在本文中，我们提出了一种在动态环境中进行多动能计划的新方法。环境被表示为时间占用网格，它赋予了所有障碍的当前以及未来/预测状态。该方法基于以前的安全走廊生成和多旋转计划的工作，以避免移动和静态障碍。它首先生成了目标的全球途径，该途径没有考虑到环境的动态方面。然后，我们使用时间安全走廊来生成机器人将来可以在离散瞬间进入的安全空间。最后，我们在优化公式中使用了时间安全走廊，该公式说明了多电流动力学以及所有障碍，以生成由多旋翼控制器执行的轨迹。我们在模拟中显示了我们方法的性能。

在本文中，我们提出了一个新的框架，用于对未知环境的多代理协作探索。提出的方法结合了映射，安全走廊生成和多代理计划中的最新算法。它首先需要我们要探索的卷，然后继续为多个代理提供不同的目标，以探索该卷的体素网格。当所有体素被发现为自由或占据时，探索结束，或者没有发现其余未发现的体素的路径。最先进的计划算法使用时间认知的安全走廊来确保机体内碰撞安全以及静态障碍的安全性。提出的方法以最多4个代理商的最高模拟器状态进行了测试。

最近在多种最先进的运动计划方法中使用了安全走廊（一系列重叠的凸形形状）。他们允许以有效的方式来代表环境中的自由空间，以避免碰撞。在本文中，我们提出了一个用于生成安全走廊的新框架。我们假设我们具有环境的体素电网表示。提出的框架改进了先前最先进的体素网格的安全走廊生成方法。它还在给定安全走廊的多面体之间创建了一个连接图，该图可以知道哪个Polyhedra相互相交。连接图可用于计划方法以减少计算时间。在计算时间，涵盖的音量，安全性，每个安全走廊的多面体数量和每个多面体的约束数量方面，将该方法与模拟中的其他最新方法进行了比较。

Voxel网格是一个最小且有效的环境表示，用于许多任务中的机器人运动规划。许多最先进的规划算法使用由自由，占用和未知的体素组成的体素网格。在本文中，我们提出了一种新的GPU加速算法，用于将空间划分为占用，自由和未知体素的体素网格。所提出的方法是低延迟，适用于高速导航。

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

This paper presents a methodology for integrating machine learning techniques into metaheuristics for solving combinatorial optimization problems. Namely, we propose a general machine learning framework for neighbor generation in metaheuristic search. We first define an efficient neighborhood structure constructed by applying a transformation to a selected subset of variables from the current solution. Then, the key of the proposed methodology is to generate promising neighbors by selecting a proper subset of variables that contains a descent of the objective in the solution space. To learn a good variable selection strategy, we formulate the problem as a classification task that exploits structural information from the characteristics of the problem and from high-quality solutions. We validate our methodology on two metaheuristic applications: a Tabu Search scheme for solving a Wireless Network Optimization problem and a Large Neighborhood Search heuristic for solving Mixed-Integer Programs. The experimental results show that our approach is able to achieve a satisfactory trade-off between the exploration of a larger solution space and the exploitation of high-quality solution regions on both applications.

We investigate the asymptotic properties of deep Residual networks (ResNets) as the number of layers increases. We first show the existence of scaling regimes for trained weights markedly different from those implicitly assumed in the neural ODE literature. We study the convergence of the hidden state dynamics in these scaling regimes, showing that one may obtain an ODE, a stochastic differential equation (SDE) or neither of these. In particular, our findings point to the existence of a diffusive regime in which the deep network limit is described by a class of stochastic differential equations (SDEs). Finally, we derive the corresponding scaling limits for the backpropagation dynamics.

Identifying the production dates of historical manuscripts is one of the main goals for paleographers when studying ancient documents. Automatized methods can provide paleographers with objective tools to estimate dates more accurately. Previously, statistical features have been used to date digitized historical manuscripts based on the hypothesis that handwriting styles change over periods. However, the sparse availability of such documents poses a challenge in obtaining robust systems. Hence, the research of this article explores the influence of data augmentation on the dating of historical manuscripts. Linear Support Vector Machines were trained with k-fold cross-validation on textural and grapheme-based features extracted from historical manuscripts of different collections, including the Medieval Paleographical Scale, early Aramaic manuscripts, and the Dead Sea Scrolls. Results show that training models with augmented data improve the performance of historical manuscripts dating by 1% - 3% in cumulative scores. Additionally, this indicates further enhancement possibilities by considering models specific to the features and the documents' scripts.

High-definition (HD) map change detection is the task of determining when sensor data and map data are no longer in agreement with one another due to real-world changes. We collect the first dataset for the task, which we entitle the Trust, but Verify (TbV) dataset, by mining thousands of hours of data from over 9 months of autonomous vehicle fleet operations. We present learning-based formulations for solving the problem in the bird's eye view and ego-view. Because real map changes are infrequent and vector maps are easy to synthetically manipulate, we lean on simulated data to train our model. Perhaps surprisingly, we show that such models can generalize to real world distributions. The dataset, consisting of maps and logs collected in six North American cities, is one of the largest AV datasets to date with more than 7.8 million images. We make the data available to the public at https://www.argoverse.org/av2.html#mapchange-link, along with code and models at https://github.com/johnwlambert/tbv under the the CC BY-NC-SA 4.0 license.

Warning: this paper contains content that may be offensive or upsetting. Considering the large amount of content created online by the minute, slang-aware automatic tools are critically needed to promote social good, and assist policymakers and moderators in restricting the spread of offensive language, abuse, and hate speech. Despite the success of large language models and the spontaneous emergence of slang dictionaries, it is unclear how far their combination goes in terms of slang understanding for downstream social good tasks. In this paper, we provide a framework to study different combinations of representation learning models and knowledge resources for a variety of downstream tasks that rely on slang understanding. Our experiments show the superiority of models that have been pre-trained on social media data, while the impact of dictionaries is positive only for static word embeddings. Our error analysis identifies core challenges for slang representation learning, including out-of-vocabulary words, polysemy, variance, and annotation disagreements, which can be traced to characteristics of slang as a quickly evolving and highly subjective language.