我们研究图形神经网络（GNN）的节点分类任务，并在群体公平性（通过统计平等和均等机会衡量）之间建立联系，以及局部分类性，即连接节点的趋势具有相似的属性。这种分类性通常是由同质性诱导的，即相似特性的节点连接的趋势。同质性在社交网络中可能很常见，在社交网络中，系统性因素迫使个人进入具有敏感属性的社区。通过合成图，我们研究了本地发生的同质和公平预测之间的相互作用，发现并非所有节点邻居在这方面都相等 - 社区以敏感属性的一类类别为主导，通常会努力获得公平的治疗，尤其是在分化本地类别和敏感属性同质。在确定存在局部同质和公平之间的关系之后，我们研究了不公平的问题是否与应用的GNN模型的设计相关联。我们表明，通过采用能够处理拆卸组标签的异性GNN设计，与真实和合成数据集中的同质设计相比，可以将本地异性邻居中的群体公平提高25％。

我们研究了以模型为简单络合物的抽象拓扑空间支撑的处理信号的线性过滤器，可以解释为解释节点，边缘，三角形面的图形的概括等，以处理此类信号，我们开发了定义为Matrix polynomials的简单卷积过滤器下霍德·拉普拉斯人的下部和上部。首先，我们研究了这些过滤器的特性，并表明它们是线性和转移不变的，以及置换和定向等效的。这些过滤器也可以以低计算复杂性的分布式方式实现，因为它们仅涉及（多个回合）上层和下相邻简单之间的简单转移。其次，着眼于边缘流，我们研究了这些过滤器的频率响应，并研究了如何使用Hodge分类来描述梯度，卷曲和谐波频率。我们讨论了这些频率如何对应于霍德拉普拉斯（Hodge laplacian）的下部和上等耦合以及上的核心，并且可以通过我们的滤波器设计独立调整。第三，我们研究设计简单卷积过滤器并讨论其相对优势的不同程序。最后，我们在几种应用中证实了简单过滤器：提取简单信号的不同频率组件，以denoise边缘流量以及分析金融市场和交通网络。

我们提出了一种方法来检测经验观察到的轨迹的异常值，在由单层复杂模型的离散或离散化歧管上的轨迹中的轨迹中的轨迹。我们的方法类似于诸如扩散图和拉普拉斯特征模式的光谱嵌入，其构建与与低特征值相关的图拉普拉斯的特征向量的顶点嵌入。在这里，我们将轨迹视为在单一的复合物上定义的边缘流动矢量，图形的更高阶的概括，并使用单层复合物的霍奇1-laplacian来导出这些边缘流的嵌入。通过将轨迹向量投影到与小特征值相关联的霍奇特1-Laplacian的成熟空间，我们可以表征轨迹相对于复合物的同源性的行为，这对应于底层空间中的孔。这使我们能够根据简单的解释，低维统计来对轨迹进行分类。我们展示了这种技术如何单一突出的轨迹（拓扑地）与典型的轨迹相比不同，并说明了我们对合成和经验数据的方法的性能。

我们通过形式化节点标签的异质性（即连接的节点倾向于具有不同的标签）和GNN与对抗性攻击的稳健性来弥合图形神经网络（GNN）的两个研究方向。我们的理论和经验分析表明，对于同质图数据，有影响力的结构攻击始终导致同质性降低，而对于异性图数据，同质级别的变化取决于节点度。这些见解对防御对现实图形的攻击具有实际含义：我们推断出分离自我和邻居限制的汇总器，这是一种已确定的设计原则，可以显着改善异性图数据的预测，还可以为增强的鲁棒性提供稳健性gnns。我们的综合实验表明，与表现最好的未接种模型相比，GNN仅采用这种设计可以提高经验和可证明的鲁棒性。此外，与表现最佳的疫苗接种模型相比，这种设计与对抗性攻击的明确防御机制相结合，可提高稳健性，攻击性能在攻击下提高18.33％。

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural networks as well as a PyTorch implementation of molecular dynamics. An optional integration with PyTorch Lightning and the Hydra configuration framework powers a flexible command-line interface. This makes SchNetPack 2.0 easily extendable with custom code and ready for complex training task such as generation of 3d molecular structures.

Antrophonegic pressure (i.e. human influence) on the environment is one of the largest causes of the loss of biological diversity. Wilderness areas, in contrast, are home to undisturbed ecological processes. However, there is no biophysical definition of the term wilderness. Instead, wilderness is more of a philosophical or cultural concept and thus cannot be easily delineated or categorized in a technical manner. With this paper, (i) we introduce the task of wilderness mapping by means of machine learning applied to satellite imagery (ii) and publish MapInWild, a large-scale benchmark dataset curated for that task. MapInWild is a multi-modal dataset and comprises various geodata acquired and formed from a diverse set of Earth observation sensors. The dataset consists of 8144 images with a shape of 1920 x 1920 pixels and is approximately 350 GB in size. The images are weakly annotated with three classes derived from the World Database of Protected Areas - Strict Nature Reserves, Wilderness Areas, and National Parks. With the dataset, which shall serve as a testbed for developments in fields such as explainable machine learning and environmental remote sensing, we hope to contribute to a deepening of our understanding of the question "What makes nature wild?".

Synthetic data generation has recently gained widespread attention as a more reliable alternative to traditional data anonymization. The involved methods are originally developed for image synthesis. Hence, their application to the typically tabular and relational datasets from healthcare, finance and other industries is non-trivial. While substantial research has been devoted to the generation of realistic tabular datasets, the study of synthetic relational databases is still in its infancy. In this paper, we combine the variational autoencoder framework with graph neural networks to generate realistic synthetic relational databases. We then apply the obtained method to two publicly available databases in computational experiments. The results indicate that real databases' structures are accurately preserved in the resulting synthetic datasets, even for large datasets with advanced data types.