机器学习和临床研究社区利用现实世界数据（RWD）的方法，包括电子健康记录中捕获的数据（EHR）截然不同。虽然临床研究人员谨慎使用RWD进行临床研究，但用于医疗团队的ML会消费公共数据集，并以最少的审查来开发新算法。这项研究通过开发和验证ML-DQA来弥合这一差距，ML-DQA是基于RWD最佳实践的数据质量保证框架。 ML-DQA框架适用于两个地理位置的五个ML项目，分别是不同的医疗状况和不同的人群。在这五个项目中，共收集了247,536名患者的RWD，共有2,999项质量检查和24份质量报告。出现了五种可推广的实践：所有项目都使用类似的方法来分组冗余数据元素表示；所有项目都使用自动实用程序来构建诊断和药物数据元素；所有项目都使用了一个共同的基于规则的转换库；所有项目都使用统一的方法将数据质量检查分配给数据元素；所有项目都使用类似的临床裁决方法。包括临床医生，数据科学家和受训者在内的平均有5.8个人参与每个项目实施ML-DQA，每个项目平均进行了23.4个数据元素。这项研究证明了ML-DQA在医疗项目中的重要性作用，并为团队提供了开展这些基本活动的框架。

Understanding customer feedback is becoming a necessity for companies to identify problems and improve their products and services. Text classification and sentiment analysis can play a major role in analyzing this data by using a variety of machine and deep learning approaches. In this work, different transformer-based models are utilized to explore how efficient these models are when working with a German customer feedback dataset. In addition, these pre-trained models are further analyzed to determine if adapting them to a specific domain using unlabeled data can yield better results than off-the-shelf pre-trained models. To evaluate the models, two downstream tasks from the GermEval 2017 are considered. The experimental results show that transformer-based models can reach significant improvements compared to a fastText baseline and outperform the published scores and previous models. For the subtask Relevance Classification, the best models achieve a micro-averaged $F1$-Score of 96.1 % on the first test set and 95.9 % on the second one, and a score of 85.1 % and 85.3 % for the subtask Polarity Classification.

The findable, accessible, interoperable, and reusable (FAIR) data principles have provided a framework for examining, evaluating, and improving how we share data with the aim of facilitating scientific discovery. Efforts have been made to generalize these principles to research software and other digital products. Artificial intelligence (AI) models -- algorithms that have been trained on data rather than explicitly programmed -- are an important target for this because of the ever-increasing pace with which AI is transforming scientific and engineering domains. In this paper, we propose a practical definition of FAIR principles for AI models and create a FAIR AI project template that promotes adherence to these principles. We demonstrate how to implement these principles using a concrete example from experimental high energy physics: a graph neural network for identifying Higgs bosons decaying to bottom quarks. We study the robustness of these FAIR AI models and their portability across hardware architectures and software frameworks, and report new insights on the interpretability of AI predictions by studying the interplay between FAIR datasets and AI models. Enabled by publishing FAIR AI models, these studies pave the way toward reliable and automated AI-driven scientific discovery.

Image segmentation is a largely researched field where neural networks find vast applications in many facets of technology. Some of the most popular approaches to train segmentation networks employ loss functions optimizing pixel-overlap, an objective that is insufficient for many segmentation tasks. In recent years, their limitations fueled a growing interest in topology-aware methods, which aim to recover the correct topology of the segmented structures. However, so far, none of the existing approaches achieve a spatially correct matching between the topological features of ground truth and prediction. In this work, we propose the first topologically and feature-wise accurate metric and loss function for supervised image segmentation, which we term Betti matching. We show how induced matchings guarantee the spatially correct matching between barcodes in a segmentation setting. Furthermore, we propose an efficient algorithm to compute the Betti matching of images. We show that the Betti matching error is an interpretable metric to evaluate the topological correctness of segmentations, which is more sensitive than the well-established Betti number error. Moreover, the differentiability of the Betti matching loss enables its use as a loss function. It improves the topological performance of segmentation networks across six diverse datasets while preserving the volumetric performance. Our code is available in https://github.com/nstucki/Betti-matching.

The new wave of digitization induced by Industry 4.0 calls for ubiquitous and reliable connectivity to perform and automate industrial operations. 5G networks can afford the extreme requirements of heterogeneous vertical applications, but the lack of real data and realistic traffic statistics poses many challenges for the optimization and configuration of the network for industrial environments. In this paper, we investigate the network traffic data generated from a laser cutting machine deployed in a Trumpf factory in Germany. We analyze the traffic statistics, capture the dependencies between the internal states of the machine, and model the network traffic as a production state dependent stochastic process. The two-step model is proposed as follows: first, we model the production process as a multi-state semi-Markov process, then we learn the conditional distributions of the production state dependent packet interarrival time and packet size with generative models. We compare the performance of various generative models including variational autoencoder (VAE), conditional variational autoencoder (CVAE), and generative adversarial network (GAN). The numerical results show a good approximation of the traffic arrival statistics depending on the production state. Among all generative models, CVAE provides in general the best performance in terms of the smallest Kullback-Leibler divergence.

