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.

We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view. Results of the ML heuristics showed the promising implementation, validation and application of thinner networks to classify 4CV with limited datasets. We conclude this work mentioning the need for (a) datasets to improve diversity of demographics, diseases, and (b) the need of further investigations of thinner models to be run and implemented in low-cost hardware to be clinically translated in the ICU in LMICs. The code and other resources to reproduce this work are available at https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-resource-countries.

This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of Rapid Human Movement. We have validated our research in a real case scenario for stroke rehabilitation at the Guttmann Institute5 (neurorehabilitation hospital), showing promising results. Our work could have a great impact in remote healthcare applications, improving the medical efficiency and reducing the healthcare costs. Future steps include more clinical validation, developing multi-modal analysis architectures (analysing data from sensors, images, audio, etc.), and exploring the application of our technology to monitor other neurodegenerative diseases.

Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field.

This paper develops a clustering method that takes advantage of the sturdiness of model-based clustering, while attempting to mitigate some of its pitfalls. First, we note that standard model-based clustering likely leads to the same number of clusters per margin, which seems a rather artificial assumption for a variety of datasets. We tackle this issue by specifying a finite mixture model per margin that allows each margin to have a different number of clusters, and then cluster the multivariate data using a strategy game-inspired algorithm to which we call Reign-and-Conquer. Second, since the proposed clustering approach only specifies a model for the margins -- but leaves the joint unspecified -- it has the advantage of being partially parallelizable; hence, the proposed approach is computationally appealing as well as more tractable for moderate to high dimensions than a `full' (joint) model-based clustering approach. A battery of numerical experiments on artificial data indicate an overall good performance of the proposed methods in a variety of scenarios, and real datasets are used to showcase their application in practice.

The library scikit-fda is a Python package for Functional Data Analysis (FDA). It provides a comprehensive set of tools for representation, preprocessing, and exploratory analysis of functional data. The library is built upon and integrated in Python's scientific ecosystem. In particular, it conforms to the scikit-learn application programming interface so as to take advantage of the functionality for machine learning provided by this package: pipelines, model selection, and hyperparameter tuning, among others. The scikit-fda package has been released as free and open-source software under a 3-Clause BSD license and is open to contributions from the FDA community. The library's extensive documentation includes step-by-step tutorials and detailed examples of use.

通常声称由软材料制成的腿部机器人比其刚性材料表现出更安全，更健壮的环境相互作用。但是，软机器人的这种激励特征需要更严格的开发才能与刚性运动进行比较。本文介绍了一个柔软的机器人平台Horton和一个反馈控制系统，并在其操作的某些方面保证了安全性。该机器人是使用一系列软肢构造的，由热形记忆合金（SMA）线肌肉作用，其位置和执行器温度的传感器。监督控制方案在机器人姿势的单独控制器操作过程中维护安全执行者状态。实验表明，霍顿可以举起腿并保持平衡姿势，这是运动的前身。在平衡过程中，通过人类交互测试在硬件中验证了主管，使所有SMA肌肉保持在温度阈值以下。这项工作代表了任何柔软的腿机器人的安全验证反馈系统的首次演示。

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.

由于促进了各种复杂的任务，因此异质自动机器人团队变得越来越重要。对于此类异质机器人，目前尚无一致的方法来描述每个机器人提供的功能。在制造领域，功能建模被认为是针对不同机器提供的语义模型功能的一种有希望的方法。这项贡献研究了如何将能力模型从制造应用到自主机器人领域，并提出了这种能力模型的方法。

社会互动网络是建立文明的基材。通常，我们与我们喜欢的人建立新的纽带，或者认为通过第三方的干预，我们的关系损害了。尽管它们的重要性和这些过程对我们的生活产生的巨大影响，但对它们的定量科学理解仍处于起步阶段，这主要是由于很难收集大量的社交网络数据集，包括个人属性。在这项工作中，我们对13所学校的真实社交网络进行了彻底的研究，其中3,000多名学生和60,000名宣布正面关系和负面关系，包括对所有学生的个人特征的测试。我们引入了一个度量标准 - “三合会影响”，该指标衡量了最近的邻居在其接触关系中的影响。我们使用神经网络来预测关系，并根据他们的个人属性或三合会的影响来提取两个学生是朋友或敌人的可能性。或者，我们可以使用网络结构的高维嵌入来预测关系。值得注意的是，三合会影响（一个简单的一维度量）在预测两个学生之间的关系方面达到了最高的准确性。我们假设从神经网络中提取的概率 - 三合会影响的功能和学生的个性 - 控制真实社交网络的演变，为这些系统的定量研究开辟了新的途径。