欧洲机械指令和相关的统一标准确实认为软件用于生成机械的安全性行为，但不考虑各种软件。特别是，未考虑基于机器学习（ML）的软件以实现与安全相关的行为。这限制了为自动移动机器人和其他自动驾驶机械引入合适的安全概念，这些机械通常取决于基于ML的功能。我们调查了此问题以及安全标准定义要针对软件故障实施的安全措施的方式。功能安全标准使用安全完整性水平（SILS）来定义应采取哪些安全措施。它们提供了确定SIL和根据SIL选择安全措施的规则的规则。在本文中，我们认为这种方法在ML和其他类型的人工智能（AI）方面很难采用。我们建议使用保证案例来争辩说，在给定情况下，我们建议使用保证案例的简单规则，而是建议使用保证案例来辩称单独选择和应用的措施就足够了。为了获得有关提案的可行性和实用性的第一个评级，我们在讲习班中与工业专家，德国法定事故保险公司，工作安全和标准化委员会以及来自各种国家，欧洲和国际工作的代表进行了讨论和讨论。处理安全和AI的小组。在本文中，我们总结了提案和研讨会的讨论。此外，我们检查我们的建议与欧洲AI ACT提案和当前的安全标准化计划有关的程度一致

基于机器学习和其他AI技术的数据驱动模型（DDM）在越来越多的自主系统的感知中起着重要作用。由于仅基于用于培训的数据而仅对其行为进行隐式定义，因此DDM输出可能会出现不确定性。这对通过DDMS实现安全 - 关键感知任务的挑战提出了挑战。解决这一挑战的一种有希望的方法是估计操作过程中当前情况的不确定性，并相应地调整系统行为。在先前的工作中，我们专注于对不确定性的运行时估计，并讨论了处理不确定性估计的方法。在本文中，我们提出了处理不确定性的其他架构模式。此外，我们在定性和定量上对安全性和性能提高进行了定量评估。对于定量评估，我们考虑了一个用于车辆排的距离控制器，其中通过考虑在不同的操作情况下可以降低距离的距离来衡量性能增长。我们得出的结论是，考虑驾驶状况的上下文信息的考虑使得有可能或多或少地接受不确定性，具体取决于情况的固有风险，从而导致绩效提高。

In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map over the "Landes de Gascogne" forest in France, a large maritime pine plantation of 13,000 km$^2$ with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-net models based on a combination of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each instrument in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020 with a mean absolute error of 2.02 m on the Test dataset. The best predictions were obtained using all available satellite layers from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement. Their success hinges on the fact that the underlying physical phenomena are continuous. For inherently discrete and categorical data such as language, various diffusion-inspired alternatives have been proposed. However, the continuous nature of diffusion models conveys many benefits, and in this work we endeavour to preserve it. We propose CDCD, a framework for modelling categorical data with diffusion models that are continuous both in time and input space. We demonstrate its efficacy on several language modelling tasks.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

刚性对象的6D姿势的估计是计算机视觉中的一个基本问题。传统上，姿势估计与确定单一最佳估计有关。但是，单个估计无法表达视觉歧义，在许多情况下，由于对象对称或识别特征的阻塞，这在许多情况下是不可避免的。无法说明姿势的歧义可能会导致后续方法的失败，这是在失败成本高时无法接受的。完全姿势分布的估计与单个估计相反，非常适合表达姿势不确定性。由此激励，我们提出了一种新颖的姿势分布估计方法。对象姿势上概率分布的隐式公式来自对象的中间表示作为一组关键点。这样可以确保姿势分布估计值具有很高的解释性。此外，我们的方法基于保守近似，这导致可靠的估计。该方法已被评估在YCB-V和T-less数据集上旋转分布估计的任务，并在所有对象上可靠地执行。

我们提出了一种整体方法，用于构建一个可实现的自然语言分类系统，以实现现实世界中的内容适度。这样一个系统的成功依赖于一系列精心设计和执行的步骤，包括内容分类法和标签说明的设计，数据质量控制，主动学习管道以捕获罕见事件以及使模型可靠的各种方法并避免过度拟合。我们的审核系统经过培训，可以检测一系列不希望的内容，包括性内容，可恨的内容，暴力，自我伤害和骚扰。这种方法概括为各种不同的内容分类法，可用于创建优于现成模型的高质量内容分类器。

颠倒地震数据以建立3D地质结构是一项艰巨的任务，这是由于大量获得的地震数据，以及由于波动方程的迭代数值解决方案而引起的最高计算负载，如行业标准的工具所要求的，例如Full WaveForm反转（FWI）。例如，在3.5公里$ \ $ 4.5公里的地面尺寸的区域中，3D模型重建需要数百个地震射击场立方体，从而导致记录数据的Terabytes。本文提出了一种深度学习解决方案，用于在地震调查中记录的田间噪声的情况下重建现实的3D模型。我们实施和分析了一个卷积编码器架构，该体系结构有效地处理了数百种地震收集立方体的整个集合。所提出的解决方案表明，在存在10dB信噪比的场噪声的情况下，可以以结构相似性指数度量（SSIM）为0.8554（在1.0中）重建现实的3D模型。

简介白质超强度（WMHS）的自动分割是磁共振成像（MRI）神经影像分析的重要步骤。流体减弱的反转恢复（FLAIR加权）是MRI对比度，对于可视化和量化WMHS，这是脑小血管疾病和阿尔茨海默氏病（AD）特别有用的。临床MRI方案迁移到三维（3D）FLAIR加权的采集，以在所有三个体素维度中实现高空间分辨率。当前的研究详细介绍了深度学习工具的部署，以使自动化的WMH分割和表征从获得的3D Flair加权图像作为国家广告成像计划的一部分获得。 DDI研究中的642名参与者（283名男性，平均年龄：（65.18 +/- 9.33）年）中的材料和方法，在五个国家收集地点进行了培训和验证两个内部网络。在642名参与者的内部数据和一个外部数据集中，对三个模型进行了测试，其中包含来自国际合作者的29个情况。这些测试集进行了独立评估。使用了五个已建立的WMH性能指标与地面真理人体分割进行比较。测试的三个网络的结果，3D NNU-NET具有最佳性能，平均骰子相似性系数得分为0.78 +/- 0.10，其性能优于内部开发的2.5D模型和SOTA DEEP DEEP BAYESIAN网络。结论MRI协议中3D Flair加权图像的使用越来越多，我们的结果表明，WMH分割模型可以在3D数据上进行训练，并产生与无需更高的或更好的无需先进的WMH分割性能用于包括T1加权图像系列。