Causal discovery, the inference of causal relations from data, is a core task of fundamental importance in all scientific domains, and several new machine learning methods for addressing the causal discovery problem have been proposed recently. However, existing machine learning methods for causal discovery typically require that the data used for inference is pooled and available in a centralized location. In many domains of high practical importance, such as in healthcare, data is only available at local data-generating entities (e.g. hospitals in the healthcare context), and cannot be shared across entities due to, among others, privacy and regulatory reasons. In this work, we address the problem of inferring causal structure - in the form of a directed acyclic graph (DAG) - from a distributed data set that contains both observational and interventional data in a privacy-preserving manner by exchanging updates instead of samples. To this end, we introduce a new federated framework, FED-CD, that enables the discovery of global causal structures both when the set of intervened covariates is the same across decentralized entities, and when the set of intervened covariates are potentially disjoint. We perform a comprehensive experimental evaluation on synthetic data that demonstrates that FED-CD enables effective aggregation of decentralized data for causal discovery without direct sample sharing, even when the contributing distributed data sets cover disjoint sets of interventions. Effective methods for causal discovery in distributed data sets could significantly advance scientific discovery and knowledge sharing in important settings, for instance, healthcare, in which sharing of data across local sites is difficult or prohibited.

The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized.

在许多重要的科学和工程应用中发现了卷数据。渲染此数据以高质量和交互速率为苛刻的应用程序（例如虚拟现实）的可视化化，即使使用专业级硬件也无法实现。我们介绍了Fovolnet - 一种可显着提高数量数据可视化的性能的方法。我们开发了一种具有成本效益的渲染管道，该管道稀疏地对焦点进行了量度，并使用深层神经网络重建了全帧。 FOVEATED渲染是一种优先考虑用户焦点渲染计算的技术。这种方法利用人类视觉系统的属性，从而在用户视野的外围呈现数据时节省了计算资源。我们的重建网络结合了直接和内核预测方法，以产生快速，稳定和感知令人信服的输出。凭借纤细的设计和量化的使用，我们的方法在端到端框架时间和视觉质量中都优于最先进的神经重建技术。我们对系统的渲染性能，推理速度和感知属性进行了广泛的评估，并提供了与竞争神经图像重建技术的比较。我们的测试结果表明，Fovolnet始终在保持感知质量的同时，在传统渲染上节省了大量时间。

通用数据模型解决了标准化电子健康记录（EHR）数据的许多挑战，但无法将其集成深度表型所需的资源。开放的生物学和生物医学本体论（OBO）铸造本体论提供了可用于生物学知识的语义计算表示，并能够整合多种生物医学数据。但是，将EHR数据映射到OBO Foundry本体论需要大量的手动策展和域专业知识。我们介绍了一个框架，用于将观察性医学成果合作伙伴关系（OMOP）标准词汇介绍给OBO铸造本体。使用此框架，我们制作了92,367条条件，8,615种药物成分和10,673个测量结果的映射。域专家验证了映射准确性，并且在24家医院进行检查时，映射覆盖了99％的条件和药物成分和68％的测量结果。最后，我们证明OMOP2OBO映射可以帮助系统地识别可能受益于基因检测的未诊断罕见病患者。

我们可以通过观看数月或数年来了解一个场景？在长时间播放中录制的视频将在多个时间范围内描绘有趣的现象，但识别和观看它们带来了挑战。该视频太长了，无法完整观看，并且某些事件的实时体验太慢，例如冰川静修。及时视频是总结长视频和可视化慢时尺度的常见方法。但是，时间段仅限于单个选择的时间频率，并且由于框架之间的混叠和时间不连续性，通常会出现闪烁。在本文中，我们提出了视频时间金字塔，该技术可以解决这些局限性并扩大可视化时间流逝的可能性。受到计算机视觉的空间图像金字塔的启发，我们开发了一种在时间域中构建视频金字塔的算法。视频时间金字塔的每个级别都可以看到不同的时间表。例如，每月时间表的视频通常非常适合可视化季节性变化，而一分钟时间尺度的视频最适合可视化日出或云层在天空中的运动。为了帮助探索不同的金字塔水平，我们还提出了一个视频频谱图，以可视化整个金字塔的活动量，从而提供了场景动力学的整体概述，并能够在时间和时间表上探索和发现现象。为了展示我们的方法，我们已经从十个户外场景中构建了视频时间金字塔，每个户外场景都包含数月或数年的数据。我们将视频颞金字塔层与天真的时间解体进行了比较，并发现我们的金字塔可以无视长期变化的别名观看。我们还证明，视频谱图通过实现概述和以细节为中心的观点来促进跨金字塔水平的现象的探索和发现。