确定与医学实体相对应的医学文本中的跨度是许多医疗保健NLP任务的核心步骤之一，例如ICD编码，医学发现提取，医学注释上下文化等等。现有的实体提取方法依赖于医疗实体的固定词汇和有限的词汇，并且难以提取以不相交跨度为代表的实体。在本文中，我们提出了一种新的基于变压器的架构，称为OSLAT，OPEL SET LABEL COATION TRUSSSIONER，它解决了先前方法的许多局限性。我们的方法使用标签 - 注意机制来隐式学习与感兴趣的实体相关的跨度。这些实体可以作为自由文本提供，包括在OSLAT培训期间看不到的实体，即使它们是不相交的，该模型也可以提取跨度。为了测试我们方法的普遍性，我们在两个不同的数据集上训练两个单独的模型，这些数据集具有非常低的实体重叠：（1）来自HNLP的公共排放笔记数据集，以及（2）更具挑战性的专有患者文本数据集“原因”相遇”（RFE）。我们发现，应用于数据集上的OSLAT模型在应用于RFE数据集以及HNLP数据集的一部分时，在数据集上训练了基于规则和模糊字符串匹配基线，其中实体由分离跨度表示。我们的代码可以在https://github.com/curai/curai-research/tree/main/oslat上找到。

我们介绍MedCod，一种医学准确，情感，多样化和可控的对话系统，具有独特的自然语言发生器模块的方法。 MedCod已经开发并专门为历史为任务进行了评估。它集成了传统模块化方法的优势，使（医学）域知识与现代深层学习技术结合起来，以产生灵活的人类自然语言表达。详细描述了Medcod的自然语言输出的两个关键方面。首先，生成的句子是情绪化的，同样地看着医生如何与患者沟通。其次，生成的句子结构和措辞是多样化的，同时保持与所需医学概念的医疗一致性（由Medcod的对话管理器模块提供）。实验结果表明了我们在创造人类医疗对话系统方面的有效性。相关代码在https://github.com/curai/curai-research/tree/main/medcod提供

在患者和医生之间的相互作用期间收集的捕获信息中，医疗谈话摘要是一体的。总结谈话用于促进医生之间的患者交出，以及将来提供护理的一部分。然而，摘要可能是生产和需要域专业知识的耗时。现代培训的培训NLP型号，如Pegasus已经成为人类总结的有能力的替代方案，在许多摘要基准上达到最先进的性能。然而，许多下游任务仍然需要至少适度尺寸的数据集来实现令人满意的性能。在这项工作中，我们（1）探讨了数据集大小对使用Pegasus的转移学习医疗会话摘要的影响，（2）在分类设置中取得成功之后，评估低数据制度的各种迭代标记策略。我们发现模型性能随着数据集大小的增加而饱和，并且各种主动学习策略评估所有显示与简单数据集大小的等效性能一致。我们还发现天真的迭代伪标签是比没有伪标签的典型或略差。我们的工作阐明了将低数据制度技术转化为医学谈话摘要的概率和挑战，帮助指导未来在这个空间中的工作。可用的相关代码在\ url {https://github.com/curai/curai-research/tree/main/medical-summarization-ml4h-2021}。

症状检查已成为收集症状和诊断患者的重要工具，最大限度地减少临床人员的参与。我们开发了一种机器学习支持的系统，智能曲线，超越传统症状，通过与电子医疗记录（EMR）紧密的双向集成。在EMR衍生的患者历史上，我们的系统将患者的首席投诉识别自由文本条目，然后询问一系列离散问题以获得相关的症状学。患者特定数据用于预测详细的ICD-10-CM代码以及药物，实验室和成像订单。然后将患者的反应和临床决策支持（CDS）预测插入EMR。要培训机器学习组件的智能路程，我们使用了超过2500万级初级保健遭遇的新型数据集和100万患者的自由文本原因的参赛作品。这些数据集用于构建：（1）基于长的短期存储器（LSTM）的患者历史表示，（2）用于首发投诉提取的微调变压器模型，（3）一个用于问题测序的随机林模型， （4）用于CDS预测的前馈网络。我们的系统总共支持337名患者的首席投诉，该投诉共同组成了Kaiser Permanente的所有初级保健费用。

We present a dynamic path planning algorithm to navigate an amphibious rotor craft through a concave time-invariant obstacle field while attempting to minimize energy usage. We create a nonlinear quaternion state model that represents the rotor craft dynamics above and below the water. The 6 degree of freedom dynamics used within a layered architecture to generate motion paths for the vehicle to follow and the required control inputs. The rotor craft has a 3 dimensional map of its surroundings that is updated via limited range onboard sensor readings within the current medium (air or water). Path planning is done via PRM and D* Lite.

