背景：获得医疗服务在很大程度上取决于资源分配，例如医疗设施的地理分布。然而，这些数据通常仅限于国家官方文件，不可公开提供。尽管某些医疗设施的数据可以作为网络上的语义资源访问，但它的建模并不一致，并且尚未集成到完整，开放和专业的存储库中。这项工作着重于生成全球医疗设施的全面语义数据集，其中包含有关此类设施地理位置的广泛信息。结果：为此，我们收集，对齐并链接了可能存在医疗设施信息的各种开源数据库。这项工作使我们能够沿着各个方面评估每个数据源，例如完整性，正确性和与其他来源相互联系，当前知识表示技术的所有关键方面。结论：我们的贡献直接受益于生物医学和健康领域（患者，医疗保健专业人员，公司，监管机构和研究人员）的利益相关者，他们现在将更好地概述获得医疗设施的访问和分配。

The decarbonization of buildings presents new challenges for the reliability of the electrical grid as a result of the intermittency of renewable energy sources and increase in grid load brought about by end-use electrification. To restore reliability, grid-interactive efficient buildings can provide flexibility services to the grid through demand response. Residential demand response programs are hindered by the need for manual intervention by customers. To maximize the energy flexibility potential of residential buildings, an advanced control architecture is needed. Reinforcement learning is well-suited for the control of flexible resources as it is able to adapt to unique building characteristics compared to expert systems. Yet, factors hindering the adoption of RL in real-world applications include its large data requirements for training, control security and generalizability. Here we address these challenges by proposing the MERLIN framework and using a digital twin of a real-world 17-building grid-interactive residential community in CityLearn. We show that 1) independent RL-controllers for batteries improve building and district level KPIs compared to a reference RBC by tailoring their policies to individual buildings, 2) despite unique occupant behaviours, transferring the RL policy of any one of the buildings to other buildings provides comparable performance while reducing the cost of training, 3) training RL-controllers on limited temporal data that does not capture full seasonality in occupant behaviour has little effect on performance. Although, the zero-net-energy (ZNE) condition of the buildings could be maintained or worsened as a result of controlled batteries, KPIs that are typically improved by ZNE condition (electricity price and carbon emissions) are further improved when the batteries are managed by an advanced controller.

本文的重点是具有复杂损失的功能输出回归（用于）。尽管大多数现有的工作都考虑了正方形损失设置，但我们利用了Huber的扩展以及$ \ Epsilon $不敏感的损失（由虚拟卷积引起的），并提出了一个灵活的框架，能够处理家庭中各种形式的异常值和稀疏。我们得出了依靠二元性来解决矢量价值复制内核希尔伯特空间中所得任务的计算算法。该方法的效率与合成基准和现实基准的经典平方损耗设置相反。

形状约束，例如非负，单调性，凸度或超模型性，在机器学习和统计的各种应用中都起着关键作用。但是，将此方面的信息以艰苦的方式（例如，在间隔的所有点）纳入预测模型，这是一个众所周知的具有挑战性的问题。我们提出了一个统一和模块化的凸优化框架，依赖于二阶锥（SOC）拧紧，以编码属于矢量值重现的载体内核Hilbert Spaces（VRKHSS）的模型对函数衍生物的硬仿射SDP约束。所提出的方法的模块化性质允许同时处理多个形状约束，并将无限数量的约束限制为有限的许多。我们证明了所提出的方案的收敛及其自适应变体的收敛性，利用VRKHSS的几何特性。由于基于覆盖的拧紧构造，该方法特别适合具有小到中等输入维度的任务。该方法的效率在形状优化，机器人技术和计量经济学的背景下进行了说明。