Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a number of clients that have access to private, potentially sensitive data. Clients are able to keep their data in their local machines and only share their locally trained model's parameters with a central server that manages the collaborative learning process. FL has delivered promising results in real-life scenarios, such as healthcare, energy, and finance. However, when the number of participating clients is large, the overhead of managing the clients slows down the learning. Thus, client selection has been introduced as a strategy to limit the number of communicating parties at every step of the process. Since the early na\"{i}ve random selection of clients, several client selection methods have been proposed in the literature. Unfortunately, given that this is an emergent field, there is a lack of a taxonomy of client selection methods, making it hard to compare approaches. In this paper, we propose a taxonomy of client selection in Federated Learning that enables us to shed light on current progress in the field and identify potential areas of future research in this promising area of machine learning.

单词如何改变他们的含义？尽管语义演化是由多种不同的因素（包括语言，社会和技术方面的）驱动的，但我们发现，有一项法律在五种主要的印欧语语言中普遍存在：这种语义演化非常宽容。使用控制基础对称性的直觉分布语义嵌入的自动管道，我们表明单词遵循含义空间中的随机轨迹，具有异常扩散指数$ \ alpha = 0.45 \ pm 0.05 \ pm 0.05 \ pm 0.05 $ 0.05 $，相反，与扩散的粒子相比之下\ alpha = 1 $。随机化方法表明，在语义变化方向上保留时间相关性是为了恢复强烈延伸的行为所必需的。但是，变化大小的相关性也起着重要作用。我们此外表明，在数据分析和解释中，强大的亚扩散是一种强大的现象，例如选择拟合位移平均值或平均单个单词轨迹的最佳拟合指数的选择。

Vehicle trajectory data has received increasing research attention over the past decades. With the technological sensing improvements such as high-resolution video cameras, in-vehicle radars and lidars, abundant individual and contextual traffic data is now available. However, though the data quantity is massive, it is by itself of limited utility for traffic research because of noise and systematic sensing errors, thus necessitates proper processing to ensure data quality. We draw particular attention to extracting high-resolution vehicle trajectory data from video cameras as traffic monitoring cameras are becoming increasingly ubiquitous. We explore methods for automatic trajectory data reconciliation, given "raw" vehicle detection and tracking information from automatic video processing algorithms. We propose a pipeline including a) an online data association algorithm to match fragments that are associated to the same object (vehicle), which is formulated as a min-cost network flow problem of a graph, and b) a trajectory reconciliation method formulated as a quadratic program to enhance raw detection data. The pipeline leverages vehicle dynamics and physical constraints to associate tracked objects when they become fragmented, remove measurement noise on trajectories and impute missing data due to fragmentations. The accuracy is benchmarked on a sample of manually-labeled data, which shows that the reconciled trajectories improve the accuracy on all the tested input data for a wide range of measures. An online version of the reconciliation pipeline is implemented and will be applied in a continuous video processing system running on a camera network covering a 4-mile stretch of Interstate-24 near Nashville, Tennessee.

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.

时间序列预测是一项强大的数据建模学科，可以分析历史观察以预测时间序列的未来价值。它已用于许多应用程序，包括但不限于经济学，气象和健康。在本文中，我们使用时间序列预测技术来建模和预测水痘的未来发生率。为了实现这一目标，我们在匈牙利收集的数据集上实现并模拟了多个模型和数据预处理技术。我们证明，在县级预测方面，LSTM模型在绝大多数实验中的所有其他模型都优于所有其他模型，而Sarimax模型在国家一级表现最佳。我们还证明，传统数据预处理方法的性能不如我们提出的数据预处理方法的性能。

