光环伴形培养基中的离子气体通过热阳光阳光层（TSZ）效应在宇宙微波背景上留下烙印。来自活性银河核（AGN）和超新星的反馈会影响晕孔集成TSZ通量的测量（$ y_ \ mathrm {sz} $），并导致其与光晕质量的关系（$ y_ \ mathrm {sz} -mm $ ）偏离病毒定理的自相似幂律预测。我们对使用骆驼，一套流体动力模拟的套件进行了全面研究，反馈处方的差异很大。我们使用两个机器学习工具（随机森林和符号回归）的组合来搜索$ y-m $关系的类似物，这对低质量的反馈过程（$ m \ sillesim 10^{14} \，h^， {-1} \，m_ \ odot $）;我们发现，仅替换$ y \ rightarrow y（1+m _*/m_ \ mathrm {gas}）$在关系中使其非常相似。这可以用作低质量簇和星系组的强大多波长质量代理。我们的方法通常对于提高其他天体分级关系的有效性领域通常也很有用。我们还预测，$ y-m $关系的测量值可以在反馈参数的某些组合和/或排除超级新闻和AGN反馈模型的主要部分，以提供百分比的约束。艺术流体动力模拟。我们的结果对于使用即将进行的SZ调查（例如SO，CMB-S4）和Galaxy Surveys（例如Desi和Rubin）来限制Baryonic反馈的性质。最后，我们发现，$ y-m _*$的另一种关系提供了有关反馈的补充信息，而不是$ y-m $。

复杂的系统（恒星，超新星，星系和群集）通常在可观察性质（例如，亮度，速度分散，振荡周期，温度）之间表现出低散射关系。这些缩放关系可以照亮底层物理，可以为估计质量和距离提供观测工具。机器学习可以在抽象的高维参数空间中寻找新的扩展关系（或对现有关系的简单扩展）提供系统的系统。我们使用称为符号回归（SR）的机器学习工具，该工具以分析方程的形式在给定的数据集中绘制模式。我们专注于Sunyaev-Zeldovich Flux $ - $群集质量关系（$ Y_ \ MATHRM {SZ} -M $），它会影响来自集群丰富数据的宇宙学参数的推断。使用SR对来自IllustrySTG流体动力学模拟的数据，我们找到了一个新的群集质量代理，它结合了$ Y_ \ MATHRM {SZ} $和电离气体的浓度（$ c_ \ mathrm {gas} $）：$ m \ propto y_ \ mathrm {ccon} ^ {3/5} \ Equiv y_ \ mathrm {sz} ^ {3/5}（1-a \，c_ \ mathrm {gas}）$。 $ y_ \ mathrm {coct} $减少预测$ m $的分散$ \ sim 20-30 $％的大型群集（$ m \ gtrsim 10 ^ {14} \，h ^ { - 1} \，m_ \ oott $）在高和低频的高频上，与使用只需$ y_ \ mathrm {sz} $相比。我们表明对$ C_ \ MATHRM {GARS} $的依赖性与展示比其郊区更大的分散的集群核心。最后，我们从骆驼项目的模拟中测试$ y_ \ mathrm {cenc} $ in clusters，并显示$ y_ \ mathrm {crc} $对宇宙学，天体物理学，划分物理学和宇宙方差的变化是稳健的。我们的结果和方法可以用于电流和即将到来的CMB和X射线调查的精确多波长簇质量估计，如ACT，所以，SPT，肌肉和CMB-S4。

制定了具有机器学习模拟（骆驼）项目的宇宙学和天体物理学，通过数千名宇宙的流体动力模拟和机器学习将宇宙学与天体物理学结合起来。骆驼包含4,233个宇宙学仿真，2,049个n-body和2,184个最先进的流体动力模拟，在参数空间中采样巨大的体积。在本文中，我们介绍了骆驼公共数据发布，描述了骆驼模拟的特性和由它们产生的各种数据产品，包括光环，次麦，银河系和空隙目录，功率谱，Bispectra，Lyman - $ \ Alpha $光谱，概率分布函数，光环径向轮廓和X射线光子列表。我们还释放了超过骆驼 - 山姆的数十亿个星系的目录：与Santa Cruz半分析模型相结合的大量N身体模拟。我们释放包含350多个Terabytes的所有数据，并包含143,922个快照，数百万光环，星系和摘要统计数据。我们提供有关如何访问，下载，读取和处理数据AT \ URL {https://camels.readthedocs.io}的进一步技术详细信息。

