变异自动编码器（VAE）是最常用的无监督机器学习模型之一。但是，尽管对先前和后验的高斯分布的默认选择通常代表了数学方便的分布通常会导致竞争结果，但我们表明该参数化无法用潜在的超球体结构对数据进行建模。为了解决这个问题，我们建议使用von Mises-fisher（VMF）分布，从而导致超级潜在空间。通过一系列实验，我们展示了这种超球vae或$ \ mathcal {s} $ - vae如何更适合于用超球形结构捕获数据，同时胜过正常的，$ \ mathcal {n} $ - vae-，在其他数据类型的低维度中。http://github.com/nicola-decao/s-vae-tf和https://github.com/nicola-decao/nicola-decao/s-vae-pytorch

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.

全球粮食需求和严峻的工作条件的上升使水果收获成为自动化的重要领域。对于任何自动化的水果收获系统来说，花梗定位是重要的步骤，因为水果分离技术对花梗位置高度敏感。大多数关于花梗本地化的工作都集中在计算机视觉上，但是由于农业环境的混乱性，花梗很难在视觉上访问。我们的工作提出了一种替代机械（而不是视觉）感知来定位花梗的替代方法。为了估算这一重要植物特征的位置，我们将扳手测量从腕部力/扭矩传感器到水果植物系统的物理模型，将水果的附着点视为要调整的参数。该方法是作为水果采摘程序的一部分进行内联执行的。使用我们的果园代理进行评估，我们证明了该技术能够将花梗定位在3.8 cm的中间距离内，中位方向误差为16.8度。

在过去几年中，水下车辆操纵器系统（UVMS）变得越来越小，越来越小，在计划和控制系统时，考虑操纵器和车辆之间的耦合力变得越来越重要。但是，处理这些力的典型方法需要媒介物的精确流体动力模型，并在操纵器上使用低级扭矩控制，这两者在现场都很少见。因此，许多UVMS控制方法都是基于运动学的，无法固有地解释这些效果。我们的工作通过训练模拟UVMS数据上的复发性神经网络来弥合运动学控制与动态之间的差距，以根据系统以前的状态预测将来车辆的音高。运动学计划者和控制者可以使用此指标来合并动态知识，而无需计算昂贵的模型，从而提高了他们执行水下操纵任务的能力。

问答（QA）系统越来越多地部署在支持现实世界决策的应用程序中。但是，最新的模型依赖于深层神经网络，这些网络很难被人类解释。固有的可解释模型或事后解释性方法可以帮助用户理解模型如何达到其预测，并在成功的情况下增加对系统的信任。此外，研究人员可以利用这些见解来开发更准确和偏见的新方法。在本文中，我们介绍了Square V2（Square的新版本），以根据图形和基于图形的说明等方法进行比较模型提供解释性基础架构。尽管显着图对于检查每个输入令牌对模型预测的重要性很有用，但来自外部知识图的基于图的解释使用户能够验证模型预测背后的推理。此外，我们提供了多种对抗性攻击，以比较质量检查模型的鲁棒性。通过这些解释性方法和对抗性攻击，我们旨在简化对可信赖的质量检查模型的研究。 Square可在https://square.ukp-lab.de上找到。

果树的休眠修剪是维持树木健康和确保高质量果实的重要任务。由于劳动力的可用性降低，修剪是机器人自动化的主要候选者。但是，修剪也代表了机器人的独特困难问题，需要在可变照明条件下以及在复杂的，高度非结构化的环境中进行感知，修剪点的确定和操纵。在本文中，我们介绍了一种用于修剪甜樱桃树的系统（在平面树建筑中，称为直立的果实分支配置），该系统整合了我们先前关于感知和操纵的工作的各种子系统。最终的系统能够完全自主运行，并且需要对环境的最低控制。我们通过在甜蜜的樱桃果园中进行现场试验来验证系统的性能，最终取得了58％的削减速度。尽管不完全稳健，并且需要改善吞吐量，但我们的系统是第一个在果树上运行的系统，并代表了将来可以改进的有用的基础平台。

尽管自动图像分析的重要性不断增加，但最近的元研究揭示了有关算法验证的主要缺陷。性能指标对于使用的自动算法的有意义，客观和透明的性能评估和验证尤其是关键，但是在使用特定的指标进行给定的图像分析任务时，对实际陷阱的关注相对较少。这些通常与（1）无视固有的度量属性，例如在存在类不平衡或小目标结构的情况下的行为，（2）无视固有的数据集属性，例如测试的非独立性案例和（3）无视指标应反映的实际生物医学领域的兴趣。该动态文档的目的是说明图像分析领域通常应用的性能指标的重要局限性。在这种情况下，它重点介绍了可以用作图像级分类，语义分割，实例分割或对象检测任务的生物医学图像分析问题。当前版本是基于由全球60多家机构的国际图像分析专家进行的关于指标的Delphi流程。

本文档提供了SNACS的详细语言描述（Adposition和Case Supersenses的语义网络； Schneider等，2018），这是52个语义标签（“ Supersenses”）的库存，这些标签（“ Supersenses”）表征了在某种程度上使用ADIP定位和案例标记的使用。粒度水平，如Streusle语料库中所示（https://github.com/nert-nlp/streusle/;版本4.5 track track track offelines guidelines guidelines版本2.6）。尽管SNACS的库存渴望成为普遍的，但该文档是特定于英语的。其他语言的文档将单独发布。版本2是Schneider等人对英语提出的超音库存的修订。 （2015，2016）（此后为“ V1”），这又基于以前的计划。本清单是在对英语的V1语料库注释进行广泛审查后开发的，以及以前未分析的属格案例所有人（Blodgett和Schneider，2018年），并考虑了希伯来语，印地语，韩国和德国的定义和案例现象的考虑。 Hwang等。 （2017）介绍了V2方案的理论基础。 Schneider等。 （2018）总结了该方案，其应用于英语语料库数据以及自动歧义任务。刘等。 （2021）提供了一个英语词法语义识别标签仪，其中包括SNACS标签的输出。该文档也可以与Xposition网站上的语料库数据一起浏览（Gessler等，2022）：http：//www.xposition.org/

Modeling lies at the core of both the financial and the insurance industry for a wide variety of tasks. The rise and development of machine learning and deep learning models have created many opportunities to improve our modeling toolbox. Breakthroughs in these fields often come with the requirement of large amounts of data. Such large datasets are often not publicly available in finance and insurance, mainly due to privacy and ethics concerns. This lack of data is currently one of the main hurdles in developing better models. One possible option to alleviating this issue is generative modeling. Generative models are capable of simulating fake but realistic-looking data, also referred to as synthetic data, that can be shared more freely. Generative Adversarial Networks (GANs) is such a model that increases our capacity to fit very high-dimensional distributions of data. While research on GANs is an active topic in fields like computer vision, they have found limited adoption within the human sciences, like economics and insurance. Reason for this is that in these fields, most questions are inherently about identification of causal effects, while to this day neural networks, which are at the center of the GAN framework, focus mostly on high-dimensional correlations. In this paper we study the causal preservation capabilities of GANs and whether the produced synthetic data can reliably be used to answer causal questions. This is done by performing causal analyses on the synthetic data, produced by a GAN, with increasingly more lenient assumptions. We consider the cross-sectional case, the time series case and the case with a complete structural model. It is shown that in the simple cross-sectional scenario where correlation equals causation the GAN preserves causality, but that challenges arise for more advanced analyses.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.