区域覆盖范围问题是使用安装在机器人（例如无人驾驶汽车（UAV）（UAV）和无人接地车辆（UGV）等机器人上的传感器有效维修给定的二维表面的任务。我们提出了一种新颖的配方，用于生成多个容量受限机器人的覆盖路线，可以根据电池寿命或飞行时间指定容量。遍历环境对具有容量限制的机器人资源产生了需求。我们方法的主要方面是将区域覆盖问题转换为线覆盖范围问题（即线性特征的覆盖范围），然后生成途径，以最大程度地减少旅行的总成本，同时尊重容量约束。我们定义了两种旅行模式：（1）维修和（2）无人机，这与机器人是否执行特定于任务的操作相对应。我们的配方允许对两种模式的单独和不对称的旅行成本和需求。此外，从细胞分解计算出来的细胞，旨在最小化转弯的数量，不需要单调多边形。我们为细胞分解和生成服务轨道开发了新的程序，这些过程可以处理有或没有孔的非符号酮多边形。我们在具有25个室内环境的地面机器人数据集和一个具有300个室外环境的空中机器人数据集上建立了算法的功效。该算法生成的解决方案的成本比最新方法平均低10％。我们还证明了我们在无人机实验中的算法。

线覆盖范围是为环境中的一组一维功能提供服务的任务。这对于检查线性基础设施（例如道路网络，电力线以及石油和天然气管道）很重要。本文通过在图上将其建模为优化问题，解决了空中和地面机器人的单个机器人线覆盖率问题。该问题属于广泛的ARC路由问题，与不对称的农村邮政问题（RPP）密切相关。本文提供了一个整数线性编程公式，并提供了正确的证明。使用最低成本流问题，我们开发近似算法，并保证解决方案质量。这些保证还改善了不对称RPP的现有结果。主要算法将问题分为三种情况，以所需图的结构，即需要维修的特征诱导的图。我们在世界上50个人口最多的城市的道路网络上评估了我们的算法。该算法以改进的启发式增强，在3s内运行，并生成最佳最佳10％以内的解决方案。我们在UNC Charlotte校园路网络上通过商业无人机在实验中展示了我们的算法。

线覆盖范围的问题是找到有效的路由，以通过一个或多个资源约束的机器人覆盖线性特征。线性具有模型环境，例如道路网络，电力线以及石油和天然气管道。我们为机器人定义了两种旅行模式：维修和陷入困境。机器人服务功能如果它执行特定于任务的操作，例如拍摄图像，则它可以遍历该功能；否则，它是无人机的。穿越环境会产生成本（例如旅行时间）和对资源的需求（例如电池寿命）。维修和无人机的成本和需求功能可能具有不同的成本和需求功能，我们进一步允许它们取决于方向。我们将环境建模为图形，并提供整数线性程序。由于问题是NP-HARD，因此我们开发了一种快速有效的启发式算法，即合并 - 默认混合物（MEM）。该算法的建设性属性使得为大图求解了多depot版本。我们进一步扩展了MEM算法，以处理转弯成本和非语言限制。我们在50个道路网络的数据集上对算法进行基准测试，并在道路网络上使用空中机器人进行了实验中的算法。

本文介绍了相关的弧定向问题（CAOP），其中的任务是找到机器人团队的路线，以最大程度地收集与环境中功能相关的奖励的收集。这些功能可以是一维或环境中的点，并且可以具有空间相关性，即访问环境中的功能可能会提供与相关功能相关的奖励的一部分。机器人在环境环境时会产生成本，并且路线的总成本受到资源限制（例如电池寿命或操作时间）的限制。由于环境通常很大，我们允许多个仓库在机器人必须启动和结束路线的地方。 CAOP概括了相关的定向问题（COP），其中奖励仅与点特征相关联以及ARC定向启动问题（AOP），其中奖励与无空间相关。我们制定了一个混合整数二次程序（MIQP），该程序正式化了问题并提供最佳的解决方案。但是，这个问题是NP-HARD，因此我们开发了一种有效的贪婪的建设性算法。我们用两种不同的应用说明了问题：甲烷气体泄漏检测和道路网络覆盖范围的信息路径计划。

RDMA超过融合以太网（ROCE），由于其与常规以太网的织物的兼容性，对数据中心网络具有重要的吸引力。但是，RDMA协议仅在（几乎）无损网络上有效，这强调了拥塞控制对ROCE网络的重要作用。不幸的是，基于优先流量控制（PFC）的本地ROCE拥塞控制方案遭受了许多缺点，例如不公平，线路阻滞和僵局。因此，近年来，已经提出许多计划为ROCE网络提供额外的拥塞控制，以最大程度地减少PFC缺点。但是，这些方案是针对一般数据中心环境提出的。与使用商品硬件构建并运行通用工作负载的一般数据中心相反，高性能分布式培训平台部署高端加速器和网络组件，并专门使用集体（全能，全能，全能）运行培训工作负载）通信库进行通信。此外，这些平台通常具有一个私人网络，将其通信流量与其他数据中心流量分开。可扩展的拓扑意识集体算法固有地设计旨在避免造成的模式并最佳地平衡流量。这些独特的功能需要重新审视先前提出的通用数据中心环境的拥塞控制方案。在本文中，我们彻底分析了在分布式培训平台上运行时的一些SOTA ROCE拥塞控制方案与PFC。我们的结果表明，先前提出的ROCE拥塞控制计划对培训工作负载的端到端表现几乎没有影响，这激发了根据分布式培训平台和分布式培训平台和特征的设计优化但低空的拥塞控制计划的必要性工作负载。

