Cell-free multi-user multiple input multiple output networks are a promising alternative to classical cellular architectures, since they have the potential to provide uniform service quality and high resource utilisation over the entire coverage area of the network. To realise this potential, previous works have developed radio resource management mechanisms using various optimisation engines. In this work, we consider the problem of overall ergodic spectral efficiency maximisation in the context of uplink-downlink data power control in cell-free networks. To solve this problem in large networks, and to address convergence-time limitations, we apply scalable multi-objective Bayesian optimisation. Furthermore, we discuss how an intersection of multi-fidelity emulation and Bayesian optimisation can improve radio resource management in cell-free networks.

In this work, we propose a communication-efficient two-layer federated learning algorithm for distributed setups including a core server and multiple edge servers with clusters of devices. Assuming different learning tasks, clusters with a same task collaborate. To implement the algorithm over wireless links, we propose a scalable clustered over-the-air aggregation scheme for the uplink with a bandwidth-limited broadcast scheme for the downlink that requires only two single resource blocks for each algorithm iteration, independent of the number of edge servers and devices. This setup is faced with interference of devices in the uplink and interference of edge servers in the downlink that are to be modeled rigorously. We first develop a spatial model for the setup by modeling devices as a Poisson cluster process over the edge servers and quantify uplink and downlink error terms due to the interference. Accordingly, we present a comprehensive mathematical approach to derive the convergence bound for the proposed algorithm including any number of collaborating clusters in the setup and provide important special cases and design remarks. Finally, we show that despite the interference in the proposed uplink and downlink schemes, the proposed algorithm achieves high learning accuracy for a variety of parameters.

Dyadic and small group collaboration is an evolutionary advantageous behaviour and the need for such collaboration is a regular occurrence in day to day life. In this paper we estimate the perceived personality traits of individuals in dyadic and small groups over thin-slices of interaction on four multimodal datasets. We find that our transformer based predictive model performs similarly to human annotators tasked with predicting the perceived big-five personality traits of participants. Using this model we analyse the estimated perceived personality traits of individuals performing tasks in small groups and dyads. Permutation analysis shows that in the case of small groups undergoing collaborative tasks, the perceived personality of group members clusters, this is also observed for dyads in a collaborative problem solving task, but not in dyads under non-collaborative task settings. Additionally, we find that the group level average perceived personality traits provide a better predictor of group performance than the group level average self-reported personality traits.

Federated learning (FL) has emerged as an instance of distributed machine learning paradigm that avoids the transmission of data generated on the users' side. Although data are not transmitted, edge devices have to deal with limited communication bandwidths, data heterogeneity, and straggler effects due to the limited computational resources of users' devices. A prominent approach to overcome such difficulties is FedADMM, which is based on the classical two-operator consensus alternating direction method of multipliers (ADMM). The common assumption of FL algorithms, including FedADMM, is that they learn a global model using data only on the users' side and not on the edge server. However, in edge learning, the server is expected to be near the base station and have direct access to rich datasets. In this paper, we argue that leveraging the rich data on the edge server is much more beneficial than utilizing only user datasets. Specifically, we show that the mere application of FL with an additional virtual user node representing the data on the edge server is inefficient. We propose FedTOP-ADMM, which generalizes FedADMM and is based on a three-operator ADMM-type technique that exploits a smooth cost function on the edge server to learn a global model parallel to the edge devices. Our numerical experiments indicate that FedTOP-ADMM has substantial gain up to 33\% in communication efficiency to reach a desired test accuracy with respect to FedADMM, including a virtual user on the edge server.

