现在，基于视觉的本地化方法为来自机器人技术到辅助技术的无数用例提供了新出现的导航管道。与基于传感器的解决方案相比，基于视觉的定位不需要预安装的传感器基础架构，这是昂贵，耗时和/或通常不可行的。本文中，我们为特定用例提出了一个基于视觉的本地化管道：针对失明和低视力的最终用户的导航支持。给定最终用户在移动应用程序上拍摄的查询图像，该管道利用视觉位置识别（VPR）算法在目标空间的参考图像数据库中找到相似的图像。这些相似图像的地理位置用于采用加权平均方法来估计最终用户的位置和透视N点（PNP）算法的下游任务中，以估计最终用户的方向。此外，该系统实现了Dijkstra的算法，以根据包括Trip Origin和目的地的可通航地图计算最短路径。用于本地化和导航的层压映射是使用定制的图形用户界面构建的，该图形用户界面投影了3D重建的稀疏映射，从一系列图像构建到相应的先验2D楼平面图。用于地图构造的顺序图像可以在预映射步骤中收集，也可以通过公共数据库/公民科学清除。端到端系统可以使用带有自定义移动应用程序的相机安装在任何可互联网的设备上。出于评估目的，在复杂的医院环境中测试了映射和定位。评估结果表明，我们的系统可以以少于1米的平均误差来实现本地化，而无需了解摄像机的固有参数，例如焦距。

先进的可穿戴设备越来越多地利用高分辨率多摄像头系统。作为用于处理所得到的图像数据的最先进的神经网络是计算要求的，对于利用第五代（5G）无线连接和移动边缘计算，已经越来越感兴趣，以将该处理卸载到云。为了评估这种可能性，本文提出了一个详细的仿真和评估，用于5G无线卸载，用于对象检测，在一个名为Vis4ion的强大新型智能可穿戴物中，用于盲目损害（BVI）。目前的Vis4ion系统是一种具有高分辨率摄像机，视觉处理和触觉和音频反馈的仪表簿。本文认为将相机数据上载到移动边缘云以执行实时对象检测并将检测结果传输回可穿戴。为了确定视频要求，纸张评估视频比特率和分辨率对物体检测精度和范围的影响。利用与BVI导航相关的标记对象的新街道场景数据集进行分析。视觉评估与详细的全堆栈无线网络仿真结合，以确定吞吐量的分布和延迟，具有来自城市环境中的新高分辨率3D模型的实际导航路径和射线跟踪。为了比较，无线仿真考虑了标准的4G长期演进（LTE）载波和高速度5G毫米波（MMWAVE）载波。因此，该工作提供了对具有高带宽和低延迟要求的应用中的MMWAVE连接的边缘计算的彻底和现实评估。

Migraine is a high-prevalence and disabling neurological disorder. However, information migraine management in real-world settings could be limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts. Our system achieved an F1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and patient sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data.

Recently, many causal estimators for Conditional Average Treatment Effect (CATE) and instrumental variable (IV) problems have been published and open sourced, allowing to estimate granular impact of both randomized treatments (such as A/B tests) and of user choices on the outcomes of interest. However, the practical application of such models has ben hampered by the lack of a valid way to score the performance of such models out of sample, in order to select the best one for a given application. We address that gap by proposing novel scoring approaches for both the CATE case and an important subset of instrumental variable problems, namely those where the instrumental variable is customer acces to a product feature, and the treatment is the customer's choice to use that feature. Being able to score model performance out of sample allows us to apply hyperparameter optimization methods to causal model selection and tuning. We implement that in an open source package that relies on DoWhy and EconML libraries for implementation of causal inference models (and also includes a Transformed Outcome model implementation), and on FLAML for hyperparameter optimization and for component models used in the causal models. We demonstrate on synthetic data that optimizing the proposed scores is a reliable method for choosing the model and its hyperparameter values, whose estimates are close to the true impact, in the randomized CATE and IV cases. Further, we provide examles of applying these methods to real customer data from Wise.

We present a method for controlling a swarm using its spectral decomposition -- that is, by describing the set of trajectories of a swarm in terms of a spatial distribution throughout the operational domain -- guaranteeing scale invariance with respect to the number of agents both for computation and for the operator tasked with controlling the swarm. We use ergodic control, decentralized across the network, for implementation. In the DARPA OFFSET program field setting, we test this interface design for the operator using the STOMP interface -- the same interface used by Raytheon BBN throughout the duration of the OFFSET program. In these tests, we demonstrate that our approach is scale-invariant -- the user specification does not depend on the number of agents; it is persistent -- the specification remains active until the user specifies a new command; and it is real-time -- the user can interact with and interrupt the swarm at any time. Moreover, we show that the spectral/ergodic specification of swarm behavior degrades gracefully as the number of agents goes down, enabling the operator to maintain the same approach as agents become disabled or are added to the network. We demonstrate the scale-invariance and dynamic response of our system in a field relevant simulator on a variety of tactical scenarios with up to 50 agents. We also demonstrate the dynamic response of our system in the field with a smaller team of agents. Lastly, we make the code for our system available.

