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.

尽管最近的自动文本识别取得了进步，但在历史手稿方面，该性能仍然保持温和。这主要是因为缺乏可用的标记数据来训练渴望数据的手写文本识别（HTR）模型。由于错误率的降低，关键字发现系统（KWS）提供了HTR的有效替代方案，但通常仅限于封闭的参考词汇。在本文中，我们提出了一些学习范式，用于发现几个字符（n-gram）的序列，这些序列需要少量标记的训练数据。我们表明，对重要的n-gram的认识可以减少系统对词汇的依赖。在这种情况下，输入手写线图像中的vocabulary（OOV）单词可能是属于词典的n-gram序列。对我们提出的多代表方法进行了广泛的实验评估。

几何深度学习最近对包括文档分析在内的广泛的机器学习领域引起了极大的兴趣。图形神经网络（GNN）的应用在各种与文档有关的任务中变得至关重要，因为它们可以揭示重要的结构模式，这是关键信息提取过程的基础。文献中的先前作品提出了任务驱动的模型，并且没有考虑到图形的全部功能。我们建议Doc2Graph是一种基于GNN模型的任务无关文档理解框架，以解决给定不同类型文档的不同任务。我们在两个具有挑战性的数据集上评估了我们的方法，以在形式理解，发票布局分析和表检测中进行关键信息提取。我们的代码可以在https://github.com/andreagemelli/doc2graph上自由访问。

在本文中，我们提出了一个文本降低不变的自动编码器（Text-Diae），这是一种旨在解决两个任务的自我监督模型，即文本识别（手写或场景文本）和文档图像增强。我们首先采用基于变压器的体系结构，该体系结构将三个借口任务作为学习目标，在预训练期间必须在不使用标签数据的情况下进行优化。每个借口目标都是专门针对最终下游任务量身定制的。我们进行了几项消融实验，以确认所选借口任务的设计选择。重要的是，所提出的模型并未基于对比损失表现出先前最新方法的局限性，而同时需要更少的数据样本来收敛。最后，我们证明我们的方法超过了手写和场景文本识别和文档图像增强的现有监督和自我监督的设置中的最新设置。我们的代码和训练有素的模型将在〜\ url {http：// on_accepters}上公开提供。

了解具有丰富布局的文档是迈向信息提取的重要一步。商业智能过程通常需要大规模从文档中提取有用的语义内容，以进行后续决策任务。在这种情况下，不同文档对象（标题，部分，图形等）的实例级分割已成为文档分析和理解社区的有趣问题。为了朝这个方向推进研究，我们提出了一个基于变压器的模型，称为\ emph {docsegtr}，用于文档图像中复杂布局的端到端实例分割。该方法适应了一个双重注意模块，用于语义推理，这有助于与最先进相比，有助于高度计算效率。据我们所知，这是基于变压器的文档细分的第一部作品。对竞争性基准等广泛的实验，例如PublayNet，Prima，“历史日语”和Tablebank，表明我们的模型比现有的最先进的方法具有可比较或更好的细分性能，平均精度为89.4、40.4、40.3、83.4和93.33 。这个简单而灵活的框架可以作为文档图像中实例级识别任务的有前途的基线。

Intensive Care Units usually carry patients with a serious risk of mortality. Recent research has shown the ability of Machine Learning to indicate the patients' mortality risk and point physicians toward individuals with a heightened need for care. Nevertheless, healthcare data is often subject to privacy regulations and can therefore not be easily shared in order to build Centralized Machine Learning models that use the combined data of multiple hospitals. Federated Learning is a Machine Learning framework designed for data privacy that can be used to circumvent this problem. In this study, we evaluate the ability of deep Federated Learning to predict the risk of Intensive Care Unit mortality at an early stage. We compare the predictive performance of Federated, Centralized, and Local Machine Learning in terms of AUPRC, F1-score, and AUROC. Our results show that Federated Learning performs equally well as the centralized approach and is substantially better than the local approach, thus providing a viable solution for early Intensive Care Unit mortality prediction. In addition, we show that the prediction performance is higher when the patient history window is closer to discharge or death. Finally, we show that using the F1-score as an early stopping metric can stabilize and increase the performance of our approach for the task at hand.

Learning universal representations across different applications domain is an open research problem. In fact, finding universal architecture within the same application but across different types of datasets is still unsolved problem too, especially in applications involving processing 3D point clouds. In this work we experimentally test several state-of-the-art learning-based methods for 3D point cloud registration against the proposed non-learning baseline registration method. The proposed method either outperforms or achieves comparable results w.r.t. learning based methods. In addition, we propose a dataset on which learning based methods have a hard time to generalize. Our proposed method and dataset, along with the provided experiments, can be used in further research in studying effective solutions for universal representations. Our source code is available at: github.com/DavidBoja/greedy-grid-search.

在过去的几年中，几项计划开始以开放方式提供对研究输出数据和元数据的访问。这些举措开发的平台正在向更广泛的公众开放科学生产，这对于基于循证的科学，技术和创新（STI）的决策是宝贵的资产。这些资源确实可以促进知识发现，并帮助确定特定感兴趣的研究领域中可用的研发资产和相关参与者。理想情况下，为了全面了解整个Sti生态系统，应相应地组合和分析这些资源所提供的信息。为了确保这一点，应至少在数据源之间保证至少一定程度的互操作性，以便可以更好地汇总和补充数据，并且为决策提供的证据更加完整和可靠。在这里，我们研究了在整个丹麦STI生态系统中绘制气候行动研究的情况，是否是通过使用4个流行的Open Access STI数据源（即OpenAire，Open Alex，Cordis和Kohesio）的情况。

科学，技术与创新（STI）决策者通常需要清楚地了解所研究的内容以及通过谁设计有效的政策。这种愿景是通过对机构界限内进行的研究活动的有效和全面映射提供的。在这种情况下要面临的一个重大挑战是访问相关数据并结合来自不同来源的信息的困难：实际上，传统上，STI数据已限制在封闭的数据源中，并且在可用的情况下，它将与不同的分类法分类。。在这里，我们介绍了一项概念验证研究，该研究使用开放资源来绘制有关可持续发展目标（SDG）13种气候行动的研究格局，该行动是整个国家的丹麦，我们在25 ERC上绘制了它面板。