We provide a brief, and inevitably incomplete overview of the use of Machine Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology. Astronomy entered the big data era with the first digital sky surveys in the early 1990s and the resulting Terascale data sets, which required automating of many data processing and analysis tasks, for example the star-galaxy separation, with billions of feature vectors in hundreds of dimensions. The exponential data growth continued, with the rise of synoptic sky surveys and the Time Domain Astronomy, with the resulting Petascale data streams and the need for a real-time processing, classification, and decision making. A broad variety of classification and clustering methods have been applied for these tasks, and this remains a very active area of research. Over the past decade we have seen an exponential growth of the astronomical literature involving a variety of ML/AI applications of an ever increasing complexity and sophistication. ML and AI are now a standard part of the astronomical toolkit. As the data complexity continues to increase, we anticipate further advances leading towards a collaborative human-AI discovery.

超越地球轨道的人类空间勘探将涉及大量距离和持续时间的任务。为了有效减轻无数空间健康危害，数据和空间健康系统的范式转移是实现地球独立性的，而不是Earth-Reliance所必需的。有希望在生物学和健康的人工智能和机器学习领域的发展可以解决这些需求。我们提出了一个适当的自主和智能精密空间健康系统，可以监控，汇总和评估生物医学状态;分析和预测个性化不良健康结果;适应并响应新累积的数据;并提供对其船员医务人员的个人深度空间机组人员和迭代决策支持的预防性，可操作和及时的见解。在这里，我们介绍了美国国家航空航天局组织的研讨会的建议摘要，以便在太空生物学和健康中未来的人工智能应用。在未来十年，生物监测技术，生物标志科学，航天器硬件，智能软件和简化的数据管理必须成熟，并编织成精确的空间健康系统，以使人类在深空中茁壮成长。

空间生物学研究旨在了解太空飞行对生物的根本影响，制定支持深度空间探索的基础知识，最终生物工程航天器和栖息地稳定植物，农作物，微生物，动物和人类的生态系统，为持续的多行星寿命稳定。要提高这些目标，该领域利用了来自星空和地下模拟研究的实验，平台，数据和模型生物。由于研究扩展到低地球轨道之外，实验和平台必须是最大自主，光，敏捷和智能化，以加快知识发现。在这里，我们介绍了由美国国家航空航天局的人工智能，机器学习和建模应用程序组织的研讨会的建议摘要，这些应用程序为这些空间生物学挑战提供了关键解决方案。在未来十年中，将人工智能融入太空生物学领域将深化天空效应的生物学理解，促进预测性建模和分析，支持最大自主和可重复的实验，并有效地管理星载数据和元数据，所有目标使生活能够在深空中茁壮成长。

口腔上皮发育不良（OED）是对口腔的病变给出的恶性肿瘤性组织病理学诊断。预测OED等级或情况是否将转型给恶性肿瘤对于早期检测和适当的治疗至关重要。 OED通常从上皮的下三分之一开始，然后以等级的严重程度向上逐步开始，因此我们提出了分割上皮层，除了单独的细胞核之外，还可以使研究人员能够评估级别/恶性预测的重要层种形态特征。我们呈现悬停网+，深度学习框架，以同时分段（和分类）核和（内部）在H＆E染色的载玻片中的核和（内）上皮层。所提出的架构由编码器分支和四个解码器分支组成，用于同时对上皮层的核和语义分割的同时分段。我们表明，拟议的模型在两个任务中实现了最先进的（SOTA）性能，而与每个任务的先前的SOTA方法相比，没有额外的成本。据我们所知，我们的是同时核实例分割和语义组织分割的第一种方法，具有用于其他类似同时任务的计算病理和对恶性预测的研究。

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variability across labs and inherent problems of the data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by the Atrous Spatial Pyramid Pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: i) background, ii) heart muscle, iii) blood and iv) scar areas. New models were compared with state-of-art models and manual quantification. Our models showed favorable performance in global segmentation and scar tissue detection relative to state-of-the-art work, including a four-fold better performance in matching scar pixels to contours produced by clinicians.

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

In recent years, reinforcement learning (RL) has become increasingly successful in its application to science and the process of scientific discovery in general. However, while RL algorithms learn to solve increasingly complex problems, interpreting the solutions they provide becomes ever more challenging. In this work, we gain insights into an RL agent's learned behavior through a post-hoc analysis based on sequence mining and clustering. Specifically, frequent and compact subroutines, used by the agent to solve a given task, are distilled as gadgets and then grouped by various metrics. This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm. We demonstrate our method by applying it to two quantum-inspired RL environments. First, we consider simulated quantum optics experiments for the design of high-dimensional multipartite entangled states where the algorithm finds gadgets that correspond to modern interferometer setups. Second, we consider a circuit-based quantum computing environment where the algorithm discovers various gadgets for quantum information processing, such as quantum teleportation. This approach for analyzing the policy of a learned agent is agent and environment agnostic and can yield interesting insights into any agent's policy.

The availability of frequent and cost-free satellite images is in growing demand in the research world. Such satellite constellations as Landsat 8 and Sentinel-2 provide a massive amount of valuable data daily. However, the discrepancy in the sensors' characteristics of these satellites makes it senseless to use a segmentation model trained on either dataset and applied to another, which is why domain adaptation techniques have recently become an active research area in remote sensing. In this paper, an experiment of domain adaptation through style-transferring is conducted using the HRSemI2I model to narrow the sensor discrepancy between Landsat 8 and Sentinel-2. This paper's main contribution is analyzing the expediency of that approach by comparing the results of segmentation using domain-adapted images with those without adaptation. The HRSemI2I model, adjusted to work with 6-band imagery, shows significant intersection-over-union performance improvement for both mean and per class metrics. A second contribution is providing different schemes of generalization between two label schemes - NALCMS 2015 and CORINE. The first scheme is standardization through higher-level land cover classes, and the second is through harmonization validation in the field.

Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body. Mouthguards are used to measure head kinematics during impacts in traumatic brain injury (TBI) studies, but deviations from reference kinematics can still occur due to potential looseness. In this study, deep learning is used to compensate for the imperfect interface and improve measurement accuracy. A set of one-dimensional convolutional neural network (1D-CNN) models was developed to denoise mouthguard kinematics measurements along three spatial axes of linear acceleration and angular velocity. The denoised kinematics had significantly reduced errors compared to reference kinematics, and reduced errors in brain injury criteria and tissue strain and strain rate calculated via finite element modeling. The 1D-CNN models were also tested on an on-field dataset of college football impacts and a post-mortem human subject dataset, with similar denoising effects observed. The models can be used to improve detection of head impacts and TBI risk evaluation, and potentially extended to other sensors measuring kinematics.