Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice. Machine-learning methods that estimate blood flow in individual patients could accelerate or replace CFD simulation to overcome these limitations. In this work, we consider the estimation of vector-valued quantities on the wall of three-dimensional geometric artery models. We employ group-equivariant graph convolution in an end-to-end SE(3)-equivariant neural network that operates directly on triangular surface meshes and makes efficient use of training data. We run experiments on a large dataset of synthetic coronary arteries and find that our method estimates directional wall shear stress (WSS) with an approximation error of 7.6% and normalised mean absolute error (NMAE) of 0.4% while up to two orders of magnitude faster than CFD. Furthermore, we show that our method is powerful enough to accurately predict transient, vector-valued WSS over the cardiac cycle while conditioned on a range of different inflow boundary conditions. These results demonstrate the potential of our proposed method as a plugin replacement for CFD in the personalised prediction of hemodynamic vector and scalar fields.

因果表示学习是识别基本因果变量及其从高维观察（例如图像）中的关系的任务。最近的工作表明，可以从观测的时间序列中重建因果变量，假设它们之间没有瞬时因果关系。但是，在实际应用中，我们的测量或帧速率可能比许多因果效应要慢。这有效地产生了“瞬时”效果，并使以前的可识别性结果无效。为了解决这个问题，我们提出了ICITRI，这是一种因果表示学习方法，当具有已知干预目标的完美干预措施时，可以在时间序列中处理瞬时效应。 Icitris从时间观察中识别因果因素，同时使用可区分的因果发现方法来学习其因果图。在三个视频数据集的实验中，Icitris准确地识别了因果因素及其因果图。

以对象为中心的表示是人类感知的基础，并使我们能够对世界进行推理，并系统地推广到新的环境。当前，大多数在无监督的对象发现上的作品集中在基于插槽的方法上，这些方法明确将单个对象的潜在表示分开。尽管结果很容易解释，但通常需要设计相关建筑的设计。与此相反，我们提出了一种相对简单的方法 - 复杂的自动编码器（CAE） - 创建分布式以对象为中心的表示。遵循对生物神经元中对象表示为基础的编码方案，其复杂值激活表示两个消息：它们的幅度表达了特征的存在，而神经元之间的相对相位差异应绑定在一起以创建关节对象表示。 。与以前使用复杂值激活进行对象发现的方法相反，我们提出了一种完全无监督的方法，该方法是端到端训练的 - 导致了性能和效率的显着提高。此外，我们表明，与最新的基于最新的插槽方法相比，CAE在简单的多对象数据集上实现了竞争性或更好的无监督对象发现性能，同时训练的速度要快100倍。

从视觉观察中了解动态系统的潜在因果因素被认为是对复杂环境中推理的推理的关键步骤。在本文中，我们提出了Citris，这是一种变异自动编码器框架，从图像的时间序列中学习因果表示，其中潜在的因果因素可能已被干预。与最近的文献相反，Citris利用了时间性和观察干预目标，以鉴定标量和多维因果因素，例如3D旋转角度。此外，通过引入归一化流，可以轻松扩展柑橘，以利用和删除已验证的自动编码器获得的删除表示形式。在标量因果因素上扩展了先前的结果，我们在更一般的环境中证明了可识别性，其中仅因果因素的某些成分受干预措施影响。在对3D渲染图像序列的实验中，柑橘类似于恢复基本因果变量的先前方法。此外，使用预验证的自动编码器，Citris甚至可以概括为因果因素的实例化，从而在SIM到现实的概括中开放了未来的研究领域，以进行因果关系学习。

计算流体动力学（CFD）是一种有价值的工具，用于动脉中血流动力学的个性化，非侵入性评估，但其复杂性和耗时的大自然在实践中禁止大规模使用。最近，已经研究了利用深度学习进行CFD参数的快速估计，如表面网格上的壁剪切应力（WSS）。然而，现有方法通常取决于表面网格的手工制作的重新参数化以匹配卷积神经网络架构。在这项工作中，我们建议使用Mesh卷积神经网络，该网状神经网络直接在CFD中使用的相同的有限元表面网格操作。我们在使用从CFD模拟中获得的地面真理培训并在两种合成冠状动脉模型的两种数据集上培训和评估我们的方法。我们表明我们灵活的深度学习模型可以准确地预测该表面网上的3D WSS矢量。我们的方法在少于5分钟内处理新网格，始终如一地实现$ \ LEQ $ 1.6 [％]的标准化平均值误差，并且在保持测试集中的90.5 [％]中位近似精度为90.5 [％]的峰值，比较以前发表的工作。这证明了CFD代理建模的可行性，使用网状卷积神经网络进行动脉模型中的血流动力学参数估计。