我们对托管银河系和andromeda星系的群众呈现出新的限制，并使用图形神经网络导出。我们的型号培训了骆驼项目的数千个最先进的流体动力模拟，仅利用属于晕圈的星系的位置，速度和恒星群体，并且能够对无似然推断进行无似的推理晕群，同时占宇宙学和天体物理的不确定性。我们的制约因素与其他传统方法的估计一致。

了解晕星连接是基本的，以提高我们对暗物质的性质和性质的知识。在这项工作中，我们构建一个模型，鉴于IT主机的星系的位置，速度，恒星群体和半径的位置。为了捕获来自星系属性的相关性及其相位空间的相关信息，我们使用图形神经网络（GNN），该网络设计用于使用不规则和稀疏数据。我们从宇宙学和天体物理学中培训了我们在Galaxies上的模型，从宇宙学和天体物理学与机器学习模拟（骆驼）项目。我们的模型，占宇宙学和天体物理的不确定性，能够用$ \ SIM 0.2欧元的准确度来限制晕群。此外，在一套模拟上培训的GNN能够在用利用不同的代码的模拟上进行测试时保留其精度的一部分精度。 GNN的Pytorch几何实现在HTTPS://github.com/pablovd/halographnet上公开可用于github上

The success of neural networks builds to a large extent on their ability to create internal knowledge representations from real-world high-dimensional data, such as images, sound, or text. Approaches to extract and present these representations, in order to explain the neural network's decisions, is an active and multifaceted research field. To gain a deeper understanding of a central aspect of this field, we have performed a targeted review focusing on research that aims to associate internal representations with human understandable concepts. In doing this, we added a perspective on the existing research by using primarily deductive nomological explanations as a proposed taxonomy. We find this taxonomy and theories of causality, useful for understanding what can be expected, and not expected, from neural network explanations. The analysis additionally uncovers an ambiguity in the reviewed literature related to the goal of model explainability; is it understanding the ML model or, is it actionable explanations useful in the deployment domain?

Human motion prediction is a complex task as it involves forecasting variables over time on a graph of connected sensors. This is especially true in the case of few-shot learning, where we strive to forecast motion sequences for previously unseen actions based on only a few examples. Despite this, almost all related approaches for few-shot motion prediction do not incorporate the underlying graph, while it is a common component in classical motion prediction. Furthermore, state-of-the-art methods for few-shot motion prediction are restricted to motion tasks with a fixed output space meaning these tasks are all limited to the same sensor graph. In this work, we propose to extend recent works on few-shot time-series forecasting with heterogeneous attributes with graph neural networks to introduce the first few-shot motion approach that explicitly incorporates the spatial graph while also generalizing across motion tasks with heterogeneous sensors. In our experiments on motion tasks with heterogeneous sensors, we demonstrate significant performance improvements with lifts from 10.4% up to 39.3% compared to best state-of-the-art models. Moreover, we show that our model can perform on par with the best approach so far when evaluating on tasks with a fixed output space while maintaining two magnitudes fewer parameters.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

This volume contains revised versions of the papers selected for the third volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.

Time series, sets of sequences in chronological order, are essential data in statistical research with many forecasting applications. Although recent performance in many Transformer-based models has been noticeable, long multi-horizon time series forecasting remains a very challenging task. Going beyond transformers in sequence translation and transduction research, we observe the effects of down-and-up samplings that can nudge temporal saliency patterns to emerge in time sequences. Motivated by the mentioned observation, in this paper, we propose a novel architecture, Temporal Saliency Detection (TSD), on top of the attention mechanism and apply it to multi-horizon time series prediction. We renovate the traditional encoder-decoder architecture by making as a series of deep convolutional blocks to work in tandem with the multi-head self-attention. The proposed TSD approach facilitates the multiresolution of saliency patterns upon condensed multi-heads, thus progressively enhancing complex time series forecasting. Experimental results illustrate that our proposed approach has significantly outperformed existing state-of-the-art methods across multiple standard benchmark datasets in many far-horizon forecasting settings. Overall, TSD achieves 31% and 46% relative improvement over the current state-of-the-art models in multivariate and univariate time series forecasting scenarios on standard benchmarks. The Git repository is available at https://github.com/duongtrung/time-series-temporal-saliency-patterns.