在自动驾驶的背景下,车辆本质上肯定会遇到更多的极端天气,在此期间必须确保公共安全。随着气候迅速变化,大暴风雪的频率有望增加,并成为安全导航的主要威胁。尽管有许多文献旨在提高对冬季条件的导航弹性,但缺乏标准指标来量化与降水有关的LIDAR传感器的可见性丧失。本章提出了一个新颖的指标,以实时量化LIDAR可见性损失,并依赖气象研究领域的可见性概念。我们在加拿大不良驾驶条件(CADC)数据集上评估了该指标,将其与基于最先进的激光雷达的本地化算法的性能相关联,并评估在本地化过程之前过滤点云的好处。我们表明,迭代最接近的点(ICP)算法令人惊讶地抵抗降雪,但是突然的事件(例如雪地)可以极大地阻碍其准确性。我们讨论了此类事件,并证明需要更好地关注这些极端事件以量化其效果。
translated by 谷歌翻译
森林中自主冬季导航所固有的挑战包括缺乏可靠的全球导航卫星系统(GNSS)信号,低特征对比度,高照明变化和变化环境。这种类型的越野环境是一个极端的情况,自治车可能会在北部地区遇到。因此,了解对自动导航系统对这种恶劣环境的影响非常重要。为此,我们介绍了一个现场报告分析亚曲率区域中的教导和重复导航,同时受到气象条件的大变化。首先,我们描述了系统,它依赖于点云注册来通过北方林地定位移动机器人,同时构建地图。我们通过在教学和重复模式下在自动导航中进行了在实验中评估了该系统。我们展示了密集的植被扰乱了GNSS信号,使其不适合在森林径中导航。此外,我们突出了在森林走廊中使用点云登记的定位相关的不确定性。我们证明它不是雪降水,而是影响我们系统在环境中定位的能力的积雪。最后,我们从我们的实地运动中揭示了一些经验教训和挑战,以支持在冬季条件下更好的实验工作。
translated by 谷歌翻译
We present a machine-learning framework to accurately characterize morphologies of Active Galactic Nucleus (AGN) host galaxies within $z<1$. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the Galaxy Morphology Network (GaMorNet) to estimate whether the host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical images from five bands of the HSC Wide Survey, we build models independently in three redshift bins: low $(0<z<0.25)$, medium $(0.25<z<0.5)$, and high $(0.5<z<1.0)$. By first training on a large number of simulated galaxies, then fine-tuning using far fewer classified real galaxies, our framework predicts the actual morphology for $\sim$ $60\%-70\%$ host galaxies from test sets, with a classification precision of $\sim$ $80\%-95\%$, depending on redshift bin. Specifically, our models achieve disk precision of $96\%/82\%/79\%$ and bulge precision of $90\%/90\%/80\%$ (for the 3 redshift bins), at thresholds corresponding to indeterminate fractions of $30\%/43\%/42\%$. The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude. No strong dependency is observed on contrast ratio. Comparing classifications of real AGNs, our models agree well with traditional 2D fitting with GALFIT. The PSFGAN+GaMorNet framework does not depend on the choice of fitting functions or galaxy-related input parameters, runs orders of magnitude faster than GALFIT, and is easily generalizable via transfer learning, making it an ideal tool for studying AGN host galaxy morphology in forthcoming large imaging survey.
translated by 谷歌翻译
Optimal transport (OT) is a framework that can guide the design of efficient resource allocation strategies in a network of multiple sources and targets. This paper applies discrete OT to a swarm of UAVs in a novel way to achieve appropriate task allocation and execution. Drone swarm deployments already operate in multiple domains where sensors are used to gain knowledge of an environment [1]. Use cases such as, chemical and radiation detection, and thermal and RGB imaging create a specific need for an algorithm that considers parameters on both the UAV and waypoint side and allows for updating the matching scheme as the swarm gains information from the environment. Additionally, the need for a centralized planner can be removed by using a distributed algorithm that can dynamically update based on changes in the swarm network or parameters. To this end, we develop a dynamic and distributed OT algorithm that matches a UAV to the optimal waypoint based on one parameter at the UAV and another parameter at the waypoint. We show the convergence and allocation of the algorithm through a case study and test the algorithm's effectiveness against a greedy assignment algorithm in simulation.
translated by 谷歌翻译
This technical report presents GPS++, the first-place solution to the Open Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular property prediction task. Our approach implements several key principles from the prior literature. At its core our GPS++ method is a hybrid MPNN/Transformer model that incorporates 3D atom positions and an auxiliary denoising task. The effectiveness of GPS++ is demonstrated by achieving 0.0719 mean absolute error on the independent test-challenge PCQM4Mv2 split. Thanks to Graphcore IPU acceleration, GPS++ scales to deep architectures (16 layers), training at 3 minutes per epoch, and large ensemble (112 models), completing the final predictions in 1 hour 32 minutes, well under the 4 hour inference budget allocated. Our implementation is publicly available at: https://github.com/graphcore/ogb-lsc-pcqm4mv2.
