在本文中，我们对数值模拟的加速感兴趣。我们专注于高超音速行星再入问题，该问题涉及耦合流体动力学和化学反应。模拟化学反应需要大部分计算时间，但另一方面，无法避免获得准确的预测。我们面临成本效率和准确性之间的权衡：模拟代码必须足够有效地在操作环境中使用，但必须足够准确，以忠实地预测现象。为了解决这个权衡，我们设计了一个混合模拟代码，将传统的流体动态求解器与近似化学反应的神经网络耦合。当在大数据上下文中应用以及它们源于其矩阵矢量结构的效率时，我们依靠它们的力量来实现重要的加速因子（$ \ tims 10 $至$ \ times 18.6 $）。本文旨在解释我们如何在实践中设计这种具有成本效益的混合模拟代码。最重要的是，我们描述了确保准确性保证的方法论，使我们能够超越传统的替代建模，并将这些代码用作参考。

通过神经网络解决新的机器学习问题总是意味着优化众多的超参数，以定义其结构并强烈影响其性能。在这项工作中，我们研究了基于希尔伯特·史克米特独立标准（HSIC）的面向目标灵敏度分析的使用，用于超参数分析和优化。超参数生活在通常复杂而尴尬的空间中。它们可以具有不同的本质（分类，离散，布尔，连续），相互作用并具有相互依存关系。所有这些使得执行经典灵敏度分析是不平凡的。我们可以减轻这些困难，以获取能够量化超参数对神经网络的最终错误的相对影响的强大分析指数。这种有价值的工具使我们能够更好地理解超参数，并使超参数优化更容易解释。我们在超参数优化的背景下说明了这些知识的好处，并得出了一种基于HSIC的优化算法，我们将其应用于MNIST和CIFAR，经典的机器学习数据集，但也适用于Runge功能和Bateman方程解决方案，兴趣解决方案的近似值，用于科学的机器学习。该方法产生既有竞争力又具有成本效益的神经网络。

在神经网络对功能的监督学习的背景下，我们声称并经验证明，当数据集的分布集中在学习功能陡峭的区域时，神经网络会产生更好的结果。我们首先使用泰勒（Taylor）扩展以数学上可行的方式来欺骗这一假设，并根据要学习的功能的导数强调新的培训分布。然后，理论推导允许构建一种我们称为基于方差的样本加权（VBSW）的方法。VBSW使用标签局部差异来加权训练点。该方法是一般，可扩展的，具有成本效益的，并且可以显着提高大量神经网络的性能，以在图像，文本和多元数据上进行各种分类和回归任务。我们通过涉及从线性模型到重新NET和BERT的神经网络的实验来强调其优势。

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

Artificial neural networks can learn complex, salient data features to achieve a given task. On the opposite end of the spectrum, mathematically grounded methods such as topological data analysis allow users to design analysis pipelines fully aware of data constraints and symmetries. We introduce a class of persistence-based neural network layers. Persistence-based layers allow the users to easily inject knowledge about symmetries (equivariance) respected by the data, are equipped with learnable weights, and can be composed with state-of-the-art neural architectures.

Quantifying motion in 3D is important for studying the behavior of humans and other animals, but manual pose annotations are expensive and time-consuming to obtain. Self-supervised keypoint discovery is a promising strategy for estimating 3D poses without annotations. However, current keypoint discovery approaches commonly process single 2D views and do not operate in the 3D space. We propose a new method to perform self-supervised keypoint discovery in 3D from multi-view videos of behaving agents, without any keypoint or bounding box supervision in 2D or 3D. Our method uses an encoder-decoder architecture with a 3D volumetric heatmap, trained to reconstruct spatiotemporal differences across multiple views, in addition to joint length constraints on a learned 3D skeleton of the subject. In this way, we discover keypoints without requiring manual supervision in videos of humans and rats, demonstrating the potential of 3D keypoint discovery for studying behavior.

Artificial intelligence is set to be deployed in operating rooms to improve surgical care. This early-stage clinical evaluation shows the feasibility of concurrently attaining real-time, high-quality predictions from several deep neural networks for endoscopic video analysis deployed for assistance during three laparoscopic cholecystectomies.

AI-based code generators are an emerging solution for automatically writing programs starting from descriptions in natural language, by using deep neural networks (Neural Machine Translation, NMT). In particular, code generators have been used for ethical hacking and offensive security testing by generating proof-of-concept attacks. Unfortunately, the evaluation of code generators still faces several issues. The current practice uses automatic metrics, which compute the textual similarity of generated code with ground-truth references. However, it is not clear what metric to use, and which metric is most suitable for specific contexts. This practical experience report analyzes a large set of output similarity metrics on offensive code generators. We apply the metrics on two state-of-the-art NMT models using two datasets containing offensive assembly and Python code with their descriptions in the English language. We compare the estimates from the automatic metrics with human evaluation and provide practical insights into their strengths and limitations.

Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure. In this work, we propose to capture each of these aspects by modeling the surgical scene with a disentangled latent scene graph representation, which we can then process using a graph neural network. Unlike previous approaches using graph representations, we explicitly encode in our graphs semantic information such as object locations and shapes, class probabilities and visual features. We also incorporate an auxiliary image reconstruction objective to help train the latent graph representations. We demonstrate the value of these components through comprehensive ablation studies and achieve state-of-the-art results for critical view of safety prediction across multiple experimental settings.

Searching for a path between two nodes in a graph is one of the most well-studied and fundamental problems in computer science. In numerous domains such as robotics, AI, or biology, practitioners develop search heuristics to accelerate their pathfinding algorithms. However, it is a laborious and complex process to hand-design heuristics based on the problem and the structure of a given use case. Here we present PHIL (Path Heuristic with Imitation Learning), a novel neural architecture and a training algorithm for discovering graph search and navigation heuristics from data by leveraging recent advances in imitation learning and graph representation learning. At training time, we aggregate datasets of search trajectories and ground-truth shortest path distances, which we use to train a specialized graph neural network-based heuristic function using backpropagation through steps of the pathfinding process. Our heuristic function learns graph embeddings useful for inferring node distances, runs in constant time independent of graph sizes, and can be easily incorporated in an algorithm such as A* at test time. Experiments show that PHIL reduces the number of explored nodes compared to state-of-the-art methods on benchmark datasets by 58.5\% on average, can be directly applied in diverse graphs ranging from biological networks to road networks, and allows for fast planning in time-critical robotics domains.