尽管动态游戏为建模代理的互动提供了丰富的范式,但为现实世界应用程序解决这些游戏通常具有挑战性。许多现实的交互式设置涉及一般的非线性状态和输入约束,它们彼此之间的决策相结合。在这项工作中,我们使用约束的游戏理论框架开发了一个高效且快速的计划者,用于在受限设置中进行交互式计划。我们的关键见解是利用代理的目标和约束功能的特殊结构,这些功能在多代理交互中进行快速和可靠的计划。更确切地说,我们确定了代理成本功能的结构,在该结构下,由此产生的动态游戏是受约束潜在动态游戏的实例。受限的潜在动态游戏是一类游戏,而不是解决一组耦合的约束最佳控制问题,而是通过解决单个约束最佳控制问题来找到NASH平衡。这简化了限制的交互式轨迹计划。我们比较了涉及四个平面代理的导航设置中方法的性能,并表明我们的方法平均比最先进的速度快20倍。我们进一步在涉及一个四型和两个人的导航设置中对我们提出的方法提供了实验验证。
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Explainable Artificial Intelligence (AI) in the form of an interpretable and semiautomatic approach to stage grading ocular pathologies such as Diabetic retinopathy, Hypertensive retinopathy, and other retinopathies on the backdrop of major systemic diseases. The experimental study aims to evaluate an explainable staged grading process without using deep Convolutional Neural Networks (CNNs) directly. Many current CNN-based deep neural networks used for diagnosing retinal disorders might have appreciable performance but fail to pinpoint the basis driving their decisions. To improve these decisions' transparency, we have proposed a clinician-in-the-loop assisted intelligent workflow that performs a retinal vascular assessment on the fundus images to derive quantifiable and descriptive parameters. The retinal vessel parameters meta-data serve as hyper-parameters for better interpretation and explainability of decisions. The semiautomatic methodology aims to have a federated approach to AI in healthcare applications with more inputs and interpretations from clinicians. The baseline process involved in the machine learning pipeline through image processing techniques for optic disc detection, vessel segmentation, and arteriole/venule identification.
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Soft actuators have attracted a great deal of interest in the context of rehabilitative and assistive robots for increasing safety and lowering costs as compared to rigid-body robotic systems. During actuation, soft actuators experience high levels of deformation, which can lead to microscale fractures in their elastomeric structure, which fatigues the system over time and eventually leads to macroscale damages and eventually failure. This paper reports finite element modeling (FEM) of pneu-nets at high angles, along with repetitive experimentation at high deformation rates, in order to study the effect and behavior of fatigue in soft robotic actuators, which would result in deviation from the ideal behavior. Comparing the FEM model and experimental data, we show that FEM can model the performance of the actuator before fatigue to a bending angle of 167 degrees with ~96% accuracy. We also show that the FEM model performance will drop to 80% due to fatigue after repetitive high-angle bending. The results of this paper objectively highlight the emergence of fatigue over cyclic activation of the system and the resulting deviation from the computational FEM model. Such behavior can be considered in future controllers to adapt the system with time-variable and non-autonomous response dynamics of soft robots.
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Different machine learning (ML) models are trained on SCADA and meteorological data collected at an onshore wind farm and then assessed in terms of fidelity and accuracy for predictions of wind speed, turbulence intensity, and power capture at the turbine and wind farm levels for different wind and atmospheric conditions. ML methods for data quality control and pre-processing are applied to the data set under investigation and found to outperform standard statistical methods. A hybrid model, comprised of a linear interpolation model, Gaussian process, deep neural network (DNN), and support vector machine, paired with a DNN filter, is found to achieve high accuracy for modeling wind turbine power capture. Modifications of the incoming freestream wind speed and turbulence intensity, $TI$, due to the evolution of the wind field over the wind farm and effects associated with operating turbines are also captured using DNN models. Thus, turbine-level modeling is achieved using models for predicting power capture while farm-level modeling is achieved by combining models predicting wind speed and $TI$ at each turbine location from freestream conditions with models predicting power capture. Combining these models provides results consistent with expected power capture performance and holds promise for future endeavors in wind farm modeling and diagnostics. Though training ML models is computationally expensive, using the trained models to simulate the entire wind farm takes only a few seconds on a typical modern laptop computer, and the total computational cost is still lower than other available mid-fidelity simulation approaches.