translated by 谷歌翻译
Privacy, security, and bandwidth constraints have led to federated learning (FL) in wireless systems, where training a machine learning (ML) model is accomplished collaboratively without sharing raw data. Often, such collaborative FL strategies necessitate model aggregation at a server. On the other hand, decentralized FL necessitates that participating clients reach a consensus ML model by exchanging parameter updates. In this work, we propose the over-the-air clustered wireless FL (CWFL) strategy, which eliminates the need for a strong central server and yet achieves an accuracy similar to the server-based strategy while using fewer channel uses as compared to decentralized FL. We theoretically show that the convergence rate of CWFL per cluster is O(1/T) while mitigating the impact of noise. Using the MNIST and CIFAR datasets, we demonstrate the accuracy performance of CWFL for the different number of clusters across communication rounds.
translated by 谷歌翻译
我们提出了一个将张量网络(TN)方法与加固学习(RL)集成的框架,以解决动态优化任务。我们考虑RL Actor-Critic方法,这是一种解决RL问题的无模型方法,并将TNS作为其政策和价值功能的近似值。我们的“带有张量网络的参与者评论”(ACTEN)方法特别适合具有大型和可分解状态和动作空间的问题。为了说明ACTEN的适用性,我们解决了在两个范式随机模型中对稀有轨迹进行指定的艰巨任务,East模型的眼镜和不对称的简单排除过程(ASEP),后者由于对其他方法特别具有挑战性缺乏详细的平衡。在与现有的RL方法中进一步集成的巨大潜力,此处介绍的方法对物理应用程序的应用和多代理RL问题都有希望。
translated by 谷歌翻译
在机器学习中,对神经网络集合(NNE)(NNE)引起了新的兴趣,从而从一组较小的模型(而不是从单个较大的模型)中获得了预测作为汇总的预测。在这里,我们展示了如何使用随机系统中稀有轨迹的技术来定义和训练NNE。我们根据模型参数的轨迹定义一个NNE,在简单的,离散的时间,扩散动力学下,并通过将这些轨迹偏向较小的时间整合损失来训练NNE,并由适当的计数领域控制,这些领域的作用是超参数。我们证明了该技术在一系列简单监督的学习任务上的生存能力。与更常规的基于梯度的方法相比,我们讨论了轨迹采样方法的潜在优势。
translated by 谷歌翻译
我们提出了LOC-NERF,这是一种基于实时视觉的机器人定位方法,结合了蒙特卡洛定位和神经辐射场(NERF)。我们的系统使用预先训练的NERF模型作为环境的地图,可以使用RGB摄像机作为机器人唯一的外部感受传感器实时定位。尽管神经辐射场在计算机视觉和图形中看到了重要的视觉渲染应用,但他们发现机器人技术的用途有限。现有的基于NERF的本地化方法需要良好的初始姿势猜测和重大的计算,这使得它们对于实时机器人技术应用不切实际。通过使用Monte Carlo定位作为使用NERF MAP模型估算姿势的主力,LOC-NERF能够比ART的状态更快地执行本地化,并且不依赖初始姿势估计。除了测试合成数据外,我们还使用ClearPath Jackal UGV收集的实际数据运行系统,并首次证明了使用神经光辉场进行实时全球定位的能力。我们在https://github.com/mit-spark/loc-nerf上公开代码。
translated by 谷歌翻译
自动面部识别是一个知名的研究领域。在该领域的最后三十年的深入研究中,已经提出了许多不同的面部识别算法。随着深度学习的普及及其解决各种不同问题的能力,面部识别研究人员集中精力在此范式下创建更好的模型。从2015年开始,最先进的面部识别就植根于深度学习模型。尽管有大规模和多样化的数据集可用于评估面部识别算法的性能,但许多现代数据集仅结合了影响面部识别的不同因素,例如面部姿势,遮挡,照明,面部表情和图像质量。当算法在这些数据集上产生错误时,尚不清楚哪些因素导致了此错误,因此,没有指导需要多个方向进行更多的研究。这项工作是我们以前在2014年开发的作品的后续作品,最终于2016年发表,显示了各种面部方面对面部识别算法的影响。通过将当前的最新技术与过去的最佳系统进行比较,我们证明了在强烈的遮挡下,某些类型的照明和强烈表达的面孔是深入学习算法所掌握的问题,而具有低分辨率图像的识别,极端的姿势变化和开放式识别仍然是一个开放的问题。为了证明这一点,我们使用六个不同的数据集和五种不同的面部识别算法以开源和可重现的方式运行一系列实验。我们提供了运行所有实验的源代码,这很容易扩展,因此在我们的评估中利用自己的深网只有几分钟的路程。
translated by 谷歌翻译