人眼目光在传递信息，交流意图和理解他人的心理状态方面起着重要作用。先前的研究表明，机器人的目光也会影响人类在互动过程中的决策和策略。然而，有限的研究已经在人类机器人相互作用方案中培训了针对基于凝视数据的人形机器人。考虑到凝视会影响社会交流的自然性并改变了观察者的决策过程，应将其视为人类机器人互动中的关键组成部分。为了研究机器人凝视对人类的影响，我们提出了一种体现的神经模型，用于进行类似人类的凝视转移。这是通过扩展社会关注模型并在吸引人的数据上训练它来实现的，该数据通过观看人类玩游戏而收集。我们将比较在人类合作游戏中采用不同凝视策略的机器人面前的人类行为表现。

可解释的人工智能的最新发展有望改变人类机器人互动的潜力：机器人决策的解释可能会影响用户的看法，证明其可靠性并提高信任。但是，尚未对解释其决定的机器人看法的影响进行彻底研究。为了分析可解释的机器人的效果，我们进行了一项研究，其中两个模拟机器人可以玩竞争性棋盘游戏。当一个机器人解释其动作时，另一个机器人只宣布它们。提供有关其行为的解释不足以改变机器人的感知能力，智力，可爱性或安全等级。但是，结果表明，解释其动作的机器人被认为是更活泼和人类的。这项研究证明了对可解释的人类机器人相互作用的必要性和潜力，以及对其效应作为新的研究方向的更广泛评估。

由于Covid-19大流行，机器人可以被视为任务中的潜在资源，如帮助人们从远程工作，维持社会疏散和改善精神或身体健康。为了提高人机互动，通过在复杂的真实环境中处理多个社会线索，机器人必须变得更加社交。我们的研究采用了凝视触发的视听跨透视整合的神经毒性范例，使ICUB机器人表达人类的社会关注反应。起初，在37名人体参与者进行行为实验。为了提高生态有效性，设计了一个具有三个蒙面动画头像的圆桌会议场景，其中包括能够进行凝视偏移的中间的一个，以及能够产生声音的其他两个。凝视方向和声音位置是一致或不一致的。掩模用于覆盖除了头像之外的所有面部视觉线索。我们观察到，阿凡达的目光可以在视听通道条件下具有更好的人类性能来引发跨型社会关注，而不是在不一致状态。然后，我们的计算模型，喘气，培训，以实现社会提示检测，视听显着性预测和选择性关注。在完成模型培训之后，ICUB机器人被暴露于与人类参与者相似的实验室条件，表明它可以将类似的关注响应作为人类的同时性和不协调性表现进行复制，而人类表现仍然优越。因此，这种跨学科工作提供了对跨型社会关注机制的新见解以及如何在复杂环境中为机器人建模的机制。

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.

Industry 4.0 aims to optimize the manufacturing environment by leveraging new technological advances, such as new sensing capabilities and artificial intelligence. The DRAEM technique has shown state-of-the-art performance for unsupervised classification. The ability to create anomaly maps highlighting areas where defects probably lie can be leveraged to provide cues to supervised classification models and enhance their performance. Our research shows that the best performance is achieved when training a defect detection model by providing an image and the corresponding anomaly map as input. Furthermore, such a setting provides consistent performance when framing the defect detection as a binary or multiclass classification problem and is not affected by class balancing policies. We performed the experiments on three datasets with real-world data provided by Philips Consumer Lifestyle BV.