通过将从地面视图摄像头拍摄到从卫星或飞机上拍摄的架空图像的图像，通过将代理定位在搜索区域内，将代理定位在搜索区域内，将代理定位在搜索区域中。尽管地面图像和架空图像之间的观点差异使得跨视图地理定位具有挑战性，但假设地面代理可以使用全景相机，则取得了重大进展。例如，我们先前的工作（WAG）引入了搜索区域离散化，训练损失和粒子过滤器加权的变化，从而实现了城市规模的全景跨视图地理定位。但是，由于其复杂性和成本，全景相机并未在现有机器人平台中广泛使用。非Panoramic跨视图地理定位更适用于机器人技术，但也更具挑战性。本文介绍了受限的FOV广泛地理定位（Rewag），这是一种跨视图地理定位方法，通过创建姿势吸引的嵌入并提供将粒子姿势纳入暹罗网络，将其概括为与标准的非填充地面摄像机一起使用，以供与标准的非卧型地面摄像机一起使用。 Rewag是一种神经网络和粒子滤波器系统，能够在GPS下的环境中全球定位移动代理，仅具有探测仪和90度FOV摄像机，其本地化精度与使用全景相机实现并提高本地化精度相似的定位精度与基线视觉变压器（VIT）方法相比，100倍。一个视频亮点，该视频亮点在https://youtu.be/u_obqrt8qce上展示了几十公里的测试路径上的收敛。

Knowledge of the symmetries of reinforcement learning (RL) systems can be used to create compressed and semantically meaningful representations of a low-level state space. We present a method of automatically detecting RL symmetries directly from raw trajectory data without requiring active control of the system. Our method generates candidate symmetries and trains a recurrent neural network (RNN) to discriminate between the original trajectories and the transformed trajectories for each candidate symmetry. The RNN discriminator's accuracy for each candidate reveals how symmetric the system is under that transformation. This information can be used to create high-level representations that are invariant to all symmetries on a dataset level and to communicate properties of the RL behavior to users. We show in experiments on two simulated RL use cases (a pusher robot and a UAV flying in wind) that our method can determine the symmetries underlying both the environment physics and the trained RL policy.

Hidden parameters are latent variables in reinforcement learning (RL) environments that are constant over the course of a trajectory. Understanding what, if any, hidden parameters affect a particular environment can aid both the development and appropriate usage of RL systems. We present an unsupervised method to map RL trajectories into a feature space where distance represents the relative difference in system behavior due to hidden parameters. Our approach disentangles the effects of hidden parameters by leveraging a recurrent neural network (RNN) world model as used in model-based RL. First, we alter the standard world model training algorithm to isolate the hidden parameter information in the world model memory. Then, we use a metric learning approach to map the RNN memory into a space with a distance metric approximating a bisimulation metric with respect to the hidden parameters. The resulting disentangled feature space can be used to meaningfully relate trajectories to each other and analyze the hidden parameter. We demonstrate our approach on four hidden parameters across three RL environments. Finally we present two methods to help identify and understand the effects of hidden parameters on systems.

为了在现实世界中安全可靠的部署，自主代理必须引起人类用户的适当信任。建立信任的一种方法是让代理评估和传达自己执行给定任务的能力。能力取决于影响代理的不确定性，使得准确的不确定性量化对于能力评估至关重要。在这项工作中，我们展示了如何使用深层生成模型的集合来量化代理商的态度和认知不确定性，当预测任务结果作为能力评估的一部分。