We develop a Bayesian semi-parametric model for the estimating the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in the phase III AAML1031 clinical trial in which patients move through a sequence of four treatment courses. At each course, they undergo treatment that may or may not include anthracyclines (ACT). While ACT is known to be effective at treating AML, it is also cardiotoxic and can lead to early death for some patients. Our task is to estimate the potential survival probability under hypothetical dynamic ACT treatment strategies, but there are several impediments. First, since ACT was not randomized in the trial, its effect on survival is confounded over time. Second, subjects initiate the next course depending on when they recover from the previous course, making timing potentially informative of subsequent treatment and survival. Third, patients may die or drop out before ever completing the full treatment sequence. We develop a generative Bayesian semi-parametric model based on Gamma Process priors to address these complexities. At each treatment course, the model captures subjects' transition to subsequent treatment or death in continuous time under a given rule. A g-computation procedure is used to compute a posterior over potential survival probability that is adjusted for time-varying confounding. Using this approach, we conduct posterior inference for the efficacy of hypothetical treatment rules that dynamically modify ACT based on evolving cardiac function.

Given a dataset of expert agent interactions with an environment of interest, a viable method to extract an effective agent policy is to estimate the maximum likelihood policy indicated by this data. This approach is commonly referred to as behavioral cloning (BC). In this work, we describe a key disadvantage of BC that arises due to the maximum likelihood objective function; namely that BC is mean-seeking with respect to the state-conditional expert action distribution when the learner's policy is represented with a Gaussian. To address this issue, we introduce a modified version of BC, Adversarial Behavioral Cloning (ABC), that exhibits mode-seeking behavior by incorporating elements of GAN (generative adversarial network) training. We evaluate ABC on toy domains and a domain based on Hopper from the DeepMind Control suite, and show that it outperforms standard BC by being mode-seeking in nature.

In computer-aided drug discovery (CADD), virtual screening (VS) is used for identifying the drug candidates that are most likely to bind to a molecular target in a large library of compounds. Most VS methods to date have focused on using canonical compound representations (e.g., SMILES strings, Morgan fingerprints) or generating alternative fingerprints of the compounds by training progressively more complex variational autoencoders (VAEs) and graph neural networks (GNNs). Although VAEs and GNNs led to significant improvements in VS performance, these methods suffer from reduced performance when scaling to large virtual compound datasets. The performance of these methods has shown only incremental improvements in the past few years. To address this problem, we developed a novel method using multiparameter persistence (MP) homology that produces topological fingerprints of the compounds as multidimensional vectors. Our primary contribution is framing the VS process as a new topology-based graph ranking problem by partitioning a compound into chemical substructures informed by the periodic properties of its atoms and extracting their persistent homology features at multiple resolution levels. We show that the margin loss fine-tuning of pretrained Triplet networks attains highly competitive results in differentiating between compounds in the embedding space and ranking their likelihood of becoming effective drug candidates. We further establish theoretical guarantees for the stability properties of our proposed MP signatures, and demonstrate that our models, enhanced by the MP signatures, outperform state-of-the-art methods on benchmark datasets by a wide and highly statistically significant margin (e.g., 93% gain for Cleves-Jain and 54% gain for DUD-E Diverse dataset).

功能配准算法表示点云为函数（例如，空间占用场），避免了常规最小二乘Quares注册算法中不可靠的对应估计。但是，现有的功能注册算法在计算上很昂贵。此外，在基于CAD模型的对象本地化等任务中，必须使用未知量表的注册能力，但是功能注册中没有这种支持。在这项工作中，我们提出了一种比例不变的线性时间复杂性功能配准算法。我们通过使用正顺序基函数在功能之间的L2距离之间有效地近似实现线性时间复杂性。正统基函数的使用导致与最小二乘配准兼容的公式。受益于最小二乘的公式，我们使用翻译反转不变测量的理论来解除尺度估计，从而实现规模不变的注册。我们在标准的3D注册基准上评估了所提出的算法，称为FLS（功能最小二乘），显示FLS的数量级比最先进的功能配准算法快，而无需损害准确性和鲁棒性。 FLS还胜过基于最小二乘的最小二乘注册算法，其精度和鲁棒性具有已知和未知量表。最后，我们证明将FLS应用于具有不同密度和部分重叠的寄存点云，同一类别中不同对象的点云以及带有嘈杂RGB-D测量值的真实世界对象的点云。

对于工业规模的广告系统，对广告点击率（CTR）的预测是一个核心问题。广告点击构成了一类重要的用户参与，通常用作广告对用户有用的主要信号。此外，在每次点击收费的广告系统中，单击费用期望值直接输入价值估计。因此，对于大多数互联网广告公司而言，CTR模型开发是一项重大投资。此类问题的工程需要许多适合在线学习的机器学习（ML）技术，这些技术远远超出了传统的准确性改进，尤其是有关效率，可重复性，校准，信用归因。我们介绍了Google搜索广告CTR模型中部署的实用技术的案例研究。本文提供了一项行业案例研究，该研究强调了当前的ML研究的重要领域，并说明了如何评估有影响力的新ML方法并在大型工业环境中有用。