Biological systems often choose actions without an explicit reward signal, a phenomenon known as intrinsic motivation. The computational principles underlying this behavior remain poorly understood. In this study, we investigate an information-theoretic approach to intrinsic motivation, based on maximizing an agent's empowerment (the mutual information between its past actions and future states). We show that this approach generalizes previous attempts to formalize intrinsic motivation, and we provide a computationally efficient algorithm for computing the necessary quantities. We test our approach on several benchmark control problems, and we explain its success in guiding intrinsically motivated behaviors by relating our information-theoretic control function to fundamental properties of the dynamical system representing the combined agent-environment system. This opens the door for designing practical artificial, intrinsically motivated controllers and for linking animal behaviors to their dynamical properties.

Modern mobile burst photography pipelines capture and merge a short sequence of frames to recover an enhanced image, but often disregard the 3D nature of the scene they capture, treating pixel motion between images as a 2D aggregation problem. We show that in a "long-burst", forty-two 12-megapixel RAW frames captured in a two-second sequence, there is enough parallax information from natural hand tremor alone to recover high-quality scene depth. To this end, we devise a test-time optimization approach that fits a neural RGB-D representation to long-burst data and simultaneously estimates scene depth and camera motion. Our plane plus depth model is trained end-to-end, and performs coarse-to-fine refinement by controlling which multi-resolution volume features the network has access to at what time during training. We validate the method experimentally, and demonstrate geometrically accurate depth reconstructions with no additional hardware or separate data pre-processing and pose-estimation steps.

Reinforcement learning can enable robots to navigate to distant goals while optimizing user-specified reward functions, including preferences for following lanes, staying on paved paths, or avoiding freshly mowed grass. However, online learning from trial-and-error for real-world robots is logistically challenging, and methods that instead can utilize existing datasets of robotic navigation data could be significantly more scalable and enable broader generalization. In this paper, we present ReViND, the first offline RL system for robotic navigation that can leverage previously collected data to optimize user-specified reward functions in the real-world. We evaluate our system for off-road navigation without any additional data collection or fine-tuning, and show that it can navigate to distant goals using only offline training from this dataset, and exhibit behaviors that qualitatively differ based on the user-specified reward function.

Recent advances in batch (offline) reinforcement learning have shown promising results in learning from available offline data and proved offline reinforcement learning to be an essential toolkit in learning control policies in a model-free setting. An offline reinforcement learning algorithm applied to a dataset collected by a suboptimal non-learning-based algorithm can result in a policy that outperforms the behavior agent used to collect the data. Such a scenario is frequent in robotics, where existing automation is collecting operational data. Although offline learning techniques can learn from data generated by a sub-optimal behavior agent, there is still an opportunity to improve the sample complexity of existing offline reinforcement learning algorithms by strategically introducing human demonstration data into the training process. To this end, we propose a novel approach that uses uncertainty estimation to trigger the injection of human demonstration data and guide policy training towards optimal behavior while reducing overall sample complexity. Our experiments show that this approach is more sample efficient when compared to a naive way of combining expert data with data collected from a sub-optimal agent. We augmented an existing offline reinforcement learning algorithm Conservative Q-Learning with our approach and performed experiments on data collected from MuJoCo and OffWorld Gym learning environments.

Monitoring changes inside a reservoir in real time is crucial for the success of CO2 injection and long-term storage. Machine learning (ML) is well-suited for real-time CO2 monitoring because of its computational efficiency. However, most existing applications of ML yield only one prediction (i.e., the expectation) for a given input, which may not properly reflect the distribution of the testing data, if it has a shift with respect to that of the training data. The Simultaneous Quantile Regression (SQR) method can estimate the entire conditional distribution of the target variable of a neural network via pinball loss. Here, we incorporate this technique into seismic inversion for purposes of CO2 monitoring. The uncertainty map is then calculated pixel by pixel from a particular prediction interval around the median. We also propose a novel data-augmentation method by sampling the uncertainty to further improve prediction accuracy. The developed methodology is tested on synthetic Kimberlina data, which are created by the Department of Energy and based on a CO2 capture and sequestration (CCS) project in California. The results prove that the proposed network can estimate the subsurface velocity rapidly and with sufficient resolution. Furthermore, the computed uncertainty quantifies the prediction accuracy. The method remains robust even if the testing data are distorted due to problems in the field data acquisition. Another test demonstrates the effectiveness of the developed data-augmentation method in increasing the spatial resolution of the estimated velocity field and in reducing the prediction error.