具有最小延迟的人工神经网络的决策对于诸如导航，跟踪和实时机器动作系统之类的许多应用来说是至关重要的。这要求机器学习硬件以高吞吐量处理多维数据。不幸的是，处理卷积操作是数据分类任务的主要计算工具，遵循有挑战性的运行时间复杂性缩放法。然而，在傅立叶光学显示器 - 光处理器中同心地实现卷积定理，使得不迭代的O（1）运行时复杂度以超过1,000×1,000大矩阵的数据输入。在此方法之后，这里我们展示了具有傅里叶卷积神经网络（FCNN）加速器的数据流多核图像批处理。我们将大规模矩阵的图像批量处理显示为傅立叶域中的数字光处理模块执行的被动的2000万点产品乘法。另外，我们通过利用多种时空衍射令并进一步并行化该光学FCNN系统，从而实现了最先进的FCNN加速器的98倍的产量改进。综合讨论与系统能力边缘工作相关的实际挑战突出了傅立叶域和决议缩放法律的串扰问题。通过利用展示技术中的大规模平行性加速卷积带来了基于VAN Neuman的机器学习加速度。

我们介绍了Galaxy动物园贴花：SDSS DR8占地面积的星系中的黑色能量相机传统调查图像的详细视觉形态学分类。更深的贴花图像（R = 23.6与SDSS的r = 22.2）显示螺旋臂，弱杆和在SDSS成像中未见的潮汐功能。为了最佳利用较大的贴花图像，志愿者从一套新的答案中选择，旨在提高对合并和酒吧的敏感性。 Galaxy动物园志愿者提供750万个单独的分类超过314,000个星系。 140,000个星系收到至少30分类，足以准确测量像条状的详细的形态，其余的收到约5.所有分类都用于培训贝叶斯卷积神经网络的集合（一种最先进的深度学习方法）预测所有314,000个星系的详细形态的后海外。当衡量自信的志愿者分类时，每个问题的网络大约有99％。形态学是每个星系的基本特征;我们的人机和机器分类是理解星系如何发展的准确和详细资源。

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

Purpose: Tracking the 3D motion of the surgical tool and the patient anatomy is a fundamental requirement for computer-assisted skull-base surgery. The estimated motion can be used both for intra-operative guidance and for downstream skill analysis. Recovering such motion solely from surgical videos is desirable, as it is compliant with current clinical workflows and instrumentation. Methods: We present Tracker of Anatomy and Tool (TAToo). TAToo jointly tracks the rigid 3D motion of patient skull and surgical drill from stereo microscopic videos. TAToo estimates motion via an iterative optimization process in an end-to-end differentiable form. For robust tracking performance, TAToo adopts a probabilistic formulation and enforces geometric constraints on the object level. Results: We validate TAToo on both simulation data, where ground truth motion is available, as well as on anthropomorphic phantom data, where optical tracking provides a strong baseline. We report sub-millimeter and millimeter inter-frame tracking accuracy for skull and drill, respectively, with rotation errors below 1{\deg}. We further illustrate how TAToo may be used in a surgical navigation setting. Conclusion: We present TAToo, which simultaneously tracks the surgical tool and the patient anatomy in skull-base surgery. TAToo directly predicts the motion from surgical videos, without the need of any markers. Our results show that the performance of TAToo compares favorably to competing approaches. Future work will include fine-tuning of our depth network to reach a 1 mm clinical accuracy goal desired for surgical applications in the skull base.

We present temporally layered architecture (TLA), a biologically inspired system for temporally adaptive distributed control. TLA layers a fast and a slow controller together to achieve temporal abstraction that allows each layer to focus on a different time-scale. Our design is biologically inspired and draws on the architecture of the human brain which executes actions at different timescales depending on the environment's demands. Such distributed control design is widespread across biological systems because it increases survivability and accuracy in certain and uncertain environments. We demonstrate that TLA can provide many advantages over existing approaches, including persistent exploration, adaptive control, explainable temporal behavior, compute efficiency and distributed control. We present two different algorithms for training TLA: (a) Closed-loop control, where the fast controller is trained over a pre-trained slow controller, allowing better exploration for the fast controller and closed-loop control where the fast controller decides whether to "act-or-not" at each timestep; and (b) Partially open loop control, where the slow controller is trained over a pre-trained fast controller, allowing for open loop-control where the slow controller picks a temporally extended action or defers the next n-actions to the fast controller. We evaluated our method on a suite of continuous control tasks and demonstrate the advantages of TLA over several strong baselines.