多语言转移技术通常改善低资源机器翻译（MT）。这些技术中的许多是不考虑数据特征的情况下应用的。我们在海地对英语翻译的背景下显示，转移效率与知识共享语言之间的培训数据和关系数量相关。我们的实验表明，对于超出真实数据阈值的某些语言，反向翻译的增强方法是适得其反的，而从足够相关的语言中的跨语言转移则是优选的。我们通过贡献了基于规则的法国人行曲拼字和句法引擎以及一种新颖的语音嵌入方法来补充这一发现。当与多语言技术一起使用时，拼字法转换使对常规方法的统计学显着改善。在非常低的牙买加MT中，用传输语言进行矫正相似的代码转换可产生6.63的BLEU点优势。

The availability of frequent and cost-free satellite images is in growing demand in the research world. Such satellite constellations as Landsat 8 and Sentinel-2 provide a massive amount of valuable data daily. However, the discrepancy in the sensors' characteristics of these satellites makes it senseless to use a segmentation model trained on either dataset and applied to another, which is why domain adaptation techniques have recently become an active research area in remote sensing. In this paper, an experiment of domain adaptation through style-transferring is conducted using the HRSemI2I model to narrow the sensor discrepancy between Landsat 8 and Sentinel-2. This paper's main contribution is analyzing the expediency of that approach by comparing the results of segmentation using domain-adapted images with those without adaptation. The HRSemI2I model, adjusted to work with 6-band imagery, shows significant intersection-over-union performance improvement for both mean and per class metrics. A second contribution is providing different schemes of generalization between two label schemes - NALCMS 2015 and CORINE. The first scheme is standardization through higher-level land cover classes, and the second is through harmonization validation in the field.

As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adapt in dynamically-changing environments, becoming Lifelong Learning Machines. Continual/lifelong learning (LL) involves minimizing catastrophic forgetting of old tasks while maximizing a model's capability to learn new tasks. This paper addresses the challenging lifelong reinforcement learning (L2RL) setting. Pushing the state-of-the-art forward in L2RL and making L2RL useful for practical applications requires more than developing individual L2RL algorithms; it requires making progress at the systems-level, especially research into the non-trivial problem of how to integrate multiple L2RL algorithms into a common framework. In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system. As an instantiation of L2RLCF, we develop a standard API allowing easy integration of novel lifelong learning components. We describe a case study that demonstrates how multiple independently-developed LL components can be integrated into a single realized system. We also introduce an evaluation environment in order to measure the effect of combining various system components. Our evaluation environment employs different LL scenarios (sequences of tasks) consisting of Starcraft-2 minigames and allows for the fair, comprehensive, and quantitative comparison of different combinations of components within a challenging common evaluation environment.

Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i.e., SOcial DeterminAnts), with pre-trained transformer models to extract social determinants of health (SDoH) for cancer patients, examine the generalizability of SODA to a new disease domain (i.e., opioid use), and evaluate the extraction rate of SDoH using cancer populations. Methods: We identified SDoH categories and attributes and developed an SDoH corpus using clinical notes from a general cancer cohort. We compared four transformer-based NLP models to extract SDoH, examined the generalizability of NLP models to a cohort of patients prescribed with opioids, and explored customization strategies to improve performance. We applied the best NLP model to extract 19 categories of SDoH from the breast (n=7,971), lung (n=11,804), and colorectal cancer (n=6,240) cohorts. Results and Conclusion: We developed a corpus of 629 cancer patients notes with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH. The Bidirectional Encoder Representations from Transformers (BERT) model achieved the best strict/lenient F1 scores of 0.9216 and 0.9441 for SDoH concept extraction, 0.9617 and 0.9626 for linking attributes to SDoH concepts. Fine-tuning the NLP models using new annotations from opioid use patients improved the strict/lenient F1 scores from 0.8172/0.8502 to 0.8312/0.8679. The extraction rates among 19 categories of SDoH varied greatly, where 10 SDoH could be extracted from >70% of cancer patients, but 9 SDoH had a low extraction rate (<70% of cancer patients). The SODA package with pre-trained transformer models is publicly available at https://github.com/uf-hobiinformatics-lab/SDoH_SODA.

电子健康记录（EHR）可获得的丰富纵向个体水平数据可用于检查治疗效果异质性。但是，使用EHR数据估算治疗效果提出了几个挑战，包括时变的混杂，重复和时间不一致的协变量测量，治疗分配和结果以及由于辍学导致的损失。在这里，我们开发了纵向数据（SDLD）算法的亚组发现，该算法是一种基于树的算法，用于使用纵向相互作用树算法结合使用纵向相互作用的一般数据驱动的方法，与纵向驱动的方法与纵向驱动的方法结合使用纵向相互作用，以发现具有异质治疗效果的亚组，并进行纵向研究。目标最大似然估计。我们将算法应用于EHR数据，以发现患有人免疫缺陷病毒（HIV）的人群的亚组，他们在接受非Dolutegravir抗逆转录病毒疗法（ART）接受非Dolutegravir抗逆转录病毒疗法（艺术）时的体重增加风险较高。

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。

证明数字搜索（PNS）和蒙特卡洛树搜索（MCT）已成功地用于一系列游戏中的决策。本文提出了一种称为PN-MCTS的新方法，该方法通过将证明和调解数字的概念纳入MCT的UCT公式来结合这两种树搜索方法。实验结果表明，PN-MCTS在包括动作线，Minishogi，Knightthrough和Awari在内的多个游戏中优于基本MCT，达到了高达94.0％的获胜率。

基于平铺，形状和图形运算符，通过其底层图描述了Ludii General Game系统的游戏板，自动检测图形元素，方向和径向序列之间的拓扑关系等重要属性。这种方法允许简单而简洁地描述最能实现的游戏板。

本文介绍了三种不同的播出优化实现，如Monte-Carlo树搜索等游戏播放算法常用。每个优化的实现都仅适用于根据其规则的特定游戏集。Ludii General游戏系统可以根据游戏的描述在其常规游戏描述语言中，是否适用任何优化的实现。经验评估展示了标准实施中的主要加速，其中运行播出的中位结果是快速的播出5.08倍，在Ludii中超过145个不同的游戏，其中一个优化的实现是适用的。

防御网络攻击的计算机网络需要及时应对警报和威胁情报。关于如何响应的决定涉及基于妥协指标的多个节点跨多个节点协调动作，同时最大限度地减少对网络操作的中断。目前，PlayBooks用于自动化响应过程的部分，但通常将复杂的决策留给人类分析师。在这项工作中，我们在大型工业控制网络中提出了一种深度增强学习方法，以便在大型工业控制网络中进行自主反应和恢复。我们提出了一种基于关注的神经结构，其在保护下灵活地灵活。要培训和评估自治防御者代理，我们提出了一个适合加强学习的工业控制网络仿真环境。实验表明，学习代理可以有效减轻在执行前几个月几个月的可观察信号的进步。所提出的深度加强学习方法优于模拟中完全自动化的Playbook方法，采取更少的破坏性动作，同时在网络上保留更多节点。学习的政策对攻击者行为的变化也比PlayBook方法更加强大。