Recurrent neural networks (RNNs) have brought a lot of advancements in sequence labeling tasks and sequence data. However, their effectiveness is limited when the observations in the sequence are irregularly sampled, where the observations arrive at irregular time intervals. To address this, continuous time variants of the RNNs were introduced based on neural ordinary differential equations (NODE). They learn a better representation of the data using the continuous transformation of hidden states over time, taking into account the time interval between the observations. However, they are still limited in their capability as they use the discrete transformations and a fixed discrete number of layers (depth) over an input in the sequence to produce the output observation. We intend to address this limitation by proposing RNNs based on differential equations which model continuous transformations over both depth and time to predict an output for a given input in the sequence. Specifically, we propose continuous depth recurrent neural differential equations (CDR-NDE) which generalizes RNN models by continuously evolving the hidden states in both the temporal and depth dimensions. CDR-NDE considers two separate differential equations over each of these dimensions and models the evolution in the temporal and depth directions alternatively. We also propose the CDR-NDE-heat model based on partial differential equations which treats the computation of hidden states as solving a heat equation over time. We demonstrate the effectiveness of the proposed models by comparing against the state-of-the-art RNN models on real world sequence labeling problems and data.

Recent advances in safety-critical risk-aware control are predicated on apriori knowledge of the disturbances a system might face. This paper proposes a method to efficiently learn these disturbances online, in a risk-aware context. First, we introduce the concept of a Surface-at-Risk, a risk measure for stochastic processes that extends Value-at-Risk -- a commonly utilized risk measure in the risk-aware controls community. Second, we model the norm of the state discrepancy between the model and the true system evolution as a scalar-valued stochastic process and determine an upper bound to its Surface-at-Risk via Gaussian Process Regression. Third, we provide theoretical results on the accuracy of our fitted surface subject to mild assumptions that are verifiable with respect to the data sets collected during system operation. Finally, we experimentally verify our procedure by augmenting a drone's controller and highlight performance increases achieved via our risk-aware approach after collecting less than a minute of operating data.

Reinforcement Learning (RL) can solve complex tasks but does not intrinsically provide any guarantees on system behavior. For real-world systems that fulfill safety-critical tasks, such guarantees on safety specifications are necessary. To bridge this gap, we propose a verifiably safe RL procedure with probabilistic guarantees. First, our approach probabilistically verifies a candidate controller with respect to a temporal logic specification, while randomizing the controller's inputs within a bounded set. Then, we use RL to improve the performance of this probabilistically verified, i.e. safe, controller and explore in the same bounded set around the controller's input as was randomized over in the verification step. Finally, we calculate probabilistic safety guarantees with respect to temporal logic specifications for the learned agent. Our approach is efficient for continuous action and state spaces and separates safety verification and performance improvement into two independent steps. We evaluate our approach on a safe evasion task where a robot has to evade a dynamic obstacle in a specific manner while trying to reach a goal. The results show that our verifiably safe RL approach leads to efficient learning and performance improvements while maintaining safety specifications.

Entity matching in Customer 360 is the task of determining if multiple records represent the same real world entity. Entities are typically people, organizations, locations, and events represented as attributed nodes in a graph, though they can also be represented as records in relational data. While probabilistic matching engines and artificial neural network models exist for this task, explaining entity matching has received less attention. In this demo, we present our Explainable Entity Matching (xEM) system and discuss the different AI/ML considerations that went into its implementation.

Vascular shunt insertion is a fundamental surgical procedure used to temporarily restore blood flow to tissues. It is often performed in the field after major trauma. We formulate a problem of automated vascular shunt insertion and propose a pipeline to perform Automated Vascular Shunt Insertion (AVSI) using a da Vinci Research Kit. The pipeline uses a learned visual model to estimate the locus of the vessel rim, plans a grasp on the rim, and moves to grasp at that point. The first robot gripper then pulls the rim to stretch open the vessel with a dilation motion. The second robot gripper then proceeds to insert a shunt into the vessel phantom (a model of the blood vessel) with a chamfer tilt followed by a screw motion. Results suggest that AVSI achieves a high success rate even with tight tolerances and varying vessel orientations up to 30{\deg}. Supplementary material, dataset, videos, and visualizations can be found at https://sites.google.com/berkeley.edu/autolab-avsi.