Chronic pain is a multi-dimensional experience, and pain intensity plays an important part, impacting the patients emotional balance, psychology, and behaviour. Standard self-reporting tools, such as the Visual Analogue Scale for pain, fail to capture this burden. Moreover, this type of tools is susceptible to a degree of subjectivity, dependent on the patients clear understanding of how to use it, social biases, and their ability to translate a complex experience to a scale. To overcome these and other self-reporting challenges, pain intensity estimation has been previously studied based on facial expressions, electroencephalograms, brain imaging, and autonomic features. However, to the best of our knowledge, it has never been attempted to base this estimation on the patient narratives of the personal experience of chronic pain, which is what we propose in this work. Indeed, in the clinical assessment and management of chronic pain, verbal communication is essential to convey information to physicians that would otherwise not be easily accessible through standard reporting tools, since language, sociocultural, and psychosocial variables are intertwined. We show that language features from patient narratives indeed convey information relevant for pain intensity estimation, and that our computational models can take advantage of that. Specifically, our results show that patients with mild pain focus more on the use of verbs, whilst moderate and severe pain patients focus on adverbs, and nouns and adjectives, respectively, and that these differences allow for the distinction between these three pain classes.

现代分布式系统受到耐故障算法的支持，例如可靠的广播和共识，即使系统的某些节点失败，也可以确保系统的正确操作。但是，分布式算法的开发是一个手动且复杂的过程，导致科学论文通常呈现单一算法或现有算法的变化。为了自动化开发此类算法的过程，这项工作提出了一种使用强化学习来生成正确且有效耐受性分布式分布式算法的智能代理。我们表明，我们的方法能够在仅12,000个学习剧集中生成正确的耐受性可靠的广播算法，而文献中的其他人则具有相同的性能。

Egocentric 3D human pose estimation with a single head-mounted fisheye camera has recently attracted attention due to its numerous applications in virtual and augmented reality. Existing methods still struggle in challenging poses where the human body is highly occluded or is closely interacting with the scene. To address this issue, we propose a scene-aware egocentric pose estimation method that guides the prediction of the egocentric pose with scene constraints. To this end, we propose an egocentric depth estimation network to predict the scene depth map from a wide-view egocentric fisheye camera while mitigating the occlusion of the human body with a depth-inpainting network. Next, we propose a scene-aware pose estimation network that projects the 2D image features and estimated depth map of the scene into a voxel space and regresses the 3D pose with a V2V network. The voxel-based feature representation provides the direct geometric connection between 2D image features and scene geometry, and further facilitates the V2V network to constrain the predicted pose based on the estimated scene geometry. To enable the training of the aforementioned networks, we also generated a synthetic dataset, called EgoGTA, and an in-the-wild dataset based on EgoPW, called EgoPW-Scene. The experimental results of our new evaluation sequences show that the predicted 3D egocentric poses are accurate and physically plausible in terms of human-scene interaction, demonstrating that our method outperforms the state-of-the-art methods both quantitatively and qualitatively.

Current abstractive summarization systems present important weaknesses which prevent their deployment in real-world applications, such as the omission of relevant information and the generation of factual inconsistencies (also known as hallucinations). At the same time, automatic evaluation metrics such as CTC scores have been recently proposed that exhibit a higher correlation with human judgments than traditional lexical-overlap metrics such as ROUGE. In this work, we intend to close the loop by leveraging the recent advances in summarization metrics to create quality-aware abstractive summarizers. Namely, we propose an energy-based model that learns to re-rank summaries according to one or a combination of these metrics. We experiment using several metrics to train our energy-based re-ranker and show that it consistently improves the scores achieved by the predicted summaries. Nonetheless, human evaluation results show that the re-ranking approach should be used with care for highly abstractive summaries, as the available metrics are not yet sufficiently reliable for this purpose.

