迁移率和加热部门的连续电气化将对分布网格运行引入新的挑战。不协调的灵活单元激活，例如，电动车辆同时充电作为对价格信号的反应，可以系统地触发变压器或线路保护。实时识别这种快速升高的灵活性激活将允许抵消以避免潜在的社会和财务成本。在这项工作中，提出了一种用于识别快速升高灵活性激活事件的新型数据处理流水线。管道结合了无监督事件检测和开放式分类的技术。实际负载数据的系统评估演示了所提出的管道的主要构建块可以通过满足分布式事件检测架构中应用的重要要求的方法来实现。为了检测灵活性激活事件，识别了上部性能限制。此外，证明了与广泛应用的闭合分类器相比，用于分类的开放式分类器的应用可以提高性能。

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.

Neural networks are increasingly applied in safety critical domains, their verification thus is gaining importance. A large class of recent algorithms for proving input-output relations of feed-forward neural networks are based on linear relaxations and symbolic interval propagation. However, due to variable dependencies, the approximations deteriorate with increasing depth of the network. In this paper we present DPNeurifyFV, a novel branch-and-bound solver for ReLU networks with low dimensional input-space that is based on symbolic interval propagation with fresh variables and input-splitting. A new heuristic for choosing the fresh variables allows to ameliorate the dependency problem, while our novel splitting heuristic, in combination with several other improvements, speeds up the branch-and-bound procedure. We evaluate our approach on the airborne collision avoidance networks ACAS Xu and demonstrate runtime improvements compared to state-of-the-art tools.

Wireless Sensor Network (WSN) applications reshape the trend of warehouse monitoring systems allowing them to track and locate massive numbers of logistic entities in real-time. To support the tasks, classic Radio Frequency (RF)-based localization approaches (e.g. triangulation and trilateration) confront challenges due to multi-path fading and signal loss in noisy warehouse environment. In this paper, we investigate machine learning methods using a new grid-based WSN platform called Sensor Floor that can overcome the issues. Sensor Floor consists of 345 nodes installed across the floor of our logistic research hall with dual-band RF and Inertial Measurement Unit (IMU) sensors. Our goal is to localize all logistic entities, for this study we use a mobile robot. We record distributed sensing measurements of Received Signal Strength Indicator (RSSI) and IMU values as the dataset and position tracking from Vicon system as the ground truth. The asynchronous collected data is pre-processed and trained using Random Forest and Convolutional Neural Network (CNN). The CNN model with regularization outperforms the Random Forest in terms of localization accuracy with aproximate 15 cm. Moreover, the CNN architecture can be configured flexibly depending on the scenario in the warehouse. The hardware, software and the CNN architecture of the Sensor Floor are open-source under https://github.com/FLW-TUDO/sensorfloor.

Insects as pollinators play a key role in ecosystem management and world food production. However, insect populations are declining, calling for a necessary global demand of insect monitoring. Existing methods analyze video or time-lapse images of insects in nature, but the analysis is challenging since insects are small objects in complex and dynamic scenes of natural vegetation. The current paper provides a dataset of primary honeybees visiting three different plant species during two months of summer-period. The dataset consists of more than 700,000 time-lapse images from multiple cameras, including more than 100,000 annotated images. The paper presents a new method pipeline for detecting insects in time-lapse RGB-images. The pipeline consists of a two-step process. Firstly, the time-lapse RGB-images are preprocessed to enhance insects in the images. We propose a new prepossessing enhancement method: Motion-Informed-enhancement. The technique uses motion and colors to enhance insects in images. The enhanced images are subsequently fed into a Convolutional Neural network (CNN) object detector. Motion-Informed-enhancement improves the deep learning object detectors You Only Look Once (YOLO) and Faster Region-based Convolutional Neural Networks (Faster R-CNN). Using Motion-Informed-enhancement the YOLO-detector improves average micro F1-score from 0.49 to 0.71, and the Faster R-CNN-detector improves average micro F1-score from 0.32 to 0.56 on the our dataset. Our datasets are published on: https://vision.eng.au.dk/mie/