Hamiltonian Monte Carlo（HMC）是Markov链算法，用于从具有密度$ e^{ - f（x）} $的高维分布中进行采样，可访问$ f $的梯度。一种特殊的感兴趣的情况是带有协方差矩阵$ \ sigma $的$ d $二维高斯分布，在这种情况下$ f（x）= x^\ top \ top \ sigma^{ - 1} x $。我们表明，HMC可以使用$ \ wideTilde {o}（\ sqrt {\ kappa} d^{1/4} \ log（1/\ varepsilon），使用$ \ varepsilon $ -close在总变化距离中取样。）$渐变查询，其中$ \ kappa $是$ \ sigma $的条件号。我们的算法对哈密顿动力学使用了长时间和随机的整合时间。这与最近的结果（并受到了）的形成对比，该结果给出了$ \ widetilde \ omega（\ kappa d^{1/2}）$查询的HMC较低限制，即使是高斯案例，也有固定的集成时间。

这项工作开发了具有严格效率的新算法，可确保无限的地平线模仿学习（IL）具有线性函数近似而无需限制性相干假设。我们从问题的最小值开始，然后概述如何从优化中利用经典工具，尤其是近端点方法（PPM）和双平滑性，分别用于在线和离线IL。多亏了PPM，我们避免了在以前的文献中出现在线IL的嵌套政策评估和成本更新。特别是，我们通过优化单个凸的优化和在成本和Q函数上的平稳目标来消除常规交替更新。当不确定地解决时，我们将优化错误与恢复策略的次级优势联系起来。作为额外的奖励，通过将PPM重新解释为双重平滑以专家政策为中心，我们还获得了一个离线IL IL算法，该算法在所需的专家轨迹方面享有理论保证。最后，我们实现了线性和神经网络功能近似的令人信服的经验性能。

5G建筑和深度学习的融合在无线通信和人工智能领域都获得了许多研究兴趣。这是因为深度学习技术已被确定为构成5G体系结构的5G技术的潜在驱动力。因此，关于5G架构和深度学习的融合进行了广泛的调查。但是，大多数现有的调查论文主要集中于深度学习如何与特定的5G技术融合，因此，不涵盖5G架构的全部范围。尽管最近有一份调查文件似乎很强大，但对该论文的评论表明，它的结构不佳，无法专门涵盖深度学习和5G技术的收敛性。因此，本文概述了关键5G技术和深度学习的融合。讨论了这种融合面临的挑战。此外，还讨论了对未来6G体系结构的简要概述，以及如何与深度学习进行融合。

在非结构化环境中工作的机器人必须能够感知和解释其周围环境。机器人技术领域基于深度学习模型的主要障碍之一是缺乏针对不同工业应用的特定领域标记数据。在本文中，我们提出了一种基于域随机化的SIM2REAL传输学习方法，用于对象检测，可以自动生成任意大小和对象类型的标记的合成数据集。随后，对最先进的卷积神经网络Yolov4进行了训练，以检测不同类型的工业对象。通过提出的域随机化方法，我们可以在零射击和单次转移的情况下分别缩小现实差距，分别达到86.32％和97.38％的MAP50分数，其中包含190个真实图像。在GEFORCE RTX 2080 TI GPU上，数据生成过程的每图像少于0.5 s，培训持续约12H，这使其方便地用于工业使用。我们的解决方案符合工业需求，因为它可以通过仅使用1个真实图像进行培训来可靠地区分相似的对象类别。据我们所知，这是迄今为止满足这些约束的唯一工作。

我们研究了在偏见的可观察性模型下，在对抗性匪徒问题中的在线学习问题，称为政策反馈。在这个顺序决策问题中，学习者无法直接观察其奖励，而是看到由另一个未知策略并行运行的奖励（行为策略）。学习者必须在这种情况下面临另一个挑战：由于他们的控制之外的观察结果有限，学习者可能无法同样估算每个政策的价值。为了解决这个问题，我们提出了一系列算法，以保证任何比较者政策与行为政策之间的自然不匹配概念的范围，从而提高了对观察结果良好覆盖的比较者的绩效。我们还为对抗性线性上下文匪徒的设置提供了扩展，并通过一组实验验证理论保证。我们的关键算法想法是调整最近在非政策强化学习背景下流行的悲观奖励估计量的概念。