自2016年成立以来，Alexa奖计划使数百名大学生能够通过Socialbot Grand Challenge探索和竞争以发展对话代理商。挑战的目的是建立能够与人类在流行主题上连贯而诱人的代理人20分钟，同时达到至少4.0/5.0的平均评分。但是，由于对话代理商试图帮助用户完成日益复杂的任务，因此需要新的对话AI技术和评估平台。成立于2021年的Alexa奖Taskbot Challenge建立在Socialbot Challenge的成功基础上，通过引入交互式协助人类进行现实世界烹饪和做自己动手做的任务的要求，同时同时使用语音和视觉方式。这项挑战要求TaskBots识别和理解用户的需求，识别和集成任务和域知识，并开发新的方式，不分散用户的注意力，而不必分散他们的任务，以及其他挑战。本文概述了Taskbot挑战赛，描述了使用Cobot Toolkit提供给团队提供的基础架构支持，并总结了参与团队以克服研究挑战所采取的方法。最后，它分析了比赛第一年的竞争任务机器人的性能。

人类AI合作（HAIC）在决策中的合作旨在在人类决策者和AI系统之间建立协同团队。学会推迟（L2D）已作为一个有前途的框架，以确定人类中的谁和人工智能应采取哪些决定，以优化联合系统的性能和公平性。然而，L2D需要几个通常不可行的要求，例如，人类对每个实例的预测可用性，或独立于上述决策者的地面标签。此外，L2D和其他方法都没有解决在现实世界中部署HAIC的基本问题，例如能力管理或处理动态环境。在本文中，我们旨在识别和审查这些局限性和其他局限性，指出HAIC未来研究的机会可能会在哪里。

在过去的十年中，许多组织制作了旨在从规范意义上进行标准化的文件，并为我们最近和快速的AI开发促进指导。但是，除了一些荟萃分析和该领域的批判性评论外，尚未分析这些文档中提出的思想的全部内容和分歧。在这项工作中，我们试图扩展过去研究人员所做的工作，并创建一种工具，以更好地数据可视化这些文档的内容和性质。我们还提供了通过将工具应用于200个文档的样本量获得的结果的批判性分析。

这项工作探讨了物理驱动的机器学习技术运算符推理（IMIPF），以预测混乱的动力系统状态。 OPINF提供了一种非侵入性方法来推断缩小空间中多项式操作员的近似值，而无需访问离散模型中出现的完整订单操作员。物理系统的数据集是使用常规数值求解器生成的，然后通过主成分分析（PCA）投影到低维空间。在潜在空间中，设置了一个最小二乘问题以适合二次多项式操作员，该操作员随后在时间整合方案中使用，以便在同一空间中产生外推。解决后，将对逆PCA操作进行重建原始空间中的外推。通过标准化的根平方误差（NRMSE）度量评估了OPINF预测的质量，从中计算有效的预测时间（VPT）。考虑混乱系统Lorenz 96和Kuramoto-Sivashinsky方程的数值实验显示，具有VPT范围的OPINF降低订单模型的有希望的预测能力，这些模型均超过了最先进的机器学习方法，例如返回和储层计算循环新的Neural网络[1 ]，以及马尔可夫神经操作员[2]。

生物医学决策涉及来自不同传感器或来自不同信道的多个信号处理。在这两种情况下，信息融合发挥着重要作用。在脑电图循环交替模式中，在这项工作中进行了深度学习的脑电图通道的特征级融合。通过两个优化算法，即遗传算法和粒子群优化优化了频道选择，融合和分类程序。通过融合来自多个脑电图信道的信息来评估开发的方法，用于夜间胸癫痫和没有任何神经疾病的患者的患者，与其他艺术艺术的工作相比，这在显着更具挑战性。结果表明，两种优化算法都选择了一种具有类似特征级融合的可比结构，包括三个脑电图通道，这与帽协议一致，以确保多个通道的唤起帽检测。此外，两种优化模型在接收器的工作特性曲线下达到了0.82的一个区域，平均精度为77％至79％，这是在专业协议的上部范围内的结果。尽管数据集是困难的数据集，所提出的方法仍处于最佳状态的上层，并且具有困难的数据集，并且具有在不需要任何手动过程的情况下提供全自动分析的优点。最终，模型显示出抗噪声和有弹性的多声道损耗。

在这项工作中，我们将注意力集中在数据分布与基于Q学基于Q学基于函数近似之间的相互作用的研究。我们提供了一个理论和实证分析，以及为什么数据分布的不同性质可以有助于调节算法不稳定性的来源。首先，我们重新审视近似动态编程算法性能的理论界限。其次，我们提供了一种新型的四态MDP，突出了在线和离线设置中具有功能近似的Q学习算法的数据分布的影响。最后，我们通过实验评估数据分布属性在离线深度Q网算法的性能中的影响。我们的结果表明：（i）数据分布需要拥有某些属性，以便在离线设置中鲁棒地学习，即距离MDP的最佳策略和高覆盖范围内的分布在状态 - 动作空间上的低距离; （ii）高熵数据分布可以有助于减轻算法不稳定性的来源。