Humans form mental images of 3D scenes to support counterfactual imagination, planning, and motor control. Our abilities to predict the appearance and affordance of the scene from previously unobserved viewpoints aid us in performing manipulation tasks (e.g., 6-DoF kitting) with a level of ease that is currently out of reach for existing robot learning frameworks. In this work, we aim to build artificial systems that can analogously plan actions on top of imagined images. To this end, we introduce Mental Imagery for Robotic Affordances (MIRA), an action reasoning framework that optimizes actions with novel-view synthesis and affordance prediction in the loop. Given a set of 2D RGB images, MIRA builds a consistent 3D scene representation, through which we synthesize novel orthographic views amenable to pixel-wise affordances prediction for action optimization. We illustrate how this optimization process enables us to generalize to unseen out-of-plane rotations for 6-DoF robotic manipulation tasks given a limited number of demonstrations, paving the way toward machines that autonomously learn to understand the world around them for planning actions.

Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc. However, some recent publications have claimed that domain mismatch prevents cross-lingual transfer, and their results show that unsupervised bilingual lexicon induction (UBLI) and unsupervised neural machine translation (UNMT) do not work well when the underlying monolingual corpora come from different domains (e.g., French text from Wikipedia but English text from UN proceedings). In this work, we show that a simple initialization regimen can overcome much of the effect of domain mismatch in cross-lingual transfer. We pre-train word and contextual embeddings on the concatenated domain-mismatched corpora, and use these as initializations for three tasks: MUSE UBLI, UN Parallel UNMT, and the SemEval 2017 cross-lingual word similarity task. In all cases, our results challenge the conclusions of prior work by showing that proper initialization can recover a large portion of the losses incurred by domain mismatch.

Learning image representations using synthetic data allows training neural networks without some of the concerns associated with real images, such as privacy and bias. Existing work focuses on a handful of curated generative processes which require expert knowledge to design, making it hard to scale up. To overcome this, we propose training with a large dataset of twenty-one thousand programs, each one generating a diverse set of synthetic images. These programs are short code snippets, which are easy to modify and fast to execute using OpenGL. The proposed dataset can be used for both supervised and unsupervised representation learning, and reduces the gap between pre-training with real and procedurally generated images by 38%.

Asymmetrical distance structures (quasimetrics) are ubiquitous in our lives and are gaining more attention in machine learning applications. Imposing such quasimetric structures in model representations has been shown to improve many tasks, including reinforcement learning (RL) and causal relation learning. In this work, we present four desirable properties in such quasimetric models, and show how prior works fail at them. We propose Interval Quasimetric Embedding (IQE), which is designed to satisfy all four criteria. On three quasimetric learning experiments, IQEs show strong approximation and generalization abilities, leading to better performance and improved efficiency over prior methods. Project Page: https://www.tongzhouwang.info/interval_quasimetric_embedding Quasimetric Learning Code Package: https://www.github.com/quasimetric-learning/torch-quasimetric

Federated Learning (FL) is a scheme for collaboratively training Deep Neural Networks (DNNs) with multiple data sources from different clients. Instead of sharing the data, each client trains the model locally, resulting in improved privacy. However, recently so-called targeted poisoning attacks have been proposed that allow individual clients to inject a backdoor into the trained model. Existing defenses against these backdoor attacks either rely on techniques like Differential Privacy to mitigate the backdoor, or analyze the weights of the individual models and apply outlier detection methods that restricts these defenses to certain data distributions. However, adding noise to the models' parameters or excluding benign outliers might also reduce the accuracy of the collaboratively trained model. Additionally, allowing the server to inspect the clients' models creates a privacy risk due to existing knowledge extraction methods. We propose CrowdGuard, a model filtering defense, that mitigates backdoor attacks by leveraging the clients' data to analyze the individual models before the aggregation. To prevent data leaks, the server sends the individual models to secure enclaves, running in client-located Trusted Execution Environments. To effectively distinguish benign and poisoned models, even if the data of different clients are not independently and identically distributed (non-IID), we introduce a novel metric called HLBIM to analyze the outputs of the DNN's hidden layers. We show that the applied significance-based detection algorithm combined can effectively detect poisoned models, even in non-IID scenarios. We show in our extensive evaluation that CrowdGuard can effectively mitigate targeted poisoning attacks and achieve in various scenarios a True-Positive-Rate of 100% and a True-Negative-Rate of 100%.