单细胞RNA-seq数据集的大小和复杂性正在增长，从而可以研究各种生物/临床环境中的细胞组成变化。可扩展的降低性降低技术需要消除它们的生物学变异，同时考虑技术和生物混杂因素。在这项工作中，我们扩展了一种流行的概率非线性维度降低的方法，即高斯过程潜在变量模型，以扩展到大量的单细胞数据集，同时明确考虑技术和生物混杂因素。关键思想是使用增强的内核，该内核可以保留下限的可分式性，从而允许快速随机变化推断。我们证明了其在Kumasaka等人中重建先天免疫的潜在潜在签名的能力。 （2021）训练时间较低9倍。我们进一步分析了一个共同数据集并在130个人群中证明了该框架，该框架可以在捕获可解释的感染签名的同时进行数据集成。具体而言，我们探讨了互联的严重程度，作为优化患者分层并捕获疾病特异性基因表达的潜在维度。

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

We describe a Physics-Informed Neural Network (PINN) that simulates the flow induced by the astronomical tide in a synthetic port channel, with dimensions based on the Santos - S\~ao Vicente - Bertioga Estuarine System. PINN models aim to combine the knowledge of physical systems and data-driven machine learning models. This is done by training a neural network to minimize the residuals of the governing equations in sample points. In this work, our flow is governed by the Navier-Stokes equations with some approximations. There are two main novelties in this paper. First, we design our model to assume that the flow is periodic in time, which is not feasible in conventional simulation methods. Second, we evaluate the benefit of resampling the function evaluation points during training, which has a near zero computational cost and has been verified to improve the final model, especially for small batch sizes. Finally, we discuss some limitations of the approximations used in the Navier-Stokes equations regarding the modeling of turbulence and how it interacts with PINNs.

Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recognition, and text categorization. Nonetheless, most Machine Learning algorithms are trained via derivative-based optimizers, such as the Stochastic Gradient Descent, leading to possible local optimum entrapments and inhibiting them from achieving proper performances. A bio-inspired alternative to traditional optimization techniques, denoted as meta-heuristic, has received significant attention due to its simplicity and ability to avoid local optimums imprisonment. In this work, we propose to use meta-heuristic techniques to fine-tune pre-trained weights, exploring additional regions of the search space, and improving their effectiveness. The experimental evaluation comprises two classification tasks (image and text) and is assessed under four literature datasets. Experimental results show nature-inspired algorithms' capacity in exploring the neighborhood of pre-trained weights, achieving superior results than their counterpart pre-trained architectures. Additionally, a thorough analysis of distinct architectures, such as Multi-Layer Perceptron and Recurrent Neural Networks, attempts to visualize and provide more precise insights into the most critical weights to be fine-tuned in the learning process.

In this paper, we propose a method for selecting the optimal footholds for legged systems. The goal of the proposed method is to find the best foothold for the swing leg on a local elevation map. We apply the Convolutional Neural Network to learn the relationship between the local elevation map and the quality of potential footholds. The proposed network evaluates the geometrical characteristics of each cell on the elevation map, checks kinematic constraints and collisions. During execution time, the controller obtains the qualitative measurement of each potential foothold from the neural model. This method allows to evaluate hundreds of potential footholds and check multiple constraints in a single step which takes 10~ms on a standard computer without GPGPU. The experiments were carried out on a quadruped robot walking over rough terrain in both simulation and real robotic platforms.

Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based on deep learning models for X-ray images often use custom architectures that employ extensive CAD models and simulated data for training purposes. Recent RGB-based methods opt to solve pose estimation problems using small datasets, making them more attractive for the X-ray domain where medical data is scarcely available. We refine an existing RGB-based model (SingleShotPose) to estimate the 6D pose of a marked cube from grayscale X-ray images by creating a generic solution trained on only real X-ray data and adjusted for X-ray acquisition geometry. The model regresses 2D control points and calculates the pose through 2D/3D correspondences using Perspective-n-Point(PnP), allowing a single trained model to be used across all supporting cone-beam-based X-ray geometries. Since modern X-ray systems continuously adjust acquisition parameters during a procedure, it is essential for such a pose estimation network to consider these parameters in order to be deployed successfully and find a real use case. With a 5-cm/5-degree accuracy of 93% and an average 3D rotation error of 2.2 degrees, the results of the proposed approach are comparable with state-of-the-art alternatives, while requiring significantly less real training examples and being applicable in real-time applications.

纳米四轮驱动器是小的，敏捷且廉价的平台，非常适合在狭窄，混乱的环境中部署。由于其有效载荷有限，这些车辆在处理能力方面受到了高度限制，从而使基于常规视觉的方法具有安全性和自主导航不兼容。最近的机器学习发展有望在低潜伏期处高性能感知，而专用的边缘计算硬件有可能增强这些有限设备的处理能力。在这项工作中，我们提出了Nanoflownet，这是一个轻巧的卷积神经网络，用于实时密集的光流估计，对边缘计算硬件。我们从最新的语义细分方面汲取灵感来设计该网络。此外，我们使用运动边界地面真实数据指导学习光流的学习，从而改善了性能而不会影响延迟。 MPI-SINTEL数据集的验证结果显示，鉴于其受限的体系结构，该网络的高性能。此外，我们通过将其部署在超低功率GAP8微处理器上，并将其应用于BitCraze Crazyflie，这是34 G纳米四轮摩托车的BitCraze Crazyflie，并将其应用于34 G Nano Quadcopter的BitCraze Crazyflie，从而成功地证明了纳米滚子的功能。

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。

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。

黑色素瘤是一种严重的皮肤癌，在后期阶段高死亡率。幸运的是，当早期发现时，黑色素瘤的预后是有希望的，恶性黑色素瘤的发病率相对较低。结果，数据集严重不平衡，这使培训当前的最新监督分类AI模型变得复杂。我们建议使用生成模型来学习良性数据分布，并通过密度估计检测出分布（OOD）恶性图像。标准化流（NFS）是OOD检测的理想候选者，因为它们可以计算精确的可能性。然而，它们的感应偏见对明显的图形特征而不是语义上下文障碍障碍的OOD检测。在这项工作中，我们旨在将这些偏见与黑色素瘤的领域水平知识一起使用，以改善基于可能性的OOD检测恶性图像。我们令人鼓舞的结果表明，使用NFS检测黑色素瘤的可能性。我们通过使用基于小波的NFS，在接收器工作特性的曲线下，面积增加了9％。该模型需要较少的参数，以使其更适用于边缘设备。拟议的方法可以帮助医学专家诊断出皮肤癌患者并不断提高存活率。此外，这项研究为肿瘤学领域的其他领域铺平了道路，具有类似的数据不平衡问题\ footNote {代码可用：