Array programming provides a powerful, compact, expressive syntax for accessing, manipulating, and operating on data in vectors, matrices, and higher-dimensional arrays [1]. NumPy is the primary array programming library for the Python language [2,3,4,5]. It plays an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, material science, engineering, finance, and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves [6] and the first imaging of a black hole [7].Here we show how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring, and analyzing scientific data. NumPy is the foundation upon which the entire scientific Python universe is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Because of its central position in the ecosystem, NumPy increasingly plays the role of an interoperability layer between these new array computation libraries.

We present a unified probabilistic model that learns a representative set of discrete vehicle actions and predicts the probability of each action given a particular scenario. Our model also enables us to estimate the distribution over continuous trajectories conditioned on a scenario, representing what each discrete action would look like if executed in that scenario. While our primary objective is to learn representative action sets, these capabilities combine to produce accurate multimodal trajectory predictions as a byproduct. Although our learned action representations closely resemble semantically meaningful categories (e.g., "go straight", "turn left", etc.), our method is entirely self-supervised and does not utilize any manually generated labels or categories. Our method builds upon recent advances in variational inference and deep unsupervised clustering, resulting in full distribution estimates based on deterministic model evaluations.

2D-to-3D reconstruction is an ill-posed problem, yet humans are good at solving this problem due to their prior knowledge of the 3D world developed over years. Driven by this observation, we propose NeRDi, a single-view NeRF synthesis framework with general image priors from 2D diffusion models. Formulating single-view reconstruction as an image-conditioned 3D generation problem, we optimize the NeRF representations by minimizing a diffusion loss on its arbitrary view renderings with a pretrained image diffusion model under the input-view constraint. We leverage off-the-shelf vision-language models and introduce a two-section language guidance as conditioning inputs to the diffusion model. This is essentially helpful for improving multiview content coherence as it narrows down the general image prior conditioned on the semantic and visual features of the single-view input image. Additionally, we introduce a geometric loss based on estimated depth maps to regularize the underlying 3D geometry of the NeRF. Experimental results on the DTU MVS dataset show that our method can synthesize novel views with higher quality even compared to existing methods trained on this dataset. We also demonstrate our generalizability in zero-shot NeRF synthesis for in-the-wild images.

This paper describes important considerations and challenges associated with online reinforcement-learning based waveform selection for target identification in frequency modulated continuous wave (FMCW) automotive radar systems. We present a novel learning approach based on satisficing Thompson sampling, which quickly identifies a waveform expected to yield satisfactory classification performance. We demonstrate through measurement-level simulations that effective waveform selection strategies can be quickly learned, even in cases where the radar must select from a large catalog of candidate waveforms. The radar learns to adaptively select a bandwidth for appropriate resolution and a slow-time unimodular code for interference mitigation in the scene of interest by optimizing an expected classification metric.

When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy? We seek insight regarding this question by examining a dynamic spectrum access scenario, in which the radar wishes to transmit in the widest unoccupied bandwidth during each pulse repetition interval. Online learning is compared to a fixed rule-based sense-and-avoid strategy. We show that given a simple Markov channel model, the problem can be examined analytically for simple cases via stochastic dominance. Additionally, we show that for more realistic channel assumptions, learning-based approaches demonstrate greater ability to generalize. However, for short time-horizon problems that are well-specified, we find that machine learning approaches may perform poorly due to the inherent limitation of convergence time. We draw conclusions as to when learning-based approaches are expected to be beneficial and provide guidelines for future study.

2型糖尿病（T2DM）的早期诊断对于及时的治疗干预措施和生活方式改变至关重要。随着医学成像数据在许多患者群体中变得更广泛可用，我们试图研究是否可以在表格学习分类器模型中利用图像衍生的表型数据来预测T2DM的发病率，而无需使用侵入性血液实验室测量。我们表明，使用图像衍生表型的神经网络和决策树模型都可以预测患者T2DM状态的召回评分高达87.6％。我们还提出了与“ Syntha1c编码器”相同的结构的新颖使用，这些结构能够输出模仿血液血红蛋白A1C经验实验室测量值的可解释值。最后，我们证明了T2DM风险预测模型对输入矢量成分中小扰动的敏感性可用于预测从以前看不见的患者人群中取样的协变量的性能。

机器人完成任务的能力在很大程度上取决于其物理设计。但是，确定最佳的物理设计及其相应的控制策略本质上是具有挑战性的。选择链接的数量，类型以及如何在组合设计空间中结果产生的自由，以及对该空间中任何设计的评估都需要得出其最佳控制器。在这项工作中，我们提出了N-LIMB，这是一种在大量形态上优化机器人设计和控制的有效方法。我们框架的核心是一种通用设计条件的控制策略，能够控制各种设计集。这项政策通过允许在设计中转移经验并降低评估新设计的成本，从而大大提高了我们方法的样本效率。我们训练这项政策，以最大程度地提高预期回报，而在设计的分布中，该政策同时更新为普遍政策下的高性能设计。通过这种方式，我们的方法收敛于设计分布，围绕高性能设计和控制器的控制器有效地进行了微调。我们在各种地形的一系列运动任务上展示了我们方法的潜力，并展示了发现小说和高性能的设计控制对。

已经表明（Amuru等人，2015年），可以有效地使用在线学习算法选择最佳的物理层参数，以与数字调制方案进行阻塞，而无需先前了解受害者的传播策略。但是，这个学习问题涉及解决一个可以非常大的混合动作空间的多军匪徒问题。结果，与最佳干扰策略的融合可能会很慢，尤其是当受害者和干扰器的符号不是完全同步时。在这项工作中，我们通过引入线性强盗算法来解决样本效率问题，该算法说明了动作之间固有的相似性。此外，我们提出了上下文特征，这些特征非常适合非连锁处理问题的统计特征，并且与先前的ART相比，表现出明显改善的收敛行为。此外，我们展示了如何将有关受害者传播的先验知识无缝整合到学习框架中。我们最终讨论了渐近状态的局限性。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

由于其在自主驾驶中的应用，因此基于单眼图像的3D感知已成为一个活跃的研究领域。与基于激光雷达的技术相比，单眼3D感知（包括检测和跟踪）的方法通常会产生较低的性能。通过系统的分析，我们确定了每个对象深度估计精度是界限性能的主要因素。在这种观察过程中，我们提出了一种多级融合方法，该方法将不同的表示（RGB和伪LIDAR）和跨多个对象（Tracklets）的时间信息结合在一起，以增强对目标深度估计。我们提出的融合方法实现了Waymo打开数据集，KITTI检测数据集和Kitti MOT数据集的每个对象深度估计的最新性能。我们进一步证明，通过简单地用融合增强的深度替换估计的深度，我们可以在单眼3D感知任务（包括检测和跟踪）方面取得重大改进。