我们介绍了一个用于无监督异常检测和本地化任务的第一个全面的3D数据集。它受到真实的视觉检查场景的启发，其中模型必须在制造产品上检测各种类型的缺陷，即使它仅在无异常数据上培训。存在缺陷，其在物体的几何结构中表现为异常。这些导致数据的3D表示中的显着偏差。我们使用高分辨率的工业3D传感器来获取10种不同的物品类别的深度扫描。对于所有对象类别，我们展示了培训和验证集，每个培训和验证集由无异常样品的扫描组成。相应的测试集包含显示诸如划痕，凹痕，孔，污染物或变形的各种缺陷的样本。为每个异常测试样品提供精确的地面说明。我们数据集上的3D异常检测方法的初始基准指示相当大的改进室。

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.

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。

从不同的随机初始化开始，经过随机梯度下降（SGD）训练的神经网络通常在功能上非常相似，从而提出了一个问题，即不同的SGD溶液之间是否存在有意义的差异。 Entezari等。最近猜想，尽管初始化不同，但在考虑到神经网络的置换不变性后，SGD发现的解决方案位于相同的损失谷中。具体而言，他们假设可以将SGD找到的任何两种解决方案排列，以使其参数之间的线性插值形成一条路径，而不会显着增加损失。在这里，我们使用一种简单但功能强大的算法来找到这样的排列，使我们能够获得直接的经验证据，证明该假设在完全连接的网络中是正确的。引人注目的是，我们发现在初始化时已经存在两个网络，并且平均它们随机，但适当排列的初始化的性能大大高于机会。相反，对于卷积架构，我们的证据表明该假设不存在。特别是在大型学习率制度中，SGD似乎发现了各种模式。

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。

我们介绍了一个新颖的联合学习框架FedD3，该框架减少了整体沟通量，并开放了联合学习的概念，从而在网络受限的环境中进行了更多的应用程序场景。它通过利用本地数据集蒸馏而不是传统的学习方法（i）大大减少沟通量，并（ii）将转移限制为一击通信，而不是迭代的多路交流来实现这一目标。 FedD3允许连接的客户独立提炼本地数据集，然后汇总那些去中心化的蒸馏数据集（通常以几个无法识别的图像，通常小于模型小于模型），而不是像其他联合学习方法共享模型更新，而是允许连接的客户独立提炼本地数据集。在整个网络上仅一次形成最终模型。我们的实验结果表明，FedD3在所需的沟通量方面显着优于其他联合学习框架，同时，根据使用情况或目标数据集，它为能够在准确性和沟通成本之间的权衡平衡。例如，要在具有10个客户的非IID CIFAR-10数据集上训练Alexnet模型，FedD3可以通过相似的通信量增加准确性超过71％，或者节省98％的通信量，同时达到相同的准确性与其他联合学习方法相比。