人类活动识别是计算机视觉中的新出现和重要领域，旨在确定个体或个体正在执行的活动。该领域的应用包括从体育中生成重点视频到智能监视和手势识别。大多数活动识别系统依赖于卷积神经网络（CNN）的组合来从数据和复发性神经网络（RNN）中进行特征提取来确定数据的时间依赖性。本文提出并设计了两个用于人类活动识别的变压器神经网络：一个经常性变压器（RET），这是一个专门的神经网络，用于对数据序列进行预测，以及视觉变压器（VIT），一种用于提取显着的变压器的变压器（VIT）图像的特征，以提高活动识别的速度和可扩展性。我们在速度和准确性方面提供了对拟议的变压器神经网络与现代CNN和基于RNN的人类活动识别模型的广泛比较。

可解释的深度学习模型的最新努力表明，基于概念的解释方法通过标准的端到端模型实现了竞争精度，并能够从图像中提取高级视觉概念的推理和干预，例如识别机翼颜色和喙长度用于鸟类分类。但是，这些概念瓶颈模型依赖于一组必要且充分的预定义概念，这对于诸如视频分类等复杂任务很棘手。对于复杂的任务，标签和视觉元素之间的关系涵盖了许多框架，例如，识别出具有各种抽象水平的鸟类飞行或捕获猎物不必要的概念。为此，我们提出了Codex，这是一个自动概念发现和提取模块，严格地构成了基于概念的视频分类的必要且充分的概念摘要集。 Codex从自然语言解释视频解释中确定了一系列复杂的概念摘要，从而需要预先定义一组无定形的概念集。为了证明我们的方法的生存能力，我们构建了两个新的公共数据集，这些数据集将现有的复杂视频分类数据集与其标签的简短，众包的自然语言解释相结合。我们的方法在自然语言中引发了固有的复杂概念摘要，以将概念 - 底层方法推广到复杂的任务。

人工智能（AI）技术越来越拓宽政府和公共部门，如权力和能源 - 这是大多数社会行动的关键基础设施。但是，由于可靠性，责任和解释性的要求，直接将基于AI的方法应用于电力系统，因此由于社会无法承担级联故障和大规模停电，这是风险的，这可能很容易花费数十亿美元。为了满足社会要求，本文提出了一种方法来开发，部署和评估能源部门的AI系统：（1）了解物理系统测量，（2）设计AI算法预测需要，（3）开发强大和责任的AI方法，（4）创建可靠的措施来评估AI模型的性能。目标是为能源公用事业用户提供高度的信心。出于说明目的，本文用作电力系统事件预测（PEF）作为示例，该示例仔细分析了通过量量测量单元（PMU）测量的同步素图案。这种物理理解导致数据驱动的框架，其减少了与物理学的维度，并以高可信度预测事件。具体地，对于维度降低，机器学习将物理信息从不同的尺寸排列，从而产生低效的信息提取。对于活动预测，监督学习模型融合了不同模型的结果，以增加信心。最后，与其他最先进的机器学习方法相比，综合实验表明了高精度，效率和可靠性。

本文评估并提出了采用，展望和维护人工智能（AI）系统的疑虑。虽然AI社区取得了迅速的进步，但在证明AI系统方面存在挑战。使用设计和操作测试和评估的程序，有机会确定性能范围以管理预期使用的期望。呈现图像数据融合的一个名字用例，以支持考虑精度与距离的AI对象识别认证性。

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

In this paper we take the first steps in studying a new approach to synthesis of efficient communication schemes in multi-agent systems, trained via reinforcement learning. We combine symbolic methods with machine learning, in what is referred to as a neuro-symbolic system. The agents are not restricted to only use initial primitives: reinforcement learning is interleaved with steps to extend the current language with novel higher-level concepts, allowing generalisation and more informative communication via shorter messages. We demonstrate that this approach allow agents to converge more quickly on a small collaborative construction task.

High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, extracting representative features from high content images that can capture subtle nuances in phenotypes remains challenging. The lack of high-quality labels makes it difficult to achieve satisfactory results with supervised deep learning. Self-Supervised learning methods, which learn from automatically generated labels has shown great success on natural images, offer an attractive alternative also to microscopy images. However, we find that self-supervised learning techniques underperform on high content imaging assays. One challenge is the undesirable domain shifts present in the data known as batch effects, which may be caused by biological noise or uncontrolled experimental conditions. To this end, we introduce Cross-Domain Consistency Learning (CDCL), a novel approach that is able to learn in the presence of batch effects. CDCL enforces the learning of biological similarities while disregarding undesirable batch-specific signals, which leads to more useful and versatile representations. These features are organised according to their morphological changes and are more useful for downstream tasks - such as distinguishing treatments and mode of action.

Objective: Imbalances of the electrolyte concentration levels in the body can lead to catastrophic consequences, but accurate and accessible measurements could improve patient outcomes. While blood tests provide accurate measurements, they are invasive and the laboratory analysis can be slow or inaccessible. In contrast, an electrocardiogram (ECG) is a widely adopted tool which is quick and simple to acquire. However, the problem of estimating continuous electrolyte concentrations directly from ECGs is not well-studied. We therefore investigate if regression methods can be used for accurate ECG-based prediction of electrolyte concentrations. Methods: We explore the use of deep neural networks (DNNs) for this task. We analyze the regression performance across four electrolytes, utilizing a novel dataset containing over 290000 ECGs. For improved understanding, we also study the full spectrum from continuous predictions to binary classification of extreme concentration levels. To enhance clinical usefulness, we finally extend to a probabilistic regression approach and evaluate different uncertainty estimates. Results: We find that the performance varies significantly between different electrolytes, which is clinically justified in the interplay of electrolytes and their manifestation in the ECG. We also compare the regression accuracy with that of traditional machine learning models, demonstrating superior performance of DNNs. Conclusion: Discretization can lead to good classification performance, but does not help solve the original problem of predicting continuous concentration levels. While probabilistic regression demonstrates potential practical usefulness, the uncertainty estimates are not particularly well-calibrated. Significance: Our study is a first step towards accurate and reliable ECG-based prediction of electrolyte concentration levels.

Inductive reasoning is a core component of human intelligence. In the past research of inductive reasoning within computer science, logic language is used as representations of knowledge (facts and rules, more specifically). However, logic language can cause systematic problems for inductive reasoning such as disability of handling raw input such as natural language, sensitiveness to mislabeled data, and incapacity to handle ambiguous input. To this end, we propose a new task, which is to induce natural language rules from natural language facts, and create a dataset termed DEER containing 1.2k rule-fact pairs for the task, where rules and facts are written in natural language. New automatic metrics are also proposed and analysed for the evaluation of this task. With DEER, we investigate a modern approach for inductive reasoning where we use natural language as representation for knowledge instead of logic language and use pretrained language models as ''reasoners''. Moreover, we provide the first and comprehensive analysis of how well pretrained language models can induce natural language rules from natural language facts. We also propose a new framework drawing insights from philosophy literature for this task, which we show in the experiment section that surpasses baselines in both automatic and human evaluations.