通常在高维生物数据集中发现的最常见的缺陷之一是特征之间的相关性。这可能导致统计和机器学习方法过度或低估这些相关预测因素,而真正相关的则被忽略。在本文中,我们将定义一种名为“成对置换算法}(PPA)的新方法,其目的是在特征重要性值中减轻相关偏差。首先,我们提供了一个理论基础,在以前的工作中建立了折射重要性。然后将PPA应用于玩具数据集,我们展示了校正相关效果的能力。我们进一步测试PPA在微生物霰弹枪数据集上,表明PPA已经能够获得生物相关的生物标志物。
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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.
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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.
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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.
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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。
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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。
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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。
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最新提出的基于变压器的图形模型的作品证明了香草变压器用于图形表示学习的不足。要了解这种不足,需要研究变压器的光谱分析是否会揭示其对其表现力的见解。类似的研究已经确定,图神经网络(GNN)的光谱分析为其表现力提供了额外的观点。在这项工作中,我们系统地研究并建立了变压器领域中的空间和光谱域之间的联系。我们进一步提供了理论分析,并证明了变压器中的空间注意机制无法有效捕获所需的频率响应,因此,固有地限制了其在光谱空间中的表现力。因此,我们提出了feta,该框架旨在在整个图形频谱(即图形的实际频率成分)上进行注意力类似于空间空间中的注意力。经验结果表明,FETA在标准基准的所有任务中为香草变压器提供均匀的性能增益,并且可以轻松地扩展到具有低通特性的基于GNN的模型(例如GAT)。
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在过去的十年中,在杂交无人驾驶空中水下车辆的研究中努力,机器人可以轻松飞行和潜入水中的机械适应水平。然而,大多数文献集中在物理设计,建筑物的实际问题上,最近,低水平的控制策略。在高级情报的背景下,如运动规划和与现实世界的互动的情况下已经完成。因此,我们在本文中提出了一种轨迹规划方法,允许避免避免未知的障碍和空中媒体之间的平滑过渡。我们的方法基于经典迅速探索随机树的变体,其主要优点是处理障碍,复杂的非线性动力学,模型不确定性和外部干扰的能力。该方法使用\ Hydrone的动态模型,提出具有高水下性能的混合动力车辆,但我们认为它可以很容易地推广到其他类型的空中/水生平台。在实验部分中,我们在充满障碍物的环境中显示了模拟结果,其中机器人被命令执行不同的媒体运动,展示了我们的策略的适用性。
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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。
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