我们为高分辨率自由呼吸肺MRI介绍了无监督的运动补偿重建方案。我们将时间序列中的图像帧模拟为3D模板图像卷的变形版本。我们假设变形图在高维空间中的光滑歧管上是点。具体地，我们在每次时刻模拟变形图作为基于CNN的发电机的输出，该发电机的输出具有由低维潜航向量驱动的所有时间框架的权重。潜伏向量的时间序列占数据集中的动态，包括呼吸运动和散装运动。模板图像卷，发电机的参数，以及潜在矢量的直接从k-t空间数据以无监督的方式学习。我们的实验结果表明，与最先进的方法相比，改进了重建，特别是在扫描期间散装运动的背景下。

我们介绍了一种无监督的深层歧管学习算法，用于运动补偿动态MRI。我们假设自由呼吸的肺部MRI数据集中的运动场在歧管上。每次即时的运动场被建模为深生成模型的输出，由捕获时间变异性的低维时变潜沿驱动。每次即时的图像都是使用上述运动字段作为图像模板的变形版本的建模。模板，深发电机的参数，以及潜伏向量以无监督的方式从K-T空间数据中学到。歧管运动模型用作规范器，使得运动场和图像的联合估计来自少数径向辐射/帧井井出良好。在运动补偿的高分辨率肺线MRI的背景下证明了算法的效用。

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。

本报告描述了一组新生儿脑电图（EEG）记录，根据背景模式中异常的严重程度分级。该数据集由来自新生儿重症监护病房记录的53个新生儿的169小时多通道脑电图组成。所有新生儿均诊断出低氧缺血性脑病（HIE），这是全年前婴儿脑损伤的最常见原因。对于每种新生儿，选择了多个1小时的高质量脑电图，然后对背景异常进行评分。分级系统评估eeg属性，例如振幅和频率，连续性，睡眠循环，对称性和同步以及异常波形。然后将背景严重程度分为4年级：正常或轻度异常，中度异常，严重异常和不活跃的脑电图。数据可用作用于HIE，用于脑电图训练目的的新生儿的多通道脑电图的参考集，或用于开发和评估自动化等级算法。

我们概述了新兴机会和挑战，以提高AI对科学发现的效用。AI为行业的独特目标与AI科学的目标创造了识别模式中的识别模式与来自数据的发现模式之间的紧张。如果我们解决了与域驱动的科学模型和数据驱动的AI学习机之间的“弥补差距”相关的根本挑战，那么我们预计这些AI模型可以改变假说发电，科学发现和科学过程本身。

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

We consider the problem of constructing minimax rate-optimal estimators for a doubly robust nonparametric functional that has witnessed applications across the causal inference and conditional independence testing literature. Minimax rate-optimal estimators for such functionals are typically constructed through higher-order bias corrections of plug-in and one-step type estimators and, in turn, depend on estimators of nuisance functions. In this paper, we consider a parallel question of interest regarding the optimality and/or sub-optimality of plug-in and one-step bias-corrected estimators for the specific doubly robust functional of interest. Specifically, we verify that by using undersmoothing and sample splitting techniques when constructing nuisance function estimators, one can achieve minimax rates of convergence in all H\"older smoothness classes of the nuisance functions (i.e. the propensity score and outcome regression) provided that the marginal density of the covariates is sufficiently regular. Additionally, by demonstrating suitable lower bounds on these classes of estimators, we demonstrate the necessity to undersmooth the nuisance function estimators to obtain minimax optimal rates of convergence.

Generative AI has matured to a point where large-scale models can generate text that seems indistinguishable from human-written text and remarkably photorealistic images. Automatically measuring how close the distribution of generated data is to the target real data distribution is a key step in diagnosing existing models and developing better models. We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images. These scores are statistical summaries of divergence frontiers capturing two types of errors in generative modeling. We explore four approaches to statistically estimate these scores: vector quantization, non-parametric estimation, classifier-based estimation, and parametric Gaussian approximations. We provide statistical bounds for the vector quantization approach. Empirically, we find that the proposed scores paired with a range of $f$-divergences and statistical estimation methods can quantify the gaps between the distributions of human-written text and those of modern neural language models by correlating with human judgments and identifying known properties of the generated texts. We conclude the paper by demonstrating its applications to other AI domains and discussing practical recommendations.

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common benchmark to allow for objective comparisons between published methods. This article addresses both issues. First, we systematically organize and connect past studies to consolidate a community that is currently fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery, satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline classifier.