最近的体积三维重建方法可以产生非常精确的结果，即使对于未观察的表面，也可以具有合理的几何形状。然而，当涉及多视图融合时，它们面临着不希望的权衡。它们可以通过全局平均来熔断所有可用视图信息，从而丢失精细的细节，或者他们可以启发式群集对本地融合的群集视图，从而限制他们共同考虑所有视图的能力。我们的关键洞察力是通过在摄像机姿势和图像内容上学习视图融合功能，可以在不限制视图多样性的情况下保留更详细的详细信息。我们建议使用变压器学习此多视图融合。为此，我们使用变压器介绍Vortx，一个端到端的体积3D重建网络，用于宽基线，多视图功能融合。我们的模型是遮挡感知的，利用变压器架构来预测初始投影场景几何估计。该估计用于避免将反射图像特征通过曲面到遮挡区域。我们在Scannet上培训我们的模型，并显示它比最先进的方法产生更好的重建。我们还展示了概括，没有任何微调，优于两个其他数据集，Tum-RGBD和ICL-Nuim的相同最先进的方法。

我们呈现3DVNET，一种新型多视图立体声（MVS）深度预测方法，该方法结合了基于深度和体积的MVS方法的优点。我们的关键思想是使用3D场景建模网络，可迭代地更新一组粗略深度预测，从而产生高度准确的预测，它达成底层场景几何形状。与现有的深度预测技术不同，我们的方法使用体积3D卷积神经网络（CNN），该网络（CNN）在所有深度图中共同地在世界空间上运行。因此，网络可以学习有意义的场景级别。此外，与现有的体积MVS技术不同，我们的3D CNN在特征增强点云上运行，允许有效地聚合多视图信息和灵活的深度映射的迭代细化。实验结果表明，我们的方法超过了Scannet DataSet的深度预测和3D重建度量的最先进的准确性，以及来自Tum-RGBD和ICL-Nuim数据集的一系列场景。这表明我们的方法既有效又推广到新设置。

多模式分类是人类以人为本的机器学习中的核心任务。我们观察到信息跨多模式融合在多模式融合之前，信息在偶像中具有高度互补的信息，因此在多模式融合之前可以彻底稀释。为此，我们呈现稀疏的融合变压器（SFT），一种用于现有最先进的方法的变压器的新型多模式融合方法，同时具有大大降低了内存占用和计算成本。我们想法的关键是稀疏池块，可在跨模式建模之前减少单峰令牌集合。评估在多个多模式基准数据集上进行，用于广泛的分类任务。在类似的实验条件下的多个基准上获得最先进的性能，同时报告计算成本和内存要求降低六倍。广泛的消融研究展示了在天真的方法中结合稀疏和多式化学习的好处。这铺平了在低资源设备上实现多模级学习的方式。

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.

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.

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.

Key Point Analysis(KPA) is a relatively new task in NLP that combines summarization and classification by extracting argumentative key points (KPs) for a topic from a collection of texts and categorizing their closeness to the different arguments. In our work, we focus on the legal domain and develop methods that identify and extract KPs from premises derived from texts of judgments. The first method is an adaptation to an existing state-of-the-art method, and the two others are new methods that we developed from scratch. We present our methods and examples of their outputs, as well a comparison between them. The full evaluation of our results is done in the matching task -- match between the generated KPs to arguments (premises).