词汇语义和认知科学指出，负担能力（即对反对支持的行为）对于理解和代表名词和动词至关重要。但是，对这些语义特征的研究尚未与当前主导语言表示研究的“基础”模型集成。我们假设随着时间的推移，对象状态的预测建模将导致“免费”编码对象负担信息的表示。我们训练神经网络在模拟交互中预测对象的轨迹，并表明我们网络的潜在表示区分了观察到的和未观察到的负担。我们发现，使用空间数据集中的3D模拟训练的模型优于传统的2D计算机视觉模型，该模型训练了类似任务，并且在初步检查时，概念之间的差异与预期功能相对应（例如，滚动需要旋转）。我们的结果提出了一种方法，即可以将现代深度学习方法与词汇表达的传统语义概念融合在一起。

阿尔茨海默氏病（AD）是最常见的神经退行性疾病，具有最复杂的病原体之一，使有效且临床上可行的决策变得困难。这项研究的目的是开发一个新型的多模式深度学习框架，以帮助医疗专业人员进行AD诊断。我们提出了一个多模式的阿尔茨海默氏病诊断框架（MADDI），以准确检测成像，遗传和临床数据中的AD和轻度认知障碍（MCI）。 Maddi是新颖的，因为我们使用跨模式的注意力，它捕获了模态之间的相互作用 - 这种域中未探讨的方法。我们执行多级分类，这是一项艰巨的任务，考虑到MCI和AD之间的相似之处。我们与以前的最先进模型进行比较，评估注意力的重要性，并检查每种模式对模型性能的贡献。 Maddi在持有的测试集中对MCI，AD和控件进行了96.88％的精度分类。在检查不同注意力方案的贡献时，我们发现跨模式关注与自我注意力的组合表现出了最佳状态，并且模型中没有注意力层表现最差，而F1分数差异为7.9％。我们的实验强调了结构化临床数据的重要性，以帮助机器学习模型将其背景化和解释其余模式化。广泛的消融研究表明，未访问结构化临床信息的任何多模式混合物都遭受了明显的性能损失。这项研究证明了通过跨模式的注意组合多种输入方式的优点，以提供高度准确的AD诊断决策支持。

我们提出了一种以最小计算成本提高广泛检索模型的性能的框架。它利用由基本密度检索方法提取的预先提取的文档表示，并且涉及训练模型以共同评分每个查询的一组检索到的候选文档，同时在其他候选的上下文中暂时转换每个文档的表示。以及查询本身。当基于其与查询的相似性进行评分文档表示时，该模型因此意识到其“对等”文档的表示。我们表明，我们的方法导致基本方法的检索性能以及彼此隔离的评分候选文档进行了大量改善，如在一对培训环境中。至关重要的是，与基于伯特式编码器的术语交互重型器不同，它在运行时在任何第一阶段方法的顶部引发可忽略不计的计算开销，允许它与任何最先进的密集检索方法容易地结合。最后，同时考虑给定查询的一组候选文档，可以在检索中进行额外的有价值的功能，例如评分校准和减轻排名中的社会偏差。

分子特性预测的深度学习模型的研究主要集中在更好的图形神经网络（GNN）架构的发展。虽然新的GNN变体继续提高性能，但它们的修改共享一个常见的主题，即减轻其基本图形到图形的内在内在的问题。在这项工作中，我们研究了这些限制，并提出了一种新的分子表现，可以完全绕过GNN的需求。与变压器模型配对时，我们的固定尺寸随机表示超出了最先进的GNN模型的性能，并提供了一种可扩展性的路径。

我们介绍了445名人员和计算机生成的文件的新型语料库，包括约27,000个条款，用于语义条款类型和相干关系，允许人工和自然话语模式的细节比较。该语料库涵盖了正式和非正式的话语，并包含使用微调GPT-2生成的文件（Zellers等，2019）和GPT-3（棕色等，2020）。我们通过提供初步证据，展示该语料库的有用性，通过提供初步证据，以提供较少，更短，更频繁的通电话条款关系与计算机生成的叙述和论点的较低质量相关。

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。