基于可穿戴传感器的人类动作识别（HAR）最近取得了杰出的成功。但是，基于可穿戴传感器的HAR的准确性仍然远远落后于基于视觉模式的系统（即RGB视频，骨架和深度）。多样化的输入方式可以提供互补的提示，从而提高HAR的准确性，但是如何利用基于可穿戴传感器的HAR的多模式数据的优势很少探索。当前，可穿戴设备（即智能手表）只能捕获有限的非视态模式数据。这阻碍了多模式HAR关联，因为它无法同时使用视觉和非视态模态数据。另一个主要挑战在于如何在有限的计算资源上有效地利用可穿戴设备上的多模式数据。在这项工作中，我们提出了一种新型的渐进骨骼到传感器知识蒸馏（PSKD）模型，该模型仅利用时间序列数据，即加速度计数据，从智能手表来解决基于可穿戴传感器的HAR问题。具体而言，我们使用来自教师（人类骨架序列）和学生（时间序列加速度计数据）模式的数据构建多个教师模型。此外，我们提出了一种有效的渐进学习计划，以消除教师和学生模型之间的绩效差距。我们还设计了一种称为自适应信心语义（ACS）的新型损失功能，以使学生模型可以自适应地选择其中一种教师模型或所需模拟的地面真实标签。为了证明我们提出的PSKD方法的有效性，我们对伯克利-MHAD，UTD-MHAD和MMACT数据集进行了广泛的实验。结果证实，与以前的基于单传感器的HAR方法相比，提出的PSKD方法具有竞争性能。

自动生物医学图像分析的领域至关重要地取决于算法验证的可靠和有意义的性能指标。但是，当前的度量使用通常是不明智的，并且不能反映基本的域名。在这里，我们提出了一个全面的框架，该框架指导研究人员以问题意识的方式选择绩效指标。具体而言，我们专注于生物医学图像分析问题，这些问题可以解释为图像，对象或像素级别的分类任务。该框架首先编译域兴趣 - 目标结构 - ，数据集和算法与输出问题相关的属性的属性与问题指纹相关，同时还将其映射到适当的问题类别，即图像级分类，语义分段，实例，实例细分或对象检测。然后，它指导用户选择和应用一组适当的验证指标的过程，同时使他们意识到与个人选择相关的潜在陷阱。在本文中，我们描述了指标重新加载推荐框架的当前状态，目的是从图像分析社区获得建设性的反馈。当前版本是在由60多个图像分析专家的国际联盟中开发的，将在社区驱动的优化之后公开作为用户友好的工具包提供。

针对组织病理学图像数据的临床决策支持主要侧重于强烈监督的注释，这提供了直观的解释性，但受专业表现的束缚。在这里，我们提出了一种可解释的癌症复发预测网络（Ecarenet），并表明没有强注释的端到端学习提供最先进的性能，而可以通过注意机制包括可解释性。在前列腺癌生存预测的用例上，使用14,479个图像和仅复发时间作为注释，我们在验证集中达到0.78的累积动态AUC，与专家病理学家（以及在单独测试中的AUC为0.77放）。我们的模型是良好的校准，输出生存曲线以及每位患者的风险分数和群体。利用多实例学习层的注意重量，我们表明恶性斑块对预测的影响较高，从而提供了对预测的直观解释。我们的代码可在www.github.com/imsb-uke/ecarenet上获得。

在神经形态计算中，人工突触提供了一种基于来自神经元的输入来设置的多重导电状态，类似于大脑。可能需要超出多重权重的突触的附加属性，并且可以取决于应用程序，需要需要从相同材料生成不同的突触行为。这里，我们基于使用磁隧道结和磁畴壁的磁性材料测量人造突触。通过在单个磁隧道结下面的畴壁轨道中制造光刻槽口，我们实现了4-5个稳定的电阻状态，可以使用自旋轨道扭矩电气可重复控制。我们分析几何形状对突触行为的影响，表明梯形装置具有高可控性的不对称性重量，而直线装置具有较高的随机性，但具有稳定的电阻水平。设备数据被输入到神经形态计算模拟器中以显示特定于应用程序突触函数的有用性。实施应用于流式的时尚 - MNIST数据的人工神经网络，我们表明梯形磁突出可以用作高效在线学习的元塑功能。为CiFar-100图像识别实施卷积神经网络，我们表明直流突触由于其电阻水平的稳定性而达到近乎理想的推理精度。这项工作显示多重磁突触是神经形态计算的可行技术，并为新兴人工突触技术提供设计指南。

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。

Making histopathology image classifiers robust to a wide range of real-world variability is a challenging task. Here, we describe a candidate deep learning solution for the Mitosis Domain Generalization Challenge 2022 (MIDOG) to address the problem of generalization for mitosis detection in images of hematoxylin-eosin-stained histology slides under high variability (scanner, tissue type and species variability). Our approach consists in training a rotation-invariant deep learning model using aggressive data augmentation with a training set enriched with hard negative examples and automatically selected negative examples from the unlabeled part of the challenge dataset. To optimize the performance of our models, we investigated a hard negative mining regime search procedure that lead us to train our best model using a subset of image patches representing 19.6% of our training partition of the challenge dataset. Our candidate model ensemble achieved a F1-score of .697 on the final test set after automated evaluation on the challenge platform, achieving the third best overall score in the MIDOG 2022 Challenge.

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

Real-life tools for decision-making in many critical domains are based on ranking results. With the increasing awareness of algorithmic fairness, recent works have presented measures for fairness in ranking. Many of those definitions consider the representation of different ``protected groups'', in the top-$k$ ranked items, for any reasonable $k$. Given the protected groups, confirming algorithmic fairness is a simple task. However, the groups' definitions may be unknown in advance. In this paper, we study the problem of detecting groups with biased representation in the top-$k$ ranked items, eliminating the need to pre-define protected groups. The number of such groups possible can be exponential, making the problem hard. We propose efficient search algorithms for two different fairness measures: global representation bounds, and proportional representation. Then we propose a method to explain the bias in the representations of groups utilizing the notion of Shapley values. We conclude with an experimental study, showing the scalability of our approach and demonstrating the usefulness of the proposed algorithms.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

Participants in political discourse employ rhetorical strategies -- such as hedging, attributions, or denials -- to display varying degrees of belief commitments to claims proposed by themselves or others. Traditionally, political scientists have studied these epistemic phenomena through labor-intensive manual content analysis. We propose to help automate such work through epistemic stance prediction, drawn from research in computational semantics, to distinguish at the clausal level what is asserted, denied, or only ambivalently suggested by the author or other mentioned entities (belief holders). We first develop a simple RoBERTa-based model for multi-source stance predictions that outperforms more complex state-of-the-art modeling. Then we demonstrate its novel application to political science by conducting a large-scale analysis of the Mass Market Manifestos corpus of U.S. political opinion books, where we characterize trends in cited belief holders -- respected allies and opposed bogeymen -- across U.S. political ideologies.