6-DOF的视觉定位系统利用植根于3D几何形状的原则方法来对图像进行准确的摄像头姿势估计图。当前的技术使用层次管道并学到了2D功能提取器来提高可扩展性并提高性能。但是，尽管典型召回@0.25m类型的指标获得了，但由于其“最差”性能领域，这些系统仍然对实际应用（如自动驾驶汽车）的实用性有限 - 在某种程度上提供不足的召回率的位置。在这里，我们研究了使用“位置特定配置”的实用性，其中将地图分割为多个位置，每个位置都有自己的配置，用于调节姿势估计步骤，在这种情况下，在多摄像机系统中选择摄像机。在福特AV基准数据集上，我们证明了与使用现成管道相比，我们证明了最大的最差案例定位性能 - 最小化数据集的百分比，该数据集的百分比降低了一定的误差耐受性，并提高了整体定位性能。我们提出的方法尤其适用于自动驾驶汽车部署的众群体模型，在该模型中，AV机队定期穿越已知的路线。

共同监督的深度学习方法的关节深度和自我运动估计可以产生准确的轨迹，而无需地面真相训练数据。但是，由于通常会使用光度损失，因此当这些损失所产生的假设（例如时间照明一致性，静态场景以及缺少噪声和遮挡）时，它们的性能会显着降解。这限制了它们用于例如夜间序列倾向于包含许多点光源（包括在动态对象上）和较暗图像区域中的低信噪比（SNR）。在本文中，我们展示了如何使用三种技术的组合来允许现有的光度损失在白天和夜间图像中起作用。首先，我们引入了每个像素神经强度转化，以补偿连续帧之间发生的光变化。其次，我们预测了每个像素的残差流图，我们用来纠正由网络估计的自我运动和深度引起的重新注入对应关系。第三，我们将训练图像降低，以提高方法的鲁棒性和准确性。这些更改使我们可以在白天和夜间图像中训练单个模型，而无需单独的编码器或诸如现有方法（例如现有方法）的额外功能网络。我们对具有挑战性的牛津机器人数据集进行了广泛的实验和消融研究，以证明我们方法对白天和夜间序列的疗效。

尽管外观和观点的显着变化，视觉地点识别（VPR）通常是能够识别相同的地方。 VPR是空间人工智能的关键组成部分，使机器人平台和智能增强平台，例如增强现实设备，以察觉和理解物理世界。在本文中，我们观察到有三个“驱动程序”，它对空间智能代理有所要求，因此vpr系统：1）特定代理包括其传感器和计算资源，2）该代理的操作环境，以及3）人造工具执行的具体任务。在本文中，考虑到这些驱动因素，包括他们的位置代表和匹配选择，在VPR区域中表征和调查关键作品。我们还基于视觉重叠的VPR提供了一种新的VPR - 类似于大脑中的空间视图单元格 - 这使我们能够找到对机器人和计算机视觉领域的其他研究领域的相似之处和差异。我们确定了许多开放的挑战，并建议未来工作需要更深入的关注的领域。

The open-radio access network (O-RAN) embraces cloudification and network function virtualization for base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). These enable the cloud-RAN vision in full, where multiple mobile network operators (MNOs) can install their proprietary or open RUs, but lease on-demand computational resources for DU-CU functions from commonly available open-clouds via open x-haul interfaces. In this paper, we propose and compare the performances of min-max fairness and Vickrey-Clarke-Groves (VCG) auction-based x-haul and DU-CU resource allocation mechanisms to create a multi-tenant O-RAN ecosystem that is sustainable for small, medium, and large MNOs. The min-max fair approach minimizes the maximum OPEX of RUs through cost-sharing proportional to their demands, whereas the VCG auction-based approach minimizes the total OPEX for all resources utilized while extracting truthful demands from RUs. We consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where PON virtualization technique is used to flexibly provide optical connections among RUs and edge-clouds at macro-cell RU locations as well as open-clouds at the central office locations. Moreover, we design efficient heuristics that yield significantly better economic efficiency and network resource utilization than conventional greedy resource allocation algorithms and reinforcement learning-based algorithms.

Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications. This is often because off-policy RL algorithms suffer from distributional shift, due to mismatch between dataset and the target policy, leading to high variance and over-estimation of value functions. In this work, we propose variance regularization for offline RL algorithms, using stationary distribution corrections. We show that by using Fenchel duality, we can avoid double sampling issues for computing the gradient of the variance regularizer. The proposed algorithm for offline variance regularization (OVAR) can be used to augment any existing offline policy optimization algorithms. We show that the regularizer leads to a lower bound to the offline policy optimization objective, which can help avoid over-estimation errors, and explains the benefits of our approach across a range of continuous control domains when compared to existing state-of-the-art algorithms.

In the process of materials discovery, chemists currently need to perform many laborious, time-consuming, and often dangerous lab experiments. To accelerate this process, we propose a framework for robots to assist chemists by performing lab experiments autonomously. The solution allows a general-purpose robot to perform diverse chemistry experiments and efficiently make use of available lab tools. Our system can load high-level descriptions of chemistry experiments, perceive a dynamic workspace, and autonomously plan the required actions and motions to perform the given chemistry experiments with common tools found in the existing lab environment. Our architecture uses a modified PDDLStream solver for integrated task and constrained motion planning, which generates plans and motions that are guaranteed to be safe by preventing collisions and spillage. We present a modular framework that can scale to many different experiments, actions, and lab tools. In this work, we demonstrate the utility of our framework on three pouring skills and two foundational chemical experiments for materials synthesis: solubility and recrystallization. More experiments and updated evaluations can be found at https://ac-rad.github.io/arc-icra2023.

This paper proposes an easy-to-compute upper bound for the overlap index between two probability distributions without requiring any knowledge of the distribution models. The computation of our bound is time-efficient and memory-efficient and only requires finite samples. The proposed bound shows its value in one-class classification and domain shift analysis. Specifically, in one-class classification, we build a novel one-class classifier by converting the bound into a confidence score function. Unlike most one-class classifiers, the training process is not needed for our classifier. Additionally, the experimental results show that our classifier \textcolor{\colorname}{can be accurate with} only a small number of in-class samples and outperforms many state-of-the-art methods on various datasets in different one-class classification scenarios. In domain shift analysis, we propose a theorem based on our bound. The theorem is useful in detecting the existence of domain shift and inferring data information. The detection and inference processes are both computation-efficient and memory-efficient. Our work shows significant promise toward broadening the applications of overlap-based metrics.

Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. This article provides a broad overview of research on the explainability and interpretability of natural language processing and information retrieval methods. More specifically, we survey approaches that have been applied to explain word embeddings, sequence modeling, attention modules, transformers, BERT, and document ranking. The concluding section suggests some possible directions for future research on this topic.

We propose a framework in which multiple entities collaborate to build a machine learning model while preserving privacy of their data. The approach utilizes feature embeddings from shared/per-entity feature extractors transforming data into a feature space for cooperation between entities. We propose two specific methods and compare them with a baseline method. In Shared Feature Extractor (SFE) Learning, the entities use a shared feature extractor to compute feature embeddings of samples. In Locally Trained Feature Extractor (LTFE) Learning, each entity uses a separate feature extractor and models are trained using concatenated features from all entities. As a baseline, in Cooperatively Trained Feature Extractor (CTFE) Learning, the entities train models by sharing raw data. Secure multi-party algorithms are utilized to train models without revealing data or features in plain text. We investigate the trade-offs among SFE, LTFE, and CTFE in regard to performance, privacy leakage (using an off-the-shelf membership inference attack), and computational cost. LTFE provides the most privacy, followed by SFE, and then CTFE. Computational cost is lowest for SFE and the relative speed of CTFE and LTFE depends on network architecture. CTFE and LTFE provide the best accuracy. We use MNIST, a synthetic dataset, and a credit card fraud detection dataset for evaluations.

Exploratory data analytics (EDA) is a sequential decision making process where analysts choose subsequent queries that might lead to some interesting insights based on the previous queries and corresponding results. Data processing systems often execute the queries on samples to produce results with low latency. Different downsampling strategy preserves different statistics of the data and have different magnitude of latency reductions. The optimum choice of sampling strategy often depends on the particular context of the analysis flow and the hidden intent of the analyst. In this paper, we are the first to consider the impact of sampling in interactive data exploration settings as they introduce approximation errors. We propose a Deep Reinforcement Learning (DRL) based framework which can optimize the sample selection in order to keep the analysis and insight generation flow intact. Evaluations with 3 real datasets show that our technique can preserve the original insight generation flow while improving the interaction latency, compared to baseline methods.