我们提出了一种基于机器学习的新型方法来解决涉及大量独立关注者的二重性程序，作为一种特殊情况，其中包括两阶段随机编程。我们提出了一个优化模型，该模型明确考虑了追随者的采样子集，并利用机器学习模型来估计未采样关注者的客观值。与现有方法不同，我们将机器学习模型培训嵌入到优化问题中，这使我们能够采用无法使用领导者决策来表示的一般追随者功能。我们证明了由原始目标函数衡量的生成领导者决策的最佳差距，该目标函数考虑了整个追随者集。然后，我们开发追随者采样算法来收紧界限和一种表示追随者功能的表示方法，可以用作嵌入式机器学习模型的输入。使用骑自行车网络设计问题的合成实例，我们比较方法的计算性能与基线方法。我们的方法为追随者的目标价值观提供了更准确的预测，更重要的是，产生了更高质量的领导者决策。最后，我们对骑自行车基础设施计划进行了现实世界中的案例研究，我们采用方法来解决超过一百万关注者的网络设计问题。与当前的自行车网络扩展实践相比，我们的方法提出了有利的性能。

抽象推理是智能系统的关键能力。大型语言模型在抽象推理任务上实现了高度的性能，但表现出许多缺陷。但是，人类的抽象推理也是不完美的，并且取决于我们对推理问题内容的知识和信念。例如，人类对在日常情况下基于逻辑规则的逻辑规则比关于抽象属性的任意规则更可靠地理解。语言模型的培训经验类似地赋予了他们先前的期望，这些期望反映了人类的知识和信念。因此，我们假设语言模型会显示出类似人类的内容对抽象推理问题的影响。我们在三个逻辑推理任务中探讨了这一假设：自然语言推论，判断三段论的逻辑有效性和ison选择任务（Wason，1968）。我们发现，最新的大语言模型（具有7或700亿个参数； Hoffman等，2022）反映了这些任务中人类在人类中观察到的许多相同模式 - 像人类一样，模型对可信情况的理由更有效地理由不现实或抽象的。我们的发现对理解这些认知效应以及有助于语言模型表现的因素具有影响。

正如人类和动物在自然世界中学习的那样，它们会遇到远非统一的实体，情况和事件的分布。通常，经常遇到相对较小的经历，而许多重要的体验很少发生。现实的高度紧密，重尾的本质构成了人类和动物通过不断发展的专业记忆系统所面临的特殊学习挑战。相比之下，大多数流行的RL环境和基准涉及属性，对象，情况或任务的大致变化。 RL算法将如何在环境特征分布的世界（如我们的）中表现出较不统一的分布？为了探讨这个问题，我们开发了三个互补的RL环境，在这些环境中，代理商的经验根据Zipfian（离散幂定律）分布而变化。在这些基准上，我们发现标准的深入RL体系结构和算法获得了对常见情况和任务的有用知识，但无法充分了解稀有的情况。为了更好地了解这一失败，我们探讨了如何调整当前方法的不同方面，以帮助提高罕见事件的性能，并表明RL目标功能，代理商的记忆系统和自我监督的学习目标都可以影响代理商的能力从罕见的体验中学习。这些结果共同表明，从偏斜的经验中进行强大的学习是应用模拟或实验室以外的深度RL方法的关键挑战，而我们的Zipfian环境为衡量未来的进步朝着这一目标提供了基础。

解释在人类学习中发挥着相当大的作用，特别是在仍然在形成抽象的主要挑战，以及了解世界的关系和因果结构的地区。在这里，我们探索强化学习代理人是否同样可以从解释中受益。我们概述了一系列关系任务，涉及选择一个在一个集合中奇数一个的对象（即，沿许多可能的特征尺寸之一的唯一）。奇数一张任务要求代理在一组对象中的多维关系上推理。我们展示了代理商不会仅从奖励中学习这些任务，但是当它们也培训以生成语言解释对象属性或选择正确或不正确时，实现> 90％的性能。在进一步的实验中，我们展示了预测的解释如何使代理能够从模糊，因果困难的训练中适当地推广，甚至可以学习执行实验干预以识别因果结构。我们表明解释有助于克服代理人来解决简单特征的趋势，并探讨解释的哪些方面使它们成为最有益的。我们的结果表明，从解释中学习是一种强大的原则，可以为培训更强大和一般机器学习系统提供有希望的道路。

