We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment are unknown. We assume a linear structural causal model with additive Gaussian noise and consider interventions that perturb their targets while maintaining the causal relationships in the system. Different models may entail the same distributions, offering competing causal explanations for the given observations. We fully characterize this equivalence class and offer identifiability results, which we use to derive a greedy algorithm called GnIES to recover the equivalence class of the data-generating model without knowledge of the intervention targets. In addition, we develop a novel procedure to generate semi-synthetic data sets with known causal ground truth but distributions closely resembling those of a real data set of choice. We leverage this procedure and evaluate the performance of GnIES on synthetic, real, and semi-synthetic data sets. Despite the strong Gaussian distributional assumption, GnIES is robust to an array of model violations and competitive in recovering the causal structure in small- to large-sample settings. We provide, in the Python packages "gnies" and "sempler", implementations of GnIES and our semi-synthetic data generation procedure.

因果学习的基本难度是通常不能根据观察数据完全识别因果模型。介入数据，即源自不同实验环境的数据，提高了可识别性。然而，改善统治性取决于每个实验中的干预措施的目标和性质。由于在实际应用实验往往是昂贵的，因此需要执行正确的干预措施，使得尽可能少。在这项工作中，我们提出了一种基于不变因果预测（ICP）的新的主动学习（即实验选择）框架（A-ICP）（Peters等，2016）。对于一般结构因果模型，我们的表征干预对所谓的稳定集的影响，由（Pfister等，2019）引入的概念。我们利用这些结果提出了用于A-ICP的几个干预选择策略，该策略快速揭示了因果图中响应变量的直接原因，同时保持ICP中固有的错误控制。经验上，我们分析了拟议的拟议政策在人口和有限政府实验中的表现。

In this paper, we address the problem of multimodal emotion recognition from multiple physiological signals. We demonstrate that a Transformer-based approach is suitable for this task. In addition, we present how such models may be pretrained in a multimodal scenario to improve emotion recognition performances. We evaluate the benefits of using multimodal inputs and pre-training with our approach on a state-ofthe-art dataset.

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.

通用数据模型解决了标准化电子健康记录（EHR）数据的许多挑战，但无法将其集成深度表型所需的资源。开放的生物学和生物医学本体论（OBO）铸造本体论提供了可用于生物学知识的语义计算表示，并能够整合多种生物医学数据。但是，将EHR数据映射到OBO Foundry本体论需要大量的手动策展和域专业知识。我们介绍了一个框架，用于将观察性医学成果合作伙伴关系（OMOP）标准词汇介绍给OBO铸造本体。使用此框架，我们制作了92,367条条件，8,615种药物成分和10,673个测量结果的映射。域专家验证了映射准确性，并且在24家医院进行检查时，映射覆盖了99％的条件和药物成分和68％的测量结果。最后，我们证明OMOP2OBO映射可以帮助系统地识别可能受益于基因检测的未诊断罕见病患者。

在从蛋白质折叠到材料发现的许多领域中，采样分子系统的相空间 - 更普遍地是通过随机微分方程有效建模的复杂系统的相位空间。这些问题本质上通常是多尺度的：可以用少数“慢速”反应坐标参数参数的低维有效自由能表面来描述它们；其余的“快速”自由度填充了反应坐标值的平衡度量。有关此类问题的抽样程序用于估计有效的自由能差以及相对于条件平衡分布的合奏平均值；后者平均值导致有效减少动态模型的关闭。多年来，已经开发了增强的采样技术与分子模拟。引人入胜的类比是与机器学习领域（ML）产生的，在该领域中，生成的对抗网络可以从低维概率分布中产生高维样品。该样本生成从有关其低维表示的信息中返回模型状态的合理高维空间实现。在这项工作中，我们提出了一种方法，该方法将基于物理学的模拟和偏置方法与基于ML的条件生成对抗网络对条件分布进行采样，以实现相同的任务。我们调节精细规模实现的“粗糙描述符”可以先验地知道，也可以通过非线性维度降低来学习。我们建议这可能会带来两种方法的最佳功能：我们证明，夫妻CGAN具有基于物理学的增强采样技术的框架可以改善多尺度SDE动力学系统采样，甚至显示出对增加复杂性系统的希望。

筛查结肠镜检查是多种3D计算机视觉技术的重要临床应用，包括深度估计，表面重建和缺失区域检测。但是，由于难以获取地面真相数据，因此在实际结肠镜检查视频中对这些技术的开发，评估和比较仍然在很大程度上是定性的。在这项工作中，我们提出了一个带有高清临床结肠镜和高保真结肠模型的结肠镜检查3D视频数据集（C3VD），用于在结肠镜检查中进行基准计算机视觉方法。我们介绍了一种新颖的多模式2D-3D注册技术，以注册光学视频序列，并以地面真实的视图对已知3D模型的视图。通过将光学图像转换为具有生成对抗网络的深度图，并通过进化优化器对齐边缘特征来注册不同的模态。在模拟实验中，这种注册方法达到了0.321毫米的平均翻译误差，平均旋转误差为0.159度，无误地面真相可用。该方法还利用视频信息，将注册精度提高了55.6％以进行翻译，与单帧注册相比，旋转60.4％。 22个简短的视频序列被注册，以生成10,015个总帧，具有配对的地面真实深度，表面正常，光流，遮挡，六个自由度姿势，覆盖范围图和3D模型。该数据集还包括胃肠病学家与配对地面真相姿势和3D表面模型获得的筛选视频。数据集和注册源代码可在urr.jhu.edu/c3vd上获得。

随着量子系统平台的快速进步，噪声量子状态的许多身体量子态重建问题成为一个重要的挑战。最近的作品在重铸量子态重建问题时使用生成神经网络模型来学习量子状态测量向量的概率分布的承诺。在这里，我们提出了“注意力的量子断层扫描”（AQT），使用基于机构的生成网络的量子状态重建，所述生成网络学习嘈杂量子状态的混合状态密度矩阵。 AQT基于Vishwani等人（2017）的“注意是您所需要的所有需要​​”的模型，该模型旨在学习自然语言句子中的远程相关性，从而优于先前的自然语言处理模型。我们不仅展示了AQT的早期基于神经网络的量子状态重建，而且可以准确地重建与IBMQ量子计算机实验地实现的嘈杂量子状态相关的密度矩阵。我们推测了AQT源于其在整个量子系统上模拟量子纠缠的能力的成功，因为自然语言处理的注意模型捕获了句子中的单词之间的相关性。

There is a dramatic shortage of skilled labor for modern vineyards. The Vinum project is developing a mobile robotic solution to autonomously navigate through vineyards for winter grapevine pruning. This necessitates an autonomous navigation stack for the robot pruning a vineyard. The Vinum project is using the quadruped robot HyQReal. This paper introduces an architecture for a quadruped robot to autonomously move through a vineyard by identifying and approaching grapevines for pruning. The higher level control is a state machine switching between searching for destination positions, autonomously navigating towards those locations, and stopping for the robot to complete a task. The destination points are determined by identifying grapevine trunks using instance segmentation from a Mask Region-Based Convolutional Neural Network (Mask-RCNN). These detections are sent through a filter to avoid redundancy and remove noisy detections. The combination of these features is the basis for the proposed architecture.

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.