While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins.

Estimating the probability of failure for complex real-world systems using high-fidelity computational models is often prohibitively expensive, especially when the probability is small. Exploiting low-fidelity models can make this process more feasible, but merging information from multiple low-fidelity and high-fidelity models poses several challenges. This paper presents a robust multi-fidelity surrogate modeling strategy in which the multi-fidelity surrogate is assembled using an active learning strategy using an on-the-fly model adequacy assessment set within a subset simulation framework for efficient reliability analysis. The multi-fidelity surrogate is assembled by first applying a Gaussian process correction to each low-fidelity model and assigning a model probability based on the model's local predictive accuracy and cost. Three strategies are proposed to fuse these individual surrogates into an overall surrogate model based on model averaging and deterministic/stochastic model selection. The strategies also dictate which model evaluations are necessary. No assumptions are made about the relationships between low-fidelity models, while the high-fidelity model is assumed to be the most accurate and most computationally expensive model. Through two analytical and two numerical case studies, including a case study evaluating the failure probability of Tristructural isotropic-coated (TRISO) nuclear fuels, the algorithm is shown to be highly accurate while drastically reducing the number of high-fidelity model calls (and hence computational cost).

Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modelling and the visualisation and analysis of materials datasets.The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. We recast this approach as tensor factorisation by exploiting the tensor structure of standard neighbour density based descriptors. In doing so, we form compact tensor-reduced representations whose size does not depend on the number of chemical elements, but remain systematically convergeable and are therefore applicable to a wide range of data analysis and regression tasks.

我们提供了证据表明，学到的密度功能理论（``dft'）的力场已准备好进行基态催化剂发现。我们的关键发现是，尽管预测的力与地面真相有很大差异，但使用从超过50 \％的评估系统中使用RPBE功能的能量与使用RPBE功能相似或较低能量的力量的力量与使用RPBE功能相似或较低的力量放松。这具有令人惊讶的含义，即学习的潜力可能已经准备好在挑战性的催化系统中替换DFT，例如在Open Catalyst 2020数据集中发现的电位。此外，我们表明，在局部谐波能量表面上具有与目标DFT能量相同的局部谐波能量表面训练的力场也能够在50 \％的情况下找到较低或相似的能量结构。与在真实能量和力量训练的标准模型相比，这种``简易电位''的收敛步骤更少，这进一步加速了计算。它的成功说明了一个关键：即使模型具有高力误差，学到的电位也可以定位能量最小值。结构优化的主要要求仅仅是学到的电位具有正确的最小值。由于学到的电位与系统大小的速度快速且尺寸为线性，因此我们的结果开辟了快速找到大型系统基础状态的可能性。

现有的数据驱动和反馈流量控制策略不考虑实时数据测量的异质性。此外，对于缺乏数据效率，传统的加固学习方法（RL）方法通常会缓慢收敛。此外，常规的最佳外围控制方案需要对系统动力学的精确了解，因此对内源性不确定性会很脆弱。为了应对这些挑战，这项工作提出了一种基于不可或缺的增强学习（IRL）的方法来学习宏观交通动态，以进行自适应最佳周边控制。这项工作为运输文献做出了以下主要贡献：（a）开发连续的时间控制，并具有离散增益更新以适应离散时间传感器数据。 （b）为了降低采样复杂性并更有效地使用可用数据，将体验重播（ER）技术引入IRL算法。 （c）所提出的方法以“无模型”方式放松模型校准的要求，该方式可以稳健地进行建模不确定性，并通过数据驱动的RL算法增强实时性能。 （d）通过Lyapunov理论证明了基于IRL的算法和受控交通动力学的稳定性的收敛性。最佳控制定律被参数化，然后通过神经网络（NN）近似，从而缓解计算复杂性。在不需要模型线性化的同时，考虑了状态和输入约束。提出了数值示例和仿真实验，以验证所提出方法的有效性和效率。

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。

我们将定量探测作为模型 - 非局部框架，用于在存在定量域知识的情况下验证因果模型。该方法被构造为基于相关的机器学习中火车/测试拆分的类似物，并增强了与科学发现逻辑一致的当前因果验证策略。在进行彻底基于模拟的研究之前，使用Pearl的洒水示例说明了该方法的有效性。通过研究示例性失败方案来识别该技术的限制，这些方案还用于提出一系列主题，以供未来的研究和改进定量探测的版本。在两个单独的开源python软件包中提供了将定量探测的代码以及基于模拟的定量探测有效性的基于仿真的研究的代码。

在2D多板磁共振（MR）采集中，平面信号通常比面内信号较低。尽管当代超分辨率（SR）方法旨在恢复基本的高分辨率量，但估计的高频信息是通过端到端数据驱动的培训隐含的，而不是明确说明和寻求。为了解决这个问题，我们根据完美的重建过滤库重新构架SR问题声明，使我们能够识别并直接估计缺失的信息。在这项工作中，我们提出了一种两阶段的方法，以近似于与特定扫描的各向异性采集相对应的完美重建过滤库。在第1阶段，我们使用梯度下降估算缺失的过滤器，在第2阶段，我们使用深网来学习从粗系数到细节系数的映射。此外，提出的公式不依赖外部训练数据，从而规避了对域移位校正的需求。在我们的方法下，特别是在“切片差距”方案中提高了SR性能，这可能是由于框架施加的解决方案空间的限制。

明显大小的时间变化（称为光曲线）是望远镜在长时间内捕获的感兴趣的观察统计。光曲线提供了空间域意识（SDA）目标（例如对象识别或姿势估计）作为潜在变量推理问题等目标的探索。与较高的精确仪器相比，来自货架上商业架子（COTS）摄像机的地面观测仍然很便宜，但是，有限的传感器可用性与嘈杂的观察结果相结合，可能会产生可能难以建模的gappy时间序列数据。这些外部因素混淆了对光曲线的自动开发，这使光曲线预测和外推成为应用的关键问题。传统上，使用基于扩散或基于示例的方法解决了图像或时间序列的完成问题。最近，由于学习复杂的非线性嵌入方面的经验成功，深度神经网络（DNNS）已成为首选工具。但是，DNN通常需要大量的培训数据，而这些数据不一定在查看单个卫星的光曲线的独特功能时可用。在本文中，我们提出了一种新的方法，可以使用高斯工艺（GPS）预测光曲线的缺失和未来数据点。 GPS是非线性概率模型，可推断后验分布在功能上并自然量化不确定性。但是，GP推理和培训的立方缩放是其在应用中采用的主要障碍。特别是，单个光曲线可以具有数十万个观测值，这远远超出了单个机器上常规GP的实际实现极限。因此，我们采用MUYGP，这是一种可扩展的框架，用于使用最近的邻居稀疏和局部交叉验证的GP模型的超参数估计。 muygps ...