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最近的研究揭示了NLP数据和模型中的不良偏见。但是,这些努力的重点是西方的社会差异,并且无法直接携带其他地质文化背景。在本文中,我们关注印度背景下的NLP公平。我们首先简要说明印度的社会差异斧头。我们为印度背景下的公平评估建立资源,并利用它们来证明沿着某些轴的预测偏见。然后,我们深入研究了地区和宗教的社会刻板印象,证明了其在Corpora&Models中的普遍性。最后,我们概述了一个整体研究议程,以重新定义印度背景的NLP公平研究,考虑印度社会背景,弥合能力,资源和适应印度文化价值的技术差距。尽管我们在这里专注于“印度”,但可以在其他地理文化背景下进行重新连接化。
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数据驱动的湍流建模正在经历数据科学算法和硬件开发后的兴趣激增。我们讨论了一种使用可区分物理范式的方法,该方法将已知的物理学与机器学习结合起来,以开发汉堡湍流的闭合模型。我们将1D汉堡系统视为一种原型测试问题,用于建模以对流为主的湍流问题中未解决的术语。我们训练一系列模型,这些模型在后验损失函数上结合了不同程度的物理假设,以测试模型在一系列系统参数(包括粘度,时间和网格分辨率)上的疗效。我们发现,以部分微分方程形式的归纳偏差的约束模型包含已知物理或现有闭合方法会产生高度数据效率,准确和可推广的模型,并且表现优于最先进的基准。以物理信息形式添加结构还为模型带来了一定程度的解释性,可能为封闭建模的未来提供了垫脚石。
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流行模型是理解传染病的强大工具。但是,随着它们的大小和复杂性的增加,它们可以迅速在计算上棘手。建模方法的最新进展表明,替代模型可用于模拟具有高维参数空间的复杂流行模型。我们表明,深层序列到序列(SEQ2SEQ)模型可以作为具有基于序列模型参数的复杂流行病模型的准确替代物,从而有效地复制了季节性和长期传播动力学。一旦受过培训,我们的代理人可以预测场景比原始模型快几千倍,从而使其非常适合策略探索。我们证明,用博学的模拟器代替传统的流行模型有助于强大的贝叶斯推断。
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动力学受部分微分方程(PDE)控制的物理系统在许多领域(从工程设计到天气预报)中找到了应用。从此类PDE中获取解决方案的过程对于大规模和参数化问题的计算昂贵。在这项工作中,使用LSTM和TCN等时间表预测开发的深度学习技术,或用于为CNN等空间功能提取而开发的,用于建模系统动力学,以占主导问题。这些模型将输入作为从PDE获得的连续时间步长的一系列高保真矢量解,并预测使用自动回归的后续时间步长的解决方案;从而减少获得此类高保真解决方案所需的计算时间和功率。这些模型经过数值基准测试(1D汉堡的方程式和Stoker的大坝断裂问题),以评估长期预测准确性,甚至在训练域之外(外推)。在向预测模型输入之前,使用非侵入性的降低订购建模技术(例如深度自动编码网络)来压缩高保真快照,以减少在线和离线阶段的复杂性和所需的计算。深层合奏被用来对预测模型进行不确定性量化,该模型提供了有关认知不确定性导致预测方差的信息。
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我们考虑在平均场比赛中在线加强学习。与现有作品相反,我们通过开发一种使用通用代理的单个样本路径来估算均值场和最佳策略的算法来减轻对均值甲骨文的需求。我们称此沙盒学习为其,因为它可以用作在多代理非合作环境中运行的任何代理商的温暖启动。我们采用了两种时间尺度的方法,在该方法中,平均场的在线固定点递归在较慢的时间表上运行,并与通用代理更快的时间范围内的控制策略更新同时进行。在足够的勘探条件下,我们提供有限的样本收敛保证,从平均场和控制策略融合到平均场平衡方面。沙盒学习算法的样本复杂性为$ \ Mathcal {o}(\ epsilon^{ - 4})$。最后,我们从经验上证明了沙盒学习算法在交通拥堵游戏中的有效性。
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在本文中,我们为不存在或无限的数据的方差提供了置信序列的扩展。置信序列提供的置信区间在任意数据依赖性停止时间时有效,自然具有广泛的应用。我们首先为有限方差案例的CATONI风格置信序列的宽度建立了一个下限,以突出现有结果的松动性。接下来,我们为数据分布提供了紧密的catoni风格的置信序列,该数据分布有一个放松的〜$ p^{th} - $ arment,其中〜$ p \ in(1,2] $,并加强了有限差异案例的结果〜$ p = 2 $。显示出比使用dubins-savage不等式获得的置信序列更好。
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