强化学习代理经常忘记过去的细节，特别是在延误或令人厌倦的任务之后。具有常见内存架构的代理努力召回和集成在过去事件的多个时间步行中，甚至会调用后跟分散的任务任务的单个时间戳的详细信息。为了解决这些限制，我们提出了一个分层块注意内存（HCAM），这有助于代理商详细记住过去。 HCAM通过将过去除以块来存储记忆，并通过首先在块的粗粗摘要上执行高级注意，然后在仅在最相关的块中进行详细关注。因此，具有HCAM的代理可以“精神上的时间旅行” - 记住过去的事件，并在不参加所有干预事件。我们展示了HCAM的代理基本上优于具有其他内存架构的代理，其任务需要长期回忆，保留或推理存储器。这些包括回顾一个对象隐藏在3D环境中的位置，迅速学习在新的邻域中有效地导航，以及快速学习和保留新的对象名称。具有HCAM的代理可以将其推断到任务序列，而不是培训的任务序列，甚至可以从元学习环境中概括为零射击，以维持跨情节的知识。 HCAM提高了代理样本效率，泛化和一般性（通过解决先前所需的专业架构的任务）。我们的工作是迈向可以学习，交互和适应复杂和时间扩展环境的代理的一步。

Traditionally, data analysis and theory have been viewed as separate disciplines, each feeding into fundamentally different types of models. Modern deep learning technology is beginning to unify these two disciplines and will produce a new class of predictively powerful space weather models that combine the physical insights gained by data and theory. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.

In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.

Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image or video by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference images to compensate for the information loss in input images. However, performing local transfer is difficult because of two gaps between input and reference images: the transformation gap (e.g., scale and rotation) and the resolution gap (e.g., HR and LR). To tackle these challenges, we propose C2-Matching in this work, which performs explicit robust matching crossing transformation and resolution. 1) To bridge the transformation gap, we propose a contrastive correspondence network, which learns transformation-robust correspondences using augmented views of the input image. 2) To address the resolution gap, we adopt teacher-student correlation distillation, which distills knowledge from the easier HR-HR matching to guide the more ambiguous LR-HR matching. 3) Finally, we design a dynamic aggregation module to address the potential misalignment issue between input images and reference images. In addition, to faithfully evaluate the performance of Reference-based Image Super-Resolution under a realistic setting, we contribute the Webly-Referenced SR (WR-SR) dataset, mimicking the practical usage scenario. We also extend C2-Matching to Reference-based Video Super-Resolution task, where an image taken in a similar scene serves as the HR reference image. Extensive experiments demonstrate that our proposed C2-Matching significantly outperforms state of the arts on the standard CUFED5 benchmark and also boosts the performance of video SR by incorporating the C2-Matching component into Video SR pipelines.

Most previous unsupervised domain adaptation (UDA) methods for question answering(QA) require access to source domain data while fine-tuning the model for the target domain. Source domain data may, however, contain sensitive information and may be restricted. In this study, we investigate a more challenging setting, source-free UDA, in which we have only the pretrained source model and target domain data, without access to source domain data. We propose a novel self-training approach to QA models that integrates a unique mask module for domain adaptation. The mask is auto-adjusted to extract key domain knowledge while trained on the source domain. To maintain previously learned domain knowledge, certain mask weights are frozen during adaptation, while other weights are adjusted to mitigate domain shifts with pseudo-labeled samples generated in the target domain. %As part of the self-training process, we generate pseudo-labeled samples in the target domain based on models trained in the source domain. Our empirical results on four benchmark datasets suggest that our approach significantly enhances the performance of pretrained QA models on the target domain, and even outperforms models that have access to the source data during adaptation.

Point-of-Care Ultrasound (POCUS) refers to clinician-performed and interpreted ultrasonography at the patient's bedside. Interpreting these images requires a high level of expertise, which may not be available during emergencies. In this paper, we support POCUS by developing classifiers that can aid medical professionals by diagnosing whether or not a patient has pneumothorax. We decomposed the task into multiple steps, using YOLOv4 to extract relevant regions of the video and a 3D sparse coding model to represent video features. Given the difficulty in acquiring positive training videos, we trained a small-data classifier with a maximum of 15 positive and 32 negative examples. To counteract this limitation, we leveraged subject matter expert (SME) knowledge to limit the hypothesis space, thus reducing the cost of data collection. We present results using two lung ultrasound datasets and demonstrate that our model is capable of achieving performance on par with SMEs in pneumothorax identification. We then developed an iOS application that runs our full system in less than 4 seconds on an iPad Pro, and less than 8 seconds on an iPhone 13 Pro, labeling key regions in the lung sonogram to provide interpretable diagnoses.