In recent years distributional reinforcement learning has produced many state of the art results. Increasingly sample efficient Distributional algorithms for the discrete action domain have been developed over time that vary primarily in the way they parameterize their approximations of value distributions, and how they quantify the differences between those distributions. In this work we transfer three of the most well-known and successful of those algorithms (QR-DQN, IQN and FQF) to the continuous action domain by extending two powerful actor-critic algorithms (TD3 and SAC) with distributional critics. We investigate whether the relative performance of the methods for the discrete action space translates to the continuous case. To that end we compare them empirically on the pybullet implementations of a set of continuous control tasks. Our results indicate qualitative invariance regarding the number and placement of distributional atoms in the deterministic, continuous action setting.

支持向量机（SVM）是机器学习工具箱中的标准方法，特别是对于表格数据。但是，非线性内核SVM通常以较长的培训时间为代价提供高度准确的预测指标。随着时间的推移，数据量的指数增长加剧了这个问题。过去，它主要是通过两种类型的技术来解决的：近似求解器和平行的GPU实现。在这项工作中，我们将这两种方法结合在一起，以设计非常快速的双SVM求解器。我们充分利用现代计算服务器的功能：多核架构，多个高端GPU和大型随机访问存储器。在这样的机器上，我们在24分钟内在ImageNet数据集上训练一个大利润分类器。

目的：脑电图（EEG）和肌电图（EMG）是两个非侵入性的生物信号，它们在人类机器界面（HMI）技术（EEG-HMI和EMG-HMI范式）中广泛用于康复，用于康复的物理残疾人。将脑电图和EMG信号成功解码为各自的控制命令是康复过程中的关键步骤。最近，提出了几个基于卷积的神经网络（CNN）架构，它们直接将原始的时间序列信号映射到决策空间中，并同时执行有意义的特征提取和分类的过程。但是，这些网络是根据学习给定生物信号的预期特征量身定制的，并且仅限于单个范式。在这项工作中，我们解决了一个问题，即我们可以构建一个单个体系结构，该架构能够从不同的HMI范式中学习不同的功能并仍然成功地对其进行分类。方法：在这项工作中，我们引入了一个称为Controanet的单个混合模型，该模型基于CNN和Transformer架构，该模型对EEG-HMI和EMG-HMI范式同样有用。 Contranet使用CNN块在模型中引入电感偏置并学习局部依赖性，而变压器块则使用自我注意机制来学习信号中的长距离依赖性，这对于EEG和EMG信号的分类至关重要。主要结果：我们在三个属于EEG-HMI和EMG-HMI范式的公开数据集上评估并比较了Contronet与最先进的方法。 Contranet在所有不同类别任务（2级，3类，4级和10级解码任务）中的表现优于其对应。意义：结果表明，与当前的最新算法状态相比，从不同的HMI范式中学习不同的特征并概述了矛盾。

表面肌电学（SEMG）的精确解码是肌肉到机器接口（MMI）的关键和它们的应用。康复治疗。由于各种因素，包括皮肤厚度，体脂百分比和电极放置，SEMG信号具有高的互类互变异性。因此，获得训练有素的SEMG解码器的高泛化质量非常具有挑战性。通常，基于机器学习的SEMG解码器可以在特定于对象的数据上培训，或者单独地为每个用户验证或至少重新校准。即使，深度学习算法也产生了几种最新的SEMG解码结果，然而，由于SEMG数据的可用性有限，深度学习模型容易过度拟合。最近，转移学习域适应改善了各种机器学习任务的培训时间减少的泛化质量。在这项研究中，我们调查了使用权重初始化进行转移学习的有效性，以重新校正在新的科目数据上的两个不同预磨削的深度学习模型，并将它们的性能与特定于学科的模型进行比较。据我们所知，这是第一项研究，即彻底调查基于体重初始化的转移学习，并比较了对象特异性建模的转移学习。我们在各种设置下在三个公开的数据库上测试了我们的模型。平均过度通过所有设置，我们的转移学习方法改善了预训练模型的5〜\％，在没有微调的情况下，在特定于课程的型号上的12〜\％点，同时平均培训22〜\％较少的时期。我们的结果表明，转让学习可以更快地培训比用户特定的型号更少，并且只要有足够的数据，可以提高预磨料模型的性能。

众所周知，步长自适应演进策略（ES）不汇总（过早地）到常规点的常规目标功能。在关键点中，需要收敛到最小值，并且对最大值的收敛易排除。然而，令人惊讶的是，在埃塞尔可以被困在马鞍点上，令人惊讶的是。在这项工作中，我们建立了即使是简单的（1 + 1）-ES在相当轻微的规律条件下可靠地克服大多数鞍座。我们的分析基于尾界的漂移。这是非标准的，因为我们甚至没有旨在估计基于漂移的击球时间。相反，在我们的情况下，表明相关时间有限，具有完全概率。

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.

In recent years, several metrics have been developed for evaluating group fairness of rankings. Given that these metrics were developed with different application contexts and ranking algorithms in mind, it is not straightforward which metric to choose for a given scenario. In this paper, we perform a comprehensive comparative analysis of existing group fairness metrics developed in the context of fair ranking. By virtue of their diverse application contexts, we argue that such a comparative analysis is not straightforward. Hence, we take an axiomatic approach whereby we design a set of thirteen properties for group fairness metrics that consider different ranking settings. A metric can then be selected depending on whether it satisfies all or a subset of these properties. We apply these properties on eleven existing group fairness metrics, and through both empirical and theoretical results we demonstrate that most of these metrics only satisfy a small subset of the proposed properties. These findings highlight limitations of existing metrics, and provide insights into how to evaluate and interpret different fairness metrics in practical deployment. The proposed properties can also assist practitioners in selecting appropriate metrics for evaluating fairness in a specific application.

Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently introducing a demand for AI-based tools that improve the efficiency with which radiologists can comfortably interpret these exams. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation. This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical Radiology workflow. We also present a taxonomy of Radiology AI use-cases. Through this report, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions.

Heating in private households is a major contributor to the emissions generated today. Heat pumps are a promising alternative for heat generation and are a key technology in achieving our goals of the German energy transformation and to become less dependent on fossil fuels. Today, the majority of heat pumps in the field are controlled by a simple heating curve, which is a naive mapping of the current outdoor temperature to a control action. A more advanced control approach is model predictive control (MPC) which was applied in multiple research works to heat pump control. However, MPC is heavily dependent on the building model, which has several disadvantages. Motivated by this and by recent breakthroughs in the field, this work applies deep reinforcement learning (DRL) to heat pump control in a simulated environment. Through a comparison to MPC, it could be shown that it is possible to apply DRL in a model-free manner to achieve MPC-like performance. This work extends other works which have already applied DRL to building heating operation by performing an in-depth analysis of the learned control strategies and by giving a detailed comparison of the two state-of-the-art control methods.

In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with traditional methods in rule learning to provide efficient and scalable algorithms for the classification of vast data sets, while remaining explainable. Apart from evaluating our approach on the common large scale data sets MNIST, Fashion-MNIST and IMDB, we present novel results on explainable classifications of dental bills. The latter case study stems from an industrial collaboration with Allianz Private Krankenversicherungs-Aktiengesellschaft which is an insurance company offering diverse services in Germany.