Reliable application of machine learning-based decision systems in the wild is one of the major challenges currently investigated by the field. A large portion of established approaches aims to detect erroneous predictions by means of assigning confidence scores. This confidence may be obtained by either quantifying the model's predictive uncertainty, learning explicit scoring functions, or assessing whether the input is in line with the training distribution. Curiously, while these approaches all state to address the same eventual goal of detecting failures of a classifier upon real-life application, they currently constitute largely separated research fields with individual evaluation protocols, which either exclude a substantial part of relevant methods or ignore large parts of relevant failure sources. In this work, we systematically reveal current pitfalls caused by these inconsistencies and derive requirements for a holistic and realistic evaluation of failure detection. To demonstrate the relevance of this unified perspective, we present a large-scale empirical study for the first time enabling benchmarking confidence scoring functions w.r.t all relevant methods and failure sources. The revelation of a simple softmax response baseline as the overall best performing method underlines the drastic shortcomings of current evaluation in the abundance of publicized research on confidence scoring. Code and trained models are at https://github.com/IML-DKFZ/fd-shifts.

Pre-trained protein language models have demonstrated significant applicability in different protein engineering task. A general usage of these pre-trained transformer models latent representation is to use a mean pool across residue positions to reduce the feature dimensions to further downstream tasks such as predicting bio-physics properties or other functional behaviours. In this paper we provide a two-fold contribution to machine learning (ML) driven drug design. Firstly, we demonstrate the power of sparsity by promoting penalization of pre-trained transformer models to secure more robust and accurate melting temperature (Tm) prediction of single-chain variable fragments with a mean absolute error of 0.23C. Secondly, we demonstrate the power of framing our prediction problem in a probabilistic framework. Specifically, we advocate for the need of adopting probabilistic frameworks especially in the context of ML driven drug design.

传统上，无监督的情感分析是通过计算存储在情感词典中的文本中的这些词，然后根据注册正面和否定词的比例分配标签的文字来执行的。尽管这些“计数”方法被认为是有益的，因为它们确定性地对文本进行评分，但当分析的文本简短或词汇与词典认为默认值的情况不同时，它们的分类率降低。本文提出的称为LEX2SENT的模型是一种无监督的情感分析方法，用于改善情感词典方法的分类。为此，对DOC2VEC模型进行了训练，以确定嵌入文档嵌入与情感词典正面和负部分的嵌入之间的距离。然后对这些距离进行评估，以在重新采样文档上多次执行DOC2VEC，并进行平均以执行分类任务。对于本文考虑的三个基准数据集，拟议的LEX2SENT优于每个评估的词典，包括Vader等最先进的词典或分类率的意见词典。

如何将新兴和全面的技术（例如AI）整合到我们社会的结构和运营中是当代政治，科学和公众辩论的问题。它从不同学科中产生了大量的国际学术文献。本文分析了有关人工智能调节（AI）的学术辩论。该系统审查包括在2016年1月1日至2020年12月31日之间发表的73份同行评审期刊文章样本。分析集中于社会风险和危害，监管责任问题以及可能基于风险的政策框架在内和基于原则的方法。主要利益是拟议的监管方法和工具。提出了各种形式的干预措施，例如禁令，批准，标准设定和披露。对所包括论文的评估​​表明该领域的复杂性，这表明其早产和剩余的缺乏清晰度。通过对学术辩论进行结构性分析，我们在经验和概念上均可更好地理解AI和监管的联系以及基本规范性决策。科学建议与拟议的欧洲AI调节的比较说明了调节的特定方法，其优势和缺点。

扩散模型是一类生成模型，与其他生成模型相比，在自然图像数据集训练时，在创建逼真的图像时表现出了出色的性能。我们引入了Dispr，这是一个基于扩散的模型，用于解决从二维（2D）单细胞显微镜图像预测三维（3D）细胞形状的反问题。使用2D显微镜图像作为先验，因此可以根据预测现实的3D形状重建条件。为了在基于功能的单细胞分类任务中展示DIPPR作为数据增强工具的适用性，我们从分组为六个高度不平衡类的单元中提取形态特征。将DISPR预测的功能添加到三个少数类别，将宏F1分数从$ f1_ \ text {macro} = 55.2 \ pm 4.6 \％$ to $ f1_ \％$ to $ f1_ \ text {macro} = 72.2 \ pm 4.9 \％$。由于我们的方法是在这种情况下第一个采用基于扩散的模型的方法，因此我们证明了扩散模型可以应用于3D中的反问题，并且他们学会了从2D显微镜图像中重建具有现实的形态特征的3D形状。