经典的Weisfeiler-Leman算法（又称颜色的细化是图形学习的基础，对于成功的图形内核和图形神经网络至关重要。该算法最初是用于图形同构测试的，它迭代地完善了顶点颜色。在许多数据集中，经过一些迭代后，可以达到稳定的着色，并且机器学习任务的最佳迭代数量通常更低。这表明颜色差异太快，定义了一个太粗糙的相似性。我们概括了颜色改进的概念，并提出了一个逐步邻里改进的框架，该框架使收敛较慢，从而提供了更细粒度的完善层次结构和顶点相似性。我们通过聚类顶点邻域来分配新颜色，从而替换原始的注射颜色分配功能。我们的方法用于得出现有图形内核的新变体，并通过有关顶点相似性的最佳分配来近似图表编辑距离。我们表明，在这两个任务中，我们的方法的表现都优于原始颜色的细化，只有在运行时间中逐渐增加，才能提高最新技术状态。

近年来，基于Weisfeiler-Leman算法的算法和神经架构，是一个众所周知的Graph同构问题的启发式问题，它成为具有图形和关系数据的机器学习的强大工具。在这里，我们全面概述了机器学习设置中的算法的使用，专注于监督的制度。我们讨论了理论背景，展示了如何将其用于监督的图形和节点表示学习，讨论最近的扩展，并概述算法的连接（置换 - ）方面的神经结构。此外，我们概述了当前的应用和未来方向，以刺激进一步的研究。

近年来，基于Weisfeiler-Leman算法的算法和神经架构，是图形同构的着名启发式问题，它被成为具有图形和关系数据的（监督）机器学习的强大工具。在这里，我们全面概述了机器学习设置中的算法使用。我们讨论了理论背景，展示了如何将其用于监督的图形和节点分类，讨论最近的扩展，以及其与神经结构的连接。此外，我们概述了当前的应用和未来方向，以刺激研究。

Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark datasets and standardized evaluation procedures is lagging, consequently hindering advancements in this area. To address this, we introduce the TUDATASET for graph classification and regression. The collection consists of over 120 datasets of varying sizes from a wide range of applications. We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools. Here, we give an overview of the datasets, standardized evaluation procedures, and provide baseline experiments. All datasets are available at www.graphlearning.io. The experiments are fully reproducible from the code available at www.github.com/chrsmrrs/tudataset.

成功的药物开发的主要障碍是临床试验的复杂性，成本和规模。临床试验数据的详细内部结构可以使常规优化难以实现。最近的机器学习进步，具体说明性结构的数据分析，有可能在改善临床试验设计方面取得重大进展。 TrimeGraph旨在应用这些方法，为开发模型的概念证明框架，可以帮助药物开发和益处患者。在这项工作中，我们首先介绍从CT.Gov，AACT和FISTTROVE数据库编译的策划临床试验数据集（n = 1191试验;代表一百万名患者）并将该数据转换为图形结构格式。然后，我们详细介绍了一系列图形机学习算法的数学依据和实现，其通常在嵌入在低维特征空间中的图形数据上使用标准机器分类器。我们培训了这些模型，以预测临床试验的副作用信息给出关于疾病，现有的医疗病症和治疗的信息。 Metapath2Vec算法表现良好，具有标准的逻辑回归，决策树，随机森林，支持向量和神经网络分类器，以及分别显示0.85,0.68,0.86,0.80和0.77的典型Roc-Auc谱分别。值得注意的是，当在等效的阵列结构数据上训练时，最好的执行分类器只能产生0.70的典型的Roc-Auc得分。我们的工作表明，图形建模可以显着提高适当的数据集上的预测准确性。改进建模假设和更多数据类型的项目的连续版本可以产生具有现实世界的药物开发应用的优秀预测因子。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

We present the interpretable meta neural ordinary differential equation (iMODE) method to rapidly learn generalizable (i.e., not parameter-specific) dynamics from trajectories of multiple dynamical systems that vary in their physical parameters. The iMODE method learns meta-knowledge, the functional variations of the force field of dynamical system instances without knowing the physical parameters, by adopting a bi-level optimization framework: an outer level capturing the common force field form among studied dynamical system instances and an inner level adapting to individual system instances. A priori physical knowledge can be conveniently embedded in the neural network architecture as inductive bias, such as conservative force field and Euclidean symmetry. With the learned meta-knowledge, iMODE can model an unseen system within seconds, and inversely reveal knowledge on the physical parameters of a system, or as a Neural Gauge to "measure" the physical parameters of an unseen system with observed trajectories. We test the validity of the iMODE method on bistable, double pendulum, Van der Pol, Slinky, and reaction-diffusion systems.

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.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

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/ .