Data-driven interatomic potentials have emerged as a powerful class of surrogate models for {\it ab initio} potential energy surfaces that are able to reliably predict macroscopic properties with experimental accuracy. In generating accurate and transferable potentials the most time-consuming and arguably most important task is generating the training set, which still requires significant expert user input. To accelerate this process, this work presents \text{\it hyperactive learning} (HAL), a framework for formulating an accelerated sampling algorithm specifically for the task of training database generation. The key idea is to start from a physically motivated sampler (e.g., molecular dynamics) and add a biasing term that drives the system towards high uncertainty and thus to unseen training configurations. Building on this framework, general protocols for building training databases for alloys and polymers leveraging the HAL framework will be presented. For alloys, ACE potentials for AlSi10 are created by fitting to a minimal HAL-generated database containing 88 configurations (32 atoms each) with fast evaluation times of <100 microsecond/atom/cpu-core. These potentials are demonstrated to predict the melting temperature with excellent accuracy. For polymers, a HAL database is built using ACE, able to determine the density of a long polyethylene glycol (PEG) polymer formed of 200 monomer units with experimental accuracy by only fitting to small isolated PEG polymers with sizes ranging from 2 to 32.

Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modelling and the visualisation and analysis of materials datasets.The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. We recast this approach as tensor factorisation by exploiting the tensor structure of standard neighbour density based descriptors. In doing so, we form compact tensor-reduced representations whose size does not depend on the number of chemical elements, but remain systematically convergeable and are therefore applicable to a wide range of data analysis and regression tasks.

通过从其计算中排除了许多随机优化的领先迭代，尾巴平均对Polyak平均的非反应行为进行了改善。实际上，具有有限数量的优化步骤和无法将其退火至零的学习率，尾巴平均可以比单个迭代或polyak平均值更接近训练损失的局部最小点。但是，引导迭代的忽略数量是重要的超参数，并且开始平均太早或太晚导致资源或次优溶液的使用效率低下。将此超参数设置为改善概括更加困难，尤其是在其他超参数和过度拟合的情况下。此外，在平均开始之前，损失只是对最终表现的淡淡信息，这使得早期停止不可靠。为了减轻这些问题，我们提出了任何时间平均变体，该变体没有超参数，并且在所有优化步骤中都近似最佳的尾巴。我们的算法基于两个运行平均值，其自适应长度以最佳的尾巴长度为界，其中一种具有一些规律性的近似最佳性。仅需要两组重量的额外存储空间和对损失的定期评估，提出的两尾平均算法是一种实用且广泛适用的方法，可用于改善随机优化。

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。

知识表示和推理（KRR）系统表示知识作为事实和规则的集合。像数据库一样，KRR系统包含有关工业企业，科学和业务等人类活动领域的信息。 KRR可以代表复杂的概念和关系，它们可以以复杂的方式查询和操纵信息。不幸的是，指定必要的知识需要大多数领域专家没有的技能，而专业知识工程师很难找到，因此KRR技术受到了阻碍。一种解决方案可能是从英语文本中提取知识，并且许多作品都尝试这样做（Openseame，Google的吊索等）。不幸的是，目前，从不受限制的自然语言中提取逻辑事实仍然是不准确的，无法用于推理，而限制语言语法（所谓的受控自然语言或CNL）对于用户来说很难学习和使用。然而，与其他方法相比，一些最近基于CNL的方法，例如知识创作逻辑机（KALM）的精度非常高，并且一个自然的问题是可以在多大程度上取消CNL限制。在本文中，我们通过将KALM框架移植到神经自然语言解析器Mstanza来解决这个问题。在这里，我们将注意力限制在创作事实和查询上，因此我们的重点是我们所说的事实英语陈述。在我们的后续工作中将考虑创作其他类型的知识，例如规则。事实证明，基于神经网络的解析器有自己的问题，并且他们犯的错误范围从言论的一部分标记到lemmatization到依赖性错误。我们介绍了许多解决这些问题并测试新系统KALMFL（即，事实语言的KALM）的技术，这些技术表明KALMFL的正确性超过95％。

我们研究在大型增长网络中找到根顶点的问题。我们证明，可以构建大小的置信集，而不是网络中包含root顶点的顶点的数量，在各种随机网络的各种模型中都具有很高的概率。这些模型包括均匀的随机递归dag和统一的库珀 - 弗里兹随机图。