained by the limited size and topical breadth of available corpora. We introduce CELLS, the largest (63k pairs) and broadest-ranging (12 journals) parallel corpus for lay language generation. The abstract and the corresponding lay language summary are written by domain experts, assuring the quality of our dataset. Furthermore, qualitative evaluation of expert-authored plain language summaries has revealed background explanation as a key strategy to increase accessibility. Such explanation is challenging for neural models to generate because it goes beyond simplification by adding content absent from the source. We derive two specialized paired corpora from CELLS to address key challenges in lay language generation: generating background explanations and simplifying the original abstract. We adopt retrieval-augmented models as an intuitive fit for the task of background explanation generation, and show improvements in summary quality and simplicity while maintaining factual correctness. Taken together, this work presents the first comprehensive study of background explanation for lay language generation, paving the path for disseminating scientific knowledge to a broader audience. CELLS is publicly available at: https://github.com/LinguisticAnomalies/pls_retrieval.

在多机构系统（例如多机构无人驾驶汽车和多机构自动驾驶水下车辆）中，羊群控制是一个重大问题，可增强代理的合作和安全性。与传统方法相反，多机构增强学习（MARL）更灵活地解决了羊群控制的问题。但是，基于MARL的方法遭受了样本效率低下的影响，因为它们需要从代理与环境之间的相互作用中收集大量的经验。我们提出了一种新颖的方法，该方法对MARL（PWD-MARL）的示范进行了预处理，该方法可以利用以传统方法预处理剂来利用非专家示范。在预审进过程中，代理人同时通过MARL和行为克隆从示范中学习政策，并阻止过度拟合示范。通过对非专家示范进行预处理，PWD-MARL在温暖的开始中提高了在线MAL的样品效率。实验表明，即使发生不良或很少的示威，PWD-MARL在羊群控制问题中提高了样本效率和政策性能。

羊群控制是一个具有挑战性的问题，在维持羊群的同时，需要达到目标位置，并避免了环境中特工之间的障碍和碰撞碰撞。多代理增强学习在羊群控制中取得了有希望的表现。但是，基于传统强化学习的方法需要代理与环境之间的相互作用。本文提出了一项次优政策帮助多代理增强学习算法（SPA-MARL），以提高样本效率。 Spa-Marl直接利用可以通过非学习方法手动设计或解决的先前政策来帮助代理人学习，在这种情况下，该策略的表现可以是最佳的。 SPA-MARL认识到次优政策与本身之间的性能差异，然后模仿次优政策，如果次优政策更好。我们利用Spa-Marl解决羊群控制问题。基于人造潜在领域的传统控制方法用于生成次优政策。实验表明，水疗中心可以加快训练过程，并优于MARL基线和所使用的次优政策。

到达状态的密度可以帮助理解安全至关重要的系统的风险，尤其是在最坏情况下的情况过于保守的情况下。最近的工作提供了一种数据驱动的方法来计算自主系统在线前进状态的密度分布。在本文中，我们研究了这种方法与模型预测控制在不确定性下的可验证安全路径计划的结合。我们首先使用学习的密度分布来计算在线碰撞的风险。如果这种风险超过可接受的阈值，我们的方法将计划在先前轨迹周围采取新的途径，并在阈值以下碰撞风险。我们的方法非常适合处理具有不确定性和复杂动力学的系统，因为我们的数据驱动方法不需要系统动力学的分析形式，并且可以通过不确定性的任意初始分布来估算正向状态密度。我们设计了两个具有挑战性的场景（自动驾驶和气垫船控制），以在系统不确定性下的障碍物中进行安全运动计划。我们首先表明我们的密度估计方法可以达到与基于蒙特卡洛的方法相似的准确性，同时仅使用0.01倍训练样本。通过利用估计的风险，我们的算法在执行超过0.99的安全速率时达到目标达到最高成功率。

我们提出了一个基于串联弹性执行器（SEA）的平行按摩机器人，提供统一的力量控制方法。首先，建立了运动和静态力模型，以获得相应的控制变量。然后，提出了一种新型的力位控制策略，以在不需要机器人动力学模型的情况下分别控制沿表面正常方向的力位和另一个两方向位移。为了评估其性能，我们实施了一系列机器人按摩实验。结果表明，所提出的按摩操纵器可以成功实现按摩任务的所需力和运动模式，从而达到高得分用户体验。

健康素养被出现为制定适当的健康决策和确保治疗结果的关键因素。然而，医学术语和该领域的专业语言的复杂结构使健康信息尤为难以解释。因此，迫切需要对自动化方法来提高生物医学文献的可访问性，以提高一般人群。这个问题可以作为医疗保健专业人员语言与公众的语言之间的翻译问题。在本文中，我们介绍了自动化生物医学科学评论的制定语言摘要的新任务，建设了一个数据集，以支持自动化方法的开发和评估，以提高生物医学文献的可访问性。我们对解决这项任务的各种挑战进行了分析，包括不仅对关键要点的总结，而且还概述了对背景知识和专业语言的简化的解释。我们试验最先进的摘要模型以及多种数据增强技术，并使用自动指标和人工评估评估其性能。结果表明，与专家专家专门开发的参考摘要相比，使用当代神经架构产生的自动产生的摘要可以实现有希望的质量和可读性（最佳Rouge-L为50.24和Flesch-Kincaid可读性得分为13.30）。我们还讨论了目前尝试的局限性，为未来工作提供了洞察和方向。

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.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.