神经操作员是一种深层建筑，可以学会解决（即学习）部分微分方程（PDE）的非线性解决方案操作员。这些模型的当前艺术状态不能提供明确的不确定性量化。可以说，这是这种任务的问题，而不是机器学习中的其他地方，因为PDE通常描述的动态系统通常表现出微妙的多尺度结构，这会使人类难以发现错误。在这项工作中，我们首先在高斯过程的形式主义中首先提供了数学上详细的贝叶斯公式（线性）版本。然后，我们使用贝叶斯深度学习的近似方法将这种分析治疗扩展到一般的深层神经操作员。我们通过为神经操作员提供不确定性量化来扩展对神经操作员的先前结果。结果，我们的方法能够识别病例，并提供结构化的不确定性估计值，而神经操作员无法很好地预测。

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.

The number of standardized policy documents regarding climate policy and their publication frequency is significantly increasing. The documents are long and tedious for manual analysis, especially for policy experts, lawmakers, and citizens who lack access or domain expertise to utilize data analytics tools. Potential consequences of such a situation include reduced citizen governance and involvement in climate policies and an overall surge in analytics costs, rendering less accessibility for the public. In this work, we use a Latent Dirichlet Allocation-based pipeline for the automatic summarization and analysis of 10-years of national energy and climate plans (NECPs) for the period from 2021 to 2030, established by 27 Member States of the European Union. We focus on analyzing policy framing, the language used to describe specific issues, to detect essential nuances in the way governments frame their climate policies and achieve climate goals. The methods leverage topic modeling and clustering for the comparative analysis of policy documents across different countries. It allows for easier integration in potential user-friendly applications for the development of theories and processes of climate policy. This would further lead to better citizen governance and engagement over climate policies and public policy research.

Predicting the presence of major depressive disorder (MDD) using behavioural and cognitive signals is a highly non-trivial task. The heterogeneous clinical profile of MDD means that any given speech, facial expression and/or observed cognitive pattern may be associated with a unique combination of depressive symptoms. Conventional discriminative machine learning models potentially lack the complexity to robustly model this heterogeneity. Bayesian networks, however, may instead be well-suited to such a scenario. These networks are probabilistic graphical models that efficiently describe the joint probability distribution over a set of random variables by explicitly capturing their conditional dependencies. This framework provides further advantages over standard discriminative modelling by offering the possibility to incorporate expert opinion in the graphical structure of the models, generating explainable model predictions, informing about the uncertainty of predictions, and naturally handling missing data. In this study, we apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.

对AI的道德影响和值得信赖系统的设计的研究需要分析使用AI系统的方案，这与“用例”的软件工程概念和“预期目的”法律术语有关。但是，没有用于涵盖使用，范围，功能要求和AI系统风险的用例文档的标准方法。在这项工作中，我们为AI用例提出了一种新颖的文档方法，特别关注情感计算领域。我们的方法基于对研究文献中记录的用例信息需求的评估以及最近提议的AI欧洲监管框架。通过此评估，我们采用并调整了统一的建模语言（UML），在过去的二十年中，这主要由软件工程师使用。然后，每个用例都由UML图和一个结构化表表示，我们提供了一组示例，说明了其在几种情感计算方案中的应用。

软机器人操纵器对于在受限环境中的医疗干预或工业检查等一系列应用都具有吸引力。文献中已经提出了无数的软机器人操纵器，但是它们的设计往往相对相似，并且通常提供相对较低的力。这限制了他们可以携带的有效载荷，因此限制了它们的可用性。在公共框架下不可用不同设计的力的比较，并且设计具有不同的直径和功能，使它们难以比较。在本文中，我们介绍了一种软机器人操纵器的设计，该设计的优化为最大化其力，同时尊重典型的应用程序约束，例如大小，工作区，有效负载能力和最大压力。此处介绍的设计具有一个优势，即它变为最佳设计，因为它被加压到朝不同方向移动，这会导致较高的横向力。该机器人是使用一组原理设计的，因此可以适应其他应用程序。我们还为软机器人操纵器提供了非二维分析，并将其应用于此处提出的设计的性能与文献中其他设计的性能。我们表明，我们的设计比同一类别中的其他设计具有更高的力量。实验结果证实了我们提出的设计的较高力量。

衡量工作头衔之间的语义相似性是自动工作建议的重要功能。通常使用有监督的学习技术来处理此任务，这需要以同等职位对的形式进行培训数据。在本文中，我们提出了一种使用嘈杂技能标签培训职位相似性模型的无监督表示学习方法。我们表明，对于文本排名和作业归一化等任务非常有效。